International Review of Neurobiology, Volume 73

International REVIEW OF Neurobiology Volume 73 International REVIEW OF Neurobiology Volume 73 SERIES EDITORS RONALD...

Author: Ronald J. Bradley | Adron R. Harris | Peter Jenner

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International REVIEW OF

Neurobiology Volume 73

International REVIEW OF

Neurobiology Volume 73 SERIES EDITORS RONALD J. BRADLEY Department of Psychiatry, College of Medicine The University of Tennessee Health Science Center Memphis, Tennessee, USA

R. ADRON HARRIS Waggoner Center for Alcohol and Drug Addiction Research The University of Texas at Austin Austin, Texas, USA

PETER JENNER Division of Pharmacology and Therapeutics GKT School of Biomedical Sciences King’s College, London, UK

EDITORIAL BOARD ERIC AAMODT PHILIPPE ASCHER DONARD DWYER MARTIN GIURFA PAUL GREENGARD NOBU HATTORI DARCY KELLEY BEAU LOTTO MICAELA MORELLI JUDITH PRATT EVAN SNYDER JOHN WADDINGTON

HUDA AKIL MATTHEW J. DURING DAVID FINK MICHAEL F. GLABUS BARRY HALLIWELL JON KAAS LEAH KRUBITZER KEVIN MCNAUGHT JOSE´ A. OBESO CATHY J. PRICE SOLOMON H. SNYDER STEPHEN G. WAXMAN

International REVIEW OF

Neurobiology EDITED BY

RONALD J. BRADLEY Department of Psychiatry, College of Medicine The University of Tennessee Health Science Center Memphis, Tennessee, USA

R. ADRON HARRIS Waggoner Center for Alcohol and Drug Addiction Research The University of Texas at Austin Austin, Texas, USA

PETER JENNER Division of Pharmacology and Therapeutics GKT School of Biomedical Sciences King’s College, London, UK

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

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CONTENTS

Contributors.........................................................................

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Chromosome 22 Deletion Syndrome and Schizophrenia NIGEL M. WILLIAMS, MICHAEL C. O’DONOVAN, I. II. III. IV. V. VI. VII. VIII. IX.

AND

MICHAEL J. OWEN

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. 22q11DS: Mechanism of the Deletion . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. 22q11DS: Clinical Phenotype . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. 22q11DS: Psychosis . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. 22q11DS: Neuropathology.. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. 22q11DS: Positional Cloning Schizophrenia Susceptibility Loci. . . . . . .. 22q11DS: Murine Models. .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Catechol-O-Methyltransferase . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Conclusions . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

1 2 4 5 6 7 9 11 18 20

Characterization of Proteome of Human Cerebrospinal Fluid JING XU, JINZHI CHEN, ELAINE R. PESKIND, JINGHUA JIN, JIMMY ENG, CATHERINE PAN, THOMAS J. MONTINE, DAVID R. GOODLETT, AND JING ZHANG I. Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. II. Materials and Methods . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. III. Results . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

v

30 31 35 96

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CONTENTS

Hormonal Pathways Regulating Intermale and Interfemale Aggression NEAL G. SIMON, QIANXING MO, SHAN HU, CARRIE GARIPPA, AND SHI-FANG LU I. II. III. IV. V.

Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Females . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Males . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Hormonal Modulation of Serotonin Function.. . . . . . . . . . . . . . . . . . . . . . . .. . . . Conclusions . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

99 103 107 113 116 117

Neuronal Gap Junctions: Expression, Function, and Implications for Behavior CLINTON B. MCCRACKEN I. II. III. IV. V. VI. VII. VIII. IX. X.

AND

DAVID C. S. ROBERTS

A Brief History of Gap Junctions . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Gap Junction Structure . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Gap Junctions in the Brain . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Electrical Coupling in the Brain . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Properties and Function of Electrical Synapses. . . . . . . . . . . . . . . . . . . . . . . .. . . . Modulation of Electrical Synapses and Gap-Junctional Coupling . .. . . . Use-Dependent Plasticity . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Local Factors: Voltage, pH, and Calcium. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Neurotransmitter and Second Messenger Modulation . . . . . . . . . . . . . . .. . . . Concluding Remarks. . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

125 127 129 132 135 137 137 138 139 141 142

Effects of Genes and Stress on the Neurobiology of Depression J. JOHN MANN I. II. III. IV. V. VI. VII. VIII. IX. X. XI. XII. XIII.

AND

DIANNE CURRIER

Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Stress and Depression.. . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Genetics and Depression . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Serotonergic System. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Candidate Gene Studies of the Serotonergic System . . . . . . . . . . . . . . . . .. . . . Current Stress and the Serotonergic System . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Gene Stress Interaction. .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Hypothalamic–Pituitary–Adrenocortical (HPA) Axis . . . . . . . . . . . . . . . . .. . . . Noradrenergic System . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Dopaminergic System in Depression . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . GABAergic System. . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Brain Derived Neurotrophic Factor . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Conclusions . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

154 154 155 157 159 163 165 166 168 170 172 173 175 175

CONTENTS

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Quantitative Imaging with the Micro-PET Small-Animal PET Tomograph PAUL VASKA, DANIEL J. RUBINS, DAVID L. ALEXOFF, AND WYNNE K. SCHIFFER I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Setup and Calibration . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Physical Corrections . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Image Reconstruction . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Data Analysis. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Conclusions . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

192 193 195 203 208 215 216

Understanding Myelination Through Studying Its Evolution RU¨DIGER SCHWEIGREITER, BETTY I. ROOTS, CHRISTINE E. BANDTLOW, AND ROBERT M. GOULD I. Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. II. Evidence that Glial Cells First Interacted with Large Axons in a ‘‘Nonmyelin’’ Relationship. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. III. Myelin-like Sheaths in Invertebrates . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. IV. Vertebrate Myelinated Nervous System . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. V. Use of Comparative Myelin Studies to Understand CNS Regeneration . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. VI. Future Studies of Myelin Evolution . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

220 221 225 231 244 255 255

Index ......................................................................................

275

Contents of Recent Volumes................................................

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CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

David L. AlexoV (191), Chemistry Department and Center for Translational Neuroimaging, Brookhaven National Laboratory, Upton, New York 11973, USA Christine Bandtlow (219), Medical University Innsbruck, Biocenter Innsbruck, Division of Neurobiochemistry, A-6020 Innsbruck, Austria Jinzhi Chen (29), Department of Medicinal Chemistry, University of Washington School of Medicine, Seattle, Washington 98104, USA Dianne Currier (153), Department of Psychiatry, Division of Neuroscience, Columbia University, New York, New York 10032, USA Jimmy Eng (29), Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA Carrie Garippa (99), Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA David R. Goodlett (29), Department of Medicinal Chemistry, University of Washington School of Medicine, Seattle, Washington 98104, USA Robert M. Gould (219), Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, Illinois 60612, USA Shan Hu (99), Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA Jinghua Jin (29), Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA Shi-fang Lu (99), Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA J. John Mann (153), Department of Psychiatry, Division of Neuroscience, Columbia University, New York, New York 10032, USA Clinton B. McCracken (125), Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA Qianxing Mo (99), Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA Thomas J. Montine (29), Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA

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CONTRIBUTORS

Michael C. O’Donovan (1), Department of Psychological Medicine, Henry Wellcome Building for Biomedical Research College of Medicine, CardiV University, CardiV, United Kingdom Michael J. Owen (1), Department of Psychological Medicine, Henry Wellcome Building for Biomedical Research College of Medicine, CardiV University, CardiV, United Kingdom Catherine Pan (29), Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA Elaine R. Peskind (29), Psychiatry and Behavioral Sciences and VA Mental Illness Research, Education, and Clinical Center, University of Washington School of Medicine, Seattle, Washington 98104, USA David C. S. Roberts (125), Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA Betty I. Roots (219), Department of Zoology, University of Toronto, Toronto, Ontario, Canada M5S 3G5 Daniel J. Rubins (191), Imaging Department, Merck Research Laboratories, Merck and Co., Inc., West Point, Pennsylvania 19486, USA Wynne K. SchiVer (191), Chemistry Department and Center for Translational Neuroimaging, Brookhaven National Laboratory, Upton, New York 11973, USA Ru¨diger Schweigreiter (219), Medical University Innsbruck, Biocenter Innsbruck, Division of Neurobiochemistry, A-6020 Innsbruck, Austria Neal G. Simon (99), Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA Paul Vaska (191), Medical Department and Center for Translational Neuroimaging, Brookhaven National Laboratory, Upton, New York 11973, USA Nigel M. Williams (1), Department of Psychological Medicine, Henry Wellcome Building for Biomedical Research College of Medicine, Cardiff University, CardiV, United Kingdom Jing Xu (29), Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA; Department of Neurosurgery, the 2nd Afﬁliated Hospital of WenZhou Medical College, Zhejiang, China Jing Zhang (29), Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA

CHROMOSOME 22 DELETION SYNDROME AND SCHIZOPHRENIA

Nigel M. Williams, Michael C. O’Donovan, and Michael J. Owen Department of Psychological Medicine, Henry Wellcome Building for Biomedical Research College of Medicine, Cardiff University, Cardiff, United Kingdom

I. II. III. IV. V. VI. VII. VIII. IX.

Introduction 22q11DS: Mechanism of the Deletion 22q11DS: Clinical Phenotype 22q11DS: Psychosis 22q11DS: Neuropathology 22q11DS: Positional Cloning Schizophrenia Susceptibility Loci 22q11DS: Murine Models Catechol-O-Methyltransferase Conclusions References

A microdeletion at chromosome 22q11 is the most frequent known interstitial deletion found in man, occurring in approximately 1 in every 4000 live births. Its occurrence is associated with a characteristic facial dysmorphology, a range of congenital abnormalities, and psychiatric problems especially schizophrenia. The prevalence of psychosis in those with 22q11 deletion syndrome is high (30%) suggesting that haploinsuYciency of a gene or genes in this region might confer a substantially increased risk. In addition, several studies provide evidence for linkage to schizophrenia on 22q, suggesting that a gene in this region could confer susceptibility to schizophrenia in nondeleted cases. Recent studies have provided compelling evidence that haploinsuYciency of TBX1 is likely to be responsible for many of the physical features associated with the deletion. A number of genes have recently been implicated as possible schizophrenia susceptibility loci, however, at this point in time these ﬁndings remain ambiguous and further detailed genetic analysis is required.

I. Introduction

Deletions at 22q11 have been associated with a heterogeneous range of clinical syndromes, which include DiGeorge syndrome, Velo-cardio-facial syndrome (VCFS), and conotruncal anomaly face syndrome (Scambler, 2000). It is INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73001-X

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now, however, widely considered that these diVerent diagnostic categories probably reﬂect variable outcomes from a single genetic mechanism (Scambler, 2000), and in this context they can be grouped together under the collective term Chromosome 22q11 Deletion Syndrome (22q11DS). As would be expected from the product of this diverse range of clinical syndromes the phenotype of 22q11DS is complex, nevertheless, it is now well established that people with 22q11DS have a greatly increased risk of developing psychosis, and in particular schizophrenia (Bassett et al., 1998; Murphy et al., 1999; Papolos et al., 1996; Pulver et al., 1994; Shprintzen et al., 1992). This chapter will assess the recent literature in order to consider the nature of this association and the evidence for the genes within the deleted region that have been claimed as susceptibility loci for schizophrenia.

II. 22q11DS: Mechanism of the Deletion

Microdeletion at chromosome 22q11 is the most frequent known interstitial deletion found in man, occurring in approximately 1 in every 4000 live births (Driscoll et al., 1992). It is inherited from an aVected parent in 5–10% of cases and occurs de novo in the remainder (Scambler, 2000). Approximately 87% of deletions include a common 3 Mb region (Fig. 1), which includes at least 48 known genes (UCSC genome browser, May 2004 freeze; http://genome.ucsc.edu), while around 8% span a smaller 1.5 Mb region (nested within the larger 3 Mb region) (Shaikh et al., 2000), which contains at least 33 genes (UCSC genome browser, May 2004 freeze; http://genome.ucsc.edu). The relative homogeneity of the common deletions (Dunham et al., 1999) is largely due to the presence of blocks of genomic sequence, known as low copy repeats (LCR22s), at the breakpoints of each deleted region. The LCR22s are believed to act as targets for anomalous intrachromosomal homologous recombination during meiosis, thereby generating the observed chromosomal rearrangements. While it is evident that the common 3 Mb/1.5 Mb microdeletions are associated with 22q11DS, a number of cases have been reported with smaller deletions at 22q11. The microdeletions have typically been identiﬁed by ﬂuorescence in situ hybridization (FISH) analysis, haplotype analysis using microsatellite markers, or more recently quantitative PCR ( Jacquet et al., 2002). Such studies suggest that the great majority of cases (85–96%) with one of the associated clinical syndromes have demonstrable microdeletions at 22q11 (Driscoll et al., 1992; Yagi et al., 2003). While microdeletions are by far the most common genomic variation associated with 22q11DS, it is important to note that other genomic instabilities that disrupt chromosome 22q11 have been reported in 22q11DS patients. In particular, there is one reported case of a balanced translocation

CHROMOSOME 22 DELETION SYNDROME AND SCHIZOPHRENIA

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FIG. 1. Comparison of the 22q11DS deleted region at human chromosome 22q11 (HSA22q11) and its syntenic region on MMU16. Figure shows the conserved synteny in the organization of the genes included in the 22q11DS deleted region and their murine orthologs at MMU16. The colored blocks represent intrachromosomal rearrangements since the divergence of the two species, which have resulted in the diVerences in gene order between the two species. The location of the deletions that were made murine models of 22q11DS is indicated. The locations of the genes COMT, PRODH, and ZDDHC8, and the microsatellite D22S944, which have all been reported to be associated with schizophrenia, as well as the gene Tbx1, which is responsible for much of the pharyngeal phenotype associated with 22q11DS are also indicated.

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t(2;22) (q14;q11) in a mother and daughter with no 22q11 deletions but who display the typical 22q11DS phenotype (Budarf et al., 1995). Microduplications mediated by the LCR22s have also been reported (Edelmann et al., 1999; Ensenauer et al., 2003), however, it is likely that as a result of limitations in the cytogenetic methods used to identify them, their prevalence has probably been underestimated (Ensenauer et al., 2003). A widely variable clinical phenotype that includes dysmorphic phenotypes, which were similar to 22q11DS have been described in the small number of cases reported to date (Ensenauer et al., 2003). Given that microdeletions account for the majority of cases of 22q11DS, the resulting phenotype is widely considered to be due to haploinsuYcieny of one or more genes within the deleted region. However, it has not been possible to correlate severity of the phenotype with the extent of the deletion or to identify a ‘‘minimal region of overlap’’ shared by all deletions or rearrangements.

III. 22q11DS: Clinical Phenotype

Given that the nature and extent of the deletions found in aVected cases are remarkably constant, it is somewhat surprising that the clinical phenotype associated with 22q11DS is so heterogenous. It consists of many diVerent birth defects and malformations, as well as behavioral and psychiatric components, with diVerent phenotypic combinations occurring sometimes even within the same family. However, it is common for cases to manifest a facial dysmorphology, which includes cleft palate and hypernasal speech (Shprintzen et al., 1978) as well as congenital cardiac anomalies, which are reported in over 75% of cases (Scambler, 2000). Some cases have a hypoplastic or absent thymus and/or parathyroid, which can lead to defects of cell-mediated immunity and hypoparathyroidism, respectively (Scambler, 2000). Many of the physical aspects of the phenotype, including the common features already noted, appear to result from abnormalities in development of structures arising from the pharyngeal arches and pouches (Lindsay et al., 2001). However, while the ‘‘pharyngeal phenotype’’ has been the main focus for clinicians and researchers, increasing interest is now being paid to behavioral and psychiatric features of the phenotype. Learning diYculties and behavioral problems are common in childhood; the median IQ is 75, with most in the range 50–100, and a range of psychiatric and behavioral phenotypes are seen including problems relating to social interaction and attention deﬁcit disorder (ADD/ADHD) (Gerdes et al., 1999; Golding-Kushner et al., 1985; Papolos et al., 1996; Swillen et al., 1997). In addition, it has also become increasingly apparent that the incidence of psychosis is markedly increased in adults who carry a deletion of 22q11.

CHROMOSOME 22 DELETION SYNDROME AND SCHIZOPHRENIA

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IV. 22q11DS: Psychosis

Children with 22q11DS have high rates of a range of psychiatric and behavioral phenotypes. Cognitive impairments are common but not ubiquitous and range in severity from minimal to severe (Swillen et al., 1997). Psychiatric disorders, such as anxiety, mood disorders, obsessive–compulsive disorder (OCD), and ADD/ADHD, are also frequently seen, as are milder behavioral abnormalities such as impulsivity, shyness, and emotional lability (Feinstein et al., 2002; Gerdes et al., 1999; Golding-Kushner et al., 1985; Graf et al., 2001; Papolos et al., 1996; Swillen et al., 1997). The ﬁndings in adults with 22q11DS are even more striking with several studies reporting high rates of psychosis, in particular schizophrenia or schizoaVective disorder (Bassett et al., 1998; Murphy et al., 1999; Papolos et al., 1996; Pulver et al., 1994; Shprintzen et al., 1992). In the largest study of psychiatric disorder in adults (n ¼ 50) with 22q11DS to date, Murphy and colleagues (Murphy et al., 1999), using standardized interviews and operational diagnostic criteria, demonstrated that people with 22q11DS have high rates of psychotic disorder (30%) of which the majority is schizophrenia (24%), and also high rates of schizotypy, a trait marker for schizophrenia susceptibility (Murphy et al., 1999). These ﬁgures represent a marked elevation in the risk for schizophrenia in adults with 22q11DS when compared with the general population risk for schizophrenia of around 1% and even when compared with the prevalence of 3% seen in people with learning disability (Fraser and Nolan, 1994). This latter point, together with the fact that there was no signiﬁcant IQ diVerence in people with 22q11DS with and without psychosis, suggests that the elevated rates of psychosis are not merely a nonspeciﬁc consequence of cognitive impairment. The demonstration that risk of schizophrenia is increased in 22q11DS raises the question of whether there is a speciﬁc childhood psychiatric phenotype associated with deletions, and whether this, or components of it, identify children at high risk of subsequent schizophrenia. Examining this issue, Feinstein et al. (2002) found very high rates of attention, anxiety, and mood disorders in children and adolescents with 22q11 deletions. However, these were equally prevalent, and in some cases more common, in a matched control group of developmentally disabled children. This implies that most, if not all, of the psychopathology found in children with 22q11DS is likely to be a nonspeciﬁc consequence of cognitive impairment (Feinstein et al., 2002). However, there may be more subtle behavioral and neurocognitive impairments that are speciﬁc to 22q11DS, and some of these might predict later onset of schizophrenia. It seems likely that the presence of these will only be veriﬁed by longitudinal studies.

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With an estimated prevalence of 1 in 4000 live births, one can estimate that 22q11DS cannot be responsible for more than a small fraction (1%) of cases of schizophrenia and this estimate is in keeping with empirical data (Arinami et al., 2001; Bassett et al., 1998; Chow et al., 1997; Gothelf et al., 1997; Ivanov et al., 2003; Karayiorgou et al., 1995). From the practical perspective, clinicians should be vigilant for 22q11DS when psychosis occurs in the presence of other features suggestive of the syndrome such as dysmorphology, mild learning disability, or a history of cleft palate or congenital heart disease. However, from the perspective of the genetic researcher, the most interesting question is whether the high rate of psychosis in 22q11DS implicates altered function of a gene or genes within the deleted region in schizophrenia in nondeleted cases.

V. 22q11DS: Neuropathology

Quantitative neuroimaging studies on people with 22q11DS have reported deﬁcits in the volume of both white and grey matter (Chow et al., 2002; van Amelsvoort et al., 2001). More recently, van Amelsvoort et al. (2004) reported the ﬁrst quantitative neuroimaging study comparing 22q11DS adults with and without schizophrenia. While it was again found that 22q11DS adults had a reduced cerebellar volume when compared with controls, this study was able to identify that only those with 22q11DS and schizophrenia had a signiﬁcant reduction in both total (grey and white) brain matter, and an increase in total and sulcal cerebrospinal ﬂuid volume when compared to 22q11DS adults without schizophrenia and controls (van Amelsvoort et al., 2004). This dataset is however limited (22q11DS with schizophrenia, n ¼ 13; 22q11DS without schizophrenia, n ¼ 12; controls, n ¼ 14) and as a result it is unclear whether it implies that schizophrenia in 22q11DS is due to a primary abnormality in grey matter development with secondary changes in white matter structure, or vice versa (van Amelsvoort et al., 2004). While it is clear that this study requires replication in a larger sample, it is interesting to note that these ﬁndings are similar to those of schizophrenia in the general population where diVerences in white matter have been implicated as a possible pathological mechanism (Davis et al., 2003). Moreover, these data are consistent with ﬁndings from global surveys of mRNA expression using microarrays, which have consistently shown that genes predominantly expressed in oligodendrocytes and implicated in the formation of myelin sheaths are downregulated in the brains of patients with schizophrenia compared to controls (Aston et al., 2004; Hakak et al., 2001; Tkachev et al., 2003), implying that altered myelination and oligodendrocyte function may underlie the subtle cytoarchitectural changes found in schizophrenia.

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VI. 22q11DS: Positional Cloning Schizophrenia Susceptibility Loci

As noted earlier, from the perspective of the genetic researcher it is of particular interest whether the high rate of psychosis in 22q11DS implicates the presence of a gene or genes within the deleted region that increases risk to schizophrenia in nondeleted cases. In support of this, a number of groups have reported evidence for linkage between schizophrenia and markers on chromosome 22q (Blouin et al., 1998; DeLisi et al., 2002; Group, 1996; Lasseter et al., 1995; Pulver et al., 1994; Shaw et al., 1998; Williams et al., 2003c), with several reporting positive data using markers in the vicinity of the typically deleted region (Blouin et al., 1998; Lasseter et al., 1995; Shaw et al., 1998; Williams et al., 2003c). More importantly, 22q11 is one of only two genomic regions to have been implicated in both meta-analyses of schizophrenia genome scans conducted to date (Badner and Gershon, 2002; Lewis et al., 2003). These ﬁndings, therefore, suggest the presence of a general schizophrenia susceptibility locus on chromosome 22q11, although not necessarily within the 22q11DS region. Association studies have provided further supportive evidence for a general schizophrenia susceptibility locus at 22q11. Our own group genotyped seven microsatellite markers spanning the entire region of the 1.5 Mb 22q11 deletions in 368 nondeleted schizophrenics and 368 controls (Williams et al., 2002). One marker, D22S944, was signiﬁcantly associated with schizophrenia, a ﬁnding that we subsequently replicated in a family-based association sample of 278 unrelated parent-proband trios (Williams et al., 2002). These ﬁndings provided support to an earlier study, which reported an association between schizophrenia and D22S944 (Li et al., 2000) in a sample of 198 Chinese parent-proband trios. In two more recent studies, Liu et al. (2002a,b) genotyped a total of 54 SNPs spanning the same 1.5 Mb 22q11 deleted region in a sample of 106 European/ American parent-proband trios with positive results being replicated in independent samples of South African Afrikaner case-controls and parent-proband trios, respectively. These studies implicated two regions within the 1.5 Mb deletion, the most signiﬁcant ﬁndings coming from a 250 kb region at the distal end (Liu et al., 2002b), while the somewhat weaker evidence at the proximal end was attributed to the gene PRODH (Liu et al., 2002a). Follow up analysis of the 250 kb region at the distal end of the 1.5 Mb deletion (Mukai et al., 2004) identiﬁed the gene ZDHHC8 as a potential schizophrenia susceptibility locus. Mukai et al. (2004) reported a signiﬁcant association between schizophrenia and allele A of SNP rs175174 (which is located in intron 4 of ZDHHC8) in females but not males, in a sample of 389 parent-proband trios of US or Afrikaner origin. ZDHHC8 encodes a putative palmitoyltransferase and is expressed throughout the brain (Mukai et al., 2004). Interestingly, rs175174 modulates the retention of intron 4 of ZDHHC8 in vitro and in turn inﬂuences

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the level of the fully functional transcript (Mukai et al., 2004), thereby oVering a mechanism by which the SNP could directly inﬂuence schizophrenia susceptibility. Even more strikingly, the gender-speciﬁc eVects found in the human genetic study were paralleled by the demonstration that ZDHHC8 knockout mice presented a sexually dimorphic abnormality in prepulse inhibition, a phenotype that has been used to model aspects of schizophrenia. To date, only one additional study has reported a positive association between rs175174 and schizophrenia (Chen et al., 2004), but these ﬁndings diVer in two crucial ways from those reported by Mukai et al. (2004). First, the risk allele in the original study (allele A) was in fact signiﬁcantly underrepresented in the cases in the replication study (Chen et al., 2004), and second, there was no evidence for a gender-speciﬁc eVect. These ﬁndings challenge the suggestion that rs175174 increases risk to schizophrenia by directly aVecting the function of ZDHHC8 in a sexually dimorphic manner. In contrast, failures to replicate an association between rs175174 and schizophrenia have been reported in ﬁve samples, which include a large sample of Bulgarian proband-parent trios and four samples of cases and ethnically matched controls from Germany, Poland, Sweden, and Japan (Saito et al., 2005) and (Glaser et al., 2005). These samples of parent-proband trios and the combined case-control samples have a respective power of 95 and 99% to detect an eVect of the magnitude reported by Mukai et al. (2004). As a result, the current balance of genetic evidence suggests that the original ﬁnding might be the result of type I error. Nevertheless, it remains possible that rs175174 could be in linkage disequilibrium (LD), which refers to the correlation between the alleles of neighboring markers located on the same chromosome with a true causative variant that increases risk to schizophrenia and that the discrepant ﬁndings are due to diVerences in LD structure between the diVerent populations studied. The same group of researchers have also reported evidence for association between PRODH and schizophrenia with particularly strong ﬁndings in a third sample of 26 parent-proband trios where the proband had a childhood onset schizophrenia (age at onset <13 years) (Liu et al., 2002a). In that same study, several amino acid changes were identiﬁed that appeared to act independently to the associated haplotype as risk factors for schizophrenia. These amino acid changes have recently been reported to have a direct functional eVect on the enzymatic activity of the PRODH protein product, proline oxidase (Bender et al., 2005). Interestingly, as with ZDHHC8, mice with an inactive PRODH gene have abnormal prepulse inhibition response compared to homozygous wild type controls (Gogos et al., 1999). Evidence supporting an association between PRODH and schizophrenia have been reported in an independent sample of 528 parentproband trios from China (Li et al., 2004), however the association was with a diVerent haplotype than that reported in the original study. No signiﬁcant association was found, however, with early onset schizophrenia. Another attempt

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to replicate the ﬁndings of Liu and colleagues proved negative (Fan et al., 2003), but this study was limited in scope in that only the most signiﬁcant SNP from Liu and colleagues was genotyped in a rather small sample of Chinese parentproband trios. Williams et al. (2003a,b) have examined PRODH in more detail in a large, well-matched UK case-control sample, a sample of 22q11DS cases from the UK and the Republic of Ireland with and without schizophrenia, and a sample of Bulgarian proband-parent trios with juvenile onset schizophrenia (age at onset <17 years). Despite each sample having >95% power to replicate the ﬁndings in the original study (Liu et al., 2002a), we did not ﬁnd evidence for association between the original PRODH risk haplotype and schizophrenia (Williams et al., 2003a). Moreover we sequenced the entire exon reported to contain the missense substitutions in 49 UK juvenile onset schizophrenia cases and 45 controls, as well as in all 44 unrelated subjects with 22q11DS all of which had FISH-conﬁrmed deletions (Williams et al., 2003a,b). Despite identifying amino acid changes, none displayed a pattern in the samples suggestive of association with psychosis, suggesting that in our samples the variants do not confer susceptibility to psychosis, and raising the possibility that the previous positive ﬁndings may have resulted from a type I error.

VII. 22q11DS: Murine Models

The genomic sequences of the 22q11 deleted region and its syntenic region on mouse chromosome 16 (MMU16) are both well characterized (Lund et al., 1999, 2000; Puech et al., 1997) (http://www.ncbi.nlm.nih.gov/Homology/). As a result of chromosomal rearrangements since divergence of the two species, the order of the genes is not conserved, however most of the genes from within the 22q11 deleted region have a murine ortholog at MMU16 (Fig. 1). This has allowed researchers to undertake gene-targeting studies to create various deletions within the syntenic region at MMU16. This work initially showed that it is possible to reproduce many of the features of human 22q11DS in mice with deletions of orthologous genes. Subsequently, further experimentation allowed the critical region for certain 22q11DS phenotypes to be reﬁned. In the ﬁrst study, Lindsay et al. (1999) deleted a 1.2 Mb segment (termed Df1) from MMU16. Mice homozygous for the deletion exhibited early embryonic lethality. Heterozygous mice (Df1/þ) were viable and showed cardiovascular abnormalities similar to those associated with human 22q11DS. Lindsay et al. (1999) were also able to create mice that carried the Df1 deletion on one chromosome, but which had the complementary duplication on the other (termed Df1/Dp1 mice). These mice did not present the abnormal cardiac

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phenotype seen in the Df1/þ mice, implying that the abnormalities were due to hemizygosity of genes within the deletion and not a result of the chromosomal rearrangement disrupting loci outside the deletion (Lindsay et al., 1999). The murine Df1 deleted region contains 23 genes (spanning from Es2-Ufd1l ), of which 21 have human orthologs that are located in a genomic segment, which is ﬂanked by the LCR22–2 and LCR22–3a and is situated within the 22q11DS deleted region. It is therefore particularly intriguing that Df1/þ mice also show sensorimotor gating and memory impairments, both of which have been implicated as endophenotypes in schizophrenia (Paylor et al., 2001) and that this is the same genomic section that the microsatellite D22S944, which has been reported to be associated with schizophrenia is located. By means of ﬁrst generating nested deletions and then single gene knockout models of MMU16, it was possible to obtain compelling evidence that haploinsuYciency of a single gene from the deleted region, Tbx1, is likely to be responsible for most, if not all, of the ‘‘pharyngeal phenotype’’ of 22q11DS (Lindsay, 2001). Tbx1 is a member of the T-box family of transcription factors that have been implicated in a variety of human syndromes associated with congenital abnormalities (Packham and Brook, 2003). Tbx1þ/ mice have cardiovascular abnormalities that, like those in 22q11DS, appear to be due to defective development of the fourth pharyngeal arch artery. Interestingly Tbx –/– mice, which die at birth, have a wide range of abnormalities in structures 1 developing from the pharyngeal apparatus including cleft palate, absence of the thymus and parathyroids, and abnormalities of the ear, jaw, and vertebrae. Recently, Yagi et al. (2003) reported evidence that haploinsuYciency of Tbx1 also plays a key role in the human phenotype by the demonstration of point mutations in patients with typical clinical features of 22q11DS but without deletions. Mutations within Tbx1 were associated with ﬁve of the major pharyngeal features of 22q11DS; conotruncal anomaly face, velopharyngeal insuYciency, cardiac defects, parathyroid dysfunction, and thymic hypolasia (Yagi et al., 2003). Interestingly, none of the ﬁve patients with point mutations in Tbx1 had mental retardation, and psychiatric disorders were not described, although only one case was adult and, therefore, in the common age of risk of psychosis. This raises the question of whether haploinsuYciency of Tbx1 also plays a key role in the development of schizophrenia in 22q11DS. This cannot be answered at present, but it is notable that expression of Tbx1 has recently been demonstrated in mouse brain, in particular in adolescence, which is the age at which, in humans, psychotic symptoms often ﬁrst manifest (Maynard et al., 2003). Unfortunately, there are as yet no reports of studies seeking sensorimotor or other abnormalities of possible relevance to schizophrenia in Tbx1þ/ mice.

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VIII. Catechol-O-Methyltransferase

Catechol-o-methyltransferase (COMT ) is a strong candidate gene for schizophrenia because it encodes an enzyme that degrades catecholamines, including dopamine. The COMT protein occurs as two distinct isoforms: a soluble form found in the cell cytoplasm (S-COMT ) and a longer, membrane-bound form (MB-COMT ). In most assayed tissues, the S-COMT form predominates, accounting for around 95% of total COMT activity (Grossman et al., 1985; JeVery and Roth, 1984), however, MB-COMT is the more prevalent species in brain (Tenhunen et al., 1994). COMT contains a nonsynonymous G > A polymorphism that produces a valine-to-methionine substitution at codons 108 and 158 in the S-COMT and MB-COMT transcripts respectively (Val(108/158)Met) (Lachman et al., 1996). The amino acid change results in altered COMT activity in both S-COMT (Lachman et al., 1996; Lotta et al., 1995) and MB-COMT (Chen et al., 2004), whereby the Val form of COMT is reported to have higher activity than the Met. Historically reports suggested that this was as much as 200–400%, however, more recent analysis at more physiological temperatures than the earlier work suggests that in brain the Val variant confers only a 40% increase in COMT activity than the Met variant (Chen et al., 2004). Although expressed widely, COMT appears to be a minor player in dopamine clearance compared with neuronal synaptic uptake by the dopamine transporter and subsequent monoamine oxidase (MAO) metabolism (Huotari et al., 2002). However, one region where this may not apply is the prefrontal cortical (PFC) where dopamine transporter expression is low (Sesack et al., 1998) and the importance of COMT appears to be greater (Gogos et al., 1998; Tunbridge et al., 2004). As reviewed below, this conclusion is supported by a considerable functional genetics literature suggesting that the Val(108/158)Met locus inﬂuences performance of certain tests of frontal lobe function. The Val allele was initially associated with poorer function as indexed by the Wisconsin Card Sorting Test and less eYcient dorsolateral prefrontal cortical response as assessed by fMRI (Egan et al., 2001) in schizophrenics and controls. Not all studies, including a very large study of 543 Greek army conscripts (Stefanis et al., 2004) have supported the original ﬁndings and there are some inconsistencies in association with speciﬁc tests, but there is now fairly consistent evidence for association between the Val allele and poorer performance in controls (Diamond et al., 2004; Goldberg et al., 2003; Malhotra et al., 2002), schizophrenics (Bilder et al., 2002; Goldberg et al., 2003; Nolan et al., 2004), the sibs of schizophrenics, though not the schizophrenics themselves (Rosa et al., 2004). One study of schizophrenics also demonstrated a biphasic eVect of the polymorphism, with

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the Val allele being associated with poorer performance in some tests, but better performance in others (Nolan et al., 2004). Recently Bearden et al. (2004) reported the ﬁrst assessment of Val(108/158)Met genotype and prefrontal cognitive function in patients with 22q11DS. Despite a small sample size (Met hemizygous, n ¼ 16; Val hemizygous, n ¼ 28) the results were analogous to those of the general population whereby individuals hemizygous for the Val allele showed reduced performance (Bearden et al., 2004). At present then, there is a large amount of data implicating the Val(108/158) Met COMT polymorphism in certain aspects of prefrontal cortical cognitive function, however, it should be noted that a major study recently failed to ﬁnd any evidence for such an eVect (Ho et al., 2005). Nevertheless, if the current body of evidence is correct it may be of relevance to schizophrenia since impaired prefrontal cognitive function has been proposed as a trait marker for schizophrenia (Callicott et al., 2003; Egan et al., 2001). However, independent of this hypothesis, COMT is an outstanding candidate gene for schizophrenia based on its role in dopamine catabolism. Two directions of allelic association are predicted by existing hypotheses. The classic hypothesis that schizophrenia results from enhanced dopaminergic neurotransmission (Carlsson, 1978; van Rossum, 1966) predicts an association with the low activity and Met allele. However, if the hypothesis that schizophrenia results from low dopamine function in the prefrontal cortex and that excess dopamine function in the mesolimbic system is secondary to this (Daniel et al., 1989; Davis et al., 1991), association will be to the Val allele. This latter hypothesis is also consistent with associations between the Val allele and poor prefrontal function and between poor prefrontal function and schizophrenia (Egan et al., 2001). On the face of it, these two competing hypotheses can be simply resolved by analysis of a single SNP, but unfortunately the vast majority of case-control studies have failed to ﬁnd evidence for association between the Val(108/158) Met locus and schizophrenia. Indeed in a meta-analysis of studies predating December 2003 (Fan et al., 2005), only 5 (Kotler et al., 1999; Kremer et al., 2003; Ohmori et al., 1998; Shifman et al., 2002; Wonodi et al., 2003) of 23 casecontrol studies yielded signiﬁcant evidence for association, 3 of which report the associated allele to be the Val and 2 reporting the Met. However, when all studies were combined, a sample size comprising 4686 cases and 7618 controls, the OR for the Val allele was 1.03 (0.94–1.11), indicative of no eVect. In the light of known ethnic diVerences in allele frequencies and of signiﬁcant evidence from an earlier meta-analysis (Glatt et al., 2003) for heterogeneity of the OR, separate analysis of case-control samples by Asian or European were performed. These also failed to provide evidence for association at this locus. Since the metaanalysis was undertaken, two other case-control studies have addressed the speciﬁc candidacy of the Val(108/158)Met locus. Galdiseri and colleagues failed to identify any association with schizophrenia in a small Italian sample (Galderisi

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et al., 2005), while a larger Turkish study provided suggestive evidence that the Met/Met genotype was associated with schizophrenia (Sazci et al., 2004). The presence of hidden population structure combined with poor study design can, where a locus has marked allele frequency diVerences in diVerent populations, result in true or false positives in case-control analysis. Family-based association studies are however robust to this. Of the ﬁve family studies reported to date (Egan et al., 2001; Fan et al., 2002; Kunugi et al., 1997; Li et al., 2000; Semwal et al., 2001), three have reported signiﬁcant evidence for association (Egan et al., 2001; Kunugi et al., 1997; Li et al., 2000), all with the Val allele. These have been included in a meta-analysis (Glatt et al., 2003), where the results are supportive of an association between the Val allele and schizophrenia (OR ¼ 1.5, CI ¼ 1.09–2.4). However, in the absence of a signiﬁcant eVect in the much larger case-control sample (Fan et al., 2005) it is diYcult to conﬁdently draw conclusions from this data alone. Given the failure to derive unambiguous signals in schizophrenia from the Val(108/158)Met locus, a number of groups have sought evidence for susceptibility variants elsewhere in the gene by including additional markers. It is however important to note that in these studies, the principle is based upon linkage disequilibrium rather than direct association. Using this approach, Li et al. (2000) reported stronger evidence for a six marker haplotype (global p ¼ 0.004) in their sample of 198 Chinese trios, which interestingly included markers in the adjacent ‘‘armadillo repeat gene deletes in velocardiofacial syndrome’’ (ARVCF). In a large study of Ashkenazi Jews (720 patients and 2970 controls), Shifman et al. (2002) genotyped six SNPs spanning COMT. Despite identifying only modest association with the Val(108/158)Met variant and schizophrenia ( p ¼ 0.024) two other polymorphisms, rs737865 (located in intron 1) and rs165599 (located in the 30 UTR), were highly signiﬁcantly associated with schizophrenia, as was the haplotype composed of all three markers (OR ¼ 1.46), where the risk haplotype was deﬁned by the alleles G-G-G. Sex-speciﬁc eVects were also reported, whereby rs165599 was strongly associated in females (p ¼ 10 5) but not males, a ﬁnding largely due to diVerences in the allele frequencies by gender in the controls rather than the cases, while rs737865 was associated in both genders. The haplotype carrying the G allele at all three loci (which at Val(108/158)Met encodes the Val) was also strongly associated in both genders (combined p ¼ 9.5 10 8). Interestingly, the three other haplotypes carrying the Val allele were underrepresented in cases. If correct, the most parsimonious explanation for this pattern is that the valine allele is unlikely to be the direct cause of the association, although it is formally possible that direct eVects on disease risk exist but are epistatic (in the sense of masked) to another functional SNP. To date there have been two published attempted replications of this ﬁnding. Like Shifman et al. (2002) a study based on 267 Irish multiplex families

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(Chen et al., 2004) revealed modest evidence for excess transmission of the Val allele using a broad deﬁnition of schizophrenia that included schizophrenia, schizoaVective disorder, psychotic mood disorder, delusional disorder, atypical psychosis, schizophreniform disorder, and paranoid, avoidant, and schizotypal personality disorders (p ¼ 0.01–0.04 depending on method of analysis). Haplotype analysis using the same markers as Shifman provided marginally stronger evidence (p ¼ 0.009), however, it is of interest that again only one of four relatively common haplotypes carrying the valine allele was signiﬁcantly overtransmitted (A-G-A) to schizophrenics, while one was signiﬁcantly undertransmitted indicating a protective eVect. The undertransmitted haplotype was actually the G-G-G risk haplotype of Shifman. In a similar manner, after genotyping the same three markers in their sample of 50 Australian Caucasian aVected sib-pair families Handoko and colleages found nominally signiﬁcant evidence for association with the Met allele (p ¼ 0.04). Again, analysis of the full three marker haplotypes revealed stronger evidence (permuted p value 0.002) (Handoko et al., 2004) where the undertransmission of the A-G-G (Val containing) haplotype (transmitted ¼ 1, nontransmitted ¼ 18) appears to have driven much of the association. Sanders et al. (2005) examined in 136 families of mixed ethnicity, mainly European American, using eight markers spanning COMT but extending into the adjacent gene ARVCF. While there was no signiﬁcant excess transmission at the Val(108/158)Met locus, a number of haplotypes including markers reaching into ARVCF did yield signiﬁcant evidence for association, with a minimum nominal global p value of around 0.002. Although it is diYcult to know how to correct this for multiple testing, given the strong LD in the region, we and the authors of the original study do not think this reﬂects a simple type I error. Moreover, examination of the individual haplotypes presented in that paper reveals that the individual speciﬁc haplotype displaying the excess transmission is almost fully characterized by G-A at Val(108/158)Met-rs165599 and therefore may be related to the associated A-G-A (rs737865-Val(108/158)Metrs165599) haplotype of Chen et al. (2004). Finally, we have recently genotyped all three markers in two independent samples of 709 cases and 710 matched controls from the UK and 488 Bulgarian parent-proband trios. However, we found no evidence for association in either sample to the Val(108/158)Met locus or to any of the Shifman markers or haplotypes (Williams et al., 2005). Analysis by gender also failed to identify any evidence for association to markers or haplotypes. The studies summarized above provide a complex pattern of data that are diYcult but not impossible, to interpret. DiVerent replication studies have reported diVerent haplotypes associated with schizophrenia (Table I), this implies allelic heterogeneity at the COMT locus. The original and to date most signiﬁcant association at the COMT locus was identiﬁed in the Ashkenazi Jewish population (Shifman et al., 2004), which were selected on the premise that they are relatively homogeneous for genetic (and environmental) risk factors. The failure of some

COMPARISON

OF

TABLE I HAPLOTYPIC ASSOCIATION STUDIES HIGHLIGHTING HIGH DEGREE HETEROGENEITY AT THE COMT LOCUS

OF THE

15

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studies to ﬁnd any evidence for association with the same core set of markers could reﬂect diVerences in the eVect size of the risk haplotypes, either due to diVerences in the extent of LD in the region or because the eVect size of the risk variant is population speciﬁc. Analysis of the marker–marker LD relationships in each sample (Table I) oVer some support to this premise, in that the LD in the UK, Irish, Australian, and Bulgarian samples is signiﬁcantly lower than in the Ashkenazi Jewish sample, in particular between markers rs737865 and Val(108/158)Met (diVerences in r2, p < 0.0001). Moreover, the frequency of the three marker haplotypes have been demonstrated to vary considerably in diVerent populations, in particular the frequency of the originally associated GG-G haplotype varies from 0% in South America to 37.1% in Southwest Asia (Palmatier et al., 2004). Such genetic heterogeneity and variation in the LD pattern in diVerent populations can greatly aVect the power of individual studies. If this premise is correct then diVerences in the genetic architecture between samples may contribute to the inconsistent ﬁndings at the COMT locus and will make interpretation of negative data diYcult. As a result, future haplotype-based studies should be encouraged to carefully deﬁne the underlying LD structure in and around the COMT locus for each population studied in order to deﬁne the appropriate ‘‘tag’’ SNPs to genotype ( Johnson et al., 2001). As a result, while the meta-analysis (Glatt et al., 2003) and our own large study (Williams et al., 2005) suggest no eVect at the COMT locus, the majority of studies employing multiple markers do appear to do so to a level beyond chance. Although all but the Shifman study have been conducted in substantially smaller samples than we have examined (Williams et al., 2005), nevertheless, we cautiously conclude that it is fairly likely, though not proven, that a susceptibility locus exists at or around COMT. If true it is possible to draw some conclusions about the role or otherwise of COMT in schizophrenia. The simplest hypothesis is that COMT activity is relevant to schizophrenia susceptibility, and that by virtue of being responsible for most or all the population variation in COMT activity, the Val(108/158)Met polymorphism is a direct risk factor for schizophrenia. This hypothesis predicts that either the Met allele will be directly associated with schizophrenia under the classic hyperdopaminergic hypothesis (Carlsson, 1978; van Rossum, 1966) or that the Val allele will be directly associated under the hypothesis that hyperdopaminergia in the stratum is secondary to hypodopaminergia in the prefrontal cortex (Daniel et al., 1989; Davis et al., 1991). Importantly, since this direct single locus hypothesis does not depend on LD, association studies should be robust to the variable LD structure at COMT (Palmatier et al., 2004). It also predicts that in all ethnic populations showing an eVect, the same allele will be associated, although the eVect size may vary as a function of the variable allele frequency observed in diVerent populations or because of variation in the frequency of other interacting genetic and environmental risk factors. It is even possible that in some populations, the eVect will be entirely masked

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(OR ¼ 1) if the eVect of COMT is only exhibited in the presence of risk factors that are population speciﬁc. The published studies are not compatible with this simple hypothesis. First, the meta-analysis and our own large studies ﬁnd no signiﬁcant eVect of this locus. This may be because the null hypothesis is true or because true associations exist with diVerent alleles in diVerent samples canceling out an overall eVect. Second, in all of the studies in which an eVect has been detected and where additional markers have been typed, it has been possible to subdivide haplotypes carrying the risk allele into risk, neutral, and even in some cases, protective haplotypes. As four out of ﬁve of these have been family-based studies, this cannot be attributed to stratiﬁcation. Moreover, in the only study to present a formal analysis (Handoko et al., 2004), the model including additional markers was more signiﬁcant than that restricted to the Val(108/158)Met locus alone, while the conditional analysis suggested two independent eVects; one that was in LD with rs737865 and another that was in LD with the Val(108/158)Met polymorphism (Handoko et al., 2004). While this has not yet been directly replicated, it is in part supported by a global survey of haplotypes at the COMT locus, which suggested that the true schizophrenia susceptibility variants could be located in the P2 promoter region of MB-COMT (Palmatier et al., 2004). From the genetic perspective, these ﬁndings suggest that if function at the Val(108/158)Met locus is relevant to schizophrenia, the relative functional properties of the Val and Met alleles can be modiﬁed, even reversed, by at least one other relatively common cis-acting variant with an inﬂuence perhaps on COMT expression or splicing. This suggestion is broadly compatible with the demonstration of cis-acting loci that modify the expression of COMT mRNA independent of the Val(108/158) Met locus (Bray et al., 2003; Zhu et al., 2004). Speciﬁcally, Bray et al. (2003) have shown that rs165599, which is included in the associated 3-marker COMT haplotypes and gives the strongest individual evidence for association with schizophrenia (Shifman et al., 2002), is actually transcribed in human brain and exhibits signiﬁcant diVerences in allelic expression, with lower relative expression of the Val allele. Moreover, the 3-marker COMT haplotype (Shifman et al., 2002) was indeed associated with lower expression of COMT mRNA (Bray et al., 2003; Zhu et al., 2004). However, in contrast, there are data that argue against the conclusion of more than one functionally relevant polymorphic locus with an appreciable frequency in COMT. Chen et al. (2004) demonstrated that brain COMT enzyme activity is associated with the Val(108/158)Met locus but none of the other Shifman markers that we and others have is associated with mRNA expression or with schizophrenia. This ﬁnding applies both to subjects of European and African ethnic origins. As enzyme activity can be reasonably assumed to be functionally more important than mRNA abundance, although weak associations between activity and the other loci would probably not have been detected, this ﬁnding provides a strong refutation of the hypothesis that the

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associations with the Shifman haplotypes are likely to be attributable to major or minor alterations in COMT expression. Unfortunately, because of the inevitable confounders like postmortem variance, age, and measurement errors in this study (Chen et al., 2004) it is diYcult to obtain an exact estimate of the proportion of the variance in COMT activity that can be attributed to the Val(108/158)Met locus. However, earlier studies with respect to peripheral COMT activity suggest that all, or almost all, COMT activity can be attributed to Val(108/158)Met (Lachman et al., 1996; Weinshilboum and Dunnette, 1981). If indeed it is correct that Val(108/158) Met is responsible for all variance in COMT function, then the haplotype data could well point to the involvement of a gene in LD with COMT. A strong candidate here is ARVCF (Li et al., 2000; Sanders et al., 2005), which is itself an excellent candidate gene for schizophrenia (Ulﬁg and Chan, 2004). Interestingly, we have recently found that the genes encoding COMTand ARVCF overlap, and that they share common exonic sequence on opposite strands. Moreover, one of the Shifman markers (rs165599) is located in the common exonic sequence and appears to inﬂuence ARVCF expression (Bray et al., 2003; Bray et al., unpublished data). It may appear to be an unlikely turn of events that evidence for association to schizophrenia at a locus as plausible as COMT might be considered to reﬂect association to an adjacent gene, however, we feel that based on the data presented in this chapter, future studies should be aware of the possibility that markers that reside anywhere in the COMT/ARVCF locus could play a functional role in schizophrenia and/or the cognitive deﬁcits reported.

IX. Conclusions

It is clear that deletions of chromosome 22q11 are associated with a complex phenotype, which includes characteristic facial dysmorphology, a range of congenital abnormalities, and psychiatric disorders. The prevalence of psychosis in those with 22q11DS is high, suggesting that haploinsuYciency of a gene or genes in this region might confer a substantially increased risk. Several linkage studies support the presence of a gene in this region that confers susceptibility to schizophrenia in nondeleted cases. The mechanism of deletion, as well as the organization of genes in the vicinity, is now well understood. However, while 22q11DS is generally considered to be caused by haploinsuYciency of one or more genes located within the deleted region it is worth noting that as some 22q11DS patients have nonoverlapping rearrangements of 22q11, it remains plausible that in some patients the associated psychiatric phenotype could be due a long range eVect on transcription of genes adjacent to the deletion. Despite being considered unlikely, this is an intriguing possibility that could potentially explain the phenotypic heterogeneity among 22q11DS patients.

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Positional cloning of the 22q11DS commonly deleted region followed by candidate gene analysis has implicated the genes PRODH and ZDHHC8 as possible schizophrenia susceptibility loci, however the general lack of support from replication studies implies that, at the time of writing, the evidence in favor of these genes is far from conclusive. The majority of schizophrenia candidate gene association studies at 22q11 have focused on the gene COMT. These have been complemented by a widely replicated association between the COMT Val allele and impaired prefrontal cognition and physiology, a ﬁnding which has also been observed in studies of patients with 22q11DS. Such deﬁcits in cognition are increasingly considered to play a role in schizophrenia and have allowed COMT to ﬁt with other possible schizophrenia susceptibility genes into current theories of schizophrenia pathology (Harrison and Owen, 2003; Harrison and Weinberger, 2005). Despite this, the genetic data regarding COMT and schizophrenia remains ambiguous. However, it does imply that we can, with a fair level of conﬁdence, reject the hypothesis that the Val(108/158)Met variant has a unidirectional eVect on schizophrenia risk. If we accept that the data implies the presence of a schizophrenia susceptibility locus at or around COMT (which we reiterate is premature), interpretation of the data requires multiple cis-acting eVects in COMT, or even eVects in an adjacent gene, the net eVect of which is unknown. Although, the absence in some studies of other sources of COMT enzyme variance out with the Val(108/158)Met site argues in favor for a contribution from an adjacent gene, neither the strength of the enzymatic ﬁndings (Chen et al., 2004; Weinshilboum and Dunnette, 1981) or the genetic ﬁndings (Li et al., 2000; Sanders et al., 2005) are strong enough to force either this conclusion. With this in mind it is important to note that when attributing a gene as a susceptibility locus it is essential that a plausible biological mechanism is complemented by strong convincing genetic support. As a result, at the time of writing there is no conclusive evidence that any gene at 22q11 is a schizophrenia susceptibility locus and further high-density genetic analysis of the 22q11DS deleted region is required. The generation of murine models of 22q11DS, by deleting sections of the chromosome 22q11 syntenic region at mouse chromosome 16, has provided compelling evidence that haploinsuYciency of Tbx1 is likely to be responsible for the ‘‘pharyngeal phenotype’’ of 22q11DS. It is of particular interest that some of the murine models of 22q11DS present sensorimotor gating and memory impairments, both of which have been implicated as endophenotypes in schizophrenia. However, despite this potentially important observation it has not been possible to unambiguously determine the loci responsible for this phenotype and consequently, in contrast to studies of the 22q11DS, pharyngeal phenotype murine models have to date provided few clues to the genetic basis for the behavioral and psychiatric phenotypes in 22q11DS. This is undoubtedly largely a result of the complexity of studying psychiatric phenotypes in animal models.

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However, in spite of this, future genetic studies will be able to apply the now routine high throughput SNP genotyping methodologies in combination with our greater understanding of the sequence of both HAS22q11 and MMU16. In combination with essential continued analysis of animal models, this will greatly improve our chances of identifying the genes and pathways relevant to schizophrenia and 22q11DS.

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Weinshilboum, R., and Dunnette, J. (1981). Thermal stability and the biochemical genetics of erythrocyte catechol-O-methyl-transferase and plasma dopamine-beta-hydroxylase. Clin. Genet. 19(5), 426–437. Williams, H. J., Williams, N., Spurlock, G., Norton, N., Ivanov, D., McCreadie, R. G., Preece, A., Sharkey, V., Jones, S., Zammit, S., Nikolov, I., Kehaiov, I., et al. (2003a). Association between PRODH and schizophrenia is not conﬁrmed. Mol. Psychiatry 8(7), 644–645. Williams, H. J., Williams, N., Spurlock, G., Norton, N., Zammit, S., Kirov, G., Owen, M. J., and O’Donovan, M. C. (2003b). Detailed analysis of PRODH and PsPRODH reveals no association with schizophrenia. Am. J. Med. Genet. 120B(1), 42–46. Williams, N. M., Norton, N., Williams, H., Ekholm, B., Hamshere, M. L., Lindblom, Y., Chowdari, K. V., Cardno, A. G., Zammit, S., Jones, L. A., Murphy, K. C., Sanders, R. D., et al. (2003c). A systematic genomewide linkage study in 353 sib pairs with schizophrenia. Am. J. Hum. Genet. 73(6), 1355–1367. Williams, N. M., Spurlock, G., Norton, N., Williams, H. J., Hamshere, M. L., Krawczak, M., Kirov, G., Nikolov, I., Georgieva, L., Jones, S., Cardno, A. G., O’Donovan, M. C., et al. (2002). Mutation screening and LD mapping in the VCFS deleted region of chromosome 22q11 in schizophrenia using a novel DNA pooling approach. Mol. Psychiatry 7(10), 1092–1100. Williams, H. J., Glaser, B., Williams, N. M., Norton, N., Zammit, S., MacGregor, S., Kirov, G. K., Owen, M. J., and O’Donovan, M. C. (2005). No association between schizophrenia and polymorphisms in COMT in two large samples. Am. J. Psychiatry 162(9), 1736–1738. Wonodi, I., Stine, O. C., Mitchell, B. D., Buchanan, R. W., and Thaker, G. K. (2003). Association between Val108/158 Met polymorphism of the COMT gene and schizophrenia. Am. J. Med. Genet. B 120(1), 47–50. Yagi, H., Furutani, Y., Hamada, H., Sasaki, T., Asakawa, S., Minoshima, S., Ichida, F., Joo, K., Kimura, M., Imamura, S., Kamatani, N., Momma, K., et al. (2003). Role of TBX1 in human del22q11.2 syndrome. Lancet 362(9393), 1366–1373. Zhu, G., Lipsky, R. H., Xu, K., Ali, S., Hyde, T., Kleinman, J., Akhtar, L. A., Mash, D. C., and Goldman, D. (2004). DiVerential expression of human COMT alleles in brain and lymphoblasts detected by RT-coupled 50 nuclease assay. Psychopharmacology (Berl) 177(1–2), 178–184.

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CHARACTERIZATION OF PROTEOME OF HUMAN CEREBROSPINAL FLUID

Jing Xu,*,¶ Jinzhi Chen,y,1 Elaine R. Peskind,z Jinghua Jin,* Jimmy Eng,§ Catherine Pan,* Thomas J. Montine,* David R. Goodlett,y and Jing Zhang* *Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98104, USA y Department of Medicinal Chemistry, University of Washington School of Medicine, Seattle, Washington 98104, USA z Psychiatry and Behavioral Sciences and VA Mental Illness Research, Education, and Clinical Center, University of Washington School of Medicine, Seattle, Washington 98104, USA § Fred Hutchinson Cancer Research Center, Seattle, Washington, 98104, USA ¶ Department of Neurosurgery, the 2nd Affiliated Hospital of WenZhou Medical College, Zhejiang, China

I. Introduction II. Materials and Methods A. Collection of Human CSF by Lumbar Puncture and Exclusion Criteria B. CSF Sample Preparation and Fractionation with SDS-PAGE C. In-Gel Digestion D. Protein Identiﬁcation Using LC Followed by LCQ-MS E. Protein Identiﬁcation Using Off-Line SCX Chromatography Followed by LTQ-FT MS F. Data Processing and Analysis III. Results A. Proteins Identiﬁed by LC-LCQ-MS/MS B. Proteins Identiﬁed by nanoLC-LTQ-FT MS/MS Following Off-Line SCX Separation C. Reexamination of Previously Identiﬁed Proteins D. Discussion References

Human cerebrospinal ﬂuid (CSF) is an ideal source for identifying biomarkers for neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB). Proteomics has been used to analyze CSF in order to discover disease-associated proteins and elucidate the basic molecular mechanisms that either cause, or result from, central nervous system disorders. However, before undertaking a rational approach to CSF protein biomarkers of neurodegenerative diseases, it is crucial to extensively characterize the proﬁles of normal human CSF proteins. In this study, to identify as many CSF proteins in 1

The author who contributed to the work equally with the ﬁrst author.

INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73002-1

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Copyright 2006, Elsevier Inc. All rights reserved. 0074-7742/06 $35.00

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well-characterized healthy young subjects as possible, SDS-PAGE gel electrophoresis was used to prefractionate the CSF proteins before further separation by multidimensional liquid chromatography and analyzed with LCQ or LTQ-FT mass spectrometry (MS). While a total of 466 proteins were identiﬁed by LCQ-MS/ MS, a total of 608 proteins were identiﬁed by LTQ-FT MS/MS, which is 30% over those identiﬁed by LCQ-MS/MS. Our results demonstrated that with improved sample preparation and better instrumentation, a much deeper analysis of the human CSF proteome was achieved. Furthermore, we searched our previous MS data obtained from the aging study with the identical database used for the current LCQ and LTQ analysis; it is surprising that a change in database had a very signiﬁcant eVect on proteomic identiﬁcation of proteins with only 66% overall overlap between two search results. When all proteins were combined, a total of 915 proteins were identiﬁed in the CSF of these young healthy subjects. In the end, issues related to sample preparation, proteomic instrumentation, and database search are discussed further in the context of characterization of human CSF proteome.

I. Introduction

Human cerebrospinal ﬂuid (CSF) circulates within the ventricles of the brain and surrounds the brain in the subarachnoid space (Blennow et al., 1993a,b,c). Secretion and absorption of CSF is closely regulated with an average circulating volume of 125–150 ml in an adult. Several reasons make human CSF an ideal source for identifying biomarkers for neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB). These include CSF’s close proximity to the site of pathology, its high availability, and the advantage of minimal ambiguities that are commonly encountered in experimental models. However, translational research using CSF to identify biomarkers in these increasingly common diseases have not been successful to date, largely due to, in our opinion, two major grounds: (1) the underlying pathogenic events are too complex to be accurately reﬂected in a single molecule or small group of molecules, and (2) the lack of a complete understanding of the composition of human CSF in normal persons. Recently, we utilized discovery based proteomics (Link et al., 1999)—the identiﬁcation of proteins in a given sample—to address both diYculties. The three fundamental steps in discovery-based proteomics are: (1) protein harvest or isolation; (2) protein identiﬁcation by mass spectrometry (MS), typically via tandem MS (MS/MS); and (3) data mining using bioinformatics tools and databases. In the ﬁrst step, proteins are often separated by three diVerent methods: two-dimensional gel electrophoresis (2-D gel) (Hochstrasser et al., 2002), liquid chromatography (LC) (Aebersold and Goodlett, 2001; Washburn et al., 2001), and more recently, ‘‘protein chips’’ or activated surfaces that bind proteins based on chemical characteristics (Yip and Lomas, 2002). For the second

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step, MS or MS/MS identiﬁcation of proteins also involves three basic steps: ionization, ion separation, and detection (Gross, 2004). Most 2-D gel and protein chip proteomic protocols are coupled with matrix-assisted laser desorption ionization (MALDI). One type of protein chip-coupled MALDI is called surface enhanced laser desorption/ionization (SELDI). In contrast, the LC-based platforms are usually interfaced to MS with electronspray ionization (ESI) (Chait and Kent, 1992). ESI-MS has emerged as one of the premier methods for examining biological molecules in solution, as it permits the direct analysis of nonvolatile compounds, such as peptides, proteins, glycoproteins, phospholipids, glycolipids, and complex carbohydrates in liquid solutions, as intact molecules without derivatization or digestion. Furthermore, ESI can be interfaced with LC readily while maintaining high sensitivity (subfemtomole). Our study utilized two popular, commercially available ESI-ion trap MS systems, the LCQ and LTQ proteomic stations made by ThermoElectron. The general method we used is referred to as shotgun proteomics, which includes methods like Multidimensional Protein Identiﬁcation Technology (MudPIT) and Isotope Coded AYnity Tag (ICAT), both of which use multidimensional LC and MS/MS to separate and fragment peptides for protein identiﬁcation (Link et al., 1999). The main advantage of shotgun proteomics over 2-D gel electrophoresis followed by MS is higher throughput. The advantages over the SELDI method (Forde and McCutchen-Maloney, 2002) include better coverage of proteins with high molecular weight and the ability to identify proteins directly (SELDI typically identiﬁes unique peaks only, i.e., pattern recognition; unique protein can be identiﬁed at a later stage with extensive oV-chip workup). With MudPIT and an LCQ system, we were able to identify more than 300 proteins in a previous study, focused on aging related changes in human CSF (Zhang et al., 2005a). Over the last year or so, however, it has become increasingly clear that the LCQ system is associated with several limitations with one of the major ones being slow scanning speed (Yi et al., 2002; Zhang et al., 2005b), which translates into lower reproducibility of samples when analyzed multiple times and less total proteins identiﬁed as compared to MS with faster scanning speeds, for example, an LTQ- Fourier transform (FT) ion trap. Thus, in the current work with a goal of identifying as many CSF proteins in well-characterized healthy young subjects as possible, we tested the LTQ-FT system as well as the LCQ system with a better sample preparation procedure to circumvent the limitation associated with LCQ.

II. Materials and Methods

A. COLLECTION OF HUMAN CSF BY LUMBAR PUNCTURE AND EXCLUSION CRITERIA Written informed consent was obtained from all subjects and the Human Subjects Division of the University of Washington approved this study. All

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subjects were compensated community volunteers consisting of 10 women and 12 men aged 22–36 (median age ¼ 25). All subjects underwent evaluation that consisted of medical history, physical and neurological examinations, laboratory tests, and brief neuropsychological assessment. Laboratory evaluation included a complete blood count and quantitative analysis of serum electrolytes, blood urea nitrogen, creatinine, glucose, vitamin B12, and thyroid stimulating hormone; all results were within normal limits. Neuropsychological evaluation for all subjects included the Mini-Mental State Exam (MMSE) (Folstein et al., 1975), TrailMaking Tests A and B (Reitan, 1958) as well as Clinical Dementia Rating Scale (CDR) (Morris, 1997). Subjects had no signs or symptoms suggesting cognitive decline or neurological disease. All subjects had a MMSE score between 28 and 30 and a CDR score of zero. In addition, heavy cigarette smoking (more than 10 packs per year), alcohol use other than socially, and any psychoactive drugs used were exclusion criteria for our study. To obtain CSF, individuals were placed in the lateral decubitus position and the L4–5 interspace was inﬁltrated with 1% lidocaine to provide local anesthesia. The lumbar puncture (LP) was performed atraumatically with a 24 g bullet-tip Sprotte spinal needle and CSF was withdrawn with sterile syringes. Individuals remained at bed rest for 1 hour following LP. Two criteria were used to control for blood contamination: (1) CSF red blood cell (RBC) count as determined by standard clinical chemistry laboratory had to be less than 10 RBC/ml, and (2) the ratio of apolipoprotein B (apoB) between serum and CSF had to be greater than 6000. This is because apoB is not synthesized in the central nervous system (CNS), therefore only a very small amount should be present in CSF unless the samples were contaminated by blood (Osman et al., 1995). CSF apoB concentrations relative to plasma were determined using Western blot analysis as previously described (Zhang et al., 2005a), with a standard curve generated by serial dilutions of plasma samples ran on the same gel. Using these criteria, we selected 22 uncontaminated samples and pooled them to generate 1 sample (20 ml total) for proteomic identiﬁcation. The protein concentrations were determined with standard Bradford assay (298 mg/ml).

B. CSF SAMPLE PREPARATION

AND

FRACTIONATION

WITH

SDS-PAGE

The pooled 20 ml CSF sample was concentrated down to 2 ml using SpeedVac (Thermo Savant, Holbrook, NY) and then mixed with 2xSDS sample buVer (Bio-Rad Laboratories, Hercules, CA) and ran on 4–15% TrisHCl criterion gels (Bio-Rad, Laboratories, Hercules, CA). The gels were stained with Coomassie blue and scanned with Versadoc (Bio-Rad, Laboratories, Hercules, CA).

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C. IN-GEL DIGESTION Stained protein bands were cut into 10 fractions according to the molecular weight and distribution of protein abundance, and then each fraction was excised into smaller pieces that were approximately 1–2 mm3. The gel pieces were destained with 50% methanol/ 5% acetic acid overnight and in-gel digestion was performed as described previously (Zhou et al., 2004). Next, the extracted peptides were desalted with a reverse-phase (RP) Atlantis dC18 column (Waters, Milford, MA). In order to obtain at least 100 mg of protein for each SDS-PAGE fraction, 100 mg of BSA were ran on the same gel and digested in parallel with CSF samples. The approximate amount of each fraction was estimated by BCA assay using BSA digests as a reference.

D. PROTEIN IDENTIFICATION USING LC FOLLOWED

BY

LCQ-MS

Around 100 mg of desalted peptides from each fraction (except fraction 5 that contained only 50 mg proteins) were separated by a two-dimensional microcapillary high performance LC system, which integrated a strong cation-exchange (SCX) column (100 mm in length 0.32 mm for inner diameter; particle size: 5 mm) with two alternating RP C18 columns (100 mm in length 0.18 mm for inner diameter), followed by analysis of each peptide with MS/MS in an LCQ DECA PLUS XP ion trap (ThermoElectron, San Jose, CA). Settings for the LCMS/MS were the following: six fractions were eluted from SCX using a binary gradient of 2–90% solvent D (1.0 M ammonium chloride and 0.1% formic acid in 5% acetonitrile) versus solvent C (0.1% formic acid in 5% acetonitrile). Each fraction was injected onto an RP column automatically with the peptides being resolved using a 200 min binary gradient of 5–80% solvent B (acetonitrile and 0.1% formic acid) versus solvent A (0.1% formic acid in water). A ﬂow rate of 160 ml/min with a split ratio of 1/80 was used. To determine the amino acid sequence, the MS operated in a data-dependent MS/MS mode in which each survey scan mass spectrum was followed by MS/MS of one of the available precursor ions from the prior survey scan. Ions selected for collision-induced dissociation (CID) were dynamically excluded for 3 min.

