Emerging and Endemic Pathogens: Advances in Surveillance, Detection and Identification (NATO Science for Peace and Security Series A: Chemistry and Biology)

Emerging and Endemic Pathogens

NATO Science for Peace and Security Series This Series presents the results of scientific meetings supported under the NATO Programme: Science for Peace and Security (SPS). The NATO SPS Programme supports meetings in the following Key Priority areas: (1) Defence Against Terrorism; (2) Countering other Threats to Security and (3) NATO, Partner and Mediterranean Dialogue Country Priorities. The types of meeting supported are generally "Advanced Study Institutes" and "Advanced Research Workshops". The NATO SPS Series collects together the results of these meetings. The meetings are coorganized by scientists from NATO countries and scientists from NATO's "Partner" or "Mediterranean Dialogue" countries. The observations and recommendations made at the meetings, as well as the contents of the volumes in the Series, reflect those of participants and contributors only; they should not necessarily be regarded as reflecting NATO views or policy. Advanced Study Institutes (ASI) are high-level tutorial courses intended to convey the latest developments in a subject to an advanced-level audience Advanced Research Workshops (ARW) are expert meetings where an intense but informal exchange of views at the frontiers of a subject aims at identifying directions for future action Following a transformation of the programme in 2006 the Series has been re-named and re-organised. Recent volumes on topics not related to security, which result from meetings supported under the programme earlier, may be found in the NATO Science Series. The Series is published by IOS Press, Amsterdam, and Springer, Dordrecht, in conjunction with the NATO Public Diplomacy Division. Sub-Series A. B. C. D. E.

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Springer Springer Springer IOS Press IOS Press

Emerging and Endemic Pathogens Advances in Surveillance, Detection and Identification edited by

Kevin P. O’Connell U.S. Army Edgewood Chemical Biological Center Aberdeen Proving Ground, MD, USA

Evan W. Skowronski U.S. Army Edgewood Chemical Biological Center Aberdeen Proving Ground, MD, USA

Alexander Sulakvelidze University of Florida Gainesville, FL, USA and

Lela Bakanidze National Center for Disease Control Tbilisi, Republic of Georgia

Published in cooperation with NATO Public Diplomacy Division

Proceedings of the NATO Advanced Research Workshop on Advances in Surveillance, Detection and Identification of Emerging and Endemic Pathogens Tbilisi, Georgia 24–26 June 2008

Library of Congress Control Number: 2010934368

ISBN 978-90-481-9639-5 (PB) ISBN 978-90-481-9636-4 (HB) ISBN 978-90-481-9637-1 (e-book)

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Preface It is a truism among biologists that an organism’s phenotype is the product of both its genotype and its environment. An organism’s genotype contains the total informational potential of the individual, while its environment shapes the expression of the genotype, influences the rate of mutation and occurrence of modifications, and ultimately determines the likelihood that the genotype (or fractions thereof) will survive into the next generation. In the relationship between host and pathogen, therefore, each forms a part of the environment of the other, mutually influencing the biology of both partners on scales ranging from the life history of individuals to the fate of populations or entire species. Molecular biologists working on problems in pathogenesis generally think of the host organism as the pathogen’s environment and perhaps occasionally consider the pathogen as part of the host’s environment. However, because “environment” can be defined at many scales, so, too, can phenotypes: if a pathogen, as a species, is considered to exist in a host, as a species, then among its phenotypes is the nature of the pandemic disease it can cause within the host community. The contributors to the proceedings of this NATO Advanced Research Workshop have treated the interplay of environment and genotype in the host–pathogen relationship and its relationship to the problem of emerging infectious disease at both the macroscopic and microscopic/ molecular levels along this continuum of scale (with some human history thrown in at times for good measure). Keynote Chapter The contribution from the meeting’s keynote speaker highlights the importance of understanding the underpinnings of pathogen phenotypes at both scales. The example of Vibrio cholerae is considered macroscopically and genetically in an examination of the factors influencing the emergence and spread of new strains of human bacterial pathogens. Citrus greening, caused by the bacterium Liberibacter asiaticus and vectored by the Asian citrus psyllid Diaphorina citri, is discussed to illustrate the effect of a vector species’ biology on disease emergence and spread. An unfortunate lesson from these examples is that diseases that have already emerged and have spread rapidly may be difficult to control; however, any hope of disease control will be founded on an understanding of the genetic and molecular basis for pathogenesis and the environmental factors (including vectors) that contribute to the transmission of the microorganism. Section I: Surveillance The next four chapters treat country-specific approaches, and their results, in one of the most fundamental tasks in combating emerging infectious disease: detecting and v

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describing the incidence of disease in a geographic region. By its very nature, this effort is labor-intensive in terms of fieldwork (both human and environmental) and in the subsequent laboratory analysis of samples. In the Balkans, the Caucasus, and the Central Asian republics, like elsewhere in the developed and developing worlds, surveillance work ranges from the basic (trapping and culturing from members of a reservoir species) to the complex (use of sensitive laboratory molecular methods, such as PCR) and the application of the resulting data to forestalling and controlling the outbreak of endemic diseases. Akimbayev et al., from Kazakhstan, and Gurbanov and Akhmedova, from Azerbaijan, provide a description of surveillance efforts in recent years that highlight the human and economic factors that influence disease transmission. From the Republic of Georgia, Bakanidze et al. provide a historical perspective that demonstrates the role that militaries have played in the development of public health methods and practices, born of necessity: throughout history, armies over time have lost more soldiers to disease than to violence. Complementing the paper by Bakanidze and colleagues, the chapter by Zhgenti et al. reports on the use of modern molecular biological techniques to differentiate closely related strains of pathogenic bacteria isolated from both environmental and clinical samples in Georgia and throughout the Caucasus. Stikova describes a syndrome-based, nationwide effort deployed in the Republic of Macedonia to report priority communicable diseases that is complementary to the routine surveillance system that reports individual confirmed disease cases. This system, called ALERT, aided in forecasting and detecting the start of the influenza season. The goal of surveillance always has been actionable information that would allow public health workers to forestall the spread of disease. “Classical” surveillance and epidemiologic reporting as described in these first four chapters, however, now also provides data that are being analyzed by advanced computational and geographic methods known collectively as Geographical Information Systems. Blackburn rounds out Section I by describing new tools that enable the fusion of climatologic, geographic, and epidemiologic data with concepts in ecological niche theory to construct models that may predict the future incidence, prevalence, and transmission of Bacillus anthracis, but the methods are generalizable to other diseases. Section II: Molecular Analysis and Tools At the scale of the bacterium and bacterial genome, the contributors to this section each provide an example of how cutting-edge molecular biological methods are being applied to answer key questions in the study of emerging infectious disease. How did the pathogens we observe in the world come to their present state? Technical challenges abound in the analysis of biological specimens for evidence of ancient infections. Aboudharam et al. describe the development of dental pulp as a target material for isolation of bacterial DNA and the diagnosis of ancient bacteremias, including Yersinia pestis infections. Key to their methodology is the development of single-use primer pairs for the detection and amplification of ancient target sequences in a method they term “suicide PCR.” What determines the severity of disease a pathogen may cause? Perry et al. demonstrate the utility of comparative genomics in identifying a putative hemagglutinin gene (“Region E”) that is present in Brucella melitensis 16M and absent in Brucella

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abortus. The data suggest that “Region E” has a host-specific influence on virulence, and the authors speculate that expressing the hemagglutinin in certain Brucella strains may improve their performance as vaccines. What genome-wide adaptations predispose a pathogen to cause severe disease? Rakin examines the contributions of both gain-of-function genetic changes (via lateral gene transfer) and negative selection (favoring what is termed pathoadaptive mutations) in the evolution of pathogenic bacteria. His analysis points out the importance of single-nucleotide polymorphisms that, besides being markers for strain identification, can have significant effects on the functions of virulence and pathogenicity genes. The implication of these results is apparent: in a selective environment or host, mutations can occur that lead to a sudden emergence of a virulent bacterial strain. What tools are available for practical studies when containment is not available or practical, but safety must be maintained? Researchers have long used non-pathogenic surrogates, or “simulants” in place of pathogens and protein toxins for reasons of convenience, safety, reduction of expense, and speed of work. Such simulants have included benign enteric bacterial species, bacteriophage (especially MS2), and proteins such as ovalbumin. Ouellette et al. review here information that suggest that baculoviruses, long used in organic agriculture and widely regarded as having no ill effects on humans, animals or plants, may serve as a new class of simulants for some viral pathogens. How do recent advances in sequencing affect the genetic analysis of pathogens? Molecular biologists are relying on the rapidly decreasing cost per base of DNA sequencing to support the continuing effort to detect and identify the genes (as is discussed by Perry et al.) or gene variants (as in Rakin) that influence bacterial pathogenicity and virulence. Khan briefly reviews the procession from Sanger dideoxy sequencing (and the dye-coupled PCR-driven variant) to so-called next-generation sequencing (NGS) methods. NGS methods have a much higher throughput than the Sanger methods but with generally smaller average read lengths. Concurrent increases in computational power allow the rapid querying of databases for bacterial identification. However, although faster computation also speeds contig formation from unique sequences, short read lengths can result in more contigs that require more effort to assemble into finished whole bacterial genomes. Fortunately, complementary technologies such as whole genome optical restriction mapping are emerging that very rapidly provide the scaffolding data needed to match the increased rate at which NGS produces contigs. Bacterial genome sequencing that 15–20 years ago required years of effort now takes weeks. The rate at which sequencing technology is accelerating has been compared with Moore’s Law in computing power, except that the rate of improvement for sequencing has proven to be steeper than the drop in the cost of memory and clock cycles over time. The “next” in NGS likely is ready to become dated in use as single molecule sequencing methods are being commercialized by at least two companies and faster methods still are certain to follow. No sequencing technology currently is employed widely outside of laboratories or core facilities. However, entrepreneurs are fervently seeking the right combinations of technology and business models that will put NGS (and beyond) into the hands of nonlaboratory end users (clinicians, epidemiologists, law enforcement, and first responders). The eventual goal is to provide a user with an encyclopedic understanding

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of the DNA sequences present in a sample, breaking the barrier that currently separates sensitivity plus specificity from speed of analysis. The possibility of a technology that will permit fast, accurate, complete data from genuinely unknown samples (unlike PCR) may at last be on the horizon.

Acknowledgements We gratefully acknowledge the assistance of the staff members of the Edgewood Chemical Biological Center, the Georgian National Center for Disease Control, and the University of Florida for their contributions to the success of this Advanced Research Workshop, whose speakers contributed to this volume. We also thank the members of the staff of ARW Secretariate for their tireless help with the logistical details of the conference. In particular, we thank Geoff Doyle of SAIC, for his outstanding organizational skills, and Rebecca Bryan for fellowship and her good humor as well as expert assistance with innumerable tasks during the conference in Tbilisi. In the production of the conference proceedings, the NATO Science Series staff, in particular Ms. Wil Bruins, provided invaluable advice and assistance. Lastly, we thank Jean McHale for the many hours spent editing, formatting and compiling the papers included in this book. Without her expert help the publishing of this book of conference proceedings would not have been possible.

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Organizers Kevin P. O’Connell Evan W. Skowronski U.S. Army Edgewood Chemical Biological Center Aberdeen Proving Ground, Maryland, USA Alexander Sulakvelidze Emerging Pathogens Institute University of Florida Gainsville, Florida, USA Lela Bakanidze National Center for Disease Control Tbilisi, Republic of Georgia Roger Hewson Centre for Applied Microbiology and Research Health Protection Agency Porton Down, Salisbury, United Kingdom

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Speakers Gérard Aboudharam Faculté de Médecine Universite de la Mediterranee Unité des Rickettsies Marseille, France e-mail: gerard.aboudharam @wanadoo fr

Philip Elzer Department of Pathobiological Sciences School of Veterinary Medicine Lousiana State University Baton Rouge, Lousiana, USA e-mail: [email protected]

Alim Aikimbayev Kazakh Scientific Center for Quarantine and Zoonotic Diseases Almaty, Kazakstan e-mail: alim.aikimbayev@mail ru

Robert Esler US Civilian Research and Development Foundation Arlington, Virginia, USA e-mail: [email protected]

Andrey Anisimov State Research Center for Applied Microbiology and Biotechnology Obolensk, Russia e-mail: [email protected]

Jason Farlow Arizona State University Tempe, Arizona, USA e-mail: [email protected]

Giorgi Babuadze National Center for Disease Control Tbilisi, Republic of Georgia e-mail: [email protected] Lela Bakanidze National Center for Disease Control Tbilisi, Republic of Georgia e-mail: [email protected] Nelli Barnabishvili National Center for Disease Control Tbilisi, Republic of Georgia e-mail: [email protected]. Jason Blackburn Emerging Pathogens Institute University of Florida Gainsville, Florida, USA e-mail: [email protected]

Stephen Francesconi US Armed Forces Institute of Pathology Washington, DC, USA e-mail:Stephen.Francesconi @afip.osd mil Amiran Gamkrelidze United Nations World Health Organization Tbilisi, Republic of Georgia e-mail: [email protected] Tatiana Gremyakova International Science and Technology Centre Moscow, Russia e-mail: [email protected]

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Shair Gurbanov Azerbaijani Republic Antiplague Station Baku, Azerbaijan e-mail: [email protected] Paata Imnadze National Center for Disease Control Tbilisi, Republic of Georgia e-mail: [email protected] Akbar Khan Defense Threat Reduction Agency Fort Belvoir, Virginia, USA e-mail: [email protected] George Khokhobashvili Georgian Research and Development Fund Tbilisi, Republic of Georgia e-mail: [email protected] J. Glenn Morris Emerging Pathogens Institute University of Florida Gainsville, Florida, USA e-mail: [email protected] C. J. Nutter Defense Threat Reduction Agency Fort Belvoir, Virginia, USA e-mail: [email protected] Tinatin Onashvili Laboratory of Ministry of Agriculture Tbilisi, Republic of Georgia e-mail: [email protected] Eka Paatashvili Ministry of Health Tbilisi, Republic of Georgia e-mail: [email protected]

SPEAKERS

Scott Petersen J. Craig Venter Institute Rockville, Maryland, USA e-mail: [email protected] Alexander Rakin Ludwig-Maximilians Universitat Max von Pettenkofer-Institut Munich, Germany e-mail: [email protected] Daniel Rock Department of Pathobiology College of Veterinary Medicine University of Illinois Urbana, Illinois, USA e-mail: [email protected] Elisaveta Stikova Faculty of Medicine University of St. Cyril and Methodius Skopje, Republic of Macedonia e-mail: [email protected] mk Nikoloz Tsertsvadze National Center for Disease Control Tbilisi, Georgia e-mail: [email protected] Natalya Vydayko Central Sanitary-Epidemiological Station Ministry of Health Kiev, Ukraine e-mail: vydaykon@ukr net Eka Zhgenti National Center for Disease Control Tbilisi, Georgia e-mail: [email protected]

Other Participants Wallace Buchholz US Army Research Office Research Triangle Park, North Carolina, USA e-mail: [email protected] Niko Burdiashvili Georgian Research and Development Fund Tbilisi, Georgia e-mail: [email protected] Geoffrey Doyle SAIC, Inc. Aberdeen Proving Ground, Maryland, USA e-mail: GEOFFREY.L.DOYLE @saic.com Ilya Elashvili Defense Threat Reduction Agency Fort Belvoir, Virginia, USA e-mail: Ilya.Elashvili@dtra mil Henry Gibbons US Army Edgewood Chemical Biological Center Aberdeen Proving Ground, Maryland, USA [email protected] mil

Roger Hewson Centre for Applied Microbiology and Research Health Protection Agency Porton Down, Salisbury, United Kingdom e-mail: [email protected] Evan Skowronski US Army Edgewood Chemical Biological Center Aberdeen Proving Ground, Maryland, USA e-mail: [email protected] Revaz Solomonia Ivane Beritashvili Institute of Physiology Tbilisi, Georgia e-mail: [email protected] Alexander Sulakvelidze Emerging Pathogens Institute University of Florida Gainsville, Florida, USA e-mail: [email protected]

David Gutierrez US Army Edgewood Chemical Biological Center Aberdeen Proving Ground, Maryland, USA e-mail: [email protected] mil

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Contents Editor’s Preface Acknowledgements Organizers Speakers Other Participants

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Keynote Contribution

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Global Effect and Prevention of Emerging and Epidemic Pathogens: Cholera and Citrus Greening as Examples J. Glenn Morris, Jr.

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Section I: Surveillance The Epidemiological Surveillance of Highly Pathogenic Diseases in Kazakhstan Alim M. Aikimbayev, Jumabek Y. Bekenov, Tatyana V. Meka-Mechenko, and Gulnara A. Temiraliyeva

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Surveillance on Plague in Natural Foci in Georgia Lela Bakanidze, Paata Imnadze, Svetlana Chubinidze, Nikoloz Tsertsvadze, Gela Mgeladze, Irakli Shalutashvili, Shota Tsanava, Merab Shavishvili, Julietta Manvelyan, Nana Ninashvili, and Guram Katsitadze

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Application of Modern Techniques for Studying Bacterial Pathogens in Georgia Ekaterine Zhgenti, Gvantsa Chanturia, Mariam Zakalashvili, and Merab Kekelidze

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Especially Dangerous Infections in Azerbaijan Sh. Gurbanov and S. Akhmedova

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Strengthening the Early-Warning Function of the Surveillance System: The Macedonian Experience Elisaveta Stikova, Dragan Gjorgjev, and Zarko Karadzovski Integrating Geographic Information Systems and Ecological Niche Modeling into Disease Ecology: A Case Study of Bacillus anthracis in the United States and Mexico Jason K. Blackburn

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Section II: Molecular Analysis and Tools

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Applications of Paleomicrobiology to the Understanding of Emerging and Re-emerging Infectious Diseases Gérard Aboudharam, Michel Drancourt, and Didier Raoult

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Characterization of a Putative Hemagglutinin Gene in the Caprine Model for Brucellosis Quinesha L. Perry, Sue D. Hagius, Joel V. Walker, Lauren Duhon, and Philip H. Elzer

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Pathoadaptation of Especially Dangerous Pathogens Alexander Rakin

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Detection of Pathogens Via High-Throughput Sequencing Akbar S. Khan

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Environmental Influences on the Relative Stability of Baculoviruses and Vaccinia virus: A Review Gary D. Ouellette, Patricia E. Buckley, and Kevin P. O’Connell

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Subject Index

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Keynote Contribution

Global Effect and Prevention of Emerging and Epidemic Pathogens: Cholera and Citrus Greening as Examples J. Glenn MORRIS, Jr. Professor and Director, Emerging Pathogens Institute, University of Florida, Gainesville, Florida Abstract. Emerging and epidemic infectious diseases have had a major effect on human history. We are just now coming to appreciate the mechanisms by which new strains emerge and the factors that permit their rapid spread within human populations. Cholera is a classic epidemic disease that causes periodic pandemics (possibly reflecting genetic changes in surface antigens of the microorganism) and seasonal epidemics that appear to be triggered by environmental factors. Citrus greening is a plant disease that kills citrus trees; a vector-borne bacterial disease, it is currently spreading rapidly across Florida. Control of these and other epidemic and emerging diseases may be difficult, particularly if the infection is already widespread in target populations. Any chance of successful control requires a comprehensive understanding of the pathogenesis and transmission pathways of the microorganism.

Emerging and epidemic pathogens have played a major role in human history. The concept of plagues – infectious and otherwise – is deeply enmeshed in the Biblical story of Moses, Pharaoh, and the departure of the Jewish people from Egypt in approximately 1300 BC. Cholera, with an ancestral home in the delta of the Brahmaputra and Ganges Rivers, is noted in the Sushruta Samhita, written about 500–400 BC; this includes the Sanskrit term generally used to refer to cholera as well as the description of a representative case. Reports of cholera can be found in the Arab literature by 900 AD, with descriptions subsequently appearing in European, Indian, and Chinese literature [1]. Plague (caused by Yersinia pestis) was responsible for the Black Death of 1348– 1349, which has been called the greatest biomedical disaster in European and possibly world history [2]. Epidemics of yellow fever in the United States were a major driver for the creation of departments of public health beginning with the formation of the Philadelphia Board of Health in 1794. Although there is general familiarity with these “classic” epidemic diseases, recent work by Jones and colleagues [3] suggests that the emergence of new pathogens is a constant, ongoing process. Based on a literature review, these authors identified 335 infectious disease emergence “events” occurring between 1940 and 2004. Zoonotic pathoens (i.e., those that have a nonhuman animal source) predominated, accounting for 60.3% of events; vector-borne diseases were responsible for 22.8% of events. The majority of pathogens identified were bacteria or rickettsia (54.3%). Although some of these events reflected small numbers of actual cases, it is clear that the potential for new disease emergence – with the threat of subsequent epidemic spread – is always present. K.P. O’Connell et al. (eds.), Emerging and Endemic Pathogens, DOI 10.1007/978-90-481-9637-1_1, © Springer Science + Business Media B.V. 2010

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There are a variety of factors that underlie the emergence of new pathogens. Emergence of a pathogen may reflect genetic changes that result in increased virulence, the ability to avoid immunologic detection by the host or killing by antibiotics, or the ability to survive in new ecologic niches. Independent of genetic changes, there may be changes in opportunities for pathogen growth and spread, often as the result of anthropogenic (human-created) changes. This may include environmental changes (such as temperature change), changes in ecologic niches, changes in host behavior, and/or introduction of a pathogen into a new geographic area (intentional or otherwise). Finally, pathogen emergence may be influenced by changes in host behavior, including loss of herd immunity (such as the diphtheria epidemics that occurred in parts of the former Soviet Union as a result of decreases in immunization rates) and immunosuppression (which may be related to disease, aging, and malnutrition) [4].

