CONTENTS
Volume 330 Issue 6012
EDITORIAL 1724 Model Organisms and Human Health Bruce Alberts >> Perspective p. 1758; Research Articles pp. 1775 & 1787
BOOKS ET AL. 1747 Evolution Since Darwin
M. A. Bell et al., Eds., reviewed by D. P. Mindell
1748
NEWS OF THE WEEK Polio Outbreak Breaks the Rules Court to Weigh University’s Decision Not to Hire Astronomer 1732 French Nobelist Escapes ‘Intellectual Terror’ to Pursue Radical Ideas in China 1733 From Science’s Online Daily News Site 1730 1731
1734
Discoverer Asks for Time, Patience Over Arsenic Bacteria Controversy
1735
From the Science Policy Blog
NEWS FOCUS 1736 What’s Next for Disease Eradication? Scientists’ New Eradication Target: A Word in Their Lexicon
Altering the Past: China’s Faked Fossils Problem 1742 CIRM: The Good, the Bad, and the Ugly 1740
LETTERS 1744 Genetic Future for Florida Panthers P. Hedrick
POLICY FORUMS 1749 The Challenge of Feeding Scientific Advice into Policy-Making R. Schenkel
1752
Boosting CITES
J. Phelps et al.
page 1736
PERSPECTIVES 1754
Ubiquitination Inhibits Neuronal Exit
C. Métin and C. Luccardini >> Report p. 1834 1755
Generating an Atmosphere
D. P. Cruikshank >> Report p. 1813 1756
Computational Physics in Film
R. Bridson and C. Batty 1758
Revealing the Dark Matter of the Genome
M. Blaxter >> Editorial p. 1724; Research Articles pp. 1775 & 1787 1759
Response
Stretching Dielectric Elastomer Performance F. Carpi et al.
W. E. Johnson et al.
1761
Biodiversity Transcends Services
Germ Cell Genes and Cancer
X. Wu and G. Ruvkun >> Report p. 1824
D. P. Faith
Response
1763
C. Perrings et al. 1746
Brain
R. DeSalle et al., curators, reviewed by A. Rabinowitz and C. E. Schoonover
Retrospective: Allan Sandage (1926–2010) D. Lynden-Bell
CORRECTIONS AND CLARIFICATIONS
page 1749
SCIENCE PRIZE ESSAY 1764
Science 101: Building the Foundations for Real Understanding A. Thanukos et al.
CONTENTS continued >>
COVER
DEPARTMENTS
An adult Caenorhabditis elegans nematode, ~1 millimeter long, pictured along with eggs and young worms. C. elegans is the first multicellular organism to have its genome fully sequenced, followed by the fruit fly Drosophila melanogaster. High-resolution genomic analyses presented on pages 1775 and 1787 provide new insights into the organization, structure, and function of the genomes of these organisms. See the related Editorial on page 1724 and Perspective on page 1758.
1722 1725 1726 1729 1766 1839 1840
This Week in Science Editors’ Choice Science Staff Random Samples AAAS News & Notes New Products Science Careers
Image: Carolina Biological Supply, Co/Visuals Unlimited, Inc.
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SCIENCE
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Published by AAAS
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CONTENTS
REVIEW 1768
1810
Has the Microbiota Played a Critical Role in the Evolution of the Adaptive Immune System?
L. Thomas et al. The current-induced motion of magnetic domain walls is controlled by the length of the current pulse.
Y. K. Lee and S. K. Mazmanian
1813
BREVIA 1774
Decreased Clearance of CNS β-Amyloid in Alzheimer’s Disease
1775
1816
pages 1755 & 1813
Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project
M. B. Gerstein et al. Extensive analysis of the Caenorhabditis elegans genome reveals regions highly occupied by multiple transcription factors. 1787
1820
High-Flux Solar-Driven Thermochemical Dissociation of CO2 and H2O Using Nonstoichiometric Ceria
W. C. Chueh et al. Solar heating of ceric oxide enables a cycle for conversion of carbon dioxide to carbon monoxide or water to hydrogen. 1801 pages 1761 & 1824
1824
1804
Brownian Motion of Stiff Filaments in a Crowded Environment
N. Fakhri et al. The thermal motion of single-walled carbon nanotubes is used to track the dynamic motion of stiff macromolecules. 1807
Tunable Field Control Over the Binding Energy of Single Dopants by a Charged Vacancy in GaAs
D. H. Lee and J. A. Gupta The electrostatic field of manganese atoms in gallium arsenide depends on its distance from a nearby arsenic vacancy site.
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Ectopic Expression of Germline Genes Drives Malignant Brain Tumor Growth in Drosophila
A. Janic et al. Inactivation of germline genes suppresses brain tumor growth in Drosophila. >> Perspective p. 1761 1827
A Pollen Factor Linking Inter- and Intraspecific Pollen Rejection in Tomato
W. Li and R. T. Chetelat The inability to cross with distant relatives in the nightshade family is linked to mechanisms preventing self-pollination.
Spin Hall Effect Transistor
J. Wunderlich et al. Manipulation of the spin degree of freedom of electrons is used to build a spin transistor without magnetic materials.
Hsp90 and Environmental Stress Transform the Adaptive Value of Natural Genetic Variation
D. F. Jarosz and S. Lindquist A molecular chaperone both buffers and potentiates the adaptive nature of genetic variation in yeast.
REPORTS 1797
Structures of C3b in Complex with Factors B and D Give Insight into Complement Convertase Formation
F. Forneris et al. A double-safety–catch mechanism controls amplification of the complement cascade during immune responses.
Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE The modENCODE Consortium et al. The Drosophila modENCODE project demonstrates the functional regulatory network of flies. >> Editorial p. 1724; Perspective p. 1758
Cassini Finds an Oxygen–Carbon Dioxide Atmosphere at Saturn’s Icy Moon Rhea
B. D. Teolis et al. Rhea’s atmosphere is maintained by chemical decomposition of surface water ice under irradiation from Saturn’s magnetosphere. >> Perspective p. 1755
K. G. Mawuenyega et al. Alzheimer’s disease is associated with reduced β-amyloid clearance from the brain.
RESEARCH ARTICLES
Dynamics of Magnetic Domain Walls Under Their Own Inertia
1830
The Social Sense: Susceptibility to Others’ Beliefs in Human Infants and Adults
A. M. Kovács et al. Knowledge of what others believe is present in 7-month-old infants. 1834
Siah Regulation of Pard3A Controls Neuronal Cell Adhesion During Germinal Zone Exit
J. K. Famulski et al. A ubiquitination cascade regulates formation of cell adhesions that immature neurons require in the developing mouse brain. >> Perspective p. 1754
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CONTENTS
SCIENCEONLINE SCIENCEXPRESS
www.sciencexpress.org
Crystal Structure of the Eukaryotic 40S Ribosomal Subunit in Complex with Initiation Factor 1
RESEARCH ARTICLE: Cyclic GMP and Protein Kinase G Control a Src-Containing Mechanosome in Osteoblasts H. Rangaswami et al.
PERSPECTIVE: Mechanosomes Carry a Loaded Message
Phosphorylation of ULK1 (hATG1) by AMP-Activated Protein Kinase Connects Energy Sensing to Mitophagy
RESEARCH RESOURCE: Phosphoproteomic Analysis Reveals Interconnected System-Wide Responses to Perturbations of Kinases and Phosphatases in Yeast
Induction of Colonic Regulatory T Cells by Indigenous Clostridium Species
K. Atarashi et al. Bacteria of the genus Clostridium promote the induction of suppressor T cells in the colons of mice. 10.1126/science.1198469
LysM-Type Mycorrhizal Receptor Recruited for Rhizobium Symbiosis in Nonlegume Parasponia R. Op den Camp et al. Parasponia uses a mycorrhizal signaling receptor essential for arbuscle formation to control rhizobium nodule symbiosis. 10.1126/science.1198181
Intramembrane Cleavage of AMA1 Triggers Toxoplasma to Switch from an Invasive to a Replicative Mode
J. M. Santos et al. Membrane proteins govern a change from invasion to replication of an intracellular parasite. 10.1126/science.1199284
SCIENCENOW
www.sciencenow.org Highlights From Our Daily News Coverage What Makes Glaciers Shake? Geologists probe the cause of icequakes. Fearless Woman Lacks Key Part of Brain Study suggests that the amygdala plays a crucial role in fear, but not other emotions.
J. P. Bidwell and F. M. Pavalko Drugs that activate protein kinase G could mimic the bone-building effects of mechanical stimulation.
B. Bodenmiller et al.
PODCAST
B. Bodenmiller et al. Targeted removal of individual enzymes elicits changes throughout the entire network of kinases and phosphatases in yeast.
PERSPECTIVE: T Cell Receptor Signaling Kinetics Takes the Stage
Y. Sykulev The kinetics of TCR signaling influence the quality of the T cell response.
SCIENCECAREERS
www.sciencecareers.org/career_magazine Free Career Resources for Scientists
The Best of Science Careers, 2010
Science Careers Staff It was a difficult year for careers in science, but another good year for Science Careers.
SCIENCETRANSLATIONAL MEDICINE
M. P. Chao et al. Calreticulin-induced phagocytosis of cancer cells can be counterbalanced by CD47 expression.
REPORT: Short-Term Monotherapy in HIV-Infected Patients with a Virus Entry Inhibitor Against the gp41 Fusion Peptide W.-G. Forssmann et al. A natural HIV-1 entry inhibitor targeting the gp41 fusion peptide shows antiviral potency in a Phase I/II clinical trial.
SCIENCEPODCAST
www.sciencemag.org/multimedia/podcast Free Weekly Show Download the 24 December Science Podcast to hear a wrap-up of some of the favorite ScienceNOW stories of 2010.
news.sciencemag.org/scienceinsider Science Policy News and Analysis
EDITORIAL: 2010: Awards Show What Translation Can Accomplish S. Desmond-Hellmann
Four of the most coveted awards in science celebrate truly translational research.
COMMENTARY: Advancing Translational Research Collaborations L. M. Portilla et al.
SCIENCESIGNALING
MEETING REPORT: Training the Translational Scientist
O. E. Sturm et al. Analysis of ERK pathway circuitry suggests appropriate targets for inhibition, providing a guide for drug development.
RESEARCH ARTICLE: Calreticulin Is the Dominant Pro-Phagocytic Signal on Multiple Human Cancers and Is Counterbalanced by CD47
www.sciencetranslationalmedicine.org Integrating Medicine and Science
A Dog’s Growl Announces Its Size Canines know how big their foes are just by listening to them.
RESEARCH ARTICLE: The Mammalian MAPK/ERK Pathway Exhibits Properties of a Negative Feedback Amplifier
J. H. Linehan and A. Chaney These five meeting reports from an NIH forum held in 2010 on promoting collaborations among stakeholders in translational medicine discuss impediments to such partnerships and ways to overcome them.
SCIENCEINSIDER
Barriers to collaboration among academia, government, and industry must be identified and overcome to maximize the clinical return on the investment in science.
www.sciencesignaling.org The Signal Transduction Knowledge Environment
S. J. Steele
MEETING REPORT: Academic/Industry Challenges for Medical Device Development
J. Rabl et al. The structure provides insight into how protein synthesis is initiated and into the evolution of the eukaryotic ribosome. 10.1126/science.1198308
D. F. Egan et al. A protein kinase links energy stores to control of autophagy. 10.1126/science.1196371
MEETING REPORT: Working with the CTSA Consortium: What We Bring to the Table
R. D. Jackson et al.
MEETING REPORT: Collaborations Among Academia, Government, and Industry in the Diagnostics Space: Barriers and Some Ideas for Solutions G. Evans and F. Austin
MEETING REPORT: Development of the First Inhaled Antibiotic for the Treatment of Cystic Fibrosis
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EDITED BY STELLA HURTLEY
Exiting the Birthplace >>
Efforts to sequence the human microbiome— the genomes of all the microbes that inhabit our bodies—have demonstrated its enormous diversity. Analyses to probe the various functions of the microbiota, particularly of those that reside in the gut, have revealed that our microbiota has a profound impact on the development and function of our immune systems. Lee and Mazmanian (p. 1768) review how the microbiota influences the development of the adaptive immune system. Specific species and families of microbiota support the differentiation of particular populations of T cells, and alterations in intestinal microbiota affect the development of inflammation and autoimmunity.
the Caenorhabditis elegans genome, and The modENCODE Consortium (p. 1787) summarize for the Drosophila melanogaster genome, full transcriptome analyses over developmental stages, genome-wide identification of transcription factor binding sites, and high-resolution maps of chromatin organization. Both studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized. Overall, the studies provide insights into the organization, structure, and function of the two genomes and provide basic information needed to guide and correlate both focused and genome-wide studies.
Tuning Semiconductor Dopants
Fuel from Heat
Dopants in semiconductors can alter their conductivity or introduce spin centers that alter their magnetic properties. Generally, the charge state of a dopant and field it creates are fixed. Lee and Gupta (p. 1807, published online 9 December) studied Mn dopants in GaAs with a low-temperature scanning tunneling microscope (STM). Using the STM to position As vacancies at different distances from the Mn dopants revealed that the As vacancy tuned the local electrostatic field of the dopant.
From Genome to Regulatory Networks For biologists, having a genome in hand is only the beginning—much more investigation is still needed to characterize how the genome is used to help to produce a functional organism (see the Perspective by Blaxter). In this vein, Gerstein et al. (p. 1775) summarize for
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Plants grow by using energy from the Sun to convert carbon dioxide into sugar-based polymers and aromatics. These compounds in turn can be stripped of their oxygen, either through millennia of underground degradation to yield fossil fuels, or through a rather more rapid process of dissolution, fermentation, and hydrogenation to yield biofuels. Can we use sunlight to turn CO2 into hydrocarbon fuel without relying on the intervening steps of plant growth and breakdown? Chueh et al. (p. 1797) demonstrate one possible approach, in which concentrated sunlight heats cerium oxide to a sufficiently high temperature (~1500°C) to liberate some oxygen from its lattice. The material then readily strips O atoms from either water or CO2, yielding hydrogen or CO, which can then be combined to form fuels.
Movement in a Tight Squeeze The motion of flexible polymer chains in a dense melt or concentrated solution is described by reptation theory, in which a single chain is considered to snake back and forth inside a virtual confining tube formed by all its neighboring chains. A number of theories have been proposed for stiffer molecules, but it has been hard to obtain experimental data to determine the thermal motion of stiff filaments. Fakhri et al. (p. 1804) visualized carbon nanotubes directly as a model system for stiff polymers diffusing in a gel, and found that even a slight increase in flexibility significantly sped up diffusion of stiff filaments under confinement. The rotational diffusion constant grew linearly with the flexibility and, counterintuitively, did not depend on the degree of crowding.
Moving Walls The current-induced movement of magnetic domain walls in magnetic nanowires is a candidate for a new architecture in logic processing and memory. Controlling the motion and position of the domain walls as they move along the wires in excess of 100 meters per second requires an understanding of the processes involved. Thomas et al. (p. 1810) investigated the dynamics of magnetic domain wall motion, looking at the acceleration, constant motion, and deceleration processes in detail. The whole process could be described in terms of the inertia of the domain wall. The distance traveled was simply proportional to the length of the current pulse used to move the wall, which should simplify implementation in a circuit or network architecture.
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CREDITS (TOP TO BOTTOM): N. KEVITIYAGALA, SOURCE: MÉTIN ET AL.; CHUEH ET AL.
A Gutsy Analysis
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In the developing mammalian brain, new neurons are not always born where they are needed. In order for immature neurons of the mouse cerebellum to leave their birthplace in the germinal zone and find their functional niche in the brain, the neurons need to migrate. Famulski et al. (p. 1834, published online 25 November; see the Perspective by Métin and Luccardini) now show that ubiquitin-mediated protein degradation regulates development of specific cell adhesions that the neurons need in order to exit their birthplace en route to their final functional location.
This Week in Science
Extraterrestrial Atmosphere The detection of oxygen in the atmospheres of Jupiter’s icy moons, Europa and Ganymede, and the presence of this gas as the main constituent of the atmosphere that surrounds Saturn’s rings, has suggested the possibility of oxygen atmospheres around the icy moons that orbit inside Saturn’s magnetosphere. Using the Ion Neutral Mass Spectrometer onboard the Cassini spacecraft, Teolis et al. (p. 1813, published online 25 November; see the Perspective by Cruikshank) report the detection of a very tenuous oxygen and carbon dioxide atmosphere around Saturn’s icy moon Rhea. As with other icy satellites, this atmosphere is maintained through the dissociation of surface molecules and ejection into the atmosphere as a result of Saturn’s magnetospheric radiation.
The complement system is an integral part of the innate immune system. When triggered, it initiates a cascade that marks intruders for elimination and stimulates immune responses. The key amplification step is cleavage of a complex comprising the complement fragment C3b and factor B (C3bB) by factor D (FD). Forneris et al. (p. 1816) now describe the crystal structure of C3bB and its complex with FD. The structures support a mechanism in which membrane-bound C3b stabilizes an open form of factor B (FB) that has its scissile bond accessible. FD binds through a site distant from its catalytic center to the open form of FB, which activates FD. The two conformational equilibria represent a double safety-catch that would allow tight regulation of this immune response pathway.
Exploiting Variation Molecular chaperones help newly synthesized proteins fold, protecting the macromolecular machinery of the cell from various stresses; for example, the highly conserved heat shock proteins (hsp) protect against elevated temperature. Hsp90 has also been suggested both to buffer against and to potentiate existing genetic variation in a population. To investigate the generality of these claims, Jarosz and Lindquist (p. 1820) screened 96 Saccharomyces cerevisiae strains from various ecological niches—soil, fruit, sake, beer, and infected humans—as well as assessed their adaptive value under different growth conditions. Hsp90 determined the adaptive value of ~20% of the genetic variation in baker’s yeast, with half of the traits being buffered, and half potentiated by hsp90.
Reds Versus Greens Self-incompatibility (SI) allows plants to prevent inbreeding. Crosses with distant relatives (outbreeding) can also be problematic and is prevented by unilateral interspecific incompatability (UI). In the nightshade family, SI functions within green-fruited species, whereas crosses between green-fruited and red-fruited species (which includes tomato) results in UI. Li and Chetelat (p. 1827) found a gene, related to known SI genes within this family, that differs in transcript length and function between individuals that are red-fruited and those that are green-fruited. A survey of species shows that the green-fruited species have a functional allele of this gene, whereas the transcript of this gene in red-fruited species, which are self-compatible, produce a putatively nonfunctional protein. These findings suggest that cultivated tomato may have lost the ability to pollinate other species within the same family, owing to the loss of this protein.
CREDIT: FORNERIS ET AL.
Mind Reading One core component of social cognition, especially of the kind practiced by humans, is the capacity to formulate a representation of what someone else believes to be true, even if that belief is not anchored in reality. Holding two such beliefs in mind—one false and one true—is no simple feat, and up until a few years ago, it was generally accepted that such a capacity did not arise until children were 3 to 4 years old. Since then, a flurry of studies, using a variety of interrogation measures, has suggested that much-younger humans might, in fact, possess this capacity, commonly referred to as a theory of mind. Kovács et al. (p. 1830) devised an ingenious behavioral paradigm and applied it both to adults and to infants, which suggests that the representations of others’ beliefs are indeed formed in the same way in adults and in infants. www.sciencemag.org SCIENCE VOL 330
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A Safety Catch on Immune Response
EDITORIAL
Model Organisms and Human Health
– Bruce Alberts
Published online 22 December 2010; 10.1126/science.1201826
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Bruce Alberts is Editorin-Chief of Science.
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the genomes of the two organisms—the fly Drosophila melanogaster and the nematode worm Caenorhabditis elegans—that serve as the best models for understanding the biology of all animals, including humans. Hundreds of scientists have collaborated in these two major studies, which have moved us far beyond the complete descriptions of the DNA molecules that make up the fly and worm genomes published a little more than a decade ago, an accomplishment that seemed amazing then. As summarized in the Perspective on p. 1758, the new findings reveal essentially all of the tens of thousands of RNA and protein molecules that each of these organisms produces, as well as how their genetic information is packaged. Extensive Web-based databases built on these data are freely available to everyone, greatly accelerating future discoveries. Strange as it may seem, this research, aimed at reaching a deep molecular understanding of how the bodies of a fly and a worm are formed and maintained, will be critical for improving human health. Most of the government funding for biomedical research in the United States is distributed through the National Institutes of Health. Its budget of $31 billion in 2010 reflects a widespread public appreciation that biomedical research will lead to great improvements in human health. Despite the many advances in our understanding of cells and tissues produced by this research, many diseases remain incurable. The disparity between the enormous amount now known about the chemistry and molecular biology of cells and our ability to intervene in human disease may seem incongruous to the public, but it is not at all surprising to the scientists involved: As we have learned more about how cells work, we have been surprised to discover how enormously sophisticated and complex are the processes that produce a human being. Consider just one example. Unlike a bacterium that keeps growing and dividing as long as food is available, each cell in an animal requires a position-detection system that causes it to proliferate only when more cells of its type are needed at its particular position in a tissue. An animal cell behaves as though it contains a tiny computer, assessing the many signals that it receives from its neighborhood and then deciding whether to maintain itself unchanged (its usual fate), grow and divide, or kill itself for the good of the entire cell collective. Powerful techniques such as those used in these two landmark studies can provide us with lists of all the molecules involved. But the crucial next challenge, thus far out of reach, is to decipher exactly how the elaborate networks of signaling molecules that exist inside a cell enable it to make its crucial decisions—a process analogous to cell “thinking.” Once scientists truly understand such processes, they will be able to create precise tools to correct harmful cell behaviors, as when cells multiply out of control in cancer or when they die inappropriately in degenerative conditions such as Alzheimer’s disease. The effort to use what we are learning about how cells and organisms work at the molecular level to improve human health is often called “translational medicine.” The ultimate success of this important endeavor will depend on gaining much more knowledge to “translate.” Because of the long evolutionary process that has given rise to the diverse array of animals that populate Earth, the molecules and mechanisms that produce humans, flies, and nematodes are nearly the same. But unlike humans, flies and worms can be experimentally manipulated, and they have short generation times that allow the complex mechanisms that form them to be deciphered with powerful genetic tools. And thus we find ourselves in a surprising position: As incredible as it seems, future research on flies and worms will quite often provide the shortest and most efficient path to curing human disease.
CREDITS: (TOP) TOM KOCHEL; (LEFT) WIKIMEDIA COMMONS
IN THIS ISSUE OF SCIENCE, WE HIGHLIGHT THE IMPRESSIVE EFFORTS TO DESCRIBE AND ANALYZE
EDITORS’CHOICE EDITED BY KRISTEN MUELLER AND JAKE YESTON
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setting in which the aftermath of a breach of trust can be studied. De Cremer et al. show that when students in the role of player 1 were treated unfairly (for instance, when receiving only €5), they judged an apology from player 2 as being less valued and less apt to induce reconciliation when the apology was actually received in comparison to a scenario where they only imagined receiving one. This disconnect also influenced their behavior; the students who received the apology were less trusting in a second round of the game, whereas those who had imagined the receipt of an apology were more willing to resume a trusting stance. Why? One possibility is that mental simulation may have enabled a more effective repair of social status via a public acknowledgment of the transgression. — GJC Psychol. Sci. 10.1177/0956797610391101 (2010). PHYSICS
BIOMEDICINE
CREDITS (TOP TO BOTTOM): COURTESY THE ALICE EXPERIMENT AT CERN; DUDA ET AL., PROC. NATL. ACAD. SCI. U.S.A. 107, 21677 (2010)
Medium-Sized Bang Most models that strive to describe the state of our universe after the Big Bang predict the existence of the quark-gluon plasma microseconds after the beginning of time. High temperatures are thought to have supported a state wherein the constituents of atomic nucleons—quarks and gluons—existed unbound. In an effort to recreate such conditions, researchers earlier collided gold ions using the Relativistic Heavy Ion Collider (RHIC) and found that the state of matter they created behaved much more like a liquid than a gas. Now, similar experiments have been carried out using the Large Hadron Collider (LHC) to collide lead ions at even higher energies than those achieved at RHIC. The results were collected and analyzed by two groups. Aamodt et al. (ALICE collaboration) found that at these higher energies and temperatures, the quark-gluon plasma still behaves like a (nearly perfect) liquid, implying that it is a strongly interacting system. This conclusion was further corroborated by Aad et al. (ATLAS collaboration), who determined that jets of particles produced by the collisions in the plasma are strongly quenched by their interaction with the surrounding medium. — JS Phys. Rev. Lett. 105, 252302; 252303 (2010).
EVOLUTION
Dark Phase Dating Molecular phylogenies indicate that grasses that transform CO2 through the C4 photosynthetic pathway developed around 30 million years ago, long after the appearance of the first C3 grasses 60 million years ago or earlier. Fossil and isotopic records do not show the presence of C4 grasses until around 20 million years ago, however, leaving their date of origin poorly constrained. It has been suggested that the C4 pathway evolved in response to the rapid decrease of atmospheric CO2 from 1000 ppm to 500 ppm that occurred between 30 and 25 million years ago, as C4 photosynthesis confers a competitive advantage over C3 photosynthesis in low-CO2 conditions. Urban et al. present evidence that C4 grasses already were abundant in southwestern Europe 34 million years ago, before the concentration of
atmospheric CO2 began to fall in the early Oligocene. The authors measured the carbon isotopic composition of morphologically indistinguishable C3 and C4 pollen grains in order to determine to which metabolic group they belonged, taking advantage of the large difference in the carbon isotopic signatures that characterize the two photosynthetic pathways. The results thus indicate that factors other than decreasing atmospheric CO2 concentrations must have driven the evolution of C4 photosynthesis. — HJS
Geology 38, 1091 (2010).
PSYCHOLOGY
Making Up Is Hard The trust game—(i) player 1 gives €10 to player 2; (ii) that amount of money is tripled; and (iii) player 2 decides how much of the €30 is given back to player 1—provides an experimental
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Travel Assistance Tumor metastases are a major cause of death from solid tumors. Evidence from preclinical models suggests that tumor cells do not metastasize alone but rather are assisted by specific host cells that modify the microenvironment of the target organ so that it can support the survival and growth of newly arriving tumor cells. Two independent studies of lung metastasis in mice converge on this theme. Kowanetz et al. show that tumor cells secrete granulocyte colony-stimulating factor, a protein that expands and mobilizes bone marrow cells of a specific type called Ly6G+Ly6C+ granulocytes and facilitates their homing into the lung before the arrival of tumor cells. Upon accumulation in the lungs, these granulocytes then secrete proteins that enhance the invasive properties of tumor cells, including matrix metalloproteinases and Bv8, a protein that stimulates tumor cell migration. Duda et al. provide evidence that the stability of circulating metastatic tumor cells is enhanced when they “cotravel” with stromal cells derived from the primary Stromal cells tumor, such as fibroblasts. (green) accompany Once these cellular clumps metastasizing tureach the lung, the stromal mor cells (red). cells appear to provide an early growth advantage to the tumor cells. Further exploration of the cells and signaling molecules identified in these studies could lead to therapies that prevent or inhibit metastases. — PAK
24 DECEMBER 2010
Proc. Natl. Acad. Sci. U.S.A. 107, 21248; 21677 (2010).
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RANDOMSAMPLES EDITED BY LAUREN SCHENKMAN
DIGITAL MAESTRO
CREDITS (TOP TO BOTTOM): RENSSELAER POLYTECHNIC INSTITUTE; NATURE BIOTECHNOLOGY 28 2010; NASA
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As a court battle rages in the United States over the legality of using taxpayer dollars for research on human embryonic stem cells (hESCs), a recent analysis has uncovered a surprising fact: The six U.S. states that fund this area now spend more on it than the federal government does. California, Connecticut, Illinois, Maryland, New Jersey, and New York launched stem cell
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States Pick Up the Slack on Stem Cells
Human Embryonic Stem Cell Research Grants
research funds after President George W. Bush limited which hESC lines could be studied with federal dollars in 2001. Aaron Levine, a profes-
Last week, a solar storm—a violent explosion from the sun’s surface also known as a coronal mass ejection—struck Earth 12 hours earlier than space scientist Chris Davis and colleagues had predicted. That would have been bad news for any astronauts relying on the forecast. But Davis, who works at Rutherford Appleton Laboratory near Didcot, U.K., was pleased: The prediction was based on data analyzed entirely by volunteers, and for the virtual team’s first effort, “half a day … isn’t bad,” he says. Davis and the Royal Observatory, Greenwich, launched the Solar Stormwatch project (http://solarstormwatch.com) on the citizen-science site Zooniverse in February. Since then, about 10,000 people have identified and tracked features in images captured by the Heliospheric Imager instruments on NASA’s twin STEREO spacecraft, which study solar activity. Davis suspects the time lag occurred because volunteers track the middle of a storm instead of the harder-to-spot front. A systematic correction, he thinks, can help future predictions rival those of the National Oceanic and Atmospheric Administration, which watches storms erupt from the sun’s surface, then calculates their arrival times (spot on for this storm) using a computer model. Bringing in about 50 images daily, all requiring human analysis, the imagers used to swamp Davis’s three-person team. Thanks to the Zooniverse volunteers, the researchers can now chronicle the sun’s current state nearly in real time, he says: “I feel very privileged having something like 10,000 research assistants.”
“
THEY SAID IT
“Space and dinosaurs are the two things that turn kids on more than anything else. If we could grow dinosaurs on the space station, we’d have this thing nailed.” —Advice on how to get more young people into science from Mark Uhran, a NASA director. Uhran spoke at a 10 December meeting about the international space station at NASA headquarters in Washington, D.C.
sor of public policy at the Georgia Institute of Technology in Atlanta, compiled a Web database (www.stemcellstates.net) of the nearly 750 grants totaling $1.25 billion that states disbursed for adult and embryonic stem cells from 2005 through 2009. Counting only research grants, since 2007 states have spent at least as much as the U.S. National Institutes of Health (NIH) on hESCs (see graph), Levine’s team reports in a letter published online 7 December in Nature Biotechnology. At least two-thirds of the scientists had no NIH grants for hESC work before 2007. That means if funds dry up in states like California, which leads the pack in funding (see p. 1742), it could hit some researchers hard, Levine says: “I think there’s a risk of some upheaval.”
Storm Chasers
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Making music can often become a battle of egos. Now a group of musician-scientists at Rensselaer Polytechnic Institute (RPI) in Troy, New York, has a solution: a computer program that decides how each musician will contribute. Saxophonist-acoustician Jonas Braasch and his colleagues wrote the program to aid them with their unusual improvisation sessions, in which groups of five or more musicians in up to four places around the world jam via the Internet. To keep everyone in sync, they tried having one musician conduct the troupe. But unfortunately, even with video monitors showing the remote groups, conductors kept favoring performers within their own groups. To overcome the human bias, Braasch went digital. With his RPI colleagues, including electronic composer Pauline Oliveros and research specialist Doug Van Nort, Braasch combined predictive algorithms similar to those used in speech recognition software with adaptive, genetic algorithms to create software that understands features such as musical timing and that can experiment with combinations of instruments. The program conducts the group via symbols that the musicians view on computer monitors as they play. “The conductor makes intelligent decisions and knows which direction you want to take,” says Braasch. After a performance, the musicians can log what they think of the conductor’s choices. ”That’s the way it learns,” he says. Once the program gains mastery, it could adopt the arrogance of real conductors: It’s also designed to give humans feedback on their performances.
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Montagnier to study “magnetic waves” from DNA
Q&A with author of arsenic bacteria paper
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Polio Outbreak Breaks the Rules Polio is a horrendous disease, but it is seldom fatal—except now. An explosive outbreak in the Republic of Congo is writing another chapter in the book on how this ancient scourge behaves. Polio usually strikes children under age 5, paralyzing one in 200 of those infected and killing at most 5%, occasionally up to 10% in developing countries. The new outbreak tearing through this West African country has so far killed an estimated 42% of its victims, who, in another unusual twist, are mostly males between the ages of 15 and 25. Since it began in early October, the outbreak has paralyzed more than 476 people and killed at least 179, according to World Health Organization (WHO) estimates from early December, making this one of the largest and deadliest polio outbreaks in recent history. And one of the most mystifying, too, says polio expert Neal Nathanson of the University of Pennsylvania: “There are too many things that don’t fit or are unexpected.” “We are scratching our heads,” says Bruce Aylward of WHO in Geneva, who runs the troubled 20-plus-year, $8 billion global program to eradicate polio (see p. 1736). When cases of acute flaccid paralysis first cropped up among adults a couple of months ago in the oil-rich city of Pointe-Noire on the Atlantic coast, no one suspected polio. The Republic of Congo—also known as CongoBrazzaville to distinguish it from the Democratic Republic of Congo, its larger and
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unrulier neighbor to the east—had rid itself of polio in 2000 through countrywide campaigns to vaccinate each and every child. Since then, routine immunization has kept CongoBrazzaville polio-free, even when outbreaks swept neighboring Angola and D.R.C. “It was not considered at high risk. That is why we were all surprised,” says Mark Pallansch, who is leading efforts to analyze the virus at the U.S. Centers for Disease Control and Prevention (CDC) in Atlanta. “Because this is not a typical outbreak that occurs in children, people initially looked for another cause,” Pallansch says. Unfortunately, that meant appropriate CONGO DEMOCRATIC REPUBLIC OF THE CONGO
ANGOLA
Wild poliovirus type 1 Cases of paralysis Brazzaville
CONGO Pointe-Noire Cabinda
Kinshasa DEM. REP. OF THE CONGO ANGOLA
One hop. The virus jumped from Angola to Congo.
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INFECTIOUS DISEASE
fecal and other samples were not collected at the outset, which has hindered subsequent efforts to reconstruct exactly what happened, he says. Even now, samples are just trickling in, and many are of very poor quality, he says. As a result, the polio labs in Kinshasa, Johannesburg, and CDC have virologically confirmed only about two dozen of the suspected cases—and some will never be confirmed, Pallansch says. Genetic analysis has determined that the culprit is a wild poliovirus type 1 that somehow jumped from northwest Angola to Congo in a single importation. That virus, in turn, came from India several years ago and has been circulating in Angola ever since—and has also recently spread from there to D.R.C., where a separate and distinct outbreak is under way. In Angola and D.R.C., it is behaving like “garden variety” polio that strikes young children, says Nathanson. “If it is the same type 1 from Angola and India, how can it be behaving so differently” in Congo? he asks. Nathanson wonders whether something in addition to polio is going on in CongoBrazzaville—perhaps a simultaneous outbreak of another deadly virus with another route of transmission, although searches for other strange viruses have so far come up empty. Until more research is complete, “I reserve judgment as to what is going on,” he says. CDC and WHO have rushed in teams of epidemiologists to help country authorities investigate. Outbreaks among adults are not unheard of—one in Namibia in 2006 was traced back to inadequate routine vaccination some 16 years earlier. But there had been no such breakdown that anyone knew of in Congo. Looking back, says Pallansch, there was some political instability in the mid-1990s around Pointe-Noire. “So maybe there was a disruption in vaccination during that time to explain the adult cases. Maybe, but it doesn’t line up nicely as it did in Namibia,” he adds. And what explains the concentration of cases in males— more than 67% of all cases? To Aylward, that’s the most interesting question. As for why it is so deadly, Pallansch says there are a couple of possibilities. When polio does strike adults, it tends to be more severe, progressing more often to the bulbar form of
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Full assault. The Republic of Congo is vaccinating adults as well as children.
China’s faked fossils
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prove it? It will be hard retrospectively to put all the pieces together.” All agree that the first priority is to snuff out the outbreak before the virus reinfects other countries. It is already spreading: Cases have been confirmed in the capital, Brazzaville, some 650 km to the east of Point-Noire, in Cabinda, a sliver of Angola that juts out into the Atlantic south of Congo, and in the adjacent province of D.R.C., Bas Congo. Massive emergency campaigns are under way to vaccinate the entire population, all ages, in Congo and in neighboring parts of Angola and D.R.C. They seem to be bringing the outbreak in check, says Pallansch: “The population is scared, so there is demand for the
vaccine.” Aylward predicts that the outbreak will be under control in 3 to 4 months, if there is enough money—in November, WHO and partners issued an emergency appeal for $23 million—and vaccination campaigns continue to go well. But there are no guarantees. Even if Aylward is right, the outbreak has raised a new, disturbing question, says Pallansch. Is the Congo-Brazzaville epidemic an anomaly, or does it suggest there are other polio-free parts of Africa with susceptible adult populations that would also be ripe for a explosive epidemic? “We don’t know where the susceptibles are,” says Aylward—“where the next Congo could be.”
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the disease, which leads to cardiac or respiratory failure. But that may typically happen in 10% of adult cases, or perhaps even 20%, says Nathanson, but nothing to rival the figure in Congo. One theory being investigated is that there is some confounding factor among those who died versus those who didn’t: “Did they have an underlying health problem? Were they all from the same location?” asks Pallansch. The other, disturbing possibility is that the Republic of Congo is in the midst of a much, much bigger polio outbreak and somehow the milder cases have been missed. “Everyone’s got an opinion. But there are few data,” says Aylward. “You can pull together a story, but will you ever be able to
Candidates for eradication
–LESLIE ROBERTS
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Court to Weigh University’s Decision Not to Hire Astronomer Is it possible to separate religious and scientific beliefs when it comes to evolution? A federal court will take up that question early next year in the case of Martin Gaskell, an astrophysicist who claims that the University of Kentucky (UK) denied him a job because he is an evangelical Christian. Pro-evolution advocates say the university was well within its rights. “It’s an employment law case,” says Eugenie Scott, executive director of the National Center for Science Education, an organization in Oakland, California, that lobbies to preserve the teaching of evolution in public schools. “Can an employer discriminate based on the scientific knowledge of an employee?” she asks. “Well, yeah.” But the case could be more complicated. “It’s a rather intriguing case,” says Ehrich Koch, an attorney in Minneapolis, Minnesota, who represented a school district whose reassignment of a biology teacher who declined to teach evolution was upheld. “It appears as though what the court is saying is both sides have arguments, and they may be able to prove their case.” On 23 November, Judge Karl Forester of the U.S. District Court for the Eastern District of Kentucky ruled that UK’s motivation for rejecting Gaskell “remains hotly contested” and needs to be examined by a jury. Gaskell is seeking damages for lost income and emotional distress. Gaskell, 57, had recently moved from
a nontenured position at the University of his e-mail, Gaskell says he is not a creationNebraska to a research fellow post at the ist or a subscriber to intelligent design, both University of Texas’s McDonald Observa- of which, to varying degrees, discount nattory when he applied to head Kentucky’s ural selection. However, his lecture notes new observatory in 2007. During the search cite work by astronomer Hugh Ross, who process, a UK committee member discov- embraces an old Earth, as geologists do, but ered an article on Gaskell’s rejects evolution as the guidpersonal Web site titled ing principle for life. “Modern Astronomy, the “I had no trouble with the Bible, and Creation.” natural selection process,” The article, based on talks Gaskell said in his deposition. Gaskell had given, “appeared But “when it comes to trying to blend science and religion,” to explain everything, and according to a brief filed by particularly the origin of life, the university. The dean of the … we just don’t have any satcollege decided that “since isfactory theory.” Gaskell’s viewpoint was disJennifer Wiseman, an cussed in a scholarly paper, astrophysicist who has known the committee should conGaskell professionally for sider whether his statements In court. It’s astrophysicist 20 years, says she doesn’t were ‘good science,’ ” accord- Martin Gaskell versus the Uni- consider Gaskell a creationversity of Kentucky in February. ist. “He doesn’t discount or ing to the brief. Gaskell, who was one of disbelieve evolution,” says three finalists, didn’t get the job, and in 2009 Wiseman, who directs the Dialogue on he sued the university. In its legal filings, UK Science, Ethics, and Religion program at says that although there were other reasons AAAS (which publishes Science). A reliGaskell wasn’t hired, “his apparent inability to gious scientist who cites ongoing puzzles in separate his personal or religious beliefs from evolution sets off more alarms than when an his scientific comments … raised concerns.” atheist makes the same point, she believes. In an e-mail to Science, Gaskell called The trial is scheduled to begin on 8 Febhimself an “old earth theistic evolution- ruary. On 1 March, Gaskell begins work as ist,” a label that deems evolution a tool God a professor at the University of Valparaiso used to develop life. In his deposition and in Chile. –JENNIFER COUZIN-FRANKEL
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NEWS OF THE WEEK
PARIS—Virologist and Nobel laureate Luc Montagnier announced earlier this month that, at age 78, he will take on the leadership of a new research institute at Jiaotong University in Shanghai. What has shocked many scientists, however, isn’t Montagnier’s departure from France but what he plans to study in China: electromagnetic waves that Montagnier says emanate from the highly diluted DNA of various pathogens. Montagnier, who won a 2008 Nobel Prize for his discovery of HIV, claims that those signals—which he described in two little-noticed papers in 2009—can reveal the bacterial or viral origins of many conditions, including autism and Alzheimer’s disease. The work could suggest novel therapies, he says. But Montagnier’s new direction evokes one of the most notorious affairs in French science: the “water memory” study by immunologist Jacques Benveniste. Benveniste, who died in 2004, claimed in a 1988 Nature paper that IgE antibodies have an effect on a certain cell type even after being diluted by a factor of 10120. His claim was interpreted by many as evidence for homeo pathy, which uses extreme dilutions that most scientists say can’t possibly have a biological effect. After a weeklong investigation at Benveniste’s lab, Nature called the paper a “delusion.” Science talked to Montagnier, who is founder and president of the World Foundation for AIDS Research and Prevention, last week. Questions and answers have been edited for brevity and clarity.
–MARTIN ENSERINK
Q: Why are you going to Shanghai? L.M.: I have been offered a professorship and a new institute, which will bear my name, to work on a new scientific movement at the crossroads of physics, biology, and medicine. The main topic will be this phenomenon of electromagnetic waves produced by DNA in water. We will study both the theoretical basis and the possible applications in medicine. Q: What exactly are these waves? L.M.: What we have found is that DNA pro-
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duces structural changes in water, which persist at very high dilutions, and which lead to resonant electromagnetic signals that we can measure. Not all DNA produces signals that we can detect with our device. The high-intensity signals come from bacterial and viral DNA. Q: What do you think are the potential medical applications? L.M.: I have found these signals coming from bacterial DNA in the plasma of many patients with autism, and also in most, if not all, patients with Alzheimer, Parkinson’s disease, and multiple sclerosis. It seems that the
bacteria we are detecting are coming from the gut. So it is quite possible that products from gut bacteria end up in the plasma and cause damage to the brain. The waves give us a biomarker to test for the presence of these bacteria, even when we can’t detect them with classical techniques like PCR. So when we treat these diseases with antibiotics, our hope is to see the pathogen disappearing. One idea is to set up a clinical trial in autism here in France. We will first show that we can detect bacterial DNA in the plasma of autistic children and not in a healthy control group. Then, if we get agreement from an ethical committee, autistic children can be treated with antibiotics to see whether the DNA signal disappears and their clinical condition improves. In the future, we may use these findings not just for diagnostics but also for treatment. It’s possible that electromagnetic waves at some frequency will kill the waves produced by bacterial DNA.
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Q: You have called Benveniste a modern Galileo. Why? L.M.: Benveniste was rejected by everybody, because he was too far ahead. He lost everything, his lab, his money. … I think he was mostly right, but the problem was that his results weren’t 100% reproducible. Q: Do you think there’s something to homeopathy as well? L.M.: I can’t say that homeopathy is right in everything. What I can say now is that the high dilutions are right. High dilutions of something are not nothing. They are water structures which mimic the original molecules. We find that with DNA, we cannot work at the extremely high dilutions used in homeopathy; we cannot go further than a 10-18 dilution, or we lose the signal. But even at 10-18, you can calculate that there is not a single molecule of DNA left. And yet we detect a signal.
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French Nobelist Escapes ‘Intellectual Terror’ to Pursue Radical Ideas in China
Q: Many of your colleagues seem to be extremely skeptical. L.M.: Well, I was skeptical myself in the beginning. But these are facts. The findings are very reproducible and we are waiting for confirmation by other labs.
Q: Can’t you pursue this research in France? L.M.: I don’t have much funding here. Because of French retirement laws, I’m no longer allowed to work at a public institute. I have applied for funding from other sources, but I have been turned down. There is a kind of fear around this topic in Europe. I am told that some people have reproduced Benveniste’s results, but they are afraid to publish it because of the intellectual terror from people who don’t understand it. Q: Are the Chinese more open to it? L.M.: I think so. I have visited Jiaotong University several times, and they are quite openminded. The editor-in-chief of [Interdisciplinary Sciences: Computational Life Sciences,] the journal in which I have published two papers on this topic, is based there as well. Q: Aren’t you worried that your colleagues will think you have drifted into pseudoscience? L.M.: No, because it’s not pseudoscience. It’s not quackery. These are real phenomena which deserve further study.
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ScienceNOW From Science’s Online Daily News Site
vast majority of icequakes are caused by calving events. That means remote observations of icequakes can serve as an early warning system for changes in calving patterns, says Larsen. And that could eventually tell glaciologists just how much ice a glacier is losing—a concern with a warming climate and the threat of sealevel rise. http://scim.ag/ice-quake
What Makes Glaciers Shake? A new study has uncovered the cause of most “icequakes,” seismic tremblings of the earth that can be felt up to 200 kilometers away. Glaciologist Chris Larsen of the Geophysical Institute in Fairbanks, Alaska, and colleague Shad O’Neel of the U.S. Geological Survey in Anchorage used 21 seismometer stations on the southeastern coast of Alaska to observe icequakes in the St. Elias mountains for a year and a half. As they will report in an upcoming issue of the Journal of Geophysical Research, 85% of the quakes originated at the ends of glaciers. Some seismologists had predicted that icequakes begin in the glacier’s interior, as the massive slab of ice slides across the ground. But given the location of the icequakes and the high rate of calving, or glacial ice falling into the sea, observed on the glaciers with the most seismic noise, the team suspects that the
Fearless Woman Lacks Key Part of Brain “SM” is a bit of an emotional anomaly. The 44-year-old mother, given those initials to preserve her anonymity, is pretty much fearless—and now scientists think they’ve figured out why. SM has interested researchers because a rare genetic condition called Urbach-Wiethe disease destroyed her amygdala, a pair of almond-shaped clusters of neurons in the brain that play a role in fear and anxiety. Justin Feinstein, a graduate student in clinical psychology at the University of Iowa in Iowa City, and colleagues wanted to test whether SM could experience fear. They recorded her reaction to snakes and spiders, horror films, and a trip to a notoriously scary haunted house. They also gave her an electronic diary that asked her to rate her current emotional state several times a day for 3 months. SM didn’t report feeling scared during her trials. Meanwhile, the emotion that received the highest average rating in her diary was “fearless.” The results suggest that “the amygdala is a critical brain region for triggering a state of fear when an individual encounters threatening stimuli,” Feinstein and his co-authors write in Current Biology. It’s the first human study to show that amygdala damage can wipe
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CREDITS (TOP TO BOTTOM): NASA/ESA AND THE HUBBLE HERITAGE TEAM (STSCI/AURA); HEMERA/THINKSTOCK; TAMÁS FARAGÓ/EÖTVÖS LORÁND UNIVERSITY, HUNGARY
Celestial Ornament This gossamer ring in the sky may look as light and lovely as a soap bubble, but its appearance belies its unimaginably violent birth: The shell of reddish gas is actually the remnants of a supernova explosion riding a shock wave and ripping through space at more than 18 million kilometers per hour. The supernova remnant, dubbed SNR 0509, was first spotted by the Hubble Space Telescope in October 2006. This newly released picture combines data from that 4-yearold image, which was taken only at wavelengths that highlight glowing hydrogen, with a visible-light image snapped just last month. http://scim.ag/red-glow
out fearful feeling, they say. But to prove it, say other researchers, the team needs to look at more than one subject. http://scim.ag/no-fear
A Dog’s Growl Announces Its Size Dogs can tell another canine’s size simply by listening to its growl, a new study reveals. Péter Pongrácz, an ethologist at Eötvös Loránd University in Hungary, and colleagues showed 24 canine test subjects images of two dogs projected onto a screen in front of them. One image showed a small dog less than 52 centimeters tall; the other image was of the same dog but projected to be 30% larger. The researchers then played recorded foodguarding growls—from either a large or a small dog—on a speaker placed between the two projected images. The scientists filmed the dogs, recording where the canines looked as they listened to the growls. Twenty of the dogs looked first and for a longer period of time at the dog whose size matched the growl, the team reports online in PLoS ONE. Matching a sound to a photograph is a complex cognitive talent previously seen only in primates, the researchers say. http://scim.ag/growl-size Read the full postings, comments, and more at http://news.sciencemag.org/sciencenow.
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NEWS OF THE WEEK Under attack. The media and scientists have deluged Felisa Wolfe-Simon with questions.
Discoverer Asks for Time, Patience Over Arsenic Bacteria Controversy Three weeks ago, Felisa Wolfe-Simon, 33, a former performance oboist with a doctorate in oceanography and a NASA fellowship in astrobiology, published a paper in Science about bacteria that can use arsenic instead of phosphorus in DNA and other biomolecules (http://scim.ag/arsenicpaper). Four days before the publication, NASA sent out a media advisory that it would hold a press briefing “to discuss an astrobiology finding that will impact the search for evidence of extraterrestrial life.” That led to wild speculations on the Web about extraterrestrial life, and when the paper was published, many headlines made the most of the “alien” nature of the discovery by Wolfe-Simon and her colleagues at the U.S. Geological Survey in Menlo Park, California. Then came a torrent of criticism by scientists. A highly critical blog post by Rosie Redfield, a microbiologist at the University of British Columbia, Vancouver, quickly drew hundreds of comments, many also finding fault with the study. WolfeSimon and her co-author Ronald Oremland then came under attack by journalists when they declined to respond to media calls for a response to these comments. On 16 December, the authors posted responses to some of the issues on http://scim.ag/arsenicresponse, and Science will publish technical comments
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and responses in early 2011. In the meantime, Wolfe-Simon agreed to share some of her thoughts in an interview with Science’s news department last week. The following has been edited for brevity; a longer version is available online at http://scim.ag/arsenicqa.
–ELIZABETH PENNISI
Q: How would you characterize your life since the press conference? F.W.-S.: Since the press conference, my life has been really busy and stressful. When the paper was accepted for publication, we told the Astrobiology Program and NASA. … And when they asked me to come in and talk about the paper, I said, “Sure.” I thought this would be great; I’ll bring the information to the public. Q: NASA approached you about doing a press conference, and you thought that was a good idea? F.W.-S.: I wouldn’t say I thought it was a good or bad idea. But we weren’t clearly prepared, in terms of understanding how it might be, again, with the new types of media that are really rather amazing, what was exactly going to happen. We thought that our findings would generate some discussion, but we didn’t anticipate the reaction we saw.
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Q: You answered questions at the press conference, but then after that, when did you stop talking to the press? F.W.-S.: For the press conference, I was prepared to talk about our findings reported in the paper. I did not show any data, nor did I describe the study as definitive. I was not giving a scientific talk, so I was really not prepared to engage in a scientific debate on that spot. Q: After Saturday [4 December], when [Rosie] Redfield’s blog came out, at least some journalists took a look at the paper again and wanted to talk to you. If my information is correct, that’s when you and NASA declined to talk to reporters anymore about this. Is that right? F.W.-S.: There are two issues. One is that, well, we wanted to be able to have that discourse in the scientific community, as a record. That’s the record, the literature record that we go back to—or has been up until now. So that was the one issue, and the other issue was the rapidness. We spent a lot of time really crafting our paper and crafting the SOM [supplemental online material] and crafting all the data, in terms of trying to show it as clearly as we thought. We wanted to give voice to that, in responses to these queries and some of the questions and issues brought up in the press, and we didn’t want to respond to it in a way that we thought would not give us the opportunity to think as deeply as we might need to. I was under a lot of pressure,
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Q: Why do you think you got the reaction that you did? F.W.-S.: I think maybe it has something to do with that there was some hype generated around it. I was receiving a lot of inquiries from all sorts of people—science journalists and scientists and other sorts of reporters—even before the paper went out under embargo. On Monday, NASA had sent out the media advisory, and it seemed to have people talking. And I thought, “Oh, we’re all talking about science.” You know, as a science communicator and a person, what I’d like to communicate is how passionate I am about science and understanding these fundamental properties and principles of nature. We, as scientists and other science communicators moving forward, need to understand how the Internet gives voice to things we can’t necessarily anticipate, and I think that that’s something I will think a lot more about.
NEWS OF THE WEEK
Q: Some researchers have suggested that it would be very easy to conclusively tell whether the arsenic is in DNA using different techniques. One was the cesium chloride density gradient ultracentrifugation. Did you do those tests? F.W.-S.: We’re aware of all these other techniques you mentioned. In fact, I have done a cesium chloride gradient experiment, and it showed what my gel showed: something unusual that we couldn’t quite explain. We could have waited until we did a really exhaustive selection of all of these alternative techniques, many requiring collaborations with groups well outside of our field, but instead, I and my co-authors, we wanted to provide a strongly suggestive and convincing argument to our community to initiate these new collaborations and really inspire other people to go out and do this. Q: Has anybody asked to collaborate or for your samples? F.W.-S.: Yes, absolutely. People have asked for cells, which we had been already working on getting to make available. That was the goal: to stimulate the discussion and [have] somebody say, “Hey, we have this technique; could we help you?” Q: But, do you have the capability of sending stuff out? F.W.-S.: We’re currently working to submit the bacteria to two culture collections so that they are available to all interested scientists. Our lab is not currently equipped to provide the cells at the scale needed to ensure fair access for everyone. We got requests from a lot of people. Q: It’s also been suggested that you didn’t wash the DNA of any arsenic that may be stuck to the DNA. What’s your comment on that? F.W.-S.: First, we take the cells, and we collect them by centrifugation, and we wash them very well. We did a standard DNA-extraction protocol, multiple phenol chloroform steps to remove all the impurities, including things like any free arsenic, which the washing will have removed. The DNA fraction that was used for further analyses, including things like PCR, would require highly purified DNA. So any of
those contaminants, if indeed they were there, would have been a problem [for PCR amplification]. So, we really don’t feel it’s a valid concern. Q: Do you think the other researchers are overstating how easy it is going to be to resolve this matter? F.W.-S.: I would immediately say, “If we’re lucky, it’s going to be very easy,” but I don’t think so. The cells are not easy to deal with. They’re kind of soft and fluffy, and they’re different. And so testing with the alternative techniques will fill in more pieces to the puzzle, and, again, no doubt will open up new questions. Q: Were you surprised by the personal tone of some of the criticism? F.W.-S.: I’ll be honest. Of course I was disturbed by the personal attacks, and I can only speak for myself right now. I’ve worked really hard on this project. I’ve solicited the advice and assistance from the top scientists across a variety of different fields—not just my co-authors, but many, many individuals. They’ve been incredibly generous with their time and expertise, and I’m deeply grateful to everyone who’s helped. Q: Are you going to start taking media calls again, or are you going to lay low for a while? F.W.-S.: That’s a hard question because, definitely, in talking to my co-authors, we want to get to work. We’re scientists, and it’s hard if all your time is taken up talking. I’m happy to explain the results, but there’s one thing, I think, to explain the results, and there’s another thing to be under what feels like an attack; it’s hard to do that. There’s only so many hours in a day. Q: It sounds like what you want to do is not really spend much time with the media right now. F.W.-S.: What we would want to iterate is that we’re thrilled that the public is talking about science. I think the media is an important part of the process. We absolutely don’t want to come off as evasive. We wanted the time to think. I think the physical volume at which questions and comments were coming in, I don’t know how others would respond. I mean, it was so much and so quick. In fact, during the press conference, I had a couple hundred, at least, e-mails coming in. I’m still on stage. I didn’t have my PDA with me. When I checked my e-mail later, they’re demanding, “Answer all my questions right now.” It’s really hard.
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From the Science Policy Blog The U.S. government should keep a close eye on the new field of synthetic biology, says a report by the president’s bioethics commission, which doesn’t think new regulations are needed. http://scim.ag/synthetic_bio_report A plan to use €1.4 billion in unused 2010 budget funds to fill a gap in 2012–13 caused by the ballooning costs of the ITER fusion reactor project in France has fallen apart. The ongoing dispute between the directly elected European Parliament and its 27 member states could raise the ultimate cost of the massive project and push it further behind schedule. http://scim.ag/ITER_budget_falters
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and I’ll be honest, I was exhausted. I would really be lying if I told you that the barrage of criticism didn’t hurt. It did. I know my colleagues in the community aren’t thrilled or happy about this delay, but, again, I’m really doing my best.
A new science law in Venezuela gives the government control over a large pool of tax money previously spent by companies on internal research projects or in collaboration with universities. Scientists fear that the changes could hinder progress across many fields. http://scim.ag/venezuela_law A South Korean court has knocked 6 months off of a 2-year suspended sentence to disgraced scientist Woo Suk Hwang while upholding his conviction. http://scim.ag/shorter_sentence Personnel moves: Harvard Provost Steven Hyman is stepping down in June to resume teaching and research. … Former NIH Director Elias Zerhouni will become head of research and development at French pharmaceutical giant Sanofi-Aventis. http://scim.ag/hyman_stepsdown http://scim.ag/zerhouni_job Beefed-up investments in malaria control are having a major impact, according to a new report by the World Health Organization (WHO), although some hard-hit countries are losing ground. … WHO needs a complete overhaul to remain relevant in the 21st century, says Jack Chow, a former high-ranking WHO official, in an online essay in Foreign Policy. http://scim.ag/antimalaria_gains http://scim.ag/WHO_overhaul For more science policy news, visit http:// news.sciencemag.org/scienceinsider.
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What’s Next For Disease Eradication?
IT WAS TIME, ONCE AGAIN, TO BASK IN the glory and share heroic tales. Late in August, approximately 260 scientists and public health leaders met in Rio de Janeiro to commemorate the 30th anniversary of what is often considered one of the major human accomplishments of the 20th century: the eradication of smallpox. Leaders of the global effort—many now in their 70s or 80s—reanalyzed the dramatic 2-decade fight to obliterate a virus that had killed countless millions of people. But many of those present in Rio wished that by now a younger generation of disease fighters would have similar victories under their belt and fresh tales to tell. Thirty years on, smallpox remains the only human disease to have been eradicated. Its demise inspired dreams that many pathogens might be wiped off the planet, and two eradication campaigns were launched in its wake. But neither has finished, and many are now questioning whether such global operations—which require extraordinary amounts of devotion and money—make sense. That’s why, just 2 days after the commemorations ended in Rio, 30 scientists and public health experts from around the world gathered
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for a weeklong meeting in the German city of Frankfurt am Main* to try to chart a new path for disease eradication in the 21st century. Their meeting was triggered by several developments. Interest in tackling global health problems has surged the past Ernst Strüngmann Forum on Disease Eradication in the Context of Global Health in the 21st Century, Frankfurt am Main, 29 August–3 September. *
SMALLPOX Agents: Variola major and minor Reported cases (since 1978): 0 Smallpox killed an estimated 2 million people a year—and grossly disfigured millions more—before 1959, when hen a worldwide eradication campaign kicked off. Victory was declared in 1980. Two labs, one in Russia and one in the United States, are allowed to retain the virus, but fears of illicit stocks linger.
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decade—as has funding—but the two ongoing eradication campaigns have proven far more difficult than predicted. In 1986, the World Health Assembly called for the eradication of the painful and disfiguring guinea worm disease; 1995 was chosen as the target date a few years later. In 1988, polio received a similar death sentence, to be carried out by 2000. The deadlines came and went, and although numbers of cases have plummeted, both pathogens are still with us. Polio is currently on a demoralizing rampage through central Africa and has struck anew in Tajikistan, fueling more doubts about its demise. Meanwhile, a key rationale for past eradication efforts—the promised financial windfall from stopping all control measures once a disease is gone—all but disappeared as a result of 9/11 and the 2001 anthrax letters. Wealthy countries in particular are determined never to let their guard down against diseases like smallpox, polio, or measles. Meanwhile, developing countries have their own questions: Why should they keep spending inordinate amounts of time and money on a disease such as polio—now down to fewer than 2000 cases a year— while their health systems are struggling
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Despite major setbacks, the idea of wiping entire diseases from the face of the planet hasn’t lost its appeal. But the rules of the game have changed
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with far more devastating diseases such as AIDS and TB? And yet, getting rid of a disease once and for all will never lose its appeal, says Walter Dowdle, a consultant for the Task Force for Global Health in Atlanta. Eradication campaigns offer the inspiring promise of perpetual benefits and the chance to write health history. The 2007 call by Bill and Melinda Gates to eradicate malaria, for instance, has reenergized many of those working on the disease—even though the couple was criticized for using the term prematurely.
POLIO Agent: Poliovirus type 1 and 3 Reported cases (2009): 1604
CREDITS: MICROSCOPIC PHOTOS (TOP TO BOTTOM): MARY NG MAH LEE/CDC; CDC (2)
Two decades into the polio-eradication on campaign, the annual number of children paralyzed by the disease has come down by more than 99%. But scientific and political problems make the home stretch frustratingly difficult, and some have suggested calling it quits.
So the Frankfurt meeting sought a new way forward. The participants, many of them involved in past and current eradications, believed that eradication campaigns should continue. But “proceed with caution” could have been the motto of the 44-page draft report cobbled together on the 5th and 6th day. Future eradication campaigns “will be put under the microscope in a way that smallpox or polio never were,” says Stephen Cochi of the U.S. Centers for Disease Control and Prevention (CDC) in Atlanta, who co-organized the meeting and has been heavily involved in the polio campaign. New eradication plans must be more evidence-based than the old ones, participants concluded. There should be an analysis of economic costs and benefits, a thorough funding plan, and new financial tricks to prevent perennial budget gaps like those hampering the polio campaign. Any new eradication program should also help poor countries build stronger health systems along the way, the report said. A smart com-
munication plan that reaches out to marginalized populations is key. Much of that was never done—or done on the fly—for polio, says Cochi. “We were wellmeaning but totally naive,” he says. “We built the boat as we sailed,” adds T. Jacob John, a member of the India Expert Advisory Group for Polio Eradication. Annihilation The concept of disease eradication has a long history of high hopes and dashed dreams. Edward Jenner, the British doctor who pioneered the smallpox vaccine, realized the huge potential of his discovery. “It now becomes too manifest to admit of controversy,” he wrote in 1801, “that the annihilation of the Small Pox, the most dreadful scourge of the human species, must be the result of this practice.” It would take 180 years—but at least it came true. In the meantime, several other eradication efforts died, and their ghosts haunt the field to this day. The problem was often that scientists underestimated the problem. In 1955, the World Health Assembly endorsed a plan to eradicate malaria through an aggressive mosquito-control program that relied on spraying DDT inside homes. In the 1960s, it became evident that the strategy wouldn’t work, in part because the insects began to develop resistance against DDT, an insecticide that came under fire from environmentalists as well. Eventually, political will and money ran out. The 1952-to-1964 attempt to eradicate yaws, an easily treatable disease caused by the spirochete Treponema pallidum pertenue that leads to disfiguring skin lesions, met a similar fate. The smallpox campaign proved that eradication was feasible given the right tools— in this case, a very effective single-dose
GUINEA WORM DISEASE Agent: Dracunculus medinensis Reported cases (Jan.-Sep. 2010): 1626 Before an eradication campaign began in 1986, 20 African and Asian countries had an estimated 3.5 million cases of this painful infection, also known as dracunculiasis. Now only a few thousand cases a year occur in four countries; southern Sudan, home to more than 95% of them, is set to become the final battleground.
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SARS Agent: The SARS coronavirus Reported cases (since 2004): 0 SARS, a highly fatal respiratory infecection that erupted in China in November er 2002, was mopped up 8 months later thanks to intense global efforts to isolate patients and quarantine their contacts. Nobody called it an eradication campaign at the time—but in hindsight, some scientists say, why not?
vaccine—and offered enthusiasts a fresh argument to forge ahead with new pathogens. The experiences since then with polio and guinea worm have provided new lessons in modesty—and new pitfalls to avoid. With polio, like with malaria, part of the problem is that the tools aren’t working as well as scientists believed. The oral polio vaccine is much less effective than the smallpox vaccine, especially in some regions of the world. And while it’s easy to recognize a smallpox case—and roll out a mop-up campaign to protect everyone in the area—polio can cause silent outbreaks; hundreds of children can be infected before one develops paralysis or dies. On top of that came what smallpox veteran William Foege calls “people problems.” War and civil unrest have sidetracked vaccination campaigns, and in 2004, suspicions about vaccine safety led to a vaccine boycott in northern Nigeria that sparked new outbreaks in 20 polio-free countries (Science, 6 February 2009, p. 702). This year, there have been 458 cases in Tajikistan—the first epidemic in the region since it was declared polio-free in 2002—and an explosive new outbreak in the Republic of Congo (see p. 1730). The battle has been so difficult that some suggested in 2006 that it was time to throw in the towel and just settle for keeping the disease in check. The eradication of guinea worm disease, led by the Carter Center in Atlanta, has had fewer setbacks; it relies on a simple change in human behavior, a strategy that has worked everywhere. Guinea worm larvae are ingested via contaminated drinking water, and they make a very painful exit, usually from the lower leg, a year later. Teaching people to filter their water and preventing those with an exiting worm from walking in sources of drinking water can interrupt transmission.
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Forever gone. An Ethiopian child is vaccinated in 1976 during the final stage of the smallpoxeradication campaign.
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LYMPHATIC FILARIASIS Agents: Wuchereria bancrofti, Brugia malayi, and Brugia timori Estimated number of people infected: 120 million use Three mosquito-borne nematodes cause LF, often called elephantiasis because of some patients’ horribly swollen limbs. A global alliance is coordinating annual rounds of mass treatment using drugs donated by GlaxoSmithKline and Merck. The target: interrupt transmission globally by 2020.
Inflammatory word One direct effect of these sobering experiences is that some prefer to avoid the word eradication in order to keep expectations low. That’s the case for the global campaign to wipe out lymphatic filariasis (LF), a mosquito-borne parasitic infection that afflicts an estimated 120 million people in more than 80 countries. LF is also known as elephantiasis for the grossly swollen limbs and scrotal sacs that it can cause. Five or six annual rounds of mass drug administration often stop transmission in affected regions, and that’s the main strategy a global partnership is using against the disease. But its stated goal is “global elimination,” not eradication, which some dismiss as a semantic difference (see sidebar). “You don’t want to use a word that is so inflammatory if you want your program to run smoothly,” says Eric Ottesen, who heads the Lymphatic Filariasis Support Center at the Task for Global Health in Atlanta. At the meeting, participants offered many recommendations to deal with the tougher climate. Some are already being
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applied in the attempt to Scientists’ New Eradication Target: put measles on the path A Word in Their Lexicon toward eradication, says CDC’s Cochi, who is Malaria has been eradicated from Europe. True or false? involved in those plans. True—malaria no longer occurs in Europe, except for “imported” Measles is an obvious cases, of course. Yet most scientists would argue that the statement next candidate because is wrong because eradication, by definition, means that a disease is it kills hundreds of thougone from the entire planet. When talking about one or more counsands of children, and tries, or even a whole continent, you have to use the second E-word: there is a very effecelimination. Smallpox has been eradicated. Malaria has been elimitive vaccine that works nated from Europe. even after a single dose. At least until now. The semantic distinction, adopted at a 1997 Stepped-up vaccinalandmark meeting on disease eradication in Berlin, has often tion efforts have led to a caused confusion, and participants at a recent follow-up meeting in plunge in global mortalFrankfurt—including some who were present 13 years ago—proity since 2000. But reimposed doing away with it. Their proposal, already contested, would portations occur fremake the assertion at the top of this story perfectly fine. quently in measles-free The debate over what constitutes eradication has been going on countries, and this year for decades. In trying to draw up a clean set of definitions, the 1997 has seen an upsurge in forum, one in a series of brainstorming sessions called the Dahlem Africa. Workshops, said that “elimination” was to become the word for anyAt WHO’s request, an thing less than the global scale, while “eradication” was reserved for expert panel has studied pathogens completely gone from the planet—except any remaining the feasibility of measles stocks in lab freezers, as is the case with the smallpox virus. (Once eradication; as part of the analysis, two independent teams have done an extensive economic analysis. Their reports, international agencies, nongovernmental presented at a July meeting in Washington, organizations, and national governments— D.C., concluded that eradication would cost should also have a solid business plan, the an estimated $7 billion to $14 billion but Frankfurt meeting concluded. This “investwould be cost-effective. ment case,” as some call it, would outline Such studies are controversial because the rationale for a disease campaign, along they rely on mathematical models, which with the risks and obstacles, to policymakers, can be a poor proxy for the real world. Wars funders, and drug- and vaccinemakers. The or political problems can add billions to the report also argued for innovative financing final tab. Still, they’re better than nothing, schemes. Because the benefits of eradication says Cochi—and they force scientists to accrue over generations to come, government take into account the new reality in which could issue bonds, for instance, just as they control measures will continue. After small- do for major capital investments such as pox was eradicated, vaccination programs roads or railways. around the world were halted, which saved billions of dollars. Today, security experts MEASLES shudder at the notion of entire populations Agent: The measles virus vulnerable to fast-spreading diseases like Estimated annual number of cases: polio or measles, and the assumption is 10 million that many countries will keep vaccinating The Americas eliminated measles in against them no matter what. 2002, and cases elsewhere have dropped ed precipitously as a result of vaccination campaigns— Both of the measles studies showed that although this year, there’s been a wave of new eradication would pay off even if current outbreaks. An expert panel advising WHO has vaccination schemes continue. Based on green-lighted eradication, but many argue it’s better to finish polio first. these and other findings, the expert group recommended in August that measles “can and should be eradicated,” and the World Health Assembly may adopt a resolution to go ahead at its next meeting, in May in Geneva. Eradication bonds A cost-benefit analysis is one step, but advocates of eradication—often an alliance of
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Although the guinea worm campaign has taken longer than anyone expected, today it is getting tantalizingly close; so far in 2010, there have been fewer than 1700 cases in four countries, down from an estimated 3.5 million cases in 20 countries 2 decades ago. More than 95% of those cases occur in southern Sudan, however, a region expected to vote for independence in a January referendum. If that leads to a new civil war, as some expect, it could mean new delays, says Donald Hopkins of the Carter Center, who has led the campaign since its inception.
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says. (Even worse, many say, is the ill-defined term “elimination as a public health problem.”) That’s why Dowdle and Stephen Cochi of the U.S. Centers for Disease Control and Prevention want to drop the e·rad·i·ca·tion (i- r˘ad i-ka-y’ shuhn) noun 1. The absence of disease in a term “elimination.” Talk about national, regional, and defined geographical area as a result of deliberate control efforts. global eradication from now on, they suggested in a discussion paper for the Frankfurt meeting. The proposal sparked yet more debate. Some called it those are destroyed as well, the third E-word applies: extinction. But this an inevitable correction of a historic mistake. “Let’s simply eradicate the word has never happened.) elimination,” quipped Ciro de Quadros, the former head of the Pan American The distinction created trouble from the start, recalls Walter Dowdle, a Health Organization. Others weren’t so sure. “Why upset a system of definiveteran of the polio-eradication effort. At a 1998 meeting in Atlanta, co- tions that has become firmly established in the medical literature?” they sponsored by the World Health Organization (WHO), polio pioneer Frederick wondered. And how exactly do you define national or regional eradication? Robbins said elimination was a vague, useless word. “What do you mean, we Those questions weren’t solved, but the proposal to eliminate the word ‘eliminated’ polio from the Americas? We eradicated it!” Dowdle recalls the “elimination” carried the day. In the process, participants agreed to also Nobel laureate yelling. “Fred’s words are still ringing in my ears.” drop the part of the 1997 definition of eradication that said that “all conAlthough scientists have generally adopted the distinction, confusion trol efforts can be stopped,” as bioterror worries have put an end to that has persisted. Journalists use the terms interchangeably. WHO often pref- promise (see main text, p. 1736). aces “eradication” with “global” or “worldwide,” which is clear but techniNot that this is the final answer. In a paper on measles elimination cally redundant. At meetings of Rotary International, whose members have published in the the 3 December issue of Weekly Epidemiological Record, donated more than $900 million for polio eradication, “I never use the for instance, WHO acknowledged the Frankfurt proposal in a footnote but word elimination,” says Robert Scott, chair of the group’s PolioPlus Com- firmly stuck to existing definitions of eradication and elimination. The conmittee. People don’t understand it, and it doesn’t inspire enthusiasm, Scott fusion is unlikely to be eliminated—or eradicated—anytime soon. –M.E.
RIVER BLINDNESS Agent: Onchocerca volvulus Estimated number of people infected: 18 million
CREDITS: MICROSCOPIC PHOTOS: CDC (2)
A problem primarily in Africa, river blindness, or onchocerciasis, is caused sed by roundworms and transmitted by black ck fli flies. An international control program relying on mass treatment has been more successful than expected, and some suggest going full throttle for eradication later this decade.
Nor can future eradication campaigns afford to bypass poor countries’ broader health concerns, like diarrhea or respiratory disease, which kill far more children, the group concluded. The relentless focus on one disease has fueled resistance to the polio campaign, for instance, in Nigeria and India. Governments of developing countries are rightly wondering whether their sacrifices for a global public health goal make sense, says Stewart Tyson, a consultant at Liverpool Associates in Tropical Health in the United Kingdom—and they should give any new eradication plan a “good grilling” before signing on, he says.
Eradication programs should not hurt existing health services by siphoning away money and effort from basic health services for an increasingly rare disease, the Frankfurt report says—and to the extent that they can, they should have a broader benef icial effect. Current eradication efforts have tried to do this. Polio vaccination has sometimes been combined with dispensing vitamin A tablets or distributing bed nets against malaria. But everyone at the Frankfurt meeting agreed that it’s not the job of an eradication campaign to fix a broken health care system. And ultimately, Dowdle points out, eradication campaigns need to be focused if they are to have any chance of success. More candidates There are other candidates for eradication, despite the more demanding environment. One is the rubella virus, which causes severe malformations in newborn babies. The total burden is considered too low to warrant a standalone eradication campaign, but rubella could piggyback on measles eradication since the vaccines are combined easily, advocates say. Some also see chances for onchocerciasis, also known as river blindness, and perhaps schistosomiasis. Yaws, which has already been eliminated from India, could once again become a candidate. But these ideas’ fortunes depend in part
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on the success—or failure—of the current campaigns. WHO is reticent to embark on a measles-eradication campaign as long as polio isn’t finished, says Cochi; it worries that two simultaneous campaigns would be too much.
MALARIA Agent: Plasmodium spp. Estimated number of cases in 2009: 225 million A flopped campaign in the 1950s made ade malaria eradication a dirty word. Now, Bill and Melinda Gates have revived the buzz. And although most scientists say global eradication is decades away, countries on the fringes of the malaria map are busy ridding themselves of the parasites.
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e·rad·i·ca·tion (i- r˘ad i-ka-y’ shuhn) noun 1. Permanent reduction to zero of the worldwide incidence of infection caused by a specific agent as a result of deliberate efforts; intervention measures are no longer needed.
Such ambivalence is one reason why many eradication enthusiasts say giving up on polio is not an option. “I’m very worried about polio,” says Hopkins. “It must succeed. If it didn’t, it would be a big setback for the whole concept of eradication.”
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–MARTIN ENSERINK
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NEWSFOCUS Crack me up. Archaeoraptor turned out to be a bird-dinosaur chimera, not a missing link.
A booming fossils market has resulted in a flood of “improved,” reconfigured, and composite specimens; many are finding their way into China’s museums BEIJING—Frozen in time, the 5-meter-long ichthyosaur embedded in dark limestone seems to be darting after prey in a turbid Triassic sea. But look more closely at the startlingly lifelike skeleton here in the Geological Museum of China, and you will see that something isn’t quite right. The beast’s lower jaw and shoulder girdle are visible, which requires a ventral view—but the lower body is a lateral-dorsal view. Such an odd juxtaposition can mean only one thing, says Li Chun, a marine reptile expert here at the Institute of Vertebrate Paleontology and Paleoanthropology (IVPP): The centerpiece of the museum’s prehistoric life exhibit is a composite of two individuals, and possibly more. Specialists and collectors around the world have long decried the flood of sham fossils pouring out of China. But Science has learned that many composites and fakes are now finding their way into Chinese museums, especially local museums. “The fake fossil problem has become very, very serious,” says Peking University paleontologist Jiang Da-yong. Li estimates that more than 80% of marine reptile specimens now on display in Chinese museums have been “altered or artificially combined to varying degrees.” Geological Museum officials are not inclined to remove the fishy ichthyosaur,
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from southwestern China. “Farmers prepared the specimen. They might have made some mistakes when they put it together, but it is not a fake. You can call it a kind of model,” says Lu Liwu, director of prehistoric life research at the museum. Nevertheless, Li is concerned about what he deems a misleading display. “This is a national museum,” he says. Another Geological Museum exhibit, a pair of huge dinosaur eggs embedded in siltstone, is also a sham: Large chunks of the shell are not the original material, says Li. Lu says the museum intends to add signage to clear up any misconceptions. Outside of Beijing, curators are not so conscientious. Chinese and Western paleontologists concur that many provincial museums are chock-full of composites, chimeras, and other phony fossils. But several contacted by Science said they are reluctant to speak out. “We would seriously jeopardize our own opportunities to work with our Chinese colleagues on very important material,” says one Western paleontologist, who requested anonymity. One consequence of the fakery is an erosion of trust in museums, which are supposed to enlighten—not con—the public. Scholars, too, pay a price: They waste time sifting authentic specimens from counterfeit chaff.
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High fidelity. Zhao Lijun’s exhibition in Zhejiang has won praise for its authenticity.
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Altering the Past: China’s Faked Fossils Problem
CREDITS (TOP TO BOTTOM): COURTESY OF ZHOU ZHONGHE; R. STONE/SCIENCE
PA L E O N T O L O G Y
And a genuine blockbuster fossil can be destroyed by attempts to enhance its appeal. “A fake part in a fossil ruins the value of the entire specimen,” says Ryosuke Motani, a paleontologist at the University of California, Davis. “Even though the genuine part of the same specimen may provide important information that is otherwise unknown,” he says, “skepticism emerges as to whether we can trust it or not.” “Normally we know right away if a fossil is fake, although it can take some time to be sure,” says IVPP Director Zhou Zhonghe. But fraudulent specimens can end up in the peerreviewed literature. For example, the holotype—for which a species is named—of Typicusichthyosaurus tsaihuae, a marine reptile from southwestern China, is “a forged specimen” with carved features, says Motani. (Li and other paleontologists agree with that analysis; the team that described the species could not be reached for comment.) More controversially, an IVPP paleontologist asserts that a 2009 report in the Proceedings of the National Academy of Sciences (PNAS) describing a new species of early cheetah is based on a forged skull; he has demanded a retraction. The authors insist the skull is authentic and stand by the report, as has the journal. Stamping out sham fossils will require a crackdown on how fossils are collected and sold in China. A new law that comes into force next month aims to protect fossils of high scientific value (Science, 17 September, p. 1453). But experts doubt that the law will pose a sufficient deterrent: Forging fossils is simply too lucrative, they say.
CREDIT: R. STONE/SCIENCE
NEWSFOCUS on mammalian fossils, says he concurs with Deng’s opinion that the skull is a composite and that the paper should be retracted. Mazák, whose birth name is Huang Ji, told Science that the skull is genuine and that Deng’s concerns amount to a “scientific dispute” because the PNAS paper did not cite Deng’s 2004 description of a primitive cheetah from Linxia, Sivapanthera linxiaensis. Mazák declined to explain how he obtained the skull, and Christiansen, now at the Zoological Garden in Ålborg, Denmark, did not respond to requests for comment. In a 4 Feb-
resulted in sizable acquisition budgets and competition for prize specimens. In October, the Shanghai History Museum invited Zhao Lijun, curator of paleontology at the Zhejiang Museum of Natural History (ZMNH) in Hangzhou, to examine fossils it intended to purchase for an exhibition hall to open in 2012. Zhao identified a dozen specimens, including a 15-meter-long ichthyosaur, that were “totally fake,” she says. “When I told them the truth, they were astonished.” To its credit, Zhao says, the Shanghai museum canceled the deal. Many other museums, how-
Pièce de contrefaçon. IVPP’s Li Chun with a composite ichthyosaur at the Geological Museum of China.
ruary 2009 letter to Deng, PNAS declined to publish Deng’s letter and stated that his observations “can be explained by sloppy preparation, incomplete preservation of the skull, or as characters that differ from Deng’s expectations that are based on an a priori hypothesis of relationship or ancestry.” Deng says he has not pursued the matter further because Mazák has declined to give him access to the skull. The growing problem of faked specimens stems from China’s fossil economy. Most fossils, including prized specimens, are unearthed by farmers, who often gussy up specimens to make them look more complete or unusual and thus fetch a higher price. Some dealers are fooled, and some also engage in such chicanery, says Zhou. Few buyers are discerning. “For officials and businessmen, beautiful fossils can upgrade their reputation. For some researchers, strange fossils mean they may have a chance to publish in a top journal and get more funding and a higher position,” says Jiang. “In this hurried and blundering situation, anything may happen.” Exacerbating the problem is a recent boom in museum building across China that has
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ever, snap up fossils with inept or derisory expert advice. As a result, Li says, many “local museums are full of fakes.” One remedy is for museums to create closer ties with academics; few now have paleontologists on staff. A rare success is “Sea Monsters,” a yearlong exhibition of marine reptile fossils that wrapped up last month at ZMNH. Zhao joined IVPP’s Li and others in the field for a few summers to collect fossils for the exhibit; other specimens were on loan from IVPP. “Without IVPP’s cooperation, we would not have been able to do this,” says ZMNH Director Kang Xi Min. Another boost would be a training program for fossil preparators. And preventing fake fossils from contaminating the scientific literature, says IVPP’s Xu Xing, “can be easily avoided by careful and experienced scientists.” But Li and others admit they don’t have a strategy for combating the root of their ills: a legion of fakers assiduously despoiling China’s paleontological riches. “Our fossils are some of the best in the world,” says Li. “But they are being destroyed, and there is little we can do about it.” –RICHARD STONE
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The art of faking fossils has a long history. Perhaps the most infamous fraud is Piltdown Man, a skull, unveiled in 1912, that was touted as a missing link between humans and apes. It was exposed as a hoax in 1923, when a German anatomist determined that Piltdown was a chimera: a modern human skull and an orangutan jawbone. In another notorious case in the annals of bogus fossils, noted paleontologist Friedrich von Huene described in 1966 a juvenile Leptopterygius from Germany. Von Huene, 91 at the time, had not realized that the ichthyosaur was a total fabrication: Its “bones” were carved from the substrate. China, too, has suffered a Piltdown moment. “Archaeoraptor,” purported to be a missing link between birds and dinosaurs, made its debut in the November 1999 issue of National Geographic. Rumors that the skeleton, unearthed in northeastern China’s Liaoning Province, was a fake began to swirl even before publication, says Zhou. Archaeoraptor was later thoroughly discredited as a chimera consisting of the body of Yanornis martini— a primitive fish-eating bird—and the tail of Microraptor zhaoianus, a feathered dinosaur. Now, an IVPP paleontologist fears that a prestigious peer-reviewed journal has published a fake. In a 13 January 2009 report in PNAS, Per Christiansen of the Zoological Museum in Copenhagen and Ji H. Mazák of the Shanghai Science and Technology Museum presented a nearly complete skull of a primitive cheetah, which they have said was unearthed from a fossil layer in Gansu’s Linxia Basin dated to around 2.2 million to 2.5 million years ago. Christiansen and Mazák describe a “unique combination of primitive and derived traits” that places the species, which they named Acinonyx kurteni, as “the most primitive cheetah known to date.” When IVPP’s Deng Tao saw the PNAS paper, he says, “I knew immediately the skull was a fake.” Deng says the published photos show that several features of the skull had been concocted from bone or plaster. For example, he wrote in a 16 January 2009 letter to PNAS, “the parietal area is glued by some bone pieces to imitate the skull of a modern cheetah, but the forger did not make the parietal crests.” With that one slip-up, Deng says, the forger “gave the game away.” According to Deng, who has collected fossils in Linxia every year since 1998, it is common there to encounter dealers peddling fake skulls. “Unqualified collectors are often cheated,” he wrote to PNAS. Because the paper’s “unfounded” conclusions are “based on a fossil forgery,” Deng urged the authors or the journal to retract the paper. IVPP’s Qiu Zhanxiang, an academician and top specialist
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CIRM: The Good, the Bad, And the Ugly
Having bolstered basic research, California’s stem cell agency must choose a new leader and figure out how to develop therapies
December has been an eventful month for the California Institute for Regenerative Medicine (CIRM), the agency created by California voters to disburse $3 billion for stem cell research. Real estate developer Robert Klein, who spearheaded the 2004 ballot initiative that created CIRM and served as its first chair, had promised to step down when his term expired on 17 December. On 15 December, CIRM’s governing board was scheduled to elect Klein’s successor. Sounds pretty straightforward, right? Not so. Klein initially backed Alan Bernstein, a Canadian scientist and executive director of the Global HIV Vaccine Enterprise, to succeed him. But Bernstein’s candidacy ran aground because of concerns that state law precludes non-U.S. citizens from holding the job. (It’s not clear that it actually does.) The governor nominated Klein for a second 6-year term. Klein accepted but said he would serve only up to 6 months more to help find a replacement. The state controller nominated a different candidate, who accepted the nomination and then withdrew 8 days later. Eleven prominent California scientists threw their support behind Klein, who is both widely admired for getting CIRM off the ground and criticized for micromanaging the agency. But the state controller wrote a letter that criticized the selection process as “fundamentally flawed” and urged the board to postpone the election and start over. Instead, they reelected Klein. “It’s the usual CIRM circus,” says Marie
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Csete, the institute’s former chief scientific officer, who now oversees research and development at Organovo, a San Diego biotech company. Controversy is nothing new for CIRM. Csete resigned abruptly last year after only 15 months on the job, for reasons she has declined to explain (Science, 17 July 2009, p. 249). In 2007, founding president Zach Hall resigned after a contentious meeting that exposed a rift between board members representing research institutions and those who are patient advocates (Science, 27 April 2007, p. 526). Watchdog groups have blasted the institute about what they see as exorbitant staff salaries and conflicts of interest on the board. And patient advocates are tired of waiting for the stem cell cures they feel they were promised during the campaign. Despite all this, many scientists insist that the institute has been a tremendous success. It has so far awarded 385 grants totaling more than $1.1 billion, money that has been used to build new labs, train scientists, and conduct research throughout the state. “I see CIRM as a major advance for the entire world of stem cell research,” says Elaine Fuchs of Rockefeller University in New York City. “Its effect has spread way beyond the state of California.” Still, even CIRM supporters say the institute has to improve its relationships with industry if it hopes to fulfill its mandate: generating stem cell therapies that help people suffering from conditions like diabetes
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CREDIT: CIRM
R E G E N E R AT I V E M E D I C I N E
A solid foundation A major impetus for CIRM was the restrictions the Bush Administration imposed in 2001 on federal funding for research using human embryonic stem cells (hESCs) (Science, 17 August 2001, p. 1242). Proposition 71 authorized California to raise $3 billion for stem cell research through the sale of state bonds, offering an alternative source of funding for the state’s scientists. “The ‘yes’ vote on Prop. 71 changed the world,” says Sean Morrison, a stem cell researcher at the University of Michigan, Ann Arbor. “Prior to that, the conversation in most states was, ‘Should we allow embryonic stem cell research?’ ” Morrison says. “But once California put that stake in the ground, the conversation shifted to, ‘How do we keep up with California?’ ” Even with the Obama Administration’s support for hESC research, some researchers see CIRM as a bulwark against political and economic turbulence. In August, a federal judge in Washington, D.C., issued a temporary injunction blocking hESC research on the grounds that it violates a law banning federal funding for research that destroys embryos (Science, 3 September, p. 1132). That injunction has been put on hold, but the legal battles continue. “If the injunction goes back into place, that would be a huge blow to the field,” says Morrison. “People in California may have a safe harbor, and that’s a big deal.” (CIRM’s portfolio now includes research on adult and induced pluripotent stem cells.) The agency also provides a buffer against increased competition for funds at the National Institutes of Health (NIH), says George Daley, a stem cell researcher at Harvard Medical School in Boston. Not surprisingly, CIRM grantees are not complaining. “I just moved into a spectacular new building,” says Arnold Kriegstein, the director of the new Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research at the University of California, San Francisco (UCSF). CIRM kicked in $35 million for the new facility, about a third of its cost. The Broad Center at UCSF is funded by one of the 12 major facilities grants awarded by CIRM. Seven of these were completed in 2010, and all but one are expected to be up and running by the end of 2011.
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and Parkinson’s disease. A recent report by a panel of external scientists convened by the CIRM board said translating basic science into therapies should be a major priority going forward. “We’ve fallen short on the clinical translation side,” says Jeff Sheehy, a CIRM board member and HIV/AIDS activist. “I think we can do better.”
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CREDITS (TOP TO BOTTOM): CIRM; NOAH BERGER/BLOOMBERG VIA GETTY IMAGES
TOP 12 CIRM GRANTEES Total awarded
Awards
Stanford University
$ 175,862,473
50
Philip A. Pizzo
Dean of medical school
University of California, Los Angeles
$ 135,154,660
40
Eugene Washington
Dean of medical school
University of California, San Francisco
$ 110,532,518
35
Sam Hawgood
Dean of medical school
University of California, San Diego
$
32
David Brenner
Dean of medical school
77,177,593
Representation on CIRM board
Position
University of Southern California
$
71,933,514
19
Carmen A. Puliafito
Dean of medical school
University of California, Irvine
$
71,878,458
27
Susan V. Bryant
Vice chancellor for research
University of California, Davis
$
61,187,635
19
Claire Pomeroy
Dean of medical school
Sanford Consortium for Regenerative Medicine
$
43,000,000
1
John C. Reed William R. Brody
Board member (CEO, Burnham Research Institute) Board member (President of Salk Institute)
City of Hope National Medical Center
$
41,586,199
9
Michael A. Friedman
CEO
Scripps Research Institute
$
37,377,357
14
Floyd E. Bloom
Professor emeritus
The Salk Institute for Biological Studies
$
36,818,181
14
William R. Brody
President
University of California, Berkeley
$
36,746,646
12
Robert Birgeneau
Chancellor
The recent external review gave CIRM includes many people in high positions at high marks for building research infrastruc- the institutions that receive the most fundture and fostering high-quality research. ing from CIRM (see table). A June 2009 The agency has so far committed $108 mil- report from the Little Hoover Commislion to training more than 600 young scien- sion, an independent state oversight comtists. CIRM says it has contributed funding mittee, found that 80% of funding had gone to research published in more than 600 jour- to institutions with representatives on the nal articles, with roughly a quarter of those in board. The commission recommended that a high-profile journals. “Progress during this smaller board with more independent voices first stage of CIRM’s development has been would have more credibility. remarkable,” the panel wrote in its report. (The fact that the Looking forward panel was chaired by Bernstein, According to the recent exterwho became a candidate to chair nal review, CIRM is now enterCIRM’s board, caused some to ing a second stage in which it question its objectivity. “Was should maintain its strength in he really going to criticize the basic research and extend its agency before he came in?” asks reach toward clinical applicaSheehy.) tions. To do that, CIRM will Even some of CIRM’s frehave to improve its ties with quent critics give the agency biotechnology companies, says credit. “If you look realistiDaley, who served on the panel: cally at the scientific work that’s Same as the old boss. “We felt that engagement with been done, there’s been very Robert Klein says his sec- industry had been underemphaimportant progress,” says John ond term will last no more sized and needs to be encourSimpson, stem cell project direc- than 6 more months. aged.” So far, slightly more tor for Consumer Watchdog in than 7% of CIRM money has Santa Monica, California. “They did manage gone to companies. to get some world-class laboratories built, and Several California biotech leaders say they did it in a clever way where they used the they have been frustrated by their interactions public money as seed money to attract match- with CIRM. “In the past, there’s been a lack ing contributions,” Simpson says. of recognition that it takes a company to actuAt the same time, Simpson criticizes what ally take a treatment forward from the bench he sees as “outrageously high” salaries for top into the clinic,” says Chris Airriess, chief CIRM staff members. The chair and presi- operating officer of California Stem Cell Inc. dent, for example, can be paid up to $529,000. in Irvine. Airriess says his company has twice (Klein has declined a salary for most of his applied, unsuccessfully, for CIRM money. He tenure.) That’s more than double the $199,700 and others place much of the blame on the paid to the director of NIH, Simpson notes— review process, which he says is structured or the $225,000 paid to the state’s governor: too much like the NIH review process for aca“That kind of largesse has often come back to demic research grants. CIRM reviewers critiembarrass the agency, and rightly.” cized his company’s applications for the lack Critics have also noted that the board of new science, but Airriess says that misses www.sciencemag.org SCIENCE VOL 330 Published by AAAS
the point. “Companies are trying to stabilize a technology and commercialize it rather than push the bleeding edge,” he says. Even companies that have succeeded say it hasn’t been easy. Earlier this year, San Francisco–based iPierian won a $6 million early translation award and a $1.5 million basic biology award. “We put a ton of effort into understanding what was being asked for,” says CEO Michael Venuti. Some companies have been discouraged from applying for CIRM funds by terms that require paying CIRM back with equity in the company or with cash equivalent to several times the original loan if a project bears commercially viable fruit, says Hans Keirstead, a stem cell researcher at UC Irvine. Such arrangements are reasonable in principle, but the terms CIRM imposes can be onerous, Keirstead says: “CIRM has to pay a lot of attention now to becoming industry-friendly.” CIRM President Alan Trounson says he is sensitive to these concerns but doesn’t think the review process is problematic: “Companies that have put in well-formed proposals have done very well.” But he acknowledges that CIRM has had difficulty attracting proposals, particularly from larger companies. He’d like to set up an industry advisory board to help improve industry relations. In the coming months, he and others will be waiting anxiously to see who succeeds Klein as chair. Patient advocates want an advocate at the helm. Scientists would prefer a scientist. Trounson, who may have to work most closely with the new boss, says he’s hoping for someone with expertise in the delivery stage of therapeutic development. “The basic science, as long as we look after it, will take care of itself,” he says. The real challenge for CIRM, he says, is getting the science into the clinic. “We need more help on how to make it all happen.” –GREG MILLER
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COMMENTARY Interactive introduction to the brain
Interacting with the public for science policy
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LETTERS I BOOKS I POLICY FORUM I EDUCATION FORUM I PERSPECTIVES
LETTERS
W. E. JOHNSON ET AL. (“GENETIC RESTORATION OF THE FLORIDA panther,” Reports, 24 September, p. 1641) document genetic changes in the Florida panther population after the 1995 introduction of eight Texas puma females. This translocation has been a great success; the population size has increased more than threefold, and several detrimental traits have substantially decreased in frequency. However, there are compelling reasons to continue the close genetic management and monitoring of the population in the future. First, only five of the eight female Texas pumas had offspring. The distribution of offspring from these five females was unequal—one female contributed nearly half of the offspring—and the total ancestry from these five females was very high. Specifically, the authors stated that “[t]he estimated relative genetic contribution[s] of the [Texas] females to the descendant population” are
Response
WE AGREE WITH HEDRICK THAT THERE ARE cogent reasons for continuing to monitor the surviving Florida panthers in the future. Inbreeding is by no means solved and may increase as available habitat is developed. It is true that the relative genetic contribution of the Texas pumas was restricted to five of the eight females released in 1995 and that they account for about 50% of the genetic heritage in Florida panthers today. Whether this represents “swamping” or natural subspecies reassortment in the aftermath of demographic and genetic perils experienced by canonical Florida panthers is a matter of opinion. What is clear is that suitable habitat must be preserved and additional populations must be
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0.20, 0.10, 0.06, 0.04, and 0.01, for a total of 41%. Ordinarily, 50% percent of the ancestry is from each sex; a contribution of 41% is equivalent to saying that about 80% of the female ancestry is from the five Texas females, nearly the maximum possible. In other words, the Texas females may have been too successful and management should evaluate whether to actively preserve the original Florida panther ancestry. Second, the success may be threatened by inbreeding and low effective population size in the current and future generations. For example, a male offspring of a Texas female and a Florida panther male mated with three of his daughters and produced seven offspring with inbreeding coefficients of 0.25. The effective population size estimate was based only on the number of breeding males and breeding females. If the variance in contributions in males is equal to that found in Yellowstone pumas (1), which resulted in the effective number of males being only 18.5% of the observed number of males, and the variance in females reflects the contributions above, the overall effective size in 2007 is probably only between 10 and 15 animals, rather than the 32.1 estimated. Overall, swamping of the Florida panther ancestry, inbreeding, and low effective population size may endanger the gains made from translocation for genetic restoration (2). PHIL HEDRICK School of Life Sciences, Arizona State University, Tempe, AZ 85287–4501, USA. E-mail:
[email protected]
References
1. M. Culver et al., Anim. Conserv. 11, 1045 (2008). 2. P. W. Hedrick, R. Fredrickson, Conserv. Genet. 11, 615 (2010).
established for the continued survival of this critically endangered group. The lessons learned from the genetic restoration project highlight the many benefits to the Florida panther population while also demonstrating that there is no quick or universally accepted solution to conserving small, endangered populations. Incorporating interdisciplinary data and the expertise of scientists from varied backgrounds can only improve the development of effective management regimes to help ensure the recovery of endangered animals, including the Florida panther.