E. PROTEIN IDENTIFICATION USING OFF-LINE SCX CHROMATOGRAPHY FOLLOWED BY LTQ-FT MS Another 100 mg of CSF digest from each SDS-PAGE fraction was loaded onto an SCX cation exchange column (0.5 mm 200 mm; PolyLC, MD, USA) that had been equilibrated in 0.05% formic acid/20% ACN and pH 3.0 (buVer

34

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A) at a ﬂow rate of 200 ml/min. Peptides were eluted by applying a linear gradient from 0 to 100% buVer B (500 mM ammonium formiate/20% ACN, pH 3.0). Six fractions were collected from each SDS-PAGE sample, dried down in a SpeedVac (Thermo Savant, Holbrook, NY), washed two times with 0.1% formic acid to remove the salts and then dissolved in 0.1% formic acid for LTQFT MS (ThermoElectron) analysis. Settings for the LTQ-FT MS were as follows: ˚ pore (a) The precolumn was 100 mm x 1.5 cm and packed with 5 mm, 200 A size Magic C-18 AQ beads (Michrome Bioresources, CA), while the analytical RP column was house-made using 75 mm i.d. 11 cm fused silica capillary (Polymicro Technologies, Phoenix, AZ) with a ESI frit, which were slurry-packed with 5 mm, 100A˚ pore size Magic C-18 AQ beads (Michrome Bioresources, CA). (b) All nano-HPLC-MS/MS experiments were performed on a Michrom Paradigm MS4B HPLC system (Michrome Bioresources, CA) online coupled to a 7-Tesla Finnigan linear quadrupole ion trap-FT (LTQ-FT) MS equipped with a nanoelectrospray ion source. The HPLC system was conﬁgured as described (Yi et al., 2003) with a few modiﬁcations. Brieﬂy, 0.1 mg of each SCX sample, that is, a total of 0.6 mg from a SDS-PAGE fraction, was loaded onto the precolumn with loading buVer (5% ACN with 0.1% formic acid in water) at a ﬂow rate of 15 ml/ min and then resolved on an analytical column at a ﬂow rate of 200 nl/min. A ternary solvent composition gradient with solvent A (100% water), solvent B (100% ACN), and solvent C (1% formic acid in water) were developed with C staying at 10% constantly, while B was increased from 5% to 45% in 60 min, then to 80% in 5 min, staying at 80% for 10 more min, dropping to 5% in 1 min, and staying at 5% for 15 min. (c) The LTQ-FT MS was operated in a data-dependent mode to switch between MS and MS/MS acquisitions. Survey full scan over m/z range 400– 1,800 were acquired in the Fourier transform ion cyclotron resonance (FTICR) with a resolution of R ¼ 85,000 at m/z 524 (with a target value of 1,000,000 in the linear ion trap). The ﬁve most intense ions were sequentially isolated and subjected to collision induced dissociation in the linear ion trap at a target value of 5,000, and then dynamically excluded for 2 min. Total MS-MS/MS scan cycle was ~1.5 s. The general MS conditions were: ESI voltage ¼ 1.5 kV; ion transfer tube temperature ¼ 200 C; collision gas pressure ¼ 1.3 mTorr; and normalized collision energy ¼ 30%. Ion selection threshold was 3,000 counts for MS2. Activation q ¼ 0.25 in MS2 acquisitions. F. DATA PROCESSING

AND

ANALYSIS

CID spectra from the micro-HPLC-LCQ or nano-HPLC-LTQ MS/MS analysis were searched using SEQUEST database engine against the IPI Human v.3.01 database. Peptide assignments were ﬁltered according to the following

PROTEOME OF HUMAN CEREBROSPINAL FLUID

35

criteria: Xcorr > 1.9 with charge state 1þ, Xcorr > 2.2 with charge state 2þ, or Xcorr > 3.75 with charge state 3þ, as well as Cn > 0.1. Also given were the proteins identiﬁed based on an error rate <0.01 as determined by ProteinProphet (Jin et al., 2005; Zhang et al., 2005b; Zhou et al., 2004). Additionally, only proteins with more than two unique tryptic peptides were considered deﬁnitive identiﬁcations; those identiﬁed by single peptide was considered provisional and presented separately.

III. Results

A. PROTEINS IDENTIFIED

BY

mLC-LCQ-MS/MS

Online MudPIT analysis of 10 SDS-PAGE fractions with the LCQ-MS/MS identiﬁed a total of 466 proteins (Tables I and II), 115 of which have the dubious distinction of being so-called single-hits (Table II). Single-hits refer to the fact that a protein is identiﬁed from the MS/MS spectrum of a single peptide and, therefore, judged as being a less reliable identiﬁcation than those proteins identiﬁed with multiple peptide tandem mass spectra. We include them here in our totals with reasons discussed later. In a previous study focused on age-related changes in the human CSF proteome, we utilized an identical approach with the exception that CSF was fractionated into three parts with organic precipitation (Zhang et al., 2005a). In that particular study, among the 315 proteins identiﬁed, only 165 of them, including single-hits, were from in-solution digestions (the remainder was identiﬁed in additional quantitative ICAT runs). Thus, SDSPAGE fractionation of CSF alone greatly enhanced the LCQ’s ability to identify many more proteins (466 versus 165 identiﬁcations). These results suggest that multiple dimensional chromatography (MDLC) can be very useful when the traditional three-dimensional type of MS (e.g., the LCQ DECA PLUS XP) is used, because these MS have a slower scanning speed and a smaller ion-trapping capacity as compared to the more advanced two-dimensional linear LTQ system.

B. PROTEINS IDENTIFIED SCX SEPARATION

BY NANOLC-LTQ-FT

MS/MS FOLLOWING OFF-LINE

When the same SDS-PAGE fractions were analyzed by nanoLC-LTQ-FT MS/MS after oV-line SCX separation, a total of 608 proteins were identiﬁed with 206 of them being single-hits, that is, an increase of about 30% over those identiﬁed by LCQ-MS/MS. However, this increase was achieved with a 0.6 mg peptide-loading amount for each SDS-PAGE fraction (i.e., less than 1% of

36

XU et al.

TABLE I TOTAL PROTEINS/GROUPS IDENTIFIED

WITH MORE THAN

TWO PEPTIDES (A TOTAL

OF

537 PROTEINS) Peptides

Group

Protein ID

p

1 2 3

IPI00385507 IPI00478742 IPI00476999 IPI00305461

1 1 1

4 5 6 7

IPI00333234 IPI00164744 IPI00479981 IPI00472011 IPI00217291 IPI00023814 IPI00334432 IPI00410714 IPI00479252 IPI00332272

1 1 1 1

8 9

0.76 1

IPI00289831

IPI00299590

IPI00293275 IPI00332271

IPI00332273

10

11

12 13 14

IPI00477336 IPI00022429 IPI00479531 IPI00412608 IPI00480034 IPI00395435 IPI00384355 IPI00019927

1

IPI00453476 IPI00385244 IPI00184598

1

1

0.84

0.99

Description

LTQ

LCQ

10-kDa protein 101-kDa protein 107-kDa protein Inter-alpha-trypsin inhibitor heavy chain h2 precursor 12-kDa protein 12-kDa protein 12-kDa protein 154-kDa protein Splice isoform 2 of neogenin precursor Splice isoform 1 of neogenin precursor 16-kDa protein Hemoglobin alpha-1 globin chain 169-kDa protein Splice isoform 3 of receptor-type tyrosine-protein phosphatase S precursor Splice isoform 1 of receptor-type tyrosine-protein phosphatase S precursor Splice isoform 5 of receptor-type tyrosine-protein phosphatase S precursor Protein tyrosine phosphatase, receptor type, sigma isoform 3 precursor Splice isoform 2 of receptor-type tyrosine-protein phosphatase S precursor Splice isoform 4 of receptor-type tyrosine-protein phosphatase S precursor 21-kDa protein Alpha-1-acid glycoprotein 1 precursor 24-kDa protein 25-kDa protein 25-kDa protein Hypothetical protein Hypothetical protein 26S proteasome non-protease regulatory subunit 7 29-kDa protein Phosphoglycerate mutase 1 (brain) 37-kDa protein

2 7 10

6

2 2 2 23

2

1

4

4

4

16

16

2

2 3 3 (Continued)

37

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

15 16 17

1 1 1

31 32 33 34 35 36 37 38 39

IPI00473015 IPI00477450 IPI00479169 IPI00430856 IPI00027780 IPI00021440 IPI00021439 IPI00015351 IPI00005908 IPI00296965 IPI00165972 IPI00019579 IPI00019943 IPI00479925 IPI00374563 IPI00065931 IPI00163646 IPI00329783 IPI00007257 IPI00465439 IPI00418262 IPI00465313 IPI00478003 IPI00020091 IPI00305457 IPI00218912 IPI00022895 IPI00029863 IPI00166729 IPI00022431 IPI00003802 IPI00008787

40 41

IPI00022426 IPI00386839

1 1

42 43 44 45 46

IPI00020012 IPI00032220 IPI00470657 IPI00032179 IPI00013941

1 1 1 1 1

47

IPI00444378 IPI00479805 IPI00304273

1

18 19 20 21 22 23 24 25 26

27 28 29 30

p

1 1 1 1 0.93 1 1 1 0.92

1 1 1 1 1 1 0.6 1 1 1 1 1 1

Description 54-kDa protein 56-kDa protein 65-kDa protein Hypothetical protein 72-kDa type IV collagenase precursor Actin, cytoplasmic 2 Actin, cytoplasmic 1 Ad039 ADAMTS-1 precursor ADAMTS-6 precursor Adipsin\complement factor D precursor Complement factor D precursor Afamin precursor Agrin Agrin precursor A-kinase anchoring protein Protein kinase A anchoring protein Ht31 Guanine nucleotide exchange factor Lbc Alcadein alpha-1 ALDOA protein ALDOC protein Alpha 2 macroglobulin Alpha-2-macroglobulin precursor Alpha-1-acid glycoprotein 2 precursor Alpha-1-antitrypsin precursor Alpha-1B-adrenergic receptor Alpha-1B-glycoprotein precursor Alpha-2-antiplasmin precursor Alpha-2-glycoprotein 1, zinc Alpha-2-HS-glycoprotein precursor Alpha-mannosidase II Alpha-N-acetylglucosaminidase precursor AMBP protein precursor Amyloid lambda 6 light chain variable region SAR Amyloid-like protein 1 precursor Angiotensinogen precursor Anti-colorectal carcinoma heavy chain Antithrombin III variant Aortic carboxypeptidase-like protein ACLP Hypothetical protein FLJ45634 APOA4 protein Apolipoprotein A-IV precursor

LTQ 6 2

LCQ 6 2

3 7

2 5

2 3

1 3 2 7

10 4

8 2 2

30 6 12 107

11 6 8 25

12 53 2 17 11 15 7 4

12 31 17 11 15 7 3

10 3

9

14 28 2 21 3

12 24

27

27

21

(Continued)

38

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

48 49 50 51 52 53 54 55 56

IPI00478761 IPI00168479 IPI00021841 IPI00021854 IPI00021856 IPI00021857 IPI00006662 IPI00021842 IPI00219029 IPI00303476

1 1 0.66 1 1 1 1 1 0.53

57

IPI00025079

0.98

IPI00024278 IPI00216251 58

IPI00007839

0.57

59 60 61

IPI00296992 IPI00003221 IPI00024284

1 0.96 1

62 63 64

IPI00298828 IPI00004656 IPI00219219

1 1 1

65

IPI00382950

1

66 67 68 69

IPI00304865 IPI00298793 IPI00218413 IPI00009619 IPI00166048

1 1 1 1

70

IPI00005794

1

71 72

IPI00007664 IPI00292304 IPI00022333

0.96 1

73

IPI00394878

1

IPI00075013

Description 45-kDa protein ApoA-I binding protein precursor Apolipoprotein A-I precursor Apolipoprotein A-II precursor Apolipoprotein C-II precursor Apolipoprotein C-III precursor Apolipoprotein D precursor Apolipoprotein E precursor Aspartate aminotransferase 1 ATP synthase beta chain, mitochondrial precursor ATP-binding cassette, sub-family C, member 9 isoform SUR2A-delta-14 Splice isoform 1 of sulfonylurea receptor 2 Splice isoform 2 of sulfonylurea receptor 2 ATP-binding cassette, sub-family G, member 5 AXL receptor tyrosine kinase, isoform 1 Ba526d8.2 Basement membrane-speciﬁc heparin sulfate proteoglycan core protein precursor Beta-2-glycoprotein I precursor Beta-2-microglobulin precursor Beta-galactosidase binding lectin precursor Beta-globin gene from a thalassemia patient, complete cds Betaglycan Beta-mannosidase precursor Biotinidase precursor Bk134p22.1 Brain immunoglobulin receptor precursor Blood plasma glutamate carboxypeptidase precursor Hypothetical protein FLJ90651 Brain protein Brain-speciﬁc angiogenesis inhibitor 1 precursor C1q and tumor necrosis factor related protein 1 Hypothetical protein FLJ23928

LTQ

LCQ

2 31 1

1 31 4 2

4 23 48 16 2

7 23 16

2

2 2 23

3 7

9 4 2

9 4

4

4 3

5 4 8

2 8

2

3

2 2 2

(Continued)

39

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

Description

LTQ

LCQ

74 75

IPI00418163 IPI00015346

1 0.94

70

70 2

76 77 78

1 1 1

3 2 2

1 2

79 80

IPI00024046 IPI00220361 IPI00479758 IPI00410600 IPI00375836 IPI00383710

81 82 83

IPI00027466 IPI00031121 IPI00479116

1 1 1

C4b1 Cadherin EGF LAG seven-pass G-type receptor 2 precursor Cadherin-13 precursor Calbindin 1 Calcium channel, alpha 2\delta subunit 2 Calcium channel, alpha 2\delta subunit 2 Cancer associated nucleoprotein Cancer-associated SCM-recognition immunedefense-suppressing and serine protease-protecting peptide, CRISPP peptide Carbonic anhydrase IV precursor Carboxypeptidase E precursor Carboxypeptidase N 83-kDa chain precursor PREDICTED: similar to carboxypeptidase N 83-kDa chain (carboxypeptidase N regulatory subunit) Cathepsin B precursor Cathepsin D precursor Cathepsin H precursor Cathepsin H Cathepsin L precursor PREDICTED: similar to cathepsin L precursor (major excreted protein) (MEP) CD59 glycoprotein precursor Cell growth regulator with EF hand domain 1 Cell growth regulator with EF hand domain 1 Cell recognition protein CASPR4 Cell surface glycoprotein MUC18 precursor Centrosomal colon cancer autoantigen protein Cerebellin 3 precursor Cerebellin precursor Ceruloplasmin precursor Chitinase-3 like protein 1 precursor Cholinesterase precursor Chondroadherin precursor Chromogranin A

0.94 1

IPI00166930

84 85 86 87

88 89

IPI00295741 IPI00011229 IPI00297487 IPI00375426 IPI00012887 IPI00457011

1 1 0.97

IPI00011302 IPI00337548

0.97 1

0.94

IPI00008584 90 91

IPI00216250 IPI00016334

1 1

92

IPI00186006

1

93 94 95 96 97 98 99

IPI00402157 IPI00011605 IPI00017601 IPI00002147 IPI00025864 IPI00014592 IPI00419463

1 0.98 1 1 0.98 1 1

2

2 12

2 2

12 2

21 1

8 11 4

1

3

1

3 3

14 3

3 2

2 2 66 9 4 12

2 2 21 9 2 4 8 (Continued)

40

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

Description

LTQ

LCQ

100 101

0.94 1

21

21

2 3

112

IPI00022394

1

113

IPI00011094

1

114

IPI00024105

1

115

IPI00296165

1

116

IPI00017696

1

117 118 119 120 121 122 123 124

IPI00303963 IPI00164623 IPI00032258 IPI00032291 IPI00418391 IPI00009920 IPI00296608 IPI00011261

1 1 1 1 1 1 1 1

125 126

IPI00022395 IPI00011264

1 1

127 128

IPI00291867 IPI00218746 IPI00477992

1 1

129

IPI00024966

1

Chromosome 7 open reading frame 3 Clusterin precursor Clusterin isoform 1 Coactosin-like protein Coagulation factor V precursor Coagulation factor V Coagulation factor XII precursor Collagen alpha 1(I) chain precursor Collagen alpha 1(III) chain precursor COL3A1 protein Collagen alpha 1(VI) chain precursor Collagen alpha 1(XV) chain precursor Type XV collagen Collagen, type XIV, alpha 1 Collectin placenta 1 Complement C1q subcomponent, A chain precursor Complement C1q subcomponent, C chain precursor Complement C1q tumor necrosis factor-related protein 4 precursor Complement C1q tumor necrosis factor-related protein 5 precursor Complement C1r subcomponent precursor Complement C1s subcomponent precursor Complement C2 precursor Complement C3 precursor Complement C4 precursor Complement C5 precursor Complement component 7 Complement component C6 precursor Complement component C7 precursor Complement component C8 gamma chain precursor Complement component C9 precursor Complement factor H-related protein 1 precursor Complement factor I precursor Complement subcomponent C1q chain B Complement component 1q subcomponent, beta polypeptide precursor Contactin 2 precursor

2 48

109 110 111

IPI00145593 IPI00291262 IPI00400826 IPI00017704 IPI00022937 IPI00419311 IPI00019581 IPI00297646 IPI00021033 IPI00167087 IPI00291136 IPI00295414 IPI00477770 IPI00176193 IPI00414467 IPI00022392

102 103 104 105 106 107 108

0.97 1 1 1 1 1 1 1 0.96 0.92

3 13 5

3

16 2

2 1

2 1

2 7

4

4

2 3

3

13

13

18

14 8 80 65

136 65 17 12 9 11 2

1 10 2

4 6

4 4

12 9

12 9

27 (Continued)

41

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

130 131 132

IPI00292791 IPI00004433 IPI00029343

1 1 1

133 134

IPI00008531 IPI00027482

1 1

135

IPI00432626 IPI00395488 IPI00032293 IPI00465315 IPI00382995 IPI00479319 IPI00008994 IPI00218109 IPI00218108 IPI00291005 IPI00333140

1

1 1 1 1 0.75 1 0.97 1

155 156 157

IPI00027547 IPI00002714 IPI00014439 IPI00470535 IPI00007778 IPI00296141 IPI00034319 IPI00012440 IPI00385460 IPI00066511 IPI00418179 IPI00419630 IPI00456969 IPI00477531 IPI00028911 IPI00004065 IPI00025447 IPI00472724 IPI00014424 IPI00303161 IPI00216171 IPI00216164

158 159

IPI00008318 IPI00005123

1 1

136 137 138

139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154

p

1 1 1

1 1

0.93 1 1 1 0.98 1

1 1 0.99

Description Contactin 3 precursor Contactin 6 precursor Contactin associated protein-like 2 precursor Corest protein Corticosteroid-binding globulin precursor Csrv314 Vasorin Cystatin C precursor Cytochrome C Cytoplasmic protein Ndr1 39-kDa protein Splice isoform 1 of NDRG2 protein Splice isoform 3 of NDRG2 protein Splice isoform 2 of NDRG2 protein Cytosolic malate dehydrogenase Delta-notch-like EGF repeat-containing transmembrane Dermcidin precursor Dickkopf related protein-3 precursor Dihydropteridine reductase Dihydropyridine receptor alpha 2 subunit Di-N-acetylchitobiase precursor Dipeptidyl-peptidase II precursor Divalent cation tolerant protein CUTA Dj20n2.5.1 Dj20n2.5.2 DNA polymerase theta DNA polymerase theta Dpkl1915 Dynein, cytoplasmic, heavy polypeptide 1 533-kDa protein Dystroglycan precursor Ecto-ADP-ribosyltransferase 4 precursor Elongation factor 1-alpha 1 Elongation factor 1 alpha-like 3 Elongation factor 1-alpha 2 Endothelial cell adhesion molecule Enolase 2 Enoyl-Coenzyme A, hydratase/ 3-hydroxyacyl Coenzyme A dehydrogenase Ephrin type-A receptor 4 precursor Ephrin-A3 precursor

LTQ

LCQ

3 5 9 7 5

5

3

1

38 6

38 3

8 3 2 19 4 30 1 4 2 2

8

8 3 1 2 2 2

2 32

32 4

10 2 2

10

2 4 2

6 2

5

(Continued)

42

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

160

IPI00301579

1

161 162 163 164

IPI00026530 IPI00002732 IPI00003351 IPI00015315 IPI00384977 IPI00027827

0.95 1 1 0.63

IPI00219684 IPI00289766 IPI00328113 IPI00471957 IPI00019439 IPI00298497 IPI00414283 IPI00023824 IPI00465038 IPI00294615 IPI00382428

1 1 1

0.99 1

181 182 183 184

IPI00025755 IPI00477747 IPI00426062 IPI00029723 IPI00294650 IPI00023673 IPI00023728 IPI00018236 IPI00418376 IPI00383814 IPI00026314 IPI00009890 IPI00003919

185

IPI00219018

1

186 187 188 189 190 191 192

IPI00478493 IPI00477597 IPI00218816 IPI00022488 IPI00453473 IPI00022371 IPI00216730 IPI00219037

1 0.99 1 1 1 1 1

165 166 167 168 169 170 171 172 173

174 175 176 177 178 179 180

p

1

0.98 1 1 1 1

0.99 1 1 1 1 1 1 0.97 0.97

Description Epididymal secretory protein E1 precursor ERGIC-53 protein precursor Exostosin-like 2 Extracellular matrix protein 1 precursor Extracellular matrix protein 2 precursor Hypothetical protein dkfzp686O0186 Extracellular superoxide dismutase [Cu-Zn] precursor Fatty acid binding protein 3 Fc receptor homolog expressed in B cells Fibrillin 1 precursor Fibrillin 1 Fibrillin 2 precursor Fibrinogen beta chain precursor Fibronectin 1 isoform 4 preproprotein Fibulin-2 precursor Fibulin 2 Fibulin-5 precursor Full-length cDNA clone CS0DI085YI08 of placenta of Homo sapiens Folate receptor beta precursor Follistatin-like 4 Hypothetical protein dkfzP686E04229 Follistatin-related protein 1 precursor Frizzled-related protein precursor Galectin-3 binding protein precursor Gamma-glutamyl hydrolase precursor Ganglioside GM2 activator precursor GM2 activator protein GBP protein isoform a Gelsolin precursor Glia derived nexin precursor Glutaminyl-peptide cyclotransferase precursor Glyceraldehyde-3-phosphate dehydrogenase Haptoglobin precursor Haptoglobin-related protein precursor Hemoglobin beta Hemopexin precursor HIST1H4F protein Histidine-rich glycoprotein precursor Histone H2A H2AFX protein

LTQ

LCQ

4

2

9 4 1

2 9 3 3

11

8

2 4

14 12 4

2 4 3 14

4

2 4

3

2 15 5 5

2 15 5 5

11 37 1 8

33 2 8

7

7

16 4 3 19 3 9

8 4 1 13 2 9 4

(Continued)

43

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

193

IPI00018524 IPI00303315 IPI00216456 IPI00102165 IPI00216457 IPI00026272 IPI00031562 IPI00081836 IPI00339274 IPI00291764 IPI00220855 IPI00472334 IPI00255316 IPI00152785 IPI00303133 IPI00003935 IPI00020101

194

195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212

IPI00455460 IPI00220403 IPI00419833 IPI00329665 IPI00018534 IPI00477495 IPI00032234 IPI00377199 IPI00152906 IPI00431656 IPI00440577 IPI00472961 IPI00419425 IPI00163446 IPI00431531 IPI00472345 IPI00021263 IPI00384931 IPI00026195 IPI00430823 IPI00448950 IPI00465248 IPI00423461 IPI00426051 IPI00426007 IPI00413451 IPI00470819

p

Description

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.57

Histone H2A.e H2A histone family, member M H2A histone family, member L Hypothetical protein FLJ10903 H2A histone family, member O Histone H2A.m Histone H2A DJ86C11.1 H2A histone family, member Q H2A histone family, member C H2A histone family, member J, isoform 2 H2A histone family, member E, Histone 1 H2ad Histone H2B H2B histone family, member J H2B histone family, member A PREDICTED: similar to Hist1h2bc protein H2B histone family, member F HIST1H2BM protein H2B histone family, member E H2B histone family, member C H2B histone family, member S H2B histone family, member T H2B histone family, member D H2B histone family, member B Histone H2B Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein dkfzp686b0286 Hypothetical protein dkfzp686c02220 Hypothetical protein dkfzp686c15213 Hypothetical protein dkfzp686g11190 Hypothetical protein dkfzp686i04222 Hypothetical protein dkfzp686i20267

1

1

LTQ

LCQ

3

2

6 5 4 4 4 3 3 3 2 2 2 4 3 3 3 3 1

1 1 2 3 1

2 3 3 3 3 2 (Continued)

44

XU et al.

TABLE I (Continued) Peptides Group

213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232

Protein ID IPI00024601 IPI00384938 IPI00418157 IPI00030385 IPI00305719 IPI00442230 IPI00024825 IPI00015834 IPI00005056 IPI00152975 IPI00442911 IPI00166339 IPI00394792 IPI00396522 IPI00384662 IPI00446503 IPI00445742 IPI00445227 IPI00443478 IPI00025363 IPI00029046 IPI00168884 IPI00171410 IPI00456036 IPI00448792 IPI00002745 IPI00015049 IPI00478417

p

1 1 1 0.96 0.95 0.86 1 1 0.78 1 1 0.99 1 1 1 1 1 0.95 1 1

233 234 235 236 237 238

IPI00382481 IPI00382499 IPI00385555 IPI00387025 IPI00387027 IPI00478600

1 1 1 1 1 1

239 240 241 242 243 244 245

IPI00387095 IPI00387097 IPI00387105 IPI00387106 IPI00387110 IPI00387113 IPI00384576

1 1 1 1 1 1 0.99

Description Carbonic anhydrase-related protein 10 Hypothetical protein dkfzp686n02209 Hypothetical protein dkfzp686o16217 Hypothetical protein FLJ13813 Selenium binding protein 1 Hypothetical protein FLJ16561 Megakaryocyte stimulating factor Hypothetical protein FLJ20421 Hypothetical protein FLJ20958 C6orf79 protein Hypothetical protein FLJ26266 Hypothetical protein FLJ33655 Hypothetical protein FLJ39837 Zinc ﬁnger protein 614 Hypothetical protein FLJ40332 Hypothetical protein FLJ41552 Hypothetical protein FLJ43465 Hypothetical protein FLJ44324 Hypothetical protein FLJ45472 Glial ﬁbrillary acidic protein, astrocyte Hypothetical protein KIAA0152 Hypothetical protein PSEC0072 Hypothetical protein PSEC0251d Hypothetical protein XP_498490 Hypothetical protein Cathepsin Z precursor Hypothetical protein Splice isoform 1 of repulsive guidance molecule A precursor Ig heavy chain V-III region BUT Ig heavy chain V-III region JON Ig kappa chain V-I region BAN Ig kappa chain V-I region DEE Ig kappa chain V-I region Gal Ig kappa chain V-I region HK102 precursor Ig kappa chain V-I region Ka Ig kappa chain V-I region Lay Ig kappa chain V-I region Mev Ig kappa chain V-I region Ni Ig kappa chain V-II region MIL Ig kappa chain V-III region B6 Ig kappa chain V-III region HIC precursor

LTQ

LCQ

2 5

2 5

1

3

7

7 2

2 2 2 2 2

7 2 3

2 3 7

3 2

2 3 4

2 2 2 6 2 5 2 2 3 4 2 3 3

3

1 1

2

3

(Continued)

45

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

246

IPI00386131

1

247 248

IPI00387119 IPI00419453

1 1

249

IPI00024138

1

250

IPI00026197 IPI00386132 IPI00386133

1

251 252 253 254 255

IPI00382426 IPI00385985 IPI00242956 IPI00382937 IPI00290411

0.99 1 1 1 1

256 257

IPI00001639 IPI00297284

0.72 1

258

IPI00029235

1

259

IPI00016915

1

260

IPI00020996

0.8

261

IPI00328829

1

IPI00451977 262

IPI00292530

1

263

IPI00382767 IPI00290456

1

264

IPI00026259

1

265 266 267 268 269

IPI00398435 IPI00023648 IPI00299083 IPI00023845 IPI00005707

1 1 1 1 1

Description Ig kappa chain V-III region IARC\BL41 precursor Ig kappa chain V-III region POM Ig kappa chain V-III region VG precursor Ig kappa chain V-III region VH precursor Ig kappa chain V-IV region precursor Ig kappa chain V-IV region JI precursor Ig kappa chain V-IV region B17 precursor Ig lambda chain V-II region TRO Ig lambda chain V-III region LOI Igg Fc binding protein IGHM protein Immunoglobulin-like domain protein MGC33530 precursor Importin beta-1 subunit Insulin-like growth factor binding protein 2 precursor Insulin-like growth factor binding protein 6 precursor Insulin-like growth factor binding protein 7 precursor Insulin-like growth factor binding protein complex acid labile chain precursor Inter-alpha trypsin inhibitor heavy chain precursor 5 isoform 1 Inter-alpha trypsin inhibitor heavy chain precursor 5 isoform 2 Inter-alpha-trypsin inhibitor heavy chain H1 precursor Intercellular adhesion molecule 5 Intercellular adhesion molecule-5 precursor Swiss-prot:p20933|trembl:q6ld43; q9uck7;q9uck8|refseq_np: np_000018|ensembl: ensp00000264595 PREDICTED: PLEXIN B2 ISLR precursor Jam-it\ve-jam Kallikrein 6 precursor KIAA0709 protein

LTQ

LCQ

3 2 4

3

2 6

2 2 13 2 3

2 6 1

2 9

9

9

9

12

12

1

2

6

6

9 5

3

2

16 3 2 13 2

10 3 (Continued)

46

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

Description

270 271 272 273 274

0.97 0.85 1 1 0.97

279 280 281 282 283 284 285

IPI00465374 IPI00180384 IPI00465184 IPI00008087 IPI00412218 IPI00395649 IPI00397768 IPI00028492 IPI00034201 IPI00002236 IPI00334005 IPI00217966 IPI00219217 IPI00013976 IPI00298281 IPI00020665 IPI00217055 IPI00022417

286

IPI00013303

1

287

IPI00186736 IPI00056478 IPI00020557

1

1 0.97

291 292 293 294

IPI00020986 IPI00289058 IPI00218851 IPI00014964 IPI00293088 IPI00019038 IPI00011218

295 296

IPI00259102 IPI00473066

1 1

297

IPI00027848 IPI00012989

0.95

298

IPI00291641

0.97

299 300

IPI00064607 IPI00003448

1 1

KIAA0736 protein KIAA0944 protein KIAA1258 protein KIAA1263 protein KIAA1409 protein KIAA1409 KIAA1579 protein KIAA1679 protein KIAA1746 protein Lactadherin precursor MFGE8 protein Lactate dehydrogenase A Lactate dehydrogenase B Laminin beta-1 chain precursor Laminin gamma-1 chain precursor Latent TGF-beta binding protein-4 Leprecan-like 1 protein Leucine-rich alpha-2-glycoprotein precursor Limbic system-associated membrane protein precursor LIR-D1 Ewi2 Low-density lipoprotein receptor-related protein 1 precursor Lumican precursor Ly-6\neurotoxin-like protein 1 precursor Hypothetical protein Lymphocyte antigen Ly-6H precursor Lysosomal alpha-glucosidase precursor Lysozyme C precursor Macrophage colony stimulating factor I receptor precursor Mammalian ependymin related protein 1 Mannose receptor, C type 1-like 1 Macrophage mannose Receptor precursor Mannosidase, alpha, class 2B, member 1 precursor Mannosyl-oligosaccharide 1,2-alphamannosidase IA MEGF10 protein Melanoma derived growth regulatory protein precursor

275 276 277 278

288 289 290

0.95 0.92 0.99 0.87 1 1 1 1 1 0.97 1

1

1 1 0.88 1

LTQ

LCQ

2 4 3

2 4 3

2 2 1 2 14 9 8 15 3 8

2 2

14

8

2

2

4

2

11 5 2

4

3 2 1 5 2 5

2 2 1

2 1

3

2 2

(Continued)

47

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

301

IPI00019157

1

302 303 304 305

IPI00032292 IPI00027166 IPI00061977 IPI00022792

1 1 1 1

306 307 308

IPI00385143 IPI00025465 IPI00029260

0.99 1 1

309 310 311

IPI00027310 IPI00024048 IPI00007893

1 1 0.97

312

IPI00007884

1

313

IPI00398220

0.98

314

IPI00009997

1

315 316 317 318

IPI00303335 IPI00016422 IPI00299059 IPI00435020

0.92 1 1 1

IPI00185362 IPI00220737 319

IPI00478109 IPI00376427

1

320 321 322 323 324 325

IPI00290085 IPI00442299 IPI00216728 IPI00441515 IPI00159927 IPI00176221 IPI00479497 IPI00220562 IPI00026946 IPI00031289 IPI00334238 IPI00016150 IPI00026944

1 1 1 1 1 1

326 327 328 329 330

1 0.75 1 1 1

Description Melanoma-associated chondroitin sulfate proteoglycan Metalloproteinase inhibitor 1 precursor Metalloproteinase inhibitor 2 precursor MGC27165 protein Microﬁbril-associated glycoprotein 4 precursor Microﬁbrillar protein 2 Mimecan precursor Monocyte differentiation antigen CD14 precursor Multiple EGF-like-domain protein 4 Muscle-cadherin precursor Myosin-reactive immunoglobulin heavy chain variable region Myosin-reactive immunoglobulin light chain variable region Myosin-reactive immunoglobulin light chain variable region N-acetyllactosaminide beta-1,3-Nacetylglucosaminyltransferase Nebulin Netrin receptor DCC precursor Neural cell adhesion molecule Neural cell adhesion molecule 1,140-kDa isoform precursor Neural cell adhesion molecule 1 Splice isoform 2 of neural cell adhesion molecule 1,120-kDa isoform precursor Neural cell adhesion molecule 2 Neural cell adhesion molecule 2 precursor Neural-cadherin precursor Neurexin 1-alpha precursor Neurexin 3-alpha Neurexin 3-alpha precursor Neurocan core protein precursor Neuronal growth regulator 1 39-kDa protein Neuronal pentraxin I precursor Neuronal pentraxin II precursor Neuronal pentraxin receptor Neuronal pentraxin receptor isoform 1 Neuroserpin precursor Nidogen precursor

LTQ

LCQ

2 5 5 2

5 5 8

3 11 12

3 11 9

29 3 2 3 2 16

16 2

10 50 12

1 12

8

5

7 27 5 8 12 7

7 3 3

7

12 1 13

12 5 13

13 5

13 1 (Continued)

48

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

331 332 333 334 335

IPI00384542 IPI00028908 IPI00289083 IPI00328703 IPI00100715 IPI00295832

1 1 0.98 0.63 0.96

336

IPI00001662

1

337 338

IPI00020990 IPI00218725 IPI00479164 IPI00479834 IPI00298888 IPI00103552

1 1

1 1 0.99 1 1 1

347

IPI00218732 IPI00163563 IPI00155647 IPI00024129 IPI00000874 IPI00027350 IPI00375400 IPI00022331

348

IPI00299503

1

339 340 341 342 343 344 345 346

0.95 0.99

1

IPI00301884 IPI00385509 349 350

IPI00169383 IPI00006114

1 1

351 352 353

IPI00026199 IPI00291866 IPI00007221

1 1 1

354 355

IPI00019580 IPI00000495 IPI00102069 IPI00479489 IPI00419262 IPI00335009 IPI00298902

1 1

356 357 358

1 1 0.97

Description NID protein Nidogen-2 precursor Novel protein NS5ATP13TP2 protein Obscurin Oligodendrocyte-myelin glycoprotein precursor Opioid binding protein\cell adhesion molecule precursor Osteomodulin precursor Otthump00000040303 343-kDa protein 344-kDa protein Otthump00000040847 Ovarian cancer related tumor marker CA125 Paraoxonase 1 PBP family protein precursor PDZ domain-containing protein AIPC Peptidyl-prolyl cis-trans isomerase C Peroxiredoxin 1 Peroxiredoxin 2 Peroxiredoxin 2 isoform b Phosphatidylcholine-sterol acyltransferase precursor Phosphatidylinositol-glycan-speciﬁc phospholipase D 1 precursor Glycosylphosphatidylinositol phospholipase D Phosphatidylinositol-glycan-speciﬁc phospholipase D 2 precursor Phosphoglycerate kinase 1 Pigment epithelium-derived factor precursor Plasma glutathione peroxidase precursor Plasma protease C1 inhibitor precursor Plasma serine protease inhibitor precursor Plasminogen precursor Pnas-125 Ga17 protein 23-kDa protein PPIB protein PREDICTED: hemicentin-2 PREDICTED: KIAA1107 protein

LTQ

6 4 3 2 1

LCQ

4 3 2

4 2 2

1

2 4 7 7 2 3 3

7 7 2

3

4 2

3 28

27

6 22 4

4 22

19

19 2

4 3 2 (Continued)

49

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

Description

359 360 361

IPI00001632 IPI00288940 IPI00300241

0.9 0.87 1

362 363

IPI00397715 IPI00376832

0.99 0.97

364

IPI00454960

1

PREDICTED: KIAA1522 protein PREDICTED: KIAA1639 protein PREDICTED: leucine rich repeat containing 4B PREDICTED: NYD-SP11 protein PREDICTED: similar to ba203i16.1 (KIAA0970 protein) PREDICTED: similar to beta-1,3-Nacetylglucosaminyltransferase lunatic fringe (O-fucosylpeptide 3-beta-Nacetylglucosaminyltransferase) Splice isoform 3 of beta-1,3-Nacetylglucosaminyltransferase lunatic fringe PREDICTED: lunatic fringe homolog PREDICTED: similar to collagen alpha 3(IX) chain precursor PREDICTED: similar to eukaryotic translation initiation factor 3, subunit 5 epsilon, 47-kDa PREDICTED: similar to hypothetical protein dkfzp434p0316 PREDICTED: similar to Ig kappa chain precursor V region (orphon V108)— human (fragment) PREDICTED: similar to Ig kappa chain V region (A2)—human PREDICTED: similar to Ig kappa chain V region (A2)—human PREDICTED: similar to KCTD11 protein PREDICTED: similar to myocyte nuclear factor PREDICTED: similar to peptidylprolyl isomerase A PREDICTED: similar to protein kinase related to Raf protein kinases PREDICTED: similar to RAB1B, member RAS oncogene family PREDICTED: similar to ribosomal protein S15 Ribosomal protein S15 PREDICTED: similar to sorbitol dehydrogenase (L-iditol 2-dehydrogenase)

IPI00472104

365

IPI00455739 IPI00454733

0.93

366

IPI00240909

1

367

IPI00255145

0.99

368

IPI00397204

1

369

IPI00454724

1

IPI00454725 370

IPI00400983

0.81

371

IPI00465123

0.82

372

IPI00248321

0.97

373

IPI00457171

0.85

374

IPI00374519

0.87

375

IPI00455479

1

376

IPI00216153 IPI00176377

0.96

LTQ

7

LCQ 2 2 7 3 2

2

2

2 2

1

2 2

2

2 2 2 2 2 2

2

(Continued)

50

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

Description

377

IPI00454963

0.95

PREDICTED: similar to ubiquitin carboxyl-terminal hydrolase 42 (ubiquitin thiolesterase 42) (ubiquitinspeciﬁc processing protease 42) (deubiquitinating enzyme 42) PREDICTED: ubiquitin speciﬁc protease 42 Prepro-alpha2(I) collagen precursor Prion protein Major prion protein precursor Probable endonuclease KIAA0830 precursor Procollagen C-proteinase enhancer protein precursor Proenkephalin A precursor Proﬁlin 1 Prosaas precursor Prostaglandin-H2 D-isomerase precursor Prostatic binding protein Protein FAM3C precursor 25-kDa protein Protein kinase C substrate 80K-H isoform 1 Glucosidase II beta subunit precursor Protein kinase C-binding protein NELL2 precursor Protein tyrosine phosphatase, non-receptor type substrate 1 precursor Protein tyrosine phosphatase, receptor type, D isoform 3 precursor Splice isoform 3 of receptor-type tyrosine-protein phosphatase delta precursor Splice isoform 1 of receptor-type tyrosine-protein phosphatase delta precursor Protein tyrosine phosphatase, receptor type, D isoform 2 precursor Protein tyrosine phosphatase, receptor type, D isoform 4 precursor Protein-L-isoaspartate (D-aspartate) O-methyltransferase Prothrombin precursor Protocadherin 9 precursor Protocadherin 9 isoform 1 precursor

IPI00375124 378 379 380

IPI00164755 IPI00382843 IPI00022284 IPI00001952

1 1 1

381

IPI00299738

1

382 383 384 385 386 387

1 1 1 1 1 1

388

IPI00000828 IPI00216691 IPI00002280 IPI00013179 IPI00219446 IPI00021923 IPI00334282 IPI00419384

389

IPI00026154 IPI00015260

1

390

IPI00332887

1

391

IPI00375548

1

1

IPI00219860

IPI00011642

IPI00375547 IPI00107819 392

IPI00411680

1

393 394

IPI00019568 IPI00006967 IPI00409626

1 1

LTQ

LCQ 2

13 8

4

7

7

17

17 2

2 10 64 10 13

3 13 10 8

2

18

3

5

2

4

1

4

4

16 2

6

(Continued)

51

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

395 396

399 400

IPI00302641 IPI00413533 IPI00103175 IPI00220644 IPI00465016 IPI00003590 IPI00465325 IPI00465186

401

IPI00480183 IPI00107831 IPI00011651

1

402 403 404

IPI00103510 IPI00289204 IPI00019176

0.99 1 1

405

IPI00480192 IPI00479848 IPI00022420 IPI00014048 IPI00005038 IPI00432405 IPI00166766 IPI00296168 IPI00306339

1

397 398

406 407 408

409

410 411 412 413 414

415

IPI00021000 IPI00218874 IPI00218875 IPI00385896 IPI00006601 IPI00009362 IPI00292071 IPI00029061 IPI00332846 IPI00178302 IPI00374169 IPI00170617 IPI00170551 IPI00465042 IPI00025257

p 1 1 1 1 1 1

1 1 1

0.93

1 1 1 1 0.97

1

Description

LTQ

LCQ

Protocadherin fat 2 precursor Putative nfkb activating protein CANT1 protein Pyruvate kinase 3 isoform 2 Quiescin Q6, isoform b Quiescin Qvsk201 Receptor-type tyrosine-protein phosphatase F precursor LAR splice variant 1 LAR Receptor-type tyrosine-protein phosphatase gamma precursor Relaxin receptor 2 Reticulon 4 receptor precursor Retinoic acid receptor responder protein 2 precursor Retinol binding protein 4, plasma Retinol binding protein 4, plasma Plasma retinol-binding protein precursor Ribonuclease pancreatic precursor Ribonuclease UK114 Sarg904 Mgc45438 protein Hypothetical protein ﬂj90761 Secreted phosphoprotein 1 (osteopontin, bone Sialoprotein I, early T-lymphocyte activation 1) Splice isoform 1 of osteopontin precursor Splice isoform 2 of osteopontin precursor Splice isoform 3 of osteopontin precursor Splice isoform 4 of osteopontin precursor Secretogranin I precursor Secretogranin II precursor Secretogranin III precursor Selenoprotein P precursor Semaphorin 6D isoform 1 Semaphorin 6D isoform 4 Semaphorin 6D isoform 5 precursor Semaphorin 6D short isoform Semaphorin 6D isoform 2 KIAA1479 protein Semaphorin 7A precursor

18 2

2

9 3

3 1

3 2

3

2

1

2

2 2 6

9

9

4 2 8

2

1

4

11 2 13 2 1

6 13 2 2

8

8

8

(Continued)

52

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

416

IPI00396348

p 1

IPI00328609

417 418 419 420 421 422 423

1 1 1 1 1 0.84 0.97

431 432

IPI00022463 IPI00165421 IPI00292950 IPI00022434 IPI00022391 IPI00022482 IPI00382750 IPI00000137 IPI00470653 IPI00102543 IPI00217146 IPI00022608 IPI00014572 IPI00296777 IPI00384293 IPI00478292 IPI00413728 IPI00003627 IPI00333383

433

IPI00027703

1

434

IPI00006608

1

424 425 426 427 428 429 430

1 0.97 1 1 1 1 0.99 0.84 1

IPI00412924 435

IPI00302146

0.99

IPI00013495 436

IPI00451624 IPI00451625 IPI00451626

1

Description Serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 4 Full-length cDNA 5-PRIME end of clone CS0DM009YC13 of Fetal liver of Homo sapiens Serotransferrin precursor SERPINC1 protein SERPIND1 protein Serum albumin precursor Serum amyloid P-component precursor Sialidase 2 Similar to protein kinase C substrate C316G12.3 Single-chain Fv SLIT and NTRK-like protein 1 precursor SLIT and NTRK-like protein 4 precursor Sortilin-related receptor precursor SPARC precursor SPARC-like protein 1 precursor SPARC-like 1 Spectrin, alpha, non-erythrocytic 1 Spectrin alpha chain, brain Splice isoform 1 of Actin-like protein 6A Splice isoform 1 of adapter-related protein complex 2 beta 1 subunit Splice isoform 1 of alpha-mannosidase iix Splice isoform 1 of amyloid beta A4 protein precursor Splice isoform 8 of amyloid beta A4 protein precursor Splice isoform 1 of ATP-binding cassette, sub-family F, member 1 Splice isoform 2 of ATP-binding cassette, sub-family F, member 1 Splice isoform 1 of cartilage acidic protein 1 precursor Splice isoform 2 of cartilage acidic protein 1 precursor Splice isoform 3 of cartilage acidic protein 1 precursor

LTQ

LCQ

5

3

111 12 8 245 4

50 12 8 82 2 3 6

6 4 2

2 3

6 32

11 10

3

5

2 1

13 20

12

2

7

5

(Continued)

53

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

437

IPI00008860

p 1

IPI00425976

438

IPI00019591

1

439

IPI00029739

1

440 441

IPI00029751 IPI00012119 IPI00219403 IPI00296337

1 1

442

0.97

IPI00376215 443

IPI00013682

1

IPI00220292 IPI00220293 444

IPI00156171

1

445

IPI00414984

1

447

IPI00418183 IPI00022418 IPI00339223 IPI00339228 IPI00339319 IPI00339227 IPI00339318 IPI00339225 IPI00479723 IPI00414283 IPI00414282 IPI00026104

448

IPI00001611

446

IPI00215977

1

0.97 1

Description Splice isoform 1 of complement C1q tumor necrosis factor-related protein 3 precursor Splice isoform 2 of complement C1q tumor necrosis factor-related protein 3 precursor Splice isoform 1 of complement factor B precursor Splice isoform 1 of complement factor H precursor Splice isoform 1 of contactin 1 precursor Splice isoform 1 of decorin precursor Splice isoform 4 of decorin precursor Splice isoform 1 of DNA-dependent protein kinase catalytic subunit Splice isoform 2 of DNA-dependent protein kinase catalytic subunit Splice isoform 1 of ecto-ADPribosyltransferase 3 precursor Splice isoform 2 of ecto-ADPribosyltransferase 3 precursor Splice isoform 3 of ecto-ADPribosyltransferase 3 precursor Splice isoform 1 of ectonucleotide pyrophosphatase\phosphodiesterase 2 Splice isoform 1 of epsilon-sarcoglycan precursor Hypothetical protein SGCE Splice isoform 1 of ﬁbronectin precursor Splice isoform 3 of ﬁbronectin precursor Splice isoform 8 of ﬁbronectin precursor Splice isoform 11 of ﬁbronectin precursor Splice isoform 7 of ﬁbronectin precursor Splice isoform 10 of ﬁbronectin precursor Splice isoform 5 of ﬁbronectin precursor Fibronectin 1 isoform 6 preproprotein Fibronectin 1 isoform 4 preproprotein Fibronectin 1 isoform 2 preproprotein Splice isoform 1 of iduronate 2-sulfatase precursor Splice isoform 1 of insulin-like growth factor II precursor Splice isoform 2 of insulin-like growth factor II precursor

LTQ

LCQ 3

22

22

62

11

2

19 2 3

4

3

46

32

3

3

5

1

2

3

(Continued)

54

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

449

IPI00294193

1

450

IPI00297124

0.97

451 452

IPI00183445 IPI00410310 IPI00410312 IPI00162547 IPI00009030

1 1

453

p

0.94

IPI00216172 IPI00427759 454

IPI00219661

1

455

IPI00396968 IPI00411478

1

456

IPI00027087

1

IPI00334532 457

IPI00007921

1

458

IPI00008944

1

459

IPI00022733

1

IPI00217778 460

IPI00387168

1

461

IPI00000024

1

IPI00215992

462

463

IPI00219455 IPI00176458 IPI00294776 IPI00241562 IPI00298066 IPI00456736

1

1

Description

LTQ

LCQ

Splice isoform 1 of inter-alpha-trypsin inhibitor heavy chain H4 precursor Splice isoform 1 of interleukin-6 receptor beta chain precursor Splice isoform 1 of latrophilin 1 precursor Splice isoform 1 of latrophilin 3 precursor Splice isoform 3 of latrophilin 3 precursor Splice isoform 2 of latrophilin 3 precursor Splice isoform 1 of lysosome-associated membrane glycoprotein 2 precursor Lysosomal-associated membrane protein 2C Splice isoform 2 of lysosome-associated membrane glycoprotein 2 precursor Splice isoform 1 of myelin proteolipid protein Proteolipid protein 1 Splice isoform 1 of neural cell adhesion molecule 1 120-kDa isoform precursor Splice isoform 1 of neural cell adhesion molecule L1 precursor Splice isoform 2 of neural cell adhesion molecule L1 precursor Splice isoform 1 of neurexin 2-alpha precursor Splice isoform 1 of neuroendocrine protein 7B2 precursor Splice isoform 1 of phospholipid transfer protein precursor Splice isoform 2 of phospholipid transfer protein precursor Splice isoform 1 of proprotein convertase subtilisin\kexin type 9 precursor Splice isoform 1 of protocadherin 1 precursor Splice isoform 2 of protocadherin 1 precursor Protocadherin 1, isoform 1 Protocadherin 1 isoform 2 precursor Splice isoform 1 of reelin precursor Splice isoform 2 of reelin precursor Splice isoform 3 of reelin precursor Splice isoform 1 of RGM domain family member B precursor

27

7

1

3

3 2

1

2

3

9 11

19

7

2 18

18

2 3

31

6

4

2

(Continued)

55

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

Description

464

IPI00022577

0.78

Splice isoform 1 of rhomboid-related protein 1 Splice isoform 2 of rhomboid-related protein 1 Splice isoform 1 of ribonuclease T2 precursor 30-kDa protein Splice isoform 1 of roundabout homolog 1 precursor Splice isoform 2 of roundabout homolog 1 precursor Splice isoform 1 of sex hormone-binding globulin precursor Splice isoform 2 of sex hormone-binding globulin precursor Splice isoform 1 of tenascin X precursor Splice isoform 1of apolipoprotein L1 precursor Splice isoform 2 of apolipoprotein L1 precursor Splice isoform 2 of amyloid-like protein 2 precursor Splice isoform 1 of amyloid-like protein 2 precursor Splice isoform 2 of brevican core protein precursor Hyaluronan binding protein Splice isoform 1 of brevican core protein precursor Splice isoform 2 of clathrin heavy chain 1 Clathrin heavy chain 1 Splice isoform 2 of collagen alpha 1(XII) chain precursor Splice isoform 1 of collagen alpha 1(XII) chain precursor Splice isoform 2 of collagen alpha 3(VI) chain precursor Alpha 3 type VI collagen isoform 2 precursor Alpha 3 type VI collagen isoform 4 precursor Splice isoform 2 of contactin 1 precursor

IPI00220135 465

IPI00414896

1

466

IPI00299103 IPI00219798

1

IPI00418121 467

IPI00023019

0.79

IPI00219583 468 469

IPI00025276 IPI00177869

1 1

IPI00186903 470

IPI00220977

0.97

IPI00031030 471

IPI00456624

1

IPI00217423 IPI00456623 472

IPI00455383

0.89

473

IPI00024067 IPI00221384

1

IPI00329573 474

IPI00220701

1

IPI00376964 IPI00022200 475

IPI00216641

1

LTQ

LCQ

2

2

3

1

3

15 2

2

3

3

4

4

2

3

9

3

56 (Continued)

56

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

476

IPI00477233 IPI00477156 IPI00179057 IPI00480142 IPI00220813

477

p 1

1

IPI00220814

IPI00029658

IPI00220815

478

IPI00029717

1

IPI00021885 479

IPI00219713

1

IPI00021891 480

IPI00414888

1

IPI00219131 481

482 483

IPI00218137 IPI00217503 IPI00002818 IPI00215894 IPI00394992

1

1 1

IPI00163207 484

485 486

IPI00442297 IPI00442294 IPI00442298 IPI00375255 IPI00219041

1

0.98 1

Description Splice isoform 2 of cullin homolog 4B Splice isoform 1 of cullin homolog 4B 103-kDa protein 105-kDa protein Splice isoform 2 of EGF-containing ﬁbulin-like extracellular matrix protein 1 precursor Splice isoform 3 of EGF-containing ﬁbulin-like extracellular matrix protein 1 precursor Splice isoform 1 of EGF-containing ﬁbulin-like extracellular matrix protein 1 precursor Splice isoform 4 of EGF-containing ﬁbulin-like extracellular matrix protein 1 precursor Splice isoform 2 of ﬁbrinogen alpha \alpha-E chain precursor Splice isoform 1 of ﬁbrinogen alpha \alpha-E chain precursor Splice isoform 2 of ﬁbrinogen gamma chain precursor Splice isoform 1 of ﬁbrinogen gamma chain precursor Splice isoform 2 of ICOS ligand precursor Splice isoform 1 of ICOS ligand precursor Splice isoform 2 of kallikrein 11 precursor Variant form hippostasin\KLK11 Splice isoform 1 of kallikrein 11 precursor Splice isoform 2 of kininogen precursor Splice isoform 2 of N-acetylmuramoyl-Lalanine amidase precursor Splice isoform 1 of N-acetylmuramoyl-Lalanine amidase precursor Splice isoform 2 of neurotrimin precursor Splice isoform 1 of neurotrimin precursor Splice isoform 3 of neurotrimin precursor Splice isoform 2 of pecanex-like protein 1 Splice isoform 2 of peptidyl-glycine alpha-amidating monooxygenase precursor

LTQ

LCQ

2

13

5

15

15

15

12

2

2

9 5

5 5

5

4

14

3 14

(Continued)

57

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

p

IPI00219042

IPI00177543

487

IPI00334175

1

IPI00183626 IPI00179964 488

IPI00291170

0.96

IPI00376689 489

IPI00216283

1

IPI00472466 IPI00291099

490

IPI00412910

1

IPI00103597 491 492 493

IPI00479016 IPI00218803 IPI00296534 IPI00333778

1 1 1

IPI00333777 IPI00333776 IPI00333781 494

IPI00219825 IPI00219824

1

Description Splice isoform 3 of peptidyl-glycine alpha-amidating monooxygenase precursor Splice isoform 4 of peptidyl-glycine alpha-amidating monooxygenase precursor Splice isoform 2 of polypyrimidine tract-binding protein 1 Polypyrimidine tract-binding protein 1, isoform a Splice isoform 1 of polypyrimidine tract-binding protein 1 Splice isoform 2 of protein KIAA1199 precursor Splice Isoform 1 of protein KIAA1199 precursor Splice isoform 2 of receptor-type tyrosine-protein phosphatase zeta precursor Tyrosine phosphatase zeta polypeptide 2 HTPZP2 Splice isoform 1 of receptor-type tyrosine-protein phosphatase zeta precursor Splice isoform 2 of VPS10 domaincontaining receptor sorcs1 precursor VPS10 domain receptor protein SORCS 1 Splice isoform 3 of contactin 4 precursor Splice isoform 3 of ﬁbulin-1 precursor Splice isoform 1 of ﬁbulin-1 precursor Splice isoform 3 of neuronal cell adhesion molecule precursor Splice isoform 2 of neuronal cell adhesion molecule precursor Splice isoform 1 of neuronal cell adhesion molecule precursor Splice isoform 5 of neuronal cell adhesion molecule precursor Splice isoform 3 of proactivator polypeptide precursor Splice isoform 2 of proactivator polypeptide precursor

LTQ

LCQ

2

2

5

1

3

8 7

7

48

10

3

3

(Continued)

58

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

IPI00012503 495

IPI00218648

0.84

IPI00296069 IPI00328418 496

IPI00220333

1

IPI00157417 497 498 499

500 501

502 503 504 505 506 507 508 509 510 511 512 513 514 515

IPI00296537 IPI00247295 IPI00386444 IPI00394655 IPI00470575 IPI00477942 IPI00415032

1 0.92 1

1

IPI00218462 IPI00027235 IPI00162735 IPI00218461 IPI00171473 IPI00033466 IPI00022314

1

IPI00218733 IPI00386630 IPI00160552 IPI00470495 IPI00307536 IPI00009028 IPI00216298 IPI00018769 IPI00328550 IPI00022892 IPI00292946 IPI00179357 IPI00386639

1 1 1 0.85

1 1 0.97

1 1 1 1 1 1 1

Description Splice isoform 1 of proactivator polypeptide precursor Splice isoform 3 of retinoblastomabinding protein 1 Splice isoform 1 of retinoblastomabinding protein 1 Splice isoform 2 of retinoblastomabinding protein 1 Splice isoform 3 of seizure 6-like protein precursor Splice isoform 1 of seizure 6-like protein precursor Splice isoform 4 of ﬁbulin-1 precursor Splice isoform 4 of nesprin 1 Splice isoform 1 of nesprin 1 Splice isoform 4 of neurofascin precursor Neurofascin isoform 1 Neurofascin isoform 2 Splice isoform 4 of neuronal cell adhesion molecule precursor Splice isoform 5 of attractin precursor Splice isoform 1 of attractin precursor Splice isoform 2 of attractin precursor Splice isoform 4 of attractin precursor Spondin 1 precursor Stem cell growth factor precursor Superoxide dismutase [Mn], mitochondrial precursor Superoxide dismutase 1, soluble TCN2 protein Tenascin-R Testis expressed sequence 11, isoform 2 Hypothetical protein FLJ32961 Tetranectin precursor Thioredoxin Thrombospondin 2 precursor Thrombospondin 4 precursor Thy-1 membrane glycoprotein precursor Thyroxine-binding globulin precursor Titin Elastic titin

LTQ

LCQ

2

8

5

5 2

30

1

47 12

3 4 3

3

2

2 8

3

4 2 10 2 2 2 5 4 9

10

1 5 3 9

(Continued)

59

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE I (Continued) Peptides Group

Protein ID

516 517 518

IPI00024102 IPI00219465 IPI00018219

1 1 1

519 520 521 522 523

IPI00022432 IPI00465028 IPI00465028 IPI00176427 IPI00166768 IPI00396304 IPI00218343 IPI00387144 IPI00180675 IPI00477796 IPI00334799 IPI00220391 IPI00220558 IPI00018276

1 1 1 1 1

IPI00437593 IPI00414694

1

524

525 526

p

0.86

1

IPI00478853 IPI00022822 IPI00418138 527 528

IPI00026320 IPI00020430

0.91 1

529

IPI00009950

1

530 531

IPI00298853 IPI00021817

1 1

532 533 534

IPI00294004 IPI00298971 IPI00420062 IPI00420061 IPI00376439

1 1 0.94

Description Transaldolase Transcobalamin II precursor Transforming growth factor-beta induced protein IG-H3 precursor Transthyretin precursor Triosephosphate isomerase 1 Triosephosphate isomerase 1 TSLC1-like 2 TUBA6 protein Tubulin, alpha, ubiquitous Tubulin alpha-6 chain Tubulin alpha-ubiquitous chain Tubulin alpha-3 chain Type 1-like ryanodine receptor Splice isoform 3 of ryanodine receptor 1 Splice isoform 1 of ryanodine receptor 1 Splice isoform 2 of ryanodine receptor 1 Type I transmembrane receptor precursor Type XVIII collagen long variant Splice isoform 2 of collagen alpha 1 (XVIII) chain precursor 150-kDa protein Splice isoform 1 of collagen alpha 1 (XVIII) chain precursor Alpha 1 type XVIII collagen isoform 3 precursor Ubiquitin–protein ligase EDD Vacuolar ATP synthase subunit S1 precursor Vesicular integral-membrane protein VIP36 precursor Vitamin D-binding protein precursor Vitamin K-dependent protein C precursor Vitamin K-dependent protein S precursor Vitronectin precursor VPS13B-1A protein VPS13B-2A protein Splice isoform 1 of cohen syndrome protein 1

LTQ

LCQ

2 4 77 6 8 3 4

6 2 77 8 2 4

2

9 6

6

2 8

8

12

12

26

26 2

3 9 2

6

(Continued)

60

XU et al.

TABLE I (Continued) Peptides Group

Protein ID

p

535 536 537

IPI00069058 IPI00010653 IPI00027038

1 0.99 1

Description

LTQ

WUGSC:H_DJ0747G18.3 protein Xpmc2h Z39Ig protein precursor

LCQ

7 2 2

5

IPI: International protein index. Proteins sharing the same peptides but with diVerent protein identiﬁcation numbers are listed in one cell. p: Probability as determined by ProteinProphet; error rate is less than 1% with the given probability. These deﬁnitions apply to all tables related to protein identiﬁcation.

TOTAL PROTEINS/GROUPS IDENTIFIED Group 1 2 3 4

5 6 7 8

9 10 11 12

13 14 15

Protein ID

p

IPI00477452 IPI00382716 IPI00161229 IPI00480035 IPI00478621 IPI00479894 IPI00477298 IPI00477714 IPI00478162 IPI00480016 IPI00455535 IPI00018146 IPI00030877 IPI00385349 IPI00247243

0.95 0.87 0.98 0.77

IPI00477432 IPI00107012 IPI00008433 IPI00013415 IPI00386010 IPI00334556 IPI00005537 IPI00472102 IPI00414018 IPI00011252 IPI00332259

0.97

0.95 0.98 0.97 0.99

0.99 0.88 0.88

0.97 0.97 0.61

TABLE II SINGLE PEPTIDE (A TOTAL

WITH A

OF

247 PROTEINS)

Description 10-kDa protein 11-kDa protein 12-kDa protein 12-kDa protein 12-kDa protein 12-kDa protein 10-kDa protein 12-kDa protein 12-kDa protein 14-kDa protein PREDICTED: similar to ig kappa variable region 14-3-3 protein tau 15-kDa selenoprotein isoform 1 precursor 19-kDa protein PREDICTED: similar to contains transmembrane (tm) region 37-kDa protein Hypothetical protein ﬂj20519 40S ribosomal protein S5 40S ribosomal protein S7 5c5 19-kDa protein 39s ribosomal protein l12, mitochondrial precursor 60-kDa heat shock protein, mitochondrial precursor 65-kDa protein Complement component c8 alpha chain precursor 93-kDa protein (Continued)

61

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE II (Continued) Group

16

17 18 19 20

21

22 23

24 25 26 27 28 29 30 31

Protein ID IPI00185885 IPI00413923 IPI00396516 IPI00479992 IPI00332623 IPI00477687 IPI00478467 IPI00010360 IPI00479031 IPI00023006 IPI00414057 IPI00021428 IPI00215919 IPI00215914 IPI00008554 IPI00030739 IPI00059685 IPI00418446 IPI00013698 IPI00032449 IPI00032453 IPI00032450 IPI00396582 IPI00433478 IPI00294834 IPI00024572 IPI00215638 IPI00472892 IPI00413734 IPI00220109 IPI00297633 IPI00220108 IPI00220107 IPI00220110 IPI00478026 IPI00157414 IPI00024887 IPI00384644 IPI00106646 IPI00020599 IPI00383751 IPI00396423 IPI00025812 IPI00010295 IPI00414455 IPI00291811

p

0.97

0.97 0.95 0.97 0.97

0.97

0.97 0.82

0.97 0.85 0.97 0.97 0.97 0.69 0.99 0.85

Description 87-kDa protein Splice isoform 3 of collagen alpha 3 Alpha 3 type iv collagen isoform 4, precursor 123-kDa protein Alpha 3 type iv collagen isoform 2, precursor 121-kDa protein Tumstatin Splice isoform 1 of collagen alpha 3 63-kDa protein Actin, alpha cardiac Actin alpha 1 skeletal muscle protein Actin, alpha skeletal muscle ADP-ribosylation factor 5 ADP-ribosylation factor 3 Angiogenin precursor Apolipoprotein M ASAH1 protein HSD-33 Acid ceramidase precursor Aspartyl beta-hydroxylase 2.8 kb transcript Junctate Junctin ASPH protein Hypothetical protein Aspartyl(Asparaginyl)beta-hydroxylase Junctin isoform 1 ATP-dependent RNA helicase A ATRX protein 282-kDa protein Splice isoform 4 of transcriptional regulator ATRX Splice isoform 1 of transcriptional regulator ATRX Splice isoform 3 of transcriptional regulator ATRX Splice isoform 2 of transcriptional regulator ATRX Splice isoform 5 of transcriptional regulator ATRX Avkl1889 Hypothetical protein ﬂj32808 Bone morphogenetic protein 6 precursor Calcium binding protein 45-kDa calcium-binding protein precursor Calreticulin precursor Calreticulin/calcium binding protein Calsyntenin-3 precursor Carbonic anhydrase-related protein 2 precursor Carboxypeptidase N catalytic chain precursor Caspase recruitment domain family, member 11 FLJ00120 protein (Continued)

62

XU et al.