Why do pathogens emerge? – Appearance of new/genetically different strains – Changes in opportunities for pathogen growth and spread (often anthropogenic) – Changes in host susceptibility

1. Cholera: A Classic Epidemic/Pandemic Pathogen Cholera remains the one disease that consistently can cause dehydrating diarrhea in a healthy adult. The symptoms of cholera are caused by cholera toxin (CT), a protein enterotoxin that elicits profuse diarrhea [5, 6]. Clinically, patients with the most severe form of the disease can pass in excess of 1 L of diarrheal stool per hour; if fluid losses are not replaced by oral or intravenous fluids, this can result in severe dehydration, shock, and death in 12–24 h. With appropriate therapy, mortality rates for cholera should be less than 1%. However, in the absence of an adequate public health infrastructure to provide treatment, mortality rates may reach or exceed 40%. This is reflected in the 2005 World Health Organization’s cholera-surveillance data (the most recent available): 131,942 cholera cases were reported in 52 countries, the majority of which had case-fatality rates below 1%; rates in excess of 1% occurred almost exclusively in sub-Saharan Africa, with multiple countries in this region reporting rates in excess of 5% [7]. Cholera is caused by strains of Vibrio cholerae that carry specific virulence factors. The species V. cholerae is free-living in the environment, often associated with phytolankton and zooplankton, and is common in estuarine areas. Growth is dependent on temperature (ideally >20°C) and salinity. Survival of the organism is facilitated by its ability to shift to a rugose phenotype (Fig. 1), involving production of an amorphous extracellular polysaccharide that serves as the basis for biofilm formation; rugose forms are fully virulent but resist killing by chlorine and ultraviolet light [8]. Survival also may be facilitated by the ability of strains to assume a viable but nonculturable form.

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Figure 1. Smooth (left) and rugose (right) colony morphologies for Vibrio cholerae.

More than 200 O-groups have been identified for V. cholerae, with epidemic cholera cases traditionally linked with O-group 1 (V. cholerae O1). Key virulence factors necessary for occurrence of cholera include CT and associated genes (carried by the CTX phage, which is capable of transfer among V. cholerae strains) and the vibrio pathogenicity island, which includes genes for toxin-coregulated pilus, a key attachment factor (and the receptor for the CTX phage). However, it appears that the ability to cause epidemic disease is dependent on additional and still poorly charcterized factors. In studies conducted in our laboratories using multilocus sequence typing (MLST), all clinical cholera strains clustered into a single MLST clonal complex, consistent with the hypothesis that strains capable of causing disease are closely related phylogenetically. In contrast, there may be striking sequence divergence between epidemic cholera strains and V. cholerae strains from other O-groups. In work that we have done with V. cholerae strain NRT-36S, an O31 strain, we found only 89% sequence homology with epidemic cholera isolates, with absence of a number of putative virulence genes [9]. Epidemiologically, cholera tends to occur in two patterns: it spreads in pandemic form, moving across continents, and, after introduction into an area, it may settle into an “endemic” pattern marked by seasonal epidemics. From the perspective of undertanding emergence of pathogens, this leads to two basic questions: what mechanisms underlie occurrence of pandemic disease, and, once the pandemic wave has passed, what are the triggers for recurrent seasonal epidemics? 1.1. Pandemic Cholera The modern history of cholera begins in 1817 with the occurrence of what has been designated as the first of seven cholera pandemics. It was during the spread of the third pandemic to London in 1854 that John Snow demonstrated the association between illness and consumption of sewage-contaminated water. His work established the role of epidemiology in public health and highlighted the efficacy of simple interventions – in this case the removal of the handle of the Broad Street pump, which had been linked with illness. The seventh (and most recent) cholera pandemic began in 1961, with an outbreak of disease in the Celebes. The strain responsible for this outbreak (V. cholerae O1 biotype El Tor) subsequently has spread through Asia, Africa, Europe, and the Americas, resulting in substantial global morbidity and mortality and leaving behind an endemic pattern of seasonal epidemics (Fig. 2) [10].

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Figure 2. Global spread of the seventh pandemic of cholera.

In 1992, against this background and outside of normal seasonal epidemic patterns, cholera began to spread rapidly across India and Bangladesh, with subsequent spread to other parts of Asia. In contrast to the traditional endemic pattern of cholera in these areas, all ages were affected, suggesting a lack of preexisting immunity within the population [11]. In subsequent studies, we and others found that the strain responsible for this “new” epidemic was from a different O-group (O139), was encapsulated, and had undergone a genetic substitution/deletion with the introduction of 35 kb of “new” DNA encoding the O139 capsule, replacing 22 kb of “original” DNA encoding the O1 antigen [12, 13]. Aside from this one substitution, the epidemic strain appeared to be identical to seventh pandemic V. cholerae O1 El Tor strains. Further studies from our laboratory have shown that the gene cluster controlling expression of the O-antigen and capsule is bounded consistently by two genes – gmhD and rjg. Genetic substitutions within this region are not uncommon and may account for the diversity of O-groups within the species [14, 15]. Although the initial epidemic due to V. cholerae O139 did not progress to pandemic disease (i.e., with involvement of multiple continents), it is clear that this new strain had pandemic potential, and there were suggestions that its appearance should be designated as the beginning of the eighth pandemic. Based on our findings with this strain, we would hypothesize that new cholera pandemics result from genetic changes leading to expression of new surface antigens (O-group and capsule), permitting rapid spread of the disease through populations that are immunologically naive to the new antigens. From the standpoint of disease control, these findings underscore the need for rapid vaccine development capabilities to permit the creation of new vaccines (for cholera as well as for other possible emergent pathogens) to match whatever new antigenic combination may appear. As a case study, based on our research, we were able to develop a polysaccharide conjugate vaccine rapidly that was protective in animals

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against the O139 cholera strain [16]. However, there was inadequate infrastructure and funding to move on to human trials, leaving us, to date, with no available vaccine for this new pandemic strain. 1.2. Endemic Cholera with Seasonal Epidemics As noted above, after passage of a pandemic wave, cholera tends to shift to an endemic pattern of seasonal epidemics. We undertook a series of studies in Lima, Peru, in the mid-1990s [17] to try to gain a better understanding of why such epidemics occur. The seventh pandemic had entered South America in 1991, appearing first in Peru and then moving across South and Central America. In subsequent years, illness settled into an endemic pattern, with epidemics occurring each summer (December–February). Over a 2-year period, we collected monthly samples from eight environmental sites in the Lima area. Detection of CT-producing V. cholerae (i.e., strains capable of causing epidemic disease) in the environment correlated significantly with occurrence of disease in the community 2 and 3 months later; the increase in counts in the environment, in turn, correlated with increases in water temperature associated with the beginning of summer. These data support a model of cholera seasonality in which initial increases in number of V. cholerae in the environment (triggered by temperature) are followed by “spillover” of illness into the human population, with these human cases further amplifying the organism as the epidemic cycle proceeds (Fig. 3). Support for the concept of temperature being an important element in triggering the epidemics has come from other investigators working with data from Peru as well as Bangladesh, with particular attention being given to the potential role of the El Nino-southern oscillation as a driver of the process [18].

Environmental Parameters

V. cholerae in environment including plankton

Cholera infections in humans

Figure 3. Model of cholera transmission.

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In further studies in two rural communities in Bangladesh (Bakerganj and Mathbaria), we used variable numbers of tandem repeats as a means of typing V. cholerae strains from clinical and environmental sources [19]. As previously noted, epidemic V. cholerae strains tend to be closely related phylogenetically, making it difficult to separate strains by MLST (or other standard molecular epidemiologictyping methods). In contrast, we found that variable numbers of tandem repeats provided us with excellent discrimination among strains. Using this technique, we evaluated 68 environmental and 56 clinical isolates from the two communities. We found that there was minimal crossover between environmental and clinical strains as well as minimal crossover between strains in Bakerganj and Mathbaria. We also found that “epidemics” in any one location, rather than being caused by a single strain, appeared to reflect the sequential appearance of different strain subsets in the human population. Although environmental V. cholerae may serve as a trigger for an epidemic, these data suggest that subsequent transmission among humans is more likely to be person-to-person. The data also suggest that distinct locales have their own strains and strain subsets; that is, an epidemic is not due to a single strain sweeping across the countryside but, rather, reflects the appearance of local strains in human populations. To further explore questions relating to person-to-person transmission, we developed a mathematic model of cholera transmission [20]. Interestingly, the best fit for the model was obtained when we incorporated the concept of a “hyperinfectious state.” This follows from laboratory experiments suggesting that passage of V. cholerae through the intestinal tract results in a short-lived increase in infectivity that decays in a matter of hours into a state of lower infectiousness. These observations help to highlight possible control strategies. Although it is unlikely that triggers for environmental proliferation of the microorganism (such as temperature) can be blocked, an awareness of the role of environmental V. cholerae in initiating epidemics may permit the focusing of resources on preventing such transmission during high-risk time periods when temperatures are elevated. Given the clear importance of person-to-person transmission, efforts also should be focused on minimizing the risk of such transmission within households, with a particular emphasis on minimizing risk of transmission of the short-lived, hyperinfectious form of the microorganism present in recently passed fecal material. 2. Citrus Greening Citrus greening is a recently emergent infectious disease that currently is estimated to infect 30% of the citrus trees in Florida, and it is spreading rapidly. Although not a human disease, it is causing major economic losses and provides some interesting, and different, perspectives on disease emergence. Citrus greening first was reported in the late 1800s in China, where it is known as huanglongbing, or yellow dragon, reflecting the pattern of leaf-yellowing within affected trees. It now has spread through citrus-growing areas in much of the world. There is no effective control once a tree is infected. Infected trees produce less fruit, and the fruit that is produced tends to be bitter and misshapen. The etiologic agent for the disease is thought to be the bacterium Liberobacter asiaticum, transmitted by an insect, the Asian

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psyllid. Although we have a basic understanding of the transmission pathways, a great deal remains to be learned about both the bacterium and the vector [21]. The Asian psyllid first was identified in Florida in September 2005, and, as shown in Fig. 4, it has spread rapidly across citrus-growing areas of the state. Efforts to control the disease have focused on quarantining infected orchards and bulldozing and burning

Figure 4. Spread of citrus greening in Florida.

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infected trees and on using insecticides to kill the psyllid vector. Neither approach has been overwhelmingly successful, with the disease continuing to spread rapidly across the state and with reports of psyllids being identified in Louisiana and, as of July 2008, in California. 3. Conclusions Emerging and epidemic pathogens have been an ongoing cause of human disease since the dawn of recorded history. Factors that drive their emergence include genetic changes, changes in opportunities for pathogen growth and spread, and changes in host susceptibility. Prevention and/or control of emergent pathogens is possible but requires early recognition and intervention; mathematic modeling that we have done [22] underscores the fact that, by the time new diseases are recognized, they often have spread to the point that control is difficult if not impossible. Citrus greening provides an excellent example: traditional control strategies are, at this point, largely ineffective owing in part to the high percentage of trees that already are infected. Prevention and control also require a comprehensive understanding of pathogenesis and transmission. Cholera provides an example of a pathogen for which better and better data are becoming available, allowing focusing of control strategies. At the same time, cholera demonstrates the complexity of these natural systems and the difficulties inherent in designing interventions even with a reasonable knowledge base. References 1.

Barua, D. History of cholera. In: Barua, D., Greenough, W.B., editors. Cholera. New York: Plenum Medical Book Company; 1992. p. 1–36. 2. Cantor, N.F. In the wake of the plague: the Black Death and the world that it made. New York: Free Press; 2001. 3. Jones, K.E., Patel, N.G., Levy, M.A., Storeygard, A., Balk, D., Gittleman, J.L., Daszak, P. 2008. Global trends in emerging infectious diseases. Nature 451:990–993. 4. Morris, J.G., Potter, M. 1997. Emergence of new pathogens as a function of changes in host susceptibility. Emerg. Infect. Dis. 3:435–441. 5. Kaper, J.B., Morris, J.G., Levine, M.M. 1995. Cholera. Clin. Microbiol. Rev. 8:48–86. 6. Morris, J.G. 2003. Cholera and other vibriosis: a story of human pandemics and oysters on the half shell. Clin. Infect. Dis. 37:272–280. 7. World Health Organization. 2006. Cholera 2005. Wkly. Epidemiol. Rec. 81:297–308. 8. Morris J.G., Jr., Sztein, M.B., Rice, E.W., Nataro, J.P., Losonsky, G.A., Panigrahi, P., Tacket, C.O., Johnson, J.A. 1996. Vibrio cholerae O1 can assume a chlorine-resistant rugose survival form that is virulent for humans. J. Infect. Dis. 174:1364–1368. 9. Chen, Y., Johnson, J.A., Pusch, G.D., Morris, J.G., Stine, O.C. 2007. The genome of non-O1 Vibrio cholerae NRT36S demonstrates the presence of pathogenic mechanisms that are distinct from O1 Vibrio cholerae. Infect. Immun. 75:2645–2647. 10. Morris, J.G. Cholera and other vibrioses. In: Encyclopedia of public health. Amsterdam: Elsevier; 2008. 11. Nair, G.B., Ramamurthy, T., Bhattacharya, S.K., Mukhopadhyay, A.K., Garg, S., Bhattacharya, M.K., Takeda, T., Shimada, T., Takeda, Y., Deb, B.C. 1994. Spread of Vibrio cholerae O139 Bengal in India. J. Infect. Dis. 169:1029–1034. 12. Johnson, J.A., Salles, C.A., Panigrahi, P., Albert, M.J., Wright, A.C., Johnson, R.J., Morris, J.G. 1994. Vibrio cholerae O139 synonym Bengal is closely related to Vibrio cholerae El Tor but has important differences. Infect. Immun. 62:2108–2110.

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13. Comstock, L.E., Maneval, D., Jr., Panigrahi, P., Joseph, A., Levine, M.M., Kaper, J.B., Morris, J.G., Johnson, J.A. 1995. Capsule and O antigen in Vibrio cholerae O139 Bengal are associated with a genetic region not present in Vibrio cholerae O1. Infect. Immun. 63:317–323. 14. Sozhamannan, S., Deng, Y.K., Li, M., Sulakvelidze, A., Kaper, J.B., Johnson, J.A., Nair, G.B., Morris, J.G. 1999. Cloning and sequence of the genes downstream of the wbf gene cluster of Vibrio cholerae serogroup O139 and analysis of the junction genes in other serogroups. Infect. Immun. 67:5033–5040. 15. Chen, Y., Bystricky, P., Adeyeye, J., Panigrahi, P., Ali, A., Johnson, J.A., Bush, C.A., Morris, J.G., Stine, O.C. 2007. The capsule polysaccharide structure and biogenesis for non-O1 Vibrio cholerae NRT36S: genes are embedded in the LPS region. BMC Microbiol. 7:20. 16. Johnson, J.A., Joseph, A., Morris, J.G. 1995. Capsular polysaccharide-protein conjugate vaccines against Vibrio cholerae O139 Bengal. Bull. l’Institut Pasteur 93:285–290. 17. Franco, A.A., Fix, A.D., Prada, A., Paredes, E., Palomino, J.C., Wright, A.C., Johnson, J.A., McCarter, R., Guerra, H., Morris, J.G. 1997. Cholera in Lima, Peru, correlates with prior isolation of Vibrio cholerae from the environment. Am. J. Epidemiol. 146:1067–1075. 18. Pascual, M., Rodo, X., Ellner, S.P., Colwell, R., Bouma, M.J. 2000. Cholera dynamics and El Ninosouthern oscillation. Science 289:1766–1769. 19. Stine, O.C., Alam, M., Tang, L., Nair, G.B., Siddique, A.K., Faruque, S.M., Huq, A., Colwell, R., Sack, R.B., Morris, J.G. 2008. Seasonal cholera from multiple small outbreaks, rural Bangladesh. Emerg. Infect. Dis. 14:831–833. 20. Hartley, D.M., Morris, J.G., Smith, D.L. 2006. Hyperinfectivity: a critical element in the ability of V. cholerae to cause epidemics? PLoS Med. 3:e7. 21. Bove, J.M. 2006. Huanglongbing: a destructive, newly emerging, century-old disease of citrus. J. Plant. Pathol. 88:7–37. 22. Smith, D.L., Harris, A.D., Johnson, J.A., Silbergeld, E.K., Morris, J.G. 2002. Antibiotic use in animals has an early but important impact on antibiotic resistance in humans. Proc. Natl. Acad. Sci. USA 99:6434–6439.

Section I Surveillance

The Epidemiological Surveillance of Highly Pathogenic Diseases in Kazakhstan Alim M. AIKIMBAYEV1, Jumabek Y. BEKENOV2, Tatyana V. MEKA-MECHENKO1, and Gulnara A. TEMIRALIYEVA1 1 M. Aikimbayev’s Kazakh Scientific Centre for Quarantine and Zoonotic Diseases, Almaty, Kazakhstan 2 Aktobe Plague Control Station, Aktobe, Kazakhstan Abstract. The Central Asian deserts’ plague focus occupies vast zones of desert and semidesert in Central Asia and Kazakhstan. The differentiation of plague strains on virulence from the plague foci of Kazakhstan testifies to its high epidemic virulence. From 1990–2003, 23 cases of human plague were registered. From 2004 to 2007, no cases human plague were registered. The growth of human plague has been caused not only by an increase in epizootic activity of the natural foci but also by the crises of social, economic, and health protection conditions in the Republic of Kazakhstan during the period of Perestroika. The same conditions challenged the increase in human anthrax, tularaemia, and brucellosis during the same period. Annually, 70,000–100,000 people are vaccinated and revaccinated with live vaccine strain tularemia. Kazakhstan is not endemic for cholera; therefore, all initial cases of cholera were imported from places such as Pakistan, Uzbekistan, Iran, Turkey, and Indonesia. For epidemiologic supervision of anthrax, the cadastre of anthrax foci is transferred in electronic format using a Geographical Information System (GIS). For Kazakh samples, 12 unique MLVA subtypes (KZ-1 through KZ-12) were used.

1. Geographical Epidemiology of Plague A considerable portion of the Republic of Kazakhstan is located in the territory of one of the biggest plague foci in the world: the Central Asian desert plague focus, which occupies vast zones of desert and semidesert in Central Asia and Kazakhstan. In Kazakhstan, the plague enzootic area covers 1,007,350 km2, that is 39% of Republic territory or 70% of the Commonwealth of Independent States’ natural plague foci. The M. Aikimbayev’s Kazakh Scientific Centre for Quarantine and Zoonotic Diseases (10 plague-control stations, 19 local plague branches, and 30 temporary antiepidemic divisions) carries out plague surveillance. More than 1,500 people work in the plaguecontrol services of Kazakhstan, including 400 people with higher and middle specialized education. All medical and biological employees are certified to do laboratory work after 2–3 months of special training. The main reservoir species of plague in the Central Asian desert plague focus are gerbils, susliks, and marmots. Marmots and the yellow suslik are hunted, and people usually are infected by fleas. For preventive measures and epidemiologic monitoring, the most important plague flea vectors were determined. The initial stages of the flea’s physiological development are the best period for using insecticides.

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Veterinary surveillance of camels is an important prevention measure because infection of these domestic animals could cause an epidemic [1]. Camels are only rarely infected by plague; on average, a plague-infected camel is registered once every 10 years. However, the meat of a slaughtered camel can cause disease not only during the slaughter, but human plague has been diagnosed in purchasers of infected meat long distances from the location of slaughter. Wintertime human plague infections caused by plague-infected camels have been connected to latent infection in the camels as a result of poor feeding and a decrease in the animals’ resistance to infection. Plague surveillance and prophylaxis consists of several synergistic elements: the control of plague transmission, epidemiologic investigation, field disinfection, settlement disinfection and deracination, vaccination of humans, work with a medical network, work with a veterinary service on camel plague prophylaxis, and sanitary and educational work with the population. Those at risk for plague infection include cattle breeders and members of their families, railway-communication workers, participants in expeditions, workers at meteorologic stations, field workers, fur-trade workers, veterinary workers, medical workers in the countryside, and inhabitants of small cities having cattle grazing on the enzootic territories. The natural factors of Aral Sea regression and Caspian Sea transgression were considered when determining plague-focus epidemic potential. The expansion of zone enzootics in the shoaled parts of the Aral Sea was revealed. The water level in the Caspian Sea rose 2 m, changing the contours of the coastal site. Raised subsoil in the waters has changed the microclimate in rodent holes, which has resulted in the dying off of fleas and the sanitation of foci. Use of landscape epidemiologic principles has allowed us to reduce epizootologic inspection of plague foci tenfold and to concentrate our field work in areas where the main part of the rodent population is located. Informative inspection in difficult economic conditions was developed and used as the reconnaissance method for inspections of gerbil foci. During a 10-day tour, the zoologist (parasitologist) collects flea probes for testing in the central laboratory. The territory around the settlements was subject to inspection; during this period, sparsely populated areas were not surveyed. Positive results in the epizootics in any part of the autonomous focus were extrapolated to the entire territory and were indications for prophylaxis. This has allowed us to reduce the number of exposed antiepidemic groups [2]. 2. Analysis of Plague Isolates Differentiation of Yersinia pestis strains from the plague foci of Kazakhstan by genetic analysis [3] suggests that the most of the strains are likely to be highly virulent in humans. The high epidemic virulence of Central Asian plague-focus isolates is proved by modern methods of genetic analyses [4] on the variability of the nucleotide sequences of the genes of rha-locus Yersinia pestis strains of the basic and nonbasic subspecies. The same research shows an evolutionary antiquity of Caucasian strains and their similarity to Yersinia pseudotuberculosis, which explains the low epidemic potential of Caucasian foci plague strains.