WARREN E. JOHNSON,1* DAVID P. ONORATO,2 MELODY E. ROELKE,3 E. DARRELL LAND,2 STEPHEN J. O’BRIEN1
24 DECEMBER 2010
Laboratory of Genomic Diversity, National Cancer Institute, Frederick, MD 21702, USA. 2Florida Fish and Wildlife Conservation Commission, Naples, FL 34114, USA. 3SAICFrederick, Laboratory of Genomic Diversity, National Cancer Institute, Frederick, MD 21702, USA. 1
*To whom correspondence should be addressed. E-mail:
[email protected]
Biodiversity Transcends Services IN THEIR POLICY FORUM “ECOSYSTEM SERVICES for 2020” (15 October, p. 323), C. Perrings et al. discuss possible missing elements in the Convention on Biological Diversity’s proposed new targets. They suggest that targets for biodiversity be based directly on ecosys-
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tem services because people will then have a stake in the program’s success. This approach undersells both biodiversity and the role of ecosystem services. Biodiversity’s value extends beyond current ecosystem services and includes likely future benefits we cannot anticipate. Recognizing the benefits of ecosystem services can reduce the cost of retaining relatively intact areas of local biodiversity, but we need to plan for larger-scale conservation. A recognized ecosystem service does more than support some local elements of biodiversity; it makes a lowcost contribution toward conserving the biodiversity of the larger region. Regionally, ecosystem services may be more important as indicators of relative cost and intactness than of biodiversity. When considering regional trade-offs, we cannot simply target ecosystem services and ignore the elements of biodiversity that are not required for the service. Adopting the ecosystem services option for a specific locality may not be as good for balanced regional biodiversity conservation as adopting full conversion of that locality (1). An example that has been used to illustrate this point is a locality offering either complete conversion to forestry logging or “sympathetic” logging with partial biodiversity retention. Adopting the ecosystem service based on sympathetic logging, while lowering opportunity costs and maintaining some biodiversity in that locality, nevertheless would mean greater regional biodiversity loss for a given level of regional forestry production. As an alternative to targets focused on current perceptions of important services, it is time to consider higher-level targets and goals in an effort to better balance overall biodiversity conservation, ecosystem services, and other needs of society. I propose
Letters to the Editor Letters (~300 words) discuss material published in Science in the previous 3 months or issues of general interest. They can be submitted through the Web (www.submit2science.org) or by regular mail (1200 New York Ave., NW, Washington, DC 20005, USA). Letters are not acknowledged upon receipt, nor are authors generally consulted before publication. Whether published in full or in part, letters are subject to editing for clarity and space.
that we implement new systematic conservation planning to more efficiently serve these different needs (2, 3). Because greater efficiency can mean more biodiversity protection for a given rate of land conversion, higher-level targets could allow us to focus on reducing the rate of biodiversity loss as opposed to the more narrow goal of maintaining ecosystem services.
DANIEL P. FAITH
The Australian Museum, Sydney, NSW 2010, Australia. E-mail:
[email protected]
References
1. D. P. Faith, Biodiversity and Regional Sustainability Analysis, (CSIRO, Canberra, 1995); http://australianmuseum. net.au/document/Biodiversity-and-regional-sustainabilityanalysis/. 2. D. P. Faith, Glob. Environ. Change Soc. Pol. Dimensions 15, 5 (2005). 3. F. Grant, J. Young, P. Bridgewater, A. D. Watt, Eds., “Targets for biodiversity beyond 2010: Research supporting policy” (Report of e-conference, 2009), p. 44; www.epbrs.org/PDF/Final%20long%20report.pdf.
Response
FAITH ARGUES THAT OUR APPROACH TO BIOdiversity conservation, which focuses on people’s interest in the benefits of ecosystem services, may deprive us of future, unanticipated benefits. This claim is misplaced. In our Policy Forum, we argue that conservation goals should reflect the benefits we get from biodiversity. The argument is not conditional on the type or timing of benefits delivered. We agree that it is not just biodiversity’s value in producing marketed commodities that matters. Its indirect value in supporting ecosystem services is often more important (1), and its potential value to future users (option value) and to future science (quasi-option value) has been recognized as the most important of all for at least three decades (2–4). Our goal was to clarify the trade-offs between these benefits, which are inevitable as we strive to meet the basic needs of a growing world population, alleviate poverty, and protect those species on which our future well-being depends (5). Only by being clear about the benefits put at risk by the loss of biodiversity now and in the future can we approach these trade-offs wisely. The ecosystem services approach helps clarify the benefits at risk, whether they are direct, indirect, or options. Science-based information on what
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we gain and lose from biodiversity change can inform decisions by those charged with representing particular constituencies in forums such as the Convention on Biological Diversity (CBD). Now that the CBD’s 2020 Targets have been set (6), the problem has shifted from goal setting to implementation. The best way, now, to prioritize and assess targets is to select appropriate indicators. For example, given that achievement of the goal for sustainable agriculture, target 7, is conditional on achievement of the goal for agricultural subsidies, target 3, the indicators for target 7 should include measures of the achievement of target 3. To implement the 2020 targets successfully, decision-makers need to be convinced that the costs of biodiversity loss are real. The ecosystem services approach provides the evidence base to argue this case. Trumpeting “intrinsic value” has had little effect in the past and is likely to have less effect in the future as other environmental concerns escalate in policy significance. Using the resources of science to identify and value the consequences of biodiversity change is likely to be the most effective strategy.
C. PERRINGS,1* S. NAEEM,2 F. AHRESTANI,2 D. E. BUNKER,3 P. BURKILL,4 G. CANZIANI,5 T. ELMQVIST,6 R. FERRATI,5 J. FUHRMAN,7 F. JASIC,8 Z. KAWABATA,9 A. KINZIG,1 G. M. MACE,10 F. MILANO,5 H. MOONEY,11 A.-H. PRIEUR RICHARD,12 J. TSCHIRHART,13 W. WEISSER14
School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA. 2Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA. 3Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA. 4Sir Alister Hardy Foundation for Ocean Science, Plymouth PL1 2PB, UK. 5Instituto Multidisciplinario sobre, Ecosistemas y Desarrollo Sustentable, Universidad Nacional del Centro, Argentina. 6The Resilience Centre, Stockholm University, SE-106 91 Stockholm, Sweden. 7Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA. 8Centro de Estudios Avanzados en Ecologia y Biodiversidad, Pontificia Universidad Católica de Chile, Chile. 9Center for Ecological Research, Kyoto University, Japan. 10Centre for Population Biology, Imperial College London, Ascot SL5 7PY, UK. 11Department of Biology, Stanford University, Stanford, CA 94305, USA. 12DIVERSITAS, Muséum National d’Histoire Naturelle, 75231 Paris Cedex 05, France. 13Department of Economics and Finance, University of Wyoming, Laramie, WY 82071, USA. 14Institut für Ökologie, Friedrich-SchillerUniversität, Jena 07743, Germany. 1
*To whom correspondence should be addressed. E-mail:
[email protected]
References
1. Millennium Ecosystem Assessment, Ecosystems and Human Well-Being: General Synthesis (Island Press, Washington, DC, 2005). 2. K. J. Arrow, A. Fisher, Q. J. Econ. 88, 312 (1974). 3. J. M. Conrad, Q. J. Econ. 94, 813 (1980). 4. A. C. Fisher, W. M. Hanemann, Nat. Res. Model. 1, 111 (1986). 5. J. D. Sachs et al., Science 325, 1502 (2009). 6. Convention on Biological Diversity, COP 10, Nagoya (2010); www.cbd.int/nagoya/outcomes/.
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LETTERS CORRECTIONS AND CLARIFICATIONS News Focus: “What shall we do with the x-ray laser?” by A. Cho (10 December, p. 1470). The story mistakenly states that Linda Young, an atomic physicist at Argonne National Laboratory in Illinois, and colleagues shined x-rays from the Linac Coherent Light Source onto xenon gas. The physicists used neon.
Research Articles: “Nonlocal transport in the quantum spin Hall state” by A. Roth et al. (17 July 2009, p. 294). An unintended duplication of figure elements was introduced during manuscript preparation. Despite their different horizontal scales, the red R14,23 curve in Fig. 1 is the same as that in Fig. 3A; likewise, the red R14,14 curve in Fig. 1 is the same as the green curve in Fig. 3A. The configuration of current contacts and voltage probes shown in Fig. 3A is fully equivalent to the four- and twoterminal configurations of a standard Hall bar as shown in Fig. 1. Therefore, this unintended duplication does not affect any claims in the paper. A corrected version of Fig. 1, based on data taken from a Hall bar device different from the one shown in Fig. 3A, is shown here. The original caption is correct. Research Article: “Identifying autism loci and genes by tracing recent shared ancestry” by E. M. Morrow et al. (11 July 2008, p. 218). The authors wish to add an
Call for Papers
acknowledgment to the contribution of the late Ahmad Teebi to the work presented here. He pioneered the study of genetic disorders in the Arab world and inspired the
idea of studying complex disorders in consanguineous populations. We are indebted to his generous collaboration and dedicate this work to his memory.
Science Translational Medicine Integrating Medicine and Science
Chief Scientific Adviser
Elias A. Zerhouni, M.D. Former Director, National Institutes of Health
Science Translational Medicine, from AAAS, the publisher of Science, focuses on the conversion of basic biomedical research into practical applications, thus bridging the research-to-application gap, linking basic scientists and researchers. For more information see ScienceTranslationalMedicine.org or contact
[email protected]
Submit your research at www.submit2scitranslmed.org
Submit your manuscripts for review in the following areas of translational medicine: • Cardiovascular Disease • Neuroscience/Neurology/ Psychiatry • Infectious Diseases • Cancer • Health Policy • Bioengineering • Chemical Genomics/ Drug Discovery • Other Interdisciplinary Approaches to Medicine
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News Focus: “Genes link epigenetics and cancer” by J. Kaiser (29 October, p. 577). The article failed to note that a Canadian team is among the researchers who have found cancer genes involved in modifying chromatin [K. C. Wiegand et al., N. Engl. J. Med. 363, 1532 (2010)].
BOOKS ET AL. EVOLUTION
viously hidden dimension for phylogeny, as well as the dynamism of evolutionary concepts (genealogy, heritability, selection) to span natural hierarchies (species, genes) and to yield new insights about the processes and consequences of change over time. The new genomics data reveal new levSketched tree of life. Estimated from RNA genes. els of complexity; however, students of organisms in nature are well aware of the limitafor 25 different hominid tions of genetics and other molecular data, species during the past 6 which are not yet well integrated, especially million years. We have not causally, with the evolution of variable detailed, long-term stud- traits, populations, species, species commuies from the field and lab nities, and ecosystems functioning. regarding the evolutionary Much of the work of integration lies ahead impacts that diverse orga- and will require syntheses of new data and nisms and species have on one expanded views on evolutionary process. In another through competition, preda- a volume to mark the centennial of Origins tion, parasitism, and mutualisms. We have that had objectives similar to those of Bell et many well-documented instances of natural al. (1), many authors worked within the tenets selection yielding adaptive change in popu- of the (then) modern synthesis. They emphalations and of speciation influenced by both sized gradual change as the dominant tempo, natural selection and chance events. We have speciation largely by geographic isolation, extensive molecular databases, including general equivalency between micro- and macsets of homologous gene sequences allowing roevolution, and strictly bifurcating phylogphylogenetic estimation for all enies, and they paid relatively living things. Growth in data little attention to processes of Evolution Since Darwin feeds growth in the analytical development, epigenetics, and The First 150 Years methods and, subsequently, ecology. The Bell et al. volume the depth at which topics can nicely illustrates how the comMichael A. Bell, Douglas J. Futuyma, Walter F. Eanes, be addressed. munity of evolutionary bioland Jeffrey S. Levinton, Eds. Our time traveler would ogists is changing the view. Sinauer, Sunderland, MA, certainly have interest in some Contributors elucidate highly 2010. 704 pp. Paper, $69.95. general truths from 50 years variable rates of evolution for ISBN 9780878934133. of evolutionary genetics. For different traits and lineages, example, all life shares one diverse speciation modes, and genetic code (with minor lateral gene transfer. Chapilluminating exceptions) and very similar ters move beyond gene-centrism for organismeans of DNA processing; a good deal of mal evolution to greater concern and data for the DNA in genomes has no apparent func- study of epigenetics, multiple levels of selection; the number of functional genes does not tion, and evolvability. correlate with organismal complexity; gene The volume reflects the dramatic expanduplication is the main source of new genes; sion of the applications of evolutionary sciboth protein function and gene expression ence in recent years. These now include are important in phenotypic evolution; both individual, public, and environmental chance and selection are important in evolu- health as well as forensics, vaccine develtionary change; and genes can move laterally opment, conservation of species, and ecobetween distinct species as well as vertically system sustainability. from parents to progeny. Back in the pub, Darwin may wonder, Lateral gene transfer, particularly com- “Does all this activity mean evolution has mon among Bacteria and Archaea, yields an lost its ability to excite fear and opposition?” atomization of phylogeny for many lineages, Not yet. As the root for natural explanations into reticulating networks of genealogy of human origins (quiescent for the current among genes and suites of genes within and news cycle) and ultimate impetus for human among organismal lineages. The pleasingly moral behavior and values, evolution remains disruptive potential to distinguish between the disturbing discovery. differing organismal and genetic histories References has been claimed by some (falsely I think) to 1. S. Tax, Ed., Evolution After Darwin: The University of invalidate Darwin’s view of the tree of life, Chicago Centennial (Univ. Chicago Press, Chicago, 1960). and it would be fun to ask the man about that. 10.1126/science.1199052 Lateral gene transfer does demonstrate a pre-
David P. Mindell
CREDIT: COURTESY DAVID HILLIS AND SINAUER ASSOCIATES
M
ore than a few biologists have fantasized about taking Charles Darwin out for a beer, to pick his brain and hear his reactions to a whirlwind update on evolutionary science. Old news items to relate would have to include the discoveries of the material of inheritance, continental drift, and radiometric dating. More recent topics to consider are equally compelling and a bit kaleidoscopic, ranging from extreme archaean lifestyles to epigenetics, macroecology, ribozymes, Hox genes, units of selection, and xenology in eukaryotes, among many others. If you could put a single book-length survey into the pigeon breeder’s hands to focus the conversation, Evolution Since Darwin: The First 150 Years, edited by Michael Bell, Douglas Futuyma, Walter Eanes, and Jeffrey Levinton, would be an excellent choice as a comprehensive guide to current understanding and controversies. The volume, based on a November 2009 workshop at Stony Brook University, brings together an accomplished set of authors representing diverse subdisciplines of evolutionary biology. In 22 chapters and 8 commentaries, they trace the history of their subjects from Darwin’s work, with particular focus on the past 50 years. Stimulating and insightful individually, the reviews combine to provide a picture of a vibrant and expanding field. Perhaps most impressive in the growth of evolutionary science has been its success in explaining change across scales. This includes extremely different scales of time and geography, hierarchical levels of biological organization, disparate groups of organisms, and even different scientific disciplines. Evolution may be unparalleled among the natural sciences in integrating so comprehensively, touching everything from cells to awe. Much of the growth in explanatory power for evolution over the past 150 years stems from growth in the kinds and richness of data. We now have a wealth of fossils with reliable age estimates, from the earliest known life forms 3.5 billion years ago to evidence The reviewer is at the California Academy of Sciences, 55 Music Concourse Drive, San Francisco, CA 94118, USA. E-mail:
[email protected]
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At the Sesquicentennial of Origin
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A Natural History of the Brain Abigail Rabinowitz1 and Carl E. Schoonover2
T
he American Museum of Natural History’s Brain: The Inside Story does not open with the customary brain numerology—the billions of neurons and synapses, the eons of evolution spent packing it all into 1.4 kg of tissue. Instead, you feel your way down a winding corridor surrounded by 680 kg of tangled electrical wire and optical fiber. Spanish artist Daniel Canogar’s installation is lit up with rapid trickles of light and sheets of shifting color—a cross between forest and funhouse. There is no better way to see the organ as many a brain scientist does: a staggeringly complex, interconnected tangle in which countless subtle signals whizz by at breakneck speed. Once the challenge of understanding the brain is made viscerally clear, the exhibition begins. Five main sections cover topics from the nervous system’s cellular workings and its role in sensation to how our brains learn and change over time. The immensely ambitious exhibition, based on a knowledge set that is still patchy, aims to explain the human brain to an audience of all ages. If you are new to gray matter, trying to understand everything from synapses to synesthesia in two hours can feel demoralizing, like cramming from an encyclopedia for a multiple-choice test. But at its best, the show moves away from textheavy placards to displays that encourage you to understand the brain intuitively. Some of this science exhibition’s most effective teaching tools are works of art. To explain how we perceive, Devorah Sperber’s visual puzzle assembles delicately tinted spools of thread into an abstract shape that, once refracted through a glass orb, resolves into a famous portrait. Her installation offers a powerful metaphorical account of how a nervous system takes in disjointed bits of information The reviewers are at 1EastWest Institute, 11 East 26th Street, 20th Floor, New York, NY 10010, USA, and 2Department of Neuroscience, Columbia University, New York, NY 10032, USA. E-mail: ces2001@ columbia.edu
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and synthesizes them into a clear, seamless cerebellum is, for instance, doesn’t help you percept—and does so in a manner that feels understand what it does. effortless to the perceiver. Just across the way, But in a few brilliant moments, the show a 1.8-m-tall homunculus with monstrously really succeeds in explaining why brain locallarge lips, hands, and feet ization matters. At a handssymbolizes the relative size on table, you are invited Brain of the somatosensory corto trace a star shape with The Inside Story tex’s representation of varia stylus while looking at Rob DeSalle, Joy Hirsch, ous body parts. (Parents, do your hand in a mirror. It’s a and Margaret Zellner, curators not fear: one oversized hand clumsy, frustrating task … at American Museum of Natural is strategically placed.) least at first. Above the staHistory, New York, through 14 The curators often tion, a panel lucidly teases August 2011. Guangdong employed art effectively, so apart why practice makes Science Center, Guangzhou, China, it is particularly unfortunate perfect: As the procedural 19 November 2011 to 30 April 2012. that they didn’t better use memory of the task becomes Parque de las Ciencias, Granada, visual cues to help unite the ingrained over time, differSpain, 14 July 2012 to 6 January 2013. Codie Idee per la Cultura, vast amount of information. ent brain areas gradually Torino, Italy, 2 March 2013 to 18 Moving from section to take over its execution. The August 2013. www.amnh.org/ section, one finds the same more frontal (“planning/ exhibitions/brain/ brain regions in panel illusthinking”) regions initially trations colored differently, recruited to solve the probwhich forces visitors to keep track of confus- lem are replaced by areas that specialize in ing new neuro-jargon to connect concepts coordinated motion, which explains why, across the exhibition. Early on, an excel- with repetition, the task requires less and less lent video linking a Juilliard dance student mental effort. It has become mindless. practicing her routine to her brain’s activity Some of the show’s most dazzling (illuminated in a clear model) gives a very moments occur at the end, in a section immediate and useful overview of the brain’s that explores advances we might look forgeography. But instead of being referenced ward to thanks to this brave new science. throughout to anchor the exhibition, it stands In one video, we encounter a scientist who alone, the point it so vividly conveys forgot- has developed a system for a disabled man ten. More generally, we felt that the show that, by capturing his neural activity, allows often devotes too much time to naming brain him to control the motion of a virtual hand regions without explaining why the informa- (and perhaps one day an actual prosthetic tion is important. Simply knowing where the one). Another uses functional magnetic resonance imaging to link musical and athletic aptitude to brain activation patterns, exploring the notion that we, our talents, our tastes, our very selves, are nothing but the product of our nervous systems. Despite our reservations about the exhibition’s lack of unity and unnecessary difficulty in parts, we found that Brain: The Inside Story presents a compelling snapshot of brain science and does so without overselling researchers’ claims. Some of the exhibition’s most moving and successful displays are also its most simple. In a section on aging, two plasticized human brains are placed side by side—one plump and healthy and one whose cortical folds have been thinned by Alzheimer’s— requiring no explanation and serving as a silent reminder of how far we have yet to go.
Tricky tracing task.
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BOOKS ET AL.
POLICYFORUM SCIENCE POLICY
The Challenge of Feeding Scientific Advice into Policy-Making
Three case studies illustrate general principles to guide scientists and policy-makers in interactions with each other and the public.
Roland Schenkel
Robust Science for Policy
CREDIT: SKB
Policy-makers today want to receive evidencebased information, including a cost-benefits Former Director-General, Joint Research Centre, European Commission, Square de Meeus 8, 1049 Brussels, Belgium. E-mail:
[email protected]
Nuclear waste. Citizens visiting the Äspo Hard Rock Laboratory as part of public consultations about the Swedish National Nuclear Repository for spent nuclear fuel.
analysis of every scenario under consideration. They also expect to receive independent, unbiased advice. Yet, this is not always obvious when decisions reflect the need to balance different opinions, as well as claims and counterclaims from interest groups, including scientists. Three case-studies spring to mind that illustrate best practices and pitfalls when feeding scientific advice into policy-making: the long-standing debate about nuclear waste management, the more recent global debate on biofuel production, and Europe’s disjointed response to the Icelandic volcanic ash crisis. In considering these studies, it would help to keep in mind five seemingly obvious, but nevertheless critical, observations: First, science is at the heart of invention and the drive to make our lives better in a globalized world. Legislative answers founded on scientific evidence increasingly shape the world we live in. Second, science should not claim to have “the” answer. Scientists from different disciplines should not be afraid to engage in a “contradictory evidence-based mode” of discussion, challenging each other with conflicting facts and uncertainties to arrive at a better-informed, yet less narrow and more harmonized view. This mode has advantages above the classical peer-review used by scientific journals and is better suited for a proper treatment of multidimensional topics, even if
it may result in “gray” literature only. Third, industry and other core interest groups have natural vested interests in policymaking. Scientific outcomes are often better if they participate in the process. Fourth, public opinion is crucial and public debate is instrumental in forming it. Scientists must speak in a language that the public understands, engaging in real dialog and moving away from the often arrogant “ex cathedra” presentation style. Finally, robust scientific advice has to be multidimensional and inclusive. It must consider economic, social, environmental, ethical, and scientific aspects, while indicating how best to deal with uncertainties. Nuclear Waste
The legacy of public opinion following the Harrisburg and Chernobyl accidents, when nobody could be seen to promote nuclear energy, least of all the nuclear energy industry, was a major impasse in having rational, evidence-based dialog for many years (1). Such was the veil of secrecy around these events and so significant were the shortcomings in communications at that time, it is easy to understand why decision-makers were paralyzed. However, in 2008, 37% of Europeans were in favor of nuclear energy, rising to 58% if the waste issue was solved (2). This represents a sea-change compared with views expressed
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B
oth the United States and the European Union are facing new challenges in terms of how science is viewed and used. There continues to be tension between scientific information and societal and political priorities. How can we explain the gap between science and policy-making while confronting misperceptions and promoting positive views of science among the public? The United States and the European Union have recently undergone major political changes, reflected in a reevaluation of the role of science in the policy-making apparatus. The Obama Administration has focused on science as a central component of the policy agenda. Concrete measures include reinvigorating the President’s Council of Advisors on Science and Technology (PCAST); increasing investment in research and education; and appointing respected scientists, such as John Holdren and Stephen Chu, to senior-level positions. Similarly in Europe, the president of the European Commission, José Manuel Barroso, has initiated the Europe 2020 agenda, which has research and innovation at its core. Perhaps less trumpeted but also important has been President Barroso’s announcement of the creation of a Chief Scientific Adviser position. However, our governments are currently faced with a significant financial crisis. Will they continue to take a long-term view and invest in the future? Or will they succumb to pressures and cut funding for science? Likewise, will the scientific community be modest enough to accept that science is just one important consideration on the table when decision-makers have to make choices? What follows is a plea for a more frequent and more issue-driven dialog between policy-makers, the scientific community, and all relevant stakeholders, based on observations that have shaped my career in providing scientific advice.
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POLICYFORUM
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Volcanic Ash
Scientists Wanted
Public engagement with all stakeholders on sensitive scientific issues is the key to success. We have to engage more on the opportunity-rich, but risk-prone, developments in nanotechnologies, genetic engineering, stem
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The Icelandic volcanic ash disruption was the largest disruption to aviation since 9/11. Thousands of tons of mineral ash were thrown into the air, creating a plume of fine particles that rose 6 to 10 km (20,000 to 35,000 feet) into the atmosphere (see the third figure). These abrasive particles had the potential to erode metal; clog fuel, sensing, and cooling systems; and melt to form glassy deposits in jet engines. What astonished many was that in such a (in principle, predictable) crisis the European Union had little real power or competence to act and that there appeared to be so little scientific evidence available. Nobody knew what kind or amount of volcanic ash would be safe for an aircraft. Nobody had rigorously tested how different engines cope with different concentrations of ash. The result was that 75% of European airspace was closed from 14 to 21 April 2010. Closures were based on a zero-tolerance strategy emanating from the precautionary principle with the weight of evidence being given to previous instances of jet engine failure when flying through some volcanic ash clouds. There are several lessons to be learned. First, in crisis situations, it is difficult to pull together the expertise of different scientific disciplines (in this case, volcanology, atmospheric science, material sciences, remote sensing, engineering, security, economics, etc.), with the aim of getting better-informed answers. Second, there were glaring methodological shortcomings. For example, existing scientific models of ash cloud movements could not be adapted effectively to the uncertainties of actual eruptions. Similarly, the technology for satellite observation of ash and SO2 in the atmosphere existed, but its ability to generate quantitative results had not been validated. Neither was there sufficient integration between airborne and ground measurements. We should not point the finger of blame at policy-makers. They reacted correctly in opting for a zero-tolerance policy in the absence of clear scientific guidance. Individual governments and various European authorities have been quick to request urgent basic research. New satellite information and dispersal models should be integrated into a common European Union platform for use by risk managers.
CREDIT: EU/JRC
two decades ago. How has it been possible— lar positions have clouded a clear focus on the in Northern Europe—to convince the public scientific evidence. One only has to think of of the long-term safety of highly active waste the enormous influence wielded by the agrostored underground for up to hundreds of economic sector or the competition between thousands of years? The history of discussions oil and biofuels in the energy sector. There in Sweden provides a good example of how to are also environmental groups concerned make progress. about issues such as monocultures, pestiStarting in the early 1990s Swedish author- cides, genetically modified (GMO) crops, ities conducted an extensive public engage- and deforestation. The developing-country ment campaign to identify candidate sites for and export perspectives are also important in deep geological waste disposal (3). In 2009, terms of interregional and international trade. the Forsmark site was In assessing the many selected as the location issues involved, one must of the Swedish National contend with considerNuclear Repository for able scientific uncertainhighly active wastes (see ties, especially in predictfirst figure). The safety ing the competitiveness assessment prepared by of biofuels, determining the Swedish Nuclear Fuel whether biofuels are susand Waste Management tainable, and calculating Company (SKB) is a reftheir overall greenhouse erence document in terms gas emissions balance of its focus on long-term ( 10). Greenhouse gas safety aspects and coverrelease from fertilizers age of plausible scenarios (11), the role of refores(4). Last July, after intense tation from fast growconsultation, the Sweding trees, and competiish government decided Biofuels. Some of the complex issues tion with other bioenergy to proceed with build- around the definition of a EU biofuel policy. sectors are just some of ing new nuclear reactors. the issues where more Sweden currently has 12 operating reactors at research is needed. We still need a robust, three sites, producing 45% of its gross elec- global map of existing biofuel production and tricity generation (5). its impacts. Uncertainties can also be “beyond What distinguishes the Swedish approach science,” when, for instance, one needs to use from previous attempts is that the nuclear assumptions of future oil prices to calculate industry placed strong emphasis on socio- competitiveness and land-use changes. economic studies, interaction with all stakeYet, there are positive lessons to be taken holders, fostering a spirit of openness and from the biofuel debate in Europe (see the transparency and above all, careful listening second figure). Early interaction among the and accurate communication (6). Represen- scientific community, interest groups, and tatives literally knocked on every door and policy-makers has helped inform decisionengaged with their most ardent critics so as making. Swift and intensive scientific conto challenge the “not in my backyard” argu- sultation (12) has resulted in policy-support ments. The result was strong local support for documents that better address many of the candidates in favor of the final repository (7– scientific questions raised, particularly con9). Perhaps there are lessons to be learned here cerning biofuel sustainability. EU legislation, for future Yucca Mountains. for example, now states that, in order to be sustainable, future energy-related agriculture Biofuels must guarantee a greenhouse gas reduction of There have been major international debates at least 35% with respect to fossil fuel. This in recent years over the production of biofuels target rises to 60% by 2018 (13). for transport and energy applications, stimuPolicy-makers worldwide must be credlated by political targets and fears about the ited for having financed a number of studenvironment and food price impacts (partic- ies (14) addressing the most uncertain issues, ularly in poorer countries). Growth, harvest- which will serve as a basis for future deciing, and processing release CO2, the amount sion-making and adaptation or revision of the of which depends on a wide range of factors, present legislative texts and targets. However, such as plant species, growth conditions, and achieving a level of transparency that can satthe soil used. isfy all stakeholders, even in large public conThe vested interests of different lobbying sultation processes, continues to be elusive groups pressing governments to take particu- (15).
CREDIT: JEFF SCHMALTZ/MODIS RAPID RESPONSE TEAM AT NASA GSFC
POLICYFORUM horizon, be they general trends in nature and society or technological developments, scientists must help policy-makers tackle the right issues at the right time. At the European Commission’s Joint Research Centre, we are trying to meet this need by setting up a scientific foresight and policy anticipation capacity. Feeding scientific advice into policy-making will be challenging. But what is important is that we defend and assert the inherent integrity of science, demonstrate openness, speak in terms the public can understand, and show that we take our duty to society seriously (17). If we strive to achieve this, then evidence-based policy may just win over policy-biased evidence. References and Notes
1. Nuclear Energy Agency, Public Attitudes to Nuclear Power (OECD, Paris, 2010), p. 45; www.oecdnea.org/ndd/ reports/2010/nea6859-public-attitudes.pdf. 2. European Commission (EC), Special Eurobarometer 227: Radioactive Waste (EC, Brussels, 2005), pp. 26 and 30; http://ec.europa.eu/public_opinion/archives/ebs/ ebs_227_en.pdf. 3. SKB (A.B. Svensk Kärnbränslehantering), Swedish Nuclear Fuel and Waste Management Co., Final Repository for Spent Fuel in Forsmark—Basis for Decision and Reasons for Site Selection (SKB, Stockholm, 2009); www.skb.se/f0a51b8e-d851-492c-a218-32bd7a857b1b. fodoc. 4. SKB, Long-Term Safety for KBS-3 Repositories at Forsmark and Laxemar—A First Evaluation: Main Report of the SR-Can Project (TR-06–09;KB, Stockholm, 2006); www.skb.se/Templates/Standard____17139.aspx. 5. EC, EU Energy and Transport in Figures (EC, Brussels, 2010), p.80; http://ec.europa.eu/energy/publications/ statistics/doc/2010_energy_transport_figures.pdf. 6. Public consultation reports of SKB, www.skb.se/Templates/Standard____24151.aspx. 7. Eight out of 10 support a final repository in Forsmark [press release] (SKB, Stockholm, 2009); www.skb.se/ Templates/Standard____29472.aspx. 8. Annual Report 2009 (SKB, Stockholm, 2009); www.skb. se/upload/publications/pdf/SKB_Verksamhet_2009_ Engelsk_web.pdf. 9. Forsmark for Swedish Nuclear Waste, World Nuclear News, 3 June 2009; www.world-nuclear-news.org/WR_ Forsmark_for_Swedish_nuclear_waste_0306091.html. 10. T. Searchinger et al., Science 319, 1238 (2008). 11. P. J. Crutzen, A. R. Mosier, K. A. Smith, W. Winiwarter, Atmos. Chem. Phys. 8, 389 (2008). 12. R. Edwards, D. Mulligan, L. Marelli, Indirect Land Use Change from Increased Biofuels Demand: Comparison of Models and Results for Marginal Biofuels Production from Different Feedstocks (EC Joint Research Center Scientific and Technical Report, EU, Luxembourg, 2010); http://re.jrc.ec.europa.eu/bf-tp/. 13. EU Announces Guidelines for Biofuel Sustainability, www.biofuelsb2b.com/B2B_news.php. 14. Intelligent Energy—Europe programme, http:// ec.europa.eu/energy/intelligent/index_en.html. 15. J. Rankin, European Voice, 3 September 2010; www.europeanvoice.com/article/imported/biofuel-policylacks-transparency-/69027.aspx 16. Gross domestic expenditure on R&D (GERD), http://epp. eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&p lugin=1&language=en&pcode=t2020_20. 17. Recommendations of the European Commission’s Joint Research Centre and the American Association for the Advancement of Science, Euroscience Open Forum, Turin, Italy, 2 to 7 July 2010.
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cell, and brain research, to name but a few, to We must engage with the media, explain remain at the forefront of research and innova- what we are doing, and speak to them in tion. Otherwise, doubters will win out, even if terms that they and their consumers can they are a minority. understand. I would like to see more scienEurope needs to analyze critically the tific organizations offering real support to damage being done by an overzealous reli- science journalism and training programs. ance on the precautionary principle. It seems Similarly, when organizing scientific conto me that if there is any doubt, we delay deci- ferences, we might consider greater political sions and others pass us by. and public engagement. Put elected officials The licensing of GMO technologies is a and third-party groups on your panels, listen clear example. It took 13 years for the Amflora to your opponents, confront them (if necesgenetically optimized starch potato to get approval for commercial application in Europe. Despite the European Food Safety Authority’s having confirmed on several occasions that Amflora is safe for humans, animals, and the environment, even now, it can only be used for industrial starch. This level of precaution when the science is clear must be challenged. The scientific community must learn to better defend its basic interests, such as funding for education, research, and innovation. In 2000, the combined total of GDP for research expenditure for EU members was 1.85%, and faced with stiff competition from the United States, China, and Japan; in particular, we declared our intention Volcanic ash over Europe. Ash from Iceland’s erupting Eyjafjallajökull Volcano had drifted over northern Europe by April to increase this to 3% by 2010. This 16, 2010. The brown ash is mixed with clouds in this photo“Lisbon Agenda” target became a like image taken by the Moderate Resolution Imaging Spectrorallying call for all, but has slowly radiometer (MODIS) on NASA’s Terra satellite. The airborne ash lost out. In 2008, the real figure grounded flights across much of northern and western Europe was still stagnating at 1.9% (16). starting on April 15. As the ash moved south, more countries Perhaps we are too good at setting began to close their airspace. targets in the EU in the knowledge that we may never achieve them. sary), but do not be content to exist in a comIt is too easy to point the finger of blame fortable, parallel universe. at the politicians. But what has the scientific There are policy-makers who bury their community really done to make the 3% tar- heads in the sand when faced with compelget happen? Have we organized ourselves to ling scientific evidence for unpopular policy protect it? Do we leave protests to students changes, believing all too easily that scionly? Do we speak up to defend the future of ence is an à la carte menu. I would like to see our countries? Do we think that things will more members of the European Parliament get any better during a period of cutbacks recruit qualified scientists into their teams. and austerity packages if we leave public The European Commission might consider debate to others? a new policy of temporarily placing highScientists should not be afraid to engage level scientists directly around its Commisin politics, from local to global. This sioners and senior teams, as well as offering increases the risk of not being perceived as to do so in the European Parliament, when independent, but this can be mitigated by working on major dossiers needing daily always sticking to the facts. We must strive scientific expertise. to be unbiased, no matter the context. This We scientists need to be a little more modimplies that we must speak up if researchers est in understanding that decisions taken by do not follow the principles of good scientific governments are, of course, ultimately politbehavior. At the same time, we must raise our ical and that science is just one of the many voices if individuals or groups try to distract elements under consideration. Given their the public from the evidence. feeling for issues that are appearing on the
10.1126/science.1197503
1751
POLICYFORUM CONSERVATION
Jacob Phelps,1*† Edward L. Webb,1* David Bickford,1† Vincent Nijman,2 Navjot S. Sodhi1
I
nternational wildlife trade remains a leading threat to biodiversity conservation (1) and is a common vector for infectious diseases (2, 3) and invasive species (4) that also affect agriculture, livestock, and public health. With 175 member countries, the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES) is the most important global initiative to monitor and regulate international trade of plants and animals (5). CITES regulates trade of nearly 34,000 species and has reduced threats associated with overharvest of imperiled species for international trade. Credible biological and trade data are core to informing decisions and garnering political will and consensus among CITES parties (6). This does not preclude party bargaining, as occurred during the March 2010 Conference of Parties (CoP) debate over bluefin tuna [e.g., (7)]. Nevertheless, CITES decisions are also frequently hindered by a lack of basic data [e.g., (8–10)]. We highlight CITES limitations and describe potential solutions related to systematic data collection, rigorous data analysis, flexible research methods, and peer review. Systematic, Standardized Data Collection
The CITES secretariat, Animals and Plant Committees (APCs), and external agencies [e.g., International Union for Conservation of Nature (IUCN) Specialist Groups] depend on national agencies to regulate trade. Yet many CITES parties fail to systematically monitor and report international wildlife trade [e.g., (11–13)]. Some of the largest exporters and importers of wildlife products are not fully compliant: Brazil, a significant source country for illegal fauna (14), lacks a functioning central mechanism for reporting wildlife confiscations (15). The United States, a leading importer of wildlife, lacks a coordinated national authority for monitoring wildlife imports (3). Many CITES parties fail to collect domestic population and harvest data, and CITES lacks a standard international reportDepartment of Biological Sciences, National University of Singapore, Singapore, 117543, Singapore. 2School of Social Sciences and Law, Oxford Brookes University, Oxford, OX3 0BP, UK. 1
*These authors contributed equally to this work. †Authors for correspondence. E-mail:
[email protected] (J.P.),
[email protected] (D.B.)
1752
ing mechanism for species-level information (16). Yet this information is central to CITES function (9, 15), as exporters must complete nondetriment f inding (NDF) reports to prove that international trade is not harming populations of regulated species (17). Such baseline data are also fundamental to listing species for CITES protection; commercially high-value species have been listed on the basis of robust, empirical population data [e.g., (6, 18)]. However, most taxa are understudied, and there is a lack of coordinated, systematic data collection within and among parties [supporting online material (SOM)]. Data collection at all levels depends on proper species identification (19), which Genera identified CITES Trade Database
Count* for each genus
Ascocentrum
5
Dendrobium
5
Rhynchostylis
10
Total count
20
Market observations
Aerides
60
Arundina
14
Ascocentrum
7
Bulbophyllum (including Cirrhopetalum)
50
Dendrobium
10
Eria
5
Vanda
6
Vanilla
16
Total count
168
Orchid trade between Lao PDR and Thailand. Comparing CITES Trade Database (2000–09) and a 1-day survey of a single market trader along the Mekong River (February 2010). *The CITES count is based on the number reported, method unreported. Observed count is based on the number of plant bundles (potentially including multiple individuals) plus the number of individuals (potentially divisions of larger plants), both recorded as single counts. This is conservative relative to traditional customs recording, but not necessarily representative of the number of genetically distinct individuals. (See SOM for details.)
To protect biodiversity, more, improved biological and trade data and analyses are needed.
remains a leading challenge. For example, more than 50% of documented live-animal imports into the United States from 2000 to 2006 were identified only by class; only about 14% were identified to species (3). Weak data sets overlook species introductions, substitutions, and exporter misidentifications [e.g., (20)]. Traditional identification protocols and methods are proving inadequate (3, 15) and require revision and innovation (19, 21). Rigorous Analysis
When data are available, analyses under the Secretariat, APCs, and their collaborators often remain insufficient to identify species threatened by trade and to detect trade inaccuracies and loop-holes. For instance, ~20% of species threatened in four mega-diversity countries (Brazil, China, Colombia, and the Philippines) have not been assessed at the international level (22). Similarly, the IUCN holds “no information” about the status of most of the Orchidaceae (23); only three species were added to the Red List of Threatened Species from 2007 to 2009, although sufficient information exists to list many others (24). A handful of studies have highlighted the need for enhanced, rigorous analysis (SOM), yet critical trade linkages often remain undetected when CITES relies on the interest, resources, and often informal or irregular input of independent researchers and organizations (25). Encouragingly, CITES partners are developing tools to enhance analysis capacity, such as the Trade Data Dashboard (26). Flexible Methods
Wildlife trade occurs openly at public border markets (27) and discrete black markets (28). Trade activity shifts and cycles among countries as wild populations are depleted (12, 29), and innovative smuggling techniques are adopted in response to enforcement pressures (28). However, trade data are collected using conventional techniques implemented along easily accessed trade routes (e.g., airports), which cannot capture the true dynamics. For example, CITES reports an insignificant fraction of CITESregulated wild orchid trade into Thailand from Lao People’s Democratic Republic (see the chart), Myanmar, Cambodia, and Vietnam. A single small-scale trader at an informal border market on the Mekong can sell
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Boosting CITES
POLICYFORUM
Solutions in Context
CITES credibility, effectiveness, and success at catalyzing consensus depend heavily on punctilious data collection, analysis, and synthesis. Yet the convention is bound by political and economic realities. General strategies through which to improve CITES (table S1) must recognize that some measures may overlap, prioritization depends on party needs and resources, and recommendations may vary in their political feasibility. CITES has improved party compliance and science-based decision-making despite political sensitivities, through provision of technical support; mission visits and recommendations; simplified reporting procedures; and legal strategies, such as warnings and threats of trade suspensions (5, 36). Such progress demonstrates CITES recognition of the importance of enhanced
enforcement and data collection. Further increasing the demands on CITES parties and secretariat is necessary, but remains administratively demanding, costly, and politically challenging. Some of the most urgent solutions (table S1) require the greatest coordination among parties and institutions. For example, collection of baseline biological data on traded species will require coordinated activities among diverse stakeholders, ranging from rural harvesters to multilateral agencies. CITES has already enhanced data-sharing and analysis through collaborations with nongovernmental organizations and partnerships, such as the Wildlife Enforcement Monitoring System. At the March 2010 CoP, CITES instituted an illegal-trade database working group to enhance data collection and analysis (38). The majority of proposed solutions depends on enhanced active, sustained, and reciprocal engagement of CITES parties with external partners. Funding remains a principal limitation to CITES, especially for on-the-ground execution of mandates and for proposed enhancements (table S1) (25). The secretariat operates on meager party donations (25, 36) of U.S. $5.2M per year for 2009–11 (39). National-level funding for CITES enforcement is similarly restricted, especially in many tropical exporting countries. There is a need for parties, particularly importing nations, to increase contributions dramatically. CITES costs should also be extended to participating industries and consumers, consistent with the “polluter pays” principle, while doing no harm to poor harvesters (40). This can be accomplished through trade levies on CITES-listed wildlife (9), increased infraction penalties ( 19), and wildlife certification schemes (41). Only through increased resources can CITES move toward proactive, real-time monitoring and regulation to strengthen enforcement and data quality. After 35 years, the CITES framework remains highly relevant, and the secretariat and CoP should continue to facilitate progress among noncompliant countries and should exercise legal tools to create consensus. However, current rigors are inadequate, and meaningful improvements will require greater financial and political commitments. We propose targeted CITES negotiations to establish new partnerships; to review financial commitments; and to develop clear rules and progressive standards for data collection, analysis, and review. A strengthened convention is essential to protecting imperiled biodiversity.
References and Notes
1. W. Sutherland et al., Conserv. Biol. 23, 557 (2009). 2. P. Daszak, A. A. Cunningham, A. D. Hyatt, Science 287, 443 (2000). 3. K. F. Smith et al., Science 324, 594 (2009). 4. P. M. Vitousek, C. M. D’Antonio, L. L. Loope, R. Westbrooks, Am. Sci. 84, 468 (1996). 5. P. H. Sand, Eur. J. Int. Law 8, 29 (1997). 6. T. Gehring, E. Ruffing, Glob. Environ. Polit. 8, 123 (2008). 7. S. Milius, Sci. News, 26 March 2010, Web edition; www.sciencenews.org/view/generic/id/57679. 8. V. Nijman, C. R. Shepherd, Wildlife Trade from ASEAN to the EU: Issues with the Trade in Captive-Bred Reptiles from Indonesia (TRAFFIC Europe Report for the EC, Brussels, 2010); www.traffic.org/species-reports/traffic_ species_reptiles26.pdf. 9. V. Nijman, Biodivers. Conserv. 19, 1101 (2010). 10. E. C. M. Parsons, N. A. Rose, T. M. Telecky, Mar. Policy 34, 384 (2010). 11. L. Yi-Ming, L. Dianmo, Biodivers. Conserv. 7, 895 (1998). 12. B. G. Giles, T. S. Ky, D. H. Hoang, C. J. Vincent, Biodivers. Conserv. 15, 2497 (2006). 13. O. G. Amir, Wildlife Trade in Somalia (Antelope Specialist Group, Northeast African Subgroup, IUCN Species Survival Commission, Geneva, 2006). 14. RENCTAS, Primer Relatorio Nacional Sobre o Trafico de Fauna Silvestre [RENCTAS (Brazilian National Network to Fight the Trafficking of Wild Animals), Brazília, 2001]; www.renctas.com.br/files/REL_RENCTAS_pt_final.pdf. 15. J. Pistoni, L. F. Toledo, Braz. Soc. Herpetol. 5, 51 (2010). 16. H. Gerson et al., Conserv. Biol. 22, 4 (2008). 17. CITES, “Non-detriment findings”; www.cites.org/eng/ prog/ndf/index.shtml. 18. A. G. Blundell, Oryx 38, 84 (2004). 19. G. E. Rosen, K. F. Smith, EcoHealth 7, 24 (2010). 20. M. Veith, J. Kosuch, R. Feldmann, H. Martens, A. Seitz, Biodivers. Conserv. 9, 333 (2000). 21. E. P. Green, H. Hendry, Coral Reefs 18, 403 (1999). 22. D. Brito et al., Biol. Conserv. 143, 1154 (2010). 23. United Nations Environment Programme–World Conservation Monitoring Centre (UNEP-WCMC), Threatened Species Database (UNEP-WCMC, Cambridge, 2010); www.unep-wcmc.org. 24. IUCN Species Survival Commission, Orchid Specialist Group General Meeting, Quito, Ecuador, 7 February 2009; www.orchidconservation.org/osg/Docs/OSG% 20Quito%20minutes.pdf. 25. L. Wilson-Wilde, Forensic Sci. Med. Pathol. 6, 221 (2010). 26. CITES, Trade Data Dashboards, http://cites-dashboards. unep-wcmc.org/about. 27. N. Van Song, J. Environ. Dev. 17, 145 (2008). 28. B. Moyle, Global Crime 10, 124 (2009). 29. A. I. Carpenter, J. M. Rowcliffe, A. R. Watkinson, Biol. Conserv. 120, 291 (2004). 30. C. R. Shepherd, V. Nijman, Biodivers. Conserv. 17, 35 (2008). 31. T. R. B. Davenport, H. J. Ndangalasi, Oryx 37, 55 (2003). 32. C. S. Olsen, N. Bhattarai, Mt. Res. Dev. 25, 37 (2005). 33. M. Mohneke, A. B. Onadeko, M. Hirschfeld, M. O. Rodel, TRAFFIC Bull. 22, 117 (2010). 34. F. Courchamp et al., PLoS Biol. 4, e415 (2006). 35. N. S. Sodhi, B. W. Brook, C. J. A. Bradshaw, Tropical Conservation Biology (Blackwell Publishing, Oxford, 2007). 36. R. Reeve, Int. Aff. 82, 881 (2006). 37. Wildlife Trade Policy Reviews, www.cites.org/eng/prog/ policy/index.shtml. 38. Gathering and analysis of data on illegal trade, www.cites.org/eng/dec/valid15/15_42-43.shtml. 39. Resolution on financing, www.cites.org/eng/res/ 14/14-01.shtml. 40. B. Dickson, Oryx 42, 548 (2008). 41. I. G. Warkentin, D. Bickford, N. S. Sodhi, C. J. Bradshaw, Conserv. Biol. 23, 1056 (2009). 42. Funding for J.P. provided by the Harry S. Truman Foundation, for E.L.W. from the Singapore Ministry of Education grant no. R-154-000-400-133.
Supporting Online Material
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more plants in a single day than reported by CITES over a 9-year period (SOM, see the charts on page 1752). Similar trade inaccuracies are evident across taxa (bears, edible tubers, medicinal plants, seahorses, bushmeat, and frogs) and regions (12, 20, 27, 30– 33). Some efforts have been made to integrate alternative, investigative approaches into CITES (e.g., the Lusaka Agreement and CITES-INTERPOL collaborations), but the overall CITES “airport bias” fails to detect the majority of illicit trade. CITES shortcomings may be overlooked because the convention lacks internal and external checks and balances. CITES relies exclusively on country self-reporting, although incentives are high for biased analyses and misreporting (34), and most CITES-listed species occur in the tropics where governance is often weak and corruption high (35). This is especially problematic when CITES National Management Authorities lack independence from their advisory Scientific Authorities (SOM) and because parties’ submissions to CITES are not publically available (36). Critical, independent peer-review offers a legitimate means of party validation, particularly when addressing contentious issues such as harvest quotas, approvals of NDFs, proof of captive breeding, and national management procedures for protected species (8). These reviews may meet with party resistance that could hamper future investigative efforts, especially if they are followed by legal action. However, the recent pilot CITES Policy Review Project in four exporting countries provides an encouraging precedent for future external reviews (37) (SOM).
10.1126/science.1195558
1753
PERSPECTIVES NEUROSCIENCE
Ubiquitination Inhibits Neuronal Exit
Newborn neurons shut down a protein destruction mechanism to migrate to their final destination.
Christine Métin and Camilla Luccardini
1754
24 DECEMBER 2010 VOL 330 SCIENCE www.sciencemag.org Published by AAAS
CREDIT: N. KEVITIYAGALA/SCIENCE
Downloaded from www.sciencemag.org on December 23, 2010
I
n the developing brain, neurons increase ing and recycling of adhesion molecules increased neuronal exit. In contrast, overextheir numbers in an “amplification com- (which enable cells to stick to each other or pressing a functional Siah in the proliferapartment,” from which they emigrate to to extracellular products) and regulate actin tive area blocks neuronal exit and/or alters colonize distant brain structures. How young cytoskeleton dynamics (2, 3). In the cerebral the direction of neuronal movements. neurons leave these compartments, however, cortex, researchers have suggested that polySiah’s target in granule cells is the partihas been unclear, although researchers have ubiquitination and degradation arrests the tioning defective-3 (Pard3) protein, which hypothesized that they turn comprises a “degron,” or bindon a biochemical “migration ing motif (sequence) that acts program” that enables them as a degradation signal (5). to exit. On page 1834 of this By observing the pattern of issue, however, Famulski et Siah expression in the develal. (1) show that one common oping cerebellum, Famulski type of neuron uses an oppoet al. showed that the radisite strategy. To exit, young ally migrating granule cells cerebellar granule neurons express higher levels of Pard3 shut down a protein ubiquiprotein than progenitor cells. tination mechanism that inhibPard3 associates to partitionits the formation of a molecing defective-6 (Pard6) and ular complex that controls a protein kinase C (PKC) to adhesion and allows the neuform the PAR polarity comrons to migrate to a final desplex, which plays a major tination. Granule cells, a large role in cell orientation. PAR population of small neurons is a major regulator of apiin the central nervous system, cal-basal polarity in epitheproliferate at the surface of the lial cells, for instance, and of developing cerebellum. They front-rear polarity in migratthen undergo a transient phase ing cells (6). Solecki et al. had of “tangential” migration near previously shown that the PAR the cerebellar surface before complex localizes to the cenextending a radial process trosome, and that Pard6 sig(sprout-like extension) and naling regulates centrosomal migrating to deeper layers farmotility by acting on microther from their birthplace (see tubules and the actomyosin the figure). The mechanisms cytoskeleton (7, 8). The presthat control the onset of radial ent study investigates another migration are poorly undercellular mechanism by which stood. In their study, Famulski No exit. Siah ubiquitin ligase inhibits the exit of developing young neurons (top) by pre- the PAR complex controls the et al. explore the role of ubiq- venting the formation of PAR3/Jam-C complexes that are required for migration along migration of granule cells. uitination, a common cellular the radial glia. After their last division, young cerebellar granule cells located near the In particular, the authors process in which the protein brain surface extend long tangential processes (middle). They are prevented from moving identified the junctional adheubiquitin bonds to proteins, deeper by Siah activity. If Siah is switched off, the cell extends a third process along the sion molecule C (JAM-C) marking them for destruction radial glia (bottom), along which the nucleus will move toward the deeper brain structure. as a molecular partner of the and recycling. Research is PAR complex in granule cells. revealing that ubiquitination is as important radial migration of neurons (4). JAM-C directly and strongly associates to as phosphorylation in regulating biological Famulski et al. combined a clever exper- Pard3 by an intracellular PDZ domain and processes. Early analyses of ubiquitination’s imental approach with sophisticated imag- localizes at apical tight junctions together role in cell migration, for instance, showed ing techniques to show that the ubiquitin with the PAR complex in epithelial cells that it can activate the intracellular traffick- ligase Seven in Absentia (Siah) regulates (9). They then performed acute gain- and the movements of neurons in the develop- loss-of-function experiments in developing ing cerebellum. Silencing Siah, or over- brain tissue to show that Pard3 controls the Institut du Fer à Moulin, INSERM U839, 75005 Paris, expressing a truncated Siah ligase in the radial migration of granule cells by controlFrance. E-mail:
[email protected];
[email protected] region where neurons proliferate, results in ling the formation of JAM-C positive cell-
PERSPECTIVES plex. A limitation of their findings, however, was that they could not directly demonstrate that the Pard3 protein is both translated and degraded by Siah-dependent ubiquitination in immature granule cells that are located near the surface of the brain structure. Indeed, as granule cells overexpessing Pard3 are induced to migrate radially, Siah invalidation that increases the Pard3 signal may also induce the exit of transfected cells toward deeper brain regions. Famulski et al. circumvented this difficulty by using in vivo imaging to visualize the extinction of a fluorescent signal linked to the degron motif of Pard3 in Siah-expressing cells located at the surface of the cerebellum. Using the cerebellum as a model system, Solecki and colleagues shed light on a previously unknown mechanism to control neuronal migration from proliferative compartments. Interestingly, their results suggest that increased migration from the compartment is associated with reduced cell cycle exit. Repressing neuronal exit by posttranslational modification of a protein in a complex appears to be a very efficient means of quickly adapting both the proliferation and the migration
of young neurons to environmental changes. It is now important to determine whether a similar mechanism controls the migration of embryonic neurons along radial glia in other brain regions, particularly in the cerebral cortex, and how it interacts with other guidance mechanisms. At the cellular level, another important question is how this new function of the PAR complex at the periphery of migrating neurons correlates with the function of the PAR complex function in the centrosome. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
References
J. K. Famulski et al., Science 330, 1834 (2010). V. H. Lobert et al., Dev. Cell 19, 148 (2010). Y. Chen et al., Mol. Cell 35, 841 (2009). L. Feng, N. S. Allen, S. Simo, J. A. Cooper, Genes Dev. 21, 2717 (2007). C. M. House et al., Proc. Natl. Acad. Sci. U.S.A. 100, 3101 (2003). S. Etienne-Manneville, Oncogene 27, 6970 (2008). D. J. Solecki et al., Nat. Neurosci. 7, 1195 (2004). D. J. Solecki et al., Neuron 63, 63 (2009). G. Mandicourt et al., J. Biol. Chem. 282, 1830 (2007). A. Chédotal, Trends Neurosci. 33, 163 (2010). C. Métin, R. B. Vallee, P. Rakic, P. G. Bhide, J. Neurosci. 28, 11746 (2008). J. Renaud et al., Nat. Neurosci. 11, 440 (2008).
Downloaded from www.sciencemag.org on December 23, 2010
cell contacts. This work identified a previously unknown function of the Pard3/JAM-C complex and shed light on a new mechanism involved in radial migration. The radial migration of newborn granule cells starts with the extension of a process along thin radial fibers in the developing brain. This process emerges in front of the centrosome, which moves in afterward (10). Several models suggest that radial migration requires forward movements of the centrosome toward the tip of the elongating process and coordinated nuclear translocations (11). Several factors that can affect this process in granule cells have been identified. In particular, previous studies have shown that the protein Semaphorin-6A and its Plexin A2 receptor control the switch from tangential to radial migration and regulate the nuclearcentrosomal coupling (12). Nevertheless, the cell mechanisms and signaling pathways involved remain poorly understood. Famulski et al. showed that Siah regulates the morphogenetic movements of granule cells through the post-translational regulation of the Pard3/JAM-C adhesive com-
10.1126/science.1200475
PLANETARY SCIENCE
Generating an Atmosphere Dale P. Cruikshank
CREDIT: NASA/JPL/SPACE SCIENCE INSTITUTE
T
he presence of water ice on most of the large satellites of the outer planets was established many years ago through near-infrared (1- to 2.5-µm wavelength) observations with ground-based telescopes. Frozen carbon dioxide, sulfur dioxide, methane, nitrogen, and other molecular ices are also found in various combinations on inner planets such as Mars to bodies far beyond Pluto. Recent discoveries of ice varieties on some asteroids and sequestered in protected regions on Mercury and the Moon point to the near-universal distribution of frozen volatiles throughout the solar system (1–3). On page 1813 of this issue, Teolis et al. (4) report the detection of a tenuous (approximately one in five trillionth of Earth’s atmospheric pressure at sea level) oxygen (O2) and carbon dioxide (CO2) atmosphere surrounding Saturn’s icy moon Rhea (diameter of 1529 km) measured as the Cassini spacecraft passed by only 97 km Astrophysics Branch, NASA Ames Research Center, Moffett Field, CA 94035–1000, USA. E-mail: dale.p.cruikshank@ nasa.gov
The oxygen and carbon dioxide atmosphere on Saturn’s moon Rhea is produced by a photochemical reaction mechanism.
Rhea close up. Image in visible light on 17 October 2010 from a distance of 44,000 km above the moon’s surface. The image was obtained with the Cassini Imaging Science Subsystem narrow-angle camera, showing ancient cratered terrain. The frame is 280 km on each side.
above the surface in March 2010 (see the figure). The Ion Neutral Mass Spectrometer onboard Cassini captured a sample of Rhea’s atmosphere and sorted the molecules by mass, confirming the presence of O2 and CO2. When these measurements are combined with data acquired by two other Cassini instruments on more distant flybys of Rhea in 2005 and 2007, a picture emerges in which O2 formed by the irradiation of water ice is ejected from the surface by charged-particle interactions, and the tenuous gas is then swept away into space. These new results expand and clarify our understanding of the pro-
cesses by which new molecules are synthesized, ejected, and then lost. Solar system ices are not static deposits that remain undisturbed for all time. Ices in the interiors of planetary satellites and comets can warm, evaporate, and burst through
www.sciencemag.org SCIENCE VOL 330 24 DECEMBER 2010 Published by AAAS
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PERSPECTIVES plex. A limitation of their findings, however, was that they could not directly demonstrate that the Pard3 protein is both translated and degraded by Siah-dependent ubiquitination in immature granule cells that are located near the surface of the brain structure. Indeed, as granule cells overexpessing Pard3 are induced to migrate radially, Siah invalidation that increases the Pard3 signal may also induce the exit of transfected cells toward deeper brain regions. Famulski et al. circumvented this difficulty by using in vivo imaging to visualize the extinction of a fluorescent signal linked to the degron motif of Pard3 in Siah-expressing cells located at the surface of the cerebellum. Using the cerebellum as a model system, Solecki and colleagues shed light on a previously unknown mechanism to control neuronal migration from proliferative compartments. Interestingly, their results suggest that increased migration from the compartment is associated with reduced cell cycle exit. Repressing neuronal exit by posttranslational modification of a protein in a complex appears to be a very efficient means of quickly adapting both the proliferation and the migration
of young neurons to environmental changes. It is now important to determine whether a similar mechanism controls the migration of embryonic neurons along radial glia in other brain regions, particularly in the cerebral cortex, and how it interacts with other guidance mechanisms. At the cellular level, another important question is how this new function of the PAR complex at the periphery of migrating neurons correlates with the function of the PAR complex function in the centrosome. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
References
J. K. Famulski et al., Science 330, 1834 (2010). V. H. Lobert et al., Dev. Cell 19, 148 (2010). Y. Chen et al., Mol. Cell 35, 841 (2009). L. Feng, N. S. Allen, S. Simo, J. A. Cooper, Genes Dev. 21, 2717 (2007). C. M. House et al., Proc. Natl. Acad. Sci. U.S.A. 100, 3101 (2003). S. Etienne-Manneville, Oncogene 27, 6970 (2008). D. J. Solecki et al., Nat. Neurosci. 7, 1195 (2004). D. J. Solecki et al., Neuron 63, 63 (2009). G. Mandicourt et al., J. Biol. Chem. 282, 1830 (2007). A. Chédotal, Trends Neurosci. 33, 163 (2010). C. Métin, R. B. Vallee, P. Rakic, P. G. Bhide, J. Neurosci. 28, 11746 (2008). J. Renaud et al., Nat. Neurosci. 11, 440 (2008).
Downloaded from www.sciencemag.org on December 23, 2010
cell contacts. This work identified a previously unknown function of the Pard3/JAM-C complex and shed light on a new mechanism involved in radial migration. The radial migration of newborn granule cells starts with the extension of a process along thin radial fibers in the developing brain. This process emerges in front of the centrosome, which moves in afterward (10). Several models suggest that radial migration requires forward movements of the centrosome toward the tip of the elongating process and coordinated nuclear translocations (11). Several factors that can affect this process in granule cells have been identified. In particular, previous studies have shown that the protein Semaphorin-6A and its Plexin A2 receptor control the switch from tangential to radial migration and regulate the nuclearcentrosomal coupling (12). Nevertheless, the cell mechanisms and signaling pathways involved remain poorly understood. Famulski et al. showed that Siah regulates the morphogenetic movements of granule cells through the post-translational regulation of the Pard3/JAM-C adhesive com-
10.1126/science.1200475
PLANETARY SCIENCE
Generating an Atmosphere Dale P. Cruikshank
CREDIT: NASA/JPL/SPACE SCIENCE INSTITUTE
T
he presence of water ice on most of the large satellites of the outer planets was established many years ago through near-infrared (1- to 2.5-µm wavelength) observations with ground-based telescopes. Frozen carbon dioxide, sulfur dioxide, methane, nitrogen, and other molecular ices are also found in various combinations on inner planets such as Mars to bodies far beyond Pluto. Recent discoveries of ice varieties on some asteroids and sequestered in protected regions on Mercury and the Moon point to the near-universal distribution of frozen volatiles throughout the solar system (1–3). On page 1813 of this issue, Teolis et al. (4) report the detection of a tenuous (approximately one in five trillionth of Earth’s atmospheric pressure at sea level) oxygen (O2) and carbon dioxide (CO2) atmosphere surrounding Saturn’s icy moon Rhea (diameter of 1529 km) measured as the Cassini spacecraft passed by only 97 km Astrophysics Branch, NASA Ames Research Center, Moffett Field, CA 94035–1000, USA. E-mail: dale.p.cruikshank@ nasa.gov
The oxygen and carbon dioxide atmosphere on Saturn’s moon Rhea is produced by a photochemical reaction mechanism.
Rhea close up. Image in visible light on 17 October 2010 from a distance of 44,000 km above the moon’s surface. The image was obtained with the Cassini Imaging Science Subsystem narrow-angle camera, showing ancient cratered terrain. The frame is 280 km on each side.
above the surface in March 2010 (see the figure). The Ion Neutral Mass Spectrometer onboard Cassini captured a sample of Rhea’s atmosphere and sorted the molecules by mass, confirming the presence of O2 and CO2. When these measurements are combined with data acquired by two other Cassini instruments on more distant flybys of Rhea in 2005 and 2007, a picture emerges in which O2 formed by the irradiation of water ice is ejected from the surface by charged-particle interactions, and the tenuous gas is then swept away into space. These new results expand and clarify our understanding of the pro-
cesses by which new molecules are synthesized, ejected, and then lost. Solar system ices are not static deposits that remain undisturbed for all time. Ices in the interiors of planetary satellites and comets can warm, evaporate, and burst through
www.sciencemag.org SCIENCE VOL 330 24 DECEMBER 2010 Published by AAAS
1755
PERSPECTIVES teorites that continuously dust the solid bodies of the solar system, including Earth. In the tenuous O2 atmosphere of Rhea, molecules rarely collide with one another, such that the rate of escape into space approximates the rate of ejection from the surface. Therefore, in the current epoch, the atmosphere is probably not increasing appreciably in density and surface pressure. However, Teolis et al. find that the rate of O2 generation in the ice exceeds the rate of ejection from it, leading to the buildup of an oxygen reservoir. The episodic or long-term release of this stored oxygen could increase the total atmospheric density, but it would still be considered tenuous. The presence of an oxygen-rich atmosphere of entirely radiolytic (photodriven) origin raises the question of using the detection of oxygen on an extrasolar planet as a criterion indicating the occurrence of life. The first detection of an oxygen-rich atmosphere on an extrasolar planet is likely to be accomplished by spectroscopy, which will require a some-
what denser atmosphere than Rhea currently has. It is notable, however, that emission of atomic oxygen in the tenuous atmospheres of Jupiter’s moons Europa and Ganymede was detected by ultraviolet spectroscopy with the Hubble Space Telescope (5) and similarly in an extrasolar planet atmosphere (6). Additional laboratory and theoretical studies of O2 production in ice by interaction with the nearby space environment and the development of a dense atmosphere should further clarify the feasibility of using this particular criterion, often cited as a hopeful sign of life in a remote planetary system (7). 1. 2. 3. 4.
References
J. B. Dalton et al., Space Sci. Rev. 153, 113 (2010). H. Campins et al., Nature 464, 1320 (2010). A. Colaprete et al., Science 330, 463 (2010). B. D. Teolis et al., Science 330, 1813 (2010); 10.1126/ science.1198366. 5. D. T. Hall, P. D. Feldman, M. A. McGrath, D. F. Strobel, Astrophys. J. 499, 475 (1998). 6. A. Vidal-Madjar et al., Astrophys. J. 604, L69 (2004). 7. L. Kaltenegger, Astrophys. J. 712, L125 (2010). 10.1126/science.1200473
COMPUTER SCIENCE
Computational Physics in Film
Numerical modeling of how objects and fluids move, collide, and break up underlies spellbinding video animations.
Robert Bridson1,2,3*and Christopher Batty1
C
omputer simulation of solid and fluid dynamics underlies many visual effects seen in films produced during the past decade. This approach not only is less expensive than filming live action but also can avoid putting actors and crews in dangerous settings and can allow visualization of the impossible. Compared with more traditional animation methods that rely chiefly on artists’ efforts, numerical solutions to the equations of physics allow computers to calculate realistic motion, such of that of smoke, fire, explosions, water, rubble, clothing, hair, muscles, and skin. Algorithmic advances now afford artists a higher-level, more efficient role in guiding the physics as they produce animation. We provide an overview here of current challenges in physics-based animation. The movement and collisions of rigid bodies have long been the mainstay of physDepartment of Computer Science, University of British Columbia, 201-2366 Main Mall, Vancouver, BC V6T 1Z4, Canada. 2Exotic Matter AB, Svardvagen 7, 182 33 Danderyd, Sweden. 3Weta Digital Ltd., 9-11 Manuka Street, Miramar, Wellington, 6022 New Zealand. 1
*To whom correspondence should be addressed. E-mail:
[email protected]
1756
ics-based animation, but modeling and integrating frictional contact remains a serious challenge. Structured stacks of blocks, highly nonconvex geometry, and delicate balances between pressure and friction all can pose torture tests for numerical methods that must exactly balance forces to keep these assemblies stable. Kaufman et al. (1) discuss new methods that use alternating projections (a way to calculate where interactions occur) to solve a constrained optimization formulation of contact. Some objects, such as hair and clothing, are naturally deformable, which complicates the collision problem. In hair simulation, modeling the contacts between individual hairs creates a problem of computational scale. Resolving all of the collisions between the 100,000 hairs on a human head overwhelms brute-force methods. McAdams et al. (2) have taken a multiscale approach by treating hair as a continuum fluid, rather than discrete strands. This approach resolves the motion of the hair as a whole by averaging the motion into a continuous vector field, but truly accurate vector-field equations have yet to be derived. Kaldor et al. (3) have taken the opposite route in clothing simulation. Rather
than use models that homogenize the twodimensional (2D) surface of clothing, they perform a full simulation of every loop and twist in the yarn of knitwear and create subtle behaviors that simpler methods cannot reproduce. However, densely woven fabrics still require more efficient modeling as isometric surfaces, ones that bend but do not stretch or shear. English and Bridson (4) recently resolved the “locking” problem plaguing earlier efforts in which isometry constraints inadvertently prevent the natural bending. Paradoxically, their solution involves allowing holes to open up in the cloth between mesh triangles (the numerical regions into which the surface is decomposed). This finding poses interesting questions in discontinuous geometry, in that the mapping from surface parameters is neither continuous nor differentiable. Volumetric elasticity—handling fully 3D deformation—is used in biomechanical models of the flesh of virtual creatures (5). Studios are rapidly increasing the anatomical detail of their models, from the complexities of muscles and tendons to delicate wrinkles in the skin. The amount of detail in the surface as well as the structures underneath the skin (muscles, tendons, bones, and other organs),
24 DECEMBER 2010 VOL 330 SCIENCE www.sciencemag.org Published by AAAS
Downloaded from www.sciencemag.org on December 23, 2010
the surface, sending jets of gas and dust into space, as we have seen emanating from Saturn’s moon Enceladus and numerous comets. Ices exposed on the cold surfaces of outer planetary moons interact with the local space environment, as the incident solar ultraviolet light and charged particles from deep space and trapped in the parent planet’s magnetosphere cause chemical changes in the ice and its evaporation into space by sputtering. Although these chemical changes occur at the molecular and even the atomic level, remotesensing instruments on Earth and on passing spacecraft can detect them directly by optical (ultraviolet through infrared wavelengths) spectroscopy and by measurements from flybys high above the surface. The origin of carbon dioxide is less clear, and requires either that CO2 is native to Rhea’s icy inventory, or that it forms at the surface from the released O2 acting on carbon-rich grains. Such grains may be native to Rhea or entrained in its ice, but a more likely source is the carbonaceous microme-
PERSPECTIVES teorites that continuously dust the solid bodies of the solar system, including Earth. In the tenuous O2 atmosphere of Rhea, molecules rarely collide with one another, such that the rate of escape into space approximates the rate of ejection from the surface. Therefore, in the current epoch, the atmosphere is probably not increasing appreciably in density and surface pressure. However, Teolis et al. find that the rate of O2 generation in the ice exceeds the rate of ejection from it, leading to the buildup of an oxygen reservoir. The episodic or long-term release of this stored oxygen could increase the total atmospheric density, but it would still be considered tenuous. The presence of an oxygen-rich atmosphere of entirely radiolytic (photodriven) origin raises the question of using the detection of oxygen on an extrasolar planet as a criterion indicating the occurrence of life. The first detection of an oxygen-rich atmosphere on an extrasolar planet is likely to be accomplished by spectroscopy, which will require a some-
what denser atmosphere than Rhea currently has. It is notable, however, that emission of atomic oxygen in the tenuous atmospheres of Jupiter’s moons Europa and Ganymede was detected by ultraviolet spectroscopy with the Hubble Space Telescope (5) and similarly in an extrasolar planet atmosphere (6). Additional laboratory and theoretical studies of O2 production in ice by interaction with the nearby space environment and the development of a dense atmosphere should further clarify the feasibility of using this particular criterion, often cited as a hopeful sign of life in a remote planetary system (7). 1. 2. 3. 4.
References
J. B. Dalton et al., Space Sci. Rev. 153, 113 (2010). H. Campins et al., Nature 464, 1320 (2010). A. Colaprete et al., Science 330, 463 (2010). B. D. Teolis et al., Science 330, 1813 (2010); 10.1126/ science.1198366. 5. D. T. Hall, P. D. Feldman, M. A. McGrath, D. F. Strobel, Astrophys. J. 499, 475 (1998). 6. A. Vidal-Madjar et al., Astrophys. J. 604, L69 (2004). 7. L. Kaltenegger, Astrophys. J. 712, L125 (2010). 10.1126/science.1200473
COMPUTER SCIENCE
Computational Physics in Film
Numerical modeling of how objects and fluids move, collide, and break up underlies spellbinding video animations.
Robert Bridson1,2,3*and Christopher Batty1
C
omputer simulation of solid and fluid dynamics underlies many visual effects seen in films produced during the past decade. This approach not only is less expensive than filming live action but also can avoid putting actors and crews in dangerous settings and can allow visualization of the impossible. Compared with more traditional animation methods that rely chiefly on artists’ efforts, numerical solutions to the equations of physics allow computers to calculate realistic motion, such of that of smoke, fire, explosions, water, rubble, clothing, hair, muscles, and skin. Algorithmic advances now afford artists a higher-level, more efficient role in guiding the physics as they produce animation. We provide an overview here of current challenges in physics-based animation. The movement and collisions of rigid bodies have long been the mainstay of physDepartment of Computer Science, University of British Columbia, 201-2366 Main Mall, Vancouver, BC V6T 1Z4, Canada. 2Exotic Matter AB, Svardvagen 7, 182 33 Danderyd, Sweden. 3Weta Digital Ltd., 9-11 Manuka Street, Miramar, Wellington, 6022 New Zealand. 1
*To whom correspondence should be addressed. E-mail:
[email protected]
1756
ics-based animation, but modeling and integrating frictional contact remains a serious challenge. Structured stacks of blocks, highly nonconvex geometry, and delicate balances between pressure and friction all can pose torture tests for numerical methods that must exactly balance forces to keep these assemblies stable. Kaufman et al. (1) discuss new methods that use alternating projections (a way to calculate where interactions occur) to solve a constrained optimization formulation of contact. Some objects, such as hair and clothing, are naturally deformable, which complicates the collision problem. In hair simulation, modeling the contacts between individual hairs creates a problem of computational scale. Resolving all of the collisions between the 100,000 hairs on a human head overwhelms brute-force methods. McAdams et al. (2) have taken a multiscale approach by treating hair as a continuum fluid, rather than discrete strands. This approach resolves the motion of the hair as a whole by averaging the motion into a continuous vector field, but truly accurate vector-field equations have yet to be derived. Kaldor et al. (3) have taken the opposite route in clothing simulation. Rather
than use models that homogenize the twodimensional (2D) surface of clothing, they perform a full simulation of every loop and twist in the yarn of knitwear and create subtle behaviors that simpler methods cannot reproduce. However, densely woven fabrics still require more efficient modeling as isometric surfaces, ones that bend but do not stretch or shear. English and Bridson (4) recently resolved the “locking” problem plaguing earlier efforts in which isometry constraints inadvertently prevent the natural bending. Paradoxically, their solution involves allowing holes to open up in the cloth between mesh triangles (the numerical regions into which the surface is decomposed). This finding poses interesting questions in discontinuous geometry, in that the mapping from surface parameters is neither continuous nor differentiable. Volumetric elasticity—handling fully 3D deformation—is used in biomechanical models of the flesh of virtual creatures (5). Studios are rapidly increasing the anatomical detail of their models, from the complexities of muscles and tendons to delicate wrinkles in the skin. The amount of detail in the surface as well as the structures underneath the skin (muscles, tendons, bones, and other organs),
24 DECEMBER 2010 VOL 330 SCIENCE www.sciencemag.org Published by AAAS
Downloaded from www.sciencemag.org on December 23, 2010
the surface, sending jets of gas and dust into space, as we have seen emanating from Saturn’s moon Enceladus and numerous comets. Ices exposed on the cold surfaces of outer planetary moons interact with the local space environment, as the incident solar ultraviolet light and charged particles from deep space and trapped in the parent planet’s magnetosphere cause chemical changes in the ice and its evaporation into space by sputtering. Although these chemical changes occur at the molecular and even the atomic level, remotesensing instruments on Earth and on passing spacecraft can detect them directly by optical (ultraviolet through infrared wavelengths) spectroscopy and by measurements from flybys high above the surface. The origin of carbon dioxide is less clear, and requires either that CO2 is native to Rhea’s icy inventory, or that it forms at the surface from the released O2 acting on carbon-rich grains. Such grains may be native to Rhea or entrained in its ice, but a more likely source is the carbonaceous microme-
CREDIT: IMAGE COURTESY OF EXOTIC MATTER AB
and the precise calculation of force response for different materials, all contribute to the ongoing challenge of controlling the motion of characters while making it appear that simulated muscles are doing the work. Objects not only move, they also break. Research efforts in depicting fracture mechanics began with O’Brien and Hodgins’s work (6) using remeshing, which improves the geometric fidelity where surfaces break by changing the mesh in that region. More recent techniques embed crack geometry in finite elements models of the mechanics (7, 8). These simulations are still difficult to control and have yet to truly break into film production. Some of the most spectacular examples of physics in film involve fluids, where nonlinearities in the underlying Navier-Stokes equations that describe fluid motion lead to accumulation of remarkable geometric complexity. A recent trend for depicting liquids has been the emergence of mesh-based surface tracking. In a preliminary attempt to follow the details of a water surface as closely as possible, Brochu et al. (9), inspired by approaches for cloth collision processing, developed a method that matches the degrees of freedom in the simulation to the geometry of the deforming surface mesh, rather than the other way around. However, the difficulties involved in making this approach truly robust are still daunting; meshes in three dimensions find endless ways to cause numerical and combinatorial troubles. Horvath and Geiger (10) have probably achieved the greatest level of detail yet in fire and smoke with a two-level approach. The hybrid particle-grid method (fluid-implicit particle, or FLIP) (11) provides a high-quality (albeit relatively low resolution) 3D simulation. They use it to guide extremely highresolution simulations on 2D slices through the volume (oriented to the camera), running in parallel on commodity graphics processing unit (GPU) hardware. This idea of getting the bulk motion from fast, low-resolution simulations and then adding localized detail with secondary simulation is being pursued by many groups, although properly accounting for dynamics within a grid element at low resolutions remains a major hurdle. The problem of scale in general looms large (and small). Consider a ship on a rough sea; the figure shows fairly convincing detail that is achievable on a single workstation today [a full movie clip is available, see (12); a compressed version is available at (13)]. This simulation still falls far short of a shot encompassing stormy waves to the horizon as well as small scales down to the tiny droplets breaking up on the rigging. Brute-force methods
Downloaded from www.sciencemag.org on December 23, 2010
PERSPECTIVES
Realistic simulations. A simulated ship upon a simulated ocean. Here, the film industry’s Naiad software is used to evolve the incompressible Navier-Stokes equations for the water, strongly coupled with the rigid-body dynamics of the longboat, with additional phenomenological simulation of foam and spray.
of today would require orders of magnitude more computing resources than are available. The other performance challenge is increasing speed at the simulation resolutions currently in use. Studios can cope with simulations running overnight, but such time scales do not allow much scope for iterative refinement. When film-quality simulations can run at real-time rates, remarkably more effective artist interaction is possible—not just more design cycles but also experimentation with continuous feedback. With more general simulations emerging that fully couple all of the solid and fluid dynamics mentioned above, we are already beginning to see the advent of “virtual practical effects.” Artists can use their natural intuition about how physics works to build virtual devices to control the virtual world of a shot, rather than awkwardly manipulating parameters in equations. The quality of physics-based animation methods is not simple to evaluate. Ultimately, success is judged by the director, and the worth of the underlying algorithms can be judged by the users based on how well it helps them do their jobs. However, given the time and effort involved in using a new method in production, and the difficulty of creating objective, quantitative metrics, researchers need something better to analyze their efforts. Classic means of evaluating algorithms, such as convergence rates for iterative schemes, have limited use. Qualitatively good results (ones that convince audiences) are usually obtained well before a model simulation converges. Psychophysical results in video compression, for example, also suggest that the mathematically convenient norms commonly used in numerical analysis are not a
good match for human-oriented evaluation. Moreover, assessing the errors in underlying models on which algorithms are based is a particular challenge for film. Unlike traditional science, reality does not necessarily provide a ground truth against which film models can be compared. Film production on a set already demands something enhanced beyond reality. Tackling the problem of objective and useful evaluation will likely demand cross-disciplinary efforts in understanding the human perception of complex dynamics. In the short term, success can continue to be judged by increasing calls for “lights, camera, simulation.” References and Notes
1. D. M. Kaufman, S. Sueda, D. L. James, D. K. Pai, ACM Trans. Graph. 27, 10.1145/1457515.1409117 (2008). 2. A. McAdams, A. Selle, K. Ward, E. Sifakis, J. Teran, ACM Trans. Graph. H 28, 10.1145/1531326.1531368 (2009). 3. J. M. Kaldor, D. L. James, S. Marschner, ACM Trans. Trans. Graph 29, 10.1145/1778765.1778842 (2010). 4. E. English, R. Bridson, A. C. M. Trans. Graph. 27, 10.1145/1360612.1360665 (2008). 5. S.-H. Lee, E. Sifakis, D. Terzopoulos, ACM Trans. Graph. 28, 10.1145/1559755.1559756 (2009). 6. J. O’Brien, J. Hodgins, in Proc. SIGGRAPH, 18 (ACM, New York, 1999), pp. 137–146. 7. N. Molino, Z. Bao, R. Fedkiw, ACM Trans. Graph. 23, 385 (2004). 8. P. Kaufmann, S. Martin, M. Botsch, E. Grinspun, M. Gross, ACM Trans. Graph. 28, 10.1145/1531326.1531356 (2009). 9. T. Brochu, C. Batty, R. Bridson, ACM Trans. Graph. 29, 10.1145/1778765.1778784 (2010). 10. C. Horvath, W. Geiger, ACM Trans. Graph. 28, 10.1145/1531326.1531347 (2009). 11. Y. Zhu, R. Bridson, ACM Trans. Graph. 24, 965 (2005). 12. www.youtube.com/watch?v=3pojNrRfJFM 13. http://people.cs.ubc.ca/~rbridson/movies/NaiadLongboat. mov 14. Supported in part by a grant from the Natural Sciences and Engineering Research Council of Canada.
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PERSPECTIVES GENETICS
Revealing the Dark Matter of the Genome
Integrated data sets from two animal model organisms provide insights into the organization, structure, and function of their genomes.
expression posttranslationally, and marking of the histone proteins on which the DNA is wound with chemical tags to define regions of the genome that are active or silent. One could analyze this complex regulatory landscape one factor or region at a time, but this would miss the big picture. The ENCODE (Encyclopedia of DNA Elements) projects are using large-scale, genome-wide assays to identify the interactions among transcription factors, ncRNA, chromatin marks, and gene expression—seeking functions for the dark genome. Initial data from the human ENCODE project (3) revealed an incredible density of regulatory marks and interactions on a small portion (1%) of the human genome. The modENCODE (model organism Encyclopedia of DNA Elements) strand of the project is using the power of model organism genomics to reveal genome-wide patterns of regulatory interactions (1, 2). Model organisms, such as the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans, are chosen for many reasons, including ease of cell culture and amenability to experimentation. In the era of genome science, one of their key benefits is their small genomes: 100 million bases Computing the organism. Integrated datasets across transcription, epigenome, and protein-DNA interactions describe (Mb) for C. elegans (5) and 180 Mb for D. melanogaster (6) [compared the dynamic regulation of gene expression in the nematode and fly model organisms. to the 3000-Mb human genome (7, tissues functioning together as organs, and is transcribed into protein-coding messen- 8)]—and a much larger proportion of their organs shaping the body’s systems; and of ger RNA (mRNA) and non–protein-coding genomes shows signatures of evolutionary individuals responding appropriately to the RNA (ncRNA), and DNA elements that con- constraint. Both models have been examined varied challenges of life and surviving to trol the expression of genes occupy another with a huge armory of genetic and molecubreed. Poisons in food are detoxified, patho- ~0.5%, suggesting that the remaining “dark lar tools, and our understanding of how their gens are killed, parasites are eliminated, and genome” is nonfunctional padding. How- embryos develop, and how the adult organpredators avoided through the deft employ- ever, 5% of the human and other mammalian isms function, exceeds that for any other animent of responses encoded in the genome. genomes are under evolutionary constraint, mal. Their role in modENCODE is to pilot It is not currently possible to compute an suggesting biological functions (3, 4). What technologies, especially those of data analorganism from its genome, performing are these functions and how are they inte- ysis, and to provide reference points for the the transformation so efficiently executed grated? Three interacting systems coordi- emerging human ENCODE data. Fruit fly nate gene expression in space and time: tran- and nematode modENCODE projects have scription factors that bind to DNA in pro- performed hundreds of experiments and proInstitute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT, UK. E-mail:
[email protected] moters of genes, ncRNA that modifies gene duced billions of data points to permit the
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by embryos, but two articles in this issue, by Gerstein et al. on page 1775 (1) and the modENCODE Consortium on page 1787 (2), bring this goal closer. The genomes of multicellular animals are big and complex, but functions have been defined for only a small proportion of them. Only 1% of the human genome
24 DECEMBER 2010 VOL 330 SCIENCE www.sciencemag.org Published by AAAS
CREDITS: DNA, Y. HAMMOND/SCIENCE; FRUIT FLY, TOMASZ ZACHARIASZ/ISTOCKPHOTO; C. ELEGANS, BOB GOLDSTEIN/WIKIMEDIA COMMONS
A
nimal embryos successfully transform the two-dimensional code of their genome into multidimensional organisms that are ready to meet the challenge of natural selection. In addition to the three dimensions of the body, animal genomes inform additional dimensions: of cells coordinating to form tissues,
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Mark Blaxter
PERSPECTIVES Both Gerstein et al. and the modENCODE Consortium report a curious class of short (100-base) elements in the genomes called highly occupied target (HOT) regions (10, 11). HOT regions were repeatedly identified as binding many different transcription factors, but are curiously not enriched in the known DNA motifs to which these factors bind, suggesting that the interactions may be indirect. HOT regions are stable and associate with gene transcription start sites, and in C. elegans they are associated with genes that are universally expressed through development at high levels. In D. melanogaster, HOT sites are also sites of binding by proteins involved in originating DNA replication. Both studies identified novel sequence motifs that are enriched in HOT regions, but these motifs are not shared between the two species and most do not match known transcription factor binding sites, suggesting that the proteins that bind to these motifs are yet to be identified. The Large Hadron Collider is the preeminent, long-term cooperative enterprise in the physical sciences, dedicated to gathering
data to fully parameterize the basic physical constants of the universe and understand dark matter. In the same way, the modENCODE and ENCODE deep genomics programs will, in time, deliver the power to model and predict organism function from multidimensional data, shine light on the dark genome, and hopefully allow a better understanding of the healthy human and how to treat human disease. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
References
M. B. Gerstein et al., Science 330, 1775 (2010). modENCODE Consortium et al., Science 330, 1787 (2010). ENCODE Project Consortium, Nature 447, 799 (2007). L. Eory, D. L. Halligan, P. D. Keightley, Mol. Biol. Evol. 27, 177 (2010). C. elegans Sequencing Consortium, Science 282, 2012 (1998). M. D. Adams et al., Science 287, 2185 (2000). J. C. Venter et al., Science 291, 1304 (2001). International Human Genome Sequencing Consortium, Nature 409, 860 (2001). U. Alon, Nat. Rev. Genet. 8, 450 (2007). S. MacArthur et al., Genome Biol. 10, R80 (2009). C. Moorman et al., Proc. Natl. Acad. Sci. U.S.A. 103, 12027 (2006). Published online 22 December 2010; 10.1126/science.1200700
MATERIALS SCIENCE
Stretching Dielectric Elastomer Performance Federico Carpi,1 Siegfried Bauer,2 Danilo De Rossi1 Devices using materials that deform in response to electricity are based on a phenomenon that was observed more than two centuries ago.
T
he idea that a solid material can deform when stimulated by electricity originated in the late-18th century with observations of ruptures in overcharged Leyden jars, the first electrical capacitors. In 1776, Italian scientist Alessandro Volta mentioned in a letter that Italian experimenter Felice Fontana had noted volume changes in the Leyden jar upon electrification (1), an observation that launched a new field of investigation—“deformable” materials affected by electricity. More than two centuries later, the concept of “electrically stretchable materials” is at the forefront of devising bioinspired robots, tactile Interdepartmental Research Centre “E. Piaggio,” School of Engineering, University of Pisa, Pisa 56100, Italy. 2Department of Soft Matter Physics, Johannes Kepler University, Linz A-4040, Austria. E-mail:
[email protected] 1
and haptic interfaces, and adaptive optical systems (2, 3). This diversity of applications took a great leap 10 years ago in a landmark study by Pelrine and colleagues (4). They reported high-speed, giant-strain, electrically actuated elastomers with unprecedented electromechanical transduction performance. These materials were demonstrated for so-called dielectric elastomer actuators, deformable capacitors made of a film of a soft insulator (such as acrylic, polyurethane, or silicone elastomer), with compliant electrodes. Upon electrical charging, purely electrostatic forces caused the elastomer film to undergo substantial thickness compression and surface expansion (4). The exceptional performance of these dielectric elastomer actuators gave rise to a scientific and technological revolution in the field of
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building of new models of gene expression regulation. These can be used to describe the idiosyncratic development and biology of each animal, but the excitement lies in the commonalities in the overall structures of the regulatory landscape they reveal. Before modeling gene expression patterns, one first has to know what genes are present. Despite the deep annotation available for the fruit fly and nematode genomes, both projects have identified many new genes and parts of genes. In C. elegans, Gerstein et al. found evidence supporting 95% of existing protein-coding gene predictions, but 1650 new genes are now added for a total of ~22,000. The number of RNA transcripts identified is now triple the previous estimate (to about three per gene), and the ncRNA set is greater by a factor of 20. Evidence obtained by the modENCODE Consortium increases the D. melanogaster gene set by a similar amount, to ~17,000 distinct genes. Both reports suggest that the gene catalogs for the two model organisms may now be essentially complete, with new discoveries inherently indistinguishable from biological noise and likely to be unimportant. The model animals have a similar complexity of patterns of chromatin marks and their correlations with gene expression. Using data for 18 different histone modifications in a D. melanogaster cell line, the modENCODE Consortium identified 30 chromatin states that are associated with different gene expression patterns and gene positions. C. elegans lacks some chromatin states found in D. melanogaster (such as heterochromatin— repressed DNA that makes up ~30% of the D. melanogaster genome), but Gerstein et al. found that chromatin states are similarly predictive of nematode gene expression patterns, including expression of ncRNAs. A third shared discovery of the modENCODE teams is the very high degree of connectivity, and beguiling simplicity, in the regulatory systems (9). Regulators (transcription factors and ncRNAs) function in hierarchies with few levels, in which master regulators control many other regulators. These, in turn, feed back in a set of simple network connection motifs involving ncRNAs. In D. melanogaster each regulator is, on average, only two (and no more than five) links away from any other. Predictive models of gene expression based on their regulatory interactions were built and tested against observed gene expression patterns. In developing D. melanogaster embryos, the model predicted 62% of the expression patterns observed in isolated cell lines. Given the stochasticity of biological systems, this is a remarkable achievement.
PERSPECTIVES Both Gerstein et al. and the modENCODE Consortium report a curious class of short (100-base) elements in the genomes called highly occupied target (HOT) regions (10, 11). HOT regions were repeatedly identified as binding many different transcription factors, but are curiously not enriched in the known DNA motifs to which these factors bind, suggesting that the interactions may be indirect. HOT regions are stable and associate with gene transcription start sites, and in C. elegans they are associated with genes that are universally expressed through development at high levels. In D. melanogaster, HOT sites are also sites of binding by proteins involved in originating DNA replication. Both studies identified novel sequence motifs that are enriched in HOT regions, but these motifs are not shared between the two species and most do not match known transcription factor binding sites, suggesting that the proteins that bind to these motifs are yet to be identified. The Large Hadron Collider is the preeminent, long-term cooperative enterprise in the physical sciences, dedicated to gathering
data to fully parameterize the basic physical constants of the universe and understand dark matter. In the same way, the modENCODE and ENCODE deep genomics programs will, in time, deliver the power to model and predict organism function from multidimensional data, shine light on the dark genome, and hopefully allow a better understanding of the healthy human and how to treat human disease. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
References
M. B. Gerstein et al., Science 330, 1775 (2010). modENCODE Consortium et al., Science 330, 1787 (2010). ENCODE Project Consortium, Nature 447, 799 (2007). L. Eory, D. L. Halligan, P. D. Keightley, Mol. Biol. Evol. 27, 177 (2010). C. elegans Sequencing Consortium, Science 282, 2012 (1998). M. D. Adams et al., Science 287, 2185 (2000). J. C. Venter et al., Science 291, 1304 (2001). International Human Genome Sequencing Consortium, Nature 409, 860 (2001). U. Alon, Nat. Rev. Genet. 8, 450 (2007). S. MacArthur et al., Genome Biol. 10, R80 (2009). C. Moorman et al., Proc. Natl. Acad. Sci. U.S.A. 103, 12027 (2006). Published online 22 December 2010; 10.1126/science.1200700
MATERIALS SCIENCE
Stretching Dielectric Elastomer Performance Federico Carpi,1 Siegfried Bauer,2 Danilo De Rossi1 Devices using materials that deform in response to electricity are based on a phenomenon that was observed more than two centuries ago.