TABLE II (Continued) Group

Protein ID

p

32 33 34 35

IPI00005559 IPI00292300 IPI00002816 IPI00017257 IPI00477751 IPI00299150 IPI00152540 IPI00171928 IPI00018396 IPI00026050 IPI00004946 IPI00019533 IPI00434467 IPI00008586 IPI00101532 IPI00385945 IPI00478943 IPI00103056 IPI00302453 IPI00005823 IPI00445211 IPI00443982 IPI00176312 IPI00218896 IPI00027507 IPI00480182 IPI00419648 IPI00301395 IPI00022977 IPI00382926 IPI00477365 IPI00216550

0.96 0.95 0.95 0.97

36 37 38 39 40 41 42 43 44

45 46

47 48 49 50 51

0.97 0.96 0.93 0.97 0.97 0.97 0.99 0.92 0.97

0.54 0.93

0.97 0.97 0.9 0.98 0.78

IPI00292069 52 53 54 55

IPI00382989 IPI00298547 IPI00042514 IPI00296058

0.8 0.8 0.98 0.98

56 57 58 59

IPI00009276 IPI00025840 IPI00019501 IPI00479082

0.81 0.83 0.99 0.87

IPI00415033

Description Caspase recruitment domain protein 15 Caspr5 Cathepsin F precursor Cathepsin O precursor 36-kDa protein Cathepsin S precursor Cd109 Cdt6 Cerebellin 4 precursor Ceroid-lipofuscinosis neuronal protein 5 Chemokine (C-X-C motif) ligand 16 Chitinase 3-like protein 2 precursor Chondroitin sulfate proteoglycan 5-III Neuroglycan C Chromosome 10 open reading frame 3 Hypothetical protein FLJ10540 53-kDa protein Chromosome 9 open reading frame 65 Ciliary dynein heavy chain 9 Dynein-related protein Hypothetical protein FLJ44071 Hypothetical protein FLJ46185 Dynein, axonemal, heavy polypeptide 17 Class I alcohol dehydrogenase, alpha subunit Complement factor H-related protein 3 precursor Hypothetical protein Cpvl Probable serine carboxypeptidase cpvl precursor Creatine kinase, B chain Decay-accelerating factor 1 ab Decay accelerating factor for complement Splice isoform 2 of complement decay-accelerating factor precursor Splice isoform 1 of complement decay-accelerating factor precursor Derp13 DJ-1 protein DKFZP564O243 protein EGF-containing ﬁbulin-like extracellular matrix protein 2 precursor Endothelial protein C receptor precursor Ephrin-A1 precursor Ephrin-B3 precursor Eukaryotic translation initiation factor 3, subunit 9 Eta, 116-kDa isoform b 89-kDa protein (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

63

TABLE II (Continued) Group

60 61 62 63 64 65 66 67 68 69 70 71 72 73

Protein ID IPI00334191 IPI00396370 IPI00290856 IPI00167881 IPI00413778 IPI00074230 IPI00217865 IPI00383767 IPI00007632 IPI00219757 IPI00001755 IPI00384288 IPI00300384 IPI00027834 IPI00384770 IPI00298388 IPI00171611 IPI00465070 IPI00472013 IPI00472918 IPI00472379 IPI00472943 IPI00473131 IPI00472711 IPI00472222 IPI00472551 IPI00004672 IPI00472088 IPI00473001 IPI00472778 IPI00472616 IPI00472592 IPI00219714

p

0.58 0.85 0.97 0.84 0.6 0.99 0.98 0.97 0.97 0.91 0.97 0.96 0.97 0.99

Description Hypothetical protein EIF3S9 Prt1 homolog Extracellular link domain containing 1 Fibrous sheath interacting protein 1 FK506-binding protein 1A FLJ21616 protein FYVE, rhogef and PH domain containing 2 GDP-mannose pyrophosphorylase A Glutamate receptor 4 precursor Glutathione S-transferase P Glypican-6 precursor Herstatin Receptor tyrosine-protein kinase erbb-2 precursor Heterogeneous nuclear ribonucleoprotein L isoform a HGFL(S) protein WUGSC:DJ515N1.2 protein Histone H3 HIST2H3C protein HLA class I histocompatibility antigen, A-33 alpha chain precursor HLA class I histocompatibility antigen, A-24 alpha chain precursor HLA class I histocompatibility antigen, A-26 alpha chain precursor HLA class I histocompatibility antigen, B-73 alpha chain precursor HLA class I histocompatibility antigen, Cw-6 alpha chain precursor Splice isoform 1 of HLA class I histocompatibility antigen, Cw-16 alpha chain precursor HLA class I histocompatibility antigen, A-74 alpha chain precursor HLA class I histocompatibility antigen, A-31 alpha chain precursor HLA class I histocompatibility antigen, alpha chain H precursor HLA class I histocompatibility antigen, Cw-12 alpha chain precursor HLA class I histocompatibility antigen, A-34 alpha chain precursor MHC class I antigen precursor HLA class I histocompatibility antigen, A-25 alpha chain precursor HLA class I histocompatibility antigen, A-69 alpha chain HLA class I histocompatibility antigen, A-3 alpha chain precursor (Continued)

64

XU et al.

TABLE II (Continued) Group

Protein ID

p

IPI00026569 IPI00472582 IPI00472882 IPI00472448 IPI00472125 IPI00472035 IPI00472825 IPI00479392 IPI00472112 IPI00472921 IPI00473123 IPI00472227 IPI00472736 IPI00478438 IPI00472855 IPI00472186 IPI00472605 IPI00472903 IPI00471951I PI00473006 IPI00472151 IPI00144014 74 75 76

IPI00019159 IPI00419590 IPI00478890 IPI00441043

0.97 0.97 0.99

Description HLA class I histocompatibility antigen, A-1 alpha chain precursor HLA class I histocompatibility antigen, A-66 alpha chain precursor HLA class I histocompatibility antigen, A-68 alpha chain precursor Splice isoform 1 of HLA class I histocompatibility antigen, A-11 alpha chain precursor HLA class I histocompatibility antigen, A-2 alpha chain precursor Splice isoform 2 of HLA class I histocompatibility antigen, Cw-16 alpha chain precursor HLA class I histocompatibility antigen, A-32 alpha chain precursor Hypothetical protein FLJ43620 Splice isoform 2 of HLA class I histocompatibility antigen, A-11 alpha chain precursor HLA class I histocompatibility antigen, A-29 alpha chain precursor HLA class I histocompatibility antigen, Cw-5 alpha chain precursor HLA class I histocompatibility antigen, A-43 alpha chain precursor HLA class I histocompatibility antigen, A-80 alpha chain precursor MHC class I antigen precursor HLA class I histocompatibility antigen, A-30 alpha chain precursor HLA class I histocompatibility antigen, A-36 alpha chain precursor HLA class I histocompatibility antigen, Cw-2 alpha chain precursor HLA class I histocompatibility antigen, Cw-8 alpha chain precursor HLA class I histocompatibility antigen, Cw-15 alpha chain precursor HLA class I histocompatibility antigen, Cw-17 alpha chain precursor HLA class I histocompatibility antigen, A-23 alpha chain precursor HLA class I histocompatibility antigen, Cw-7 alpha chain precursor HP47 protein Hsaj1454 Splice isoform 1 of testican-3 precursor Hypothetical protein (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

65

TABLE II (Continued) Group

Protein ID

p

77 78 79 80

IPI00385332 IPI00386785 IPI00441042 IPI00431444 IPI00375453 IPI00455878

1 1 0.99 0.97

81 82 83

IPI00430842 IPI00433678 IPI00032575 IPI00007102 IPI00448800 IPI00410319 IPI00018057 IPI00160131 IPI00396169 IPI00419922 IPI00385651 IPI00297160 IPI00418465 IPI00305064 IPI00419219 IPI00470804 IPI00006653 IPI00306471 IPI00413065 IPI00395997 IPI00303387 IPI00153049 IPI00386879 IPI00479084 IPI00396060 IPI00306719 IPI00045536 IPI00301185 IPI00442168 IPI00010154 IPI00442909 IPI00056537 IPI00043654 IPI00065229 IPI00477484 IPI00445690 IPI00168459 IPI00410259 IPI00166266

0.96 0.96 0.95

84 85 86 87

88

89 90 91 92 93 94 95 96

97 98 99 100 101 102

103

0.74 0.55 0.64 0.85

0.99

0.8 0.93 0.9 0.97 0.99 1 0.92 0.81

0.91 0.97 0.89 0.81 0.89 0.94

0.99

Description Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Protein kinase cAMP-dependent, regulatory, type I, beta PREDICTED: similar to cAMP-dependent protein kinase type I-beta regulatory chain Hypothetical protein Hypothetical protein Hypothetical protein CGI-150 protein Hypothetical protein Hypothetical protein Hypothetical protein dkfzp434f1016 Myosin-ixa Hypothetical protein dkfzp434p1219 IQ motif containing E 74-kDa protein Hypothetical protein dkfzp451k1918 CD44 antigen isoform 2 precursor Splice isoform 1 of CD44 antigen precursor CD44 antigen Hypothetical protein dkfzp686h22230 Probable calcium-transporting atpase KIAA0703 Hypothetical protein FLJ10374 Hypothetical protein FLJ10916 Hypothetical protein Hypothetical protein FLJ14008 Hypothetical protein FLJ14363 Hypothetical protein FLJ14473 Hypothetical protein FLJ14701 Hypothetical protein dkfzp686g1990 Hypothetical protein FLJ14805 Hypothetical protein Hypothetical protein PSEC0104 Hypothetical protein FLJ16632 Rab GDP dissociation inhibitor alpha Hypothetical protein FLJ26301 Hypothetical protein FLJ30102 Hypothetical protein FLJ30927 Hypothetical protein FLJ32871 Hypothetical protein FLJ35677 Hypothetical protein FLJ43567 Hypothetical protein LL5 beta protein Hypothetical protein FLJ37558 (Continued)

66

XU et al.

TABLE II (Continued) Group

Protein ID

p

104

IPI00384669 IPI00019190 IPI00446210 IPI00301143 IPI00418960 IPI00217784 IPI00008269 IPI00472607 IPI00446498 IPI00443534 IPI00168336 IPI00063333 IPI00328722 IPI00179044 IPI00000138

0.83

IPI00250397 IPI00032387 IPI00382482 IPI00382488 IPI00382474 IPI00382497 IPI00003111 IPI00387024 IPI00387100 IPI00387111 IPI00385252 IPI00387116 IPI00387117 IPI00387118 IPI00383808 IPI00386575 IPI00382423 IPI00335356 IPI00450309 IPI00013438 IPI00020906 IPI00029236 IPI00477987 IPI00031821 IPI00472202 IPI00028600 IPI00455000 IPI00479403 IPI00043215 IPI00004503

0.76 0.53 0.95 0.97 0.71 0.58 1 0.9 0.91 0.97 1 0.51 1 0.97 1 0.97 0.82 1 0.71 0.91 0.97 0.91 0.98

105 106

107 108 109 110 111

112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139

0.97 0.99

0.95 0.86 0.84 0.55 0.99

0.76 0.83 0.99 0.97 0.73

Description Hypothetical protein FLJ39991 Myocilin precursor Hypothetical protein FLJ42598 Hypothetical protein PSEC0164 Hypothetical protein FLJ44611 Splice isoform 4 of NDRG4 protein Hypothetical protein FLJ16174 Splice isoform 2 of NDRG4 protein Hypothetical protein FLJ42011 Hypothetical protein FLJ46889 Hypothetical protein FLJ90478 Hypothetical protein MGC14376 Hypothetical protein MGC43026 Hypothetical protein PSEC0120 Alpha-1,3-mannosyl-glycoprotein 2-beta-Nacetylglucosaminyltransferase Hypothetical protein XP_294778 Hypothetical UPF0080 protein KIAA0186 Ig heavy chain V-III region CAM Ig heavy chain V-III region HIL Ig heavy chain V-III region TRO Ig heavy chain V-III region TUR Ig kappa chain V-I region AU Ig kappa chain V-I region CAR Ig kappa chain V-I region Roy Ig kappa chain V-II region TEW Ig kappa chain V-III region GOL Ig kappa chain V-III region NG9 precursor Ig kappa chain V-III region Ti Ig kappa chain V-III region WOL Ig kappa chain V-IV region STH Ig lambda chain V-I region EPS Ig lambda chain V-II region TOG IGHM protein IGLC2 protein Immunoglobulin lambda-like polypeptide 1 precursor Inositol-1 Insulin-like growth factor binding protein 5 precursor Integral membrane protein 2B Integral membrane protein 2B Integrin, alpha 1 precursor Kallikrein 7 precursor Kallikrein 7 splice variant 3 Keratin 6C KIAA0364 protein LAMP1 protein (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

67

TABLE II (Continued) Group

Protein ID

p

Description

140

IPI00410152

0.97

Latent transforming growth factor beta binding protein 1 isoform LTBP-1L Latent transforming growth factor beta binding protein 1 isoform LTBP-1S Latent transforming growth factor beta binding protein, isoform 1L precursor Legumain precursor Full-length cDNA clone CS0DI002YH20 of placenta of Homo sapiens Full-length cDNA clone CS0DB001YK19 of neuroblastoma of Homo sapiens Leukocyte elastase inhibitor LRAP protein LRAP protein Leukocyte-derived arginine aminopeptidase long form variant M130 antigen cytoplasmic variant 2 precursor CD163 antigen, isoform a Malignant melanoma metastasis-suppressor kiss-1 Matrix Gla-protein precursor Mitochondrial 28S ribosomal protein S28 Mu-crystallin homolog Multimerin 2 precursor Muscle-speciﬁc DNase I-like precursor Myosin-reactive immunoglobulin heavy chain variable region Myosin-reactive immunoglobulin light chain variable region Nectin-like protein 2 Hypothetical protein PSEC0200 N-ethylmaleimide-sensitive factor Neural cell adhesion molecule Neurogenic locus notch homolog protein 3 precursor Neuropilin-2 soluble isoform 9 Splice isoform 1 of neuropilin-2 precursor 104-kDa protein Splice isoform 3 of neuropilin-2 precursor Neuropilin-2b Splice isoform 2 of neuropilin-2 precursor Neuropilin-2b NOTCH2 protein Neurogenic locus notch homolog protein 2 precursor Notch homolog 2 NOV protein homolog precursor Olfactory receptor, family 4, subfamily F, member 21 Olfactory receptor 4F3 Olfactory receptor OR1-1

IPI00302679 IPI00220249 141

IPI00293303 IPI00384155

0.82

IPI00384156 142 143

IPI00027444 IPI00439750 IPI00382999 IPI00465261

0.81 0.98

144

IPI00104074 IPI00409669 IPI00301950 IPI00028714 IPI00022276 IPI00000949 IPI00015525 IPI00026125 IPI00384403

0.97

IPI00384399 IPI00003813 IPI00166392 IPI00006451 IPI00385035 IPI00029819 IPI00398766 IPI00029693 IPI00478241 IPI00218781 IPI00300890 IPI00375877 IPI00300891 IPI00450962 IPI00297655 IPI00480098 IPI00011140 IPI00470608 IPI00419364 IPI00465263

0.97 0.97

145 146 147 148 149 150 151 152 153 154 155 156 157

158

159 160

0.94 0.97 0.97 0.96 0.97 0.99 0.88

0.91 0.87 0.91 0.96

0.88

0.96 0.83

(Continued)

68

XU et al.

TABLE II (Continued) Group

Protein ID

p

Description

161

IPI00300685 IPI00166807 IPI00386964 IPI00002412 IPI00432592 IPI00445654 IPI00449450 IPI00219703 IPI00329775 IPI00006702 IPI00382635 IPI00178047 IPI00430079 IPI00480187 IPI00003168

0.97

IPI00240988 IPI00398046 IPI00374481 IPI00374485 IPI00374914 IPI00016112 IPI00174237 IPI00455552 IPI00457325 IPI00037070 IPI00397340

0.96 0.87 0.8 0.81 0.72 0.95 0.94 0.82 0.96

Oxidation protection protein Oxidation resistance 1 P41 Palmitoyl-protein thioesterase 1 precursor Papilin Hypothetical protein FLJ43670 Papilin Parvalbumin PCPB protein Pelp1 Proline and glutamic acid rich nuclear protein isoform 108-kDa protein PHD ﬁnger protein 8 OTTHUMP00000061869 Phosphoribosyl pyrophosphate synthetase-associated protein 2 PREDICTED: FLJ00133 protein PREDICTED: hypothetical protein LOC116068 PREDICTED: hypothetical protein XP_373599 PREDICTED: hypothetical protein XP_373606 PREDICTED: hypothetical protein XP_379250 PREDICTED: melanoma associated gene PREDICTED: similar to CG3104-PA PREDICTED: similar to FKSG30 PREDICTED: similar to heat shock cognate 71-kDa protein Splice isoform 2 of heat shock cognate 71-kDa protein PREDICTED: similar to heat shock cognate 70-kDa protein (44-kDa Atpase N-Terminal Fragment) (E.C.3.6.1.3) Mutant With Asp 206 Replaced By Ser (d206s) Splice isoform 1 of heat shock cognate 71-kDa protein PREDICTED: similar to heat shock cognate 71-kDa protein PREDICTED: similar to immunoglobulin superfamily, member 3 PREDICTED: similar to OG-2 homeodomain protein-like PREDICTED: similar to ribosome biogenesis protein BMS1 homolog PREDICTED: similar to adrenoleukodystrophy protein (ALDP) PREDICTED: similar to Ig kappa chain V region (Z4)—human PREDICTED: similar to ribosome biogenesis protein BMS1 homolog Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 precursor Progesterone receptor membrane component 1 Putative protein c21orf30 Putative ras-related C3 botulinum toxin substrate 4

162 163 164

165 166 167

168 169 170 171 172 173 174 175 176 177 178

0.8 0.97 0.97

0.61 0.83 0.66

0.65 0.99

179

IPI00003865 IPI00455889 IPI00240905

0.75

180 181

IPI00061033 IPI00253009

0.82 0.73

IPI00397198 IPI00376441 IPI00455927 182 183 184 185

IPI00027192 IPI00220739 IPI00004827 IPI00001352

0.95 0.97 0.99 0.9

(Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

69

TABLE II (Continued) Group

Protein ID

p

Description

186 187 188

IPI00177476 IPI00419883 IPI00383866 IPI00002579 IPI00003817 IPI00004114 IPI00299573 IPI00026271 IPI00418125 IPI00452748 IPI00022368 IPI00006146 IPI00419724 IPI00019399 IPI00297188 IPI00029817 IPI00425902 IPI00258979

0.89 0.97 0.98

IPI00383591 IPI00217882 IPI00456631

0.97

Putative ubiquinone biosynthesis protein aarf RAB26, member RAS oncogene family Retinoblastoma binding protein 2 homolog 1 RB-binding protein Rho GDP-dissociation inhibitor 2 Ribonuclease K6 precursor Ribosomal protein l7a Ribosomal protein S14 Rpgt208 SAA1 protein Serum amyloid A protein precursor Serum amyloid A2 Semaphorin C Serum amyloid A-4 protein precursor Seven transmembrane helix receptor Sialidase 1 precursor Signal sequence receptor, beta Similar to sporulation-induced transcript 4-associated protein Sortilin 1 preproprotein Sortilin precursor Splice isoform 1 of amine oxidase ﬂavin containing domain protein 2 Splice isoform 2 of amine oxidase ﬂavin containing domain protein 2 Splice isoform 1 of cadherin-6 precursor Splice isoform 2 of cadherin-6 precursor Splice isoform 1 of calcium\calmodulin-dependent protein kinase type II alpha chain Splice isoform 2 of calcium\calmodulin-dependent protein kinase type II alpha chain Splice isoform 1 of dipeptidyl aminopeptidase-like protein 6 Hypothetical protein FLJ44657 Splice isoform 2 of dipeptidyl aminopeptidase-like protein 6 Splice isoform 1 of GDNF family receptor alpha 2 precursor Splice isoform 2 of GDNF family receptor alpha 2 precursor Splice isoform 1 of glutaminase, kidney isoform, mitochondrial precursor Splice isoform 3 of glutaminase, kidney isoform, mitochondrial precursor Splice isoform 1 of heterogeneous nuclear ribonucleoproteins A2\B1 Splice isoform 2 of heterogeneous nuclear ribonucleoproteins A2\B1 37-kDa protein Splice isoform 1 of neuropilin-1 precursor Neuropilin-1 soluble isoform 11

189 190 191 192 193 194

195 196 197 198 199 200 201 202

0.92 0.94 0.95 0.97 0.97 0.99

0.99 0.94 0.97 0.97 0.81 0.97

0.84

IPI00217540 203 204

IPI00024035 IPI00217314 IPI00098624

0.97 0.97

IPI00215715 205

206 207

IPI00301512 IPI00445089 IPI00252731 IPI00011732 IPI00398301 IPI00289159

0.99

0.91 0.61

IPI00215687 208

IPI00396378

0.92

IPI00414696

209

IPI00477522 IPI00299594 IPI00398715

0.92

(Continued)

70

XU et al.

TABLE II (Continued) Group

Protein ID

p

210

IPI00165438 IPI00291549

0.75

IPI00329727 211

IPI00411730

0.85

IPI00478255 212

IPI00027239

0.91

IPI00218935 IPI00218017 IPI00218937 213

IPI00410216 IPI00410218 IPI00012039 IPI00410217 IPI00410220 IPI00410125 IPI00012036 IPI00410124 IPI00328245 IPI00165947 IPI00455176 IPI00477172 IPI00005142 IPI00216859 IPI00220983 IPI00410219

0.97

Description Muscle type neuropilin 1 Splice isoform 1 of RAP guanine-nucleotide-exchange factor 3 Splice isoform 2 of RAP guanine-nucleotide-exchange factor 3 Splice isoform 1 of serine\threonine phosphatase 4 regulatory subunit 1 Splice isoform 2 of serine\threonine phosphatase 4 regulatory subunit 1 Splice isoform 1 of tumor necrosis factor ligand superfamily member 13 precursor Splice isoform 2 of tumor necrosis factor ligand superfamily member 13 precursor TWE-PRIL Splice isoform 3 of tumor necrosis factor ligand superfamily member 13 precursor Splice isoform 10 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 12 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 18 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 11 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 14 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 9 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 3 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 8 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 7 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 4 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 6 of basic ﬁbroblast growth factor receptor 1 precursor Fibroblast growth factor receptor 1 isoform 9 precursor Splice isoform 1 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 2 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 16 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 13 of basic ﬁbroblast growth factor receptor 1 precursor (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

71

TABLE II (Continued) Group

Protein ID

p

IPI00332838 214

215 216 217

218

219

220 221

IPI00220631 IPI00220632 IPI00220635 IPI00220634 IPI00000779 IPI00019906 IPI00220572 IPI00015117 IPI00216474 IPI00021907 IPI00216475 IPI00216477 IPI00216478 IPI00479524 IPI00007236 IPI00176424 IPI00479936 IPI00307328 IPI00215777 IPI00022202 IPI00031766 IPI00003907 IPI00334667

0.95

0.67 0.89 0.96

0.97

0.78

0.97 0.9

IPI00024289 IPI00473056 IPI00472249 IPI00334666 222

223

224

IPI00220532 IPI00027977 IPI00442334 IPI00219620 IPI00030431 IPI00071177 IPI00219619 IPI00292156

0.57

0.89

0.98

IPI00290239 225

IPI00221007 IPI00221008

0.96

Description Splice isoform 5 of basic ﬁbroblast growth factor receptor 1 precursor Splice isoform 2 of ADAM 22 precursor Splice isoform 3 of ADAM 22 precursor Splice isoform 5 of ADAM 22 precursor Splice isoform 4 of ADAM 22 precursor Splice isoform 1 of ADAM 22 precursor Splice isoform 2 of basigin precursor Splice isoform 2 of laminin gamma-2 chain precursor Splice isoform 1 of laminin gamma-2 chain precursor Splice isoform 2 of myelin basic protein Splice isoform 1 of myelin basic protein Myelin basic protein Splice isoform 5 of myelin basic protein Splice isoform 6 of myelin basic protein Splice isoform 3 of myelin basic protein Splice isoform 2 of neuroligin 1 precursor Neuroligin 2 precursor 94-kDa protein Splice isoform 1 of neuroligin 1 precursor Splice isoform 2 of phosphate carrier protein, mitochondrial precursor SLC25A3 protein Splice isoform 2 of protocadherin gamma C5 precursor Splice isoform 1 of protocadherin gamma C5 precursor Splice isoform 2 of receptor-type tyrosine-protein phosphatase N2 precursor KIAA0387 protein 110-kDa protein Protein tyrosine phosphatase, receptor type, N polypeptide 2 isoform 2 precursor Splice isoform 1 of receptor-type tyrosine-protein phosphatase N2 precursor Splice isoform 2 of transmembrane protease, serine 3 Transmembrane protease serine 3 isoform 5 Hypothetical protein FLJ16093 Splice isoform 4 of anthrax toxin receptor 1 precursor Splice isoform 1 of anthrax toxin receptor 1 precursor Splice isoform 2 of anthrax toxin receptor 1 precursor Splice isoform 3 of anthrax toxin receptor 1 precursor Splice isoform 4 of calcium\calmodulin-dependent protein kinase kinase 2 Splice isoform 1 of calcium\calmodulin-dependent protein Kinase kinase 2 Splice isoform 5 of transcription factor 7-like 2 Splice isoform 6 of transcription factor 7-like 2 (Continued)

72

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TABLE II (Continued) Group

226

227 228 229

230 231 232 233 234 235 236 237 238 239 240 241

242

243

Protein ID IPI00221010 IPI00221011 IPI00221004 IPI00479483 IPI00221009 IPI00164708 IPI00221006 IPI00221005 IPI00335587 IPI00215631 IPI00009802 IPI00215628 IPI00219551 IPI00009049 IPI00003016 IPI00096066 IPI00164300 IPI00299086 IPI00478874 IPI00216138 IPI00032197 IPI00005292 IPI00166123 IPI00013621 IPI00299026 IPI00451422 IPI00455477 IPI00020416 IPI00298237 IPI00007752 IPI00023598 IPI00182840 IPI00160897 IPI00335160 IPI00292496 IPI00018511 IPI00031370 IPI00013475 IPI00011654 IPI00142634 IPI00013683 IPI00152453 IPI00456429 IPI00296120 IPI00328348

p

0.73

0.87 0.93 0.56

0.97 0.89 0.98 0.86 0.59 0.79 0.97 0.89 0.83 0.99 0.97 0.97

0.97

0.97

Description Splice isoform 8 of transcription factor 7-like 2 Splice isoform 9 of transcription factor 7-like 2 Splice isoform 2 of transcription factor 7-like 2 Transcription factor 7-like 2 Splice isoform 7 of transcription factor 7-like 2 Splice isoform 1 of transcription factor 7-like 2 Splice isoform 4 of transcription factor 7-like 2 Splice isoform 3 of transcription factor 7-like 2 Splice isoform 10 of transcription factor 7-like 2 Splice isoform 5 of versican core protein precursor Splice isoform 1 of versican core protein precursor Splice isoform 2 of versican core protein precursor SRY-box 13 Splice isoform 1 of SOX-13 protein Striatin 4 Succinyl-CoA ligase [GDP-forming] beta-chain, mitochondrial precursor PREDICTED: similar to succinyl-CoA ligase Syntenin 1 Syntenin isoform 2 TAGLN protein T-box transcription factor TBX19 Testican-1 precursor Tetratricopeptide repeat domain 5 Thiamine triphosphatase Tissue alpha-L-fucosidase precursor TRA @ protein TRIM9-like protein TNL Tripeptidyl peptidase II Tripeptidyl-peptidase I precursor Tubulin beta-2 chain Tubulin beta-5 chain 47-kDa protein Tubulin, beta 5,50-kDa protein Beta-tubulin 4Q Tubulin beta-4q chain Tubulin, beta polypeptide paralog Beta tubulin Tubulin beta-1 chain Tubulin beta-5 chain Tubulin beta-4 chain Tubulin beta 4 Ubiquitin and ribosomal protein L40 precursor PREDICTED: similar to Zgc:66168 protein 16-kDa protein (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

73

TABLE II (Continued) Group

Protein ID

p

244 245 246

IPI00418813 IPI00387164 IPI00456666 IPI00179330 IPI00291175 IPI00032050 IPI00431738

0.97 0.99 0.97

247

IPI00183487

0.97

Description Hypothetical protein FLJ46113 Hypothetical protein FLJ32377 UBC protein Ubiquitin B Vinculin isoform VCL WW domain binding protein 2 X-linked interleukin-1 receptor accessory protein-like 1 precursor Xylosyltransferase I

proteins used for LCQ-MS/MS). The list of identiﬁed proteins can also be found in Table I (identiﬁed with more than two peptides) and Table II (single-hits). It should be noted that among the 466 proteins identiﬁed by LCQ, 287 (or more than 60% of them) were also observed in LTQ analysis. When LCQ and LTQ data were combined, a total of 785 proteins with 248 single-hits were identiﬁed in the CSF of these well-characterized young subjects. As seen in Fig. 1, the 785 proteins can be divided into the following categories with a similar classiﬁcation scheme used previously (Zhang et al., 2005a): inﬂammation/immune, extracellular matrix/cell adhesion, transportation/neurotransmission, protein synthesis/ degradation, signaling/apoptosis, metabolism, and unknown functions.

C. REEXAMINATION

OF

PREVIOUSLY IDENTIFIED PROTEINS

It is noted that the current human protein sequence database is incomplete. The number as well as the nature of proteins identiﬁed is signiﬁcantly inﬂuenced by how and which database is used to search the peptide tandem mass spectra. Thus, to make a fair comparison, we searched our previous MS data obtained from the ‘‘aging study’’ with the identical database used for the current LCQ and LTQ analysis. Interestingly but not surprisingly, only 292 proteins were identiﬁed, that is, about a 10% decrease from the total number of 315 proteins identiﬁed previously. The new search results are shown in Tables III and IV, and overlaps between these two search results are shown in Table V. It is important to emphasize that the overlaps between these two searches are only 83.7% and 52.1% for proteins identiﬁed with more than two peptides and a single peptide, respectively. The other issue worth noting is that not all proteins identiﬁed previously were seen in the current study (about 66% overlap). Thus, if

74

XU et al.

FIG. 1. Classiﬁcation of human CSF proteins identiﬁed by LCQ and LTQ. This chart is generated with all identiﬁed proteins about 29% of which do not have known functions. Of note, when a protein can be assigned to multiple classes of functions, it was assigned to the one that is best known.

proteins unique to the previous study (new search results) were added to our current list, a grand total of 915 unique proteins were identiﬁed for the proteome of young healthy individuals.

D. DISCUSSION Using state-of-the-art proteomics we identiﬁed close to 1,000 proteins in the CSF of well-characterized healthy young subjects. Not only does extensive characterization of the human proteome expand current knowledge regarding the proﬁles of human CSF proteins, it also supplies the necessary information to appropriately interpret protein biomarkers of age-related neurodegenerative diseases. Comparing the results reported in the literature (including our own), the current analysis was a much deeper analysis into the CSF proteome, largely owing to better sample preparation (i.e., extensive fractionation prior to MS analysis) and better mass spectrometric instrumentation. It is not too surprising that a change in database searched had a very signiﬁcant eVect on proteomic identiﬁcation of proteins, but we were surprised at the overall low overlap between two searches with the identical MS data. Each of these areas will be discussed later in more detail with emphasis on the issues pertaining to the current study. Finally, a short perspective discussion on future investigation, particularly the discovery of CSF biomarkers for CNS diseases, is also provided.

75

PROTEOME OF HUMAN CEREBROSPINAL FLUID

SEARCH RESULTS

Group 1

2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24

OF

TABLE III OLD MS DATA AGAINST NEW DATABASE (PROTEINS/GROUPS IDENTIFIED MORE THAN TWO PEPTIDES; A TOTAL OF 128 PROTEINS)

Protein ID

p

Descrition

IPI00415045 IPI00414707 IPI00002821 IPI00069693 IPI00479531 IPI00022429 IPI00477336 IPI00479497 IPI00176221 IPI00008433 IPI00479705 IPI00003918 IPI00471915 IPI00291419 IPI00007257 IPI00413959 IPI00465313 IPI00478003 IPI00305457 IPI00022895 IPI00029863 IPI00022431 IPI00020012 IPI00032220 IPI00032179 IPI00021841 IPI00021854 IPI00006662 IPI00021842 IPI00298828 IPI00009619 IPI00166048 IPI00418163 IPI00453459 IPI00032258 IPI00396423 IPI00383709

0.88

22-kDa protein 24-kDa protein 60S ribosomal protein l14 PREDICTED: similar to ribosomal protein l14 24-kDa protein Alpha-1-acid glycoprotein 1 precursor 21-kDa protein 39-kDa protein Neuronal growth regulator 1 40S ribosomal protein S5 22-kDa protein 60S ribosomal protein L4 48-kDa protein Acetyl-CoA acetyltransferase, cytosolic Alcadein alpha-1 Calsyntenin-1 precursor Alpha 2 macroglobulin Alpha-2-macroglobulin precursor Alpha-1-antitrypsin precursor Alpha-1B-glycoprotein precursor Alpha-2-antiplasmin precursor Alpha-2-HS-glycoprotein precursor Amyloid-like protein 1 precursor Angiotensinogen precursor Antithrombin III variant Apolipoprotein A-I precursor Apolipoprotein A-II precursor Apolipoprotein D precursor Apolipoprotein E precursor Beta-2-glycoprotein I precursor Bk134p22.1 Brain immunoglobulin receptor precursor C4b1 Complement Component 4b proprotein Complement C4 precursor Calsyntenin-3 precursor Cancer-associated SCM-recognition immunedefense-suppressing and serine protease-protecting peptide, CRISPP peptide Cancer-associated SCM-recognition immunedefense-suppressing and serine protease-protecting peptide, CRISPP peptide

IPI00383710

1

1 1 0.93 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.95 1

1 1

WITH

Peptides 2

6

2 2 2 2 2 7 27 5 3 5 12 7 2 10 6 3 24 10 2 22

2 2

(Continued)

76

XU et al.

TABLE III (Continued) Group

Protein ID

p

IPI00383708

25 26 27 28

IPI00011302 IPI00017601 IPI00002147 IPI00220289

1 1 1 0.57

29

IPI00400826 IPI00291262 IPI00291136 IPI00296165 IPI00017696 IPI00478843 IPI00164623 IPI00294395

1

IPI00032293 IPI00002714 IPI00026621 IPI00477279 IPI00024933 IPI00419098 IPI00018350 IPI00419630 IPI00479747 IPI00064667

1 1 1

0.99

41 42

IPI00386639 IPI00179357 IPI00301579 IPI00027827

1 1

43 44 45 46 47 48

IPI00418433 IPI00026314 IPI00218130 IPI00478493 IPI00022488 IPI00383296

1 1 1 1 1 1

30 31 32 33 34 35 36 37

38 39

40

1 0.99 1 1 1

0.99 1

IPI00171903 49 50 51 52

IPI00022371 IPI00217787 IPI00426051 IPI00399007

1 0.94 1 1

Descrition Cancer-associated SCM-recognition immunedefense-suppressing and serine protease-protecting peptide, CRISPP peptide CD59 glycoprotein precursor Ceruloplasmin precursor Chitinase-3 like protein 1 precursor Chromodomain heliCase DNA binding protein 6 Clusterin isoform 1 Clusterin precursor Collagen alpha 1(VI) chain precursor Complement C1r subcomponent precursor Complement C1s subcomponent precursor 60-kDa protein Complement C3 precursor Complement component C8 beta chain precursor Cystatin C precursor Dickkopf related protein-3 precursor Dj999l4.1 21-kDa protein 60S ribosomal protein l12 Hypothetical protein DNA replication licensing factor MCM5 Dpkl1915 56-kDa protein Glutamate carboxypeptidase-like protein 2 precursor Elastic titin Titin Epididymal secretory protein E1 precursor Extracellular superoxide dismutase [Cu-Zn] precursor Fatty acid synthase Gelsolin precursor GlycoGen phosphorylase Haptoglobin precursor Hemopexin precursor Heterogeneous nuclear ribonucleoprotein M isoform b Heterogeneous nuclear ribonucleoprotein M isoform a Histidine-rich glycoprotein precursor Hypothetical protein dkfzp547d2210 Hypothetical protein dkfzp686c15213 Hypothetical protein dkfzp686i04196

Peptides

3 18 2 2 12 2 2 4 21 2 4 8 2

2 3

3 2 2 3 11 4 5 10 2

2 3 4 8 (Continued)

77

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE III (Continued) Group

Protein ID

53

1

57 58

IPI00418153 IPI00168728 IPI00472345 IPI00301019 IPI00448800 IPI00154742 IPI00430806 IPI00430848 IPI00472961 IPI00419425 IPI00430820 IPI00440577 IPI00441043 IPI00448985 IPI00026195 IPI00419424 IPI00385058 IPI00430847 IPI00430808 IPI00430839 IPI00448707 IPI00473097 IPI00448845 IPI00004574 IPI00004618 IPI00305380

59

IPI00029235

1

60

IPI00016915

1

61 62 63 64

IPI00023845 IPI00220327 IPI00019359 IPI00013303

1 1 1 1

65

0.99

66 67

IPI00186736 IPI00056478 IPI00289058 IPI00012989

68 69

IPI00025465 IPI00029260

1 1

70

IPI00007856 IPI00001753

1

54 55

56

p

1 1 1

1

1 1

1 0.91

Descrition Hypothetical protein dkfzp686i15212 FLJ00385 protein Hypothetical protein Hypothetical protein FLJ33674 Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Ig kappa chain C region IGHG4 protein Insulin-like growth factor binding protein 4 precursor Insulin-like growth factor binding protein 6 precursor Insulin-like growth factor binding protein 7 precursor Kallikrein 6 precursor Keratin 1 Keratin 9 Limbic system-associated membrane protein precursor Lir-D1 EwI2 Ly-6\neurotoxin-like protein 1 precursor Mannosidase, alpha, class 2B, member 1 precursor Mimecan precursor Monocyte differentiation antigen CD14 precursor Myosin heavy chain, skeletal muscle, adult 2 Myosin heavy chain, skeletal muscle, fetal

Peptides 7

2 6

8

4 3 2 4 4 5 6 2 2 2 2 2 4 6

(Continued)

78

XU et al.

TABLE III (Continued) Group

Protein ID

p

71

IPI00009997

1

72 73

IPI00299059 IPI00385035 IPI00185362 IPI00435020

1 1

IPI00220737 IPI00411478 74 75 76 77

IPI00159927 IPI00016150 IPI00013701 IPI00332499

0.98 1 0.91 1

IPI00179953 IPI00219821

1 0.99

80

IPI00383746 IPI00099996 IPI00472718 IPI00374732 IPI00027388 IPI00022331

1 0.98

81 82 83

IPI00006114 IPI00291866 IPI00218084

1 0.99 1

78 79

1

IPI00376005

85

IPI00411704 IPI00006935 IPI00382843 IPI00022284 IPI00299738

1

86 87 88 89

IPI00000828 IPI00002280 IPI00013179 IPI00015260

1 1 1 1

90

IPI00019568

1

84

Descrition N-acetyllactosaminide beta-1,3-Nacetylglucosaminyltransferase Neural cell adhesion molecule Neural cell adhesion molecule Neural cell adhesion molecule 1 Neural cell adhesion molecule 1, 140-kDa isoform precursor Splice isoform 2 of neural cell adhesion molecule 1, 120-kDa isoform precursor Splice isoform 1 of neural cell adhesion molecule 1, 120 isoform precursor Neurocan core protein precursor Neuroserpin precursor Nociceptin precursor Nuclear autoantigenic sperm protein isoform 1 Splice isoform 1 of nuclear autoantigenic sperm protein Splice isoform 2 of nuclear autoantigenic sperm protein NY-REN-49 antigen HNYA Peptidylprolyl isomerase A isoform 2 PREDICTED: similar to PPIA protein 19-kDa protein Phosphatidylcholine-sterol acyltransferase precursor Pigment epithelium-derived factor precursor Plasma protease C1 inhibitor precursor PREDICTED: similar to eukaryotic translation initiation factor 5A (eif-5A) (eif-4D) (Rev-binding factor) Eukaryotic initiation factor 5A isoform I variant A Eukaryotic translation initiation factor 5A Eukaryotic translation initiation factor 5AII Prion protein Major prion protein precursor Procollagen C-proteinase enhancer protein precursor Proenkephalin A precursor Prosaas precursor Prostaglandin-H2 D-isomerase precursor Protein kinase C-binding protein NELL2 precursor Prothrombin precursor

Peptides 2 5 2

2 2 2 2

2 2 2 2 14 2 3

2 3 5 4 9 2 3 (Continued)

79

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE III (Continued) Group

Protein ID

p

Descrition

91

IPI00019176

0.94

92 93

IPI00014048 IPI00419880 IPI00472119

1 1

94 95 96 97 98 99 100

IPI00026800 IPI00006601 IPI00009362 IPI00292071 IPI00022463 IPI00022434 IPI00383164 IPI00386879 IPI00426060 IPI00478462 IPI00336074 IPI00449920 IPI00477450 IPI00472226 IPI00430842 IPI00473015 IPI00014572 IPI00296777 IPI00384293 IPI00451624

1 1 1 1 1 1 1

IPI00451625

1

104

IPI00019591

1

105

IPI00156171

1

Retinoic acid receptor responder protein 2 precursor Ribonuclease pancreatic precursor Ribosomal protein s3a PREDICTED: similar to ribosomal protein s3a Scrapie-responsive protein 1 precursor Secretogranin I precursor Secretogranin II precursor Secretogranin III precursor Serotransferrin precursor Serum albumin precursor SNC66 protein Hypothetical protein FLJ14473 Hypothetical protein dkfzp686j11235 SNC73 protein MGC27165 protein Hypothetical protein FLJ90170 56-kDa protein Ig alpha-1 chain C region Hypothetical protein 54-kDa protein SPARC precursor SPARC-like protein 1 precursor SPARC-like 1 Splice isoform 1 of cartilage acidic protein 1 precursor Splice isoform 2 of cartilage acidic protein 1 Precursor Splice isoform 1 of complement factor B precursor Splice isoform 1 of ectonucleotide pyrophosphatase\phosphodiesterase 2 Splice isoform 2 of ectonucleotide pyrophosphatase\phosphodiesterase 2 Splice isoform 1 of inter-alpha-trypsin inhibitor heavy chain H4 precursor Splice isoform 2 of inter-alpha-trypsin inhibitor heavy chain H4 precursor Splice isoform 1 of N-acetylmuramoyl-Lalanine amidase precursor Splice isoform 2 of N-acetylmuramoyl-Lalanine amidase precursor Splice isoform 1 of NKG2D ligand 4 precursor Splice isoform 3 of NKG2D ligand 4 precursor

101 102 103

1 1 1

IPI00303210 106

IPI00294193

0.93

IPI00218192 107

IPI00163207

1

IPI00394992 108

IPI00154722 IPI00411860

0.96

Peptides 3 3 2

2 3 4 6 55 123 4

4 5 2

4 7

2

3

2

(Continued)

80

XU et al.

TABLE III (Continued) Group

Protein ID

p

IPI00411858 109

110

IPI00021000 IPI00218874 IPI00306339

IPI00218875 IPI00385896 IPI00018522

1

0.63

IPI00382516 111

IPI00025273

1

IPI00218408 112

1

113 114

IPI00216641 IPI00029751 IPI00215894 IPI00470716

115

IPI00220814

1

1 1

IPI00220813

IPI00029658

IPI00220815

116

117

IPI00339223 IPI00339228 IPI00339226 IPI00022418 IPI00470919 IPI00339227 IPI00339318 IPI00479723 IPI00414283 IPI00414282 IPI00333778

1

1

Descrition Splice isoform 2 of NKG2D ligand 4 precursor Splice isoform 1 of osteopontin precursor Splice isoform 2 of osteopontin precursor Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) Splice isoform 3 of osteopontin precursor Splice isoform 4 of osteopontin precursor Splice isoform 1 of protein arginine N-methyltransferase 1 Splice isoform 3 of protein arginine N-methyltransferase 1 Splice isoform 1 of trifunctional purine biosynthetic protein adenosine-3 Splice isoform 2 of trifunctional purine biosynthetic protein adenosine-3 Splice isoform 2 of contactin 1 precursor Splice isoform 1 of contactin 1 precursor Splice isoform 2 of kininogen precursor Splice isoform 2 of neuroendocrine protein 7B2 precursor Splice isoform 3 of EGF-containing ﬁbulinlike extracellular matrix protein 1 precursor Splice isoform 2 of EGF-containing ﬁbulinlike extracellular matrix protein 1 precursor Splice isoform 1 of EGF-containing ﬁbulinlike extracellular matrix protein 1 precursor Splice isoform 4 of EGF-containing ﬁbulinlike extracellular matrix protein 1 precursor Splice isoform 3 of ﬁbronectin precursor Splice isoform 8 of ﬁbronectin precursor Splice isoform 6 of ﬁbronectin precursor Splice isoform 1 of ﬁbronectin precursor Hypothetical protein dkfzp686k08164 Splice isoform 7 of ﬁbronectin precursor Splice isoform 10 of ﬁbronectin precursor Fibronectin 1 isoform 6 preproprotein Fibronectin 1 isoform 4 preproprotein Fibronectin 1 isoform 2 preproprotein Splice isoform 3 of neuronal cell adhesion molecule precursor

Peptides

4

2

3

4 4 8 2

3

4

(Continued)

81

PROTEOME OF HUMAN CEREBROSPINAL FLUID

TABLE III (Continued) Group

Protein ID

p

IPI00333777 IPI00333776 IPI00333781 IPI00415032 118

IPI00219825

1

IPI00219824 IPI00012503 119 120

IPI00296537 IPI00412681

1 1

IPI00219187 IPI00412568 IPI00219186 IPI00006608 IPI00219183 IPI00219185 IPI00412924 121 122 123 124 125 126 127 128

IPI00298994 IPI00005292 IPI00009028 IPI00022892 IPI00022432 IPI00298853 IPI00069058 IPI00183487

1 1 1 1 1 1 1 1

Descrition Splice isoform 2 of neuronal cell adhesion molecule precursor Splice isoform 1 of neuronal cell adhesion molecule precursor Splice isoform 5 of neuronal cell adhesion molecule precursor Splice isoform 4 of neuronal cell adhesion molecule precursor Splice isoform 3 of proactivator polypeptide precursor Splice isoform 2 of proactivator polypeptide precursor Splice isoform 1 of proactivator polypeptide precursor Splice isoform 4 of ﬁbulin-1 precursor Splice isoform 9 of amyloid beta A4 protein precursor Splice isoform 7 of amyloid beta A4 protein precursor Splice isoform 4 of amyloid beta A4 protein precursor Splice isoform 6 of amyloid beta A4 protein precursor Splice isoform 1 of amyloid beta A4 protein precursor Splice isoform 3 of amyloid beta A4 protein precursor Splice isoform 5 of amyloid beta A4 protein precursor Splice isoform 8 of amyloid beta A4 protein precursor Talin 1 Testican-1 precursor Tetranectin precursor Thy-1 membrane glycoprotein precursor Transthyretin precursor Vitamin D-binding protein precursor WUGSC:H_DJ0747G18.3 protein Xylosyltransferase I

Peptides

2

7 9

2 3 3 2 13 17 2 3

82

XU et al.

TABLE IV SEARCH RESULTS OF OLD MS DATA AGAINST NEW DATABASE (PROTEINS/GROUPS IDENTIFIED WITH ONE PEPTIDE; A TOTAL OF 163 PROTEINS) Group

Protein ID

p

Description

IPI00478205 IPI00387120 IPI00478443 IPI00030104 IPI00479205 IPI00453116 IPI00306382

0.99

0.79

17

IPI00477971 IPI00383410 IPI00029210 IPI00479902 IPI00009865 IPI00295684 IPI00383111 IPI00413380 IPI00030153 IPI00023006 IPI00021428 IPI00455552 IPI00021440 IPI00021439 IPI00455547 IPI00003269 IPI00025416 IPI00008603 IPI00013897 IPI00016621 IPI00019943 IPI00479925 IPI00374563 IPI00020091 IPI00166729 IPI00479805 IPI00304273 IPI00478761 IPI00303207 IPI00215638 IPI00155723 IPI00024284

18 19 20 21

IPI00004656 IPI00013452 IPI00218413 IPI00218414

0.82 1 0.94 1

10-kDa protein Ig kappa chain v-iv region len 128-kDa protein Myosin-binding protein c, fast-type 38-kDa protein Scamp3 protein Splice isoform 1 of secretory carrier-associated membrane protein 3 56-kDa protein Transducin beta-like 3 Wd-repeat protein sazd 57-kDa protein Keratin, type i cytoskeletal 10 Keratin 10 Keratin 10 85-kDa protein Otthump00000016553 Actin, alpha cardiac Actin, alpha skeletal muscle PREDICTED: similar to FKSG30 Actin, cytoplasmic 2 Actin, cytoplasmic 1 PREDICTED: similar to POTE2A PREDICTED: similar to RIKEN cDNA 4732495G21 gene Actin, gamma-enteric smooth muscle Actin, aortic smooth muscle ADAM 10 precursor Adapter-related protein complex 2 alpha 2 subunit Afamin precursor Agrin Agrin precursor Alpha-1-acid glycoprotein 2 precursor Alpha-2-glycoprotein 1, zinc APOA4 protein Apolipoprotein A-IV precursor 45-kDa protein ATP-binding cassette sub-family E member 1 ATP-dependent RNA helicase A DHX9 protein Basement membrane-speciﬁc heparan sulfate proteoglycan core protein precursor Beta-2-microglobulin precursor Bifunctional aminoacyl-tRNA synthetase Biotinidase precursor Carbonic anhydrase II

1 2 3

4

5

6 7

8 9 10 11 12 13 14

15 16

1 0.99

0.6

0.6 0.81

1 0.89 0.79 0.72 0.96 0.97 0.8

0.94 0.9 0.95

(Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

83

TABLE IV (Continued) Group

Protein ID

p

22 23 24 25

IPI00031121 IPI00295741 IPI00217158 IPI00290315 IPI00419463 IPI00100453 IPI00383566 IPI00019581 IPI00304962 IPI00164755 IPI00022392 IPI00418391 IPI00296608 IPI00027507 IPI00480182 IPI00479952 IPI00029739 IPI00218999 IPI00291867 IPI00477992 IPI00218746

0.99 0.93 0.5 1

IPI00027717 IPI00012075 IPI00289647 IPI00215637 IPI00383592 IPI00293616 IPI00031836 IPI00257508 IPI00298547 IPI00018281 IPI00028911 IPI00478090 IPI00217934 IPI00032405 IPI00013068 IPI00006969 IPI00003351 IPI00017569 IPI00216070 IPI00220332 IPI00298497 IPI00386323 IPI00465059 IPI00163187 IPI00023673

0.62 1 0.8 0.95

26 27 28 29 30 31 32

33 34

35 36 37 38

39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

0.59 0.51 0.86 0.97 0.53 0.92 0.8

0.64 0.71

0.58 0.85 0.79 0.83 0.97 0.52 0.97 0.68 0.93 0.98 1 0.97 0.54 0.67 0.71

Description Carboxypeptidase E precursor Cathepsin B precursor CDKL3 protein Chromogranin A precursor Chromogranin A Chromosome 9 open reading frame 55 CLN3 protein Coagulation factor XII precursor Collagen alpha 2(I) chain precursor,Prepro-alpha2(I) Collagen precursor Complement C1q subcomponent, A chain precursor Complement component 7 Complement component C7 precursor Complement factor H-related protein 3 precursor Hypothetical protein 49-kDa protein Splice isoform 1 of complement factor H precursor Splice isoform 2 of complement factor H precursor Complement factor I precursor Complement subcomponent C1q chain B Complement component 1, q subcomponent, beta polypeptide precursor Component of gems 4 C-type natriuretic peptide precursor Cytochrome P450 2C18 DEAD\H (Asp-Glu-Ala-Asp\His) box polypeptide 3 DDX3Y protein DEAD-box protein 3, Y-chromosomal Developmentally regulated GTP-binding protein 1 Dihydropyrimidinase related protein-2 DJ-1 protein DNA repair and recombination protein RAD54B Dystroglycan precursor Early hematopoietic zinc ﬁnger EHZF protein Endothelin B receptor-like protein-2 precursor Eukaryotic translation initiation factor 3 subunit 6 Extracellular matrix protein 1 Extracellular matrix protein 1 precursor Fas apoptotic inhibitory molecule 2 Fast skeletal myosin alkali light chain 1 isoform 1F Fast skeletal myosin alkali light chain 1 isoform 3F Fibrinogen beta chain precursor FLJ00042 protein Mitochondrial Rho 2 FSCN1 protein Galectin-3 binding protein precursor (Continued)

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TABLE IV (Continued) Group

Protein ID

p

54

IPI00418376 IPI00018236 IPI00414676 IPI00248321 IPI00334775 IPI00015505 IPI00382470 IPI00455463

0.6

IPI00025409 IPI00385665 IPI00012835 IPI00470798 IPI00015479 IPI00386524 IPI00423462 IPI00166866 IPI00446503 IPI00396002 IPI00329574 IPI00167233 IPI00446534 IPI00430844 IPI00439491 IPI00426056 IPI00433678 IPI00471965 IPI00384948 IPI00426070 IPI00444204 IPI00328520 IPI00434728 IPI00376966 IPI00001399 IPI00418183 IPI00414984 IPI00386604 IPI00025491 IPI00472610 IPI00430826 IPI00423463 IPI00423445 IPI00430840 IPI00382606 IPI00448938 IPI00439447 IPI00448984

0.73 0.84

55

56 57

58 59 60 61 62

63 64 65

66 67 68 69 70 71 72 73

0.73

0.57 0.91

0.99 0.87 0.98

0.63 0.57 1

0.88 1 0.78 0.5 1 0.64 0.74 1

Description GM2 activator protein Ganglioside GM2 activator precursor Heat shock 90-kDa protein 1, beta PREDICTED: similar to peptidylprolyl isomerase A Hypothetical protein dkfzp761k0511 Hermansky-Pudlak syndrome 6 protein Hsp89-alpha-delta-N PREDICTED: similar to heat shock protein HSP 90-alpha (HSP 86) Hypothetical protein Hypothetical protein dkfzp434b0914 C-terminal binding protein 1 Hypothetical protein dkfzp686e23209 Hypothetical protein FLJ21415 Hypothetical protein FLJ25298 Hypothetical protein dkfzp686k18196 MGC27165 protein Hypothetical protein FLJ41552 Hypothetical protein FLJ39369 Hypothetical protein FLJ35387 Hypothetical protein FLJ40941 Hypothetical protein FLJ41981 Hypothetical protein Hypothetical protein Hypothetical protein dkfzp686l19235 Hypothetical protein Immunoglobulin heavy chain variant Hypothetical protein dkfzp686c02218 Hypothetical protein dkfzp686m08189 Hypothetical protein FLJ45773 Hypothetical protein FLJ90091 Hypothetical protein FP1523 Hypothetical protein MGC40069 Hypothetical protein MOT8 Hypothetical protein SGCE Splice isoform 1 of epsilon-sarcoglycan precursor Hypothetical protein Eukaryotic initiation factor 4A-I Hypothetical protein Hypothetical protein Hypothetical protein dkfzp686o01196 Hypothetical protein dkfzp686p15220 Hypothetical protein Factor VII active site mutant immunoconjugate IGHG1 protein Hypothetical protein Hypothetical protein (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

85

TABLE IV (Continued) Group

74

75

76 77

78 79 80

Protein ID IPI00472762 IPI00426007 IPI00448925 IPI00426057 IPI00332161 IPI00423464 IPI00384938 IPI00385332 IPI00423466 IPI00386140 IPI00382472 IPI00470652 IPI00382455 IPI00382493 IPI00382491 IPI00382497 IPI00382478 IPI00387111 IPI00477090 IPI00430856 IPI00335356 IPI00479708 IPI00382937 IPI00385264 IPI00479169 IPI00297284 IPI00021304 IPI00024289 IPI00450961 IPI00473056 IPI00334667

p

0.76

0.92

0.99 0.95

0.97 0.99 1

IPI00472249 IPI00334666 81 82

83 84 85 86

IPI00465044 IPI00064125 IPI00479942 IPI00063048 IPI00011662 IPI00217694 IPI00103279 IPI00401911 IPI00006932 IPI00216804 IPI00410027

0.87 0.97

0.71 0.88 0.68 0.98

Description IGHG1 protein Hypothetical protein dkfzp686g11190 Hypothetical protein Hypothetical protein dkfzp686c11235 Ig gamma-1 chain C region Hypothetical protein dkfzp686k03196 Hypothetical protein dkfzp686n02209 Hypothetical protein Hypothetical protein dkfzp686h20196 Ig heavy chain V-I region Mot Ig heavy chain V-I region SIE Single-chain Fv Ig heavy chain V-I region EU Ig heavy chain V-III region WAS Ig heavy chain V-III region POM Ig heavy chain V-III region TUR Ig heavy chain V-III region TIL Ig kappa chain V-II region TEW Ig mu chain C region Hypothetical protein IGHM protein IGHM protein IGHM protein Ig mu heavy chain disease protein 65-kDa protein Insulin-like growth factor binding protein 2 precursor Keratin, type II cytoskeletal 2 epidermal KIAA0387 protein PTPRN2 protein 110-kDa protein Splice isoform 2 of receptor-type tyrosine-protein phosphatase N2 precursor Protein tyrosine phosphatase receptor type, N polypeptide 2 isoform 2 precursor Splice isoform 1 of receptor-type tyrosine-protein phosphatase N2 precursor KIAA1470 protein KIAA1877 protein Alpha 2,6-sialyltransferase ST6GaIII protein Kunitz-type protease inhibitor 2 precursor LOC124773 protein LOC150159 protein,PREDICTED: similar to hypothetical protein LUC7-like 2 Splice isoform 2 of putative RNA-binding protein Luc7-like 2 Splice isoform 3 of putative RNA-binding protein Luc7-like 1 (Continued)

86

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TABLE IV (Continued) Group

Protein ID

87 88

IPI00071318 IPI00478353 IPI00410026 IPI00020986 IPI00166749

89

90 91 92 93 94

95

96 97 98

99 100 101 102 103 104 105 106 107 108 109 110 111 112

IPI00412087 IPI00025092 IPI00384170 IPI00410261 IPI00025879 IPI00007858 IPI00002352 IPI00007884 IPI00384401 IPI00428511 IPI00479602 IPI00442299 IPI00216728 IPI00441515 IPI00414249 IPI00031289 IPI00334238 IPI00001506 IPI00465432 IPI00329352 IPI00333985 IPI00413732 IPI00411937 IPI00024621 IPI00295832 IPI00163563 IPI00019580 IPI00000044 IPI00334195 IPI00419595 IPI00142762 IPI00397808 IPI00179330 IPI00396918 IPI00397875 IPI00291922 IPI00021923 IPI00334282 IPI00332887 IPI00375705

p

0.99 0.94 0.7

0.89 0.76 1 0.95 0.88

0.99

1 0.99 0.64

0.87 0.95 1 0.9 0.73 0.63 0.9 0.75 0.75 0.58 0.56 0.85 0.99 0.54

Description Splice isoform 1 of putative RNA-binding protein Luc7-like 1 48-kDa protein Splice isoform 2 of putative RNA-binding protein Luc7-like 1 Lumican precursor Mitochondrial processing peptidase alpha subunit, Mitochondrial precursor Myosin binding protein C, slow type isoform 4 Myosin-binding protein C, slow-type Hypothetical protein dkfzp451f042 Hypothetical protein dkfzp451l023 Myosin heavy chain, skeletal muscle, adult 1 Myosin heavy chain, skeletal muscle, extraocular Myosin light chain 2 Myosin-reactive immunoglobulin light chain variable region Myosin-reactive immunoglobulin kappa chain variable region Neurexin 1-beta precursor 47-kDa protein Neurexin 1-alpha precursor Neurexin 3-alpha Neurexin 3-alpha precursor Neurexin 3 isoform alpha precursor Neuronal pentraxin receptor Neuronal pentraxin receptor isoform 1 Neuropeptide Y precursor Nodal modulator 2 isoform 1 Protein pm5 precursor Nodal modulator 2 isoform 2 Nodal modulator 3 Nucleolar protein Nop56 Olfactomedin-like protein 3 precursor Oligodendrocyte-myelin glycoprotein precursor PBP family protein precursor Plasminogen precursor Platelet-derived growth factor, B chain precursor Platelet-derived growth factor beta isoform 2, preproprotein Podocalyxin-like protein PREDICTED: hypothetical protein XP_117408 PREDICTED: similar to bA92K2.2 (similar to ubiquitin) Ubiquitin and ribosomal protein S27a precursor PREDICTED: similar to hypothetical protein MGC56918 PREDICTED: similar to tudor domain containing 6 protein Proteasome subunit alpha type 5 Protein FAM3C precursor 25-kDa protein Protein tyrosine phosphatase, non-receptor type substrate 1 precursor Hypothetical protein dkfzp686a05192 (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

87

TABLE IV (Continued) Group

113

114 115 116

117 118 119 120 121 122 123 124 125

126 127

128

Protein ID IPI00232311 IPI00479267 IPI00376503 IPI00296864 IPI00470610 IPI00220644 IPI00479186 IPI00465344 IPI00427809 IPI00479848 IPI00480192 IPI00022420 IPI00465081 IPI00218236 IPI00479703 IPI00292950 IPI00297188 IPI00017964 IPI00000130 IPI00383591 IPI00217882 IPI00031030 IPI00220977 IPI00027235 IPI00479117 IPI00478671 IPI00162735 IPI00218462 IPI00218461 IPI00301317 IPI00220032 IPI00306710 IPI00221162 IPI00221163 IPI00296337

p

0.81

0.99 0.51 0.6

0.88 1 0.53 0.78 1 0.71 0.7 0.98 1

0.99 1

0.9

IPI00376215 129 130

0.92 0.98

132

IPI00003865 IPI00001611 IPI00215977 IPI00183445 IPI00410210 IPI00015881

133

IPI00022733

0.98

131

0.61 0.78

Description 54-kDa protein 44-kDa protein Pyrroline-5-carboxylate reductase 1 isoform 2 Pyrroline-5-carboxylate reductase Pyrroline 5-carboxylate reductase Pyruvate kinase 3 isoform 2 Pyruvate kinase 3 isoform 1 RARG protein Retinoic acid receptor gamma-2 Retinol binding protein 4, plasma Retinol binding protein 4, plasma Plasma retinol-binding protein precursor Ribonuclease\angiogenin inhibitor Serine\threonine protein phosphatase PP1-beta catalytic subunit 41-kDa protein SERPIND1 protein Seven transmembrane helix receptor Small nuclear ribonucleoprotein Sm D3 Somatostatin precursor Sortilin 1, preproprotein Sortilin precursor Splice isoform 1 of amyloid-like protein 2 precursor Splice isoform 2 of amyloid-like protein 2 precursor Splice isoform 1 of attractin precursor OTTHUMP00000030104 134-kDa protein Splice isoform 2 of attractin precursor Splice isoform 5 of attractin precursor Splice isoform 4 of attractin precursor Splice isoform 1 of catenin delta-2 Splice isoform 2 of catenin delta-2 Splice isoform 1 of chordin precursor Splice isoform 4 of chordin precursor Splice isoform 5 of chordin precursor Splice isoform 1 of DNA-dependent protein kinase catalytic subunit Splice isoform 2 of DNA-dependent protein kinase catalytic subunit Splice isoform 1 of heat shock cognate 71-kDa protein Splice isoform 1 of insulin-like growth factor II precursor Splice isoform 2 of insulin-like growth factor II precursor Splice isoform 1 of latrophilin 1 precursor Splice isoform 2 of latrophilin 1 precursor Splice isoform 1 of macrophage colony stimulating factor-1 precursor Splice isoform 1 of phospholipid transfer protein precursor (Continued)

88

XU et al.

TABLE IV (Continued) Group

Protein ID

134

IPI00217778 IPI00021833

p

1

IPI00220454 135

0.59

136

IPI00398749 IPI00398750 IPI00022184 IPI00382936

137

IPI00215975

0.71

0.58

IPI00297991 IPI00007334 138

139

140

IPI00218390 IPI00295133 IPI00299450 IPI00456624 IPI00217423 IPI00456623 IPI00455210

0.56

0.9

0.57

IPI00000846

141 142 143

144

145

IPI00152535 IPI00445286 IPI00335930 IPI00165230 IPI00029717 IPI00021885 IPI00219713 IPI00411626 IPI00021891 IPI00217291 IPI00472011 IPI00023814 IPI00219041

0.96 0.97 1

0.93

0.97

IPI00219042 IPI00177543 IPI00219043 146

IPI00216435

0.63

Description Splice isoform 2 of phospholipid transfer protein precursor Splice isoform 1 of platelet-derived growth factor, A chain precursor Splice isoform 2 of platelet-derived growth factor, A chain precursor Splice isoform 1 of pumilio homolog 2 Splice isoform 2 of pumilio homolog 2 Splice isoform 3 of pumilio homolog 2 Splice isoform 1 of ralbp1 associated Eps domain containing protein 2 Splice isoform 2 of apoptotic chromatin condensation inducer in the nucleus Splice isoform 3 of apoptotic chromatin condensation inducer in the nucleus Splice isoform 1 of apoptotic chromatin condensation inducer in the nucleus Splice isoform 2 of B-cell receptor CD22 precursor Splice isoform 1 of B-cell receptor CD22 precursor CD22 protein Splice isoform 2 of brevican core protein precursor Hyaluronan binding protein Splice isoform 1 of brevican core protein precursor Splice isoform 2 of chromodomain helicase-DNA-binding protein 4 Splice isoform 1 of chromodomain helicase-DNA-binding protein 4 Chromodomain helicase-DNA-binding protein 5 Hypothetical protein FLJ44024 Splice isoform 2 of DAZ-associated protein 1 Splice isoform 1 of DAZ-associated protein 1 Splice isoform 2 of ﬁbrinogen alpha\alpha-E chain precursor Splice isoform 1 of ﬁbrinogen alpha\alpha-E chain precursor Splice isoform 2 of ﬁbrinogen gamma chain precursor Hypothetical protein dkfzp779n0926 Splice isoform 1 of ﬁbrinogen gamma chain precursor Splice isoform 2 of neogenin precursor 154-kDa protein Splice isoform 1 of neogenin precursor Splice isoform 2 of peptidyl-glycine alpha-amidating monooxygenase precursor Splice isoform 3 of peptidyl-glycine alpha-amidating monooxygenase precursor Peptidylglycine alpha-amidating monooxygenase isoform a preproprotein Splice isoform 4 of peptidyl-glycine alpha-amidating monooxygenase precursor Splice isoform 2 of protein tyrosine kinase 2 beta (Continued)

PROTEOME OF HUMAN CEREBROSPINAL FLUID

89

TABLE IV (Continued) Group

147

148 149

Protein ID IPI00443767 IPI00029702 IPI00241562 IPI00294776 IPI00479053 IPI00298066 IPI00218803 IPI00296534 IPI00398722

p

0.81

0.99 0.91

IPI00333125 IPI00219666 IPI00473134 IPI00409587 IPI00396139 IPI00376382 IPI00398724 IPI00219664 150

151

IPI00442298 IPI00442297 IPI00442294 IPI00219210

0.75

0.72

IPI00219212 IPI00219208 IPI00219211 IPI00012009 IPI00172409 152

153 154 155

IPI00296039 IPI00014581 IPI00442894 IPI00455050 IPI00031556 IPI00171473 IPI00384856

1

1 0.55 0.99

Description Hypothetical protein FLJ46514 Splice isoform 1 of protein tyrosine kinase 2 beta Splice isoform 2 of reelin precursor Splice isoform 1 of reelin precursor 49-kDa protein Splice isoform 3 of reelin precursor Splice isoform 3 of ﬁbulin-1 precursor Splice isoform 1 of ﬁbulin-1 precursor Splice isoform 3 of myelin-oligodendrocyte glycoprotein precursor MOG protein Splice isoform 6 of myelin-oligodendrocyte glycoprotein precursor Splice isoform 8 of myelin-oligodendrocyte glycoprotein precursor Myelin oligodendrocyte glycoprotein isoform 6 Splice isoform 5 of myelin-oligodendrocyte glycoprotein precursor Splice isoform 7 of myelin-oligodendrocyte glycoprotein precursor Splice isoform 9 of myelin-oligodendrocyte glycoprotein precursor Splice isoform 2 of myelin-oligodendrocyte glycoprotein precursor Splice isoform 3 of neurotrimin precursor Splice isoform 2 of neurotrimin precursor Splice isoform 1 of neurotrimin precursor Splice isoform 4 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 6 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 2 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 5 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 1 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 3 of granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor Splice isoform 4 of tropomyosin 1 alpha chain Splice isoform 1 of tropomyosin 1 alpha chain Hypothetical protein FLJ26372 Sarcomeric tropomyosin kappa Splicing factor U2AF 65-kDa subunit Spondin 1 precursor Sulfatase 2 isform b precursor (Continued)

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XU et al.