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3. Incidence of Human Cases of Plague As a result of the epizootologic investigations of the past two decades, new plague foci or sites have been discovered in the Central Asian desert plague focus. From 1990 to 2003 in Kazakhstan, 23 cases of human plague were diagnosed in 17 geographical foci of human plague. Morbidity increased fourfold in comparison with the foregoing period (1977–1989), during which six cases of human plague were registered. Of the cases diagnosed from 1990 to 2003, 11 cases of human plague were caused by flea bites. The main causes of mortality in these cases were delayed medical attention, incorrect primary diagnosis, and accompanying chronic disease [2]. The growth of human plague has been caused not only by an increase in epizootic activity of the natural foci but also by the crises of social and economic conditions in the Republic of Kazakhstan, which did not allow adequate funds for preventive action. The negative social effects during the period of Perestroika reduced the immune status of the population and the resistance of the inhabitants of the Commonwealth of Independent States not only to plague but also to other infectious diseases. The decreasing immune status was caused by stressful living conditions, including unemployment and a falling standard of living. The stress accompanied by an increase in the hormone level of corticosteroids resulted in an immune-depressive action and a decrease in organisms’ resistance to infections. For example, earlier patients were infected with bubonic plague by multiple flea bites; in 2003, one trace of flea bite was found in a child who died of plague. Approximately 25,000 bacteria – the quantity of plague microbe delivered by one flea bite – was enough for plague transmission. From 2004 to 2007, no cases of human plague were registered. Since 2006, the medical service has used a definition of cases of especially dangerous infection regulated by the Order of the Ministry of Health RK #623 (15.12.2006), in which stages of the diagnosis are subdivided into suspect, presumptive, and confirmed plague. 4. Treatment of Plague Infections The first stage in the treatment plan for plague patients [1] is detoxification by introduction a 0.5-L salt solution with diuretic. The use of bacteriostatic antibiotics then is preferred. It allows avoiding the Jarisch-Herxher reaction. The daily dose of antibiotic should not exceed 2.0 g (3 g in combination). The antibiotics and the salt solution should be administered in a 1:1 ratio (1.0 g of antibiotic to 1.0 L of solution). We prefer gentamycin for replacement bacteriostatic antibiotics for bactericidal treatments on the second day. This antibiotic penetrates well through the hematoencephalic barrier and prevents infection with meningoencephalitis. Meningoencephalitis complicates 50% of cases in children younger than 14 years [1]. This antibiotic also counteracts microbe endotoxin. In cases of bubonic plague, 80.0 mg of gentamycin was injected three times per day; in cases of pneumonic and septic plague, 80.0 mg of gentamycin was injected three to four times per day. The use of oxacillin is not recommended because it can cause necrosis at the point of injection. Doxycycline in combination with ciprofloxacin has no expected synergistic action. It is recommended to replace doxycycline with amikacin or rifampicin or, in their absence, cefotaxim.

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The use of corticosteroids in indicated where the collapse of the patient’s condition is progressive, but corticosteroids suppress phagocytosis [5]. The antibiotic-resistant cells multiply, which may cause a relapse in 2–3 days. In this case, we recommend using other group reserve antibiotics. For treatment of the plague-related skin ulcers, we recommend applying polyphytoleum (“Kyzylmay,” manufactured in Kazakhstan). We have proved the safety of discharging patients diagnosed with plague from the hospital after treatment and one negative test instead of three. This allows closing the epidemic foci 4 days earlier, and it already is regulated by the new Ministry of Health standard. 5. Surveillance and Treatment of Cholera Kazakhstan is not endemic for cholera; therefore, all initial cases of cholera were imported from places such as Pakistan, Uzbekistan, Iran, Turkey, and Indonesia [6]. The cholera infection then spread through household contact. To determine whether Vibrio cholerae was in the water of the Syr-Darya River, we tested the rivers upstream and discovered a camp of nonlegal immigrants – workers from Kara-Kalpak (Uzbekistan). The bacteriological tests were negative because the workers took antibiotics, but cholera infection was confirmed by the presence of vibriocidal antibodies [7]. The highest case rate occurred in 1993, when 65 cholera patients came by air from Karachi (Pakistan) to Almaty over the course of 3 days. For the temporary isolation of passengers arriving from countries with cholera outbreaks, an area was set up in Almaty airport with room for 500 people. Those who had contact with people who were sick were sent to city hospitals, where they received preventive treatment with the antibiotic Ciflox. Travellers leaving Kazakhstan received a certificate of epidemiologic safety for their countries of residence. A total of 119 patients were isolated in 1993. Kazakh tourists and migrants were infected with cholera in Pakistan, Indonesia, Tajikistan, and Uzbekistan. In 1996, Kazakh tourists who were travelling to countries with cholera outbreaks were given the licensed intestinal antiseptic Intetrix (Beaufour IPSEN International, France) for chemoprophylaxis if there was a likelihood of secondary transmission. These measures prevented the introduction of cholera into Kazakhstan in 1996 [8]. In Almaty in 2000, a strain of V. cholerae O-139 Bengal was isolated from a patient. Kazakh Scientific Centre for Quarantine and Zoonotic Diseases (KSCQZD) immunoglobulin erythrocyte diagnosticum is used to test for V. cholerae O-139. 6. Surveillance and Prophylaxis for Tularemia The natural tularemia foci in Kazakhstan occupy 552,400 km2 (26% of the territory of the Republic). The most effective method of prophylaxis is vaccination by live vaccine strain Francisella tularensis holarctica (Russian), which provides reliable immunity for 5 years. Annually, between 70,000 and 100,000 people are vaccinated and revaccinated. We have patented the strain F. tularensis mediasiatica KA-29 for creation of a domestic vaccine that is highly immunogenic, non reactogenic, and will induce crossimmunity [9].

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7. Surveillance of Anthrax and Analysis of Bacillus anthracis Strains In Kazakhstan, there are 2,598 anthrax soil foci; 1,767 stationary anthrax settlements are registered in a 2,100 km2 area. For epidemiologic supervision of anthrax the cadastre of anthrax foci is transferred in electronic format. The geographic distribution of Bacillus anthracis strains throughout Kazakhstan are monitored using GIS methods. For Kazakh samples, MLVA was performed on 93 anthrax strains from the KSCQZD collection using eight VNTR markers developed in the laboratory of Р. Keim. A UPGMA dendrogram of 88 isolates from the KSCQZD B. anthracis collection revealed 12 unique MLVA subtypes: KZ-1 through KZ-12. Genotyping of Kazakh anthrax strains according to Keim et al. [10] and Pazylov et al. [11] has shown that they fall under genetic group “A,” which is widely spread throughout the world. The majority of isolates (n = 79) belong to the previously described A1a genetic cluster. Similar strains were isolated in China, Turkey, and Europe. Nine isolates belong to the A3b, A4 clusters and novel genetic lineages A5, A6. In accordance with veterinary laws in the Republic of Kazakhstan, the owners of livestock suspected of having anthrax receive financial compensation at market price if they give the ill animals to the veterinary service. This has been the practice in Kazakhstan since1968 and has had high preventive efficiency. 8. Brucellosis The problem of brucellosis is caused by animal industries and the prevalence of small cattle (such as sheep and goats), which are the carriers of the most pathogenic Brucella species, Brucella melitensis. A total of 3,000 people are sickened annually. Lambingtime work attracts many teenagers, and it could result in their becoming disabled. Therefore, the first step in preventing infection is explaining safety measures to those involved in lambing-time work. The most important measure is immunization of livestock. 9. Outlook By 2015, the government of the Republic of Kazakhstan is planning to spend up to 4% of the gross national product on public health services. The steps for preventing highly pathogenic diseases are the scientific development of bases of epidemiologic supervision, the introduction of new technologies, and the perfection of normative documents and legislations. References 1. 2.

Aikimbayev, A. The plague [Russian]. Almaty: Kazinformcenter; 1992. Aikimbayev, A., Atshabar, B.B., et al. The Kazakh natural plague foci epidemic potential [Russian]. Almaty: DOIVA; 2006.

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Aikimbayev, A., Kisselev U., et al. The effect of human plague isolates on eukaryotic cells [Russian]. The plague epidemiological surveillance and prophylaxis measures. Almaty: Kazakhstan, 1992, pp. 45–48. 4. Kukleva, L.M., Eroshenko, G.A., Kuklev, V.E., Shavina, N.Iu., Krasnov, Ia.M., Guseva, N.P., Kutyrev, V.V. 2008. A study of the nucleotide sequence variability of rha locus genes of Yersinia pestis main and non-main subspecies [Russian]. Mol. Gen. Microbiol. Virusol. 2:23–27. 5. Aikimbayev, A., Temiralyeva, G. Cortisones for plague diagnostic and treatment [Russian]. Almaty: Kazakhstan, 1994. 6. Temiralyeva, G., Meka-Mechenko, T., et al. Dangerous zoonotic diseases control and biosafety system in Kazakhstan. Proceedings of the 7th National Symposium on Biosafety: Managing Risk in Animal Care and Use. Atlanta: Eagleson Institute; 2002. pp. 159–165. 7. Mussagaliyeva, R., Zholsharinov, A., et al. The cholera spreading ways in Atyrau [Russian]. Almaty: Kazakhstan, 2002. pp. 106–110. 8. Aikimbayev, A., Temiralyeva, G. Chemoprophylaxis and diagnostics of cholera [Russian]. Health protection of Kazakhstan. Almaty: Kazakhstan, 1996; pp. 26–29. 9. Aikimbayev, A., Chimirov, O. The strain Francisella mediaasiatica 240, attenuated, candidate of tularemia vaccine. Patent #312. Astana: Committee on intellectual property rights of the Ministry of Justice of Republic Kazakhstan; 2002. 10. Keim, P., Price, L.B., Klevytska, A.M., Smith, K.L., Schupp, J.M., Okinaka, R., Jackson, P.J., HughJones, M.E. 2000. Multiple-locus variable-number tandem repeat analysis reveals genetic relationships within Bacillus anthracis. J. Bacteriol. 182:2928–2936. 11. Pazylov, Y., Meka-Mechenko, T., Easterday, W., Van Ert, M., Keim, P., Hadfield, T., Francesconi, S., Blackburn, J., Hugh-Jones, M., Aikimbayev, A , Lukhnova, L., Zakaryan, S., Temiraliyeva, G. Molecular diversity of Bacillus anthracis in Kazakhstan. 17th European Congress of Clinical Microbiology and Infectious Diseases. Oxford: Blackwell Publishing; 2007. p. 155. 3.

Surveillance on Plague in Natural Foci in Georgia Lela BAKANIDZE, Paata IMNADZE, Svetlana CHUBINIDZE, Nikoloz TSERTSVADZE, Gela MGELADZE, Irakli SHALUTASHVILI, Shota TSANAVA, Merab SHAVISHVILI, Julietta MANVELYAN, Nana NINASHVILI, and Guram KATSITADZE National Center for Disease Control and Public Health and Medical Statistics of Georgia, Tbilisi, Georgia Abstract. Plague is one of the oldest and most devastating recorded human diseases. Several epidemics of plague have occurred in the territory of Georgia. In 1933, the Transcaucasian Anti-Plague Center was established in Tbilisi. There are two natural foci of plague in the territory of Georgia: plain–foothill and high mountainous. The Georgian Anti-Plague Station carried out active surveillance on natural foci. In the plain–foothill focus, plague epizootics were established in 1966 and in 1968–1971. In the high-mountainous focus, plague epizootics were established in 1979–1983 and in 1992–1997. A total of 122 strains of Yersinia pestis were isolated in Georgia – 83 in the plain–foothill focus and 39 in the highmountainous focus; 46 strains are kept at the National Center for Disease Control and Public Health’s Microbial Library. Although no new isolates were obtained in recent years, the plague foci in Georgia are so close to populated areas that they must be under permanent control to be able to respond rapidly to emergencies.

1. Introduction Plague is one of the oldest recorded human diseases, and it likely originated in the Himalayan region during the pre-Christian era. One of the first recorded outbreaks of plague was described in Athens in 430 BC during the Peloponnesian War. The outbreak caused an estimated 300,000 deaths. During the Christian era, epidemics of plague occurred in 5- to 12-year cycles grouped in three pandemics. The first pandemic, called Justinian plague (AD 541–750), spread from Egypt through the Middle East and Mediterranean basin. Population loss was 50–60%. The second pandemic, called The Black Death, started in central Asia in AD 1330. The pandemic killed an estimated 17–28 million Europeans. The third pandemic, which is currently ongoing, started in 1855 in the Yunnan region of China and spread to Hong Kong, India (where it killed an estimated 12.5 million people from 1898 to 1918), Africa, South and North America, and much of the rest of the world. An estimated 200 million deaths have been attributed to Yersinia pestis infection throughout recorded history. It is the first bacterium used as a biological warfare agent, when the Tatars catapulted plague-infected corpses into the sieged Black Sea port of Kaffa (currently Feodossia in the Ukraine) in 1346. During World War II, the Japanese

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infected fleas with Y. pestis and released them into several Chinese cities, causing small epidemics of plague. Y. pestis also was the key component of biological weapon development programs in the United States, the former Soviet Union, and other countries, and it is also likely to be one of the preferred agents to be used during possible bioterrorism attacks in the United States and elsewhere. 2. History of Plague in Georgia Georgia is located at the crossroads of Europe and Asia, and it was a major link in the chain of the Great Silk Road. It always was a vivid transition point for voyagers, thus it was not protected from the spread of different epidemics – among them plague. In Georgian folklore, plague was named zhami (in old Georgian, it means misfortune). Plague first was mentioned in Georgian manuscripts in 11th century, when clinical manifestations (bubonic form, epidemic character in densely populated areas) were described. Later, in the 15th century, more detailed information on plague was given in another Georgian manuscript – “Book of Medical Treatment-Ustsoro Karabadini” (“Peerless Karabadini”); particularly, the clinical manifestation of the bubonic form was described. While information on individual cases of plague in the 16th and 17th centuries can be found, official registration of plague cases started in the 19th century after Georgia was joined to Russia. Three plague epidemics were registered in Georgia during the 19th century: 1803–1807, 1811–1812, and 1838–1843. The epidemics started mainly in the south of Georgia – Akhaltsikhe and surrounding territories – and later spread east, north, west, and to Tbilisi. There were special quarantine checkpoints arranged at the entrances to the capital city, and disease surveillance was also conducted in military units. On February 2, 1804, after several cases of plague had been identified, the Russian Tsar’s representative in Georgia issued an order containing special measures that were to be carried out against plague epidemics. These measures were not effective, however, and did not prevent the spread of epidemics that had a devastating effect on the population of the northern part of Georgia. Many villages were emptied because of the disease. In 1807, in the Larsi citadel in the north of the country near the border with Russia, 1,596 cases of plague were registered, out of which 1,144 (71.7%) people died. The population of the mountainous regions in the north of Georgia built tombs to isolate those who showed signs of plague. In Khevsureti and Tusheti – regions in the north of the country – we still find such tombs today, called anatora after the village Anatory, the entire population of which died of plague. In 1811, the epidemics spread to Tbilisi. The city major reported to the Tsar’s representative the necessity of taking anti-epidemic measures. Shortly thereafter, the disease spread to neighboring villages. Later plague epidemics expanded to the central part of Georgia and, despite quarantines, reached many villages not only in the central but also in the eastern part of the country. Later in 19th century, plague epidemics were registered in Georgia from 1803 to 1807, from 1811 to 1812, and from 1838 to 1843. Usually they started in the southern

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regions, particularly in Akhaltsikhe. The population of Akhaltsikhe, especially the adults, was more protected from plague compared with those in other parts of Georgia. There was a tradition of gathering and saving necrotic parts of plague pustules; during the subsequent epidemic, they were ground and drunk with water. We speculate that partial protection from plague might have resulted from this practice. The first officially documented plague outbreak in western Georgia was in 1836 in Batumi. There were subsequent plague outbreaks in Batumi in 1901, 1910, and 1920. In 1927, by decision of the Narkomzdrav (People’s Commissariat of Public Health) of the Georgian Soviet Socialist Republic, a specialized anti-plague (AP) laboratory was set up at the Batumi port to carry out the quarantine monitoring of the ships coming from countries considered to be risks with regard to plague. In 1934, the Batumi port AP laboratory joined the centralized AP system of the USSR as a department of especially dangerous infections. The reason for the repeated occurrences of plague was believed to be the poor sanitary conditions in the city. The fact that the initial cases of plague always were discovered in proximity to the port facilities led Soviet epidemiologists to conclude that the plague was brought to Georgia by foreign naval vessels from Turkey and other Middle Eastern countries where unsatisfactory epidemiological conditions prevailed. The last officially registered human plague case in Georgia also was registered in Batumi, in 1924. It was imported by a sailor on a foreign ship. 3. Establishing an Anti-plague System in Georgia In 1933, under the initiative of Professor Giorgi Eliava, the Transcaucasian AP Center was created in Tbilisi at the Institute of Bacteriology. In 1937, the Transcaucasian AP Center became an independent organization and was renamed the Tbilisi AP Monitoring Station. The main function of this organization was to carry out epidemiological monitoring of the Tbilisi city territories and surrounding districts. In 1939, under the leadership of Nikoloz Abashidze, the functions of the Tbilisi AP Monitoring Station expanded. Georgian AP specialists began to study epidemiological outbreaks of unknown etiology and undertook the epizootic monitoring of areas near the Turkish border. Later, the Georgian AP Station became an integral part of the centralized AP system, controlled by the Main Department of Quarantinous Diseases of the Ministry of Health of the USSR. The AP system had a very well-defined hierarchy; and the supervisor for the Georgian AP Station was the Stavropol AP Institute. All new isolates of especially dangerous pathogens (if any) had to be sent to Stavropol for confirmation, after which the isolates were to be destroyed. In 1953, plague epizootics was discovered on Apsheron Peninsula in Azerbaijan among Libyan jirds (Meriones libicus erythrourus). The Georgian AP system organized and sent the first epidemiological team to look for a natural plague focus in Eastern Georgia on the then-administrative border with Azerbaijan. In 1956, the continuous plague epizootics in neighboring Armenia and Azerbaijan prompted the reorganization of the Tbilisi AP Monitoring Station into the Georgian Republic AP Station, and active surveillance on plague had started. In 1958, by decision of the Ministry of Health (MOH) of the USSR, the Batumi port AP laboratory was upgraded into a field AP station and placed under the administrative control of the Georgian Republic AP

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Station in Tbilisi. In 1979, another field AP station was established in Tsitelitskaro (now Dedoplistskaro) to conduct epidemiological monitoring in eastern Georgia. The Georgian AP system consisted of the Georgian AP Station in Tbilisi, two field AP stations in Batumi and Tsitelitskaro, and four seasonal AP laboratories in Aspindza, Dmanisi, Jandara, and Ninotsminda. As a result of the activities at the stations and laboratories of the Georgian AP system, the existence of two natural foci of plague in the territory of Georgia – plain/ foothill and high mountainous – was established (see Fig. 1).

Figure 1. Natural foci of plague in eastern Georgia.

The main reservoir in the plain/foothill focus is the jird Meriones erythrourus; the main vectors between rodents are fleas (most commonly Xenopsylla conformis and Ceratophyllus laeviceps). This information was determined by sampling not only rodents themselves, but also from analysis of the contents of rodent burrows (Fig. 2). High-mountainous focus first was identified in 1958. The main reservoir here is the common vole Microtus arvalis; main vectors are Callopsylla caspia, Nosopsillus consimilis, and Ctenophthalmus teres. As was mentioned above, all isolates of Y. pestis were sent to the Stavropol AP Institute. Later, however, at the request of the head of the Georgian AP Station, Professor Levan Sakvarelidze, several strains of Y. pestis, including isolates from Georgia, Dagestan, Kyrgyzia, and Armenia, were returned to the Museum of Live Cultures at the Georgian AP station in Tbilisi for research. After the collapse of the Soviet Union, independent Georgia went through economic difficulties, and there was a considerable decrease in state funding. Limited financial resources forced the Georgian AP Station to cut back on epizootic surveillance and epidemiological monitoring of natural plague foci. Remaining seasonal field work was limited, and field-team size was reduced.

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Figure 2. Entrance to rodents’ burrow.

Since 1989, the Georgian AP Station has changed its name several times, expanding its activities and duties. It now is called the National Center for Disease Control and Public Health (NCDC) and is an integral part of the Georgian health care system. It runs several state programs, among them the state program of surveillance and control of epidemics (surveillance, control, and prevention of especially dangerous and other communicable diseases). The NCDC has established and owns the National Collection of Live Microorganisms, which consists of live cultures including strains of especially dangerous pathogens such as Yersinia pestis, Francisella tularensis, Bacillus anthracis, and Vibrio cholerae. In total, the collection consists of more than 1,000 microorganisms. State funding for the NCDC had been very low until recently, and the concern of plague specialists was that the limited scope of epidemiological monitoring in the known natural foci of especially dangerous infections could result in an undetected epizootic, which could, in turn, lead to an outbreak, especially in densely populated areas. Owing to the shortage of relevant vaccines, the NCDC could no longer offer even limited vaccinations to groups of people who were at greater risk of contracting especially dangerous infectious diseases because of their occupations or places of residence. Consequently, the risk of an outbreak was increased. In the beginning of 2000, construction of the Baku–Tbilisi–Ceyhan main oil export pipeline began. It crossed territories of plague and tularemia natural foci. The pipeline’s main stockholder, British Petroleum, contracted the NCDC and the S. Imamaliyev Republic AP Station (Baku, Azerbaijan) to carry out short-term, commercially-funded epi-zoological surveys of areas along the pipeline route. At the same time, the NCDC started preparing scientific research projects on different pathogens. U.S. governmental organizations such as the Department of Health and Human Services (under the Biotechnology Engagement Program, or BTEP), the Department of Homeland Security, the Department of Energy, and the Department of

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Defense funded these projects, including projects on especially dangerous pathogens. In 2003 and 2004, the U.S. Department of Homeland Security-funded project “Application of Molecular Fingerprinting to Geographical Characterization and Epidemiological Surveillance of Natural Foci of Yersinia pestis and Francisella tularensis,” in collaboration with Lawrence Livermore National Laboratories in Livermore, California, and the Department of Defense/Defense Threat Reduction Agency-funded project “Ecology, Genetic Clustering, and Virulence of Yersinia pestis Strains Isolated from Natural Foci of Plague in Georgia,” in collaboration with the University of Maryland, Baltimore and the U.S. Army Edgewood Chemical Biological Center (ECBC), allowed the NCDC to carry out active surveillance on plague natural foci. We followed the timeframes for field work for both foci. An example of the scope of the field work during the implementation of the research projects is given in Table 1. TABLE 1. Scope of Field Work: 2005–2007 Dedoplistskaro 2005 S Territories 100 investigated (1,000 ha) Sampled rodents Main 59 reservoir Other 326 rodents Ectoparasites obtained Fleas 964 Ticks 2,270

F

2006 S

F

Gardabani 2007

S

2005

F

S

F

Ninotsminda

2006 S

F

2007 S

2005 2006 2007

F

120 130

130 195

170

100

100 100

100 100

100 250 250 250

126 118

136 114

6

102

37

56

24

27

188 258

134 206

82

11

86

33

130 115

39

319 291 178

124 16

4

1

555 24 561 746 184 346 146 187 321 1,334 397 2,241 3,669 3,675 1,179 1,140 1,001 4,211 1,875 1,052 920 1,077 1,172 1,485 2,720 827 801 3,163

S, Spring; F, Fall.

Although no new isolates have been obtained in recent years, the plague foci in Georgia are so close to populated areas that they must remain under permanent surveillance to be able to respond rapidly to emergencies. References 1.