T
he idea that a solid material can deform when stimulated by electricity originated in the late-18th century with observations of ruptures in overcharged Leyden jars, the first electrical capacitors. In 1776, Italian scientist Alessandro Volta mentioned in a letter that Italian experimenter Felice Fontana had noted volume changes in the Leyden jar upon electrification (1), an observation that launched a new field of investigation—“deformable” materials affected by electricity. More than two centuries later, the concept of “electrically stretchable materials” is at the forefront of devising bioinspired robots, tactile Interdepartmental Research Centre “E. Piaggio,” School of Engineering, University of Pisa, Pisa 56100, Italy. 2Department of Soft Matter Physics, Johannes Kepler University, Linz A-4040, Austria. E-mail:
[email protected] 1
and haptic interfaces, and adaptive optical systems (2, 3). This diversity of applications took a great leap 10 years ago in a landmark study by Pelrine and colleagues (4). They reported high-speed, giant-strain, electrically actuated elastomers with unprecedented electromechanical transduction performance. These materials were demonstrated for so-called dielectric elastomer actuators, deformable capacitors made of a film of a soft insulator (such as acrylic, polyurethane, or silicone elastomer), with compliant electrodes. Upon electrical charging, purely electrostatic forces caused the elastomer film to undergo substantial thickness compression and surface expansion (4). The exceptional performance of these dielectric elastomer actuators gave rise to a scientific and technological revolution in the field of
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building of new models of gene expression regulation. These can be used to describe the idiosyncratic development and biology of each animal, but the excitement lies in the commonalities in the overall structures of the regulatory landscape they reveal. Before modeling gene expression patterns, one first has to know what genes are present. Despite the deep annotation available for the fruit fly and nematode genomes, both projects have identified many new genes and parts of genes. In C. elegans, Gerstein et al. found evidence supporting 95% of existing protein-coding gene predictions, but 1650 new genes are now added for a total of ~22,000. The number of RNA transcripts identified is now triple the previous estimate (to about three per gene), and the ncRNA set is greater by a factor of 20. Evidence obtained by the modENCODE Consortium increases the D. melanogaster gene set by a similar amount, to ~17,000 distinct genes. Both reports suggest that the gene catalogs for the two model organisms may now be essentially complete, with new discoveries inherently indistinguishable from biological noise and likely to be unimportant. The model animals have a similar complexity of patterns of chromatin marks and their correlations with gene expression. Using data for 18 different histone modifications in a D. melanogaster cell line, the modENCODE Consortium identified 30 chromatin states that are associated with different gene expression patterns and gene positions. C. elegans lacks some chromatin states found in D. melanogaster (such as heterochromatin— repressed DNA that makes up ~30% of the D. melanogaster genome), but Gerstein et al. found that chromatin states are similarly predictive of nematode gene expression patterns, including expression of ncRNAs. A third shared discovery of the modENCODE teams is the very high degree of connectivity, and beguiling simplicity, in the regulatory systems (9). Regulators (transcription factors and ncRNAs) function in hierarchies with few levels, in which master regulators control many other regulators. These, in turn, feed back in a set of simple network connection motifs involving ncRNAs. In D. melanogaster each regulator is, on average, only two (and no more than five) links away from any other. Predictive models of gene expression based on their regulatory interactions were built and tested against observed gene expression patterns. In developing D. melanogaster embryos, the model predicted 62% of the expression patterns observed in isolated cell lines. Given the stochasticity of biological systems, this is a remarkable achievement.
PERSPECTIVES
New frontiers. Future applications of electrically charged dielectric elastomers include replacing traditional Braille printed pages with refreshable Braille displays, using millimeter-sized actuators that control the tactile pattern of dots.
resilience; are light weight, scalable, shocktolerant, and noise- and heat-free; and are inexpensive (2–5). Possible future applications of dielectric elastomer actuators that have been under development over the past decade, and seem to be promising, deal with haptics and optics. For example, they are expected to be used in electronic smart devices such as mobile phones to provide users with vibro-tactile feedback, transmitting clicks and vibrations through the sense of touch. Smooth touch screens do not provide key sensorial experience (see the photo, above). Dielectric elastomer actuators may deliver feedback that integrates visual, audio, and tactile responses. Adaptive optical devices, such as lenses and diffraction gratings, are another area in an advanced stage of development (6). Lenses with electrically tunable focal length are potentially useful in autofocus cameras. Miniaturization of dielectric elastomer actuators is a new frontier of research (2, 3, 7) in which millimeter-sized actuators could, for example, control the tactile pattern
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tinuous increase of the length of the band” (10). Today, this experiment is a milestone in the historical background of dielectric elastomer actuation, as the first example of a charge-controlled electrode-free actuator. Indeed, this result was reproduced in a study this year (11) using the acrylic elastomer tested by Pelrine et al. A process called corona charging was used, in which a piece of elastomer is electrified by ionizing the surrounding medium with high voltages. Corona-charged electrified actuators performed better than those controlled directly by electrodes. This is because the former overcome the elastomer “pull-in” insta-
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of Braille interfaces, allowing for simple and compact electrically refreshable tactile displays (2, 3). Key early investigations on electrically induced deformations of insulators include studies in 1775 by the French scientist Nicolas-Phillipe Ledru (also known as Comus), who observed the rise and fall of mercury within glass tubes upon electrification (8). In 1776, Volta explained Fontana’s observations on the Leyden jar: “The glass is strongly compressed…by the two armatures, i.e., exterior metallic leaf, and interior water, which…armatures weight, I will say so, one against the other, because they are
Touch screens that are used in smart devices and laptop computers that lack tactile feedback could have this new dimension. Dielectric elastomer actuators could produce effects that enhance the user’s experience with such devices.
oppositely electric...So...it behaves alike, both when it is interiorly charged by excess, and when it is charged by defect” (1). Volta was the first to interpret the observations as electrostatically induced deformations of the solid dielectric, independent of the voltage polarity. Today, this appears as a sort of unaware anticipation of the physical principle that underpins dielectric elastomer actuation, introduced two centuries later. Moreover, electromechanical effects experienced in those early capacitors that were filled with conductive liquids have recently inspired contractile “hydrostatically coupled” dielectric elastomer actuators (9), which exactly follow Volta’s explanation. Here, an incompressible liquid confined between elastomer membranes allows for electrically safe transmission of forces to loads. In 1880 German physicist Wilhelm Conrad Röntgen reported that a “caoutchouc stripe...[is] electrified by...an isolated comb of needles...connected... to...a strong Holtz influence machine...As...the caoutchouc becomes electrified...one observes a con-
bility—that is, the mechanical collapse of the elastomer when the electrostatic force exceeds the elastic force. At present, the greatest challenge for dielectric elastomer actuation is reducing the driving voltages (on the order of 1 kV for electrode separations of 10 to 100 um). To this end, developing high–dielectric constant elastomers and processing them as thin films is strategic (2, 3). There are promising advances in boosting the dielectric constant of a material while preserving low mechanical stiffness and high dielectric strength (12). Reaching voltages comparable to those of piezoelectrics (on the order of 100 V) may be feasible through thin-film processing in a few years. This would allow the dielectric elastomer actuator technology to permeate an enormous variety of products in haptic, automation, robotic, fluidic, biomedical, optical, and acoustic systems (2, 3). Fault tolerance is increased with “selfhealing” electrodes; on this front, conductive polyaniline nanofiber or carbon nano-
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CREDIT: (LEFT) COMSTOCK; (RIGHT) NICOLAS LORAN/ISTOCKPHOTO
electroactive polymers, materials that can undergo electrically induced deformations. Since that milestone study, elastomers with improved electromechanical properties have been developed, such as interpenetrating networks of acrylic polymers (3). The most widely recognized potential of dielectric elastomer actuators is for creating artificial muscles (2, 3). Indeed, electrically driven elastomers have already exceeded the performance of natural muscles in terms of strain (up to 380% in area), stress (up to 7.2 MPa), and elastic energy density (up to 3.4 J cm–3) (3). Moreover, they show fast response and long lifetime; have high
PERSPECTIVES Current challenges include the development of optimized cycles to maximize electromechanical efficiency at any strain. The field of dielectric elastomer transducers is rapidly maturing and broadening, and the limits of their applications surely will be stretched. The question is whether future applications will be enabled by the two key factors that have thus far prompted their vast and diverse impacts: a simple and reliable physical principle, and the possibility of effective implementation with inexpensive and off-the-shelf materials. References
1. G. I. Montanari, Ed., Lettere inedite di Alessandro Volta, (Stamperia Nobili, Pesaro, Italy, 1835), pp. 15–17. 2. F. Carpi, D. De Rossi, R. Kornbluh, R. Pelrine, P. SommerLarsen, Eds, Dielectric Elastomers as Electromechanical Transducers (Elsevier, Amsterdam, 2008). 3. P. Brochu, Q. Pei, Macromol. Rapid Commun. 31, 10
(2010). 4. R. E. Pelrine, R. D. Kornbluh, Q. Pei, J. P. Joseph, Science 287, 836 (2000). 5. G. Kovacs, L. Düring, S. Michel, G. Terrasi, Sens. Actuators A Phys. 155, 299 (2009). 6. M. Aschwanden, A. Stemmer, Opt. Lett. 31, 2610 (2006). 7. S. Rosset, M. Niklaus, P. Dubois, H. Shea, Adv. Funct. Mater. 19, 470 (2009). 8. Scelta di opuscoli interessanti, Nuova edizione, Tomo II (Stamperia di G. Galeazzi, Milano, 35, 1782), p. 33. 9. F. Carpi, G. Frediani, D. De Rossi, IEEE ASME Trans. Mech. 15, 308 (2010). 10. W. C. Röntgen, Ann. Phys. Chem. 247, 771 (1880). 11. C. Keplinger, M. Kaltenbrunner, N. Arnold, S. Bauer, Proc. Natl. Acad. Sci. U.S.A. 107, 4505 (2010). 12. H. Stoyanov, M. Kollosche, D. N. McCarthy, G. Kofod, J. Mater. Chem. 20, 7558 (2010). 13. S. Chiba, M. Waki, R. Kornbluh, R. Pelrine, Proc. SPIE 6927, 692715 (2008). 14. S. J. A. Koh, X. Zhao, Z. Suo, Appl. Phys. Lett. 94, 262902 (2009).
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tube electrodes, wherein dielectric breakdown results in isolated local burns, show promise (3). Ion-implanted electrodes (7) are paving the way to dielectric elastomerbased microelectromechanical systems and microfluidic devices, such as electrically controlled valves (3). The use of dielectric elastomer transduction technology in a reverse mode to convert mechanical energy into electrical energy (especially from renewable sources, such as offshore ocean waves) is one of the latest frontiers. The voltage is increased when the force of a stretched and charged elastomer is reduced (2). Here, high-voltage operation is of utmost advantage for energy distribution. While experimental assessments are in progress (13), theoretical estimates anticipate energy densities around 6 J/g ( 14).
10.1126/science.1194773
CANCER
Germ Cell Genes and Cancer
In fruit flies, genes that help to program germ cells also play a role in a brain cancer.
Xiaoyun Wu1,2 and Gary Ruvkun1,2
C
ancer cells and germ cells share several characteristics. For instance, both have the ability to rapidly proliferate, typically do not lose the ability to divide as they age (lack senescence), and exist in undifferentiated states. Although some genes involved in cancer may initiate disease simply by activating the cell division cycle, others may spur tumors by activating early developmental pathways associated with programming for multipotency (the ability to differentiate into different cell types). On page 1824 of this issue, Janic et al. (1) show that in fruit flies a number of genes typically involved in early programming of germline cells are also involved in the formation of one type of malignant brain tumor. They also show that inactivating these germ cell genes—some of which have related genes shown to be abnormally expressed in certain human cancers—can suppress tumor growth, suggesting new avenues for therapy. Janic et al. studied brain tumors of the fruit fly Drosophila melanogaster that are caused by a mutation in the tumor suppressor gene lethal (3) malignant brain tumor [l(3)mbt] (see the figure). Gene expression profiles of tumor and normal brain cells revealed that
one-fourth of the genes that had increased activity in the tumors (up-regulated) have germline-associated functions. Among these genes are two (vasa and nanos) involved in a germ cell organelle called the nuage, or polar granule, and four (spn-E, piwi, aubergine, and ago3) that express proteins in the Piwi-interacting RNA (piRNA) pathway. The germline organelles have been intensively studied. Drosophila developmental geneticists first discovered their role as couriers of maternally contributed germline specification and early patterning sig-
Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. 2Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. E-mail:
[email protected];
[email protected]. harvard.edu
Out of place? In normal fruit fly brain cells (left, blue), vasa, a gene normally associated with germ cell organelle function, is not active. Flies with a mutation in the tumor suppressor gene l(3)mbt develop cancerous brain tumors, and vasa shows activity in tumor cells (right, red). The expression of vasa and other germ cell genes appear to be essential for tumor growth, and inactivating these genes can suppress tumors.
1
nals to the developing embryo (2). Then, over the past decade, small RNA biologists discovered that some of the same genes in the polar granule pathway also mediate small RNA control of transposons (movable segments of DNA) (3). Germline cells are more active than other cells in small RNA surveillance, perhaps to protect the genome from damage by transposons and viruses, or perhaps also to carry out small RNA–mediated regulation of maternally encoded mRNAs and the chromatin-regulated deshrouding of the zygotic genome
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PERSPECTIVES Current challenges include the development of optimized cycles to maximize electromechanical efficiency at any strain. The field of dielectric elastomer transducers is rapidly maturing and broadening, and the limits of their applications surely will be stretched. The question is whether future applications will be enabled by the two key factors that have thus far prompted their vast and diverse impacts: a simple and reliable physical principle, and the possibility of effective implementation with inexpensive and off-the-shelf materials. References
1. G. I. Montanari, Ed., Lettere inedite di Alessandro Volta, (Stamperia Nobili, Pesaro, Italy, 1835), pp. 15–17. 2. F. Carpi, D. De Rossi, R. Kornbluh, R. Pelrine, P. SommerLarsen, Eds, Dielectric Elastomers as Electromechanical Transducers (Elsevier, Amsterdam, 2008). 3. P. Brochu, Q. Pei, Macromol. Rapid Commun. 31, 10
(2010). 4. R. E. Pelrine, R. D. Kornbluh, Q. Pei, J. P. Joseph, Science 287, 836 (2000). 5. G. Kovacs, L. Düring, S. Michel, G. Terrasi, Sens. Actuators A Phys. 155, 299 (2009). 6. M. Aschwanden, A. Stemmer, Opt. Lett. 31, 2610 (2006). 7. S. Rosset, M. Niklaus, P. Dubois, H. Shea, Adv. Funct. Mater. 19, 470 (2009). 8. Scelta di opuscoli interessanti, Nuova edizione, Tomo II (Stamperia di G. Galeazzi, Milano, 35, 1782), p. 33. 9. F. Carpi, G. Frediani, D. De Rossi, IEEE ASME Trans. Mech. 15, 308 (2010). 10. W. C. Röntgen, Ann. Phys. Chem. 247, 771 (1880). 11. C. Keplinger, M. Kaltenbrunner, N. Arnold, S. Bauer, Proc. Natl. Acad. Sci. U.S.A. 107, 4505 (2010). 12. H. Stoyanov, M. Kollosche, D. N. McCarthy, G. Kofod, J. Mater. Chem. 20, 7558 (2010). 13. S. Chiba, M. Waki, R. Kornbluh, R. Pelrine, Proc. SPIE 6927, 692715 (2008). 14. S. J. A. Koh, X. Zhao, Z. Suo, Appl. Phys. Lett. 94, 262902 (2009).
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tube electrodes, wherein dielectric breakdown results in isolated local burns, show promise (3). Ion-implanted electrodes (7) are paving the way to dielectric elastomerbased microelectromechanical systems and microfluidic devices, such as electrically controlled valves (3). The use of dielectric elastomer transduction technology in a reverse mode to convert mechanical energy into electrical energy (especially from renewable sources, such as offshore ocean waves) is one of the latest frontiers. The voltage is increased when the force of a stretched and charged elastomer is reduced (2). Here, high-voltage operation is of utmost advantage for energy distribution. While experimental assessments are in progress (13), theoretical estimates anticipate energy densities around 6 J/g ( 14).
10.1126/science.1194773
CANCER
Germ Cell Genes and Cancer
In fruit flies, genes that help to program germ cells also play a role in a brain cancer.
Xiaoyun Wu1,2 and Gary Ruvkun1,2
C
ancer cells and germ cells share several characteristics. For instance, both have the ability to rapidly proliferate, typically do not lose the ability to divide as they age (lack senescence), and exist in undifferentiated states. Although some genes involved in cancer may initiate disease simply by activating the cell division cycle, others may spur tumors by activating early developmental pathways associated with programming for multipotency (the ability to differentiate into different cell types). On page 1824 of this issue, Janic et al. (1) show that in fruit flies a number of genes typically involved in early programming of germline cells are also involved in the formation of one type of malignant brain tumor. They also show that inactivating these germ cell genes—some of which have related genes shown to be abnormally expressed in certain human cancers—can suppress tumor growth, suggesting new avenues for therapy. Janic et al. studied brain tumors of the fruit fly Drosophila melanogaster that are caused by a mutation in the tumor suppressor gene lethal (3) malignant brain tumor [l(3)mbt] (see the figure). Gene expression profiles of tumor and normal brain cells revealed that
one-fourth of the genes that had increased activity in the tumors (up-regulated) have germline-associated functions. Among these genes are two (vasa and nanos) involved in a germ cell organelle called the nuage, or polar granule, and four (spn-E, piwi, aubergine, and ago3) that express proteins in the Piwi-interacting RNA (piRNA) pathway. The germline organelles have been intensively studied. Drosophila developmental geneticists first discovered their role as couriers of maternally contributed germline specification and early patterning sig-
Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. 2Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. E-mail:
[email protected];
[email protected]. harvard.edu
Out of place? In normal fruit fly brain cells (left, blue), vasa, a gene normally associated with germ cell organelle function, is not active. Flies with a mutation in the tumor suppressor gene l(3)mbt develop cancerous brain tumors, and vasa shows activity in tumor cells (right, red). The expression of vasa and other germ cell genes appear to be essential for tumor growth, and inactivating these genes can suppress tumors.
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nals to the developing embryo (2). Then, over the past decade, small RNA biologists discovered that some of the same genes in the polar granule pathway also mediate small RNA control of transposons (movable segments of DNA) (3). Germline cells are more active than other cells in small RNA surveillance, perhaps to protect the genome from damage by transposons and viruses, or perhaps also to carry out small RNA–mediated regulation of maternally encoded mRNAs and the chromatin-regulated deshrouding of the zygotic genome
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in the early embryo. Consistent with these roles, Janic et al. found that many of the small RNAs (such as micro-RNAs and piRNAs) that accumulate at high levels in fruit fly brain tumors are also highly expressed in normal ovaries. Therefore, tumor cells deficient in l(3)mbt “reanimate” multiple germ cell characteristics. This unusual, “ectopic” expression of germ cell genes in somatic tumor cells may constitute a pathway for multipotent cell specification, perhaps leading to tumorous attributes. Indeed, Janic et al. show that some of the ectopically expressed genes are essential for tumor growth. Loss of the germ plasm component genes vasa and nanos or of the piRNA pathway genes piwi and aubergine substantially reduced l(3) mbt tumor mass or almost completely prevented tumor growth. What triggers the reanimation of germ cell characteristics in these brain tumors? Ectopic expression of germline genes was not associated with malignant Drosophila brain tumors caused by other genetic lesions (1), and thus are unique to the l(3) mbt-derived tumors. L(3)MBT is an MBT domain–containing chromatin factor that represses the transcription of particular suites of genes, some of which we now know are germline-specific. It is known to interact with other transcriptional repressors such as the Rb-containing dREAM/MMB complex (4), which contains homologs of the retinoblastoma protein (Rb) tumor suppressor pathway involved in human retinoblastoma, an eye cancer. Intriguingly, inactivation of components of dREAM/MMB in cultured fly cell lines also led to the up-regulation of germline genes including (but not limited to) vasa, nanos, spn-E, piwi, and zpg (5). Therefore, preventing ectopic germline gene expression in somatic cells requires transcriptional repression by retinoblastoma protein (Rb) and its associated chromatin cofactors. An analogous repression of germline genes in somatic cells has been observed in Caenorhabditis elegans. Mutations in genes encoding homologs of dREAM/MMB complex components and two other functionally related chromatin repressors (MEP-1 and Mi2) cause ectopic expression of the germline granule proteins PGL-1, PGL-3, and GLH-1 (a VASA homolog) (6, 7). These similarities suggest that the retinoblastoma pathway genes repress germline expression in somatic cells in many animal systems, perhaps including mammals. There is precedent for exploring the links between tumors and cellular programming for totipotency (the ability to produce
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all kinds of differentiated cells). Years ago, investigators reported that testis or germline antigens normally expressed only in germ cells were highly expressed in certain human solid tumors (8, 9). Higher levels of these antigens are often correlated with advanced tumors and are associated with a poor prognosis, suggesting that these antigens play a role in tumor etiology. Janic et al. show that interfering with this program of germline expression can blunt the growth of the brain tumors induced by the recessive l(3)mbt oncogene mutation. The ectopically expressed germ cell genes that are required for tumor growth are present in the very earliest stages of germ cells and germline stem cells. Janic et al. detected ectopic expression of these genes in the proliferative neuroblast cell regions of the tumorous brain. Similarly, the most convincing germline antigens in mammalian tumors are those that are normally expressed in male germline stem cells (8, 9). Moreover, homologs of vasa along with nanos or piwi are expressed in somatic stem cells in other animals such as planaria, polychaetes, and cnidaria (10–12). The expression of these totipotency factors, normally limited to early developmental stages, may inappropriately specify cancer stem cells that divide and differentiate into heterogeneous cell types in tumors. Loss or inhibition of these stem cell program genes may prevent the formation of cancer stem cells or lead to their death, and may thereby prevent tumor formation. These findings suggest some new paths for research. If the expression of germline characteristics is common in tumors, for instance, it should be seen in gene expression analyses of human tumors. Indeed, the Piwil2 protein, a human Piwi family member, was found to be widely expressed in a variety of solid tumors (13). It should be feasible to examine more systematically the expression of germ cell genes, including vasa and nanos, in human tumors by microarray or deep RNA sequencing. The retinoblastoma tumor that triggered the study of this pathway is a good candidate for studying germline gene activity in tumorigenesis. In addition, mutations in the human homologs of L(3)MBT, Rb, and its chromatin cofactors may be common in cancer genomes as they are sequenced (14). A query of the human homologs of these genes at the Cosmic web site (www.sanger.ac.uk/genetics/CGP/ cosmic), for instance, revealed somatic mutations in L(3)MBT, Rb, and CHD3 (an Mi2 homolog) in a small fraction of tumors. Because there are so many mutations in
these tumors, however, a more sophisticated statistical analysis is needed. The up-regulation of germline pathways in the l(3)mbt brain tumors and the required role for some of these genes in tumor growth also suggest new possibilities for tumor therapy. Screens in C. elegans Rb pathway mutants have identified 32 other chromatin factors that reverse the developmental defects potentially induced by somatic misexpression of germline genes (6, 7, 15, 16). Interestingly, many of these suppressor genes were also identified by RNA interference and genetic screens for defects in small RNA pathways (15, 17– 19), suggesting that the somatic cell specification defects in l(3)mbt-type tumors may be mainly small RNA–based. Inactivation of the Drosophila homologs of these suppressor genes would be predicted to suppress tumor formation in the l(3)mbt brain tumor model. These genes are also conserved in mammals and could be potential targets for drugs that treat tumors similar to those analyzed by Janic et al. References
1. A. Janic, L. Mendizabal, S. Llamazares, D. Rossell, C. Gonzalez, Science 330, 1824 (2010). 2. S. Strome, R. Lehmann, Science 316, 392 (2007). 3. M. Ghildiyal, P. D. Zamore, Nat. Rev. Genet. 10, 94 (2009). 4. P. W. Lewis, E. L. Beall, T. C. Fleischer, D. Georlette, A. J. Link, M. R. Botchan, Genes Dev. 18, 2929 (2004). 5. D. Georlette, S. Ahn, D. M. MacAlpine, E. Cheung, P. W. Lewis, E. L. Beall, S. P. Bell, T. Speed, J. R. Manak, M. R. Botchan, Genes Dev. 21, 2880 (2007). 6. Y. Unhavaithaya, T. H Shin, N Miliaras, J. Lee, T. Oyama, C. C. Mello, Cell 111, 991 (2002). 7. D. Wang, S. Kennedy, D. Conte, Jr., J. K Kim, H. W. Gabel, R. S. Kamath, C. C. Mello, G. Ruvkun, Nature 436, 593 (2005). 8. O. L. Caballero, Y. T. Chen, Cancer Sci. 100, 2014 (2009). 9. A. J. Simpson, O. L. Caballero, A. Jungbluth, Y. T. Chen, L. J. Old, Nat. Rev. Cancer 5, 615 (2005). 10. N. Rebscher, F. Zelada-González, T. U. Banisch, F. Raible, D. Arendt, Dev. Biol. 306, 599 (2007). 11. N. Shibata, L. Rouhana, K. Agata, Dev. Growth Differ. 52, 27 (2010). 12. C. G. Extavour, K. Pang, D. Q. Matus, M. Q. Martindale, Evol. Dev. 7, 201 (2005). 13. J. H. Lee, D. Schütte, G. Wulf, L. Füzesi, H.-J. Radzun, S. Schweyer, W. Engel and K. N. Hum. Mol. Genet. 15 (2005). 14. S. van den Heuvel, N. J. Dyson, Nat. Rev. Mol. Cell Biol. 9, 713 (2008). 15. M. Cui, E. B. Kim, M. Han, PLoS Genet. 2, e74 (2006). 16. E. C. Andersen, X. Lu, H. R. Horvitz, Development 133, 2695 (2006). 17. J. K. Kim, H. W. Gabel, R. S. Kamath, M. Tewari, J.-F. Pasquinelli, Science 308, 1164 (2005). 18. T. Takasaki, Z. Liu, Y. Habara, K. Nishiwaki, J.-I. Nakayama, K. Inoue, H. Sakamoto, S. Strome, Development 134, 757 (2007). 19. N. L. Vastenhouw, K. Brunschwig, K. L. Okihara, F. Müller, M. Tijsterman, R. H. A. Plasterk, Nature 442, 882 (2006).
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PERSPECTIVES
PERSPECTIVES RETROSPECTIVE
Allan Sandage (1926–2010)
An astronomer launched the field of observational cosmology and influenced our view of the universe over the past half-century.
Donald Lynden-Bell
CREDIT: CARNEGIE INSTITUTION OF WASHINGTON
Institute of Astronomy, Cambridge University, Cambridge, CB3 0HA UK. E-mail:
[email protected]
sars (quasi-stellar radio sources), active galaxies, and the Virgo cluster. He studied the theory of how stars evolve into red giants under Martin Schwarzschild at Princeton, and in 1963 they were jointly awarded the Eddington medal of the Royal Astronomical Society. The ages of the globular star clusters estimated from theory were uncomfortably large (at times reaching 18 billion years rather than the true 12 billion years). The time scale found from the rate of expansion of the universe was shorter—closer to 10 billion years—but depended inversely on the size of the Hubble constant (the measure of how fast the universe expands per unit distance; the speeds of galaxies moving away from us are approximately proportional to their distances). Sandage was convinced that nature must be consistent, and worked unceasingly with Tammann to refine distances and redetermine the Hubble constant. For years their best estimate was around 52 ± 5 km s−1 Mpc−1, but their paper this year on the linearity of the Hubble flow, based on more accurate distance estimators, gives a value of 62.3 km s−1 Mpc−1. That paper gives the most accurate relative distances of local structures in the universe, but the current consensus, which is not always right, is that Hubble’s constant is about 72 km s−1 Mpc−1. Sandage was elected to the National Academy of Sciences in 1963 but resigned when the Academy failed to elect Olin Eggen. Eggen, Sandage, and I had collaborated on a seminal paper about the formation of the galaxy. This paper gave birth to galactic archaeology, in which the ages, chemical constitutions, and galactic orbits of stars are used to discuss the formation of the galaxy they are in. In 1961, Sandage was the first to determine the spectrum of a quasar called 3C48,
but could not find a reasonable interpretation of it. He consulted colleagues Ira Bowen and Jesse Greenstein about it, and meanwhile showed that the light of “radio-star” 3C48 varied over a few months, which demonstrated that it must be only a few lightmonths across, much smaller than a galaxy. It was the discovery by Maarten Schmidt of the large redshift in another radio source (3C273) two years later that led to the interpretation of Sandage’s spectrum. When Schmidt and Greenstein finally understood and published the spectra of 3C48, Sandage felt excluded. A few years later, he discovered that there are many more radio-quiet quasi-stellar objects than quasars. This contributed to the current view that most large galaxies contain giant black holes (dead quasi-stellar objects) in their nuclei. On one occasion, Sandage showed me a photographic plate of a quasar that he was studying. It lay near a bright galaxy, and on inspection I saw a faint mark that led from the quasar to the galaxy. Sandage pointed to the office of Halton Arp and said with a laugh, “Perhaps he could be right after all.” That possibility did not survive. The mark was merely one side of the residue left by a droplet that had dried after the plate had been developed. Arp’s claims of connections between objects of very different redshifts were never substantiated, although Fred Hoyle believed this. Sandage was awarded the Crafoord prize in 1991, was the joint recipient of the first Gruber prize for Cosmology in 2000, and was a Foreign Member of the Royal Society. He was also awarded all the major astronomical medals in the United States and the United Kingdom. His public lectures were filled with wonder and enthusiasm for astronomy, and his atlases of galaxies influenced young observers and theorists alike. Sandage was open to new ideas and was generous and helpful to those who worked with him. He was a true gentleman and expected equally high standards of honor and acknowledgment from others. He never stopped working and has left us with a wider understanding of the scale of the universe and a greater wonder at the remarkable objects in it.
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llan Sandage spent long nights leading observational cosmology until studies of the cosmic microwave background led to a new dawn. For 60 years, he surveyed and measured the universe. He died on 13 November at age 84. His fascination with stars began as a youth in Iowa and led to his study of physics at the University of Illinois and a Ph.D. in astronomy from the California Institute of Technology (CalTech) in 1953, where he was a student of Walter Baade. He joined the Carnegie Observatories in Pasadena (where he remained), became Edwin Hubble’s assistant a year before Hubble’s sudden death, and inherited Hubble’s program to determine the size and age of the universe. Hubble had used the magnitudes of the brightest stars in galaxies to determine their distances. Sandage realized that many of these were actually tight groups of bright stars, together brighter than any star in the Milky Way, and this doubled the distances estimated to those galaxies. Thus began his main work improving distance measurements to determine the age of the universe. Refining the distance estimates to variable stars became a recurring theme of his work. These estimates were needed to determine the size of the first step on the ladder of distances through which the scale of the universe was measured. By comparing the brightness of stars of the same period in different galaxies, the ratio of their distances can be found. However, a star’s color and chemical composition can influence its intrinsic brightness, and interstellar dust can change its color. Accuracy can only be achieved by learning how to compensate for such effects. Sandage discovered good secondary distance indicators in the brightest galaxies of clusters and supernovae. He attributed this use of supernovae to the brilliant and opinionated astronomer Fritz Zwicky, but it was only through detailed refinements, to which Sandage and his longtime collaborator Gustav Tammann contributed greatly, that type IA supernovae became the standard candles for measuring large cosmological distances. At different times, Sandage’s attention was dominated by the latest new discoveries: x-ray sources, the origin of the Galaxy, qua-
10.1126/science.1201221
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ESSAY SPORE* SERIES WINNER
Science 101: Building the Foundations for Real Understanding
Two online projects offer one-stop shopping for teaching evolution, as well as the nature and process of science.
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Since its launch in 2004, Understanding Evolution’s impact has grown. The site now averages more than a million page accesses per month during the academic year, is available in Spanish and Turkish (www.sesbe.org/ evosite/evohome.html, www.evrimianlamak. org/e/Ana_Sayfa), and has been distributed in Tibetan as part of the Dalai Lama’s EmoryTibet Science Initiative. Additionally, site resources have been shown to improve teachers’ and students’ understandings of evolution and to increase instructors’ confidence in their ability to teach this challenging, and sometimes charged, material (4). As we developed Understanding Evolution and noticed similar tensions and misinformation arising around topics like climate change, we realized that much of the public’s mistrust of evolution stems from more basic and even more important issues: poor understanding of how science works to build reliable knowledge and confusion about the strengths and limitations of this process. Hence, we envisioned a Web site that would leverage the format of Understanding Evolution toward the goal of helping teachers reinforce the true nature and process of science throughout their teaching. The Understanding Science Web site (www.understandingscience.org) was launched in January 2009. Its development process followed that for Understanding Evolution, bringing together scientists, philosophers of science, teachers, writers, and Web designers to conceive, develop, and vet content. The site is unique in its straightforward presentation of science, not as an esoteric collection of vocabulary and facts, but as an intensely human endeavor—a multifaceted process that both students and scientists can use to better understand the natural world. Instead of oversimplifying science into a five-step recipe, the site emphasizes the dynamic and iterative nature of the process, as well as the roles of creativity and community in scientific progress. Understanding Science is designed to give students and the general public the tools they need to recognize the relevance of science to their lives and to keep pace with the ways in which science informs personal and societal decision-making. These ideas are communicated through
24 DECEMBER 2010 VOL 330 SCIENCE www.sciencemag.org Published by AAAS
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I
t’s not just about evolution anymore. Growing antiscience sentiment in the United States now infuses public discourse on conservation, vaccination, distribution of research funds, and climate change (1). Low rates of scientific literacy (2) exacerbate the problem. Although the public recognizes its indebtedness to the products of scientific knowledge, few understand much about the nature of that knowledge or the processes that generated it (3). Without a basic understanding of how science Teaching the process of science. Mark Stefanski, a teacher adworks, the public is vulnerable viser, uses the Science Flowchart with high-school biology students to antiscience propaganda, which at Marin Academy, where the science faculty employs the flowchart engenders distrust of science in lab activities to help students focus on the process of science. when it comes to social issues, consumer choices, and policy decisions. pedagogy; review and editing by scientific The University of California Museum of experts; field testing through teacher advisers; Paleontology’s interest in this issue stemmed revision of materials with additional expert from a project on evolution education, which review; and summative evaluation performed expanded into an effort to support more by the evaluation firm Rockman et al. (4). effective teaching of the nature and process The result of this process was the Underof science (see the first figure). In 2000, we standing Evolution Web site, which provides hosted a conference on evolution instruc- educational materials targeting teachers of tion that brought together stakeholders from kindergarten through college, students, and education, academia, and the media. Partic- the general public (see the second figure). ipants identified a critical need for a collec- Teacher advisers requested resources that tion of vetted tools for teaching evolution- engage students with data, explore scientific ary biology. Understanding Evolution (www. reasoning and science as a human endeavor, understandingevolution.org) was built, in and demonstrate the relevance of evolution to collaboration with the National Center for everyday life. Site resources that respond to Science Education, to meet this need and to these needs include (i) “Evo in the News,” a provide a clear and comprehensive reference monthly feature that reveals the evolutionary for the general public. science behind a current news story and inteUnderstanding Evolution brought together grates data from the primary literature with an advisory board of scientists, Web design- discussion questions and background readers, authors, and master teachers to create a ing; (ii) research profiles and case studies, vision for the project and develop content. which follow a particular scientist or invesKey aspects of this process included teacher tigation and step students through the logic guidance on content types, Web features, and of testing evolutionary hypotheses; and (iii) interactive investigations (e.g., Visualizing Life on Earth, http://evolution.berkeley.edu/ 1 University of California Museum of Paleontology, Univerevolibrary/article/ldg_01) that ask students to sity of California, Berkeley, Berkeley, CA, 94720–4780, put scientific reasoning into practice. Many USA. 2Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, 94720–4780, USA. such resources will be housed in our Undergraduate Library, an area devoted to college*SPORE, Science Prize for Online Resources in Education; level evolution instruction, which will open www.sciencemag.org/special/spore/. †Author for correspondence. E-mail:
[email protected] in January 2011.
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Anastasia Thanukos,1 Judith G. Scotchmoor,1 Roy Caldwell,1,2† David R. Lindberg1,2
ESSAY
About the Authors From left to right: Roy Caldwell, Josh Frankel, David R. Lindberg, Judith G. Scotchmoor, Anastasia Thanukos, and David Smith. R. Caldwell and D. R. Lindberg, co-principal investigators on the project, are curators in the University of California Museum of Paleontology (UCMP) and professors of Integrative Biology at the University of California, Berkeley. J. G. Scotchmoor, project coordinator, is an assistant director at UCMP, in charge of education and outreach. A. Thanukos, primary author, is principal editor at UCMP. J. Frankel and D. Smith work in education and outreach at UCMP and direct Web design for the project. Although less than 2 years old, Understanding Science has had far-reaching impacts and now averages more than 60,000 page accesses per month during the school year. The project is endorsed by organizations such as the American Institute of Biological Sciences, and materials from the site have been incorporated into middle- and high-school textbooks from major publishers. Encouragingly, an evaluation of a yearlong in-service training program indicates that site materials generate a high level of
Educational resources available through Understanding Evolution and Understanding Science Target Audience
Resource Understanding Evolution
Students
K–12 Teachers
Undergraduate Instructors
General Public
Searchable lesson database Tips, strategies, and teaching help Common misconceptions and explanations Conceptual framework Image library Evolution 101 and basic content Advanced tutorials Interactive online labs Research profiles and case studies Evo in the News articles
Understanding Science
PHOTO CREDIT: MOLLY WRIGHT/UCMP
teacher buy-in, meaningful increases in student understanding, and reports of increased student motivation (6). In the current climate of both funding constraints and concern for the future of science education in the United States, we see opportunities for additional contributions from these projects, such as new resources and collaborations with scientists, as well as challenges, such as maintaining vibrant and freely accessible teaching materials while seeking a sustainable funding model. Fortunately, many other initiatives have also set their sights on improving science literacy (7), and this complements a growing movement within the scientific community itself to reach out to students and the broader public. We are proud to be a part of this campaign and are committed to working with scientists, scientific agencies, the media, and educators to build a more scientifically literate society. References and Notes
Individual lessons and activities Searchable lesson database Tips, strategies, and teaching help Guidelines for modifying lessons Tips from the education research literature Common misconceptions and explanations Conceptual framework First-hand instructor reports Understanding Science 101 and basic content Advanced supplementary content Science in Action stories Currently available
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a friendly primer on the nature of science, as well as through “Science in Action” features, which use stories from the history of science, animations, and graphics to reinforce basic scientific concepts and show how science works. By providing a comprehensive, practical resource for teaching the nature and process of science, Understanding Science also fills a major gap in the landscape of science education materials. Teaching resources on the site (second figure) are informed by educational research showing that instruction in this area is most effective when it is explicit, reflective, and reinforced in many contexts (5). Three tools from the site help teachers put these guidelines into practice in multiple instructional settings. The Science Checklist helps students identify key characteristics of science in different investigations. The Science Flowchart provides a more accurate and appealing representation of the scientific process than the rigid Scientific Method. The Science Toolkit helps students analyze policies and media messages to get to the science behind the spin.
Launching this academic year
Educational resources. The Understanding Evolution and Understanding Science Web sites offer a wealth of resources for teaching and learning evolutionary biology and the nature and process of science. K–12, kindergarden to high school.
1. C. Mooney, S. Kirshenbaum, Unscientific America (Basic Books, New York, 2009). 2. R. Bybee, B. McCrae, R. Laurie, J. Res. Sci. Teach. 46, 865 (2009). 3. National Science Board, Science and Engineering Indicators 2010 (National Science Foundation, Arlington, VA, 2010). 4. Rockman et al., Understanding Evolution Evaluation, http://evolution.berkeley.edu/evolibrary/evaluation.php. 5. N. G. Lederman, in Handbook of Research on Science Education, S. K. Abell and N. G. Lederman, Eds. (Lawrence Erlbaum Associates, Mahwah, NJ, 2007), pp. 831–880. 6. M. A. M. Stuhlsatz, Final Evaluation Report for the University of California Museum of Paleontology: Understanding Science (ER 2010-08, Biological Sciences Curriculum Study, Colorado Springs, CO, 2010); http:// undsci.berkeley.edu/BERKreport_7_12_10.doc. 7. American Association for the Advancement of Science, Center for Public Engagement with Science and Technology, www.aaas.org/programs/centers/pe/. 8. Supported by the NSF 0096613, EAR-0624436, DUE0918741; Howard Hughes Medical Institute 51003439; and the University of California Museum of Paleontology. Published online 2 December 2010; 10.1126/science.1186994
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AAASNEWS&NOTES
EDITED BY EDWARD W. LEMPINEN
dous resource that can be tapped for the purposes of development of our continent.” Science education and training are crucial to the region’s ability to address its challenges, said Romain Murenzi, formerly a minister in the Kagame administration overseeing education, science, and communication, and KIGALI —High-ranking government lead- Rwanda’s neighbors are embracing a similar now director of the AAAS Center for Sciers from six African nations have pledged approach to strong economic growth. ence, Technology and Sustainable Developto expand their collaboration in science and The conference, convened 8 to 9 Decem- ment. But with less than 1% of East Africa’s science education to further economic and ber in Rwanda’s capital, reflected the region’s 200 million people holding college degrees, human development in their resource-rich energy and optimism. It attracted about 60 he said, “regional integration is critical to but long-impoverished region. influential science leaders—government efficiently use this scarce resource.” At a landmark meeting organized by the science and education ministers, heads of Before the conference, Jo Ellen RoseRwandan Ministry of Education and AAAS, national science organizations, and univer- man, director of AAAS’s Project 2061 science leaders from the East African nations of sity rectors, along with top officials from the literacy initiative, gave a 2-hour briefing on the Burundi, Kenya, Rwanda, Tanzania, program’s extensive science learning and Uganda, along with the Demoresources to about 40 administrators cratic Republic of Congo, agreed to and staff at Rwanda’s National Curestablish a forum for their science riculum Development Centre. ministers. By working together on At the conference, some 40 a range of issues—from education undergraduate and graduate students and health to energy and the environwon praise for their research postment—they hope to support sustainers, many focused on agriculture, able economic growth while advanchydrology, and health, and most with ing East Africa’s emerging commitdirect practical applications. ment to regional integration. Speakers from both Africa and “The integration spirit in the the United States put particular region is very high,” said Charles focus on the need to recruit more Murigande, Rwanda’s minister of women into science and engineereducation. “The objective of this con- Regional partners. (left to right): Alexis Kanyenye, deputy direc- ing studies. Mabel Imbuga, vice ference was to bring us together. We tor of cabinet, Ministry of Scientific Research, Democratic Republic of chancellor of the Jomo Kenyatta did not want to come together once the Congo; Romain Murenzi, director of the AAAS Center for Science, University of Agriculture and Techand end it there, and so I expected … Technology and Sustainable Development; Julien Nimubona, minister nology, described Kenya’s incenthat we should continue to meet and of higher education and scientific research, Burundi; Ignace Gatare, tives to bring women into the sciexchange ideas on how we can pro- minister in the office of the president for Information Communication ences. Uganda and Tanzania are mote science and technology.” Technologies, Rwanda; Nelson G. Gagawala, minister of state for trade, making similar efforts, speakers That could be a crucial oppor- Uganda; Alan I. Leshner, CEO of AAAS; Charles Murigande, minister of said. Advocates need to demontunity for harmonizing science and education, Rwanda; Charles Kitwanga, deputy minister of communi- strate more forcefully that bringeducation goals and procedures in cation, science, and technology, Tanzania; and Shaukat A. Abdulrazak, ing more women into science has the region, said AAAS Chief Execu- executive secretary, Kenya National Council for Science and Technology. a positive economic impact, said tive Officer Alan I. Leshner, who led Shirley Malcom, head of Education a delegation to the conference. “This is a very African Development Bank and UNESCO. and Human Resources at AAAS. important and promising development,” said W. Stuart Symington, the U.S. ambassador to Vaughan Turekian, AAAS’s chief internaLeshner, the executive publisher of Science. Rwanda, also spoke at the event. tional officer, was among many speakers who “If we’re going to be able to work for the betDiscussions were collegial and candid, urged science leaders to build on momenterment of humankind on a global scale, the and they ranged broadly across issues— tum created by the conference. “Cooperating scientific community has to be able to func- from Internet technology and data collection across borders to improve the lives of people tion as a global community in and of itself.” to food security and approaches to scien- and drive prosperity represents one of the Rwanda, devastated by genocide in 1994, tific collaboration. But participants returned greatest goals of our scientific and education has become an international model for build- repeatedly to the importance of education. enterprise,” he said. “We have to seize on this ing economic strength with education, sci“Our continent has reached the milestone opportunity through sustained action.” ence, and technology; Rwandan President of 1 billion people,” said Boukary Savadogo, The science ministers forum can help Paul Kagame discussed that strategy and division manager for education, science, and lead that effort, said Ugandan Trade Minister plans for the future in an hour-long meet- technology at the African Development Bank. Nelson G. Gagawala. “We need action,” he ing with Leshner and the AAAS delegation. “These 1 billion brains constitute a tremen- said. “We need action in real time.” INTERNATIONAL
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Seeking Growth, East Africa Expands Science, Education Ties
PUBLIC ENGAGEMENT
Science, Law Enforcement Build Biotech Bridges pioneer J. Craig Venter announced development of the first cell controlled by a synthetic genome. That breakthrough underscored that biotech will likely create unpredictable implications for science and society. In a recent appearance at AAAS, bioethicist Thomas H. Murray said synthetic biology—fundamentally altering life or creating new life forms—offers “mind-boggling” possible benefits, from production of new pharmaceuticals to cleaning up oil spills. But, he added, the benefits must be weighed against bioterrorism and other hard-to-define risks. “If I didn’t think the potential benefits... were massive, there would be no point in having this conversation,” said Murray, president and chief executive officer of the Hastings Center, in the annual AAAS-Hitachi Lecture on Science & Society on 28 October. Finding the best balance of benefits and risks is the rationale for the collaboration between the FBI and AAAS, said AAAS biosecurity expert Kavita Berger, an associate program director in the Center for Science, Technology and Security Policy. Just a few years ago, Berger contributed to a survey of researchers that found only a third were comfortable sharing their research with agents, and a mere 14% felt comfortable with the FBI having a role in monitoring research.
ANNUAL MEETING
In Washington, Researchers Seek Science Without “Silos” Some of the most compelling scientific work of the 21st century depends on researchers who seek inspiration and partnerships across disciplines and national borders. It’s an approach that Frances H. Arnold, a plenary speaker at the 2011 AAAS Annual Meeting, uses when she combines mechanical engineering, chemistry, and evolutionary biology to design new enzymes for medical and energy research. It’s also the driving force behind the work of Colin Phillips, one of the meeting’s topical speakers, who employs computer science, anthropology, and neuroscience in his studies of human grammar. Under the banner “Science Without Borders,” Arnold, Phillips, and scientists and engineers from more than 50 countries will convene from 17 to 21 February at the 177th Annual Meeting in Washington, D.C. Their innovative projects “cross conventional borders or break out from silos, especially in groundbreaking areas of research,” said AAAS President Alice S. Huang. The program will also highlight the international nature of scientific collaborations, said Huang,
who has consulted on sciience policy for government nt agencies in China, Taiwan, n, and Singapore. The 2011 meeting willl continue AAAS’s tradition of boundary-crossing science, featuring multidisciplinary research on oceans, human health, sustainability, and next-generation generation engineering. Special events include seminars on neuroscience and robotics, molecular machines, the search for Earth-like planets, and a plenary panel on emerging issues in biosecurity. For registration and other information about the 2011 Annual Meeting, see www.aaas.org/ meetings. Information from the D.C. gathering will also be posted at the Annual Meeting News site at http://news.aaas.org, at the 2011 AAAS Annual Meeting and ScienceNOW pages on Facebook, and on Twitter at #AAASmtg. —Becky Ham
Genspace President Ellen Jorgensen (left) and FBI Supervisory Special Agent Edward H. You (right).
But if science and security couldn’t build a working relationship, she thought, then policy-makers, acting out of mistrust or fear, might impose rules that impede research without affecting real security concerns. Collaboration, she said, is “ultimately going to be a lot more productive and a lot more useful in reaching the end goals of security and science.” In professional conferences organized by the FBI and AAAS, You and Berger have had agents and researchers work through simulated problems related to biotech and biosecurity. In the process, they learned about each other’s values, perspectives, and practices. Now the uneasiness is giving way to closer interaction between researchers and law enforcement, with major universities offering to host the conferences. “We’re seeing a paradigm shift,” said You, who had worked in gene therapy and cancer research before joining the FBI. AAAS is helping forge a similar relationship with amateur biologists, who number an estimated 4000 or more nationwide. An iinformal meeting this fall brought three of tthem together with You and others from the F FBI, along with government and AAAS oofficials. The DIY speakers described how a love of science and commitment to public en engagement has led them to hold exhibits at st street fairs and form community labs. Ellen Jorgensen, an assistant professor in pathology at New York Medical College and president of the Genspace community lab in New York City, acknowledged that cooperation with agents does not come easily for many in the DIY movement. But, she said, “I think that the meetings we have had were very useful in terms of fostering some trust between the FBI and the DIY biocommunity…To kill a movement that embodies a reawakened public enthusiasm about science due to concerns about biosecurity would be a terrible shame.” —Brian Vastag contributed to this report.
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With scientists working to create new life forms and amateur biology clubs springing up nationwide, it stands to reason that the U.S. security community would be concerned that one rogue researcher or one innocent error might create a grave problem. But before uneasiness could turn to conflict, the FBI, working closely with AAAS, embraced a new strategy. The Bureau held conferences with university and private sector researchers, attended synthetic biology science fairs, and spent time with do-it-yourself (DIY) biologists. The message, though tailored for each audience, was consistent. “We want science and security communities to come to an understanding to promote a culture of responsibility,” says Edward H. You, an experienced researcher and now the FBI supervisory special agent guiding the outreach effort. By bringing those communities together, “we can…identify what some of the risks and gaps might be, and then come up with strategies that make sense to both communities to mitigate those risks and gaps.” A certain amount of uneasiness was inevitable after the deadly blitz of anthrax letters that followed the 9/11 terror attacks and, more recently, the stunning advances and increasing accessibility of biotech research. In research published last May in Science, genomics
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Has the Microbiota Played a Critical Role in the Evolution of the Adaptive Immune System? Yun Kyung Lee and Sarkis K. Mazmanian* Although microbes have been classically viewed as pathogens, it is now well established that the majority of host-bacterial interactions are symbiotic. During development and into adulthood, gut bacteria shape the tissues, cells, and molecular profile of our gastrointestinal immune system. This partnership, forged over many millennia of coevolution, is based on a molecular exchange involving bacterial signals that are recognized by host receptors to mediate beneficial outcomes for both microbes and humans. We explore how specific aspects of the adaptive immune system are influenced by intestinal commensal bacteria. Understanding the molecular mechanisms that mediate symbiosis between commensal bacteria and humans may redefine how we view the evolution of adaptive immunity and consequently how we approach the treatment of numerous immunologic disorders. e are (fortunately) not alone: Humans provide residence to numerous microbial communities comprising hundreds of individual bacterial species. Although teleological design may predict that the immune system evolved to eliminate infectious microbes, we now know that almost every environmentally exposed surface of our bodies is teeming with symbiotic microbes (Fig. 1). These polymicrobial communities contribute profoundly to the architecture and function of the tissues they inhabit and thus play an important role in the balance between health and disease. The notion that commensal microbes critically affect tissue and cell development in humans can be rationalized when this process is viewed from an evolutionary perspective. Bacteria populated Earth 2 billion years before the first signs of eukaryotic life, and they occupy almost every terrestrial and aquatic niche on our planet. Mitochondria and chloroplasts of eukaryotic cells are descended from bacteria, which suggests that bacteria may have had an active role in the evolution of higher organisms. As multicellular metazoans evolved more complex body plans, bacteria acquired the ability to inhabit new anatomical niches. Animals represent a stable, nutrient-rich ecosystem for microbes to thrive; hence, host health is paramount to the microbiota. In turn, the host benefits from a diverse commensal microbiota that helps to digest complex carbohydrates and provide essential nutrients to mammals. Symbionts are not the only microbes the host encounters, however. An important challenge faced by the host immune system is to distinguish between beneficial and pathogenic microbes, be-
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Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. *To whom correspondence should be addressed. E-mail:
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cause they share similar molecular patterns that are recognized by the innate immune system (such as lipopolysaccharide, peptidogycan, lipoproteins, and flagellin). Discrimination between specific microbes may be a feature of the adaptive immune system, which can recognize discrete molecular sequences and mount both pro- and anti-inflammatory responses depending on the nature of the antigen. In particular, CD4+ T cells are quite plastic and differentiate into numerous subsets after development in the thymus and thus are capable of sensing environmental cues from the microbiota. As adaptive immunity evolved in higher vertebrates, the ability of this system to recognize and respond to specific microorganisms may have been driven by evolutionary forces provided by the microbiota itself, resulting in immune functions beyond simply clearing microbial pathogens (which in theory also helps the microbiota by improving host health). Recent evidence shows that the commensal microbiota “programs” many aspects of T cell differentiation, thus augmenting the developmental instructions of the host genome to engender the full function of the adaptive immune system. Here, we review concepts derived from gnotobiology (Greek for “known life”) to unravel how commensal bacteria promote the development and function of adaptive immunity. In particular, we explore how CD4+ T helper cell subsets within the gastrointestinal and systemic immune system are shaped (perhaps even controlled) by our microbiota and theorize how and why gut bacteria evolved to so profoundly influence immunologic well-being. Understanding human coevolution with our microbiota may lead to a philosophical and conceptual redefinition of the microbial world and may yield clinical advances toward the treatment of autoimmunity and inflammatory diseases by harnessing the immunomodulatory properties of human commensal bacteria.
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How Does the Microbiota Shape Host Immune Development and Function? Although microbes reside in several anatomical locations including the skin, vagina, and mouth, the lower gastrointestinal tract of mammals harbors the greatest density and diversity of commensal microorganisms. These include bacteria, archaea, fungi, viruses, protozoans, and (in some cases) multicellular helminths; however, bacteria predominate and reach 100 trillion microbial cells in the colon. Recent efforts to sequence the bacterial genomes of the microbiota (known as the microbiome) have begun to reveal its genetic identity (1) and suggest that our microbiome contains more than 150 times as many nonredundant genes as in the human genome (2). For decades, microbiological techniques to culture bacteria in the laboratory have only identified cultivatable microorganisms, which represent a minority of the microbial species of the gut. The aggregate human microbiota likely contains 1000 to 1150 bacterial species (spread among all people sampled), with each person harboring about 160 bacterial species (2). This suggests that an individual’s microbiome is relatively distinct in composition and is adaptable to environmental changes and/or host genetics. Germ-free animals (born and raised in the absence of all microbes) provide important insights into how the microbiota affects the host immune system. The development of gut-associated lymphoid tissue (GALT), the first line of defense for the intestinal mucosa, is defective in germfree mice. Germ-free mice display fewer and smaller Peyer’s patches, smaller and less cellular mesenteric lymph nodes, and less cellular lamina propria of the small intestine relative to animals with a microbiota (3–7). Besides developmental defects in tissue formation, the cellular and molecular profile of the intestinal immune system is also compromised in the absence of symbiotic bacteria. In germ-free mice, intestinal epithelial cells (IECs), which line the gut and form a physical barrier between luminal contents and the immune system, exhibit reduced expression of Toll-like receptors (TLRs) and class II major histocompatibility complex (MHC II) molecules (8, 9), which are involved in pathogen sensing and antigen presentation, respectively. Interspersed between epithelial cells is a specialized population of T cells known as intraepithelial lymphocytes (IELs). IELs from germ-free mice are reduced in number, and their cytotoxicity is compromised (10, 11). Microbial colonization expands specific subsets of intestinal gd T cells (12). Germ-free mice also have reduced numbers of CD4+ T cells in the lamina propria (13). The development of isolated lymphoid follicles, specialized intestinal structures made of mostly dendritic cells and B cell aggregates, is also dependent on the microbiota (14). Therefore, multiple populations of intestinal immune cells require the microbiota for their development and function.
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REVIEW to the peritoneal cavity in response to Listeria infection is impaired in germ-free mice (20). The contributions of the microbiota to the development and function of the immune system appear to be fundamental. A more robust immune system, equipped with a diverse arsenal of cells and molecules, is better able to combat microbial pathogens and ultimately provides a healthier residence for commensal bacteria. This view implies that host mechanisms and the microbiota may have evolved to collaborate against infectious agents. Indeed, several reports show an antagonistic relationship between overt pathogens and the microbiota. For example, Salmonella triggers intestinal inflammation, which reduces the numbers and diversity of the microbiota—a process that promotes bacterial infection (21). Depletion of the microbiota diminishes intestinal immune responses that help to control enteric
infections by Citrobacter rodentium and Campylobacter jejuni (22). Given the role of the microbiota in immune system function, harnessing the immunomodulatory capabilities of the microbiota may offer novel avenues for the development of antimicrobial therapies for infectious disease. How Does the Microbiota Provide Signals to Instruct Peripheral Regulatory T Cell Differentiation? Although many cell types are influenced by the microbiota, we focus here on the emerging role of the microbiota on effector CD4+ T cell differentiation. After lineage commitment in the thymus, naïve CD4+ T cells enter the periphery, where they sense environmental signals that further instruct their maturation and function. During an infection, microbial and host signals provide cues to naïve CD4+ T cells to induce their differentia-
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The absence of a microbiota also leads to several extra-intestinal defects, including reduced numbers of CD4+ T cells in the spleen, fewer and smaller germinal centers within the spleen, and reduced systemic antibody levels, which suggests that the microbiota is capable of shaping systemic immunity (15–17). Beyond development, the microbiota also influences functional aspects of intestinal and systemic immunity, including pathogen clearance. Germ-free mice are more susceptible to infectious agents such as Shigella flexneri, Bacillus anthracis, and Leishmania (18). Peptidoglycan from the microbiota enhances neutrophil cytotoxicity after systemic infections by Streptococcus pneumoniae and Staphylococcus aureus (19). During challenge with Listeria monocytogenes, sterile mice harbored an increased bacterial burden in the liver, spleen, and peritoneal cavity (20). Moreover, trafficking of T lymphocytes
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Fig. 1. The microbiome of various anatomical locations of the human body. Numerous bacterial species colonize the mouth, upper airways, skin, vagina, and intestinal tract of humans. The phylogenetic trees show the speciation of bacterial clades from common ancestors at each anatomical site. Although the communities in different www.sciencemag.org
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regions of the body share similarities, they each have a unique site-specific “fingerprint” made of many distinct microbes. Each site has a very high level of diversity, as shown by the individual lines on the dendrograms. Data are from the NIH-funded Human Microbiome Project; circles represent bacterial species whose sequences are known. VOL 330
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tion into various pro- and anti-inflammatory subsets. For instance, infection by intracellular pathogens drives the development of T helper 1 (TH1) cells, whereas extracellular pathogens induce the differentiation of TH2 and TH17 subsets (23). These proinflammatory cells coordinate many aspects of the innate and adaptive immune response to clear microbial invaders. CD4+ T cells can also adopt an anti-inflammatory phenotype. Regulatory T cells (Tregs) control unwanted immune system activation and dampen inflammation after microbial infection. Expression of the Treg cell–specific transcription factor Foxp3 (forkhead box P3) induces regulatory phenotypes and functions by CD4+ T cells (24). Foxp3+ T cells develop in the thymus shortly after birth, and deletion or depletion of Foxp3+ T cells leads to severe multi-organ lymphoproliferative disease and autoimmunity (24). Besides the thymus-derived CD4+Foxp3+ T cells (“natural” Tregs), various subsets of Tregs can be generated in the gut from naïve T cells (“inducible” Tregs), some of which produce the anti-inflammatory cytokine interleukin-10 (IL-10) (25–28). Moreover, intestinal bacteria may be critically involved in the differentiation of some gut Treg subsets (29–31). Accordingly, several commensal bacteria (e.g., Bifidobacteria infantis, Faecalibacterium prausnitzii) have been shown to induce Foxp3+ Tregs and IL-10 production in the gut (32, 33). Members of the genus Bacteroides are prominent in the mammalian gastrointestinal tract and are also potent stimulators of the mucosal immune system of mammals (34). The gut microorganism Bacteroides fragilis has emerged as a model system for the study of immune-bacterial symbiosis. During colonization of mice with B. fragilis, the bacterial molecule polysaccharide A (PSA) directs the cellular and physical development of the immune system (16). Moreover, B. fragilis is able to prevent intestinal pathology in two independent models of experimental colitis in a PSA-dependent manner (35). Furthermore, in mouse models of experimental colitis, oral treatment of mice with purified PSA protects against weight loss, decreases proinflammatory cytokine expression in the gut, and inhibits lymphocyte infiltration that is associated with disease (35). The protective effects of PSA were likely mediated by CD4+ T cell production of IL-10, because CD4+ T lymphocytes from mesenteric lymph nodes of PSA-treated mice produced elevated amounts of IL-10. IL-10–deficient CD4+ T cells abolished the protective effects of PSA in colitis models. These studies identify PSA as a beneficial microbial molecule that suppresses inflammation-driven host pathology. No consensus has been reached about whether Foxp3+ Treg cells in the intestinal tissues of germ-free mice are defective (36–40); however, production of IL-10 is reduced within the GALT of germ-free animals (13, 36, 41). Foxp3+ Treg cells in the colon of germ-free mice exhibit reduced IL-10 expression, and monocolonization with PSA-producing bacteria (but not PSA-
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deficient B. fragilis) restores IL-10 expression (42). PSA increases Foxp3 expression by Treg cells, and colonization of germ-free animals with B. fragilis augments the in vitro suppressive activity of Tregs in a PSA-dependent manner (42). PSA protects and cures animals from experimental colitis by inducing Foxp3+ Treg cells and IL-10 production (42). Recently, it was shown that a defined set of Clostridium strains induce Foxp3+ Tregs that produce IL-10 in the colon and protect animals from colitis (43). These findings imply that optimal Foxp3+ Treg cell differentiation in the colon requires signals from the microbiota and the host genome. They also suggest that specific commensal bacteria may have evolved to promote Treg cell differentiation in the gut to actively engender mucosal tolerance. If validated in human disease, these findings may lead to probiotic therapies for colitis based on microbial-driven Treg induction. How Does the Microbiota Instruct T Helper Cell Differentiation? Although the microbiota has been shown to affect the TH1-TH2 balance in systemic immune compartments (44), studies have not yet observed symbiotic microbial effects on TH1 or TH2 cells at mucosal surfaces. In contrast, TH17 cell development in the gut is specifically affected by commensal bacteria (45). Germ-free mice are deficient in the production of IL-17 from CD4+ T cells (the hallmark cytokine of TH17 cells) of the small intestinal lamina propria (39). Only a minor defect was noted for gd T cells, which suggests that the lack of TH17 cells was not due to an overall deficiency in immune activation and that specific features of the immune response are sensitive to the microbiota. One mechanism of intestinal TH17 cell differentiation may be production of adenosine 5′-triphosphate (ATP) in the lamina propria by commensal bacteria, which drives the production of TH17-inducing cytokines by resident lamina propria cells (46). Germ-free animals display a reduction in fecal ATP amounts, and treatment of mice with a nonhydrolyzable ATP analog increased the number of gut TH17 cells (46). Not all bacterial species of the microbiota are similar in their ability to promote nonpathogenic T cell responses during normal colonization of animals. Of the numerous bacterial phylotypes that constitute the normal microbiota of mice, only segmented filamentous bacteria (SFBs) have been shown to direct intestinal T helper cell development. A role for SFBs was identified by reconstituting germ-free mice with various subsets of bacterial consortia and measuring cytokine production in gut mucosal tissues (41). SFBs, which are known to tightly adhere to the intestinal mucosa (and to Peyer’s patches of the ileum), induced the development of T helper cells in the lamina propria and in cell aggregates of Peyer’s patches. This activity was greatly reduced even when very complex groups of bacteria were tested if they were missing SFBs. In a contemporary report, a comparison of the micro-
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biota of mice that contained TH17 cells with mice deficient in these cells identified SFBs as being sufficient to restore TH17 cells to germ-free mice and conventionally raised mice that lack TH17 cells (47). Gene expression analysis showed that SFBs induce a spectrum of intestinal immune responses including production of cytokines and chemokines, antimicrobial peptides, and serum amyloid A (SAA), which was shown in vitro to support TH17 cell differentiation (47). SFB colonization protected animals from intestinal infection with C. rodentium, a bacterial pathogen of animals that causes acute intestinal inflammation similar to enteropathogenic Escherichia coli in humans (47). Thus, commensal SFBs induce a tonic (or controlled) inflammatory response in the gut through TH17 cell development that does not cause pathology and is protective against infection with pathogenic bacteria. These new studies build on research done several decades ago, which showed that SFBs promote germinal center development, mucosal immunoglobulin A responses, and recruitment of intraepithelial lymphocytes (48–50). Collectively, it appears that only a particular subset of bacteria from the gut microbiota directly influences TH17 immune responses during steady-state colonization. Are Noninfectious Human Diseases Influenced by the Microbiota? Numerous autoimmune diseases result from dysregulation of the adaptive immune system. The incidences of autoimmune diseases such as multiple sclerosis (MS), type 1 diabetes (T1D), and rheumatoid arthritis (RA) are rapidly increasing in Western societies, suggesting alterations in environment factors that regulate the adaptive immune system. As appreciation for the immunomodulatory potential of commensal bacteria has increased, we and others have proposed that lifestyle changes have caused a fundamental alteration in our association with the microbial world (51, 52). Altered diets, widespread antibiotic use, and other societal factors in developed countries may result in an unnatural shift in the community composition of a “healthy” microbiota, leading to altered microbial colonization known as dysbiosis. Whether dysbiosis causes any human disease is yet unproven (insights may come from microbiome sequencing projects); however, evidence in mice suggests that dysbiosis may affect autoimmunity by altering the balance between toleragenic and inflammatory members of the microbiota (Fig. 2). PSA from B. fragilis, previously shown to treat experimental colitis in the gut, is also able to prevent and cure experimental autoimmune encephalomyelitis (EAE, an animal model for multiple sclerosis) (53). Oral treatment of animals with PSA reduced TH17 cell development and increased Treg numbers in the central nervous system (CNS). Furthermore, germ-free animals display reduced TH17 cell numbers in the spleen and spinal cords, and do not develop RA or EAE (inflammation in joints and in the CNS, respectively) (54, 55). The inflammatory responses in
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REVIEW tract for an invading pathogen. SFBs are not overt pathogens and colonize animals as symbionts, and thus TH17 induction may lead to more enhanced immune responses that protect against acute infectious agents (such as C. rodentium). Besides this beneficial outcome, it appears that SFB colonization also leads to adverse host effects. TH1 and TH17 cells of the adaptive immune system promote autoimmunity. As a result, microbes that stimulate T helper cell development may (inadvertently) also increase the inherent immune reactivity of the host, potentially leading to host-destructive pathologies mediated by the adaptive immune system. This notion is supported by a role for SFBs in promoting RA and EAE during induced animal models, both of which involve TH17 cell inflammation (54, 55).
Human microbiome B. fragilis
The enhancement of RA and EAE by SFBs establishes that the microbiota can adversely influence autoimmune disease outside the gut. Therefore, SFBs can colonize healthy animals without causing illness; however, when the host is immunocompromised or under inflammatory conditions, SFBs can be detrimental. We propose that certain microbes, such as SFBs, that can peacefully coexist with a healthy host but still retain pathogenic potential be termed “pathobionts” to distinguish them from opportunistic pathogens that are acquired from the environment and cause acute infections (56). Pathobionts may represent microorganisms on the evolutionary continuum between acute pathogens and commensal microbes, whose sustained relationships with the host induce the development of additional
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Fig. 2. How the microbiome and the human genome contribute to inflammatory disease. In a simplified model, the community composition of the human microbiome helps to shape the balance between immune regulatory (Treg) and proinflammatory (TH17) T cells. The molecules produced by a given microbiome network work with the molecules produced by the human genome to determine this equilibrium. (A) In a healthy microbiome, there is an optimal proportion of both pro- and anti-inflammatory organisms (represented here by SFBs and B. fragilis), which provide signals to the developing immune system (controlled by the host genome), leading to a balance of Treg and TH17 cell activities. In this scenario, the host genome can contain “autoimmune-specific” mutations (represented by the stars), but disease does not develop. (B and C) The genomes of patients with multiple www.sciencemag.org
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both RA and EAE are promoted by TH17 cells and prevented by Tregs, which suggests that the effects of gut bacteria on the adaptive immune system likely extend beyond the gastrointestinal tract to influence autoimmune diseases that are seemingly unrelated to microbial infections. Why only specific commensal bacteria induce TH17 cell differentiation remains unclear. TH17 responses are critical at mucosal surfaces to control infections by extracellular pathogens. IL17 production recruits neutrophils to the site of infection and induces antimicrobial peptide expression and other mediators of immunity. If there is an evolutionary rationale for the ability of SFBs to induce TH17 cell differentiation, one interpretation is that they mediate a state of “controlled inflammation” that prepares the gastrointestinal
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sclerosis, type I diabetes, rheumatoid arthritis, and Crohn’s disease contain a spectrum of variants that are linked to disease by genome-wide association studies [reviewed in (63)]. Environmental influences, however, are risk factors in all of these diseases. Altered community composition of the microbiome due to life-style, known as dysbiosis, may represent this disease-modifying component. An increase in proinflammatory microbes (for example, SFBs in animal models) may promote TH17 cell activity to increase and thus predispose genetically susceptible people to TH17-mediated autoimmunity (B). Alternatively, a decrease or absence in anti-inflammatory microbes—for example, B. fragilis in animal models—may lead to an underdevelopment of Treg cell subsets (C). The imbalance between TH17 cells and Tregs ultimately leads to autoimmunity. VOL 330
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Fig. 3. A model for the coevolution of adaptive immunity with the microbiota. (A) The adaptive immune system develops under the control of the vertebrate genome to produce various cell types. The evolutionarily ancient molecule TGFb directs the differentiation of Foxp3+ Treg cells. Although the earliest mammals contained a gut microbiota, bacteria may or may not have influenced features of the primordial adaptive immune system. (B) Over millennia of coevolution, commensal microbes (B. fragilis used as an example here) produced molecules that networked with the primordial immune system to help expand various Treg cell subsets (for example, IL-10–producing Foxp3+ Treg cells). This process may have evolved to allow these microorganisms to colonize the gut by inducing antigen-specific tolerance to the microbiota. (C) Proinflammatory pathobionts layers of mucosal defense while promoting the unwanted side effect of autoimmune disease. The importance of TH17 cell–inducing microorganisms (such as SFBs) to animal models of autoimmunity remains to be further established; caveats exist, such as the fact that animals from colonies devoid of SFBs can develop autoimmune disease. Also, it remains to be determined how the microbiota may contribute to human autoimmunity. SFB colonization of animals, however, does provide a model system for testing concepts linking specific gut bacteria to nonintestinal immune disorders. The identification of bacterial molecules required for SFBs to induce TH17 cell responses may reveal why this particular microorganism is capable of promoting the development of proinflammatory T cells. Furthermore, studies that delineate the gene regulatory networks induced by SFB colonization may enhance our understanding of the evolutionary forces that resulted in TH17 lineage development. Autoimmune diseases such as MS, T1D, and RA are associated with a spectrum of genetic polymorphisms, as shown by recent genome-wide association studies. Given that concordance rates
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(such as SFBs) may have induced TH17 cell differentiation to increase mucosal defenses against enteric pathogens. (D) The modern adaptive immune system may have arisen from two distinct events: Tregs and TH17 cell types evolved independently [(A) to (B) and (A) to (C)] or through the sequential development of TH17 cells from Treg cell precursors [(A) to (B) to (C) to (D)]. This may have been achieved by a combinatorial signal of TGFb, augmented by the addition of IL-6 to promote TH17 cell evolution over time (inset). Together, the modulation of Tregs and TH17 cells by commensal microorganisms and pathobionts, respectively, appears to shape the immune status of the host and thus represents a possible risk factor for autoimmune diseases that appear to depend on balanced Treg-TH17 proportions.
for disease among monozygotic twins are 20 to 40% on average, environmental factors are crucial for the manifestations of symptoms (57). We predict that autoimmunity can result from the combination of an altered human genome and an altered microbiome (Fig. 2). Patients with autoimmunity likely have a genetic landscape that predisposes them to self-reactivity, and in some cases, certain gut bacteria may promote disease by activating the adaptive immune system. Potential future treatments for autoimmunity may include treatment of dysbiosis, because whereas the human genome is static and intransigent to manipulation, the microbiome is conceivably more amenable to therapeutic alterations. Understanding the molecular mechanisms of how symbiotic microbes affect immune reactions to self antigens may provide insight into the causes, and potential cures, for autoimmune diseases. Did the Microbiota Influence the Evolution of Adaptive Immunity? The adaptive immune system distinguishes between self and foreign antigens and mounts an
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appropriate response to clear invading pathogens by recognizing non-self molecules. The microbiota presents a challenge to the adaptive immune system because it contains an enormous foreign antigenic burden, which must be either ignored or tolerated to maintain health. One hypothesis for how this occurs is “immunologic ignorance,” whereby spatial separation of bacteria from the immune system or down-modulation of innate immunity prevents overt inflammation (58). This notion rests on the inability of the innate immune system to distinguish pathogens from symbionts because they share similar molecular patterns (such as TLR ligands). Rather than ignorance, tolerance could also be induced by the microbiota, given the capacity of gut bacteria to induce Treg lineage differentiation. Molecules produced by our microbiome may be considered “self,” because inflammatory bowel disease is thought, in part, to involve a loss of tolerance to antigens of the microbiota. Therefore, it appears that we may tolerate the microbiota in the same way that we tolerate antigens encoded by our own genome. This then raises the question
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of whether symbiotic bacteria evolved mechanisms to suppress unwanted inflammation toward the microbiota by actively inducing mucosal tolerance. Several studies now suggest this to be the case (32, 33, 42). The necessity for the microbiota to induce tolerance as a requirement for colonization, if true, provides a rationale for why symbiotic bacteria may have influenced critical aspects of the adaptive immune system throughout mammalian evolution. Although T cells can adopt numerous effector cell fates (such as TH1, TH2, TH3, TH9, etc.), there are common mechanistic foundations to TH17 and Treg cell development. The differentiation of both lineages is promoted by transforming growth factor b (TGFb); Tregs require TGFb (and retinoic acid), whereas TGFb and IL6 promote TH17 development (23). The central transcription factors for Treg cells and TH17 cells (Foxp3 and RORgt, respectively) are coexpressed in naïve and effector CD4+ T cells, physically interact with each other, and differentially respond to cytokine stimulation to help determine lineage commitment between Foxp3+ Treg and TH17 differentiation (59). Furthermore, TH17 cells can develop from Foxp3+ Treg cell precursors (60). As mentioned above, germ-free animals show decreased TH17 cell development in several anatomic locations (39, 46, 54, 55), and recolonization with a microbiota (containing SFBs) promotes TH17 cells (47, 54, 55). On the basis of this knowledge, we propose a hypothetical model for how specific commensal bacteria network with an evolving adaptive immune system (Fig. 3). Thymically derived Foxp3+ Treg cells developed under the control of the evolutionarily ancient molecule TGFb. It is tempting to speculate that commensal microbes (for example, B. fragilis) may have “learned” to augment this process by further promoting the differentiation of existing Treg cells into expanded subsets, such as those producing IL10 at mucosal surfaces. This expanded Treg cell repertoire could have provided the host with a mechanism to tolerate foreign antigens of the microbiota. Pathobionts such as SFBs may have further modified the development of adaptive immunity by promoting the differentiation of TH17 cells, in part from Foxp3+ precursors. In support of this notion, IL-17 family members have been found only in vertebrates (61). Other proinflammatory cytokines (IL-6, IL-21, and IL-1) along with TGFb are required to induce IL-17 production, perhaps suggesting that TH17 cells might be a more recent invention than Treg cells. Perhaps the evolution of specific immune responses was not mainly driven by pathogens (as is popular assumption), but instead by organisms that developed more sustained relationships with the host such as commensals and pathobionts. As these new symbiotic relationships were forged through host-microbial coevolution, novel additions to the immune system were introduced over millennia. Treg and TH17 cells provide a powerful means by which mucosal
surfaces can be protected from unwanted inflammatory responses to the microbiota (by Treg cells) while still being capable of potently responding to microbial infections (with TH17 cells). B. fragilis and SFBs represent model organisms that have been experimentally validated to provide these functions; other microbes may possess similar activities. Although this concept requires experimental validation, the coordination of Treg and TH17 responses appears ideally suited to benefit both the microbiota and the host, and may represent an important evolutionary partnership for human health. Co-opting an antigen-specific adaptive immune system by the microbiota may extend beyond simply a host-derived process for controlling microbial infections. During cohabitation with the microbiota, evolution of the vertebrate genome occurred under the influence of signals from symbiotic bacteria. In fact, the evolutionary forces that contributed to immune system development during lifelong microbial associations may be dominant relative to those of transient encounters by microbial pathogens (which are rare and opportunistic) (62). Thus, symbiotic microbes may have influenced features of adaptive immune system evolution and function more profoundly than pathogens, possibly to protect both host and microbiota from invading infections. As an increasing body of knowledge links the microbiota to Treg and TH17 phenotypes that mediate autoimmunity, it is imperative to determine how signals from the microbiota shape gene regulatory networks within CD4+ T cells after their thymic development. If these coevolutionary interactions are relatively recent inventions, is autoimmunity an unwanted side effect from fine-tuning of the peripheral adaptive immune system by the microbiota? As the influences of the microbiota on autoimmune diseases are unraveled, it can be envisioned that harnessing the ability of the microbiota to induce tolerance through Treg cells may provide novel treatments for autoimmunity by correcting immunologic imbalances found in an evolving adaptive immune system. Finally, because we harbor 10 times as many bacterial cells as human cells, explorations into how the microbiota may have influenced the evolution of adaptive immunity might redefine how we view our “microbial selves.” References and Notes 1. P. J. Turnbaugh et al., Nature 449, 804 (2007). 2. J. Qin et al., Nature 464, 59 (2010). 3. P. G. Falk, L. V. Hooper, T. Midtvedt, J. I. Gordon, Microbiol. Mol. Biol. Rev. 62, 1157 (1998). 4. A. J. Macpherson, N. L. Harris, Nat. Rev. Immunol. 4, 478 (2004). 5. M. Pollard, N. Sharon, Infect. Immun. 2, 96 (1970). 6. H. Hoshi et al., Tohoku J. Exp. Med. 166, 297 (1992). 7. J. R. Glaister, Int. Arch. Allergy Appl. Immunol. 45, 719 (1973). 8. A. Lundin et al., Cell. Microbiol. 10, 1093 (2008). 9. S. Matsumoto, H. Setoyama, Y. Umesaki, Gastroenterology 103, 1777 (1992). 10. A. Imaoka et al., Eur. J. Immunol. 26, 945 (1996). 11. Y. Umesaki, H. Setoyama, S. Matsumoto, Y. Okada, Immunology 79, 32 (1993).
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12. J. Duan, H. Chung, E. Troy, D. L. Kasper, Cell Host Microbe 7, 140 (2010). 13. J. H. Niess, F. Leithäuser, G. Adler, J. Reimann, J. Immunol. 180, 559 (2008). 14. D. Bouskra et al., Nature 456, 507 (2008). 15. M. C. Noverr, G. B. Huffnagle, Trends Microbiol. 12, 562 (2004). 16. S. K. Mazmanian, C. H. Liu, A. O. Tzianabos, D. L. Kasper, Cell 122, 107 (2005). 17. H. Bauer et al., Am. J. Pathol. 42, 471 (1963). 18. K. Smith, K. D. McCoy, A. J. Macpherson, Semin. Immunol. 19, 59 (2007). 19. T. B. Clarke et al., Nat. Med. 16, 228 (2010). 20. H. Inagaki, T. Suzuki, K. Nomoto, Y. Yoshikai, Infect. Immun. 64, 3280 (1996). 21. B. Stecher et al., PLoS Biol. 5, e244 (2007). 22. C. Lupp et al., Cell Host Microbe 2, 119 (2007). 23. E. Bettelli et al., Nature 441, 235 (2006). 24. J. D. Fontenot et al., Immunity 22, 329 (2005). 25. A. Foussat et al., J. Immunol. 171, 5018 (2003). 26. M. Battaglia, S. Gregori, R. Bacchetta, M. G. Roncarolo, Semin. Immunol. 18, 120 (2006). 27. S. Sakaguchi et al., Cell 133, 775 (2008). 28. Y. P. Rubtsov et al., Immunity 28, 546 (2008). 29. J. L. Coombes et al., J. Exp. Med. 204, 1757 (2007). 30. M. Boirivant, W. Strober, Curr. Opin. Gastroenterol. 23, 679 (2007). 31. J. L. Coombes, K. J. Maloy, Semin. Immunol. 19, 116 (2007). 32. C. O’Mahony et al., PLoS Pathog. 4, e1000112 (2008). 33. H. Sokol et al., Proc. Natl. Acad. Sci. U.S.A. 105, 16731 (2008). 34. S. K. Mazmanian, D. L. Kasper, Nat. Rev. Immunol. 6, 849 (2006). 35. S. K. Mazmanian, J. L. Round, D. L. Kasper, Nature 453, 620 (2008). 36. U. G. Strauch et al., Gut 54, 1546 (2005). 37. S. Östman, C. Rask, A. E. Wold, S. Hultkrantz, E. Telemo, Eur. J. Immunol. 36, 2336 (2006). 38. B. Min et al., Eur. J. Immunol. 37, 1916 (2007). 39. I. I. Ivanov et al., Cell Host Microbe 4, 337 (2008). 40. C. Zaph et al., J. Exp. Med. 205, 2191 (2008). 41. V. Gaboriau-Routhiau et al., Immunity 31, 677 (2009). 42. J. L. Round, S. K. Mazmanian, Proc. Natl. Acad. Sci. U.S.A. 107, 12204 (2010). 43. K. Atarashi et al., Science 10.1126/science.1198469 (2010). 44. R. Dobber, A. Hertogh-Huijbregts, J. Rozing, K. Bottomly, L. Nagelkerken, Dev. Immunol. 2, 141 (1992). 45. I. I. Ivanov et al., Cell 126, 1121 (2006). 46. K. Atarashi et al., Nature 455, 808 (2008). 47. I. I. Ivanov et al., Cell 139, 485 (2009). 48. J. Snel et al., Can. J. Microbiol. 44, 1177 (1998). 49. G. L. Talham, H. Q. Jiang, N. A. Bos, J. J. Cebra, Infect. Immun. 67, 1992 (1999). 50. Y. Umesaki, Y. Okada, S. Matsumoto, A. Imaoka, H. Setoyama, Microbiol. Immunol. 39, 555 (1995). 51. M. C. Noverr, G. B. Huffnagle, Clin. Exp. Allergy 35, 1511 (2005). 52. J. L. Round, S. K. Mazmanian, Nat. Rev. Immunol. 9, 313 (2009). 53. J. Ochoa-Repáraz et al., Mucosal Immunol. 3, 487 (2010). 54. H. J. Wu et al., Immunity 32, 815 (2010). 55. Y. K. Lee et al., Proc. Natl. Acad. Sci. U.S.A. 10.1073/ pnas.1000082107 (2010). 56. J. Chow, S. K. Mazmanian, Cell Host Microbe 7, 265 (2010). 57. S. E. Baranzini et al., Nature 464, 1351 (2010). 58. L. V. Hooper, Nat. Rev. Microbiol. 7, 367 (2009). 59. L. Zhou et al., Nature 453, 236 (2008). 60. X. O. Yang et al., Immunity 29, 44 (2008). 61. C. T. Weaver, R. D. Hatton, P. R. Mangan, L. E. Harrington, Annu. Rev. Immunol. 25, 821 (2007). 62. M. McFall-Ngai, Nature 445, 153 (2007). 63. S. E. Baranzini, Curr. Opin. Immunol. 21, 596 (2009). 64. We thank members of the Mazmanian laboratory for their critical review of the manuscript. Supported by NIH grants DK078938, DK083633, and AI088626; the Damon Runyon Cancer Research Foundation; and the Crohn’s and Colitis Foundation of America (S.K.M.).
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REVIEW
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Decreased Clearance of CNS b-Amyloid in Alzheimer’s Disease Kwasi G. Mawuenyega,1 Wendy Sigurdson,1,2 Vitaliy Ovod,1 Ling Munsell,1 Tom Kasten,1 John C. Morris,1,2,3 Kevin E. Yarasheski,4 Randall J. Bateman1,2,5* lzheimer’s disease (AD) is characterized P = 0.98). The average clearance rate of Ab42 was by increased amounts of soluble and in- slower for AD individuals compared with that for soluble b-amyloid (Ab), predominantly in cognitively normal controls (5.3%/hour versus the form of Ab42 in amyloid plaques and Ab40 in 7.6%/hour, P = 0.03), as was the average clearamyloid angiopathy. The amyloid hypothesis pro- ance rate of Ab40 (5.2%/hour for AD individuals poses that AD is caused by an imbalance between versus 7.0%/hour for controls; P = 0.01). To determine the balance of Ab production to Ab production and clearance (1), resulting in increased amounts of Ab in various forms such as clearance rates in AD versus controls, we meamonomers, oligomers, insoluble fibrils, and pla- sured the ratios of production to clearance (fig. S2). ques in the central nervous system (CNS). High The ratio of Ab42 production to clearance rates levels of Ab then initiate a cascade of events cul- was balanced for cognitively normal participants minating in neuronal damage and death manifesting (0.95); however, because of decreased clearance in as progressive clinical dementia of the Alzheimer’s the AD participants, there was an imbalance in the Ab42 production to clearance ratio (1.35). Simitype (2). In rare cases of AD, genetic alterations increase larly, we observed an imbalance in the AD Ab40 the production of Ab (3). However, Ab dysregula- production to clearance ratio (1.37) compared with tion in the far more common late-onset “sporadic” the ratio in cognitively normal participants (0.99). The technique of measuring Ab production and AD is less well understood. Possible mechanisms of increased Ab production for late-onset AD in- clearance has been used to measure effects of drugs clude alterations in gamma or beta secretase activ- that target Ab generation, demonstrating decreases ity. Alternatively, impaired clearance of Ab may in production (5). We found that late-onset AD is also cause late-onset AD through interactions with associated with a 30% impairment in the clearance ApoE4, decreased catabolism of Ab via reduced of both Ab42 and Ab40, indicating that Ab clearproteolysis, impaired transport A B across the blood-brain barrier, or impaired cerebrospinal fluid (CSF) transport. To measure the production and clearance of Ab in AD, we developed a method to measure human CNS Ab production and C D clearance (fig. S1) (4) and compared Ab42 and Ab40 production and clearance rates in individuals with symptomatic AD and in cognitively normal persons to determine whether either or both are altered in AD. We plotted the average time E F course results of labeled Ab42 and Ab40 for the production phase (hours 5 to 14) and the clearance phase (hours 24 to 36) (Fig. 1). The production and clearance rates were calculated for each participant and compared by group status (AD versus control). The average Ab42 production rate did not differ Fig. 1. Ab kinetics in the CNS of 12 AD participants (red triangles) and 12 between the control (6.7%/hour) controls (blue circles). The amount of labeled Ab42 and Ab40 was meaand AD (6.6%/hour) groups sured and compared between groups to measure production and clearance (P = 0.96), nor did Ab40 pro- rates of both Ab species. Error bars indicate SEM. (A) Normalized labeled duction rate differ between Ab42 production phase. (B) Ab42 clearance phase. (C) Normalized labeled groups (6.8%/hour for controls Ab40 production phase. (D) Ab40 clearance phase. (E) Fractional synthesis and 6.8%/hour for the AD group; rates of Ab42 and Ab40. (F) Fractional clearance rates of Ab42 and Ab40.
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ance mechanisms may be critically important in the development of AD (6). Estimates based on a 30% decrease in Ab clearance rates suggest that brain Ab accumulates over about 10 years in AD. The impaired clearance of both Ab40 and Ab42 is consistent with prior findings of deposition of Ab40 and Ab42 in parenchymal amyloid plaques and the substantial deposition of Ab40 in cerebral amyloid angiopathy in about 80% of cases of AD (7). Limitations of this study include the relatively small numbers of participants (12 in each group) and the inability to prove causality of impaired Ab clearance for AD. In addition to decreased CNS Ab clearance, CSF Ab42 concentrations are decreased in AD compared with those in controls (fig. S3). Taken together, these may be consistent with decreased Ab42 clearance (efflux) from the brain to the CSF. However, the relationship between decreased concentrations of CSF Ab42 and decreased CNS clearance of labeled Ab (fig. S4) is not fully understood. Additional possibilities include more than one pool of Ab in CSF, undetected pools of Ab in CSF by enzyme-linked immunosorbent assay (e.g., oligomers), or a combined increase in Ab production with impaired efflux from parenchyma to CSF. Overall, these results suggest impaired metabolism of Ab in AD compared with that in controls. References and Notes 1. 2. 3. 4. 5. 6. 7. 8.
J. Hardy, D. J. Selkoe, Science 297, 353 (2002). J. L. Cummings, N. Engl. J. Med. 351, 56 (2004). D. Scheuner et al., Nat. Med. 2, 864 (1996). R. J. Bateman et al., Nat. Med. 12, 856 (2006). R. J. Bateman et al., Ann. Neurol. 66, 48 (2009). R. B. DeMattos et al., Neuron 41, 193 (2004). R. J. Ellis et al., Neurology 46, 1592 (1996). We are grateful to the participants for their time and effort. The authors thank J. X. Wang for gas chromatography–mass spectrometry (MS) analysis and R. Connors and R. Potter for immunoprecipitation-MS analysis. Special thanks to D. M. Holtzman for mentoring, support, and review of the manuscript. This work was supported by grants from NIH (nos. K08 AG027091, K23 AG030946, R01 NS065667, P50 AG05681, P01 AG03991, UL1 RR024992, P41 RR000954, P60 DK020579, and P30 DK056341), an anonymous foundation, Eli Lilly research, the Knight Initiative for Alzheimer’s Research, and the James and Elizabeth McDonnell Fund for Alzheimer’s Research. R.J.B. and D. M. Holtzman are cofounders of a company (C2N Diagnostics) that has licensed a pending Washington University patent on the technology described in this article.
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1197623/DC1 Materials and Methods Figs. S1 to S4 References 10 September 2010; accepted 11 November 2010 Published online 9 December 2010; 10.1126/science.1197623 1 Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA. 2Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA. 3Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA. 4Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. 5Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
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BREVIA
Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project Mark B. Gerstein,1,2,3*† Zhi John Lu,1,2* Eric L. Van Nostrand,4* Chao Cheng,1,2* Bradley I. Arshinoff,5,6* Tao Liu,7,8* Kevin Y. Yip,1,2* Rebecca Robilotto,1* Andreas Rechtsteiner,9* Kohta Ikegami,10* Pedro Alves,1* Aurelien Chateigner,11* Marc Perry,5* Mitzi Morris,12* Raymond K. Auerbach,1* Xin Feng,5,22* Jing Leng,1* Anne Vielle,13* Wei Niu,14,15* Kahn Rhrissorrakrai,12* Ashish Agarwal,2,3 Roger P. Alexander,1,2 Galt Barber,16 Cathleen M. Brdlik,4 Jennifer Brennan,10 Jeremy Jean Brouillet,4 Adrian Carr,11 Ming-Sin Cheung,13 Hiram Clawson,16 Sergio Contrino,11 Luke O. Dannenberg,17 Abby F. Dernburg,18 Arshad Desai,19 Lindsay Dick,38 Andréa C. Dosé,18 Jiang Du,3 Thea Egelhofer,9 Sevinc Ercan,10 Ghia Euskirchen,14 Brent Ewing,20 Elise A. Feingold,21 Reto Gassmann,19 Peter J. Good,21 Phil Green,20 Francois Gullier,11 Michelle Gutwein,12 Mark S. Guyer,21 Lukas Habegger,1 Ting Han,23 Jorja G. Henikoff,24 Stefan R. Henz,29 Angie Hinrichs,16 Heather Holster,17 Tony Hyman,26 A. Leo Iniguez,17 Judith Janette,15 Morten Jensen,10 Masaomi Kato,28 W. James Kent,16 Ellen Kephart,5 Vishal Khivansara,23 Ekta Khurana,1,2 John K. Kim,23 Paulina Kolasinska-Zwierz,13 Eric C. Lai,30 Isabel Latorre,13 Amber Leahey,20 Suzanna Lewis,31 Paul Lloyd,5 Lucas Lochovsky,1 Rebecca F. Lowdon,21 Yaniv Lubling,32 Rachel Lyne,11 Michael MacCoss,20 Sebastian D. Mackowiak,33 Marco Mangone,12 Sheldon McKay,34 Desirea Mecenas,12 Gennifer Merrihew,20 David M. Miller III,27 Andrew Muroyama,19 John I. Murray,20 Siew-Loon Ooi,24 Hoang Pham,18 Taryn Phippen,9 Elicia A. Preston,20 Nikolaus Rajewsky,33 Gunnar Rätsch,25 Heidi Rosenbaum,17 Joel Rozowsky,1,2 Kim Rutherford,11 Peter Ruzanov,5 Mihail Sarov,26 Rajkumar Sasidharan,2 Andrea Sboner,1,2 Paul Scheid,12 Eran Segal,32 Hyunjin Shin,7,8 Chong Shou,1 Frank J. Slack,28 Cindie Slightam,35 Richard Smith,11 William C. Spencer,27 E. O. Stinson,31 Scott Taing,7 Teruaki Takasaki,9 Dionne Vafeados,20 Ksenia Voronina,19 Guilin Wang,15 Nicole L. Washington,31 Christina M. Whittle,10 Beijing Wu,35 Koon-Kiu Yan,1,2 Georg Zeller,25,36 Zheng Zha,5 Mei Zhong,14 Xingliang Zhou,10 modENCODE Consortium,‡ Julie Ahringer,13† Susan Strome,9† Kristin C. Gunsalus,12,37† Gos Micklem,11† X. Shirley Liu,7,8† Valerie Reinke,15† Stuart K. Kim,4,35† LaDeana W. Hillier,20† Steven Henikoff,24† Fabio Piano,12,37† Michael Snyder,4,14† Lincoln Stein,5,6,34† Jason D. Lieb,10† Robert H. Waterston20† We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome. omplete genome sequences provide a view of the full instruction set of an organism. However, understanding the functional content of a genome requires more than DNA sequence. To address this need, in 2003 the U.S. National Human Genome Research Institute (NHGRI) initiated the Encyclopedia of DNA Elements (ENCODE) project in order to study the human genome in greater depth (1). Recognizing the importance of well-annotated model genomes, in 2007 the NHGRI initiated the model organism ENCODE (modENCODE) project on Caenorhabditis elegans and Drosoph-
C
ila melanogaster so as to systematically annotate the functional genomic elements in these organisms (2). Given its intermediate complexity between single-celled eukaryotes and mammals, C. elegans offers an outstanding system for studies of genome organization and function. C. elegans was the first multicellular organism with a fully defined cell lineage, a nervous system reconstructed through serial-section electron microscopy, and a sequenced genome (3–5). Its 100.3-Mb genome is only about eight times larger than that of S. cerevisiae, and yet it contains almost as many
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genes as a human and all of the information necessary to specify the major tissues and cell types of metazoans. From the project start in 2007 (2), the C. elegans modENCODE groups had by February 2010 collected 237 genome-wide data sets (table S1) bearing on gene structure, RNA expression profiling, chromatin structure and regulation, and evolutionary conservation. To ensure the completeness and standardization of modENCODE data, all data sets were submitted to the modENCODE Data Coordinating Center; hand curated with extensive, structured metadata; validated for completeness; and checked for consistency before release at www.modencode.org. Analyses of these data reveal (i) directly supported protein-coding genes containing 5′ and 3′ ends and alternative splice junctions; (ii) sets of noncoding RNAs, including RNAs belonging to known classes and previously unknown types; (iii) gene expression and transcription factor (TF)– binding profiles across developmental stages; (iv) genomic locations bound by many of the TFs analyzed, designated as HOT (high-occupancy target) regions; (v) a hierarchy of candidate regulatory interactions among TFs and its relationship to the network of microRNAs (miRNAs) and their targets; (vi) differences in histone modifications and nuclear-envelope interactions between the centers and arms of autosomes and between autosomes and the X chromosome; (vii) evidence for chromatin-mediated epigenetic transmission of the memory of gene expression from adult germ cells to embryos; and (viii) predictive models that relate chromatin state to TF-binding sites and to expression levels of protein- and miRNAencoding genes. The summation of features annotated through these functional data sets provides a potential explanation for most of the conserved sequences in the C. elegans genome and lays the foundation for further study of how the genome of a multicellular organism accurately directs development and maintains homeostasis.