TABLE IV (Continued) Group

156 157 158

159 160 161

162 163

Protein ID IPI00297252 IPI00218733 IPI00026606 IPI00220828 IPI00299633 IPI00376165 IPI00376163 IPI00180240 IPI00477225 IPI00216694 IPI00171716 IPI00175036 IPI00018276 IPI00306470 IPI00419722 IPI00018352 IPI00298971

COMPARISON

>Two peptides Single peptides Total

p

Description

0.99 0.97 0.52

0.86 0.66 0.93

0.94 0.96

OF

Extracellular sulfatase sulf-2 precursor Superoxide dismutase 1, soluble Surfeit locus protein 6 Thymosin, beta 4 DJ1071L10.1 5-kDa protein 5-kDa protein Thymosin-like 3 T-plastin Plastin 3 TPR repeat containing protein KIAA1043 OTTHUMP00000028696 Type I transmembrane receptor precursor SEZ6L2 protein PSK-1 Ubiquitin carboxyl-terminal hydrolase isozyme L1 Vitronectin precursor

TABLE V SAME MS DATA SEARCHED AGAINST DIFFERENT DATABASE New results

Old results

Overlap

129 163 292

187 128 315

108 (83.7%) 85 (52.1%) 193 (66%)

1. Sample Preparation a. Collection of CSF and Quality Controls. A good proteomic study begins with sample preparation; this is especially true for CSF because CSF protein proﬁles are very similar to those of plasma or serum, whose protein concentration is about 200 times higher than that in CSF (Jiang et al., 2004). As a result, any meaningful proteomic analysis of human CSF has to use samples with minimal blood contamination. Several methods have been considered. First and foremost is visual inspection: all samples with blood contamination grossly should be eliminated from the study. In the last few years, the likelihood of getting grossly contaminated samples has dropped substantially due to the adoption of a less traumatic LP method (i.e., tapping CSF with a 24 g bullet-tip Sprotte spinal needle). Use of a 24 g Sprotte atraumatic spinal needle was initially recommended for the prevention of post-LP headaches, because it greatly reduces the

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frequency of post-LP headaches (<1%) (Kuntz et al., 1992). This new approach also minimizes the chances of damaging vessels when the needle is inserted into the cistern. It is obvious that, however, visual inspection, though helpful, is not reliable, as a small amount of blood contamination may not be detected readily with the naked eye but can have signiﬁcant negative impact on the CSF proteome, given the very high ratios in protein concentration between serum and CSF. To get around this problem, we have recently employed two additional criteria to determine the extent of blood contamination: (1) CSF RBC count as determined by standard clinical laboratory methods, and (2) CSF apoB concentration as determined by semiquantitative Western blot analysis. More speciﬁcally, CSF samples containing more than 10 RBC/ml and/or apoB concentration with a ratio of serum:CSF <6,000 were considered as contaminated (Osman et al., 1995). Using apoB as an additional criterion is particularly helpful for samples that were obtained years ago when standard clinical chemistry was not routinely performed during CSF taps and thus, no RBC count information was available. While the scheme developed earlier is probably the best method currently available to control the quality of CSF for qualitative proteomics, a few additional variables that may inﬂuence the relative levels of proteins in each individual CSF sample need to be controlled for quantitative proteomics. For instance, the CSF to be compared should be obtained during a similar time of day to limit the variability secondary to circadian ﬂuctuations. Furthermore, samples to be compared should come from comparable fractions of CSF (e.g., from 20th to 25th ml) to limit the variability from rostro-caudal concentration gradients. Finally, factors, such as cigarette smoking, alcohol use other than socially, and any psychoactive drugs, should also be compared and matched for quantitative proteomics. b. Fractionation of CSF. As mentioned earlier, the protein proﬁles in CSF are similar to those in serum/plasma, including the fact that albumin and immunoglobulins (IgGs) constitute >50% and >15% of the total protein content, respectively (Blennow et al., 1993a). The dynamic range of protein concentrations is about 109 as opposed to a dynamic range of 108 for typical cell lysates (Corthals et al., 2000). These realities create major challenges to any given proteomic techniques aimed at the identiﬁcation of low abundance proteins, as all current MS is inherently biased toward abundant proteins, that is, abundant proteins are analyzed preferentially or exclusively at times (Yuan et al., 2002). In fact, this is the primary reason why many investigators choose the SELDI approach for studying biomarkers in CSF as well as in serum/plasma (Marshall, 2005; Suzuyama et al., 2004), because it enriches proteins in low abundance using chips with various binding properties (Seibert et al., 2004). Nonetheless, recent studies have indicated two major problems associated with the SELDI technique: (1) SELDI shows high variations (i.e., low reproducibility) when the same samples are run with the same chips but diVerent lot numbers (BischoV and Luider, 2004); and (2) SELDI requires labor-intensive oV chip work-up when protein identiﬁcation is necessary.

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While traditional two-dimensional gel-based or recently invented two-dimensional LC-based proteomics largely circumvent these problems, both have their own limitations. One major limitation is that both methods require prefractionation of samples or removal of albumin and IgGs before low abundance proteins can be analyzed. Several competing methods are available with relative strengths and weaknesses. For instance, removing albumin or IgGs with aYnity puriﬁcation before proteomics (Li and Lee, 2004) eVectively enriches low abundance proteins, but proteins that are potentially interesting but trapped by albumin during puriﬁcation will also be absent. This problem may be negligible if the goal is to simply identify low abundance proteins; however, it can become a signiﬁcant concern when trying to determine which proteins change signiﬁcantly in CSF in a given disease setting, since low abundance proteins could be removed diVerentially along with albumin and IgGs. An alternative method is to focus on a subset of the proteome (e.g., glycosylated proteins, an approach proposed for serum proteomics for biomarker discovery in cancer patients) (Block et al., 2005; Zhang et al., 2003). While this method remains viable in biomarker discovery, it oVers little help when the goal is to characterize the total proteome of CSF or serum/plasma. Thus, to deal with this diYculty, we have recently developed a method called graduated organic fractionation of CSF, which is suitable for both qualitative and quantitative proteomics. With the precolumn organic fractionation, we have identiﬁed more than 300 CSF proteins in young healthy subjects in a previous study (Zhang et al., 2005a), which is many times more than those reported in the in-literature combined. However, our current results clearly demonstrated that organic separation of CSF into a few fractions is not suYcient for LCQ type MS, at least for extensive protein identiﬁcation, given its slow scanning speed. This limitation can be approached easily by better separation of samples before LCQ runs. This has been clearly demonstrated by our current study in which further SDS-PAGE gel electrophoresis of CSF proteins before proteomics signiﬁcantly increased the numbers of proteins identiﬁed by LCQ when other parameters were comparable. Notably, however, SDS-PAGE separation of CSF proteins, though very useful for the current purpose (i.e., identiﬁcation of deeper CSF proteome), cannot be applied to typical quantitative proteomics that are currently used (e.g., ICAT and iTRAQ) as quantiﬁcation of proteins is almost impossible after SDS-PAGE separation of samples. 2. Mass Spectrometry Instrumentation and Bioinformatics a. Instrumentation. With only about 1% of the proteins used in LCQ studies, comparable LTQ-FT runs identiﬁed 30% more proteins (Tables I and II, respectively), clearly demonstrating that better instrumentation is also imperative in achieving a more extensive protein identiﬁcation of the CSF proteome. The LTQ-FT is a hybrid ion trap—FTICR-MS. For each duty cycle, while the

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LTQ conducts CID of ions in data-dependent mode, the FTICR is acquiring precursor ion scans. Thus two detectors are operating simultaneously. The major advantages of the LTQ over LCQ are threefold: (1) a segmented, linear ion trap with hyperbolic quadrupoles allowing for about 40 times more ions to be trapped than a traditional three-dimensional trapping MS (e.g., our LCQ DECA PLUS XP), which translates into a signiﬁcant increase in overall sensitivity; (2) increased sensitivity by radial ejection of ions to two detectors; and (3) increased scan rates 3–5 times faster, depending on the modes of operation. The obvious unfair advantage is that the LTQ is now also coupled to an FTICR, which in our experiments was used to generate parent ion scans at mush higher mass accuracy than any ion trap (~2 ppm). This additional information in the accurate mass measurements may be used to decrease the false matches in the peptide library and increase protein identiﬁcation conﬁdence. b. Search Engine and Database Issues. Good sample preparation and obtaining high quality MS data (qualitative or quantitative) with better instruments are only half the challenge for proteomic analysis, as data mining and validation of identiﬁed proteins are just as important to the ultimate success of proteomics. Presently, the two most widely used commercially available database search algorithms, SEQUEST (invented by our colleagues at UW) and MASCOT, infer protein identiﬁcations with MS/MS information with various protein databases. Although both have unique aspects, neither is perfect. The underlying score function in MASCOT has not been revealed in detail and is thus considered a ‘‘black box’’ to users. On the other hand, the underlying operation of the widely used SEQUEST algorithm is publicly described. SEQUEST generates a theoretical product ion mass spectrum and compares it against a preprocessed mass spectrum acquired experimentally using a cross correlation algorithm (Yates et al., 1995). However, this method as originally developed, did not assimilate the composite peptide scores into an overall protein score as MASCOT does. Such functions have been developed recently and are embedded in the programs known as: (1) PeptideProphet (Keller et al., 2002), to allow identiﬁed peptides to be sorted according to a statistical probability; and (2) ProteinProphet that assimilates these peptide scores into an overall protein probability score (Nesvizhskii et al., 2003). In the last few years, we have utilized these tools in the characterization of proteins related to -synuclein (Zhou et al., 2004), neuroﬁbrillary tangles (Wang et al., 2005), and animals treated with a parkinsonian toxicant, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) ( Jin et al., 2005), as well as in determining the proteome of human CSF in aging (Zhang et al., 2005a ) and AD-related changes (Zhang et al., 2005b). It should be noted that our recent experiments also indicate that the data searched with diVerent algorithms are not necessarily comparable, as most investigators might believe. For instance, we have searched an identical LCQ

94

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spectrum against SEQUEST versus MASCOT, and to our surprise, when similar error rates were allowed, the overlap was only about 60% between these two search engines (unpublished data). Databases is another issue that has been emphasized in recently published proteomic guidelines (Carr et al., 2004). Currently, there is no single database that is considered to be the best for searching, as all of them suVer from major problems such as incompleteness and redundant entries. Notably, though, ‘‘redundancy’’ could be biological, as many genomes have multiple copies of similar genes as well as splice variants. Thus, multiple proteins with similar (if not identical) sequences are often identiﬁed by the search engine using subsets of the same group of mass spectra, and in practice, all of these proteins should be listed, as we did in our current as well as our previous publications. However, this approach does not necessarily solve the problem when someone wants to followup a candidate protein with more extensive biologic questions, since he or she will have diYculty determining which one to study ﬁrst. Furthermore, a more serious issue is the incompleteness of the current database. The human databases are being continuously updated and remain incomplete. Thus, proteins identiﬁed previously may be lost in future searches and new proteins will be identiﬁed as we have demonstrated in Tables III, IV, and V, where the same MS data were searched against two diVerent databases. It is surprising that the overlap between these two searches was only about 66%. However, it is clear that proteins identiﬁed by more than two peptides are more likely to be replicated than those identiﬁed by a single-hit, which is why the guideline suggests that proteins identiﬁed by a single peptide should be considered provisional (Carr et al., 2004). Nonetheless, we have included proteins indented by a single peptide in this chapter for two reasons. First, some singlehits in previous studies (e.g., superoxide dismutase 1, ﬁbrinogen beta chain precursor, and vitronectin precursor) were identiﬁed in the current study as proteins with more than two peptides whereas others identiﬁed as single-hits in the current studies (e.g., calsyntenin-3 precursor, xylosyltransferase I, testican-1 precursor) were actually identiﬁed as proteins with more than two peptides previously. Thus it should be noted that data-dependent ion selection during LC sample introduction on even the newer LTQ is poorly reproducible, as we have shown in prior studies using the LCQ DECA PLUS XP (Yi et al., 2002; Zhang et al., 2005a). Second, some proteins were identiﬁed as single-hits (e.g., creatine kinase) even though they are determined to be present in CSF by other biological experiments. That said, it should be emphasized that proteomics should be considered only as a discovery tool that generates lists of candidate proteins that, if judged important biologically, the roles of which must be conﬁrmed/refuted by corroborating non-MS methods such as Western blotting.

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3. Prospective in Further Characterization of the CSF Proteome and Discovering Unique Proteins for CNS Diseases Identiﬁcation of close to 1000 proteins in the current study is signiﬁcant in its own right; nonetheless, complete characterization of the CSF proteome will remain a challenge for years to come. Clearly missing from the list of our CSF proteome (Tables I–IV) are several proteins that are well known to be present in human CSF (e.g., -synuclein) (Yuan and Desiderio, 2005) and gesolin (Yuan et al., 2002). This brings up another limitation of current MS-based proteomics, namely variations in ionization and sampling during MS/MS run, that is, not all peptides are ionized or identiﬁed when ionized (Aebersold and Goodlett, 2001; Gross, 2004). Typical reproducibility for a traditional LCQ MS is only about 30% whereas it has been claimed that LTQ can reach about 60–70% when a moderately complex sample is analyzed multiple times. Thus, theoretically more extensive coverage could be achieved if the same samples are injected multiple times ideally with faster MS machines like LTQ-FT. In other words, it is likely that more proteins could have been identiﬁed if we had gained unlimited access to the most sensitive LTQ-FT and ran the SDS-PAGE samples multiple times. It should be noted that when more sensitive methods are applied, it becomes harder to diVerentiate the native CSF proteins from a trace amount of proteins contaminated by serum/plasma obtained during CSF tap. There is currently no perfect solution for dealing with this diYculty. In theory, CSF can be obtained in a controlled surgical setting where no blood contamination is present; however, this is unlikely in practice, given the stringent requirement for human studies. An alternative would be obtaining ventricular ﬂuids during autopsies from subjects who died due to disease unrelated to the CNS, although the integrity of the premortem blood–brain barrier could become a confounding factor that cannot be assessed eVectively. Consequently, a more realistic approach is probably to perform quantitative proteomics (e.g., ICAT and iTRAQ based assays), which help diVerentiate proteins speciﬁc to CSF for reasons of biological function from those entering through nonspeciﬁc means (Zhang et al., 2005a,b). Characterization of the human CSF proteome is just the beginning of our pursuit to identify biomarkers useful in assisting the diagnosis of various neurological diseases clinically and/or monitoring the progression of these diseases. The major challenges for this line of productive inquiry are heterogeneity of the diseases and accuracy of diagnosis without autopsy conﬁrmation. A panel of markers discovered via proteomics can be an eVective approach to deal with the ﬁrst diYculty. The second issue, that is, accuracy of diagnosis of CNS disorders, neurodegenerative diseases like AD, PD, and DLB in particularly, can be very signiﬁcant. For instance, the clinical diagnosis of PD is based on identiﬁcation of a combination of the cardinal motor signs (i.e., bradykinesia, rigidity, tremor, and

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postural instability). Although experts in movement disorders have greater accuracy in the initial diagnosis of PD (Hughes et al., 2002; Jankovic et al., 2000), several clinical pathological studies have indicated that, in general, the diagnostic accuracy for PD with the current diagnostic criteria is only about 65% at the ﬁrst evaluation, which can be improved to about 80% before patients die (Hughes et al., 1992b; Litvan et al., 1998; Rajput et al., 1991). Therefore, despite the advancement in diagnostic techniques, as many as one in ﬁve individuals with pathological PD receives a misdiagnosis clinically (Hughes et al., 1992a,b; Rajput et al., 1991). Thus, successful identiﬁcation of unique biomarkers for various neurodegenerative diseases relies heavily on the accuracy of disease diagnosis, and it is optimal to use cases with pathological conﬁrmation, which is the only current gold standard for the accurate diagnosis of these diseases.

Acknowledgments

Proteomic characterization of human CSF is supported by NIH grants (S10RR17262 and P30ES007033) to DRG and NIH grants (R01AG025327 and R01ES012703) to JZ.

References

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HORMONAL PATHWAYS REGULATING INTERMALE AND INTERFEMALE AGGRESSION

Neal G. Simon, Qianxing Mo, Shan Hu, Carrie Garippa, and Shi-fang Lu Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA

I. Introduction A . Common Regulatory Concepts in Males and Females II. Females A . DHEA as a Neurosteroid III. Males A . Regulation in the Adult B . Neural Steroid Receptors IV. Hormonal Modulation of Serotonin Function V. Conclusions References

I. Introduction

Characterization of the mechanisms involved in the regulation of aggression by androgens is a major objective in behavioral endocrinology. Advances in our understanding of molecular, cellular, and biochemical processes that mediate androgenic eVects in target cells (Lee and Chang, 2003) continue to drive revisions in increasingly sophisticated models of behavioral regulation in animal models. Investigations that seek to discern hormonal contributions to aggression in clinical populations have not seen comparable progress. These studies face signiﬁcant methodological limitations that, combined with wide variation in indices of aggression, frequently lead to equivocal results (Archer, 1991, 1988; van der Pahlen, 2005). As subtypes of aggression, such as irritability, impulsivity, hostility, and dominance are employed as target behaviors versus a global construct termed ‘‘aggression’’ in human studies, advances in deﬁning the relationship between hormones and speciﬁc behavioral forms should emerge (Simon, 2002). OVensive aggression between conspeciﬁc males and conspeciﬁc females can serve as model systems to exemplify our understanding of androgenic eVects on aggressive behavior. This form of aggression between same-sex conspeciﬁcs is a productive behavior because it determines dominance status and access to resources. The use of oVensive aggression in males as a model is based on its widely documented dependence on testosterone (T), the principal testicular INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73003-3

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androgen (Nelson, 1995). For well over 100 years, it has been recognized that gonadectomy reduces conspeciﬁc aggression in males (Freeman et al., 2001). Including females as a representative system for exemplifying androgenic eVects on aggression may seem unusual given the numerous failures to identify a positive relationship between T and this behavior in female mammals. However, several studies clearly demonstrated that females housed in small groups displayed aggression toward other females, juvenile males, or gonadectomized adult males (Brain and Haug, 1992) and that dehydroepiandrosterone (DHEA), an androgenic neurosteroid synthesized in the brains of humans and other mammals (Baulieu et al., 2001; Compagnone and Mellon, 2000), played an important role in regulating this behavior. Assessments of seasonal variation in aggression in avian species supported the concept that DHEA also may inﬂuence the display of male-typical aggression, particularly outside the breeding season (Hau et al., 2004; Soma and Wingﬁeld, 2001). A systems perspective has been adopted in our laboratory to frame the relationship between androgens and conspeciﬁc oVensive aggression in males and females (Fig. 1). This approach draws on recent developments in functional genomics, cell biology, biochemistry, and molecular biology to build hypotheses and develop regulatory models that span gene function through behavioral expression. Environmental inﬂuences on behavior and adaptive responses to these events are additional important features of the systems approach. This aspect of the model recognizes the inﬂuence of factors, such as age, cognition, experience, diet, and culture on signaling pathways.

FIG. 1. A graphic outline of the components that require characterization for the development of a system’s model for the regulation of conspeciﬁc oVensive aggression. Progress in developing the model will require a multidisciplinary approach that draws on molecular and cell biology, bioinformatics, physiology, ethology, ecology, and evolutionary biology. The application of a systems analysis to the relationship between androgens and oVensive aggression should yield a model that integrates events from the gene level to behavioral expression and subsequent adaptations based on experience. Adapted from Simon and Lu, in press, and reprinted with permission from Oxford Press.

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1. Metabolism Aromatization is widely recognized as an important step in the promotion of aggression by T (Balthazart et al., 2003). Evidence has also accumulated showing that androgens can directly induce male-typical aggression (Simon, 2002). These observations demonstrate that deﬁning the role of T in male-typical, oVensive aggression must include a discussion of the contributions of E2 and dihydrotestosterone (DHT), the metabolites produced by the activity of aromatase and 5reductase, respectively. The distribution of these enzymes in the CNS (Melcangi et al., 1998; Naftolin et al., 2001; Silverin et al., 2000) and their localization within sites implicated in male-typical aggression are important considerations. Several methods and strategies have been employed to assess the eVects of these metabolites. Among the most common are behavioral assessments in mice with disruptions of speciﬁc steroid receptor genes or key enzymes (ER, ER , aromatase), descriptions of aggressive phenotypes in mice with naturally occurring mutations that aVect receptor function or critical enzyme activity (e.g., Tfm, 5-reductase deﬁcient), pharmacological manipulations (enzymatic inhibitors, antagonists), and comparisons among outbred strains in the postcastration response to speciﬁcally acting androgenic and estrogenic hormones. The modulatory actions of DHEA on female-typical aggression may involve multiple metabolites of this steroid, a circumstance that would parallel observations in males. The synthetic and metabolic pathways for DHEA have been well deﬁned (Compagnone and Mellon, 2000; Labrie, 2003) (Fig. 2). For aggression, the 3 -hydroxysteroid dehydrogenase (3 -HSD), hydroxysteroid sulfotransferase (HST), steroid sulfatase (SST), and CYP7B pathways all may be involved. The activity of 3 -HSD has attracted substantial interest because it leads to androstenedione (AE) formation, which can serve as substrate for the production of more potent androgens and estrogens. The balance between SST and HST may determine the contribution of DHEA sulfate (DHEA-S) versus DHEA, and CYP7B family activity leads to the production of 7- and 7 -hydroxy DHEA. The activity of CYP7B enzymes has been neglected in the context of female-typical aggression, which may represent a signiﬁcant gap in the literature because the 7hydroxylated metabolite of DHEA appears to be the major form recovered in the CNS (Cui and Belshams, 2003; Jellinck et al., 2001, 2005). Further complicating characterization of the mechanisms through which DHEA aVects aggression in females are data showing that both genomic and nongenomic eVects may be involved. Direct androgenic eVects of DHEA itself, and the observation that more potent androgens are formed from DHEA in peripheral tissues (Labrie, 2003; Lu et al., 2003; Mo et al., 2004) have provided evidence that establishes genomic activity. In relation to nongenomic eVects of DHEA, metabolism is important

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FIG. 2. A summary of the metabolism of DHEA in the central nervous system. Three pathways have been identiﬁed with DHEA as the initial substrate: (A) to DHEA sulfate, a reversible path involving hydroxysteroid sulfotransferase and steroid sulfatase, (B) to 7- or 7 -hydroxy DHEA, which involves CYP7B pathways, and (C) to androstenedione, which utilizes 3 -hydroxysteroid dehydrogenase and provides the possibility for the formation of more potent androgens and estrogens. Adapted from Simon (2002) and reprinted with permission from Academic Press.

because there are diVerences in the potency of DHEA versus DHEA-S as negative modulators of the GABAA receptor (Imamura and Prasad, 1998). 2. Neuromodulator Hypothesis A conceptual model that enables the integration of androgenic eVects on aggression in males and females would have broad utility. If T and DHEA are seen as modulators of neurochemical systems, it becomes possible to propose a model that bridges observed eVects in both sexes. We have termed this model the

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neuromodulator hypothesis. The strength of this conceptual approach is a strong emphasis on integration among hormonal, neurochemical, and peptide systems that inﬂuence aggression and, if validated, the capacity to bridge ﬁndings about aggression in animal models with clinical issues related to androgen excess or deﬁciency. To illustrate the utility of the neuromodulator hypothesis, the inﬂuence of T and DHEA on representative neurochemical systems is presented in both females and males. II. Females

A widely held premise of eVorts to understand the hormonal contribution to female-typical aggression has been that aggressive behavior exhibited by females follows the same regulatory processes observed in males, that is, an emphasis on a facilitative contribution of T. Not surprisingly, when this position has been tested experimentally, mixed to outrightly negative outcomes have been obtained (Albert et al., 1993; Giammanco et al., 2005; von Engelhardt et al., 2000). We believe that a diVerent conceptual approach to hormone function in femaletypical oVensive aggression is needed. Based on over 20 years of ﬁndings, which show that the neurosteroid DHEA inhibits female-typical aggression when administered chronically (Baulieu, et al., 2001; Young et al., 1995, 1996), a model that focuses on the eVects and mechanism of action of this compound may have utility. Intact or ovariectomized females reliably display attack behavior toward intruder females that are intact, ovariectomized, or lactating (Brain and Haug, 1992; Simon, 2002). This type of aggression appears to be under GABAergic control and is modulated by DHEA (Simon, 2002; Young et al., 1995, 1996), a neurosteroid synthesized in the CNS (Baulieu et al., 2001; Compagnone and Mellon, 2000). The demonstration that extended treatment with DHEA inhibited aggression by intact or OVX females toward females or lactating females generated interest in this steroid. In addition to modulating GABA function, DHEA exerts androgenic eVects through the androgen receptor and also may serve as substrate for more potent steroidal metabolites (Fig. 2). These recent ﬁndings raise the possibility that the eVects of DHEA on female-typical aggression are exerted through multiple mechanisms. A. DHEA

AS A

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DHEA modulates GABAA, NMDA, and 1 receptors (Compagnone and Mellon, 2000), although eVects exerted at the GABAA receptor complex have received the most attention (Dubrovsky, 2005; Rupprecht, 2003; Rupprecht et al.,

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2001). The emphasis on GABAA receptors in female-typical aggression is supported by numerous studies, which showed speciﬁc eVects of GABA on oVensive aggression (Miczek et al., 2003; Siegel et al., 1999). Also, the NMDA receptor, to the extent it has been studied in relation to aggression (Adamec, 1997; Blanchard et al., 2002; Gould and Cameron, 1997) is linked to defensive behavior. Last, to the best of our knowledge, the 1 receptor has little inﬂuence on aggression, although an indirect role cannot be excluded. This is because 1 receptor can eVect NMDA-mediated responses (Maurice et al., 1999). 1. Mechanism of Action The prevailing position concerning the modulation of female-typical aggression by DHEA is that it produces a reduction in pregnenolone sulfate (Preg-S), a potent negative modulator of the GABAA receptor (Majewska and Schwartz, 1987), through competition for HST. By decreasing Preg-S, GABA function is enhanced, which in turn inhibits oVensive aggression (Robel and Baulieu, 1995). DHEA also can act at membrane sites rapidly to alter receptor conformation (a nongenomic eVect) and aVect long-term processes by itself or through neurosteroid metabolites (a genomic eVect). The resulting changes in gene expression could then alter membrane receptor function by, for example, producing changes in GABAA subunit composition (Herbison and Fenelon, 1995). An important feature of GABAA receptors is that modulation can be achieved at multiple sites because of its pentameric structure. Included are sites that bind GABA, the benzodiazepines, the C1 ionophore, barbiturates, and an as yet unidentiﬁed neurosteroid binding site (Majewska, 1995; Majewska and Schwartz, 1987; Majewska et al., 1990). For female-typical aggression, however, prevailing models have focused on the reduction in Preg-S as a critical eVect rather than a direct action on GABAA receptor. Interpreting the eVects of DHEA on aggression is not a straightforward proposition. DHEA itself is a negative modulator of GABAA receptor (albeit weaker than Preg-S) and a positive modulator of NMDA receptor (Bergeron et al., 1996; Imamura and Prasad, 1998; Majewska, 1992; Sousa and Ticku, 1997). A positive association between aggression and DHEA levels was reported in some avian species and adolescents with conduct disorder, which suggests a potential facilitative eVect (Hau et al., 2004; Soma and Wingﬁeld, 2001; van Goozen et al., 2000). The aggression-enhancing eVects, however, were noted in males and, in these circumstances, likely reﬂect direct androgenic eVects of DHEA (Lu et al., 2003; Mo et al., 2004). The recent ﬁnding of direct androgenic eVects of DHEA may provide new insights into underlying mechanisms. DHEA exhibited characteristics of typical androgenic compounds, which included upregulation of androgen receptor (AR) protein expression (Fig. 3) and conferring AR transcriptional activity in a dosedependent manner (Lu et al., 2003; Mo et al., 2004). Western analysis of brain

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FIG. 3. Upregulation of AR by DHEA in mouse brain. Female mice were ovariectomized one week before being treated with 80, 320, or 1280 mg or vehicle (n ¼ 3) for four consecutive days (s. c.). Five hours after the last injection, major limbic system regions were blocked and analyzed by Western blot for relative concentration of AR (Lu et al., 1998). DHEA treatment augmented cellular AR level in a dosedependent manner. A trend analysis showed the eVect of DHEA dosage was signiﬁcant ( p < 0.01). Data shown are mean integrated band densities (IBD þ SEM) for 97-kD AR bands. *; signiﬁcantly diVerent from controls ( p < 0.05). Adapted from Lu et al. (2003) and reprinted with permission.

extracts from LS, BNST, and MPOA showed a dose-dependent increase in AR content in response to DHEA treatment, and a similar regulatory eVect also was seen in GT1-7 cells, which are AR-expressing hypothalamic cell lines. Importantly, the upregulation of AR by DHEA was not blocked by trilostane, an inhibitor of 3 -hydroxysteroid dehydrogenase activity responsible for the conversion of DHEA to androstenedione, a more potent androgen. The androgenic activity of DHEA was further conﬁrmed when it was shown that DHEA induced intracellular translocation of AR-GFP and formation of nuclear clusters (Mo et al., 2006). When COS-7 cells transfected with an ARGFP expression vector were treated with 10 7 M DHEA for 24 hours, AR-GFP protein translocated from the cytoplasm into the nucleus and led to the formation of punctate ﬂuorescent foci (Fig. 4, Mo et al., 2006). The androgenic activity of DHEA represents a novel mechanistic ﬁnding that also may be a potential component of the antiaggressive mechanism. Another mechanistic component tied to genomic eVects of DHEA or its metabolites is an alteration of GABAA subunit structure, which potentially can inﬂuence the extent of modulation (Herbison and Fenelon, 1995; Mehta and Ticku, 1999). The direct androgenic eVects of DHEA and its metabolites thus

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FIG. 4. AR-GFP protein intracellular traslocation and nuclear clustering in response to DHEA or control treatment. COS-7 cells were transfected with AR-GFP expression plasmid pEGFP-N1-AR. AR-GFP fusion proteins were detected in living cells by excitation with 488 nm line from an argon laser of a Zeiss LSM-510 confocal microscope. (A) Typical COS-7 cells without treatment. (B) COS-7 cells were treated with 10 7 M DHEA for 24 hours. Bar ¼ 5 mm.

may represent a crosstalk cellular signaling system (Katzenellenbogen, 2000; Rupprecht, 2003) linked to its antiaggressive eVect. It is critically important to deﬁne the interrelationship among DHEA, its androgenic eVects, the subunit structure of GABAA receptor, and attendant changes in function to elaborate the mechanism of action of this neurosteroid and how it modulates the expression of female-typical aggression. Establishing the functional signiﬁcance of observed alterations in GABAA structure, whether produced directly at the membrane level or through the AR, requires additional steps. A neuroanatomy of female-typical aggression needs to be deﬁned. This is particularly important because modulation of GABAA

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receptor by DHEA can occur through multiple mechanisms that diVer across regions. For example, GABAA receptor subunit structure varies regionally and is a limiting factor in steroidal eVects (Mehta and Ticku, 1999). In addition, AR distribution and the GABA system only partially overlap. Thus, androgenic activity of DHEA may play an important role in some regions, while nongenomic eVects of DHEA are essential elsewhere.

III. Males

The ability of T to facilitate the display of intermale aggressive behavior is recognized as a fundamental relationship in behavioral endocrinology. Over the past 30 years, the focus of research in the ﬁeld has shifted largely to mechanistic questions that addressed neuroanatomical, cellular, and molecular processes involved in hormonal responsiveness. Rodent models have been a primary tool in these investigations and their utility for providing data directly relevant to humans has been buttressed by genomic data and molecular conservation of steroidal systems in mammals (Choong et al., 1998). Animal models have broadened considerably to include species ranging from ﬁsh to lizards and, increasingly, birds (Elofsson et al., 2000; Godwin and Crews, 2002; Panksepp, 2003; Wingﬁeld et al., 1997; Woolley et al., 2004). The overarching goal of these investigations has been to characterize CNS pathways in the adult brain that underlie the ability of T to promote aggressive behavior. Comparisons of sex and strain diVerences in the response to T and its major metabolites, E2 and DHT, as well as studies using enzymatic inhibitors and receptor antagonists, were important steps in elaborating these pathways (Simon, 2002). Models of steroid receptor function and cellular mechanisms involved in the hormonal regulation of aggression have grown increasingly sophisticated. The complexity of these models has resulted from eVorts to carefully deﬁne the molecular processes that determine cellular sensitivity to the aggressionpromoting property of gonadal steroids. Achieving a comprehensive regulatory model may well require more than an understanding of hormonal systems in isolation; elaboration of interactions between steroidal and relevant neurochemical systems will be needed. At present, the largest amount of data in this area is in regard to hormonal inﬂuences on serotonin function in males. For females, the modulation of GABAA receptor function by DHEA has been a major focus. In the following sections, hormonal inﬂuences on these target neurochemical systems will be used to exemplify the utility of the neuromodulator hypothesis.

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A. REGULATION

IN THE

ADULT

The metabolism of T peripherally and in CNS target cells established a physiological basis for multiple steroidal pathways regulating aggression in males (Simon, 2002). Four distinct steroid-sensitive pathways have been identiﬁed: (i) Androgen-sensitive, which responds to T itself or its 5-reduced metabolite, DHT (ii) Estrogen-sensitive, which uses E2 derived by aromatization of T (iii) Synergistic or combined, in which both the androgenic and estrogenic metabolites of T are used to facilitate behavioral expression (iv) Direct T-mediated, which utilizes T itself It is important to note that a given male does not necessarily express all four systems; genotype is the major determinant of the functional pathway. Estrogen is the most typical active hormone, which is consistent with a prominent role for aromatization and estrogen receptor. The regulatory pathways share a basic feature of high sensitivity in males. It takes only 2–3 days of hormone treatment with the appropriate steroid at physiological doses to restore aggression, a time course in keeping with a genomic mechanism of action.

B. NEURAL STEROID RECEPTORS The characterization of multiple neuroendocrine pathways through which T can facilitate aggressive behavior provided a basis for assessing the contributions of androgen receptor (AR) and estrogen receptor (ER) in these systems. The time frame for postcastration restoration of aggressive behavior in mice and other rodents strongly supports a focus on classical genomic processes, that is, on these receptors as transcription factors. Recent developments have added support to the critical role of AR (Sato et al., 2004), and the role of ER subtypes has been more clearly deﬁned (PfaV et al., 2002). 1. Androgen Receptor The importance of AR in male typical aggression has not been well appreciated, in part due to an emphasis on aromatization, the formation of E2, and subsequent activation of ER-mediated signaling pathways (Balthazart et al., 2003; PfaV et al., 2002). The importance of AR in the expression of aggressive behavior was reinforced by results that showed that AR gene knockout (ARKO) in male mice led to the ablation of male-typical aggressive behavior (Sato et al., 2004). Further, the impaired male typical behavior in female ER knockout (ERKO) mice was restored by DHT treatment. Developmental experiments revealed that

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perinatal DHT treatment of female ERKO mice established sensitivity to both E2 and DHT for the induction of male-typical behaviors, but that brain masculinization was abolished upon AR inactivation. These ﬁndings indicate that: (1) AR, as well as ER, is required for the expression of male typical behaviors in both sexes; (2) enhanced activation in an androgenic signaling pathway is adequate to compensate for the loss of ER function; and (3) AR plays an organizational role in brain masculinization during development. Speciﬁc contributions of the androgenic and estrogenic metabolites of T in masculinization of the regulatory pathways for intermale aggression had been reported previously (Simon et al., 1996). Several regions that are part of the neuroanatomical substrate for conspeciﬁc aggression, including the bed nucleus of the stria terminalis (BNST), lateral septum (LS), medial preoptic area (MPOA), and medial amygdala (MAMYG), exhibit strong positive immunoreactivity for AR in rodents and nonhuman primates (Lu et al., 1998, 2006). These descriptive ﬁndings are useful for deﬁning functional circuitry and can help elucidate the androgenic signaling cascading that eventually inﬂuences behavior. For example, a robust feature of androgen action in target cells, including the brain, is the autoregulation of AR by its cognate hormones. The characterization of sex, genotypic, or regional diVerences in androgen-induced AR autoregulation represent possible mechanisms that could underlie variation in sensitivity to the aggression-promotion property of androgen. The postcastration regulation of AR by diVerent androgens and estrogens in multiple brain regions and species has been tested. In CF-1 male and female mice, for example, a strain that is highly responsive to direct androgenic stimulation (Simon, 2002), AR regulation was dose- and ligand-dependent in multiple brain regions. Castration led to a rapid and pronounced loss of AR immunoreactivity, and T replacement (50–1000 mg) produced a dose-dependent linear increase in AR protein (Lu et al., 1998). Further, DHT, which is a more potent androgen than T, produced greater upregulation for a more extended period of time. The ﬁndings in mice recently were extended to a nonhuman primate model with similar results (Lu et al., 2006). Male cynomolgus monkeys were gonadectomized and treated with silastic implants containing E2 or DHT; control males were sham operated. The results (Fig. 5) showed that GDX þ DHT males exhibited the strongest AR immunoreactivity in the hypothalamus, while AR protein expression in GDX þ E2 males was signiﬁcantly lower than controls. Identical AR regulatory processes in female mouse brain to those seen in males indicates that a rapid increase in AR protein is only one component of the processes mediating responsiveness to the aggression-promoting property of androgen. Supporting this position are repeated demonstrations that the induction of male-typical aggression in ovariectomized females requires 16–21 days of androgen treatment (Simon, 2002). Because AR level can be increased dramatically within 3 hours of androgen administration, it is likely that increased

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FIG. 5. EVects of steroid hormones on AR content in ventromedial hypothalamus of male cynomolgus monkey brain. Animals were either sham operated or gonadectomized (GDX) and given silastic implants containing vehicle, dihydrotestosterone, or estradiol. The treatment groups were: sham þ vehicle; GDX þ vehicle; GDX þ estradiol (E2); and, GDX þ dihydrotestosterone (DHT). After 12 weeks of treatment, brains were immediately frozen at necropsy and stored at 70 C until use. Brain regions were isolated, ﬁxed in 3.7% formaldehyde/PBS for 24 hours, sections frozen on a microtome, and processed for AR immunohistochemical (IHC) staning. Representative AR IHC staining from VMH in each of the four groups is shown in 2(A), and 2(B) provides a graphical representation of semiquantitative image analysis, * indicates signiﬁcant diVerence from GDX group. Bar ¼ 50 mm. From Lu et al., 2006.

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cellular AR triggers enhanced (or suppressed) transcription of other androgenregulated genes. A reasonable hypothesis is that the changes in gene function bring about alterations in neuronal structure and neurotransmitter function that enables the expression of aggressive behavior. More speciﬁcally, the time frame required to induce male-like aggression in females raises the possibility that AR mediates elaboration of an androgen-dependent circuit through interactions with growth factors (Bimonte-Nelson et al., 2003; Fusani et al., 2003; Yang and Arnold, 2000; Yang et al., 2004). The pronounced sexual dimorphisms in neural pathways mediating reproductive behaviors are consistent with androgen-mediated circuit remodeling (Hutton et al., 1998; Simerly, 1998). Several of these structures, including the vomeronasal organ, accessory olfactory bulbs, medial and posterior nuclei of the amygdala, and BNST, process pheromonal and other olfactory stimuli (Segovia and Guillamon, 1993; Simerly, 1998). Because intermale aggression is triggered by a pheromonal stimulus, androgenic stimulation may function to establish this pathway in females and maintain it in normal males. AR-induced circuit remodeling in mammals may be similar to a testosterone-dependent increase in BDNF in adult male canary brain, which seems to play an important role in the viability of high vocal center neurons (Rasika et al., 1999). 2. Estrogen Receptor Elucidating the potential role of ER in the regulation of aggression became a more formidable challenge when ER , a novel form, was cloned from a variety of species including rat and human (Koehler et al., 2005; Matthews and Gustafson 2003; Sierens, 2004; Wilkinson et al., 2002). The and ER subtypes are highly conserved across species and share signiﬁcant amino acid sequence homology, particularly in the DNA-binding and ligand-binding domains (Ogawa et al., 1998). However, ER diVers from ER in two important aspects: in relative tissue distribution and cellular localization within the CNS (Shughrue et al., 1997, 1998) and in the relative aYnity of both naturally occurring and synthetic ligands (Kuiper et al., 1997; Sun et al., 1999). Both receptor subtypes are involved in mediating the eVects of estrogen on male-typical aggression, but their respective actions diVer markedly. Studies in estrogen receptor knockout mice (ERKO) have demonstrated that ER is the primary facilitator of oVensive aggression (PfaV et al., 2002). In the residentintruder paradigm, oVensive attacks were rarely displayed by ERKO males while wild-type (WT) and heterozygous males showed normal attack durations. Castration-hormone replacement studies built on these observations by showing that daily TP injections were ineVective in promoting aggression in ERKO males but highly eVective in gonadectomized WT males. Results with ER knockout males ( ERKO) provided additional support for the facilitative role for ER because ERKO males exhibited normal or enhanced attack behavior compared to WT males.

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ER appears to be a negative modulator of oVensive aggression. In addition to the ER gene knockout studies noted earlier, a recent investigation in nonhuman primates suggested several mechanisms through which ER could aVect aggression (Simon et al., 2004). More speciﬁcally, male cynomolgus macaques were fed diets containing high or low levels of soy phytoestrogens for 15 months. Those on the high soy diet exhibited signiﬁcantly higher levels of agonistic behavior compared to controls (Fig. 6). Soy estrogens preferentially bind to ER , are less active than estradiolER complexes in transcriptional activity in reporter assays ( JeVerson et al., 2002), and function as ‘‘weaker agonists’’ at ER compared to naturally occurring E2 (An et al., 2001; JeVerson et al., 2002; Yi et al., 2002). These properties, combined with the alterations in agonistic behavior, suggest multiple processes for ER -driven modulation of aggression. In limbic system regions that are part of the neuroanatomical substrate for intermale aggression (Simon, 2002), a substantial portion of target neurons for estrogen express both forms of ER (Gundlah et al., 2002; Mitra et al., 2002; Shughrue and Merchenthaler, 2001). In these cells, two ER -mediated mechanisms may contribute to the modulation of agonism in males. One involves changes in the transactivation function of ER ( JeVerson et al., 2002). In keeping with this concept, studies of cell proliferation in immature mouse uterus and the regulation of cyclin D1 gene expression have shown that E2–ER complexes negatively modulate ER induced eVects (Liu et al., 2002; Weihua et al., 2000). Another more speculative mechanism involves changes in the function of ER/ER heterodimers. In vitro studies have shown the formation of ER/ER heterodimers that retain DNA binding ability

FIG. 6. Frequencies (mean þ SEM) of episodes of intense aggression and submission among male cynomolgus monkeys who were on an isoﬂavone-free casein and lactalbumin-based diet (C/L) (n ¼ 14), a diet based in soy protein isolate containing 0.94 mg/g of isoﬂavone (Lo Iso) (n ¼ 15), and a diet based in soy protein isolate containing 1.88 mg/g isoﬂavone (Hi Iso) (n ¼ 15). * ¼ p < 0.05 relative to C/L group. From Simon et al. (2004) and reprinted with permission.

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(Petterson et al., 1997). The ER component of the heterodimer may normally diminish ER-mediated transcription. In both cases, the inhibitory eVect of ER in regions, such as the medial preoptic area, bed nucleus of the stria terminalis, and medial amygdala would diminish the facilitative eVort ER function and, as a consequence, decrease agonistic behavior. A third potential mechanism for contributions of ER to agonistic behavior involves eVects in target cells that express only this form of the receptor. The modulation of serotonergic tone in the rhesus monkey provides an example of this hypothesized process. In nonhuman primates, only ER are found in 5-HT neurons (Bethea et al., 2002; Gundlah et al., 2002). Estradiol normally acts in these cells to enhance serotonergic tone by increasing tryptophan hydroxylase synthesis and decreasing 5-HT transporter expression (Bethea et al., 2002; Lu et al., 2003). Thus, ER , as a modulator of serotonin, would decrease the propensity for aggression by maintaining normal serotonergic tone. Enhanced or reduced ER function in this region potentially could exert dramatic eVects on agonistic behavior.

IV. Hormonal Modulation of Serotonin Function

Pharmacological and molecular biological studies indicates that serotonin (5-HT) is a critical regulatory signal in the control of aggression in numerous species (Birger et al., 2003; Ferris, 2000; Kravitz, 2000; Olivier, 2004; Panksepp et al., 2003). The studies have shown that lower serotonergic tone is associated with increased aggression while enhanced serotonergic function reduces the expression of aggressive behavior. These relationships have been demonstrated in species ranging from crustaceans to rodents to primates, including humans (Birger et al., 2003; Kravitz, 2000) and the breadth of the ﬁndings has engendered a compelling basis for the extensive analysis of serotonergic tone and aggressive behavior. Broadly, agonists with selective aYnity for 5-HT1 receptors, particularly the 5-HT1A and 5-HT1B subtypes, speciﬁcally and selectively reduced oVensive intermale aggression (Olivier, 2004). From the perspective of the neuromodulator hypothesis, testicular hormones may inﬂuence behavioral activation by altering serotonin function in brain regions that either constitute or project to the neuroanatomical substrate for intermale aggression. Support for this concept can be found in both autoradiographic and in situ hybridization ﬁndings that, in combination, show overlapping distributions of estrogen-, androgen-, and serotonin-concentrating neurons as well as receptor gene expression in these regions (Herbison, 1995, 1998; Mengod et al., 1996; Simerly et al., 1990; Wright et al., 1995). Such ﬁndings, although

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clearly of interest and suggesting interactions, are insuYcient for the establishment of neuromodulation. To test whether 5-HT function is aVected by gonad requires evidence which shows that: (1) androgens or estrogens diVerentially aVect the ability of 5-HT1A, 5-HT1B, or combined agonist treatments to alter the display of oVensive intermale aggression; (2) neuronal populations where these eVects are produced are identiﬁed; and (3) androgen or estrogen inﬂuences 5-HT1A or 5-HT1B function in these regions by altering particular aspects of serotonin function. Our laboratory conducted two investigations that assessed androgenic and estrogenic eVects on 5-HT1A and 5-HT1B functions in the context of oVensive aggression (Cologer-CliVord et al., 1997, 1999). In the initial study, systemic treatments were used to identify the relationship between functional hormonal pathways and the modulation of serotonergic eVects. Interestingly, serotonergic 1A and 1B agonists were far more eVective in reducing the display of ﬁghting behavior in the presence of speciﬁcally acting androgens compared to estrogen. If estrogens were present, either alone or as a metabolic product, the ability of 5-HT1A and 5-HT1B agonists to inhibit oVensive aggression was restricted. When aggression was promoted by a direct androgenic eVect, however, 5-HT1A and 5-HT1B agonists were very eVective in decreasing the expression of oVensive behaviors. Neuroanatomical localization of the modulatory eVects of androgen and estrogen was assessed in a second study. Likely sites included the LS, MPOA, MAMYG, and DR based on receptor distribution maps and our understanding of neuroanatomical substrates for intermale aggression. When selective 5-HT1A and 5-HT1B agonists were microinjected into these regions, there were pronounced diVerences in the observed eVects. In the presence of diethylstilbestrol (DES), a potent speciﬁcally acting estrogen, microinjections of either 1A or 1B agonists into the LS had essentially no eVect on behavioral expression. When gonadectomized males were implanted with DHT, aggressive behavior was decreased with 1B agonist microinjection alone or in combination with the 1A agonist 8-OH-DPAT. The eVects of CGS12066B microinjection were speciﬁc because motor behavior was unaVected. At the level of the LS, then, an androgen-sensitive pathway that facilitates aggression can be attenuated by the action of serotonin at 1B receptor sites. In the MPOA, observed eVects were robust. Signiﬁcantly reduced oVensive aggression in the presence of either androgen or estrogen was seen with both 5-HT1A or 5-HT1B agonist microinjections and the eVects were obtained without any impact on activity level. The MPOA may thus be a major integrative site for gonadal hormone-serotonin interactions in the regulation of T-dependent aggression. The alteration by gonadal hormones of the ability of serotonergic 1A and 1B agents to eVect T-dependent intermale aggression supports the

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neruomodulator hypothesis. Additional examples of comparable hormone-neurotransmitter interactions have been described in other systems, particularly in regard to reproductive behavior (Etgen, 2002; Fink et al., 1999; Melton, 2000; Uphouse, 2000), anxiety, and mood disorders (Bethea et al., 2002; Fink et al., 1998, 1999). An interesting aspect of our studies was diVerences in the ability of estrogens and androgens to attenuate the aggression-inhibiting eVects of 5-HT1A and 5-HT1B agonists and the regional variation in these eVects. If additional studies continue to ﬁnd regional and cellular variation as well, regulatory models would necessarily take on an even more complex structure. Areas of inquiry requiring attention in light of our ﬁndings include mechanistic studies that address steroidal enhancement or repression of the ability of 5-HT1A and 5-HT1B agonists to attenuate oVensive intermale aggression. Estrogens can, for example, alter 5-HT1A gene expression or inﬂuence ligand availability through eVects on synthetic or degradative processes (Gundlah et al., 2002; Lu et al., 2003; McQueen et al., 1999; Mize and Alper, 2002). One step in establishing a direct eVect on 5-HT1A gene function would be the identiﬁcation of a functional ERE in the promoter region of the 5-HT1A receptor gene. Interestingly, both mouse and human 5-HT1A receptor genes contain a putative ERE (Table I). The spacer element is a clear diVerence between the postulated motifs and the consensus sequence, which is ﬁve nucleotides rather than three. However, nonconsensus EREs with diVerent spacer lengths are responsive to estrogenic regulation (Berry et al., 1989; Hall et al., 2002; Klungland et al., 1994; Shupnik and Rosenzweig, 1990; Sohrabji et al., 1995). For example, the salmon GnRH and rat BDNF genes have ERE motifs with eight or nine nucleotide spacers and can bind activated estrogen receptors in vitro (Klungland et al., 1994; Sohrabji et al., 1995). TABLE I ESTROGEN RESPONSE ELEMENTS WITH VARIABLE SPACERS Species and gene Traditional spacer (n ¼ 3) Xenopus vitellogenin A2 Chicken vitellogenin II Chicken ovalbumin Human c-fos Rat prolactin Nontraditional spacer (n > 3) Rat LH- Rat BDNF Salmon GnRH Salmon GnRH Putative human 5-HT1A motif Putative mouse 5-HT1A motif

Starting position

DNA sequence

331 625 177 1209 1572

GGTCACAGTGACC GGTCAGCGTGACC GGTAACAATGTGT CGGCAGCGTGACC TGTCACTATGTCC

1173 1045 1501 1569 429 426

GGACA[N]5TGTCC GGTGA[N]9TGACC GGTCA[N]8TGTCC AGTCA[N]9TGACC GGTCA[N]5TGACC

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Estrogen also can aVect serotonin function through processes that require multiple steps. For example, E2 treatment reportedly alters 5-HT1A receptor binding and ligand availability (Osterlund et al., 2000) as a result of modulating tryptophan hydroxylase activity and/or transporter gene expression (Bethea et al., 2000; McQueen et al., 1999; Pecins-Thompson and Bethea, 1998). It is important to recognize that to the extent that such eVects have been observed in whole animal models, they have been deﬁned only in females. Moving from these studies to intermale aggression requires caution. In addition, ER potentially may have direct eVects on serotonergic function in DR (Alves et al., 2000; Bethea et al., 2002) and may modulate the regulatory actions of ER.

V. Conclusions

Characterization of the hormonal processes involved in the expression of conspeciﬁc aggression has progressed diVerently in males and females. In adult males, our understanding is far more developed compared to female-typical behavior. In males, the importance of hormone metabolism has been demonstrated; aromatization and 5-reduction of T in males are critical steps. In females, the contribution of DHEA and its metabolites as androgens may represent important system components but this has not been established experimentally. Several target neurochemical systems have been identiﬁed in males, but extensive work is needed to deﬁne the cellular processes that are aVected and the genomic and nongenomic mechanisms that mediate these eVects. A systems model of oVensive aggression that encompasses gene regulation, functional circuitry, behavioral expression, and adaptation depends on progress in this area. The neuromodulator hypothesis represents a working model that can help deﬁne the hormonal contribution to sex-typical oVensive aggression. The emphasis on neuromodulation provides a broad conceptual framework; strength is that it can incorporate ﬁndings that show that key hormonal systems produce diVerent eVects in each sex. More speciﬁcally, in males the gonadal steroids are neutral or facilitative, while in females hormonal inﬂuences seem to be largely inhibitory. Neuronanatomical substrates for aggression are still not completely deﬁned in males, while in females little if any work has been done and characterization of cell/molecular mechanisms is in its infancy. In males, elucidating the cell/ molecular interactions among T, its metabolites, and components of the 5-HT (and no doubt other) systems is needed. In females, the various levels and processes involved in the modulation of GABAA receptor functions have not been systematically deﬁned. These are formidable tasks, but at the same time represent only a partial list based on the representative systems covered in this chapter.

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Finally, we chose to focus on gonadal steroids in males and one neurosteroid in females rather than take an approach that brieﬂy touched on the many neurotransmitters that are involved in aggression that are modulated by steroids. Other areas of interest, for example, are the eVects of corticosteroids and interactions of these and other hormones with the serotonin and vasopression systems (Ferris, 2000; Haller et al., 2000a,b). Research with animal models demonstrates the complex nature of hormonal modulation and the need for increasingly reﬁned regulatory models of oVensive aggression. The complexity and extent of interactions also indicates that focusing on a single genetic or physiological marker as a cause of aggression is a diYcult proposition with limited utility. A systems perspective is required, one that recognizes when hormones may have a role, that physiological eVects of hormones are modulatory, and that social structure, life events, and subsequent adaptations are reﬂected in alterations in cellular signaling pathways. Acknowledgments

The preparation of this chapter was supported in part by grants from NIH (R01 MH59300) and the HF Guggenhiem Foundation to NGS.

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NEURONAL GAP JUNCTIONS: EXPRESSION, FUNCTION, AND IMPLICATIONS FOR BEHAVIOR

Clinton B. McCracken and David C. S. Roberts Department of Physiology and Pharmacology, Wake Forest University School of Medicine Winston-Salem, North Carolina 27157, USA

I. II. III. IV. V. VI. VII. VIII. IX. X.

A Brief History of Gap Junctions Gap Junction Structure Gap Junctions in the Brain Electrical Coupling in the Brain Properties and Function of Electrical Synapses Modulation of Electrical Synapses and Gap-Junctional Coupling Use-Dependent Plasticity Local Factors: Voltage, pH, and Calcium Neurotransmitter and Second Messenger Modulation Concluding Remarks References

In this chapter, we will review what is currently known (and not known) about gap junction expression in neurons. We will discuss the composition of neuronal gap junctions, the functions of neuronal gap junctions acting as ‘‘electrical synapses,’’ and attempt to highlight some of the many controversies surrounding these issues. The latter portion of this chapter will be devoted to modulation and plasticity of junctional communication between neurons, with a particular emphasis on the potential consequences of alterations in neuronal coupling for neural function and behavior. This chapter is not directed at those who are currently studying gap junction neurobiology per se, rather, it is an attempt to convince neuroscientists less familiar with the subject of the importance of direct intracellular communication between neurons in brain function. Often assumed to be static, we know now that gap-junctional communication is plastic and subject to modulation, and this plasticity is likely to have meaningful consequences for neural activity. I. A Brief History of Gap Junctions

The concept that neurons interact through direct transfer of electrical current is not new. Indeed, this form of communication was championed by one faction of early neuroscientists as the primary mechanism of neural transmission (Eccles, INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73004-5

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1982), until pivotal studies by Loewi, Dale, and others produced incontrovertible evidence that neurons communicate using chemical messengers. This led to the dogma that neurons communicate by chemical neurotransmission only, and interest in electrical interactions between neurons rapidly faded. As such, the study of gap junction-mediated intracellular communication between neurons has only recently begun to attract attention from most contemporary neuroscientists. It has been known for some time that invertebrate neurons are electrically coupled (Furshpan and Potter, 1959; Watanabe, 1958), and this type of connection was also documented in vertebrates (Bennett et al., 1959). At the time, however, these direct neuronal interactions were thought to be a hallmark of lower organisms and of little signiﬁcance in mammalian brain. Even with the demonstration of neuronal coupling in rodents (Baker and Llinas, 1971; Korn et al., 1973; Llinas et al., 1974), there remained very little study devoted to the subject, save for a small core of researchers. Development of more sophisticated microscopy techniques enabled identiﬁcation of the morphological correlate of this coupling, from studies on heart, liver, and brain (Barr et al., 1965; Pappas et al., 1971; Revel et al., 1971). This structure was named the ‘‘gap junction,’’ from the studies of Revel and Karnovsky (1967). Gap junctionmediated communication has now been documented in virtually all cell types and tissues, and the bulk of our understanding of direct intracellular communication comes not from brain, where it was ﬁrst characterized, but from examination of expression and function in other tissues. Studies of gap-junctional communication between neurons have generally remained on the periphery of neuroscience, even as breakthroughs were made in other disciplines regarding the molecular biology and biophysical properties of these channels. As shown in Fig. 1, studies of gap junctions in brain comprise only a small fraction of total gap junction studies. Major advances in the study of gap junctions included the cloning of a gap junction subunit and the determination of the gap junction channel crystal structure. These eVorts greatly facilitated the study of gap junction biology, and enabled comprehensive analysis of gap junctions in expression systems. Still, as indicated by the limited number of studies, these channels were thought to be of limited relevance to neuronal function. At the same time, critical roles for intercellular communication were being shown in development, cell growth, and diVerentiation; cardiovascular, hepatic, endocrine, and immune system functions; as well as in certain pathologies. The last 10 years or so have seen an explosion in studies of gap junctions in brain, largely due to advances in electrophysiology and molecular biology. As we will discuss below, a neuron-speciﬁc connexin has been identiﬁed, and has been shown to mediate current transfer between neurons in a number of brain areas using paired intracellular recordings. This has led to a reassessment of the functional signiﬁcance of neuronal gap junctions in adult mammals. Indeed, where in past years neuroscience textbooks either had no mention of

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FIG. 1. Publications involving the structure and function of gap junctions published in the period 1971 through 2005, broken down into 5-year epochs. Bars represent the total number of publications during each interval as reported by PubMed. Search terms were ‘‘gap junction’’ and ‘‘gap junction AND brain.’’

gap junctions, they are now being accorded more attention. For example, Kandel et al. (2000) and Bear et al. (2001) give only passing reference to gap junctions; however the most recent edition of Fundamental Neuroscience (Squire et al., 2002) devotes a full chapter to the subject, reﬂecting the recent advances in the ﬁeld. These developments have raised a plethora of new experimental questions. Whereas the main question was once ‘‘are they relevant?’’ the question has now become ‘‘how are they relevant?’’ Here we will outline some of the properties of gap junction channels, before discussing the modulation of junctional communication and its relevance for brain function.

II. Gap Junction Structure

Gap junctions were traditionally identiﬁed by their characteristic structural features when viewed with thin section electron microscopy. They appear as a close apposition of thickened plasma membranes separated by a small (2–3 nm) gap of extracellular space (Fig. 2A). Based on these observations, Revel and Karnovsky (1967) coined the term ‘‘gap junction’’ to describe these structures. Freeze-fracture electron microscopy showed that the close membrane appositions

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FIG. 2. Gap junction ultrastructure. (A) Thin-section micrograph showing a gap junction between glial cells. Arrows indicate gap junction. Scale bar—200 nm. Adapted from Atlas of Ultrastructural Neurocytology. (B) Freeze-fracture micrograph of a gap-junction plaque. Each small dot on the plaque is a gap-junction channel (large black spots are gold beads). Scale bar—0.1 mm. Adapted from Rash et al. (2001b).

were formed by ‘‘plaques’’ of hundreds to thousands of individual channels connecting the two cells (Fig. 2B). Full understanding of the gap junction structure was facilitated greatly by cloning of a gap-junction subunit, or connexin (Paul, 1986). This allowed detailed examination of gap junctions using the entire arsenal of molecular biological techniques. As mentioned, gap-junction channels are formed by connexin subunits, and more than 20 diVerent connexin genes have been identiﬁed (Evans and Martin, 2002; Willecke et al., 2002). These genes are named according to apparent molecular weight of the expressed protein (e.g., Cx43 is approximately 43 kDa). Individual connexin subunits have four membrane spanning domains that are highly conserved across the gene family (Saez et al., 2003), with two extracellular loops, and intracellular N- and C-termini. Six individual connexin subunits combine to form a hexameric hemichannel, or connexon, and two connexons from apposed membranes form the gap-junction channel (Fig. 3). Connexon hexamers can be homo- or heteromeric, and as well, gap-junction channels can be homo- or heterotypic. Only certain connexons can form functional heterotypic gap junctions, and compatibility is determined by individual connexin subtypes. It is believed that this may allow cells expressing multiple connexins to establish distinct functional connections. The gap-junction channel pore is approximately 1.2 nm in diameter, allowing transmission of ions and small molecules under 1 kD in size such as cAMP and IP3. Channel conductance, selective permeability, and gating inﬂuences all vary depending on connexin subtype composition (Kumar and Gilula, 1996), however, not all subtypes have been fully characterized in native tissue.

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FIG. 3. Schematic of a gap-junction plaque and connexin subunit hemichannels in apposed plasma membranes of neighbouring cells can dock to each other and form gap junction channels. Inset: Connexin protein subunits have four transmembrane domains. Adapted from Sohl et al. (2004).

III. Gap Junctions in the Brain

The study of gap junction expression and function in the brain has until recently proceeded along separate lines of research—physiology, anatomy, or molecular biology—that have only recently begun to converge. The relationship between electrical synapses and gap junctions was beginning to be clariﬁed more than 30 years ago. At approximately the same time electrical synapses were discovered in mammals, ultrastructurally identiﬁed gap junctions between neurons were also reported in the mammalian brain (Sloper, 1972), and it has been known for some time that a number of connexin proteins are found in brain (Dermietzel et al., 1989; Shiosaka et al., 1989). Deﬁnitively establishing the connexin constituents of gap junctions between neurons remains a high priority. As mentioned, channels formed by diVerent connexins show diVerent properties such as channel conductance, selectivity of permeant molecules, and gating. Accordingly, gap junctions comprising diVerent connexins may aVect neuronal coupling in diVerent ways. Considerable progress has been made in determining the cellular localization of various connexins in the CNS, although a good deal of controversy remains.

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One confound is the fact that expression levels of many connexins show dramatic changes with development. Some conﬂicting reports of neuronal connexin expression could possibly be due to animals of diVerent ages being used. Intercellular coupling plays a signiﬁcant role in neuronal development and development of functional neuronal architecture (Montoro and Yuste, 2004; Rozental et al., 2000), which is beyond the scope of this chapter. A number of other factors have impeded this line of work. Low expression levels, of both mRNA and protein, and protein expression at sites distal to the soma have impeded immunocytochemical studies. In situ hybridization and immunohistochemistry studies may be confounded by cross-reactions, and conﬁrmation of antibody and probe speciﬁcity in knockout animals is essential (Meier et al., 2002). As well, studies performed using light microscopy lack the spatial resolution (i.e., less than 0.2 mm) to deﬁnitively assign connexin signals to particular cell types. The most deﬁnitive technique for identiﬁcation of connexin constituents of gap junctions is freezefracture immunogold labeling (FRIL) (Nagy et al., 2004), which uses a combination of electron microscopy and immunolabeled antibodies that provides very high resolution. However, this technique is labor-intensive and has not been used in many brain areas, and gap-junction plaques formed by a small number of channels could conceivably escape detection. A relatively novel method for examining connexin expression involves replacing the coding region of the gene with a reporter gene such as -galactosidase (e.g., Deans et al., 2001; Zhang et al., 2000). This technique has limitations as well, as ectopic expression may occasionally be observed due to interference with upstream regulatory elements (Sohl et al., 2004). As the ﬁeld has progressed, it has become clear that all techniques have their advantages and disadvantages, and combinatorial approaches are necessary to achieve consensus. While approximately half of the currently known connexins are expressed in brain, only Cx36 has been unequivocally shown to be expressed in neurons. This connexin was ﬁrst cloned in 1998 and shown to be highly expressed in neurons using in situ hybridization (Condorelli et al., 1998; Sohl et al., 1998). Cx36 expression is found in almost all brain areas, including neocortex, brainstem, basal ganglia, hippocampus, and cerebellum, and shows a developmentally regulated expression proﬁle, with highest expression on postnatal day 7, declining to lower levels in adulthood. Subsequent studies combining in situ hybridization with immunolabeling for a neuronal marker conﬁrmed that Cx36 was not only expressed in neurons, but also appeared to be neuron speciﬁc (Belluardo et al., 2000; Condorelli et al., 2000). Development of antibodies to Cx36 enabled direct study of protein localization to ultrastructurally-identiﬁed gap junctions between neurons in a number of brain regions using FRIL (Rash et al., 2000, 2001a,b), and, on a more macroscopic scale, using immunohistochemical techniques (Liu and Jones, 2003; Meier et al., 2002; Teubner et al., 2000). Data from these Cx36

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protein studies were in general concordance with the mRNA data. Further support for neuronal expression of this connexin was obtained using transgenic animals (generated by several groups) that expressed a reporter gene in the place of Cx36 (Deans et al., 2001; Degen et al., 2004; Landisman et al., 2002; Long et al., 2002). The bulk of these studies indicate that Cx36 is predominantly expressed in GABAergic neurons, usually interneurons, although expression has been observed elsewhere. In these Cx36-deﬁcient animals, there is virtually no electrical coupling of interneurons, which adds considerably to the accumulated evidence for Cx36 contributing to at least one kind of electrical synapse. Evidence for other neuronal connexins is less robust. While Cx45 was originally characterized as an oligodendrocytic connexin (Dermietzel et al., 1997; Kunzelmann et al., 1997), recent reports have suggested that Cx45 may be expressed by restricted populations of neurons. In situ histochemistry indicated neuronal expression in most brain regions in young animals (Condorelli et al., 2003), although expression in nonneuronal cells was also observed. Using the same technique, another group also showed Cx45 expression in mature olfactory neurons (Zhang and Restrepo, 2002). Replacing the Cx45 coding region with a reporter gene showed that in adult animals, expression was conﬁned to subregions of the hippocampus, thalamus, and cerebellum, with no signal detected in nonneuronal cells (Maxeiner et al., 2003). In addition to Cx45, several other connexins have been proposed to be in neurons. Before the discovery of Cx36, Cx32 was thought to be a major candidate for a neuronal connexin, although it is predominantly expressed in oligodendrocytes and Schwann cells (Scherer et al., 1995). Light microscopic studies have shown apparent neuronal expression of Cx32 mRNA and protein in mature animals (Dermietzel et al., 1989; Micevych and Abelson, 1991, 1996; Micevych et al., 1996; Nadarajah and Parnavelas, 1999; Nadarajah et al., 1996). As well, single-cell RT-PCR studies on electrically coupled neurons indicated the presence of Cx32 mRNA, although it was less common than Cx36 (Venance et al., 2000, 2004). However, the animals used in these studies were still juvenile, and the presence of Cx32 mRNA does not necessarily guarantee that the protein is expressed. Of interest is the fact that the Cx32 transcript found by Venance et al. (2000) in neurons is apparently a splice variant of the more common oligodendrocytic Cx32 transcript, and may represent a diYcult-to-detect neuronal subtype. Cx26 has also been reported in neurons (Honma et al., 2004; Solomon et al., 2001), although other studies suggest it is speciﬁc to astrocytes (Altevogt and Paul, 2004; Nagy et al., 2001). Further complicating matters, a reporter gene study showed Cx26 promoter activity in leptomeningeal cells only (Filippov et al., 2003). While Cx43 is generally acknowledged to be in astrocytes (Altevogt and Paul, 2004; Nagy et al., 2003; Ochalski et al., 1997), some studies have indicated this connexin may also be found in neurons (Nadarajah et al., 1996; Priest et al., 2001; Simburger et al.,

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1997), and particularly, in olfactory neurons (Theis et al., 2003; Zhang et al., 2000). These are selected examples of the kinds of discrepancies seen in the literature. It must be noted that of all the connexins mentioned, only Cx36 has been shown to participate in ultrastructurally deﬁned neuronal gap junctions using FRIL, while Cx26, Cx32, and Cx43 have only been found in gap junctions connecting glial cells (Rash et al., 2000, 2001a,b). These studies remain somewhat limited due to the number of brain regions examined, and rely on antibody detection, which does not preclude the possibility of other neuronal connexins. Obviously, though, further studies are needed for these issues regarding cellular localization to be resolved. It will be necessary to determine whether connexin expression guarantees functional coupling, which may not always be the case. The cloning of the human, rat, and mouse genomes led to the proposition that new connexin genes are unlikely to be discovered (Willecke et al., 2002). However, recent developments indicate that connexins may not be the only gap junction-forming protein in mammals. Invertebrate gap junctions are formed from connexin homologs known as innexins (Phelan and Starich, 2001) that share structural but very little sequence homology with the connexin family. Recently, innexin-like genes were discovered in mammals (Panchin et al., 2000). These genes, referred to as pannexins, form functional gap-junction channels in expression systems, and one subtype shows expression in brain (Bruzzone et al., 2003; Weickert et al., 2005). Whether these proteins form functional gap junctions and/or electrical synapses between neurons has yet to be determined, but certainly presents intriguing possibilities for a novel substrate of direct intracellular communication between neurons.