2.

Abesadze, B., Chkheidze, G., Maskharashvili, P., Nersesov, V., Jmukhadze, I., Dzneladze, M. On natural endemicity of plague in semi-desert zone of East Georgia. Especially dangerous infections in Caucasus. Proceedings of the Second Scientific Conference of Anti-Plague Institutions of Caucasus on Epidemiology, Epizootology, and the Prevention of Especially Dangerous Infections, 1st ed. Stavropol; 1970. pp. 7–10. Pilipenko, V. Outcomes and prospective of the study of natural foci of plague in Caucasus. Especially dangerous infections. Stavropol: Russia, 1978.

SURVEILLANCE ON PLAGUE IN NATURAL FOCI IN GEORGIA 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.

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Surveillance and control of communicable diseases: guidelines for public health services in Georgia. Cambridge, MA: Abt Associates Inc.; 2005. Thacker, S.B , Parrish, R.G., Trowbridge, F.L. 1988. A method for evaluating systems of epidemiological surveillance. World Health Stat. Q. 41:11–8. Kukhalashvili, I. Gvelesiani, G. Archive data on plague in Georgia in XIX century. Especially dangerous infections in Caucasus. Stavropol: Russia, 1864. pp. 209–210. Nersesov, V , Tsikhistavi, Sh. Assessment of epizootic factors of focus in Southern Georgia and heightening of effectiveness of epi-surveillance. Materials of jubilee conference. Tbilisi: Georgia, 1988. pp. 23–28. Anonymous. General instructions on the parasitological work in anti-plague institutes in the USSR. Stavropol: Russia, 1978. Anonymous. General instructions on parasitological works: methodological guidelines. MY 3.1 1027-01, April 6, 2001. Anonymous. Guidelines on calculating the quantity of rodents for anti-plague institutions. Stavropol: Russia, 1978. Anonymous. Instructions on epi-surveillance in natural foci of plague in Caucasus. Stavropol: Russia, 1986. Anonymous. Handbook of plague prevention. Saratov: Russia, 1972. Petrov, P. Adamyan, A., Rozanova, G., et al. Current study of high mountainous focus of plague. Especially dangerous infections. Stavropol: Russia, 1984. pp. 57–58. Tsikhistavi, Sh. Problems of especially dangerous infections. Saratov: Russia, 1972. 25, pp. 69–74. Babenshev, V., Emalianov, P., Jmukhadze, I. Ecology of Microtus arvalis in Javakheti Plateau (South Georgia). Especially dangerous infection in Caucasus. Edition 1. Stavropol: Russia, 1970. pp. 47–51. Klimenko, I., Lobanov, T. Some peculiarities of the reflection of epizootologic process in the plain foothills focus endemic focus of South Caucasus. Especially dangerous infections in Caucasus. Stavropol: Russia, 1978. pp. 39–40. Manvelian, D., Chautidze, A., Tarasov, M., Beyer, A. Peculiarities of epizootics of plague in scare settlements of Microtus arvalis and tactics of epizootological investigation in the years of low epizootics of focus. Especially dangerous infections. Stavropol: Russia, 1987. pp. 91–93. Tsikhistavi, Sh., Magradze, G., Tarasov, M., Goncharov, A. Results of epizootological investigation of southern slopes of Big Caucasus. Especially dangerous infections. Stavropol: Russia, 1987. pp. 111–112. Emelianov, P. Climate of high mountainous Caucasus as a possible factor of long-term maintenance of plague microbe in rodents’ burrows. Especially dangerous infections. Stavropol: Russia, 1987. pp. 80–82. Petrov, P., Adamov, A., Naiden, P., Khajakian, G. Some issues of the follow up study of epizootics of plague in mountainous focus of South Caucasus. Prevention of endemic infections. Stavropol: Russia, 1983.pp. 95–97. Aliev, M., Savelev, B., Djaganov, M., Djaganov, M., Ismailov, A., Mekhtiev, A. Some peculiarities of plague epizootics in common vole on the territory of Shakhbuz rayon in the Nakhchevan Autonomous Republic. Especially dangerous infections. Stavropol: Russia, 1987. pp. 61–64. Denisenko, I., Denisov, P., Podsvirov, A., et al. Peculiarities of epizootics of plague in 1985-1986 within the scope of activity of Elistin anti-plague station. Especially dangerous infections. Stavropol: Russia, 1987. pp. 73–75. Fedorov, V., Rall, Yu. Epizootological regularities and epidemiological peculiarities in natural plague foci of different types. Works of scientific research anti-plague institutions of Caucasus and South Caucasus. Edition 4. pp. 36–47. Chautidze, A., Manvelian, D., Tarasov, M., Goncharov, A. Observation on restoring the quantity of common vole after deep depression on Javakheti Plateau. Especially dangerous infections. Stavropol: Russia, 1987. pp. 314–315. Petrov, P., Adamyan, A., Mnatsakanyan, A. On reasons for the persistence of plague in certain parts of the high mountainous focus of the South Caucasus. Especially dangerous infections. Stavropol: Russia, 1978. pp. 65–67. Bezsonova, A. 1928. About two varieties of B. pestis, which can be distinguished while growing on glycerol-containing media. Herald Microbiol. Epidemiol. Parasitol. 7:250–253. Sakvarelidze, L., Nersesov, B., Georgadze, T., Kulev, A. Relative cultural and morphological characteristics of strains of Yersinia isolated in the territory of Georgia. Especially dangerous infections in the Caucasus. Stavropol: Russia, 1984. pp. 111–112. Kozlov, M.P. Plague (natural focality, epizootology, epidemiological manifestations). Moscow: Meditsina Press; 1979. Anisimov, A.P. 2002. Yersinia pestis factors assuring circulation and maintenance of the plague pathogen in natural foci ecosystems. Report 1. Mol. Gen. Mikrobiol. Virusol. 3:3–23. Aparin, G.P., Golubinskii, E.P. Plague microbiology. Manual. Irkutsk: Irkutsk State University; 1989.

28 30. 31. 32. 33. 34. 35. 36.

L. BAKANIDZE ET AL. Plague manual. Epidemiology, distribution, surveillance and control. Geneva: World Health Organization; 1999. p. 63. Evans, A., Brachman, P.S., editors. Bacterial infections of humans. Epidemiology and control. Third Edition. 1998. p. 547. Perry, R.D., Fetherston, J.D. 1997. Yersinia pestis – etiologic agent of plague. Clin. Microbiol. Rev. 10:35–66. Rudnev, G.P. Clinical picture of plague. Moscow: Medgiz; 1940. Anisimov, A., Linder, L.E., Pier, G.B. Intra-specific diversity of Yersinia pestis. Clinical microbiology reviews. 2004. p. 695. Kuklev, E. Quantitative assessment of epidemic potential of natural plague foci. Sc.D. thesis. Russian Research Anti-Plague Institute “Microbe.” Saratov: Russia, 1999. Kuklev, E., Kokushkin, A., Kutireb, B. 2001. Quantitative assessment of indicators of epidemiological potential in natural foci of plague and optimization of epidemiological surveillance on the infection. J. Epidemiol. Infect. Dis. 2001. 5:10–13.

Application of Modern Techniques for Studying Bacterial Pathogens in Georgia Ekaterine ZHGENTI, Gvantsa CHANTURIA, Mariam ZAKALASHVILI, and Merab KEKELIDZE National Center for Diseases Control and Public Health of Georgia, Tbilisi, Georgia Abstract. Over the last few years several molecular methods for rapid identification and fingerprinting of different pathogens have been applied at the National Center for Disease Control and Public health of Georgia (NCDC). These modern techniques strengthen the NCDC Georgia laboratory’s capabilities to rapidly respond to urgent public health threats and assist in the detection and tracking of these diseases. Application of genotyping techniques for investigation of NCDC Live Culture Collections enables Georgian public health scientists to determine the extent of strain variability as well as define the ability of new molecular techniques to characterize potential biothreat organisms circulating in the wild. Molecular methods, such as pulsed-field gel electrophoresis (PFGE), IS (insertion sequence) element fingerprinting, multiple-locus variable number tandem repeat analysis (MLVA) have been applied at NCDC in order to characterize bacterial isolates of Yersinis pestis and Francisella tularensis obtained from a variety of different foci. Such characterization has helped close a significant gap in our knowledge of strains present in this geographical region.

1. Introduction The National Center for Disease Control and Public Health of Georgia, former AntiPlague Station, carries out surveillance on especially dangerous pathogens (EDPs) in the country. During Soviet times, mainly traditional bacteriological methods were used, such as culture isolation, standard biochemical tests, microscopy of stained preparations, characteristics of growth in liquid and solid media, lyses of pure cultures by specific diagnostic bacteriophages, etc. Surveillance on EDPs in Georgia was stopped for some period, or minimized after the collapse of the Soviet Union. After Health Care System reform, the State Program of Epidemiological Surveillance and Control of Epidemics, Surveillance, Control and Prevention of Quarantinous, Particularly Dangerous and other Communicable Diseases were launched. Many U.S. governmental organizations, like the Department of Health and Human Services (DHHS), Department of Homeland Security (DHS), Department of Defense (DoD), etc. implemented in Georgia scientific projects that deal with very important Georgian Public Health issues, and are directed toward the improvement of public health. Particularly, the National Center for Disease Control and Public Health of Georgia (NCDC) has implemented projects on botulism, amebiasis, hepatitis B, tuberculosis, leishmaniasis, antibiotic resistance, viral meningitis, plague and tularemia, among

K.P. O’Connell et al. (eds.), Emerging and Endemic Pathogens, DOI 10.1007/978-90-481-9637-1_4, © Springer Science + Business Media B.V. 2010

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others. These projects have contributed to the modernization of the epidemiological and surveillance tools available to public health scientists of the Republic of Georgia. These programs also provided an opportunity for young and qualified staff to be trained in leading laboratories all over the world. These projects also made possible the purchase of new equipment for molecular biology, such as thermocyclers, ultracentrifuges, pulsed-field gel electrophoresis (PFGE) equipment, a Light Cycler (real-time PCR sequence detection system), and a DNA sequencer. With this training and equipment, Georgian scientists have applied molecular methods to the identification and fingerprinting of different pathogens. These modern techniques strengthen the NCDC Georgia laboratory’s capabilities to rapidly respond to urgent public health threats and assist in the detection and tracking of these diseases. 2. Rapid Diagnosis of Especially Dangerous Pathogens The rapid and accurate laboratory diagnosis of especially dangerous pathogens is a key to monitoring the presence of these organisms in natural foci and in cases of human disease. Human cases of Anthrax, Tularemia and Brucellosis still occur in Georgia; only during the last year 42 cases of anthrax, 169 cases of brucellosis, 31 cases of tularemia were registered. Twenty-six of the tularemia derived from a tularemia outbreak in vil. Rene, Kaspi region. In all cases, the rapid identification of these infectious agents was essential to ensure proper medical intervention. For detection of especially dangerous bacterial and viral agents such as Bacillus anthracis, Francisella tularensis, Yersinia pestis, Brucella species and influenza A/H5, real-time PCR using a LightCycler 2.0 (Roche) and reagents (Idaho Technologies) has been applied at NCDC. This modern rapid diagnostic test was very useful in detection of first influenza A/H5 cases in Georgia. In 2006, according to the information received by telephone hot-line, 10 dead wild swans were found in the Ajara region. Samples from two swans tested by real-time RT-PCR at NCDC were positive for A/H5. Samples were sent to a reference laboratory in the United Kingdom for independent confirmation. 3. Molecular Investigation of Especially Dangerous Pathogens In addition to rapid diagnostics, Georgian public health scientists at the laboratory of NCDC carry out molecular-epidemiological investigations of especially dangerous infections and characterization of pathogens kept in the Live Culture Collection of NCDC, as well as newly isolated strains. The molecular typing methods used at NCDC include: • • •

Pulse Field Gel Electrophoresis (PFGE) IS (Insertion Sequence) element fingerprinting Multiple-Locus Variable Number Tandem Repeat Analysis (MLVA)

Detailed characterization of both the naturally occurring pathogens, as well as those from NCDC Live Culture Collections have great importance to understand the range of variability and the distribution of these dangerous pathogens. In this study we

APPLICATION OF MODERN TECHNIQUES

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will review the results obtained by application of above mentioned methodologies on Y. pestis and F. tularensis. 3.1. PFGE Typing of Y. pestis Strains Pulsed-field gel electrophoresis is a technique that detects large restriction fragment size differences and is considered the “gold standard” of the methods of molecular typing, being highly discriminatory and useful for many bacterial pathogens [6, 9]. Genomic DNA patterns generated by pulsed-field gel electrophoresis are specific for different strains of an organism and have significant value in epidemiologic investigations of infectious-disease outbreaks. The PFGE technique was used for typing 46 Y. pestis strains kept in the Live Culture Collection of NCDC. The genomic DNAs of Y. pestis were prepared in agarose plugs as described previously [5]. Genomic DNA was digested with restriction endonuclease SfiI (New England Biolabs). The restriction fragments were resolved by PFGE with a CHEF-DRII apparatus (Bio-Rad Laboratories). The electrophoretic conditions used were as follows: initial switch time, 2 s; final switch time, 30 s; run time, 20 h; 6.0 V/cm. 1

2

3

4

5

6

7

8

9

10

Figure 1. Pulse-field gel electrophoresis patterns of SfiI-digested DNA of Y. pestis strains. Lane 1, low range PFGE markers (New England BioLabs). Lanes 2 through 9, Y. pestis strains isolated from Georgia and Armenia. Lane 10, Lambda ladder PFGE marker (New England Biolabs).

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1

2

3

4

5

6

7

8

9

10

Figure 2. Pulse-field gel of genomic DNA of Y. pestis strains, after digestion with SfiI. Lane 1, Lambda ladder PFG marker (New England Biolabs). Lane 2, isolate from the Dedoplistskaro region. Lane 3, isolate from Kirgizia. Lanes 4, 7, 8, 9, isolates from the Ninotsminda region. Lanes 5, 6, isolates from Dagestan. Lane 10, low range PFGE marker (New England BioLabs).

An analysis of the SfiI macro-restriction patterns generated by PFGE showed that 39 Georgian isolates from the Ninotsminda region and two Armenian isolates had an identical PFGE profile (Fig. 1). These results may be due to the close geographic proximities of these regions. A distinct pattern was observed from the strains isolated from Dedoplistskaro region, Georgia (Fig. 2, lane 2). Additional, separate groups were formed by strains isolated in Dagestan (North Caucasus) (Fig. 2, lanes 5 and 6), Azerbaijan and Kirgizia (Fig. 2, lane 3). Overall, five distinct SfiI-based PFGE profiles were obtained among the 46 Y. pestis strains: I.

Thirty-nine strains isolated from the Ninotsminda region, Georgia and two strains from Armenia II. Strain isolated from the Dedoplistskaro region, Georgia III. Two strains isolated from Dagestan

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IV. Strain isolated from Azerbaijan V. Strain isolated from Kirgizia 3.2. IS100 Fingerprinting of Y. pestis Strains Several classes of insertion sequences (IS100, IS285, IS1541, IS1661) are found in multiple copies in the Y. pestis genome [1, 2, 8]. A PCR-based genotyping system that detects divergence of IS100 locations within the Y. pestis genome (IS-positional genotyping) was used to characterize Y. pestis strains in the NCDC collection of isolates. The method is based on measuring a fragment polymorphism originated from the amplification of the region located between one or both ends of an IS 100 element and the neighboring ORF. A set of 82 locus-specific primers (developed at the Lawrence Livermore National Laboratory, University of California) designed from the sequence of Y. pestis strain CO92, and two unique primers corresponding to sequences at the right and left ends of the IS100 element (ISfor1754: GGTGATGCAGCACTGACCTC, ISRev216: GCTCAGATTTTGCCTGCAAA) were used to amplify fragments between the end of IS100 and its neighboring gene.

Figure 3. IS100-based fingerprinting of 46 Y. pestis strains. The dendrogram was constructed using the programs FreeTree and TreeView.

All 46 Y. pestis strains gave PCR-amplified fragments identical in size to those produced by C092 when amplified with 33 out of the 82 locus-specific primers. Thirty other locus-specific primers failed to amplify in all 46 strains, thus indicating that none of these strains carry corresponding IS100 copies at the same locus as in CO92, or that these regions’ positions are shifted relative to their positions in CO92. Strain-specific

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E. ZHGENTI ET AL.

differences were observed when genomic DNA was amplified with 19 of the locusspecific primers. Each strain was given a score of 1 (present) or 0 (absent) for each possible band position. IS100-based fingerprinting study reveals totally five distinct patterns (Fig. 3). I. II. III. IV. V.

39 strains from Ninotsminda region and two Armenian isolates formed a homogeneous group with identical genotype (36 positive loci amplified out of 82 tested). One strain isolated from Dedoplistskaro Region, Georgia, was considerably different from other Georgian strains (47 positive loci amplified out of 82 tested). Two strains isolated from Daghestan resembled a single strain isolated from Dedoplistskaro, but differed from it by amplification of one additional band. One strain isolated from Azerbaijan was positive to amplify 45 primers and was different from strain C771 multiple amplifications on two primers and failed to amplify one band. One strain from Kirgizia gave positive amplification on 51 primers.

The results obtained by IS100-based fingerprinting study resembled those obtained by PFGE typing. However, additionally we found out one unique Georgian strain, isolated from Dedoplistskaro region showing more similarity with North Caucasian and Azerbaijani isolates compared to other Georgian isolates from the Ninotsminda region. 3.3. PFGE Typing of F.tularensis Strains The PFGE technique was used for typing 52 Georgian isolates of F. tularensis and the F. tularensis Live Vaccine Strain (LVS) as well. The genomic DNAs were prepared in agarose plugs, using the CDC PulseNet protocol (http://www.cdc.gov/pulsenet/ protocols htm) for PFGE typing of Salmonella serotypes, with a few modifications. DNA was digested with several restriction endonucleases (XhoI, BamHI, XbaI, SpeI); the clearest results were obtained following XhoI digestion. The electrophoretic conditions used were as follows: initial switch time, 0.1 s; final switch time, 10 s; run time, 20 h; 6.0 V/cm; temperature, 14°C. XhoI -macrorestriction patterns generated by PFGE revealed that all strains had an identical PFGE profile except LVS (which showed a slightly different pattern). The substantial genetic similarity among F. tularensis strains makes their differentiation difficult [4] (Fig. 4). 3.4. MLVA Typing of F. tularensis Strains A recently acquired DNA sequencer gave us the opportunity to use another modern technique for bacterial identification, multiple-locus variable number tandem repeat analysis (MLVA). MLVA is a higher-resolution technique and permits better differentiation of F.tularensis strains that had an identical PFGE pattern. Variable-Number Tandem Repeats (VNTRs) are genomic regions with potentially extreme variation and has a great strain discrimination capacity [7]. MLVA has been used for individual strain discrimination within several bacterial species with little genomic variation.

APPLICATION OF MODERN TECHNIQUES

1

2

3

4

5

6

7

8

35

9

10

Figure 4. Pulsed-field gel of genomic DNA of Georgian F. tularensis strains and F. tularensis strain LVS after digestion with XhoI. Lanes 1-8, F. tularensis strains isolated in Georgia. Lane 9, F. tularensis strain LVS. Lane 10, low range PFGE marker (New England BioLabs).

A set of 25 PCR primers [3, 7] flanking the repeat motif were used to characterize the repeat diversity among 17 Georgian isolates of F.tularensis and strain LVS. Fluorescently labeled amplicons were visualized by capillary electrophoresis (CE) on a SEQ 8000 Beckman Coulter instrument using a fragment analysis program. The degree of VNTR variability for a locus was assessed by the number of alleles observed. Typespecific C1–C4 markers reveal that all F. tularensis strains from NCDC Live Culture Collection belong to subsp. Holarctica (type B). Twenty five MLVA marker systems revealed a total three unique genotypes among 17 F. tularensis isolates which differed from the LVS and SCHU-S4 (Fig. 5).

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Figure 5. Dendrogram of MLVA of 17 F. tularensis strains constructed using all 25 VNTR marker loci and the programs FreeTree and TreeView.

Diversity among the isolates was detected on three out of 25 MLVA markers. The most variable VNTR locus was Ft-M3. The observed diversity identified across a limited collection of isolates suggests the presence of a heterogeneous population of F. tularensis holarctica (type B) in Georgia. 4. Future Plans The ability to characterize and determine relatedness among bacterial isolates involved in an infectious disease outbreak is a prerequisite for informative epidemiological investigations. A detailed characterization of both the naturally occurring isolates as well as reference strains from collections are of great importance to understand the range of variability and the distribution of these dangerous pathogens. In addition, this

APPLICATION OF MODERN TECHNIQUES

37

level of characterization contributes to our understanding of natural foci of infectious agents in this region. Of the techniques used here, MLVA methodology showed the highest discriminatory power and can assist in completion of the epidemiological analysis. Such typing methods may be of significant value in identifying the natural reservoirs of F. tularensis, including the elucidation of transmission routes between water, bloodfeeding arthropods, and animals. It will be of great importance to complete MLVA typing for the rest of the F. tularensis strains kept in NCDC’s Live Culture Collection and to type newly isolated strains as well. The MLVA technique can be applied for typing other EDPs (B. anthracis, Y. pestis, Brucella spp., and so on). In the nearest future we plan to implement other modern DNA sequence based high-resolution subtyping systems at NCDC. The application of various modern techniques will provide a valuable approach to comprehensive epidemiological understanding of different pathogens. References 1. 2. 3. 4. 5. 6. 7.

8.

9.