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The Transcriptome Accurate and comprehensive annotation of all RNA transcripts (the transcriptome) provides a framework for interpreting other genomic features, such as TF-binding sites and chromatin marks. At the project’s inception [WS170; WormBase versions used for specific analyses can be found in (6)], the C. elegans genome lacked direct experimental support for about one third of predicted splice junctions, and some of these predictions were erroneous (7, 8). Many genes lacked transcript start sites and polyadenylate [poly(A)] addition sites; exons and even whole genes were missing. To address these deficiencies, cDNA-based evidence was obtained through highthroughput sequencing (RNA-seq), reverse transcription polymerase chain reaction (RT-PCR)/ rapid amplification of cDNA ends (RACE), and tiling arrays from a variety of stages, conditions,
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and tissues (tables S1, S3, and S4). Analysis of the data yielded previously unrecognized proteincoding genes, refined the structure of known protein-coding genes, revealed the dynamics of expression and alternative splicing, provided evidence of pseudogene transcription, and suggested previously unknown noncoding RNAs (ncRNAs). Through mass spectrometry, we verified predicted proteins and distinguished short singleexon protein-coding transcripts from ncRNAs. Protein-coding genes. We used RNA-seq to generate more than 1 billion uniquely aligned short sequence reads from 19 different nematode populations, including all major developmental stages (embryonic, larval, dauer, and adult), embryonic and late L4 males, animals exposed to pathogens, and selected mutants (fig. S3) (9, 10). Data sets targeting the 3′ ends of poly(A)-plus transcripts were also collected, and additional sequence tags representing polyadenylated 3′ ends that were acquired by using 3P-Seq [poly(A)position profiling by sequencing] were made available to the consortium (11, 12). RNA-seq reads were mapped exhaustively and, together with the 3P-Seq data, allowed us to detect with nucleotide resolution features of protein-coding genes independently of previous WormBase models (fig. S7). The number of confirmed splice junctions increased from 70,028 at project start to 111,786, with 8174 of these not previously represented in WormBase (Fig. 1A and fig. S8). The number of genes with a transspliced leader (either Spliced Leader 1 or 2) at the 5′ end increased from 6012 to 12,413, covering 20,515 different trans-spliced transcript start sites (TSSs), and the number of poly(A) sites associated with genes increased from 1330 to 28,199 (table S2A) (13). RT-PCR/RACE and mass spectrometry provided direct support for 40,114 splice junctions (6). About 95% of these
overlapped with those detected with RNA-seq, providing independent support for 37,830 of these features (fig. S9). In addition, mass spectrometry proved that of 359 tested, 73 singleexon genes produced protein. We used several avenues to estimate how many features of protein-coding genes remain to be supported in C. elegans. Of predicted WormBase transcripts, only 1108 (5%) do not have support through RNA-seq (table S2B). Of these, 369 are members of rapidly evolving gene families implicated in environmental response and may be nonfunctional or only expressed under specific conditions. The yield of new features discovered with additional RNA-seq samples is clearly diminishing, and features such as newly discovered exons are approaching saturation (fig. S10). Intersection of the data sets produced here with previous evidence from WormBase suggests as few as 2000 to 3000 exons (2 to 3%) remain undetected (fig. S10). However, we continue to detect rare splice-junction and splicedleader events, particularly those associated with more abundantly expressed genes. These could be biologically important but might also result from RNA-processing errors. Gene models. We built probable gene models from the results of transcript sequencing, allowing for multiple transcripts (isoforms) from a given region (10). These models, called genelets because they could be fragments of full genes, were initiated with the most highly represented splice junction in a region and extended in each direction so as to incorporate regions covered by above-threshold sequence reads and splice junctions (6). The model was terminated when either a transcript start or stop signal was encountered or when coverage was interrupted (fig. S5). By iterating the process, we generated alternative isoforms. We used the longest open reading frame
to annotate protein-coding sequences (CDSs) and 5′ and 3′ untranslated regions (UTRs). For each of the 19 stages and conditions, we built transcript sets purely on the basis of RNA-seq data from a given stage (stage-specific RNA-seq– only genelets), along with three aggregate sets: (i) aggregate RNA-seq–only genelets; (ii) aggregate integrated genelets, which combined RNAseq data with available ESTs (expressed sequence tags), cDNAs, and OSTs (open reading-frame sequence tags) (7, 8, 11), as well as the RT-PCR/ RACE and mass spectrometry data produced in the project; and (iii) aggregate integrated transcripts, which incorporates all evidence from “(ii)” above and allows WormBase predictions to fill small coverage gaps within exons. The last set incorporates all of the splice junctions and spliced-leader sites, as well as multiple poly(A) addition sites, and thus often contains multiple isoforms. Altogether, we generated 64,824 transcripts from 21,733 genes, as compared with 23,710 transcripts from 20,082 genes in WormBase at the project start. Our gene models, which come from direct experimental evidence, exactly match the internal splice junction pattern for 10,123 WormBase transcripts, but we provide revised 5′- or 3′UTRs for many of these. For 6418 models, the internal gene structure was unchanged from WormBase, but new 5′ or 3′ exons and associated splice junctions were added. The remaining fall into three categories: Our models overlap WormBase transcripts but differ in splice junctions (3292); they fail to cover all of the splice junctions (2235); or they are not represented in WormBase at all (1952). Expression dynamics. To determine the dynamics of gene expression during development and in specific cell types, we analyzed tiling array data from 42 biological samples, comprising 17 different growth stages and conditions from
1 Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA. 2Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA. 3Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA. 4Department of Genetics, Stanford University Medical Center, Stanford, CA 94305, USA. 5Ontario Institute for Cancer Research, 101 College Street, Suite 800, Toronto, Ontario M5G 0A3, Canada. 6Department of Molecular Genetics, University of Toronto, 27 King’s College Circle, Toronto, Ontario M5S 1A1, Canada. 7Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA. 8Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. 9Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA. 10Department of Biology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. 11Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK, and Cambridge Systems Biology Centre, Tennis Court Road, Cambridge CB2 1QR, UK. 12Center for Genomics and Systems Biology, Department of Biology, New York University, 1009 Silver Center, 100 Washington Square East, New York, NY 10003–6688, USA. 13Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK. 14Department of Molecular, Cellu-
lar, and Developmental Biology, Yale University, New Haven, CT 06824, USA. 15Department of Genetics, Yale University School of Medicine, New Haven, CT 06520–8005, USA. 16 Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064 USA. 17Roche NimbleGen, 500 South Rosa Road, Madison, WI 53719, USA. 18 Howard Hughes Medical Institute, Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA, and Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. 19Ludwig Institute Cancer Research/Department of Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093–0653, USA. 20Department of Genome Sciences, University of Washington School of Medicine, William H. Foege Building S350D, 1705 NE Pacific Street, Post Office Box 355065, Seattle, WA 98195–5065, USA. 21Division of Extramural Research, National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Suite 4076, Bethesda, MD 20892–9305, USA. 22Department of Biomedical Engineering, State University of New York at Stonybrook, Stonybrook, NY 11794, USA. 23Life Sciences Institute, Department of Human Genetics, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI 48109–2216, USA. 24Basic Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA. 25Friedrich Miescher Laboratory of the Max Planck Society, Spemannstrasse 39, 72076 Tübingen, Germany. 26Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307
Dresden, Germany. 27Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37232–8240, USA. 28Department of Molecular, Cellular and Developmental Biology, Post Office Box 208103, Yale University, New Haven, CT 06520, USA. 29Max Planck Institute for Developmental Biology, Spemannstrasse 37-39, 72076 Tübingen, Germany. 30Sloan-Kettering Institute, 1275 York Avenue, Post Office Box 252, New York, NY 10065, USA. 31 Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 64-121, Berkeley, CA 94720 USA. 32 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel. 33MaxDelbrück-Centrum für Molekulare Medizin, Division of Systems Biology, Robert-Rössle-Strasse 10, D-13125 Berlin-Buch, Germany. 34Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11542 USA. 35Department of Developmental Biology, Stanford University Medical Center, 279 Campus Drive, Stanford, CA 94305–5329, USA. 36European Molecular Biology Laboratory, 69117 Heidelberg, Germany. 37New York University, Abu Dhabi, United Arab Emirates. 38David Rockefeller Graduate Program, Rockefeller University, 1230 York Avenue New York, NY 10065, USA. *These authors contributed equally to this work. †To whom correspondence should be addressed. E-mail:
[email protected] ‡The modENCODE Consortium is a group of NHGRI-funded investigators defining genomic elements in C. elegans and D. melanogaster.
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simple alternative exons to more complicated patterns, such as splicing or retention of an entire series of introns in different stages (Fig. 1C and fig. S6). We also developed algorithms that infer quantitative transcript-level expression by distributing sequence reads among alternative isoforms in a probabilistic manner (6). Pairwise comparisons of staged samples showed that overall, isoform usage does not change dramatically between stages: Of 12,875 genes with multiple isoforms, 280 on average switch isoform usage between any two stages, totaling 1324 genes with switching (Fig. 1B and fig. S14) (6). Using a different approach, we grouped transcript-level expression profiles across many stages into 48 distinct clusters (figs. S15 and S16). We identified 1320 genes for which one isoform fell into a separate cluster from all the others and then classified these according to the type of processing events that distinguish them (figs. S17 and S18) (6). These analyses illustrate the range of alternative mRNA processing that takes place during development.
files at a given stage with all other stages. For simplicity, we focused on a set of 8428 genes with non-overlapping transcripts and found that profiles over the time course cluster into distinct embryo and larval phases (Fig. 2A) (6). This division was consistent with a principal-components analysis on the tiling-array data from matched tissues from embryo and L2 (Fig. 2C) (6). The RNA for the embryos and larvae was isolated through different procedures, but on the basis of a number of controls and comparisons these differences are unlikely to confound the analysis (6). Alternative splicing. Alternative mRNA processing, including selection of alternative splice junctions, promoters, or poly(A) addition signals, provides another mechanism for differential transcript generation. To discover prominent stagespecific alternative isoforms among the aggregate integrated transcript models, we identified genes with two or more isoforms whose abundance changed more than fivefold during development; differential splice junction usage ranged from
whole animals, and 25 samples from different isolated cell and tissue types (table S3) (6). For almost all whole-animal samples, RNA-seq data were also obtained from the same or similarly prepared samples. Calibration and processing were done to facilitate the integration of sequencing and arrays for both RNA-seq and for chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq), allowing them to be used for a merged data set (figs. S1, S2, and S4) (6, 14). Overall, we found that only a small number of genes (~100 per stage) showed strong stage-specific expression in the whole-animal samples, but fewer than half of the genes were detectably expressed in all stages by means of RNA-seq, and tiling arrays suggest that >75% of genes show a greater than twofold range of expression across all the tissues (figs. S11 and S12) (15). To investigate the relationship between gene expression and developmental stages in greater detail, we correlated the RNA-seq expression pro-
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comparisons across seven developmental stages. A fractional difference close to 1 indicates large differences in the relative composition. (C) Representative example (F01G12.5; let-2), illustrating alternative exon usage across stages. (D) Example of a differentially transcribed pseudogene creating a ncRNA. Rows are normalized signal tracks for the various developmental stages, showing the expression pattern of the parent gene (T01B11.7.1; orange) and an associated duplicated pseudogene (PP00501, green). VOL 330
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Pseudogenes. Several gene models derived from RNA-seq fell in regions previously annotated as pseudogenes. Pseudogenes are DNA sequences similar to protein-coding genes that are generally thought not to produce functioning proteins (16). However, some pseudogenes are transcribed and may potentially act as endo-siRNA (endogenous small-interfering RNA) regulators of their parent genes (17). Using computational methods, we identified 1293 probable pseudogenes in the C. elegans genome, adding 173 to and removing 213 from the previous annotation set (WS170), and established the probable source (parent) gene for 1198 of them (fig. S19) (6). Using RNA-seq data, we found evidence of transcription for 323 pseudogenes (6). For 191 of the 323, we determined that the transcription was clearly independent of the parent gene, ruling out potential mismapping artifacts. Of these 191, 104 had a discordant expression pattern across stages relative to the parent (Fig. 1D), and 87 were greater than two times more expressed than the parent (6). Intriguingly, 17 of the transcribed pseudogenes have a unique peptide match through mass spectrometry, suggesting that they are translated and may create novel short peptides. ncRNAs. The genome produces a variety of transcripts that do not code for proteins but instead function directly as noncoding RNA (ncRNA). At the start of the project, there were 1061 known ncRNAs in C. elegans (table S5). These include small nucleolar RNAs (snoRNAs), RNAs involved in mRNA translation and splicing [such as ribosomal RNAs (rRNAs) and tRNAs], miRNAs, piwi-associated RNAs (piRNAs, called 21U-RNAs in C. elegans), and multiple classes of endo-siRNAs (18). To provide a more comprehensive annotation of small ncRNAs, we profiled small-RNA gene expression using RNA-seq on size-fractionated total RNA. In particular, using 81 million aligned
reads from 11 different stages enabled us to identify 154 out of 174 previously annotated miRNA genes (19, 20). Most of these are products of the canonical Drosha-Dicer cleavage pathway. However, four are mirtrons—miRNAs for which the precursor hairpins are generated directly by intron splicing (21). Our computational and experimental analysis validated 13 previously unidentified mirtrons (6, 22). Small-RNA data also defined 102 additional candidate canonical miRNAs and thousands of 21U-RNAs, although these latter were from previously identified loci (6, 19, 23). To identify other candidate ncRNAs, particularly ones longer than those discussed above, we combined all the transcriptome data sets to integrate both tiling-array and RNA-seq data. We found that in comparison to other genomic “elements” (such as well-curated CDSs, UTRs, or intergenic regions), the known ncRNAs tend to have a higher small RNA-seq signal and very little poly(A)-plus RNA-seq signal. However, no single transcriptome feature was able to reliably distinguish them (fig. S21A) (24). Therefore, we developed a multivariate machine-learning model combining all the transcriptome data sets and found support for 21,521 previously unknown ncRNAs (4.3 Mb in total), which we call the 21kset of ncRNAs (tables S6 to S8 and fig. S20) (6). Because identifying ncRNAs by using tiling arrays can be problematic (14), we added conservation and RNA secondary structure to our model. However, doing so restricted the predictions of this second model to only the ~15% of the C. elegans genome that was readily alignable to C. briggsae. Overall, the second model predicted 7237 previously unidentified ncRNA candidates (the 7k-set, comprising 1.0 Mb), with an estimated positive-predictive value of 91% (from testing against an independent validation set of known ncRNAs) (24). Of these, 1678 ncRNA candidates (181 kb) fell in intergenic regions,
with the remainder in introns, pseudogenes, or regions antisense to exons (fig. S21B). We tested a number of these intergenic candidates to validate expression: RT-PCR detected RNA products for 14 of 15, and Northern blots detected expression for three of five (24). The 7k-set contains many RNA structural motifs, including some not found in known RNA secondary structure families (24). Additionally, these ncRNA candidates tend to be differentially expressed across development (24), with many preferentially expressed in the embryo. Comparing the expression profiles of the 7k-set with those of well-characterized genes allowed us to identify putative functions for some candidate ncRNAs (table S9) (6). Lastly, in comparing the 7k and 21k sets of ncRNAs the overlap was small, with just 1259 overlaps. Thus, when conservation and structure were considered we detected candidate ncRNAs not found from the expression data alone; conversely, many previously uncharacterized transcripts in C. elegans may occur in nonconserved parts of the genome. Thus, the 7k and 21k sets provide complementary types of ncRNA candidates for further study. In summary, the improved annotation of transcribed portions of the genome from these data sets provides the community with new substrates for further experimentation. However, gaps remain in some transcript models, some protein-coding genes remain to be discovered, and direct evidence is needed to support the candidate ncRNAs. Regulatory Sites and Interactions Accurate annotation of sites bound by TFs is central to understanding the regulatory networks underlying development and homeostasis. However, at the start of the project very few TF-binding sites had been annotated in the nematode ge-
Fig. 2. Expression and binding dynamics. (A) Spearman correlations of gene expression and RNA Pol II binding across seven stages. Expressionlevel correlations are shown above the diagonal; RNA Pol II–binding correlations appear below. For both expression and binding, there is a notable transition between embryonic and larval stages. (B) Correlation of RNA Pol II–binding levels with gene expression. Although RNA Pol II–binding in embryonic stages shows low correlation with gene expression in larval and young adult stages, expression in the embryo correlates moderately well with RNA Pol II– binding later. (C) Principal components analysis (PCA) of six matched tissue samples from mixed embryo (MxE) and L2 (7). GABA, g-aminobutyric acid.
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nome, in part because of a lack of suitable methods with which to assay binding sites in whole animals (25). We developed these methods and have applied them to map the binding sites for 23 green fluorescent protein (GFP)–tagged fusion proteins and RNA polymerase II (RNA Pol II) using ChIP-seq (table S10) (6, 26). Most factors were assayed at their stage of highest expression, but both PHA-4 (a well-studied factor required for pharyngeal development) and RNA Pol II were analyzed at six developmental stages. Some of the factors were expressed in as few as 10% of the cells in the whole animal. TF-binding sites, motifs, and targets. Binding sites were identified by first finding relatively broad regions of enrichment and then, for some analyses, refining these to narrow [≤200 base pairs (bp)] peak summits (figs. S24 and S46). Most TF-binding sites defined by means of ChIPseq peaks for protein-coding genes lie within 500 bp upstream of transcript start sites. Binding sites assigned to known ncRNAs are even closer to the 5′ end of the transcript (fig. S22C). On the
basis of their proximity to the TSS, we were able to assign most sites to specific protein-coding or known ncRNA genes, creating a set of candidate targets for each TF (6); however, some sites were ambiguously located and remain unassigned. Although most factors target both protein-coding and known ncRNA genes, GEI11 preferentially targets ncRNAs (Fig. 3D and fig. S22, A and B). Analysis of TF-binding sites adjacent to ncRNA candidates from the 7k-set showed that 59% are potential targets of the 22 TFs examined, which is significantly more than would be expected by chance (P < 0.001, derived from a z score assuming a normal distribution of random sequences) (6, 24). Pairwise correlation of target genes revealed that factors with related functions often show substantial overlap in their protein-coding gene targets (fig. S23A). Three homeobox (HOX) genes involved in establishing the body plan provide particularly striking examples (mab-5, lin-39, and egl-5) (26). In contrast, pairwise correlation of targeted miRNAs shows that the factors bound to them
Fig. 3. Integrated miRNA-TF regulatory network. (A) TFs are organized hierarchically, and those miRNAs either regulating or being regulated by the TFs are shown. (TF names are in fig S36.) All larval TF-TF interactions in HOT regions were removed. Tissue specificity and number of protein-protein interactions are shown for each of the hierarchical levels (6). (B) TF network after filtering out edges that do not show a significant correlation in their expression patterns. www.sciencemag.org
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tend to cluster together more by stage than by factor type (fig. S23B), which is consistent with observations that expression of miRNAs tends to show strong stage-specific enrichment (19). To further characterize TF-binding sites, we searched for 8- to 12-bp cis-regulatory motifs within the ChIP-seq peaks (6) and found strong motifs for eight TFs (BLMP-1, CEH-14, CEH-30, EGL-5, HLH-1, LIN-39, NHR-6, and PHA-4) (fig. S35). Two of these are similar to previously described motifs (PHA-4 and HLH-1). The binding sites (defined from narrow peaks) cover a total of 5,165,949 bp (5.2% of the genome) and target 8859 protein-coding genes, as well as 652 known ncRNAs, indicating that each gene may have sites for many factors. Clustered binding in HOT regions. We identified 304 short binding regions (average length of ~ 400 bp) that were significantly enriched (q value < 1e-5) in most TF ChIP-seq experiments despite the fact that the 22 analyzed factors have diverse functions and expression patterns. These regions, which we term HOT regions, were bound
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Also shown is a schematic representation of the target genes of the 18 larval TFs. (C) One of the three significantly enriched network motifs (other two are in fig. S37). (D) Enrichment of binding targets and signal of TFs in noncoding versus coding genes. Max signal equals the ratio of maximum binding signal of a TF at noncoding versus coding genes. Target fraction represents the ratio of target percentage in noncoding genes to that in coding genes (fig. S22A). VOL 330
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by 15 or more factors (Fig. 4, A and B, and fig. S25A) (6). Control experiments revealed that these regions are not enriched in input DNA, nor do they appear in control ChIPs from strains lacking GFP-tagged TFs (fig. S26) (6). The number of factors bound to HOT regions was relatively insensitive to the width of the peaks used to identify them because peak summits occur within 100 bp for over 80% of HOT regions (fig. S25B) (6). In addition to the HOT regions, most TFs also cross-link to “factor-specific” DNA regions (bound by one to four total factors) (Fig. 4A). Using HLH-1, a typical factor with both known tissue specificity and a known binding motif, we compared these two different classes of sites (HOT and factor-specific) for functional differences. HLH-1 drives muscle development in C. elegans (27) and is associated with 598 factorspecific and 165 HOT regions. Relative to HOT regions, factor-specific HLH-1 ChIP-seq regions were over twofold enriched for the HLH-1– binding motif (Fisher’s exact test, P < 0.0001) (28), and genes associated with these regions were more than ninefold enriched for musclespecific expression (Fisher’s exact test, P < 0.01)
(fig. S27, A and C) (29). Similar enrichment for motifs and tissue-specific expression of targets was also observed for other TFs when factorspecific sites were compared with HOT regions (fig. S27B) (6), suggesting that factor-specific and HOT regions are functionally distinct. Genes associated with HOT regions are distinguished by several other measures. HOT-region genes assayed for expression at the individualcell level in L1 larvae are expressed in most or all cell types, whereas other genes mostly showed tissue-specific expression (Fig. 4C and fig. S29) (30). Genes associated with HOT regions were also expressed at higher levels in whole-animal and tissue-enriched measurements and were less likely to be stage-specific (fig. S28) (6). Compared with 3% of genes associated with factorspecific regions, 21% of the HOT region– associated genes are essential (P < 1e-40; c2 test) (fig. S27C) (6, 31). Gene Ontology (GO) (32) analysis revealed a variety of biological processes highly represented in HOT-associated genes, including growth, reproduction, and larval and embryonic development (each P < 1e-15), as well as 19 ribosomal protein genes (>12×
enrichment, P < 1e-12) (table S11). In comparison, GO analysis of the remaining (non-HOT) targeted genes identified functional terms that are consistent with the known tissue specificity and function of the individual TFs (26). Extensive overlap in binding sites between TFs with disparate functions has previously been observed in both limited (33) as well as wholegenome ChIP-chip experiments (34, 35). Using ChIP-seq data, we have shown that hundreds of regions in C. elegans are bound by the majority of TFs within a 100-bp window. Our results suggest that many TFs that are cross-linked to HOT regions are not directly associated with DNA via specific binding, which is consistent with findings for highly occupied regions in Drosophila (34). Rather, they suggest that association with HOT regions may be driven by protein-protein interactions to a currently unknown set of HOT region– associated DNA-binding factors. We searched for sequence motifs that might be broadly associated with HOT regions and found a few that were significantly enriched (fig. S35), but the protein factors that bind directly to these motifs are currently unknown.
Fig. 4. HOT regions. (A) A HOT Factor-specific B Chromosome 50 random 304 HOT regions region regions TF-binding peaks at a HOT regions # of HOT region and two “factorPES-1 I II III IV V X I II III IV V X regions bound GEI-11 specific regions” on chro304/304 EOR-1 L3 PQM-1 302 MDL-1 L1 mosome III: 7,206,000 MAB-5 301 SKN-1 L1 299 PES-1 L4 LIN-39 to 7,220,000. Top tracks 298 LIN-15B L3 EGL-5 show read density (scaled 294 MEP-1 MxE LIN-15B 293 PHA-4 L1 EOR-1 based on the total mapped 291 LIN-39 L3 LIN-11 284 LIN-13 MxE reads) from 22 ChIP-seq CEH-14 268 CEH-30 LE ALR-1 experiments. Bottom tracks 267 MAB-5 L3 221 EGL-5 L3 UNC-130 show ChIP-seq controls, 220 ELT-3 L1 SKN-1 220 LIN-11 L2 MDL-1 RNA-seq expression lev212 BLMP-1 L1 ELT-3 els, and ChIP-chip signals 209 EGL-27 L1 EGL-27 185 CEH-14 L2 for two histone modificaBLMP-1 175 ALR-1 L2 CEH-30 165 HLH-1 MxE tions. (B) 304 HOT regions 164 PQM-1 L3 MEP-1 bound by 15 or more 133 UNC-130 L1 LIN-13 62 GEI-11 L4 PHA-4 factors and 50 randomly DPY-27 MxE 29 HLH-1 chosen TF-bound regions. EGL-27 IgG Peak significance Input Each row represents a TF, -log10(q-value) 20 30 70 90 100 40 50 60 80 0 5 10 H3K27ac and each region is colored H3K4me C Pol II by enrichment q value (6). Expression in 363 L1 cells RNA-seq Promoter (C) Genes associated with neu. hyp. blast int. b.w.m. Refseq(+) Contains: HOT regions are broadly R151.2 rpl-6 A1 expressed. Single-cell gene HOT region(s) expression measurements of 93 mCherry reporters (30) are shown separated by A4 B1 whether the promoter contains a HOT region, contains a region bound by 10 to 14 Region(s) factors, or contains only regions bound by 0 to 9 factors (gene names are in fig. S29). bound by 10-14 factors The x axis represents 363 specific cells present in L1-stage animals. B7 C1
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RESEARCH ARTICLES miRNAs fall into distinct levels, paralleling the arrangement of TFs (Fig. 3A). Moreover, the network reveals two different classes of miRNAs: those that are more strongly regulated by TFs versus those that predominantly regulate TFs (Fig. 3A, bottom right versus top left, respectively). We can further analyze our integrated network in terms of motifs, which is a common approach used to decompose a complex network into simple building blocks (36). Many different types of network motifs exist; as a simple example, we observed miRNA-TF loops in our integrated network, in which a miRNA regulates a TF and the same TF regulates the miRNA (39). Of particular interest are patterns that are overrepresented as compared with randomized, rewired null models (6). We observed three overrepresented motifs in the integrated miRNA-TF network (fig. S37) (6). One example is a miRNAmediated feed-forward loop, in which a TF regulates a miRNA and, together with the miRNA, regulates a target coding gene (Fig. 3C). This particular motif structure is potentially responsible for buffering noise and maintaining target protein homeostasis (40). RNA Pol II binding and expression. We profiled RNA Pol II and the specific factor PHA-4 in each of the main stages of C. elegans development and compared their binding profiles with the corresponding RNA-seq data. Similar to the above approach for gene-expression dynamics, for RNA Pol II we focused on a set of 8428 genes with non-overlapping transcripts and used the binding profiles at promoters to generate correlation matrices between each stage. We found a similar differential clustering of the embryonic and larval stages (Fig. 2A). This embryonic-larval division was also observed for PHA-4 binding across stages (fig. S30) and presumably reflects the different transcriptional programs between embryos and larvae. Next, we correlated the RNA Pol II–binding profiles with expression profiles across all the stages. As expected, the same-stage correlation was fairly high (0.64 to 0.70) (Fig. 2B) but was notably lower for embryonic stages than for larval ones, perhaps reflecting the presence of maternal transcripts in embryos (6, 41, 42). Unexpectedly, we found expression at earlier developmental stages more tightly correlated with binding at later stages, rather than RNA Pol II–binding anticipating RNA production (Fig. 2B). Specifically, the correlation is low initially, reaches a maximum at the matching stage, and then remains high for later stages. This can be interpreted as RNA Pol II binding to genes at the same developmental stage at which they are initially expressed, and Pol II then remaining bound in later stages, even if expression drops. The initial round of transcription may affect the accessibility of the promoter, which may then remain unaltered in later stages for nondividing cells. Alternatively, this result may reflect paused RNA Pol II at genes with reduced expression at later stages. We have found several examples of genes
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in which RNA Pol II binding remains high in later stages but gene expression is low [such as isl-1 and pgp-2 (fig. S31)], which is consistent with RNA Pol II stalling. Overall, we have shown how the analysis of relatively few TFs allows the construction of a fairly elaborate network. To improve these networks in the future, we will need to identify the precise cells and stages in which the TFs and miRNAs are expressed. Chromatin Organization and Its Implications One modENCODE goal is to identify elements that control chromosome behavior and regulate the function of DNA elements. C. elegans chromosomes have several distinctive features. Instead of having centromeres embedded in highly repeated sequences, its chromosomes are holocentric, with microtubule attachment sites distributed along their length. In hermaphrodites (XX), gene expression from both X chromosomes is down-regulated in somatic cells by a dosage compensation mechanism and so better match expression in males, which have one X chromosome (XO) (43). Furthermore, the entire X chromosome is under-expressed relative to the autosomes in the germline cells of both hermaphrodites and males (44). C. elegans autosomes have distinct domains—a central region flanked by two distal “arms” that together comprise more than half of the chromosome. Compared with the centers, the arms have higher meiotic recombination rates, lower gene density, and higher repeat content (5, 45, 46). Arms are not as sharply defined on the X chromosome. Chromosome-scale domains of histone modification. The distribution of 19 histone modifications and two key histone variants (C. elegans homologs of H2A.z and H3.3) revealed striking, broad domains of histone modification states on the autosomes, with relatively sharp boundaries between the central region of each autosome and the arms (Fig. 5, A to C) (47–49). Modifications traditionally associated with gene activity and euchromatin such as acetylation and H3K4 and H3K36 methylation are enriched in the central regions of the chromosomes. In contrast, H3K9 mono-, di-, and trimethylation marks associated with transcriptional repression and heterochromatin formation are relatively depleted from the central regions and enriched on the arms of the autosomes (Fig. 5A). These megabasescale chromosomal domains are not homogeneous; there are small zones of repressive marks within the generally active central regions and active marks within the generally repressed arms. The chromosome-scale domains of histone modification do not vary substantially in composition or position between embryos and L3 larvae. Despite the biased distribution of repressive marks, the arms of the chromosomes do not appear heterochromatic through 4´,6´-diamidino2-phenylindole (DAPI) staining or classical banding techniques (50). Although our samples did not include appreciable meiotic tissue, the broad
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Building a TF hierarchy. Following the assignment of binding sites to target genes, we investigated the resulting “binding network,” as had previously been done in yeast and Escherichia coli (36). The network for 18 factors assayed in larval stages (Fig. 3, A and B, and fig. S36) is relatively dense, with each TF bound to an average of 828 genes, including TFs and other gene targets. We pruned the network to the strongest interactions, using the fact that the expression profile of a TF tends to be more strongly correlated over the time course with that of its targets than nontargets, being positive for activators and negative for repressors (table S12) (6). The pruned network shows a high level of autoregulation among the factors. Within the network, we organized TFs hierarchically according to the degree to which they target other TFs (top of the hierarchy) or are themselves targets for other TFs (bottom) (37). We observed clear differences between the TFs at each level (Fig. 3, A and B). TFs at the lower levels tended to be more uniformly expressed across multiple tissues (P = 0.07, Student’s t test) (6). Consistent with this, TFs at the bottom level were essential more often than those at the top. In contrast, members of the Hox family were more often at the top of the hierarchy— among the six Hox TFs examined, four were at the top layer of nine TFs—perhaps reflecting their role in modulating specific developmental processes across multiple tissues. Lastly, TFs showed connectivity in the existing C. elegans protein-protein interaction network so that those at the hierarchy top tended to have significantly fewer protein-protein interactions than those below (P = 0.002, Student’s t test) (38). This suggests that TFs in the middle and bottom layers act as “mediators” or “effectors,” more likely to exchange information with other proteins. Although the predicted larval-stage TF network here is small and one cannot make strong statistical statements, these conclusions follow a pattern that is consistent with regulatory hierarchies in yeast and E. coli, in which essential and highly connected “workhorse” regulators tend to occupy lower levels whereas overall modulators are on the top (37). An integrated miRNA-TF network and its motifs. Next, we added miRNAs to our TF hierarchy in order to enable us to explore the interplay between transcriptional and posttranscriptional regulation. In particular, we identified the targets of miRNAs on the basis of annotated 3′UTRs and sequence conservation (table S13) (6). We then constructed an integrated network between miRNAs expressed during larval stages and the above 18 TFs (all assayed in the same stages). For simplicity in this network, we describe connections between two entities as “A regulates B”—though more properly, we should describe them as “A is predicted to bind near B and regulate it.” In the integrated network, the level of a miRNA was assigned according to the highestlevel TF it regulates or, if it does not regulate a TF, the lowest-level TF that regulates it. The
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The X chromosome. Gene density, recombination rates, and repeat content are more uniformly distributed along the X chromosome than autosomes (5). Consistent with this, chromatin marks on the X are more uniformly distributed. A high density of repressive marks, similar to that seen throughout the autosome arms, is associated with only two narrow ~300-kb regions at the left end of the X that flank the meiotic pairing center (Fig. 5B). The genomic distribution of DPY-26, DPY-27, DPY-28, and SDC-3, proteins mediating dosage compensation, is highly enriched on the X chromosome (Fig. 5B) (25, 54, 55). H4K20me1, a modification linked in mammals to chromosome maturation
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and X-chromosome inactivation (56), is also enriched on the X. This X-enrichment is detectable in early embryo populations, when some embryos have initiated dosage compensation, and becomes more pronounced in L3 animals, when dosage compensation is fully established. Chromosomes and nuclear envelope interactions. Interactions between the genome and the nuclear envelope were determined by means of ChIP of LEM-2, a transmembrane protein associated with the nuclear lamina (57). In embryos, LEM-2 interacts with the repeat-rich, H3K9-methylated arms of the autosomes but not with the autosome centers (Fig. 5, A and D). Similar to H3K9 methylation, the transition be-
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Fig. 5. Chromosome-scale domains of chromatin organization. (A and B) Whole-genome ChIP-chip data for various histone modifications and chromatinassociated proteins, along with relevant genome annotations, were normalized, placed into 10-kb bins, and displayed as a heat map. Red indicates a stronger signal, and blue indicates a weaker signal. The continuous black line plots the relationship between physical (x axis) and genetic (y axis) distance. Three major groups were identified by hierarchical clustering. Group 1 contains H3K9 methylation marks and LEM-2, which tend to be enriched at distal autosomal regions, and correlate with repetitive DNA and a high recombination rate.
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Group 2 contains dosage compensation complex members and H4K20me1, which are highly enriched on X. Group 3 contains marks associated with active chromatin. Generally, signals for active marks are weaker on the X chromosome than the autosomes. This megabase-scale chromatin organization persists through all stages examined. (A) Chromosome III is representative of autosomes. (B) X has a distinct chromatin configuration. (C) H3K9me1, - 2, and -3 signals decrease gradually at the boundaries between the central and distal domains, whereas the boundaries defined by LEM-2 are relatively sharp. (D) A schematic representation of key findings. SCIENCE
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domains of histone modifications correspond to regions defined by differences in recombination rate, with the boundaries located at the recombination rate inflection points (Fig. 5A) (5, 46). On each chromosome, one arm contains a meiotic pairing center that mediates homologous pairing and synapsis (50, 51). As previously reported, H3K9me3 is more highly enriched on that arm (Fig. 5A) (52). However, methylation is not particularly enriched within the pairing center regions themselves (53). H3K9me3 is also highly enriched on silent genes on arms, and all forms of H3K9 methylation are enriched in repetitive elements, which are more prevalent on chromosome arms (fig. S32).
tween LEM-2–enriched arms and the central chromosomal regions is relatively sharp, coinciding with the transition between regions of high and low meiotic recombination rate (Fig. 5B). Within the arm regions, LEM-2 enrichment exhibits a complex underlying subdomain structure (57). On the X chromosome, LEM-2 interacts with only the small regions on the left end that harbor repressive chromatin marks (Fig. 5B). This suggests a particular organization for the X chromosome within the nucleus (Fig. 5D). Histone mono-methylation. We plotted the distribution of each chromatin mark relative to transcript starts and ends and further subdivided these plots by the expression level of the associated gene on autosomes versus the X chromosome (Fig. 6 and fig. S34). Overall, the results are consistent with the known distributions and functions of chromatin marks in other eukaryotes (58). However, the distribution of several mono-methyl marks—including H4K20me1, H3K9me1, and H3K27me1—are associated more with the bodies of highly transcribed genes on the X chromosome than with similarly expressed genes on autosomes. Further, H3K36me1 is con-
fined sharply to gene bodies on X, in contrast to broader enrichment that spans promoters and 3′ UTRs on autosomal genes. Conversely, H3K36me3 and H3K36me2 are more associated with autosomal genes than with X-linked ones (Fig. 6 and fig. S34). Differences in several marks are observed between early embryogenesis and more differentiated L3 animals—most notably a redistribution of H3K27me1 and H3K27me3 (Fig. 6 and fig. S34, bottom row). Nucleosome organization. Consistent with micrococcal nuclease (MNase) nucleosome-mapping experiments (52, 59, 60), both X and autosomal genes exhibit a typical nucleosome-depleted region upstream of TSSs, a well-positioned +1 nucleosome, and nucleosome depletion at the 3′ ends. However, we observed that the average nucleosome occupancy immediately upstream of the +1 nucleosome on the X chromosome was 1.6-fold higher than that of genes on autosomes (at –300 to +200 bp relative to the TSS; P < 2.2e−16, Wilcoxon rank-sum test) (61). Relative to autosomal genes, promoters of X-linked genes have higher GC content, which is predictive of high nucleosome occupancy in vitro (fig. S33)
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(61–63). We observed a similar difference between X and autosomal promoters when naked DNA was digested with MNase, although this result was expected because the known DNA sequence preferences of MNase are similar to the sequence preferences of linker DNA (64, 65). DNA sequences associated with nucleosome occupancy evolve according to expression requirements (66, 67), suggesting that the higher GC content on X promoters may relate to mechanisms of X-specific gene regulation in the soma and germline. Epigenetic transmission of chromatin state to progeny. The activity of the C. elegans protein MES-4—a histone H3K36 methyltransferase required for the survival of nascent germ cells in developing animals—mediates the transmission of information about the pattern of germline gene expression from mother to progeny. Similar to other H3K36 methyltransferases, MES-4 is associated with gene bodies. However, in contrast to previously studied H3K36 methyltransferases (68) MES-4 is able to associate with genes in an RNA Pol II–independent manner (69). In the embryo, MES-4 is preferentially bound to genes that were highly expressed in the maternal germline but may no longer be expressed in embryos (69). Conversely, MES-4 is not associated with genes expressed specifically in early embryos, despite recruitment of RNA Pol II to those genes (69). Therefore, RNA Pol II association with genes is neither necessary nor sufficient to recruit MES-4 in embryos (69). These findings suggest that MES-4, which is required for fertility, functions as a maintenance histone methyltransferase and propagates the memory of gene expression from the maternal germline to the cells of the next generation (69). Models relating chromatin to TF binding. To integrate chromatin with other types of modENCODE data, we sought to relate the patterns of histone marks with the observed TFbinding sites. Across the whole genome, we observed only weak direct correlations between the two (fig. S38A). However, the relationship between chromatin and TFs may involve complex, nonlinear relationships. To probe these, we built machine-learning models to identify TFbinding peaks from chromatin features (fig. S39). Investigating the association of individual histone marks with TF-binding sites, we found some that discriminate TF-binding sites from the genomic background with reasonable accuracy (Fig. 7A). Often, this is connected with their actual presence at binding sites; for example, when comparing the background to binding peaks, on average, some marks have stronger signals, whereas others have weaker ones [such as H3K4me3 versus H3K9me3 (fig. S41)]. Individual chromatin marks and RNA Pol II–binding signals could also distinguish HOT regions from the genomic background, highlighting the association with active transcription in these regions. Because chromatin features work in combination to influence binding-site selection (70),
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Fig. 7. Statistical models predicting TF-binding and gene expression from chromatin features. (A) Modeling TF-binding sites with chromatin features. The color of each cell represents the accuracy of a statistical model in which a chromatin feature or a set of features acts as predictor for TF binding or HOT regions. (B) An example of combining chromatin and sequence features. Potential binding sites of HLH-1 were predicted by using only sequence motifs, only chromatin features, or both. (C) Correlation pattern for a number of chromatin features in 100-bp bins around the TSS (T 4 kb) and TTS (T 4 kb) of transcripts at the early embryo (EE) stage. The Spearman correlation coefficient of each chromatin feature with gene-expression levels was calculated for each bin. (D) Chromatin features can predict expression levels for both proteincoding genes and miRNAs. (Top) A model involving all chromatin features. (Bottom) The model for protein-coding genes can also be used to predict accurately miRNA expression levels.
By combining all features at each of the 160 bins, we built a model for gene expression, predicting the quantitative expression levels of transcripts with support vector regression (SVR) (6). Predicted expression levels were highly correlated with measured ones [correlation coefficient (r) = 0.75, cross-validated]. As an overall benchmark, we compared our chromatin model with one based on the level of RNA Pol II–binding alone (r = 0.37); our model achieves better prediction accuracy for expression levels. To find the relative importance for gene expression of the 160 possible bin locations, we divided genes into highly and lowly expressed classes and predicted the class of each gene from each bin. The best predictions were obtained from bins immediately after the TSS and just before the TTS. With increasing distance upstream of the TSS, predictive power decreased smoothly. Intriguingly, the predictive capability of chromatin features extended as much as 4 kb upstream of the TSS and 4 kb downstream of the TTS, even when we restricted the analysis to widely separated genes with distant neighbors. This may indicate a long-range influence of chromatin on gene expression. In contrast to protein-coding genes, the association between histone modifications and miRNA
To provide additional predictive power, we incorporated into our models the information from the specific sequence motif recognized by a TF, summarized by a position-weight matrix. The combined models with both chromatin and sequence information were more accurate than were models involving either type of information alone (Fig. 7B and fig. S43). Thus, chromatin features enable one to predict TF-accessible regions and broad classes of binding sites, and motifs provide additional information on the exact sites bound by particular factors, chosen from these broad classes. Models relating chromatin to gene expression. Next, we developed a model to relate chromatin marks to gene expression levels. We divided the regions around each TSS and transcript termination site (TTS) into small (100 bp) bins and calculated the average signal of each chromatin feature and RNA Pol II (13 features in total) in a set of 160 bins up to 4 kb upstream and downstream of these two anchors (to include even long-range effects). Then at each bin, we correlated the chromatin signals with the stagematched gene expression value (Fig. 7C). There is clear variation across the bins in this correlation, with the effect of making activating marks more sensitive than are repressive ones to their exact positioning relative to the TSS or TTS.
we combined all the histone marks together in a classifier. The resulting models could identify binding sites better than those based on any individual mark (Fig. 7A and figs. S38B and S40A). We further observed that chromatin features are particularly good at identifying the binding peaks of some specific TFs. For example, H3K4me2 and H3K4me3, which are usually enriched in promoters, identified the binding peaks of a group of five factors (CEH-14, CEH30, LIN-13, LIN-15B, and MEP-1) better than the other TFs. This association is specifically due to a relative enrichment of these H3K4me2 and H3K4me3 at the binding peaks of this group of five TFs (fig. S41). It further suggests that the chromatin features can be useful in discriminating not only binding sites from the genomic background but also the sites of specific TFs in comparison with other TFs. Indeed, we were able to build integrated models to do this with reasonable accuracy (fig. S40B). The same approach was also successful in discriminating HOT regions from all TF-binding regions (fig. S40B). Our models perform best when chromatin features are measured at the same stage as the TFs, suggesting a dynamic relationship between chromatin and binding sites across developmental stages (fig. S42).
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expression has not been explored in detail. Because protein-coding and miRNA genes are both transcribed by RNA Pol II, we applied the above chromatin model, derived from protein-coding genes, to the regions around candidate premiRNAs. We then predicted expression levels for 162 microRNAs, for which genomic locations are provided by miRBase (71), and compared these predictions to the measurements in the modENCODE small RNA-seq data set. We found a correlation of 0.60 (r = 0.62 for just miRNAs far from known genes) (Fig. 7D). That expression of miRNAs can be predicted accurately by using a chromatin model trained on protein-coding genes is consistent with miRNAs and protein-coding gene regulation sharing similar mechanistic connections to histone marks. Conservation Analysis Because mutations are constantly accumulating over evolutionary time, purifying selection slows the rate of divergence of functional relative to nonfunctional sequences (72). For this reason, evolutionarily constrained regions can assist in identifying functional elements (73). Although some functional sequences may not be conserved, are conserved in a way that we are unable to detect, or are under positive selection (resulting in accelerated divergence), the coverage of constrained bases by identified functional elements is a valuable measure of the completeness of our understanding of the genome. We characterized regions of the C. elegans genome under evolutionary constraint by constructing a multiple alignment among the nematodes C. elegans, C. remanei, C. briggsae, C. brenneri, C. japonica, and Pristionchus pacificus using methods previously developed (1). We then calculated conservation scores with PhastCons (6, 74). These
procedures identified 59,504 constrained blocks that cover 29.6% of the C. elegans genome as a whole and range from 27.4% of chromosome IV to 31.9% of chromosome X. The single largest constrained block was 3558 bp on chromosome V, but conserved blocks were typically much smaller (mean 49 T 58.6 bp). These conserved regions are highly correlated with functional elements. We first examined the proportion of evolutionarily constrained regions that overlap experimentally annotated portions of the genome (Fig. 8A and fig. S44). In the last WormBase freeze before the incorporation of modENCODE data (6), 50.8% of the constrained regions were covered by annotations supported by direct experimental evidence. Adding modENCODE protein-coding gene evidence increased the coverage of constrained bases to 58.3%. Other modENCODE increases came from the 7k-set of ncRNAs (1.9%), TF-binding sites, (5.9%), dosage compensation (9.3%), and other chromatin-associated factors (2.8%). Thus, modENCODE explains an additional 27.4% (8.1 Mb) of the constrained portion of the genome; together with remaining unconfirmed WormBase gene predictions (0.7%) and pseudogenes (0.6%), coverage now totals 79.5% of constrained bases. We then estimated the extent of constraint on different functional elements by plotting the distribution of the PhastCons conservation scores for each type of element (Fig. 8, B and C, and fig. S45). The most constrained elements were ncRNAs (both known and the 7k-set), presumably reflecting the fact that conservation was a criterion used to identify them. Next came protein-coding elements, followed by miRNAs, TF-binding sites, and other chromatin factor–binding sites. Pseudogenes, introns, and regions of the genome not
covered by modENCODE data sets all have low levels of conservation. We then used the genome structure correction (GSC) statistic (1, 75) to calculate confidence intervals on the degree of overlap between evolutionarily constrained bases and functional elements defined by modENCODE and other sources. This demonstrated that coding regions, ncRNAs, TF-binding sites, and other chromatin factor–binding sites are significantly more constrained than would be expected by chance, whereas regions covered by pseudogenes, introns, and unannotated regions are significantly depleted in constrained regions relative to chance. Roughly 20.5% of the constrained genome remains uncovered by known functional elements, but a portion of this sequence directly abuts known functional elements. If the borders of transcribed regions and chromatin-associated protein-binding sites are extended across all constrained blocks that neighbor them, ~ 4.1 Mb (14%) in isolated constrained blocks remains. These residual constrained bases are highly enriched in introns and intragenic regions (table S14), are moderately enriched in the 1-kb regions upstream of TSSs, and are depleted in the 1-kb regions downstream of TTSs. One potential explanation for the residual constrained bases is that they correspond to the binding sites of untested TFs. Indeed, a plot of coverage of constrained sequence against numbers of TF experiments shows that the relatively small numbers of TFs studied here are far from saturating constrained bases (fig. S47), implying that additional TFs may explain part of the remaining constrained bases in these regions. Other explanations for the residual constrained regions include other intronic regulatory sites, transcribed regions that are expressed only under rare circumstances, and possibly as-yet unknown classes of functional elements.
Fig. 8. Relative proportion of annotations among constrained sequences. (A) Relative proportion of constrained and unconstrained bases in the C. elegans genome. Within the constrained region, the stacked bar chart shows the cumulative proportion covered by various classes of annotated genomic elements. (B) Fraction of element classes covering (red) constrained and (gray) unconstrained bases. The error bars show the 95% confidence interval for random placement of elements calculated with GSC. If the ends of the columns are outside the confidence interval, then it is unlikely that the fraction of the element class overlapping constrained and/or unconstrained bases could have occurred by chance. (C) Constraint profiles of broad categories of elements. The x axis indicates the PhastCons score of bases covered by the element ranging from 0 (no conservation) to 1.0 (perfect conservation). The y axis indicates the log ratio of the number of bases with the given score covered, relative to what would be expected by random element placement (dotted line) (fig. S45 shows more detail). www.sciencemag.org
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Discussion Our analysis illustrates patterns at multiple genomic scales: individual gene, chromosomal domain, and whole-chromosome. At the first scale, in addition to improving annotation of protein-coding genes, we identified transcribed pseudogenes and many previously unidentified ncRNAs, mapped binding sites of TFs, built regulatory networks, and constructed models predicting binding location and expression levels from chromatin marks. On a larger scale, we found chromosomal domains—characterized by repressive marks and interactions with the nuclear envelope on the autosome arms—and noted how the boundaries in these domains align with changes in recombination frequency. We also identified additional properties of the entire X chromosome, including the preferential accumulation of multiple mono-methylated histone marks. Our large-scale approach also discovered unexpected biological phenomena that would be difficult to uncover in conventional studies. In particular, upon profiling the binding sites of 23 factors we identified regions of clustered binding (HOT regions). One limitation of the modENCODE strategy is that we cannot readily distinguish low levels of biochemical noise, such as a rare nonfunctional transcription splice form, from biologically important phenomena. The presence of such noise may be an unavoidable part of the cell regulatory machinery (76) and will only be distinguished from biologically important signals through careful follow-up experimentation. Another limitation is that almost all experiments were performed in populations of whole animals composed of multiple tissues. Future studies will increase the tissue-specific resolution of the data. Model organisms such as C. elegans have long served as key experimental systems for developing technology and providing fundamental insights into human biology. Comparing our modENCODE results with the ENCODE pilot, which assessed functional elements in 1% of the human genome, we can already begin to see commonalities (6). For instance, for some aggregated binding signals (such as for RNA Pol II) the overall shape of the signal distributions relative to the TSS are quite similar between human and C. elegans. Likewise, the overall amount (per base pair) of transcription and binding by TFs is comparable (fig. S49 and tables S15 and S16). However, there are differences in the shape of the aggregated signal distributions for a few matched histone modifications (Fig. 6 versus fig. S50). Moreover, the relative proportion of constrained genome covered by experimental annotation is quite different in human and nematode, perhaps reflecting evolutionary pressures for a compact genome in the latter (fig. S48). A more comprehensive comparison, including the Drosophila genome data presented in the accompanying article, must await genome-wide analysis of human cells—an effort currently underway in the ENCODE project.
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The modENCODE data sets are intended as an enduring resource for the genomics community. All raw and analyzed data, metadata, and interpreted results are available at www. modencode.org, where they can be searched, displayed, and downloaded. Raw sequencing reads and microarray data are archived at the Short-read Archive and the Gene Expression Omnibus, and higher-order results are being incorporated into WormBase (77). In addition, we have assembled a compact guide to the data sets used (at www.modencode.org/publications/ integrative_worm_2010) (table S1) (6) and have populated a community cloud-computing resource with the data and analysis tools to facilitate further investigation by interested researchers (6). We expect that analyses of these data sets in the coming years will provide additional insights into general principles of genome organization and function, which will ultimately aid in annotating and deciphering the human genome.
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44. 45. 46. 47. 48. 49.
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RESEARCH ARTICLES Biology and Genetics. Raw microarray data are available from the Gene Expression Omnibus archive, and raw sequencing data are available from the SRA archive (accessions are in table S18). We appreciate help from S. Anthony, K. Bell, C. Davis, C. Dieterich, Y. Field, A. S. Hammonds, J. Jo, N. Kaplan, A. Manrai, B. Mathey-Prevot, R. McWhirter, S. Mohr, S. Von Stetina, J. Watson, K. Watkins, C. Xue, and Y. Zhang, and B. Carpenter. We thank C. Jan and D. Bartel for sharing data on poly(A) sites before publication, WormBase curator G. Williams for assistance in quality checking and preparing the transcriptomics data sets for publication, as well as his
Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE The modENCODE Consortium,* Sushmita Roy,1,2† Jason Ernst,1,2† Peter V. Kharchenko,3† Pouya Kheradpour,1,2† Nicolas Negre,4† Matthew L. Eaton,5† Jane M. Landolin,6† Christopher A. Bristow,1,2† Lijia Ma,4† Michael F. Lin,1,2† Stefan Washietl,1† Bradley I. Arshinoff,7,18† Ferhat Ay,1,33† Patrick E. Meyer,1,30† Nicolas Robine,8† Nicole L. Washington,9† Luisa Di Stefano,1,31† Eugene Berezikov,23‡ Christopher D. Brown,4‡ Rogerio Candeias,1‡ Joseph W. Carlson,6‡ Adrian Carr,10‡ Irwin Jungreis,1,2‡ Daniel Marbach,1,2‡ Rachel Sealfon,1,2‡ Michael Y. Tolstorukov,3‡ Sebastian Will,1‡ Artyom A. Alekseyenko,11 Carlo Artieri,12 Benjamin W. Booth,6 Angela N. Brooks,28 Qi Dai,8 Carrie A. Davis,13 Michael O. Duff,14 Xin Feng,13,18,35 Andrey A. Gorchakov,11 Tingting Gu,15 Jorja G. Henikoff,8 Philipp Kapranov,16 Renhua Li,17 Heather K. MacAlpine,5 John Malone,12 Aki Minoda,6 Jared Nordman,22 Katsutomo Okamura,8 Marc Perry,18 Sara K. Powell,5 Nicole C. Riddle,15 Akiko Sakai,29 Anastasia Samsonova,19 Jeremy E. Sandler,6 Yuri B. Schwartz,3 Noa Sher,22 Rebecca Spokony,4 David Sturgill,12 Marijke van Baren,20 Kenneth H. Wan,6 Li Yang,14 Charles Yu,6 Elise Feingold,17 Peter Good,17 Mark Guyer,17 Rebecca Lowdon,17 Kami Ahmad,29 Justen Andrews,21 Bonnie Berger,1,2 Steven E. Brenner,28,32 Michael R. Brent,20 Lucy Cherbas,21,24 Sarah C. R. Elgin,15 Thomas R. Gingeras,13,16 Robert Grossman,4 Roger A. Hoskins,6 Thomas C. Kaufman,21 William Kent,34 Mitzi I. Kuroda,11 Terry Orr-Weaver,22 Norbert Perrimon,19 Vincenzo Pirrotta,27 James W. Posakony,26 Bing Ren,26 Steven Russell,10 Peter Cherbas,21,24 Brenton R. Graveley,14 Suzanna Lewis,9 Gos Micklem,10 Brian Oliver,12 Peter J. Park,3 Susan E. Celniker,6§|| Steven Henikoff,25§|| Gary H. Karpen,6,28§|| Eric C. Lai,8§|| David M. MacAlpine,5§|| Lincoln D. Stein,18§|| Kevin P. White,4§|| Manolis Kellis1,2|| To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation. everal years after the complete genetic sequencing of many species, it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. The Encyclopedia of DNA Elements (ENCODE) (1) and model organism ENCODE (modENCODE) (2) projects use diverse genomic assays to comprehensively annotate the Homo sapiens (human), Drosophila melanogaster (fruit fly), and Caenorhabditis elegans (worm) genomes,
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through systematic generation and computational integration of functional genomic data sets. Previous genomic studies in flies have made seminal contributions to our understanding of basic biological mechanisms and genome functions, facilitated by genetic, experimental, computational, and manual annotation of the euchromatic and heterochromatic genome (3), small genome size, short life cycle, and a deep knowledge of development, gene function, and chromosome
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fellow curator P. Davis for reviewing and hand-checking the list of pseudogenes.
Supporting Online Material www.sciencemag.org/cgi/content/science.1196914/DC1 Materials and Methods Figs. S1 to S50 Tables S1 to S18 References 24 August 2010; accepted 18 November 2010 Published online 22 December 2010; 10.1126/science.1196914
biology. The functions of ~40% of the proteinand nonprotein-coding genes [FlyBase 5.12 (4)] have been determined from cDNA collections (5, 6), manual curation of gene models (7), gene mutations and comprehensive genome-wide RNA interference screens (8–10), and comparative genomic analyses (11, 12). The Drosophila modENCODE project has generated more than 700 data sets that profile transcripts, histone modifications and physical nucleosome properties, general and specific transcription factors (TFs), and replication programs in cell lines, isolated tissues, and whole organisms across several developmental stages (Fig. 1). Here, we computationally integrate these data sets and report (i) improved and additional genome annotations, including full-length proteincoding genes and peptides as short as 21 amino acids; (ii) noncoding transcripts, including 132 candidate structural RNAs and 1608 nonstructural transcripts; (iii) additional Argonaute (Ago)– associated small RNA genes and pathways, including new microRNAs (miRNAs) encoded within protein-coding exons and endogenous small interfering RNAs (siRNAs) from 3′ untranslated regions; (iv) chromatin “states” defined by combinatorial patterns of 18 chromatin marks that are associated with distinct functions and properties; (v) regions of high TF occupancy and replication activity with likely epigenetic regulation; (vi) mixed TF and miRNA regulatory networks with hierarchical structure and enriched feed-forward loops; (vii) coexpression- and co-regulation–based functional annotations for nearly 3000 genes; (viii) stage- and tissue-specific regulators; and (ix) predictive models of gene expression levels and regulator function. Overview of data sets. Our data sets provide an extensive description of the transcriptional, epigenetic, replication, and regulatory landscapes of the Drosophila genome (table S1). Experimental assays include high-throughput RNA sequencing (RNA-seq), capturing-small and large RNAs and splice variants; chromatin immunoprecipitation (ChIP)–chip and ChIP followed by high-throughput sequencing (ChIP-seq), profiling chromosomal and RNA binding or processing proteins; tilingarrays, identifying and measuring replication patterns, nucleosome solubility, and turnover; and genomic DNA sequencing, measuring copynumber variation. We conducted most assays in the sequenced strain y; cn bw sp (13), with multiple developmental samples (30 for RNA expres-
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78. Funding for this work came from the NHGRI of the NIH as part of the modENCODE project, NIH (grant R01GM088565), Muscular Dystrophy Association, and the Pew Charitable Trusts (J.K.K.); the Helmholtz-Alliance on Systems Biology (Max Delbrück Centrum Systems Biology Network) (S.D.M.); the Wellcome Trust (J.A.); the William H. Gates III Endowed Chair of Biomedical Sciences (R.H.W.); and the A. L. Williams Professorship (M.B.G.). M. Snyder has an advisory role with DNANexus, a DNA sequence storage and analysis company. Transfer of GFP-tagged fosmids requires a Materials Transfer Agreement with the Max Planck Institute of Molecular Cell
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RESEARCH ARTICLES Biology and Genetics. Raw microarray data are available from the Gene Expression Omnibus archive, and raw sequencing data are available from the SRA archive (accessions are in table S18). We appreciate help from S. Anthony, K. Bell, C. Davis, C. Dieterich, Y. Field, A. S. Hammonds, J. Jo, N. Kaplan, A. Manrai, B. Mathey-Prevot, R. McWhirter, S. Mohr, S. Von Stetina, J. Watson, K. Watkins, C. Xue, and Y. Zhang, and B. Carpenter. We thank C. Jan and D. Bartel for sharing data on poly(A) sites before publication, WormBase curator G. Williams for assistance in quality checking and preparing the transcriptomics data sets for publication, as well as his
Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE The modENCODE Consortium,* Sushmita Roy,1,2† Jason Ernst,1,2† Peter V. Kharchenko,3† Pouya Kheradpour,1,2† Nicolas Negre,4† Matthew L. Eaton,5† Jane M. Landolin,6† Christopher A. Bristow,1,2† Lijia Ma,4† Michael F. Lin,1,2† Stefan Washietl,1† Bradley I. Arshinoff,7,18† Ferhat Ay,1,33† Patrick E. Meyer,1,30† Nicolas Robine,8† Nicole L. Washington,9† Luisa Di Stefano,1,31† Eugene Berezikov,23‡ Christopher D. Brown,4‡ Rogerio Candeias,1‡ Joseph W. Carlson,6‡ Adrian Carr,10‡ Irwin Jungreis,1,2‡ Daniel Marbach,1,2‡ Rachel Sealfon,1,2‡ Michael Y. Tolstorukov,3‡ Sebastian Will,1‡ Artyom A. Alekseyenko,11 Carlo Artieri,12 Benjamin W. Booth,6 Angela N. Brooks,28 Qi Dai,8 Carrie A. Davis,13 Michael O. Duff,14 Xin Feng,13,18,35 Andrey A. Gorchakov,11 Tingting Gu,15 Jorja G. Henikoff,8 Philipp Kapranov,16 Renhua Li,17 Heather K. MacAlpine,5 John Malone,12 Aki Minoda,6 Jared Nordman,22 Katsutomo Okamura,8 Marc Perry,18 Sara K. Powell,5 Nicole C. Riddle,15 Akiko Sakai,29 Anastasia Samsonova,19 Jeremy E. Sandler,6 Yuri B. Schwartz,3 Noa Sher,22 Rebecca Spokony,4 David Sturgill,12 Marijke van Baren,20 Kenneth H. Wan,6 Li Yang,14 Charles Yu,6 Elise Feingold,17 Peter Good,17 Mark Guyer,17 Rebecca Lowdon,17 Kami Ahmad,29 Justen Andrews,21 Bonnie Berger,1,2 Steven E. Brenner,28,32 Michael R. Brent,20 Lucy Cherbas,21,24 Sarah C. R. Elgin,15 Thomas R. Gingeras,13,16 Robert Grossman,4 Roger A. Hoskins,6 Thomas C. Kaufman,21 William Kent,34 Mitzi I. Kuroda,11 Terry Orr-Weaver,22 Norbert Perrimon,19 Vincenzo Pirrotta,27 James W. Posakony,26 Bing Ren,26 Steven Russell,10 Peter Cherbas,21,24 Brenton R. Graveley,14 Suzanna Lewis,9 Gos Micklem,10 Brian Oliver,12 Peter J. Park,3 Susan E. Celniker,6§|| Steven Henikoff,25§|| Gary H. Karpen,6,28§|| Eric C. Lai,8§|| David M. MacAlpine,5§|| Lincoln D. Stein,18§|| Kevin P. White,4§|| Manolis Kellis1,2|| To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation. everal years after the complete genetic sequencing of many species, it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. The Encyclopedia of DNA Elements (ENCODE) (1) and model organism ENCODE (modENCODE) (2) projects use diverse genomic assays to comprehensively annotate the Homo sapiens (human), Drosophila melanogaster (fruit fly), and Caenorhabditis elegans (worm) genomes,
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through systematic generation and computational integration of functional genomic data sets. Previous genomic studies in flies have made seminal contributions to our understanding of basic biological mechanisms and genome functions, facilitated by genetic, experimental, computational, and manual annotation of the euchromatic and heterochromatic genome (3), small genome size, short life cycle, and a deep knowledge of development, gene function, and chromosome
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fellow curator P. Davis for reviewing and hand-checking the list of pseudogenes.
Supporting Online Material www.sciencemag.org/cgi/content/science.1196914/DC1 Materials and Methods Figs. S1 to S50 Tables S1 to S18 References 24 August 2010; accepted 18 November 2010 Published online 22 December 2010; 10.1126/science.1196914
biology. The functions of ~40% of the proteinand nonprotein-coding genes [FlyBase 5.12 (4)] have been determined from cDNA collections (5, 6), manual curation of gene models (7), gene mutations and comprehensive genome-wide RNA interference screens (8–10), and comparative genomic analyses (11, 12). The Drosophila modENCODE project has generated more than 700 data sets that profile transcripts, histone modifications and physical nucleosome properties, general and specific transcription factors (TFs), and replication programs in cell lines, isolated tissues, and whole organisms across several developmental stages (Fig. 1). Here, we computationally integrate these data sets and report (i) improved and additional genome annotations, including full-length proteincoding genes and peptides as short as 21 amino acids; (ii) noncoding transcripts, including 132 candidate structural RNAs and 1608 nonstructural transcripts; (iii) additional Argonaute (Ago)– associated small RNA genes and pathways, including new microRNAs (miRNAs) encoded within protein-coding exons and endogenous small interfering RNAs (siRNAs) from 3′ untranslated regions; (iv) chromatin “states” defined by combinatorial patterns of 18 chromatin marks that are associated with distinct functions and properties; (v) regions of high TF occupancy and replication activity with likely epigenetic regulation; (vi) mixed TF and miRNA regulatory networks with hierarchical structure and enriched feed-forward loops; (vii) coexpression- and co-regulation–based functional annotations for nearly 3000 genes; (viii) stage- and tissue-specific regulators; and (ix) predictive models of gene expression levels and regulator function. Overview of data sets. Our data sets provide an extensive description of the transcriptional, epigenetic, replication, and regulatory landscapes of the Drosophila genome (table S1). Experimental assays include high-throughput RNA sequencing (RNA-seq), capturing-small and large RNAs and splice variants; chromatin immunoprecipitation (ChIP)–chip and ChIP followed by high-throughput sequencing (ChIP-seq), profiling chromosomal and RNA binding or processing proteins; tilingarrays, identifying and measuring replication patterns, nucleosome solubility, and turnover; and genomic DNA sequencing, measuring copynumber variation. We conducted most assays in the sequenced strain y; cn bw sp (13), with multiple developmental samples (30 for RNA expres-
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78. Funding for this work came from the NHGRI of the NIH as part of the modENCODE project, NIH (grant R01GM088565), Muscular Dystrophy Association, and the Pew Charitable Trusts (J.K.K.); the Helmholtz-Alliance on Systems Biology (Max Delbrück Centrum Systems Biology Network) (S.D.M.); the Wellcome Trust (J.A.); the William H. Gates III Endowed Chair of Biomedical Sciences (R.H.W.); and the A. L. Williams Professorship (M.B.G.). M. Snyder has an advisory role with DNANexus, a DNA sequence storage and analysis company. Transfer of GFP-tagged fosmids requires a Materials Transfer Agreement with the Max Planck Institute of Molecular Cell
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Developmental Stages
junctions in 14,016 distinct alternative transcripts [35% supported by cDNAs, reverse transcription polymerase chain reaction products, and long poly(A)+ RNA-seq (14)]. Overall, 74% of annotated genes show at least one previously undescribed or modified exon or alternative splice form, despite extensive previous annotation efforts, illustrating the importance of probing additional cell types. Of the 21,071 newly predicted exons expressed in S2 cells, 89% are associated with chromatin signatures characteristic of transcribed regions (17). We also characterized the shapes and transcription start site (TSS) distributions for 56% of annotated genes (70% of embryonically expressed genes). We discovered and validated 2075 alternative promoters for known genes. Of 427 discovered alternative promoters adjacent to
Larva
Embryo
RNAPolymerase Transcription
Transcription Start Site
Pre-Replicative Complex
Intron Extract RNA
mRNA hnRNA ncRNA etc
miRNA piRNA siRNA etc
Transcription/Splicing
Nucleosome physical properties
Chromatin & Histone Binding Proteins
Origin Mapping Timing Differential Replication
Salt Fractionation
Histone Modifications & Variants
Nucleosomes
Chromatin ImmunoPrecipitation (ChIP)
Chromatin Generate Antibodies or Tagged Lines
Microarray or Sequence
Replication
Nucleus
Transcription Factors
Chromosomes
Histone tails
RNA
Long RNA Short RNA
Cell lines
Adult
splicing
DNA
OR
Pupae
Replication Origins
Spliceosome
active S2 cell transcripts, 72.5% are supported by promoter-associated chromatin marks in that cell type (18), confirming predictions and suggesting that these regions contain regulatory elements. Similarly, comparison to chromatin marks in whole animals yielded 1117 additional validated promoters (19). We detect all but 1498 (9.9%) of previously annotated D. melanogaster genes (4) in either the poly(A)+ or total RNA-seq samples. Undetected genes include members of multicopy gene families [e.g., ribosomal RNAs, paralogs, small nucleolar RNAs (snoRNAs), tRNAs] and those with known low or constrained expression. We discovered new snoRNAs, scaRNAs, and pri-miRNA transcripts in the total embryonic RNA-seq data alone, even without including larval, pupal, or adult samples.
Epigenetics
Transcription Regulation
Fig. 1. Overview of Drosophila modENCODE data sets. Range of genomic elements and trans factors studied, with relevant techniques and resulting genome annotations. hnRNA, heterogeneous nuclear RNA.
1 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. 2Broad Institute of MIT and Harvard, Cambridge, MA 02140, USA. 3Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA. 4 Institute for Genomics and Systems Biology, Department of Human Genetics, The University of Chicago, 900 East 57th Street, Chicago, IL 60637, USA. 5Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710, USA. 6Department of Genome Dynamics, Lawrence Berkeley National Laboratory (LBNL), 1 Cyclotron Road, Berkeley, CA 94720 USA. 7Department of Molecular Genetics, University of Toronto, 27 King’s College Circle, Toronto, Ontario M5S 1A1, Canada. 8Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, NY 10065, USA. 9Genome Sciences Division, LBNL, 1 Cyclotron Road, Berkeley, CA 94720, USA. 10 Department of Genetics and Cambridge Systems Biology Centre, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. 11Department of Medicine and Department of Genetics, Brigham and Women’s Hospital, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. 12 Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, MD 20892, USA. 13Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. 14Department of Genetics and Developmental Biology, University of Connecticut Stem Cell Institute, 263 Farmington,
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CT 06030–6403, USA. 15Department of Biology CB-1137, Washington University, Saint Louis, MO 63130, USA. 16 Affymetrix, Santa Clara, CA 95051, USA. 17Division of Extramural Research, National Human Genome Research Institute, NIH, 5635 Fishers Lane, Suite 4076, Bethesda, MD 20892– 9305, USA. 18Ontario Institute for Cancer Research, 101 College Street, Suite 800, Toronto, Ontario M5G 0A3, Canada. 19 Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. 20Center for Genome Sciences, Washington University, 4444 Forest Park Boulevard, Saint Louis, MO 63108, USA. 21Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, IN 47405–7005, USA. 22Whitehead Institute, Cambridge, MA 02142, USA. 23Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center Utrecht, Utrecht, Netherlands. 24Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, IN 47405–7005, USA. 25Basic Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA. 26Division of Biological Sciences, Section of Cell and Developmental Biology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. 27Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08854, USA. 28Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA. 29Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, MA 02115, USA.
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30 Machine Learning Group, Université Libre de Bruxelles, CP212, Brussels 1050, Belgium. 31Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, MA 02129, USA. 32Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA. 33Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. 34Center for Biomolecular Science and Engineering, School of Engineering and Howard Hughes Medical Institute (HHMI), University of California Santa Cruz, Santa Cruz, CA 95064, USA. 35Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
*The complete list of authors appears at the end of the paper. †These authors contributed equally to this work. ‡These authors contributed equally to this work (listed alphabetically). §These authors contributed equally to this work (listed alphabetically). ||To whom correspondence should be addressed. E-mail:
[email protected] (M.K.) (integrative analysis); celniker@fruitfly. org (S.E.C.) (transcripts);
[email protected] (G.H.K.) (chromatin);
[email protected] (K.P.W.) (transcription factors);
[email protected] (D.M.M.) (replication);
[email protected] (E.C.L.) (small RNAs);
[email protected] (S.H.) (nucleosomes);
[email protected] (L.D.S.) (data availability)
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sion and 12 for TF and histone studies), and in cultured cells, predominantly with four lines (S2, BG3, Kc, and Cl.8; table S2). Annotation of gene transcripts and their promoter regions. To comprehensively characterize transcribed sequences, we performed RNA-seq using poly(A)+ and total RNA, cap analysis of gene expression, rapid amplification of cDNA ends, and produced expressed sequence tags (table S1) (14–16) and cDNAs. These data support more than 90% of annotated genes, exons, and splice junctions and provide experimental evidence for a total of 17,000 protein-coding and noncoding genes, of which 1938 are previously unannotated. In addition to genes, we discovered 52,914 previously undescribed or modified exons (65% supported by cDNAs) and 22,965 new splice
Protein-coding, structural, and noncoding transcripts. We searched for evolutionary signatures of conserved protein-coding DNA sequences in alignments of 12 Drosophila genomes (12, 20) and for similarity to known proteins. Only 57 of 1938 previously undescribed gene models (17) contain a complete, conserved open reading frame (ORF) likely to represent unidentified protein-coding genes (Fig. 2A). An additional 81 gene models are likely to be incompletely reconstructed coding genes, because they contain at least one protein-coding exon but lack clearly identifiable translation start or stop sites (17). These 138 genes show nearly sixfold lower average expression than known protein-coding genes [fragments per kilobase of transcript per million fragments sequenced (FPKM) of 6.7 versus 34.8], and 40% have expression restricted to late larvae, pupae, and adult males, providing a potential explanation for why they were missed in previous annotations. For the remaining 1800 gene models, we find no evidence of protein-coding selection using PhyloCSF and no similarity to known protein sequences using blastx, suggesting that they are unlikely to represent protein-coding genes (20). We looked for properties of noncoding RNAs (ncRNAs) among the 1740 transcripts (excluding 60 snoRNA and miRNA transcripts) detected by RNA sequencing that do not appear to encode proteins. We examined folding thermodynamics and comparative evidence of local secondary structures in the predicted ncRNAs and in 140 ncRNAs listed in FlyBase (4) that do not belong to major classes of structural RNAs, such as miRNAs and snoRNAs. We predicted highconfidence structures for 132 transcripts (7.6%) using the RNAz program (21), suggesting conserved function as structural RNAs, similar to the fraction (7.8%) of transcripts with predicted structure observed in FlyBase ncRNAs (4). We revealed candidate structural RNAs in the newly
predicted transcripts (Fig. 2B), as well as previously unidentified structural elements in wellstudied ncRNAs, including sex-chromosome dosage compensation regulator roX2 and heatshock regulator HSRw (fig. S1) (17). However, the lack of highly structured regions in the vast majority of ncRNAs suggests functions independent of secondary structure. Argonaute-associated small regulatory RNAs. Our analysis of deeply sequenced ~18- to 28nucleotide (nt) RNAs dramatically extended the catalog of Ago-dependent small regulatory RNAs (22), including miRNAs, siRNAs, and piwiassociated RNAs (piRNAs). In the canonical miRNA pathway, ~21- to 24-nt RNAs are cleaved from hairpin precursors by Drosha and Dicer-1 ribonuclease (RNase) III enzymes and loaded into AGO1 effector complexes to repress mRNA targets. We annotated 61 additional canonical miRNAs, 12 of which are derived from the antisense strands of known miRNA loci (23), which may provide an efficient route for the evolution of new miRNA activities. We unexpectedly detected miRNAs that overlap mRNAs, including nine cases where conserved proteincoding regions harbor RNA hairpins cleaved into duplexes of miRNA and partner strand miRNA* species, many of which are found in AGO1 complexes (e.g., Fig. 2C). It remains to be seen whether these mRNA-resident miRNAs have detectable trans-regulatory activities, affect their host transcripts in the cis configuration, or are simply neutral substrates. We identified 15 additional mirtrons that generate miRNAs by splicing of short hairpin introns (24), doubling the number of known cases from 14 to 29. We defined up to seven hybrid mirtrons bearing 3′ tails, which appear to require processing by the exosome before dicing (25). In total, we recognize at least three miRNA biogenesis strategies, producing miRNAs from at least 240 genomic loci.
Fig. 2. Coding and noncoding genes and structures. (A) Extended region of male-specific expression in chromosome 2R including new protein-coding and noncoding transcripts. MIP03715 contains two short ORFs of 23 and 21 codons, respectively. ORF multispecies alignments (color coded) show abundant synonymous (bright green) and conservative (dark green) substitutions and a depletion of nonsynonymous substitutions (red), indicative of protein-coding selection [ratio of nonsynonymous to synonymous substitutions (dN/dS) < 1 for both, P < 10−7 and P < 10−11, respectively, likelihood ratio test]. Surrounding regions show abundant stop codons (blue, magenta, yellow) and frame-shifted positions www.sciencemag.org
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We and others recognized several classes of endogenous siRNAs (endo-siRNAs), 21-nt RNAs that are processed by Dicer-2 RNase III enzyme and preferentially loaded into AGO2 (26–31). Endo-siRNAs derive from three distinct sources: (i) diverse transposable elements (TEs), whose activity they restrict; (ii) seven genomic regions encoding long inverted-repeat transcripts, which direct the cleavage of specific mRNA targets; and (iii) bi-directionally transcribed regions. This last class mostly comprises convergent transcripts that overlap in their 3′ untranslated regions (3′ UTRs), termed 3′ cis-natural antisense transcripts (3′ cisNATs). Our current analysis doubled the number of 3′ cis-NAT–siRNA regions to 237, including nearly one-quarter of overlapping 3′ UTRs (table S4). Lastly, piRNAs are ~24- to 30-nt RNAs bound by the largely gonadal Piwi-class Argonautes, Piwi, Aubergine (Aub), and AGO3. The majority of piRNAs match TEs in sense or antisense orientation and are essential to repress their activity (32). Though many Drosophila piRNAs map uniquely to tens of master loci that serve as genetic repositories for TE defense (32), we found that the 3′ UTRs of hundreds of cellular transcripts also generate abundant Piwi-loaded primary piRNAs in somatic ovarian follicle cells (33–35). This suggests that beyond transposon control, the piRNA pathway may play a more general role in cellular gene regulation. Large-scale organization of the chromatin landscape. Eukaryotic genomes are organized into large domains (~10 kb to megabases) that exhibit distinct chromatin properties, such as heterochromatic regions that cover one-third of the genome and are typically known for transcriptional silencing (36). Our analyses show that the chromatin composition, organization, and boundaries of heterochromatin display surprising complexity and plasticity among cell types (37). We find surprisingly active heterochromatic regions,
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(orange). (B) A transcribed region in chromosome 3R (26,572,290 to 26,573,456), identified by RNA-seq and supported by promoter-specific and transcriptionassociated chromatin marks, shows RNA secondary-structure conservation in eight Drosophila species. (C) Example of a new miRNA derived from a protein-coding exon of CG6700, with 21- to 23-nt RNAs indicative of Drosha/Dicer-1 processing and also recovered in AGO1-immunoprecipitate libraries from S2 cells and adult heads indicative of Argonaute loading. Evolutionary evidence suggests protein-coding constraint, no conservation for the mature arm, and conservation of the star arm. Red boxes indicate 8-mer “seed” sequence potentially mediating 3′ UTR targeting. VOL 330
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with expression of 45% of pericentric heterochromatin genes (compared with 50% for euchromatic genes), and enrichment for both active and silent marks in active heterochromatic genes. Conversely, we find that domains enriched for heterochromatic marks (e.g., H3K9me2) cover a surprisingly large proportion of euchromatic sequences (12% in BG3 cells and 6% in S2) (37). We identified large domains with similar replication patterns by characterizing the Drosophila DNA replication program in cell lines, and we observed that the temporal replication program is determined by local chromatin environment (18, 38) and the density of replication initiation factors (39). We also found that specific euchromatic regions up to 300 kb were under-replicated in a tissue-specific manner in the polytene salivary glands, larval midgut, and fat bodies (40), which suggests that copy-number variation may help regulate gene expression levels. Chromatin signatures characteristic of functional elements. Many genomic regulatory regions are difficult to identify because of a lack of characteristic sequence signatures, but they are often marked by specific histone modifications, variants, and other epigenetic factors (41, 42). To identify such signatures, we assayed 18 histone modifications and variants by ChIP-chip in multiple cell lines (18) and developmental stages (19), and we defined the physical properties of nucleosomes (43, 44). We correlated this information with gene annotations, transcriptome data sets, binding site profiles for replication factors,
insulator-binding proteins, and TFs to characterize chromatin signatures of each type of element (Fig. 3A). TSS-proximal regions were marked by H3K4me3 enrichment (45), depletion of nucleosome density, increased nucleosome turnover, and enrichment in the pellet chromatin fraction (43, 44). Gene bodies showed H2B ubiquitination covering the entire transcribed region and a 3′biased enrichment of H3K36me3 and K3K79me1 marks. Moreover, large introns are enriched for H3K36me1, H3K18ac, and H3K27ac; specific chromatin remodelers; high nucleosome turnover; the H3.3 histone variant; and DNase I hypersensitive sites, all suggestive of regulatory functions (18). These features are generally absent from short genes and from genes with a low fraction of intronic sequence. Most transcriptionally silent genes lack pronounced chromatin signatures, except when positioned within Pc domains (H3K27me3) or heterochromatin (H3K9me2/3, HP1a, H3K23ac depletion) (37). Positional correlation analysis identified relationships between histone marks and nucleosome physical properties. Active marks [e.g., H3K27Ac, RNA polymerase II (RNA Pol II), H3K4me3] correlate with high chromatin solubility and high nucleosome-turnover rates, whereas marks associated with silent chromatin (e.g., H3K27me3, H1, H3K9me2/3) show the opposite, correlating with increased nucleosome density (fig. S2). High chromatin solubility indicates less stable nucleosomes (44), and high levels of nucleosome turnover are indicative of a dynamic chromatin
Fig. 3. Chromatin-based annotation of functional elements. (A) Average enrichment profiles of histone marks, chromosomal proteins, and physical chromatin properties at genes, origins of replications, insulator proteins, and TF binding positions. Each panel shows 4 kb centered at a specified location, either proximal to TSS (prox.) or distal (dist.). (B) Example of a transcript predicted by chromatin signatures associated with promoter (red trace) and gene bodies (blue box) and supported by cDNA evidence. Strong RNA Pol II and H3K4me3 peaks in the promoter region and strong H2B ubiquitination extending toward the previously annotated luna gene are confirmed by RNA-
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structure (43), consistent with the biological functions associated with the corresponding marks. We mapped origins of replication activated early in the S phase of the cell cycle and binding sites of the origin recognition complex (ORC), a conserved replication initiation factor that exhibits little, if any, sequence specificity in vitro (46, 47). ORC-associated sequences are often found at TSSs and depleted for bulk nucleosomes, but are enriched for the variant histone H3.3 (39) and undergo active nucleosome turnover (43). These findings suggest that local nucleosome occupancy and organization are determinants of ORC binding in Drosophila, as in yeast (48, 49). By subdividing the ORC sites into TSS-proximal and -distal sites, we found that local enrichment for GAGA factor (GAF), and H4Ac tetra, H3K27Ac, H4K8Ac, and H3K18Ac are common to both, whereas H3K36me1 appears to be specific for TSS-distal ORC sites (Fig. 3A). ORC marks sites of cohesin complex loading in Drosophila (38); H3K36me1, which is also enriched at cohesin sites (18), may be required in the absence of TSS-associated marks to promote ORC binding and subsequent cohesin loading (50, 51). Insulator elements and proteins (e.g., CP190, CTCF, SUHW, and BEAF) block enhancerpromoter interactions and restrict the spread of histone modifications (52). Analysis of the genomic distributions of insulator proteins showed that BEAF32, CP190, and ZW5 preferentially bind upstream of TSSs, whereas SUHW binds
seq junction reads that were not used in the prediction. (C) Intergenic H3K36me1 chromatin signatures predict replication activity. Enrichment of multiple chromatin marks were used to identify putative large (>10 kbp) intergenic H3K36me1/H3K18ac domains located outside of annotated genes. Although these marks generally correspond to long introns within transcripts, their intergenic domains were enriched for replication activity (fig. S5). In this example from BG3 cells, such a domain was found upstream of the bi locus and is associated with early replication, contains an early origin, is enriched for ORC binding, and is further supported by NippedB binding. SCIENCE
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almost exclusively distal to TSSs, with CTCF binding both equally (53). Insulator regions displayed distinct chromatin signatures (Fig. 3A), but most of the variation is explained by the differences between TSS-proximal and -distal chromatin contexts, suggesting that specific marks are not required for insulator binding or function. However, nucleosome depletion is a common feature of both TSS-proximal and -distal insulator binding sites, as in mammals (54), a property that may facilitate insulator binding or reflect the ability of insulator proteins to displace nucleosomes. Chromatin-based annotation of functional elements. Chromatin signatures associated with TSSs and transcribed regions (45) identified genes and promoters missed by transcript-based annotation. We developed a predictive model for active promoters in cell lines using positional enrichments of 18 histone marks, ORC complex localization, and nucleosome stability and turnover in the 1-kb regions surrounding validated active promoters. Our logistic regression classifier achieved 93.7% sensitivity at a 21.5% false discovery rate (FDR) (fig. S4) and predicted 2203 additional promoter positions at least 500 base pairs (bp) away from annotated TSSs (17). These included promoters for 10 primary miRNA transcripts, of which 7 were also identified by RNA-seq (14). We also used H3K36me3/H2Bubiquitination signatures (fig. S3) to identify 53 transcribed gene bodies outside annotated genes, 11 of which are additionally supported by promoter predictions (e.g., Fig. 3B). These included four primary miRNA transcripts, of which three are also supported by RNA-seq (14) and one is also supported by our promoter predictions (for mir-317). Chromatin signatures also identify functional elements involved in other chromosomal processes such as duplication and segregation. We identified 133 sites in BG3 and 78 sites in S2 cells that contained large (>10-kbp) intergenic domains of H3K36me1. In BG3 cells, 90 and 68%
of the intergenic H3K36me1 domains overlapped with cohesin (18) and early origin activity, respectively, as observed for a 20-kb region upstream of the bi gene (Fig. 3C and fig. S5). Although only 15% of early replication origins appear to be defined by intergenic H3K36me1 domains, the overlap with cohesion enrichment (18) suggests a shared mechanism to ensure faithful chromosome inheritance. De novo discovery of combinatorial chromatin states. Multiple histone modifications act in concert to determine genome functions producing combinatorial chromatin states (55). We used two unsupervised, multivariate hidden Markov models to segment the genome on the basis of the combinatorial patterns of 18 histone marks in S2 and BG3 cells (Fig. 4 and fig. S6) (18). We did not seek a true number of distinct chromatin states; instead, we sought to identify models that balance resolution and interpretability given the available chromatin marks, as more states led to increased enrichment for specific genomic features but captured progressively smaller fractions of each type of feature (fig. S7). From these considerations, we focused on a 9-state, intensity-based model reflecting broad classes of chromatin function (continuous model states c1 to c9) and a 30-state model that identifies combinatorial patterns at a finer resolution (discrete model states d1 to d30) (Fig. 4, left panel) (17). These showed distinct functional and genomic enrichments (Fig. 4, right panel) associated with different chromosomes (chromosome 4, male X), regulatory elements (promoters, enhancers), gene length and exonic structure (e.g., long first introns), gene function (e.g., developmental regulators), and gene expression levels (high or medium, low, or silent). Intergenic regions and silent genes are associated with state d30 (c9) in euchromatin (covering 51% of the genome and lacking enrichments for any of the marks examined) and with states
Fig. 4. Discovery and characterization of chromatin states and their functional enrichments. Combinatorial patterns of chromatin marks in S2 and BG3 cells reveal chromatin states associated with different classes of functional elements. A discrete model (states d1 to d30) captures the presence/absence information, and a continuous model (states c1 to c9) also www.sciencemag.org
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d26, d28, and d29 (c7 and c8) in heterochromatin (characterized by H3K9me2/3 enrichment and H3K23ac depletion). These states lack enrichments for other mapped factors [e.g., insulators, histone deacetylases (HDACs), TFs] and exhibit low levels of chromatin solubility and nucleosome turnover. In contrast, expressed genes display numerous and complex enrichments for several factors and chromatin properties. Most active TSSs were associated with state c1, defined by known promoterassociated marks H3K4me3 and H3K9ac (45). Other active TSSs were additionally enriched for H3K36me1 and multiple acetylations (d13). Even within c1, some TSSs showed higher association with nucleosome turnover, group 1 insulator proteins and HDACs (d1, d3), whereas others were associated with heterochromatic genes of medium (d5) or low expression (d6). The state analysis also captured the correlation between ORC binding and TSSs for both euchromatin and heterochromatin, as well as the correlation between early origins and open chromatin in euchromatic regions. However, ORC binding is largely limited to a subset of TSSassociated states (d1, d5, d6, d13, d17, and not d3 or d24), and some states enriched for ORC binding are not found at TSSs (d11, d14, d21). Early origins are primarily associated with states c3 (active intron, enhancer) and c4 (open chromatin) and often display distinct state enrichments from ORC binding in accord with the broad domains they cover, compared with the near nucleotide resolution of the ORC binding data. Our states showed some similarities with the recently published five “colors” of chromatin from DNA adenine methyltransferase identification– mapped chromosomal proteins in Kc cells (56), but even highly specific states were sometimes split across multiple colors (fig. S8). This suggests a more complex picture with many highly specific chromatin states with specific functional enrichments.
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incorporates mark intensity information (22). States were learned solely from mapped locations of marks (left) and were associated with modENCODE-defined elements (right) with most pronounced patterns in euchromatin (green) and heterochromatin (blue) shown here (additional variations shown in fig. S6). VOL 330
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Chromatin and motif properties of highoccupancy TF binding sites. Extensive overlap in the binding profiles of multiple TFs has revealed highly occupied target (HOT) regions or hotspots (19, 57–61). Using the binding profiles of 41 TFs in early embryo development, we assigned a TF complexity score to each of 38,562 distinct TF binding sites corresponding to the number of distinct TFs bound (from 1 to ~21), resulting in 1962 hotspots with TF complexity of eight or greater, corresponding to ~10 overlapping factors bound (19). We correlated these regions with our and other data sets to gain insight into the possible mechanisms of HOT region establishment and how they may impact or be affected by chromatin properties. We studied the enrichment of regulatory motifs for 32 TFs for which we have both genomewide bound regions and well-established regulatory motifs (Fig. 5A). We sorted each TF on the basis of its average complexity [the average number of TFs that co-bind (19)], which ranges from 10.8 for KNI to 1.3 for FTZ-F1. We studied the relative enrichment of each factor’s known motif in bound regions and found eight factors (KNI, DLL, GT, PRD, KR, SNA, DA, and TWI) with average complexity greater than four that showed significant differences in motif enrichment at
varying complexity levels. In all eight cases, motif matches were preferentially found in regions of lower complexity, which is suggestive of nonspecific binding. For an additional 9 TFs, bound regions were enriched in the known motif, but no bias for lower-complexity regions was found; for another 10 factors, the known motif did not show a substantial enrichment in bound regions, suggesting that either the motif is incorrect, or a larger fraction of TFs than previously expected binds in non–sequence-specific ways. We found a strong correlation between HOT spots of increasing TF complexity and decreased nucleosome density (fig. S9A) (19), increased nucleosome turnover (fig. S9B), and histone variant H3.3, which is associated with nucleosome displacement (fig. S9C), but a surprising depletion in previously annotated enhancers (19), suggesting potentially distinct roles for these elements. We observed enrichment for HOT regions across a wide range of complexity values for several chromatin states associated with TSS and open chromatin regions (d1, d5, d6, d13, d14, d21), whereas some states (d3 and d24) were enriched only at lower complexity (fig. S9D). In contrast, transcriptional elongation (d7 to d9), intergenic (d30), and heterochromatic states (d26, d27, d29) were strongly depleted
Fig. 5. High-occupancy TF binding regions and their relation to motifs, ORC, and chromatin. (A) Enrichment of known motifs for regions bound by corresponding TF, sorted by average complexity, denoting the number of distinct TFs bound in the same region. For eight TFs, motifs are depleted (blue) for highercomplexity regions, suggesting non–sequence-specific recruitment. In seven of eight cases, known motifs were enriched in bound regions (Enrich), suggesting sequence-specific recruitment in lower-complexity regions. For each factor, binding sites were highly reproducible between replicates (Reprod). (B) ORC versus TF complexity. The relation between HOT spot complexity (x axis) and enrichment in ORC binding (y axis). (C) Discovered motifs in high- or low-complexity regions (boxed range) and their enrichment in regions of higher (red) or lower (blue) complexity. M1 to M5 are candidate “drivers” of HOT region establishment.