IV. Electrical Coupling in the Brain

While electrical coupling between neurons in mammalian brain was discovered some time ago, there has been very little research on the subject until recently. The presence of electrical synapses was inferred from electron micrographs showing gap junctions between neurons in a number of brain regions (Kosaka, 1983; Kosaka and Hama, 1985; Sloper, 1972; Sloper and Powell, 1978; Sotelo et al., 1974). Technical limitations made it extremely diYcult to study electrical coupling between neurons directly using paired intracellular recordings. As such, a common functional assay to assess gap-junctional coupling was to examine transfer of dye from an injected cell to its neighbors, presumably through gap-junction channels (Stewart, 1978, 1981). This technique has been used to provide physiological evidence for gap-junctional communication in many brain areas (Andrew et al., 1981; Gutnick and Prince, 1981; MacVicar

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and Dudek, 1981a). Validation of dye coupling as a correlate of electrical coupling, through gap junctions was provided in a series of experiments where transfection of connexin mRNA into connexin-deﬁcient cells induces both electrical and dye coupling (Eghbali et al., 1990, 1991; Moreno et al., 1991). However, dye coupling has limitations; unless great care is taken with slice preparation and dye loading, this technique is prone to artifact (Connors and Long, 2004; Gutnick et al., 1985), so caution is required. Moreover, while dye coupling implies electrical coupling, lack of dye coupling does not imply lack of electrical coupling (Gibson et al., 1999) due to any number of factors, including dye molecule size or size of the coupled network. The obstacles to paired intracellular recordings were largely overcome with the development of infrared diVerential interference contrast microscopy (IR-DIC) (Stuart et al., 1993). In 1999, using IR-DIC, several groups showed that pairs of interneurons in a number of brain regions were electrically coupled (Galarreta and Hestrin, 1999; Gibson et al., 1999; Koos and Tepper, 1999; Mann-Metzer and Yarom, 1999). Moreover, this coupling synchronized ﬁring among coupled cells in the network. Here, we present a general overview of electrical synapses in brain (for in-depth review, see Bennett and Zukin, 2004; Connors and Long, 2004). Electrically coupled neurons have now been documented in many brain regions. Most of the electrically coupled neurons identiﬁed using dual recordings are GABAergic, and coupling seems to be generally restricted to neurons of the same class, although neither of these rules is absolute. In neocortex, four electrophysiologically identiﬁed subclasses of GABAergic interneurons—low threshold spiking cells (Gibson et al., 1999), fast spiking cells (Galarreta and Hestrin, 1999; Gibson et al., 1999), multipolar bursting cells (Blatow et al., 2003), and late spiking cells (Chu et al., 2003)—show extensive coupling among cells of the same type, but almost never to other classes of cells. This seems to be a general principle in electrical coupling. Supporting this notion, Galarreta et al. (2004) showed over 90% of tested cell pairs of an inhibitory interneuron subclass characterized by irregular spiking, and CB1 receptor expression was electrically coupled. In addition to cortical interneurons, paired recordings have revealed electrical synapses between hippocampal interneurons (Hormuzdi et al., 2001; Venance et al., 2000), inferior olivary (IO) neurons (Devor and Yarom, 2002; De Zeeuw et al., 2003; Long et al., 2002), cerebellar interneurons (Mann-Metzer and Yarom, 1999), thalamic reticular neurons (Landisman et al., 2002; Long et al., 2004), suprachiasmatic neurons (Long et al., 2005), and striatal interneurons (Koos and Tepper, 1999), all of which are GABAergic. A potential concern is that most paired recordings are done in juvenile animals when myelination is incomplete. This is because slices from adult animals are considerably more opaque, making visual identiﬁcation of neurons using IR-DIC

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much more diYcult. Given the prominent developmental role for gap junctions and intercellular coupling, it is possible that these studies do not reﬂect coupling in the mature animal. These concerns were largely allayed in two studies where a reporter gene was used in the place of parvalbumin, a calcium-binding protein that is a marker gene for a subset of GABAergic interneurons (Galarreta and Hestrin, 2002; Meyer et al., 2002). Both groups showed that parvalbumin-containing interneurons showed similar incidences of coupling in juvenile and adult animals, although the strength of coupling appeared weaker and less common in adults. Of note, coupling between inhibitory and excitatory cells reported earlier in the literature (Venance et al., 2000), was seen in juvenile but not adult animals, lending credence to the notion that electrical synapses are found primarily between cells of the same type. Although there is a report showing gap junctions and coupling between neurons and glia in immature locus coeruleus (AlvarezMaubecin et al., 2000), these ﬁndings have been disputed as being artifactual (Nagy et al., 2004), and further studies are necessary for clariﬁcation. While studies on electrical synapses between neurons have generally shown coupling between similar interneurons and not between principal eVerent cells, this is not an absolute rule. A series of studies using electron microscopy, dye coupling, and dual intracellular recordings suggested hippocampal pyramidal cells were coupled via gap junctions (MacVicar and Dudek, 1980, 1981b; MacVicar et al., 1982), although subsequent reexamination of the electron micrographs led to the conclusion that cells connected by gap junctions were not actually neurons (Nagy et al., 2004). Support for the idea of electrical synapses between pyramidal cells came from both computational modeling studies, that suggested a role for this type of coupling in certain types of high-frequency oscillations (Draguhn et al., 1998; Traub et al., 2002), and experimental evidence, using dye coupling and antidromic stimulation in CA1 pyramidal cells (Schmitz et al., 2001). However, as pointed out by Connors and Long (2004), while there is abundant evidence for morphological gap junctions (Fukuda and Kosaka, 2000; Katsumaru et al., 1988; Kosaka, 1983; Kosaka and Hama, 1985) and electrical synapses (Hormuzdi et al., 2001; Venance et al., 2000) between hippocampal interneurons, none of these studies reported gap junctions or electrical coupling between pyramidal cells. A similar situation exists in the cortex. As mentioned above, there are numerous reports documenting gap junctions and electrical coupling between cortical interneurons, but very little evidence indicating the principal neurons of the cortex are coupled. While mature cortical pyramidal neurons have been reported to be dye coupled (Gutnick and Prince, 1981), subsequent studies indicated that dye coupling was present in immature animals and declined substantially in adulthood (Connors et al., 1983; Peinado et al., 1993; Rorig and Sutor, 1996b). Recent studies indicate principal output cells of other brain regions may also be functionally coupled. Medium spiny neurons, the GABAergic output neurons

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of the striatum, show dye transfer in mature animals (Cepeda et al., 1989; O’Donnell and Grace, 1993) and have recently been shown to be electrically coupled using paired intracellular recording (Venance et al., 2004). Dopaminergic neurons of the substantia nigra pars compacta have also been reported to be dye coupled (Grace and Bunney, 1983), although the opposite has also been reported (Lin et al., 2003). A very recent study used dye coupling and dual recordings to demonstrate that these neurons showed both electrical and tracer dye coupling (Vandecasteele et al., 2005). Interestingly, coupling disappeared between postnatal days 15–20 and reappeared between days 20–25. This may help explain the previous discrepant results. Another example of coupling of principal neurons is found in the inferior olivary nucleus, where climbing ﬁbers, the main output neurons of this nucleus, are extensively coupled (Devor and Yarom, 2002).

V. Properties and Function of Electrical Synapses

What are the functional roles of electrical synapses? For in depth reviews on the electrophysiological properties of electrical synapses, see Bennett (1997); Bennett and Zukin (2004); and Galarreta and Hestrin (2001a). In invertebrates, electrical synapses allow very rapid transmission of electrical signals, however, at higher mammalian body temperatures electrical transmission is not signiﬁcantly faster than chemical synaptic transmission (Bennett and Zukin, 2004). The unique aspect of electrical synapses is their reciprocity. Of the electrical synapses studied, almost all show equivalent coupling strength in both directions. As such, electrical synapses are neither inhibitory nor excitatory per se, but rather, synchronizing (Bennett and Zukin, 2004) as the eVect of a depolarizing current ﬂowing from cell A to B will result in cell A becoming less depolarized. In eVect, electrical coupling normalizes the voltage diVerence between two coupled cells. This process is more eYcient for slow changes in membrane potential than fast, due to gap junctions acting like low-pass ﬁlters (Bennett, 1997; Galarreta and Hestrin, 2001a; Gibson et al., 2005). Low-pass ﬁltering in eVect means that action potentials are not transmitted with 1:1 ﬁdelity, but instead are greatly attenuated in the postjunctional cell, appearing as small amplitude ‘‘spikelets.’’ Slower and lower-amplitude changes in membrane potential are transmitted more eVectively. Across many of the paired recording studies described above, ‘‘coupling coeYcients’’—deﬁned as the change in postjunctional voltage divided by the change in prejunctional (i.e., current-injected cell) voltage; expressed as a percentage—for small membrane potential changes range from 2 to 20%, with an average of approximately 8%. For action potentials the range of coupling coeYcients is from 0.5 to 2% (Galarreta and Hestrin, 2001a). Although action potentials are not faithfully transmitted through electrical synapses, rapid spikelet

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transmission allows submillisecond action potential coordination, and transmission of subthreshold membrane potential changes also enhances synchronous ﬁring among the coupled cells (Galarreta and Hestrin, 1999). A detailed analysis of the biophysical properties of electrical synapses between cortical interneurons extended these ﬁndings, showing that electrical synapses synchronize interneuron ﬁring at all ﬁring frequencies (Gibson et al., 2005). Many electrically coupled cells are also coupled by reciprocal inhibitory chemical connections, which allows for very complex modulation of spiking. Though synchronous ﬁring in inhibitory interneurons may be engendered by inhibitory chemical synapses alone, electrical coupling sharpens this synchrony considerably (Bartos et al., 2002; Placantonakis et al., 2004). What are the implications of gap junction-mediated synchrony for larger neuronal networks? Electrical coupling, by virtue of its properties of being fast, synchronizing, and bidirectional, is thought to be involved in the coordination of the synchronized, rhythmic ﬁring oscillations of interneurons and principal cells seen in neocortex and hippocampus (Traub et al., 2003). Oscillations at diVerent discrete frequencies correlate with diVerent behavioral states (Buzsaki and Chrobak, 1995), and some oscillations, in particular those in the gammaband frequency (30–80 Hz), have been proposed to be involved in synchronizing neural activity across brain areas, and in emergent properties, such as consciousness (Buzsaki and Draguhn, 2004; Singer, 2001; Singer and Gray, 1995 ). Tamas et al. (2000), showed that the combination of inhibitory chemical synapses and electrical synapses was able to entrain gamma-frequency ﬁring. As well, pharmacological gap-junction blockade reduced the synchrony of gamma oscillations in interneuron networks (Traub et al., 2001a). Further support for the role of electrical synapses between interneurons in these oscillations has come from studies of the Cx36 knockout (KO) mouse, which showed impaired gamma activity both in vitro (Hormuzdi et al., 2001) and in vivo (Buhl et al., 2003). Another type of synchronous network behavior, ultrafast oscillations (>200 Hz), has been proposed to depend on coupling between pyramidal cells based on modeling studies (Traub et al., 1999) and in vitro studies (Draguhn et al., 1998). These studies oVer the intriguing possibility that electrical coupling may modulate higher cognitive ability. Electrical coupling of neurons may also subserve other physiological functions. Computational studies (Marder, 1998) and experimental evidence (Galarreta and Hestrin, 2001b) suggest that electrical synapses may also act as coincidence detectors in interneuronal networks, where coincident inputs will promote cell ﬁring, but noncoincident inputs will reduce network ﬁring due to transmission of the afterhyperpolarization to the coupled cells. With regards to pathophysiology, gap junctions may also contribute to seizure activity. The ultrafast oscillations mentioned above may be involved in seizure initiation (Traub, 2003; Traub et al., 2001b), and gap-junctional communication has been proposed to contribute to

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the hypersynchronous ﬁring seen in seizures (Gajda et al., 2003; Jahromi et al., 2002; Margineanu and Klitgaard, 2001; Uusisaari et al., 2002). The role of gap junctions in seizure activity has been reviewed elsewhere (Carlen et al., 2000; Perez Velazquez and Carlen, 2000) and will not be discussed further here. In addition to transfer of current, gap junctions between neurons allow passage of second messengers such as IP3 and cAMP (Kumar and Gilula, 1996). This neuronal biochemical coupling is not well deﬁned in adults, but may aid in modulation of other aspects of the coupled network, coordinating metabolic eVects and ensuring neurons of the network act in concert. Movement of IP3 through gap junctions plays a pivotal role in shaping cortical architecture in development (Kandler and Katz, 1998); however, further studies are needed to determine the relevance of this metabolic coupling in adult animals. VI. Modulation of Electrical Synapses and Gap-Junctional Coupling

The preceding studies have contributed a wealth of information regarding the electrophysiological interaction of neurons via electrical synapses. However, none of the aforementioned studies eVectively addresses plasticity and modulation of gap-junctional communication, and other than in the most tangential sense, the potential impact on behavior. While it was long thought that gap junctions were merely static, selectively permeable membrane pores, a considerable evidence has accumulated indicating that junctional coupling is plastic and can be aVected in myriad ways by a number of factors. In this section, we will discuss various manipulations of gap-junctional communication, both short-term and long-term, and highlight developments especially relevant for behavior. VII. Use-Dependent Plasticity

While activity-dependent plasticity is a common feature of chemical synapses (e.g., long-term potentiation), there are currently no data to support this phenomenon in mammalian electrical synapses. However, activity-dependent potentiation and depression have been shown to occur at electrical synapses between the club endings of goldﬁsh Mauthner cells (Pereda and Faber, 1996; Pereda et al., 1998; Yang et al., 1990). This potentiation is dependent on activation of closely associated NMDA receptors (found at the same nerve terminal; these terminals with both chemical and electrical transmission are known as ‘‘mixed synapses’’) and is thought to be mediated via phosphorylation. Interestingly, the connexin mediating this plasticity is Cx35, the ﬁsh homolog of Cx36. These proteins share several consensus sites for phosphorylation (Mitropoulou and

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Bruzzone, 2003), suggesting that Cx36 could be modulated in this way. In support of this notion, mixed synapses have been observed throughout mammalian brain and spinal cord (Fukuda and Kosaka, 2003; Rash et al., 1996, 2000; Sloper and Powell, 1978), and Cx36 and the NR1 subunit of the NMDA receptor has been observed in close proximity to Cx36-containing gap junctions (Rash et al., 2004). Although further evidence is needed, activity-dependent plasticity in electrical synapses in a manner analogous to chemical synapses could have implications for the molecular mechanisms of learning and memory.

VIII. Local Factors: Voltage, pH, and Calcium

Some of the most well-known and best-characterized modulators of gapjunctional coupling are transjunctional voltage, intracellular calcium levels, and intracellular pH. In vitro, acidiﬁcation decreases junctional conductance, and alkalinization does the converse (Spray et al., 1981, 1984). Transjunctional voltage refers to the diVerence in internal voltage between the coupled cells, and current ﬂow through the junctional channel is maximal when transjunctional voltage is zero (Kumar and Gilula, 1996). These factors enable rapid changes in channel conductance and permeability through gating mechanisms similar to those used by voltage-gated ion channels, and sensitivity to this modulation is generally determined by individual connexin subtypes (Harris, 2001). On the surface, it would seem that voltage gating might play a signiﬁcant role in neuronal gap junctions, considering the large ﬂuctuations in membrane potential exhibited by neurons. Ironically, the principal neuronal connexin shows the weakest voltage sensitivity of all connexins studied to date (Srinivas et al., 1999), and it is unlikely that this voltage-dependent gating is physiologically relevant (Connors and Long, 2004). Consistent with this notion, no voltage dependence was observed in electrically coupled cortical interneurons (Gibson et al., 1999). Calcium has long been known to inhibit gap-junctional coupling in vitro (Peracchia, 1978), possibly indirectly via calmodulin, which physically blocks the channel pore (Peracchia et al., 2000). This type of gating also oVers interesting possibilities regarding modulation of neuronal coupling, given importance of calcium in many other aspects of neuronal function. However, some reports indicate that the concentrations of intracellular calcium needed for inhibition of coupling are so high that they are not within the realm of normal physiology (Rozental et al., 2001). pH may play a more signiﬁcant role at physiological conditions. Intracellular pH can ﬂuctuate signiﬁcantly as a function of neural activity (Chesler, 2003), and Cx36-mediated electrical coupling is eliminated by intracellular acidiﬁcation (Teubner et al., 2000). pH manipulations have also been shown to eVect changes

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in dye coupling (Church and Baimbridge, 1991; Rorig et al., 1996) and spikelet amplitude in brain slices (Schmitz et al., 2001). The functional roles for this modulation of coupling are not known; one possibility might be that acidiﬁcation produced by excessive neural activity inhibits coupling to prevent additional depolarization from electrically coupled neighbors.

IX. Neurotransmitter and Second Messenger Modulation

Perhaps the strongest argument for the relevance of neuronal coupling in behavior comes from studies of neurotransmitter eVects on dye coupling. Stimulation of various neurotransmitter receptors, especially by exogenous ligands, often has distinct behavioral eVects and allows correlation between observed behavioral eVects and speciﬁc modulation in dye coupling. Further research will hopefully begin to clarify the speciﬁc roles subserved by electrical and chemical synapses, respectively, in the actions of various neurotransmitters. Modulation of the dopamine (DA) system has potent eVects on junctional coupling. DA was ﬁrst shown to modulate gap-junctional coupling in the retina, where both exogenously applied and endogenous DA decreased dye coupling in turtle horizontal cells (Piccolino et al., 1984; 1987). This ﬁnding was extended to mammalian retina shortly thereafter (Hampson et al., 1992, 1994). This modulation of coupling is thought to occur via a cAMP-dependent protein kinase resulting in phosphorylation of the gap-junctional channel and a subsequent decrease in probability of channel opening (Hampson et al., 1994; Lasater, 1987; McHahon et al., 1989; Mills and Massey, 1995). These studies provided the rationale for the examination of DAergic modulation of neuronal coupling in other brain areas, such as the striatum, which receives very dense DAergic innervation (Ungerstedt, 1971). As in the retina, manipulation of DAergic transmission alters neuronal coupling in the striatum. Electrolytic or 6-hydroxydopamine lesions of DA cell bodies signiﬁcantly increased dye coupling between striatal output neurons (Cepeda et al., 1989; Onn and Grace, 1999). Studies using more subtle modulation of the DA system showed complex eVects on dye coupling. In vitro, activation of the D1 receptor decreased coupling in the nucleus accumbens (NAc), a ventral striatal area, while D2 stimulation enhanced coupling (O’Donnell and Grace, 1993). However, the eVects were slightly diVerent in diVerent subdivisions of the NAc (i.e., core versus shell), and eVects varied along a rostro-caudal gradient. In vivo, D2 stimulation (at doses suYcient to produce locomotor stimulation) enhanced dye coupling in the striatum, with no eVect of D1 modulation. Repeated treatment with the antipsychotic drugs haloperidol (a classical D2 antagonist) and clozapine (an atypical D2 antagonist) aVected dye coupling both in vivo and in vitro (O’Donnell and Grace,

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1995; Onn and Grace, 1996). Interestingly, the changes in dye coupling were only evident after extended treatment, and paralleled the time course of the delayed onset of therapeutic eVects seen with these drugs (Pickar, 1988). Also of note, clozapine that results in less motor impairment than haloperidol, did not produces changes in ‘‘motor-related’’ striatal regions. The above studies provide correlational evidence for the behavioral signiﬁcance of DA modulation of neuronal coupling. While studies showing a causal relationship between DA, gap-junctional coupling, and behavior are exceedingly rare, it has been reported that pharmacological gap junction blockade inhibited the expression of certain DA-mediated stereotyped behaviors (Moore and Grace, 2002). There has been considerably less study of eVects other neurotransmitters have on gap-junctional communication. During development, junctional communication between neurons is aVected by norepinephrine (Rorig et al., 1995a), serotonin (Rorig and Sutor, 1996c), and nitric oxide (Rorig and Sutor, 1996a) in addition to DA (Rorig et al., 1995b). However, these neurotransmitters all reduced neuronal dye coupling, and this may be related speciﬁcally to developmental processes. The fact that some of these transmitters share eVector systems with DA opens the possibility that they mediate functionally signiﬁcant eVects on neuronal coupling in mature animals. Many drugs of abuse, and particularly psychostimulants, have eVects mediated at least in part by DA. Repeated administration of psychostimulant drugs can produce enduring changes in both DA transmission and behavior (Vanderschuren and Kalivas, 2000), and evidence is beginning to accumulate that these drugs also produce persistent changes in gap-junctional communication. Withdrawal from repeated amphetamine administration has been shown to produce long-lasting changes in dye coupling between neurons (Onn and Grace, 2000) and has recently been reported to produce changes in Cx36 expression (McCracken et al., 2005a). Of note, the changes in both dye coupling and Cx36 expression parallel the behavioral changes induced by repeated amphetamine (Paulson and Robinson, 1995), in that a drug-free period is necessary for these alterations to manifest. A sensitizing regimen of cocaine self-administration also alters Cx36 expression in a similar manner to amphetamine (McCracken et al., 2005b). A withdrawal period is also necessary for cocaineinduced changes in Cx36, and these changes are present at a time point when behavioral sensitization is observed. While these studies are correlational, they suggest that alterations in gap-junctional communication between neurons may be a contributing mechanism to the lasting behavioral eVects produced by psychostimulants. There have been reports of other miscellaneous behaviorally active substances that may produce some of their eVects through actions on gap junctions. Ethanol has been reported to inhibit coupling in PC12 cells (Wentlandt et al., 2004), possibly due to eVects on the membrane, as is thought to be the

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mechanism for gap-junction blockade by longer chain alcohols (Bernardini et al., 1984) and volatile anesthetics (Burt and Spray, 1989). Ethanol has also been shown to aVect a measure of coupling in a population of ventral tegmental area GABAergic projection neurons (Stobbs et al., 2004). Oleamide, a sleep-inducing endogenous lipid, has also been reported to inhibit junctional coupling (Boger et al., 1998a,b; Guan et al., 1997) as has a related compound, the endogenous cannabinoid anandamide (Venance et al., 1995). A recent report demonstrated that the abused solvent toluene also inhibits gap-junction communciation in cultured cells (Del Re and Woodwald, 2005). The eVects of these compounds were observed on astrocytic gap junctions and whether neuronal coupling is aVected by these factors is not yet known. Perhaps the most-convincing demonstration of the functional importance of a particular gene comes from deﬁcits engendered by the gene’s deletion. While the study of connexin-deﬁcient transgenic animals is in its infancy, some reports do exist. The Cx36 KO mouse was initially thought to be rather normal in phenotype, save for impaired night vision due to lack of Cx36 in the retina (Guldenagel et al., 2001). However, detailed examination revealed impairments in complex memory tasks as well as motor behavior (Frisch et al., 2005). Moreover, a number of compensatory adaptations have been documented in the Cx36 KO mouse (De Zeeuw et al., 2003). These compensations involve changes in membrane electrical properties, which result in neurons from Cx36 KO animals behaving very similarly to wild-type neurons—suggesting the true degree of impairment due to Cx36 deletion may not yet be known. Surprisingly, deletion of the astrocytic connexins Cx30 (Dere et al., 2003) or Cx43 (Frisch et al., 2003) revealed altered behaviors and neurochemistry in these mice. This raises the very interesting possibility that manipulation of gap junctions between astrocytes, in addition to neurons, may have substantial implications for behavior.

X. Concluding Remarks

There is now considerable accumulated evidence regarding the roles of neuronal coupling via gap junctions in neural function. While a number of unresolved issues remain, it has become clear that this form of neuronal communication is both more prevalent and more signiﬁcant than was once thought. The coming years will likely see major strides in this ﬁeld, as the multidisciplinary approaches necessary for the study of the functional signiﬁcance of neuronal coupling are becoming the norm in neuroscience. Understanding the eVects of neuronal coupling will greatly enhance our knowledge of basic mechanisms of brain function, and further our comprehension of the relationship between brain and behavior.

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EFFECTS OF GENES AND STRESS ON THE NEUROBIOLOGY OF DEPRESSION

J. John Mann and Dianne Currier Department of Psychiatry, Division of Neuroscience, Columbia University, New York, New York 10032, USA

I. II. III. IV. V.

VI. VII. VIII.

IX.

X.

XI.

XII.

XIII.

Introduction Stress and Depression Genetics and Depression Serotonergic System Candidate Gene Studies of the Serotonergic System A. Serotonin Transporter B. Serotonin Receptors C. Other Serotonin Receptors D. Tryptophan Hydroxylase Current Stress and the Serotonergic System A. Early Life Stress and the Serotonergic System Gene Stress Interaction Hypothalamic–Pituitary–Adrenocortical (HPA) Axis A. Genetics and the HPA Axis B. Early Life Stress and the HPA Axis Noradrenergic System A. Genetics and the Noradrenergic System B. Current Stress and the Noradrenergic System C. Early Life Stress and the Noradrenergic System Dopaminergic System in Depression A. Genetics and the Dopaminergic System B. Current Stress and the Dopaminergic System C. Early Life Stress and the Dopaminergic System GABAergic System A. Genetics and the GABAergic System B. Early Life Stress and the GABAergic System Brain Derived Neurotrophic Factor A. Genetics and BDNF B. Current Stress and BDNF C. Early Life Stress and BDNF Conclusions References

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I. Introduction

Mood disorders, including major depressive disorder and bipolar disorder, are common and serious illnesses. Major depressive disorder, also termed unipolar depression, is characterized by recurrent episodes of major depression each involving greater than two weeks of depressed mood and/or reduced or absent capacity for pleasure on most days, accompanied by additional symptoms such as disturbed sleep and appetite, reduced concentration and energy, excessive guilt, slowed or agitated movements, and suicidal thoughts or acts (American Psychiatric Association, 1994). These episodes generally last for months and are separated by periods of normal mood lasting years. Bipolar disorder, in addition to episodes of major depression, is characterized by episodes of mania or hypomania. Both disorders are chronic and generally have a recurrent episodic course (Keller et al., 1986; Mueller et al., 1999). About 20% of patients have a chronic depression with only partial improvement between episodes. Epidemiological surveys report yearly mood disorder prevalence rates in the United States of 5.1–11.1% of the general population with yearly prevalence rates of 4.5–10.1% for a major depressive episode, 4–8.9% for major depressive disorder, 0.5–1.3% for bipolar I, and 0.2% for bipolar II disorder (Kessler et al., 1994; Narrow et al., 2002; Regier et al., 1993). Women have higher rates of major depressive disorder, approximately twice that of men, whereas bipolar disorder is equally prevalent in men and women (Kessler et al., 1994). Globally, major depression is the leading cause of disease burden among females aged 15– 44 years, and has been projected to become the second leading cause of disability in the entire population worldwide by 2020 (The Global Burden of Disease, 1996). Depressive disorders are pleomorphic in terms of symptomatology (Oquendo et al., 2004) and it is not clear if they share a single common cause or the cause and pathogenesis is more diverse and complex. Although the speciﬁc causes of mood disorders are not fully known, evidence exists for genetic predisposition and early life trauma being causal, and environmental factors such as stressful life events acting as triggers or precipitants of episodes.

II. Stress and Depression

Stressful life events are causally associated with the onset of episodes of major depression (Kendler et al., 1999). Life events occurring after the onset of major depression are often due to the adverse eVects of the depression but then add to the burden of disease. However, the majority of individuals in the general

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population experience stress without experiencing major depression indicating the presence of a susceptibility to depression in speciﬁc individuals. DiVerent types of stressful events have been found to increase risk for depressive episodes. There are gender diVerences in reactivity to stressful life events, with women more likely to experience a depressive episode in relation to stress connected with social networks and interaction, and men in relation to work, or divorce or separation (Kendler et al., 2001). Moreover, in females ‘‘dependent’’ events, that is, events resulting from an individual’s own behavior, were more strongly associated with onset of a depressive episode than ‘‘independent’’ events that are not the result of an individual’s behavior, such as accidents (Kendler et al., 1999). These gender diVerences in sensitivity to the depressogenic eVects of stressors may contribute to higher rates of depression in women. Early life stress, including childhood neglect, physical and sexual abuse, and early separation from parents, is a major risk factor for the onset of depression in adolescence and adulthood (Kendler et al., 1992; Nelson et al., 2002; Young et al., 1997). Early life stressors, presumably by producing an enduring eVect such as on biologic and psychologic development including heightening sensitivity of stress response systems, may enhance the sensitivity of some individuals to stress in adulthood. Vulnerability to the depressogenic eVects of stress is also thought to have genetic underpinnings. Individuals with greater genetic liability for mood disorders are more likely to experience onset of a depressive episode in response to a stressful life event than those with no genetic liability (Caspi et al., 2003; Kendler et al., 1995; Zalsman et al., 2006). Genetic vulnerability to depression has also been found to modulate the eVect of childhood trauma on the subsequent development of depression (Caspi et al., 2003; Eley et al., 2004; Kaufman et al., 2004). This is not a simple causal model, however as those who have a genetic liability for major depression have also been shown to have increased likelihood of experiencing stressful life events, perhaps because they tend to ﬁnd themselves in high risk environments (Brostedt and Pedersen, 2003; Kendler and KarkowskiShuman, 1997).

III. Genetics and Depression

The heritability of major depression and bipolar disorders are well established by family, twin and adoption studies (Smoller and Finn, 2003; Sullivan et al., 2000; Tsuang and Faraone, 1996). In twin studies, monozygotic twins have two- to three-fold higher concordance for major depressive disorder than dizygotic twins (Tsuang and Faraone, 1996) and four- to eight-fold higher

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concordance in bipolar disorder (Smoller and Finn, 2003). Adoption studies observe higher rates of depression in biological parents than adoptive parents amongst depressed adoptees (Wender et al., 1986). Genetic factors are estimated to account for 37–75% of the liability for major depressive disorder (McGuYn et al., 1996; Sullivan et al., 2000) and 60–85% of the liability for bipolar disorder (McGuYn et al., 2003; Smoller and Finn, 2003). Studies of major depression have demonstrated a genetic component, but as yet we are unable to deﬁnitively identify the responsible genes. Genetic linkage studies aim to identify loci on chromosomes associated with the disease. Two genetic loci or a gene and a clinical phenotype are considered to be ‘‘linked’’ when they are not transmitted independently to oVspring. An approach is to utilize several polymorphisms near or within a gene of interest and to track the inheritance, within fewer extended pedigrees or many smaller family units, of a disease-causing mutation in that gene relative to a disease or clinical suspected phenotype. Association studies examine variants such as single nucleotide polymorphisms (SNPs) in candidate genes that are often selected on the basis of a presumed functional relevance of that gene for the disease and compare the frequencies of such alleles or genotypes in control and disease populations. Candidate genes are identiﬁed based on current knowledge of the neurobiological correlates of major depression, or antidepressant pharmacology, or by microarray study results indicating altered gene expression or follow-up bioinformatics approaches. Functional polymorphisms in coding and promotor or regulatory regions of candidate genes are of particular interest because they may be components of the primary pathophysiology, or cause, as well as ‘‘markers’’ of disease. Genetic linkage studies have identiﬁed chromosomal regions including 8p, 11p, 11q, 15q, and 12q associated with MDD (Abkevich et al., 2003; Holmans et al., 2004; Zubenko et al., 2002) and linkage on chromosome 2q, particularly in females (Zubenko et al., 2003). Possible susceptibility genes for bipolar disorder are in chromosome regions 4p, 12q, 18p, 16p, 21q, 18q, 22q, Xq, 1q, 6o, 10p, 10q, and 13q (Baron, 2002; Berrettini, 2001; Craddock and Jones, 1999). Replication of ﬁndings from genetic linkage studies of mood disorders has been diYcult and reasons include diVerences in ascertainment and sample composition, modest estimated eVects sizes, the broadness of the chromosomal regions of interest, and the overlap of some of the chromosome regions with other psychiatric phenotypes. Moreover given the variability and complexity of the phenotype of major depression and the patterns of inheritance it is unlikely that any single locus could account for it. We have found a pleomorphic clinical manifestation of major depression in successive episodes within the same individuals suggesting that these variants of major depression all belong to a super family of mood disorders (Oquendo et al., 2004) that may have a common set of causal genes. The genetic underpinnings of major depression likely involve

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multiple genes and neurobiological systems, as well as epistatic genetic eVects. In addition the interaction between genes and environment likely plays a crucial role. Genetic associations studies investigating candidate genes based on the current knowledge of the neurobiological underpinnings of depression, need to also identify potential associations with more basic biologic or psychopathological endophenotypes as opposed to the global disorder or syndrome to achieve greater experimental power. Stress can play a mediating role between genotype and depression, or sensitivity to stress itself may have genetic causes. An explanatory model of the neurobiology of depression must account for the role of stress and genetics as well as their interaction. This model is illustrated in Fig. 1 and also shows how early stress can have developmental eVects including induction of stress sensitivity. The next part of this chapter will address the neurobiologic systems that mediate these eVects, including possible candidate genes identiﬁed in association studies.

IV. Serotonergic System

Serotonin is a monoamine widely distributed in the brain and involved in mood and impulse control. The serotonergic system plays a role in the regulation of a range of basic biological functions including sleep, appetite, circadian rhythm, and cognition, many of which are disrupted in major depression. Studies of serotonin function in major depression suggest a model of hypofunction and accompanying compensatory alterations to increase serotonergic activity (Mann et al., 2005). Evidence of underactivity of serotonergic system in the pathogenesis of depression is suggested by ﬁndings such as lower levels of serotonin and/or the serotonin metabolite 5-HIAA in postmortem brainstem and cerebrospinal ﬂuid, the relapse of depression with acute depletion of tryptophan, fewer serotonin transporter sites in prefrontal cortex and other brain regions, and the antidepressant properties of selective serotonin reuptake inhibitors (SSRIs) that enhance serotonergic transmission (Malone and Mann, 1993; Mann et al., 2005). Reports of more 5-HT2A receptor binding in the frontal cortex of depressed individuals who committed suicide, fewer brainstem 5-HT1A autoreceptors, fewer serotonin transporters in the cortex and greater tryptophan hydroxylase (TPH) immunoreactivity in serotonin nuclei in the brainstem, all point to homeostatic changes designed to increase deﬁcient serotonergic transmission in major depression (Boldrini et al., 2005). Increased TPH appears to be a healthy homeostatic mechanism to counter adverse eVects of low serotonin activity. Impairment of this pathway by eVects such as a catalytically compromised TPH variant will potentially increase risk of major depression, suicide or aggressive behaviors (Fig. 2).

158 FIG. 1. A genetic and environmental model of major depression.

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FIG. 2. Serotonergic system—model of the interaction of genes and stress in depression.

V. Candidate Gene Studies of the Serotonergic System

The serotonergic system has been most extensively studied with respect to candidate genes in major depression. Association and linkage studies are reported including the transporter, several receptor subtypes, as well as TPH, the ratelimiting enzyme in serotonin synthesis.

A. SEROTONIN TRANSPORTER The serotonin transporter (5-HTT) is responsible for serotonin reuptake into presynaptic neurons and regulates the concentration of serotonin in the synaptic cleft. 5-HTT is the site of action for tricyclic antidepressants and SSRI’s that work by inhibiting serotonin reuptake. Two polymorphisms in the serotonin transporter gene, located on chromosome 17q11.2, have been extensively investigated: a variable number of tandem repeats (VNTR) in intron 2, reported to act as a transcriptional regulatory element of 5-HTT (Lovejoy et al., 2003; MacKenzie and Quinn, 1999) and, a functional 44-bp insertion/deletion in the serotonergic transporter gene upstream promoter region (5-HTTLPR). The functional 5-HTTLPR polymorphism has been shown to aVect in vitro gene transcription (Collier et al., 1996; Lesch et al., 1996) and in vitro transporter activity (Stoltenberg et al., 2002), although the relationship to in vivo serotonin transporter binding is uncertain (Heinz et al., 2001; Parsey et al., 2006; Shioe et al., 2003; Willeit et al., 2001). Within this polymorphism there are actually three

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alleles (Hu et al., 2005; Nakamura et al., 2000) designated low expressing (S or LG) or high expressing (LA). Almost all published studies have examined the biallelic variants originally reported by Lesch et al. (1996) and this introduced errors in functional classiﬁcation particularly in Caucasians and African–Americans. There have been numerous positive and negative reports on the association of 5-HTTLPR genotype and bipolar disorder and MDD (Anguelova et al., 2003). Meta-analyses, too produce conﬂicting results, with two reporting no association of genotype with MDD (Anguelova et al., 2003; Lasky-Su et al., 2005) and two others studies reporting positive associations (Furlong et al., 1998b; Lotrich and Pollock, 2004). In bipolar disorder, ﬁndings are more consistent with three metaanalyses reporting an association between bipolar and the 5-HTTLPR genotype (Anguelova et al., 2003; Furlong et al., 1998b; Lasky-Su et al., 2005), one ﬁnding a nonsigniﬁcant trend (Lotrich and Pollock, 2004), and one ﬁnding no association (Craddock et al., 2001). The low expressing 5-HTTLPR allele modulates eVects of early adversity on major depression in childhood, adolescence and adulthood, as well as the sensitivity to recent life events in terms of developing depression and the severity of depression (Caspi et al., 2003; Eley et al., 2004; Kaufman et al., 2004; Zalsman et al., 2006). The serotonin transporter knockout mouse has a depressive behavioral phenotype (Lira et al., 2003). The same behavioral phenotype is observed in mice with early postnatal SSRI administration, yet in adulthood SSRIs have antidepressant eVects (Ansorge et al., 2004). This suggests early low expression of 5-HTT or SSRI exposure may produce a downstream developmental eVect that results in vulnerability to develop major depression in the face of life stressors later in life (Fig. 1). If the role of serotonin in major depression reverses with development, in adulthood it is necessary to look downstream to ﬁnd the biologic phenotype underlying the predisposition to major depression and the sensitivity to eVects of current life events. One possible pathway or mechanism involves the amygdala. Several studies have shown that in individuals with lower expressing alleles of 5-HTTLPR, the amygdala manifests greater activation when the individuals look at fearful faces or frightful pictures (Furmark et al., 2004; Hariri et al., 2002, 2005; Heinz et al., 2005). Thus, the lower expressing 5-HTT alleles may sensitize individuals to the eVects of adversity in childhood resulting in more intense emotional memories being encoded with consequences for responses to stress and adversity later in life. EVects of serotonin-related gene polymorphisms on CNS serotonergic function are reported to vary due to both ethnicity and gender (Williams et al., 2003). Heterogeneity of behavioral phenotypes further complicate demonstrating relationships of serotonin related genes to biologic intermediate phenotypes and major depression. To identify such relationships, studies have examined genetic associations with behavioral traits and/or intermediate biologic phenotypes such

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as alterations in neurobiological function and gene expression, rather than the full illness syndrome of major depression. Various lines of evidence indicate decreased serotonin function associated with the low expressing 5-HTTLPR allele including; blunted neuroendocrine response to fenﬂuramine (the serotonin reuptake inhibitor and releasing agent) (Reist et al., 2001), lower platelet serotonin uptake (Greenberg et al., 1999), and lower cerebrospinal ﬂuid concentrations of serotonin metabolites (5-hydroxyindoleacetic acid, 5-HIAA) in women (Williams et al., 2003). Smith et al. (2004) report these alleles are associated with blunted prolactin and cortisol response and greater decreases in left frontal, precentral and middle temporal gyri compared to the higher expressing 5-HTTLPR genotype in response to the SSRI citalopram in healthy volunteers. The latter allele carriers show greater decreases in right frontal, insula, and superior temporal gyrus compared to s/s genotype. Other studies also report the low expression genotype is associated with poorer antidepressant response to SSRIs (Arias et al., 2003; Pollock et al., 2000; Zanardi et al., 2000, 2001). A large postmortem study comparing depressed suicides and nonsuicides to nondepressed controls, found more heterozygotes in major depression, however genotype did not explain the lower transporter binding observed in the prefrontal cortex, anterior cingulate, and brainstem raphe nuclei in depressed individuals (Mann et al., 2000). It is possible such an eVect is present but obscured by regulatory eVects at nerve terminals such as rate of transporter internalization, which is inﬂuenced by the intrasynaptic level of serotonin. Imaging studies report increased amygdala response to fearful or aversive stimuli in individuals carrying the low expressing 5-HTTLPR allele (Hariri et al., 2002, 2005; Heinz et al., 2005) and in individuals diagnosed with social phobia (Furmark et al., 2004). The amygdala sensitivity to stress in association with the low expressing 5-HTTLPR allele may contribute to the interaction of the allele with early life adversity on stress response in adulthood (Fig. 1). There have been conﬂicting reports of association with bipolar and MDD for the VNTR in the serotonin transporter (Anguelova et al., 2003; Bellivier et al., 2002). Meta-analysis of studies found no association with MDD (Anguelova et al., 2003; Furlong et al., 1998b; Lasky-Su et al., 2005). Anguelova et al.’s (2003) meta-analysis of bipolar disorder reports a positive association, however one large study is the main source of positive result, and two other meta-analyses ﬁnd no association (Furlong et al., 1998b; Lasky-Su et al., 2005). Functional diVerences associated with allelic variation in the polymorphic region in the second intron have been observed with diVerent levels of reporter gene expression in embryonic stem cells (Fiskerstrand et al., 1999) and in mouse embryo (MacKenzie and Quinn, 1999), and individual repeat elements within the VNTR domain diVered in their enhancer activity in an embryonic stem cell model (Lovejoy et al., 2003). Investigations of functional consequences of the

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VNTR-2 polymorphism report no eVect of the genotype on platelet 5-HT uptake (Kaiser et al., 2002) or on CSF 5-HIAA level ( Jonsson et al., 1998).

B. SEROTONIN RECEPTORS There are 15 known mammalian 5-HT receptor genes, some of which encode additional receptor variants that mediate serotonin action in the brain as postsynaptic receptors on target neurons (Barnes and Sharp, 1999). Most association studies of serotonin receptors and mood disorders have focused on polymorphisms in the 5-HT2A receptor, principally the T102C polymorphism, with mixed results (Anguelova et al., 2003). Meta-analysis found no evidence of an association with either bipolar disorder or MDD for this locus (Anguelova et al., 2003; Craddock et al., 2001). Recent large European multicenter studies also found no association with MDD (Oswald et al., 2003) or between the 5-HT2A 1438G/A and the His452Tyr polymorphisms and bipolar disorders (Etain et al., 2004). There have been conﬂicting reports on the functional consequences of allelic variation. Genotype variation in the T102C polymorphism has been associated with altered 5-HT2A receptor binding with the T/T genotype being associated with higher platelet 5-HT2A Bmax (Khait et al., 2005). Turecki et al. (2003) found that higher 5-HT2A binding in the brain is associated with the T102C polymorphism, although others did not (Hrdina and Du, 2001). A postmortem brain study reported higher expression levels with the T allele than the C allele in both schizophrenics and controls (Polesskaya and Sokolov, 2002), however an in vivo examination of mRNA in several cortical regions found no diVerence in expression between T and C carriers (Bray et al., 2004).

C. OTHER SEROTONIN RECEPTORS There have been fewer studies of other serotonin receptors, and with little replication. No association was reported between bipolar disorder and polymorphisms in the 5-HT1A, 5-HT1B, and 5-HT1D alpha or beta (Vincent et al., 1999). Major depression (and substance abuse) appeared associated with the 5-HT1B G861C locus (Huang et al., 2003), while a German study found an association between alcohol dependence and HTR1B 861G allele (Fehr et al., 2000). Associations have been reported between bipolar disorder and the 5-HT3A variant C178T in 156 patients, but not the C195T (Niesler et al., 2001); and with 5-HT4A in a small Japanese sample (Ohtsuki et al., 2002). In the 5-HT5A receptor, allelic association was reported by some between the 19G/C polymorphism and bipolar disorder and MDD, and also MDD and the 12A/T polymorphism (Birkett et al., 2000), although others found no association between

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12A/T and bipolar or MDD (Arias et al., 2001). Volt and associates reported an association between a 5-HT6 (267C) polymorphism and bipolar disorder in a small European sample (Vogt et al., 2000), however no association between 5-HT6 polymorphism (C267T) and bipolar or MDD was observed in a Taiwanese group (Hong et al., 1999) or with MDD antidepressant response in a Taiwanese sample (Wu et al., 2001). Vincent et al. (1999) found no association between 5-HT7 and bipolar disorder.

D. TRYPTOPHAN HYDROXYLASE Tryptophan hydroxylase (TPS), the rate-limiting enzyme in serotonin synthesis exists in two isoforms, TPH1 and TPH2, and has been examined as another possible site of disruption of serotonin neurotransmission. The TPH1 A218C polymorphism was associated with small increase in susceptibilitly to bipolar disorder in European sample (Bellivier et al., 1998) but not replicated in other studies (Furlong et al., 1998a; Kirov et al., 1999; Kunugi et al., 1999; McQuillin et al., 1999; Vincent et al., 1999) and in large multicenter European study of bipolar and MDD (Souery et al., 2001), or family association study (Rietschel et al., 2000). We reported an association with mood disorders (Mann et al., 1997). There have also been conﬂicting reports on the association of TPH1 with suicidal behavior (Stefulj et al., 2004). A postmortem study found the A/A genotype of the A218C TPH polymorphism associated with increased TPH immunoreactivity and lower 5-HT2A receptor density in the prefrontal cortex in suicides and nonsuicides, compared to the C allele (Ono et al., 2002). The recently identiﬁed second TPH isoform (TPH2) expressed only in the brain in humans, has shown promise, with report of an association between MDD and one SNP (rs1386494), and a positive haplotype association (Zill et al., 2004a). Similar results were reported by the same group for suicide (Zill et al., 2004b), although one other study found no association with suicidal behavior in a large bipolar sample (De Luca et al., 2004). Clearly further studies of TPH1 and TPH2 are needed in larger cohorts of depressed patients.

VI. Current Stress and the Serotonergic System

The role of serotonin in stress is demonstrated in studies, mostly of animal models, examining changes in extracellular level of serotonin in diVerent brain areas including hypothalamus, amygdala, frontal cortex, and raphe nuclei after exposure to stressors. When subjected to stress, rats increase brain tryptophan availability and 5-HT level in the hypothalamus (Shimizu et al., 1992), increase

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5-HT metabolism in amygdala (Adell et al., 1997), hypothalamus, and prefrontal cortex (Dunn, 1988; Hashimoto et al., 1999). Immobilization stress increases TPH mRNA and immunoreactivity in median and dorsal raphe nuclei (Chamas et al., 1999, 2004). In rodent studies, administration of 5-HT2A or 5-HT2B receptor agonists produces an increase in stress hormones including ACTH and corticosteroid (Bagdy, 1996; Rittenhouse et al., 1994; Van de Kar et al., 2001), and 5-HT1A receptors stimulate release of ACTH, oxytocin, and corticosterone (Bagdy, 1996; Calogero et al., 1989; Serres et al., 2000). These eVects may be mediated by the serotonergic innervation of CRH-containing neurons in the paraventricular nucleus of the hypothalamus, which project to the median eminence and release CRH into the venous portal circulation (Bagdy and Makara, 1994). 5-HT actions in the hypothalamus may thereby mediate hormonal responses to stress (Van de Kar and Blair, 1999). In rats, destruction of hypothalamic 5-HT neurons with 5,7-dihydroxytryptamine enhances the inhibitory eVect of dexamethasone on the adrenocortical response to stress (Feldman and Weidenfeld, 1991). Injecting CRF into the dorsal raphe nucleus inhibits the ﬁring rate of 5-HT neurons resulting in reduced extracellular levels of 5-HT in the rat striatum (Price and Lucki, 2001).

A. EARLY LIFE STRESS

AND THE

SEROTONERGIC SYSTEM

Animal and human studies suggest that stress in early life has a lasting eVect on the functioning of the serotonergic system. Adult rats exposed to maternal separation 180 min/day on postnatal days 2–14 exhibit greater decreases in 5-HT cell ﬁring in the raphe nuclei in response to increasing dose of the SSRI citalopram (Arborelius et al., 2004). This suggests a persistent alteration in 5-HT transporter, 5-HT1A autoreceptors, or both after early stress. In depressed children, those who have experienced abuse show increased prolactin, but normal cortisol responses, to L-5-hydroytrptophan, a precursor of 5-HT, compared to nonabused depressed children and controls (Kaufman et al., 1998). Pine et al. (1997) reported greater prolactin response to fenﬂuramine challenge in boys in a juvenile detention facility who had experienced adverse rearing environments. In adult borderline personality disorder women, a history of severe childhood abuse was correlated with blunted prolactin responses to the serotonergic agonist meta-chlorophenylpiperazine (m-CPP) (Rinne et al., 2000). Since prolactin release is mediated via 5-HT1A and 5-HT2A receptors, these ﬁndings suggest sensitization of these receptors due to early life stress. Animal stress studies report upregulation of 5-HT2A receptors. Therefore stress acutely increases serotonin synthesis and turnovers but long-term eVects are mostly on 5-HT1A and 5-HT2A receptors, and in maternally deprived monkeys with the

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lower expressing 5-HTTLPR allele, there is low CSF 5-HIAA suggesting a longterm reduction in serotonin release persisting into adulthood.

VII. Gene Stress Interaction

Animal models support an interaction between stress response, genotype, and behavioral or neurobiological anomalies considered analogous with depressive psychopathology in humans. In mice there is an association between 5-HTTLPR genotype, stress, and increased fearful behavior (Murphy et al., 2001), and an interaction between genotype, stressful-rearing conditions, and decreased serotonergic function in rhesus monkeys (Bennett et al., 2002). At 6 months of age, macaques with the low expressing 5-HTTLPR allele had higher ACTH levels when exposed to social separation stress than those without that allele. Moreover peer-reared monkeys with that allele genotypes had markedly higher increases in ACTH release after stress exposure than maternally-reared equivalent genotypes (Barr et al., 2004). Thus allele not only increases vulnerability to stress, but early-life stress interacts with genotype to further increase sensitivity to subsequent stressful events. In humans, presence of the low expressing alleles of the 5-HTTLPR increases the likelihood of onset of a depressive episode in the face of current stress in adulthood or a history of childhood abuse (Caspi et al., 2003) and the severity of major depressive symptoms in response to current stressful life events (Zalsman et al., 2006). The low expressing 5-HTTLPR alleles are associated with more severe depression relative to severity of current life events (Zalsman et al., 2006). Caspi et al. (2003), found that childhood maltreatment predicted adult depression only in those with the low expressing allele. Kaufman (2004) found that in children the low expressing allele confers vulnerability to depression only in individuals with histories of signiﬁcant stress. In the absence of such a history this allele did not signiﬁcantly contribute to the development of the depression. Moreover, the relation between genotype and depression in maltreated children was further moderated by the availability and quality of social support. Where high levels of support were available low expressing genotype children with a history of maltreatment had signiﬁcantly lower risk of depression, only slightly above that of control subjects, than maltreated children with low social support. A study of adolescent depression found that, low expressing genotype girls with high family environmental risk are twice as likely to have depression compared with the low expression genotype with low environmental risk (Eley et al., 2004). Together, these studies suggest that the causal pathway between genetic susceptibility and disease onset is moderated by stress, particularly during early life.

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VIII. Hypothalamic–Pituitary–Adrenocortical (HPA) Axis

The HPA axis is one of the major stress response systems. Stress-conveying signals rapidly activate immediate early genes in CRH-expressing neurons of the central nucleus of the amygdala. CRH release in the central nucleus of the amygdala is thought to activate CRH expressing neurons in the hypothalamic paraventricular nucleus to secrete CRH into the hypothalamo-pituitary portal system, inducing ACTH and glucocorticoid secretion from the pituitary and adrenal, respectively. In response to stress, CRH gene expression is also activated rapidly. Glucocorticoids exert a negative feedback on the hypothalamic paraventricular nucleus (PVN), both directly and via the hippocampus, however they also activate CRH gene expression in the amygdala, creating capacity for enhanced CRH release in this region (Brunson et al., 2001). Altered function of the cortical hypothalamic–pituitary–adrenal axis in major depression and bipolar disorder has been observed including: elevated corticotropin-releasing factor (CRF) concentrations in CSF (Arato´ et al., 1986; Banki et al., 1987, 1992); blunted adrenocorticotropic hormone (ACTH) and -endorphin responses after intravenous CRF administration (Kathol et al., 1989; Young et al., 1990); lower postmortem CRF binding in prefrontal cortex (Raadsheer et al., 1995); pituitary (Krishnan et al., 1991) and adrenal (Amsterdam et al., 1987) gland enlargement; hypercortisolemia and elevated CSF cortisol concentrations (Sachar et al., 1970); blunted plasma glucocorticoid, ACTH, and -endorphin suppression after dexamethasone administration (Carroll, 1968); and higher ACTH and cortisol responses to CRF after dexamethasone pretreatment (Amsterdam et al., 1988).

A. GENETICS

AND THE

HPA AXIS

There have been relatively few studies in humans of genetic liability for HPA axis dysfunction. In studies of healthy volunteers, there is evidence that genes play a role in basal HPA axis function, limited and conﬂicting reports on genetic role in HPA axis activity in response to various challenges or stressors, although a meta-analysis did conﬁrm a genetic role (Wust et al., 2004). In a twin study of healthy volunteers, Wust et al. (2005) found no evidence of association between the Bcl1 and the N363S variant of the GR gene and cortisol response habitation to repeated stress exposure. A small number of clinical studies in depressed patients have explored the genetic inﬂuence on HPA axis function using various candidate genes and methods. Binder et al. (2004) found associations of response to antidepressants

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167

and recurrence of depressive episodes with polymorphisms in the FKBP5 gene—a glucocorticoid receptor-regulating co-chaperone. These SNPs were associated with greater intracellular FKBP5 protein expression which triggers adaptive changes in glucocorticoid receptor and thereby HPA axis regulation. Individuals carrying the associated genotypes had less HPA axis hyperactivity during depressive episodes. In depressed patients, no associations have been found with CRH and AVP. Baghai et al. (2002) found that DST/CRH test results were related to the insertion (I)/deletion (D) polymorphism within the angiotensin-converting enzyme (ACE). ACE plays a modulatory role in HPA axis system activity. D/d genotypes showed higher cortisol after test admission than I/I homozygotes. After successful antidepressant treatment and attenuation of HPA axis system hyperactivity, there were no detectable diVerences between genotypes. A European study of 4 single-nucleotide polymorphisms (SNPs) in the arginine vasopressin (AVP) 1b receptor gene in depressed patients found allele and genotype associations with SNP AVPR1b-s3, in a Swedish cohort, and a similar trend for SNP AVPR1b-s5 in a Belgian cohort. The SNP AVPR1b-s1-5 appeared to be a protective haplotype for major depression (van West et al., 2004).

B. EARLY LIFE STRESS

AND THE

HPA AXIS

Early stress can lead to lasting alterations in HPA axis stress response through mechanisms such as long-lasting alteration of CRH expression in limbic regions that are involved in the regulation of the HPA axis (Brunson et al., 2001). In animal models, maternal separation has an eVect on glucocorticoid receptor gene expression in hippocampus and frontal cortex, brain regions implicated in the negative-feedback regulation of CRH and vasopressin (Liu et al., 1997; Meaney et al., 1996), resulting in excessive corticosterone and ACTH release under stress in adulthood. Alterations in glucocorticoid receptor gene expression can change the sensitivity of the system to the inhibitory eVects of glucocorticoids on CRH and vasopressin synthesis in hypothalamic neurons. Changes in CRH and vasopressin in turn determine responsivity of the HPA axis to subsequent stress whereby increased production of these neuropeptides leads to increased HPA response to stress (Meaney et al., 1996). Thus stress in early life can alter gene expression within the brain leading to permanent modiﬁcation of the HPA axis which later in life result in abnormal molecular and hormonal responses to further stressful stimuli (Avishai-Eliner et al., 2001; Plotsky and Meaney, 1993). In humans, early adversity or abuse has been associated with abnormal HPA axis function in adulthood. Death of a parent in childhood is associated with both increased plasma cortisol concentration and psychiatric illness in adults

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(Breier, 1989). Women with varied psychiatric diagnoses, and a history of childhood abuse had hypersuppression of salivary cortisol concentrations in response to dexamethasone, indicating supersensitivity of the corticosteroid feedback inhibitory mechanisms (Stein et al., 1997). Lower basal plasma cortisol concentration has also been reported in women with a history of childhood abuse (Heim et al., 2001). Abused women with or without current depression show markedly increased plasma ACTH, and increased cortisol responses, in response to laboratory psychosocial stress compared to controls and depressed women without early life stress (Heim et al., 2000). Thus, early life stress has lasting consequences for HPA axis stress reactivity, and may be one mechanism through which earlylife stress increases risk for depressive disorders (see Fig. 1). The role of genes abnormalities in HPA axis function in depression requires further investigation.

IX. Noradrenergic System

Noradrenaline is involved in learning and memory, sleep, arousal, and adaptation and there is evidence of involvement in both major depression and the regulation of stress response. Norepinephrine is broadly distributed in the brain with the source neurons located in the brainstem locus coeruleus (Leonard, 1997). The hypothalamus as a major integrative center for the neuroendocrine response also receive innervation from norepinephrine-containing neurons (Habib et al., 2001). Cortical adrenergic receptor density is altered in depression. Lower -adrenoceptor density and alpha2-adrenergic binding is reported in prefrontal cortex in suicide victims (De Paermentier et al., 1990). We have observed fewer noradrenergic neurons in the locus coeruleus and less tyrosine hydroxylase immunoreactivity in depressed suicides (Arango et al., 1996; Wiste et al., 2006). Lower levels of norepinephrine transporters have been observed in the locus coeruleus of depressed patients (Klimek et al., 1997). Further evidence of the role of the noradrenergic system in depressive disorders is seen in the eVect of -methyl-ptyrosine (AMPT), which induces catecholamine depletion by inhibiting tyrosine hydroxylase, the rate-limiting step in catecholamine synthesis. AMPT has negligible eVects on mood in healthy subjects, but produces a return of depressive symptoms in recovered depressed patients treated with noradrenaline reuptake inhibitors (Charney, 1998; Delgado et al., 1993). Cerebrospinal ﬂuid levels of the norepinephrine metabolite 3-methoxy-4-hydroxphenylglycol (MHPG) are normal or elevated in major depression (Koslow et al., 1983). Thus, the evidence is mixed and suggested both increased noradrenergic activity and NE depletion. Perhaps a tendency to an exaggerated stress response in terms of NE release and the risk of depletion explains these observations (Fig. 1).

EFFECTS OF GENES AND STRESS ON THE NEUROBIOLOGY OF DEPRESSION

A. GENETICS

AND THE

169

NORADRENERGIC SYSTEM

There have been few genetic studies of the noradrenergic system and depressive disorders, and with largely negative ﬁndings. No signiﬁcant diVerence was found between controls and MDD (Owen et al., 1999; Zill et al., 2002) in genotype or allelic frequencies in the 1287g/a polymorphism in exon 9 of the norepinephrine transporter (NET) gene. Also no association was found with bipolar disorder (Leszczynska-Rodziewicz et al., 2002). Lower t/t genotype frequency in MDD subjects compared to controls in the T-182C polymorphism in the NET gene was reported in a Korean MDD sample (Ryu et al., 2004), but this was not replicated in a European sample (Zill et al., 2002). A Japanese study found no association between a polymorphism in the promoter region of the alpha2-adrenergic receptor and mood disorder, or any association with clinical characteristics (Ohara et al., 1998).

B. CURRENT STRESS

AND THE

NORADRENERGIC SYSTEM

General activation of the norepinephrine neurons has been described with respect to diVerent stressors in animal models (Abercrombie and Jacobs, 1987; Cassens et al., 1980; Curtis et al., 2002; Morilak et al., 1987). Prolonged exposure to stress decreases alpha2A-adrenergic receptor density in amygdala and hippocampus of the tree shrew (Fuchs and Flugge, 2003). We did not ﬁnd altered alpha2A-adrenergic receptor binding in the prefrontal cortex of suicides although changes in hippocampus or amygdala would be consistent with a stress response (Arango et al., 1993). Further studies of such brain regions are needed. In rats, foot shocks produce immediate increase in brain level of MHPG, a major metabolite of norepinephrine (Cassens et al., 1980) and increased tyrosine hydroxylase, the rate-limiting enzyme in norepinephrine synthesis (Melia et al., 1992). Tail shock elicits a release of endogenous norepinephrine in the hippocampus of chronically stressed rats (Nisenbaum et al., 1991). Immobilization stress in rats elevates tyrosine hydroxylase gene expression in the CNS and periphery, most likely through altered transcriptional activation (Nankova et al., 1999; Sabban et al., 2004). The eVect of stressors on noradrenergic activity appears to be sensitized by previous exposure to diVerent stressors, with chronically stressed rats having increased noradrenergic response to a new stressor than stress naive ones (Cassens et al., 1980; Nisenbaum and Abercrombie, 1993). We have reported that depressed suicides have fewer neuromelanin positive or noradrenergic neurons in the rostral locus coeruleus (Arango et al., 1996) and, have hypothesized that this lower reserve capacity for NE may result in more rapid depletion in individuals whose childhood adversity has sensitized their NA system to stress

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in terms of excessive NE release (Mann, 2003) (Fig. 1). Such accentuated NE depletion may lead more rapidly and completely to depression and hopelessness. Another mechanism of noradrenergic related stress response may be its interaction with the HPA axis. There are reciprocal neural connections between CRF neurons in the hypothalamic paraventricular nucleus and noradrenergic neurons in the locus coeruleus (Habib et al., 2001). We have conﬁrmed these ﬁndings in human brainstem and the locus coeruleus (Austin et al., 1995). Moreover, both alpha- and beta-adrenergic receptors regulate the secretion of ACTH (Al Damluji et al., 1988; Whitnall et al., 1993) and there is also substantial evidence for the regulation of noradrenergic activity by CRF (Curtis et al., 1997; Jedema and Grace, 2004; Smagin et al., 1995). Most of the evidence, but not all, suggests that CRF acts as a neurotransmitter in the locus coeruleus, mediating noradrenergic activation by diVerent stressors (Carrasco and Van de Kar, 2003).

C. EARLY LIFE STRESS

AND THE

NORADRENERGIC SYSTEM

Early stress aVects noradrenergic function. Rhesus monkeys exposed to maternal separation in infancy have elevated CSF norepinephrine measured monthly over 22 months in adulthood (Kraemer et al., 1989). In rat studies early life stress had lasting consequences for noradrenergic response to stress in adulthood. PVN levels of norepinephrine during a 20 min restraint stress were higher in rats which had been exposed to maternal separation compared to non handled control animals (Liu et al., 2000). Following 15 min of restraint stress, noradrenaline level in adult rats was signiﬁcantly lower in the hypothalamus and hippocampus and MHPG levels signiﬁcantly lower in the frontal cortex in those exposed to maternal separation in infancy compared to nonexposed rats (Daniels et al., 2004). This may reﬂect NE depletion. Heim et al. (2000, 2002) examined stress response in women with history of childhood abuse and found a greater heart rate, ACTH and cortisol response in those who developed major depression as an adult suggesting they were more sensitized. The heart rate response suggests an excessive catecholamine response. As yet there are no published studies that examine noradrenergic function in the context of interactions between genetic vulnerability, stress, and depression.

X. Dopaminergic System in Depression

Some studies report low levels of cerebrospinal ﬂuid homovanillic acid (HVA) in major depression and HVA is the main metabolite of dopamine (DA) (Asberg et al., 1984). Parkinson’s disease, a disease characterized by DA neuron

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171

degeneration is frequently complicated by major depression unrelated to the level of motor impairment (Mayeux et al., 1981). Such studies (for a review see Mann and Kapur, 1992) indicate that a DA deﬁcit is part of the cause of major depression.

A. GENETICS

AND THE

DOPAMINERGIC SYSTEM

The dopamine receptor DRD3 has been of interest as a candidate gene for association studies because it is almost exclusively expressed in limbic regions of the brain—areas associated with cognitive and emotion function (SokoloV et al., 1990). A meta-analysis of 11 case-control studies comprising 980 bipolar patients 1100 controls found no evidence of association between the DRD3 Bal1 polymorphism and bipolar disorder (Elvidge et al., 2001). Chiaroni et al. (2000) noted a gender distribution diVerence for the Bal1 polymorphism in bipolar disorder, with female preferentially heterozygous and males homozygous. A later multicenter European study also found no association with DRD3 receptor and either bipolar of MDD, however reported an association between the DRD2 and bipolar disorder but not MDD (Massat et al., 2002b). A positive association between a functional DRD2 promoter variant and the DRD2 taq1A polymorphism was reported in Chinese bipolar disorder patients, but not Caucasian bipolar patients (Li et al., 1999) while, Furlong et al. (1998a) found no association between D2 and bipolar or MDD. A DRD2 polymorphism was not associated with MDD (Manki et al., 1996; Serretti and Smeraldi, 1999), nor with symptomology, including excitement, depression, delusion, and disorganization symptoms in bipolar, or the outcome of lithium prophylaxis in mood disorders (Serretti et al., 1999). Other associations studies of genes in the dopaminergic system found no diVerence between MDD patients and healthy controls in genotypic or allelic distribution of DRD4 receptor (DRD4) or dopamine transporter DAT1 (Frisch et al., 1999), and no association between polymorphisms in DRD4 or DRD2 and bipolar disorder (De bruyn et al., 1994), or MDD in a Croatian sample (Oruc et al., 1997b).

B. CURRENT STRESS

AND THE

DOPAMINERGIC SYSTEM

Dopaminergic system response to stress is complex. In rats acute stress increases dopamine turnover in the prefrontal cortex and nucleus accumbens, while generally no response is observed in the neostriatum (Finlay and Zigmond, 1997). Prior exposure to chronic stress enhances response of mesocortical dopamine neurons to an acute novel stressor, but not in subcortical sites (Finlay and

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Zigmond, 1997). Low intensity stressors activate mesoprefrontal dopamine neurons (Horger and Roth, 1996).

C. EARLY LIFE STRESS

AND THE

DOPAMINERGIC SYSTEM

There have been few studies on the eVect of early stress on dopaminergic function. Maternal separation during ﬁrst 14 days of life, results in excessive response to mild stressor (tail pinch) manifested by greater increases in nucleus accumbens dopamine levels compared to normally reared and handled rats (Brake et al., 2004). In humans, childhood exposure to stress did not predict depression or anxiety by variants of the DA3 polymorphism (Henderson et al., 2000). Clearly further studies are needed of both DA related stress responses and modulations by genetic eVects.