Chain, P. S., Carniel, E., Larimer, F. W., Lamerdin, J., Stoutland, P. O., Regala, W. M., Georgescu, A. M., Vergez, L. M., Land, M. L. et al. 2004. Insights into the evolution of Yersinia pestis through wholegenome comparison with Yersinia pseudotuberculosis. Proc. Natl. Acad. Sci. U S A 101:13826–13831. Chain, P. S. G., Hu, P., Malfatti, S. A., Radnedge, L., Larimer, F., Vergez, L. M., Worsham, P., Chu, M. C. & Andersen, G. L. 2006. Complete genome sequence of Yersinia pestis strains Antiqua and Nepal516: evidence of gene reduction in an emerging pathogen. J. Bacteriol. 188:4453–4463. Farlow, J., Smith, K. L., Wong, J., Abrams, M., Lytle, M. & Keim, P. 2001. Francisella tularensis strain typing using multiple-locus, variable-number tandem repeat analysis. J. Clin. Microbiol. 39:3186–3192. Garcia del Blanco, N., Dobson, M. E., Vela, A. I. et al. 2002. Genotyping of Francisella tularensis strains by pulsed-field gel electrophoresis, amplified fragment length polymorphism fingerprinting, and 16S rRNA gene sequencing. J. Clin. Microbiol. 40:2964–2972. Gautom, R. K. 1997. Rapid pulsed-field gel electrophoresis protocol for typing of Escherichia coli O157:H7 and other gram-negative organisms in 1 day. J. Clin. Microbiol. 11:2977–2980. Huang, X. Z., Chu, M. C., Engelthaler, D. M. & Lindler, L. E. 2002. Genotyping of a homogeneous group of Yersinia pestis strains isolated in the United States. J. Clin. Microbiol. 40:1164–1173. Johansson, A., Farlow, J., Larsson, P., Dukerich, M., Chambers, E., Bystrom, M., Fox, J., Chu, M., Forsman, M., Sjostedt, A. & Keim, P. 2004. Worldwide genetic relationships among Francisella tularensis isolates determined by multiple-locus variable-number tandem repeat analysis. J. Bacteriol. 186:5808–5818. Motin, V. L., Georgescu, A. M., Elliott, J. M., Hu, P., Worsham, P. L., Ott, L. L., Slezak, T. R., Sokhansanj, B. A., Regala, W. M., Brubaker, R. R. & Garcia, E. 2002. Genetic variability of Yersinia pestis isolates as predicted by PCR-based IS100 genotyping and analysis of structural genes encoding glycerol-3-phosphate dehydrogenase (glpD). J. Bacteriol. 184:1019–1027. Revazishvili, T., Bakanidze, L., Gomelauri, T., Zhgenti, E., Chanturia, G., Kekelidze, M., Rajanna, C., Kreger, A. & Sulakvelidze, A. 2006. Genetic background and antibiotic resistance of Staphylococcus aureus strains isolated in the Republic of Georgia. J. Clin. Microbiol. 44:3477–3483.

Especially Dangerous Infections in Azerbaijan Sh. GURBANOV and S. AKHMEDOVA Republican Anti-Plague Station, Baku, Azerbaijan

Abstract. A recent history in Azerbaijan of the epidemiology of especially dangerous pathogens is given, with emphasis on brucellosis, plague, anthrax, and cholera. Brucellosis in humans is driven seasonally by the influx of dairy products from rural areas into the cities, emphasizing the importance of domesticated animals as the reservoir. Similarly, livestock are an apparent reservoir for the anthrax bacillus. Conversely, native wild rodent species, both highland and lowland, have been noted as reservoirs for the plague organism. Cholera has a long history in Azerbaijan and also a recent history. Both food and water sources are an apparent vector.

1. Brucellosis 1.1. Brucellosis in Cattle Brucellosis first was detected in Azerbaijan in 1922. Brucellosis was registered in 20 regions of the Republic by 1945 and had reached 48 regions by 1950; during the subsequent years, it spread throughout the country. The largest pockets of disease are found in the big (from Northwestern Georgia to the Caspian coast of northern Azerbaijan) and small (or Lesser, from southern Georgia and Armenia into southwestern Azerbaijan) Caucasus and in the Kura-Aras lowland (the plains in Azerbaijan between the Caucasus and Lesser Caucasus). The Kura-Aras lowland area contains most of the winter pastures in the Republic, and, during their use, large numbers of cattle are driven there, which explains the high level of disease among the area’s inhabitants. When large herds of cattle are kept in unsanitary conditions, sick cattle mix with healthy cattle, thereby increasing the infection rate among the animals and creating the risk of human infection. The situation is complicated further because the animal industries in Azerbaijan are distantly located, and the cattle are driven to seasonal pastures across many regions, making use of the same pastures and sources of water. Cattle are moved gradually from the foothills and mountain areas to the lowlands and vice versa. This situation, a result of military conflict in the Republic, also affects the epidemiologic and epizootic state of brucellosis. Thus, as a result of the loss of 20% of territory of the Republic and without consideration for any quarantine protocols, cattle-breeding farms from these regions were transferred to territories of other regions.

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1.2. Brucellosis in Sheep and Goats According to research conducted at laboratories at the Republican Anti-Plague Station and its branches, a majority of cases occur in the spring–summer season. This indicates that the goat–sheep type of brucellosis is characteristic of the Republic. That is, a spring–summer increase in disease is characteristic of this type of brucellosis owing to lambing of goats and sheep, which, as a rule, takes place during early spring, and the lactation period, which proceeds during the spring and summer. Our long-term observations also indicate that a prevalence of brucellosis in small livestock in Azerbaijan, where 113 strains of Brucella were isolated for the last period, of which 100 (88.5%) were identified as Brucella melitensis and 13 (11.5%) as Brucella abortus. 1.3. Incidence of Brucellosis in Humans Data on rates of brucellosis infection by season show that it is identical in cities compared with rural settlements. While it is taken for granted that human infection by brucellosis may take place at any time of the year, the seasonal increase in infection rates in cities undoubtedly is connected with the mass influx of dairy products, particularly cheese, produced in rural areas of the Republic. Alimentary routes of infection are unique to the Republic of Azerbaijan. 2. Anthrax The epidemiologic situation regarding anthrax in the Republic does not inspire optimism. The increase of disease in humans started in the early 1990s: 33 human infections were identified in 1992, 55 in 1993, and 59 in 1994. Later, there was an insignificant decrease in the rate of infection. To conduct an epidemiologic assessment of anthrax in Azerbaijan, it is necessary to pay attention to the so-called “hearths” of disease, which are numerous and persistent. Presently, 115 stationary, nonsatisfactory foci of infection, encompassing all physical and geographical regions of Azerbaijan, are registered. 2.1. Anthrax in Cattle Analysis of our data indicates that the source of infection is basically infected cattle, which are found at individual farms. It is necessary to point out that not all cattle from individual farms are vaccinated against anthrax by the veterinary service, and cattle owners admit to secretly slaughtering diseased animals without veterinary control. The highest rate of anthrax infection in cattle also is seen during the summer, and occurrences of the disease in cattle decrease as the temperature falls. 2.2. Anthrax in Humans Seasonal fluctuations in human anthrax infections in Azerbaijan are rather sharp; it is most predominant in summer, reaching maximum levels in August. Given the observation above that the infection rate in cattle peaks in summer, a direct link between anthrax infection in people and cattle is obvious.

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Analysis of human anthrax infection by profession of the infected patient indicates that, for the most recent period, cattle owners and their family members and those who purchased meat from them are most often infected with anthrax. The difference in infection rate between the sexes is insignificant. It stands to reason, then, that infection of men is connected with the slaughter and handling of infected animals and infection of women is connected with cattle care, culinary meat processing, cleaning of slaughter houses, and wool washing. People of all ages are infected with anthrax in the Republic. The highest percentage of disease occurs in the 25–34 (23.8%) and 35–44 (29.7%) age groups, i.e., diseases caused by anthrax prevail among the adult population. Disease among children younger than 14 (8.2%) also raises interest. Anthrax-related diseases among children are connected with the specifics of life in the countryside, where children have close contact with the remains of cattle that died from anthrax. Considering the above-mentioned link and the availability of a large number of stationary, nonsatisfactory areas of anthrax within the territory of the Republic, weak veterinary control (especially in the private sector, which now is considered the core issue in the animal industries of Azerbaijan), and the uncontrolled sale of meat and dairy products at markets in cities and other regions of the Republic, it is likely that the unfavorable epizootic situation of brucellosis and anthrax and its interrelation with the epidemiologic situation within the territory of the Republic will continue in forthcoming years. 3. Plague The natural occurrence of plague is considered to be an ecologic and geographic problem. We will attempt to summarize data about plague centers in Azerbaijan since their introduction. It is well-established that currently there are three natural plague centers within the territory of the Azerbaijan Republic: the trans-Caucasian lowlandfoothills, the near Aras, and the trans-Caucasian high-mountainous hearth. 3.1. Plague in the Trans-Caucasian Lowland-Foothills In the first of these centers, plague is connected with settlements of the Libyan jird, located in the arid plains and foothills of Azerbaijan and East Georgia. This rodent is the main carrier of the plague microbe in this hearth. The main carriers are the fleas Xenopsylla conformis and Ceratophyllus laeviceps. 3.2. Plague in the Near-Aras Research conducted in the near-Aras hearth shows that plague is supported here because of the Libyan jird of Vinogradov, which is highly sensitive to plague and which is the main carrier of this infection in the semidesert and upland steppe of the left-bank of the Aras (Nakhichevan Аutonomous Region). The main carriers are the fleas Xenopsylla conformis and Ceratophyllus iranus. In the territory of this hearth, strains of the “sandy” race of Yersinia pestis, possessing high virulence, circulate. Hearth indications of plague in the near Aras part of the Nakhichevan Аutonomous Region were observed in 1967, and only one epizootic wave is registered. Therefore, insufficient material has been accumulated, and consequently the plague strains from this territory are less well characterized.

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3.3. Plague in the Trans-Caucasus High-Mountain Hearth The first data about epizootic plague infections in the trans-Caucasian uplands appeared in 1958. The study of the development of hearth indications in this area is quite difficult because of the complexity of the landscape, the inaccessibility of some areas for observation, and seasonal limitations on periods of field work. Observations in the trans-Caucasian high-mountainous hearth led to the conclusion that the epizootic nature of plague has its reservoir in settlements of ordinary field mice, which are located mainly in meadows and steppes and on the subalpine meadow. The flea Ceratophyllus caspius is considered to be the leading carrier. Ceratophyllus consimilis is the secondary carrier. Strains of plague microbe detected in the hearth are significantly different from the previously mentioned strains. Along with other indications, hearth strains have selective virulence in relation to laboratory animals and yearly fluctuations in level. Although these hearths are not distantly located from the others described above, they are sufficiently ecologically and geographically separated from them that the natural history of plague, to the extent it has been observed and recorded, appears significantly different from other biomes in the country. 4. Cholera 4.1. Historical Outbreaks Analysis of the literature and multiple archive records shows that penetration of cholera into the territory of the Russian empire in the past occurred mostly through Azerbaijan [1–4]. This was facilitated by several factors: (1) travel along the rivers Aras and Kura, originating abroad in territories infected by cholera, (2) the intensive trade relations between Azerbaijan and countries in the Near East, (3) the availability of numerous caravan trails, (4) climatic conditions favorable for infection, and (5) an extremely low level of household sanitation. It is possible presently to bring cholera infection into the Republic of Azerbaijan from countries whose conditions are not hospitable to it but that share borders or have established close economic, tourist, or other relations. During six pandemics of cholera, Azerbaijan suffered a total of 24 outbreaks. Seven of these outbreaks originated in Iran (1823, 1830, 1846, 1866, 1871, 1872, and 1904), seven originated in Russia (1871, 1872, 1907, 1908, 1920, 1921, and 1923), two originated in Turkey (1847 and 1918), and one outbreak originated in Uzbekistan (1828). Seven other outbreaks of cholera in Azerbaijan evidently originated within the country (1905, 1909, 1910, 1911, 1915, 1916, and 1917) [5]. During all six pandemics, cholera penetrated into Russia through its southern borders and Azerbaijan was the first territory affected because it was on the transportation route between the Russian empire and eastern and southern countries. Azerbaijan also had a number of natural and climatic features that particularly encouraged the spread of cholera. The poor sanitary conditions of the population, a weak water supply, and the absence of a sewage system also contributed to the wide spread of cholera in Azerbaijan during the first six pandemics.

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4.2. Modern Outbreaks Certainly, some of these same factors played a role in choleras penetration into the territory of Azerbaijan during the seventh pandemic. Outbreaks of high or low intensiveness have been occurring in the Republic almost annually since 1970. Abundant seeding of many open reservoirs by Vibrio cholerae has been observed and those that have high importance for the national economy have been used by the population for various purposes. Azerbaijan’s on-going importance as a point of distribution of cholera into other regions and countries is defined by several factors: (1) its central geographical position, (2) the possibility of penetration of cholera infection into foreign regions, (3) the presence of endemic cholera in its waterways, and (4) the continuing relatively low sanitary and hygienic standards of its population, including its insufficient water supply and problems with the sewage systems of populated settlements. Cholera outbreaks, registered in Azerbaijan in 1970 (42 cases), 1977 (62 cases), 1981 (19 cases), 1985 (48 cases), 1988–1989 (68 cases), 1993 (40 cases), 1994 (22 cases), 1995 (66 cases), and 1998 (19 cases), were caused by V. cholerae El Tor (hemolysisnegative, enterotoxigenic). During these years (almost annually), sporadic cases of cholera caused by hemolysis-positive V. cholerae El Tor were also registered. Factors of infection transfer were water from open reservoirs being used for recreational purposes, drinking, household requirements, and pipes. In most cases, cholera outbreaks in Azerbaijan in 1977, 1981, 1985, 1988, 1989, 1995, and 1998 occurred in settlements located in the basins of the Kura (lower stream) and Aras rivers and mainly along the waterways of the Aras river or on the banks of irrigating channels taking water from this river. Human infection from tap water containing V. cholerae El Tor was noted in Baku in 1985, 1993, and 1995. It is important to note that one of the branches of the Baku water pipeline originates below the junction of the Aras and Kura rivers. During the cholera outbreak in Baku in 1985 (Karadagh district), the main factor in the transfer of infection was pilaw (rice) given in commemoration of a woman who had died of food poisoning (V. cholerae El Tor was extracted from her gall bladder posthumously). Therefore, two means of transfer of cholera infection were observed in Azerbaijan: water (1977–1998) and food (1985). Disease and contamination rates varied in the outbreak years from 7 to 24 and from 20 to 65 per 100,000 of population. References 1.

2. 3. 4. 5.

Barbieri, E., Fladanzo, L., Fiorentini, C., Pianetti, A., Affone, W., Fabbri, A., Matarrese, P., Casiere, A., Katouli, M., Kuhn, I., Mollby, R., Bruscolini, F., Donelli, G. 1999. Occurrence, diversity and pathogenicity of halophilic Vibrio spp. and non-O1 Vibrio cholerae from estuarine waters along the Italian Adratic coast. Appl. Environ. Microbiol. 65:2748–2753. Birger, O. Manual of microbiology methods. Moscow: Medkniga; 1982. Centers for Disease Control and Prevention (CDC). 1993. Update: cholera – Western Hemisphere in 1992. MMWR Morb. Mortal. Wkly. Rep. 42:89–91. Kepner, R. J., Pratt, J. 1994. Use of fluorochromes for direct enumeration of total bacteria in environmental samples: past and present. Microbiol. Rev. 58:603–615. Gurbanov, S. 2001. Cholera in the Republic of Azerbaijan [Russian]. Zh. Mikrobiol. Epidemiol. Immunobiol. 6:50–55.

Strengthening the Early-Warning Function of the Surveillance System: The Macedonian Experience Elisaveta STIKOVA1,2, Dragan GJORGJEV1,2, and Zarko KARADZOVSKI2 1 National Public Health Institute, Skopje, Republic of Macedonia 2 Medical Faculty, University “Ss Cyril and Methodius,” Skopje, Republic of Macedonia Abstract. Epidemics and pandemics can place sudden and intense demands on health systems. The world requires a global system that can identify and contain public health emergencies rapidly and reduce panic and disruption of trade, travel, and society in general. Strengthening public health preparedness requires establishing an integrated global alert and response system for epidemics and other public health emergencies along the lines of the World Health Organization’s International Health Regulations. The revised International Health Regulations provide a global framework to address these needs through a collective approach to the prevention, detection, and timely response to any public health emergency of international concern. A standardized approach for readiness and response to major epidemic-prone diseases should be developed. An early-warning and rapid-alert system is one of the possibilities to improve readiness at the local, regional, national, and international level to limit the spread of disease and to reduce health, economic, and social damage. The Republic of Macedonia, with World Health Organization support, has implemented an earlywarning system (ALERT) for priority communicable diseases to complement the routine surveillance system that reports individual confirmed cases. ALERT relies on reporting of eight syndromes by primary care facilities. Data are analyzed weekly at the regional level and transmitted to national epidemiologists. It is perceived to be a simple and flexible tool for detecting and triggering timely investigation and control of outbreaks. ALERT was identified as a useful instrument for forecasting and detecting the start of the influenza season.

1. Introduction We live in a world of new and evolving threats [1-7]. There are six clusters of threats with which the world must be concerned now and in the decades ahead: war between states; violence within states, including civil wars, large-scale human rights abuses and genocide; poverty, infectious disease, and environmental degradation; nuclear, radiological, chemical, and biological weapons; terrorism; and transnational organized crime [8, 9]. In today’s world, a threat to one is a threat to all. Globalization means that a major terrorist attack anywhere in the industrial world would have devastating consequences for the well-being of millions in the developing world. Any one of 700 million international airline passengers every year can be a carrier of a deadly infectious disease. K.P. O’Connell et al. (eds.), Emerging and Endemic Pathogens, DOI 10.1007/978-90-481-9637-1_6, © Springer Science + Business Media B.V. 2010

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At the beginning of the 21st century, the world still confronts: • • •

The emergence of new or newly recognized pathogens such as Nipah virus, Ebola virus, Marburg virus, severe acute respiratory syndrome (SARS) corona virus, and influenza A/H5N1 virus The recurrence of well-characterized epidemic-prone diseases such as cholera, dengue, influenza, measles, meningitis, shigellosis, and yellow fever The accidental release or deliberate use of biological agents such as anthrax [7]

Globally, from 1998 to December 2006, the World Health Organization (WHO) identified 2,031 syndromes and diseases that were potential public health emergencies of international concern. Of these events, 195 subsequently were verified in the WHO European Region [10]. TABLE 1. Syndromes and Diseases Associated with Verified Events That Were Potential Public Health Emergencies of International Concern in the WHO European Region, 1998–2006 Syndrome/Disease Foodborne or waterborne diseases Acute respiratory syndrome Acute hemorrhagic fever syndrome Other zoonotic diseases Acute neurological syndrome Vector-borne disease Vaccine-preventable diseases Influenza (A/H5 virus) Influenza (novel virus, not H5) Cholera Yellow fever Plague Others Unknown Total

Number of Events 42 34 32 20 16 11 10 2 2 4 3 2 8 9 195

Percent of Total 22 17 16 10 8 6 5 1 1 2 2 1 4 5 100

In addition to the events described in Table 1, 10 member states in the European Region reported 34 cases of SARS, including one death, from February through July 2003. This figure corresponds to 4% of the cases reported worldwide over the same period. Communicable diseases in the European Region account for 9% of the disease burden measured in disability-adjusted life years. This is largely attributable to high rates of tuberculosis and growing rates of HIV infection, particularly in central and eastern European countries and in central Asia, and to emerging and reemerging epidemic-prone diseases. Some of the most prominent public health programs currently being undertaken are the eradication of smallpox, the ongoing efforts to eradicate poliomyelitis and to eliminate measles, the Expanded Programme on Immunization, the Stop TB Partnership, the coordination of the global epidemic response to control SARS, and the ongoing efforts to contain the spread of influenza A/H5N1 virus (avian influenza) and to prepare for pandemic influenza. We must be aware, however, that widening development gaps, the collapse of public health infrastructure, poverty, urbanization, civil strife, environmental change and degradation, and the globalization of travel and trade can contribute to the new challenges posed by epidemic-prone and emerging communicable diseases worldwide.

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These are reasons for public health-capacity building at the local, national, and international level and strengthening of public health preparedness and response systems around the world [11, 12]. Avian influenza is a major challenge for the international community and a real public health threat. Globally, as of June 19, 2008, 385 laboratory-confirmed human cases of influenza A/H5N1 virus infection, including 243 fatal cases (case-fatality rate about 60%), had been registered in 15 member states. In the European Region in 206, 20 human cases including nine deaths were reported in Turkey (12 cases and four deaths) and Azerbaijan (eight cases and five deaths) [13]. Many international organizations, including WHO, and experts are working together to coordinate activities regarding key actions, including controlling avian influenza in animals and reducing opportunities for human infection; strengthening the earlywarning system; containing or delaying the spread at the source; reducing morbidity, mortality, and social disruption; and conducting research to guide response measures. The challenges that epidemic-prone diseases, including avian influenza, pose to WHO are: • • •

How to minimize the risk of international spread How to assist countries in preparing for and controlling epidemics How to coordinate and focus global resources when no single institution has the necessary capacity [14, 15]

The revised International Health Regulations (IHR) [4], which entered into force in June 2007, provide a legal framework to assist countries in protecting the health of their populations against any potential public health emergency of international concern, implementing the necessary measures, and contributing to making the world more secure [16, 17]. National and international partnerships will maximize the benefit of strengthening surveillance and response [18, 19]. To ensure the timely detection of events that are potential public health emergencies of international concern, the WHO Regional Office for Europe, aside from relying on official reports from national health authorities, systematically screens a wide range of formal and informal sources of information in several languages. The monitoring and control of communicable diseases are facilitated by wellfunctioning surveillance systems. Surveillance systems provide information for early detection of potential outbreaks and help to identify disease trends, risk factors, and the need for interventions [20, 21]. They provide information for priority setting, planning, implementation, resource allocation, and for evaluating preventive programs and control measures. Surveillance systems are set up to detect and control communicable diseases in humans regardless of the cause and manner of transmission. Their principal aim is to prevent further transmission of the disease to other persons by epidemiologic investigation [22, 23]. The timely detection of outbreaks at the regional and national level is a priority function of communicable disease surveillance systems. In the process of implementing its IHR [4], WHO included the requirement for member states to maintain an adequate core capacity to detect and respond to significant public health threats. This requires that member states develop effective early-warning systems and strengthen their investigation and response capabilities [24, 25].