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across all complexity ranges. We also found concordance between HOT regions and ORC binding sites (Fig. 5B), with the likelihood of ORC binding increasing monotonically with the complexity of the TF-bound regions. Coupled with the lack of a detectable specific sequence for ORC binding in Drosophila (39), this suggests hotspots as an alternative mechanism for ORC localization via nonspecific binding in highaccessibility regions, as well as widespread interplay between chromatin regulation, TF binding, and DNA replication. Given the high agreement between embryo and cell-line data sets, we propose that hotspots are stable genomic regions, kept open via recruitment of specific chromatin marks or remodelers, that facilitate binding of additional TFs at their motifs or nonspecifically. We looked for potential “driver” motifs that may be recognized by TFs potentially involved in establishing HOT regions (Fig. 5C). Applying our motif-discovery pipelines (19) within bound regions of varying complexity resulted in seven distinct motifs associated with hotspots of different complexities. Motifs M2 and M3 were similar to the BEAF-32 and Trl/GAF insulator motifs, suggesting interplay between hotspots and insulator proteins. Motif M1 differed in only one position from the known Sna motif and was strongly enriched for high-complexity regions (Fig. 5C), whereas the Sna motif was depleted in Sna-bound regions of higher complexity (Fig. 5A), suggesting that the single-nucleotide difference may be important for recognition. The other four motifs did not match any known TFs, suggesting that yet-uncharacterized potential sequence-specific regulators may be involved in the establishment of hotspots. Fraction of the genome assigned to candidate functions. We assigned candidate functions to the fraction of the nonrepetitive genome covered by the data sets, excluding large blocks of repeats and low-complexity sequences (Fig. 6A). Protein-coding exons cover 21% of the genome, and adding Argonaute-associated small regulatory RNAs, UTRs, other ncRNAs, bases covered by Pol II, the binding sites of TFs, and other chromatin-interacting factors brings the total genome coverage to 73%. Inclusion of Pc and ORC binding sites, and derived chromatin states, brings the total genome coverage to 81.5%, and the addition of transcribed intronic positions raises the total coverage to more than 89% (Fig. 6A). Compared with previous annotations [FlyBase (4)], we have increased coverage of the Drosophila genome with putative associated functions by 26.3% (47 Mb). Euchromatic regions had much higher coverage than heterochromatic regions (90.6 versus 69.5%) in a comparison of the respective nonrepetitive portions. We next determined the overlap between our predicted functional elements and PhastCons evolutionarily conserved elements across 12 Drosophila species, mosquitoes, honeybees, and beetles (62). These elements cover 38% of the D. melanogaster genome in 1.2 million blocks, over which
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we repeated our previous individual and cumulative calculations. Thirty-two percent of constrained bases are covered by protein-coding exons alone, increasing to a cumulative total of 80% for transcribed and regulatory elements and 91.8% after inclusion of specific chromatin states (Fig. 6A). Nearly all modENCODE-defined functional elements were more likely to cover constrained bases than is expected by chance, providing additional independent evidence for the predicted elements (fig. S10). The only exceptions were some less active chromatin states, as expected, and introns, UTRs, and ncRNAs (63) providing additional independent evidence for the predicted elements. Overlap among the annotations produced by different types of elements resulted in dense multiple coverage (Fig. 6B), even for regions that
previously lacked any annotation (Fig. 6C). Even though the genome coverage average is 2.8 data sets, 10.8% of the genome is covered by 15 or more data sets, and coverage peaks at 103 data sets overlapping a single region on chromosome 3R. We found strong positive correlations between bound regulators and transcribed element densities, as well as regulators and chromatin element densities (fig. S11). In the case of chromatin data sets, additional chromatin marks resulted in higher accuracy in chromatin-state recovery (fig. S12), and we expect similar additional data sets to have an effect on other classes of functional elements. TF targets and physical regulatory network inference. We examined the network of regulatory relationships between TFs, miRNAs, and
Fig. 6. Genome coverage by modENCODE data sets. (A) Unique (bars) and cumulative (lines) coverage of nonrepetitive (blue line) and conserved (red line) genomes. (B) Multiple coverage for data sets grouped into transcribed elements (red), bound regulators (blue), and chromatin domains (green) (17). Across all three classes (black), 10.8% of the genome is covered 15 or more times, and 69.5% is covered at least twice. (C) Increased coverage in a Chr2R region with no prior annotation (left half), now showing multiple overlapping data sets. Coverage by different tracks is highly clustered (fig. S11), with some regions showing little coverage and others densely covered by many types of data. www.sciencemag.org
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their target genes. In these networks, “nodes” represent the transcriptional and posttranscriptional regulators and target genes, and “edges” or “connections” represent their directed regulatory relationships. We inferred a physical regulatory network of TF binding and miRNA targeting, where connections represent physical contact between regulators and genomic regions of their target genes. The structural properties of the physical regulatory network were inferred from the experimentally derived binding profiles of 76 TFs (table S5) and genome-wide occurrences of 77 distinct evolutionarily conserved miRNA seed motifs for 105 miRNAs (17). The structure of the resulting network shows high connectivity and rapid spread of regulatory information, requiring traversal of only ~two regulatory connections, on average, between any two genes and no more than five connections between any pair of genes. Target genes are regulated by ~12 TFs, on average, and can have up to 54 regulatory TFs (17). The most heavily targeted genes are associated with increased pleiotropy, as measured by the number of distinct functional processes and tissues with which they are associated (17). The physical regulatory network includes both pre- and posttranscriptional regulators, identifying the interplay between these two types of regulation. We organized the TFs of the physical regulatory network into five levels (Fig. 7A and fig. S13) on the basis of the relative proportion of TF targets versus TF regulators for each TF (64), and we augmented this network with the miRNA regulators most closely interacting with each level. The presumed “master regulator” TFs at the top level targeted almost all of the other TFs in the network, whereas only 8% of lower-level edges pointed upward to higher levels, supporting a hierarchical nature and suggesting little direct feedback control of master regulators among the TFs surveyed. We also observed that even though the number of TF targets decreases for TFs at lower levels of the hierarchy, the number of their miRNA targets increases (0.58 miRNA targets per TF for the two topmost levels versus 1.55 for the two lowest levels, fold enrichment of 2.66). This suggests that at least some feedback from the lower levels to the master regulators may occur indirectly through miRNA regulators. We next searched for significantly overrepresented network connectivity patterns, or “network motifs” (Fig. 7B), likely to represent building blocks of gene regulation (65). We found eight network motifs in the physical regulatory network (66), five of which correspond to TF cooperation (motifs 1, 2, 4, 7, and 8), confirming observations of cobinding and cotargeting (57–61). In all five motifs, at least two TFs bind each other’s promoter regions, suggesting extensive positive and negative feedback. Two other motifs correspond to mixed feed-forward loops involving cooperation of TFs and miRNAs (motifs 3 and 6), which can lead to different delay properties in the expression of target genes depending
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on the activating or repressive action of the TF. Lastly, one motif (motif 5) corresponds to a feedback loop of a downstream TF targeting an upstream TF through a miRNA, which is also observed as a means for feedback in the hierarchical network layout (17). Data set integration predicts a functional regulatory network. We integrated the physical network with patterns of coordinated activity of regulators and targets to derive a functional regulatory network (fig. S14A). Although TF binding is strongly associated with the true regulatory targets, binding alone can occur without a sequencespecific TF-motif interaction and does not always result in changes in gene expression (60). Thus, a functional regulatory network should consider both binding and its functional consequences, such as changes in expression or chromatin, which are correlated with gene function (fig. S15). Neither network is a strict subset of the other, as some physical connections may not lead to functional changes, and functional connections may be indirect or simply missing in the physical regulatory map. We integrated multiple types of evidence including conserved sequence motifs of 104 TFs in promoter regions across the genome (table S5), ChIP-based TF binding for 76 factors, and the correlation between chromatin marks and gene expression patterns of regulators and their target genes (fig. S16). We combined these lines of evidence with unsupervised machine learning to infer the confidence of each regulatory edge between 707 proteins classified as TFs (17) and 14,444 targets for which at least one line of evidence was available (17). We compared the resulting functional network to the physical network inferred from TF binding, a predicted physical network constructed from motif occurrences, and the REDfly literature-
curated functional network (17). The functional network included a similar number of target genes as both the binding and motif physical networks (~10,000 targets each), but more regulators overall (576 versus 104 and 76, respectively) and more regulators per target (24 versus 7 and 13, respectively) (fig. S14B). The functional network showed similarity to both the motif and binding networks, which were both used as input evidence; connections of the functional network showed more than fourfold enrichment in both networks, even though the two only showed a 1.6-fold enrichment to each other’s connections (fig. S14C). Compared with either the motif or the binding network, the functional network showed the strongest connectivity similarity to the REDfly network, even though it was not specifically trained to match known edges. The functional regulatory network showed increased biological relevance compared with both the motif and binding networks, including increased functional similarity, increased expression correlation, and increased protein-protein interactions of cotargeted genes (fig. S14D) (17). The REDfly network slightly outperformed the functional network, confirming the relevance of the metrics. However, the functional network contains 100 times more targets (9436 versus 88) and 1000 times more connections (231,181 versus 233) than the REDfly network, suggesting it will be more valuable for predicting gene function and gene expression at the genome scale. Predicting gene function from the functional regulatory network. We provided candidate functional annotations for genes that lack Gene Ontology (GO) terms on the basis that targets of similar regulators and with similar expression are likely to share similar functions. We probabilistically assigned genes to 34 expression clusters
Fig. 7. Properties of the physical regulatory network. (A) Hierarchical view of mixed ChIP-based/miRNA physical regulatory network that combines transcriptional regulation by 76 TFs (green) from ChIP experiments and posttranscriptional regulation by 52 miRNAs (red). TFs are organized in a five-level hierarchy on the basis of their relative proportion of TF targets versus TF regulators. miRNAs are separated into two groups: the ones that are regulated by TFs (left) and the ones that only regulate TFs (right). The horizontal position of the TFs in each level shows whether they regulate miRNAs (left), have no regulation to or from miRNAs
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(fig. S15) (17) and predicted likely functional GO terms for every gene with a guilt-byassociation approach that uses GO terms of annotated genes to predict likely functions of unannotated genes, allowing for multiple annotation predictions for each gene (17). This resulted in a higher predictive power than the use of expression or regulators alone (Fig. 8). At FDR < 0.25, we predicted GO terms for 1286 previously unannotated genes and additional terms for 1586 previously annotated genes (fig. S17, table S6, data set S15). In general, tissue-specific enrichments of new GO predictions matched those of known genes in the same GO terms (fig. S18), providing an independent validation of our approach. Predicting stage-specific regulators of gene expression. We predicted stage-specific regulators of gene expression on the basis of transcriptional changes during development. With the Dynamic Regulatory Events Miner (DREM) (67), we searched for splits (a point at which previously coexpressed genes begin to exhibit divergence into two or three distinct expression patterns) among a set of more than 6000 genes with the largest expression changes occurring during the developmental time course (Fig. 9A and fig. S19). We mined the physical and functional regulatory networks to predict stagespecific regulators from the over-representation of regulator targets along specific trajectories or “paths” from each split (17). Several predictions agreed with literature support. For example, TIN, a known regulator of organ development (68), was a predicted regulator of genes with an early increase in expression and enriched for organ development (P < 10–53), and E2F2, a known cellcycle regulator (69), was a predicted regulator of genes with an early decrease in expression and enriched for cell-cycle function (P < 10–100).
(middle), or do not regulate but are targeted by miRNAs (right). Different shades of green and red represent the total number of target genes for TFs and miRNAs, respectively (darker nodes indicate more targets). Ninety-two percent of TF regulatory connections are downstream connections from higher levels to lower levels (green), and only 8% are upstream (blue). miRNA regulatory connections are red. (B) Highly enriched network motifs in a mixed physical regulatory network including TFs (green), miRNAs (red), and target genes (black). For each motif, five examples are shown. Known activators, blue; known repressors, red; other TFs, black. SCIENCE
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To provide additional support for regulator predictions made using the physical network, we examined the time-course expression profiles of the regulators, which were not directly used in the prediction scheme. Even though several caveats could hinder this analysis, the time-course expression of the regulators was often consistent with DREM’s predictions. For example, a sharp decline in SU(HW) expression coincides with sharp expression increase of its targets (Fig. 9A), consistent with a repressive role (70). We generally observed a notable correspondence among the
stage-specific expression changes of predicted regulators at developmental stages that correspond with concomitant expression changes in their target genes. Regulators predicted to be associated with a split had, on average, a significantly greater absolute expression change than those not associated with a split (P < 10−10) (fig. S19) (17). Predicting cell type–specific regulators of chromatin activity. We computed enrichments of conserved regulatory motif instances in cell type–specific annotations for 22 chromatin factors in both S2 and BG3 cells. We defined signa-
Fig. 8. Gene function prediction from coexpression and co-regulation patterns. Receiver operator characteristic curves for GO terms with predicted new members and area-under-the-curve statistics. False negatives for each GO term are predictions for genes previously annotated for “incompatible” GO terms, defined as pairs of GO terms that have less than 10% common genes relative to the union of their gene sets.
Fig. 9. Predictive models of regulator, region, and gene activity. (A) Dynamic regulatory map produced by DREM predicts stage-specific regulators associated with expression changes (y axis, log space relative to first time point) across developmental stages (x axis) (17). Each path (colored lines) indicates the average expression of a group of genes (solid circles) and its standard deviation (size of circle). Predicted bifurcation events, or splits, (open circles) are numbered 1 through 19. The colored insets show the expression level of each individual gene going through the split and ranked regulators from the physical (black) or functional (blue) regulatory network associated with the higher (H), lower (L), or middle (M) path. The uncolored inset shows the expression of repressor SU(HW), whose expression decrease coincides with an expression increase of its targets (red asterisk). (B) Predicted S2 activators www.sciencemag.org
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tures of cell-type–specific activators and repressors probably involved in establishing the chromatin differences between S2 and BG3 cells (Fig. 9B) by comparing these enrichments to the expression patterns of the TFs that recognize these motifs in the same cell types (17). Activators were defined as TFs whose cell type–specific expression coincided with activation of their predicted targets, and repressors were defined as TFs whose cell type–specific expression was correlated with repression of their predicted targets. This resulted in one to eight predicted regulators for each cell, including, for example, CREBA as a predicted S2 activator, H as a predicted BG3 repressor, and factors with the stereotypical homeobox binding motif (HOX-like) as a predicted BG3 activator. For most regulatory motifs, enrichment in activating chromatin marks was coupled with depletion in repressive chromatin marks. This coupling leads to more robust predictions of activators and repressors and also enables a highlevel distinction between active and repressive chromatin marks that agrees with previous studies and with our chromatin-state analysis (Fig. 4) (18, 19). For a small number of motifs, however, the chromatin enrichments did not show a consistent picture of opposite enrichments in activating versus repressive marks. These could be false positives and not actually associated with chromatin regulation, or they could be active in other cell types and not relevant to the distinction between S2 and BG3 chromatin marks.
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(top group) or repressors (bottom group), based on the coherence between relative expression of the TF in S2 (yellow) versus BG3 (green) and the relative motif enrichment (red) or depletion (blue) in S2 versus BG3 for activating (left columns) or repressive marks (right columns). (C) True (top of shaded area) and predicted (dotted blue line) expression levels for target genes, from the expression levels of inferred activators (red) and repressors (green). Only the top five positive and negative regulators are shown, ranked by their contribution to the expression prediction (weight of linear-regression model). Examples are shown from 8 of 1487 predictable genes, ranked by prediction quality scores (rank in upper right corner), evaluated as the averaged squared error between predicted and true expression levels across the time course. An expanded set of examples is shown in fig S23. VOL 330
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Predicting target gene expression from regulator expression. Developmental regulatory programs are defined by multiple interacting regulators contributing to observed changes in gene or region activity (71). We sought to predict the specific expression levels of target genes across numerous stages and cell lines on the basis of the expression levels of their regulators. With the 30 distinct measurements of expression levels obtained by RNA-seq across development (14), we represented the expression level of each target gene as a linear combination of its regulators, as defined by the functional regulatory network (Fig. 9C). We split the time course into 10 intervals of three samples each and learned stable coefficients for linear combinations of TFs across 9 intervals to predict expression in the tenth (17). We predicted the expression levels of 1991 genes better than random control networks (23.6% of genes), a 2.5-fold enrichment (control networks perform better on 9.5% of genes) (figs. S20 and S21). In contrast, physical networks showed almost no predictive value over the randomized networks (table S7), suggesting that they are best used when combined with additional information for inferring functional regulatory networks. Genes whose expression levels are predictable from the expression levels of their regulators (those with consistently lower errors than random) may be more precisely regulated and, thus, associated with less noisy expression patterns. Indeed, the expression correlation between the 30–time-point data set used for expression prediction (14) and an independently generated 12– time-point data set sampled at longer intervals (19) was significantly higher for predictable genes compared with unpredictable genes (KolmogorovSmirnov test P value < 1E–7) (fig. S22). These results validate our methodology for gene expression prediction and suggest that unpredictable genes may be due to intrinsic variability in gene expression levels. We also tested whether the regulatory models obtained with whole-embryo time-course data sets can predict gene expression under novel conditions: specifically the Cl.8+, Kc167, BG3, and S2-DRSC cell lines. For each “predictable” gene, the expression levels of its regulators were combined, as dictated by the weights learned in the time-course experiment, and used to predict target gene expression. The expression of 932 predictable genes also showed better-than-random predictions (compared with 296 genes for the binding network and 214 genes for the motif network). Overall, 62% of embryo-defined predictable genes were also predictable in cell lines, compared with only 10 to 15% for embryo-based unpredictable genes, providing further validation of our methodology. Our results suggest that the primary data sets are highly relevant for inferring functional regulatory relations that are predictive of expression (Fig. 9C and figs. S20 and S23). However, genomescale gene expression prediction remains an enormously difficult problem, as only one-quarter of
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all genes was predictable, a fraction that we expect to improve with additional data sets generated from more and more genome-scale projects. Discussion. This first phase of the modENCODE project has provided the foundation for integrative studies of metazoan biology, enhancing existing genome annotations; broadening the number and diversity of small RNA genes and pathways; revealing chromatin domains and signatures; and elucidating the interplay between replication, chromatin, and TF binding in highoccupancy regions. Together, our resulting annotations cover 82% of the genome, a nearly fourfold increase compared with previously annotated proteincoding exons, and have important implications for interpreting the molecular basis of genetically linked phenotypes. Our integrative analysis revealed connections between elements in physical and functional regulatory networks, enabling the prediction of gene function, tissue- and stage-specific regulators, and gene expression levels. Though our initial results are promising, only one-quarter of all genes showed predictable expression, suggesting the need for continued mapping of regulatory interconnections and functional data sets, as well as new predictive models. It remains to be seen how the general regulatory principles elucidated here will be conserved across the animal kingdom and especially in humans, through comparison across the ENCODE and modENCODE projects. Toward this end, we are expanding our exploration of functional elements, cell types, and developmental stages and prioritizing orthologous assays and conditions across species. Given the extensive conservation of biological molecules and processes between flies and vertebrates (72), these will not only improve our understanding of fly biology, but can also serve as a template for understanding of human biology and disease. References and Notes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
www.genome.gov/10005107 S. E. Celniker et al., Nature 459, 927 (2009). R. A. Hoskins et al., Science 316, 1625 (2007). Compared to FlyBase release 5.12 (October 2008), available at http://fb2008_09.flybase.org/ M. Stapleton et al., Genome Biol. 3, RESEARCH0080 (2002). K. H. Wan et al., Nat. Protoc. 1, 624 (2006). R. Drysdale, FlyBase Consortium, Methods Mol. Biol. 420, 45 (2008). G. Dietzl et al., Nature 448, 151 (2007). S. Mohr, C. Bakal, N. Perrimon, Annu. Rev. Biochem. 79, 37 (2010). H. J. Bellen et al., Genetics 167, 761 (2004). Drosophila 12 Genomes Consortium, Nature 450, 203 (2007). A. Stark et al., Nature 450, 219 (2007). M. D. Adams et al., Science 287, 2185 (2000). B. Graveley, Nature, 10.1038/nature09715. L. Cherbas et al., Genome Res., 10.1101/gr.112961.110. R. A. Hoskins et al., Genome Res., 10.1101/gr.112466.110. Supplemental text and materials and methods are available as supporting material on Science Online. P. V. Kharchenko et al., Nature, 10.1038/nature09725. TF binding, hotspots, TF motif instances, promoter and enhancer validations, 12-point expression, and chromatin time course are available at www.cistrack.org.
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Complete Author List Kellis (integration): Sushmita Roy, Jason Ernst, Pouya Kheradpour, Christopher A. Bristow, Michael F. Lin, Stefan Washietl, Ferhat Ay, Patrick E. Meyer, Luisa Di Stefano, Rogerio Candeias, Irwin Jungreis, Daniel Marbach, Rachel Sealfon, Manolis Kellis Celniker (transcription): Jane M. Landolin, Joseph W. Carlson,
Benjamin Booth, Angela N. Brooks, Carrie A. Davis, Michael O. Duff, Philipp Kapranov, Anastasia A. Samsonova, Jeremy E. Sandler, Marijke J. van Baren, Kenneth H. Wan, Li Yang, Charles Yu, Justen Andrews, Steven E. Brenner, Michael R. Brent, Lucy Cherbas, Thomas R. Gingeras, Roger A. Hoskins, Thomas C. Kaufman, Norbert Perrimon, Peter Cherbas, Brenton R. Graveley, Susan E. Celniker, Charles L. G. Comstock, Alex Dobin, Jorg Drenkow, Sandrine Dudoit, Jacqueline Dumais, Delphine Fagegaltier, Srinka Ghosh, Kasper D. Hansen, Sonali Jha, Laura Langton, Wei Lin, David Miller, Aaron E. Tenney, Huaien Wang, Aarron T. Willingham, Chris Zaleski, Dayu Zhang Karpen (chromatin): Peter V. Kharchenko, Michael Y. Tolstorukov, Artyom A. Alekseyenko, Andrey A. Gorchakov, Tingting Gu, Aki Minoda, Nicole C. Riddle, Yuri B. Schwartz, Sarah C. R. Elgin, Mitzi I. Kuroda, Vincenzo Pirrotta, Peter J. Park, Gary H. Karpen, David Acevedo, Eric P. Bishop, Sarah E. Gadel, Youngsook L. Jung, Cameron D. Kennedy, Ok-Kyung Lee, Daniela Linder-Basso, Sarah E. Marchetti, Gregory Shanower White (transcription factors): Nicolas Nègre, Lijia Ma, Christopher D. Brown, Rebecca Spokony, Robert L. Grossman, James W. Posakony, Bing Ren, Steven Russell, Kevin P. White, Richard Auburn, Hugo J. Bellen, Jia Chen, Marc H. Domanus, David Hanley, Elizabeth Heinz, Zirong Li, Folker Meyer, Steven W. Miller, Carolyn A. Morrison, Douglas A. Scheftner, Lionel Senderowicz, Parantu K. Shah, Sarah Suchy, Feng Tian, Koen J. T. Venken, Robert White, Jared Wilkening, Jennifer Zieba MacAlpine (replication): Matthew L. Eaton, Heather K. MacAlpine, Jared T. Nordman, Sara K. Powell, Noa Sher, Terry L. Orr-Weaver, David M. MacAlpine, Leyna C. DeNapoli, Queying Ding, Thomas Eng, Helena Kashevsky, Sharon Li, Joseph A. Prinz
Lai (small RNAs): Nicolas Robine, Eugene Berezikov, Qi Dai, Katsutomo Okamura, Eric C. Lai, Qi Dai, Gregory J. Hannon, Martin Hirst, Marco Marra, Michelle Rooks, Yongjun Zhao Henikoff (nucleosomes): Jorja G. Henikoff, Akiko Sakai, Kami Ahmad, Steven Henikoff, Terri D. Bryson Stein (data coordination center): Bradley I. Arshinoff, Nicole L. Washington, Adrian Carr, Xin Feng, Marc D. Perry, William J. Kent, Suzanna E. Lewis, Gos Micklem, Lincoln D. Stein, Galt Barber, Aurelien Chateigner, Hiram Clawson, Sergio Contrino, Francois Guillier, Angie S. Hinrichs, Ellen T. Kephart, Paul Lloyd, Rachel Lyne, Sheldon McKay, Richard A. Moore, Chris Mungall, Kim M. Rutherford, Peter Ruzanov, Richard Smith, E. O. Stinson, Zheng Zha Oliver (comparative transcription): Carlo G. Artieri, Renhua Li, John H. Malone, David Sturgill, Brian Oliver, Lichun Jiang, Nicolas Mattiuzzo RNA structure: Sebastian Will, Bonnie Berger Program management: Elise A. Feingold, Peter J. Good, Mark S. Guyer, Rebecca F. Lowdon
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1198374/DC1 Materials and Methods SOM Text Figs. S1 to S23 Tables S1 to S7 Data Sets S1 to S17 (available at www.modencode.org/ publications/integrative_fly_2010/) 28 September 2010; accepted 30 November 2010 Published online 22 December 2010; 10.1126/science.1198374
REPORTS High-Flux Solar-Driven Thermochemical Dissociation of CO2 and H2O Using Nonstoichiometric Ceria William C. Chueh,1 Christoph Falter,2 Mandy Abbott,1 Danien Scipio,1 Philipp Furler,2 Sossina M. Haile,1* Aldo Steinfeld2,3* Because solar energy is available in large excess relative to current rates of energy consumption, effective conversion of this renewable yet intermittent resource into a transportable and dispatchable chemical fuel may ensure the goal of a sustainable energy future. However, low conversion efficiencies, particularly with CO2 reduction, as well as utilization of precious materials have limited the practical generation of solar fuels. By using a solar cavity-receiver reactor, we combined the oxygen uptake and release capacity of cerium oxide and facile catalysis at elevated temperatures to thermochemically dissociate CO2 and H2O, yielding CO and H2, respectively. Stable and rapid generation of fuel was demonstrated over 500 cycles. Solar-to-fuel efficiencies of 0.7 to 0.8% were achieved and shown to be largely limited by the system scale and design rather than by chemistry. ong-term storage and long-range transport of the vast, yet intermittent and unevenly distributed, solar energy resource is essential for a transition away from fossil energy (1).
L
1 Materials Science, California Institute of Technology, MC 309-81, Pasadena, CA 91125, USA. 2Department of Mechanical and Process Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, 8092 Zürich, Switzerland. 3Solar Technology Laboratory, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland.
*To whom correspondence should be addressed. E-mail:
[email protected] (S.M.H.);
[email protected] (A.S.)
Chemical fuels, derived from CO2 and/or H2O, offer exceptional energy density and convenience for transportation, but their production using solar energy input has remained a grand challenge (2–9). Solar-driven thermochemical approaches to CO2 and H2O dissociation inherently operate at high temperatures and use the entire solar spectrum; as such, they provide an attractive path to solar fuel production at high rates and efficiencies in the absence of precious metal catalysts (10). In contrast to direct thermolysis of CO2 and H2O, two-step ther-
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mochemical cycles using metal oxide redox reactions further bypass the CO-O2 or H2-O2 separation problem (11). Among candidate redox materials, ferrite-based oxides exhibit relatively slow reaction rates, degradation in rates because of sintering, and losses because of uncontrolled volatilization, whereas ZnO, SnO2, and analogous volatile oxides that sublime during decomposition require rapid quenching of gaseous products to avoid recombination (10–18). Cerium oxide (ceria) has emerged as a highly attractive redox active material choice for twostep thermochemical cycling because it displays rapid fuel production kinetics and high selectivity (17, 19–24), where such features result, in part, from the absence of distinct oxidized and reduced phases. However, ceria-based thermochemical studies to date have largely been limited to bench-top demonstrations of components or individual steps of the solar fuel production cycle; assessment of cyclability has been limited, and the energy conversion efficiency has remained uncertain because of the relatively low gravimetric fuel productivity inherent to the nonstoichiometric process. Here, we demonstrate high-rate solar fuel production from both CO2 and H2O using a solar reactor subjected directly to concentrated radiation under realistic operating conditions relevant to large-scale industrial implementation, without the need for complex material microstructures and/or system design (e.g., additional quench or separation steps). The results provide compelling evidence for the viability of thermochemical approaches to solar fuel
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U01HG004279 (D.M.M.), U01HG004261 (E.L.), U01HG004274 (S.H.), and U41HG004269 (L.S.). Awards to S.E.C. and G.H.K. were carried out at LBNL under contract no. DE-AC02-05CH11231. Additional support was provided by the NSF under grant 0937060 to the Computing Research Association for the CIFellows Project (S.R.) and under award no. 0905968 (J.E.), a Natural Sciences and Engineering Research Council of Canada (NSERC) fellowship (B.A.), T. Kahveci (F.A.), the Japan Society for the Promotion of Science (K.O.), the Swedish Research Council (Q.D.), a NIH National Research Service Award postdoctoral fellowship (C.A.B.), a National Defense Science and Engineering Graduate Fellowship (R.S.), an Erwin Schrödinger Fellowship of the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (S.W.), a Leukemia and Lymphoma Society fellowship (S.W.), a Lilly-Life Sciences Research Foundation fellowship (C.D.B.), a NSERC postdoctoral fellowship (C.G.A.), Affymetrix (T.G.R.), a fellowship from the Swiss National Science Foundation (D.M.), a German Research Foundation grant WI 3628/1-1 (S.W.), a HHMI Damon Runyon Cancer Research fellowship (J.T.N.), the Indiana Genomics Initiative (T.C.K.), H. Smith and the NIDDK genomics core laboratory (B.O.), NIH R01HG004037, NSF CAREER award 0644282, and the Sloan Foundation (M.K.). A full list of author contributions is available in the SOM.
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Complete Author List Kellis (integration): Sushmita Roy, Jason Ernst, Pouya Kheradpour, Christopher A. Bristow, Michael F. Lin, Stefan Washietl, Ferhat Ay, Patrick E. Meyer, Luisa Di Stefano, Rogerio Candeias, Irwin Jungreis, Daniel Marbach, Rachel Sealfon, Manolis Kellis Celniker (transcription): Jane M. Landolin, Joseph W. Carlson,
Benjamin Booth, Angela N. Brooks, Carrie A. Davis, Michael O. Duff, Philipp Kapranov, Anastasia A. Samsonova, Jeremy E. Sandler, Marijke J. van Baren, Kenneth H. Wan, Li Yang, Charles Yu, Justen Andrews, Steven E. Brenner, Michael R. Brent, Lucy Cherbas, Thomas R. Gingeras, Roger A. Hoskins, Thomas C. Kaufman, Norbert Perrimon, Peter Cherbas, Brenton R. Graveley, Susan E. Celniker, Charles L. G. Comstock, Alex Dobin, Jorg Drenkow, Sandrine Dudoit, Jacqueline Dumais, Delphine Fagegaltier, Srinka Ghosh, Kasper D. Hansen, Sonali Jha, Laura Langton, Wei Lin, David Miller, Aaron E. Tenney, Huaien Wang, Aarron T. Willingham, Chris Zaleski, Dayu Zhang Karpen (chromatin): Peter V. Kharchenko, Michael Y. Tolstorukov, Artyom A. Alekseyenko, Andrey A. Gorchakov, Tingting Gu, Aki Minoda, Nicole C. Riddle, Yuri B. Schwartz, Sarah C. R. Elgin, Mitzi I. Kuroda, Vincenzo Pirrotta, Peter J. Park, Gary H. Karpen, David Acevedo, Eric P. Bishop, Sarah E. Gadel, Youngsook L. Jung, Cameron D. Kennedy, Ok-Kyung Lee, Daniela Linder-Basso, Sarah E. Marchetti, Gregory Shanower White (transcription factors): Nicolas Nègre, Lijia Ma, Christopher D. Brown, Rebecca Spokony, Robert L. Grossman, James W. Posakony, Bing Ren, Steven Russell, Kevin P. White, Richard Auburn, Hugo J. Bellen, Jia Chen, Marc H. Domanus, David Hanley, Elizabeth Heinz, Zirong Li, Folker Meyer, Steven W. Miller, Carolyn A. Morrison, Douglas A. Scheftner, Lionel Senderowicz, Parantu K. Shah, Sarah Suchy, Feng Tian, Koen J. T. Venken, Robert White, Jared Wilkening, Jennifer Zieba MacAlpine (replication): Matthew L. Eaton, Heather K. MacAlpine, Jared T. Nordman, Sara K. Powell, Noa Sher, Terry L. Orr-Weaver, David M. MacAlpine, Leyna C. DeNapoli, Queying Ding, Thomas Eng, Helena Kashevsky, Sharon Li, Joseph A. Prinz
Lai (small RNAs): Nicolas Robine, Eugene Berezikov, Qi Dai, Katsutomo Okamura, Eric C. Lai, Qi Dai, Gregory J. Hannon, Martin Hirst, Marco Marra, Michelle Rooks, Yongjun Zhao Henikoff (nucleosomes): Jorja G. Henikoff, Akiko Sakai, Kami Ahmad, Steven Henikoff, Terri D. Bryson Stein (data coordination center): Bradley I. Arshinoff, Nicole L. Washington, Adrian Carr, Xin Feng, Marc D. Perry, William J. Kent, Suzanna E. Lewis, Gos Micklem, Lincoln D. Stein, Galt Barber, Aurelien Chateigner, Hiram Clawson, Sergio Contrino, Francois Guillier, Angie S. Hinrichs, Ellen T. Kephart, Paul Lloyd, Rachel Lyne, Sheldon McKay, Richard A. Moore, Chris Mungall, Kim M. Rutherford, Peter Ruzanov, Richard Smith, E. O. Stinson, Zheng Zha Oliver (comparative transcription): Carlo G. Artieri, Renhua Li, John H. Malone, David Sturgill, Brian Oliver, Lichun Jiang, Nicolas Mattiuzzo RNA structure: Sebastian Will, Bonnie Berger Program management: Elise A. Feingold, Peter J. Good, Mark S. Guyer, Rebecca F. Lowdon
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1198374/DC1 Materials and Methods SOM Text Figs. S1 to S23 Tables S1 to S7 Data Sets S1 to S17 (available at www.modencode.org/ publications/integrative_fly_2010/) 28 September 2010; accepted 30 November 2010 Published online 22 December 2010; 10.1126/science.1198374
REPORTS High-Flux Solar-Driven Thermochemical Dissociation of CO2 and H2O Using Nonstoichiometric Ceria William C. Chueh,1 Christoph Falter,2 Mandy Abbott,1 Danien Scipio,1 Philipp Furler,2 Sossina M. Haile,1* Aldo Steinfeld2,3* Because solar energy is available in large excess relative to current rates of energy consumption, effective conversion of this renewable yet intermittent resource into a transportable and dispatchable chemical fuel may ensure the goal of a sustainable energy future. However, low conversion efficiencies, particularly with CO2 reduction, as well as utilization of precious materials have limited the practical generation of solar fuels. By using a solar cavity-receiver reactor, we combined the oxygen uptake and release capacity of cerium oxide and facile catalysis at elevated temperatures to thermochemically dissociate CO2 and H2O, yielding CO and H2, respectively. Stable and rapid generation of fuel was demonstrated over 500 cycles. Solar-to-fuel efficiencies of 0.7 to 0.8% were achieved and shown to be largely limited by the system scale and design rather than by chemistry. ong-term storage and long-range transport of the vast, yet intermittent and unevenly distributed, solar energy resource is essential for a transition away from fossil energy (1).
L
1 Materials Science, California Institute of Technology, MC 309-81, Pasadena, CA 91125, USA. 2Department of Mechanical and Process Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, 8092 Zürich, Switzerland. 3Solar Technology Laboratory, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland.
*To whom correspondence should be addressed. E-mail:
[email protected] (S.M.H.);
[email protected] (A.S.)
Chemical fuels, derived from CO2 and/or H2O, offer exceptional energy density and convenience for transportation, but their production using solar energy input has remained a grand challenge (2–9). Solar-driven thermochemical approaches to CO2 and H2O dissociation inherently operate at high temperatures and use the entire solar spectrum; as such, they provide an attractive path to solar fuel production at high rates and efficiencies in the absence of precious metal catalysts (10). In contrast to direct thermolysis of CO2 and H2O, two-step ther-
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mochemical cycles using metal oxide redox reactions further bypass the CO-O2 or H2-O2 separation problem (11). Among candidate redox materials, ferrite-based oxides exhibit relatively slow reaction rates, degradation in rates because of sintering, and losses because of uncontrolled volatilization, whereas ZnO, SnO2, and analogous volatile oxides that sublime during decomposition require rapid quenching of gaseous products to avoid recombination (10–18). Cerium oxide (ceria) has emerged as a highly attractive redox active material choice for twostep thermochemical cycling because it displays rapid fuel production kinetics and high selectivity (17, 19–24), where such features result, in part, from the absence of distinct oxidized and reduced phases. However, ceria-based thermochemical studies to date have largely been limited to bench-top demonstrations of components or individual steps of the solar fuel production cycle; assessment of cyclability has been limited, and the energy conversion efficiency has remained uncertain because of the relatively low gravimetric fuel productivity inherent to the nonstoichiometric process. Here, we demonstrate high-rate solar fuel production from both CO2 and H2O using a solar reactor subjected directly to concentrated radiation under realistic operating conditions relevant to large-scale industrial implementation, without the need for complex material microstructures and/or system design (e.g., additional quench or separation steps). The results provide compelling evidence for the viability of thermochemical approaches to solar fuel
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U01HG004279 (D.M.M.), U01HG004261 (E.L.), U01HG004274 (S.H.), and U41HG004269 (L.S.). Awards to S.E.C. and G.H.K. were carried out at LBNL under contract no. DE-AC02-05CH11231. Additional support was provided by the NSF under grant 0937060 to the Computing Research Association for the CIFellows Project (S.R.) and under award no. 0905968 (J.E.), a Natural Sciences and Engineering Research Council of Canada (NSERC) fellowship (B.A.), T. Kahveci (F.A.), the Japan Society for the Promotion of Science (K.O.), the Swedish Research Council (Q.D.), a NIH National Research Service Award postdoctoral fellowship (C.A.B.), a National Defense Science and Engineering Graduate Fellowship (R.S.), an Erwin Schrödinger Fellowship of the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (S.W.), a Leukemia and Lymphoma Society fellowship (S.W.), a Lilly-Life Sciences Research Foundation fellowship (C.D.B.), a NSERC postdoctoral fellowship (C.G.A.), Affymetrix (T.G.R.), a fellowship from the Swiss National Science Foundation (D.M.), a German Research Foundation grant WI 3628/1-1 (S.W.), a HHMI Damon Runyon Cancer Research fellowship (J.T.N.), the Indiana Genomics Initiative (T.C.K.), H. Smith and the NIDDK genomics core laboratory (B.O.), NIH R01HG004037, NSF CAREER award 0644282, and the Sloan Foundation (M.K.). A full list of author contributions is available in the SOM.
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REPORTS generation while clarifying the efforts required to transform the concept into a central technology in a sustainable energy future.
Thermochemical H2O-CO2-splitting cycles over a nonstoichiometric oxide are described by the following pairs of reactions:
Higher temperature, TH 1 1 1 d MO2 → d MO2d þ =2 O2 ðgÞ
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Concentrated Solar Radiation
Lower temperature, TL CO2 ðgÞ þ 1 d MO2d → 1 =d MO2 þ COðgÞ ð2bÞ Net H2O dissociation
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s Purge Ga
where M in the present case is Ce or a combination of Ce and a dopant element. At the relatively high temperatures of the present study (>800°C), equilibria of reaction (2b) involving carbonaceous species can be neglected (21). The solar reactor constructed for the purposes of demonstrating these cycles is schematically shown in Fig. 1. It consists of a cavity receiver with a windowed aperture through which concentrated solar radiation enters. The selected dimensions ensure multiple internal reflections and efficient capture of incoming solar energy; the apparent absorptivity exceeds 0.94, approaching the ideal blackbody limit (25). Porous, monolithic ceria, assembled from quarter-circular-arc pieces in the form of a cylinder (325 g in mass, 35 mm in outer diameter, 102 mm in height, and 80% in porosity as fabricated), is placed inside the cavity and subjected to multiple heat-cool cycles under appropriate gases to induce fuel production (Fig. 2). No additional support or absorber was used.
Alumina Insulation Porous Ceria Outlet Oxygen Evolution Half-Cycle Fuel Production Half-Cycle
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Fig. 1. Schematic of the solar reactor for the two-step, solar-driven thermochemical production of fuels. It consists of a thermally insulated cavity receiver containing a porous monolithic ceria cylinder. Concentrated solar radiation enters through a windowed aperture and impinges on the ceria inner walls. Reacting gases flow radially across the porous ceria toward the cavity inside, whereas product gases exit the cavity through an axial outlet port at the bottom. (Inset) The scanning electron micrograph of the porous ceria tube after 23 cycles. Blue arrows indicate ceria reduction (Eq. 1); red arrows indicate oxidation (Eqs. 2a and 2b).
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Fig. 2. Thermochemical cycling of ceria (325 g) using the solar reactor with (A) CO2 and (B) H2O as oxidant. The oxygen and fuel evolution rates as well as the total volume of gas evolved are shown. Temperatures were measured at three positions along the height of the ceria tube. Maximum temperatures (Tmax) attained in the reactor are shown. Conditions for (A) were as follows: Ar sweep gas at a flow rate of 0.0062 liter min−1 g−1 of ceria during the
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ceria reduction half-cycle, and CO2/Ar at pCO2 = 0.78 atm and a flow rate of 0.035 liter min−1 g−1 of ceria during the ceria oxidation half-cycle. Conditions for (B) were as follows: Ar sweep gas at a flow rate of 0.023 liter min−1 g−1 of ceria during the ceria reduction half-cycle, and H2O/Ar at pH2O = 0.78 atm and a flow rate of 0.035 liter min−1 g−1 of ceria during the ceria oxidation half-cycle. SCIENCE
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Net CO2 dissociation
With this arrangement, the porous ceria cylinder is directly exposed to concentrated solar radiation impinging on its inner walls. An annular gap between the ceria cylinder and the alumina insulation tiles suppresses undesired reactions between the two components. Reacting gases are injected into this annular gap and directed to flow radially across the porous ceria cylinder toward the cavity inside, whereas product gases exit the cavity through an axial outlet port at the bottom. Complete experimental details, including procedures for estimating uncertainty, are given in (25). Post situ x-ray diffraction showed that, with the exception of the portion in direct contact with the insulation material, the ceria remained phase-pure and free of detectable alumina incorporation (fig. S1). To drive oxygen evolution (Eq. 1), we purged the solar reactor with flowing argon [partial pressure of O2 (pO2) = 10–5 atm] and ramped the incident radiation power to about 1.9 kWat a mean solar flux intensity over the aperture of 1500 suns (1 sun = 1 kW m−2), typical of a commercial solar dish or a tower concentration system. The temperature of the ceria tube rose to values between 1420° and 1640°C, with the exact temperature dependent on the position within the reactor and on the cycle (Fig. 2A). The rise in temperature was rapid below 1250°C, averaging 140°C min−1, but slowed to about 8°C min−1 as the temperature approached a steady-state value because of increasing heat dissipation by re-radiation through the aperture and conduction heat transfer through the insulation. Oxygen evolution from ceria was observed at an onset temperature of about 900°C, consistent with equilibrium thermogravimetry measurements (fig. S2). The rate of evolution increased with temperature, reaching a peak value of 34 T 2 ml min−1 (STP, standard temperature and pressure) and an average value of 16 T 1 ml min−1 (averaged over Fig. 3. Comparison of (A) O2, (B) H2, and (C) CO evolution between the solar reactor (dotted lines) and the differential reactor (solid lines). Experimental conditions for the solar reactor are the same as those for Fig. 2. In the differential reactor, 0.429 g of CeO2 was cycled between 1500°C (Ar sweep gas flow rate = 2.3 liter min−1 g−1 of ceria, 20 min) and 800°C (for CO2 splitting, pCO2 = 0.50 atm, flow rate = 1.2 liter min−1 g−1 of ceria, 10 min; for H2O splitting, pH2O = 0.44 to 0.52 atm, flow rate = 2.1 to 2.5 liter min−1 g−1 of ceria, 10 min).
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of O2, CO, or H2 evolution under corresponding reaction conditions. The characteristics of solar-thermochemical fuel production from ceria reveal several important features of the cycling process. Although the behavior is generally reproducible between cycles, some run-to-run variations are evident. The oxygen evolution reaches a peak value between 17 and 34 ml min−1, whereas the total amount evolved ranges from 0.54 to 0.94 liter for 325 g of ceria. As noted, these differences are correlated with the peak reactor temperature obtained (Fig. 2), variations in which are attributed to unsteady heat transfer. Taking this temperature variability into account, the total oxygen evolution is found to be generally consistent with thermodynamic expectations (fig. S2). Mass balance considerations further dictate a 2-to-1 molar (and hence volumetric) ratio of fuel produced to oxygen released for full utilization of the ceria nonstoichiometry. For CO2 dissociation, the CO:O2 ratio ranged from 1.6 T 0.2:1 to 2.0 T 0.2:1, whereas for H2O dissociation, the H2:O2 ratio was 1.6 T 0.2:1. The slight deviation from the ideal value is attributed to small leaks in the system and to the accuracies of the electronic mass flow controller and measured gas composition. Perhaps the most obvious feature of the cycling behavior in Fig. 2 is the much faster rate of fuel production than that of O2 release. It was observed that lowering the purge gas flow rate during ceria reduction by a factor of 4 had negligible impact on the oxygen evolution rate (Fig. 2), indicating that the convective transport of oxygen gas in the reactor is not likely the rate-limiting step. Furthermore, the substantial difference in the O2 evolution and CO2 dissociation rate suggests that gas-phase transport through the pores of ceria is probably not rate-limiting. We considered, as an alternative, that the oxygen evolution kinetics in the solar reactor are
the time required to reach 90% of the extent of reaction) on the first cycle (Fig. 2A). When the rate dropped to 20% of the peak value, the evolution reaction was terminated by decreasing the intensity of the incident radiation flux. Upon cooling to ~900°C, CO2 was injected into the solar reactor. Production of CO was immediately observed, reaching a remarkable peak rate of 1.5 × 103 T 0.1 × 103 ml min−1 (STP) and an average rate of 5.9 × 102 T 0.4 × 102 ml min−1 (STP) (Fig. 2A). Consistent with the fact that there was no water present in the reactant stream, no gas-phase C1, C2, or C3 hydrocarbons were detected by the gas chromatograph. Carbon-neutral balance (<3% C unaccounted for, well within error) was achieved by summing the flow rates of CO2 in the reactant stream and CO2 and CO in the product steam. As verification, temperature-programmed oxidation was performed on the ceria after the CO2 dissociation reaction by flowing oxygen into the solar reactor while ramping the temperature to 1000°C (26). Both CO and CO2 levels were below the detection limit, confirming that no appreciable amount of carbonaceous species was deposited onto ceria during CO2 dissociation and that a 100% selectivity toward CO production was achieved. Upon the termination of CO production, the radiation flux was increased and the entire cycle repeated. A slight decline over the four cycles in the nominal reactor temperature attained during the O2 release step (from 1624° to 1581°C) is responsible for the observed slight decline in O2 release and CO yield. An analogous set of experiments was also performed for H2O dissociation, with H2 production rate reaching a peak value of 7.6 × 102 T 0.8 × 102 ml min−1 (STP) and a maximum average value of 3.1 × 102 T 0.3 × 102 ml min−1 (STP) (Fig. 2B). A total of 23 cycles were performed. An experimental run performed without the ceria confirmed the absence
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Cycle limited by the heating rate, a factor that does not affect fuel production because this step occurs isothermally. If the heating rate is slow relative to the surface reaction and solid-state diffusion steps involved in oxygen release, we can express the oxygen evolution rate as dd=dt ≈ ðdd=dT ÞT ¼TðtÞ ðdT =dtÞ, where d is ceria oxygen nonstoichiometry (Eq. 1), T is temperature, and t is time. By using the spatially averaged temperature profile in the first cycle in Fig. 2A, we compute a maximum oxygen evolution rate of 65 ml min−1, which, given the approximate nature of the calculation, is comparable to the observed rate. To eliminate the effect of gas-phase mass and heat transfer, we carried out thermochemical cycling of ceria by using identically prepared monolithic porous samples (annealed for 50 hours to simulate cycling conditions) in a smaller-scale infrared imaging furnace that uses only a 0.4-g sample. Such an experimental setup permitted thermochemical cycling under high-flow, differential reactor conditions, in which the sample temperature could be changed rapidly (average ramp rate of 1700°C min−1) and the gas composition approached uniformity. Under these ideal conditions in which only surface chemical reactions and solid-state oxygen diffusion in ceria limit the overall reaction rate, oxygen evolution (Fig. 3) attained a peak instantaneous rate ~80 times faster than in the solar reactor and an associated average rate of 2.2 T 0.2 ml min−1 g−1 of ceria; CO2 and H2O dissociation reactions were about two and four times faster (although the reactant partial pressures, temperatures, and initial extents of reduction differ slightly between the differential and solar reactor), with rates of 5.1 T 0.4 and 5.3 T 0.4 ml min−1 g−1 of ceria, respectively. These rates support the proposition that oxygen evolution kinetics in the solar reactor are limited predominantly by the heating rate. The solar-to-fuel energy conversion efficiency is defined as h¼
rfuel DHfuel Psolar þ rinert Einert
ð4Þ
where rfuel is the molar fuel production rate, DHfuel is the higher heating value of the fuel,
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Psolar is the incident solar radiation power, rinert is the flow rate of the inert gas during oxygen evolution, and Einert is the energy required to separate the inert sweep gas from air (usually N2; Ar was used in this work entirely for reasons of experimental convenience). Based on the experimental data, the peak instantaneous efficiencies for CO2 and H2O dissociations reached 0.8% and 0.7%, respectively [for detailed calculation procedures, see (25)]. No heat recuperation strategy was used. Upon averaging the efficiencies over the time required to produce 80% of the fuel, the efficiencies become 0.4% (for both CO2 and H2O dissociations). These experimentally measured efficiencies reflect the cycle irreversibilities resulting from intrinsic material properties as well as solar reactor design and operation. An energy-balance analysis (25) reveals that 50% of the energy loss resulted from heat conduction through the reactor wall and 41% resulted from reradiation through the aperture. The former energy penalty can be dramatically reduced by improving thermal insulation and by scaling up to increase the volume-toarea ratio. The latter can be minimized by augmenting the solar flux such that the aperture size can be reduced. Decreasing heat loss also has the added benefit of increasing the temperature ramp rate. As shown in the earlier comparison of the oxygen evolution kinetics under slow and rapid heating rates, the oxygen evolution kinetics and conversion efficiency are closely coupled to the rates at which the active ceria materials can be heated and cooled. Beyond efficiency, material stability is an essential criterion for a viable thermochemical process. With use of the differential reactor system, which enables rapid access to multiple cycles, 500 cycles of water dissociation were performed without interruption. The results (Fig. 4), reported in part in an earlier work (21), indicate that, after an initial stabilization period of ~100 cycles, both the oxygen and hydrogen evolution rates remain essentially constant for a subsequent 400 cycles. Scanning electron microscopy examination of samples of porous ceria that underwent heat treatment under similar conditions (fig. S3) revealed
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that the decrease in reaction rate is accompanied by an increase in particle size. The morphology stabilized after 24 hours of heat treatment at 1500°C, much as the fuel production rate stabilized after an initial period. That ceria can be cycled between two oxidation states without substantial loss of activity can be attributed to its sufficiently large change in oxygen nonstoichiometry at moderate homologous temperatures (<0:6 Tm ). In sum, the feasibility of a solar-driven thermochemical cycle for dissociating H2O and CO2 using nonstoichiometric ceria has been demonstrated in terms of materials, reaction rates, cyclability, reactor technology, and energy conversion efficiency. Essential to this demonstration is a simple and scalable reactor design using porous ceria directly exposed to concentrated solar radiation that enables high-temperature heat transfer to the reaction sites, as required for performing both steps of the cycle. The solar-to-fuel energy conversion efficiency obtained in this work for CO2 dissociation is about two orders of magnitude greater than that observed with state-of-the-art photocatalytic approaches (3, 9).The gravimetric hydrogen production rate exceeds that of other solar-driven thermochemical processes by more than an order of magnitude (27, 28). Both the efficiency and the cycling rates in the reactor were limited largely by thermal losses, resulting from conductive and radiative heat transfer. A thermodynamic analysis of efficiency based solely on the material properties of CeO2 indicates that values in the range of 16 to 19% are attainable, even in the absence of sensible heat recovery (21). Thus, with reactor optimization and system integration substantial increases in both efficiency and fuel production rates are anticipated. The material stability, showing stable fuel production over 500 thermochemical cycles, is already suitable for realistic applications. Furthermore, the abundance of cerium, which is comparable to that of copper (29), is such that the approach is applicable at scales relevant to global energy consumption (21). References and Notes 1. N. S. Lewis, D. G. Nocera, Proc. Natl. Acad. Sci. U.S.A. 103, 15729 (2006). 2. H. Takeda, K. Koike, H. Inoue, O. Ishitani, J. Am. Chem. Soc. 130, 2023 (2008). 3. S. C. Roy, O. K. Varghese, M. Paulose, C. A. Grimes, ACS Nano 4, 1259 (2010). 4. Y. Hori, K. Kikuchi, S. Suzuki, Chem. Lett. 14, 1695 (1985). 5. V. P. Indrakanti, J. D. Kubicki, H. H. Schobert, Energy Environ. Sci. 2, 745 (2009). 6. T. Inoue, A. Fujishima, S. Konishi, K. Honda, Nature 277, 637 (1979). 7. O. Khaselev, J. A. Turner, Science 280, 425 (1998). 8. K. R. Thampi, J. Kiwi, M. Gratzel, Nature 327, 506 (1987). 9. O. K. Varghese, M. Paulose, T. J. Latempa, C. A. Grimes, Nano Lett. 9, 731 (2009). 10. E. A. Fletcher, J. Sol. Energy Trans. Am. Soc. Mech. Eng. 123, 63 (2001). 11. A. Steinfeld, Sol. Energy 78, 603 (2005). 12. L. O. Schunk, A. Steinfeld, AIChE J. 55, 1497 (2009). 13. L. O. Schunk, W. Lipinski, A. Steinfeld, Chem. Eng. J. 150, 502 (2009). 14. T. Kodama, N. Gokon, Chem. Rev. 107, 4048 (2007). 15. M. Chambon, S. Abanades, G. Flamant, Chem. Eng. Sci. 65, 3671 (2010).
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Fig. 4. O2 (black) and H2 (red) evolution rates for 500 water-splitting cycles. CeO2 was cycled between 1500°C (pO2 =10−5 atm, flow rate = 3.2 liter min−1 g−1 of ceria, 10 min, ramp rate = 100°C min−1) and 800°C (pH2O = 0.13 to 0.15 atm, flow rate = 0.75 to 0.76 liter min−1 g−1 of ceria, 10 min). The gas evolution rate is calculated by averaging the instantaneous rate over the time required to reach 90% of the gas produced.
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Spin Hall Effect Transistor Jörg Wunderlich,1,2*† Byong-Guk Park,1* Andrew C. Irvine,3* Liviu P. Zârbo,2 Eva Rozkotová,4 Petr Nemec,4 Vít Novák,2 Jairo Sinova,5,2 Tomás Jungwirth2,6 The field of semiconductor spintronics explores spin-related quantum relativistic phenomena in solid-state systems. Spin transistors and spin Hall effects have been two separate leading directions of research in this field. We have combined the two directions by realizing an all-semiconductor spin Hall effect transistor. The device uses diffusive transport and operates without electrical current in the active part of the transistor. We demonstrate a spin AND logic function in a semiconductor channel with two gates. Our study shows the utility of the spin Hall effect in a microelectronic device geometry, realizes the spin transistor with electrical detection directly along the gated semiconductor channel, and provides an experimental tool for exploring spin Hall and spin precession phenomena in an electrically tunable semiconductor layer.
Fig. 1. (A) Schematics of the measurement setup with optically injected spin-polarized electrical current propagating through the Hall bar and corresponding experimental Hall effect signals at crosses H1 and H2. The Hall resistances, RH = VH/IPH, for the two opposite helicities of the incident light are plotted as a function of the focused (∼1 mm) light spot position, i.e., of the position of the injection point. Increasing x corresponds to shifting the spot further away from the Hall detectors. (The focused laser beam is indicated by the yellow cylinder in the schematics.) The optical current IPH is independent of the helicity of the incident light and varies only weakly with the light spot position. The applied bias voltage VB = −15 V, the laser intensity is 1000 W/cm2, and the laser wavelength is 870 nm. (B) Same as (A) for measurement geometry in which electrical current is closed before the first detecting Hall cross H1. (C) Schematics of the diffusive transport of injected spin-polarized electrons and Monte-Carlo simulations of the out-of-plane component of the spin of injected electrons averaged over the 1-mm bar cross section assuming Rashba field a = 5.5 meV Å, Dresselhaus field b = −24 meV Å, and different values of the mean free path l.
www.sciencemag.org/cgi/content/full/330/6012/1797/DC1 Materials and Methods Figs. S1 to S3 15 September 2010; accepted 23 November 2010 10.1126/science.1197834
triguing quantum-relativistic physics (16–19) has been established before the first experimental observations (20, 21), but the field is still striving to turn the phenomenon into a concrete device functionality. We demonstrate the applicability of the spin Hall effect in a new type of spin transistor. The active semiconductor channel in our devices is a two-dimensional electron gas (2DEG) in which the spin-orbit coupling induced spin precession is controlled by external gate electrodes and detection is provided by transverse spin Hall 1
Hitachi Cambridge Laboratory, Cambridge CB3 0HE, UK. Institute of Physics ASCR, v.v.i., Cukrovarnická 10, 162 53 Praha 6, Czech Republic. 3Microelectronics Research Centre, Cavendish Laboratory, University of Cambridge, CB3 0HE, UK. 4 Faculty of Mathematics and Physics, Charles University in Prague, Ke Karlovu 3, 121 16 Prague 2, Czech Republic. 5Department of Physics, Texas A&M University, College Station, TX 77843–4242, USA. 6School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK. 2
*These authors contributed equally to this work. †To whom correspondence should be addressed. E-mail:
[email protected]
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lished from the outset by Datta and Das (3). The ensuing research has focused on the fundamental physical problems related to the resistance mismatch between the transistor’s components and to the spin manipulation in the semiconductor via spin-orbit coupling effects (4–15). By contrast, in the spin Hall effect case, much of the related in-
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wo major themes in semiconductor spintronics research, the spin transistors and the spin Hall effects, have followed distinct and independent scientific paths (1, 2). In the transistor case, the target device concept of a ferromagnetic spin injector and detector connected by a semiconductor channel was estab-
under award no. DMR 08-43934. We thank the technical staff of the Solar Technology Laboratory of the Paul Scherrer Institute for supporting the experimental activities at the High-Flux Solar Simulator. W.C.C. designed the experiments, and C.F. designed the solar reactor. Samples were prepared by M.A. and D.S. W.C.C, C.F., P.F., and D.S. executed the experiments. S.M.H. and A.S. supervised the project.
Downloaded from www.sciencemag.org on December 23, 2010
28. Fuel production rate is compared with other thermochemical processes by normalizing the average fuel production rate (as defined in the text) by the mass of the material, including inactive support used to prevent material sintering. The rate for oxygen evolution is not included. 29. G. B. Haxel, J. B. Hedrick, G. J. Orris, “Rare earth elements—Critical resources for high technology” (U.S. Geological Survey Fact Sheet 087-02, Reston, VA, 2002). 30. This work was funded in part by NSF (CBET-0829114), the Initiative for Renewable Energy and the Environment (under subcontract from the University of Minnesota), and the Swiss National Science Foundation (contract no. 200021-126512). Additional travel support was provided by the International Materials Institutes program of NSF
IPH [ nA ]
16. D. Gstoehl, A. Brambilla, L. O. Schunk, A. Steinfeld, J. Mater. Sci. 43, 4729 (2008). 17. J. E. Miller et al., J. Mater. Sci. 43, 4714 (2008). 18. C. Perkins, A. W. Weimer, AIChE J. 55, 286 (2009). 19. P. Singh, M. S. Hegde, Chem. Mater. 22, 762 (2010). 20. W. C. Chueh, S. M. Haile, ChemSusChem 2, 735 (2009). 21. W. C. Chueh, S. M. Haile, Philos. Trans. R. Soc. London Ser. A 368, 3269 (2010). 22. S. M. Haile, W. C. Chueh, U.S. Patent application 20,090,107,044 (2009). 23. S. Abanades et al., J. Mater. Sci. 45, 4163 (2010). 24. H. Kaneko et al., Energy 32, 656 (2007). 25. Materials and methods are detailed in supporting material at Science Online. 26. C. Li, L. Minh, T. C. Brown, J. Catal. 178, 275 (1998). 27. M. Roeb et al., Int. J. Hydrogen Energy 34, 4537 (2009).
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Spin Hall Effect Transistor Jörg Wunderlich,1,2*† Byong-Guk Park,1* Andrew C. Irvine,3* Liviu P. Zârbo,2 Eva Rozkotová,4 Petr Nemec,4 Vít Novák,2 Jairo Sinova,5,2 Tomás Jungwirth2,6 The field of semiconductor spintronics explores spin-related quantum relativistic phenomena in solid-state systems. Spin transistors and spin Hall effects have been two separate leading directions of research in this field. We have combined the two directions by realizing an all-semiconductor spin Hall effect transistor. The device uses diffusive transport and operates without electrical current in the active part of the transistor. We demonstrate a spin AND logic function in a semiconductor channel with two gates. Our study shows the utility of the spin Hall effect in a microelectronic device geometry, realizes the spin transistor with electrical detection directly along the gated semiconductor channel, and provides an experimental tool for exploring spin Hall and spin precession phenomena in an electrically tunable semiconductor layer.
Fig. 1. (A) Schematics of the measurement setup with optically injected spin-polarized electrical current propagating through the Hall bar and corresponding experimental Hall effect signals at crosses H1 and H2. The Hall resistances, RH = VH/IPH, for the two opposite helicities of the incident light are plotted as a function of the focused (∼1 mm) light spot position, i.e., of the position of the injection point. Increasing x corresponds to shifting the spot further away from the Hall detectors. (The focused laser beam is indicated by the yellow cylinder in the schematics.) The optical current IPH is independent of the helicity of the incident light and varies only weakly with the light spot position. The applied bias voltage VB = −15 V, the laser intensity is 1000 W/cm2, and the laser wavelength is 870 nm. (B) Same as (A) for measurement geometry in which electrical current is closed before the first detecting Hall cross H1. (C) Schematics of the diffusive transport of injected spin-polarized electrons and Monte-Carlo simulations of the out-of-plane component of the spin of injected electrons averaged over the 1-mm bar cross section assuming Rashba field a = 5.5 meV Å, Dresselhaus field b = −24 meV Å, and different values of the mean free path l.
www.sciencemag.org/cgi/content/full/330/6012/1797/DC1 Materials and Methods Figs. S1 to S3 15 September 2010; accepted 23 November 2010 10.1126/science.1197834
triguing quantum-relativistic physics (16–19) has been established before the first experimental observations (20, 21), but the field is still striving to turn the phenomenon into a concrete device functionality. We demonstrate the applicability of the spin Hall effect in a new type of spin transistor. The active semiconductor channel in our devices is a two-dimensional electron gas (2DEG) in which the spin-orbit coupling induced spin precession is controlled by external gate electrodes and detection is provided by transverse spin Hall 1
Hitachi Cambridge Laboratory, Cambridge CB3 0HE, UK. Institute of Physics ASCR, v.v.i., Cukrovarnická 10, 162 53 Praha 6, Czech Republic. 3Microelectronics Research Centre, Cavendish Laboratory, University of Cambridge, CB3 0HE, UK. 4 Faculty of Mathematics and Physics, Charles University in Prague, Ke Karlovu 3, 121 16 Prague 2, Czech Republic. 5Department of Physics, Texas A&M University, College Station, TX 77843–4242, USA. 6School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK. 2
*These authors contributed equally to this work. †To whom correspondence should be addressed. E-mail:
[email protected]
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lished from the outset by Datta and Das (3). The ensuing research has focused on the fundamental physical problems related to the resistance mismatch between the transistor’s components and to the spin manipulation in the semiconductor via spin-orbit coupling effects (4–15). By contrast, in the spin Hall effect case, much of the related in-
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wo major themes in semiconductor spintronics research, the spin transistors and the spin Hall effects, have followed distinct and independent scientific paths (1, 2). In the transistor case, the target device concept of a ferromagnetic spin injector and detector connected by a semiconductor channel was estab-
under award no. DMR 08-43934. We thank the technical staff of the Solar Technology Laboratory of the Paul Scherrer Institute for supporting the experimental activities at the High-Flux Solar Simulator. W.C.C. designed the experiments, and C.F. designed the solar reactor. Samples were prepared by M.A. and D.S. W.C.C, C.F., P.F., and D.S. executed the experiments. S.M.H. and A.S. supervised the project.
Downloaded from www.sciencemag.org on December 23, 2010
28. Fuel production rate is compared with other thermochemical processes by normalizing the average fuel production rate (as defined in the text) by the mass of the material, including inactive support used to prevent material sintering. The rate for oxygen evolution is not included. 29. G. B. Haxel, J. B. Hedrick, G. J. Orris, “Rare earth elements—Critical resources for high technology” (U.S. Geological Survey Fact Sheet 087-02, Reston, VA, 2002). 30. This work was funded in part by NSF (CBET-0829114), the Initiative for Renewable Energy and the Environment (under subcontract from the University of Minnesota), and the Swiss National Science Foundation (contract no. 200021-126512). Additional travel support was provided by the International Materials Institutes program of NSF
IPH [ nA ]
16. D. Gstoehl, A. Brambilla, L. O. Schunk, A. Steinfeld, J. Mater. Sci. 43, 4729 (2008). 17. J. E. Miller et al., J. Mater. Sci. 43, 4714 (2008). 18. C. Perkins, A. W. Weimer, AIChE J. 55, 286 (2009). 19. P. Singh, M. S. Hegde, Chem. Mater. 22, 762 (2010). 20. W. C. Chueh, S. M. Haile, ChemSusChem 2, 735 (2009). 21. W. C. Chueh, S. M. Haile, Philos. Trans. R. Soc. London Ser. A 368, 3269 (2010). 22. S. M. Haile, W. C. Chueh, U.S. Patent application 20,090,107,044 (2009). 23. S. Abanades et al., J. Mater. Sci. 45, 4163 (2010). 24. H. Kaneko et al., Energy 32, 656 (2007). 25. Materials and methods are detailed in supporting material at Science Online. 26. C. Li, L. Minh, T. C. Brown, J. Catal. 178, 275 (1998). 27. M. Roeb et al., Int. J. Hydrogen Energy 34, 4537 (2009).
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nel, resulting in a spatially varying magnitude and sign of the Hall signals on several successive Hall crosses. Because of the limited number of discrete detection points, these experiments did not provide a detailed picture of the spin precession of injected electrons. To better visualize the effect, we use here the optical activity of the device presented in Fig. 1, which extends over a several-micrometer range from the lateral p-n junction into the unetched p-type side of the epilayer. By shifting the focused laser spot, we can smoothly change the position of the spin injection point with respect to the detection Hall crosses. This results in damped oscillatory Hall resistance, RH = VH/IPH, measured at each of the two successive Hall crosses labeled as H1 and H2 in Fig. 1, placed 6 and 8 mm from the lateral p-n junction. (VH is the Hall voltage and IPH is the photocurrent.) The oscillations at each Hall cross and the phase shift between signals at the two Hall crosses are consistent with a micrometerscale spin precession period and with a spindiffusion length that extends over more than one precession period. Experiments in Fig. 1 are performed in two distinct electrical measurement configurations. In Fig. 1A, we show data obtained with the source and drain electrodes at the far ends of the p- and n-type sides of the lateral junction, respectively. In this geometry, spin-polarized electrical currents reach the detection Hall crosses, similar to experiments performed in (22). In Fig. 1B, the electrical current is drained 4 mm before the first
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detection Hall cross H1. In this case, only pure spin current (25–27) reaches crosses H1 and H2. The experiments in Fig. 1 demonstrate that in our 2DEG microchannel, we can realize the Hall effect detection of injected spin-polarized electrical currents, as well as pure spin currents. [For additional measurements of ungated devices, see (23).] The conventional field-effect transistor functionality in our 2DEG channel achieved by the p-layer top gate is demonstrated in Fig. 2A, where we show the gate voltage dependence of the channel current and mobility underneath the gate. At zero gate voltage, we detect only a small residual channel current consistent with the partial depletion of the 2DEG in the unetched part of the heterostructure. By applying forward or reverse voltages of an amplitude less than 1 V, we can open or close the 2DEG channel, respectively, at negligible gate-channel leakage current. Within the range of measurable signals, we detect gate voltage induced changes of the channel current by five orders of magnitude while the mobility changes by two orders of magnitude. The main effect of the gate voltage on the channel current is therefore via direct charge depletion or accumulation of the 2DEG, but mobility changes are also important. With increasing reverse gate voltage, the mobility decreases because the 2DEG is shifted closer to the ionized donors on the other side of the AlGaAs/GaAs heterojunction and because screening of the donor impurity potential by the 2DEG decreases with depletion.
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effect voltages (22) measured along the 2DEG Hall bar. For spin injection, we use an optical method described in (22) that permits all three components of the spin transistor to be realized within an all-semiconductor structure. The optical injection method is less scalable than electrical injection from ferromagnetic contacts, yet it does not require any magnetic elements or external magnetic fields for the operation of the device. Because of the nondestructive nature of the spin Hall effect detection, one semiconductor channel can accommodate multiple gates and Hall cross detectors and is therefore directly suitable for realizing spin logic operations. Semiconductor heterostructures used in our experiments, described in detail in (23), comprise a modulation p-doped AlGaAs/GaAs heterojunction on top of the structure, 90 nm of intrinsic GaAs, and an n-doped AlGaAs/GaAs heterojunction underneath. In the unetched part of the wafer, the top heterojunction is populated by holes, whereas the 2DEG at the bottom heterojunction is partly depleted. The n-side of the coplanar p-n junction is formed by removing the p-doped surface layer from a part of the wafer, thereby populating the 2DEG. At zero or reverse bias, the device is not conductive in the dark due to charge depletion at the lateral p-n junction. Counterpropagating electron and hole currents can be generated by illumination at subgap wavelengths (22). Because of the optical selection rules, the out-ofplane spin polarization of injected electrons is determined by the sense and degree of the circular polarization of vertically incident light. The n-region is patterned by electron-beam lithography into a 1-mm-wide Hall bar along the ½110 crystallographic axis. The effective width of individual Hall contacts for local spin detection is 50 to 100 nm, and separation between neighboring Hall crosses is 2 mm. Electrical gates controlling the spin currents are placed between one or more pairs of the Hall crosses. The gates are realized by the p-type surface layer areas of the heterostructure, which were locally masked and remained unetched during the fabrication of the n-channel Hall bar (24). The laser beam is focused to a ∼1- to 2-mm spot at the lateral p-n junction or near the junction on the p-side of the epilayer. For further details on the fabrication of the series of devices used in our study, employed experimental techniques, and the theory of the measured spin-dependent Hall signals, see (23). All experimental data presented below were measured at 4 K. As illustrated in (22, 23), our ungated and gated devices operate also at high temperatures. In Fig. 1, we show experimental results on a control device in which we did not pattern the gate electrodes. These measurements extend previous demonstration of the spin injection Hall effect in similar ungated structures (22). In the previous work, we observed that injected spinpolarized electrical currents produce Hall effect signals that are proportional to the out-of-plane component of the local spin polarization. We also demonstrated that spins precess along the chan-
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VG [V] Fig. 2. (A) Schematics of the measurement setup corresponding to the conventional field-effect transistor and experimental dependence of the electrical current (blue) through the channel and mobility (black) underneath the gate on the gate voltage. (B) Schematics of the setup of the spin Hall transistor and experimental Hall signals as a function of the gate voltage at a Hall cross placed behind the gate electrode for two light spot positions with a relative shift of 1 mm and the dashed black curve corresponding to the spot shifted further away from the detection Hall cross. The applied bias voltage VB = −10 V, the laser intensity is 700 W/cm2, and the laser wavelength is 870 nm. The data demonstrate the realization of the spin Hall effect transistor.
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The main result of our work (Fig. 2B) is the sensitivity of the measured Hall signal at the cross placed behind the gate on the voltage applied to the gate electrode. To exclude any potential gate voltage dependence of spin-injection conditions in our device, we performed the experiments with the electrical current drained before the gated part of the channel (Fig. 2B). The data show two regimes of operation of our spin transistor. At large reverse voltages, the Hall signals disappear as the diffusion of spin-polarized electrons from the injection region toward the detecting Hall cross is blocked by the repulsive potential of the intervening gate electrode. Upon opening the gate, the Hall signal first increases, in analogy to the operation of the conventional field-effect transistor. We emphasize, however, that while the optically generated current IPH is kept constant, the electrical current in our experiments in the manipulation and detection parts of the transistor channel remains zero at all gate voltages. The onset of the output transverse electrical signal upon opening the gate is a result of a pure spin current. The mechanism by which the spin current generates the output signal cannot be ascribed to a normal charge Hall effect because of the absence of magnetic field and charge current underneath the cross.
The initial increase of the detected output signal upon opening the gate is followed by a non-monotonic gate voltage dependence of the Hall voltage (Fig. 2B). This is in marked contrast to the monotonic increase of the normal electrical current in the channel observed in the conventional field-effect transistor measurement in Fig. 2A. Apart from blocking the spin current at large reverse gate voltages, the intermediate gate electric fields are modifying spin precession of the injected electrons and therefore the local spin polarization at the detecting Hall cross when the channel is open. This is the spin manipulation regime analogous to the original Datta-Das proposal of a spin transistor. We further demonstrated the presence of this regime in our device by comparing two measurements shown in Fig. 2B: one where the laser spot is aligned close to the lateral p-n junction on the p-side (red solid line), and the other with the spot shifted by ~1 mm in the direction away from the detecting Hall crosses (black dashed line). The reverse voltage at which the Hall signals disappear is the same in the two measurements. For gate voltages at which the channel is open, the signals are shifted with respect to each other in the two measurements and have opposite sign at certain gate voltages, and the overall magnitude of the signal is
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Fig. 3. (A) Scanning electron micrograph and schematics of the device with two detecting Hall crosses H1 and H2 and one gate placed before cross H1 and the second gate placed behind cross H1 and before cross H2. Gates and p-side of the lateral p-n junction are highlighted in red. The focused laser beam is indicated by the yellow spot. (B) Hall signals at cross H1 measured as a function of the first gate voltage. These gating characteristics are similar to those of the single-gate device in Fig. 2B and have much weaker dependence on the second gate voltage. (C) Hall signals at cross H2 measured as a function of the second gate voltage. The curves show strong dependence on the voltages on both gates. (D) Demonstration of the spin AND logic function by operating both gates (input signals) and measuring the response at Hall cross H2 (output signal). Measured data at cross H1 are also shown for completeness. The applied bias voltage VB = −10 V, the laser intensity is 400 W/cm2, and the laser wavelength is 870 nm. www.sciencemag.org
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larger for smaller separation between injection and detection points, all confirming the spin precession origin of the observed effect. [For additional measurements of gated devices see (23).] One of the important attributes of our nondestructive spin detection method integrated, together with the electrical spin manipulation, along the semiconductor channel is the possibility of fabricating devices with a series of Hall cross detectors and also with a series of gates. In Fig. 3, we demonstrate the feasibility of this concept and of the ensuing logic functionality on a spin Hall effect transistor structure with two gates, the first placed before cross H1 and the second before H2. The scanning electron micrograph of the device is shown in Fig. 3A. The measured data plotted in Fig. 3B demonstrate that Hall cross H1 responds strongly to the electric field on the first gate, with gate voltage characteristics similar to those observed in the single-gate device in Fig. 2. As expected for the relative positions of the injection point, of Hall cross H1, and of the two gates in the device, the dependence of the signal at cross H1 on the second gate is much weaker. By contrast, Hall cross H2 responds strongly to both gates (Fig. 3C). Before the spin can reach the detecting Hall cross H2, it is manipulated by two external parameters. This is analogous to the measurement in Fig. 2B in which the position of the injection point played the role of the second parameter. The analogy between results in Figs. 2B and 3C further demonstrates the spin origin of the functionality of our transistor structures. In Fig. 3D we demonstrate a simple AND logic functionality by operating both gates and by measuring the Hall electrical signal at cross H2. Intermediate gate voltages on both gates represent the input value 1 and give the largest electrical signal at H2 (positive for s− helicity of the incident light), representing the output value 1. When we apply to any of the two gates a large reverse (negative) gate voltage, representing input 0, the electrical signal at H2 disappears, i.e., the output is 0. Note that additional information is contained in the polarization dependence of the detected Hall signals, as illustrated in Fig. 3D. Proceeding to the theoretical analysis of the measured data, we first characterize the transport regime in which our devices operate. The 2DEG mobilities in the etched, n-type part of the wafer and underneath the p-layer gates are ≲3 × 103 cm2/Vs, corresponding to a mean-free path ≲102 nm. This is much smaller than the precession length and the length of our 2DEG channel, i.e., the experiments are done in the diffusive, strong-disorder weak spin-orbit coupling regime. As explained in (22), the Hall effect and the spinprecession effect can be decoupled in this regime. The Hall effect measures the local out-of-plane component of the spin polarization of carriers and originates from the spin-orbit coupling induced skew scattering. [See (23) for quantitative estimates of the Hall signals that are consistent with experiment.] In the following analysis, we
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focus on the spin-precession and spin-diffusion lengths. The possibility of observing and using spin precession of an ensemble of electrons in the diffusive regime is demonstrated by our numerical Monte Carlo simulations (22, 28) shown in Fig. 1C. The numerically obtained spin-precession period is well described by an analytical formula derived from the dynamics of the spin-density matrix (28), LSO = pħ2/m*(|a| + |b|); m* = 0.067 is the electron effective mass in GaAs. There are two regimes in which spin precession can be observed in the diffusive transport regime. In one regime, the width of the channel is not relevant and a spin-diffusion length larger than the precession length occurs as a result of the singleparticle transport analog of the spin helix state (9) realized at 2DEG Rashba and Dresselhaus spin-orbit fields of equal or similar strengths, a ≈ −b for our bar orientation. When the two spin-orbit fields are not tuned to similar strengths, the spin-diffusion length is approximately given by ∼L2SO =w and spin precession is therefore observable only when the width w of the channel is comparable to or smaller than the precession length (28–30). The complex design of our semiconductor heterostructure provides simultaneously the means for spin injection, electrical gating, and detection, so we did not rely on further fine tuning of the internal spin-orbit fields to realize the spin helix state condition. Instead, we fabricated narrow Hall bars whose width is smaller than the precession length and used a strongly focused light spot for spin injection. As shown in Fig. 1C, several precessions are readily observable in this quasi one-dimensional geometry even in the
diffusive regime and for a ≠ −b, and the spinprecession and spin-diffusion lengths in this regime are independent of the mean-free-path, i.e., of the mobility of the 2DEG channel (28). The strength of the confining electric field of the 2DEG underneath the gate changes by up to a factor of ∼2 in the range of applied gate voltages in our experiments. This result implies (22) comparably large changes in the strength of the internal spin-orbit fields in the 2DEG channel. The dependence on the spin-orbit field strength shown in the above equation and confirmed by Monte Carlo simulations (28) (and the independence on the momentum of injected electrons) implies also comparably large changes in the spin-precession length. These estimates corroborate the observed spin manipulation in our spin Hall effect transistors by external electric fields applied to the gates. References and Notes
1. I. Zˇ utić, J. Fabian, S. Das Sarma, Rev. Mod. Phys. 76, 323 (2004). 2. T. Dietl, D. D. Awschalom, M. Kaminska, H. Ohno, Eds., Spintronics, vol. 82 of Semiconductors and Semimetals (Elsevier, Amsterdam, 2008). 3. S. Datta, B. Das, Appl. Phys. Lett. 56, 665 (1990). 4. J. M. Kikkawa, D. D. Awschalom, Nature 397, 139 (1999). 5. H. J. Zhu et al., Phys. Rev. Lett. 87, 016601 (2001). 6. P. R. Hammar, M. Johnson, Phys. Rev. Lett. 88, 066806 (2002). 7. G. Schmidt, L. W. Molenkamp, Semicond. Sci. Technol. 17, 310 (2002). 8. J. Schliemann, J. C. Egues, D. Loss, Phys. Rev. Lett. 90, 146801 (2003). 9. B. A. Bernevig, J. Orenstein, S.-C. Zhang, Phys. Rev. Lett. 97, 236601 (2006). 10. X. Jiang et al., Phys. Rev. Lett. 94, 056601 (2005). 11. S. A. Crooker et al., Science 309, 2191 (2005). 12. C. P. Weber et al., Phys. Rev. Lett. 98, 076604 (2007).
Brownian Motion of Stiff Filaments in a Crowded Environment Nikta Fakhri,1 Frederick C. MacKintosh,2 Brahim Lounis,3 Laurent Cognet,3 Matteo Pasquali1* The thermal motion of stiff filaments in a crowded environment is highly constrained and anisotropic; it underlies the behavior of such disparate systems as polymer materials, nanocomposites, and the cell cytoskeleton. Despite decades of theoretical study, the fundamental dynamics of such systems remains a mystery. Using near-infrared video microscopy, we studied the thermal diffusion of individual single-walled carbon nanotubes (SWNTs) confined in porous agarose networks. We found that even a small bending flexibility of SWNTs strongly enhances their motion: The rotational diffusion constant is proportional to the filament-bending compliance and is independent of the network pore size. The interplay between crowding and thermal bending implies that the notion of a filament’s stiffness depends on its confinement. Moreover, the mobility of SWNTs and other inclusions can be controlled by tailoring their stiffness. rowding greatly constrains the transversal mobility of a filament and causes anisotropic diffusion, which is limited to the filament axial direction. In the case of polymer solutions or melts, understanding the motion of a single polymer chain confined by the meshwork of its neighbors was key to a number of advances in polymer science. In their seminal work, de
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Gennes, Doi, and Edwards (1–3) modeled the effect of crowding on polymer dynamics by introducing the concept of a confining tube, together with preferential motion along the polymer’s axis, known as reptation because of its resemblance to the slithering of a snake (Fig. 1A, inset). This model captured many bulk dynamical properties of flexible polymer melts and solutions (4), al-
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13. X. Lou et al., Nat. Phys. 3, 197 (2007). 14. B. Huang, D. J. Monsma, I. Appelbaum, Phys. Rev. Lett. 99, 177209 (2007). 15. H. C. Koo et al., Science 325, 1515 (2009). 16. M. I. Dyakonov, V. I. Perel, Phys. Lett. A 35, 459 (1971). 17. J. E. Hirsch, Phys. Rev. Lett. 83, 1834 (1999). 18. S. Murakami, N. Nagaosa, S.-C. Zhang, Science 301, 1348 (2003). 19. J. Sinova et al., Phys. Rev. Lett. 92, 126603 (2004). 20. Y. K. Kato, R. C. Myers, A. C. Gossard, D. D. Awschalom, Science 306, 1910 (2004). 21. J. Wunderlich, B. Kaestner, J. Sinova, T. Jungwirth, Phys. Rev. Lett. 94, 047204 (2005). 22. J. Wunderlich et al., Nat. Phys. 5, 675 (2009). 23. Materials and methods are available on Science online. 24. B. Kaestner, J. Wunderlich, T. J. B. M. Janssen, J. Mod. Opt. 54, 431 (2007). 25. S. O. Valenzuela, M. Tinkham, Nature 442, 176 (2006). 26. C. Brüne et al., Nat. Phys. 6, 448 (2010). 27. E. S. Garlid, Q. O. Hu, M. K. Chan, C. J. Palmstrøm, P. A. Crowell, Phys. Rev. Lett. 105, 156602 (2010). 28. L. P. Zârbo, J. Sinova, I. Knezevic, J. Wunderlich, T. Jungwirth, Phys. Rev. B 82, 205320 (2010). 29. A. A. Kiselev, K. W. Kim, Phys. Rev. B 61, 13115 (2000). 30. S. Kettemann, Phys. Rev. Lett. 98, 176808 (2007). 31. We acknowledge support from European Union grant FP7-215368 SemiSpinNet; Czech Republic grants AV0Z10100521, MSM0021620834, KAN400100652, LC510, and Preamium Academiae; and U.S. grants NSF-MRSEC DMR-0820414, ONR-N000140610122, DMR-0547875, and SWAN-NRI. J.S. is a Cottrell Scholar of Research Corporation. In connection with this work, we have two pending patent applications with the European Patent Office, patent numbers EP 2 224 500 A2 and EP 2 190 022 A.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1801/DC1 Materials and Methods SOM Text Figs. S1 to S10 Table S1 References 29 July 2010; accepted 22 November 2010 10.1126/science.1195816
though direct experimental evidence validating this powerful theoretical intuition came over two decades later, when reptation of flexible and semiflexible filaments was observed directly by imaging fluorescently labeled DNA (5) and actin (6). In contrast, little is known about the thermal motion of stiff filaments such as carbon nanotubes, biopolymers, and stiff fibers in a network. In particular, the role of the bending stiffness of such inclusions remains controversial, with longstanding conflicting theoretical predictions (7–11). Doi predicted that rotational diffusion is independent of stiffness (7), whereas Odijk concluded that such diffusion should be enhanced by flexibility (9) and Sato concluded the opposite (11). Bulk experiments by means of birefringence and dichroism (12–14) have also given conflicting 1 Department of Chemical and Biomolecular Engineering, Department of Chemistry, Smalley Institute for Nanoscale Science and Technology, Rice University, Houston, TX 77005, USA. 2 Department of Physics and Astronomy, Vrije Universiteit, 1081 HV Amsterdam, Netherlands. 3Centre de Physique Moléculaire Optique et Hertzienne, Université de Bordeaux CNRS, Talence F-33405, France.
*To whom correspondence should be addressed. E-mail:
[email protected]
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focus on the spin-precession and spin-diffusion lengths. The possibility of observing and using spin precession of an ensemble of electrons in the diffusive regime is demonstrated by our numerical Monte Carlo simulations (22, 28) shown in Fig. 1C. The numerically obtained spin-precession period is well described by an analytical formula derived from the dynamics of the spin-density matrix (28), LSO = pħ2/m*(|a| + |b|); m* = 0.067 is the electron effective mass in GaAs. There are two regimes in which spin precession can be observed in the diffusive transport regime. In one regime, the width of the channel is not relevant and a spin-diffusion length larger than the precession length occurs as a result of the singleparticle transport analog of the spin helix state (9) realized at 2DEG Rashba and Dresselhaus spin-orbit fields of equal or similar strengths, a ≈ −b for our bar orientation. When the two spin-orbit fields are not tuned to similar strengths, the spin-diffusion length is approximately given by ∼L2SO =w and spin precession is therefore observable only when the width w of the channel is comparable to or smaller than the precession length (28–30). The complex design of our semiconductor heterostructure provides simultaneously the means for spin injection, electrical gating, and detection, so we did not rely on further fine tuning of the internal spin-orbit fields to realize the spin helix state condition. Instead, we fabricated narrow Hall bars whose width is smaller than the precession length and used a strongly focused light spot for spin injection. As shown in Fig. 1C, several precessions are readily observable in this quasi one-dimensional geometry even in the
diffusive regime and for a ≠ −b, and the spinprecession and spin-diffusion lengths in this regime are independent of the mean-free-path, i.e., of the mobility of the 2DEG channel (28). The strength of the confining electric field of the 2DEG underneath the gate changes by up to a factor of ∼2 in the range of applied gate voltages in our experiments. This result implies (22) comparably large changes in the strength of the internal spin-orbit fields in the 2DEG channel. The dependence on the spin-orbit field strength shown in the above equation and confirmed by Monte Carlo simulations (28) (and the independence on the momentum of injected electrons) implies also comparably large changes in the spin-precession length. These estimates corroborate the observed spin manipulation in our spin Hall effect transistors by external electric fields applied to the gates. References and Notes
1. I. Zˇ utić, J. Fabian, S. Das Sarma, Rev. Mod. Phys. 76, 323 (2004). 2. T. Dietl, D. D. Awschalom, M. Kaminska, H. Ohno, Eds., Spintronics, vol. 82 of Semiconductors and Semimetals (Elsevier, Amsterdam, 2008). 3. S. Datta, B. Das, Appl. Phys. Lett. 56, 665 (1990). 4. J. M. Kikkawa, D. D. Awschalom, Nature 397, 139 (1999). 5. H. J. Zhu et al., Phys. Rev. Lett. 87, 016601 (2001). 6. P. R. Hammar, M. Johnson, Phys. Rev. Lett. 88, 066806 (2002). 7. G. Schmidt, L. W. Molenkamp, Semicond. Sci. Technol. 17, 310 (2002). 8. J. Schliemann, J. C. Egues, D. Loss, Phys. Rev. Lett. 90, 146801 (2003). 9. B. A. Bernevig, J. Orenstein, S.-C. Zhang, Phys. Rev. Lett. 97, 236601 (2006). 10. X. Jiang et al., Phys. Rev. Lett. 94, 056601 (2005). 11. S. A. Crooker et al., Science 309, 2191 (2005). 12. C. P. Weber et al., Phys. Rev. Lett. 98, 076604 (2007).
Brownian Motion of Stiff Filaments in a Crowded Environment Nikta Fakhri,1 Frederick C. MacKintosh,2 Brahim Lounis,3 Laurent Cognet,3 Matteo Pasquali1* The thermal motion of stiff filaments in a crowded environment is highly constrained and anisotropic; it underlies the behavior of such disparate systems as polymer materials, nanocomposites, and the cell cytoskeleton. Despite decades of theoretical study, the fundamental dynamics of such systems remains a mystery. Using near-infrared video microscopy, we studied the thermal diffusion of individual single-walled carbon nanotubes (SWNTs) confined in porous agarose networks. We found that even a small bending flexibility of SWNTs strongly enhances their motion: The rotational diffusion constant is proportional to the filament-bending compliance and is independent of the network pore size. The interplay between crowding and thermal bending implies that the notion of a filament’s stiffness depends on its confinement. Moreover, the mobility of SWNTs and other inclusions can be controlled by tailoring their stiffness. rowding greatly constrains the transversal mobility of a filament and causes anisotropic diffusion, which is limited to the filament axial direction. In the case of polymer solutions or melts, understanding the motion of a single polymer chain confined by the meshwork of its neighbors was key to a number of advances in polymer science. In their seminal work, de
C
1804
Gennes, Doi, and Edwards (1–3) modeled the effect of crowding on polymer dynamics by introducing the concept of a confining tube, together with preferential motion along the polymer’s axis, known as reptation because of its resemblance to the slithering of a snake (Fig. 1A, inset). This model captured many bulk dynamical properties of flexible polymer melts and solutions (4), al-
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13. X. Lou et al., Nat. Phys. 3, 197 (2007). 14. B. Huang, D. J. Monsma, I. Appelbaum, Phys. Rev. Lett. 99, 177209 (2007). 15. H. C. Koo et al., Science 325, 1515 (2009). 16. M. I. Dyakonov, V. I. Perel, Phys. Lett. A 35, 459 (1971). 17. J. E. Hirsch, Phys. Rev. Lett. 83, 1834 (1999). 18. S. Murakami, N. Nagaosa, S.-C. Zhang, Science 301, 1348 (2003). 19. J. Sinova et al., Phys. Rev. Lett. 92, 126603 (2004). 20. Y. K. Kato, R. C. Myers, A. C. Gossard, D. D. Awschalom, Science 306, 1910 (2004). 21. J. Wunderlich, B. Kaestner, J. Sinova, T. Jungwirth, Phys. Rev. Lett. 94, 047204 (2005). 22. J. Wunderlich et al., Nat. Phys. 5, 675 (2009). 23. Materials and methods are available on Science online. 24. B. Kaestner, J. Wunderlich, T. J. B. M. Janssen, J. Mod. Opt. 54, 431 (2007). 25. S. O. Valenzuela, M. Tinkham, Nature 442, 176 (2006). 26. C. Brüne et al., Nat. Phys. 6, 448 (2010). 27. E. S. Garlid, Q. O. Hu, M. K. Chan, C. J. Palmstrøm, P. A. Crowell, Phys. Rev. Lett. 105, 156602 (2010). 28. L. P. Zârbo, J. Sinova, I. Knezevic, J. Wunderlich, T. Jungwirth, Phys. Rev. B 82, 205320 (2010). 29. A. A. Kiselev, K. W. Kim, Phys. Rev. B 61, 13115 (2000). 30. S. Kettemann, Phys. Rev. Lett. 98, 176808 (2007). 31. We acknowledge support from European Union grant FP7-215368 SemiSpinNet; Czech Republic grants AV0Z10100521, MSM0021620834, KAN400100652, LC510, and Preamium Academiae; and U.S. grants NSF-MRSEC DMR-0820414, ONR-N000140610122, DMR-0547875, and SWAN-NRI. J.S. is a Cottrell Scholar of Research Corporation. In connection with this work, we have two pending patent applications with the European Patent Office, patent numbers EP 2 224 500 A2 and EP 2 190 022 A.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1801/DC1 Materials and Methods SOM Text Figs. S1 to S10 Table S1 References 29 July 2010; accepted 22 November 2010 10.1126/science.1195816
though direct experimental evidence validating this powerful theoretical intuition came over two decades later, when reptation of flexible and semiflexible filaments was observed directly by imaging fluorescently labeled DNA (5) and actin (6). In contrast, little is known about the thermal motion of stiff filaments such as carbon nanotubes, biopolymers, and stiff fibers in a network. In particular, the role of the bending stiffness of such inclusions remains controversial, with longstanding conflicting theoretical predictions (7–11). Doi predicted that rotational diffusion is independent of stiffness (7), whereas Odijk concluded that such diffusion should be enhanced by flexibility (9) and Sato concluded the opposite (11). Bulk experiments by means of birefringence and dichroism (12–14) have also given conflicting 1 Department of Chemical and Biomolecular Engineering, Department of Chemistry, Smalley Institute for Nanoscale Science and Technology, Rice University, Houston, TX 77005, USA. 2 Department of Physics and Astronomy, Vrije Universiteit, 1081 HV Amsterdam, Netherlands. 3Centre de Physique Moléculaire Optique et Hertzienne, Université de Bordeaux CNRS, Talence F-33405, France.
*To whom correspondence should be addressed. E-mail:
[email protected]
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results, mainly because polydispersity, aggregation, attractive forces, and strong coupling between translational and rotational diffusivities of the filaments all complicate the interpretation of the results. We directly visualized single-walled carbon nanotubes (SWNTs) reptating in a gel and established that flexibility substantially speeds up diffusion of stiff filaments under confinement, which is in accord with Odijk’s theory (9). We found that the rotational diffusion constant grows linearly with the bending flexibility and, counterintuitively, is independent of degree of crowding. A natural measure of the stiffness of a filament is its persistence length, Lp = k/kBT, which measures its thermal bending by Brownian forces.