XI. GABAergic System

Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in almost all areas of the CNS and regulates many CNS functions. Lower GABAergic activity may play a role in depression. Magnetic resonance spectroscopy studies have observed lower GABA levels in occipital cortex of depressed patients (Sanacora et al., 1999), and lower CSF GABA levels and plasma concentrations of GABA have also been reported (Gold et al., 1980; Petty and Schlesser, 1981). Fewer GABA neurons in anterior cingulate, dorsolateral prefrontal cortex and entorhinal cortex have been observed in bipolar disorders (Beasley et al., 2002; Benes et al., 2000, 2001). As no statistically signiﬁcant diVerences in pyramidal cells or glia and no diVerence in the size of pyramidal cells is reported, this suggests a deﬁcit in local circuit neurons or GABA cells in layer II of anterior cingulate in bipolar disorders.

A. GENETICS

AND THE

GABAERGIC SYSTEM

There is some evidence that genes coding for the GABA receptor may be involved in the etiology of major depression. In MDD associations have been reported between a CA-repeat in the GABA R3 gene and MDD in females but not males (Henkel et al., 2004) and with bipolar disorder in a European multicenter study (Massat et al., 2002a). Though not all studies ﬁnd this association (Papadimitriou et al., 2001). Other associations have been reported between GABA1 receptor gene and bipolar disorder, but not MDD, in a Japanese study

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(Horiuchi et al., 2004) and the GABAA receptor alpha-5 subunit gene and MDD but not bipolar disorder (Oruc et al., 1997a). Another study observed the opposite—an association with bipolar but not MDD patients or controls (Papadimitriou et al., 1998). Others found no association with GABAA receptor alpha-1 subunit receptor gene and bipolar or MDD (Serretti et al., 1998) or with the outcome of lithium prophylaxis in mood disorders (Serretti, 1999). No association was found between the GABAR3, GABAR5, and GABARB3 subunits of the GABAA receptor and lithium-responsive bipolar disorder (DuVy et al., 2000). B. EARLY LIFE STRESS

AND THE

GABAERGIC SYSTEM

Adult rats who had been exposed to maternal separation for the ﬁrst 14 postnatal days have lower GABAA receptor levels in the locus coeruleus and nucleus tractus solitarius, and reduced levels of the mRNA for the gamma2 subunit of the GABAA receptor complex (Caldji et al., 2000). Nothing is known of GABA-related stress responsiveness and major depression. XII. Brain Derived Neurotrophic Factor

Brain derived neurotrophic factor (BDNF) is part of a growth factor cascade which inﬂuence cellular plasticity and resilience. Neurotrophic factors such as BDNF increase cell survival by suppressing intrinsic cellular apoptotic machinery (Mamounas et al., 1995). Other elements in the BDNF cascade include the TrkB receptor, cAMP-response element-binding protein (CREB), and Bcl-2 (a major antiapoptotic protein) (Manji et al., 2001). cAMP-response element is a cis-acting enhancer element in the regulatory region of various genes. CREB is activated by BDNF, and the BDNF gene promoter also contains cAMP-response element (Fang et al., 2003; Tabuchi et al., 2002). There is some evidence that this cascade is involved in major depression. Antidepressants in rodents upregulate the CREB cascade and expression of BDNF (Duman et al., 1999). BDNF and the serotonergic system may regulate reciprocal function; 5-HTT function is modulated by BDNF (Mossner et al., 2000), which in turn was found to be elevated in the hippocampus and frontal cortex after antidepressant treatment (Nibuya et al., 1995). A. GENETICS

AND

BDNF

Genetic studies of BDNF have produced mixed reports. The met allele of the val66met SNP is associated with reduced hippocampal synaptic activity and poorer episodic memory, and impaired activity-dependent BDNF secretion in

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transfected neuronal cells (Egan et al., 2003). One study reported excess transmission of the val allele of amino acid 66 of BDNF in bipolar parent-proband trios compared to controls (Sklar et al., 2002). No association has been reported for MDD (Tsai et al., 2003), bipolar and MDD in a Chinese study sample (Hong et al., 2003), bipolar disorder in a Japanese cohort (Nakata et al., 2003), and childonset mood disorder in a mixed Caucasian and African–American sample (Strauss et al., 2004). A family study examining association between child-onset mood disorder and the BDNF receptor tropomyosin related kinase (TrkB) also found no association (Adams et al., 2005). In a related study of recurrent early-onset familial MDD Zubenko et al. (2002) found signiﬁcant evidence of linkage of MDD to a 451 kb region of 2q33–34 ﬂanked by D2S2321 and D2S2208. That region contains the CREB1 gene. Linkage, however, was observed only in female aVected pairs.

B. CURRENT STRESS

AND

BDNF

Stress can decrease the expression of BDNF and lead to atrophy of the hippocampal neurons (Duman et al., 1997). Dysregulation of the BDNFERK1/2-CREB-Bcl-2 cascade may be a mechanism by which prolonged stress induced atrophy of vulnerable neurons, distal dendrites, or both (Manji et al., 2001). Chronic stress also aVects this cascade. A pronounced and enduring hyperphosphorylation of ERK1 and -2 (the extracellular signal-related kinase) in dendrites of outer prefrontal cortical layers, and reduced phospho-CREB in several cortical regions including medial prefrontal cortex and cingulate cortex were observed in rodents exposed to chronic foot shock (Trentani et al., 2002).

C. EARLY LIFE STRESS

AND

BDNF

The eVect of childhood stress on BDNF and related signaling pathways has yet to be fully investigated, although it has been shown that sustained HPA axis dysregulation has deleterious eVects on cellular plasticity, neurogenesis, and signal transduction. Given the mounting evidence that early life stress can have both acute and lasting impact on HPA axis functioning, it is possible that eVects will be seen on BDNF and its downstream signaling pathways. Studies are needed to speciﬁcally investigate the eVects of stress in early life on these systems. Such studies should also include a consideration of the role genetic vulnerability plays in modulating risk of neurobiological abnormalities.

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XIII. Conclusions

It is clear that genes have a role in the etiology of mood disorders. The speciﬁc genes are beginning to be identiﬁed. The eVects of genes may be direct and indirect such as modulation of the eVects of early life stress as well as current stress. Stress, particularly in early life, also contributes to the risk for mood disorders. Although there are many gaps in current knowledge, a model is emerging whereby early life stress can interact with genetic vulnerability to lead to increased risk for mood disorder. This emerging model also identiﬁes targets for both pharmacologic and psychosocial intervention and has greater potential for prevention than previous therapeutic approaches that have focused on treatment of acute episodes of major depression or mania or prevention of recurrent episodes. Further treatments may target eVects of early childhood stress in at-risk individuals. References

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QUANTITATIVE IMAGING WITH THE MICRO-PET SMALL-ANIMAL PET TOMOGRAPH

Paul Vaska,* Daniel J. Rubins,y David L. Alexoff,z and Wynne K. Schifferz *Medical Department and Center for Translational Neuroimaging Brookhaven National Laboratory, Upton, New York 11973, USA y Imaging Department, Merck Research Laboratories, Merck and Co., Inc. West Point, Pennsylvania 19486, USA z Chemistry Department and Center for Translational Neuroimaging Brookhaven National Laboratory, Upton, New York 11973, USA

I. Introduction II. Setup and Calibration A. Scanner Setup B. Calibration C. Quality Control D. External Radioactivity Measurements E. Animal Positioning III. Physical Corrections A. Randoms B. Deadtime C. Attenuation D. Scatter E. Normalization F. Natural Background Radioactivity of LSO IV. Image Reconstruction V. Data Analysis A. Anatomical Segmentation and Image Registration B. Partial Volume Correction VI. Conclusions References

Quantitative imaging of complex biological processes is a critical technology of the post-sequencing era. In particular, positron emission tomography (PET), using small-animal models, has emerged as a powerful technique to explore physiology in a ﬂexible, noninvasive, and potentially highly quantitative way. With the recent advent of commercial high-resolution, small-animal imagers, such as the microPET scanners from Siemens (formerly Concorde Microsystems), functional imaging of rodent models using PET has found increasing acceptance. However, a broad class of PET research, particularly neuroimaging, requires quantitative accuracy which, for the new small-animal systems, has generally been slow to INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73006-9

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reach the standards of state-of-the-art clinical research cameras. An essential ﬁrst step in a quantitative PET study is the generation of a faithful representation of the radioactivity distribution in the subject as a function of time, which can be subsequently interpreted in terms of biological processes using methods such as tracer kinetic modeling. Since the accuracy of the input images is critical to the eVectiveness of such models, the development of methods to improve image quantiﬁcation is an important endeavor. These issues in the physics of imaging comprise the focus of this manuscript. Many factors impact PET image quantiﬁcation including system setup and calibration, prereconstruction corrections for physical eVects (e.g., deadtime, randoms, scatter, and attenuation), the type of image reconstruction algorithm, and postreconstruction methods that delineate anatomical regions and correct for spatial-resolution eVects (i.e., partial volume eVects). While most of these quantitative issues are applicable to all smallanimal PET systems, they will be described in the speciﬁc context of the popular micro-PET R4 rodent tomograph in order to provide concrete recommendations.

I. Introduction

Small-animal PET is becoming an increasingly common tool to explore biological function noninvasively, particularly in the neurosciences. However, it is a relatively new phenomenon with its own special challenges. Although smallanimal PET scans suVer less from scatter and attenuation than their human counterparts due to the smaller subject size, background rates can be signiﬁcant. And while the spatial resolution of 2 mm was much better than for human scanners, the improvement did not scale with the size of the regions of interest in rats and mice, making the partial volume eVect relatively more problematic. The ﬁrst commercial systems were the micro-PET scanners from Concorde Microsystems (Knoxville, TN), which appeared around the year 2000. Concorde (now part of Siemens) produced the rodent micro-PET systems R4 (Knoess et al., 2003) and Focus 120 (Laforest et al., 2004), and the primate systems P4 and Focus 220 (Tai et al., 2001). The micro-PET R4 scanner was the ﬁrst system and has perhaps the largest installed base and thus will serve as a speciﬁc context for the issues discussed later. Recently several other commercial small-animal PET systems have been oVered, including the HIDAC (Oxford Positron Systems, Oxfordshire, UK) (Jeavons et al., 1999; Schafers et al., 2005), MOSAIC (Philips Medical Systems, Philadelphia, PA) (Surti et al., 2003), X-PET (Gamma Medica, Northridge, CA), and YAP-PET (ISE, Pisa, Italy) (Del Guerra et al., 1998). Many of the concepts discussed herein can be applied to these scanners as well. The initial micro-PET systems were robust in terms of hardware, but the processing software had few of the quantitative corrections that researchers had

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come to expect of state-of-the-art human systems. In the last few years the software has improved signiﬁcantly, but careful attention to quantitative issues remains important to maximize its power to resolve subtle biological eVects in small regions.

II. Setup and Calibration

Reliable quantitation begins with a careful setup of the system and daily monitoring of quantitative stability. Accurate cross-calibrations with external radioactivity measurements are also essential, and animal positioning devices are important to reduce intersubject measurement variations. A. SCANNER SETUP The setup procedures devised by Concorde are generally well documented and robust, with manual tools to assist automated methods. However, the methods for crystal identiﬁcation and ﬁtting of energy spectra are far from 100% eVective and it is highly recommended that the position and energy spectrum of each crystal are veriﬁed and corrected as necessary to ensure optimum performance, despite the additional eVort required. Default settings for coincidence time window (10 ns) and energy window (250–750 keV) are suYcient for many studies, but may be adjusted for speciﬁc study types (e.g., using a narrower time window to minimize random coincidences in high-count rate studies). A common way to optimize these parameters is to compare noise equivalent count rate (NEC) (Strother et al., 1990) using phantoms similar in size to the subject of interest. While this is a well-deﬁned quantitative method, it is not comprehensive in that it does not account for image artifacts or distortions that commonly occur at high-count rates such as degradation of spatial resolution (Badawi et al., 2004). At a minimum, more subjective measures of image quality should be performed to conﬁrm predictions based on NEC. B. CALIBRATION To ensure the highest accuracy in absolute calibration, a uniform phantom is typically used and the actual study conditions should be replicated as closely as possible. This means using a phantom of approximately the same size as the object being imaged and in the same position in the ﬁeld-of-view (FOV), and a count rate in the range of actual studies. Ideally, a short-lived isotope, such as 11C or 13N, allows calibration over a range of count rates. Image processing should be identical to that used in actual studies and regions-of-interest on the phantom

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should be placed far enough from the edges to avoid partial volume eVects. The calibration factor is the ratio of activity concentration as measured in a calibrated well counter (decay-corrected to the scan start time), to an averaged region of interest (ROI) value. In the micro-PET software, this is manually inserted into the appropriate header location in the normalization ﬁle. C. QUALITY CONTROL Quantitative quality control methods are important but not provided with the system. Blank scans acquired with the transmission source can be visually inspected for obvious detector failures, but subtle changes over time are diYcult to detect. The Brookhaven group has developed code that quantitatively compares a daily blank scan to a similar scan, which was acquired immediately following system normalization. The algorithm calculates a single ﬁgure of merit representing the diVerence between the two scans, equal to the bin-by-bin root-meansquare diVerence between the sinograms scaled by the expected counting error (assuming Poisson statistics) and accounting for source decay. This ﬁgure is very sensitive to a dead block as well as slow drifts in the electronics over time, and suggests recalibration every few months on the micro-PET R4. D. EXTERNAL RADIOACTIVITY MEASUREMENTS Repeated blood sampling is diYcult in rodents, but the radioactivity levels in plasma as a function of time (the input function) represent critical data in many quantitative studies involving tracer kinetic modeling. Cross-calibration of the scanner and well counter as described previously is a requirement, as are quantitative corrections to the well counter measurements themselves, including corrections for deadtime, background rates, and sample geometry. Other simple but often overlooked issues include a consistently applied correction for positron decay branching fraction, and the synchronization of well counter and scanner clocks for accurate decay correction. Time errors can be a signiﬁcant problem with short-lived isotopes, especially considering the notorious inaccuracy of the system clocks in standard PCs. Automatic time synchronization software must be employed to eliminate such errors.

E. ANIMAL POSITIONING Scanner performance is dependent on the position within the FOV due to variations in spatial resolution and uniformity. Accurate positioning of animals helps ensure that these variations will not confound measurements across subjects

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or among scans of the same subject. Holders should be of low mass and in positions that minimize attenuation for lines of response through the region of study, especially because of diYculties in attenuation correction (see the following section). The Brookhaven group has adapted a rat-head holder from David Kopf Instruments (Tujunga, CA), originally designed for MRI studies, to small-animal PET, as discussed below in more detail.

III. Physical Corrections

Real PET data deviates signiﬁcantly from the idealized projections of activity, which are required for faithful image reconstruction. Statistical noise from photon counting is one eVect that cannot be fundamentally corrected due to its random nature, but there are many sources of bias, which can be corrected in principle. These include deadtime and variable counting eYciencies of the detectors and electronics, gamma-ray scatter and attenuation by the subject, and acceptance of random coincidences. Various methods have been developed over the years to correct for these eVects, with varying degrees of accuracy and complexity to ﬁt the particular imaging needs at hand. Accuracy is especially important for quantitative studies, in contrast to diagnostic imaging for example. Speed, simplicity, and robustness against failure are competing practical issues to facilitate eYcient operations with a minimum of user intervention. The micro-PET has required signiﬁcant development by the vendor and installed sites to validate and improve its quantitative ability to acceptable levels. Brookhaven received one of the ﬁrst micro-PET systems (in late 2000) when many of the basic corrections did not exist. In the few years since, much progress has been made, ranging from improvements by the vendor, to validation studies at BNL and elsewhere. For example, methods for scatter and attenuation corrections have recently become available. However, much validation and optimization remains to be done as the accuracy and practicality of various approaches have not been fully explored.

A. RANDOMS Random (aka chance or accidental) coincidences are subtracted using the standard delayed-coincidence method, which generally exhibits minimal bias, although it introduces some additional statistical noise because it is itself a countlimited measurement. Future improvements may take advantage of the highstatistical accuracy in the measured singles rates via a calculation based on those rates and the coincidence time window (Brasse et al., 2005).

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B. DEADTIME Deadtime refers to the counts lost due to electronic processing of events and includes rate limitations at the detector as well as at higher level processing components such as the coincidence processor. In the micro-PET systems, a single overall correction factor is calculated for each time frame from measured count rates. System deadtime was studied by allowing a uniform cylindrical phantom to decay while scanning. The resulting decay-corrected activity concentrations were plotted as a function of known activity concentration, determined by counting an aliquot of the mixture in a calibrated well counter, as shown in Fig. 1. The ideal result would be a horizontal line, at least over the range of typical activity levels. The original method supplied by the vendor (‘‘old’’) was based on a ratio of prompt and delayed (random) coincidence rates. It had the undesirable properties that at low activities, when deadtime is negligible in reality, the correction was not negligible (1.1 instead of 1.0) and furthermore, it was unstable due to statistical noise caused by the very low-randoms rate. That method was superseded by one, which assumed a ﬁxed deadtime per event at the coincidence processor. The two methods are compared to no correction in Fig. 1, which shows that the new method is an improvement.

C. ATTENUATION Despite the small size of rodents, gamma-ray attenuation in rat brain studies, for example, reaches 20–25% in central regions. The most basic way to account

FIG. 1. Deviation of microPET activity measures from linearity, using a 37-mm diameter uniform cylinder ﬁlled with a 11C mixture.

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for attenuation is to calibrate the system with a uniform cylinder approximately the same size as the animal. This corrects for the average eVect of attenuation, but not variations across the FOV (Chow et al., 2005), which are about 10–15% depending on position within the head. This level of error can be signiﬁcant for quantitative studies. The standard correction method of transmission scanning using the supplied rotating 68Ge point source has been problematic. Since the micro-PET is a fully three-dimensional camera with no septa, scatter acceptance is large, especially with the default 250 keV lower-energy threshold. Coincidence transmission scanning could potentially eliminate most scattered events from the transmission data by rejecting those coincidences that are not collinear with the known position of the point source. However, the potential of this spatial ‘‘windowing’’ technique appears to be unrealized in this system, and instead singles mode transmission scanning is recommended by the vendor. An advantage of singles mode is higher sensitivity since only one of the two photons need be detected. This further enables higher acquisition rates by allowing a much stronger source since the detectors near the source are not needed and thus do not contribute to deadtime. However, singles mode accepts a large number of scattered events, which depress measured attenuation coeYcients by up to a factor of 3. Concorde has provided two methods to correct for scattered events in the transmission data. The ﬁrst is a manual segmentation algorithm in which the transmission data is reconstructed, and regions in the transmission image above a speciﬁed threshold set to the ‘‘known’’ value (usually assumed to be the attenuation coeYcient of water, 0.095 cm 1). This relies on a somewhat subjective determination of the appropriate threshold and is rather impractical for routine use. The second method involves a new scatter correction method applied directly to the transmission sinograms. It seems to restore coeYcients to the correct range, but has not been published or well documented and will require further validation. An additional problem with the above transmission attenuation correction methods is statistical noise. Despite acquisitions of up to 30 min with an 0.5 mCi source and ﬁlling tens of GB of disk space with listmode data, the transmission data are still too noisy to use directly on emission data without propagating signiﬁcant noise into the resulting image. A new method has been recently provided by the vendor, using a strong (5 mCi) 57Co singles source. The lower gamma energies (mostly 122 keV) undergo greater attenuation in tissue, providing higher contrast in the data for small subjects. However, the attenuation coeYcients need to be scaled to values appropriate for 511 keV gamma rays, and the lower energy limits the ability to reject scattered coincidences due to poorer energy resolution. A promising method for rat-brain studies is to ﬁx the head position in a head holder so that it can be accurately repositioned with high accuracy. In this case, a long transmission scan can be done only once and the results applied to all such

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FIG. 2. Rat-head holder under development by the Brookhaven group, with ear bars and bite bar.

studies, assuming that the attenuating distribution remains the same even across multiple subjects. Rat brains, even for diVerent strains and sex, are very similar in size for a given weight range (Paxinos et al., 1985), so this is potentially an excellent solution that requires no additional time for each subject other than the time to accurately reposition the subject. While this is not a general solution, it can be a very practical one for studies with large numbers of similar-sized subjects. As noted previously, a new rat-head holder is being developed based on a design by Kopf, shown in Fig. 2, in order to provide the necessary repositioning capability. Modiﬁcations have been made to reduce any additional attenuation caused by the head holder itself.

D. SCATTER Object scatter is also a signiﬁcant problem for emission data. Since there was initially no correction method for the micro-PET scanners, the Brookhaven group developed an automated, fast, and robust sinogram tail-ﬁtting method (AlexoV et al., 2003). Recently the vendor has supplied a rather sophisticated scatter correction method, based on the single scatter simulation method used in many human scanners (Watson et al., 1997). In principle, the new method can be more accurate than tail ﬁtting, but it requires an attenuation map, which is problematic for the reasons discussed earlier. Also, validation studies have yet to be published. An initial comparison of scatter proﬁles using both methods has been carried out using actual rat-brain data. Figure 3 shows that while the new method is

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FIG. 3. Comparison of scatter correction proﬁles in a radial projection of a central sinogram of a rat-brain study, using a simple quadratic tail-ﬁt and the new single-scatter simulation algorithm.

perhaps more accurate within the rat head (based on the general shape of scatter proﬁles in the literature), scatter is clearly underestimated in the tails. Possible sources of the additional background events in the data, which are not accounted for in the new correction method include scatter originating outside the ﬁeld-ofview and background from the natural radioactivity of the scintillator LSO, discussed in the following section. Monte Carlo simulation codes can also be used to estimate the scatter distribution on a per-study basis (Holdsworth et al., 2002). While this method also requires attenuation data and can be slow, the accuracy is unparalleled and speed can be increased with future computing power, making it an attractive strategy in the event that the alternatives don’t provide suYcient accuracy. A number of PET simulation codes are freely distributed, including SimSET (Lewellen et al., 1998) and more recently GATE (Santin et al., 2003) based on the GEANT4 physics package. E. NORMALIZATION The normalization correction in PET accounts for variable detector eYciencies and geometric eVects (Bailey et al., 1996). There are a number of ways to

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FIG. 4. Residual image nonuniformities in a microPET scan of a uniform cylinder. (A) sinogram with summed radial projection below, (B) reconstructed image plane with projection through center, and (C) axial distribution of image.

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accomplish this. The originally supplied point-source, direct-inversion method leaves residual image artifacts—fully corrected scans of a uniform cylinder of activity do not produce a uniform image. Figure 4 shows a residual dip at the center of the ﬁeld-of-view in the transverse plane, and in the axial direction, the end planes have higher values than the center. The other corrections and reconstruction algorithm were ruled out as the cause by Monte Carlo simulations, which reproduced the eVect using data that contained no scatter, attenuation, randoms, or deadtime eVects, as shown in Fig. 5. These simulations are based on the SimSET code, which was modiﬁed by our group to reproduce the micro-PET block detector geometry and the micro-PET sinogram binning, among other enhancements. A new normalization method has recently been supplied by the vendor, using a ‘‘model-based’ approach (Bai et al., 2002), but it will also require further validation before use in routine studies.

F. NATURAL BACKGROUND RADIOACTIVITY

OF

LSO

The scintillator used in the micro-PET is the relatively new lutetium oxyorthosilicate (LSO) (Melcher, 2000). LSO is an excellent scintillator for PET due

FIG. 5. Proﬁles across uniform phantom images, reconstructed from real and simulated microPETsinogram data under the conditions stated.

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to its high-stopping power, high-light output, and fast decay. A known drawback is that it contains radioactive 176Lu (T1/2 ¼ 4 1010 years, E ,max ¼ 595 keV). The conventional wisdom was that the coincidence requirement of PET eliminates the LSO background because the beta decay is essentially a ‘‘singles’’ event. And that even if it contributes to the randoms rate, the standard delayed-coincidence randoms correction method will remove it. However, the decay scheme shows that not only does the beta spectrum fall largely within the PET energy window, but coincident gamma rays are also emitted, most notably at 307 keV. With the default settings of the micro-PET R4 (250– 750 keV energy window), this results in a background true coincidence rate of >1000 cps (Vaska and AlexoV, 2003). This is likely the cause of the increasing background fractions observed in the later time frames in dynamic neuroreceptor studies in rats (AlexoV et al., 2003), which reached levels >60%. EVects of the LSO background were determined as a function of the lowerenergy threshold for the micro-PET R4. To conﬁrm that the source of the background is the beta–gamma coincidence from LSO, a 96-hour transmission attenuation measurement was performed using no external sources of any kind. The background true rates were measured. And to quantitatively compare scanner performance at the diVerent thresholds, NEC curves were calculated using a uniform cylinder ‘‘rat’’ phantom in a brain-study position. The sourceless transmission and blank scans produced a remarkably artifact-free and nearly quantitative -map image of a complex phantom, as shown in Fig. 6. Compared to the default 250-keV threshold, the 350-keV case cuts the background rate by a factor >6. However, it also reduces system sensitivity by 30%, and the NEC curves shown in Fig. 6 are thus lower all the way down to 65 nCi/cc, below which the 350-keV case is better due to the reduced scatter and LSO background. Although a higher threshold of 350 keV slashes the background rate, it also signiﬁcantly reduces the system sensitivity, giving lower NEC for all but the lowest activity levels. Thus, despite the higher background, the 250-keV threshold should be superior for dynamic rat-brain studies in the micro-PET if a background subtraction technique is used to remove the bias. While the physical origin of this background is now understood, the best methods to correct for it have not yet been fully investigated. Assuming that an unbiased estimate of the LSO background can be subtracted, measured NEC curves at the 250-keV threshold demonstrate a marginal improvement in signalto-noise at the lowest count rates. For some studies (such as most FDG studies), the higher NEC may not be a signiﬁcant beneﬁt, and a simple solution is to use the higher, 350-keV threshold, which essentially fully rejects the background from the data. However, for low-activity studies, such as neuroreceptor studies in rats and mice, the increase in NEC from the lower threshold might be more beneﬁcial and a subtraction method would need to be employed to take advantage of it.

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FIG. 6. (A) Three-dimensional, surface-rendered -map images of Lucite rat-head holder and water-ﬁlled, cylindrical phantom in the microPET, acquired using only the LSO background radiation as a transmission source. (B) NEC curves for rat-sized phantom in microPET with two diVerent lower-energy thresholds.

The LSO background is very uniform across the sinogram, and its distribution appears similar to scatter. Indeed, a tail-ﬁt scatter correction method should correct for the bulk of the eVect, since it subtracts all relatively ﬂat backgrounds from the sinogram. However, tail-ﬁtting will somewhat over-correct for the LSO background within the object, because attenuation of the LSO gamma rays lowers the background in the object relative to the tails (this can be seen in transmission sinograms). On the other hand, the single scatter simulation correction method does not account for LSO background at all, and may even perform less eVectively if results are scaled to the sinogram tails as is sometimes done in these techniques.

IV. Image Reconstruction

The goal of PET image reconstruction is to produce an accurate spatial map of radioactivity concentration in the subject, using projection data (sinograms) that reﬂect the distribution of coincidence events in detectors arranged around the subject. Reconstruction algorithms based on idealized data are relatively straightforward and have existed for many years. Most commonly, reconstruction is done using an analytical technique called ﬁltered backprojection (FBP).

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Although it is simple, fast, and predictable and therefore widely used, FBP cannot fully accommodate certain physical eVects described below, and so is prone to image errors and artifacts. In addition, it is essentially a two-dimensional method, reconstructing one image plane at a time, using only projection data from within that plane. Modern systems acquire fully three-dimensional data. This improves the counting statistics, but before FBP can be used with three-dimensional data, additional processing (rebinning or reprojection) is required, creating more potential errors. Unfortunately, clinical constraints and the immutable physics of the detection process conspire to corrupt the data with numerous sources of noise and bias, which may distort the resulting image. Substantial statistical noise is caused by limits on the amount of radioactivity that can be administered, based on scannercount rate limitations and/or the requirement of maintaining tracer levels of injected radioligand. Further errors are introduced by correction of the data for random coincidences and for attenuation by the subject, especially since these corrections are themselves usually based on separate, noisy measurements. Even after the corrections described earlier are carried out, the impact of these errors can be severe. This has driven the development of sophisticated reconstruction algorithms to improve image ﬁdelity (Leahy and Qi, 2000). Due to ever-increasing computer power, these newer iterative reconstruction techniques have become more feasible, promising improved performance by modeling the physics of the detection process. As noise is properly controlled and spatial resolution improved, quantitation beneﬁts accordingly. Many algorithms have now been proposed, and each has variable input parameters, which aVect performance in ways often not obvious. Iterative methods take advantage of the fact that the forward detection process that is, starting with the activity distribution and then going to the projection data, can be determined in a relatively straightforward manner. The problem is posed as a simple matrix equation y ¼ Ax, where x is a vector representing all the image voxels, y is a vector of all the projection elements, and A is the so-called ‘‘projection’’ or ‘‘system’’ matrix, which can be computed such that it contains all the desired physical eVects connecting the two. In the most common maximum likelihood (ML) approach, the idea is to iteratively change the image x, until the projections Ax best match the measured projections, within the limitations of the known noise properties of the projections. A principal challenge is to derive image update formulas such that they converge and are reasonably fast, since iterative methods impose potentially huge computational burdens (reconstruction times can be hours per image versus minutes for FBP). Modern variations on the ML method include ordered subsets expectation maximization (OSEM) (Hudson and Larkin, 1994) and maximum a posteriori (MAP) (Qi and Leahy, 2000; Qi et al., 1998) methods, which converge more

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FIG. 7. FBP (left) and MAP (right) reconstructions, respectively, of the same data.

18

F-FDG rat-brain

quickly and control noise more eVectively. A comparison of FBP and MAP reconstructions of the same rat-brain data is shown in Fig. 7. Two-dimensional FBP and OSEM algorithms are provided by Concorde, along with the required rebinning code called FORE (Defrise et al., 1997) to reduce the three-dimensional data to two-dimensional. More recently fully threedimensional OSEM and MAP codes have been introduced. MAP algorithms inherently include the MLEM algorithm when the smoothing term is turned oV. Since iterative techniques promise to better control statistical noise, a comparison between FBP and OSEM was made using an early time frame from a dynamic 11 C-raclopride study of a rat brain. Figure 8 shows that OSEM indeed performs better in a qualitative sense. FBP displays signiﬁcant streak artifacts outside the head, while OSEM does not. To demonstrate the potential spatial resolution performance of each algorithm, a miniature Derenzo-type phantom was scanned in the micro-PET and reconstructed using FBP, OSEM, and MAP. The results are shown in Fig. 9. Since a large number of counts were collected, the eVects of statistical noise are not expected to be important, and this experiment should primarily show diVerences in terms of image artifacts and spatial resolution. Clearly, there is a dramatic improvement in resolution in the MAP case, due most likely to eVective modeling of the spatial resolution in the system matrix. Since OSEM is also an iterative method, it has similar potential to improve spatial resolution. The lack of improvement in OSEM compared to FBP implies that the resolution was modeled poorly or not at all in the system matrix (a fairly common feature of traditional iterative algorithms). This makes an important point that the system

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FIG. 8. FBP and OSEM reconstructions of a transverse slice through a rat brain from an early time frame in a 11C-raclopride study on the microPET R4.

FIG. 9. Transverse slice through Derenzo phantom, scanned in microPET R4, reconstructed using three diVerent algorithms. Images were not corrected for scatter.

matrix is a key component of the overall algorithm. A fully-deﬁned iterative reconstruction method speciﬁes not only the mathematical algorithm, but also the extent to which physical eVects are included in the system matrix, the number

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of iterations required, and ﬁnally, the details of any smoothing procedures and associated parameter values. An important fact is that there is no single optimal method for all types of PET imaging. The best method depends on both the data (noise, detector resolution, etc.) as well as the task for which the images will be used. For example, an algorithm that is optimized to visually localize cancerous lesions in wholebody scans may produce large errors in quantitative brain studies. For a given reconstruction algorithm, the ROI accuracy depends on the parameters used, in particular on those controlling smoothing. With low smoothing, statistical noise in the ROI may be large, giving a large error. At higher smoothing, the noise will be reduced, but a bias may be introduced from averaging in the activities in adjoining background regions (partial volume eVect). Thus, there is an optimum level of smoothing which produces the most accurate result. The trade-oV is best represented in a plot of bias versus noise, where the points on the curve represent diVerent degrees of smoothing. Accurately analyzing the noise is not straightforward, and much of the literature overlooks important subtleties. Most papers report the variance of the pixels within a single ROI (Chatziioannou et al., 2000), but this has been shown to be quite inaccurate (Dahlbom, 2002). The best estimate of the noise is the variance of the ROI means over repeated, equivalent measurements. The use of a phantom provides a spatially stable radioactivity distribution, but the decaying activity in the phantom complicates the acquisition of equivalent data sets. One possible solution is to use so-called ‘‘bootstrap’’ resampling methods to draw a number of statistically similar data sets from a single large data set (Dahlbom, 2002). Of course, Monte Carlo simulation methods can be used for this purpose as well, with special care given to creating statistically independent data sets. By properly measuring bias and noise as a function of smoothing parameter for a given reconstruction algorithm, the optimum smoothing parameter can be determined as the value which minimizes the overall error, which can be deﬁned as the quadrature sum of bias and noise. An initial investigation of the noise/bias trade-oV on the micro-PET R4 was carried out with the MLEM algorithm (MAP with smoothing parameter set to zero) run to convergence and postreconstruction Gaussian smoothing. This reconstruction strategy is supported by the work of Nuyts and Fessler (Nuyts and Fessler, 2003) as a more straightforward but equally accurate method compared to MAP with an optimized smoothing paradigm. Figure 10 shows a representative FBP image and the corresponding MLEM images for various iteration numbers up to 100. Without smoothing, the MLEM images appear noisier at high-iteration numbers. However, striatal ROI data shown in the graph demonstrate that the ROI mean has fully converged by 40 iterations and does not deteriorate with more iterations despite an increasing standard deviation within the ROI. Postreconstruction smoothing or a

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FIG. 10. Sagittal slice through rat brain in 11C-raclopride microPET study, showing FBP image and MLEM images after the speciﬁed number of iterations. Upper right image is the 100-iteration MLEM image after 1.5-mm FWHM Gaussian smoothing. Graph represents mean and standard deviation (error bars) of a striatal ROI as a function of MLEM iteration number, with a 0.4-mm voxel size and no smoothing, 0.4-mm voxel size and the Gaussian smoothing, and 0.6-mm voxel size and no smoothing.

larger voxel size decrease the standard deviation signiﬁcantly, while creating only a small negative bias most likely due to the partial volume eVect.

V. Data Analysis

A. ANATOMICAL SEGMENTATION

AND IMAGE

REGISTRATION

Quantiﬁcation in PET studies usually depends on the measurement of radioactivity concentrations within distinct regions (e.g., organs, brain structures). To

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accomplish this, a two-dimensional ROI or a three-dimensional volume of interest (VOI) that corresponds to that structure must be deﬁned within the PET image volume. PET studies are commonly analyzed with ROIs drawn manually on the PET image, but this method has many disadvantages. For example, the ROIs are based on the observed radioactivity distribution and are without anatomical reference. As a result, changes in the appearance of PET image slices can result in inconsistent delineation of regional boundaries with this method. Further, manual drawing of ROIs is time-consuming and becomes problematic when the biological activity within the structure of interest has been altered such that the corresponding structural boundaries are no longer delineated by the observed radioactivity distribution. Lastly, if the structure of interest covers multiple planes of the PET image volume, commonly used single-plane ROIs will not cover the entire brain structure, resulting in increased statistical noise in the measurement of radioactivity concentration within the ROI. Alternatively, independently obtained anatomical information can be used for the deﬁnition of regional boundaries in PET studies, making possible the deﬁnition of VOIs for structures that are not visually identiﬁable in the PET image. Further, the drawing of VOIs that fully contain complex three-dimensional brain structures is facilitated, maximizing the signal-to-noise ratio of measurements. Such VOIs also eliminate the potential bias in measurements caused by the arbitrary selection of a single plane from the PET image volume during manual ROI analysis. Techniques have been developed for brain studies of humans and nonhuman primates that register PET studies with either a corresponding anatomical image volume obtained by magnetic resonance (MR) imaging or a single brain atlas (Talairach and Tournoux, 1988). Image registration techniques range from automated, voxel intensity-based measures, for example, mutual information (Studholme et al., 1997; Wells et al., 1996), AIR (Woods et al., 1998), to landmarkbased approaches that require the user to select corresponding locations in the two images (Habboush et al., 1996). Registration techniques also vary in degrees of freedom (i.e., rigid-body versus warping) and level of automation. The need for an anatomical image of the individual for studies of humans and nonhuman primates arises from the often large diVerences in brain anatomy between individuals. With an anatomical image, rigid-body registration (i.e., limited to translation and rotation) can be applied for successful anatomical deﬁnition, while atlas-based approaches may require more complex warping techniques. However, atlas-based approaches have several advantages. Atlasderived VOIs can be obtained without an additional scanning procedure, and consistency of VOI deﬁnition is improved because the atlas-derived VOI need be carefully deﬁned only once, thereby reducing variability in PET measurements across studies. In contrast to humans and nonhuman primates, rat brains are very similar anatomically across individuals in a given weight range, even for diVerent

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FIG. 11. Atlas-derived VOIs are shown superimposed on (A) coronal, (B) sagittal, and (C) horizontal slices of the rat-brain atlas, demonstrating the relative positions of the VOI: striatum (red), hippocampus (orange), cerebral cortex (green), cerebellum (yellow), thalamus (blue), and whole brain (purple) (Rubins et al., 2003).

strains and sex (Paxinos et al., 1985). Thus, a single brain atlas can be used to provide accurate deﬁnition of anatomical structures, and PET images can be registered to an atlas with simple rigid-body registration. An automated method for placement of rat brain atlas-derived VOIs (shown in Fig. 11) onto PET studies has recently been designed and evaluated (Rubins et al., 2003). This method was used to register various PET studies with an anatomical atlas. A set of previously obtained micro-PET studies of control rats with 11 C-raclopride (n ¼ 4, 3 scanned twice) (AlexoV et al., 2003) was analyzed in this manner. Time activity curves (TAC) were obtained for striatum and cerebellum, and distribution volumes (DVR) were calculated using the Logan method (Logan et al., 1996). While TACs obtained with the atlas method were smoother than those obtained with conventional hand-drawn ROIs (representative study shown in Fig. 12), and the average calculated striatal DVR was signiﬁcantly higher (2.83, SD ¼ 0.21, with the atlas-based VOIs versus 2.36, SD ¼ 0.20, with conventional hand-drawn ROIs), no diVerence was found in the consistency of striatal DVR values between the methods. In addition to automated VOI placement, PET-atlas registration can aid in the visual assessment of PET images through image fusion techniques. This is particularly beneﬁcial for PET studies with novel radiotracers. In Fig. 13, PET images are shown superimposed on slices of the anatomical atlas. Visual assessment of fused images enabled conﬁrmation that the nicotinic compound 18 F-A85380 primarily accumulated within the thalamus and cortex, as expected. If the animal can be scanned using diVerent image modalities during a single session of anesthesia, registration issues can be simpliﬁed dramatically.

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FIG. 12. Time activity curves of the same 11C-raclopride PET study of the rat brain, analyzed with the automated method for placement of rat brain atlas-derived VOIs, and also with conventional hand-drawn ROIs.

To facilitate image registration of a given subject across diVerent imaging modalities, Concorde supplies an optional mounting platform for the micro-PET, which accepts beds from commercial small-animal CT and MR systems. If the animal is positioned on a single bed and is undisturbed across all scans, a onetime, accurate registration of the bed among the diVerent scanners can achieve submillimeter registration accuracy between the images in a fast and completely automated fashion (Meei-Ling et al., 2005).

B. PARTIAL VOLUME CORRECTION Recent advances in the synthesis of PET radiotracers and in the design of PET scanners have led to a demand for quantitative PET measurements of increasingly small organs and structures. This has been especially true for PET studies of the brain. In studies of human and nonhuman primates, the availability of highly speciﬁc radiotracers has led to interest in PET measurements of brain substructures, such as prefrontal cortex, raphe nuclei, and nucleus accumbens,

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FIG. 13. PET studies of the rat brain with PET radiotracer (A) 11C-ﬂumazenil, (B) 11C-raclopride, and (C) 18F-A85380 are shown superimposed on coronal (left), sagittal (center), and horizontal (right) slices of the rat-brain atlas, using methods of Rubins et al. (2003).

while the commercial availability of small-animal scanners has led to widespread interest in PET measurements of brain structures in the rat and mouse. In quantitative PET measurements of small objects, the misplacement of radioactive decay events by distances on the order of the spatial resolution of the PET scanner can cause the measured radioactivity concentration to be signiﬁcantly aVected by the radioactivity concentration in surrounding regions. In cases where the concentration is underestimated due to low concentration in surrounding regions, this underestimation is referred to as partial volume error (PVE). The corresponding overestimation of radioactivity concentration due to high concentrations in surrounding regions is often referred to as

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spillover. A rough rule of thumb is that PVE may signiﬁcantly aVect quantitative radioactivity measurements for any structure with a diameter less than twice the spatial resolution of the PET scanner (HoVman et al., 1979). A simple method of PVE correction (PVC) is to image a phantom containing a region of known size ﬁlled with a known concentration of radioactivity, and compare the radioactivity concentration measured with PET to the true value. Measurements can be made with various background radioactivity concentrations, and results can then be used for PVC in PET studies of objects of similar size, with similar background radioactivity levels, that are imaged with the same PET scanner. However, such a method assumes there is homogeneous radioactivity concentration outside the measured region, and in most PET applications, this assumption is not valid. For example, radiotracers used in PET studies of the brain often accumulate in varying amounts throughout the brain based on physiological variations between brain structures (e.g., changes in receptor density, glucose utilization), and therefore require a more complex method of PVC. Measurements of radioactivity concentration that are aVected by limited spatial resolution can lead to incorrect calculation of biological parameters. While such errors might apply to all measurements in an experimental data set in the same manner, in many types of PET studies they can have unpredictable results. For example, if the eVect of an intervention on radiotracer accumulation in a brain structure is under investigation, alterations in adjacent brain structures due to the same intervention can aVect measurement of the structure of interest by changing the amount of PVE/spillover between the regions. These aVects can have a particularly large impact on dynamic PET studies analyzed with tracer kinetic modeling, as the PET radiotracer may clear from a brain structure of interest at a diVerent rate than surrounding regions, altering the shape of time activity curves (TACs), and thus changing the calculated values determined by the model. The spatial resolution of a PET scanner can be measured in detail, thereby allowing the contribution of radioactivity from any location within the camera ﬁeld-of-view to each voxel in the resulting image to be known. Unfortunately, complete voxel-by-voxel deconvolution for PVE eVects in PET images is prohibitively computationally complex, and also results in an undesirable ampliﬁcation of statistical noise (Aston et al., 2002; Rousset et al., 1998). However, for many types of PET studies, the radioactivity distribution is often known (or at least assumed) to be homogeneous across a given anatomical region. By combining the voxels within each region, the mathematical complexity and the statistical noise are greatly reduced, making deconvolution feasible. If the size, shape, and relative orientation of the anatomical regions are known, in addition to the spatial resolution of the PET scanner, the contribution of each region to other regions can be determined and the measured values can be corrected. Methods based on this principle have been developed for human brain PET studies, with regional

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deﬁnition based on segmentation of a corresponding MR image. The geometric transfer matrix (GTM) method relies on a region-based approach (Rousset et al., 1998), while alternate approaches use a voxel-based approach (Meltzer et al., 1996). In a recent comparison of leading PVC methods, the GTM method showed the best performance (Quarantelli et al., 2004). In order to investigate PVC for rodent studies, a simulation approach was used because the input activity distribution is known exactly and thus any deviations in the reconstructed image could be directly determined. Another advantage is that the eVects of scatter and attenuation can be investigated independently since they can be easily controlled in the simulation. To accomplish this, the Monte Carlo PET scanner simulation program, SimSET, was modiﬁed to accurately simulate images generated with the micro-PET R4. The modiﬁcations enabled simulation of the correct block detector geometry of the crystals and also provided standard micro-PET-format sinograms for the output. Sinogram normalization and image reconstruction were accomplished using the standard micro-PET software (normalization data was simulated and processed in the same manner as real data, using a simulated rotating point-source scan). This procedure was validated in part through the observation of similar spatial resolutions for point sources in both real and simulated data. As an initial test, images of concentric cylinders were simulated, and volumetric analysis was applied with and without PVC using the GTM method based on an assumed constant Gaussian spatial resolution of 2.6 mm FWHM. Concentric cylinders simulated with an inner:outer radioactivity concentration ratio of 10 yielded a measured ratio of 7.68 without PVC and 9.49 with PVC. To further examine the baseline performance of the method, a simulation with a ratio of 1 (i.e., no diVerence between inner and outer cylinders) yielded a measured ratio of 1.01 without PVC, and 0.93 with PVC. Low values of the corrected inner:outer cylinder ratios were likely due to an observed dip in voxel values at the center of images, which also was seen in real micro-PET images and appears to be related to a problematic normalization correction in the micro-PET software. However, a more realistic calculation of the GTM using the improved SimSET method may enable the inclusion of the eVects of image nonuniformities, assuming that the nonuniformities are accurately reproduced by the model. In order to estimate the order of magnitude of the PVE problem for the more relevant situation of rat-brain studies, a PET image of each region of the rat brain represented by a VOI was simulated using the modiﬁed SimSET method. The fraction of radioactivity detected in each VOI was then extracted. The recovery fraction, representing the fraction of activity within a brain structure that is measured within that same structure in the reconstructed image, is listed for each region in Table I. The low recovery fractions (all <0.5) indicate the importance of PVC in rat-brain PET studies.

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TABLE I RECOVERY FRACTION FOR RAT BRAIN STRUCTURES DETERMINED MONTE CARLO SIMULATION OF MICROPET IMAGING

BY

Brain structure

Recovery fraction

Striatum Hippocampus Cerebellum Cerebral cortex Thalamus

0.33 0.30 0.46 0.47 0.35

Most PET radiotracers accumulate throughout the body, and many accumulate at a high concentration just outside of the brain in the harderian glands (Fukuyama et al., 1998). Since areas outside the brain are not held in place by the skull, they can vary in relative position and orientation between scans. As a result, a priori assumptions about their shape and orientation may not be entirely accurate, and accounting for extra-brain radioactivity requires more careful attention. Another approach to the PVC problem is to incorporate the anatomical information into the image reconstruction algorithm itself, as opposed to using PVC as an independent post-processing step. This can be accomplished within the framework of iterative reconstruction methods by changing the image basis functions from voxels to VOIs (Carson, 1986). Advantages include simpler processing and more quantitative results, which account for the eVects of partial volume directly. The computational requirements of the iterative reconstruction would be greatly reduced by a large decrease in the number of eVective ‘‘voxels.’’ A potential drawback is the necessity to divide the entire ﬁeld-of-view into contiguous regions (each assumed to be homogeneous) as opposed to having to deﬁne regions only near the organs of interest.

VI. Conclusions

Highly quantitative studies with the new generation of small-animal PET tomographs are possible, but compared to more mature human PET systems they currently require a greater understanding of a number of complicating physical eVects. It should be noted that diVerent types of PET studies may require emphasis on diVerent physical eVects, and a tailored and prioritized approach to quantiﬁcation is the most practical given limited resources.

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UNDERSTANDING MYELINATION THROUGH STUDYING ITS EVOLUTION

Ru¨diger Schweigreiter,* Betty I. Roots,y Christine E. Bandtlow,* and Robert M. Gouldz *Medical University Innsbruck, Biocenter Innsbruck, Division of Neurobiochemistry A-6020 Innsbruck, Austria y Department of Zoology, University of Toronto, Toronto, Ontario, Canada M5S 3G5 z Department of Anatomy and Cell Biology, University of Illinois at Chicago Chicago, Illionis 60612, USA

I. Introduction II. Evidence that Glial Cells First Interacted with Large Axons in a ‘‘Nonmyelin’’ Relationship III. Myelin-like Sheaths in Invertebrates A. Morphological Considerations B. Physiological Properties of Invertebrate Myelinated Fibers C. Biochemical Properties of Invertebrate Myelinated Fibers D. Concluding Remarks on Invertebrate Fiber Myelination IV. Vertebrate Myelinated Nervous System A. Morphological Features B. Biochemical and Molecular Features of Vertebrate Myelin Sheaths C. Differentiation of Myelinating CNS and PNS Glial Cells V. Use of Comparative Myelin Studies to Understand CNS Regeneration A. Myelin Inhibition, a Historical Perspective B. Characterization of Regeneration Inhibitors in Mammals C. Phylogeny of CNS Regeneration D. Comparative Genomics of the Myelin Inhibitory Machinery E. CNS Regeneration Despite Myelin Inhibitors—Does the Immune System Make the Difference? F. Conclusion: Collision of Biological Necessity and Clinical Needs VI. Future Studies of Myelin Evolution References

All vertebrate and some invertebrate neurons use multilayered coverings of their axons and clustered ion channels to speed the ﬂow of current toward target cells. This process, termed saltatory conduction, which involves the jumping of nerve impulses from the axon initial segment to consecutive, hot spots (nodes of Ranvier) that are evenly spaced between successive myelin internodes, evolved in the common vertebrate ancestor and independently in several invertebrate clades. Studies with many diVerent types of animals are needed to understand how myelinated nervous systems evolved. In this chapter we discuss many studies INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 73 DOI: 10.1016/S0074-7742(06)73007-0

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that provide insights into the origins of central (CNS) and peripheral nervous system (PNS) myelination and into reasons why CNS regeneration in adult vertebrates is restricted to ﬁsh and amphibians. As in other ﬁelds, studying biological processes, here myelination, through their evolutionary history, in their own right, provides important and novel insights into human diseases in which myelin sheaths develop abnormally and/or following normal development they disassemble and are degraded. Myelinated axons dominate both the CNS and PNS of all extant vertebrates, an estimated 50,000 species of which nearly half are ﬁsh (VolV, 2004; Young, 1981, also http://www.embl-heidelberg.de/uetz/db-info/HowManyReptileSpecies.html). Knowledge of many facets of myelination in nonmammals and most mammals is nonexistent and in those that are used in laboratories, it generally lags far behind the large and rapidly growing knowledge of myelination in mice, rats, and humans. The widespread use of rodents as experimental models and the broad spectrum of reagents and techniques available from decades of research lead to the question: ‘‘What place will future studies of myelination in less studied species, particularly nonmammals, have in developing understanding of myelination and features of myelination that facilitate/inhibit regeneration and repair?’’ To answer this question, we consider how published studies that use nonmammalian species have and will continue to contribute to our overall understanding of myelination. Because of the widely diVerent environments animals with myelinated nervous systems thrive in, we hope that this chapter will lead investigators to study mechanisms for the adaptation of sophisticated regulation that underlies myelination for animals living under conditions far diVerent from our own, for example, without thermoregulation or diVerent osmotic environments. Among the animal models with a rapidly growing following is the zebraﬁsh (Key and Devine, 2003; Sprague et al., 2003 and www.zﬁn. org), which is being used more frequently by ‘‘myelin’’ scientists (as discussed later). As more and more sequences from genome-sequencing projects relevant to vertebrate and invertebrate evolution become available, appreciation of evolutionary adaptations made by the plethora of molecules associated with diVering facets of the myelination process will be realized. As in the emerging ﬁeld that links evolution and development, evo-devo (Carroll, 2005), cross-fertilization of studies in nonmammalian and mammalian species will continue to nurture advances that would not be possible with research restricted to a few, highly derived, laboratory mammals.

I. Introduction

Clearly, the vertebrate myelinated nervous system originated in a common vertebrate ancestor for CNS, and PNS myelin sheaths of all extant vertebrates

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examined are highly similar to one another both in structural features and biochemical makeup. Furthermore, it is becoming ever more evident that neuron-Schwann cell precursor and neuron-oligodendrocyte precursor interactions that developed in such an exacting manner in the common vertebrate ancestor are recapitulated among all extant lineage dependents. These developmental events, whose goal is eVective saltatory impulse propagation, provide tight control over the formation of each of the many consecutive myelin internodes, the enlargements in internodal axon diameters underlying them, and the assembly of functional nodal regions. Many features of the highly synchronized programs that developed in ancestors roughly 500 million years ago are seen at work in all extant vertebrates. Each of the multitude of nodes and surrounding paranodes (regions where myelin lamellae terminate in cytoplasmic loops that abut the axonal membrane) are constructed and maintained with lipids and proteins carried by axonal transport from sites of synthesis in neuronal soma. Clearly to understand the highly sophisticated relationships involved in producing and maintaining myelinated ﬁbers, we need to seek knowledge of its evolutionary history, a history that was essential for the evolution of the enormously complex and diverse circuitry that deﬁnes each vertebrate nervous system. Although not nearly as ubiquitous as in vertebrates, most likely many (only a few species have been examined) nervous systems of invertebrates evolved large axons capable of saltatory nerve impulse propagation based on multilayered glial cell wrappings. In this chapter, we ﬁrst consider the nonmyelin-forming interactions of glial cells with large axons, and then properties of myelination in invertebrate and vertebrate nervous systems. Finally we touch upon diVerences in ability, or lack thereof, of the CNS in diVerent species to foster repair following injury. Comparisons of our knowledge in each of these areas allow us to see some of the future directions that should be considered.

II. Evidence that Glial Cells First Interacted with Large Axons in a ‘‘Nonmyelin’’ Relationship

In all animals except coelenterates glial cells accompany axons. As brains became larger and more complex, ratios of glial cells to neurons are increased (Pfrieger and Barres, 1995; Reichenbach, 1989). Axons may be ensheathed individually or in the case of smaller axons in groups. Individual axons less than 0.5–1 mm in diameter in vertebrate CNS and PNS do not have their own sheath. It seems reasonable to suggest that two factors led to the glial cell ensheathment of axons. One is the evolution of a role in the development of the architecture of nervous systems with glial cells providing both scaVolding and guidance. This process would entail communication between neurons and glial cells via the development of small molecule-based signaling systems (Engler et al., 2002; Fields

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and Stevens-Graham, 2002; Roots, 1986). The other is the need for metabolic support, a trophic function for glial cells that developed as axons became larger. For larger animals to develop, larger and longer axons expectedly developed the need for glial cell-based neurotrophic support. Detailed discussion of two-way axon–glial cell signaling is beyond the scope of this chapter, it should be noted that evidence is accumulating at a rapid rate (Allen and Barres, 2005). For example, proteolipid protein (PLP), the major myelin protein in tetrapod CNS and a member of the lipophilin family (Gow, 1997), has been suggested to be involved in a signaling pathway in oligodendrocyte diVerentiation (Hudson and Nadon, 1992; Timsit et al., 1992). Although a PLP family member has not yet been identiﬁed in invertebrate myelin, genes of the lipophilin family exist in the tunicate, Ciona intestinalis (Gould et al., 2005), fruit ﬂy, Drosophila melanogaster, and silkworm, Bombyx mori (Stecca et al., 2000). It is tempting to speculate that lipophilin proteins, ancient proteins initially involved in glial cell–axon signaling, were subsequently recruited to become structural components of tetrapod CNS myelin sheaths (see the later section). In many invertebrate phyla, Platyhelminthes, Nematoda, Annelida, Arthropoda, and Mollusca, the nerve cell bodies are invaginated by satellite glial cells, a condition ﬁrst observed in the snail, Helix (Holmgren, 1900) and subsequently termed ‘‘trophospongium’’ (Holmgren, 1901). The advent of electron microscopy led to detailed investigations of this relationship, ﬁrst in insects (Hess, 1958) and then in other species. Since ganglia in mollusks and arthropods (with the exception of some crustaceans and cephalopods) are not vascularized, and the nerve cell bodies do not have dendritic branches to increase their surface areas, the role of invaginating glial cells is most probably trophic in function. With the evolution of large axons allowing greater conduction velocities and, therefore, both faster reactions and greater body size, the neurotrophic relationship with ensheathing glial cells was likely extended from soma to axon. To give a few examples, invagination of large axons by glial cells has been described in mollusks, Aplysia (Batham, 1961), several crustaceans, crayﬁsh, Procambarus (Peracchia, 1974) and shrimp Penaeus japonicus (Hama, 1966), and in insects Drosophila and other dipteran ﬂies in which the glial infoldings have a specialized structure, termed capitate projections (Kretzschmar and Pﬂugfelder, 2002; Stark and Carlson, 1986; Trujillo-Cenoz and Melamed, 1973). Almost all polychaetes (at least 28 families) and oligochaetes (7 superfamilies) have giant axons. None of them are invaginated by glial cells, perhaps because they are syncytial (Bullock and Horridge, 1965). Although not apparent from the structure of the axoplasm, each giant axon is composed of contributions from many nerve cells, the bodies of which are distributed at regular intervals along the axon. This organization together with the vascularization of the nervous system found in annelids would obviate the need for trophic support from glial cells.

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Squid giant axons, also formed from a syncytium of neurons, are ensheathed though not invaginated by glial cells. Nevertheless transfer of nutrients/proteins has been amply demonstrated (Gainer et al., 1977; Lasek et al., 1974, 1977; Sheller et al., 1995; Tytell et al., 1986). Thus, in the cephalopods a trophospongium has been superseded by a transfer mechanism that does not require the increase in glial/axonal contact provided by a trophospongium. The independent appearances of large axons capable of attracting glial cells to form regularly spaced myelin sheaths and concentrated membrane domains with high conductance properties in both vertebrates and invertebrates likely arose following the earlier, selective association of glial cells with larger caliber axons; for many extant invertebrates have their larger but not smaller caliber axons separated from neighbors by glial cell coverings. As one example, large caliber squid axons, including the giant axons (Brown and Abbott, 1993; Villegas and Villegas, 1984), are all surrounded by their own complement of glial cells. The overwhelming commitment of glial cells to large caliber axons can be realized from the estimate that approximately 62,000 Schwann cells cover each cm2 of giant axon surface. Another example is sea lamprey spinal cord, a preparation that is widely used for studies of locomotion (Buchanan, 2001), regeneration (Zhang et al., 2004), and evolution (Murakami et al., 2005). The selective covering of large axons, some reaching diameters of 0.05–0.1 mm, by glial cells is illustrated in Fig. 1. Not only the large caliber axons in lamprey CNS (Schultz et al., 1956), but also large caliber PNS axons (Nakao and Ishizawa, 1987; Peters, 1960) are individually packaged by complements of glial cells. With the shift from covering axons to forming myelin sheaths that in vertebrates and many invertebrates places compact membrane between the bulk of the glial cell cytoplasm and the axon, one may wonder what had to have been compromised in close-knit communication that includes, at least in the case of glial cells surrounding the squid giant axon, transfer of glial proteins to axons (see an earlier section), and likely similar metabolic support that allows severed crayﬁsh axons separated from their soma to survive (Bittner, 1991; Hoy et al., 1967). It is possibly related to the extent of vascularization. Compact sheaths developed only where the nervous system is well vascularized, for example, in earthworms, crabs, and vertebrates and where the need for a trophic function for ensheathing glial cells is reduced. It is curious that cephalopods, which have vascularized ganglia did not develop compact myelin sheaths. Even with intervening myelin sheaths, glial cells have the ability to inﬂuence axon caliber through augmenting neuroﬁlament phosphorylation/axon caliber (Gould and Brady, 2005) and to regulate transport to and support of presynaptic nerve terminal specializations (Edgar et al., 2004; Yin et al., 2004). Whether or not the same and/or related signaling pathways that regulate neuroﬁlament phosphorylation and axon caliber (HoVman et al., 1988) regulate neuroﬁlament phosphorylation and axon caliber in species lacking myelin, such as sea lamprey

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FIG. 1. Transverse sections through the spinal cord of the adult sea lamprey, Petromyzon marinus. (A) Toluidine blue stained semithin section showing about half the avascularized cord. (B) Electron micrograph of region with large caliber ﬁbers, each with a complement of glial cells. Several small

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(Pijak et al., 1996), will need further experimentation. Also, whether myelin wrappings inﬂuence support of terminal specializations in invertebrate myelinated axons and whether glial cell ensheathment inﬂuences axonal transport are questions not yet tackled. Although we may never know the reasons why glial cells ﬁrst developed in ancient nervous systems nor why selective association with larger axons evolved, clearly the high frequency of associations between glial cells and large axons in both nonmyelinating and myelinating conditions suggests an importance of this association in nervous system evolution. In addition to the possible involvement in augmenting axon caliber, many potential functions of glial cells that associate with large-caliber axons have been considered, and likely many more will be realized through further investigations. III. Myelin-like Sheaths in Invertebrates

Unlike vertebrates, only a small portion of invertebrate axons in individual species are myelinated. We use the term loosely and include any large axon that is covered with multilayered glial cell membrane. A. MORPHOLOGICAL CONSIDERATIONS Whereas visual images of vertebrate myelin sheaths all look very similar, images of invertebrate sheaths vary considerably; some are loosely wound as in the crayﬁsh, Procambarus clarkii, nerve cord (Fig. 2A) (Cardone and Roots, 1991, 1996), whereas others are tightly compacted as in the eyestalk of the crab, Cancer irroratus (McAlear et al., 1958), and in the median giant ﬁber of the earthworm, Lumbricus terrestris (Fig. 2B) (Gu¨nther, 1973, 1976; Roots and Lane, 1983). In the annelids, members of three families of oligochaetes—includes two species of Lumbricidae, Eisenia foetida (Hama, 1959) and Lumbricus terrestris (Gu¨nther, 1973, 1976; Roots and Lane, 1983), one species of Lumbriculidae, Lumbriculus variegates (Drewes and Brinkhurst, 1990), and one Tubiﬁcid, Branchiura sowerbyi (Zoran et al., 1988), produce myelin-like wrappings around large axons. Multilayered membrane sheaths have been described in members of three families of polychaetes: Capitellidae, Spionidae, and Maldanidae (Nicol, 1948). Among crustacea, malacostracan crustacea, several shrimps, prawns, crayﬁsh, and crabs (Cardone and Roots, 1991; Govind and Pearce, 1988; caliber axons in the lower left of the ﬁgure are not enveloped by glial cells. (C) Electron micrograph of a region of dorsal cord with small caliber axons. These axons are adjacent to one another and no intervening glial cells are visible. Abbreviations in (A): CC ¼ central canal, LF ¼ large ﬁbers, SF ¼ small ﬁbers. Figures (B) and (C) were kindly provided by Dr Michael E. Selzer, University of Pennsylvania School of Medicine.

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FIG. 2. Electron micrographs: (A) Loosely wrapped myelin sheaths of two neighboring axons in the ventral nerve cord of the crayﬁsh, Procambarus clarkii. (B) Portion of the sheath of the median giant

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Hama, 1966; Heuser and Doggenweiler, 1966; Holmes, 1942; Kusano, 1966; McAlear et al., 1958; Xu and Terakawa, 1999), and three superfamilies of copepods (Davis et al., 1999; Lenz et al., 2000; Weatherby et al., 2000) have axons covered with myelin-like sheaths. Oligochaete sheaths resemble those of vertebrates; both are spirally wound. As in vertebrates, sheath thickness is related to axon caliber and, consequently, overall lamellar numbers vary widely. For example, Eisenia foetida ﬁbers contain between 2 and 30 lamellae, whereas Lumbricus terrestris ﬁbers contain 60 to 200 lamellae. Also, interlamellar spacing is highly variable. The Lumbricus terrestris median giant ﬁber, in particular, contains some regions with tightly compacted membranes that resemble vertebrate myelin, and other regions, especially in redundant loops formed where the sheath buckles that retain substantial cytoplasm between membranes (Fig. 2B), stacks of desmosome-like structures run serially in register across some sheaths, attaching lamellae to each other (Fig. 2B). Although these structures resemble vertebrate desmosomes in electron micrographs, they diVer both in their intramembranous organization, as revealed by freeze-fracture (Roots and Lane, 1983), and in their protein composition (Pereyra and Roots, 1988). The sheaths found in crustaceans diVer from those of annelids and vertebrates; they are concentric rather than spiral. Moreover, concentric sheaths come in two patterns. In the prawn Palaemonetes vulgaris (Heuser and Doggenweiler, 1966) and in shrimps, genus Penaeus (Xu and Terakawa, 1999), the concentric laminae meet at a short seam that is reminiscent of vertebrate sheath mesaxons. The arrangement of the seams is very regular with those of alternate laminae located on opposite sides of the axon. In the copepods Undinula vulgaris, Neocalanus gracilis, and Euchaeta rimana, the lamellae form complete circles and lack seams (Weatherby et al., 2000). Another distinguishing feature of crustacean myelin is the location of glial cell nuclei, which are found in varying positions within the sheath (Heuser and Doggenweiler, 1966; Xu and Terakawa, 1999). The conditions whereby glial cell nuclei and perinuclear cytoplasm lie close to axons would tend to favor metabolic transfer of glial proteins and other metabolites to axons. The myelin-like sheath of the crab, Cancer irroratus, in particular, bears a striking resemblance to vertebrate myelin. Glial cell nuclei lie outside the sheaths, compaction is similar to that of vertebrate myelin, and structures resembling Schmidt-Lanterman incisures (SLI) and nodes of Ranvier are present (McAlear et al., 1958). Further studies are needed to determine whether the sheath is spirally or concentrically wound.

ﬁber of the earthworm, Lumbricus terrestris. Note the desmosome-like structures running in register across the sheath, and the pockets of cytoplasm retained between the layers. Reprinted with permission (Roots, 1995).

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As in annelids, in crustaceans, myelin thickness correlates with axon caliber. Lamellar numbers vary from 1 or 2 up to more than 50. In copepods, interlamellar spacing varies between 3 and 30 nm while the compact intralamellar (fused membranes) spacing remains constant, around 18 nm in thickness (Weatherby et al., 2000). In prawns, the interlamellar periodicity is over 20 nm (Heuser and Doggenweiler, 1966), whereas in Penaeus shrimps it is far less, 8 or 9 nm (Xu and Terakawa, 1999). Periodicity is species-dependent in both invertebrates and vertebrates (see the following section); moreover, it is aVected by tissue processing methods (Kirschner and Blaurock, 1992; Roots, 1993). The periodicity of the fully compact myelin of Penaeus setiferus, determined by X-ray diVraction (probably the most accurate measurement), is 16 nm, a value similar to that in teleost peripheral myelin (Blaurock, 1986). Interruptions, functionally comparable to vertebrate nodes of Ranvier, occur in both annelid and crustacean sheaths. In many decapod crustaceans (prawns, shrimps, and crabs), the nodes are strikingly similar to vertebrate nodes, both in general morphology (Holmes, 1942; Holmes et al., 1941; Retzius, 1890) and in the presence of surrounding paranodal loops, including structures that resemble septate desmosomes (Heuser and Doggenweiler, 1966; McAlear et al., 1958). Internodal distances are, in general, shorter than in vertebrates (Holmes et al., 1941) (For a more detailed comparison of this type of crustacean node with vertebrate nodes, see Roots, 1984). Other shrimps, six species of the genus Penaeus, a number of copepods (Hsu and Terakawa, 1996; Weatherby et al., 2000; Xu and Terakawa, 1999), and the earthworms, Eisenia foetida and Lumbricus terrestris (Gu¨nther, 1973, 1976; Hama, 1959), have completely diVerent nodes. They have circular openings in the myelin sheath, which are referred to as focal or fenestration nodes. In L. terrestris, there are two nodes of 10–15 mm in diameter in each segment. In Penaeus shrimps, node diameter and internodal distance are both approximately proportional to ﬁber diameter. Node diameter varies between 5 and 50 mm and internodal distance from 3 to 12 mm (Xu and Terakawa, 1999). Another morphological feature, suggestive of a novel mechanism of fast nerve conduction, occurs in shrimps of the genus Penaeus. A large gel-ﬁlled space is present between the axon and myelin sheath. This submyelinic space increases eVective axon diameter and, as a consequence, conduction velocity. It is tightly sealed at the nodal regions permitting saltatory conduction (Hsu and Terakawa, 1996; Xu and Terakawa, 1999).