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Since December 2000, central and eastern Europe and the Baltic countries have worked together to strengthen surveillance and early-warning and response systems [26]. To ensure a rapid and effective response to events (including emergencies) related to communicable diseases, an early-warning and response system has been put in place in Macedonia. This is a Web-based system linking the 10 Regional Public Health Institutes (RPHIs) with the National Public Health Institute (NPHI) and the Ministry of Health. 2. Overview of Syndromic Surveillance Innovative electronic surveillance systems are being developed to improve early detection of outbreaks attributable to biologic and other causes of threats. A review of the rationale, goals, definitions, and realistic expectations for these surveillance systems is a crucial first step toward establishing a framework for further research and development in this area [27]. Syndromic surveillance has been used for early detection of outbreaks; to follow the size, spread, and tempo of outbreaks; to monitor disease trends; and to provide reassurance that an outbreak has not occurred [28]. Syndromic surveillance systems seek to use existing health data in real time to provide immediate analysis and feedback to those charged with the investigation and follow-up of potential outbreaks. Optimal syndrome definitions for continuous monitoring and specific data sources best suited to outbreak surveillance for specific diseases have not been determined [29, 30]. Broadly applicable signal-detection methodologies and response protocols that would maximize detection while preserving scant resources are being sought [31, 32]. Stakeholders need to understand the advantages and limitations of syndromic surveillance systems. Syndromic surveillance systems might enhance collaboration among public health agencies, health-care providers, information-systems professionals, academic investigators, and industry. However, syndromic surveillance does not replace traditional public health surveillance, nor does it substitute for direct physician reporting of unusual or suspect cases of public health importance [33, 34]. Specific definitions for syndromic surveillance are lacking, and the name itself is imprecise. Diverse names used to describe public health surveillance systems for early outbreak detection include: • • • • • • •

Early-warning systems Prodrome surveillance Outbreak-detection systems Information system-based sentinel surveillance Biosurveillance systems Health-indicator surveillance Symptom-based surveillance

However, syndromic surveillance is the term that has persisted. The fundamental objective of syndromic surveillance is to identify illness clusters early, before diagnoses are confirmed and reported to public health agencies, and to

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mobilize a rapid response, thereby reducing morbidity and mortality. Syndromic surveillance aims to identify a threshold number of early symptomatic cases, allowing detection of an outbreak earlier than would conventional reporting of confirmed cases [35]. The ability of syndromic surveillance to detect outbreaks earlier than conventional surveillance methods depends on such factors as the size of the outbreak, the population dispersion of those affected, the data sources and syndrome definitions used, the criteria for investigating threshold alerts, and health-care providers’ ability to detect and report unusual cases [36]. Syndromic surveillance focuses on the early symptom (prodrome) period before clinical or laboratory confirmation of a particular disease and uses both clinical and alternative data sources. Strictly defined, syndromic surveillance gathers information about patients’ symptoms (e.g., cough, fever, shortness of breath). The analytic challenge in using syndromic surveillance for outbreak detection is to identify a signal corresponding to an outbreak or cluster amid substantial “background noise” in the data [37]. However, signal-detection methods have not yet been standardized. Temporal and spatio-temporal methods have been used to assess day-to-day and day and place variability of data from an expected baseline [38, 39]. 3. International Health Regulations and Surveillance Systems The new IHR [4] entered into force on June 15, 2007. The IHR are (1) a legal framework for surveillance of international health threats, (2) a procedure for WHO’s recommendations to counteract public health emergencies of international concern, and (3) a set of rules concerning routine measures against international disease spread. Here we will briefly review the first of these features. In the globalized world, diseases can spread far and wide via international travel and trade. A health crisis in one country can affect livelihoods and economies in many parts of the world. Such crises can result from emerging infections such as SARS or a new human influenza pandemic. The IHR also can apply to other public health emergencies such as chemical spills, leaks, and dumping or nuclear accidents [19]. The IHR aim to limit interference with international traffic and trade, ensuring public health through the prevention of disease spread. The IHR require countries to report certain disease outbreaks and public health events to WHO [5, 6, 40]. Building on the unique experience of WHO in global disease surveillance, alert, and response, the IHR define the rights and obligations of countries to report public health events and establish a number of procedures that WHO must follow in its work to uphold global public health security. Within the framework of the IHR [4], seven areas of work have been identified to achieve the goals described above. The first area of work aims to strengthen global partnerships; the second and third address countries’ capacities to meet IHR requirements; the fourth and fifth areas of work focus on surveillance, prevention, control, and response systems at the international level; and the sixth and seventh address awareness of rules and legal aspects and measuring progress (Table 2).

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E. STIKOVA ET AL. TABLE 2. Seven Areas of Work to Guide IHR Implementation

Area of Work Global partnership 1. Foster global partnerships

Goal WHO, all countries and all relevant sectors (e.g. health, agriculture, travel, trade, education, and defence) are aware of the new rules and collaborate to provide the best available technical support and, where needed, mobilize the necessary resources for effective implementation of IHR (2005).

Strengthen national capacity 2. Strengthen national disease surveillance, prevention, control and response systems

Each country assesses its national resources in disease surveillance and response and develops national action plans to implement and meet IHR (2005) requirements, thus permitting rapid detection and response to the risk of international disease spread. 3. Strengthen public health security in travel The risk of international spread of disease is minimized and transport through effective permanent public health measures and response capacity at designated airports, ports and ground crossings in all countries. Prevent and respond to international public health emergencies 4. Strengthen WHO global alert and response Timely and effective coordinated response to international systems public health risks and public health emergencies of international concern. 5. Strengthen the management of specific risks Systematic international and national management of the risks known to threaten international health security, such as influenza, meningitis, yellow fever, SARS, poliomyelitis, food contamination, chemical and radioactive substances. Legal issues and monitoring 6. Sustain rights, obligations and procedures New legal mechanisms as set out in the Regulations are fully developed, and upheld; all professionals involved in implementing IHR (2005) have a clear understanding of, and sustain, the new rights, obligations and procedures laid out in the Regulations. 7. Conduct studies and monitor progress Indicators are identified and collected regularly to monitor and evaluate IHR (2005) implementation at national and international level. WHO Secretariat reports on progress to the World Health Assembly. Specific studies are proposed to facilitate and improve implementation of the Regulations.

There are three groups of events that may constitute public health emergencies of international concern: Group 1. A case of the following diseases is unusual or unexpected and may have serious public health effects and thus shall be reported: • • • •

Smallpox Poliomyelitis due to wild-type poliovirus Human influenza caused by a new subtype Severe acute respiratory syndrome (SARS)

Group 2. An event involving the following diseases shall always lead to use of the algorithm because these diseases have demonstrated the ability to have serious public health effects and to spread rapidly internationally:

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Cholera Pneumonic plague Yellow fever Viral haemorrhagic fevers (Ebola, Lassa, Marburg) West Nile fever Other diseases that are of special national or regional concern, e.g., dengue fever, Rift valley fever, and meningococcal disease

Group 3. Any event of potential international public health concern, including those of unknown causes or sources and those involving events or diseases other than those listed above shall lead to use of the algorithm and criteria from Annex 2 of the IHR [4], e.g.: • • • • • • • • •

Anthrax antimicrobial resistance Arboviruses (e.g., Rift valley fever, West Nile fever) Dengue HIV/AIDS Malaria Measles and other vaccine-preventable diseases Meningococcal meningitis Tuberculosis Severe emerging zoonoses affecting humans

4. Early-Warning Alert Response System in the Republic of Macedonia – Results The Republic of Macedonia has population of about two million people. The territory is divided into 123 municipalities. In 1993, in the framework of the new public health system, one national and 10 RPHIs were established. They adopted a previously established system for routine surveillance for registration and notification of communicable diseases, which included 62 diseases. In 2004, new recommendations for protecting the population from communicable diseases were adopted, and a new obligatory list of 48 diseases was introduced. Past work has shown an absence of case definitions, a lack of laboratory confirmation, significant delays in reporting between surveillance levels, delayed and inadequate outbreak response, lack of feedback to reporting level, lack of training, lack of analysis at the peripheral level, under-reporting of unconfirmed cases or outbreaks, and poor motivation of healthcare staff. In 2005, the NPHI, with WHO support, started to develop a syndromic early-warning alert response system (EWARS), called ALERT, with an ultimate goal of strengthening the early detection of outbreaks of epidemicprone and emerging infectious diseases. A panel of Macedonian experts in the field of epidemiology and microbiology has assessed the needs and priorities for disease surveillance using a standardized questionnaire. The aim of this assessment was to define what the most important diseases are in Republic of Macedonia, from their point of view. The results of the assessment are shown in Table 3.

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TABLE 3. Results of Assessment Performed Among Epidemiologists and Microbiologists Targeted to Define Priorities for Disease Surveillance Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Microbiologist HIV+ Influenza Salmonellosis Toxoinfectio alimentaris AIDS Hepatitis A Brucellosis Hepatitis B Shigellosis Enterocolitis Tuberculosis Hepatitis C Hepatitis-nonclassified SARS Hemophilus influenza B

Epidemiologist Influenza HIV+ AIDS Enterocolitis Hepatitis A Toxoinfectio alimentaris Hepatitis B Tuberculosis Salmonellosis Brucellosis Hepatitis-nonclassified Hepatitis C Shigellosis Varicella Meningitis epidemica

TABLE 4. Syndromic Events That Are Included in the Early-Warning Alert Response System and Their Case Definitions Number 1 2 2-a

Syndromic Event Suspicion of an upper respiratory tract infection

Case Definition Rhinitis (serious or purulent nose secretions), dry coughing, throat redness and/or throat pain, with or without swelling and painful sensitivity of the lymph glands on the neck Suspicion of an lower Increased body temperature, coughing with or without sputum respiratory tract infection (productive or nonproductive), acute dyspnea, with or without general exhaustion and chest pains Suspicion of acute lower Every child younger than 5 with the following signs or symptoms: respiratory tract infection cough or difficult breathing, breathing 50 or more times per minute in children younger than 5 for infants aged 2 months to 1 year, breathing 40 or more times per minute for children aged 1–5 years

3

Suspicion of rash fevers, excluding varicellae

Acute beginning, increased body temperature and maculopapularpapulose rash, with or without throat redness and/or throat pain, hyperaemia of the upper respiratory tract, cervical lymphadenopathy

4

Suspicion of meningitis/meningoencephalitis

5

Acute watery diarrhea

6

Acute bloody diarrhea

7

Suspicion of acute infective hepatitis

8

Suspicion of acute hemorrhagic fever

Acute beginning, increased body temperature, heavy and diffuse headache, vomiting without nausea, painful neck stiffness, with or without photophobia, nausea, pharingitis with excudate, consciousness disorders, neurological attacks, petechial or purpural rash Dehydratation, stomach pains, stomach cramps, with or without vomiting Mucous stools containing (visible) blood in the previous 24 h, with or without dehydratation, stomach pain, and cramps Acute jaundice (yellow skin and sclera colour), weakness and exhaustion, dark urine, light stool, anorexia, nausea, pain below the right rib arch Acute beginning of fever in a period shorter than 3 weeks in a very ill patient and any two of the following signs/symptoms: petechial or purpural rash, nose bleeding, hematemesis, hemoptysis, oliguria or anuria, bloody stool, any other hemorrhagic manifestation without known cause

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A list of eight health events was included in ALERT on the basis of the results of this study. To unify the reporting process for all participants in the system, a case definition is necessary. A case definition is a combination of symptoms and signs that have to be present in a patient for the patient to be placed in a certain category. The list of eight syndromes that are part of the national EWARS are presented in Table 4 with case definitions for each. After the decision was made about the syndromic diseases that would be included in the EWARS, the next challenge facing the expert panel was to make a decision about the threshold limits. Two different approaches have been used for threshold definition. Regarding the severity of the disease and expecting threats for three of the syndromic diseases, the fixed number of cases was used. For the other five syndromic diseases, threshold limits were established on the basis of previous epidemiologic data and already-registered cases. Using these two methodologies, threshold limits were established for all ten surveillance units. They are shown in Table 5. TABLE 5. Threshold Limits for All Syndromic Diseases Included in the Early-Warning Alert Response System Stip Strumica Bitola Ohrid Prilep Veles Kocani Skopje Kumanovo

URTI 470 305 520 700 330 930 580 2,800 870

LRTI 100 102 122 200 56 450 140 300 234

RF 3 2 6 3 4 5 5 20 5

M&ME 1 1 1 1 1 1 1 1 1

AWD 90 90 75 80 35 215 55 700 176

ABD 1 1 1 1 1 1 1 1 1

AVH 9 6 11 6 4 8 7 8 7

AHF 1 1 1 1 1 1 1 1 1

URTI, upper respiratory tract infection; LRTI, lower respiratory tract infection; RF, rash fever; M&ME, meningitis/meningo-encephalitis; AWD, acute watery diarrhea; ABD, acute bloody diarrhea; AVH, acute infective hepatitis; AHF, acute hemorrhagic fever.

Anytime the defined number of syndrome cases listed in Table 5 is exceeded, the alert will go out automatically. For three groups of syndromic events – meningitis and meningoencefalitis, acute bloody diarrhea, and acute hemorrhagic fever – an alert will be declared after every registered case. For the other five syndromic events, an alert will be declared after the defined number of cases specific for each region of surveillance is exceeded. 5. Reporting and Surveillance Units Reporting units all are comprised of primary care physicians who work in different segments of the health system in the Republic of Macedonia. Currently there are 1,014 primary health units, but only 30–40% of them are included in EWARS. Through a written, standardized surveillance form, they report weekly the aggregated number of new cases in four age groups to the corresponding collecting units at the municipality level or directly to the local and regional surveillance units. They send aggregated data by mail or fax. There are ten regional surveillance units equipped and trained to process

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data from the reporting units. The regional surveillance units are RPHIs and their epidemiologic departments. At the regional level, data are computerized and electronically transmitted to the NPHI. The NPHI prepares a report and sends it to the Ministry of Health and initiates and performs all requested interventions and additional activities. Feedback is sent electronically from the NPHI to the regional institutes. In addition, the RPHIs can send information to the reporting units. Epidemiologists from the RPHIs and the NPHI are responsible for data control and regularity, reporting any unusual changes and undertaking urgent activities. A program has been developed using public-domain software for relational data entry (EpiData) and production of interactive reports (EpiInfo). It includes features for data entry (with quality checks) at the RPHI level and electronic transfer of records to the NPHI. It provides links with Excel and Word. The application produces a weekly epidemiologic bulletin in Word and allows interactive browsing of tables, charts, and maps in HTML format. The system generates alert reports based on disease-specific thresholds. The communicable diseases surveillance system in the Republic of Macedonia is shown in Fig. 1.

Figure 1. Early-warning alert response surveillance system in the Republic of Macedonia.

6. Discussion The system is considered simple and flexible. Users emphasized that ALERT has improved communication between reporting and surveillance units and strengthened the surveillance network. The acceptability of the system is higher at the national level mainly because data from ALERT are received in a timely fashion, which allows the surveillance department at RIPH to monitor potential outbreaks at the national level.

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Using syndromic case definitions allows remote areas that do not usually report, because of lack of confirmation capacity, to report, thereby providing valuable early-warning information. Moreover, for some rare and serious diseases such as those targeted by the hemorrhagic fever syndrome, ALERT is used as zero reporting. Syndromic surveillance is simple and often the only available surveillance tool at the primary health care level when laboratory confirmation of disease is not possible [41]. It allows detection of potential outbreaks of targeted diseases earlier than with the diagnosis-based routine surveillance system and leads to field investigations for confirmation and control [19, 20]. Experience has shown that reporting units at the primary health care level are not the most appropriate source of notification for early detection of some epidemic-prone diseases. Some specific syndromes may be seen first in emergency departments, private clinics, or pharmacies [21]. Syndromes such as hemorrhagic fever, as an indicator for hantavirus or CrimeanCongo hemorrhagic fever, are sensitive and specific enough to detect outbreaks. Because it is a serious and uncommon syndrome, each individual case reported is an alert and triggers an action. For other diseases, such as influenza, targeted by acute respiratory illness, the alert for action is a rise in reported syndrome cases, indicating the onset of the influenza season. ALERT was able to detect this increase during the 2008 season. However, other categories of syndromes have not been sensitive or specific enough to detect outbreaks in a timely fashion. Timely detection of public health threats relies on proper analysis of early-warning data at each level. ALERT software produces automated tables, charts, and maps highlighting increases. Epidemiologists should use those resources to trigger actions when individual confirmed cases are reported. The evaluation of the effects of implementation of the pilot project for the EWARS have shown us that sensitivity and usefulness should be increased. There are many possible ways to do this, such as adding emergency departments as notification sources for some syndromes, better defining the role of the laboratory to confirm the suspicion of outbreaks, revising the list and definition of syndromes to adjust their sensitivity and specificity for detecting the targeted diseases, and strengthening data analysis through training. Our experience shows that the role of training should not be overlooked. It is a change of paradigm, which is impossible to induce by simply implementing new surveillance tools, difficult to induce by short training, and best induced by coaching programs such as field-epidemiology training programs. Although the process for implementing the EWARS was piloted by the ministry of health, the ALERT reporting procedures were not incorporated into public health laws. ALERT does not interrupt the continuity of the existing reporting system, regulated by law. All obligations and responsibilities prescribed by it still remain. The final goal is, by comparing the advantages and disadvantages of both systems, to enable the creation (establishment) of a new, combined system that would be more functional, safer, and more economically sustainable. On the basis of our experiences, the obligation for syndromic reporting through EWARS will be laid down in our national law. Some additional measures, such as financial copayment for reporting units, should be discussed.

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References 1. 2. 3. 4. 5. 6. 7.

8. 9. 10. 11. 12.

13. 14. 15. 16. 17. 18.

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19. European Center for Disease Prevention and Control. Surveillance of communicable diseases in the European Union, a long-term strategy 2008–2013. Available at: http://www.ecdc.europa.eu/en/ activities/surveillance/Pages/StrategiesPrinciples_Long-termStrategy.aspx. 20. Krause, G., Benzler, J., Reiprich, G., Görgen, R. 2006. Improvement of a national public health surveillance system through use of a quality circle. Eurosurveillance. Available at: http://www. Eurosurveillance.org/ViewArticle.aspx?ArticleId=659. 21. Krause, G., Ropers, G., Stark, K. 2005. Notifiable disease surveillance and practicing physicians. Emerg. Infect. Dis. 11:442–445. 22. Kaiser, R., Coulombier, D., Baldari, M., Morgan, D., Paquet, C. 2006. What is epidemic intelligence, and how is it being improved in Europe? Eurosurveillance. Available at: http://www.eurosurveillance. org/ViewArticle.aspx?ArticleId=2892. 23. Reingold, A. If syndromic surveillance is the answer, what is the question? 2003. Biosecur. Bioterror. 1:1–5. 24. Green, M.S. Kaufman, Z. 2002. Surveillance for early detection and monitoring of infectious disease outbreaks associated with bioterrorism. Isr. Med. Assoc. J. 4:503–506. 25. Mykhalovskiy, E., Weir, L. 2006. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health. Can, J. Public Health 97:42–44. 26. Valenciano, M., Bergeri, I., Jankovic, D., Milic, N., Parli, M., Coulombier, D. 2004. Strengthening early warning function of surveillance in the Republic of Serbia: lessons learned after a year of implementation. Eurosurveillance. Available at: http://www.eurosurveillance.org/ViewArticle.aspx? ArticleId=465. 27. Wagner, M.M., Tsui, F.C., Espino, J.U., Dato, V.M., Sittig, D.F., Caruana, R.A., McGinnis, L.F., Deerfield, D.W., Druzdzel, M.J., Fridsma, D.B. 2001. The emerging science of very early detection of disease outbreaks. J. Public Health Manag. Pract. 7:51–59. 28. Mostashari, F., Hartman, J. 2003. Syndromic surveillance: a local perspective. J. Urban Health 80(Suppl 1):1–7. 29. Henning, K.J. Syndromic surveillance. In: Smolinski, M.S., Hamburg, M.A., Lederberg, J., editors. Microbial threats to health: emergence, detection, and response. Washington, DC: National Academies Press; 2003. pp. 309–350. 30. Buehler, J.W., Berkelman, R.L., Hartley, D.M., Peters, C.J. 2002. Syndromic surveillance and bioterrorism-related epidemics. Emerg. Infect. Dis. 9:1197–1204. 31. Das, D., Weiss, D., Mostashari, F., Treadwell, T., McQuiston, J., Hutwagner, L., Karpati, A., Bornschlegel, K., Seeman, M., Turcios, R., Terebuh, P., Curtis, R., Heffernan, R., Balter, S. 2003. Enhanced drop-in syndromic surveillance in New York City following September 11, 2001. J. Urban Health 80(Suppl. 1):i76–88. 32. Duchin, J.S. Epidemiological response to syndromic surveillance signals. 2003. J. Urban Health 80(Suppl. 1):i115–116. 33. German, R.R., Lee, L.M., Horan, J.M., Milstein, R.L., Pertowski, C.A., Waller, M.N., Guidelines Working Group Centers for Disease Control and Prevention (CDC). 2001. Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm. Rep. 50(RR13):1–36. 34. Paquet, C.L., Coulombier, D., Kaiser, R., Ciotti, M. 2006. Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Eurosurveillance 11:212–214. 35. Pavlin, J.A. 2003. Investigation of disease outbreaks detected by syndromic surveillance systems. J. Urban Health 80(Suppl 1):i107–114. 36. Heffernan, R., Mostashari, F., Das, D., Karpati, A., Kulldorff, M., Weiss, D. 2004. Syndromic surveillance in public health practice, New York City. Emerg. Infect. Dis. 10:858–864. 37. Heymann, D.L., Rodier, G. 2001. Hot spots in a wired world: WHO surveillance of emerging and reemerging infectious diseases. Lancet Infect. Dis. 1:345–353. 38. Kluger, M.D., Sofair, A.N., Heye, C.J., Meek, J.I., Sodhi, R.K., Hadler, J.L. 2001. Retrospective validation of a surveillance system from unexplained illness and death: New Haven County, Connecticut. Am J Public Health 91:1214–1219. 39. Centers for Disease Control and Prevention. Syndrome definitions for diseases associated with critical bioterrorism-associated agents. Atlanta: Centers for Disease Control and Prevention. Available at: http://www.bt.cdc.gov/surveillance/syndromedef/index.asp. 40. Guglielmetti, P., Coulombier, D., Thinus, G., Van Loock, F., Schreck, S. 2006. The early warning and response system for communicable diseases in the EU: an overview from 1999 to 2005. Eurosurveillance 11:215–220. 41. Epidemic alert and verification: summary report for 2005. 2006. Wkly. Epidemiol. Rec. 81:357–362.