Here, k is the bending stiffness, T is the temperature, and kB is Boltzmann’s constant. Doi (7) postulated that as long as the rods are stiff (L < Lp), flexibility does not affect diffusion, and that such a stiff filament of length L confined in a tube of diameter x << L would explore an angle q ≈ x/L in the reptation time trep = L2/D‖ needed to diffuse a length L. This yields a rotational diffusivity DDoi r = q2/trep = kBTx2/hL5, where D‖ ~ kBT/hL is the translational diffusivity of an isolated filament in a solvent of viscosity h. In contrast, Odijk (9) argued that whenever the amplitude of the thermal undulations u = (L3/Lp)1/2 exceeds the pore diameter, confinement results in the independent deflection of segments of length l = (Lpx2)1/3, of which there are
Fig. 1. (A) (x, y) center-of-mass trajectories of a SWNT reptating in 1.5 w/w % agarose gel and representative NIR images of the SWNT, illustrating the effect of flexibility on reorientation of SWNT in different pores (scale bar, 5 mm). (Inset) Schematic of a stiff filament in a fixed network: L is the length of the filament, l is the deflection length, and x is the pore size of the network. (B) Individual SWNT emission spectrum with peak at 985 nm, implying a (6, 5) structure with a diameter of 0.76 nm and persistence length of 26 mm. (C) Angular MSD showing the subdiffusive-to-diffusive behavior which occurs at disengagement time td. The line is the best fit to the data. Dr is calculated from the long time (diffusive behavior). (Inset) Representative image of a SWNT in x-y lab frame; the orientation angle q is the angle between the x axis and the major axis of the best-fitted ellipse to the shape of the SWNT. Fig. 2. Normalized rotational diffusivity of 35 SWNTs with different length and persistence length [denoted by different symbols (table S1)], reptating in different concentrations of agarose gel versus normalized length by deflection length. Doi’s theory is shown by a dashed line and predicts a power law with scaling exponent –3 across the whole range of normalized length. Odijk’s theory is denoted by a solid line and predicts a plateau at ~1 for L > l.
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L/l (15). As the filament reptates a distance l, the ends of the filament reorient by an angle dq ~ x/l. After a reptation time, this results in a mean-square angular deflection of the filament of q2 ~ x2L/l3 and an angular diffusivity of DOdijk = kBT/hL2Lp. r Doi’s theory is recovered for filaments shorter than l, a length typically much shorter than the persistence length, at which the flexibility becomes irrelevant. Otherwise, Odijk theory predicts that rotational diffusion speeds up by a factor 3 DOdijk /DDoi r r = (L/l) , for example, by three orders of magnitude for a 10–mm-long, stiff (Lp = 100 mm) filament moving through 100-nm pores. (9) SWNTs are the ideal system to study confined dynamics of stiff filaments. SWNTs are slender (typical diameters of d ≈ 0.7 to 1.2 nm), sufficiently long to be visualized through optical microscopy (L ≈ 3 to 15 mm), and share many dynamical characteristics with polymers (17, 18). SWNTs are considered stiff because their persistence length ranges from 20 to 150 mm and scales with their diameter cubed, Lp ~ d 3, similar to the bending stiffness of a macroscopic hollow pipe (19). Individual semiconducting SWNTs can be visualized directly because of their bright near-infrared (NIR) luminescence, and their diameter can be determined simultaneously spectroscopically (20). We image the quasi-two-dimensional dynamics of these individual SWNTs in agarose gel (21), a permanent network with pores x ≈ 0.1 to 1 mm [depending on agarose concentration (22, 23)], which mimics the reptation ansatz of a filament moving in a fixed network of frozen obstacles (1–3). The diameter (hence the persistence length) of each SWNT was determined from its emission spectrum (19, 20). By means of image analysis, we extracted frame-by-frame each SWNT’s center-of-mass position ri = [xi, yi] in the lab coordinates and its orientation qi relative to the x axis (i represents the frame number spaced by 30 ms acquisition time). Figure 1A depicts the centerof-mass trajectory of a 4.5-mm-long (6,5) SWNT [deduced from its emission spectrum (Fig. 1B)], with a 0.76 nm diameter and Lp = 26 mm (19) in a 1.5% w/w agarose gel (x ≈ 0.2 mm); this figure and the accompanying video (24) show unequivocally snake-like motion. NIR fluorescence snapshots demonstrate that flexibility substantially affects reorientation of the SWNT in a new confining tube. At first, the SWNT slides back and forth partially out of the confining tube. By bending slightly, the end of the SWNT has more freedom to explore various paths while translating along its length, even though most of the SWNT is still caged and thus restricted to a certain orientation. Eventually, the SWNT completely slides out of the original confining tube and reorients in another tube. We quantify rotational motion by the statistics of the angle qi. A typical time-evolution of the meansquare angular displacement (MSAD), 〈Dq2 〉, is shown in Fig. 1C. At short times, the SWNT’s angular diffusion is subdiffusive (〈Dq2 〉º tn , n << 1), reflecting the confinement in the initial tube. At longer times, the SWNT diffuses out of the initial tube, and the mean angular displacement
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Fig. 3. (A) The disengagement time normalized by deflection length l2 scales linearly with length L. (B) Time domains in translational MSDs parallel (Ds2) and perpendicular (Dn2) to the time-averaged reptation path (corresponding to the SWNT in Fig. 1A). t < td is the dynamics inside the tube. td < t < tr is the crossover region between anisotropic and isotropic diffusion. t > tr is the isotropic diffusive long-time dynamics. behaves diffusively 〈Dq2 〉 ¼ 2Dr t (25), yielding the value of the rotational diffusivity Dr. We measured the rotational diffusivity of 35 SWNTs with different lengths (2 to 10 mm) and persistence lengths (26 to 60 mm), reptating in agarose gels of several concentrations (hence pore sizes). We collapsed the rotational diffusivity on a master curve (Fig. 2) by plotting the normalized rotational diffusivities Dr / DOdijk = r DrhL2Lp/kBT versus normalized length L/l. In such a plot, Doi’s theory predicts a power law with scaling exponent –3 [(L/l)–3] across the whole range of normalized length (Fig. 2, dashed line), whereas Odijk’s theory predicts a plateau at ~1 for L > l (Fig. 2, solid line). The data show that when L ≥ l, flexibility does not affect mobility (which is in agreement with both Doi and Odijk), whereas for L > l flexibility clearly speeds up long-time diffusion, which follows Odijk’s scaling. Therefore, the effective rigidity of a filament depends on its degree of confinement. Whereas in the absence of confinement Brownian filaments can be considered essentially as rigid when L < Lp, confined filaments (x < L) behave as rigid when L > l. We next turned to the short-time subdiffusive dynamics of the MSAD (Fig. 1C). To cross over from short time subdiffusive behavior to longtime diffusive motion, a filament must diffuse by a length l out of its initial confining tube. This occurs on a time scale known as the disengagement time td, which is the time scale a SWNT needs to reptate by a deflection length and is determined from the free parallel diffusion constant of the center of the mass, td = l2/D‖ ~ hl2L/kBT (9). At times shorter than td, the SWNT wiggles “freely” inside its initial confining tube, with minimal angular reorientation (q < x/L, hence the subdiffusive behavior of MSAD in Fig. 1C). At times longer than td, the SWNT slides out of the initial confining tube and starts exploring the other accessible tubes. Show in Fig. 3 are the disengagement times normalized to l2 obtained for 11 (6,5) SWNTs by fitting the MSAD with 〈Dq2 〉 ¼ q20 þ 2Dr t and settingtd ¼ q20 =2Dr . We
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found that the measured td normalized by deflection length l2 scales linearly with length L, confirming Odijk’s prediction (9) for short-time translational diffusion (26) and showing that flexibility speeds up disengagement. Because SWNTs explore orientation space by reptating in and out of pores, rotational and translational diffusion should be strongly coupled at time scales below the rotational diffusion time tr = 1/2Dr . Such coupling occurs even in the much simpler case of two-dimensional Brownian motion of an unconstrained ellipsoid and is well described in terms of Perrin-Smoluchowski theory (27). Theoretical calculations and simulations have recently shown that this same theory can capture such coupling in the motion of confined rigid rods (infinite Lp) (28). To investigate this coupling experimentally, we measured the time evolution of the center-of-mass mean square displacements (MSDs) parallel (Ds2) and perpendicular (Dn2) to the orientation of the reptation tube, averaged over the same time window (23). The parallel and perpendicular MSDs versus time (normalized by the rotational diffusion time) are shown in Fig. 3B. At short times (t < td), SWNT diffusion is anisotropic—Ds2 >> Dn2; SWNTs diffuse much faster parallel than perpendicular to the tube axis. In this time regime, the dynamics of center of mass is dominated by the relaxation of thermally excited elastic bending modes of the SWNT, with relaxation times t nr e hln4 =k, where ln is the mode wavelength (29). For a given time t, long-wave modes ðtnr > tÞ are effectively “frozen,” whereas short-wave modes ðtnr < tÞ evolve and contribute to the amplitude of thermal undulations. At time t, the longest (dominant) bending mode has a wavelength of l(t) ~ (kt/h)1/4. The mean square amplitude of the transverse fluctuations (Du2) of this mode dominate the transverse diffusion of the center of mass and evolves with time (29–31) as Du2 ≈ Dn2 ~ l(t)3/Lp ~ t3/4, which is indeed the subdiffusive power law t3/4 we measured (Fig. 3B). The same time dependence, t3/4, is also expected for the mean square amplitude of the
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longitudinal fluctuations of the SWNT (29, 31); these dominate the mean square longitudinal displacement of the center of mass Ds2, as shown in Fig. 3B. At longer times, Ds2 crosses over to a linear diffusion regime, indicating that the SWNT has fully reptated along its length (Ds2 ~ t). In this crossover regime, the transverse MSD Dn2 grows super-linearly with time because reptation occurs along a curved path—a motion that couples rotation and translation (Dn2 ~ D‖tDq2 ~ D‖Drt2) (10, 28). Thus on intermediate time scales between disengagement and rotational diffusion times (td and tr), translational diffusion perpendicular to the filament is also enhanced by flexibility. At times longer than rotational diffusion time tr , the SWNT loses memory of its initial orientation, and its diffusion becomes isotropic. On these time scales, translational diffusion is weakly reduced by flexibility (32). By varying SWNT surface modifications (33), we can selectively tune the sensitivity of the carbon nanotubes to the different physical properties of the porous media for transport and sensing applications (such as a cellular crowded environment). The orientation dynamic behavior of SWNTs in a fixed network is a starting point to study the dynamics of concentrated solutions of SWNTs as well as SWNT composite materials. Our results indicate that the SWNT shapes are altered by the presence of the pores and that bent shapes can be very long lived. Rotational diffusion and coupling between translational and rotational motion of SWNTs can provide a useful counterpart to translational diffusion approaches in microrheology techniques and render the ability to probe different viscoelastic modes or local heterogeneity in complex fluids and biological media. References and Notes 1. P. G. de Gennes, J. Chem. Phys. 55, 572 (1971). 2. M. Doi, S. F. Edwards, J. Chem. Soc., Faraday Trans. II 74, 1789 (1978). 3. S. F. Edwards, Proc. Phys. Soc. Lond. 92, 9 (1967). 4. M. Doi, S. F. Edwards, The Theory of Polymer Dynamics (Oxford Univ. Press, Oxford, 1986). 5. T. T. Perkins, D. E. Smith, S. Chu, Science 264, 819 (1994). 6. J. Käs, H. Strey, E. Sackmann, Nature 368, 226 (1994). 7. M. Doi, J. Phys. 36, 607 (1975). 8. F. Hofling, T. Munk, E. Frey, T. Franosch, Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 77, 060904R (2008). 9. T. Odijk, Macromolecules 16, 1340 (1983). 10. S. Ramanathan, D. C. Morse, Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76, 010501 (2007). 11. T. Sato, Y. Takada, A. Teramoto, Macromolecules 24, 6220 (1991). 12. M. Tracy, R. Pecora, Annu. Rev. Phys. Chem. 43, 525 (1992). 13. S. S. Wijmenga, A. Maxwell, Biopolymers 25, 2173 (1986). 14. K. M. Zero, R. Pecora, Macromolecules 15, 87 (1982). 15. This characteristic length scale has been estimated from thermal fluctuations of flexible filaments in an ordered polymer background (16). 16. Z. Dogic et al., Phys. Rev. Lett. 92, 125503 (2004). 17. M. J. Green, N. Behabtu, M. Pasquali, W. W. Adams, Polymer (Guildf.) 50, 4979 (2009). 18. R. Duggal, M. Pasquali, Phys. Rev. Lett. 96, 246104 (2006). 19. N. Fakhri, D. A. Tsyboulski, L. Cognet, R. B. Weisman, M. Pasquali, Proc. Natl. Acad. Sci. U.S.A. 106, 14219 (2009). 20. D. A. Tsyboulski, S. M. Bachilo, R. B. Weisman, Nano Lett. 5, 975 (2005). 21. L. Cognet et al., Science 316, 1465 (2007).
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27. 28. 29.
30. 31. 32. 33. 34.
does not substantially affect the measurement of the subdiffisive regime due to the short-time dynamics in the system. Y. Han et al., Science 314, 626 (2006). T. Munk, F. Hofling, E. Frey, T. Franosch, Europhys. Lett. 85, 30003 (2009). F. Gittes, F. C. MacKintosh, Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 58, R1241 (1998). E. Farge, A. C. Maggs, Macromolecules 26, 5041 (1993). R. Granek, J. Phys. II 7, 1761 (1997). M. Doi, J. Polymer Sci. Polymer Symp. 73, 93 (1985). J. G. Duque et al., J. Am. Chem. Soc. 130, 2626 (2008). This work was supported by the NSF Center for Biological and Environmental Nanotechnology (EEC-0118007 and
Tunable Field Control Over the Binding Energy of Single Dopants by a Charged Vacancy in GaAs D. H. Lee and J. A. Gupta* Local manipulation of electric fields at the atomic scale may enable new methods for quantum transport and creates new opportunities for field control of ferromagnetism and spin-based quantum information processing in semiconductors. We used a scanning tunneling microscope to position charged arsenic (As) vacancies in the gallium arsenide 110 [GaAs(110)] surface with atomic precision, thereby tuning the local electrostatic field experienced by single manganese (Mn) acceptors. The effects of this field are quantified by measuring the shift of an acceptor state within the band gap of GaAs. Experiments with varying tip-induced band-bending conditions suggest a large binding energy for surface-layer Mn, which is reduced by direct Coulomb repulsion when the As vacancy is moved nearby.
T
Department of Physics, Ohio State University, Columbus, OH 43210, USA. *To whom correspondence should be addressed. E-mail:
[email protected]
Mn acceptors in GaAs. Experiments were performed with a custom-built ultra-high vacuum (UHV) STM operated at 7.3 K (19). The semiconductor sample is a commercial p-GaAs wafer doped with 2 × 1018 cm−3 Zn atoms for nonzero conductivity at low temperature. Arsenic vacancies, VAs, in the GaAs(110) surface formed during cleavage appeared as dark
B
A
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1804/DC1 Materials and Methods Fig. S1 Table S1 References Movie S1 9 September 2010; accepted 16 November 2010 10.1126/science.1197321
depressions at negative sample voltage (e.g., Fig. 1A) and could be positioned on the surface by applying a positive voltage pulse (~+1.7 V) (fig. S1). Fig. 1, A to C, shows STM images with VAs at three different positions. The average distance (~8 Å) and direction of the motion could not always be controlled and changed with different atomic-scale terminations of the STM tip. Thermaland electro-migration of VAs have been studied previously by STM (20, 21). The nanometer-scale depression around VAs reflects downward band bending associated with its +1e charge in p-GaAs (20). However, we could reversibly switch VAs to a neutral state by applying a smaller voltage pulse (~+1.3 V). The neutral state of VAs in Fig. 1D appears as a protrusion, in contrast to the apparent depression in Fig. 1, A to C. We suggest that this state results from the capture of a tunneling electron by VAs+. The neutral state is stable indefinitely at 7 K but is readily switched back to VAs+ by the STM tip under typical imaging conditions. We studied the influence of the electrostatic field provided by VAs on single Mn acceptors, which were formed using an STM-based substitution technique (15). Mn adatoms adsorbed onto the surface at 7 K were exchanged with Ga atoms
E dI/dV (nA A/V)
he scaling of electronic devices such as field-effect transistors to nanometer dimensions requires more precise control of individual dopants in semiconductor nanostructures, because statistical fluctuations in dopant distributions are beginning to affect the performance and functionality of current devices (1–4). Proposals for next-generation quantum- and spin-based electronics also rely on the tuning of the charge, spin, and interactions of dopant atoms with local electric fields [e.g., P in Si (5) or Mn in GaAs (6)]. On Si surfaces, the scanning tunneling microscope (STM) has been used to probe the influence of charged dangling bonds on molecular conductance (7). In III-V semiconductors, recent STM studies of single Si, Zn, and Mn dopants have shown that the electronic and magnetic properties of such impurities depend on proximity to the surface (8–14), other impurities (15), interactions with the STM tip (9, 14, 16, 17), and local strain fields (18). Here, we demonstrate control of single-dopant properties by using the local electrostatic field emanating from a charged vacancy. Using an STM, we can position this vacancy with atomic precision or reversibly switch it to a neutral state to tune the binding energy of holes to individual
EEC-0647452), the Welch Foundation (grant C-1668), the Advanced Energy Consortium (www.beg.utexas.edu/ aec), the Région Aquitaine, the Agence Nationale pour la Recherche (ANR PNANO), the European Research Council (grant n 232942), and the Foundation for Fundamental Research on Matter (FOM), which is part of the Netherlands Organisation for Scientific Research (NWO).
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22. T. K. Attwood, B. J. Nelmes, D. B. Sellen, Biopolymers 27, 201 (1988). 23. N. Pernodet, M. Maaloum, B. Tinland, Electrophoresis 18, 55 (1997). 24. Materials and methods are available as supporting material on Science Online. 25. Rotational diffusion is characterized in two dimensions by a single diffusion coefficient, Dr, and associated diffusion time, tr = 1/2Dr. 26. The errors introduced by the limited angular resolution in our measurements can affect the interpretation of the short time dynamics. The microscope angular resolution is ≈a/L, where a is the pixel size. Therefore, resolution limits our experiments below a resolution time of tresolution ≅ a2Lpph/2kBT. In the experimental conditions of Fig. 3A, tresolution/td = [(l/L)(a/x)2]/4 ranges from 0.01 to 0.26; therefore, tresolution << td and resolution
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Fig. 1. Shift of Mn acceptor resonance due to VAs . (A to D) STM topographic images of a Mn acceptor and As vacancy in the (110) surface layer of p-GaAs (V = –1.3 V, I = 0.5 nA). Scale bar, 1 nm. Under these imaging conditions, the bright dumbbell-like shape of the Mn acceptor reflects the influence on neighboring As atoms (15). The Mn atomic position is indicated with a circle in (A). Positively charged VAs appears as a dark depression. (A to C) Manipulation of VAs+ to three positions (1.42 nm, 2.47 nm, and 5.48 nm from Mn). (D) A voltage pulse switches the vacancy to a neutral state, which is imaged as a protrusion. (E) Corresponding differential conductance (dI/dV) spectra taken on the Mn acceptor. The in-gap resonance associated with the Mn acceptor shifts toward lower voltage as VAs+ is moved closer. The peak shifts back to its unperturbed position when VAs is switched to the neutral state.
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27. 28. 29.
30. 31. 32. 33. 34.
does not substantially affect the measurement of the subdiffisive regime due to the short-time dynamics in the system. Y. Han et al., Science 314, 626 (2006). T. Munk, F. Hofling, E. Frey, T. Franosch, Europhys. Lett. 85, 30003 (2009). F. Gittes, F. C. MacKintosh, Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 58, R1241 (1998). E. Farge, A. C. Maggs, Macromolecules 26, 5041 (1993). R. Granek, J. Phys. II 7, 1761 (1997). M. Doi, J. Polymer Sci. Polymer Symp. 73, 93 (1985). J. G. Duque et al., J. Am. Chem. Soc. 130, 2626 (2008). This work was supported by the NSF Center for Biological and Environmental Nanotechnology (EEC-0118007 and
Tunable Field Control Over the Binding Energy of Single Dopants by a Charged Vacancy in GaAs D. H. Lee and J. A. Gupta* Local manipulation of electric fields at the atomic scale may enable new methods for quantum transport and creates new opportunities for field control of ferromagnetism and spin-based quantum information processing in semiconductors. We used a scanning tunneling microscope to position charged arsenic (As) vacancies in the gallium arsenide 110 [GaAs(110)] surface with atomic precision, thereby tuning the local electrostatic field experienced by single manganese (Mn) acceptors. The effects of this field are quantified by measuring the shift of an acceptor state within the band gap of GaAs. Experiments with varying tip-induced band-bending conditions suggest a large binding energy for surface-layer Mn, which is reduced by direct Coulomb repulsion when the As vacancy is moved nearby.
T
Department of Physics, Ohio State University, Columbus, OH 43210, USA. *To whom correspondence should be addressed. E-mail:
[email protected]
Mn acceptors in GaAs. Experiments were performed with a custom-built ultra-high vacuum (UHV) STM operated at 7.3 K (19). The semiconductor sample is a commercial p-GaAs wafer doped with 2 × 1018 cm−3 Zn atoms for nonzero conductivity at low temperature. Arsenic vacancies, VAs, in the GaAs(110) surface formed during cleavage appeared as dark
B
A
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1804/DC1 Materials and Methods Fig. S1 Table S1 References Movie S1 9 September 2010; accepted 16 November 2010 10.1126/science.1197321
depressions at negative sample voltage (e.g., Fig. 1A) and could be positioned on the surface by applying a positive voltage pulse (~+1.7 V) (fig. S1). Fig. 1, A to C, shows STM images with VAs at three different positions. The average distance (~8 Å) and direction of the motion could not always be controlled and changed with different atomic-scale terminations of the STM tip. Thermaland electro-migration of VAs have been studied previously by STM (20, 21). The nanometer-scale depression around VAs reflects downward band bending associated with its +1e charge in p-GaAs (20). However, we could reversibly switch VAs to a neutral state by applying a smaller voltage pulse (~+1.3 V). The neutral state of VAs in Fig. 1D appears as a protrusion, in contrast to the apparent depression in Fig. 1, A to C. We suggest that this state results from the capture of a tunneling electron by VAs+. The neutral state is stable indefinitely at 7 K but is readily switched back to VAs+ by the STM tip under typical imaging conditions. We studied the influence of the electrostatic field provided by VAs on single Mn acceptors, which were formed using an STM-based substitution technique (15). Mn adatoms adsorbed onto the surface at 7 K were exchanged with Ga atoms
E dI/dV (nA A/V)
he scaling of electronic devices such as field-effect transistors to nanometer dimensions requires more precise control of individual dopants in semiconductor nanostructures, because statistical fluctuations in dopant distributions are beginning to affect the performance and functionality of current devices (1–4). Proposals for next-generation quantum- and spin-based electronics also rely on the tuning of the charge, spin, and interactions of dopant atoms with local electric fields [e.g., P in Si (5) or Mn in GaAs (6)]. On Si surfaces, the scanning tunneling microscope (STM) has been used to probe the influence of charged dangling bonds on molecular conductance (7). In III-V semiconductors, recent STM studies of single Si, Zn, and Mn dopants have shown that the electronic and magnetic properties of such impurities depend on proximity to the surface (8–14), other impurities (15), interactions with the STM tip (9, 14, 16, 17), and local strain fields (18). Here, we demonstrate control of single-dopant properties by using the local electrostatic field emanating from a charged vacancy. Using an STM, we can position this vacancy with atomic precision or reversibly switch it to a neutral state to tune the binding energy of holes to individual
EEC-0647452), the Welch Foundation (grant C-1668), the Advanced Energy Consortium (www.beg.utexas.edu/ aec), the Région Aquitaine, the Agence Nationale pour la Recherche (ANR PNANO), the European Research Council (grant n 232942), and the Foundation for Fundamental Research on Matter (FOM), which is part of the Netherlands Organisation for Scientific Research (NWO).
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Downloaded from www.sciencemag.org on December 23, 2010
22. T. K. Attwood, B. J. Nelmes, D. B. Sellen, Biopolymers 27, 201 (1988). 23. N. Pernodet, M. Maaloum, B. Tinland, Electrophoresis 18, 55 (1997). 24. Materials and methods are available as supporting material on Science Online. 25. Rotational diffusion is characterized in two dimensions by a single diffusion coefficient, Dr, and associated diffusion time, tr = 1/2Dr. 26. The errors introduced by the limited angular resolution in our measurements can affect the interpretation of the short time dynamics. The microscope angular resolution is ≈a/L, where a is the pixel size. Therefore, resolution limits our experiments below a resolution time of tresolution ≅ a2Lpph/2kBT. In the experimental conditions of Fig. 3A, tresolution/td = [(l/L)(a/x)2]/4 ranges from 0.01 to 0.26; therefore, tresolution << td and resolution
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Fig. 1. Shift of Mn acceptor resonance due to VAs . (A to D) STM topographic images of a Mn acceptor and As vacancy in the (110) surface layer of p-GaAs (V = –1.3 V, I = 0.5 nA). Scale bar, 1 nm. Under these imaging conditions, the bright dumbbell-like shape of the Mn acceptor reflects the influence on neighboring As atoms (15). The Mn atomic position is indicated with a circle in (A). Positively charged VAs appears as a dark depression. (A to C) Manipulation of VAs+ to three positions (1.42 nm, 2.47 nm, and 5.48 nm from Mn). (D) A voltage pulse switches the vacancy to a neutral state, which is imaged as a protrusion. (E) Corresponding differential conductance (dI/dV) spectra taken on the Mn acceptor. The in-gap resonance associated with the Mn acceptor shifts toward lower voltage as VAs+ is moved closer. The peak shifts back to its unperturbed position when VAs is switched to the neutral state.
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Fig. 2. Insensitivity of the Mn peak to varying TIBB. (A) Black dots indicate measured in-gap peak positions of surface-layer Mn with four different tip materials [*, W-tip data from (15)]. Colored points represent peak positions predicted from the rigid band-bending model, based on simulated TIBB with varying work function, tip apex radius r, and shank angle q. The shaded region indicates the broad range of predicted peak positions expected in the experiments. (B) Comparison of tunneling spectra with varying set current for surface-layer Mn and subsurface Zn. The resonance peak of Zn shifts according to the rigid bandbending model, whereas no shift is observed for Mn. The dashed green lines are guides to the eye.
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Fig. 3. Peak shift versus distance to VAs. (A) The peak positions of five different Mn acceptors are plotted as a function of distance d to VAs. The solid lines are Coulomb fits with the same coefficient but with different offsets [i.e., V(∞)]. This variation reflects the different local electrostatic environments due to –1e charged Zn neighbors (inset). (B) Data from 22 Mn acceptors collapse to a single curve after the offset corrections. The data are well fit with a Coulomb-like law, DV = A/d, where the fit parameter A = –0.22 V nm. The inset shows statistics of 106 Mn acceptors isolated from VAs. From the 1.65 s point of the normal distribution, we chose a value, V0 = 0.74 V, as the binding energy of an isolated Mn-hole complex (green arrow). error (attributed to variations in local environment, as discussed further below), the Mn peak appeared at ~0.8 V regardless of tip material. For comparison, we performed three-dimensional simulations of TIBB using Poisson’s equation (22), which then allowed us to calculate the peak positions predicted by the rigid band-bending model. In this model, the predicted peak position corresponds to the bias voltage at which the Mn bulk acceptor level [0.11 eV (27)] crosses the Fermi energy of the sample (fig. S4A). We used bulk work function values of the tip materials (28, 29) and considered both sharp and blunt tip terminations. The yellow region in Fig. 2A indicates that a broad range of peak positions is expected, in contrast to our observations. To further explore this issue, we systematically varied TIBB by varying the tunneling set current for spectroscopy. Higher set current corresponds to a smaller tip-sample distance and, thus, larger TIBB. Figure 2B compares tunneling spectra taken
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from surface-layer Mn and subsurface Zn over a range in set current. Consistent with the rigid bandbending model and previous studies (23, 25), subsurface Zn exhibits resonance peaks that shifted toward higher voltage (by ~ 0.13 V) as current was increased. Surface-layer Mn, however, showed little if any shift (0.008 T 0.008 V), again suggesting that such impurities do not respond to TIBB. A similar insensitivity to varying TIBB conditions was observed for Fe adatoms on InAs (30), presumably reflecting the stronger localization of adatom states compared with subsurface impurities that hybridize with the host lattice. Our studies suggest that surface-layer Mn exhibits intermediate characteristics between bulk acceptor and adatom. Whereas STM images reveal a holeacceptor complex whose asymmetry reflects the GaAs crystal structure (15, 31, 32), the acceptor energy levels are decoupled from the valence band edge, a characteristic of so-called “deep” impurities. Recent tight-binding model calculations support
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Tip Work function (eV)
n resonance peak (V) Mn
in the surface layer by applying a positive voltage pulse with the STM tip (~+1.3 V). Figure 1A shows a Mn acceptor formed in this way; under these conditions, the acceptor is imaged as a bright, dumbbell-like feature. Tunneling spectra taken with the tip positioned over Mn (Fig. 1E) show a resonance peak within the GaAs band gap, which is associated with the Mn acceptorhole complex (15). The peak systematically shifts toward lower voltage as VAs is moved closer and returns to its unperturbed position when VAs is switched to the neutral state (Fig. 1E, red). To understand this peak shift, we must consider two possible influences of VAs on acceptor-hole complexes: band bending and direct Coulomb repulsion. Vacancy-induced band bending can be understood in analogy to tip-induced band bending (TIBB). When brought into tunneling range less than 1 nanometer from the surface, the STM tip affects the local carrier distribution and locally bends the semiconductor bands by an amount that depends on the voltage, tip work function, and geometric factors such as tip radius and shank angle (22). TIBB has been invoked in previous STM studies to explain why the in-gap resonances of subsurface impurities such as Mn and Zn in III-V semiconductors (8, 13, 23–25) occur at voltages not expected from the bulk binding energies. The acceptor state is thought to rigidly follow the valence band, whose bending changes as the voltage is varied in tunneling spectroscopy. A resonance peak is produced when the acceptor state crosses the sample Fermi energy. The peak’s position does not necessarily locate the acceptor energy level and is sensitive to TIBB conditions. For example, tunneling spectroscopy of subsurface Mn in InAs indicates a peak at ~0.8 V, even though the acceptor level in the bulk lies only ~28 meV above the valence band (8, 16). This peak shifts by ~0.1 V with varying TIBB conditions (16). We will refer to this interpretation as a “rigid band-bending model” of the impurity resonances (fig. S2A). In our experiments, TIBB is negative (i.e., downward band bending) for all voltages less than the flat-band voltage (fig. S4A). Similar to TIBB, VAs+ causes an additional downward band bending (20). As a result, the crossing of acceptor level and sample Fermi energy should occur at a larger positive voltage. The rigid band-bending model therefore predicts that the Mn resonance should shift toward higher positive voltage as VAs is moved closer, in contrast to the shift toward lower voltage shown in Fig. 1E. This contradiction was initially surprising and led us to hypothesize that the rigid band-bending model holds for subsurface impurities (e.g., native Zn acceptors) (fig. S3) but does not hold for surface-layer Mn in GaAs. This hypothesis is consistent with our observation that the resonance peak for surface-layer Mn does not shift with varying TIBB conditions [e.g., tip material, tip termination, and tip-sample distance (26)]. Figure 2A shows the Mn peak positions as measured with four different tip materials whose bulk work function ranges from 4.2 eV (Ag) to 5.2 eV (Ir). Within experimental
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Fig. 4. Multiple vacancies and a charged adatom. (A to C) STM topographic images of a Mn acceptor with multiple As vacancies: (A) three, (B) two, and (C) one VAs. (D) Corresponding dI/dV spectra taken on the Mn acceptor show that the electrostatic potential can be enhanced with multiple VAs. (E to G) STM images and dI/dV spectra of a Mn acceptor with a Ga adatom. The peak shift of the Mn acceptor suggests that the Ga adatom is positively charged (V = –1.3 V, I = 0.5 nA). Scale bar, 1 nm. this suggestion that surface-layer Mn is a deep acceptor with a more localized wave function compared with the bulk (32). Given that the acceptor level appears to be independent of TIBB, we reconsidered the physical interpretation of the 0.8 V peak for surfacelayer Mn. In the absence of any evidence for Fermi-level pinning (15, 33), the peak’s separation from the valence band edge at ~0 V directly corresponds to the acceptor-hole binding energy. This value, ~0.8 eV, is much greater than the bulk value of 0.11 eV but is consistent with theoretical predictions for surface-layer dopant impurities such as Mn (32) and Si (34). For example, Strandberg et al. estimate that the binding energy for surface-layer Mn acceptors is an order of magnitude greater than the bulk (32). These predictions are consistent with recent STM experiments in GaAs, which show an increase in the binding energy of subsurface Mn (13) and Si (14) with proximity to the surface. Underlying mechanisms for an increased binding energy at or near the surface include surface effects such as strain fields induced by relaxation (9, 12, 14), a reduction in the effective dielectric constant at the vacuum interface (14), or danglingbond states (33, 34). Because the peak shift in Fig. 1E could not be caused by vacancy-induced band bending, its direction suggests that VAs reduces the binding energy of surface Mn by the direct Coulomb repulsion between the positively charged hole and VAs+. The change in binding energy, DE, can be expressed within an effective model Hamiltonian, which includes a Coulomb potential from VAs e2 4pe0 eeff d e2 jy 〉 DE ¼ 〈yMn j 4pe0 eeff d Mn H ¼ H0 þ
ð1Þ
where H0 is the unperturbed Hamiltonian, d is the distance between Mn and VAs, eeff is an effective
dielectric constant at the surface, and yMn is Mn wave function. A simple hydrogenic model calculation with 1s wave functions predicts a reduction of the binding energy with a Coulomblike 1/d dependence when a positive point charge is moved nearby (35). To test this prediction, Fig. 3A shows the peak positions of five different Mn acceptors as a function of distance. These data are well fit with the same Coulomb curve but are offset from each other by tens of meV. These offsets result from variations in the local electrostatic environment created by the native Zn acceptors that are negatively charged under our tunneling conditions (Fig. 3A, inset). We verified that the resonance peak shifts toward higher voltage for Mn acceptors near Zn acceptors, opposite to the shift due to VAs+. Thus, the immobile Zn acceptors contributed a finite positive offset to the peak position, whose value was different for each Mn acceptor. We define the peak shift as DV(d) = V(d ) – V0, where V0 is the binding energy of a Mn-hole complex (isolated from both Zn and VAs) and V(d) is the resonance peak position, corrected for variations in local environment. From statistics of 106 Mn acceptors, we chose a value V0 = 0.74 V, which represents the 1.65 s point of the normal distribution of resonance peak positions (Fig. 3B, inset). By measuring V(~ ∞), we then calculated the offset for each Mn. After this correction, data from 22 different Mn were fit using a single Coulomb-like curve (Fig. 3B, red line). These experiments can be extended in several ways to characterize and engineer local electrostatic fields at the nanometer scale. For example, larger effects can be achieved by introducing additional vacancies (Fig. 4, A to D). We find that the electrostatic potential experienced by a Mn acceptor is the superposition of the individual potentials from each VAs. We have also achieved similar field control using other charged species such as adatoms (Ga, Mn, or
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Ag) or native dopants (Zn). For example, Fig. 4, E to G, shows the peak shift produced by a positively charged Ga adatom, which was positioned 1.2 nm from a Mn acceptor using atom manipulation. These data suggest a new and direct method for quantifying the charge of adsorbates (e.g., adatoms or molecules) as well as defects (e.g., vacancies, antisites, and interstitials) at semiconductor surfaces. We also anticipate that manipulation of charged species can be used to tune the interactions between pairs of dopants, which may provide insight into the mechanisms for ferromagnetism in semiconductors (15, 36), and new methods for quantum information processing in solids (5, 6). Although the experiments demonstrated here required an STM tip to position the As vacancies, a similar control may also be achieved in future devices using conventional electrodes (4). References and Notes 1. P. S. Peercy, Nature 406, 1023 (2000). 2. T. Shinada, S. Okamoto, T. Kobayashi, I. Ohdomari, Nature 437, 1128 (2005). 3. G. P. Lansbergen et al., Nat. Phys. 4, 656 (2008). 4. J. J. Yang et al., Nat. Nanotechnol. 3, 429 (2008). 5. B. E. Kane, Nature 393, 133 (1998). 6. J. M. Tang, J. Levy, M. E. Flatté, Phys. Rev. Lett. 97, 106803 (2006). 7. P. G. Piva et al., Nature 435, 658 (2005). 8. F. Marczinowski et al., Phys. Rev. Lett. 99, 157202 (2007). 9. S. Loth, M. Wenderoth, R. G. Ulbrich, Phys. Rev. B 77, 115344 (2008). 10. J. K. Garleff et al., Phys. Rev. B 78, 075313 (2008). 11. J. M. Jancu et al., Phys. Rev. Lett. 101, 196801 (2008). 12. C. C. Celebi et al., Phys. Rev. Lett. 104, 086404 (2010). 13. J. K. Garleff et al., Phys. Rev. B 82, 035303 (2010). 14. A. P. Wijnheijmer et al., Phys. Rev. Lett. 102, 166101 (2009). 15. D. Kitchen, A. Richardella, J. M. Tang, M. E. Flatté, A. Yazdani, Nature 442, 436 (2006). 16. F. Marczinowski, J. Wiebe, F. Meier, K. Hashimoto, R. Wiesendanger, Phys. Rev. B 77, 115318 (2008). 17. K. Teichmann et al., Phys. Rev. Lett. 101, 076103 (2008). 18. A. M. Yakunin et al., Nat. Mater. 6, 512 (2007). 19. Materials and methods are available as supporting material on Science Online. 20. P. Ebert, Surf. Sci. Rep. 33, 121 (1999). 21. G. Lengel, J. Harper, M. Weimer, Phys. Rev. Lett. 76, 4725 (1996). 22. R. M. Feenstra, S. Gaan, G. Meyer, K. H. Rieder, Phys. Rev. B 71, 125316 (2005). 23. S. Loth, M. Wenderoth, R. G. Ulbrich, S. Malzer, G. H. Dohler, Phys. Rev. B 76, 235318 (2007). 24. A. M. Yakunin et al., Phys. Rev. Lett. 92, 216806 (2004). 25. S. Loth et al., Phys. Rev. Lett. 96, 066403 (2006). 26. D. Kitchen et al., J. Appl. Phys. 101, 09G515 (2007). 27. M. Linnarsson, E. Janzen, B. Monemar, M. Kleverman, A. Thilderkvist, Phys. Rev. B 55, 6938 (1997). 28. H. B. Michaelson, J. Appl. Phys. 48, 4729 (1977). 29. Because the work function varies with different crystal orientations, the work function of a sharp-ended tip may differ from the bulk value. 30. T. Matsui, C. Meyer, L. Sacharow, J. Wiebe, R. Wiesendanger, Phys. Rev. B 75, 165405 (2007). 31. J. M. Tang, M. E. Flatté, Phys. Rev. Lett. 92, 047201 (2004). 32. T. O. Strandberg, C. M. Canali, A. H. MacDonald, Phys. Rev. B 80, 024425 (2009). 33. A. Richardella, D. Kitchen, A. Yazdani, Phys. Rev. B 80, 045318 (2009). 34. J. Wang, T. A. Arias, J. D. Joannopoulos, G. W. Turner, O. L. Alerhand, Phys. Rev. B 47, 10326 (1993).
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REPORTS Mabel Beckman Foundation and the Center for Emergent Materials at Ohio State University, an NSF Materials Research Science and Engineering Center (DMR-0820414).
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1197434/DC1 Materials and Methods
Dynamics of Magnetic Domain Walls Under Their Own Inertia Luc Thomas,* Rai Moriya, Charles Rettner, Stuart S. P. Parkin* The motion of magnetic domain walls induced by spin-polarized current has considerable potential for use in magnetic memory and logic devices. Key to the success of these devices is the precise positioning of individual domain walls along magnetic nanowires, using current pulses. We show that domain walls move surprisingly long distances of several micrometers and relax over several tens of nanoseconds, under their own inertia, when the current stimulus is removed. We also show that the net distance traveled by the domain wall is exactly proportional to the current pulse length because of the lag derived from its acceleration at the onset of the pulse. Thus, independent of its inertia, a domain wall can be accurately positioned using properly timed current pulses. lectrical current passing through a magnetic material becomes spin-polarized along the local magnetization direction. When the current traverses a magnetic domain wall (DW), spin angular momentum is transferred from the current to the magnetization, thereby inducing a torque on the DW and leading to DW motion (1, 2). Such spin-transfer torque (STT)–driven DW motion has distinct characteristics that make it very useful for magnetic memory-storage devices (3). In particular, two or more adjacent DWs can be moved in the same direction, contrary to the case when DWs are driven by a magnetic field. Advances in our understanding of current-driven DW dynamics have resulted from various experimental (4–13) and theo-
E
IBM Almaden Research Center, 650 Harry Road, San Jose, CA, USA. *To whom correspondence should be addressed. E-mail:
[email protected] (L.T.);
[email protected] (S.S.P.P.)
retical (14–18) studies. However, many aspects of the underlying physical mechanisms remain unclear. An important question, from both fundamental and technological standpoints, is whether DWs, driven solely by current, exhibit inertial effects similar to those observed when they are driven by a magnetic field (19). Two contributions to STT have been identified: the adiabatic and nonadiabatic (field-like) contributions (14–18). The inertial response of the DW depends on the relative magnitude of these two terms. Their relative contributions can be quantified by the ratio b/a, where b and a are dimensionless constants that reflect the strengths of the nonadiabatic STT and the Gilbert damping, respectively. Although there is still considerable debate as to the precise origin and value of b/a, many experimental studies have concluded that b/a > 1 for various magnetic materials (10, 20–24). Under these circumstances, theory predicts that
Fig. 1. (A) Scanning electron micrograph image of a 12-mm-long permalloy nanowire with electrical contact lines at the left and right ends of the nanowire, shown in beige. Schematics of the electrical connections and the injection (red) and shift (blue) current pulses are shown. The blue arrow indicates the direction of the electron flow. The shapes of the ends of the nanowire, which are hidden under the contact lines, are shown schematically in the insets at top left and bottom right. The dc resistance is measured through the low-frequency port of a bias tee (bottom left contact), and the shift pulse is applied through the high-frequency port of the bias tee. (B) Probability Pout of a DW exiting a 6-mm-long nanowire after a shift pulse is applied. (C) Shift pulse length tout required to move a DW out of the nanowire with a 50% probability, as a function of the nanowire length. Error bars corresponding to 20/80% probabilities are shown. (they are about the same size as the symbols). (D) tout as a function of the inverse number of shift current pulses Np, for two nanowires with lengths of 6 and 12 mm (open and solid symbols, respectively). Error bars show 20/80% probabilities.
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SOM Text Figs. S1 to S4 References 6 September 2010; accepted 29 November 2010 Published online 9 December 2010; 10.1126/science.1197434
DWs should exhibit inertial effects when driven by current (14–18). Inertial effects have indeed been reported, but only when DWs are excited while confined at a trapping site (3, 20, 25–28). In contrast, recent reports of current-driven propagation of DWs over long distances of several micrometers indicate that the distance traveled by DWs in response to current pulses varies linearly with the length of the pulse (5, 13). This would seemingly indicate that the DWs move steadily at a fixed velocity without any inertia. Our experiments were carried out using 20nm-thick permalloy (Ni81Fe19) nanowires with widths of 200 nm and lengths between 6 and 15 mm. The devices were composed of the magnetic nanowire and two electrical contact lines, which were used to write, shift, and detect DWs (Fig. 1A) (29). A DW was first written into the nanowire by applying a burst of current pulses through the injection line (injection pulse), and the dc resistance of the nanowire was then measured. A current pulse [shift pulse (30)] was then applied through the nanowire to move the DW along the nanowire, and the resistance was measured for a second time. This procedure was repeated 100 times under identical conditions, except that the sign of the injection current was reversed in successive experiments so as to write, alternately, head-to-head (HH) and tail-to-tail (TT) DWs. The shift pulse polarity was maintained unchanged, so that electrons always flowed in the same direction along the nanowire (from right to left in Fig. 1A), and, through STT, drove the DWs, whether HH or TT, in the same direction as the electron flow (5, 13). The shift pulse voltage was chosen to give a current density in the nanowire of ~1.2 ×
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35. P. D. Robinson, Proc. Phys. Soc. 71, 828 (1958). 36. J. M. D. Coey, S. A. Chambers, MRS Bull. 33, 1053 (2008). 37. We thank D. R. Daughton and X. H. Qiu for help with the STM construction and A. J. Heinrich, S. Loth, M. E. Flatté, and A. H. MacDonald for helpful discussions. We are grateful for support from the Arnold and
REPORTS Mabel Beckman Foundation and the Center for Emergent Materials at Ohio State University, an NSF Materials Research Science and Engineering Center (DMR-0820414).
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1197434/DC1 Materials and Methods
Dynamics of Magnetic Domain Walls Under Their Own Inertia Luc Thomas,* Rai Moriya, Charles Rettner, Stuart S. P. Parkin* The motion of magnetic domain walls induced by spin-polarized current has considerable potential for use in magnetic memory and logic devices. Key to the success of these devices is the precise positioning of individual domain walls along magnetic nanowires, using current pulses. We show that domain walls move surprisingly long distances of several micrometers and relax over several tens of nanoseconds, under their own inertia, when the current stimulus is removed. We also show that the net distance traveled by the domain wall is exactly proportional to the current pulse length because of the lag derived from its acceleration at the onset of the pulse. Thus, independent of its inertia, a domain wall can be accurately positioned using properly timed current pulses. lectrical current passing through a magnetic material becomes spin-polarized along the local magnetization direction. When the current traverses a magnetic domain wall (DW), spin angular momentum is transferred from the current to the magnetization, thereby inducing a torque on the DW and leading to DW motion (1, 2). Such spin-transfer torque (STT)–driven DW motion has distinct characteristics that make it very useful for magnetic memory-storage devices (3). In particular, two or more adjacent DWs can be moved in the same direction, contrary to the case when DWs are driven by a magnetic field. Advances in our understanding of current-driven DW dynamics have resulted from various experimental (4–13) and theo-
E
IBM Almaden Research Center, 650 Harry Road, San Jose, CA, USA. *To whom correspondence should be addressed. E-mail:
[email protected] (L.T.);
[email protected] (S.S.P.P.)
retical (14–18) studies. However, many aspects of the underlying physical mechanisms remain unclear. An important question, from both fundamental and technological standpoints, is whether DWs, driven solely by current, exhibit inertial effects similar to those observed when they are driven by a magnetic field (19). Two contributions to STT have been identified: the adiabatic and nonadiabatic (field-like) contributions (14–18). The inertial response of the DW depends on the relative magnitude of these two terms. Their relative contributions can be quantified by the ratio b/a, where b and a are dimensionless constants that reflect the strengths of the nonadiabatic STT and the Gilbert damping, respectively. Although there is still considerable debate as to the precise origin and value of b/a, many experimental studies have concluded that b/a > 1 for various magnetic materials (10, 20–24). Under these circumstances, theory predicts that
Fig. 1. (A) Scanning electron micrograph image of a 12-mm-long permalloy nanowire with electrical contact lines at the left and right ends of the nanowire, shown in beige. Schematics of the electrical connections and the injection (red) and shift (blue) current pulses are shown. The blue arrow indicates the direction of the electron flow. The shapes of the ends of the nanowire, which are hidden under the contact lines, are shown schematically in the insets at top left and bottom right. The dc resistance is measured through the low-frequency port of a bias tee (bottom left contact), and the shift pulse is applied through the high-frequency port of the bias tee. (B) Probability Pout of a DW exiting a 6-mm-long nanowire after a shift pulse is applied. (C) Shift pulse length tout required to move a DW out of the nanowire with a 50% probability, as a function of the nanowire length. Error bars corresponding to 20/80% probabilities are shown. (they are about the same size as the symbols). (D) tout as a function of the inverse number of shift current pulses Np, for two nanowires with lengths of 6 and 12 mm (open and solid symbols, respectively). Error bars show 20/80% probabilities.
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SOM Text Figs. S1 to S4 References 6 September 2010; accepted 29 November 2010 Published online 9 December 2010; 10.1126/science.1197434
DWs should exhibit inertial effects when driven by current (14–18). Inertial effects have indeed been reported, but only when DWs are excited while confined at a trapping site (3, 20, 25–28). In contrast, recent reports of current-driven propagation of DWs over long distances of several micrometers indicate that the distance traveled by DWs in response to current pulses varies linearly with the length of the pulse (5, 13). This would seemingly indicate that the DWs move steadily at a fixed velocity without any inertia. Our experiments were carried out using 20nm-thick permalloy (Ni81Fe19) nanowires with widths of 200 nm and lengths between 6 and 15 mm. The devices were composed of the magnetic nanowire and two electrical contact lines, which were used to write, shift, and detect DWs (Fig. 1A) (29). A DW was first written into the nanowire by applying a burst of current pulses through the injection line (injection pulse), and the dc resistance of the nanowire was then measured. A current pulse [shift pulse (30)] was then applied through the nanowire to move the DW along the nanowire, and the resistance was measured for a second time. This procedure was repeated 100 times under identical conditions, except that the sign of the injection current was reversed in successive experiments so as to write, alternately, head-to-head (HH) and tail-to-tail (TT) DWs. The shift pulse polarity was maintained unchanged, so that electrons always flowed in the same direction along the nanowire (from right to left in Fig. 1A), and, through STT, drove the DWs, whether HH or TT, in the same direction as the electron flow (5, 13). The shift pulse voltage was chosen to give a current density in the nanowire of ~1.2 ×
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35. P. D. Robinson, Proc. Phys. Soc. 71, 828 (1958). 36. J. M. D. Coey, S. A. Chambers, MRS Bull. 33, 1053 (2008). 37. We thank D. R. Daughton and X. H. Qiu for help with the STM construction and A. J. Heinrich, S. Loth, M. E. Flatté, and A. H. MacDonald for helpful discussions. We are grateful for support from the Arnold and
108 A/cm2. The presence of one or more DWs in the nanowire between the contacts was inferred from the dc resistance of the nanowire. Owing to the anisotropic magnetoresistance of permalloy, the nanowire resistance was reduced by a fixed amount of ~180 milliohm for each additional DW located in the nanowire (31). In these experiments, we focused on DWs with a vortex structure (29). The probability that a single DW, located in the nanowire after the injection pulse, exits the nanowire after a shift pulse of length tsh exhibits a clear threshold in tsh above which almost all the DWs exit (Fig. 1B). We define tout as the value of tsh corresponding to an exit probability of 50%. tout increases linearly with the nanowire’s length, with a slope corresponding to a velocity of ~138 m/s (Fig. 1C). The small offset when tout = 0 is due to the distance traveled by the DW along the nanowire from its injection point during its creation (13). This first set of experiments indicates that the DW propagates at constant velocity along the nanowire. One possibility, however, is that the current pulses used are long compared to the time scale of any inertial effects, so in a second set of experiments we explored much shorter shift pulse lengths. We used a train of up to eight shift pulses to move the DWs, instead of one long pulse. The interval between these pulses was set to be ~6 times their length. Figure 1D shows that tout varies as the inverse number of pulses 1/Np for 6-mm-
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long (open symbols) and 12-mm-long (solid symbols) nanowires (here tout is the length of just one of the shift pulses). This means that the overall time that the current needs to be applied for the DW to traverse the nanowire remains the same, irrespective of the length of the individual pulses. This again shows that the distance traveled by the DW during a single pulse is directly proportional to the pulse length, even for current pulses as short as a few nanoseconds. Τhe value of b/a can be directly derived from the DW’s steady-state velocity, which is given by v = (b/a)u, where u has the dimension of a velocity and is given by u = (mB/eMs)PJ, where mB is the Bohr magneton, e is the electron charge, Ms is the saturation magnetization (~800 electromagnetic units/cm3 for permalloy), P is the spin polarization of the current, and J is the current density (16, 17). For a DW velocity of ~138 m/s for J ~ 1.2 × 108 A/cm2, and assuming P = 0.5, as reported recently in permalloy wires of similar thicknesses (32), we find that b/a ~ 3.2. Thus, because b/a is much larger than 1, the DWs should theoretically display inertial behavior. Because of the very small resistance of the DW, the dynamics of DWs in the nanowires were probed by their presence or absence using quasistatic resistance measurements, which take much longer than the typical time scale of the DW dynamics. Thus, to detect any possible inertial mo-
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Fig. 2. (A) Schematic of the shift pulse sequence applied to the nanowire. Two pulses of opposite 60 polarities are applied sequentially, separated by a waiting time twait during which the current is zero. Cartoons show the DW motion during this pulse sequence. Different colors are used to highlight the 40 motion of the DW during the two current pulses (blue and red) and under its own inertia (green). (B) Probability of DW exiting the nanowire as a function of the 20 0 20 40 60 80 100 shift pulse length tsh, for various waiting times twait (from left to right, twait = 25, 12, 7, and 0 ns), for a 6-mm-long nanowire. (C) The shift pulse length tout for which Pout = 50% versus twait for 6-mm-long (green diamonds), 12-mm-long (red circles), and 15-mm-long (blue squares) nanowires. Error bars show 20/80% probabilities. Solid lines show fits to an exponential decay. www.sciencemag.org
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tion of the DWs shortly after the end of the current pulse, we used a second shift current pulse. This pulse, with opposite polarity from the first, was applied after a waiting time twait, which was varied on a nanosecond time scale (Fig. 2A). If the DW had already exited the nanowire when the second pulse was applied, the DW exit probability Pout was not affected. However, if the DW was still located within the nanowire at the onset of the second pulse (Fig. 2A), because the current flow was now reversed, the DW would be pushed back into the nanowire, thereby modifying Pout. Figure 2B shows the dependence of Pout on the shift pulse length tsh for several values of twait for a 6-mm-long nanowire. As twait was decreased from 25 to 0 ns, the length of the shift pulse required for the DW to exit the nanowire significantly increased, from ~32 T 2 to 42 T 2 ns. For three nanowires with different lengths, tout decreased as twait was increased, until twait ~ 25 T 5 ns, above which tout was approximately constant (Fig. 2C). Thus, if no time was allowed for the DW to move after the end of the shift pulse, a longer shift pulse was needed to drive the DW out of the nanowire, indicating that the DW kept on moving after the end of the shift pulse while decelerating to zero velocity. The dependence of tout on twait can be fitted to an exponential form exp(–twait/t), giving a deceleration time of t ~11.5 T 2 ns, which is independent of nanowire length. During this deceleration period, we estimate that the DW moved ~1.4 T 0.6 mm. This distance is calculated from the extra time needed to move the DW along the length of the nanowire when twait = 0 (Dtout ~10 T 4 ns), during which the DW is moving at its terminal velocity (v = 138 m/s). To quantify the distance over which the DW accelerated, we created a virtual nanowire of variable length by pre-positioning the DW at a given location along the nanowire, using a current pulse of length tpos (Fig. 3). The longer the prepositioning time is, the shorter is the distance dx between the DW’s new position and the exit point (at the left contact). Then, after waiting ~100 ms to ensure that the DW was completely relaxed, a first current pulse was applied, followed by a second current pulse of the opposite polarity, separated by a waiting time. Here we consider just the two cases where twait = 0 and 100 ns, which correspond to either zero or a full contribution of the DW’s motion during its after-pulse deceleration. When twait = 0, we find that tout initially increases rapidly as dx is increased from zero before reaching a linear regime. We attribute this dependence to the initial acceleration of the DW toward its terminal velocity, given by the slope in the linear regime. By fitting the data with an exponential function (29), we find an acceleration time tacc = 13.3 T 4 ns, which is comparable to the deceleration time found above. In contrast, when twait = 100 ns, tout varies linearly with dx for all dx, with the same slope as for twait = 0 for large dx. In this case, where both acceleration and deceleration contribute, we find that the effective DW velocity is independent of distance traveled, which
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is consistent with the first set of results shown in Fig. 1. Thus, the additional distance that the DW travels during deceleration after the current is
turned off exactly makes up for the distance lagged during acceleration to its terminal velocity. From the data in Fig. 3, we estimate that the
Fig. 3. Shift pulse length tout for which Pout = 50% versus the length of a current pulse tpos used to position the DW at a distance dx from the exit point of the 12-mm-long nanowire. Open and solid symbols correspond to twait = 0 and 100 ns, respectively. Solid lines show fits to the data as discussed in the text. Error bars show 20/80% probabilities. The inset shows a diagram of the nanowire with the DW positioned at a distance dx from the exit. The offset in dx between the two curves (dotted line) represents the distance moved by the DW during its deceleration after the end of the current pulse.
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Fig. 4. (A) DW position and (B) velocity versus time in response to a 100-ns-long current pulse, calculated using the 1D model, for the same steady-state velocity, v = (b/a)u = 138 m/s, but two different b/a ratios: b/a = 1 and u = 138 m/s (blue line), and b/a = 3.2 and u = 43.1 m/s (red line). (C) DW position versus time for a shift current pulse sequence of the same form as shown in Fig. 2A with amplitude u = 43.1 m/s, b/a = 3.2, and three waiting times twait. Each pulse is 100 ns long. Details of the curves highlighting inertia-driven motion at the end of the first shift current pulse are shown in (D). The vertical dashed line at 100 ns indicates the end of this first pulse. The solid diamonds at 101 and 110 ns show the onset of the second shift pulse. (E) Velocity (red) and position (black) of a vortex DW versus time in response to a stepwise change in current calculated by micromagnetic simulations in a 20-nm-thick, 200-nm-wide nanowire, for the same set of parameters as found experimentally (b/a ~3.2, J = 1.2 × 108 A/cm2, P = 0.5). The blue solid line is a fit to the data as described in the text. (F) Snapshots of the vortex DW structure at zero time and when moving at terminal velocity (60 ns). The snapshots are centered on the DW. The direction of magnetization is color-coded, as indicated by the black arrows. The wall is moving from right to left.
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distance over which the DW accelerates to 90% of its terminal velocity is ~2.5 mm, whereas the distance moved by the DW during deceleration is ~1.45 mm (shown by the horizontal dashed line in Fig. 3). The latter is in close agreement with the data described earlier. To support our experimental findings, we analyzed the response of a DW to a current pulse using the well-known one-dimensional (1D) model of DW dynamics (15, 17, 19, 20, 29). Figure 4A and B show the calculated position and instantaneous velocity of a DW in response to a 100-ns-long current pulse, for two different values of b/a (1 and 3.2) but the same terminal velocity (b/a)u = 138 m/s. Clearly, the DW’s inertial response to the current pulse is strongly influenced by the value of b/a, even though the total distance traveled by the DW is the same in both cases. The DW’s relaxation after the pulse exactly offsets the acceleration at the beginning of the pulse (15), in agreement with our experiments. In particular, the effective DW velocity (the total displacement divided by the pulse length) is equal to the DW’s terminal velocity in both cases. Deceleration also offsets acceleration even if the current pulses are shorter than the DW’s acceleration time, as in the experiments of Fig. 1D. The effect of a bipolar pulse is shown in Fig. 4, C and D, for 100-ns-long pulses and different waiting times twait between the two pulses. We find that in all cases, the DW comes back to its starting position, but as shown in Fig. 4D, its excursion is greater when the waiting time between the pulses is long. Analytical expressions of the time-dependent DW velocity can be derived within a linear approximation that is valid for small currents, allowing us to calculate the distance lagged due to acceleration (which is the same as the deceleration distance). This distance is ~1.1 mm, which is slightly smaller than the value found experimentally. The 1D model does not take into account the structure of the vortex DW used in our experiments. To confirm our findings and enable a more quantitative description, we used micromagnetic simulations to calculate the response of a vortex DW to a current step (Fig. 4E). Snapshots of the DW structure during motion show that the acceleration is associated with the lateral motion of the vortex core (Fig. 4F). The DW velocity versus time is very well described by the analytical model. The Gilbert damping a was varied to match the experimental value of t (29). Best agreement was found for a = 0.008 T 0.002, which leads to b ~ 0.026. The distance lagged due to acceleration was ~1.0 mm, which is in close agreement with the analytical model. Notwithstanding our findings that a DW, irrespective of its inertia, can be precisely moved a distance proportional to the temporal length of a current pulse, the DW’s inertial response means that its position at a given point in time is not simply linearly related to the time elapsed since the beginning of the current pulse. This must be taken into account when devising clocking schemes for memory or logic devices.
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REPORTS 14. 15. 16. 17. 18. 19.
20. 21. 22. 23. 24. 25. 26. 27.
G. Tatara, H. Kohno, Phys. Rev. Lett. 92, 086601 (2004). Z. Li, S. Zhang, Phys. Rev. Lett. 92, 207203 (2004). S. Zhang, Z. Li, Phys. Rev. Lett. 93, 127204 (2004). A. Thiaville, Y. Nakatani, J. Miltat, Y. Suzuki, Europhys. Lett. 69, 990 (2005). S. E. Barnes, S. Maekawa, Phys. Rev. Lett. 95, 107204 (2005). A. P. Malozemoff, J. C. Slonczewski, Magnetic Domain Walls in Bubble Materials (Academic Press, New York, 1979). L. Thomas et al., Nature 443, 197 (2006). R. Moriya et al., Nat. Phys. 4, 368 (2008). I. M. Miron et al., Phys. Rev. Lett. 102, 137202 (2009). T. A. Moore et al., Phys. Rev. B 80, 132403 (2009). M. Eltschka et al., Phys. Rev. Lett. 105, 056601 (2010). E. Saitoh, H. Miyajima, T. Yamaoka, G. Tatara, Nature 432, 203 (2004). D. Bedau et al., Phys. Rev. Lett. 99, 146601 (2007). L. Thomas et al., Science 315, 1553 (2007).
Cassini Finds an Oxygen–Carbon Dioxide Atmosphere at Saturn’s Icy Moon Rhea B. D. Teolis,1* G. H. Jones,2,3 P. F. Miles,1 R. L. Tokar,4 B. A. Magee,1 J. H. Waite,1 E. Roussos,5 D. T. Young,1 F. J. Crary,1 A. J. Coates,2,3 R. E. Johnson,6 W.-L. Tseng,6 R. A. Baragiola6 The flyby measurements of the Cassini spacecraft at Saturn’s moon Rhea reveal a tenuous oxygen (O2)–carbon dioxide (CO2) atmosphere. The atmosphere appears to be sustained by chemical decomposition of the surface water ice under irradiation from Saturn’s magnetospheric plasma. This in situ detection of an oxidizing atmosphere is consistent with remote observations of other icy bodies, such as Jupiter’s moons Europa and Ganymede, and suggestive of a reservoir of radiolytic O2 locked within Rhea’s ice. The presence of CO2 suggests radiolysis reactions between surface oxidants and organics or sputtering and/or outgassing of CO2 endogenic to Rhea’s ice. Observations of outflowing positive and negative ions give evidence for pickup ionization as a major atmospheric loss mechanism. n 2 March 2010, the Cassini spacecraft executed a flyby of Saturn’s icy moon Rhea, with a trajectory inbound toward Saturn passing 97 km over the surface at 81° north latitude. The Ion Neutral Mass Spectrometer (INMS)—a quadrupole mass analyzer equipped with an antechamber and electron-impact ionizer for in situ collection and detection of neutral gas (1) —was operated during the flyby with the antechamber inlet pointed favorably at an angle of 44° to Cassini’s trajectory, enabling the measurement of neutral species. INMS detected a tenuous atmosphere of oxygen and carbon dioxide in mass channels 32 and 44 daltons, reaching peak densities along the trajectory of 5 and 2 T 1 × 1010 molecules per m3, respectively. A highly non-uniform
O
1 Southwest Research Institute, Space Science and Engineering Division, 6220 Culebra Road, San Antonio, TX 78238, USA. 2 Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London (UCL), Holmbury St. Mary, Dorking, Surrey RH5 6NT, UK. 3The Centre for Planetary Sciences at UCL/Birkbeck, Gower Street, London WC1E 6BT, UK. 4 Los Alamos National Laboratory, Space Science and Applications, Los Alamos, NM 87545, USA. 5Max-Planck-Institut für Sonnensystemforschung, Max-Planck-Strasse 2, 37191 KatlenburgLindau, Germany. 6University of Virginia, Department of Materials Science and Engineering, 116 Engineer’s Way, Charlottesville, VA 22903, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
atmosphere was observed, with the CO2 seen almost exclusively on the outbound portion of the trajectory over the day-lit hemisphere (Fig. 1). In contrast, the O2 profile is more symmetrical about the point of closest approach, but it is nevertheless shifted slightly outbound to the day side (Fig. 1). Spectra from the Cassini Plasma Spectrometer (CAPS) (2), acquired during the more distant 502and 5736-km flybys on 26 November 2005 and 30 August 2007, also show clear signatures (Fig. 2) symptomatic (3) of outflowing streams of positive and negative ions, which are produced by ionization of the atmosphere and electron capture, respectively. These ions are subsequently swept up into Saturn’s rotating magnetosphere (4). The timing of the positive and negative ion signatures inbound and outbound from Rhea (Fig. 2) is consistent → → with the expected E × B cycloidal trajectories (where → → E and B are the electric and magnetic fields, respectively) of pickup ions in the mass ranges of 26 to 56 daltons (possibly O2+ or CO2+ ) and 13 to 26 daltons, respectively; thus, we tentatively identify the negative species as O–. The mass uncertainty results from the CAPS energy and angular resolution (2), as well as the still-uncertain corotation electric field and corotation speed in Rhea’s plasma wake (5). Unlike the 2005 encounter, only positive ions were detected during the 11 times more distant 2007 flyby, suggesting rapid (6) removal of
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28. L. Bocklage et al., Phys. Rev. B 78, 180405 (2008). 29. Materials and methods can found as supporting material on Science Online. 30. The rise and fall times of the shift current pulses, measured in a transmission geometry through the devices using a real-time oscilloscope and corresponding to a variation of 20/80% of the pulse amplitude, were ~0.5 ns. 31. M. Hayashi et al., Phys. Rev. Lett. 97, 207205 (2006). 32. V. Vlaminck, M. Bailleul, Science 322, 410 (2008). 33. We thank S.-H. Yang, X. Jiang, and B. Hughes for useful discussions and help with sample fabrication.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1810/DC1 Materials and Methods Figs. S1 to S3 References 7 September 2010; accepted 16 November 2010 10.1126/science.1197468
loosely bound electrons from the negative ions by photo or electron impact ionization as the ions move away from Rhea. The in situ detection of O2 and CO2 at Rhea is consistent with remote observations of Jupiter’s icy moons, where the Galileo spacecraft’s NearInfrared Mapping Spectrometer observed resonantly scattered 4.26-mm infrared emission from atmospheric CO2 at Callisto (7), and the Hubble Space Telescope measured 1304 and 1356 Ǻ ultraviolet fluorescence from electron-impact dissociatively excited atmospheric O2 at Europa and Ganymede (8). Oxygen at Europa and Ganymede is generated by radiation chemistry and sputtered from the surface ice into the atmosphere by bombarding ions and electrons from Jupiter’s magnetosphere (8). The Jupiter findings, and the detection by Cassini of O2 from ultraviolet (UV) photodecomposition of ice in Saturn’s rings (9), have long suggested the possibility of oxygen atmospheres around the saturnian icy satellites (10), which orbit inside Saturn’s magnetosphere. Ganymede’s ice (11) and that of Europa and Callisto (12) also exhibit the weak 5770 and 6275 Ǻ optical absorption signatures of trapped radiolytic O2 (13), which has been shown in laboratory experiments to lead to ozone as a byproduct (14), along with eventual O2 ejection from the surface through sputtering (15). Rhea and Saturn’s icy moon Dione are especially interesting because O3 is present in their surface ices (16), a trait that they share with Ganymede (17). Together with the existence of ozone in Rhea’s ice, the detection of an O2 atmosphere is consistent with surface radiolysis, as seen at other icy satellites, and indicative of O2 trapped in the surface ice. On the basis of CAPS and Magnetospheric Imaging Instrument (MIMI) measurements of the saturnian ion and electron plasma, as well as updated laboratory estimates of O2 production and desorption from ice irradiated with different projectiles and energies, we have modeled the expected production of O2 from different radiation sources (18). The principal oxygen source in the model is bombardment by water group ions (W+) from Saturn’s corotating plasma (Table 1), which sweep past Rhea along its orbit while preferentially bombarding its trailing hemisphere. The oxygen is, therefore, produced preferentially on the
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References and Notes 1. L. Berger, J. Appl. Phys. 55, 1954 (1984). 2. L. Berger, Phys. Rev. B 33, 1572 (1986). 3. S. S. P. Parkin, M. Hayashi, L. Thomas, Science 320, 190 (2008). 4. J. Grollier et al., Appl. Phys. Lett. 83, 509 (2003). 5. A. Yamaguchi et al., Phys. Rev. Lett. 92, 077205 (2004). 6. M. Yamanouchi, D. Chiba, F. Matsukura, H. Ohno, Nature 428, 539 (2004). 7. N. Vernier, D. A. Allwood, D. Atkinson, M. D. Cooke, R. P. Cowburn, Europhys. Lett. 65, 526 (2004). 8. M. Kläui et al., Phys. Rev. Lett. 95, 026601 (2005). 9. D. Ravelosona, D. Lacour, J. A. Katine, B. D. Terris, C. Chappert, Phys. Rev. Lett. 95, 117203 (2005). 10. M. Hayashi et al., Phys. Rev. Lett. 98, 037204 (2007). 11. S. Yang, J. L. Erskine, Phys. Rev. B 75, 220403 (2007). 12. G. Meier et al., Phys. Rev. Lett. 98, 187202 (2007). 13. M. Hayashi, L. Thomas, R. Moriya, C. Rettner, S. S. P. Parkin, Science 320, 209 (2008).
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20. 21. 22. 23. 24. 25. 26. 27.
G. Tatara, H. Kohno, Phys. Rev. Lett. 92, 086601 (2004). Z. Li, S. Zhang, Phys. Rev. Lett. 92, 207203 (2004). S. Zhang, Z. Li, Phys. Rev. Lett. 93, 127204 (2004). A. Thiaville, Y. Nakatani, J. Miltat, Y. Suzuki, Europhys. Lett. 69, 990 (2005). S. E. Barnes, S. Maekawa, Phys. Rev. Lett. 95, 107204 (2005). A. P. Malozemoff, J. C. Slonczewski, Magnetic Domain Walls in Bubble Materials (Academic Press, New York, 1979). L. Thomas et al., Nature 443, 197 (2006). R. Moriya et al., Nat. Phys. 4, 368 (2008). I. M. Miron et al., Phys. Rev. Lett. 102, 137202 (2009). T. A. Moore et al., Phys. Rev. B 80, 132403 (2009). M. Eltschka et al., Phys. Rev. Lett. 105, 056601 (2010). E. Saitoh, H. Miyajima, T. Yamaoka, G. Tatara, Nature 432, 203 (2004). D. Bedau et al., Phys. Rev. Lett. 99, 146601 (2007). L. Thomas et al., Science 315, 1553 (2007).
Cassini Finds an Oxygen–Carbon Dioxide Atmosphere at Saturn’s Icy Moon Rhea B. D. Teolis,1* G. H. Jones,2,3 P. F. Miles,1 R. L. Tokar,4 B. A. Magee,1 J. H. Waite,1 E. Roussos,5 D. T. Young,1 F. J. Crary,1 A. J. Coates,2,3 R. E. Johnson,6 W.-L. Tseng,6 R. A. Baragiola6 The flyby measurements of the Cassini spacecraft at Saturn’s moon Rhea reveal a tenuous oxygen (O2)–carbon dioxide (CO2) atmosphere. The atmosphere appears to be sustained by chemical decomposition of the surface water ice under irradiation from Saturn’s magnetospheric plasma. This in situ detection of an oxidizing atmosphere is consistent with remote observations of other icy bodies, such as Jupiter’s moons Europa and Ganymede, and suggestive of a reservoir of radiolytic O2 locked within Rhea’s ice. The presence of CO2 suggests radiolysis reactions between surface oxidants and organics or sputtering and/or outgassing of CO2 endogenic to Rhea’s ice. Observations of outflowing positive and negative ions give evidence for pickup ionization as a major atmospheric loss mechanism. n 2 March 2010, the Cassini spacecraft executed a flyby of Saturn’s icy moon Rhea, with a trajectory inbound toward Saturn passing 97 km over the surface at 81° north latitude. The Ion Neutral Mass Spectrometer (INMS)—a quadrupole mass analyzer equipped with an antechamber and electron-impact ionizer for in situ collection and detection of neutral gas (1) —was operated during the flyby with the antechamber inlet pointed favorably at an angle of 44° to Cassini’s trajectory, enabling the measurement of neutral species. INMS detected a tenuous atmosphere of oxygen and carbon dioxide in mass channels 32 and 44 daltons, reaching peak densities along the trajectory of 5 and 2 T 1 × 1010 molecules per m3, respectively. A highly non-uniform
O
1 Southwest Research Institute, Space Science and Engineering Division, 6220 Culebra Road, San Antonio, TX 78238, USA. 2 Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London (UCL), Holmbury St. Mary, Dorking, Surrey RH5 6NT, UK. 3The Centre for Planetary Sciences at UCL/Birkbeck, Gower Street, London WC1E 6BT, UK. 4 Los Alamos National Laboratory, Space Science and Applications, Los Alamos, NM 87545, USA. 5Max-Planck-Institut für Sonnensystemforschung, Max-Planck-Strasse 2, 37191 KatlenburgLindau, Germany. 6University of Virginia, Department of Materials Science and Engineering, 116 Engineer’s Way, Charlottesville, VA 22903, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
atmosphere was observed, with the CO2 seen almost exclusively on the outbound portion of the trajectory over the day-lit hemisphere (Fig. 1). In contrast, the O2 profile is more symmetrical about the point of closest approach, but it is nevertheless shifted slightly outbound to the day side (Fig. 1). Spectra from the Cassini Plasma Spectrometer (CAPS) (2), acquired during the more distant 502and 5736-km flybys on 26 November 2005 and 30 August 2007, also show clear signatures (Fig. 2) symptomatic (3) of outflowing streams of positive and negative ions, which are produced by ionization of the atmosphere and electron capture, respectively. These ions are subsequently swept up into Saturn’s rotating magnetosphere (4). The timing of the positive and negative ion signatures inbound and outbound from Rhea (Fig. 2) is consistent → → with the expected E × B cycloidal trajectories (where → → E and B are the electric and magnetic fields, respectively) of pickup ions in the mass ranges of 26 to 56 daltons (possibly O2+ or CO2+ ) and 13 to 26 daltons, respectively; thus, we tentatively identify the negative species as O–. The mass uncertainty results from the CAPS energy and angular resolution (2), as well as the still-uncertain corotation electric field and corotation speed in Rhea’s plasma wake (5). Unlike the 2005 encounter, only positive ions were detected during the 11 times more distant 2007 flyby, suggesting rapid (6) removal of
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28. L. Bocklage et al., Phys. Rev. B 78, 180405 (2008). 29. Materials and methods can found as supporting material on Science Online. 30. The rise and fall times of the shift current pulses, measured in a transmission geometry through the devices using a real-time oscilloscope and corresponding to a variation of 20/80% of the pulse amplitude, were ~0.5 ns. 31. M. Hayashi et al., Phys. Rev. Lett. 97, 207205 (2006). 32. V. Vlaminck, M. Bailleul, Science 322, 410 (2008). 33. We thank S.-H. Yang, X. Jiang, and B. Hughes for useful discussions and help with sample fabrication.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1810/DC1 Materials and Methods Figs. S1 to S3 References 7 September 2010; accepted 16 November 2010 10.1126/science.1197468
loosely bound electrons from the negative ions by photo or electron impact ionization as the ions move away from Rhea. The in situ detection of O2 and CO2 at Rhea is consistent with remote observations of Jupiter’s icy moons, where the Galileo spacecraft’s NearInfrared Mapping Spectrometer observed resonantly scattered 4.26-mm infrared emission from atmospheric CO2 at Callisto (7), and the Hubble Space Telescope measured 1304 and 1356 Ǻ ultraviolet fluorescence from electron-impact dissociatively excited atmospheric O2 at Europa and Ganymede (8). Oxygen at Europa and Ganymede is generated by radiation chemistry and sputtered from the surface ice into the atmosphere by bombarding ions and electrons from Jupiter’s magnetosphere (8). The Jupiter findings, and the detection by Cassini of O2 from ultraviolet (UV) photodecomposition of ice in Saturn’s rings (9), have long suggested the possibility of oxygen atmospheres around the saturnian icy satellites (10), which orbit inside Saturn’s magnetosphere. Ganymede’s ice (11) and that of Europa and Callisto (12) also exhibit the weak 5770 and 6275 Ǻ optical absorption signatures of trapped radiolytic O2 (13), which has been shown in laboratory experiments to lead to ozone as a byproduct (14), along with eventual O2 ejection from the surface through sputtering (15). Rhea and Saturn’s icy moon Dione are especially interesting because O3 is present in their surface ices (16), a trait that they share with Ganymede (17). Together with the existence of ozone in Rhea’s ice, the detection of an O2 atmosphere is consistent with surface radiolysis, as seen at other icy satellites, and indicative of O2 trapped in the surface ice. On the basis of CAPS and Magnetospheric Imaging Instrument (MIMI) measurements of the saturnian ion and electron plasma, as well as updated laboratory estimates of O2 production and desorption from ice irradiated with different projectiles and energies, we have modeled the expected production of O2 from different radiation sources (18). The principal oxygen source in the model is bombardment by water group ions (W+) from Saturn’s corotating plasma (Table 1), which sweep past Rhea along its orbit while preferentially bombarding its trailing hemisphere. The oxygen is, therefore, produced preferentially on the
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References and Notes 1. L. Berger, J. Appl. Phys. 55, 1954 (1984). 2. L. Berger, Phys. Rev. B 33, 1572 (1986). 3. S. S. P. Parkin, M. Hayashi, L. Thomas, Science 320, 190 (2008). 4. J. Grollier et al., Appl. Phys. Lett. 83, 509 (2003). 5. A. Yamaguchi et al., Phys. Rev. Lett. 92, 077205 (2004). 6. M. Yamanouchi, D. Chiba, F. Matsukura, H. Ohno, Nature 428, 539 (2004). 7. N. Vernier, D. A. Allwood, D. Atkinson, M. D. Cooke, R. P. Cowburn, Europhys. Lett. 65, 526 (2004). 8. M. Kläui et al., Phys. Rev. Lett. 95, 026601 (2005). 9. D. Ravelosona, D. Lacour, J. A. Katine, B. D. Terris, C. Chappert, Phys. Rev. Lett. 95, 117203 (2005). 10. M. Hayashi et al., Phys. Rev. Lett. 98, 037204 (2007). 11. S. Yang, J. L. Erskine, Phys. Rev. B 75, 220403 (2007). 12. G. Meier et al., Phys. Rev. Lett. 98, 187202 (2007). 13. M. Hayashi, L. Thomas, R. Moriya, C. Rettner, S. S. P. Parkin, Science 320, 209 (2008).
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Fig. 1. (A) INMS 32-dalton measurement (32) of the O2 density along Cassini’s trajectory versus time durA ing the 2 March 2010 Rhea encounter. The black vertical dotted line indicates that the closest approach (CA) was 17:40:39 UT at 96.8-km altitude and nearly simultaneous (later by ~0.05 s) with solar terminator traversal to B Rhea’s day side. The blue dashed curve denotes along-track density predicted by a Monte Carlo simulation of the O2 atmosphere that assumes 100/40 K day/night surface temperatures, respectively (25). (B) Same as Cassini (8.6 km/s) 1012 C (A) for CO2 in the 4417:46 17:36 dalton mass channel. (C) E 1011 Diagrammatic equatoriSaturn al view of Rhea looking B perpendicular to the Cas1010 26 Nov 2005 sini 2010 trajectory (red Trajectory 1000 km line in Rhea’s reference 109 frame) on the same time scale as in (A) and (B). The vantage point at 81.8° longitude and 8.9° north latitude is near the apex (90° longitude) of Rhea’s leading hemisphere. Cassini’s motion toward Saturn at 8.6 km/s was nearly perpendicular (at 88.8°) to the daynight terminator (shown at the time of CA), with CA at 81.1° north latitude, 263.4° longitude. Rhea’s orbit and Saturn’s corotation direction point out of the page and perpendicular to the magnetic and corotation → → electric fields B and E . Also shown is the O2 density cross section predicted by the Monte Carlo model. 1000 km
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Fig. 2. (A) Diagrammatic Rhea north polar view with the 26 November 2005 Cassini flyby trajectory (black line in Rhea’s reference frame) during which CAPS detected pickup ions. The time scale is matched to that of (B) and (C). The day and night hemispheres are shown during CA at 22:37:39 UT. The trajectory traversed Rhea’s plasma wake, with CA at 502-km altitude, 226 km south (Fig. 1) of the equator. Our model prediction of the O2 density (226-km south cross section) is also shown. The O2+ and O– (orange) and CO2+ trajectories (blue) are those required to enter anodes 4 and 3 (33) of the CAPS Electron Spectrometer (ELS) and Ion Mass Spectrometer (IMS) at the time and energy of the ion signatures. The trajectories assume (in Saturn’s reference frame) a → → B of 26 nT (34) and a corotation electric field E [within uncertainty (5)] of – + + 1.77 (O2 , O ) or 1.51 (CO2 ) V/km. Before ionization, most atmospheric neu→ trals have thermal speeds less than 1 km/s, so jE j is optimized such that ions backtracked from Cassini come nearly to rest (the trajectory starting point). FOV, field of view. (B) ELS negative particle flux spectrogram from anode 4 (20° FOV), which had optimal pointing. Negative pickup ions are indicated by the sharp feature near 22:41 UT (T0.35 min) and 1.14 (T0.15) keV over the electron background. (C) Positive ions from IMS anode 3: Pickup ions produce the sharp 22:32 UT (T0.5 min), 2.06 (T0.2)–keV signature over the background of (mostly) corotating H+/W+ (31).
trailing hemisphere. Relative to the amount of energy deposited by the different radiation sources, the W+ ions are also the most efficient O2 producers (Table 1). Because of their mass, these ions are the most effectively stopped on impact within the ice; that is, they deposit the most energy close to the material surface where, according to experiments, O2 synthesis is favored most (18). The predicted total global production rate of ~2.2 × 1024 O2 molecules per second is within the ~0.4 to 4 × 1024 s–1 range implied by the elevated O2+ densities seen by CAPS near Rhea’s orbit (19). In contrast to O2, knowledge of Rhea’s CO2 source is much less well constrained. Atmospheric CO2 might result from sputtering of primordial CO2 in Rhea’s ice or from radiolysis reactions between surface water molecules, radiolytic oxygen, and carbonaceous minerals or organics possibly present in the surface ice (13, 20, 21) and/or deposited by micrometeorite bombardment (22). The trailing hemispheres of Rhea and Dione both show a darkening in their visible-infrared reflectance spectrum, which is indicative of such nonice material. At Dione, the Cassini Visible and Infrared Mapping Spectrometer (VIMS) detected the 4.26-mm absorption of CO2 in the dark regions (22). However, the Rhea measurements are inconclusive: A possible detection of the CO2 absorption in Rhea’s global spectrum (22) was not confirmed in subsequent VIMS mapping measurements (23) of the surface. A completely endogenic CO2 source is also possible: for instance, outgassing of primordial CO2 or of CO2 produced by aqueous chemistry from Rhea’s interior, similar to scenarios suggested at Enceladus (24) and Callisto (7).
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Table 1. Estimated O2 production from different radiation sources. Radiation source
Energy deposition (× 1026 eV/s)
Estimated O2 production (× 1022 O2/s)
W+ H+ Electrons Solar UV Total
14.8 9.5 73 8.1 105
170 7.4 38 4.2 220
The surface source processes compete with atmospheric loss mechanisms to determine the atmospheric O2 and CO2 abundances. The loss mechanisms are Jeans escape and atmosphereplasma interactions—that is, ionization, dissociation, charge exchange, and electron capture. The plasma-interaction channels result in fast neutral and ionized species that, depending on their point of origin (Fig. 2), either (i) collide with Rhea’s surface (and implant into the ice or adsorb or react on the surface)→ or →(ii) escape into space directly or (for ions) by E × B pickup, as seen by CAPS. We used a Monte Carlo approach to model the atmosphere by initializing O2 molecules according to the expected surface position–dependent production (18) while allowing the molecules to execute random ballistic trajectories between surface impacts. The simulation assumed no surface adsorption, thermally equilibrated molecules with the surface on impact by reinitializing the speed with a MaxwellBoltzmann distribution at the local surface temperature (25), and destroyed the molecules in mid-flight, according to the loss rate from plasma interactions or on leaving the Hill sphere (Jeans escape). Our model predicts a day-side bulge due to the higher (25) temperatures (and, therefore, increased scale height) that is well matched by the outbound O2 tail seen by INMS (Fig. 1); i.e., the warmer day-side temperatures expand the atmospheric gas to the altitude of Cassini’s trajectory in this hemisphere. The bulge is also consistent with the predicted origin of the positive pickup ions at high altitudes during the 2005 flyby (Fig. 2). Non-negligible night-side O2 adsorption could account (26) for the model’s slight overestimate of the INMS inbound measurements (Fig. 1). The mean free path is ~6 to 30 × 103 km at the predicted day-night surface densities of ~9 to 40 × 1010 O2 m–3 (26); therefore, the atmosphere can be considered as collisionless. The estimated Jeans escape of 6(T1) × 1022 O2 s–1 is ~2.7% of the estimated total O2 produced (~2.2 × 1024 O2 s–1), with the remainder lost because of plasma interactions and pickup. The total atmospheric O2 abundance is estimated to be 2.5(T0.5) × 1029 molecules, corresponding to an average atmospheric molecule lifetime of ~105 seconds, or ~1 day. Whereas for O2 the sticking times are short, CO2 is much less volatile (27); thus, the night side could act as a much more effective cold trap for CO2 condensation, possibly explaining the almost nonexistent CO2 signal inbound on the night side
(Fig. 1). Locally condensed CO2 would be rereleased by solar heating as the dawn terminator advances across the surface, although some CO2 might be trapped for longer periods in shadowed polar regions [analogous to lunar (28) volatiles]. Although surface CO2 on the night side would be undetectable by Cassini VIMS (which measures reflected sunlight), CO2 near the faintly illuminated poles or dawn terminator might be observable. The estimated mean atmospheric O2 column density of 3.4(T0.7) × 1016 m–2 over Rhea’s surface is two orders of magnitude below the 2.4 to 14 and 1 to 10 × 1018 m–2 abundances at Europa and Ganymede (8), respectively, a difference likely attributable to the greater O2 desorption flux from the warmer and more intensely irradiated Galilean satellites (29). Rhea’s atmospheric abundance is also well below the 1018 m–2 detection limit of MIMI and the Cassini Ultraviolet Imaging Spectrograph, explaining why earlier attempts by these instruments to detect an atmosphere remotely were unsuccessful (30). In comparison, laboratory measurements on irradiated ice imply that 1019 to 1020 O2 m–2 (14, 15) are synthesized by penetrating ions as trapped molecules inside the bulk H2O solid, from which diffusive loss is expected to be slow; thus, it is likely that a large fraction of Rhea’s oxygen is actually locked inside the moon’s ice. The laboratory column densities correspond to ~0.4 to 4 × 104 metric tons of trapped O2 globally on Rhea, but these are a lower limit because diffusion and micrometeorite gardening can disperse O2 into the subsurface ice.
20. 21. 22. 23.
24. 25.
26.
27.
28. 29.
References and Notes 1. 2. 3. 4.
5.
6.
7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
J. H. Waite Jr. et al., Space Sci. Rev. 114, 113 (2004). D. T. Young et al., Space Sci. Rev. 114, 1 (2004). A. J. Coates et al., Icarus 206, 618 (2010). CAPS did not detect pickup ions during the 2010 flyby because Cassini’s path north of Rhea did not intersect the allowable ion trajectories (Figs. 1 and 2). Field strengths of 1.77 and 1.51 V/km, consistent with O2+ and CO2+ (Fig. 2), yield bulk plasma speeds |E|/|B| = 68 and 58 km/s, respectively, both of which are compatible with published estimates [(31) and supplemental reference 44 (S44)]. Assuming a 49-km/s bulk plasma speed with respect to Rhea (18, 31), pickup ions reaching Cassini’s 2007 position are approximately 2 min old, compared with ages of 10 (T5) s in 2005. R. W. Carlson, Science 283, 820 (1999). D. T. Hall, P. D. Feldman, M. A. McGrath, D. F. Strobel, Astrophys. J. 499, 475 (1998). W.-L. Tseng, W.-H. Ip, R. E. Johnson, T. A. Cassidy, M. K. Elrod, Icarus 206, 382 (2010). J. Saur, D. F. Strobel, Astrophys. J. 620, L115 (2005). J. R. Spencer, W. M. Calvin, M. J. Person, J. Geophys. Res. 100, 19049 (1995). J. R. Spencer, W. M. Calvin, Astron. J. 124, 3400 (2002). J. Spencer, Icarus 136, 349 (1998). B. D. Teolis, M. J. Loeffler, U. Raut, M. Fama, R. A. Baragiola, Astrophys. J. 644, L141 (2006). B. D. Teolis, J. Shi, R. A. Baragiola, J. Chem. Phys. 130, 134704 (2009). K. S. Noll, T. L. Roush, D. P. Cruikshank, R. E. Johnson, Y. J. Pendleton, Nature 388, 45 (1997). K. S. Noll, R. E. Johnson, A. L. Lane, D. L. Domingue, H. A. Weaver, Science 273, 341 (1996). Materials, methods, and further discussion are available as supporting material on Science Online. CAPS measurements throughout the Saturn system imply an average relative O2+/W+ density of 0.31 T 0.01% at 4.5 to
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30. 31. 32.
33. 34. 35.
7.5Rs (where Rs is the radius of Saturn) from photoionization of ring-atmosphere O2 and a statistically greater 0.41 T 0.02% at 7.5 to 10.5Rs (S45). Rhea orbits at 8.75Rs. D. P. Cruikshank et al., Icarus 206, 561 (2010). D. P. Cruikshank et al., Icarus 175, 268 (2005). R. N. Clark et al., Icarus 193, 372 (2008). K. Stephan et al., in 40th Lunar and Planetary Science Conference, Abstract 1377 (Lunar and Planetary Institute, The Woodlands, TX, 23 to 27 March 2009). C. R. Glein, M. Y. Zolotov, E. L. Shock, Icarus 197, 157 (2008). Considering the day/night hemisphere orientation at the times of the 2 March 2010 and 26 November 2005 flybys, we reconstructed a temperature map based on Cassini Composite Infrared Spectrometer (CIRS) measurements (S16) showing 100/40 K max/min day/night temperatures, respectively. Because the CIRS coverage did not include the poles, we assume a constant 35/75 K (26 Nov 2005, Saturn winter) and 35/35 K (2 March 2010, near Saturn equinox) beyond 75° north/south latitude. The predicted day-night surface densities correspond to surface pressures of 1.2 and 2.2 × 10−12 mbar at 100 and 40 K, respectively. The values are 100 × 1010 O2/m3, 4.8 × 10−12 mbar, and 3 × 103 km mean free path for 35 K minimum at the poles. Any night-side or polar O2 adsorption [e.g., into the porous surface regolith (S46)] would lower the overlying atmospheric density (Fig. 1); hence, the night-side and polar values are upper limits. The O2 and CO2 sublimation energies are 0.095 eV (S47) and 0.271 eV (S48) from the pure solid. Extrapolation from available equilibrium vapor pressure data (S49, S50) yields O2 and CO2 values of 5.69 × 10−5 and 3.15 × 10−32 mbar at 35 K, as well as 2547 and 2.11 × 10−4 mbar at 100 K. J. M. Sunshine et al., Science 326, 565 (2009); 10.1126/science.1179788. Compared with Rhea [40 to 100 K (S16)], the warmer Europa/Ganymede ice [80 to 132 K/90 to 152 K (S51, S52)] is expected to exhibit O2 yields roughly one order of magnitude greater (15). For instance, Europa receives a mean of ~1.2 and 3.6 × 1017 eV/m2/s (S53) from ions (H+, W+, S+) and electrons, respectively, and mean O2 sources are ~1013 to 1015 O2/m2/s for both moons (S24–S28), which exceed the Rhea values: ~3.3 (ions) and 9.9 (electrons) × 1014 eV/m2/s and 3.0 × 1011 O2/m2/s. G. H. Jones et al., Science 319, 1380 (2008). R. J. Wilson, R. L. Tokar, W. S. Kurth, A. M. Persoon, J. Geophys. Res. 115, A05201 (2010). Mass channels are sampled one at a time by integrating detector counts during 0.031-s intervals. During the 2010 flyby, CO2 and O2 were sampled every 2.3 and 6.9 s, respectively. We assumed ions entering the center of the field of view, which is 20°. K. K. Khurana, C. T. Russell, M. K. Dougherty, Icarus 193, 465 (2008). We thank E. Roussos, D. Mitchell, and the Cassini MIMI team for providing the MIMI data used in figs. S1 and S4 and N. G. Petrik, A. G. Kavetsky, and G. A. Kimmel at Pacific Northwest National Laboratory, Richland, Washington, USA, for contributing the O2 electronstimulated desorption measurements in fig. S9. The INMS and CAPS teams acknowledge support from NASA and the Jet Propulsion Laboratory under SwRI subcontracts 1283095 and 1356497, respectively. G.H.J. and A.J.C. acknowledge support for CAPS-ELS operations and analysis by the United Kingdom Space Agency, and G.H.J. was supported by a UK Science and Technology Facilities Council Advanced Fellowship. B.D.T. and R.A.B. acknowledge support by the NSF Astronomy and Astrophysics Program through grant AST0807830.
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Supporting Online Material www.sciencemag.org/cgi/content/full/science.1198366/DC1 Materials and Methods SOM Text Figs. S1 to S10 References and Notes 28 September 2010; accepted 12 November 2010 Published online 25 November 2010; 10.1126/science.1198366
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Structures of C3b in Complex with Factors B and D Give Insight into Complement Convertase Formation Federico Forneris,1 Daniel Ricklin,2 Jin Wu,1 Apostolia Tzekou,2 Rachel S. Wallace,1 John D. Lambris,2* Piet Gros1* Activation of the complement cascade induces inflammatory responses and marks cells for immune clearance. In the central complement-amplification step, a complex consisting of surface-bound C3b and factor B is cleaved by factor D to generate active convertases on targeted surfaces. We present crystal structures of the pro-convertase C3bB at 4 angstrom resolution and its complex with factor D at 3.5 angstrom resolution. Our data show how factor B binding to C3b forms an open “activation” state of C3bB. Factor D specifically binds the open conformation of factor B through a site distant from the catalytic center and is activated by the substrate, which displaces factor D’s self-inhibitory loop. This concerted proteolytic mechanism, which is cofactor-dependent and substrate-induced, restricts complement amplification to C3b-tagged target cells. he human complement system contributes to a variety of processes, ranging from immunosurveillance to maintenance of cell homeostasis, which depend on a balance of complement activation and regulation (1). Besides a low level of steady activation through hydrolysis of the central complement component, C3, the cascade can be specifically triggered by various structures on the surfaces of foreign and apoptotic cells (1, 2). These events initiate distinct pathways that converge into a central amplification loop, which relies on an efficient enzyme complex that allows rapid turnover of C3 (3). This C3 convertase complex is generated when factor B (FB) interacts with surface-bound complement fragment C3b and forms the pro-convertase C3bB, which is cleaved by factor D (FD) to yield the active yet unstable C3 convertase C3bBb (4, 5). The C3 convertase cleaves C3 into anaphylatoxin C3a and opsonin C3b (2, 6–8). Newly generated C3b molecules become covalently attached to cell surfaces proximate to sites of activation and form additional convertases, thereby rapidly amplifying complement response on targeted cells (9–11). In order to prevent excessive activation and attack of host tissue, convertase activity has to be tightly controlled (12). Although recent studies provided insight into structure, function, stability, and regulation of the assembled C3 convertase (13, 14), molecular details about the convertase formation and activation step remained elusive. The published crystal structure of free FB showed a “locked” conformation in which the scissile bond (Arg234–Lys235) is protected from being cleaved by FD (15). Moreover, a recent structure of FB in complex with the C3b homolog
T
1 Crystal and Structural Chemistry, Bijvoet Center for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH Utrecht, Netherlands. 2 Department of Pathology and Laboratory Medicine, University of Pennsylvania, 401 Stellar Chance, Philadelphia, PA 19104, USA.