B. PHYSIOLOGICAL PROPERTIES

OF INVERTEBRATE

MYELINATED FIBERS

Conduction velocity has been measured in only a few invertebrate nerves. In the median giant ﬁber of the earthworm, Lumbricus terrestris, which is 90 mm in diameter, it is 30 m/s (Gu¨nther, 1976). In the shrimp, Penaeus japonicus, it is

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90–190 m/s in ﬁbers 120 mm in diameter (Kusano, 1966; Kusano and LaVail, 1971). For comparison, a rat ﬁber of 4.5 mm in diameter, with a sheath that is roughly 50% of the total diameter, conducts at 59 m/s. Thus, in general vertebrate sheaths are far more eVective in increasing conduction velocity. Reaction times for the escape responses of calanoid copepods have been measured (Lenz et al., 2000). The fastest responses were recorded in myelinated species, the escape response being initiated two to ﬁve times more rapidly than in the nonmyelinated species. Myelin is found only in the more recently evolved copepod superfamilies, which also live in more diverse habitats. They not only live in neritic and deep-water environments but also in regions of the ocean where faster reaction times are essential for avoiding predators (Hays et al., 1997; Hayward and McGowan, 1979; Lenz et al., 2000; Park, 1986). Thus, during the course of copepod evolution, the development of myelin has allowed them to occupy new habitats. Very little is known about the distribution of sodium channels in invertebrates. Studies on the shrimp, Penaeus japonicus, indicate that, as in vertebrates, sodium channels are concentrated at the nodes at a density of 530 channels/mm2 (Hsu and Terakawa, 1996; Xu and Terakawa, 1999). In the earthworm, Lumbricus terrestris, sodium channels also are concentrated at the nodes (Gu¨nther, 1976; Roots, 1984, 1995).

C. BIOCHEMICAL PROPERTIES

OF INVERTEBRATE

MYELINATED FIBERS

Information on the chemical compositions of diVerent invertebrate myelin sheaths is limited. Fortunately, techniques used to purify myelin membranes from vertebrates are adaptable to invertebrates (Pereyra and Roots, 1988; Waehneldt et al., 1989). Analysis of lipid and protein compositions in annelid and crustacean myelin show marked diVerences from vertebrate myelin (see in a later section) and from one another. Birefringence studies of earthworm myelin showed the sheath to be qualitatively similar to that of frog sciatic nerve, with protein contributing 30 to 40% of the total birefringence and the rest attributed to lipids (Taylor, 1940). In the shrimp, Penaeus duorarum, a strikingly high proportion of lipids is found in isolated myelin; the lipid: protein ratio being 15:1 (Okamura et al., 1986). Galactolipids, major constituents of vertebrate myelin sheaths, with importance in signal transduction (Morell and Quarles, 1999; Popko, 2000) and paranodal stability (Bosio et al., 1998; Coetzee et al., 1996; Honke et al., 2002) are not found in annelid or crustacean myelin. Instead, glucocerebrosides in amounts equivalent to galactocerebrosides in vertebrate myelin are present in crustacean, though not in earthworm myelin. Sphingomyelin is not present in earthworm nerve cord and, although it is found in crayﬁsh (Cambarus clarkia) nerves, it is

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structurally quite diVerent from vertebrate sphingomyelin (Komai et al., 1973; Okamura et al., 1985). Thus, there is an evolutionary trend in which glucocerebrosides in protostomes are replaced by galactolipids in deuterostomes. Although deuterostomes can also synthesize glucocerebroside (Tamai et al., 1992), replacement by galactosphingolipids (Roots, 1995a) may reﬂect changes needed for structure and/or signaling in vertebrate myelin (Boggs and Wang, 2001; Popko, 2000). Based largely on antibody cross-reactivity, the protein components of annelid (earthworm, Lumbricus terrestris) and crustacean (crayﬁsh, Procambarus clarkii, and pink shrimp, Penaeus duorarum), myelin sheaths appear to be totally diVerent from vertebrate myelin proteins. As in vertebrates, the protein pattern of earthworm myelin is relatively simple, with 80- and 42-kDa proteins predominating and 28- to 32-kDa proteins as minor components. The structure of these proteins is unknown and there is no cross-reactivity with antibodies to myelin basic protein (MBP), PLP, myelin-associated glycoprotein (MAG), or 20 ,30 -cyclic nucleotide 30 -phosphodiesterase (CNP) (Cardone and Roots, 1990; Pereyra and Roots, 1988) associated with vertebrate myelin (see in a later section). However, in another crustacean, the crab Ucides cordatus, CNP-like immunoreactivity has been demonstrated in the visual system (da Silva et al., 2003), though enzyme activity was not examined. In the pink shrimp, four major proteins, 21.5, 40, 78, and 85 kDa, and four minor proteins, 36, 41.5, 43, and 50 kDa, are found in puriﬁed sheath membranes. None of these proteins shows cross-reactivity with antibodies that recognize mammalian MBP or PLP or trout MBP, 36K or myelin protein 0 (P0) (Okamura et al., 1986; Waehneldt et al., 1989). A monoclonal antibody generated to earthworm myelin-like membranes and showing crossreactivity to 30–32- and 40-kDa proteins cross-reacts with 60–65-, 42-, and 40-kDa proteins in crayﬁsh (Procambarus clarkii ) axon ensheathing membranes (Cardone and Roots, 1996). Thus, earthworm and crayﬁsh membrane proteins seem to have some antigenic epitopes in common. EVorts to understand possible relationships between vertebrate and invertebrate myelin will be augmented when a genome-sequencing project is performed on an invertebrate that forms myelin-like membranes.

D. CONCLUDING REMARKS

ON INVERTEBRATE

FIBER MYELINATION

The presence of a myelin sheath confers several advantages in invertebrate survival. The startle reactions of earthworms, escape responses of crayﬁsh, shrimp, and copepods, and retraction of eyestalks in crabs are behaviors in which speed is of paramount importance (Bullock, 1984). Faster conduction may be achieved by simply increasing axon diameter as in squid, which have unusually large ‘‘giant’’ axons (Villegas and Villegas, 1984; Young, 1936). Although lack of

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myelin in general limits the size of organisms, some squid achieve enormous sizes (Roper and Boss, 1982). A more eYcient means of increasing conduction speed and, therefore, the rapidity with which escape mechanisms take place is the development of myelin sheaths. The nodes of invertebrate nerve ﬁbers serve the same function as vertebrate nodes of Ranvier; they allow saltatory conduction to occur. It should be noted that the points of emergence of small collaterals serve as nodes in both vertebrates (Roots, 1984), and in shrimps, Penaeus chinensis and P. japonicus (Xu and Terakawa, 1999). IV. Vertebrate Myelinated Nervous System

Contrasting the highly varied nature of myelin sheaths in invertebrates, the structural properties among all vertebrate myelin sheaths are highly conserved. Light and electron microscopic images of myelinated ﬁbers and/or cells producing myelin from numerous nonmammalian vertebrates reveal the same structural and ultrastructural features as those described for mammals (Peters et al., 1991). In fact, with light and electron micrographs, it is (almost) impossible to know whether myelinated tissues are from mammals or some other vertebrate. So too, developmental and biochemical features of vertebrate myelination are highly conserved. Coupled with rapid advances in knowledge of myelination in mammals (Baumann and Pham-Dinh, 2001; Colognato and Vrench-Constant, 2004; Hildebrand and Mohseni, 2005; Jessen and Mirsky, 2004; Kagawa et al., 2001; Lobsiger et al., 2002; Miller and Reynolds, 2004) there is an enormous base to use for directing studies of character-novel properties, for example, changes with activity (Carr and Boudreau, 1993) or temperature, of myelination in nonmammals. A. MORPHOLOGICAL FEATURES The ﬁrst electron microscopic illustrations of the spiraling nature of peripheral nerve myelin formation were obtained from chick (Geren, 1954) and chameleon (Robertson, 1955) nerves. Subsequently, the similar spiraling nature of CNS myelin sheaths was demonstrated with electron microscopic observations of frog (Maturana, 1960) and toad (Stensaas and Stensaas, 1968) brains. Many other structural features of the myelinated nervous system, for example, concentrations of sodium channels at nodes of Ranvier (Rosenbluth, 1976) and the presence of astrocytic processes at nodes (Bodega et al., 1987), to name a few, were identiﬁed in samples from nonmammals, that is, frogs and lizards. For other examples, references showing features of myelination seen in birds (Ono et al., 1995), reptiles (Nadon et al., 1995), amphibians (Chvatal et al., 2001; Tabira et al., 1978), teleost ( Jeserich and Rauen, 1990; Lyons et al., 2005), and cartilaginous

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ﬁshes (Gould et al., 1995b), to name a few, are available. Clearly the many successful innovations of the original myelinated nervous system have been retained, unaltered, through hundreds of millions of years of vertebrate evolution. Because of the marked structural similarity of CNS and PNS myelination among diVerent vertebrate classes, no publications have detailed morphological similarities that occur. To partly address this deﬁciency, we include micrographs that represent some of key features of myelination seen through micrographs from the CNS and PNS of a cartilaginous ﬁsh, the spiny dogﬁsh (Squalus acanthias). This species represents elasmobranches, as they lie at the far end of the vertebrate spectrum; that is, the lineage leading to modern-day elasmobranches separated from other vertebrate lineages, before any others (Rasmussen and Arnason, 1999; Venkatesh et al., 2001). Hemisections from spinal cords of an adult dogﬁsh (Fig. 3A) and an adult rat (Fig. 3B) are placed face-to-face. Clearly they are highly similar in both overall organization and structural details. Abundant white matter tracts completely surround centrally positioned gray matter represented by distinct dorsal (DH) and ventral horns (VH), though in the dogﬁsh the gray matter is not horn shaped. In the dogﬁsh spinal cord, unlike in rat and other mammals, a small tract of particularly large caliber axons, the fasciculus medianus of Stieda (Smeets, 1998), is located above the VH. Developmentally, the myelination in this ﬁber tract commences with myelination in the ventral funiculus (Gould, unpublished observation). Because these tracts are well separated from one another and lie at diVerent distances from the notochord, the notion that a gradient of diVusible sonic hedgehog regulates oligodendrocyte diVerentiation, must be balanced with the notion that enlarging axons that appear at the time that myelination commences in both tracts, participate in causing oligodendrocyte progenitors to diVerentiate at an appropriate time. Perhaps like oligodendrocytes that arise in dorsal spinal cord (Miller, 2005), several factors, working in concert regulate the availability of oligodendrocytes and their ability to send out processes to ﬁnd, ensheath, and myelinate the axons that are ﬁrst ready. Higher power comparisons between the myelinated ﬁbers in adult spiny dogﬁsh (Fig. 3C) and rat (Fig. 3D) ventral funiculi show the same classical oligodendrocyte-based CNS myelinated ﬁbers with myelin sheath thicknesses adjusted to axon caliber. In contrast to similarity in size of the smallest myelinated ﬁbers in both species, the far larger size of myelinated ﬁbers in the dogﬁsh ventral spinal cord likely reﬂects the presence of much larger axons, presumably to command movements in the longer body. Because of the extensive developmental growth of the myelinated internodes in ventral spinal cord ﬁbers of both species, few oligodendrocyte cell bodies are seen in single light microscopic sections. Several examples of Schmidt–Lanterman incisures (SLI) are seen in myelinated ﬁbers from spiny dogﬁsh. Unlike, mammals, where SLI in the CNS are rare (Blakemore, 1969), numerous SLI are seen in adult spiny dogﬁsh samples.

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FIG. 3. Transverse sections through adult spiny dogﬁsh and rat spinal cords. (A) Hemisection of adult spiny dogﬁsh spinal cord. (B) Hemisection of rat spinal cord at same magniﬁcation as (A). (C) High-power image of ventral funiculus of spiny dogﬁsh spinal cord. (D) Ventral funiculus of rat spinal cord printed at same magniﬁcation as (C). Abbreviations in A and B: VH ¼ ventral horn, VF ¼ ventral funiculus, VR ¼ ventral root, DR ¼ dorsal root. (*) Asterisks in (C) and (D) locate some oligodendrocyte soma. Arrows in (C) point to possible SLI.

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FIG. 4. Transverse section through (A) single myelinated ﬁber, the axoplasm contains two mitochondria (m), microtubules (black arrow), and neuroﬁlaments (white arrow). Compact myelin

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There are striking similarities in ultrastructure of adult spiny dogﬁsh myelinated ﬁber morphology with that in other nonmammalian and mammalian species. However, characteristics of ﬁber ultrastructure in developing dogﬁsh provide additional insights (Fig. 4). Micrographs of single ﬁbers (Fig. 4A) and ﬁber bundles (Fig. 4B) from the spinal cord of a 13-cm fetus (dogﬁsh are 26–27 cm at birth, and require a 2-year gestation) (Hisaw and Albert, 1947; Kormanik, 1988), match myelinated ﬁber images from many other vertebrates. At high power, the axoplasm is seen to be dominated by parallel arrays of microtubules and neuroﬁlaments (Fig. 4A). Mitochondria and smooth membrane structures are also present. A layer of abaxonal (close to the axon) glial cell cytoplasm contains the inner mesaxon and corresponding inner tongue. Outside the compact myelin lamellae are located outer tongue (ot) processes with included outer mesaxon (Fig. 4A). Because of the extensive growth of developing myelin sheaths, both inner and outer tongue processes are ﬁlled with mitochondria, rough and smooth endoplasmic reticulum, Golgi apparatus (not seen in this image), and free polyribosomes, all involved in early stages of myelin sheath assembly. Unlike peripheral nerves, where individual sheaths are well separated from one another, in the CNS, many myelin sheaths form with common intraperiod lines with their neighbors (Fig. 4B, inset). Nodes (not shown) and paranodes (Fig. 4C) are structurally indistinguishable from those seen in mammals. Not unexpectedly, early CNS myelination develops in an identical manner in spiny dogﬁsh as reported for other vertebrates (Fig. 5). The ﬁrst axons to be myelinated, in spinal cords from both species, are in the ventral funiculus. They appear in 4–4.5-cm long spiny dogﬁsh fetuses (Fig. 5A) and also in newborn rat pups (Fig. 5B) (Schwab and Schnell, 1989). At this stage, very few oligodendrocytes are seen and these locate the ﬁrst axons that enlarge and, using both their cell bodies and their processes, they separate the axons from all the smaller axons. In micrographs from slightly later stages of development, here from spinal cords from a 6 cm spiny dogﬁsh fetus (Fig. 5C) and a postnatal day 2 rat pup (Fig. 5D), more oligodendrocytes appear and at the ultrastructural level; each is characterized by organelle-ﬁlled (mitochondria, rough and smooth endoplasmic reticulum, Golgi complexes, polyribosomes, and microtubules) perinuclear cytoplasm needed for elaborating myelin sheaths. In addition to dense organelle-ﬁlled cytoplasm and multiple ﬁne processes, a typical feature of myelinating oligodendrocytes

lamellae surround an inner layer of abaxonal oligodendrocyte cytoplasm with an inner mesaxon (IM). The compact layers are surrounded by abaxonal oligodendrocyte cytoplasm with characteristic outer mesaxon (OM) contained within the outer tongue process (ot). Below the myelinated ﬁber lies an astrocytic process (AP) with longitudinally oriented ﬁbrils. (B) Three similarly-sized myelinated ﬁbers that are joined together and share common intraperiod lines (see insert, area enclosed in white rectangle). Typical inner (it) and outer tongue (ot) processes are seen. (C) Typical paranodal loop (PL) structure is seen in this longitudinal section from a 25 cm spiny dogﬁsh fetus spinal cord.

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FIG. 5. Transverse light and electron microscopic images of developing spiny dogﬁsh and rat ventral spinal cords. (A) Ventral funiculus of 4.5 cm spiny dogﬁsh fetus showing the presence of oligodendrocytes (OLs) and beginning ring-shaped myelin sheaths (arrows). (B) Ventral funiculus of newborn rat pup showing similarity in appearances of early oligodendrocytes (OLs) and beginning myelin sheaths (arrows). (C) Electron micrograph from a 6 cm spiny dogﬁsh fetus spinal cord showing an oligodendrocyte with typical darkened organelle-rich cytoplasm and neighboring large caliber axons in early stages of myelination. (D) Electron micrograph from a P2 neonatal rat pup showing a similarly-structured oligodendrocyte with surrounding large axons in early stages of myelination. Abbreviations in (C) and (D): MF ¼ myelinated ﬁber.

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is a round-to-oval nucleus with a rim of karyoplasm bordering the nuclear envelope. At these relatively early stages of myelination, oligodendrocytes clearly disregard the many very small axons that remain fasciculated. As development proceeds, more oligodendrocytes are formed and recruited to ensheath and myelinate the axons as they attain suYcient caliber (Hildebrand et al., 1993). Thus, upon reaching adulthood, virtually all spinal cord axons in both species are myelinated with sheaths that correlate in size and length with axon caliber (Fig. 3). Myelination of the spiny dogﬁsh PNS is clearly diVerent from that in its CNS; as in all other vertebrates, PNS myelination is Schwann cell-based (Fig. 6). In a 25-cm spiny dogﬁsh fetus (Fig. 6A), all the axons in the trigeminal nerve are large and already well myelinated. In comparison, most of the axons in the trigeminal nerve from a 5-cm spiny dogﬁsh fetus (Fig. 6B) are ensheathed in Schwann cell processes, but not yet myelinated. By the time the dogﬁsh fetus reaches 13 cm (Fig. 6C), all axons are covered by multilayered myelin sheaths. As in the mammalian PNS, each myelinated PNS ﬁber unit in spiny dogﬁsh is covered with a basal lamina (Fig. 6D) and surrounded by supportive collagen ﬁbers. As in mammalian PNS, there are many SLI in adult PNS myelin sheaths (not shown) and nodes of Ranvier look like typical PNS nodes in mammals and in other vertebrates. In addition to using morphological criteria to assess myelination, the high conservation of many proteins and lipids among diVerent vertebrate lineages, investigators have used antibodies raised against mammalian ‘‘myelin’’ homologs to visually monitor aspects of myelination in nonmammalian species (Gould et al., 1995b; Jeserich and Rauen, 1990; Jeserich and Stratmann, 1992; Jeserich et al., 1990b; Nguyen and Jeserich, 1998; Ono et al., 1995; Yoshida, 1997). Coupled with a growing availability of comparative genomics data and bioinformatics programs (Aparicio et al., 2001, 2002; Gilchrist et al., 2004; Venkatesh and Yap, 2005) as well as the use of high-throughput genetic screens, for example, in zebraﬁsh (Lyons et al., 2005; Rossant and Hopkins, 1992; Sprague et al., 2003), it will become continually easier to identify structural/developmental similarities and diVerences in myelination among diVerent vertebrate species. X-ray diVraction measurements of lamellar spacing in fresh and in ﬁxed optic and sciatic nerves indicate that phylogenetic diVerences among vertebrates are located at the intraperiod line and depend on whether the dominant myelin protein is P0 or PLP (Kirschner and Blaurock, 1992; Kirschner et al., 1984, 1989). Coupled with immunoblot studies (Waehneldt et al., 1986), these X-ray diVraction measurements demonstrate the conversion of a prominently P0-based CNS myelin in ﬁsh to a PLP-based CNS myelin in tetrapods (see in a later section). Abaxonal cytoplasmic channels (called superﬁcial cytoplasmic channels in Schwann cells and outer tongue processes in oligodendrocytes) anastomose over

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FIG. 6. Transverse sections of trigeminal nerves from spiny dogﬁsh. (A) Light micrograph from 26-cm spiny dogﬁsh fetus. Two regions with enlarged ‘‘perinuclear’’ Schwann cell cytoplasm are indicated (arrows). (B) Electron micrograph from the nerve of a 5 cm fetal spiny dogﬁsh. All of the axons here have reached the stage where they are separated from neighboring axons by Schwann cell processes. Nuclei (SC nuc) from three Schwann cells can be seen here. (C) Electron micrograph from the nerve of a 13 cm spiny dogﬁsh fetus. Typically by this stage all of the ﬁbers are covered with myelin sheaths. (D) Higher power of a portion of a myelinated ﬁber from a 13 cm spiny dogﬁsh fetus. A basement membrane (arrow) surrounds the ﬁber as does extracellular collagen (co).

the surface of the outermost compact lamellae and, at least in the mammalian PNS, are regions of myelin sheath assembly (Gould, 1990). By assembly we mean that these regions contain organelles/machinery that both synthesize select ‘‘myelin’’ proteins and lipids and integrate these with lipids and proteins that are synthesized in perinuclear regions and then transported to these regions. Circumferential-running channels including internodal SLI (Ghabriel and Allt, 1981; Hall and Williams, 1970; Robertson, 1958; Scherer, 1996) and paranodal loops, endpoints of myelin lamellae usually attaching to axolemma in transverse bands (Fig. 4C), (Girault and Peles, 2002; Scherer and Arroyo, 2002; Tao-Cheng and Rosenbluth, 1983), play minimal role in myelin assembly/maintenance per se

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(Gould and Mattingly, 1990). Surprisingly, whereas in all other species MBP is synthesized in Schwann cell and oligodendrocyte processes (based both on kinetic criteria and mRNA localization (Barbarese et al., 1999; Brophy et al., 1993; Carson et al., 2001) including zebraﬁsh (Brosamle and Halpern, 2002), in elasmobranches, MBP synthesis is conﬁned to oligodendrocyte (and presumably Schwann cell) soma (Gould, unpublished observations). The function(s) of SLI, rare in mammalian CNS myelin (Blakemore, 1969), common in both P0-based CNS of spiny dogﬁsh and all vertebrate (P0-based) peripheral nerves (Hall and Williams, 1970; Small et al., 1987), are not known. Numbers are increased in shiverer mouse nerves, perhaps because MBP limits their formation (Gould et al., 1995a). Although SLI are not involved in RNA transport and protein synthesis in mature peripheral nerves (Gould and Mattingly, 1990), they may act as conduits in glial cell-axonal communication (Scherer and Arroyo, 2002). Further studies are needed to determine whether alterations in P0, PLP/DM20, and MBP inﬂuence SLI appearance/functioning as well as structural modiﬁcations that accompany Wallerian degeneration (Ghabriel and Allt, 1979a,b). Abaxonal cytoplasm, which separates compact myelin from the axolemma (axon plasma membrane, called inner tongue processes in oligodendrocytes), is where MAG resides and possibly functions in glial/axon communication (Georgiou et al., 2004; Quarles, 2002) (see the following section). During development, these regions may contain smooth endoplasmic reticulum, mitochondria, actin ﬁlaments, and microtubules (e.g., Fig. 4A) and participate in myelin sheath assembly/spiraling. Radial component, a characteristic of mammalian CNS (Peters, 1961), though not PNS myelin (Kosaras and Kirschner, 1990), is present in amphibian CNS myelin as well (Schnapp and Mugnaini, 1976; Tabira et al., 1978). Because proteins that purify with radial component, including exon-2 containing MBP isoforms (Karthigasan et al., 1996) and myelin-associated oligodendrocytic basic proteins (MOBP) (Yamamoto et al., 1999), have not been identiﬁed in amphibians and other nonmammalian species (following sections), further studies are needed to determine if these proteins are, indeed, expressed and, thus, contribute to radial component structure. Furthermore, experiments that reduce or alter the expression of these proteins may shed further light on their involvement in radial component and its function(s). Nodes of Ranvier, regions between internodal myelin and bracketed by paranodal loops, contain high concentrations of sodium channels and other proteins that are delivered to these sites via axonal transport (Poliak and Peles, 2003; Salzer, 2003). In the PNS, they are covered by microvillae, extensions of myelinating Schwann cells. In the CNS, they are covered by a diVerent cell type, which is related to CNS astrocytes/NG2-positive cells (Black et al., 1989; Bodega et al., 1987; Butt et al., 1999; Vrench-Constant et al., 1986). Structural features of mammalian CNS and PNS are conserved among nonmammalian vertebrates.

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MOLECULAR FEATURES

OF

VERTEBRATE MYELIN SHEATHS

Based on structural diVerences with other cell membranes, that is, highabundance lipid content and large expanses of compact membrane layers, myelin membranes are relatively easy to purify in brain and peripheral nerve by subcellular fractionation. These methods developed in the 1960s (Morell and Quarles, 1999), proved to be readily adaptable to the isolation of myelin-like membranes from invertebrate neural tissues (see an earlier section) and from nonmammalian vertebrate neural tissues (Agrawal et al., 1971; Cuzner et al., 1965; Franz et al., 1981; Mehl and Halaris, 1970; Waehneldt et al., 1984). Analyses of myelin fractions from species representing each group of modern-day gnathostomes conﬁrmed that, as in mammalian myelin preparations, they contain an abundance of lipids (70–80% of isolated membrane dry weight) including high portions of cholesterol, galactolipids (mainly galactocerebroside) and sulfatide, as well as ethanolamine plasmalogen (Bu¨rgisser et al., 1986; Jeserich et al., 1990a; Kirschner and Blaurock, 1992), with an overall simple protein proﬁle (Waehneldt et al., 1986). The major proteins in mammalian myelin, like those in invertebrate myelin, are of low molecular weight. The fact that both invertebrate and vertebrates contain an abundance of low molecular weight proteins in their sheaths may be an indication that recruitment of small proteins lacking large extracellular or cytoplasmic processes, including tetraspan proteins (see in a later section), may have been an important innovation in the evolution of myelin. For example, the transition from compact myelin to noncompacted abaxonal membrane in mammalian peripheral nerves has been suggested to involve the replacement of the larger ﬁve immunoglobulin (Ig) domain MAG with the smaller, single Ig domain P0 protein (Quarles and Trapp, 1984). As mentioned above for immunocytochemical studies, antibodies produced to recognize mammalian myelin proteins usually also recognize homologs in nonmammals and vice versa (Waehneldt et al., 1986). Based mainly on immunoblot studies of proteins present in myelin isolated by subcellular fractionation, vertebrate myelin sheaths contain a single dominant protein, either P0 (one or two isoforms) or PLP, usually accompanied by an alternatively spliced variant, DM20 (Waehneldt, 1990). Although these data were interpreted to mean that a switch from a P0-based CNS myelin in ﬁsh to a PLP-based CNS myelin in tetrapods had occurred (Waehneldt, 1990; Waehneldt et al., 1986; Yoshida and Colman, 1996), the abundance of DM20 proteins in a variety of vertebrate CNS myelin from many species (Yoshida and Colman, 1996) coupled with their ancient origin (Gould et al., 2005; Stecca et al., 2000) suggest that these tetraspan proteins might have been associated with myelin development from the beginning. As suggested above, they may have been involved in axon–glial cell communication and, along with retention of such a role (Edgar and Garbern, 2004),

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also were adapted for a structural role in myelin sheath compaction. Certainly there was a plethora of studies that use antibody speciﬁcity (still the best approach with invertebrate myelination) to characterize expression of myelin-related proteins in diVerent vertebrates. These and many other studies, for example, Matthieu et al. (1986); Waehneldt et al. (1987a,b) to cite a few, set the stage for molecular studies that demonstrate the value of myelin protein sequence information in establishing phylogenetic relationships among vertebrate groups (Tohyama et al., 2000b; Venkatesh et al., 2001). P0, a single membrane spanning Ig-like protein, is the major protein of all vertebrate peripheral nerve myelin, and all ﬁsh CNS myelin (Eichberg, 2002; Kirschner et al., 2004; Spiryda, 1998). Although a single isoform is present in mammals, several isoforms have been identiﬁed in teleost and cartilaginous ﬁshes (Lanwert and Jeserich, 2001; Tai and Smith, 1984; Waehneldt et al., 1984). They are all glycoproteins, having HNK-1 reactive epitopes in most, if not all, species (Quarles, 2002). The HNK-1 reactive structure, originally shown to react with natural killer cells, identiﬁes an antigen, which plays important roles in cell–cell interactions, including the homophilic P0 interaction that underlies compaction at the intraperiod line (Kirschner et al., 1996; Quarles, 2002; Shapiro et al., 1996). Besides glycosylation, other posttranslational modiﬁcations including sulfation, fatty acylation, and phosphorylation occur in mammalian P0. In fact, in addition to glycosylation, palmitoylation of trout P0 has been detected (Waehneldt and Jeserich, 1984). P0, involved not only in adhesion at the intraperiod, but also the major dense line, is subject to mutations in humans that underlie the prominent form of Charcot-Marie-Tooth disease 1B as well as other related neuropathies (Hanemann et al., 2001; Kochanski, 2004; Nelis et al., 1999). The large cytoplasmic domain of P0 functions in both extracellular (Wong and Filbin, 1994) and intracellular adhesion and the smaller cytoplasmic domain of P0 in ﬁsh (trout and zebraﬁsh) appears to be less eVective, based on cell adhesion assays, in augmenting membrane–membrane interactions (Lanwert and Jeserich, 2001). Early expression of P0 in chick PNS (Bhattacharyya et al., 1991) may suggest that it plays other roles in Schwann cell lineage commitment. No P0 homologs are present in the ascidian, Ciona intestinalis (Gould et al., 2005), suggesting either that this protein appeared with genome duplications that occurred during vertebrate evolution or that the gene was lost during ascidian evolution. PLP and DM20 are tetraspan proteins of an ancient family (Campagnoni and SkoV, 2001; Gould et al., 2005; Gow, 1997; Hudson, 2004), with both N- and C-terminal regions in the cytoplasm. Although PLP is mainly restricted to tetrapod CNS myelin; PLP is also expressed by mammalian Schwann cells (Puckett et al., 1987), DM20 and related M6 glycoproteins are present in myelin/myelinating cells and other cells of amphibians (Yoshida and Colman, 1996), teleost ﬁshes (Brosamle and Halpern, 2002; Tohyama et al., 1999, 2000a), and elasmobranches (Kitagawa et al., 1993; Sinoway et al., 1994). A recent study,

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showing that the promoter region of mouse PLP causes selective expression in developing zebraﬁsh oligodendrocytes (Yoshida and Macklin, 2005), suggests that oligodendrocyte lineage speciﬁcity of PLP/DM20/M6 ‘‘lipophilin’’ family members occurred evolutionarily early. Coupled with the ﬁnding that portions of the trout P0 promoter are shared with the mammalian PLP promoter ( Jeserich et al., 1997), this ﬁnding indicates that common elements directing cell-type expression of these proteins were developed prior to the split of ﬁsh and tetrapods. Evidence that the lipophilin family originated in protostomes early deuterostomes was uncovered by searches of ﬂy and ascidian genome databases (Gould et al., 2005; Stecca et al., 2000). Whether promoters that regulate the expression of the ﬂy and ascidian homologs of these proteins already have elements developed for expression in myelinating cells remains to be studied. MBP is a group of alternatively spliced proteins that are present in all CNS and PNS myelin sheaths, including birds and elasmobranches (Campagnoni, 1988; Martenson and Uyemura, 1992; Saavedra et al., 1989; Spivack et al., 1993). It is part of a larger gene family, called GOLLI (Genes of Oligodendrocyte Lineage), with other members, generated from diVerent promoters, having no known functions in myelination per se (Campagnoni and Campagnoni, 2004; Givogri et al., 2001). Based on structural considerations (Karthigasan et al., 1992; Stoner, 1990), the classic MBPs may hold cytoplasmic surfaces of adjacent myelin lamellae together in the CNS. These surfaces lack an association in mutant mice, shiverer and mld, that fail to express MBP (Ganser and Kirschner, 1980; Matthieu et al., 1984; Privat et al., 1979). The ability of PNS myelin to retain nearly normal myelin structure in shiverer mice is believed to be due to the basic nature of the cytoplasmic tail of P0 (Lemke, 1988). As mentioned above, exon 2-containing isoforms of MBP, possibly involved in early events in glial cell diVerentiation and myelination (Allinquant et al., 1991; Barbarese et al., 1977) have not been identiﬁed in nonmammals. In addition to P0, MBP, and PLP/DM20, there are a growing number of proteins associated with myelin sheaths proper and the process of myelination; compare Braun (1984); Campagnoni (1988) with chapters in Lazzarini et al. (2004). Among proteins related to myelination that are receiving growing attention are peripheral myelin protein-22 (PMP22) (Suter, 2004), MAG (Georgiou et al., 2004; Quarles, 2002; Schachner and Bartsch, 2000), MOBP (Yoshikawa, 2001), myelin oligodendrocyte glycoprotein (MOG) ( Johns and Bernard, 1999), and oligodendrocyte myelin glycoprotein (OMgp) (Vourc’h and Andres, 2004). Among these, identiﬁcations of homologs in nonmammalian vertebrates are growing. Antibody-based evidence that MAG is ubiquitously expressed among vertebrates (Matthieu et al., 1986), had been disputed (Hammer et al., 1993; Zand et al., 1991) and the issue was not resolved until recent screening of puVerﬁsh and zebraﬁsh genomes identifying several MAG, also called siglec-4 (sialic acid

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binding protein) isoforms in these ﬁsh (Lehmann et al., 2004). Finding ﬁsh homologs of MAG is having an impact on eVorts to understand the evolutionary loss of CNS regeneration (see Section VI). CNP, an enzyme long associated with mammalian myelin (Braun et al., 2004), is present in birds and amphibians (Kasama-Yoshida et al., 1997), though not in ﬁsh (Moll et al., 2003). A related protein, gRICH (goldﬁsh regeneration-induced CNPase homolog), which lacks CNPase activity, has been identiﬁed in goldﬁsh and zebraﬁsh (Ballestero et al., 1995, 1997, 1999). This protein obviously is suggested to play an important role in CNS regeneration in ﬁsh (Braun et al., 2004). A ﬁsh homolog of PMP-22 has been identiﬁed in zebraﬁsh (Wulf et al., 1999) and puVerﬁsh (Gould et al., 2005) and a possible homolog has been identiﬁed in the urochordate, Ciona intestinalis database (Gould et al., 2005). A myelin protein unique to ﬁsh myelin known as a 36-kDa protein ( Jeserich, 1983), has recently been shown to be a member of the short-chain dehydrogenase protein family (Moll et al., 2003; Morris et al., 2004). Knowledge of diVerences among myelin protein homolog sequences has the potential to aid ﬁnding domains within these proteins used in the interactions required in formation and stabilization of myelin. It also can lead to greater understanding of how control of expression of these proteins foster myelination. Besides proteins incorporated into myelin, there are a growing number of proteins associated with myelination, including those involved in formation and maintenance of nodal and paranodal specializations, as well as those involved in specifying the development of glial cells to the myelination lineage (see Section V.C.). It is becoming clearer that most, if not all of the proteins characterized so far, have multiple expressions and varied roles in nervous system development/ evolution. Their recruitment to participate in myelination can be more readily studied from the point of view of gaining new expression/targeting than changing the character of the protein, though it is possible that myelin-speciﬁc alternatively spliced isoforms will be identiﬁed in the future. In this regard, a recent paper (Haenisch et al., 2005) has identiﬁed and characterized a protein important for node formation, contactin-1, in ﬁsh. This protein (contactin-1/F3/ F11) is targeted to mammalian paranodes (Rios et al., 2000) and along with caspr, it interacts in a trans fashion with neurofascin-155 (Charles et al., 2002) to stabilize paranodal junctions and cause a diVusion barrier that holds proteins/ lipids in nodes of Ranvier (Rios et al., 2003). Knockout mice lacking contactin-1 (Boyle et al., 2001) fail to develop proper paranodes and allow the spreading of voltage-gated sodium channels along the axolemma. In addition, contactin-1 interacts with notch on oligodendrocytes to cause activation of signal transduction pathways (Hu et al., 2003). Haenisch et al. demonstrated that ﬁsh (zebraﬁsh, goldﬁsh, and puVerﬁsh) contain an additional contactin-1 protein, presumably the result of gene duplication, and further studies may demonstrate added functions that evolved with the new molecule.

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C. DIFFERENTIATION

OF

MYELINATING CNS

AND

PNS GLIAL CELLS

The importance of transcription factors and other proteins involved in controlling the fate of myelinating oligodendrocytes is beyond the scope of this chapter and relevant information can be found in recent literature (Colognato and Vrench-Constant, 2004; Jessen and Mirsky, 2005; Rowitch, 2004; Sherman and Brophy, 2005; Shimizu et al., 2005). As in other areas, research on chickens, amphibians, and ﬁsh has markedly contributed to understanding the role of ventral (notochord and ﬂoor plate) (Cameron-Curry and Le Douarin, 1995; Orentas et al., 1999; Park et al., 2004; Pringle et al., 1998) and related transcription factors (Zhou et al., 2000, 2001) as well as signal transduction pathways (Park and Appel, 2003). A recent study demonstrating the role of erbB receptors in Schwann cell migration in zebraﬁsh is of particular note (Lyons et al., 2005).

V. Use of Comparative Myelin Studies to Understand CNS Regeneration

Traumatic injury to the adult mammalian spinal cord typically results in severe and irreversible motor and sensory defects. In contrast, during fetal and early postnatal (depending on species) stages of mammalian development and throughout life in ﬁshes and many amphibians, the CNS responds to traumatic injury with extensive regeneration and functional repair. DiVerences between regeneration-competent and -incompetent states have been investigated in great detail from both the ontogenetic and phylogenetic perspective. A major conclusion from these studies is that a number of myelin-associated proteins seem to negatively aVect the regenerative potential of the CNS. Here, we consider how studies of nonmammalian nervous systems have and will further impact understanding of the responses that facilitate or block CNS regeneration.

A. MYELIN INHIBITION,

A

HISTORICAL PERSPECTIVE

Until the 1980s there was no clear explanation of why adult mammalian CNS axons do not regenerate. The ﬁrst ideas came from studies showing that the lack of CNS regeneration was not intrinsic to the nerve ﬁbers themselves, but rather to their glial cell environment. For if transected axons, from adult rat spinal cord are exposed to peripheral nerve grafts, they grow through the graft only stopping when reentering CNS tissue (David and Aguayo, 1981; Richardson et al., 1980). Thus, there is a fundamental diVerence between the CNS and PNS tissue/environment; the former blocks and the latter supports CNS axon

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regrowth. Next it was found that oligodendrocytes actively inhibit axon outgrowth (Schwab and Caroni, 1988; Schwab and Thoenen, 1985), and, thus, the impaired axon regrowth in CNS was found to be unrelated to a missing trophic factor, a notion originally proposed by Tello and Ramon y Cajal (Ramon y Cajal, 1928; Tello, 1911). Three lines of evidence support the concept that although spinal cord injury (SCI) involves a complex interplay of events that include inﬂammation, cell death, and scar formation, the active block of severed axon regrowth is caused by oligodendrocyte (myelin) components. First, when oligodendrocytes are deleted or myelination is delayed regeneration of spinal cord ﬁbers in several model organisms succeeds (Dyer et al., 1998; Keirstead et al., 1992). Second, application of antibodies directed against myelin components, most notably Nogo-A, enhances regenerative sprouting and axonal regrowth in a rat model of SCI (Bregman et al., 1995; Caroni and Schwab, 1988; Schnell and Schwab, 1990). Third, autoimmunization of mice or rats with MAG plus Nogo-A augments regeneration and/or sprouting of corticospinal axons following spinal cord hemisection; however, myelin immunization has a greater overall eVect than MAG/Nogo-A immunization (Hauben et al., 2001b; Huang et al., 1999; Sicotte et al., 2003). In addition, there is a remarkable temporal correlation between onset of myelination and loss of regenerative capability during development. In the chick CNS, the critical switch from a regenerative to a nonregenerative state occurs at embryonic day E13 (Shimizu et al., 1990), a time that coincides with the appearance of the ﬁrst myelin proteins (Macklin and Weill, 1985). Similarly, in the opossum Monodelphi domestica, the regenerative ability signiﬁcantly drops around postnatal days P12-P14 (Saunders et al., 1998), the time that myelination commences (Ghooray and Martin, 1993).

B. CHARACTERIZATION

OF

REGENERATION INHIBITORS

IN

MAMMALS

Several proteins that are present in CNS myelin have been identiﬁed as neurite growth inhibitors: (1) MAG (McKerracher et al., 1994; Mukhopadhyay et al., 1994), (2) chondroitin-sulfate proteoglycans (CSPG) versican V2 and brevican (Niederost et al., 1999; Schmalfeldt et al., 2000), (3) Nogo-A/RTN4-A, the largest isoform encoded by the nogo/rtn4 gene (Chen et al., 2000; GrandPre´ et al., 2000; Prinjha et al., 2000), (4) OMgp (Kottis et al., 2002; Wang et al., 2002b), and (5) ephrin-B3 (Benson et al., 2005). In neurons, only two receptor types are known that mediate the inhibitory activity of these myelin-associated ligands: EphA4, a receptor tyrosine kinase speciﬁcally interacting with B-class ephrins including ephrin-B3 and hitherto best described in the context of developmental axon path ﬁnding (Benson et al., 2005), and two members of the Nogo-66 receptor family, NgR and NgR2. Whereas

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NgR was not only shown to bind to one inhibitory domain of Nogo-A, called Nogo-66 (Fournier et al., 2001, 2002), but also to MAG (Domeniconi et al., 2002; Liu et al., 2002) and OMgp (Wang et al., 2002b), NgR2 displays selective aYnity for MAG (Venkatesh et al., 2005). Importantly, NgR binds to Nogo-66, an inhibitory sequence stretch shared by all Nogo isoforms, but not to NiG, the central inhibitory domain which is speciﬁc for Nogo-A (Niedero¨st et al., 2002; Oertle et al., 2003; Schweigreiter et al., 2004). For CSPGs and NiG no receptor has been identiﬁed as yet. Since both NgR and NgR2 are glycosylphosphatidylinositol (GPI)-anchored proteins lacking an intracellular domain, they must team up with transmembrane receptor molecules to allow signaling. Two of these coreceptors were identiﬁed so far; both are members of the tumor necrosis factor receptor (TNFR) superfamily. Both of these, the neurotrophin receptor p75NTR (Wang et al., 2002a; Wong et al., 2002) and TROY/TAJ (Park et al., 2005; Shao et al., 2005), are believed to function as the signal transducing constituent in a receptor complex that includes NgR and also LINGO-1 (Mi et al., 2004). Although the details of the intracellular neuronal signal transduction response are still sketchy, the small GTPase RhoA has emerged as a major signaling hub for various myelin-associated inhibitor-triggered pathways (Fig. 7). Rho GTPases are best known for their role in regulating actin cytoskeleton dynamics in general (Hall, 1998; Kaibuchi et al., 1999) and in neurons in particular (Bito, 2003; Dickson and Senti, 2002; Luo, 2002). Since a high level of RhoA activity is believed to suppress actin cytoskeleton dynamics (Burridge and Wennerberg, 2004), this would oVer an explanation as to how (re)extension of neurites is blocked upon exposure to myelin inhibitors. For a more thorough discussion of the neuronal signaling machinery see a recent review by Schweigreiter and Bandtlow.

C. PHYLOGENY

OF

CNS REGENERATION

As mentioned above, the CNS is not only permissive for regeneration in early stages of ontogenetic development of mammals, but also in many ﬁshes and amphibians throughout their whole life (Clarke and Ferretti, 1998). Speciﬁc examples of successful CNS regeneration in adult animals include: cyclostomes (lamprey) (Lurie and Selzer, 1991), teleost ﬁsh (Murray, 1976; Stuermer, 1988a,b), amphibians, and reptiles. In urodele amphibians (newts and salamanders) spinal cord, retina, and even part of the telencephalon regenerate (ChernoV, 1996; Clarke et al., 1988; Stensaas, 1983), whereas in anurans (frogs and toads) CNS regeneration is restricted to the optic nerve (Gaze, 1970). In reptiles, CNS regeneration studies are limited to a few lizards. These were found to regenerate their tail including spinal cord (DuVy et al., 1992; Egar et al., 1970) and optic nerve (Beazley et al., 1997). However, lizards, unlike anurans, are unable to restore a

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FIG. 7. CNS myelin inhibitory signaling molecules. Schematic showing the inhibitory proteins expressed by oligodendrocytes/myelin and their neuronal receptor complexes. The signaling pathways of all inhibitors investigated so far converge at Rho GTPases, RhoA, and Rac1. Note that the stimulation of RhoA activity by EphA4 has not been conﬁrmed in the context of myelin inhibitory signaling yet. The activity status of Rho GTPases determines the neuron’s capability to extend neurites. Straight lines represent direct interactions, dashed lines indirect or incompletely deﬁned ones.

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retinotopic map and thus stay functionally blind after their optic nerves are severed. In addition, in lizard in vitro retina explant experiments, axons extend from retinal ganglion cells (RGC) only 2–3 months after optic nerve transection (Lang et al., unpublished observation), whereas optic nerve regeneration in ﬁsh commences within only 2–3 weeks (Wanner et al., 1995). Taken together, the ability for CNS regeneration in the adult organism is gradually lost from the evolutionary stage of urodeles ‘‘upward’’ toward birds and mammals, where it became conﬁned to early ‘‘pre-myelin’’ developmental stages. These results lead to the question of what molecular changes caused the decline and disappearance of CNS regenerative capacity in the course of vertebrate evolution.

D. COMPARATIVE GENOMICS

OF THE

MYELIN INHIBITORY MACHINERY

A plethora of molecular data on myelin inhibition available in mammals (Section VI.C.) coupled with a rapidly growing number of completed vertebrate genome sequences has led to recent studies designed to identify changes that account for phylogenetic loss of CNS regeneration in adult birds and mammals. Studies of this nature will clearly shed light on the evolutionary origins of myelin and more speciﬁcally on myelin-associated inhibitory mechanisms. Understanding the evolutionary context of myelin inhibition might in turn help to conceptualize strategies for designing therapeutic intervention applicable to CNS regeneration in humans. As a ﬁrst step, investigators have begun to look for correlations across taxa between the appearance of ‘‘marker’’ genes and loss of regenerative ability in adult CNS. Apart from mammals, most eVort to identify components of the myelin inhibitory machinery has focused on two teleost ﬁsh species, Danio rerio, zebraﬁsh, and Takifugu rubripes, puVerﬁsh/fugu and two amphibians, the clawed frog, Xenopus laevis, and the tropical frog, Xenopus tropicalis, as these animals couple regenerative capacity with completed genome sequences. In both zebraﬁsh and fugu, a single ortholog of the mammalian nogo/rtn4 gene was recently identiﬁed, termed (DANRE)rtn4 and (FUGRU)rtn4, respectively (Diekmann et al., 2005). Like the nogo gene, each ﬁsh ortholog gives rise to several isoforms by means of alternative splicing and promoter usage. Whereas at the C-termini encoded proteins exhibit a high degree of identity to Nogo, including the region harbouring the inhibitory Nogo-66 domain, at the N-termini, in contrast, similarity, including the central inhibitory domain of mammalian Nogo-A, NiG, is missing indicating diVerences in genomic origin. This ﬁnding was unexpected, given that ﬁsh RGC respond to a peptide from within rat NiG in an outgrowth inhibition assay (Diekmann et al., 2005). Fish neurites are indeed not only inhibited by rat CNS myelin in outgrowth inhibition assays, indicating the expression of

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functional receptors for inhibitory myelin components, but also, albeit to a less extent, by ﬁsh myelin (Sivron et al., 1994; Wanner et al., 1995). It would thus be very interesting to test whether the two ﬁsh RTN4 proteins exert inhibitory activity on rat/ﬁsh neurite growth in vitro. Expression data suggest a potential role of ﬁsh RTN4 as myelin inhibitor, although a more reﬁned analysis needs to be carried out and the expression proﬁle clearly lacks the CNS speciﬁcity as shown for mammalian Nogo-A (Diekmann et al., 2005). Speciﬁcity of inhibitory action of the ﬁsh RTN4 proteins, however, might be conferred by the expression pattern of zebraﬁsh NGR and fugu NGR, two orthologs of the mammalian ngr gene, which have been detected recently (He et al., 2003; Klinger et al., 2004b). As in the mouse, in adult ﬁsh orthologs of the Nogo-66 receptor are predominantly expressed in the brain, a ﬁnding consistent with a potential role in the regulation of axon growth and plasticity. Not only orthologs of ngr, but also additional ngr family members, ngr2 and ngr3 (Barton et al., 2003; Pignot et al., 2003), have been found both in the zebraﬁsh and fugu genome (Klinger et al., 2004b). Like many ﬁsh counterparts of mammalian genes they come in pairs, a phenomenon which is presumably due to a genome duplication event in the teleost ﬁsh lineage (Amores et al., 1998). Following each genome duplication, two copies often adopt diVerent fates by sequence diversiﬁcation either in the coding or the regulatory region leaving only one true ortholog of the original gene. This phenomenon also appears to be the case with the ﬁsh rtn4 and ngr homologous genes. Along with ﬁsh ngr, ﬁsh ngr2 and ngr3 genes are expressed in a pattern similar to that in mammals probably conveying evolutionary conserved functions. If either of the ﬁsh NgR orthologs, with similar structural motifs as in mammals, is to function as receptor for myelin-associated inhibitors, it will have to recruit a coreceptor for transmembrane signal transduction just as it has been described for mammalian NgR. While no such partner has been characterized yet, two sequences homologous to p75NTR have been identiﬁed in both the zebraﬁsh and fugu genome with one being a true ortholog of p75NTR, and the other one corresponding to nrh1 (neurotrophin receptor homolog 1), encoding a transmembrane receptor that is closely related to p75NTR and found in all nonmammalian vertebrates (Kanning et al., 2003). Finally, in the zebraﬁsh genome also orthologs of MAG and OMgp (Lehmann et al., 2004, data not shown) as well as ephrin B-3 and EphA4 (Coulthard et al., 2002) were shown to exist. Taken together, teleost ﬁsh seem to encode most constituents of the myelin inhibitory machinery as it has so far been characterized for mammals. Thus, it is not surprising that in Xenopus alike, orthologs of several myelinassociated inhibitors and neuronal receptors have been detected including (XENLA) RTN4 (Klinger et al., 2004a), Xenopus brevican (XBcan) (Sander et al., 2001), and Xenopus p75NTR (Hutson and Bothwell, 2001; Kanning et al., 2003), each with an expression pattern that largely resembles that in mammals. Furthermore, orthologs of ephrin B-3 (Helbling et al., 1999) and A-type Eph

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receptors (Helbling et al., 1998) were identiﬁed in the Xenopus genome. In addition, Xenopus spinal neurons are responsive to MAG and loose this responsiveness upon treatment with PI-PLC, an enzyme that releases GPI-anchored proteins from the cell surface, strongly indicating the existence of a Xenopus ortholog of NgR (Song et al., 1998; Wong et al., 2002). Interestingly, it is in Xenopus that, in evolutionary terms and as investigated so far, the NiG domain appears for the ﬁrst time. In fact, (XENLA) RTN4 is homologous to the mammalian nogo gene throughout the whole cDNA giving rise to three major isoforms that correspond to mammalian Nogo-A, -B, and -C. Moreover, the tissue distribution of all three isoforms is comparable to that in mammals including a predominant expression of frog nogoA in the CNS. Speciﬁcally, Xenopus Nogo-A protein within the CNS is not only detected in myelinated ﬁber tracts but also in what seems to be neurons and neuropil, just as has been demonstrated for mammalian Nogo-A (Klinger et al., 2004a). In overall, with regard to Nogo/RTN4, there seems to be a close match to the mammalian situation only at the evolutionary level of anuran amphibians. Whether this contributes to the increasing lack of regenerative capability at the transition from ﬁsh to amphibians depends on the in vivo signiﬁcance of the proposed inhibitory activity of NiG and on its presence in urodelian amphibians. Both issues still await clariﬁcation.

E. CNS REGENERATION DESPITE MYELIN INHIBITORS—DOES SYSTEM MAKE THE DIFFERENCE?

THE IMMUNE

Evidently, the correlation between the genomic presence of known myelinassociated inhibitors plus neuronal receptor components and the lack of regenerative capability of the CNS is poor. Already in teleost ﬁsh, which exhibit a high regenerative potential in the adult CNS, virtually all of the myelin inhibitory machinery, as it is characterized today, is present. The only exception seems to be the absence of one inhibitory domain of RTN4, NiG. It is highly unlikely, though, that this accounts for the good regeneration in ﬁsh, since on the one hand NiG is present in Xenopus and obviously does not hamper regeneration in the visual system, and on the other hand deletion of the whole nogo gene in the mouse does hardly, if at all, improve regeneration in in vivo lesion models (Kim et al., 2003; Simonen et al., 2003; Zheng et al., 2003). Neither seems the diVerence between regenerative and nonregenerative species be due to diVerent expression patterns of myelin related marker genes. Expression of ngr and ngr2 orthologs in ﬁsh occurs predominantly in the brain and thus coincides with the mouse expression pattern. Likewise, ﬁsh RTN4, even if it lacks the CNS speciﬁcity of Nogo-A, is abundantly found in the brain and spinal cord, as is the case with mammalian Nogo-A (Huber et al., 2002; Josephson et al., 2001). Furthermore, in

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Xenopus, the RTN4 ortholog is readily detected in the optic nerve, thus belying any correlation between expression and regenerative capability. How can this apparent discrepancy between lack of correlation as outlined above and the postulated adverse role of the myelin inhibitory machinery for regeneration, which is supported by a large body of both in vitro and in vivo data, be resolved? The potential answer is fourfold and speculative in nature. (1) First of all, the known ensemble of myelin inhibitors and cognate receptors is likely to be incomplete. Deleting the genes encoding components of the myelin inhibitory machinery in the mouse did not yield a substantial eVect on CNS regeneration in any case, as has been investigated so far. Thus, while there is certainly redundany among ligands and receptors blurring the analysis of individual genes, the existence of as yet uncharacterized components, which are possibly conﬁned to the nonregenerative CNS of higher vertebrates, cannot be excluded. (2) Second, it has not been proven yet that the orthologs of the mammalian myelin inhibitory machinery in ﬁsh and Xenopus do actually exert an inhibitory function on neurite growth. There is still complete lack of biochemical evidence that ﬁsh RTN4 or MAG or OMgp bind to ﬁsh NgR, that ﬁsh NgR interacts with the p75NTR ortholog, and that the activation of such a receptor complex in ﬁsh neurons would cause growth cone collapse and block of neurite extension. The only evidence for an inhibitory activity of ﬁsh myelin has been documented in ﬁsh retina explant experiments. Outgrowth of RGC axons is impeded on a substrate coated with ﬁsh myelin in comparison to ﬁsh membrane or polylysine only, albeit signiﬁcantly less than on rat myelin (Sivron et al., 1994; Wanner et al., 1995), strongly suggesting the existence of myelin-inhibitory activity and cognate neuronal receptors. This raises a key question: if not inhibition of neurite growth, what might be the function of the ﬁsh orthologs? Considering a diVerent functionality for the ﬁsh orthologs of mammalian myelin-associated inhibitors plus receptors is in fact not far-fetched. A great many data implicate constituents of the mammalian myelin inhibitory machinery in mechanisms unrelated to block of neurite growth. p75NTR is involved in the regulation of a variety of cellular programmes including apoptosis and diVerentiation (Barker, 2004). MAG was originally characterized as major myelin-associated member of the Ig-superfamily promoting neurite outgrowth of a number of diVerent neurons ( Johnson et al., 1989). It was later realized that depending on the developmental stage of neurons it either inhibits neurite growth or promotes it (DeBellard et al., 1996; Turnley and Bartlett, 1998), a functional bidirectionality that seems to be determined by the neuronal level of cyclic nucleotides, notably cAMP, and [Ca2þ]i (Cai et al., 2001; Henley et al., 2004; Song et al., 1998). Moreover, most of Nogo-A is not localized at the plasma membrane but at the intracellular membrane system of

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the endoplasmic reticulum and the Golgi apparatus (GrandPre´ et al., 2000; Oertle et al., 2003). A lot of eVort is currently devoted toward elucidating the functional relevance of intracellular Nogo-A, in particular when neuronally expressed. Taken together, myelin inhibitors and their receptors are involved in a variety of mechanisms unrelated to neurite growth inhibition in higher vertebrates. Without unequivocal biochemical evidence, however, it is pure speculation to hypothesize that the inhibitory activity on neurons has been acquired as a novel feature at some time between the transition from ﬁsh to amphibians, for example, by coupling myelin ligand/receptor pairs to inhibitory signaling pathways. This notion is neither supported by the relatively high conservation of sequences of putative myelin inhibitors between vertebrate phyla nor by the largely unaltered expression pattern throughout evolution. (3) Third, the activity of myelin-derived inhibitors might be overriden by the presence of growth-promoting molecules in the regeneration permissive CNS. In the zebraﬁsh, a number of glial and neuronal cell adhesion molecules, which are known to promote axonal growth in vitro, are upregulated upon lesion in the optic nerve or spinal cord, including P0 and L1 orthologs (Becker et al., 1998; Schweitzer et al., 2003). Silencing the expression of L1 in axotomized neurons in the zebraﬁsh in vivo signiﬁcantly impairs regrowth and functional recovery upon spinal cord transection (Becker et al., 2004). Importantly, lesioninduced upregulation or even expression of either P0 or L1 has not been observed in the mammalian CNS (Sommer and Suter, 1998; Zhang et al., 1995). In this context, the apparent switch from a P0-based CNS myelin in ﬁsh to a PLP-based CNS myelin in tetrapods (see Section V.B.) is of particular note. Thus, ﬁsh CNS neurons might readily regrow after lesion due to the presence of conducive molecules both in the glial environment and in the neurons themselves. Whether this occurs despite the activity of putative myelin-associated inhibitors remains to be investigated. (4) The fourth approach for resolving the discrepancy outlined above brings into play the immune system. This concept basically states that the diVerence between a regenerative and a nonregenerative CNS is not so much due to the tissue-speciﬁc expression of myelin-associated inhibitors plus receptors but is rather explained by the reaction of the immune system to an injury of the CNS. Speciﬁcally, in a regenerative CNS, an immune reaction is mounted upon lesion— as in any other tissue—resulting in clearance of the injury site from inhibitory myelin debris and concomitant generation of an environment that promotes both proliferation of beneﬁcial glial cells and survival of nerve cells; in sum, these mechanisms are thought to enable reextension of axons and functional recovery. In a nonregenerative CNS, conversely, the immune reaction is too weak as to have any beneﬁcial eVects, presumably due to suppression of the presence and activity of immune cells as part of the immune-privileged status of the CNS. This neuroimmunological theory of CNS regeneration is supported by a large body of

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experimental evidence from both regenerative and nonregenerative systems and implicates both the innate and adaptive arm of the immune system in form of macrophages and T cells, respectively (Schwartz, 2000; Schwartz and Moalem, 2001). In lesioned ﬁsh CNS, as in the injured PNS of higher vertebrates, the lesion site is rapidly invaded by blood-derived macrophages. This cell type not only phagocytoses myelin debris, but also secretes myelin-degrading proteases including plasminogen activator (Cammer et al., 1978; Perry et al., 1987; Salles et al., 1990; Wilson et al., 1992). Moreover, macrophages have been shown to regulate the glial response to injury in two beneﬁcial ways. On the one hand they secrete chemokines, such as IL-1, which acts as astroglial as well as (indirect) neuronal growth factor (Giulian et al., 1988; Lindholm et al., 1987). Rapid and persistent repopulation of the lesion site with reactive astrocytes has been correlated with regeneration permissiveness (Blaugrund et al., 1993; Cohen et al., 1994). On the other hand macrophages are thought to confer cytotoxicity to oligodendrocytes presumably by secretion of soluble factors that reduce oligodendrocytes in number, which would otherwise eYciently present myelin-associated inhibitors to axons that are about to regenerate (Sivron et al., 1990, 1991). In line with this notion, implanting activated macrophages to the lesioned CNS in the rat resulted in marked improvement of nerve regrowth and functional recovery (LazarovSpiegler et al., 1996; Rapalino et al., 1998). Along with macrophages, T cells seem to play an equally important neuroprotective and regeneration promoting role. Upon injury, a systemic T-cell response is mounted in both the PNS and CNS, however, a signiﬁcantly higher number of T cells accumulate at the PNS than in the CNS (Moalem et al., 1999b; Popovich et al., 1996). Interestingly, when trying to boost the number of T cells in the lesioned spinal cord via implantation experiments, only those subtypes of T cells which are speciﬁc to a CNS myelin-associated self-antigen that is, CNS myelin speciﬁc autoimmune T cells, seem to have a beneﬁcial eVect on regeneration and functional recovery (Hauben et al., 2000; Moalem et al., 1999a). Self-antigens are not necessarily neurite growth inhibitors. A comparable beneﬁcial eVect on CNS regeneration is achieved when actively immunizing rats with MBP (Hauben et al., 2000, 2001a) or Nogo-A (Hauben et al., 2001b). The underlying mechanism is stated to involve activation of antigenspeciﬁc T cells, which is consistent with the T-cell implantation experiments. This, however, does not conform with results obtained in another immunization experiment using MBP and Nogo-A as antigens (Sicotte et al., 2003). Whereas the regeneration promoting eVect could be conﬁrmed in this experimental set-up, it is attributed to the production of antigen-speciﬁc antibodies, which are thought to bind to and neutralize respective myelin proteins at the lesion site. Mechanistically, this experimental approach might therefore resemble direct administration of function-blocking antibodies directed against CNS myelin proteins (Bregman et al., 1995; Schnell and Schwab, 1990). Due to considerable diVerences in the experimental protocols, however, it is difﬁcult to compare the studies by Hauben

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et al. and Sicotte et al. It is tempting to speculate, though, that immunization with CNS myelin speciﬁc autoantigens elicits a twofold immune response involving the production of myelin neutralizing antibodies and the activation of speciﬁc T cells, one beneﬁcial mechanism of which might be their secretion of neurotrophins at the lesion site (Moalem et al., 2000). Handling myelin speciﬁc autoimmune T cells is, not unexpectedly, a delicate balance between their protective eVect and the development of an autoimmune disease, an aspect that has to be considered when trying to boost the T- cell response for therapeutic means (Kipnis et al., 2001).

F. CONCLUSION: COLLISION

OF

BIOLOGICAL NECESSITY AND CLINICAL NEEDS

Summarizing these studies, one solution to the discrepancy as outlined above might be the good correlation between regenerative capability and the physical, rather than the genomic, absence of myelin-associated inhibitors upon injury. What seems to have changed throughout vertebrate evolution is not so much the genomics of the myelin inhibitory machinery, but rather the immune response to a lesion in the CNS. Thus, it might not be the expression of myelin-derived inhibitors per se, as is widely believed, but the immunological response upon injury that makes the diVerence between a regeneration competent and incompetent CNS. While in the lesioned adult CNS of lower vertebrates a signiﬁcant immunological reaction is initiated leading to physical removal of myelin inhibitors and concomitant provision of a regeneration permissive environment by glial cells, in the adult CNS of higher vertebrates the immune response is apparently too weak as to evoke suYcient beneﬁcial eVects. When aiming for therapeutic intervention, one way might be to mask myelin-associated inhibitors or their receptors with antisera. Alternatively, modulating the immune system in an appropriate way appears to have a similar supportive eVect. The intrinsic weakness of the immune response in higher vertebrates is likely to be attributed to the CNS’ elaborated immune-privileged character. The phenomenon of ‘‘immune privilege,’’ involving restricted access to and activity of immune cells in the CNS, might be an evolutionary adaptation developed to protect the intricate neuronal circuits of the CNS from potentially detrimental inﬂuences by the immune system (Lotan and Schwartz, 1994). Thus, lack of ability to regenerate and recover upon CNS injury might be the price to pay by higher vertebrates for highly integrated neural functions. In a biological sense, this price was not diYcult to pay since injuries to the CNS, typically the spinal cord, would usually be lethal before functional recovery after a few weeks of regenerative nerve growth was achieved. Lack of selective forces to promote repair of the injured CNS might also explain how myelin-associated inhibitors could evolve in the ﬁrst place. They possibly arose from the need to prevent further axonal sprouting after embryonic and early postnatal development thus stabilizing the basic neural circuits in the white matter as opposed to the still plastic connections in the

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unmyelinated gray matter (Chiquet, 1989). In fact, by making use of the paradigm of ocular dominance plasticity in the mouse, it was recently shown that Nogo and NgR contribute to suppress plasticity in the visual cortex at the end of the juvenile age (McGee et al., 2005).

VI. Future Studies of Myelin Evolution

Hopefully, by putting studies of vertebrates/gnathostomes and invertebrates together, one can better appreciate the importance of research as seen from an evolutionary perspective. Particularly in invertebrates, a number of strategies have developed that expand the role of glial cells that associate with larger axons, to one in which rapid and eYcient saltatory conduction is prioritized. Research on these strategies will provide a broader and clearer understanding of the mechanisms most important for the evolution of myelin and saltatory conduction. Since we are unaware of plans to sequence genomes of any invertebrates with myelinated axons, the most straightforward approach to identify proteins involved in invertebrate myelination will be proteomics using isolated myelin as starting material. Additionally, with expanding knowledge of sites of glial-axonal interactions, axonal partners of glial proteins may be used in novel ways to identify glial cell proteins implicated in the evolution of myelin. Even if ones goal is not to understand the origins of myelinated nervous systems, expansion of studies to other, less frequently used, animal models may pave the way to fresh insights not only of how myelin developed, but also how it became less permissive to CNS regeneration and to other facets important for understanding and treating human diseases. Acknowledgments

We would like to acknowledge the support of the National Science Foundation (grant IBN0402188, to RMG) and grants from the NIH NS23868 and NS23320, and grant A6052 from the Natural Sciences and Engineering Council of Canada, to BIR, without whose support this chapter would not have been written.