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Additional Reading Biological and chemical terrorism: strategic plan for preparedness and response. Recommendations of the CDC Strategic Planning Workgroup. MMWR Recomm. Rep. 2000:49(RR-4):1–14. Centers for Disease Control and Prevention (CDC). 2002. Syndromic surveillance for bioterrorism following the attacks on the World Trade Center–New York City, 2001. MMWR Morb. Mortal. Wkly. Rep. 51(Spec. No.):13–15. Hutwagner, L., Thompson, W., Seeman, G.M., Treadwell, T. 2003. The bioterrorism preparedness and response early aberration reporting system (EARS). J. Urban Health 80(Suppl. 1):89–96. Kaiser, R., Coulombier, D. Different approaches to gathering epidemic intelligence in Europe. Eurosurveillance. Available at: http://www.eurosurveillance.org/ew/2006/060427.asp#1. Ostroff, S.M. 2001. The epidemic intelligence service in the United States. Eurosurveillance 6:34–36. Pavlin, J.A., Mostashari, F., Kortepter, M.G., Hynes, N.A., Chotani, R.A., Mikol, Y.B., Ryan, M.A., Neville, J.S., Gantz, D.T., Writer, J.V., Florance, J.E., Culpepper, R.C., Henretig, F.M., Kelley, P.W. 2003. Innovative surveillance methods for rapid detection of disease outbreaks and bioterrorism: results of an interagency workshop on health indicator surveillance. Am. J. Public Health 93:1230–1235.

Integrating Geographic Information Systems and Ecological Niche Modeling into Disease Ecology: A Case Study of Bacillus anthracis in the United States and Mexico Jason K. BLACKBURN Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, California State University, Fullerton, Fullerton, California Abstract. This chapter provides an overview of geographic information systems, spatial analysis and spatial statistics, and predictive ecological niche modeling as they apply to disease ecology. I provide a conceptual model of the epidemiology and outbreak ecology of anthrax and the landscape ecology of the pathogen Bacillus anthracis. I apply Anselin’s exploratory spatial data analysis process to these two components of the anthrax-transmission and spore-survival model. Spatial clustering statistics are reviewed in the context of outbreak epidemiology and potential mechanical vector transmission. I then provide a primer on ecological niche theory and apply ecological niche modeling to estimate the potential geographic distribution of B. anthracis on the landscape of the contiguous United States under current and future climate scenarios and to estimate the unknown distribution of B. anthracis in Mexico.

1. Introduction This chapter will briefly introduce geographic information systems (GIS), geographic information science (GISc), data development with GIS and remote sensing, and predictive ecological niche modeling (ENM) and then illustrate their uses in investigating the spatial distribution of Bacillus anthracis, the causative agent of anthrax. Secondarily, I will define the epidemiology of this disease in wildlife and livestock and indicate uses of GIS and ENM that can enhance our understanding of the disease. Although this chapter is limited to the spatial ecology of anthrax, readers are encouraged to think of parallel applications to other disease systems and public health issues. 2. Epidemiology of Anthrax and Landscape Ecology of Bacillus anthracis To illustrate the application of GIS, spatial analyses and statistics, and ENM to disease ecology, it is first necessary to review the ecology and transmission of B. anthracis, the

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causative agent of anthrax and the model disease system for this chapter. It is also important to distinguish between the epidemiology of the disease and the ecology of the disease agent. Anthrax disease is a continuing zoonosis in livestock and wildlife throughout many countries of the world [2, 3]. Although the disease still affects animal and human populations, its ecology is complex and its distribution remains poorly understood [2] despite recent efforts to model the spatial distribution of the agent [4]. The causative agent, B. anthracis, is a ubiquitous, spore-forming, Gram-positive bacterium known to survive in soils for long periods of time, resulting in some areas experiencing regularly occurring outbreaks [2]. Although a number of studies have addressed the physiological and ecological constraints on the species [2, 5–7], few studies have evaluated the geographic potential of the species and the distribution of environmental parameters that can promote spore survival and subsequent outbreaks. The study of the disease and the agent can be divided into two discrete bodies of research, which then can be further subdivided. Figure 1 illustrates a conceptual model of the transmission cycle for B. anthracis and recognizes these two research components. Similar work has been developed by Hugh-Jones and De Vos [3] for wildlife transmission in Africa with an emphasis on outbreak ecology once an animal has become infected. In this chapter, I attempt to expand this conceptual model with a discussion on the landscape ecology of the bacterium and the geographic limitations on B. anthracis in the contiguous United States.

Figure 1. A working model of the landscape ecology of Bacillus anthracis and the hypotheses of transmission pathways. Solid arrows indicate established transmission pathways between bacilli and animals. Dashed arrows represent hypothesized or poorly understood transmission pathways. This chapter is focused primarily on B. anthracis landscape ecology so spores are outlined.

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2.1. Epidemiology and Outbreak Ecology First, the study of anthrax can be defined by the body of work on the epidemiology and ecology of outbreaks and the disease transmission cycle. This research is focused on a variety of climatic and local weather events that promote outbreaks and the ecology of affected species, which promotes outbreaks or larger epizootics. Although still lacking a full body of literature, a number of studies have documented the climatic conditions (and associated local weather patterns) that promote livestock outbreaks [8, 9] and wildlife outbreaks [3, 10–12]. In a synthesis on anthrax and wildlife, Hugh-Jones and De Vos [3] define the anthrax season as approximately late spring to early fall. There is a need to expand quantitative analyses of seasonality and outbreak periodicity, particularly outside of African wildlife populations. This area of research also is focused on the movement ecology of wildlife species in relation to outbreaks and anthrax seasonality [12]. Likewise, studies on the potential role of necrophilic flies to increase the number of browsing wildlife cases locally [13] would fit into this category of anthrax research. Hugh-Jones and De Vos [3] reviewed this phenomenon in African wildlife and suggest that a similar mode of transmission could be at work in the United States with deer. Blackburn [12] confirmed the presence of B. anthracis in necrophilic flies collected on and around disease-positive, dead white-tailed Odocoileus virginianus in west Texas and named the Hugh-Jones and De Vos [3] concept the “case multiplier hypothesis” of anthrax transmission. This states that necrophilic fly species likely increase cases in browsers by feeding on disease-positive carcasses and then depositing B. anthracis spores on nearby preferential browse; other animals will feed on the contaminated vegetation and contract the disease. Although data on this mode of transmission are quite limited from field investigations [12–14] given our understanding of deer feeding preferences in the United States, this is a plausible and testable hypothesis. There is a second working hypothesis on mechanical transmission by hematophagous flies that also was reviewed by Hugh-Jones and De Vos [3] and was evident in the literature for much of the previous century [15–18]. In recent work, at least 21 different species from five genera of the Tabanidae family of flies have been confirmed under experimental conditions to transmit anthrax bacilli on their body parts [19]. This potential mode of transmission suggests that biting flies can pick up spores on their large mouth parts or legs during a blood meal from a bacterimic animal and then transmit them to other animals during subsequent blood meals. In an early study by Rao and Mohiyadeen [16], the researchers were able to isolate bacilli from the edema of bacterimic cattle and biting flies feeding on these animals. Given that hematophagous flies have greater flight strength and potential to travel farther distances, Hugh-Jones and De Vos [3] suggest that this mode of transmission may explain the wave-like pattern of large and fast-moving outbreaks, particularly those that expand beyond individual herds or fenced pastures. This also might explain interspecific infections during outbreaks in which species with different feeding ecologies are infected during the same outbreak. In North America, for example, infections in deer of the Cervidae family and cattle or bison in the Bovidae family have been documented in single outbreaks [12]. Although cervids may feed on some low-growing herbs and forbs, Texas deer primarily feed on browse during the summer period when anthrax outbreaks

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are most likely [20]. Gates et al. [21] suggest that there might be a relationship between high biting-fly populations and large outbreaks in Canadian wood bison (Bison bison athabascae) but researchers have yet to quantify this phenomenon or isolate bacilli from biting flies during an outbreak. Blackburn [12] provided limited evidence that there is a positive, quantitative relationship in biting fly densities and the spatial pattern of positive deer cases on a study ranch in west Texas. This study directly employed spatial statistics and geographic information to quantify spatial clusters of high fly counts during trapping periods in relation to individual deer movements (from VHF telemetry) and current and historical case locations of dead deer. Although this study was limited to spatial relationships and lacks the “smoking gun” of spore-positive flies, it does suggest that a “spatial multiplier hypothesis” is plausible and worth further investigation. This hypothesis suggests that interspecific transmission between wildlife species, or wildlife and livestock, during large outbreaks may be due to mechanical transmission by biting flies and that fly movement patterns may expand the spatial footprint of an outbreak. A third testable hypothesis during multispecies epizootics is that both the case multiplier and spatial multiplier hypotheses are working in tandem. Studies on each of the hypotheses presented in this section can directly employ spatio-temporal analyses of climate data [9] and spatial analyses, such as spatial cluster statistics [12, 22], for fly–animal relationships. 2.2. Landscape Ecology of Bacillus anthracis Each of the patterns and processes in the section above aim to identify or describe anthrax outbreaks in relation to transmission pathways and species interactions. However, for any of the interactions above to take place and promote the transmission and subsequent infection of anthrax bacilli, the bacilli have to be present. This means that, for naturally occurring outbreaks to occur, we first must understand the landscape-level patterns that promote spore survival and subsequent exposure to populations. I define this area of anthrax research as the landscape ecology of B. anthracis. To understand the ecology of outbreaks presented above, and given that most research on B. anthracis currently suggests that germination and multiplication occurs in the host while spore survival occurs in the soil [2], it is necessary to identify the geographic area where bacilli spores can thrive for long periods of time. Landscape ecology provides a useful perspective of scale for such analyses. Haines-Young et al. [23] provide an overview of landscape ecology and the role that GIS can play to test hypotheses within this theoretical framework. For this chapter, we can define landscape ecology as the study of relationships between the biological requirements of the bacilli in spore form and the ecological conditions that support spore survival and the geographic areas where these requirements are met. This landscape perspective can be useful for understanding the broad-scale geographic distribution of the bacterium [4, 5] and identifying areas where wildlife or livestock may be at risk. Likewise, micro-level studies on spore survival and bacilli multiplication in soil are key parts of this approach [2, 7, 24].

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Understanding the geographic areas and ecological characteristics that promote B. anthracis emergence, propagation, and subsequent exposure to populations at risk is critical to developing or improving disease surveillance and control programs. This is especially true of anthrax given the complexity of transmission and spore survival described in this chapter. Oftentimes, determining the complete spatial distribution of a disease agent or its host, reservoir, or vector requires the use of spatially explicit predictive models and any available data on the known occurrences of outbreak foci or host/reservoir or vector populations. Because of this, GIS and spatial analyses have become important tools in the study of anthrax epidemiology and the landscape ecology of B. anthracis. The bulk of the analyses presented in this chapter focuses on the landscape ecology perspective and aims to define the potential geographic distribution of B. anthracis in the lower 48 states of the United States, under both current ecological conditions and a future climate-change scenario, and across the country of Mexico, where surveillance data on this disease are lacking. 3. Geographic Information Systems and Geographic Information Science Today, GIS and GISc play an ever-increasing role in international research that focuses on disease distributions [25], ecology [4], and epidemiology [26]. The application of spatial data management and spatial analyses intuitively fits into research agendas that focus on a wide variety of topics, such as resource management [27], health and disease surveillance [28, 29], basic ecology [30], and socio-environmental [31] and socioeconomic patterns [32]. As the application of GIS technology and the GISc paradigm grows, so, too, does the number of GIS techniques, computer applications and hardware, and GISc-trained personnel. 3.1. Geographic Information Systems 3.1.1. Geographic Information Systems for Disease Studies Although GIS has been defined in great detail [33, 34] and has a growing body of literature, I will define it briefly in the context of disease surveillance and the modeling of disease ecology. GIS is a combination of computer hardware, computer software, and database technologies that allows for the storage, management/editing, visualization (mapping), and analysis of spatial data [35]. The primary component of a GIS is the ability to establish relationships within data sets and to analyze them spatially. Spatial here is defined as geographic relationships between data sets (such as disease outbreak locations and environmental parameters such as temperature). Disease studies readily lend themselves to GIS-based analyses because, simply stated, diseases are spatial in nature. Pathogen biology and transmission are linked to specific ecologies that promote the long-term survival and fitness of the pathogen, its reservoir or host populations or environment that sustain its population (e.g., soil foci for B. anthracis [2] or populations

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of small mammals that circulate Yersinia pestis [36]), or vector species that transmit disease agents from host to host (e.g., tabanid flies that move B. anthracis spores [12], mosquitoes that vector malaria [25] or ticks that move Nairovirus species for CrimeanCongo hemorrhagic fever [37]). In these examples, we can evaluate geographic phenomena, such as habitats, that allow for the interaction of disease agents, vectors, and hosts. This is done through the integration of multiple disparate (and often idiosyncratic) data sets linked through relational databases, dynamic maps, and spatially explicit modeling techniques and spatial statistics. In GIS, the visualization component equates to digital map development or graphics that depict spatial relationships (such as histograms). The disparate data sets used to develop a spatial visualization (map) of habitats might include satellite-derived or GIS-interpolated precipitation maps, elevation maps, soil types, or forest cover (see Fig. 2, inset 1, for an example of environmental maps). Likewise, the data sets used to map a disease distribution might be known case locations (perhaps based on serology, laboratory diagnostics, or case definitions), known reservoir species’ ranges, or agent-positive vector sampling sites.

Figure 2. The six steps of the predictive ecological niche modeling [ENM] process for modeling Bacillus anthracis in the contiguous U.S. and Mexico: (1) environmental variables are processed in GIS/remote sensing and used in the ENM process to develop the Hutchinsonian N dimensional hypervolume; (2) Locality data from culture-positive B. anthracis are input into the ENM application; (3) the modeling system iteratively develops a rule-set of logic strings that describes the ecological space that supports the species; (4) the rule-set is applied to the landscape to develop a map of presence/absence; (5) the rule-set from the known locations can be applied to a environmental coverage set for an area lacking occurrence data; and (6) the projected rule-set is applied to the unknown landscape for a first prediction.

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By evaluating each of these data sets in a multivariate paradigm, a single map can be produced to evaluate the intersections in geographic space where appropriate environmental conditions exist to promote the target species or disease-transmission cycle [4, 25, 38]. In short, GIS is a methodology and digital infrastructure for evaluating data in space. This is done through integrating databases with map visualizations. With today’s technology, databases are limited only by intellectual imagination. As long as we invest effort and forethought into our database infrastructure, we will be able to expand and scale our GIS applications to capture data on multiple diseases, disease ecosystems, and study designs. This can be done across scales from the local or micro environment to the national or regional macro scale. Employing GIS requires two infrastructures. The first, a physical infrastructure of computers, servers, network capabilities, and software, is necessary to employ the technology. In the ever-growing computer market, the availability of high-end computers at low prices has made access to high-powered computers tenable in developed and developing countries. Secondly (and more importantly), a data infrastructure of spatial data sets, disease data sets, and environmental data sets is required to construct GIS data layers and build multilayer maps that reflect meaningful epidemiological relationships. Within the context of how GIS is employed in disease studies, it is important to distinguish how data sets are organized, particularly as this chapter is focused on more sophisticated applications of GIS for constructing predictive ENM scenarios for B. anthracis. Data within a GIS can be organized into three data formats. First, there are two basic GIS data types: vector and raster. The main difference between these two formats is how the geographic data values are stored within a database. Vector GIS defines geographic features into three major geometric objects: points (e.g., a disease case location or sampling site), lines (e.g., road networks, streams, railroad lines), and polygons (e.g., political boundaries, building footprints, water bodies; Fig. 2, inset 2, illustrates a map made entirely of vector features). Descriptive data for each point, line, or polygon are stored as attributes in an accompanying data table that links respective vector geometry with appropriate data values. A raster GIS uses a spatial grid with symmetrical cells to store the data. With raster data, attributes are assigned to each grid cell in the database and numerical values are used to represent various features (Fig. 2, insets 1 and 5, illustrate raster layers). For example, a 1-km2 raster file of temperature data would represent equally sized square grid cells each with a numerical value representing the temperature for that 1 × 1-km pixel. In this way, a variety of raster files, such as soil pH, vegetation, or elevation, all could be stored in a GIS and represent these values with equal pixel sizes. Because each cell occupies the same space, multiple spatial layers can be compared within each cell, making raster GIS useful for layering multiple environmental data sets and finding associations between them. Vector GIS is well-suited for identifying clusters [22] and diffusion patterns [39] across a landscape or study area. The third data format managed within the GIS is the aspatial database table (those tables without spatial data assigned). For example, data developed by a collaborating laboratory, such as molecular genetic data about the disease agent, can be managed and stored within the GIS for later data joins or assignment to spatial locations. For a detailed review of GIS methods for developing data in all three of these formats, see Curtis et al. [31].

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3.1.2. Visualizing Data within a Geographic Information System – Maps The primary visualization outputs from GIS analyses are maps (Fig. 2). Simply stated, maps are easy to read. Although GIS is more dynamic than static map production, it is important to realize the value of maps for improving our understanding of complex systems such as disease-transmission cycles [37] or the relationship between human settlement patterns and infectious diseases [28, 40]. We need to remember that maps are only as good as the data we invest into making them, but maps provide easy tools for visualizing disease distributions. Imagine trying to describe the distribution of Crimean-Congo hemorrhagic fever across a country as large as Kazakhstan (the ninth largest country by land mass) without a map to keep track of all the areas you are trying to define. That would be quite difficult compared with showing a single map of the disease distribution. Now imagine trying to relate that message to health managers who might control eradication strategies and trying to sort out which areas should be prioritized first. Managers are likely to communicate and make decisions more clearly through maps. It is important that these maps have the best data available, including precise spatial locality data and accurate data attributes. That is where GIS and data management become important. Although maps are an important output from GIS analyses, it is important to realize that map development is only one of several tools in the GIS toolbox for improving our understanding of disease distribution, ecology, and epidemiology. GIS provides a set of tools for developing data sets for advanced spatial analyses and statistics to test explicit hypotheses (often completed outside of the primary GIS software environment, such as in a statistics program or programming environment). 3.2. Geographic Information Science and Spatial Data Analysis GISc can be summarized as the paradigm for formulating geospatial hypotheses and proper employment of the tools of GIS to solve spatially driven research problems systematically. Goodchild [41] first introduced the term, and the field has grown since. Mark [42] provides a detailed review of the evolution of naming the term, Goodchild [43] discusses the terminology further, and Goodchild [44] reviews the roles of GISc as split between the study of GIS to advance its technology and the use of GIS technology to advance scientific fields of study within a spatial framework. In the increasingly fastgrowing world of computer technology and push-button analytical tools, GISc provides a systems approach to compartmentalizing, evaluating, and organizing these tools. GISc could be considered the academic discipline for organizing GIS and promoting a proper educational structure to prepare researchers for sustaining GIS and for employing it within a hypothesis-testing framework focused on addressing scientific research. In addition to training basic GIS techniques and data manipulation, GISc can integrate formal training in spatial analysis (such as point-pattern analysis, clustering algorithms, and probability-based testing), geographic processes, biogeography, and spatial ecology to examine spatial phenomena. In the context of spatial epidemiology, and this chapter, GISc should be defined as that body of work that advances our knowledge of disease ecology through an improved understanding of the spatial patterns and processes that promote disease

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propagation and transmission. With that in mind, we can consider a three-step process for (1) evaluating the distribution of a disease visually, (2) exploring patterns of disease (such as outbreaks or vector population dynamics) using spatial statistics, and (3) modeling geographic distributions of disease with spatially explicit predictive models. Anselin [1] provides a generic overview of this process and a review of the classical literature from the field. Here I attempt to integrate those steps into the study of B. anthracis ecology and anthrax to establish a framework for advancing our knowledge of this pathogen and its natural ecology and epidemiology. 3.2.1. A Framework for Integrating Geographic Information Systems and Spatial Analysis into Disease Ecology As an expansion of section 2.1.2 above, the development of maps that relate the spatial position of anthrax outbreaks to the landscape illustrates Anselin’s concept of exploratory spatial data analysis (ESDA) [1]. Briefly, data visualization can be considered a first step in evaluating a data set (or data sets) to determine the spatial nature of the data and evaluate the spatial patterns. This often leads to the development of more than maps, including histrograms, graphs, box plots, or animations, that allow the researcher to explore the data and identify possible outliers (spatial or attribute data) or visual clusters (aggregations of data observations in close spatial proximity). Figure 2, inset 2, illustrates a simple point map of the distribution of anthrax outbreaks in the contiguous United States from 1957 to 2005. Although there is no statistical analysis applied to the data in Fig. 2, inset 2, the map alone is informative for determining the location of outbreaks across the lower 48 states over a relatively long time period. Although we will discuss more sophisticated analytical tools in later sections of this chapter, this first step does provide useful information. For example, it is clear from this map that outbreaks are concentrated in two areas (southwestern Texas and the Dakotas), with smaller numbers of outbreaks in the western-most states and eastern Oklahoma. Knowing this, one now can think about how this pattern may have developed. How did B. anthracis get from the southern states to the northern states? How did it move from east to west? Is there some environmental gradient in the central portion of the landscape that prohibits outbreaks in the eastern states during this period? These questions are easier to pose with the map in hand, illustrating an important part of the ESDA process: the development of hypotheses to test on this spatial distribution. Anselin’s [1] second step in the ESDA process is the use of measures of spatial autocorrelation to identify statistically significant patterns within data sets. Here these patterns refer to disease outbreaks. There is a growing body of spatial statistics that employs local measures of spatial autocorrelation to determine whether statistically significant spatial patterns exist within a data distribution. Geographic areas in which more outbreaks occur than would be expected by random chance are defined as hotspots; those areas with significantly fewer outbreaks than would be expected randomly are defined as cold spots [45]. These statistics are performed through iterative algorithms that evaluate the relationship between outbreak occurrences (at some aggregated level, such as a polygon grid surface or a political boundary) and neighboring occurrences. These neighbors can be defined through a neighbor relationship, in which some boundary of these polygons is shared [1] or based on proximity within a distance

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threshold [22]. Two known studies have employed spatial autocorrelation in the study of anthrax epidemiology. 3.2.2. Employing Local Indicators of Spatial Autocorrelation to Anthrax Data Blackburn [12] employed the Getis statistic to determine whether significant local spatial clusters occurred for any biting fly-trapping events during the 2005 anthrax season on a study ranch in west Texas. The goal of the study was to determine whether there were areas within the outbreak zone that had significantly high tabanid fly catches. The Gi*(d) statistic tests for local spatial clusters in group-level data and assesses the association of the variable of interest within a set distance of each observation in the data set tested [46]. Gi*(d) is useful for identifying individual members of local cluster events [22]. The Blackburn [12] study identified both cold spots of low fly-catch rates and hotspots of high fly-catch rates. This then was coupled with the spatial distribution of anthrax-positive deer carcasses for that anthrax season plus historical cases from 2001 to 2005 (Fig. 3). Although not confirming any direct relationship between outbreaks and fly densities, the spatial association between clusters of biting flies and the location of carcasses certainly warrants further investigation into possible

Figure 3. Spatial clusters of tabanid flies on a study ranch during the 2005 Texas anthrax season. Setups equal three time periods that divide the sampling season. Critical distances represent the spatial scale at which any given trapping location was part of a spatial cluster. Gray circles represent sampling areas that were not significant, red circles indicate spatial hotspots of significantly high catch rates for tabanid flies, and blue circles indicate significantly lower than expected catch rates. Black dots across the eastern portion of the study represent carcass locations of anthrax-positive deer. Notice the spatial overlap between fly hotspots and anthrax locations. Photograph insert: a Nzi fly trap setup used to collect flies. Adapted from Blackburn [12].