*To whom correspondence should be addressed. E-mail:
[email protected] (P.G.);
[email protected] (J.D.L.)
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cobra-venom factor (CVF) indicated how the propeptide segment of FB, Ba (residues 1 to 234), mediates the Mg2+-dependent binding of the protease segment Bb (residues 235 to 739) to the C terminus of CVF through the metal-ion-dependent adhesion site (MIDAS) of the Von Willebrand factor type-A (VWA) domain in Bb (16). Unexpectedly, the structure of CVFB did not show conformational changes in FB that lead to exposure of its scissile bond. Single-particle reconstruction of negatively stained electron microscopy (EM) data for the C3bB complex, however, indicated the coexistence of two conformations: one corresponding to a closed form as observed in CVFB and one to an open form that putatively exposes the scissile bond in FB (17). Allosteric roles have been proposed for the regulatory MIDAS and C-terminal helix a7 of the VWA domain of Bb [structural elements that play a key role in signaling through the homologous I domains in integrins (18, 19)] and the three complement-control protein (CCP) domains and C-terminal helical linker aL of the Ba segment (15, 20, 21). Whereas it is likely that binding of FB to C3b induces conformational changes that expose the scissile bond (Arg234– Lys235) for cleavage by FD, the mechanisms involved in this process have not yet been elucidated. FD is a single-domain serine protease, which circulates in plasma in a mature but self-inhibited form (22, 23). Even when the inhibition was prevented by mutagenesis, the activity of FD against peptides remained poor when compared with other serine proteases like trypsin or thrombin (24–26). Therefore, it is likely that proteolytic activity and specificity are controlled during the assembly and activation of the central enzyme complex. We determined crystal structures of the proconvertase C3bB stabilized by Ni2+ (17, 27) at 4.0-Å resolution and of the pro-convertase C3bB in complex with a catalytically inactive FD mutant Ser183→Ala183 [abbreviated as S183A (28)] (S195A, chymotrypsin numbering is given in parentheses) in the presence of Mg2+ at 3.5-Å resolution, which we refer to as C3bBD* (Fig. 1A) (29). In addition,
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we solved the structures of two FD mutants, S183A (S195A) and R202A (R218A) in their unbound form. Refinement statistics and the quality of the final models are described in table S1 and fig. S1. C3b in both C3bB and C3bBD* resembles free C3b (fig. S2) (30, 31). Two polypeptide chains, termed b (residues 1 to 645) and a′ (residues 727 to 1641) chains, together form a core of eight macroglobulin (MG1 to MG8) domains with a linker domain (LNK) inserted into domain MG6, a “complement C1r/C1s, Uegf, Bmp1” (CUB) and a thioester-containing domain (TED) in between MG7 and MG8, and a C-terminal C345C domain attached to MG8 (Fig. 1). Most of the C3b domains (MG1 to MG8, LNK, and CUB) show only minor rearrangements (with rotations up to 7°) compared to free C3b; however, we observed larger rotations for the peripheral domains TED and C345C (up to 32° and 15° rotations for C3bB and C3bBD*, respectively) (fig. S2). FB consists of five domains grouped into two segments: three N-terminal CCP domains that together with the helical linker aL form the Ba segment and a central VWA domain and a C-terminal trypsinlike serine protease (SP) domain that form the protease segment Bb (Fig. 1). The CCP1-3 and VWA domains of FB bind C3b in an identical orientation as in CVFB (16) (Fig. 1B and fig. S2D). In contrast, the SP domain of FB in C3bB is rotated by 84°, which effectively shifts the catalytic serine residue by 65 Å and creates additional contacts between FB and C3b (Fig. 1, A and B). Furthermore, the structure of C3bBD* shows that FD binds this new conformation of FB without making any contacts to C3b (Fig. 1C), which is confirmed by surface plasmon resonance (SPR) measurements showing that C3b interacts with FB but not with FD (fig. S3). In the presence of the physiologically relevant ion Mg2+, two conformations of C3bB have been observed in EM experiments at a ratio of 35%:65%, which were referred to as closed and open forms, respectively (17). When using Ni2+, however, the equilibrium was shifted to a 2%:98% ratio (17). We used Ni2+ to crystallize the open form of C3bB. The crystal structure of C3bB (Ni2+) (Fig. 2A) fits well, with a correlation coefficient of 0.87 (fig. S4), to the shape of the open form of the proconvertase seen in the EM experiments (17, 32). In contrast, the crystal structure of CVFB, which was confirmed by small-angle x-ray scattering solution studies (16), correlates with the closed form observed in EM. The closed conformation of CVFB possibly explains why it is processed ~100-fold slower than C3bB. However, despite the good agreement between the C3bB crystal structure and the EM reconstruction of the open form, our findings do not agree with the proposed structural model of domain rearrangements that was derived from the EM data (32). In particular, the x-ray data show a significant rotation of the SP domain rather than the CCP1-3 domains, as suggested by the EM model. The highly similar conformation of FB in the structure of C3bB in complex with FD (that is, C3bBD* crystallized in the presence of Mg2+) confirms that C3bB (Ni2+)
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adopts a physiological open conformation that is cleavable by FD. In all three structures (i.e., CVFB, C3bB, and C3bBD*), the C terminus of CVF or C3b is chelated to the metal ion in the regulatory MIDAS of the VWA domain in FB. The electron densities at 4 Å and 3.5 Å resolution for C3bB and C3bBD*, respectively, are consistent with highaffinity configurations for the MIDAS in both structures, as was observed for the structure of CVFB determined at 2.2 Å resolution (16). However, the limited resolution of the data precludes a detailed interpretation of the conformational changes that may be induced when Mg2+ is substituted by Ni2+ (fig. S5). The open form of C3bB is characterized by new interactions between the SP domain of FB and the MG2 and CUB domains of C3b, which result from the large rotation of the SP domain in FB. Mutations in FB at this new contact site reduced the C3bB cleavage rate (Fig. 2B and fig. S6), which shows that the interface observed in the crystal structure of C3bB in the open conformation is important for activity. In both free FB and the closed CVFB complex, the P1 residue of the scissile bond (Arg234) is buried inside the VWA domain and forms salt
bridges with Glu207 of helix aL and Glu446 of helix a7 (15, 16) (Fig. 2C). However, in the open C3bB structure concerted changes at the VWA-SP interface result in exposure of FB’s scissile loop (Fig. 2C). Rotation of the SP domain repositions the VWA-SP linker (residues 447 to 453) and elongates helix aL by two helical turns. This elongation is stabilized by residues Ile217 and Val220, which are docked into hydrophobic pockets of the VWA domain that were formerly occupied by residues Ile236 and Leu238 of the scissile loop (fig. S7A). A new interface is formed between four glutamates (residues 215 and 218 of aL and 422 and 424) of VWA and a cluster of four arginines (residues 580, 583, 683, and 685) in the SP domain (fig. S7B). In combination, these changes release Arg234 from its stabilizing interactions and expose the scissile loop (residues 224 to 239), which appears disordered in the structure of C3bB (fig. S7C). This mechanism differs markedly from that observed for the I domains of integrins, where a coupling of the MIDAS to helix a7 is implicated in the allosteric activation (18, 33). In the case of FB, however, the conformational changes involve predominantly the C-terminal
Fig. 1. Structures of C3bB and C3bBD*. (A) Overall structure of C3bB (left) and C3bBD* (right). C3b is shown as gray transparent surface; FB is shown as orange (Ba), green (VWA), and blue (SP) cartoons; and FD is represented as a magenta cartoon. The black spheres highlight the metal ions (Ni2+ for C3bB, Mg2+ for C3bBD*) at the MIDAS site. (B) Opened view of the footprint of the www.sciencemag.org
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end of helix aL, and to a lesser extent helix a7 and the interactions with the succeeding SP domain. FD binds FB in the open C3bB configuration at the VWA-SP interface through a binding site 25 Å away from the catalytic center (Fig. 3A). This interaction of FD induces a downward and sideways shift (of ~4 Å) of the VWA helix a7 in FB and unwinds the C-terminal part of the helix (residues 442 to 446) (Fig. 2C). FD binds the unwound helix a7 and its surroundings, yielding a total interface area of ~700 Å2 dominated by 10 hydrogen bonds and nine salt bridges. In agreement with the structural findings, mutations at this site of FD [i.e., His133 (His146), Val203 (Val219), and Arg157 (Arg170)] abolish cleavage of FB (Fig. 3, B and C, and fig. S8) and render FD unable to amplify complement response (Fig. 3, D to F). Similarly, biotinylation of FD at residue Lys208 (Lys223) causes loss of activity (26), likely because biotin blocks the binding of FD to FB. In addition, Glu446 of FB, which plays an important role in binding the P1-Arg234 in the closed form, contacts His133 (His146) of FD in the C3bBD* complex, and mutation of this residue (i.e., FB E446A) strongly reduces convertase formation (fig. S9). This contact
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C3b-FB interaction, highlighting the domains of FB on the C3b surface (top) and the domains of C3b on FB (bottom). (C) Opened view of the footprint of the FB-FD interaction, highlighting the domains of FB on the FD surface (left) and the single domain of FD on FB (right). The scheme indicates the domain compositions and color codes of C3b, FB, and FD used in (B) and (C). VOL 330
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site in FD is formed by four loops: residues 132 to 135 (145 to 149), 155 to 159 (169 to 171), 173 to 176 (185 to 188), and 203 to 209 (220 to 224). In coagulation factor VIIa, loop 155 to 159 (169 to 171) is part of the zymogen activation domain (i.e., tissue-factor binding site). Loops 132 to 135 (145 to 149), 173 to 176 (185 to 188), and 203 to 209 (220 to 224) and the self-inhibitory loop of FD overlap with the Na+ ion–binding region in thrombin (34, 35). In FD, this exosite is critical for binding of the open form of FB in C3bB. In the C3bBD* structure, the self-inhibitory loop of FD is rearranged, resulting in an active configuration of the catalytic triad in FD (Fig. 4A and fig. S10, A and B). In contrast, the structure of the unbound form of the corresponding FD mutant S183A (S195A), determined at 1.2 Å resolution (29), shows a self-inhibited state identical to the wild-type enzyme (22, 36) (fig. S10, A and B). We observe no induced structural changes at the exosite (fig. S10C). The C3bBD* structure and all eight available structures of free FD and mutants thereof (which occur in three different crystal forms) show well-defined and virtually identical conformations of the exosite loops. Thus, an induced activation,
either structurally or dynamically, of FD through binding of FB at the exosite is not supported by the present data. Instead, the rearrangements in FD are restricted to the self-inhibitory loop [residues 196 to 202 (212 to 218)]. In the self-inhibited state, Arg202 (Arg218) of FD forms a salt bridge with Asp177 (Asp189) at the bottom of the S1 pocket, whereas the same residue points outward in C3bBD* (Fig. 4A). Mutation R202A (R218A) in FD increased the activity against peptides threefold (fig. S11A) because of rearrangements in the selfinhibitory loop that induce the active conformation of the catalytic triad (fig. S10A) (25). In contrast, the same R202A (R218A) mutation reduced cleavage of FB to ~20% and thus reduced the convertase formation rate (fig. S11, B to D), indicating a role for Arg202 (Arg218) in substrate specificity, as predicted earlier (23, 26, 36–38). However, published data showed complete inactivity for FD mutant R202D-V203G (R218D-V219G) toward both peptides and FB (37). We mimicked this by using a R202G (R218G) mutant of FD, which yielded almost no activity (fig. S11B). Unexpectedly, we observed no electron density for the scissile loop of FB in C3bBD*. The
Fig. 2. Comparison between the closed and open states in the pro-convertase C3bB. (A) Surface representation of the closed (CVFB, left) (16) and open (C3bB, right) conformations of the pro-convertase. Colors are the same as in Fig. 1A. Red triangles indicate the position of the catalytic site of FB during the conformational changes. (B) FD-mediated cleavage of C3bB performed by using SDS– polyacrylamide gel electrophoresis (PAGE) shows that mutations (28) in FB located at the interface between the SP and CUB domains in the open C3bB proconvertase lower convertase formation rates. The histogram shows the relative pro-
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apparent poor binding of this loop to FD is consistent with the observed low activity of FD toward short peptides even when the self-inhibitory loop is displaced (25). Starting from residues 223 and 240 that are visible in the electron density map, we could model the scissile loop of FB (224 to 239) in an extended fashion, placing residues 233 to 235 into the S2, S1, and S1′ binding pockets of FD. This modeling places the P5-residue Glu230 in the vicinity of the exposed Arg202 (Arg218) of FD (Fig. 4B). Mutating Glu230 in FB (E230A) reduced the cleavage rate, indicating a putative interaction between Arg202 (Arg218) in FD and Glu230 in FB. (fig. S11B). Overall, we interpreted the specificity and activity of FD for cleaving FB in C3bB as primarily determined by its exositemediated binding to the open form of FB (with nanomolar affinity, fig. S3C), which allows the comparatively weaker interacting scissile loop of FB to induce activation of FD. Lastly, cleavage of FB releases the propeptide segment Ba from the complex, inducing additional structural changes in the residual Bb fragment that remains bound to C3b (13). In this step, the helix aL of the Ba fragment is removed
convertase activation rates compared with those of wild-type FB (see also fig. S6). Error bars represent deviations from the mean observed in multiple experiments (n > 3). (C) Cartoon diagram of the VWA domain of FB, highlighting conformational changes in the transition from the closed (left) to the open state of the proconvertase in absence (center) or in presence of FD (right). The aL helix is colored in orange and the a7 helix in green. The putative orientation of the loop containing the scissile bond of FB is shown with a dashed line. The positions of the C-a atoms located at the N-termini of aL and a7 helix are shown as spheres. SCIENCE
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REPORTS affinity ligand binding configuration. Similar to the SP rearrangement in the pro-convertase, rearrangement of the a7 helix affects the VWA-SP linker, resulting in a rotation of ~140° for the SP domain
(fig. S12). As derived from the crystal structure of the SCIN-inhibited convertase C3bBb (13), this arrangement allows cleavage of the substrate C3 thereby amplifying the complement response.
Fig. 3. Analysis of FD exosite. (A) Surface diagram of FD highlighting its exosite in blue and its catalytic site in red (28). Exosite mutations are shown in yellow, whereas the biotinylation site associated with FD inactivation (26) is shown in green. (B) FDmediated cleavage of C3bB pro-convertases monitored by SPR; FD mutants were injected onto surface-bound C3bB, and the cleavage activity was compared with that of wild type (wt). A drop in SPR response upon FD injection corresponds to the removal of the Ba fragment. Wild-type FD was injected at the end of each experiment to ensure cleavage sensitivity. (C) FD-mediated cleavage of C3bB performed by using SDS-PAGE with FD exosite mutants. The histogram shows the relative pro-convertase activation
rates compared with those of wild-type FD. Error bars represent deviations from the mean observed in multiple experiments (n > 3). (D) Effect of FD exosite mutations on the formation and decay of the C3bBb convertase monitored by SPR; wild-type FB was premixed with various FD mutants and injected over immobilized C3b. RU, resonance units. (E) Reconstitution of complement activity in FD-depleted plasma using FD mutants determined as C3b deposition on the enzyme-linked immunosorbent assay (ELISA) plate. O.D., optical density. (F) Hemolytic activity assays using FD-depleted plasma reconstituted with different mutants of FD: Lysis of rabbit erythrocytes was monitored by colorimetry and compared with 100% haemolysis in water.
Fig. 4. Analysis of FD catalytic site. (A) Conformational changes observed in FD catalytic site in C3bBD* structure. Superposition of the structure of FD S183A (S195A) from C3bBD* (magenta) with wild-type free FD (green, PDB ID 1DSU) (22, 28) showing the displacement of the self-inhibitory loop and flipping of the
side chain of His41 (His57) to the catalytic conformation. (B) Zoomed view of FD catalytic site with modeled FB scissile bond loop bound (dark gray). The model highlights the putative interaction between Glu230 of FB and Arg202 (Arg218) of FD and the P1 residue Arg234 making a salt bridge with Asp177 (Asp189).
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from its groove in the VWA domain, and helix a7 takes its place (fig. S7A). This results in a canonical arrangement of the VWA domain with the MIDAS-a7 presumably arranged in a high-
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Our data reveal a highly concerted and specific activation mechanism based on cofactor-dependent and substrate-induced proteolysis, which provides an important “double-safety” catch to restrict complement amplification to C3b-tagged target cells. These detailed mechanistic insights into the central and transient, and therefore low-abundance, C3bBD complex of complement activation offer opportunities for the development of new therapeutics to fight complement-mediated diseases (39) such as age-related macular degeneration, atypical hemolytic uremic syndrome, membranoproliferative glomerulonephritis, and chronic inflammations that are associated with excessive or poorly controlled activation of complement. References and Notes
14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.
1. D. Ricklin, G. Hajishengallis, K. Yang, J. D. Lambris, Nat. Immunol. 11, 785 (2010). 2. M. J. Walport, N. Engl. J. Med. 344, 1058 (2001). 3. M. K. Pangburn, H. J. Müller-Eberhard, Biochem. J. 235, 723 (1986). 4. P. Gros, F. J. Milder, B. J. Janssen, Nat. Rev. Immunol. 8, 48 (2008). 5. Y. Xu, S. V. Narayana, J. E. Volanakis, Immunol. Rev. 180, 123 (2001). 6. J. R. Dunkelberger, W. C. Song, Cell Res. 20, 34 (2010). 7. S. K. Law, A. W. Dodds, Protein Sci. 6, 263 (1997). 8. P. J. Haas, J. van Strijp, Immunol. Res. 37, 161 (2007). 9. M. C. Carroll, Mol. Immunol. 41, 141 (2004). 10. H. J. Müller-Eberhard, Annu. Rev. Immunol. 4, 503 (1986). 11. M. van Lookeren Campagne, C. Wiesmann, E. J. Brown, Cell. Microbiol. 9, 2095 (2007). 12. A. P. Sjöberg, L. A. Trouw, A. M. Blom, Trends Immunol. 30, 83 (2009). 13. S. H. Rooijakkers et al., Nat. Immunol. 10, 721 (2009).
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36. 37.
J. Wu et al., Nat. Immunol. 10, 728 (2009). F. J. Milder et al., Nat. Struct. Mol. Biol. 14, 224 (2007). B. J. Janssen et al., EMBO J. 28, 2469 (2009). E. Torreira, A. Tortajada, T. Montes, S. Rodríguez de Córdoba, O. Llorca, J. Immunol. 183, 7347 (2009). T. A. Springer, Structure 14, 1611 (2006). T. Xiao, J. Takagi, B. S. Coller, J. H. Wang, T. A. Springer, Nature 432, 59 (2004). A. A. Bhattacharya, M. L. Lupher Jr., D. E. Staunton, R. C. Liddington, Structure 12, 371 (2004). J. Hinshelwood, S. J. Perkins, J. Mol. Biol. 298, 135 (2000). S. V. Narayana et al., J. Mol. Biol. 235, 695 (1994). H. Jing et al., J. Mol. Biol. 282, 1061 (1998). C. M. Kam et al., J. Biol. Chem. 262, 3444 (1987). S. Kim, S. V. Narayana, J. E. Volanakis, J. Biol. Chem. 270, 24399 (1995). F. R. Taylor et al., Biochemistry 38, 2849 (1999). Z. Fishelson, H. J. Müller-Eberhard, J. Immunol. 129, 2603 (1982). Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr. Materials and methods are available as supporting material on Science Online. B. J. Janssen, A. Christodoulidou, A. McCarthy, J. D. Lambris, P. Gros, Nature 444, 213 (2006). C. Wiesmann et al., Nature 444, 217 (2006). E. Torreira, A. Tortajada, T. Montes, S. Rodríguez de Córdoba, O. Llorca, Proc. Natl. Acad. Sci. U.S.A. 106, 882 (2009). M. Shimaoka et al., Cell 112, 99 (2003). P. E. Bock, P. Panizzi, I. M. Verhamme, J. Thromb. Haemost. 5 (suppl. 1), 81 (2007). B. C. Lechtenberg, D. J. Johnson, S. M. Freund, J. A. Huntington, Proc. Natl. Acad. Sci. U.S.A. 107, 14087 (2010). S. Kim, S. V. Narayana, J. E. Volanakis, J. Immunol. 154, 6073 (1995). S. Kim, S. V. Narayana, J. E. Volanakis, Biochemistry 33, 14393 (1994).
Hsp90 and Environmental Stress Transform the Adaptive Value of Natural Genetic Variation Daniel F. Jarosz1 and Susan Lindquist1,2* How can species remain unaltered for long periods yet also undergo rapid diversification? By linking genetic variation to phenotypic variation via environmental stress, the Hsp90 protein-folding reservoir might promote both stasis and change. However, the nature and adaptive value of Hsp90-contingent traits remain uncertain. In ecologically and genetically diverse yeasts, we find such traits to be both common and frequently adaptive. Most are based on preexisting variation, with causative polymorphisms occurring in coding and regulatory sequences alike. A common temperature stress alters phenotypes similarly. Both selective inhibition of Hsp90 and temperature stress increase correlations between genotype and phenotype. This system broadly determines the adaptive value of standing genetic variation and, in so doing, has influenced the evolution of current genomes. any vital proteins have difficulty reaching their final folds or are inherently unstable when they do. To contend with such problems, organisms employ proteinremodeling factors and chaperones, including a subset known as heat-shock proteins (Hsps) (1). Unlike more general chaperones, Hsp90 specializes in folding metastable signal transducers (2) and key components of multiprotein complexes. These are hubs in interaction networks (3), and
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Hsp90 is thereby a “hub of hubs” in regulatory circuits. Also unlike most chaperones, Hsp90 is constitutively expressed at much higher levels than required to fulfill its normal functions. The Hsp90 chaperone system, then, constitutes a large but highly specific protein-folding reservoir (4). Environmental stresses can destabilize Hsp90 clients and produce additional unfolded substrates, straining the capacity of this buffer. We have suggested that these unusual features of the Hsp90
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38. H. Jing et al., EMBO J. 18, 804 (1999). 39. D. Ricklin, J. D. Lambris, Nat. Biotechnol. 25, 1265 (2007). 40. Coordinates and structure factors of C3bB, C3bBD*, FD (S183A), and FD (R202A) have been deposited in the Protein Data Bank (PDB) with accession numbers 2XWJ, 2XWB, 2XW9, and 2XWA, respectively. We gratefully thank the European Synchrotron Radiation Facility (ESRF) and the Swiss Light source (SLS) for the provision of synchrotron radiation facilities and beamline scientists of the SLS, ESRF, and the European Molecular Biology Laboratory for assistance. This work was supported by a “Top” grant (700.54.304 to P.G.) by the Council for Chemical Sciences of the Netherlands Organization for Scientific Research (NWO-CW) and NIH grants (AI030040, AI068730, AI072106, and GM062134 to J.D.L.). Author contributions: F.F. expressed and purified all FB and FD mutants, purified C3b, generated protein complexes, performed crystallization experiments, collected diffraction data, and solved the structures; F.F. and P.G. analyzed the structures; J.W. and R.S.W. helped with cloning and optimization of protein expression and purification; F.F. performed kinetic studies; D.R. and A.T. performed the SPR binding studies and hemolytic assays; F.F., D.R., J.D.L., and P.G. conceived the experiments; F.F. prepared the figures; and F.F., D.R., and P.G. wrote the manuscript. P.G. and J.D.L. co-supervised this work. Competing financial interests: P.G., F.F., D.R., and J.D.L. are co-inventors of a patent application titled “Structure of C3bB-factor D complex and use for rational drug design.”
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1816/DC1 Materials and Methods Figs. S1 to S12 Table S1 References 29 July 2010; accepted 4 November 2010 10.1126/science.1195821
chaperone system alter relationships between genotypes and phenotypes under conditions of environmental stress (5–8) and, in so doing, provide at least two routes to the rapid evolution of new traits: (i) Acting as a potentiator, Hsp90’s folding reservoir allows individual genetic variants to immediately create new phenotypes; when the reservoir is compromised, the traits previously created by potentiated variants disappear. (ii) Acting as a capacitor, Hsp90’s excess chaperone capacity buffers the effects of other variants, storing them in a phenotypically silent form; when the Hsp90 reservoir is compromised, the effects of these variants are released, allowing them to create new traits (5). To date, however, only two types of potentiated variants have been defined (2, 6), and the nature of buffered variants remains completely enigmatic. Some buffered traits map to specific chromosomal regions, suggesting a dependence on preexisting genetic variation. But similar phenotypes can be produced by epigenetic variation (9, 10) and transposon activation (11), providing alternative explanations for their ap1 Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142, USA. 2Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
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Our data reveal a highly concerted and specific activation mechanism based on cofactor-dependent and substrate-induced proteolysis, which provides an important “double-safety” catch to restrict complement amplification to C3b-tagged target cells. These detailed mechanistic insights into the central and transient, and therefore low-abundance, C3bBD complex of complement activation offer opportunities for the development of new therapeutics to fight complement-mediated diseases (39) such as age-related macular degeneration, atypical hemolytic uremic syndrome, membranoproliferative glomerulonephritis, and chronic inflammations that are associated with excessive or poorly controlled activation of complement. References and Notes
14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.
1. D. Ricklin, G. Hajishengallis, K. Yang, J. D. Lambris, Nat. Immunol. 11, 785 (2010). 2. M. J. Walport, N. Engl. J. Med. 344, 1058 (2001). 3. M. K. Pangburn, H. J. Müller-Eberhard, Biochem. J. 235, 723 (1986). 4. P. Gros, F. J. Milder, B. J. Janssen, Nat. Rev. Immunol. 8, 48 (2008). 5. Y. Xu, S. V. Narayana, J. E. Volanakis, Immunol. Rev. 180, 123 (2001). 6. J. R. Dunkelberger, W. C. Song, Cell Res. 20, 34 (2010). 7. S. K. Law, A. W. Dodds, Protein Sci. 6, 263 (1997). 8. P. J. Haas, J. van Strijp, Immunol. Res. 37, 161 (2007). 9. M. C. Carroll, Mol. Immunol. 41, 141 (2004). 10. H. J. Müller-Eberhard, Annu. Rev. Immunol. 4, 503 (1986). 11. M. van Lookeren Campagne, C. Wiesmann, E. J. Brown, Cell. Microbiol. 9, 2095 (2007). 12. A. P. Sjöberg, L. A. Trouw, A. M. Blom, Trends Immunol. 30, 83 (2009). 13. S. H. Rooijakkers et al., Nat. Immunol. 10, 721 (2009).
29. 30. 31. 32. 33. 34. 35.
36. 37.
J. Wu et al., Nat. Immunol. 10, 728 (2009). F. J. Milder et al., Nat. Struct. Mol. Biol. 14, 224 (2007). B. J. Janssen et al., EMBO J. 28, 2469 (2009). E. Torreira, A. Tortajada, T. Montes, S. Rodríguez de Córdoba, O. Llorca, J. Immunol. 183, 7347 (2009). T. A. Springer, Structure 14, 1611 (2006). T. Xiao, J. Takagi, B. S. Coller, J. H. Wang, T. A. Springer, Nature 432, 59 (2004). A. A. Bhattacharya, M. L. Lupher Jr., D. E. Staunton, R. C. Liddington, Structure 12, 371 (2004). J. Hinshelwood, S. J. Perkins, J. Mol. Biol. 298, 135 (2000). S. V. Narayana et al., J. Mol. Biol. 235, 695 (1994). H. Jing et al., J. Mol. Biol. 282, 1061 (1998). C. M. Kam et al., J. Biol. Chem. 262, 3444 (1987). S. Kim, S. V. Narayana, J. E. Volanakis, J. Biol. Chem. 270, 24399 (1995). F. R. Taylor et al., Biochemistry 38, 2849 (1999). Z. Fishelson, H. J. Müller-Eberhard, J. Immunol. 129, 2603 (1982). Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr. Materials and methods are available as supporting material on Science Online. B. J. Janssen, A. Christodoulidou, A. McCarthy, J. D. Lambris, P. Gros, Nature 444, 213 (2006). C. Wiesmann et al., Nature 444, 217 (2006). E. Torreira, A. Tortajada, T. Montes, S. Rodríguez de Córdoba, O. Llorca, Proc. Natl. Acad. Sci. U.S.A. 106, 882 (2009). M. Shimaoka et al., Cell 112, 99 (2003). P. E. Bock, P. Panizzi, I. M. Verhamme, J. Thromb. Haemost. 5 (suppl. 1), 81 (2007). B. C. Lechtenberg, D. J. Johnson, S. M. Freund, J. A. Huntington, Proc. Natl. Acad. Sci. U.S.A. 107, 14087 (2010). S. Kim, S. V. Narayana, J. E. Volanakis, J. Immunol. 154, 6073 (1995). S. Kim, S. V. Narayana, J. E. Volanakis, Biochemistry 33, 14393 (1994).
Hsp90 and Environmental Stress Transform the Adaptive Value of Natural Genetic Variation Daniel F. Jarosz1 and Susan Lindquist1,2* How can species remain unaltered for long periods yet also undergo rapid diversification? By linking genetic variation to phenotypic variation via environmental stress, the Hsp90 protein-folding reservoir might promote both stasis and change. However, the nature and adaptive value of Hsp90-contingent traits remain uncertain. In ecologically and genetically diverse yeasts, we find such traits to be both common and frequently adaptive. Most are based on preexisting variation, with causative polymorphisms occurring in coding and regulatory sequences alike. A common temperature stress alters phenotypes similarly. Both selective inhibition of Hsp90 and temperature stress increase correlations between genotype and phenotype. This system broadly determines the adaptive value of standing genetic variation and, in so doing, has influenced the evolution of current genomes. any vital proteins have difficulty reaching their final folds or are inherently unstable when they do. To contend with such problems, organisms employ proteinremodeling factors and chaperones, including a subset known as heat-shock proteins (Hsps) (1). Unlike more general chaperones, Hsp90 specializes in folding metastable signal transducers (2) and key components of multiprotein complexes. These are hubs in interaction networks (3), and
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Hsp90 is thereby a “hub of hubs” in regulatory circuits. Also unlike most chaperones, Hsp90 is constitutively expressed at much higher levels than required to fulfill its normal functions. The Hsp90 chaperone system, then, constitutes a large but highly specific protein-folding reservoir (4). Environmental stresses can destabilize Hsp90 clients and produce additional unfolded substrates, straining the capacity of this buffer. We have suggested that these unusual features of the Hsp90
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38. H. Jing et al., EMBO J. 18, 804 (1999). 39. D. Ricklin, J. D. Lambris, Nat. Biotechnol. 25, 1265 (2007). 40. Coordinates and structure factors of C3bB, C3bBD*, FD (S183A), and FD (R202A) have been deposited in the Protein Data Bank (PDB) with accession numbers 2XWJ, 2XWB, 2XW9, and 2XWA, respectively. We gratefully thank the European Synchrotron Radiation Facility (ESRF) and the Swiss Light source (SLS) for the provision of synchrotron radiation facilities and beamline scientists of the SLS, ESRF, and the European Molecular Biology Laboratory for assistance. This work was supported by a “Top” grant (700.54.304 to P.G.) by the Council for Chemical Sciences of the Netherlands Organization for Scientific Research (NWO-CW) and NIH grants (AI030040, AI068730, AI072106, and GM062134 to J.D.L.). Author contributions: F.F. expressed and purified all FB and FD mutants, purified C3b, generated protein complexes, performed crystallization experiments, collected diffraction data, and solved the structures; F.F. and P.G. analyzed the structures; J.W. and R.S.W. helped with cloning and optimization of protein expression and purification; F.F. performed kinetic studies; D.R. and A.T. performed the SPR binding studies and hemolytic assays; F.F., D.R., J.D.L., and P.G. conceived the experiments; F.F. prepared the figures; and F.F., D.R., and P.G. wrote the manuscript. P.G. and J.D.L. co-supervised this work. Competing financial interests: P.G., F.F., D.R., and J.D.L. are co-inventors of a patent application titled “Structure of C3bB-factor D complex and use for rational drug design.”
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1816/DC1 Materials and Methods Figs. S1 to S12 Table S1 References 29 July 2010; accepted 4 November 2010 10.1126/science.1195821
chaperone system alter relationships between genotypes and phenotypes under conditions of environmental stress (5–8) and, in so doing, provide at least two routes to the rapid evolution of new traits: (i) Acting as a potentiator, Hsp90’s folding reservoir allows individual genetic variants to immediately create new phenotypes; when the reservoir is compromised, the traits previously created by potentiated variants disappear. (ii) Acting as a capacitor, Hsp90’s excess chaperone capacity buffers the effects of other variants, storing them in a phenotypically silent form; when the Hsp90 reservoir is compromised, the effects of these variants are released, allowing them to create new traits (5). To date, however, only two types of potentiated variants have been defined (2, 6), and the nature of buffered variants remains completely enigmatic. Some buffered traits map to specific chromosomal regions, suggesting a dependence on preexisting genetic variation. But similar phenotypes can be produced by epigenetic variation (9, 10) and transposon activation (11), providing alternative explanations for their ap1 Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142, USA. 2Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
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pearance. Further, the adaptive value of buffered traits remains untested. To broadly determine the adaptive value of Hsp90’s effects on the relationship between genotype and phenotype, we examined 102 genetically diverse strains of Saccharomyces cerevisiae, from soil, fruit, wine, sake, beer, infected human patients, etc. (table S1). We measured growth in 100 conditions, including alternative carbon sources, oxidative stressors, antifungals, DNA-damaging agents, osmotic stressors, and small molecules that perturb varied cellular processes (fig. S1 and table S2). Using two chemically unrelated Hsp90 inhibitors, radicicol (Rad) and geldanamycin A (GdA) (12, 13), we then determined the effects of reducing the Hsp90 reservoir. We used concentrations that did not induce a general stress response (fig. S2) or inhibit the growth of any strain in standard medium. However, every strain exhibited substantial changes in growth under specific conditions. These varied widely and were sometimes positive, sometimes negative (Fig. 1A).
Rad and GdA produced very similar changes (table S3), confirming that they derived from Hsp90 inhibition, not off-target drug effects. The reproducible character of the traits suggests that they arose from preexisting variation rather than de novo mutation. To investigate quantitative trait loci (QTLs) that might underlie such traits, we analyzed 104 densely mapped haploid progeny from a cross between BY4716 (BY), a common laboratory strain originally obtained from a rotten fig, and RM11-1a (RM), a vineyard isolate (14). The coding sequences of these strains diverge by approximately 0.5%. Most genes were polymorphic in this cross but, importantly, Hsp90 was not. Our results agreed well with earlier studies under 23 overlapping conditions (15), demonstrating the robust consequences of the variation segregating in this cross. Again, we reduced the Hsp90 reservoir, and again, this strongly affected growth in specific strains in different conditions [Fig. 1B, fig. S3, and supporting online material (SOM)]. With the genetic diver-
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Fig. 1. Reducing the Hsp90 reservoir creates diverse phenotypes. Representative growth changes elicited by Hsp90 inhibition. The scale bar indicates log2 of the ratio of growth, with and without 5 mM Rad, in each condition for (A) wild strains and (B) BY × RM progeny. (C to F) Examples of rank-ordered growth distributions after 64 hours of growth of BY × RM progeny with (orange bars) and without (gray bars) 5 mM Rad. www.sciencemag.org
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sity present in just these two parents, the patterns of growth changes in their progeny were highly diverse (Fig. 1C-F), establishing the independent heritable segregation of many Hsp90-contingent alleles. No QTLs influenced growth in 5 mM Rad or GdA alone: Variations in drug pumps or detoxification pathways did not confound the analysis. In three conditions (trichostatin A, iodoacetate, and chlorhexidine), we could not map Hsp90-contingent QTLs. This might reflect epigenetic effects or the segregation of too many underlying variants (SOM). The vast majority of Hsp90-contingent phenotypes, however, could be mapped (table S5 and fig. S4). The QTLs responsible derived equally from BY and RM: Relaxed pressures of laboratory cultivation did not skew the nature of the accumulated variation. Hsp90 acted as a potentiator of variation almost as frequently as a capacitor: 44 previously apparent QTLs disappeared when the reservoir was reduced, and 63 previously silent QTLs became apparent (tables S4 and S5). With a false discovery rate of 0.05, at most three would have been expected by chance. As previously suggested (7, 8), reducing the Hsp90 reservoir produced genetically complex traits in a single step: Fully one-third of the traits involved two QTLs, and 15% involved three or more. As expected in such analyses, the QTLs encompassed many polymorphic alleles. To identify causative variants, we dissected four. In each case, we used three otherwise diverse progeny that carried BY sequence in the relevant region and three that carried RM sequence. In these four sets of six strains, we substituted every candidate gene, one by one, with the allele of the opposite parent. For the first QTL, Hsp90 acted as a capacitor for rapamycin resistance latent in the RM genome. Segregants with both RM and BY sequence were sensitive to the compound. When the Hsp90 reservoir was reduced, those with RM sequence gained the ability to grow. The QTL spanned eight genes, but all Hsp90-dependent growth effects were conferred by the open reading frame (ORF) of NFS1 (Fig. 2A). Nfs1 is a cysteine desulfurase that acts as a sulfur donor in tRNA thiolation (16). Rapamycin targets the highly conserved TOR proteins, which regulate growth in all eukaryotes, primarily via the protein synthesis machinery (17). Other mutations in this same tRNA modification pathway confer rapamycin sensitivity. Furthermore, Nfs1 function is known to depend on Hsp90 (18). Thus, changes in the Hsp90 reservoir are logically linked to polymorphisms in this region. For the second QTL, Hsp90 acted as a potentiator for deoxycholate (DOC) resistance conferred by standing variation in the RM genome. Segregants carrying RM sequence were DOCresistant, whereas those carrying BY sequence were sensitive. When the Hsp90 reservoir was reduced, strains carrying RM sequence lost resistance. Allele replacements demonstrated that this resistance arose entirely from the RM PDR8 ORF (Fig. 2B).
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Fig. 2. Genetic dissection of Hsp90-contingent alleles. The growth of allelereplacement strains with (solid bars) and without (open bars) 5 mM Rad is normalized to that of the BY allele–replacement strain in each condition without Rad. (A) QTLs conferring Hsp90-buffered rapamycin resistance, due to the RM NFS1 allele (44 hours). (B) QTLs conferring Hsp90-potentiated DOC resistance, due to the RM PDR8 allele. (C). QTLs conferring Hsp90-buffered
HU resistance, due to the BY MEC1 allele (25 hours). Hsp90-potentiated resistance to UV irradiation was due to the same allele (20 J/m2; 25 hours after irradiation). (D) QTLs conferring CDNB resistance, due to polymorphisms in the 3′ untranslated region (UTR) of RM NDI1 (44 hours). Overexpression of BY NDI1 rescues CDNB toxicity. Error bars in the entire figure represent the standard deviation of three biological replicates.
Fig. 3. Environmental stress recapitulates phenotypic effects of Hsp90 inhibition. Calculations and symbols are as in Fig. 2. Growth of allelereplacement strains at 23°C, 39°C, or after a deletion of one of the Hsp90 genes, HSP82, at 23°C, is shown. (A) NFS1 (0.5 mM rapamycin; 44 hours). (B)
PDR8 (1 mM DOC; 80 hours). (C) MEC1 (25 mM HU; 25 hours) (D) RM intergenic region between NDI1 and GTR1 (5 mM CDNB; 44 hours). Because the HSP82 deletion reduces Hsp90 function more than does 5 mM Rad, it often creates stronger phenotypes.
DOC facilitates fat emulsification in the intestine and acts as an antimicrobial agent (19). PDR8 encodes a transcription factor not known to depend on Hsp90. To determine whether RM polymorphisms caused Pdr8 to become an Hsp90 client, we examined other Pdr8-dependent phenotypes: growth in NaCl, hygromycin B, and LiCl (20). Reducing Hsp90 did not affect any of these (fig. S6), suggesting that RM Prd8 does not require Hsp90 for function. More likely, RM polymorphisms exert their effects via Hsp90’s interaction with another, DOC-specific element of Pdr8’s circuitry. For the third QTL (Fig. 2C), Hsp90 acted as a capacitor for hydroxyurea (HU) resistance latent
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in the BY genome. Segregants carrying BY sequence were initially more sensitive than those carrying RM sequence. Reducing the Hsp90 reservoir increased the resistance of segregants carrying BY sequence but not RM sequence. This trait proved to be conferred by MEC1. HU reduces intracellular deoxynucleotide triphosphate concentrations, eliciting replication stress (21). Mec1 coordinates multicomponent repair and checkpoint pathways that differ for different damage responses (22). A major QTL that conferred resistance to ultraviolet (UV) radiation also proved to map to MEC1. In this case, however, Hsp90 acted as a potentiator. The UV resistance of strains carrying the BY allele was lost when
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the Hsp90 reservoir was reduced. Because Hsp90 inhibition affected two Mec1 functions in different ways, these results suggest that Mec1 is an Hsp90 client, whose partitioning between diverse complexes is affected by Hsp90-contingent polymorphisms. For the fourth QTL, Hsp90 acted as a capacitor for CDNB (1-chloro-2,4-nitrobenzene) resistance latent in the RM genome. Segregants with either RM or BY sequence were sensitive to this oxidative stressor. When the Hsp90 reservoir was compromised, those with RM sequence gained the ability to grow. Allele replacements established RM NDI1 as the causative variant, but here, the polymorphisms resided in
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Clustering by phenotype with reduced Hsp90 reservoir Fig. 4. Hsp90 inhibition and environmental stress improve the corrrelations between genotype and phenotype. Phylogenetic clustering is derived from (27) and (25). Phenotypic clustering is described in the SOM.
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Clustering by phenotype the 3′ untranslated region (Fig. 2D) rather than in the ORF. NDI1 encodes an oxidoreductase that defends against oxidative stresses (23). We found that CDNB normally had little effect on NDI1 mRNA levels. But when the Hsp90 reservoir was reduced, transcripts produced by NDI1 in response to CDNB stress increased by ~100-fold in segregants with RM relative to those with BY sequence. Increased NDI1 transcripts fully explained the phenotype: Forced overexpression of the normally ineffective BY allele (using a Gal1 promoter) was sufficient to confer CDNB resistance (Fig. 2D). How might changes in Hsp90 affect the expression of genetic variation in nature? Hsp90 is induced by environmental stress (4). We have postulated that this increase is sometimes insufficient to maintain the folding reservoir, changing the manifestation of genetic variation (24). Indeed, all four alleles analyzed above were affected by a simple temperature stress (growth at 39°C) in the same manner as by Hsp90 inhibition (Fig. 3AD). Moreover, the same phenotypes were elicited by genetic deletion of one of two Hsp90 alleles, confirming that they are due to changes in Hsp90 function. Far more broadly, we find that even with the abundant genetic diversity present in the wild strains, the effects of temperature on phenotypic transitions were similar to those of Rad and GdA (Pearson correlation ~0.61 and ~0.56, respectively, and see SOM). We took advantage of the fact that 48 of these strains have been sequenced to ask whether their genomes carry an impress of Hsp90’s selective forces. As previously reported in other strains and circumstances (25), across the ~100,000 polymorphisms present here with the 100 growth con-
ditions we used, the correlation between genoptye and phenotype was relatively weak (Spearman correlation ~0.35) in the absence of Hsp90 inhibition. Similar strains often had divergent phenotypes, and divergent genotypes often produced similar phenotypes. The correlation between genotype and phenotype became much stronger when the Hsp90 reservoir was reduced (Spearman correlation ~0.54; Fig. 4). Ten million random data permutations did not produce a single increase of such magnitude (P < 10−7). This transition was evident across diverse ecological niches. A simple increase in growth temperature had a similar effect (Spearman correlation ~0.48). It is difficult to imagine how environmental stress in general, and Hsp90 in particular, could have such a strong impact on genotype-phenotype correlations unless it had acted though the evolutionary history of these strains to influence the retention of a broad swath of genetic variation. Our hypothesis that Hsp90 plays a role in evolutionary processes remains controversial because of a paucity of hard evidence (26). Here we establish that Hsp90 operates on roughly 20% of the preexisting genetic variation in S. cerevisiae to both preserve phenotypic robustness and provide a broad conduit to diversification. Further, environmental stress creates a dynamic interface for transitioning between these effects in a manner that has left an impress on current genomes. Half of the traits buffered by Hsp90 and half potentiated by it had beneficial effects on growth; the other half were detrimental. What might maintain such contrasting adaptive effects? Many proteins in regulatory hubs are metastable, essential for life, and constitutively dependent on Hsp90. The need to preserve these functions during environmental
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stress might provide all the selective pressure needed to maintain this protein-folding reservoir. The accumulation of new Hsp90-contingent alleles might simply be an inevitable consequence of its existence. Once established, however, the capacity of the reservoir to facilitate the appearance of new traits—evolvability—might have provided an additional selective advantage. Theory holds that natural selection is unable to sustain mechanisms for evolvability because genetic recombination would inevitably separate evolvability genes from the alleles on which they act (5, 24). Negating this objection, Hsp90-contingent polymorphisms are dispersed throughout the genome; loss of some through genetic reassortment would be balanced by the gain of others. A particular advantage of the Hsp90 system is that it provides a route to genetically complex traits in a single step, via combinatorial gain and loss of phenotypic variation in response to environmental stress. Under selective pressure, multiple mechanisms could lead to the fixation of such traits (5, 24). In Drosophila, at least, Hsp90 can also create new traits by affecting epigenetic variation (10) and transposon-mediated mutagenesis (11), and it probably affects genome stability by other mechanisms as well (5). The strength of the Hsp90 buffer and the wealth of mechanisms by which it creates heritable new traits in response to environmental change may help to explain two long-puzzling features of evolution: the stability of phenotypes over long periods despite the accumulation of genetic variation and their rapid appearance of heritable new phenotypes in response to changing environments.
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References and Notes 1. S. Lindquist, E. A. Craig, Annu. Rev. Genet. 22, 631 (1988). 2. L. Neckers, J. Biosci. 32, 517 (2007).
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REPORTS 15. E. O. Perlstein, D. M. Ruderfer, D. C. Roberts, S. L. Schreiber, L. Kruglyak, Nat. Genet. 39, 496 (2007). 16. S. Leidel et al., Nature 458, 228 (2009). 17. A. Ramanathan, S. L. Schreiber, J. Biol. 6, 3 (2007). 18. A. J. McClellan et al., Cell 131, 121 (2007). 19. T. Inagaki et al., Proc. Natl. Acad. Sci. U.S.A. 103, 3920 (2006). 20. I. Hikkel et al., J. Biol. Chem. 278, 11427 (2003). 21. E. Vitols, V. A. Bauer, E. C. Stanbrough, Biochem. Biophys. Res. Commun. 41, 71 (1970). 22. A. M. Friedel, B. L. Pike, S. M. Gasser, Curr. Opin. Cell Biol. 21, 237 (2009). 23. B. B. Seo, M. Marella, T. Yagi, A. Matsuno-Yagi, FEBS Lett. 580, 6105 (2006). 24. T. A. Sangster, S. L. Lindquist, C. Queitsch, Bioessays 26, 348 (2004). 25. G. Liti et al., Nature 458, 337 (2009). 26. T. Mitchell-Olds, C. A. Knight, Science 296, 2348 (2002).
Ectopic Expression of Germline Genes Drives Malignant Brain Tumor Growth in Drosophila Ana Janic,1 Leire Mendizabal,1* Salud Llamazares,1 David Rossell,2 Cayetano Gonzalez1,3† Model organisms such as the fruit fly Drosophila melanogaster can help to elucidate the molecular basis of complex diseases such as cancer. Mutations in the Drosophila gene lethal (3) malignant brain tumor cause malignant growth in the larval brain. Here we show that l(3)mbt tumors exhibited a soma-to-germline transformation through the ectopic expression of genes normally required for germline stemness, fitness, or longevity. Orthologs of some of these genes were also expressed in human somatic tumors. In addition, inactivation of any of the germline genes nanos, vasa, piwi, or aubergine suppressed l(3)mbt malignant growth. Our results demonstrate that germline traits are necessary for tumor growth in this Drosophila model and suggest that inactivation of germline genes might have tumor-suppressing effects in other species. he Drosophila tumor-suppressor gene lethal (3) malignant brain tumor [l(3)mbt] was identified as a temperature-sensitive mutation that caused malignant growth in the larval brain (1). Other l(3)mbt mutant alleles obtained later show the same temperature-sensitive phenotype (2). L(3)mbt’s closest homologs, Drosophila Scm (Sex comb on midleg) and Sfmbt (Scm-related gene containing four mbt domains), encode Polycomb Group proteins (3). L3MBTL1, the human homolog of Drosophila L(3)MBT (3), is a transcriptional repressor (4) that is found in a complex with core histones, heterochromatin protein 1g (HP1g), and RB (Retinoblastoma protein) and can compact nucleosomes (5). Drosophila L(3)MBT is a substoichiometric component of the dREAM-MMB complex, which includes
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1 Cell Division Group, Institute for Research in Biomedicine (IRBBarcelona), PCB, c/Baldiri Reixac 10-12, Barcelona, Spain. 2 Bioinformatics and Biostatistics Unit, IRB-Barcelona, PCB, c/Baldiri Reixac 10-12, Barcelona, Spain. 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain.
*Present address: Genomics Core Facility, Vall d'Hebrón Institute of Oncology, Passeig Vall d'Hebron 119, 08035 Barcelona, Spain. †To whom correspondence should be addressed. E-mail:
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the two Drosophila Retinoblastoma-family proteins and the Myb-MuvB (MMB) complex (6). Depletion of components of the dREAM/MMB complex in Drosophila Kc cells by RNA interference results in genome-wide changes in gene expression (7). These data strongly suggest that l(3)mbt function might contribute to establishing and maintaining certain differentiated states through the stable silencing of specific genes (3, 7). To identify the genes whose misexpression might account for the growth of l(3)mbt tumors (henceforth referred to as mbt tumors), we carried out genome-wide gene expression profiling of l(3)mbt E2 and l(3)mbt ts1 homozygous and transheterozygous larval brains raised at restrictive temperature (29°C). We also analyzed l(3)mbt ts1 tumors at the 1st, 5th, and 10th rounds of allograft culture in adult flies (T1, T5, and T10, respectively). Brains from homozygous white1118 (w1118), l(3)mbt E2, or l(3)mbt ts1 larvae raised at permissive temperature (17°C) were used as controls. For comparison, we also profiled larval brain malignant neoplasms caused by mutation in brain tumor (brat) as well as allograft cultures at T1,T5, and T10 of tumors caused by mutants in brat, lethal giant larvae (lgl), miranda (mira), prospero (pros), and partner of inscuteable (pins) (8).
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27. J. Schacherer, J. A. Shapiro, D. M. Ruderfer, L. Kruglyak, Nature 458, 342 (2009). 28. We are grateful to L. Kruglyak, E. Perlstein, L. Cowen, S. Schreiber, A. Regev, I. Barassa, E. Louis, S. Dietzmann, and F. Dietrich for materials, discussion, and help. S.L. is a Howard Hughes Medical Institute (HHMI) investigator, and D.F.J. is an HHMI fellow of the Damon Runyon Cancer Research Foundation (DRG-1964-08). This work was supported by grants from the Broad Institute, the G. Harold and Leila Y. Mathers Foundation, and HHMI.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1820/DC1 Materials and Methods Figs. S1 to S6 Tables S1 to S5 References 22 July 2010; accepted 18 November 2010 10.1126/science.1195487
Hierarchical clustering plots of these data (table S1) reveal three distinct clusters that include control larval brains, mbt larval brain tumors, and cultured l(3)mbt ts1 tumors, respectively (fig. S1). From these data, we identified 151 genes that were either overexpressed (n = 125) or underexpressed (n = 26) in all three larval mbt tumor types compared to all three controls (table S2). From this list, we removed those genes that were also up- (n = 23) or downregulated (n = 14) in larval brat neoplasms and, hence, likely to encode functions generally required for larval brain tumor growth. We refer to the expression levels of the remaining 102 up-regulated genes as the mbt signature (MBTS) (table S3). MBTS is notably enhanced in cultured mbt tumors and can be used unequivocally to distinguish mbt tumors from other cultured malignant brain neoplasms like lgl, mira, pros, pins, or brat (Fig. 1A and table S3). Individual MBTS genes, however, are also up-regulated in some of these tumors. The function of most MBTS genes remains unknown. However, a quarter of them (26 of 102) are genes required in the germ line (Fig. 1B and table S4A). For instance, nanos (nos), female sterile(1)Yb ( fs(1)Yb), and zero population growth (zpg) function in the establishment of the pole plasm in the egg and cystoblasts differentiation (9). The gonad-specific thioredoxins ThioredoxinT (TrxT) and deadhead (dhd), giant nuclei (gnu), corona (cona), hold'em (hdm), matotopetli (topi), and the female germline-specific gTUB37C isoform function during oocyte differentiation, meiosis, and syncytial embryo development (10–15). Also piwi, aubergine (aub), krimper (krimp), and tejas (tej) are involved in the biogenesis of Piwiinteracting RNAs (piRNAs) that protect germline cells against transposable elements and viruses (16, 17). Some of these genes also have functions that are not germline related. For instance, some piwi alleles display synthetic lethality (18), and nos is required during nervous system development (19). Driven by the high percentage of MBTS genes that have germline functions, we looked for other germline-related genes that do not meet the stringent criteria applied to select the 102 MBTS genes, but are overexpressed in mbt tumors (table S4B). Among these we found the genes that encode the
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3. Y. Gong et al., Mol. Syst. Biol. 5, 275 (2009). 4. K. A. Borkovich, F. W. Farrelly, D. B. Finkelstein, J. Taulien, S. Lindquist, Mol. Cell. Biol. 9, 3919 (1989). 5. D. F. Jarosz, M. Taipale, S. Lindquist, Annu. Rev. Genet. 44, 189 (2010). 6. L. E. Cowen, S. L. Lindquist, Science 309, 2185 (2005). 7. C. Queitsch, T. A. Sangster, S. Lindquist, Nature 417, 618 (2002). 8. S. L. Rutherford, S. Lindquist, Nature 396, 336 (1998). 9. M. Tariq, U. Nussbaumer, Y. Chen, C. Beisel, R. Paro, Proc. Natl. Acad. Sci. U.S.A. 106, 1157 (2009). 10. V. Sollars et al., Nat. Genet. 33, 70 (2002). 11. V. Specchia et al., Nature 463, 662 (2010). 12. S. V. Sharma, T. Agatsuma, H. Nakano, Oncogene 16, 2639 (1998). 13. L. Whitesell, E. G. Mimnaugh, B. De Costa, C. E. Myers, L. M. Neckers, Proc. Natl. Acad. Sci. U.S.A. 91, 8324 (1994). 14. R. B. Brem, G. Yvert, R. Clinton, L. Kruglyak, Science 296, 752 (2002).
REPORTS 15. E. O. Perlstein, D. M. Ruderfer, D. C. Roberts, S. L. Schreiber, L. Kruglyak, Nat. Genet. 39, 496 (2007). 16. S. Leidel et al., Nature 458, 228 (2009). 17. A. Ramanathan, S. L. Schreiber, J. Biol. 6, 3 (2007). 18. A. J. McClellan et al., Cell 131, 121 (2007). 19. T. Inagaki et al., Proc. Natl. Acad. Sci. U.S.A. 103, 3920 (2006). 20. I. Hikkel et al., J. Biol. Chem. 278, 11427 (2003). 21. E. Vitols, V. A. Bauer, E. C. Stanbrough, Biochem. Biophys. Res. Commun. 41, 71 (1970). 22. A. M. Friedel, B. L. Pike, S. M. Gasser, Curr. Opin. Cell Biol. 21, 237 (2009). 23. B. B. Seo, M. Marella, T. Yagi, A. Matsuno-Yagi, FEBS Lett. 580, 6105 (2006). 24. T. A. Sangster, S. L. Lindquist, C. Queitsch, Bioessays 26, 348 (2004). 25. G. Liti et al., Nature 458, 337 (2009). 26. T. Mitchell-Olds, C. A. Knight, Science 296, 2348 (2002).
Ectopic Expression of Germline Genes Drives Malignant Brain Tumor Growth in Drosophila Ana Janic,1 Leire Mendizabal,1* Salud Llamazares,1 David Rossell,2 Cayetano Gonzalez1,3† Model organisms such as the fruit fly Drosophila melanogaster can help to elucidate the molecular basis of complex diseases such as cancer. Mutations in the Drosophila gene lethal (3) malignant brain tumor cause malignant growth in the larval brain. Here we show that l(3)mbt tumors exhibited a soma-to-germline transformation through the ectopic expression of genes normally required for germline stemness, fitness, or longevity. Orthologs of some of these genes were also expressed in human somatic tumors. In addition, inactivation of any of the germline genes nanos, vasa, piwi, or aubergine suppressed l(3)mbt malignant growth. Our results demonstrate that germline traits are necessary for tumor growth in this Drosophila model and suggest that inactivation of germline genes might have tumor-suppressing effects in other species. he Drosophila tumor-suppressor gene lethal (3) malignant brain tumor [l(3)mbt] was identified as a temperature-sensitive mutation that caused malignant growth in the larval brain (1). Other l(3)mbt mutant alleles obtained later show the same temperature-sensitive phenotype (2). L(3)mbt’s closest homologs, Drosophila Scm (Sex comb on midleg) and Sfmbt (Scm-related gene containing four mbt domains), encode Polycomb Group proteins (3). L3MBTL1, the human homolog of Drosophila L(3)MBT (3), is a transcriptional repressor (4) that is found in a complex with core histones, heterochromatin protein 1g (HP1g), and RB (Retinoblastoma protein) and can compact nucleosomes (5). Drosophila L(3)MBT is a substoichiometric component of the dREAM-MMB complex, which includes
T
1 Cell Division Group, Institute for Research in Biomedicine (IRBBarcelona), PCB, c/Baldiri Reixac 10-12, Barcelona, Spain. 2 Bioinformatics and Biostatistics Unit, IRB-Barcelona, PCB, c/Baldiri Reixac 10-12, Barcelona, Spain. 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain.
*Present address: Genomics Core Facility, Vall d'Hebrón Institute of Oncology, Passeig Vall d'Hebron 119, 08035 Barcelona, Spain. †To whom correspondence should be addressed. E-mail:
[email protected]
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the two Drosophila Retinoblastoma-family proteins and the Myb-MuvB (MMB) complex (6). Depletion of components of the dREAM/MMB complex in Drosophila Kc cells by RNA interference results in genome-wide changes in gene expression (7). These data strongly suggest that l(3)mbt function might contribute to establishing and maintaining certain differentiated states through the stable silencing of specific genes (3, 7). To identify the genes whose misexpression might account for the growth of l(3)mbt tumors (henceforth referred to as mbt tumors), we carried out genome-wide gene expression profiling of l(3)mbt E2 and l(3)mbt ts1 homozygous and transheterozygous larval brains raised at restrictive temperature (29°C). We also analyzed l(3)mbt ts1 tumors at the 1st, 5th, and 10th rounds of allograft culture in adult flies (T1, T5, and T10, respectively). Brains from homozygous white1118 (w1118), l(3)mbt E2, or l(3)mbt ts1 larvae raised at permissive temperature (17°C) were used as controls. For comparison, we also profiled larval brain malignant neoplasms caused by mutation in brain tumor (brat) as well as allograft cultures at T1,T5, and T10 of tumors caused by mutants in brat, lethal giant larvae (lgl), miranda (mira), prospero (pros), and partner of inscuteable (pins) (8).
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27. J. Schacherer, J. A. Shapiro, D. M. Ruderfer, L. Kruglyak, Nature 458, 342 (2009). 28. We are grateful to L. Kruglyak, E. Perlstein, L. Cowen, S. Schreiber, A. Regev, I. Barassa, E. Louis, S. Dietzmann, and F. Dietrich for materials, discussion, and help. S.L. is a Howard Hughes Medical Institute (HHMI) investigator, and D.F.J. is an HHMI fellow of the Damon Runyon Cancer Research Foundation (DRG-1964-08). This work was supported by grants from the Broad Institute, the G. Harold and Leila Y. Mathers Foundation, and HHMI.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1820/DC1 Materials and Methods Figs. S1 to S6 Tables S1 to S5 References 22 July 2010; accepted 18 November 2010 10.1126/science.1195487
Hierarchical clustering plots of these data (table S1) reveal three distinct clusters that include control larval brains, mbt larval brain tumors, and cultured l(3)mbt ts1 tumors, respectively (fig. S1). From these data, we identified 151 genes that were either overexpressed (n = 125) or underexpressed (n = 26) in all three larval mbt tumor types compared to all three controls (table S2). From this list, we removed those genes that were also up- (n = 23) or downregulated (n = 14) in larval brat neoplasms and, hence, likely to encode functions generally required for larval brain tumor growth. We refer to the expression levels of the remaining 102 up-regulated genes as the mbt signature (MBTS) (table S3). MBTS is notably enhanced in cultured mbt tumors and can be used unequivocally to distinguish mbt tumors from other cultured malignant brain neoplasms like lgl, mira, pros, pins, or brat (Fig. 1A and table S3). Individual MBTS genes, however, are also up-regulated in some of these tumors. The function of most MBTS genes remains unknown. However, a quarter of them (26 of 102) are genes required in the germ line (Fig. 1B and table S4A). For instance, nanos (nos), female sterile(1)Yb ( fs(1)Yb), and zero population growth (zpg) function in the establishment of the pole plasm in the egg and cystoblasts differentiation (9). The gonad-specific thioredoxins ThioredoxinT (TrxT) and deadhead (dhd), giant nuclei (gnu), corona (cona), hold'em (hdm), matotopetli (topi), and the female germline-specific gTUB37C isoform function during oocyte differentiation, meiosis, and syncytial embryo development (10–15). Also piwi, aubergine (aub), krimper (krimp), and tejas (tej) are involved in the biogenesis of Piwiinteracting RNAs (piRNAs) that protect germline cells against transposable elements and viruses (16, 17). Some of these genes also have functions that are not germline related. For instance, some piwi alleles display synthetic lethality (18), and nos is required during nervous system development (19). Driven by the high percentage of MBTS genes that have germline functions, we looked for other germline-related genes that do not meet the stringent criteria applied to select the 102 MBTS genes, but are overexpressed in mbt tumors (table S4B). Among these we found the genes that encode the
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3. Y. Gong et al., Mol. Syst. Biol. 5, 275 (2009). 4. K. A. Borkovich, F. W. Farrelly, D. B. Finkelstein, J. Taulien, S. Lindquist, Mol. Cell. Biol. 9, 3919 (1989). 5. D. F. Jarosz, M. Taipale, S. Lindquist, Annu. Rev. Genet. 44, 189 (2010). 6. L. E. Cowen, S. L. Lindquist, Science 309, 2185 (2005). 7. C. Queitsch, T. A. Sangster, S. Lindquist, Nature 417, 618 (2002). 8. S. L. Rutherford, S. Lindquist, Nature 396, 336 (1998). 9. M. Tariq, U. Nussbaumer, Y. Chen, C. Beisel, R. Paro, Proc. Natl. Acad. Sci. U.S.A. 106, 1157 (2009). 10. V. Sollars et al., Nat. Genet. 33, 70 (2002). 11. V. Specchia et al., Nature 463, 662 (2010). 12. S. V. Sharma, T. Agatsuma, H. Nakano, Oncogene 16, 2639 (1998). 13. L. Whitesell, E. G. Mimnaugh, B. De Costa, C. E. Myers, L. M. Neckers, Proc. Natl. Acad. Sci. U.S.A. 91, 8324 (1994). 14. R. B. Brem, G. Yvert, R. Clinton, L. Kruglyak, Science 296, 752 (2002).
synaptonemal complex protein Crossover suppressor on 3 of Gowen [C(3)G] and the cell cycle kinase Pan gu (PNG), which interact with the proteins encoded by the MBTS genes cona and gnu, respectively (11, 13). The same applies to Squash (SQU), Spindle-E (SPN-E), Maelstrom (MAEL), and AGO3, components of the piRNA machinery, which colocalize with other MBTS proteins in nuage (16, 17). To determine whether the mRNAs that we found ectopically expressed in mbt tumors are translated, we checked for protein expression with a selected number of currently available antibodies. Given the key role of VASA in the assembly of the pole plasm and germline development (20), we included it in this study, even though vasa mRNA levels are not significantly increased in mbt tumors. By Western blot, we confirmed that PIWI, AUB, and VASA are ectopically expressed in mbt tumors (Fig. 2A). Immunofluorescence studies also revealed the ectopic expression in l(3)mbt ts1 brains raised at 29°C of C(3)G, SQU, and VASA (Fig. 2B). These results show that some of the germline genes ectopically expressed in mbt tumors are translated. However, we have not been able to confirm the expression of other proteins, including MAEL, ORB, BAM, GNU, and TOPI, which sug-
gests that, possible technical problems aside, either the corresponding mRNAs are not translated or these proteins might be unstable in such an ectopic environment. The expression of VASA, by contrast, suggests that other mRNAs whose levels are not appreciably increased in mbt tumors might actually be ectopically translated. Prompted by the expression in l(3)mbt ts1 brains of several genes involved in the biogenesis and regulation of piRNAs, we sequenced 23- to 30-nucleotide RNAs from l(3)mbt ts1 larval brain tumors and from wild-type brains and ovaries. We found 117 known piRNAs and microRNAs (miRNAs) in l(3)mbt ts1 larval brain tumor samples (table S5). Of these, 31 are either not expressed in wild-type brains or are expressed there at less than 10% their level in larval brain tumors. Most of them are highly expressed in wild-type ovaries, thus substantiating further the ectopic acquisition of germline traits that characterizes mbt tumors. We do not know which, if any, of the germline genes that are up-regulated in mbt tumors are direct targets of l(3)mbt or if their ectopic expression is a downstream consequence of intermediate events. The putative direct targets of l(3)mbt are many. The dREAM-MMB complex, of which L(3)MBT is a substoichiometric component (6),
Fig. 1. Gene expression profile of mbt tumors. (A) Heatmap of expression levels of the genes that are most significantly up-regulated in larval brain mbt tumors (mbt tumor signature, MBTS). Samples include wild-type larval brains, larval mbt tumors, and different types of larval brain malignant neoplasms in the 1st, 5th, and 10th rounds of allograft culture (T1, T5, and T10, respectively). (B) MBTS genes with known germline functions. www.sciencemag.org
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has been found to be promoter-proximal to 32% of Drosophila genes, and MMB factors are known to regulate transcription of a wide range of genes in Drosophila Kc cells (7). In addition, we do not have an estimate for the number of proteins like VASA that, despite their low mRNA expression levels, might be up-regulated in mbt tumors. Indeed, many of these genes, as well as the piRNAs and miRNAs expressed in mbt tumors, might themselves regulate the basal transcription and translation machineries, adding a further layer of gene expression modulation (21–23). We then determined the extent to which ectopic expression of germline genes contributes to mbt tumor growth. To this end, we first quantified larval brain growth in individuals that were mutant for l(3)mbt ts1 alone, or double mutant for l(3)mbt ts1 and one of several of the germline genes that are ectopically expressed in mbt tumors (Fig. 3). Measured as the total amount of protein, the average brain size in l(3)mbt ts1 (21 T 6 mg of protein per brain, n = 5) is about seven times as large (P < 0.0001) as that in control w1118 larvae, a difference that is not significantly reduced by the additional loss of zpg, Pxt, or AGO3. However, brain overgrowth is reduced to a size similar to that of the control in l(3)mbt ts1 larvae that are also mutant for either piwi (P < 0.0001), vasa (P < 0.0001), aub (P = 0.0003), or nos (P = 0.001) (Fig. 3). The loss of piwi does not prevent brain overgrowth in brat k06028 mutant larvae (P = 0.72). We then quantified tumor growth after allograft in adult flies (Fig. 3). The frequency with which l(3)mbt ts1 homozygous larval brain tissue develops tumors in this assay (70%, n = 67) is not significantly reduced by the additional loss of zpg or AGO3 and is only moderately reduced by the loss of Pxt (P = 0.03), but it is markedly reduced by the additional loss of piwi (P < 0.0001), vasa (P < 0.0001), aub (P = 0.0002), or nos (P < 0.0001). The frequency of brat k06028 tumor formation (80%, n = 10) is not affected by the loss of piwi (73%, n = 15) or nos (86%, n = 7, P = 1). These results demonstrate that the ectopic expression of germline genes, particularly piwi, vasa, nos, and aub, significantly contributes to mbt tumor growth. A closely reminiscent soma-to-germline transformation observed in mutants in the Caenorhabditis elegans Rb homolog LIN-35, as well as in long-lived C. elegans strains (20, 24, 25), has led some to propose that the acquisition of germline characteristics by somatic cells might contribute to increased fitness and survival, a mechanism that could contribute to the transformation of mammalian cells (24, 25). Also in humans, some genes that are predominantly expressed in germline cells and have little or no expression in somatic adult tissues become aberrantly activated in various malignancies, including melanoma and several types of carcinomas (26, 27). These are known as cancertestis (CT) genes or cancer-germline (CG) genes (28). A subset of these CG genes encode antigens that are immunogenic in cancer patients and are being pursued as biomarkers and as targets for therapeutic cancer vaccines (29, 30).
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Fig. 2. Ectopic expression of germline proteins in l(3)mbt ts1 larval brain tumors. (A) Western blot. PIWI, AUB, and VASA are ectopically expressed in l(3)mbt ts1 brain tumors. aTUB is used as a loading control. (B) Immunofluorescence. VASA, SQU, and C(3)G are overexpressed in l(3)mbt ts1 brains raised at 29°C. Lowmagnification views (left) reveal VASA staining concentrated in the outer proliferative center (OPC) and in undifferentiated cells of the central brain (CB) (scale bar, 50 mm). High-magnification views (middle and right) show that SQU and C(3)G localize in the cytoplasm and on condensed chromatin, respectively (scale bar, 10 mm). Brains were counterstained with DAPI (4´,6´-diamidino-2-phenylindole) (DNA) and antibodies against the neuroblast marker MIRA. Fig. 3. The role of ectopically expressed germline genes in mbt tumor growth. Brain micrographs were taken from larvae of the corresponding genotypes raised at 29°C (scale bars, 100 mm). Larval brain size is shown as mean T SD (in micrograms) of protein per brain (n = number of brains). Adult fly micrographs were taken 10 days after implantation of green fluorescent protein (GFP)– labeled larval brain tissue. In the absence of tumor growth, GFP signal is either undetectable or is localized to a very small piece of green tissue that is about the size of the implant (arrows). Tumor growth was quantified as the percentage (%) of n hosts in which the implanted tissue (green) grew over the entire abdomen of the host. P-values refer to the difference between each double-mutant combination and l(3)mbt ts1, or between piwi1 brat K06028 and brat k06028. Human CG genes are suspected to contribute to oncogenesis germline traits like immortality, invasiveness, and hypomethylation (28), but their actual role in cancer remains unknown. Our results demonstrate that ectopic germline traits are necessary for tumor growth in Drosophila mbt tumors, suggesting that their inactivation might have tumor-suppressing effects in other species. Some germline genes up-regulated in mbt tumors are orthologs of human CG genes like PIWIL1/piwi
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(31, 32), NANOS1/nanos (33), and SYCP1 /c(3)G (34). The list of genes up-regulated in mbt tumors includes many other germline genes that might also be relevant in human cancer. References and Notes 1. E. Gateff, T. Löffler, J. Wismar, Mech. Dev. 41, 15 (1993). 2. C. B. Yohn, L. Pusateri, V. Barbosa, R. Lehmann, Genetics 165, 1889 (2003). 3. R. Bonasio, E. Lecona, D. Reinberg, Semin. Cell Dev. Biol. 21, 221 (2010).
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4. P. Boccuni, D. MacGrogan, J. M. Scandura, S. D. Nimer, J. Biol. Chem. 278, 15412 (2003). 5. P. Trojer et al., Cell 129, 915 (2007). 6. P. W. Lewis et al., Genes Dev. 18, 2929 (2004). 7. D. Georlette et al., Genes Dev. 21, 2880 (2007). 8. C. Gonzalez, Nat. Rev. Genet. 8, 462 (2007). 9. M. D. Wong, Z. Jin, T. Xie, Annu. Rev. Genet. 39, 173 (2005). 10. M. J. Svensson, J. D. Chen, V. Pirrotta, J. Larsson, Chromosoma 112, 133 (2003). 11. L. A. Lee, D. Van Hoewyk, T. L. Orr-Weaver, Genes Dev. 17, 2979 (2003).
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REPORTS 26. www.cancerimmunity.org/CTdatabase/ 27. www.cta.lncc.br/ 28. A. J. Simpson, O. L. Caballero, A. Jungbluth, Y. T. Chen, L. J. Old, Nat. Rev. Cancer 5, 615 (2005). 29. L. J. Old, Cancer Immun. 8 (suppl. 1), 1 (2008). 30. E. Jäger, D. Jäger, A. Knuth, Curr. Opin. Immunol. 14, 178 (2002). 31. H. Taubert et al., Oncogene 26, 1098 (2007). 32. L. F. Grochola et al., Br. J. Cancer 99, 1083 (2008). 33. A. Bonnomet et al., Oncogene 27, 3692 (2008). 34. O. Türeci et al., Proc. Natl. Acad. Sci. U.S.A. 95, 5211 (1998). 35. We thank E. Gateff, R. Lehmann, P. Zamore, A. Spradling, F. Azorin, P. Lasko, S. Hawley, M. Siomi, T. Kai, T. Orr-Weaver, H. White-Cooper, D. McKearin, T. Schupbach, Hybridoma Bank, and Bloomington and Tübingen Drosophila Stock centers for antibodies and fly stocks; H. Auer, the IRB Functional Genomics Facility, and the European Molecular Biology Laboratory Genomics Core Facility for invaluable technical guidance;
J. Januschke and members of our laboratory for discussions; and M. Llamazares for proofreading. Work in our laboratory is supported by ONCASYM-037398, BFU200605813, BFU2009-07975, SGR2005, SRG200, CEN20091016, and Consolider-Ingenio CSD2006-23. A.J. is a recipient of a Ministerio de Ciencia e Innovacion Formacion de Personal Investigador fellowship. Array data sets are deposited at Gene Expression Omnibus (accession no. GSE24917).
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1824/DC1 Material and Methods Fig. S1 Tables S1 to S5 References 22 July 2010; accepted 9 November 2010 10.1126/science.1195481
near the S locus in Solanum (5, 6). Expression of S-RNase transgenes in Nicotiana established their function in rejecting pollen from related SC species but also uncovered S-RNase–independent pollen rejection pathways (7). However, the differential timing of rejection of self versus interspecific pollen tubes, as well as exceptions to the SI × SC rule—including rejection of interspecific pollen by accessions that lack S-RNase—demonstrate that there are also differences between SI and UI (8–11). Tomato and its wild relatives (Solanum sect. Lycopersicon and allied species) exhibit a wide range of mating systems, from autogamy to strict allogamy, with variation between and within species (12). Cultivated tomato (S. lycopersicum) and the other red- or orange-fruited species are all SC, and their pollen is rejected by UI on pistils of all the green-fruited taxa, which can be either SI or SC (13). Besides impeding the transfer of cytoplasmic traits (maternally inherited in Solanum), UI may also contribute to reproductive isolation, because SI and SC tomato species are often sympatric in their native regions. Our goal was to isolate the male gametophytic factors underlying pollen rejection by UI. As selective pistils, we used allotriploid S. lycopersicum × S. lycopersicoides hybrids, composed of two genomes from cultivated tomato and one from the
A Pollen Factor Linking Inter- and Intraspecific Pollen Rejection in Tomato Wentao Li and Roger T. Chetelat* Self-incompatibility (SI)—intraspecific pollen recognition systems that allow plants to avoid inbreeding—in the Solanaceae (the nightshade family) is controlled by a polymorphic S locus where “self” pollen is rejected on pistils with matching S alleles. In contrast, unilateral interspecific incompatibility (UI) prevents hybridization between related species, most commonly when the pollen donor is self-compatible (SC) and the recipient is SI. We observed that in Solanum, a pollen-expressed Cullin1 gene with high similarity to Petunia SI factors interacts genetically with a gene at or near the S locus to control UI. Cultivated tomato and related red- or orange-fruited species (all SC) exhibit the same loss-of-function mutation in this gene, whereas the green-fruited species (mostly SI) contain a functional allele; hence, similar biochemical mechanisms underlie the rejection of both “self” and interspecific pollen.
19E05-21
19E05-29
19E05-36
73H07-11
A
19E05-16 19E05-17
*To whom correspondence should be addressed. E-mail:
[email protected]
C
GT AG
Mi
Department of Plant Sciences, University of California, Davis, CA 95616, USA.
SpCUL1
6
1614
D
unilateral interspecific incompatibility (UI). In the nightshade family (Solanaceae), SI specificity is determined by an S locus, encoding S-ribonucleases (S-RNases) expressed in the pistil (2) and S-locus F-box (SLF) proteins expressed in pollen (3). Most cases of UI occur in crosses between SI and self-compatible (SC) species, when the pollen source is the SC species (referred to as the SI × SC rule) (4). The general validity of the SI × SC rule in the Solanaceae suggests the two systems are related. Indeed, quantitative trait loci for both pistil and pollen-side UI have been detected at or 19E05-15
arwin noted that many flowering plants reject both self pollen (too similar) and pollen from foreign species (too dissimilar), but allow fertilization between individuals of the same species (1). Although the molecular mechanisms underlying self-incompatibility (SI) have been well studied, much less is known about
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12. E. F. Joyce, S. N. Tanneti, K. S. McKim, Genetics 181, 335 (2009). 13. S. L. Page et al., PLoS Genet. 4, e1000194 (2008). 14. L. Perezgasga et al., Development 131, 1691 (2004). 15. G. Tavosanis, S. Llamazares, G. Goulielmos, C. Gonzalez, EMBO J. 16, 1809 (1997). 16. C. Klattenhoff, W. Theurkauf, Development 135, 3 (2008). 17. V. S. Patil, T. Kai, Curr. Biol. 20, 724 (2010). 18. T. K. Smulders-Srinivasan, H. Lin, Genetics 165, 1971 (2003). 19. B. Ye et al., Curr. Biol. 14, 314 (2004). 20. S. Strome, R. Lehmann, Science 316, 392 (2007). 21. T. Kai, D. Williams, A. C. Spradling, Dev. Biol. 283, 486 (2005). 22. N. Bushati, S. M. Cohen, Annu. Rev. Cell Dev. Biol. 23, 175 (2007). 23. H. Lin, Science 316, 397 (2007). 24. D. Wang et al., Nature 436, 593 (2005). 25. S. P. Curran, X. Wu, C. G. Riedel, G. Ruvkun, Nature 459, 1079 (2009).
SlCUL1
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1478
1463
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Position/kb
1456 1457
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SL1.50sc04013
Recombinants 19-89 44-32 50-58 71-61
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B
Leaf
Cullin1
WD-40
Pollen
Bud
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Stem Cullin1 WD-40 Actin
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TAG
Fig. 1. Map of the ui6.1 region and analysis of candidate genes. (A) Physical map showing the genotypes of four recombinants and two candidate genes. Open bars, homozygous for S. lycopersicum allele; hatched, heterozygous; solid, homozygous S. pennellii. The pollen phenotypes of recombinants on pistils of the allotriploid (I, incompatible; C, compatible) place ui6.1 between markers 19E0516 and 19E05-21, a region that contains a WD-40 domain gene and a Cullin1 (CUL1) gene. (B) Expression of CUL1 and WD-40 in pollen of S. pennellii by reverse transcription PCR (RT-PCR). (C) Gene structure of SlCUL1 (S. lycopersicum allele) and SpCUL1 (S. pennellii allele). Both alleles include 19 introns and 20 exons, but SlCUL1 contains a 436-bp deletion in the seventh intron, which shifts 46 bp of intron sequence (solid segment) to the seventh exon, introducing a premature stop codon (TAG).
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REPORTS 26. www.cancerimmunity.org/CTdatabase/ 27. www.cta.lncc.br/ 28. A. J. Simpson, O. L. Caballero, A. Jungbluth, Y. T. Chen, L. J. Old, Nat. Rev. Cancer 5, 615 (2005). 29. L. J. Old, Cancer Immun. 8 (suppl. 1), 1 (2008). 30. E. Jäger, D. Jäger, A. Knuth, Curr. Opin. Immunol. 14, 178 (2002). 31. H. Taubert et al., Oncogene 26, 1098 (2007). 32. L. F. Grochola et al., Br. J. Cancer 99, 1083 (2008). 33. A. Bonnomet et al., Oncogene 27, 3692 (2008). 34. O. Türeci et al., Proc. Natl. Acad. Sci. U.S.A. 95, 5211 (1998). 35. We thank E. Gateff, R. Lehmann, P. Zamore, A. Spradling, F. Azorin, P. Lasko, S. Hawley, M. Siomi, T. Kai, T. Orr-Weaver, H. White-Cooper, D. McKearin, T. Schupbach, Hybridoma Bank, and Bloomington and Tübingen Drosophila Stock centers for antibodies and fly stocks; H. Auer, the IRB Functional Genomics Facility, and the European Molecular Biology Laboratory Genomics Core Facility for invaluable technical guidance;
J. Januschke and members of our laboratory for discussions; and M. Llamazares for proofreading. Work in our laboratory is supported by ONCASYM-037398, BFU200605813, BFU2009-07975, SGR2005, SRG200, CEN20091016, and Consolider-Ingenio CSD2006-23. A.J. is a recipient of a Ministerio de Ciencia e Innovacion Formacion de Personal Investigador fellowship. Array data sets are deposited at Gene Expression Omnibus (accession no. GSE24917).
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1824/DC1 Material and Methods Fig. S1 Tables S1 to S5 References 22 July 2010; accepted 9 November 2010 10.1126/science.1195481
near the S locus in Solanum (5, 6). Expression of S-RNase transgenes in Nicotiana established their function in rejecting pollen from related SC species but also uncovered S-RNase–independent pollen rejection pathways (7). However, the differential timing of rejection of self versus interspecific pollen tubes, as well as exceptions to the SI × SC rule—including rejection of interspecific pollen by accessions that lack S-RNase—demonstrate that there are also differences between SI and UI (8–11). Tomato and its wild relatives (Solanum sect. Lycopersicon and allied species) exhibit a wide range of mating systems, from autogamy to strict allogamy, with variation between and within species (12). Cultivated tomato (S. lycopersicum) and the other red- or orange-fruited species are all SC, and their pollen is rejected by UI on pistils of all the green-fruited taxa, which can be either SI or SC (13). Besides impeding the transfer of cytoplasmic traits (maternally inherited in Solanum), UI may also contribute to reproductive isolation, because SI and SC tomato species are often sympatric in their native regions. Our goal was to isolate the male gametophytic factors underlying pollen rejection by UI. As selective pistils, we used allotriploid S. lycopersicum × S. lycopersicoides hybrids, composed of two genomes from cultivated tomato and one from the
A Pollen Factor Linking Inter- and Intraspecific Pollen Rejection in Tomato Wentao Li and Roger T. Chetelat* Self-incompatibility (SI)—intraspecific pollen recognition systems that allow plants to avoid inbreeding—in the Solanaceae (the nightshade family) is controlled by a polymorphic S locus where “self” pollen is rejected on pistils with matching S alleles. In contrast, unilateral interspecific incompatibility (UI) prevents hybridization between related species, most commonly when the pollen donor is self-compatible (SC) and the recipient is SI. We observed that in Solanum, a pollen-expressed Cullin1 gene with high similarity to Petunia SI factors interacts genetically with a gene at or near the S locus to control UI. Cultivated tomato and related red- or orange-fruited species (all SC) exhibit the same loss-of-function mutation in this gene, whereas the green-fruited species (mostly SI) contain a functional allele; hence, similar biochemical mechanisms underlie the rejection of both “self” and interspecific pollen.
19E05-21
19E05-29
19E05-36
73H07-11
A
19E05-16 19E05-17
*To whom correspondence should be addressed. E-mail:
[email protected]
C
GT AG
Mi
Department of Plant Sciences, University of California, Davis, CA 95616, USA.
SpCUL1
6
1614
D
unilateral interspecific incompatibility (UI). In the nightshade family (Solanaceae), SI specificity is determined by an S locus, encoding S-ribonucleases (S-RNases) expressed in the pistil (2) and S-locus F-box (SLF) proteins expressed in pollen (3). Most cases of UI occur in crosses between SI and self-compatible (SC) species, when the pollen source is the SC species (referred to as the SI × SC rule) (4). The general validity of the SI × SC rule in the Solanaceae suggests the two systems are related. Indeed, quantitative trait loci for both pistil and pollen-side UI have been detected at or 19E05-15
arwin noted that many flowering plants reject both self pollen (too similar) and pollen from foreign species (too dissimilar), but allow fertilization between individuals of the same species (1). Although the molecular mechanisms underlying self-incompatibility (SI) have been well studied, much less is known about
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12. E. F. Joyce, S. N. Tanneti, K. S. McKim, Genetics 181, 335 (2009). 13. S. L. Page et al., PLoS Genet. 4, e1000194 (2008). 14. L. Perezgasga et al., Development 131, 1691 (2004). 15. G. Tavosanis, S. Llamazares, G. Goulielmos, C. Gonzalez, EMBO J. 16, 1809 (1997). 16. C. Klattenhoff, W. Theurkauf, Development 135, 3 (2008). 17. V. S. Patil, T. Kai, Curr. Biol. 20, 724 (2010). 18. T. K. Smulders-Srinivasan, H. Lin, Genetics 165, 1971 (2003). 19. B. Ye et al., Curr. Biol. 14, 314 (2004). 20. S. Strome, R. Lehmann, Science 316, 392 (2007). 21. T. Kai, D. Williams, A. C. Spradling, Dev. Biol. 283, 486 (2005). 22. N. Bushati, S. M. Cohen, Annu. Rev. Cell Dev. Biol. 23, 175 (2007). 23. H. Lin, Science 316, 397 (2007). 24. D. Wang et al., Nature 436, 593 (2005). 25. S. P. Curran, X. Wu, C. G. Riedel, G. Ruvkun, Nature 459, 1079 (2009).
SlCUL1
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Fig. 1. Map of the ui6.1 region and analysis of candidate genes. (A) Physical map showing the genotypes of four recombinants and two candidate genes. Open bars, homozygous for S. lycopersicum allele; hatched, heterozygous; solid, homozygous S. pennellii. The pollen phenotypes of recombinants on pistils of the allotriploid (I, incompatible; C, compatible) place ui6.1 between markers 19E0516 and 19E05-21, a region that contains a WD-40 domain gene and a Cullin1 (CUL1) gene. (B) Expression of CUL1 and WD-40 in pollen of S. pennellii by reverse transcription PCR (RT-PCR). (C) Gene structure of SlCUL1 (S. lycopersicum allele) and SpCUL1 (S. pennellii allele). Both alleles include 19 introns and 20 exons, but SlCUL1 contains a 436-bp deletion in the seventh intron, which shifts 46 bp of intron sequence (solid segment) to the seventh exon, introducing a premature stop codon (TAG).
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(fig. S1). A 436–base pair (bp) deletion was observed in SlCUL1 relative to SpCUL1 (fig. S1). A combination of rapid amplification of cDNA ends (RACE) and the polymerase chain reaction (PCR) determined that the SpCUL1 cDNA comprised 2223 bp and SlCUL1 2269 bp (Fig. 1C and fig. S2). Alignment of the genomic and cDNA sequences showed both alleles are comprised of 20 exons and 19 introns, and differ only in the seventh exon and intron (Fig. 1C and fig. S1), where the 436-bp deletion in SlCUL1 is located. The deleted fragment contains the splice recognition sites; this suggests that a small intron may have been lost during evolution in SlCUL1. The deletion shifts 46 bp of intron to the seventh exon (Fig. 1C), resulting in a stop codon that is translated into a truncated protein (252 rather than 740 amino acids; fig. S3). To test for function in pollen-side UI, we introduced a construct containing the full-length SpCUL1 cDNA driven by the LAT52 pollenspecific promoter (19) into S. lycopersicum via Agrobacterium-mediated transformation. Because both S. pennellii factors, ui1.1 and ui6.1, are required for pollen compatibility on pistils of the allotriploid hybrid, T0 plants were crossed to a S. lycopersicum stock homozygous for ui1.1 introgressed from S. pennellii, to produce “T1BC1” plants. Transformant 1 exhibited T-DNA inheritance consistent with a single insertion, whereas transformant 2 most likely had two independent insertions. The SpCUL1 transgene was expressed at high levels in anthers of transgenic plants (Fig. 2A). The function of SpCUL1 was tested by placing transgenic pollen on pistils of the allotriploid. Pollen tubes of T0 plants (which lack ui1.1) and
wild parent, which is SI (14). UI in pistils of the allotriploid hybrid is weakened relative to pure S. lycopersicoides: Arrest of incompatible pollen tubes occurs lower in the style, and fewer pollen factors are required to overcome the incompatibility, thus presenting a simplified barrier to interspecific pollen (5, 15). On the pollen side, S. lycopersicum stocks containing two gametophytic factors (ui1.1 and ui6.1) introgressed from an SC biotype of S. pennellii (most are SI) are compatible on the allotriploid (5, 15). We previously mapped the ui6.1 locus to a 160-kb region on the short arm of chromosome 6, between markers 73H07-11 and Mi (15). We refined the candidate region to a ~22-kb interval (Fig. 1A and table S1) containing two open reading frames: a WD-40 repeat domain gene and a Cullin1 gene. The Cullin1 (hereafter CUL1) protein was homologous (75% sequence identity) to PiCul1-G from Petunia inflata (16) and PhCullin1 from P. hybrida (17), which interact with the SIrelated factors, SBP1 (S-RNase binding protein) and SSK1 (SLF-interacting Skp1-like1), respectively. These proteins form either a canonical SCF (Skp1-Cullin1–F box) complex or a SLFSBP1-CUL1 complex, which most likely functions as an E3 ubiquitin ligase (SCFSLF) to target a pistil SI factor for degradation. WD-40 repeat domains function in protein-protein interactions and contribute to the substrate specificity of F-box proteins (18). Because the WD-40 gene was expressed at low levels in pollen of S. pennellii, whereas CUL1 mRNA was more abundant in pollen (Fig. 1B), we hypothesized that CUL1 was a more likely candidate for ui6.1. The S. lycopersicum (SlCUL1) and S. pennellii (SpCUL1) alleles were obtained by sequencing
A
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T1BC1(-) anthers
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T1BC1 plants lacking the transgene were incompatible with the allotriploid (Fig. 2, B and C), consistent with our earlier findings (5, 15). In contrast, pollen of transgenic plants containing both the SpCUL1 transgene and ui1.1 from S. pennellii were compatible on allotriploid styles (Fig. 2, B and C), confirming that CUL1 underlies ui6.1. Because the SlCUL1 gene of cultivated tomato contains a loss-of-function mutation abolishing pollen compatibility on pistils of SI species (or their hybrids), we examined other SC red- or orange-fruited tomato species (i.e., S. pimpinellifolium, S. cheesmaniae, and S. galapagense) whose pollen is also rejected on styles of the SI (and green-fruited SC) taxa. We determined that the CUL1 deletion was also present in these species, based on the length of the seventh intron (Fig. 3A and table S2). In contrast, the green-fruited species (mostly SI, some SC) all contained a full length intron in CUL1. Both mutant and full-length alleles were detected in S. pimpinellifolium, a species that includes facultative outcrossing populations and is more diverse than the other red- or orange-fruited species (20); this finding suggests that the intron deletion may have originated in S. pimpinellifolium. We found that all green-fruited species produce a normal length (i.e., shorter) mRNA, whereas all the accessions with the CUL1 deletion produce the longer mRNA seen in S. lycopersicum (Fig. 3B and table S2). These observations confirm that variation in length of the seventh intron is responsible for differences in mRNA sizes, consistent with a single mutation event in the red/orange clade. In addition, we tested the function of CUL1 alleles from three diverse SI green-fruited species (S. peruvianum, S. habrochaites, and S. lycopersicoides) using inT1BC1(-)
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Fig. 2. Expression and functional analysis of S. lycopersicum plants transformed with LAT52-SpCUL1. Primary transformants (T0) were crossed to a ui1.1 homozygote to produce T1BC1 plants with and without the transgene [T1BC1(+) and T1BC1(–), respectively]. (A) Analysis of SpCUL1 expression by RT-PCR. SpCUL1 mRNA was abundant in anthers of transgenic plants. (B) Pollen tube phenotypes of transgenic plants on the allotriploid hybrid stigma, style and ovaries. T0 plants, which lack ui1.1 (left column), and T1BC1 plants without the transgene [T1BC1 (–), center column] show an incompatible phenotype, whereas plants with the transgene and ui1.1 [T1BC1 (+), right column] are compatible. (C) Pollen tube growth on pistils of the allotriploid hybrid. Controls, ui1.1 and/or ui6.1 introgressions from S. pennellii [data from (15)], and multiple transformants of T0, T1BC1(–), and T1BC1(+) are shown. Values are averages T SE.