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INDEX

A Abaxonal cytoplasm, 239 Abaxonal cytoplasmic channels, 238–239 ACE. See Angiotensin-converting enzyme Activity-dependent plasticity, 137 Aggression behavior, 99–100, 117 in females, DHEA role as neurosteroid, 103–107 in males, 107–108 neural steroid receptors, 108–113 regulation in adult, 108 regulatory concepts in males and females metabolism, 101–102 neuromodulator hypothesis, 102–103 serotonin function for regulation of, 113–116 Amygdala sensitivity, 161 Anatomical segmentation, 208–211 Androgenic activity, of DHEA, 104–107 Androgen(s), 99, 101. See also Dehydroepiandrosterone effects of steroid hormones on, 106 receptors(AR), 110–111 Angiotensin-converting enzyme (ACE), 167 AR. See Androgen(s), receptors Astrocytic connexins Cx30, deletion of, 141 B BDNF. See Brain derived neurotrophic factor BDNF ERK1/2-CREB-Bcl-2, dysregulation of, 174 Biological processes, quantitative imaging of, 191 Bipolar disorder, 154 Blank scans, 194 Blood contamination, criteria for extent of, 90–91 Blood sampling, 194 ‘‘Bootstrap’’ resampling methods, 207 Brain electrical coupling in, 132–133 gap junctions, 129–132 Brain derived neurotrophic factor (BDNF)

current stresses, 174 early life stresses, 174 effect of childhood stress on, 174 genetic studies, 174 Brevican, 245

C cAMP-response element-binding protein (CREB), 173 Capitate projections, 222 Catechol-o-methyl transferase (COMT ), 12–18 isoforms of, 11 Cephalopods, 223 Cerebrospinal ﬂuid. See CSF; Human CSF proteins Cerebrospinal ﬂuid homovanillic acid (HVA), low levels of, 170–171 Chondroitin-sulfate proteoglycans (CSPG) versican V2, 245 Chromosome 22q11 deletion syndrome (22q11DS), 1 clinical phenotype of, 4 microdeletion mechanism of, 2–4 murine models of, 9–10 neuropathology, 6 positional cloning schizophrenia susceptibility loci at, 7–9 psychosis in, 5–6 Chronic stress, 171, 174 CNP, 243 CNS regeneration despite myelin inhibitors, 250–254 inhibitors, characterization in mammals, 245–246 phylogeny of, 246, 248 Collision of biological necessity and clinical needs, 254–255 COMT. See Catechol-o-methyl transferase Connexon hexamers, 128 Contactin-1, 243 275

276

INDEX

Cortical adrenergic receptor density, 168 CREB. See CAMP-response element-binding protein CRH gene expression, 166 CSF, biomarkers for CNS diseases CSF proteome and discovering unique proteins for CNS diseases, future aspects, 95–96 mass spectrometry instrumentation and bioinformatics proteomic instrumentation, 92–93 search engine and database issues, 93–94 sample preparation collection of CSF and quality controls, 90–91 fractionation of CSF, 91–92 Cx36 expression in brain, 130–132 knockout (KO) mouse, 136 D DA. See Dopamine Dehydroepiandrosterone (DHEA), 100 metabolism in, 101–102 role in aggression in females, 103 mechanism of action, 104–107 Depression and genetics, 155–157 and stress, 154–155 Depression, dopaminergic system in brain derived neurotrophic factor (BDNF), 173 current stress, 171–172 early life stress, GABAergic system, 172 genetics, 171 and GABAergic system, 172 Depressive episode, 155 DES. See Diethylstilbestrol Dexamethasone, inhibitory effect of, 164 DHEA. See Dehydroepiandrosterone DHT. See Dihydrotestosterone Diethylstilbestrol (DES), 114 Dihydrotestosterone (DHT), 101 Discovery-based proteomics, steps in, 30–31 DM20, 240 Dopamine (DA), 139–140, 170–171 receptor DRD3, 171 Dopaminergic system, to stress, 171–172 Dye coupling, 134 changes in, 140

E Electrically coupled neurons, 133 Electrical synapses in invertebrates, 135 modulation of, 137 properties and function of, 135–137 ER. See also Estrogen receptors gene, 112–113 gene, 112 Estrogen receptors (ER), 111–113 effect on serotonin function, 116 Exclusion criteria for human CSF collection, 31–32 F Facial dysmorphology, 4 FBP. See Filtered backprojection Females ‘‘dependent’’ events, 155 Females-typical aggression. See also Male-typical aggression role of DHEA as neurosteroid in, 103–107 Fiber myelination in invertebrates, 230–231 Filtered backprojection (FBP), 204 FORE, 205 Freeze-fracture electron microscopy, 128 Freeze-fracture immunogold labeling (FRIL), 130 FRIL. See Freeze-fracture immunogold labeling G GABA. See also Gamma-aminobutyric acid neurons, 172 receptor, genes coding for, 172–173 GABAA receptors, 104–107 GABAergic interneurons, 133 GABAergic projection neurons, 141 Galactolipids, 229 Gamma-aminobutyric acid (GABA), 172 Gap junction(s) in brain, 129–132 cloning of, 126 expression in neurons, 125 local factors, 138–139 structure, 127–129 ultrastructure, 128 Gap junctional coupling, modulators of, 138 Gap junction-mediated communication, 126 Gap-junction plaque, 128, 129

INDEX

Gene’s deletion, 141 Gene stress interaction, animal model, 165 Genetic liability for mood disorders, 155 Genetic linkage, 156 Geometric transfer matrix (GTM), 214 Glial cells ensheathment of axons, 221–222 evidence of interaction with axons, 221–225 Glucocorticoid receptor gene expression, alterations in, 167 Glucocorticoids, 166 Glycosylphosphatidylinositol (GPI)-anchored proteins, 246 GOLLI (Genes of Oligodendrocyte Lineage), 242 GRICH (goldﬁsh regeneration-induced CNPase homolog), 243 GTM. See Geometric transfer matrix

277

data processing and analysis, 34–35 exclusion criteria and LP for collection of, 31–32 in-gel digestion, 33 preparation and fractionation with SDS-PAGE, 32 protein identiﬁcation using LC followed by LCQ-MS, 33 protein identiﬁcation using off-line SCX chromatography followed by LTQ FT MS, 33–34 Human genome, cloning of, 132 Hypothalamic–pituitary–adrenocortical (HPA) axis, 166 genetics early life stress, 167–168 noraderenergic system, 168–169 Hypothalamus, 5-HTactions in, 164

H Haploinsuffciency, 10 Haplotypes associated with schizophrenia, 15 HNK-1, 241 Homovanillic acid (HVA), 170–171 Hormonal modulation of serotonin function, 113–116 HPA. See also Hypothalamic–pituitary–adrenocortical axis axis dysfunction, genetic liability for, 166–167 axis dysregulation, 174 5-HT. See Serotonin Human CSF proteins, 29–30 characterization results proteins identiﬁed by LC-LCQ-MS/MS, 35 proteins identiﬁed by nanoLC-LTQ FT MS/MS after off-line SCX separation, 36–73 reexamination of previously identiﬁed proteins, 73–74 discovery of CSF biomarkers for CNS diseases future aspects of CSF proteome and discovering unique proteins for CNS diseases, 95–96 mass spectrometry instrumentation and bioinformatics, 92–94 sample preparation, 90–92 identiﬁed by LCQ and LTQ, classiﬁcation of, 73 material and methods for characterization

I IL-1, 253 Image processing, 193–194 Inferior olivary (IO) neurons, 133 Infrared differential interference contrast microscopy (IR-DIC), 133 In-gel digestion of CSF, 33 Inner tongue processes, 239 Innexins, 132 Innexin-like genes, 132 Invertebrate ﬁber myelination, 230–231 Invertebrate gap junctions, 132 IP3, 137 Isotope coded afﬁnity tag (ICAT), 31 K 36-kDa protein, 243 L LC-LCQ-MS/MS, for CSF protein identiﬁcation, 35 Lithium prophylaxis in mood disorders, 171, 173 Low copy repeats (LCR22), 2–4 Low expression genotype, 161 LSO. See Lutetium oxyorthosilicate LTQ FT MS, for CSF protein identiﬁcation, 33–34 disadvantage of LCQ-MS over, 92–93

278 Lumbar puncture (LP), 31–32 Lutetium oxyorthosilicate (LSO), 202 M Major depression genetic and environmental model of, 158 heritability of, 155 neurobiological correlates of, 156 Major depressive disorder. See Unipolar depression Male-typical aggression, 107 Mammalian 5-HT receptor genes, 162 Mammalian myelin, major proteins DM20, 240 36-kDa protein, 243 P0, 241 MASCOT database search algorithm, 93 Mass spectrometry (MS) with electronspray ionization (ESI-MS), 31 Maximum likelihood (ML) approach, 204 MBP. See Myelin basic protein Medial preoptic area (MPOA), 114 Metabolism of DHEA, 101–102 of T, 108 3-Methoxy-4-hydroxyphenylglycol (MHPG), 168–169 -Methyl-p-tyrosine (AMPT), 168 MHPG. See 3-Methoxy-4-hydroxyphenylglycol Microdeletion at 22q11, 2–4 MicroPET, 195–198 R4, axial uniformity of, 200 scan, residual image nonuniformities in, 200 scanners, 192 scintillator in, 202 MLEM algorithm, 205 Monte Carlo PET scanner simulation program, 214 Monte Carlo simulation codes, 199, 207 Mood disorders, genetic linkage studies, 156 mRNA, examination of, 162 Multidimensional protein identiﬁcation technology (Mud-PIT), 31 Multiple dimensional chromatography (MDLC), 35 Murine models of 22q11DS, 9–10

INDEX

Myelin-associated glycoprotein (MAG), 230, 239, 245, 251 Myelin-associated oligodendrocytic basic proteins (MOBP), 239 Myelinated axons, 220 Myelinated ﬁbers in invertebrates biological properties of in Cambarus clarkia, 229–230 in Lumbricus terrestris, 229–230 in Penaeus duorarum, 229–230 in Procambarus clarkii, 230 in Ucides cordatus, 230 physiological properties of in copepod superfamilies, 229 in Lumbricus terrestris, 228–229 in Penaeus japonicus, 228–229 Myelinated nervous system, morphology of in Squalus acanthias, 232, 233–234, 235, 236, 237–239 Myelinated nervous system in vertebrates biological and molecular features, 240–243 differentiation of myelinating CNS and PNS glial cells, 244 morphological features, 231–232, 235, 237–239 Myelinating CNS and PNS glial cells, differentiation of, 244 Myelin basic protein (MBP), 230, 242 Myelin inhibition, historical perspective, 244–245 Myelin inhibitory machinery, comparative genomics of, 248–250 Myelin-like sheaths in invertebrates biochemical properties of invertebrate myelinated ﬁbers, 229–230 invertebrate ﬁber myelination, 230–231 morphological considerations, 225, 227–228 physiological properties of invertebrate myelinated ﬁbers, 228–229 Myelin sheaths in invertebrates, morphology in Branchiura sowerbyi, 225 in Cancer irroratus, 225–227 in copepods, 227 in crustaceans in Palaemonetes vulgaris, 227–228 in Penaeus, 227–228 in Eisenia foetida and Lumbricus, 228 in Lumbriculus variegates, 225 in Lumbricus terrestris, 225 in oligochaete, 227

INDEX

279

synthesis, rate-limiting enzyme in, 169 transporter (NET) gene, 169 Nucleus accumbens (NA), 139

Eisenia foetida, 227 Lumbricus terrestris, 227 in polychaetes, 225 in Procambarus clarkii, 225, 226 Myelin studies, comparative, use in understanding CNS regeneration characterization of regeneration inhibitors in mammals, 245–246 CNS reneration despite myelin inhibitors-does the immune system make the difference?, 250–254 collision of biological necessity and clinical needs, 254–255 comparative genomics of myelin inhibitor machinery, 248–250 myelin inhibition, historical perspective, 244–245 phylogeny of CNS regeneration, 246, 248

Object scatter, 198 Offensive aggression, 99–100. See also Aggression behavior regulation of, 100 Oligochaete sheaths, 227 Oligodendrocytic connexin, 131 Oligodendrocytic Cx32 transcript, 131 Ordered subsets expectation maximization (OSEM), 204–205 OSEM. See Ordered subsets expectation maximization Outer tongue processes. See Abaxonal cytoplasmic channels

N

P

NEC. See Noise equivalent count rate Neocortex, 133 NET. See Norepinephrine transporter gene Neural steroid receptors, 108 AR, 108–111 ER, 111–113 Neuroimaging studies on 22q11DS, 6 Neuromodulator hypothesis for aggression, 102–103, 113 Neuronal biochemical coupling, 137 Neuronal connexin, 138 Neuronal coupling in rodents, 126 Neuron(s) electric coupling of, 136 gap junction expression in, 125 speciﬁc connexin, 126 Neuropathology, of 22q11DS, 6 Neurotransmitter, 139–141 Nodes of Ranvier, 219, 239 Nogo-A, 245 Noise equivalent count rate (NEC), 193 Noraderenergic system current stress, 168–170 early stress, 170 genetics, 168–169 Noradrenaline, 168 Noradrenergic activity, effect of stressors on, 169 Norepinephrine, 168 neurons, activation of, 169

P0, 241 Paranodes, 221 Paraventricular nucleus (PVN), 166 Parkinson’s disease, 170–171 Partial volume error (PVE), 212–213 PET. See Positron emission tomography PET image reconstruction, goal of, 203–204 pH, 138–139 Pharmacological gap junction blockade, 136 Photon, statistical noise, 195 PLP. See Proteolipid protein Positron decay branching fraction, 194 Positron emission tomography (PET) data analysis image registration, 208–211 partial volume correction, 211–215 image quantiﬁcation factors, 192 image reconstruction, 203–208 physical corrections attenuation, 196–198 deadtime, 196 normalization, 199–201 radioactivity of LSO, 202–203 randoms, 195 scatter, 198–199 quantiﬁcation in, 208–209 setup and calibration animal positioning, 194–195 calibration, 193–194

O

280

INDEX

Positron emission tomography (PET) (cont.) external radioactivity measurements, 194 quality control, 194 scanner setup, 193 Postjunctional voltage, change in, 135 Pregnenolone sulfate (Preg-S), 104 Preg-S. See Pregnenolone sulfate PRODH gene, 8–9 Protein identiﬁcation in CSF, 33–34 Proteins/groups identiﬁed with a single peptide, search results of old MS data against new database for, 82–90 Proteins/groups identiﬁed with a single peptide using LCQ and LTQ, 60–73 Proteins/groups identiﬁed with more than two peptides, search results of old MS data against new database for, 75–81 Proteins/groups identiﬁed with more than two peptides using LCQ and LTQ, 36–60 Proteolipid protein (PLP), 222, 241 Psychiatric disorders, 5 Psychosis in 22q11DS, 5–6 PVE correction (PVC), 214 PVN. See Paraventricular nucleus Q 22q11DS. See Chromosome 22q11 deletion syndrome 22q11DS deleted region at HSA22q11 and MMU16 , comparison of, 3 Quantitative quality control, 194 R Radial component, 239 Radiotracers, 213 Rat brain, gamma-ray attenuation in, 196–198 Rat-brain studies, 197 Region of interest (ROI) value, 194, 207 Retina, modulate gap-junctional coupling in, 139 Rhesus monkeys, 170 Rs175174, 7–8 S Salivary cortisol concentrations, hypersuppression of, 168 Saltatory conduction, 219

Scatter correction proﬁles, comparison of, 199 Schizophrenia haplotypes associated with, 15 susceptibility loci on 22q11, 7–9 Schmidt-Lanterman incisures (SLI), 232, 239 SDS-PAGE gel electrophoresis, 29 Second messenger modulation, 139–141 Selective serotonin reuptake inhibitors (SSRI), 157 SEQUEST database search algorithm, 33–34, 93–94 Serotonergic system(s), 157–158 candidate gene studies of, 159 other serotonin receptors, 162–163 serotonin receptors, 162 serotonin transporter, 159–162 tryptophan hydroxylase (TPS), 163 current stresses early life stress, 164–165 Serotonergic transporter gene upstream promoter region (5-HTTLPR), 159–161 Serotonin, 157 for aggression, 113–116 receptors, 162 role of, 163–164 transporter (5-HTT), 159–160 Serotonin-related gene polymorphisms, effects of, 160 Shotgun proteomics, 31 Siglec-4 (sialic acid binding protein), 242 SimSET method, 214 Single nucleotide polymorphisms (SNP), 156, 167 Sinograms, 203–204 SLI. See Schmidt-Lanterman incisures Small-animal PET, 192 SNP. See Single-nucleotide polymorphisms Spinal cord injury (SCI), 245 Sprotte atraumatic spinal needle, 90 Squalus acanthias, 232 Squid, 231–232 giant axons, 223 SSRI. See Selective serotonin reuptake inhibitors Steroid-sensitive pathways for aggression regulation, 108 Superﬁcial cytoplasmic channels. See Abaxonal cytoplasmic channels Surface enhanced laser desorption/ionization (SELDI), 31, 95 Synchronous ﬁring, 136

INDEX

System deadtime, 196 ‘‘System’’ matrix, 204 T TAC. See Time activity curves Tail shock, 169 TBX1 gene, 10 T102C polymorphism, 162 Time activity curves (TAC), 213 T metabolism in males, 108 TPH. See Tryptophan hydroxylase TPH1 A218C polymorphism, 163 Transmission scanning, correction method of, 197 Trilostane, 105 Trophospongium, 222 Tryptophan hydroxylase (TPH), 157, 163

281

U Unipolar depression, 154 Use-dependent plasticity, 137–138 V Vertebrate myelin sheaths, biochemical and molecular features of, 240–243 W Western analysis of brain extracts, 104–105 Z ZDHHC8 gene, 7–8

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CONTENTS OF RECENT VOLUMES

Volume 37

Memory and Forgetting: Long-Term and Gradual Changes in Memory Storage Larry R. Squire

Section I: Selectionist Ideas and Neurobiology in

Implicit Knowledge: New Perspectives on Unconscious Processes Daniel L. Schacter

Population Thinking and Neuronal Selection: Metaphors or Concepts? Ernst Mayr

Section V: Psychophysics, Psychoanalysis, and Neuropsychology

Selectionist and Neuroscience Olaf Sporns

Instructionist

Ideas

Selection and the Origin of Information Manfred Eigen

Phantom Limbs, Neglect Syndromes, Repressed Memories, and Freudian Psychology V. S. Ramachandran

Section II: Populations

Neural Darwinism and a Conceptual Crisis in Psychoanalysis Arnold H. Modell

Development

and

Neuronal

Morphoregulatory Molecules and Selectional Dynamics during Development Kathryn L. Crossin

A New Vision of the Mind Oliver Sacks

Exploration and Selection in the Early Acquisition of Skill Esther Thelen and Daniela Corbetta

index

Population Activity in the Control of Movement Apostolos P. Georgopoulos

Volume 38

Section III: Functional Integration in the Brain

Segregation

and

Reentry and the Problem of Cortical Integration Giulio Tononi Coherence as an Organizing Principle of Cortical Functions Wolf Singerl

Regulation of GABAA Receptor Function and Gene Expression in the Central Nervous System A. Leslie Morrow Genetics and the Organization of the Basal Ganglia Robert Hitzemann, Yeang Olan, Stephen Kanes, Katherine Dains, and Barbara Hitzemann

Section IV: Memory and Models

Structure and Pharmacology of Vertebrate GABAA Receptor Subtypes Paul J. Whiting, Ruth M. McKernan, and Keith A. Wafford

Selection versus Instruction: Use of Computer Models to Compare Brain Theories George N. Reeke, Jr.

Neurotransmitter Transporters: Biology, Function, and Regulation Beth Borowsky and Beth J. Hoffman

Temporal Mechanisms in Perception Ernst Po¨ppel

283

Molecular

284

CONTENTS OF RECENT VOLUMES

Presynaptic Excitability Meyer B. Jackson

Volume 40

Monoamine Neurotransmitters in Invertebrates and Vertebrates: An Examination of the Diverse Enzymatic Pathways Utilized to Synthesize and Inactivate Biogenic Amines B. D. Sloley and A. V. Juorio

Mechanisms of Nerve Cell Death: Apoptosis or Necrosis after Cerebral Ischemia R. M. E. Chalmers-Redman, A. D. Fraser, W. Y. H. Ju, J. Wadia, N. A. Tatton, and W. G. Tatton

Neurotransmitter Systems in Schizophrenia Gavin P. Reynolds

Changes in Ionic Fluxes during Cerebral Ischemia Tibor Kristian and Bo K. Siesjo

Physiology of Bergmann Glial Cells Thomas Mu¨ller and Helmut Kettenmann

Techniques for Examining Neuroprotective Drugs in Vitro A. Richard Green and Alan J. Cross

index

Volume 39

Techniques for Examining Neuroprotective Drugs in Vivo Mark P. Goldberg, Uta Strasser, and Laura L. Dugan

Modulation of Amino Acid-Gated Ion Channels by Protein Phosphorylation Stephen J. Moss and Trevor G. Smart

Calcium Antagonists: Their Role in Neuroprotection A. Jacqueline Hunter

Use-Dependent Regulation Receptors Eugene M. Barnes, Jr.

GABAA

Sodium and Potassium Channel Modulators: Their Role in Neuroprotection Tihomir P. Obrenovich

Synaptic Transmission and Modulation in the Neostriatum David M. Lovinger and Elizabeth Tyler

NMDA Antagonists: Their Role in Neuroprotection Danial L. Small

of

The Cytoskeleton and Neurotransmitter Receptors Valerie J. Whatley and R. Adron Harris

Development of the NMDA Ion-Channel Blocker, Aptiganel Hydrochloride, as a Neuroprotective Agent for Acute CNS Injury Robert N. McBurney

Endogenous Opioid Regulation of Hippocampal Function Michele L. Simmons and Charles Chavkin

The Pharmacology of AMPA Antagonists and Their Role in Neuroprotection Rammy Gill and David Lodge

Molecular Neurobiology of the Cannabinoid Receptor Mary E. Abood and Billy R. Martin

GABA and Neuroprotection Patrick D. Lyden

Genetic Models in the Study of Anesthetic Drug Action Victoria J. Simpson and Thomas E. Johnson Neurochemical Bases of Locomotion and Ethanol Stimulant Effects Tamara J. Phillips and Elaine H. Shen Effects of Ethanol on Ion Channels Fulton T. Crews, A. Leslie Morrow, Hugh Criswell, and George Breese index

Adenosine and Neuroprotection Bertil B. Fredholm Interleukins and Cerebral Ischemia Nancy J. Rothwell, Sarah A. Loddick, and Paul Stroemer Nitrone-Based Free Radical Traps as Neuroprotective Agents in Cerebral Ischemia and Other Pathologies Kenneth Hensley, John M. Carney, Charles A. Stewart, Tahera Tabatabaie, Quentin Pye, and Robert A. Floyd

CONTENTS OF RECENT VOLUMES

Neurotoxic and Neuroprotective Roles of Nitric Oxide in Cerebral Ischemia Turgay Dalkara and Michael A. Moskowitz

Sensory and Cognitive Functions Lawrence M. Parsons and Peter T. Fox

A Review of Earlier Clinical Studies on Neuroprotective Agents and Current Approaches Nils-Gunnar Wahlgren

Skill Learning Julien Doyon

index

Volume 41

Section V: Clinical and Neuropsychological Observations Executive Function and Motor Skill Learning Mark Hallett and Jordon Grafman

Section I: Historical Overview

Verbal Fluency and Agrammatism Marco Molinari, Maria G. Leggio, and Maria C. Silveri

Rediscovery of an Early Concept Jeremy D. Schmahmann

Classical Conditioning Diana S. Woodruff-Pak

Section II: Anatomic Substrates

Early Infantile Autism Margaret L. Bauman, Pauline A. Filipek, and Thomas L. Kemper

The Cerebrocerebellar System Jeremy D. Schmahmann and Deepak N. Pandya Cerebellar Output Channels Frank A. Middleton and Peter L. Strick Cerebellar-Hypothalamic Axis: Basic Circuits and Clinical Observations Duane E. Haines, Espen Dietrichs, Gregory A. Mihailoff, and E. Frank McDonald Section III. Physiological Observations Amelioration of Aggression: Response to Selective Cerebellar Lesions in the Rhesus Monkey Aaron J. Berman Autonomic and Vasomotor Regulation Donald J. Reis and Eugene V. Golanov Associative Learning Richard F. Thompson, Shaowen Bao, Lu Chen, Benjamin D. Cipriano, Jeffrey S. Grethe, Jeansok J. Kim, Judith K. Thompson, Jo Anne Tracy, Martha S. Weninger, and David J. Krupa

Olivopontocerebellar Atrophy and Friedreich’s Ataxia: Neuropsychological Consequences of Bilateral versus Unilateral Cerebellar Lesions The´re`se Botez-Marquard and Mihai I. Botez Posterior Fossa Syndrome Ian F. Pollack Cerebellar Cognitive Affective Syndrome Jeremy D. Schmahmann and Janet C. Sherman Inherited Cerebellar Diseases Claus W. Wallesch and Claudius Bartels Neuropsychological Abnormalities in Cerebellar Syndromes—Fact or Fiction? Irene Daum and Hermann Ackermann Section VI: Theoretical Considerations Cerebellar Microcomplexes Masao Ito

Visuospatial Abilities Robert Lalonde

Control of Sensory Data Acquisition James M. Bower

Spatial Event Processing Marco Molinari, Laura Petrosini, and Liliana G. Grammaldo

Neural Representations of Moving Systems Michael Paulin

Section IV: Functional Neuroimaging Studies Linguistic Processing Julie A. Fiez and Marcus E. Raichle

285

How Fibers Subserve Computing Capabilities: Similarities between Brains and Machines Henrietta C. Leiner and Alan L. Leiner

286

CONTENTS OF RECENT VOLUMES

Cerebellar Timing Systems Richard Ivry

Volume 43

Attention Coordination and Anticipatory Control Natacha A. Akshoomoff, Eric Courchesne, and Jeanne Townsend

Early Development of the Drosophila Neuromuscular Junction: A Model for Studying Neuronal Networks in Development Akira Chiba

Context-Response Linkage W. Thomas Thach

Development of Larval Body Wall Muscles Michael Bate, Matthias Landgraf, and Mar Ruiz Gmez Bate

Duality of Cerebellar Motor and Cognitive Functions James R. Bloedel and Vlastislav Bracha Section VII: Future Directions Therapeutic and Research Implications Jeremy D. Schmahmann

Volume 42 Alzheimer Disease Mark A. Smith Neurobiology of Stroke W. Dalton Dietrich Free Radicals, Calcium, and the Synaptic Plasticity-Cell Death Continuum: Emerging Roles of the Trascription Factor NFB Mark P. Mattson AP-I Transcription Factors: Short- and LongTerm Modulators of Gene Expression in the Brain Keith Pennypacker

Development of Electrical Properties and Synaptic Transmission at the Embryonic Neuromuscular Junction Kendal S. Broadie Ultrastructural Correlates of Neuromuscular Junction Development Mary B. Rheuben, Motojiro Yoshihara, and Yoshiaki Kidokoro Assembly and Maturation of the Drosophila Larval Neuromuscular Junction L. Sian Gramates and Vivian Budnik Second Messenger Systems Underlying Plasticity at the Neuromuscular Junction Frances Hannan and Yi Zhong Mechanisms of Neurotransmitter Release J. Troy Littleton, Leo Pallanck, and Barry Ganetzky Vesicle Recycling at the Drosophila Neuromuscular Junction Daniel T. Stimson and Mani Ramaswami Ionic Currents in Larval Muscles of Drosophila Satpal Singh and Chun-Fang Wu

Ion Channels in Epilepsy Istvan Mody

Development of the Adult Neuromuscular System Joyce J. Fernandes and Haig Keshishian

Posttranslational Regulation of Ionotropic Glutamate Receptors and Synaptic Plasticity Xiaoning Bi, Steve Standley, and Michel Baudry

Controlling the Motor Neuron James R. Trimarchi, Ping Jin, and Rodney K. Murphey

Heritable Mutations in the Glycine, GABAA, and Nicotinic Acetylcholine Receptors Provide New Insights into the Ligand-Gated Ion Channel Receptor Superfamily Behnaz Vafa and Peter R. Schofield

Volume 44

index

Human Ego-Motion Perception A. V. van den Berg Optic Flow and Eye Movements M. Lappe and K.-P. Hoffman

CONTENTS OF RECENT VOLUMES

The Role of MST Neurons during Ocular Tracking in 3D Space K. Kawano, U. Inoue, A. Takemura, Y. Kodaka, and F. A. Miles Visual Navigation in Flying Insects M. V. Srinivasan and S.-W. Zhang Neuronal Matched Filters for Optic Flow Processing in Flying Insects H. G. Krapp A Common Frame of Reference for the Analysis of Optic Flow and Vestibular Information B. J. Frost and D. R. W. Wylie Optic Flow and the Visual Guidance of Locomotion in the Cat H. Sherk and G. A. Fowler Stages of Self-Motion Processing in Primate Posterior Parietal Cortex F. Bremmer, J.-R. Duhamel, S. B. Hamed, and W. Graf Optic Flow Perception C. J. Duffy

Analysis

for

Self-Movement

Neural Mechanisms for Self-Motion Perception in Area MST R. A. Andersen, K. V. Shenoy, J. A. Crowell, and D. C. Bradley Computational Mechanisms for Optic Flow Analysis in Primate Cortex M. Lappe Human Cortical Areas Underlying the Perception of Optic Flow: Brain Imaging Studies M. W. Greenlee

287

Brain Development and Generation of Brain Pathologies Gregory L. Holmes and Bridget McCabe Maturation of Channels and Receptors: Consequences for Excitability David F. Owens and Arnold R. Kriegstein Neuronal Activity and the Establishment of Normal and Epileptic Circuits during Brain Development John W. Swann, Karen L. Smith, and Chong L. Lee The Effects of Seizures of the Hippocampus of the Immature Brain Ellen F. Sperber and Solomon L. Moshe Abnormal Development and Catastrophic Epilepsies: The Clinical Picture and Relation to Neuroimaging Harry T. Chugani and Diane C. Chugani Cortical Reorganization and Seizure Generation in Dysplastic Cortex G. Avanzini, R. Preafico, S. Franceschetti, G. Sancini, G. Battaglia, and V. Scaioli Rasmussen’s Syndrome with Particular Reference to Cerebral Plasticity: A Tribute to Frank Morrell Fredrick Andermann and Yuonne Hart Structural Reorganization of Hippocampal Networks Caused by Seizure Activity Daniel H. Lowenstein Epilepsy-Associated Plasticity in gammaAmniobutyric Acid Receptor Expression, Function and Inhibitory Synaptic Properties Douglas A. Coulter

What Neurological Patients Tell Us about the Use of Optic Flow L. M. Vaina and S. K. Rushton

Synaptic Plasticity and Secondary Epileptogenesis Timothy J. Teyler, Steven L. Morgan, Rebecca N. Russell, and Brian L. Woodside

index

Synaptic Plasticity in Epileptogenesis: Cellular Mechanisms Underlying Long-Lasting Synaptic Modifications that Require New Gene Expression Oswald Steward, Christopher S. Wallace, and Paul F. Worley

Volume 45 Mechanisms of Brain Plasticity: From Normal Brain Function to Pathology Philip. A. Schwartzkroin

Cellular Correlates of Behavior Emma R. Wood, Paul A. Dudchenko, and Howard Eichenbaum

288

CONTENTS OF RECENT VOLUMES

Mechanisms of Neuronal Conditioning David A. T. King, David J. Krupa, Michael R. Foy, and Richard F. Thompson

Biosynthesis of Neurosteroids and Regulation of Their Synthesis Synthia H. Mellon and Hubert Vaudry

Plasticity in the Aging Central Nervous System C. A. Barnes

Neurosteroid 7-Hydroxylation Products in the Brain Robert Morfin and Luboslav Sta´rka

Secondary Epileptogenesis, Kindling, and Intractable Epilepsy: A Reappraisal from the Perspective of Neuronal Plasticity Thomas P. Sutula Kindling and the Mirror Focus Dan C. McIntyre and Michael O. Poulter Partial Kindling and Behavioral Pathologies Robert E. Adamec The Mirror Focus and Secondary Epileptogenesis B. J. Wilder Hippocampal Lesions in Epilepsy: A Historical Review Robert Naquet Clinical Evidence for Secondary Epileptogensis Hans O. Luders Epilepsy as a Progressive (or Nonprogressive ‘‘Benign’’) Disorder John A. Wada Pathophysiological Aspects of Landau-Kleffner Syndrome: From the Active Epileptic Phase to Recovery Marie-Noelle Metz-Lutz, Pierre Maquet, Annd De Saint Martin, Gabrielle Rudolf, Norma Wioland, Edouard Hirsch, and Chriatian Marescaux

Neurosteroid Analysis Ahmed A. Alomary, Robert L. Fitzgerald, and Robert H. Purdy Role of the Peripheral-Type Benzodiazepine Receptor in Adrenal and Brain Steroidogenesis Rachel C. Brown and Vassilios Papadopoulos Formation and Effects of Neuroactive Steroids in the Central and Peripheral Nervous System Roberto Cosimo Melcangi, Valerio Magnaghi, Mariarita Galbiati, and Luciano Martini Neurosteroid Modulation of Recombinant and Synaptic GABAA Receptors Jeremy J. Lambert, Sarah C. Harney, Delia Belelli, and John A. Peters GABAA-Receptor Plasticity during LongTerm Exposure to and Withdrawal from Progesterone Giovanni Biggio, Paolo Follesa, Enrico Sanna, Robert H. Purdy, and Alessandra Concas Stress and Neuroactive Steroids Maria Luisa Barbaccia, Mariangela Serra, Robert H. Purdy, and Giovanni Biggio

Local Pathways of Seizure Propagation in Neocortex Barry W. Connors, David J. Pinto, and Albert E. Telefeian

Neurosteroids in Learning and Processes Monique Valle´e, Willy Mayo, George F. Koob, and Michel Le Moal

Multiple Subpial Assessment C. E. Polkey

Neurosteroids and Behavior Sharon R. Engel and Kathleen A. Grant

Transection:

A

Clinical

The Legacy of Frank Morrell Jerome Engel, Jr. Volume 46 Neurosteroids: Beginning of the Story Etienne E. Baulieu, P. Robel, and M. Schumacher

Memory

Ethanol and Neurosteroid Interactions in the Brain A. Leslie Morrow, Margaret J. VanDoren, Rebekah Fleming, and Shannon Penland Preclinical Development of Neurosteroids as Neuroprotective Agents for the Treatment of Neurodegenerative Diseases Paul A. Lapchak and Dalia M. Araujo

CONTENTS OF RECENT VOLUMES

Clinical Implications of Circulating Neurosteroids Andrea R. Genazzani, Patrizia Monteleone, Massimo Stomati, Francesca Bernardi, Luigi Cobellis, Elena Casarosa, Michele Luisi, Stefano Luisi, and Felice Petraglia Neuroactive Steroids and Central Nervous System Disorders Mingde Wang, Torbjo¨rn Ba¨ckstro¨m, Inger Sundstro¨m, Go¨ran Wahlstro¨m, Tommy Olsson, Di Zhu, Inga-Maj Johansson, Inger Bjo¨rn, and Marie Bixo Neuroactive Steroids in Neuropsychopharmacology Rainer Rupprecht and Florian Holsboer Current Perspectives on the Role of Neurosteroids in PMS and Depression Lisa D. Griffin, Susan C. Conrad, and Synthia H. Mellon

289

Processing Human Brain Tissue for in Situ Hybridization with Radiolabelled Oligonucleotides Louise F. B. Nicholson In Situ Hybridization of Astrocytes and Neurons Cultured in Vitro L. A. Arizza-McNaughton, C. De Felipe, and S. P. Hunt In Situ Hybridization on Organotypic Slice Cultures A. Gerfin-Moser and H. Monyer Quantitative Analysis of in Situ Hybridization Histochemistry Andrew L. Gundlach and Ross D. O’Shea Part II: Nonradioactive in Situ hybridization Nonradioactive in Situ Hybridization Using Alkaline Phosphatase-Labelled Oligonucleotides S. J. Augood, E. M. McGowan, B. R. Finsen, B. Heppelmann, and P. C. Emson

index

Volume 47

Combining Nonradioactive in Situ Hybridization with Immunohistological and Anatomical Techniques Petra Wahle

Introduction: Studying Gene Expression in Neural Tissues by in Situ Hybridization W. Wisden and B. J. Morris

Nonradioactive in Situ Hybridization: Simplified Procedures for Use in Whole Mounts of Mouse and Chick Embryos Linda Ariza-McNaughton and Robb Krumlauf

Part I: In Situ Hybridization with Radiolabelled Oligonucleotides In Situ Hybridization with Oligonucleotide Probes Wl. Wisden and B. J. Morris

index

Cryostat Sectioning of Brains Victoria Revilla and Alison Jones

Volume 48

Processing Rodent Embryonic and Early Postnatal Tissue for in Situ Hybridization with Radiolabelled Oligonucleotides David J. Laurie, Petra C. U. Schrotz, Hannah Monyer, and Ulla Amtmann

Assembly and Intracellular GABAA Receptors Eugene Barnes

Trafficking

of

Processing of Retinal Tissue for in Situ Hybridization Frank Mu¨ller

Subcellular Localization and Regulation of GABAA Receptors and Associated Proteins Bernhard Lu¨scher and Jean-Marc Fritschy D1 Dopamine Receptors Richard Mailman

Processing the Spinal Cord for in Situ Hybridization with Radiolabelled Oligonucleotides A. Berthele and T. R. To¨lle

Molecular Modeling of Ligand-Gated Ion Channels: Progress and Challenges Ed Bertaccini and James R. Trudel

290

CONTENTS OF RECENT VOLUMES

Alzheimer’s Disease: Its Diagnosis and Pathogenesis Jillian J. Kril and Glenda M. Halliday DNA Arrays and Functional Genomics in Neurobiology Christelle Thibault, Long Wang, Li Zhang, and Michael F. Miles

The Treatment of Infantile Spasms: An Evidence-Based Approach Mark Mackay, Shelly Weiss, and O. Carter Snead III

index

ACTH Treatment of Infantile Spasms: Mechanisms of Its Effects in Modulation of Neuronal Excitability K. L. Brunson, S. Avishai-Eliner, and T. Z. Baram

Volume 49

Neurosteroids and Infantile Spasms: The Deoxycorticosterone Hypothesis Michael A. Rogawski and Doodipala S. Reddy

What Is West Syndrome? Olivier Dulac, Christine Soufflet, Catherine Chiron, and Anna Kaminski

Are there Specific Anatomical and/or Transmitter Systems (Cortical or Subcortical) That Should Be Targeted? Phillip C. Jobe

The Relationship between encephalopathy and Abnormal Neuronal Activity in the Developing Brain Frances E. Jensen

Medical versus Surgical Treatment: Which Treatment When W. Donald Shields

Hypotheses from Functional Neuroimaging Studies Csaba Juha´sz, Harry T. Chugani, Ouo Muzik, and Diane C. Chugani Infantile Spasms: Unique Sydrome or General Age-Dependent Manifestation of a Diffuse Encephalopathy? M. A. Koehn and M. Duchowny

Developmental Outcome with and without Successful Intervention Rochelle Caplan, Prabha Siddarth, Gary Mathern, Harry Vinters, Susan Curtiss, Jennifer Levitt, Robert Asarnow, and W. Donald Shields Infantile Spasms versus Myoclonus: Is There a Connection? Michael R. Pranzatelli

Histopathology of Brain Tissue from Patients with Infantile Spasms Harry V. Vinters

Tuberous Sclerosis as an Underlying Basis for Infantile Spasm Raymond S. Yeung

Generators of Ictal and Interictal Electroencephalograms Associated with Infantile Spasms: Intracellular Studies of Cortical and Thalamic Neurons M. Steriade and I. Timofeev

Brain Malformation, Epilepsy, and Infantile Spasms M. Elizabeth Ross

Cortical and Subcortical Generators of Normal and Abnormal Rhythmicity David A. McCormick Role of Subcortical Structures in the Pathogenesis of Infantile Spasms: What Are Possible Subcortical Mediators? F. A. Lado and S. L. Moshe´ What Must We Know to Develop Better Therapies? Jean Aicardi

Brain Maturational Aspects Relevant to Pathophysiology of Infantile Spasms G. Auanzini, F. Panzica, and S. Franceschetti Gene Expression Analysis as a Strategy to Understand the Molecular Pathogenesis of Infantile Spasms Peter B. Crino Infantile Spasms: Criteria for an Animal Model Carl E. Stafstrom and Gregory L. Holmes index

CONTENTS OF RECENT VOLUMES

Volume 50 Part I: Primary Mechanisms How Does Glucose Generate Oxidative Stress In Peripheral Nerve? Irina G. Obrosova Glycation in Diabetic Neuropathy: Characteristics, Consequences, Causes, and Therapeutic Options Paul J. Thornalley Part II: Secondary Changes

Nerve Growth Factor for the Treatment of Diabetic Neuropathy: What Went Wrong, What Went Right, and What Does the Future Hold? Stuart C. Apfel Angiotensin-Converting Enzyme Inhibitors: Are there Credible Mechanisms for Beneficial Effects in Diabetic Neuropathy? Rayaz A. Malik and David R. Tomlinson Clinical Trials for Drugs Against Diabetic Neuropathy: Can We Combine Scientific Needs With Clinical Practicalities? Dan Ziegler and Dieter Luft

Protein Kinase C Changes in Diabetes: Is the Concept Relevant to Neuropathy? Joseph Eichberg

index

Are Mitogen-Activated Protein Kinases Glucose Transducers for Diabetic Neuropathies? Tertia D. Purves and David R. Tomlinson

Volume 50

Neurofilaments in Diabetic Neuropathy Paul Fernyhough and Robert E. Schmidt Apoptosis in Diabetic Neuropathy Aviva Tolkovsky Nerve and Ganglion Blood Flow in Diabetes: An Appraisal Douglas W. Zochodne Part III: Manifestations Potential Mechanisms of Neuropathic Pain in Diabetes Nigel A. Calcutt Electrophysiologic Measures of Diabetic Neuropathy: Mechanism and Meaning Joseph C. Arezzo and Elena Zotova Neuropathology and Pathogenesis of Diabetic Autonomic Neuropathy Robert E. Schmidt Role of the Schwann Cell in Diabetic Neuropathy Luke Eckersley

291

Energy Metabolism in the Brain Leif Hertz and Gerald A. Dienel The Cerebral Glucose-Fatty Acid Cycle: Evolutionary Roots, Regulation, and (Patho) physiological Importance Kurt Heininger Expression, Regulation, and Functional Role of Glucose Transporters (GLUTs) in Brain Donard S. Dwyer, Susan J. Vannucci, and Ian A. Simpson Insulin-Like Growth Factor-1 Promotes Neuronal Glucose Utilization During Brain Development and Repair Processes Carolyn A. Bondy and Clara M. Cheng CNS Sensing and Regulation of Peripheral Glucose Levels Barry E. Levin, Ambrose A. Dunn-Meynell, and Vanessa H. Routh

Part IV: Potential Treatment

Glucose Transporter Protein Syndromes Darryl C. De Vivo, Dong Wang, Juan M. Pascual, and Yuan Yuan Ho

Polyol Pathway and Diabetic Peripheral Neuropathy Peter J. Oates

Glucose, Stress, and Hippocampal Neuronal Vulnerability Lawrence P. Reagan

292

CONTENTS OF RECENT VOLUMES

Glucose/Mitochondria in Neurological Conditions John P. Blass Energy Utilization in the Ischemic/Reperfused Brain John W. Phillis and Michael H. O’Regan

Stress and Secretory Immunity Jos A. Bosch, Christopher Ring, Eco J. C. de Geus, Enno C. I. Veerman, and Arie V. Nieuw Amerongen Cytokines and Depression Angela Clow

Diabetes Mellitus and the Central Nervous System Anthony L. McCall

Immunity and Schizophrenia: Autoimmunity, Cytokines, and Immune Responses Fiona Gaughran

Diabetes, the Brain, and Behavior: Is There a Biological Mechanism Underlying the Association between Diabetes and Depression? A. M. Jacobson, J. A. Samson, K. Weinger, and C. M. Ryan

Cerebral Lateralization and the Immune System Pierre J. Neveu

Schizophrenia and Diabetes David C. Henderson and Elissa R. Ettinger

Behavioral Conditioning of the Immune System Frank Hucklebridge Psychological and Neuroendocrine Correlates of Disease Progression Julie M. Turner-Cobb

Psychoactive Drugs Affect Glucose Transport and the Regulation of Glucose Metabolism Donard S. Dwyer, Timothy D. Ardizzone, and Ronald J. Bradley

The Role of Psychological Intervention in Modulating Aspects of Immune Function in Relation to Health and Well-Being J. H. Gruzelier

index

index

Volume 52 Volume 53 Neuroimmune Relationships in Perspective Frank Hucklebridge and Angela Clow Sympathetic Nervous System Interaction with the Immune System Virginia M. Sanders and Adam P. Kohm Mechanisms by Which Cytokines Signal the Brain Adrian J. Dunn Neuropeptides: Modulators of Responses in Health and Disease David S. Jessop

Immune

Brain–Immune Interactions in Sleep Lisa Marshall and Jan Born Neuroendocrinology of Autoimmunity Michael Harbuz Systemic Stress-Induced Th2 Shift and Its Clinical Implications Ibia J. Elenkov Neural Control of Salivary S-IgA Secretion Gordon B. Proctor and Guy H. Carpenter

Section I: Mitochondrial Structure and Function Mitochondrial DNA Structure and Function Carlos T. Moraes, Sarika Srivastava, Ilias Kirkinezos, Jose Oca-Cossio, Corina van Waveren, Markus Woischnick, and Francisca Diaz Oxidative Phosphorylation: Structure, Function, and Intermediary Metabolism Simon J. R. Heales, Matthew E. Gegg, and John B. Clark Import of Mitochondrial Proteins Matthias F. Bauer, Sabine Hofmann, and Walter Neupert Section II: Primary Respiratory Chain Disorders Mitochondrial Disorders of the Nervous System: Clinical, Biochemical, and Molecular Genetic Features Dominic Thyagarajan and Edward Byrne

CONTENTS OF RECENT VOLUMES

Section III: Secondary Respiratory Chain Disorders Friedreich’s Ataxia J. M. Cooper and J. L. Bradley Wilson Disease C. A. Davie and A. H. V. Schapira

293

The Mitochondrial Theory of Aging: Involvement of Mitochondrial DNA Damage and Repair Nadja C. de Souza-Pinto and Vilhelm A. Bohr index

Hereditary Spastic Paraplegia Christopher J. McDermott and Pamela J. Shaw Cytochrome c Oxidase Deficiency Giacomo P. Comi, Sandra Strazzer, Sara Galbiati, and Nereo Bresolin Section IV: Toxin Induced Mitochondrial Dysfunction Toxin-Induced Mitochondrial Dysfunction Susan E. Browne and M. Flint Beal Section V: Neurodegenerative Disorders Parkinson’s Disease L. V. P. Korlipara and A. H. V. Schapira Huntington’s Disease: The Mystery Unfolds? A˚sa Peterse´n and Patrik Brundin Mitochondria in Alzheimer’s Disease Russell H. Swerdlow and Stephen J. Kish Contributions of Mitochondrial Alterations, Resulting from Bad Genes and a Hostile Environment, to the Pathogenesis of Alzheimer’s Disease Mark P. Mattson Mitochondria and Amyotrophic Lateral Sclerosis Richard W. Orrell and Anthony H. V. Schapira

Volume 54 Unique General Anesthetic Binding Sites Within Distinct Conformational States of the Nicotinic Acetylcholine Receptor Hugo R. Ariaas, William, R. Kem, James R. Truddell, and Michael P. Blanton Signaling Molecules and Receptor Transduction Cascades That Regulate NMDA ReceptorMediated Synaptic Transmission Suhas. A. Kotecha and John F. MacDonald Behavioral Measures of Alcohol Self-Administration and Intake Control: Rodent Models Herman H. Samson and Cristine L. Czachowski Dopaminergic Mouse Mutants: Investigating the Roles of the Different Dopamine Receptor Subtypes and the Dopamine Transporter Shirlee Tan, Bettina Hermann, and Emiliana Borrelli Drosophila melanogaster, A Genetic Model System for Alcohol Research Douglas J. Guarnieri and Ulrike Heberlein index

Section VI: Models of Mitochondrial Disease Models of Mitochondrial Disease Danae Liolitsa and Michael G. Hanna

Volume 55

Section VII: Defects of Oxidation Including Carnitine Deficiency

Section I: Virsu Vectors For Use in the Nervous System

Defects of Oxidation Including Carnitine Deficiency K. Bartlett and M. Pourfarzam

Non-Neurotropic Adenovirus: a Vector for Gene Transfer to the Brain and Gene Therapy of Neurological Disorders P. R. Lowenstein, D. Suwelack, J. Hu, X. Yuan, M. Jimenez-Dalmaroni, S. Goverdhama, and M.G. Castro

Section VIII: Mitochondrial Involvement in Aging

294

CONTENTS OF RECENT VOLUMES

Adeno-Associated Virus Vectors E. Lehtonen and L. Tenenbaum Problems in the Use of Herpes Simplex Virus as a Vector L. T. Feldman Lentiviral Vectors J. Jakobsson, C. Ericson, N. Rosenquist, and C. Lundberg Retroviral Vectors for Gene Delivery to Neural Precursor Cells K. Kageyama, H. Hirata, and J. Hatakeyama

Processing and Representation of SpeciesSpecific Communication Calls in the Auditory System of Bats George D. Pollak, Achim Klug, and Eric E. Bauer Central Nervous System Control of Micturition Gert Holstege and Leonora J. Mouton The Structure and Physiology of the Rat Auditory System: An Overview Manuel Malmierca Neurobiology of Cat and Human Sexual Behavior Gert Holstege and J. R. Georgiadis

Section II: Gene Therapy with Virus Vectors for Specific Disease of the Nervous System

index

The Principles of Molecular Therapies for Glioblastoma G. Karpati and J. Nalbatonglu

Volume 57

Oncolytic Herpes Simplex Virus J. C. C. Hu and R. S. Coffin

Cumulative Subject Index of Volumes 1–25

Recombinant Retrovirus Vectors for Treatment of Brain Tumors N. G. Rainov and C. M. Kramm

Volume 58

Adeno-Associated Viral Vectors for Parkinson’s Disease I. Muramatsu, L. Wang, K. Ikeguchi, K-i Fujimoto, T. Okada, H. Mizukami, Y. Hanazono, A. Kume, I. Nakano, and K. Ozawa HSV Vectors for Parkinson’s Disease D. S. Latchman Gene Therapy for Stroke K. Abe and W. R. Zhang Gene Therapy for Mucopolysaccharidosis A. Bosch and J. M. Heard index

Volume 56 Behavioral Mechanisms and the Neurobiology of Conditioned Sexual Responding Mark Krause NMDA Receptors in Alcoholism Paula L. Hoffman

Cumulative Subject Index of Volumes 26–50

Volume 59 Loss of Spines and Neuropil Liesl B. Jones Schizophrenia as a Disorder of Neuroplasticity Robert E. McCullumsmith, Sarah M. Clinton, and James H. Meador-Woodruff The Synaptic Pathology of Schizophrenia: Is Aberrant Neurodevelopment and Plasticity to Blame? Sharon L. Eastwood Neurochemical Basis for an Epigenetic Vision of Synaptic Organization E. Costa, D. R. Grayson, M. Veldic, and A. Guidotti Muscarinic Receptors in Schizophrenia: Is There a Role for Synaptic Plasticity? Thomas J. Raedler

CONTENTS OF RECENT VOLUMES

295

Serotonin and Brain Development Monsheel S. K. Sodhi and Elaine Sanders-Bush

Volume 60

Presynaptic Proteins and Schizophrenia William G. Honer and Clint E. Young

Microarray Platforms: Introduction and Application to Neurobiology Stanislav L. Karsten, Lili C. Kudo, and Daniel H. Geschwind

Mitogen-Activated Protein Kinase Signaling Svetlana V. Kyosseva Postsynaptic Density Scaffolding Proteins at Excitatory Synapse and Disorders of Synaptic Plasticity: Implications for Human Behavior Pathologies Andrea de Bartolomeis and Germano Fiore Prostaglandin-Mediated Signaling in Schizophrenia S. Smesny Mitochondria, Synaptic Plasticity, and Schizophrenia Dorit Ben-Shachar and Daphna Laifenfeld Membrane Phospholipids and Cytokine Interaction in Schizophrenia Jeffrey K. Yao and Daniel P. van Kammen Neurotensin, Schizophrenia, and Antipsychotic Drug Action Becky Kinkead and Charles B. Nemeroff Schizophrenia, Vitamin D, and Brain Development Alan Mackay-Sim, Franc¸ois Fe´ron, Darryl Eyles, Thomas Burne, and John McGrath Possible Contributions of Myelin and Oligodendrocyte Dysfunction to Schizophrenia Daniel G. Stewart and Kenneth L. Davis Brain-Derived Neurotrophic Factor and the Plasticity of the Mesolimbic Dopamine Pathway Oliver Guillin, Nathalie Griffon, Jorge Diaz, Bernard Le Foll, Erwan Bezard, Christian Gross, Chris Lammers, Holger Stark, Patrick Carroll, Jean-Charles Schwartz, and Pierre Sokoloff S100B in Schizophrenic Psychosis Matthias Rothermundt, Gerald Ponath, and Volker Arolt Oct-6 Transcription Factor Maria Ilia NMDA Receptor Function, Neuroplasticity, and the Pathophysiology of Schizophrenia Joseph T. Coyle and Guochuan Tsai index

Experimental Design and Low-Level Analysis of Microarray Data B. M. Bolstad, F. Collin, K. M. Simpson, R. A. Irizarry, and T. P. Speed Brain Gene Expression: Genomics and Genetics Elissa J. Chesler and Robert W. Williams DNA Microarrays and Animal Models of Learning and Memory Sebastiano Cavallaro Microarray Analysis of Human Nervous System Gene Expression in Neurological Disease Steven A. Greenberg DNA Microarray Analysis of Postmortem Brain Tissue Ka´roly Mirnics, Pat Levitt, and David A. Lewis index

Volume 61 Section I: High-Throughput Technologies Biomarker Discovery Using Molecular Profiling Approaches Stephen J. Walker and Arron Xu Proteomic Analysis of Mitochondrial Proteins Mary F. Lopez, Simon Melov, Felicity Johnson, Nicole Nagulko, Eva Golenko, Scott Kuzdzal, Suzanne Ackloo, and Alvydas Mikulskis Section II: Proteomic Applications NMDA Receptors, Neural Pathways, and Protein Interaction Databases Holger Husi Dopamine Transporter Network and Pathways Rajani Maiya and R. Dayne Mayfield Proteomic Approaches in Drug Discovery and Development Holly D. Soares, Stephen A. Williams,

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CONTENTS OF RECENT VOLUMES

Peter J. Snyder, Feng Gao, Tom Stiger, Christian Rohlff, Athula Herath, Trey Sunderland, Karen Putnam, and W. Frost White Section III: Informatics Proteomic Informatics Steven Russell, William Old, Katheryn Resing, and Lawrence Hunter Section IV: Changes in the Proteome by Disease Proteomics Analysis in Alzheimer’s Disease: New Insights into Mechanisms of Neurodegeneration D. Allan Butterfield and Debra Boyd-Kimball

Rene L. Olvera, David C. Glahn, Sheila C. Caetano, Steven R. Pliszka, and Jair C. Soares Chemosensory G-Protein-Coupled Receptor Signaling in the Brain Geoffrey E. Woodard Disturbances of Emotion Regulation after Focal Brain Lesions Antoine Bechara The Use of Caenorhabditis elegans in Molecular Neuropharmacology Jill C. Bettinger, Lucinda Carnell, Andrew G. Davies, and Steven L. McIntire index

Proteomics and Alcoholism Frank A. Witzmann and Wendy N. Strother Proteomics Studies of Traumatic Brain Injury Kevin K. W. Wang, Andrew Ottens, William Haskins, Ming Cheng Liu, Firas Kobeissy, Nancy Denslow, SuShing Chen, and Ronald L. Hayes Influence of Huntington’s Disease on the Human and Mouse Proteome Claus Zabel and Joachim Klose Section V: Overview of the Neuroproteome Proteomics—Application to the Brain Katrin Marcus, Oliver Schmidt, Heike Schaefer, Michael Hamacher, AndrA˚ van Hall, and Helmut E. Meyer index

Volume 62 GABAA Receptor Structure–Function Studies: A Reexamination in Light of New Acetylcholine Receptor Structures Myles H. Akabas Dopamine Mechanisms and Cocaine Reward Aiko Ikegami and Christine L. Duvauchelle Proteolytic Dysfunction in Neurodegenerative Disorders Kevin St. P. McNaught Neuroimaging Studies in Bipolar Children and Adolescents

Volume 63 Mapping Neuroreceptors at work: On the Definition and Interpretation of Binding Potentials after 20 years of Progress Albert Gjedde, Dean F. Wong, Pedro Rosa-Neto, and Paul Cumming Mitochondrial Dysfunction in Bipolar Disorder: From 31P-Magnetic Resonance Spectroscopic Findings to Their Molecular Mechanisms Tadafumi Kato Large-Scale Microarray Studies of Gene Expression in Multiple Regions of the Brain in Schizophrenia and Alzeimer’s Disease Pavel L. Katsel, Kenneth L. Davis, and Vahram Haroutunian Regulation of Serotonin 2C Receptor PREmRNA Editing By Serotonin Claudia Schmauss The Dopamine Hypothesis of Drug Addiction: Hypodopaminergic State Miriam Melis, Saturnino Spiga, and Marco Diana Human and Animal Spongiform Encephalopathies are Autoimmune Diseases: A Novel Theory and Its supporting Evidence Bao Ting Zhu Adenosine and Brain Function Bertil B. Fredholm, Jiang-Fan Chen, Rodrigo A. Cunha, Per Svenningsson, and Jean-Marie Vaugeois index

CONTENTS OF RECENT VOLUMES

297

Volume 64

G-Protein–Coupled Receptor Deorphanizations Yumiko Saito and Olivier Civelli

Section I. The Cholinergic System John Smythies

Mechanistic Connections Between Glucose/ Lipid Disturbances and Weight Gain Induced by Antipsychotic Drugs Donard S. Dwyer, Dallas Donohoe, Xiao-Hong Lu, and Eric J. Aamodt

Section II. The Dopamine System John Symythies Section III. The Norepinephrine System John Smythies Section IV. The Adrenaline System John Smythies

Serotonin Firing Activity as a Marker for Mood Disorders: Lessons from Knockout Mice Gabriella Gobbi

Section V. Serotonin System John Smythies

index

index

Volume 66

Volume 65

Brain Atlases of Normal and Diseased Populations Arthur W. Toga and Paul M. Thompson

Insulin Resistance: Causes and Consequences Zachary T. Bloomgarden

Neuroimaging Databases as a Resource for Scientific Discovery John Darrell Van Horn, John Wolfe, Autumn Agnoli, Jeffrey Woodward, Michael Schmitt, James Dobson, Sarene Schumacher, and Bennet Vance

Antidepressant-Induced Manic Conversion: A Developmentally Informed Synthesis of the Literature Christine J. Lim, James F. Leckman, Christopher Young, and Andre´s Martin Sites of Alcohol and Volatile Anesthetic Action on Glycine Receptors Ingrid A. Lobo and R. Adron Harris Role of the Orbitofrontal Cortex in Reinforcement Processing and Inhibitory Control: Evidence from Functional Magnetic Resonance Imaging Studies in Healthy Human Subjects Rebecca Elliott and Bill Deakin

Modeling Brain Responses Karl J. Friston, William Penny, and Olivier David Voxel-Based Morphometric Analysis Using Shape Transformations Christos Davatzikos The Cutting Edge of f MRI and High-Field f MRI Dae-Shik Kim Quantification of White Matter Using DiffusionTensor Imaging Hae-Jeong Park

Common Substrates of Dysphoria in Stimulant Drug Abuse and Primary Depression: Therapeutic Targets Kate Baicy, Carrie E. Bearden, John Monterosso, Arthur L. Brody, Andrew J. Isaacson, and Edythe D. London

Perfusion f MRI for Functional Neuroimaging Geoffrey K. Aguirre, John A. Detre, and Jiongjiong Wang

The Role of cAMP Response Element–Binding Proteins in Mediating Stress-Induced Vulnerability to Drug Abuse Arati Sadalge Kreibich and Julie A. Blendy

Neural Modeling and Functional Brain Imaging: The Interplay Between the Data-Fitting and Simulation Approaches Barry Horwitz and Michael F. Glabus

Functional Near-Infrared Spectroscopy: Potential and Limitations in Neuroimaging Studies Yoko Hoshi

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CONTENTS OF RECENT VOLUMES

Combined EEG and fMRI Studies of Human Brain Function V. Menon and S. Crottaz-Herbette

W. Gordon Frankle, Mark Slifstein, Peter S. Talbot, and Marc Laruelle index

index

Volume 68 Volume 67 Distinguishing Neural Substrates of Heterogeneity Among Anxiety Disorders Jack B. Nitschke and Wendy Heller Neuroimaging in Dementia K. P. Ebmeier, C. Donaghey, and N. J. Dougall Prefrontal and Anterior Cingulate Contributions to Volition in Depression Jack B. Nitschke and Kristen L. Mackiewicz Functional Imaging Research in Schizophrenia H. Tost, G. Ende, M. Ruf, F. A. Henn, and A. Meyer-Lindenberg Neuroimaging in Functional Somatic Syndromes Patrick B. Wood Neuroimaging in Multiple Sclerosis Alireza Minagar, Eduardo Gonzalez-Toledo, James Pinkston, and Stephen L. Jaffe Stroke Roger E. Kelley and Eduardo Gonzalez-Toledo Functional MRI in Pediatric Neurobehavioral Disorders Michael Seyffert and F. Xavier Castellanos Structural MRI and Brain Development Paul M. Thompson, Elizabeth R. Sowell, Nitin Gogtay, Jay N. Giedd, Christine N. Vidal, Kiralee M. Hayashi, Alex Leow, Rob Nicolson, Judith L. Rapoport, and Arthur W. Toga Neuroimaging and Human Genetics Georg Winterer, Ahmad R. Hariri, David Goldman, and Daniel R. Weinberger Neuroreceptor Imaging in Psychiatry: Theory and Applications

Fetal Magnetoencephalography: Viewing the Developing Brain In Utero Hubert Preissl, Curtis L. Lowery, and Hari Eswaran Magnetoencephalography in Studies of Infants and Children Minna Huotilainen Let’s Talk Together: Memory Traces Revealed by Cooperative Activation in the Cerebral Cortex Jochen Kaiser, Susanne Leiberg, and Werner Lutzenberger Human Communication Investigated With Magnetoencephalography: Speech, Music, and Gestures Thomas R. Kno¨sche, Burkhard Maess, Akinori Nakamura, and Angela D. Friederici Combining Magnetoencephalography and Functional Magnetic Resonance Imaging Klaus Mathiak and Andreas J. Fallgatter Beamformer Analysis of MEG Data Arjan Hillebrand and Gareth R. Barnes Functional Connectivity Analysis in Magnetoencephalography Alfons Schnitzler and Joachim Gross Human Visual Processing as Revealed by Magnetoencephalographys Yoshiki Kaneoke, Shoko Watanabe, and Ryusuke Kakigi A Review of Clinical Applications of Magnetoencephalography Andrew C. Papanicolaou, Eduardo M. Castillo, Rebecca Billingsley-Marshall, Ekaterina Pataraia, and Panagiotis G. Simos index

CONTENTS OF RECENT VOLUMES

299

Volume 69

Spectral Processing in the Auditory Cortex Mitchell L. Sutter

Nematode Neurons: Anatomy and Anatomical Methods in Caenorhabditis elegans David H. Hall, Robyn Lints, and Zeynep Altun

Processing of Dynamic Spectral Properties of Sounds Adrian Rees and Manuel S. Malmierca

Investigations of Learning and Memory in Caenorhabditis elegans Andrew C. Giles, Jacqueline K. Rose, and Catharine H. Rankin

Representations of Spectral Coding in the Human Brain Deborah A. Hall, PhD

Neural Specification and Differentiation Eric Aamodt and Stephanie Aamodt Sexual Behavior of the Caenorhabditis elegans Male Scott W. Emmons The Motor Circuit Stephen E. Von Stetina, Millet Treinin, and David M. Miller III Mechanosensation in Caenorhabditis elegans Robert O’Hagan and Martin Chalfie

Volume 70 Spectral Processing by the Peripheral Auditory System Facts and Models Enrique A. Lopez-Poveda Basic Psychophysics of Human Spectral Processing Brian C. J. Moore Across-Channel Spectral Processing John H. Grose, Joseph W. Hall III, and Emily Buss Speech and Music Have Different Requirements for Spectral Resolution Robert V. Shannon Non-Linearities and the Representation of Auditory Spectra Eric D. Young, Jane J. Yu, and Lina A. J. Reiss Spectral Processing in the Inferior Colliculus Kevin A. Davis Neural Mechanisms for Spectral Analysis in the Auditory Midbrain, Thalamus, and Cortex Monty A. Escab and Heather L. Read

Spectral Processing and Sound Source Determination Donal G. Sinex Spectral Information in Sound Localization Simon Carlile, Russell Martin, and Ken McAnally Plasticity of Spectral Processing Dexter R. F. Irvine and Beverly A. Wright Spectral Processing In Cochlear Implants Colette M. McKay index

Volume 71 Autism: Neuropathology, Alterations of the GABAergic System, and Animal Models Christoph Schmitz, Imke A. J. van Kooten, Patrick R. Hof, Herman van Engeland, Paul H. Patterson, and Harry W. M. Steinbusch The Role of GABA in the Early Neuronal Development Marta Jelitai and Emı´lia Madarasz GABAergic Signaling in the Developing Cerebellum Chitoshi Takayama Insights into GABA Functions in the Developing Cerebellum Mo´nica L. Fiszman Role of GABA in the Mechanism of the Onset of Puberty in Non-Human Primates Ei Terasawa Rett Syndrome: A Rosetta Stone for Understanding the Molecular Pathogenesis of Autism Janine M. LaSalle, Amber Hogart, and Karen N. Thatcher

300

CONTENTS OF RECENT VOLUMES

GABAergic Cerebellar System in Autism: A Neuropathological and Developmental Perspective Gene J. Blatt

A Systematic Examination of Catatonia-Like Clinical Pictures in Autism Spectrum Disorders Lorna Wing and Amitta Shah

Reelin Glycoprotein in Autism and Schizophrenia S. Hossein Fatemi

Catatonia in Individuals with Autism Spectrum Disorders in Adolescence and Early Adulthood: A Long-Term Prospective Study Masataka Ohta, Yukiko Kano, and Yoko Nagai

Is There A Connection Between Autism, Prader-Willi Syndrome, Catatonia, and GABA? Dirk M. Dhossche, Yaru Song, and Yiming Liu Alcohol, GABA Receptors, and Neurodevelopmental Disorders Ujjwal K. Rout Effects of Secretin on Extracellular GABA and Other Amino Acid Concentrations in the Rat Hippocampus Hans-Willi Clement, Alexander Pschibul, and Eberhard Schulz Predicted Role of Secretin and Oxytocin in the Treatment of Behavioral and Developmental Disorders: Implications for Autism Martha G. Welch and David A. Ruggiero Immunological Findings in Autism Hari Har Parshad Cohly and Asit Panja Correlates of Psychomotor Symptoms in Autism Laura Stoppelbein, Sara Sytsma-Jordan, and Leilani Greening GABRB3 Gene Deficient Mice: A Potential Model of Autism Spectrum Disorder Timothy M. DeLorey The Reeler Mouse: Anatomy of a Mutant Gabriella D’Arcangelo Shared Chromosomal Susceptibility Regions Between Autism and Other Mental Disorders Yvon C. Chagnon index

Are Autistic and Catatonic Regression Related? A Few Working Hypotheses Involving GABA, Purkinje Cell Survival, Neurogenesis, and ECT Dirk Marcel Dhossche and Ujjwal Rout Psychomotor Development and Psychopathology in Childhood Dirk M. J. De Raeymaecker The Importance of Catatonia and Stereotypies in Autistic Spectrum Disorders Laura Stoppelbein, Leilani Greening, and Angelina Kakooza Prader–Willi Syndrome: Atypical Psychoses and Motor Dysfunctions Willem M. A. Verhoeven and Siegfried Tuinier Towards a Valid Nosography and Psychopathology of Catatonia in Children and Adolescents David Cohen Is There a Common Neuronal Basis for Autism and Catatonia? Dirk Marcel Dhossche, Brendan T. Carroll, and Tressa D. Carroll Shared Susceptibility Region on Chromosome 15 Between Autism and Catatonia Yvon C. Chagnon Current Trends in Behavioral Interventions for Children with Autism Dorothy Scattone and Kimberly R. Knight

index

Case Reports with a Child Psychiatric Exploration of Catatonia, Autism, and Delirium Jan N. M. Schieveld

Volume 72

ECT and the Youth: Catatonia in Context Frank K. M. Zaw

Classification Matters for Catatonia and Autism in Children Klaus-Ju¨rgen Neuma¨rker

Catatonia in Autistic Spectrum Disorders: A Medical Treatment Algorithm Max Fink, Michael A. Taylor, and Neera Ghaziuddin

CONTENTS OF RECENT VOLUMES

Psychological Approaches to Chronic CatatoniaLike Deterioration in Autism Spectrum Disorders Amitta Shah and Lorna Wing

301

Blueprints for the Assessment, Treatment, and Future Study of Catatonia in Autism Spectrum Disorders Dirk Marcel Dhossche, Amitta Shah, and Lorna Wing