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REPORTS Cullin1 proteins function as scaffolds in recruiting other components, including Skp1, Rbx1, and F-box proteins, to assemble an SCF-type E3 ubiquitin ligase complex that targets proteins recognized by the F-box for degradation by the proteasome (21). Biochemical evidence suggests that the ubiquitin-proteasome pathway is involved in the degradation of a pistil-expressed factor required for S-RNase–based SI (16, 17, 22). The putative SCFSLF complex has been proposed to target nonself S-RNases for degradation in the
trogression lines containing the ui6.1 region bred into the genetic background of cultivated tomato (Fig. 3C). When combined with the ui1.1 allele introgressed from S. pennellii, pollen bearing any of the three independent ui6.1 introgressions was compatible on pistils of the allotriploid hybrid. This demonstrates that CUL1 genes from SI species function in interspecific incompatibility, and that they interact genetically with the ui1.1 gene from SC S. pennellii to change the pollen phenotype.
pollen tube, with multiple SLF proteins recognizing different S-RNase alleles (23). Our results suggest that UI is regulated by a similar biochemical mechanism. Although we have not yet identified the product of ui1.1, its map location at or near the S locus, its gametophytic gene action, and its genetic interaction with CUL1 in controlling pollen-side UI are consistent with a model in which ui1.1 encodes an SLF protein. Other genetic evidence suggests that CUL1 functions in an S-RNase–dependent pathway of
S. habrochaites
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ii orick S. ne
Fig. 3. Analysis of CUL1 alleles in wild tomato and allied Solanum species. (A) Distribution of a CUL1 intron deletion. Five representative accessions (table S2) from each species were compared to S. lycopersicum cv. VF36 (436-bp deletion in the seventh intron) and S. pennellii LA0716 (intact intron). All green-fruited species examined contained only the full-length intron, whereas the red- or orange-fruited taxa all contained the intron deletion (both alleles were present in S. pimpinellifolium). (B) Analysis of the seventh exon of CUL1 in accessions of each species. (C) Lines of S. lycopersicum containing the ui6.1 (CUL1) region introgressed from S. peruvianum (S. peruv.), S. habrochaites (S. habro.), or S. lycopersicoides (S. lycds.) (28–30) were crossed to a stock homozygous for ui1.1 from S. pennellii (ui1.1p/p) to produce doubly heterozygous lines. Pollen from the ui6.1 single introgressions was incompatible on pistils of the allotriploid, whereas the corresponding ui1.1 + ui6.1 double introgressions were compatible, indicating functional CUL1 alleles.
ides
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pollen rejection. In progeny of some interspecific tomato hybrids, distorted segregation ratios for CUL1-linked markers are consistent with the selective elimination of pollen bearing the nonfunctional SlCUL1 allele (15). Distorted transmission of CUL1 is only observed in progeny of crosses between SC cultivated tomato and SI wild species, and only when the F1 hybrid is used as pistillate parent. Moreover, two SC accessions of mostly SI species that either accumulate no S-RNase in the pistil [S. pennellii LA0716 (11)] or express a mutant protein lacking RNase activity [S. arcanum LA2157 (24)] exhibit normal F2 segregation for CUL1-linked markers in hybrids with cultivated tomato (25, 26). These observations suggest that selection against CUL1-deficient pollen requires S-RNase activity in the pistil. If so, loss of CUL1 function in the red- or orange-fruited species was likely preceded by a loss of S-RNase expression [styles of S. lycopersicum do not accumulate S-RNase (11)]. On the other hand, pistils of LA0716 and LA2157 reject pollen from cultivated tomato (9, 27) without expressing functional S-RNases, which suggests that pollen rejection by UI can also be mechanistically distinct from SI. Although our results are from an analysis of interspecific Solanum hybrids, they may be relevant to UI in other solanaceous plants, and possibly to other families that use the S-RNase–based SI system.
References and Notes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
13.
14. 15. 16. 17. 18. 19. 20. 21. 22.
B. A. McClure, J. Exp. Bot. 60, 1069 (2009). B. A. McClure et al., Nature 342, 955 (1989). P. Sijacic et al., Nature 429, 302 (2004). D. Lewis, L. K. Crowe, Heredity 12, 233 (1958). R. T. Chetelat, J. W. DeVerna, Theor. Appl. Genet. 82, 704 (1991). D. Bernacchi, S. D. Tanksley, Genetics 147, 861 (1997). J. Murfett et al., Plant Cell 8, 943 (1996). N. G. Hogenboom, Euphytica 22, 219 (1973). J. J. Hardon, Genetics 57, 795 (1967). B. E. Liedl, S. McCormick, M. A. Mutschler, Sex. Plant Reprod. 9, 299 (1996). P. A. Covey et al., Plant J. 64, 367 (2010). C. M. Rick, in Plant Evolutionary Biology, L. D. Gottlieb, S. K. Jain, Eds. (Chapman & Hall, London, 1988), pp. 133–147. M. A. Mutschler, B. E. Liedl, in Genetic Control of Self-Incompatibility and Reproductive Development in Flowering Plants, E. G. Williams, A. C. Clarke, R. B. Knox, Eds. (Kluwer, Dordrecht, Netherlands, 1994), pp. 164–188. C. M. Rick, J. W. De Verna, R. T. Chetelat, M. A. Stevens, Proc. Natl. Acad. Sci. U.S.A. 83, 3580 (1986). W. Li, S. Royer, R. T. Chetelat, Genetics 185, 1069 (2010). Z. Hua, T. H. Kao, Plant Cell 18, 2531 (2006). L. Zhao et al., Plant J. 62, 52 (2010). C. Cenciarelli et al., Curr. Biol. 9, 1177 (1999). D. Twell, J. Yamaguchi, S. McCormick, Development 109, 705 (1990). C. M. Rick, J. F. Fobes, M. Holle, Plant Syst. Evol. 127, 139 (1977). N. Zheng et al., Nature 416, 703 (2002). H. Qiao et al., Plant Cell 16, 582 (2004).
The Social Sense: Susceptibility to Others’ Beliefs in Human Infants and Adults Ágnes Melinda Kovács,1,2,3* Erno˝ Téglás,1,2,3 Ansgar Denis Endress3,4 Human social interactions crucially depend on the ability to represent other agents’ beliefs even when these contradict our own beliefs, leading to the potentially complex problem of simultaneously holding two conflicting representations in mind. Here, we show that adults and 7-month-olds automatically encode others’ beliefs, and that, surprisingly, others’ beliefs have similar effects as the participants’ own beliefs. In a visual object detection task, participants’ beliefs and the beliefs of an agent (whose beliefs were irrelevant to performing the task) both modulated adults’ reaction times and infants’ looking times. Moreover, the agent’s beliefs influenced participants’ behavior even after the agent had left the scene, suggesting that participants computed the agent’s beliefs online and sustained them, possibly for future predictions about the agent’s behavior. Hence, the mere presence of an agent automatically triggers powerful processes of belief computation that may be part of a “social sense” crucial to human societies.
H
umans are guided by internal states such as goals and beliefs. Without an ability to infer others’ mental states, society would
1 Institute for Psychology, Hungarian Academy of Sciences, H-1132 Budapest, Hungary. 2Cognitive Development Centre, Central European University, H-1015, Budapest, Hungary. 3 Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), I-34014 Trieste, Italy. 4Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge MA 02139, USA.
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be hardly imaginable. Social interactions, from collective hunting to playing soccer to criminal justice, critically depend on the ability to infer others’ intentions and beliefs. Such abilities are also at the foundation of major evolutionary conundra. For example, the human aptitude at inferring mental states might be one of the crucial preconditions for the evolution of the cooperative social structure in human societies (1). However, despite their importance for all aspects of social life, the question of how such “theory of mind” (ToM) abilities (2) emerge and develop, and what
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23. K. Kubo et al., Science 330, 796 (2010). 24. J. Royo et al., Proc. Natl. Acad. Sci. U.S.A. 91, 6511 (1994). 25. A. W. van Heusden et al., Theor. Appl. Genet. 99, 1068 (1999). 26. T. M. Fulton, R. Van der Hoeven, N. T. Eannetta, S. D. Tanksley, Plant Cell 14, 1457 (2002). 27. C. M. Rick, in Solanaceae Biology and Systematics, W. G. D’Arcy, Ed. (Columbia Univ. Press, New York, 1986), pp. 475–495. 28. J. Y. Ho et al., Plant J. 2, 971 (1992). 29. A. J. Monforte, S. D. Tanksley, Genome 43, 803 (2000). 30. M. A. Canady, V. Meglic, R. T. Chetelat, Genome 48, 685 (2005). 31. We thank J. DeVerna, P. March, K. Smith, and the C. M. Rick Tomato Genetics Resource Center (TGRC) staff for providing seed or cuttings of key genotypes; P. March for photos of fruit; B. McClure and P. Bedinger for comments on the manuscript; and S. Tanksley for providing seed of S. habrochaites introgression lines. Supported by NSF grant DBI 0605200. Sequence data have been deposited in GenBank under accession numbers HQ610200 and HQ610201. Seed requests submitted to the TGRC are subject to a material transfer agreement (http://tgrc.ucdavis.edu/MTA/TGRC-MTA.pdf).
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1827/DC1 Materials and Methods Tables S1 and S2 Figs. S1 to S3 References 17 September 2010; accepted 18 November 2010 10.1126/science.1197908
their functional characteristics are, is still the topic of important debates. Decades of research seem to suggest that ToM emerges after the age of four. Developmental transitions in ToM have often been assessed using so-called “false-belief tasks” (3). In such tasks, children typically have to predict a person’s behavior based on the person’s false belief while ignoring their own true belief. For example, children are presented with a situation in which another child (say, John) places a toy in a cupboard and leaves the scene. In his absence, the toy is moved to a different location (say, a basket). Threeyear-olds typically predict that, upon his return, John will search in the basket rather than in the cupboard, because they themselves know that the toy is in the basket. That is, at least in their overt responses, 3-year olds fail to take into account that John cannot know that the toy is in the basket and must, therefore, (falsely) believe the toy to be in the cupboard. In contrast, older children (and adults) take into account John’s false belief and predict that he will search in the cupboard. Based on such findings, it has been argued that ToM requires complex computations and emerges relatively late in development (4). However, such data are not necessarily inconsistent with the possibility that ToM is automatic and innate (5–8). Children might possess ToM abilities early on; however, these might be masked by the slower development of other abilities involved in such tasks, such as inhibition and selection (6) or problem solving (8). That is, young children might well be able to represent
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REPORTS
pollen rejection. In progeny of some interspecific tomato hybrids, distorted segregation ratios for CUL1-linked markers are consistent with the selective elimination of pollen bearing the nonfunctional SlCUL1 allele (15). Distorted transmission of CUL1 is only observed in progeny of crosses between SC cultivated tomato and SI wild species, and only when the F1 hybrid is used as pistillate parent. Moreover, two SC accessions of mostly SI species that either accumulate no S-RNase in the pistil [S. pennellii LA0716 (11)] or express a mutant protein lacking RNase activity [S. arcanum LA2157 (24)] exhibit normal F2 segregation for CUL1-linked markers in hybrids with cultivated tomato (25, 26). These observations suggest that selection against CUL1-deficient pollen requires S-RNase activity in the pistil. If so, loss of CUL1 function in the red- or orange-fruited species was likely preceded by a loss of S-RNase expression [styles of S. lycopersicum do not accumulate S-RNase (11)]. On the other hand, pistils of LA0716 and LA2157 reject pollen from cultivated tomato (9, 27) without expressing functional S-RNases, which suggests that pollen rejection by UI can also be mechanistically distinct from SI. Although our results are from an analysis of interspecific Solanum hybrids, they may be relevant to UI in other solanaceous plants, and possibly to other families that use the S-RNase–based SI system.
References and Notes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
13.
14. 15. 16. 17. 18. 19. 20. 21. 22.
B. A. McClure, J. Exp. Bot. 60, 1069 (2009). B. A. McClure et al., Nature 342, 955 (1989). P. Sijacic et al., Nature 429, 302 (2004). D. Lewis, L. K. Crowe, Heredity 12, 233 (1958). R. T. Chetelat, J. W. DeVerna, Theor. Appl. Genet. 82, 704 (1991). D. Bernacchi, S. D. Tanksley, Genetics 147, 861 (1997). J. Murfett et al., Plant Cell 8, 943 (1996). N. G. Hogenboom, Euphytica 22, 219 (1973). J. J. Hardon, Genetics 57, 795 (1967). B. E. Liedl, S. McCormick, M. A. Mutschler, Sex. Plant Reprod. 9, 299 (1996). P. A. Covey et al., Plant J. 64, 367 (2010). C. M. Rick, in Plant Evolutionary Biology, L. D. Gottlieb, S. K. Jain, Eds. (Chapman & Hall, London, 1988), pp. 133–147. M. A. Mutschler, B. E. Liedl, in Genetic Control of Self-Incompatibility and Reproductive Development in Flowering Plants, E. G. Williams, A. C. Clarke, R. B. Knox, Eds. (Kluwer, Dordrecht, Netherlands, 1994), pp. 164–188. C. M. Rick, J. W. De Verna, R. T. Chetelat, M. A. Stevens, Proc. Natl. Acad. Sci. U.S.A. 83, 3580 (1986). W. Li, S. Royer, R. T. Chetelat, Genetics 185, 1069 (2010). Z. Hua, T. H. Kao, Plant Cell 18, 2531 (2006). L. Zhao et al., Plant J. 62, 52 (2010). C. Cenciarelli et al., Curr. Biol. 9, 1177 (1999). D. Twell, J. Yamaguchi, S. McCormick, Development 109, 705 (1990). C. M. Rick, J. F. Fobes, M. Holle, Plant Syst. Evol. 127, 139 (1977). N. Zheng et al., Nature 416, 703 (2002). H. Qiao et al., Plant Cell 16, 582 (2004).
The Social Sense: Susceptibility to Others’ Beliefs in Human Infants and Adults Ágnes Melinda Kovács,1,2,3* Erno˝ Téglás,1,2,3 Ansgar Denis Endress3,4 Human social interactions crucially depend on the ability to represent other agents’ beliefs even when these contradict our own beliefs, leading to the potentially complex problem of simultaneously holding two conflicting representations in mind. Here, we show that adults and 7-month-olds automatically encode others’ beliefs, and that, surprisingly, others’ beliefs have similar effects as the participants’ own beliefs. In a visual object detection task, participants’ beliefs and the beliefs of an agent (whose beliefs were irrelevant to performing the task) both modulated adults’ reaction times and infants’ looking times. Moreover, the agent’s beliefs influenced participants’ behavior even after the agent had left the scene, suggesting that participants computed the agent’s beliefs online and sustained them, possibly for future predictions about the agent’s behavior. Hence, the mere presence of an agent automatically triggers powerful processes of belief computation that may be part of a “social sense” crucial to human societies.
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umans are guided by internal states such as goals and beliefs. Without an ability to infer others’ mental states, society would
1 Institute for Psychology, Hungarian Academy of Sciences, H-1132 Budapest, Hungary. 2Cognitive Development Centre, Central European University, H-1015, Budapest, Hungary. 3 Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), I-34014 Trieste, Italy. 4Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge MA 02139, USA.
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be hardly imaginable. Social interactions, from collective hunting to playing soccer to criminal justice, critically depend on the ability to infer others’ intentions and beliefs. Such abilities are also at the foundation of major evolutionary conundra. For example, the human aptitude at inferring mental states might be one of the crucial preconditions for the evolution of the cooperative social structure in human societies (1). However, despite their importance for all aspects of social life, the question of how such “theory of mind” (ToM) abilities (2) emerge and develop, and what
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23. K. Kubo et al., Science 330, 796 (2010). 24. J. Royo et al., Proc. Natl. Acad. Sci. U.S.A. 91, 6511 (1994). 25. A. W. van Heusden et al., Theor. Appl. Genet. 99, 1068 (1999). 26. T. M. Fulton, R. Van der Hoeven, N. T. Eannetta, S. D. Tanksley, Plant Cell 14, 1457 (2002). 27. C. M. Rick, in Solanaceae Biology and Systematics, W. G. D’Arcy, Ed. (Columbia Univ. Press, New York, 1986), pp. 475–495. 28. J. Y. Ho et al., Plant J. 2, 971 (1992). 29. A. J. Monforte, S. D. Tanksley, Genome 43, 803 (2000). 30. M. A. Canady, V. Meglic, R. T. Chetelat, Genome 48, 685 (2005). 31. We thank J. DeVerna, P. March, K. Smith, and the C. M. Rick Tomato Genetics Resource Center (TGRC) staff for providing seed or cuttings of key genotypes; P. March for photos of fruit; B. McClure and P. Bedinger for comments on the manuscript; and S. Tanksley for providing seed of S. habrochaites introgression lines. Supported by NSF grant DBI 0605200. Sequence data have been deposited in GenBank under accession numbers HQ610200 and HQ610201. Seed requests submitted to the TGRC are subject to a material transfer agreement (http://tgrc.ucdavis.edu/MTA/TGRC-MTA.pdf).
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1827/DC1 Materials and Methods Tables S1 and S2 Figs. S1 to S3 References 17 September 2010; accepted 18 November 2010 10.1126/science.1197908
their functional characteristics are, is still the topic of important debates. Decades of research seem to suggest that ToM emerges after the age of four. Developmental transitions in ToM have often been assessed using so-called “false-belief tasks” (3). In such tasks, children typically have to predict a person’s behavior based on the person’s false belief while ignoring their own true belief. For example, children are presented with a situation in which another child (say, John) places a toy in a cupboard and leaves the scene. In his absence, the toy is moved to a different location (say, a basket). Threeyear-olds typically predict that, upon his return, John will search in the basket rather than in the cupboard, because they themselves know that the toy is in the basket. That is, at least in their overt responses, 3-year olds fail to take into account that John cannot know that the toy is in the basket and must, therefore, (falsely) believe the toy to be in the cupboard. In contrast, older children (and adults) take into account John’s false belief and predict that he will search in the cupboard. Based on such findings, it has been argued that ToM requires complex computations and emerges relatively late in development (4). However, such data are not necessarily inconsistent with the possibility that ToM is automatic and innate (5–8). Children might possess ToM abilities early on; however, these might be masked by the slower development of other abilities involved in such tasks, such as inhibition and selection (6) or problem solving (8). That is, young children might well be able to represent
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others’ beliefs, but, to correctly predict that, in the example above, John will search for the toy where he (falsely) believes it to be, children also need efficient inhibitory abilities. Specifically, they need to deal with two conflicting representations, namely John’s (false) belief that the ball is in the cupboard and their own (true) belief that the ball is in the basket, and to inhibit their own belief when they predict John’s behavior. In line with such views, newer research suggests that ToM abilities are present in young children, for instance in 3year-olds when testing populations with enhanced inhibitory abilities (9), or even in infants in their second year when using simpler testing procedures (10–12). Although these data suggest that ToM abilities may emerge well before the age of four, another question has received little empirical attention. If such abilities are essentially an innate “social sense,” they should be spontaneous and automatic, and others’ beliefs should be com-
puted online and effortlessly, just as we compute representations of what we perceive in the environment. The experiments below will address this issue. Our experiments will also address a second issue that, to our knowledge, has not been investigated directly. Representations about the environment and representations about others’ beliefs can differ in many aspects. Most important, unlike most aspects of the environment, others’ mental states are not directly observable and are sometimes inconsistent with the true state of affairs. We can represent that “Mary thinks that John is at home” even if we know that “John is not at home.” Thus, our knowledge of the environment does not reliably predict the contents of others’ beliefs, nor do others’ beliefs reliably predict the state of the environment. Accordingly, representations of others’ beliefs might be stored in a way reflecting that their content is not referenced to the current state of affairs. Crucially, if
Fig. 1. Logical structure of events in Experiment 1. (A) In all four conditions, the agent enters the scene, placing a ball on a table (13) (Movie S1). The ball then rolls behind an occluder. (B) In the agent’s presence, the ball stays behind the occluder (a and c), or leaves the scene (b and d). As a result, the agent (A) “believes” either that the ball is behind the occluder or that there is no ball behind the occluder. Then, the agent leaves the scene. (C) In the agent’s absence, the ball leaves the scene (c), returns behind the occluder (d), or does not move (a and b). Thus, the participant (P) either believes the ball to be behind the occluder (a and d), or to have left (b and c). (D) The agent reenters the scene, and the occluder is lowered. In half of the trials of all conditions, participants see the ball behind the occluder. We measure ball detection latencies as a function of (i) the participant’s belief (P+, ball behind occluder, versus P–, no ball behind occluder) and (ii) the agent’s “belief” (A+, ball behind occluder, versus A–, no ball behind occluder), resulting in two true belief conditions and two false belief conditions. The figure does not reflect the actual timing of the events. To control for the timing differences, we used pairs of conditions matched for their timing properties (13). www.sciencemag.org
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such representations are not referenced to the environment, they should not affect how we interact with the environment either. Although this conjecture seems consistent with the proposal that representations about others’ beliefs are computed by specialized mechanisms (5), there is a more parsimonious hypothesis. We clearly have mechanisms in place to compute representations about what we experience in the environment. Perhaps we use very similar mechanisms to compute others’ beliefs based on what they experience. If so, representations about others’ beliefs should have fundamentally similar properties as representations about the environment. This hypothesis predicts that representations of others’ beliefs should be referenced to the environment just as our own beliefs, and thus can affect our behavior. We test this idea in the experiments below. Here, we develop a method for investigating ToM mechanisms that, in contrast to variants of the standard false belief task, is implicit, makes no reference to others’ beliefs, and requires no behavioral predictions of what agents will do on the basis of their beliefs. Specifically, we use an object detection task to investigate two questions. First, are belief computations automatically triggered by the mere presence of an agent in adults and in infants as young as 7 months, even when the beliefs are entirely irrelevant to the task participants have to perform? Second, are beliefs about others’ beliefs stored in a format sufficiently similar to our own representations about the environment that both types of representations can affect our behavior? In Experiment 1, adults (N = 24) performed a visual detection task while watching 40 animated movies (13). As shown in Fig. 1, movies started with an agent placing a ball on a table in front of an occluder. Then the ball rolled behind the occluder. After this, the movies could continue in four ways depending on the experimental conditions. Our critical manipulations involved the participant’s beliefs about the ball’s presence and the “beliefs” of the agent, such that the agent, the participant, both, or neither could believe that the ball was behind the occluder. This was achieved by varying (i) the final location of the ball and (ii) the time at which the agent left the scene. Specifically, (i) participants saw the ball either staying behind the occluder or leaving the scene and (ii) the agent left the scene either before or after the ball had reached its final location (leading to a true/false belief). That is, the agent had a true belief about the ball’s location if he left the scene after the ball had reached its final location; if he left the scene before the ball reached its final location, his belief was false. At the end of each movie, the agent reentered the scene and the occluder was lowered. The four conditions were paired with two outcomes, in which the ball was either present or absent behind the occluder. Participants were instructed to press a button as soon as they detected the ball. Notably, the agent’s beliefs were never mentioned and were irrelevant to the task.
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We will discuss the experimental conditions in terms of the beliefs of the participant and the agent, respectively, rather than in terms of the events displayed in the animations. (The agent’s “belief ” is what he might believe based on what he “experiences” in the scenes if he were a real person.) We predicted that participants should be faster to detect the ball when they believed that the ball is behind the occluder than when they do not believe so. Crucially, and as mentioned above, the agent’s beliefs were completely irrelevant to the task. As a result, if others’ beliefs are computed through explicit processes requiring effortful computations, the agent’s “beliefs” should have no effect on reaction times (RTs), because participants were not required to perform belief computations. However, if participants automatically compute the agent’s beliefs and store them in a way similar to that of their own representations about the environment, their detection latencies should also be faster when only the agent “believes” that the ball is behind the occluder. We will compare the experimental conditions to a baseline condition where neither the participant nor the agent believed the ball to be behind the occluder; as a result, there were no belief representations that could speed up RTs. To validate our paradigm, we compared RTs in this baseline condition to the condition where both the participant and the agent believed the ball to be behind the occluder. Compared to the baseline condition, participants detected the ball faster when they (and the agent) believed that the ball was behind the occluder [t(23) = 3.47, P = 0.002] (Fig. 2A) (13). Likewise, participants were faster than in the baseline when they but not the agent believed that the ball was behind the occluder [t(23) = 3.43, P = 0.002]. Our critical comparison involves the baseline condition and the condition where only the agent believed that the ball was behind the occluder. Results showed that participants were faster than in the baseline condition when only the agent believed the ball to be behind the occluder [t(23) = 2.42, P = 0.02]. This suggests that they computed
the agent’s belief and that this belief influenced their behavior even though it was inconsistent with their own belief. Moreover, RTs did not differ significantly between the condition where the participants themselves believed that the ball was behind the occluder and the condition where only the agent believed so [t(23) = 0.99, P = 0.33]. Thus, both types of belief representations speeded up the participants’ RTs to similar extents, a result consistent with the view that the agent’s beliefs are stored similarly to participants’ own representations about the environment. In Experiment 1, participants simply had to detect a ball after watching a scene involving an agent whose presence was irrelevant to the task. Nevertheless, the agent’s beliefs influenced the participants’ RTs to the same extent as their own beliefs, suggesting that just seeing the agent automatically made participants compute his beliefs and that these beliefs were represented and sustained similarly to participants’ own beliefs. These results may also provide an important clue to a question that has remained elusive for the last three decades, namely how false beliefs might be computed. In Experiment 1, the agent had a false belief because he left the scene before the ball reached its final position. Possibly, participants compute online the agent’s last belief and, unless there is evidence that he updated it, maintain it in parallel with their own beliefs. If so, the agent’s belief should continue to influence their RTs even in the agent’s absence; if the environment changes, the agent’s beliefs will necessarily become false. We tested this prediction in Experiment 2 with a new group of participants (N = 24). This experiment was identical to Experiment 1 except that the agent did not return in the last phase of the movies; instead, a pile of boxes entered the scene before the occluder was lowered (13). The results of Experiment 2 were similar to those of Experiment 1. Participants were faster than in the baseline condition when they and the agent (who was not present when the occluder was lowered) believed the ball to be behind the occluder [t(23) = 2.83, P = 0.009] (Fig. 2B). Likewise, RTs were faster than in the baseline
Fig. 2. (A) Results of Experiment 1 (adults; agent present in the last scene). Ball detection latencies in adults. Bars represent average latencies, and error bars show SEM (see Fig. 1 for condition labels). (B) Results of Experiment 2 (adults; agent absent in the last scene). Ball detection latencies in adults when a pile of boxes replaced the agent in the very last scene (corresponding to Fig. 1D). (C) Results of Experiment 3 (adults; agent absent in the entire movie). Ball detection latencies in adults when no agent was present at all but a stationary pile of boxes (represented by B in the panel) was present during the entire movie. The signs in square brackets indicate the beliefs the agent would have had if he had been present, as in Experiments 1 and 2. Thus, P+B[–] indicates the
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condition when they but not the agent believed that the ball was there [t(23) = 3.2, P = 0.004]. Crucially, RTs were also faster than in the baseline condition when only the agent believed that the ball was behind the occluder [t(23) = 2.1, P = 0.04]. Thus, although the agent was not present when participants detected the ball, his beliefs continued to influence participants’ behavior. Taken together, data from Experiments 1 and 2 show that when participants had to detect the presence of the ball, their RTs were faster not only when they believed the ball to be behind the occluder but also when the agent believed so, irrespective of whether or not the agent was present when participants gave their responses. Although these results appear to reflect computations of the agent’s beliefs, differences in the ordering of events in the different experimental conditions might possibly affect participants’ RTs as well. Specifically, in our experimental design, some conditions required the agent to leave the scene before the ball reached its final location (resulting in a false belief), whereas other conditions required the agent to leave after the ball reached its final location (resulting in a true belief). Despite its plausibility, further analysis of our results did not support this possibility, as the ordering differences (e.g., the number of events that occur after the ball reached its target location) did not predict participants’ RTs in the four experimental conditions (13). Experiment 3 was designed to further confirm that the RT differences between the crucial conditions in Experiments 1 and 2 were due to the agent’s beliefs and not to other perceptual differences between the conditions. In Experiment 3, participants (N = 24) were presented with movies that were similar to those shown in Experiments 1 and 2, except that the agent did not appear in the movies at all. Instead, a stationary pile of boxes was present in all movies during their entire duration. However, all other events (e.g., the movements of the ball) were as in Experiments 1 and 2. Hence, while participants in Experiment 3 could not compute another agent’s beliefs (because there was no agent present), they experienced
condition where the participant believes that the ball is behind the occluder, and the motion of the ball corresponds to a condition in Experiments 1 and 2 where the agent did not believe that the ball was behind the occluder. SCIENCE
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motion paths of the ball identical to those in the different belief conditions from Experiments 1 and 2. If the differences between the critical conditions in Experiments 1 and 2 were due to perceptual differences rather than to belief computations, Experiment 3 should yield the same results, because the motion of the ball is identical. In contrast, if these differences were due to belief computations rather than to perceptual differences, RTs in Experiment 3 should be affected only by participants’ own beliefs, since participants never saw an agent and, therefore, could not compute his beliefs. Results showed that RTs were faster in the two conditions when participants believed the ball to be behind the occluder compared to the two conditions when participants did not believe the ball to be behind the occluder, with no other differences [all P’s < 0.05; see (13) for details] (Fig. 2C). Contrary to Experiments 1 and 2, RTs did not differ between the P–B[–] and P–B[+] conditions [t(23) = 0.76, P = 0.45], which correspond to the P–A– and P–A+ conditions in the first two experiments. To further confirm that RTs did not differ between these conditions, likelihood ratio analyses showed that the null hypoth-
esis was about 3 times more likely than the alternative hypothesis (13). Hence, when the agent was not present, participants’ RTs were influenced only by their own beliefs about the presence of the ball but not by other perceptual differences between the conditions. Together, Experiments 1 to 3 suggest that adults automatically compute and store the beliefs of other agents; the resulting representations appear to be similarly accessible to other cognitive processes as are their own beliefs. Once a belief is computed, it seems to remain active even in the absence of the agent, possibly to be used for future predictions about the agent’s behavior. If adults track others’ beliefs automatically, such processes may well be present in young infants. We thus asked whether 7-month-olds would automatically compute the beliefs of an agent and whether these beliefs would influence their looking times in a violation of expectation paradigm. We tested this possibility in four experiments involving four different groups of infants (N = 56). Whereas Experiments 1 and 2 measured how beliefs about the presence of a ball influenced RTs when adult participants saw the ball, Experiments 4 to 7 measured infants’ “sur-
Fig. 3. Results of Experiments 4 to 7. Looking times in 7-month-old infants. Bars represent average looking times, and error bars show SEM (see Fig. 1 for condition labels). (A) Results of Experiment 4 (true belief). Looking times for the condition when infants (and the agent) believed the ball to be behind the occluder (P+A+) and for the condition when neither the infants nor the agent believed the ball to be behind the occluder (P–A–). (B) Results of Experiment 5 (false belief; agent present in the last scene). Looking times for the condition when only the agent (falsely) believed the ball to be behind the occluder (P–A+) (Movie S1), and for the condition when neither they nor the agent believed the ball to be behind the occluder (P–A–). (C) Results of Experiment 6 (no outcome control). Looking times for two conditions that were identical to the ones used in Experiment 4, except that the occluder was not lowered at the end of the movies. Thus, infants did not see whether the ball was present behind the occluder. As a result, there were no confirmed nor violated beliefs. (D) Results of Experiment 7 (false belief; agent absent in the last scene). Looking times for the two conditions where the agent was replaced with a pile of boxes in the very last scene (corresponding to Fig. 1D). We compared the condition where only the agent (falsely) believed the ball to be behind the occluder (P–A+) with the condition where neither the infants nor the agent believed the ball to be behind the occluder (P–A–). www.sciencemag.org
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prise” (indicated by longer looking times) when no ball was found, although the participant and/or the agent believed the ball to be behind the occluder. In each experiment, the condition where the participant and/or the agent believed the ball to be behind the occluder was compared to a condition where neither the participant nor the agent believed the ball to be behind the occluder. In Experiment 4, infants watched two movies from Experiment 1 (13). In the baseline condition, neither the infant nor the agent believed that the ball was behind the occluder. We compared this baseline to a condition where both the infant and the agent believed that the ball was behind the occluder. When the occluder revealed no ball, infants looked longer in this condition than in the baseline condition [F(1,13) = 5.65, P = 0.03] (Fig. 3A), which suggests that their expectations modulated their looking behavior. Experiment 5 presents the crucial comparison from Experiment 1. In this experiment, infants’ looking times were compared in the baseline condition and in a condition where only the agent believed the ball to be behind the occluder (Movie S1). When no ball appeared behind the occluder, infants looked longer in this condition than in the baseline [F(1,13) = 7.29, P = 0.01] (Fig. 3B). This suggests that infants computed the agent’s belief and looked longer when this belief was not confirmed, possibly also expecting the agent to be surprised. Thus, the beliefs of the agent influenced the infants’ looking behavior, even though they clashed with the infants’ own beliefs. Experiment 6 controls for the possibility that infants’ looking times in Experiment 5 were not influenced by the agent’s beliefs but rather by some visual differences between the movies occurring before the occluder was lowered. (The movies were identical after the occluder was lowered.) We exclude this possibility by replicating Experiment 5, but without lowering the occluder at the end. Thus, infants did not see whether the ball was present behind the occluder, and neither their own nor the agent’s beliefs were confirmed or disconfirmed. If the results of Experiment 5 were due to visual differences rather than to belief computations, Experiment 6 should yield the same results. In contrast to this prediction, no differences were observed when the occluder was not lowered [F(1,13) = 0.05, P = 0.81] (Fig. 3C). This suggests that the differences in Experiment 5 were not due to visual differences between the movies, but rather that infants did indeed compute the agent’s beliefs. In analogy to Experiment 2, Experiment 7 asked whether infants would maintain others’ beliefs even in the agent’s absence. Specifically, infants were presented with the baseline condition (where both the infant and the agent believed that the ball was not there) and a condition where only the agent believed the ball to be behind the occluder. Before the occluder was lowered, however, a pile of boxes, rather than the agent, entered the scene. As in Experiment 5, infants looked longer than in the baseline condition when the agent
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REPORTS least in implicit tasks like ours, others’ (false) beliefs can influence infants’ and adults’ behavior similarly to their own (true) beliefs. The finding that others’ beliefs can be similarly accessible as our own beliefs might seem problematic for an individual, because it may make one’s behavior susceptible to others’ beliefs that do not reliably reflect the current state of affairs. However, the rapid availability of others’ beliefs might allow for efficient interactions in complex social groups. These powerful mechanisms for computing others’ beliefs might, therefore, be part of a core humanspecific “social sense,” and one of the cognitive preconditions for the evolution of the uniquely elaborate social structure in humans. References and Notes 1. E. Herrmann, J. Call, M. V. Hernàndez-Lloreda, B. Hare, M. Tomasello, Science 317, 1360 (2007). 2. D. Premack, G. Woodruff, Behav. Brain Sci. 1, 515 (1978). 3. S. Baron-Cohen, A. M. Leslie, U. Frith, Cognition 21, 37 (1985). 4. J. Perner, Understanding the Representational Mind (MIT Press, Cambridge, MA, 1991). 5. B. J. Scholl, A. M. Leslie, Child Dev. 72, 696 (2001).
Siah Regulation of Pard3A Controls Neuronal Cell Adhesion During Germinal Zone Exit Jakub K. Famulski,*† Niraj Trivedi,* Danielle Howell, Yuan Yang,‡ Yiai Tong, Richard Gilbertson, David J. Solecki§ The brain’s circuitry is established by directed migration and synaptogenesis of neurons during development. Although neurons mature and migrate in specific patterns, little is known about how neurons exit their germinal zone niche. We found that cerebellar granule neuron germinal zone exit is regulated by proteasomal degradation of Pard3A by the Seven in Absentia homolog (Siah) E3 ubiquitin ligase. Pard3A gain of function and Siah loss of function induce precocious radial migration. Time-lapse imaging using a probe to measure neuronal cell contact reveals that Pard3A promotes adhesive interactions needed for germinal zone exit by recruiting the epithelial tight junction adhesion molecule C to the neuronal cell surface. Our findings define a Siah-Pard3A signaling pathway that controls adhesion-dependent exit of neuronal progenitors or immature neurons from a germinal zone niche. he migration of neurons from a germinal zone (GZ) to their final laminar positions is essential for morphogenesis of the developing brain (1–3); aberrations in this process are linked to profound neurodevelopmental and
T
Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. *These authors contributed equally to this work. †Present address: Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada. ‡Present address: Department of Physiology, Development and Neuroscience, Anatomy Building, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK. §To whom all correspondence should be addressed. E-mail:
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cognitive disorders (4). Although the substrates (5–7), guidance mechanisms (8–10), cytoskeletal elements (11–13), and posttranslational modifications (14–16) required for neuronal migrations are well established, the cell-intrinsic machinery regulating when neurons gain access to permissive migration pathways to exit their GZs are unidentified (17). Developing cerebellar granule neurons (CGNs) are an excellent model to analyze the mechanisms regulating GZ exit and to elucidate migration pathway selection, because they undergo two migration phases (18–20): tangential migration near the cerebellar surface followed by radial migration away from the external granular layer (EGL) where CGNs cross the molecular layer (ML) to eventually reside within the internal
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6. A. M. Leslie, O. Friedman, T. P. German, Trends Cogn. Sci. 8, 528 (2004). 7. P. Bloom, T. P. German, Cognition 77, B25 (2000). 8. J. A. Fodor, Cognition 44, 283 (1992). 9. Á. M. Kovács, Dev. Sci. 12, 48 (2009). 10. K. H. Onishi, R. Baillargeon, Science 308, 255 (2005). 11. V. Southgate, A. Senju, G. Csibra, Psychol. Sci. 18, 587 (2007). 12. L. Surian, S. Caldi, D. Sperber, Psychol. Sci. 18, 580 (2007). 13. Materials and methods are available as supporting material on Science Online. 14. G. Csibra, Cognition 107, 705 (2008). 15. This research was supported by the New and Emerging Science and Technology PATHFINDER initiative CALACEI and the Marie Curie Disorders and Coherence of the Embodied Self Research Training Network. We thank G. Csibra, G. Gergely, J. Perner, H. Wellman, M. Hauser, P. Jacob, L. Bonatti, and J. Mehler for comments.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1830/DC1 Materials and Methods SOM Text References Movie S1 12 April 2010; accepted 22 November 2010 10.1126/science.1190792
granule layer (IGL). In this study, we examined the roles of the partitioning-defective (PAR) polaritysignaling complex and an upstream regulator in controlling CGN migration from the EGL, a GZ niche (fig. S1). The PAR complex is an evolutionarily conserved multiprotein complex containing orthologs of partitioning defective-6 (Pard6), partitioning defective-3 (Pard3) and protein kinase Cz (PKCz) that regulates many polarized cellular processes, like cell motility, asymmetric cell division, and epithelial junction formation (21). Because Pard3A protein expression is low in the EGL (Fig. 1, A to C), we examined whether elevated Pard3A activity induces CGN GZ exit. Expression constructs for Pard3A and the fluorescent nuclear reporter H2B-mCherry were co-electroporated into the cerebellar cortices of postnatal day 8 (P8) mice, and cerebellar slices were cultured ex vivo (22). Whereas control CGNs remained within the EGL after 24 hours (fig. S2A), CGNs expressing elevated Pard3A entered the ML and IGL (fig. S2B), suggesting that elevated Pard3A expression is sufficient to induce precocious GZ exit. We next examined the role of Siah, a PAR complex–interacting E3 ubiquitin ligase (fig. S3) expressed in the EGL (Fig. 1D), in regulating Pard3A protein level and PAR complex–dependent GZ exit. The role of Siah ligases in the morphogenesis of the vertebrate nervous system has not previously been examined (23). Epitope-tagged Siah1B immunoprecipitated Pard3A when coexpressed in human embryonic kidney 293 (HEK293) cells (fig. S3B), an interaction that required an intact Siah substrate-binding domain (fig. S3D). Furthermore, Siah1B expression reduced expression of Pard3A, but not Pard6 or PKCz, protein (Fig. 1E) and induced Pard3A ubiquitination (Fig.
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(who was not present when the occluder was lowered) believed that the ball was behind the occluder [F(1,13) = 6.75, P = 0.02] (Fig. 3D). Hence, like adults in Experiment 2, infants seem to compute others’ beliefs online and to maintain them even in the absence of the agent. Possibly, the boxes could have prompted participants to think of the agent and his beliefs, although there was no relation between the boxes and the agent. However, even if the boxes reminded participants the agent, our results can be explained only if participants computed the agent’s beliefs and sustained them even though the agent was not present. Together, our results suggest that the mere presence of social agents is sufficient to automatically trigger online belief computations not only in adults, but also in 7-month-old infants. Once the beliefs have been computed, adults and infants maintain them even in the absence of the agent, presumably for later use in social interactions. Hence, from 7 months on, an age by which infants attribute goals and intentionality (14), humans automatically compute other’s beliefs and seem to hold them in mind as alternative representations of the environment. As a result, at
REPORTS least in implicit tasks like ours, others’ (false) beliefs can influence infants’ and adults’ behavior similarly to their own (true) beliefs. The finding that others’ beliefs can be similarly accessible as our own beliefs might seem problematic for an individual, because it may make one’s behavior susceptible to others’ beliefs that do not reliably reflect the current state of affairs. However, the rapid availability of others’ beliefs might allow for efficient interactions in complex social groups. These powerful mechanisms for computing others’ beliefs might, therefore, be part of a core humanspecific “social sense,” and one of the cognitive preconditions for the evolution of the uniquely elaborate social structure in humans. References and Notes 1. E. Herrmann, J. Call, M. V. Hernàndez-Lloreda, B. Hare, M. Tomasello, Science 317, 1360 (2007). 2. D. Premack, G. Woodruff, Behav. Brain Sci. 1, 515 (1978). 3. S. Baron-Cohen, A. M. Leslie, U. Frith, Cognition 21, 37 (1985). 4. J. Perner, Understanding the Representational Mind (MIT Press, Cambridge, MA, 1991). 5. B. J. Scholl, A. M. Leslie, Child Dev. 72, 696 (2001).
Siah Regulation of Pard3A Controls Neuronal Cell Adhesion During Germinal Zone Exit Jakub K. Famulski,*† Niraj Trivedi,* Danielle Howell, Yuan Yang,‡ Yiai Tong, Richard Gilbertson, David J. Solecki§ The brain’s circuitry is established by directed migration and synaptogenesis of neurons during development. Although neurons mature and migrate in specific patterns, little is known about how neurons exit their germinal zone niche. We found that cerebellar granule neuron germinal zone exit is regulated by proteasomal degradation of Pard3A by the Seven in Absentia homolog (Siah) E3 ubiquitin ligase. Pard3A gain of function and Siah loss of function induce precocious radial migration. Time-lapse imaging using a probe to measure neuronal cell contact reveals that Pard3A promotes adhesive interactions needed for germinal zone exit by recruiting the epithelial tight junction adhesion molecule C to the neuronal cell surface. Our findings define a Siah-Pard3A signaling pathway that controls adhesion-dependent exit of neuronal progenitors or immature neurons from a germinal zone niche. he migration of neurons from a germinal zone (GZ) to their final laminar positions is essential for morphogenesis of the developing brain (1–3); aberrations in this process are linked to profound neurodevelopmental and
T
Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. *These authors contributed equally to this work. †Present address: Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada. ‡Present address: Department of Physiology, Development and Neuroscience, Anatomy Building, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK. §To whom all correspondence should be addressed. E-mail:
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cognitive disorders (4). Although the substrates (5–7), guidance mechanisms (8–10), cytoskeletal elements (11–13), and posttranslational modifications (14–16) required for neuronal migrations are well established, the cell-intrinsic machinery regulating when neurons gain access to permissive migration pathways to exit their GZs are unidentified (17). Developing cerebellar granule neurons (CGNs) are an excellent model to analyze the mechanisms regulating GZ exit and to elucidate migration pathway selection, because they undergo two migration phases (18–20): tangential migration near the cerebellar surface followed by radial migration away from the external granular layer (EGL) where CGNs cross the molecular layer (ML) to eventually reside within the internal
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6. A. M. Leslie, O. Friedman, T. P. German, Trends Cogn. Sci. 8, 528 (2004). 7. P. Bloom, T. P. German, Cognition 77, B25 (2000). 8. J. A. Fodor, Cognition 44, 283 (1992). 9. Á. M. Kovács, Dev. Sci. 12, 48 (2009). 10. K. H. Onishi, R. Baillargeon, Science 308, 255 (2005). 11. V. Southgate, A. Senju, G. Csibra, Psychol. Sci. 18, 587 (2007). 12. L. Surian, S. Caldi, D. Sperber, Psychol. Sci. 18, 580 (2007). 13. Materials and methods are available as supporting material on Science Online. 14. G. Csibra, Cognition 107, 705 (2008). 15. This research was supported by the New and Emerging Science and Technology PATHFINDER initiative CALACEI and the Marie Curie Disorders and Coherence of the Embodied Self Research Training Network. We thank G. Csibra, G. Gergely, J. Perner, H. Wellman, M. Hauser, P. Jacob, L. Bonatti, and J. Mehler for comments.
Supporting Online Material www.sciencemag.org/cgi/content/full/330/6012/1830/DC1 Materials and Methods SOM Text References Movie S1 12 April 2010; accepted 22 November 2010 10.1126/science.1190792
granule layer (IGL). In this study, we examined the roles of the partitioning-defective (PAR) polaritysignaling complex and an upstream regulator in controlling CGN migration from the EGL, a GZ niche (fig. S1). The PAR complex is an evolutionarily conserved multiprotein complex containing orthologs of partitioning defective-6 (Pard6), partitioning defective-3 (Pard3) and protein kinase Cz (PKCz) that regulates many polarized cellular processes, like cell motility, asymmetric cell division, and epithelial junction formation (21). Because Pard3A protein expression is low in the EGL (Fig. 1, A to C), we examined whether elevated Pard3A activity induces CGN GZ exit. Expression constructs for Pard3A and the fluorescent nuclear reporter H2B-mCherry were co-electroporated into the cerebellar cortices of postnatal day 8 (P8) mice, and cerebellar slices were cultured ex vivo (22). Whereas control CGNs remained within the EGL after 24 hours (fig. S2A), CGNs expressing elevated Pard3A entered the ML and IGL (fig. S2B), suggesting that elevated Pard3A expression is sufficient to induce precocious GZ exit. We next examined the role of Siah, a PAR complex–interacting E3 ubiquitin ligase (fig. S3) expressed in the EGL (Fig. 1D), in regulating Pard3A protein level and PAR complex–dependent GZ exit. The role of Siah ligases in the morphogenesis of the vertebrate nervous system has not previously been examined (23). Epitope-tagged Siah1B immunoprecipitated Pard3A when coexpressed in human embryonic kidney 293 (HEK293) cells (fig. S3B), an interaction that required an intact Siah substrate-binding domain (fig. S3D). Furthermore, Siah1B expression reduced expression of Pard3A, but not Pard6 or PKCz, protein (Fig. 1E) and induced Pard3A ubiquitination (Fig.
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(who was not present when the occluder was lowered) believed that the ball was behind the occluder [F(1,13) = 6.75, P = 0.02] (Fig. 3D). Hence, like adults in Experiment 2, infants seem to compute others’ beliefs online and to maintain them even in the absence of the agent. Possibly, the boxes could have prompted participants to think of the agent and his beliefs, although there was no relation between the boxes and the agent. However, even if the boxes reminded participants the agent, our results can be explained only if participants computed the agent’s beliefs and sustained them even though the agent was not present. Together, our results suggest that the mere presence of social agents is sufficient to automatically trigger online belief computations not only in adults, but also in 7-month-old infants. Once the beliefs have been computed, adults and infants maintain them even in the absence of the agent, presumably for later use in social interactions. Hence, from 7 months on, an age by which infants attribute goals and intentionality (14), humans automatically compute other’s beliefs and seem to hold them in mind as alternative representations of the environment. As a result, at
REPORTS mutant in CGNs). Lastly, Venus-Pard3A fluorescence signal in purified CGNs was diminished by Siah1B coexpression but not by Siah1B-DRING (fig. S3G). Therefore, Siah regulates Pard3A protein level through VxP degron-dependent ubiquitination. Siah1 and 2 immunoreactivity in the EGL is elevated at P6, the peak time of CGN neurogenesis (Fig. 1D) and declines at P15 (see fig. S5 for comparison of Siah1 versus Siah2 expression). To detect sites of Siah activity in the developing cerebellum, we fused the Siah degron motif to the Venus fluorescent protein (fig. S6) and electroporated expression vectors for this
Siah sensor in conjunction with H2B-mCherry into P8 EGL. Siah sensor fluorescence was scant in the EGL but visible in the ML and IGL (fig. S7), whereas a negative control sensor containing the NxN mutations showed fluorescence in all cerebellar layers. These results suggest that Siah activity is high in CGNs within the EGL and decreases during GZ exit. To assess Siah function in GZ exit, we electroporated short hairpin RNAs (shRNAs) silencing Siah1B and Siah2 (fig. S8A) or an expression construct for dominant-negative Siah1B-DRING into P8 EGL. After 1 day of ex vivo culture, CGNs
Fig. 1. Siah is highly exFLAG-Pard3A FLAG-Par6 FLAG-PKCζ sense E Pard3A B pressed in the EGL and A α−FLAG ubiquitinates Pard3A protein. (A and B) In situ α-myc hybridization shows that Pard3A mRNA is exβ actin pressed throughout the P6 EGL. Scale bar indicates + + + Siah1B-myc 80 mm. (C) ImmunohistoF D C Siah1B-myc + chemistry of P6 mouse Siah1B-∆RING-myc + cerebellum for Pard3A Venus-Pard3A + (green) and Tuj1 (red). + + Pard3A expression is low oEGL HA-Ubq + + + oEGL in the outer EGL (oEGL) iEGL Venus-Pard3A and higher in differenti(α GFP) ated CGNs in the inner iEGL EGL (iEGL). Scale bar, 50 α HA mm. (D) Immunohistochemistry of P6 mouse Pard3A/ Tuj1 Siah1/2/ Tuj1 cerebellum for Siah1 and IP-α-GFP 2 (green) and Tuj1 (red). Siah expression is high in the oEGL and absent in the iEGL. (E) Expression of Siah1B-myc reduces Pard3A but not Par6 or PKCz protein levels in HEK293 cells. (F) Siah1B-myc but not Siah1B-DRING-myc can induce ubiquitination of Venus-Pard3A in HEK293 cells. IP, immunoprecipitation; GFP, green fluorescent protein.
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1F). Pard3A protein levels were also reduced by Siah2 but not by a dominant negative mutant ligase lacking the catalytic RING domain (Siah1BDRING, fig. S3E). The Pard3A protein sequence contains two Siah degron recognition sequences Pro-x(Ala,Thr,Arg)x-Val-x-Pro (where x is any amino acid) (fig. S3C) (24). Mutation of the Val-x-Pro (VxP) core of both Pard3A degrons to Asn-x-Asn (NxN) or treatment with MG132 attenuated Siahmediated reduction of Pard3A protein (fig. S3E) and blocked Siah-induced ubiquitination (fig. S3F; see figs. S2, B and C, and S4 for additional functional differences between Pard3A and the NxN
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Fig. 2. Siah activity attenuates GZ exit by negatively regulating Pard3A. P8 EGL was co-electroporated with the indicated expression constructs and H2B-mCherry. After 24 (A) or 48 (C) hours of ex vivo culture, the migration distance of H2B-labeled CGNs from the pial layer (outer dashed line) was analyzed in three imaging experiments. (A) Most control and Siah1Bsilenced cells remain within the EGL (dashed lines) at 24 hours, whereas Siah2-silenced and Siah1B-DRING–overexpressing cells prematurely entered the ML and IGL. (B) Migration distance versus frequency plot of conwww.sciencemag.org
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trol (n = 872, black), Siah1B-silenced (n = 960, red), Siah2-silenced (n = 926, blue), and Siah1B-DRING–overexpressing (n = 927, green) CGNs. (C) Whereas control cells entered the ML and IGL after 48 hours, Siah1Band Siah2-overexpressing cells remained in the EGL. Addition of Pard3A to Siah-expressing CGNs restored migration. (D) Migration distance versus frequency plot of control (n = 909, black), Siah1B-expressing (n = 970, red), Siah2-expressing (n = 921, blue), and Siah1B+Pard3A-expressing (n = 919, green) CGNs. VOL 330
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transfected with a control shRNA remained in the EGL. Silencing of Siah2, but not of Siah1B, and expression of Siah1B-DRING increased CGN migration toward the IGL (Fig. 2, A and B, and fig. S9A for additional analysis). We next evaluated whether Siah activity regulates CGN GZ exit by using a gain-of-function approach. Expression vectors for mouse Siah1B or Siah2 were electroporated into P8 EGL. After 2 days of ex vivo culture, control CGNs entered the ML and IGL (Fig. 2, C and D, and fig. S9B for additional analysis), whereas Siah1B- or Siah2expressing CGNs remained within the EGL. EGL exit was similarly blocked by Pard3A silencing (fig. S8, B and C). Elevated Pard3A expression in Siah-expressing cells rescued the Siah phenotype, confirming that Siah-mediated blockade of EGL exit is not only reversible but also Pard3Adependent (Fig. 2, C and D). Lastly, we assessed proliferation status of CGNs in our experiments by using 5-ethynyl-2'-deoxyuridine (EdU) in-
corporation. Whereas elevated Siah2 activity has no effect on EdU incorporation, Siah2 silencing, and Pard3A gain of function, conditions that induce early GZ exit significantly reduced EdU incorporation, suggesting a linkage between migration initiation and cell cycle exit (fig. S10). Having found that Siah activity controls CGN GZ exit, we next examined whether Siah regulates CGN migration mode. We introduced expression vectors for mouse Siah1B or Siah1B plus Pard3A into purified CGNs via nucleofection and examined migration by time-lapse microscopy of microcultures. Control CGNs exhibited radial-like migration that persisted in the direction of leading process extension (Fig. 3A, movie S1, and figs. S11 and S12). Siah-expressing cells were motile but did not elaborate long leading processes or neurites and were less directionally persistent (Fig. 3A and movie S2). Ubiquitin ligase activity was required for the Siah phenotype, because Siah1B-DRING expression did
not alter migration (movie S3). Lastly, increased Pard3A expression restored normal leading process extension (fig. S11C) and directional persistence to Siah-expressing neurons (Fig. 3A and movie S4). Therefore, the antagonistic relation between Siah activity and Pard3A regulates the directional persistence of migrating CGNs but not cell motility. To examine CGN migration pathway selection, we electroporated P8 EGL with Siah1B or Siah1B plus Pard3A expression constructs and examined the migration of H2B-mCherry–labeled CGNs by long-term time-lapse microscopy. Control CGNs migrated extensively parallel to the cerebellar slice surface before migrating radially toward the IGL (Fig. 3B, movie S5, and fig. S12E). Elevated Siah activity reduced radial migration and increased the percentage of neurons migrating toward the pial surface, whereas the percentage of CGNs migrating tangentially within the EGL was unaffected; this phenotype
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Fig. 3. Siah activity and Ex vivo migration analysis A In vitro migration analysis B Pard3A regulate transiMinutes 0 25 50 75 100 125 150 tion from tangential to radial migration. (A) Pu0 0 25 rified CGNs were nucleo12 20 fected with Centrin2-Venus, 8 15 H2B-mCherry, and the in10 4 EGL dicated constructs. After 5 18 hours in culture, frame270 0 90 0 90 270 to-frame migration angle ML of H2B-mCherry–labeled nuclei was analyzed in IGL three separate experiments. Pseudocolored nuclei show 180 180 representative migration angle paths. Control cells (n = 0 0 25 12 3140) and Siah1B+Pard3Aangle 20 expressing cells (n = 8 15 EGL 4743) migrated predomi10 4 nantly forward (315° to 5 ML 45°), whereas the migra270 0 90 0 90 270 tion pattern of Siah1Bangle expressing cells (n = IGL 4480) was randomized. The quadrant (315° to 45°, 45° to 135°, 135° to 225°, 180 180 and 225° to 315°) distribution of Siah1B-expressing 0 0 25 12 cells differed significantly 20 from that of controls (P = 8 15 0.001, c2 test), whereas 10 4 EGL that of Siah1B+Pard3A did 5 not (P = 0.670, c2 test). (B) 0 90 270 0 90 270 ML P8 EGL was electroporated with H2B-mCherry and the indicated constructs. IGL Cerebellar slices were incu180 bated for 28 hours and 180 then imaged for 20 hours. Migration endpoint angles of H2B-mCherry–labeled nuclei were tracked, whereas Siah1B-expressing cells (n = 454) display predominantly tangential binned, and plotted in three separate experiments. Colored lines indicate and pial-directed migration (285° to 75°). Migration of cells expressing migration paths; arrowheads indicate direction of migration. Control cells Siah1B differed significantly from that of controls (P = 2.056 × 10−8, c2 (n = 431) display tangential migration parallel to the EGL (255° to 285°/75° test), whereas that of cells expressing Siah1B+Pard3A did not (n = 452, P = to 105°) and radial migration perpendicular to the EGL (105° to 255°), 0.301, c2 test). VOL 330
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was rescued by increased Pard3A expression (Fig. 3B and movies S6 and S8). Expression of Siah1B-DRING, which appeared to drive GZ exit (Fig. 2A), did not adversely affect movement to the IGL, in vitro directional persistence, or migration pathway selection (figs. S12 and S13). These results show that Siah activity intrinsically regulates CGN migration mode and suggest that Siah regulation of Pard3A activity constitutes a switch controlling tangential versus radial migration as CGNs exit the EGL. Although the PAR complex activates cytoskeletal elements that propel migration (13), we hypothesized that the Siah-Pard3A module controls adhesion during GZ exit because Pard3A is essential for junction formation in epithelial cells. We therefore focused our approach on the analysis of junctional adhesion molecule C (JAM-C), a tight-junction component that directly interacts
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tween JAM-C and Pard3A is required for normal and Siah2 loss of function–induced GZ exit (fig. S15C). Having found that JAM-C is required for GZ exit, we next asked whether JAM-C mediated cell contacts are regulated by the Siah-Pard3A pathway in Madin-Darby canine kidney (MDCK) polarized epithelial cells, a well-established system to assay PAR complex–dependent adhesion. Elevated Siah1B expression dissolved ZO-1, JAM-C, and Pard3A-labeled MDCK junctions, and these effects were reversed by Pard3A expression (fig. S16A). Elevated Siah1B expression also altered the localization of endogenous JAM-C in cultured CGNs (fig. S16B). To directly measure JAM-C contact in live CGNs, we developed a novel JAM-C–based fluorescent probe by fusing super ecliptic pHluorin, a pH-sensitive conditional fluorophore (26), with the extracel-
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with Pard3A and is required for epithelial adhesion (25). Immunostaining revealed that JAM-C is not only expressed in differentiating CGNs but also labels sites of CGN contact (fig. S14, A and B). We examined JAM-C function in GZ exit by electroporating P8 EGL with a shRNA silencing JAM-C (fig. S15A) or with a dominant-negative fragment of the JAM-C cytoplasmic domain (JAM-C-DN) previously shown to competitively inhibit Pard3A binding to endogenous JAM-C and block epithelial junction formation (25) (fig. S15B). After 2 days of ex vivo culture, both JAMC–silenced and JAM-C-DN–expressing CGNs remained predominantly within the EGL, but migration was unaffected by expression of JAMC-DND9, a JAM-C cytoplasmic domain lacking the Pard3A binding motif (fig. S15C). JAM-CDN expression inhibited migration induced by Siah2 silencing, indicating that interaction be-
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Fig. 4. Siah activity regulates GZ exit by modulating the formation of Pard3A-dependent JAM-C adhesions. (A) Time-lapse imaging of CGNs electroporated to express JAM-C–pHluorin. Fluorescence is low before cell contact. Upon establishment of stable contacts, JAM-C-pHluorin signal fluorescence intensifies. (B) Purified CGNs were electroporated to coexpress JAM-C–pHluorin and the indicated constructs. After 18 hours, the abundance of JAM-C–pHluorin contacts was analyzed. Control cells (n = 64 cells, 16045 puncta) displayed robust JAM-C contacts. Siah expression (n = 37 cells, 2579 puncta) significantly reduced JAM-C–pHluorin–positive puncta. Siah1B-DRING (n = 30 cells, 7589 puncta) and Siah1B+Pard3A (n = 32 cells, 8069 puncta) cells were similar to controls, whereas Siah2 silencing (n = 34 cells, 10709 puncta) increased JAM-C–pHluorin contact. (C and D) CGNs in www.sciencemag.org
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P8 EGL were co-electroporated with the indicated constructs and H2BmCherry. After 24 or 48 hours of culture, the distance of H2B-labeled cells from the pial layer (outer dashed line) was analyzed in three separate experiments. Gray shading shows percentage of cells found in the EGL; red overlay indicates the average migration distribution of control cells (error bars, SD). (C) JAM-C overexpression (n = 891) did not induce migration from the EGL, but the JAM-C–Nectin3 fusion molecule (n = 874), a Pard3Aindependent JAM-C variant, induced CGN migration from the EGL at 24 hours. Control versus JAM-C, P = 0.58, and versus JAM-C–Nectin3, P = 5.50 × 10−26 (c2 test). (D) JAM-C–Nectin3 (n = 971) expression rescues migration of Siah1B-expressing cells. Control versus Siah1B+JAM-C-Nectin3, P = 0.95 (c2 test). Error bars indicate SD. VOL 330
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lular domain of JAM-C (fig. S17A). High JAMC-pHluorin signal revealed sites of neuron-neuron or neuron-glial contact (as indicated by f-actin and Pard3A accumulation), validating the JAM-C probe (Fig. 4A, fig.S17B, and movies S9 and S10). We expressed JAM-C-pHluorin and Siah1B, Siah1B-DRING, Siah1B plus Pard3A, or Siah2 shRNA into purified CGNs via nucleofection and assayed cell contacts by time-lapse microscopy of microcultures. Control CGNs displayed robust contacts with neighboring cells, whereas Siah1B gain of function inhibited JAM-C contacts (Fig. 4B and movies S11 and S12). Increased Pard3A expression restored contact formation to near wildtype levels (movie S14). Lastly, Siah2 silencing increased JAM-C contact nearly twofold (movie S15). Therefore, Siah activity inhibits Pard3Adependent JAM-C adhesion. If Pard3A binding to the JAM-C cytoplasmic domain is essential for JAM-C–dependent GZ exit, as it is for epithelial tight junction formation (25), we hypothesized that swapping the JAM-C cytoplasmic domain with that of a Pard3independent adhesion receptor would create a Siah-insensitive receptor. We fused extracellular domains of JAM-C to the cytoplasmic domain of Nectin-3, a non-Pard3A–binding adhesion receptor required for epithelial cell adherens junctions (see fig. S15B for schematic) and electroporated P8 EGL with expression vectors for wild-type mouse JAM-C and JAM-C–Nectin3. Whereas CGNs expressing wild-type JAM-C remained in the EGL, expression of JAM-C– Nectin3 spurred early EGL exit (Fig. 4C). JAMC–Nectin3 expression also fully rescued the EGL exit of CGNs expressing Siah1B, directly demonstrating that elevated JAM-C cell contact overcomes Siah inhibition of GZ exit (Fig. 4D). Newborn neurons must exercise plasticity in order to integrate into the vertebrate brain (27–29).
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Our results show that posttranslational ubiquitination of Pard3A by Siah controls whether cerebellar granule neurons will follow the tangential migration that keeps them within the EGL of the mouse cerebellum or the radial migration path they use to exit their germinal zone and migrate to the IGL. Migration pattern plasticity is invoked by the production of tight junction JAM-C cell contacts necessary for the integration of new neurons into the developing cerebellar cortex. These cell interactions resemble those known to regulate mesenchymal epithelial transitions as new epithelial cells integrate into developing epithelia (30, 31). References and Notes 1. P. Rakic, J. Comp. Neurol. 145, 61 (1972). 2. M. E. Hatten, Science 297, 1660 (2002). 3. C. Métin, R. B. Vallee, P. Rakic, P. G. Bhide, J. Neurosci. 28, 11746 (2008). 4. M. Kato, W. B. Dobyns, Hum. Mol. Genet. 12, R89 (2003). 5. G. Fishell, M. E. Hatten, Development 113, 755 (1991). 6. E. S. Anton, J. A. Kreidberg, P. Rakic, Neuron 22, 277 (1999). 7. L. A. Elias, D. D. Wang, A. R. Kriegstein, Nature 448, 901 (2007). 8. F. Polleux, K. L. Whitford, P. A. Dijkhuizen, T. Vitalis, A. Ghosh, Development 129, 3147 (2002). 9. P. Zhou et al., Neuron 55, 53 (2007). 10. J. Renaud et al., Nat. Neurosci. 11, 440 (2008). 11. B. T. Schaar, S. K. McConnell, Proc. Natl. Acad. Sci. U.S.A. 102, 13652 (2005). 12. J. W. Tsai, K. H. Bremner, R. B. Vallee, Nat. Neurosci. 10, 970 (2007). 13. D. J. Solecki et al., Neuron 63, 63 (2009). 14. G. N. Patrick, P. Zhou, Y. T. Kwon, P. M. Howley, L. H. Tsai, J. Biol. Chem. 273, 24057 (1998). 15. S. Suetsugu et al., Biochem. J. 384, 1 (2004). 16. O. Karakuzu, D. P. Wang, S. Cameron, Development 136, 943 (2009). 17. P. Rakic, in The Cell in Contact: Adhesion and Junctions of Morphogenetic Determinants, G. M. Edelman, J. P. Thiery, Eds. (Wiley, New York, 1985), pp. 67–91. 18. J. C. Edmondson, M. E. Hatten, J. Neurosci. 7, 1928 (1987). 19. E. F. Ryder, C. L. Cepko, Neuron 12, 1011 (1994).
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20. H. Komuro, E. Yacubova, E. Yacubova, P. Rakic, J. Neurosci. 21, 527 (2001). 21. D. J. Solecki, E. E. Govek, T. Tomoda, M. E. Hatten, Genes Dev. 20, 2639 (2006). 22. Materials and methods are available as supporting material on Science Online. 23. C. M. House, A. Möller, D. D. Bowtell, Cancer Res. 69, 8835 (2009). 24. C. M. House et al., Structure 14, 695 (2006). 25. K. Ebnet et al., EMBO J. 20, 3738 (2001). 26. G. Miesenböck, D. A. De Angelis, J. E. Rothman, Nature 394, 192 (1998). 27. S. C. Noctor, V. Martínez-Cerdeño, L. Ivic, A. R. Kriegstein, Nat. Neurosci. 7, 136 (2004). 28. R. S. Bultje et al., Neuron 63, 189 (2009). 29. J. J. Yi, A. P. Barnes, R. Hand, F. Polleux, M. D. Ehlers, Cell 142, 144 (2010). 30. J. P. Thiery, J. P. Sleeman, Nat. Rev. Mol. Cell Biol. 7, 131 (2006). 31. C. L. Chaffer, E. W. Thompson, E. D. Williams, Cells Tissues Organs 185, 7 (2007). 32. We are grateful to J. Morgan, M. Dyer, M. Roussel, and N. Ayad for critically reading the manuscript; A. Sawa, S. Zakharenko, and B. Schulman for insightful discussions; S. Connell, R. Baird, and K. Kilborn (Intelligent Imaging Innovations) for timely data analysis advice and support; G. Chan for providing space for some of the revision experiments; and S. Naron for excellent editorial assistance. G. Miesenbock and Memorial Sloan-Kettering Cancer Center provided the pHluorin reporter, A. Miyawaki provided Venus, F. Polleux provided pCIG2, M. Aurrand-Lions provided anti-JAM-C antiserum, I. Dikic provided the hemagglutinin-ubiquitin (HA-Ubq) construct, and A. Sawa shared Siah reagents. Supported by American Lebanese Syrian Associated Charities (ALSAC, D.J.S.), a National Cancer Institute Cancer Center Support Grant (D.J.S.), and a March of Dimes Basil O’Connor Starter Scholar Research Award (D.J.S.).
Supporting Online Material www.sciencemag.org/cgi/content/full/science.1198480/DC1 Materials and Methods Figs. S1 to S17 References Movies S1 to S15 30 September 2010; accepted 10 November 2010 Published online 25 November 2010; 10.1126/science.1198480
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The new Syncronis range of high-performance liquid chromatography (HPLC) columns unerringly delivers consistent, predictable separations, from run to run and column to column. When developing a new method, one of the key goals for the chromatographer is to achieve a consistent, reproducible separation. The selection of a highly reproducible HPLC column is essential to attaining this goal. The new Syncronis column range has been engineered to deliver exceptional reproducibility by providing highly pure, high surface-area silica, dense bonding, and double endcapping, all controlled and characterized through rigorous testing. New enhanced, automated column packing methods used within the Syncronis range deliver greater consistency, and every column is individually tested to ensure that it meets the required specifications. These extensive testing and quality control procedures ensure the delivery of a completely consistent product, column after column. Syncronis columns are available with 5 µm particle size for conventional HPLC applications and with 1.7 µm particle size for highspeed, high-efficiency UHPLC separations. Thermo Fischer Scientific For info: 800-532-4752
www.thermoscientific.com/Syncronis
Pressure/Flow Rate Sensors The new Mitos Sensor Units provide a flexible system for measuring and displaying pressure and flow rates in microfluidic systems. With a real-time display and low internal volumes, each sensor is designed to minimize interference with the liquid flow. All data is clearly displayed on the Mitos Sensor Display unit, which interfaces with each of the sensor units using a simple push-and-click action. Comprising five flow rate sensors and one pressure sensor (-0.5–30 bar), the complete range is fully interchangeable and each device can run stand-alone or integrated with a Mitos P-Pump for the efficient logging of flow rate output. All data is easily transferrable to a PC for further analysis via USB. Ongoing enhancements to the system will soon enable the flow rate sensors to communicate with the Mitos P-Pump to create a closed loop pumping system. This will enable the user to automatically control flow rates using pressure, providing the ultimate in smooth liquid flow. Dolomite For info: +44-1763-242491
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controlled rate freezer The Bio-Cool is the only controlled rate freezer that does not require expendable liquid nitrogen and the associated pumping, refilling, and storage challenges posed by a cryogenic liquid. The Bio-Cool, simply plugs into a standard electrical outlet and quietly provides low temperature cooling to either -40ºC or -80ºC. Unlike liquid nitrogen systems, which surround samples with a cold vapor phase, the Bio-Cool immerses samples into a well-circulated, cold liquid bath. The circulating liquid allows more efficient heat transfer to the samples and maintains a more consistent temperature profile at all locations. The Bio-Cool comes standard with an easy-to-use programmable microprocessor controller that enables programming
and storage of up to eight different protocols, each with up to eight distinct segments specifying ramp rate, hold temperature, and hold time. Elimination of liquid nitrogen handling and usage, better freezing results, simple straightforward operation, and world-class service support all make the Bio-Cool the controlled rate freezer of choice for most clinical, commercial, and research applications. SP Scientific For info: 845-255-5000
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IMAGING CAMERA Engineered for high-sensitivity and high-speed performance in low-light imaging applications, the Rolera EM-C2 camera aids researchers in applications such as spinning-disc confocal imaging, total internal reflection fluorescence (TIRF) microscopy, ratiometric ion imaging, and fluorescence recovery after photobleaching (FRAP). Fast frame rates essential to cutting-edge live cell imaging studies are enabled by the camera’s 40 MHz pixel clock rate. Up to 34.2 full-resolution (one megapixel) frames per second can be read out and transferred over an optimized 800 Mb/s implementation of the IEEE 1394b FireWire protocol. The Rolera EM-C2 delivers very low noise and high-sensitivity across a broad spectrum common to fluorescence experiments. This camera introduces an EasyEM mode, which optimizes camera EM Gain settings with a single click. Researchers no longer have to gauge sensitivity using the arbitrary sliders that are required with the use of existing cameras to estimate necessary EM Gain. The camera includes the QCapture Suite software, which can be installed and operational within minutes. QImaging For info: 800-874-9789
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Electronically submit your new product description or product literature information! Go to www.sciencemag.org/products/newproducts.dtl for more information. Newly offered instrumentation, apparatus, and laboratory materials of interest to researchers in all disciplines in academic, industrial, and governmental organizations are featured in this space. Emphasis is given to purpose, chief characteristics, and availability of products and materials. Endorsement by Science or AAAS of any products or materials mentioned is not implied. Additional information may be obtained from the manufacturer or supplier.
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HPLC Columns
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NEW PRODUCTS HPLC COLUMNS The new Syncronis range of high-performance liquid chromatography (HPLC) columns unerringly delivers consistent, predictable separations, from run to run and column to column. When developing a new method, one of the key goals for the chromatographer is to achieve a consistent, reproducible separation. The selection of a highly reproducible HPLC column is essential to attaining this goal. The new Syncronis column range has been engineered to deliver exceptional reproducibility by providing highly pure, high surface-area silica, dense bonding, and double endcapping, all controlled and characterized through rigorous testing. New enhanced, automated column packing methods used within the Syncronis range deliver greater consistency, and every column is individually tested to ensure that it meets the required specifications. These extensive testing and quality control procedures ensure the delivery of a completely consistent product, column after column. Syncronis columns are available with 5 µm particle size for conventional HPLC applications and with 1.7 µm particle size for highspeed, high-efficiency UHPLC separations. Thermo Fischer Scientific For info: 800-532-4752
www.thermoscientific.com/Syncronis
PRESSURE/FLOW RATE SENSORS The new Mitos Sensor Units provide a flexible system for measuring and displaying pressure and flow rates in microfluidic systems. With a real-time display and low internal volumes, each sensor is designed to minimize interference with the liquid flow. All data is clearly displayed on the Mitos Sensor Display unit, which interfaces with each of the sensor units using a simple push-and-click action. Comprising five flow rate sensors and one pressure sensor (-0.5–30 bar), the complete range is fully interchangeable and each device can run stand-alone or integrated with a Mitos P-Pump for the efficient logging of flow rate output. All data is easily transferrable to a PC for further analysis via USB. Ongoing enhancements to the system will soon enable the flow rate sensors to communicate with the Mitos P-Pump to create a closed loop pumping system. This will enable the user to automatically control flow rates using pressure, providing the ultimate in smooth liquid flow. Dolomite For info: +44-1763-242491
www.dolomite-microfluidics.com
CONTROLLED RATE FREEZER The Bio-Cool is the only controlled rate freezer that does not require expendable liquid nitrogen and the associated pumping, refilling, and storage challenges posed by a cryogenic liquid. The Bio-Cool, simply plugs into a standard electrical outlet and quietly provides low temperature cooling to either -40ºC or -80ºC. Unlike liquid nitrogen systems, which surround samples with a cold vapor phase, the Bio-Cool immerses samples into a well-circulated, cold liquid bath. The circulating liquid allows more efficient heat transfer to the samples and maintains a more consistent temperature profile at all locations. The Bio-Cool comes standard with an easy-to-use programmable microprocessor controller that enables programming
and storage of up to eight different protocols, each with up to eight distinct segments specifying ramp rate, hold temperature, and hold time. Elimination of liquid nitrogen handling and usage, better freezing results, simple straightforward operation, and world-class service support all make the Bio-Cool the controlled rate freezer of choice for most clinical, commercial, and research applications. SP Scientific For info: 845-255-5000
www.spscientific.com
IMAGING CAMERA Engineered for high-sensitivity and high-speed performance in low-light imaging applications, the Rolera EM-C2 camera aids researchers in applications such as spinning-disc confocal imaging, total internal reflection fluorescence (TIRF) microscopy, ratiometric ion imaging, and fluorescence recovery after photobleaching (FRAP). Fast frame rates essential to cutting-edge live cell imaging studies are enabled by the camera’s 40 MHz pixel clock rate. Up to 34.2 full-resolution (one megapixel) frames per second can be read out and transferred over an optimized 800 Mb/s implementation of the IEEE 1394b FireWire protocol. The Rolera EM-C2 delivers very low noise and high-sensitivity across a broad spectrum common to fluorescence experiments. This camera introduces an EasyEM mode, which optimizes camera EM Gain settings with a single click. Researchers no longer have to gauge sensitivity using the arbitrary sliders that are required with the use of existing cameras to estimate necessary EM Gain. The camera includes the QCapture Suite software, which can be installed and operational within minutes. QImaging For info: 800-874-9789
www.qimaging.com
Electronically submit your new product description or product literature information! Go to www.sciencemag.org/products/newproducts.dtl for more information. Newly offered instrumentation, apparatus, and laboratory materials of interest to researchers in all disciplines in academic, industrial, and governmental organizations are featured in this space. Emphasis is given to purpose, chief characteristics, and availability of products and materials. Endorsement by Science or AAAS of any products or materials mentioned is not implied. Additional information may be obtained from the manufacturer or supplier.
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2011 AAAS ANNUAL MEETING
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UNIVERSITY OF VERMONT COLLEGE OF MEDICINE The Department of Pathology at the University of Vermont College of Medicine seeks an MD or MD/PhD pathologist with fellowship training in a subspecialty area of Surgical Pathology for a tenure track position at the Assistant or Associate Professor level. The applicant must be Board certified (or eligible) in anatomic pathology with subspecialty fellowship training (or have equivalent qualification/experience). The successful applicant will devote 25% of their effort to clinical practice and 75% time to research. We are most interested in individuals with research programs relevant to environmental pathology and carcinogenesis, particularly in the lung. The Department of Pathology has strong research programs in cardiovascular biology, redox signaling, fiber carcinogenesis, pulmonary fibrosis and other aspects of environmental pathology. The Department has had an Environmental Pathology Training Grant from the NIEHS for 25 years. The College of Medicine and University support modern core facilities for microscopic imaging, genomics/proteomics, mouse transgenics and other contemporary technologies. The applicant is expected to direct an independently funded translational research program, participate in teaching, and contribute to Departmental leadership. Salary, laboratory space and startup support will be commensurate with qualifications, professional accomplishments and research activities. Clinical responsibilities will include interpretation of surgical pathology specimens and training of housestaff and medical students, activities that are shared with the other full-time surgical pathologists. The surgical pathology volume is approximately 37,000 specimens per year, with large active subspecialty practices. The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through their research, teaching, and/or service. Applicants are requested to include in their cover letter information about how they will further this goal. Applications will be accepted until the position is filled, but we strongly encourage the submission of application materials by March 1, 2011. Applicants should submit a letter of interest and detailed curriculum vitae with three references to Ms. Jennifer Diaz, Faculty Search Coordinator, Department of Pathology, University of Vermont College of Medicine, the Courtyard at Given S267 BeaumontAve, Burlington, Vermont 05405.Applications may also be submitted electronically to Jennifer Diaz at
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Associate Director of Basic Sciences The Medical College of Wisconsin is actively recruiting for an Associate Director for Basic Sciences in its newly developing Cancer Center. The Associate Director should have a PhD and/or MD degree and a distinguished record of achievement in a basic science discipline of cancer research. This position will be responsible for assisting the basic science program leaders in program development, in identifying areas of potential research collaboration, and in facilitating translational research from the basic science laboratories to the clinic. The Associate Director will also assist in the development of and oversee all basic science core facilities in the Center. The successful candidate will be a member of the Executive Committee of the Cancer Center and report directly to the Center Director. The academic appointment will be in a mutually agreed upon department at the Medical College of Wisconsin where teaching, research and service requirements of a departmental appointment are expected. Rank is open and commensurate with experience. This leader could also potentially qualify for an endowed chair. The successful candidate will bring a significant extramural funding portfolio and focus on research funding. In addition to an active research program, this position will include limited administrative activities within the Cancer Center and service to the institution. Salary is competitive and will depend on rank and experience. Qualified individuals are encouraged to send, via e-mail (
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ENDOCRINOLOGY FACULTY POSITIONS The Endocrinology, Diabetes and Nutrition Division is currently seeking six new faculty members to join a vibrant group of 27 current faculty (see http: //medschool.umaryland.edu/medicine/default.asp). We seek two physician-scientists at the Assistant/Associate and Professor or Associate Professor level (Position #’s 03-309-591-592). The successful candidates will be accomplished independently funded investigators in clinical/translational or basic science research who will also contribute to the clinical, teaching and service missions of the University. The additional four positions are for clinician educators/investigators specializing in diabetes management and thyroid/pitituary disorders at the Assistant Professor to Associate Professor level (Position #’s 03-309-525,526,579,593). Clinical responsibilities will include management of diabetes in a comprehensive multidisciplinary setting at the University of Maryland Medical Center, and general endocrinology in both outpatient and inpatient settings. Successful applicants will also have opportunities to engage in clinical/translational research and contribute to the teaching duties of the Division. All candidates must be board eligible/certified in Endocrinology. Tenure status/salary dependent upon candidate background. Send CV and list of 4 references to Kristi Silver, M.D., Associate Professor, Division of Endocrinology, Diabetes and Nutrition, c/o JoAnn Gibbs, Academic Programs Office, Department of Medicine, N3E09, University of Maryland Medical Center, 22 S. Greene St., Baltimore, MD 21201-1595. Please reference appropriate position # when submitting your application. The UM,B encourages women and minorities to apply and is an AA/EEO/ADA Employer.
The 2011 Department of Energy Joint Genome Institute (DOE JGI)
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March 22-24 Walnut Creek, California http://go.usa.gov/1Gl
topics include:
confirmed speakers include:
Synthetic Biology
Peer Bork, European Molecular Biology Laboratory (EMBL) Ed Buckler, Cornell University
Christopher Scholin, Monterey Bay Aquarium Research Institute (MBARI)
Dan Distel, Ocean Genome Legacy
Stephan Schuster, Penn State University
Hardware and Software Trends in Genomics Supercomputing
Dusko Ehrlich, French National Institute for Agricultural Research (INRA)
Pam Silver, Harvard
Computational Approaches to Massive Short Read Metagenomic Data Sets
Terry Hazen, Lawrence Berkeley National Laboratory (LBNL)
Mike Thomashow, Michigan State University
Genomics of Biofuel Crops
Scott Hodges, University of California, Santa Barbara
Ecogenomics and Ecoresilience of the Gulf Oil Spill
Behavioral Genetics of Pollinating Bees
Tom Juenger, University of Texas at Austin
Microbiome Analyses from Humans to Shipworms
Rob Knight, University of Colorado
Metatranscriptomics of Marine Microbial Communities
Ruth Ley, Cornell University
Successful Transposable Elements Secrets
Magnus Nordborg, Gregor Mendel Institute
Great Prairie Soil Metagenomics
Gene Robinson, University of Illinois at Urbana-Champaign
Jim Tiedje, Michigan State University
Jerry Tuskan, Oak Ridge National Laboratory/DOE JGI Sue Wessler, University of California, Riverside Katherine Yelick, National Energy Research Scientific Computing Center (NERSC) NL L at LBNL
Mary Ann Moran, University of Georgia
workshops include: Integrated Microbial Genomes; Mycocosm Fungal Genomics Portal; Phytozome; RNA Technologies & Analysis
POSITIONS OPEN DNA Sequencing and Computational Biology Core Facility Director Health and Human Services (HHS) National Institutes of Health (NIH) National Heart, Lung and Blood Institute (NHLBI)
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An expert is sought in the area of Computational Biology with an emphasis on next-generation DNA sequence analysis at the DNA Sequencing and Computational Biology (DSCB) Core Facility, Division of Intramural Research (DIR), National Heart, Lung and Blood Institute (NHLBI), NIH in Bethesda, Maryland USA. The successful applicant will participate in analyzing large-scale data sets consisting of a wide spectrum of sequencing applications, including but not limited to chromatin immunoprecipitation sequencing (ChIP-Seq), RNA-seq, targeted and whole-genome DNA sequencing, microRNA sequencing. He/she is expected to work closely with the core director and interact with DIR Principal Investigators in establishing a comprehensive sequence analysis pipeline, in basic bioinformatics studies for the presentation of sequence output to end users, and in original collaborative research in genomics and systems biology. The DSCB Core Facility is part of an NHLBI DIR Initiative in Systems Biology, and the candidate is also expected to interact closely with scientists within the DSCB Core and/or other independently operated DIR Facilities (e.g. proteomics) to facilitate data integration. Although the DSCB Core Facility is oriented toward providing service and conducting collaborative research, the position will also have the opportunity to undertake research initiative in the area of computational biology. The mission of the DIR is to improve the health of all Americans through basic and clinical research, research training, and translation of discoveries to new tools to be applied directly to the field of medicine. We are seeking an experienced scientist (with Ph.D. or equivalent) with an outstanding track record in computational biology research. Salary will be commensurate with qualifications and experience. More detailed information about the NHLBI Division of Intramural Research may be found at: http://dir.nhlbi.nih.gov/ . Applicants should submit the following: cover letter highlighting key qualifications; current curriculum vitae with complete bibliography; names and addresses of four references; and a one-page description of current and future research interests. Applications should be received by Feb. 1, 2011 but the advertisement will remain open until the position is filled. PDF versions of documents sent by electronic mail are strongly preferred. Materials should be sent to Dr. Jun Zhu c/o: Trina Gregory, Administrative Officer, NHLBI, by email:
[email protected]; or by regular mail: Building 10, Room 7N220, 10 Center Drive MSC 1670, Bethesda, MD 20892-1670.
HHS and NIH are Equal Opportunity Employers
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THE 2011 LOUISA GROSS HORWITZ PRIZE FOR BIOLOGY OR BIOCHEMISTRY The Louisa Gross Horwitz Prize was established under the will of the late S. Gross Horwitz through a bequest to Columbia University and is named to honor the donor’s mother. Louisa Gross Horwitz was the daughter of Dr. Samuel David Gross (1805-1889), a prominent surgeon of Philadelphia and author of the outstanding Systems of Surgery who served as President of the American Medical Association. Each year since its inception in 1967, the Louisa Gross Horwitz Prize has been awarded by Columbia University for outstanding basic research in the fields of biology or biochemistry. The purpose of this award is to honor a scientific investigator or group of investigators whose contributions to knowledge in either of these fields are deemed worthy of special recognition. The Prize consists of an honorarium and a citation which are awarded at a special presentation event. Unless otherwise recommended by the Prize Committee, the Prize is awarded annually. Dr. Elizabeth H. Blackburn, University of California, San Francisco, CA; Dr. Joseph G. Gall, Carnegie Institute, Baltimore, MD; Dr. Carol W. Greider, Johns Hopkins University, Baltimore, MD were the 2007 awardees.
QUALIFICATIONS FOR THE AWARD The Prize Committee recognizes no geographical limitations. The prize may be awarded to an individual or a group. When the prize is awarded to a group, the honorarium will be divided among the recipients, and each member will receive a citation. Preference will be given to work done in the recent past. Nominations must be submitted electronically at: http://www.cumc.columbia.edu/horwitz/ Nominations should include: 1. A summary, preferably less than 500 words, of the research on which this nomination is based. 2. A summary, preferably less than 500 words, of the significance of this research in the fields of biology or biochemistry. 3. A brief biographical sketch of the nominee, including positions held and awards received by the nominee. 4. A listing of up to ten of the nominee’s most significant publications relating to the research noted under item 1. 5. A copy of the nominee’s curriculum vitae.
Deadline date: January 31, 2011
POSITIONS OPEN
Dean, College of Arts and Sciences Georgia State University, a leading research university located in the heart of downtown Atlanta, is conducting a search for the Dean of the College of Arts and Sciences. The Search Committee invites nominations, applications (letter of interest, complete CV, and references), or expressions of interest to be submitted to the search firm assisting Georgia State University. Confidential review of materials will begin immediately. It is preferred that all nominations and applications be submitted prior to February 28, 2011. For a complete position description, refer to Current Opportunities on www.parkersearch.com. For additional information, please visit GSU online at www.gsu.edu or the College of Arts and Sciences at www.cas.gsu.edu. Laurie C. Wilder, Senior Vice President Katie Bain, Principal 770-804-1996 ext: 108
[email protected] Georgia State University, a unit of the University System of Georgia, is an Equal Opportunity Educational Institution and is an Equal Opportunity/Affirmative Action Employer.
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PRIZES
AAAS is here – connecting government to the scientific community. As a part of its efforts to introduce fully open government, the White House is reaching out to the scientific community for a conversation around America’s national scientific and technological priorities. To enable the White House’s dialogue with scientists, AAAS launched Expert Labs, under the direction of blogger and tech guru Anil Dash. Expert Labs is building online tools that allow government agencies to ask questions of the scientific community and then sort and rank the answers they receive. On April 12, 2010, AAAS asked scientists everywhere to submit their ideas to the Obama administration and at the same time launched the first of Expert Labs tools, Think Tank, to help policy makers collect the subsequent responses. The result was thousands of responses to the White House’s request, many of which are already under consideration by the Office of Science and Technology Policy. As a AAAS member, your dues support our efforts to help government base policy on direct feedback from the scientific community. If you are not already a member, join us. Together we can make a difference.
To learn more, visit aaas.org/plusyou/expertlabs
AAAS is here – helping scientists achieve career success. Every month, over 400,000 students and scientists visit ScienceCareers.org in search of the information, advice, and opportunities they need to take the next step in their careers. A complete career resource, free to the public, Science Careers offers a suite of tools and services developed specifically for scientists. With hundreds of career development articles, a grants and scholarships database, webinars and downloadable booklets filled with practical advice, a community forum providing real-time answers to career questions, and thousands of job listings in academia, government, and industry, Science Careers has helped countless individuals prepare themselves for successful careers. As a AAAS member, your dues help AAAS make this service freely available to the scientific community. If you’re not a member, join us. Together we can make a difference.
To learn more, visit aaas.org/plusyou/sciencecareers
AAAS is here – promoting universal science literacy. In 1985, AAAS founded Project 2061 with the goal of helping all Americans become literate in science, mathematics, and technology. With its landmark publications Science for All Americans and Benchmarks for Science Literacy, Project 2061 set out recommendations for what all students should know and be able to do in science, mathematics, and technology by the time they graduate from high school. Today, many of the state standards in the United States have drawn their content from Project 2061. Every day Project 2061 staff use their expertise as teachers, researchers, and scientists to evaluate textbooks and assessments, create conceptual strand maps for educators, produce groundbreaking research and innovative books, CD-ROMs, and professional development workshops for educators, all in the service of achieving our goal of universal science literacy. As a AAAS member, your dues help support Project 2061 as it works to improve science education. If you are not yet a AAAS member, join us. Together we can make a difference.
To learn more, visit aaas.org/plusyou/project2061
Science & Technology Policy Fellows
The science and engineering challenges that society faces today are far more complex than those of 40 to 50 years ago. The best available scientific, technical, and economic information is required to establish priorities, make decisions, and develop best practices. AAAS manages the Science & Technology Policy Fellowships in four areas to provide the opportunity for accomplished scientists and engineers to contribute to the federal policymaking process while learning firsthand about the intersection of science and policy. And this is just one of the ways that AAAS is committed to advancing science to support a healthy and prosperous world. Join us. Together we can make a difference. aaas.org/plusyou/fellows
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POSITIONS OPEN
POSITIONS OPEN
TENURE-TRACK POSITION ASSISTANT PROFESSOR CHEMIST/BIOCHEMIST Department of Food Science and Technology University of California, Davis The Department of Food Science and Technology is currently seeking to fill a faculty position. We are interested in individuals who have or can establish a strong extramurally funded research program in an advanced contemporary area of food chemistry/biochemistry. The specific areas of research investigation we seek in the broad area of food chemistry include, but are not limited to: (i) food components and their interactions (including polymers, colloids, and emulsions); (ii) carbohydrates, especially carbohydrate polymers; (iii) food formulations; and, (iv) health-promoting bioactive food components, including encapsulation and delivery of nutrients or otherwise functional components. Candidates are expected to have a Ph.D. (or equivalent) and demonstrated ability in chemistry, biochemistry, food science, or a related discipline. Postdoctoral experience is highly desirable. Selection will be based in part on a record of research publications in internationally recognized peer-reviewed journals, and the ability to obtain extramural funding. The specific research program will depend upon the expertise and interests of the candidate. The successful applicant will be expected to develop an independent, internationally recognized research program and contribute to the mission of the CAES and its associated Agricultural Experiment Station, teach at the undergraduate and graduate level, and supervise graduate student thesis research. Applicants should submit online at website: https:// secure.caes.ucdavis.edu/Recruitment a letter of application, curriculum vitae (including list of publications), a statement of research, a separate statement describing teaching interests, and background; reprints of three publications; academic transcripts; and names, addresses including e-mail, and telephone numbers of three references. The position is open until filled; but to assure full consideration, completed online applications should be submitted no later than February 28, 2011, for a targeted start date of July 1, 2011. UC Davis is an Affirmative Action/Equal Employment Opportunity Employer and is dedicated to recruiting a diverse faculty community. We welcome all qualified applicants to apply, including women, minorities, individuals with disabilities, and veterans.
TENURE-TRACK FACULTY POSITION in Health Sciences Purdue University, School of Health Sciences The School of Health Sciences invites applications for a tenure-track position at the rank of ASSISTANT PROFESSOR. The successful candidate is expected to develop and maintain extramurally funded research programs in neurotoxicology, metal/pesticide toxicology, or related research areas. Applicants with expertise in molecular, cellular, genetic, or neuroimaging approaches in understanding the mechanisms of neurodegenerative diseases are encouraged to apply. Candidates must have a Ph.D., M.D., or equivalent degree and at least two years of relevant postdoctoral research experience. The position is competitive with regard to salary, startup funds, and laboratory space. Please electronically send curriculum vitae, a brief statement of current and future research interests, and contact information for three references to Dr. Wei Zheng, Head of the School of Health Sciences, at e-mail:
[email protected]. Review of applicants will begin February 1, 2011, and will continue until the position is filled. Applicants are encouraged to apply by January 31, 2011 for full consideration. Purdue University is an Equal Opportunity/Equal Access/ Affirmative Action Employer fully committed to achieving a diverse workforce. THE METHODIST HOSPITAL RESEARCH INSTITUTE Weill Cornell Medical College The Molecular Imaging Program at the Department of Radiology develops novel agents and new technologies to image molecular processes and treat diseases. The research focuses on cancer, cardiovascular disease, neurodegeneration, cell therapy, and nanomedicine. Several POSTDOCTORAL FELLOW positions are currently open for application. Self-motivated scientists with expertise in liposomes, peptides, nanoparticles, radiochemistry, photodynamic therapy, and MR physics are encouraged to join our dynamic research team. Please electronically send curriculum vitae and contact information of three references to Dr. Ching H. Tung at e-mail:
[email protected]. The Methodist Hospital Research Institute is centrally located in the world largest medical center in Houston, Texas.
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ASSISTANT, ASSOCIATE, AND FULL PROFESSOR OF PHYSIOLOGY University of California, Merced The University of California, Merced invites applicants for a faculty position in Physiology. The appointment will be made at either the tenure-track Assistant Professor or tenured Associate or Full Professor rank. We seek an outstanding individual with research interests in and expertise in any area of Physiology. We welcome applicants using experimental approaches working at the cellular and/or organism level. We seek distinguished scholars who will help establish a program of international repute in Physiology research at UC Merced, and who will participate actively in the development of innovative, interdisciplinary curricula and in the teaching and mentoring of a diverse student population. For more information and to apply visit website: http:// jobs.ucmerced.edu/n/academic/listings.jsf?seriesId01. The application deadline is February 1, 2011. Affirmative Action/Equal Opportunity Employer.
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EAST TENNESSEE STATE UNIVERSITY— College of Medicine Department of Physiology— POSTDOCTORAL positions available to study the molecular mechanisms of cardiac myocyte apoptosis and myocardial remodeling. Highly motivated individuals (M.D. and/or Ph.D. required) with experience in molecular signaling, cardiac physiology, biochemistry, or cell biology will be preferred. Apply to the positions at website: https://jobs.etsu.edu. Contingent upon grant funding. Affirmative Action/Equal Opportunity Employer.
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