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causal relationships. Even in the absence of a direct diagnostic link between flies and disease transmission, there is direct overlap between high catch rates of biting flies and diseased carcasses. There may be an indirect effect between immune suppression in deer and seasonal inundation with flies. This alone may serve to increase the number of susceptible hosts in a given outbreak season. A second study employed the same Getis statistic to the spatial distribution of anthrax outbreaks in Kazakhstan for four separate decades from 1960 to 1990 [47]. In the second study, only hotspots, or significantly high outbreak counts, were evaluated for statistical significance. In this latter study, cattle and sheep outbreaks were aggregated to fixed-width grid cells for four separate decades and the spatial statistic was calculated for each decade separately. This provided a time series of four maps to evaluate areas of long-term outbreak persistence across the country and the time period. Figure 4 illustrates the decadal hotspots of cattle outbreaks in Kazakhstan. Both of these studies illustrate the use of ESDA for evaluating disease ecology within a hypothesis-testing framework. However, these measures of spatial autocorrelation are inferential and do not provide any causality of high fly-catch rates or counts of cattle outbreaks but rather where these events cluster in space.

Figure 4. Decadal spatio-temporal clusters of anthrax outbreaks in cattle from Kazakhstan during the period 1960–1999. Grayscale ramp indicates the spatial scale of the cluster as determined by the critical distance for that g-score. Black arrows indicate areas in which significant clusters of outbreaks disappeared during the study period; the red arrow indicates an area in which a cluster developed in the latter decades of the study period. Cluster data adapted from Sagiyev et al. [47].

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It is also important to note that both of these examples of cluster analysis focus on the spatial patterns of mechanical vectors or individual outbreaks. Both of these studies represent the use of GIS and spatial analysis for evaluating components of disease transmission and ecology and do not employ the landscape approach or evaluate direct spatial relationships between the environment and B. anthracis. The third approach presented in Anselin [1] is to employ a predictive modeling approach to relate parameters to spatial patterns. As mentioned in previous sections, spatial patterns identified during a naturally occurring anthrax outbreak must be occurring in geographic regions in which bacilli in spore form can be maintained in the environment. To identify these areas, it is necessary to merge Anselin’s third concept with a modeling approach that can address the landscape ecology of B. anthracis and relate culture-positive outbreak locations to an ecological signature that defines the range limits of the disease agent. ENM provides an ideal tool for predicting species’ distributions. In the remainder of the chapter, I will define ENM and present examples of its application to B. anthracis ecology. 4. Evaluating Landscape Ecology with Predictive Ecological Niche Modeling 4.1. A Primer on Ecological Niche Theory To understand how ENM can be useful for predicting the geographic space in which anthrax outbreaks may occur naturally, it is first important to define the concept of the ecological niche and provide a conceptual framework for the modeling process. Hutchinson [48] provides an excellent overview of ecological niche theory and provides a reference to the original introduction of the term “niche” in the ecological literature. Hutchinson argues that Johnson [49] was the first to use the term, though a more rigorous and technical definition was provided by Grinnell [50], which often serves as a key reference in many niche-modeling articles. Grinnell defines the ecological niche as a limited range of ecological variables that could maintain a population without immigration. As part of this definition, Grinnell [50] states that no two species could occupy a single niche. This definition later was expanded into a quantifiable ecological space by Hutchinson in two articles [51, 52]. While examining relationships between phytoplankton and chemical properties within a lake system, Hutchinson [51, page 20, footnote 5] proposed that the ecological niche can be “…defined as the sum of all environmental factors acting on the organism; the niche thus defined is a region of an n-dimensional hyper-space…” Hutchinson [52] then expanded this definition to an n-dimensional hyper-volume of parameters that could be ordered linearly to define the fundamental niche or the potential ecological space that could maintain a species. Hutchinson [52], and later MacArthur [53], also define the realized niche as that limited portion of ecological space that is actually used by a species owing to biological interactions (such as competition, dispersal limits, and historical events like local extirpation). Under the Hutchinsonian definition, there is acknowledgement that biological interactions play a role in limiting the available ecological space and actual geographic space in which a species can survive. Morrison and Hall [54] argue that the Grinellian niche definition can be considered a dimension

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of the Hutchinsonian niche. For a further review of ecological niche theory, see Morrison and Hall [54] and Chase and Leibold [55]. In the context of this chapter, the ecological niche is used as a construct to evaluate spatially explicit modeling approaches to define the potential geographic distribution of a species. This of course is not limited to disease studies and, in fact, has been tested rigorously across a wide range of taxa (see Blackburn [12] for a partial review of taxa modeled). When attempting to understand geographic patterns of disease distribution (or any species for that matter), it is important to define clearly the ecological space being evaluated [56] and the ecological theory that is being tested [54]. 4.2. Predictive Ecological Niche Modeling Predictive ENM provides an ideal tool for determining disease agent or vector distributions using GIS data and remotely sensed data to model the environments that might support disease outbreaks [56, 57]. Currently, several modeling approaches are available to estimate species’ distributions (e.g., Stockwell and Peters [58] – genetic algorithm for rule-set prediction [GARP]; Rogers [25, 59] – Discriminant Function; Phillips et al. [60] – MaxEnt). In all of these models, the goal is to identify the geographic region and ecological environments that can sustain the disease agent or promote interaction/infection through host–vector interaction. Identifying these regions is done through computationally intensive iterative algorithms that pattern match disease case or vector species’ localities with environmental data layers. The goal is to identify nonrandom relationships between case locations and the environment, either through pattern matching with post hoc statistical evaluation of those patterns or through direct statistical relationships. Once these relationships are identified, the user can data mine the modeling outputs for biological information. Several recent articles have employed ENM successfully to describe the distribution of diseases or as predictions of host/ reservoir populations [38], vector populations [61], or disease-agent presence from outbreak data [4, 62]. Although the software application is described in some detail below, it is useful to provide a conceptual overview of the modeling process employed. Figure 2 provides a diagram of the modeling system and the input data sets required to produce potential geographic distributions for B. anthracis. I have divided the modeling process into six parts. First, an environmental coverage set of ecological variables is input into the modeling software (Fig. 2, inset 1). Next, input data of disease occurrence are input into the modeling software (Fig. 2, inset 2). In step three, the rule set (see below) that describes the relationships between B. anthracis presence and absence is developed. Figure 2, inset 3, illustrates this with a set of if/then logic strings (the actual rule set from the modeling software) and with a simplified visualization of ecological space with two variables. In the examples used in this figure, the n-dimensional hyper-volume would be constructed from five ecological variables. Figure 2, inset 4, illustrates the next step in the modeling process, in which the rule set is applied to the landscape to produce a potential geographic distribution for the species. One advantage of the GARP modeling system is the opportunity to project the rule set onto landscapes that lack occurrence data. Step five illustrates this, with the rule set from the U.S. model being applied to the landscape (and variable) space of Mexico. Notice it is an identical data

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set to the U.S. coverage set in Fig. 2, inset 1, but for the geographic space of Mexico. In step six, the rule-set relationships for Mexico are applied to the landscape to create a potential geographic distribution for B. anthracis. Steps five and six could be replaced here with data from future climate scenarios to illustrate the concept for modeling climate change. 4.3. The Genetic Algorithm for Rule-Set Prediction In this chapter, I present three ENM scenarios to describe the potential geographic distribution of B. anthracis on the landscape of the lower 48 contiguous United States and Mexico under current climatic conditions and provide a first estimate of the geographic potential for the species in 2050. For all three modeling scenarios, I employ the GARP modeling algorithm. The GARP modeling system has been explained in detail elsewhere [58], as have examples of the application of GARP to disease systems [4, 40]. In brief, this study employed the DesktopGARP version 1.1.6 [DG] application to develop all GARP models (available from www.lifemapper.org/desktopgarp). GARP is a presence-only modeling technique that determines nonrandom associations between point localities (anthrax outbreak locations) and environmental parameters (environmental “coverages”) [58, 63]. Results are in the form of presence/absence predictions based on a set of heterogeneous rules. GARP modeling is stochastic in nature, owing in part to both the genetic algorithm for building models and the random partitioning of input-locality data. In other words, GARP is a random walk through variable space. Because of this, GARP can generate multiple solutions across multiple model runs. To evaluate this potential intermodel variation, it is critical to develop multiple models. Optimal models are those that compromise between omission (exclusion of known locations from the model) and commission (inclusion of areas with no known cases) [64]. DG employs a “best subset” procedure to optimize model outputs by selecting models with user-defined omission and commission thresholds. The modeling approach is a two-step process wherein rules are generated to describe presence and absence in variable space. This rule set then is applied to the landscape pixel by pixel to create a spatially explicit prediction of presence and absence. GARP outputs are rasterized coverages of the study area representing presence and absence pixels that can be manipulated in a GIS. These individual models can be summated to identify geographic areas in which none, some, or all of the models predict presence or absence [65]. The greater the number of models that agree, the more certainty there is in the prediction classification [62]. Likewise, similarity across models indicates stability in the modeling system. For an in-depth review of employing DG, see McNyset [66]. 4.3.1. Input Data: Occurrences of Bacillus anthracis A GIS database of specific anthrax outbreak localities within the 48 contiguous United States was developed from a variety of data sources for the period 2000 to 2005, with the exception of a 1957 outbreak report that could be mapped at the point level for

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Oklahoma and a 1968 outbreak for east central California. To be included in the final modeling data set, laboratory diagnostics had to confirm the presence of bacilli. Suspect cases were removed from this modeling process. Point data representing confirmed wildlife and livestock outbreaks were available from six states representing three regions of anthrax outbreaks for the contiguous 48 states: (1) the Dakotas Region (North Dakota, South Dakota, Minnesota), (2) the Southern Region (Oklahoma, Texas), and (3) the Western Region (Nevada, California; Fig. 2, inset 2). Table 1 summarizes the sample sizes and methods of data collection for each of the states used in this analysis. These occurrence data were used in all three modeling scenarios included in this chapter. TABLE 1. Data Sources for Anthrax-Outbreak Localities (1957–2005) Used to Develop Ecological

Niche Models of Bacillus anthracis in the Contiguous United States and to Project the Distribution in Mexico Data Outbreak locality data Minnesota North Dakota South Dakota Oklahoma Texas Nevada California

Data source

Minnesota Board of Animal Health North Dakota State University Veterinary Diagnostic Laboratory South Dakota State University Agriculture Extension and GIS Center for Excellence Oklahoma Department of Agriculture U.S. Centers for Disease Control and Prevention Louisiana State University Field Investigations U.S. Department of Agriculture Animal and Plant Health Inspection Service U.S. Centers for Disease Control and Prevention California Department of Food and Agriculture Dr. Frank Paterson

4.3.2. Input Data: Environmental Coverages 4.3.2.1. Current-day conditions (1950–2000) For the two current-day modeling scenarios (B. anthracis in the United States and the distribution in Mexico), a set of environmental coverages was constructed from publicly available climatic and biophysical parameters. Nineteen variables were downloaded from the WorldClim data set representative of various temperature and precipitation measurements (www.worldclim.org) [67]. Thirteen additional environmental variables, including temperature and vegetation measures (e.g., normalized difference vegetation index [NDVI]), were provided by the TALA research group at Oxford University [68]. All environmental coverages were resampled to 0.10 degree2 (~8 × 8 km) and clipped to the boundary of the 48 contiguous United States and again separately for Mexico. All data sets were prepared using ERDAS Imagine version 8.7 (Leica GeoSystems, St. Gallen, Switzerland), ArcGIS 9.2, and ArcView 3.2a (ESRI, Redlands, CA). To select the variables used in any modeling scenario, a rigorous culling methodology was applied. This is described in detail in Blackburn et al. [4]. Variables in the coverage sets are presented in Table 2.

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TABLE 2. Environmental Coverages Used to Develop Predictive Ecological Niche Models of Bacillus anthracis for the Contiguous United States and to Project the Potential Geographic Distribution of the Pathogen in Mexico and the United States in the Year 2050 Data Current-day coverages (United States and Mexico study) Mean annual temperature ( C) Annual precipitation (mm) Elevation (m above mean sea level) TFP mean NDVI TFP NDVI annual amplitude Current-day/future-scenario coverages (HADCM3 B2 scenario) Annual temperature ( C) Maximum annual temperature Minimum annual temperature Annual precipitation (mm) Annual solar radiation Annual wind Elevation (m above sea level) Soil pH Soil moisture

Data Sources WorldClim data set; Hijmans et al. [67] WorldClim data set; Hijmans et al. [67] TALA Research Group, Hay et al. [68] TALA Research Group, Hay et al. [68] TALA Research Group, Hay et al. [68]

IPCC (2001) IPCC (2001) IPCC (2001) IPCC (2001) IPCC (2001) IPCC (2001) TALA Research Group, Hay et al. [68] Blackburn et al. [4]; STATSGO database Blackburn et al. [4]; STATSGO database

TFP, temporal Fourier processed [68]; NDVI, normalized difference vegetation index.

4.3.2.2. Future data set (2050) A third modeling scenario was constructed for this chapter to illustrate the potential changes in B. anthracis distribution on the landscape in 2050 under a climate-change scenario. For this modeling experiment, I used the HADCM3 B2 climate data set for 2050 [69]. This scenario is constructed from a 30-year average based around 2055, excluding effects of potential increased climate variability [70]. The B2 scenario is a conservative estimate of overall temperature increase, ranging from 2.1–3.9°C, defined as “a world in which emphasis is on local solutions to economic, social, and environmental sustainability. It is…a heterogeneous world with less rapid and more diverse technological change but a strong emphasis on community initiative and social innovation to find local, rather than global solutions” [69]. For this modeling scenario, a selection of temperature, precipitation, wind, and solar radiation variables were selected from the B2 data set and combined with two continuous soil parameters from the STATSGO data set (soil moisture, soil pH; www ncgc nrcs.usda.gov/products/ datasets/statsgo). These were used to incorporate measures of known ecological factors that promote spore survival. Elevation also was included in this coverage set. It was assumed for this study that these soil values would not change significantly in the 50-year period. Both soil variables were rasterized for inclusion in the ENM. All data for this coverage set were resampled to 0.5 degrees (the resolution of the HADCM3 data) and clipped to the contiguous United States.

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4.3.3. Modeling Parameters For modeling the current distribution scenarios, 177 spatially unique anthrax-outbreak locations were available for model building (Table 1). Before initiating DG, a randomly selected, independent hold-out sample of ~25% (n = 47) of the original data was withheld for later calculation of accuracy metrics. The remaining ~75% of the data (n = 130) was used for model building. A training/testing partition (50%/50%, respectively) internal to DG was used for model building. To maximize DG performance, 1,000 models were developed and the best subset procedure was employed to select the 20 best models under a 10% hard omission threshold and a 50% commission threshold for a final 10-model best subset. The final 10 models were summated within the GIS to visualize the geographic areas of presence/absence predicted across the best subsets. The same parameters were selected for the future modeling scenario. However, given the larger pixel size of the ecological coverages, only 57 sampling sites were used to build the model. A post hoc sample of 27 points was used to calculate accuracy metrics for the future scenario. 4.3.4. Model Accuracy Metrics An area under the curve (AUC) in a receiver operating characteristic (ROC) analysis was used to evaluate the predictive performance of the 10-best model subset using measures of specificity (absence of commission error) and sensitivity (absence of omission error) following other GARP studies [66, 71]. The ROC analysis is a thresholdindependent assessment of model quality derived from a plot of sensitivity (true positive rate; y-axis) versus 1 – specificity – (error or true negative rate; x-axis) constructed from the best subset to determine whether models are predicting better than random [72, 73]. Likewise, AUCs, as employed here, are based on all pixels of presence and all pixels of absence. The AUC of a given model set is compared with that of a random prediction using a z-test. Successful models have AUC scores approaching 1.0 (a perfect model or a measure of reality); the higher the AUC score, the better the model is predicting presence/absence. Models predicting no better than random will have an AUC approaching 0.5 [74]. The ROC was derived from the 25% independent test data points withheld from the original GARP model-building data sets [66]. Two measures of omission were calculated from the 10-best model subset and the independent test data [66]. First, total omission was calculated as the total number of independent test points predicted absent by the summated grid of all ten best models. Second, an average omission was calculated as the average omission across each of the ten best models. Omission indices are useful for evaluating the success of GARP at predicting known localities not included in model building. Two commission indices also were developed. First, total commission was calculated as the total number of pixels predicted present across all ten models divided by the total number of pixels in the study area. Second, an average commission was calculated as the average of the total number of cells predicted present divided by the total number of pixels within the study area on a model-by-model basis for each of the ten models in the best subset. Little difference between these two measures indicates little variation in the rule sets across the models, whereas a large difference indicates high variation across the models.

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5. Ecological Niche Modeling Modeling Scenarios of Bacillus anthracis in North America 5.1. Modeling Scenario 1: Modeling the Current Distribution of Bacillus anthracis in the United States Blackburn et al. [4] provided the first ENM-based geographic predictions for the potential distribution of B. anthracis for the lower 48 states. That modeling scenario was based on a six-variable niche definition and included two soil parameters (soil moisture content and soil pH). These parameters are not available for Mexico, so to project the geographic distribution of an unknown data set, it is necessary to construct models for both countries with the same ecological parameters. This required the development of a modified coverage set that included variables available for both countries. Results from the first modeling effort for B. anthracis [4] indicated that mean NDVI was the most limiting variable in the rule set. For this chapter, I provide a fivevariable coverage set to define the ecological niche for B. anthracis. Soil parameters were removed from the coverage set and replaced with an additional NDVI variable – annual amplitude [68]. 5.1.1. Model Results The U.S. model predicted the known distribution of outbreaks with high predictive accuracy (Fig. 5), with 95.6% of the independent test points predicted correctly and a statistically significant AUC score of 0.823 (Table 3), both indicating that the ecological

Figure 5. The potential geographic distribution of Bacillus anthracis for the contiguous United States based on a five-variable ecological niche modeling experiment using the GARP modeling system. The grayscale ramp indicates model agreement among the ten best models in the subset. Minimum threshold for the acceptable model agreement was set at five models.

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niche models are accurate [66]. The geographic distribution was similar to that of Blackburn et al. [4], with a south-to-north corridor from southwest Texas to northwestern Minnesota, then westward across the Dakotas, through much of Montana, and through the Snake River drainage. A second east-to-west corridor, though patchier, is present from west Texas through southern Arizona, with a disjunct area predicted in the Californian Central Valley. The results of this geographic prediction based on a fivevariable niche definition are consistent with that of Blackburn et al. [4]. TABLE 3. Accuracy Metrics for the U.S. GARP Models Based on Current Climatic Conditions Metric Model specifications Mexico scenario N to build models 130* N to test models (independent) 47 Total omission 4.40% AUC 0.832†‡ HADCM3 B2 scenario (present day) N to build models 57* N to test models (independent) 27 Total omission 0.0% AUC 0.846§|| AUC, area under the curve. *N was divided into 50% training/50% testing at each model iteration. † z = 10.31 (p < 0.01). ‡ SE = 0.0367. § z = 8.55 (p < 0.01). || SE = 0.0471.

For this study, models were developed with point occurrences and coverages that represent the contiguous United States in the current time period (~1950–2000). Accuracy metrics are used to test these current models with post hoc validation points of known outbreaks in the U.S. to evaluate the quality of the projections onto the Mexican and future U.S. climate scenarios. 5.1.2. Evaluating Scenario 1 Relative to White-Tailed Deer, a Primary Wildlife Host Following the Grinnellian definition of the niche, no two species can occupy the same niche. Because of this, it is important to conceptualize how the niche for the disease agent is defined when occurrence data were dependent on either livestock or wildlife outbreaks. In one such disease study, Peterson et al. [38] evaluated the potential geographic distribution of sylvatic Trypanosoma cruzi, the parasite that causes Chagas disease, across Mexico. In that study, Peterson et al. [38] suggests that the potential geographic distribution was likely at the intersection of the reservoir species (Neotoma wood rats) and the Triatomine insects that vector the parasite. To develop models, Peterson et al. constructed individual niche modeling experiments for each rat species and each insect species and then overlaid them in a GIS to identify potential areas of overlap where reservoirs and vectors might interact. Although this process was laborious, it adheres to the Grinnellian niche definition.

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In the study of B. anthracis, where a specific vector is not required to transmit the disease (flies most likely serve as secondary vectors once an outbreak has started), it is important to determine whether or not the models reflect the potential distribution of bacterium or the host species. To test this hypothesis, I compared the geographic area predicted by the B. anthracis model to the known distribution of the white-tailed deer, O. virginianus. The published range distribution for deer was downloaded from the NatureServe Web site (www.natureserve.org) [75] and clipped to the extent of the lower 48 states to match the extent of the modeling experiment. I then converted the range limit for deer to a raster file and recoded those pixels representing the deer range to a value of 1. I then set a threshold limit of five or more models for the B. anthracis best subset and recoded presence to a value of 1 and used map algebra to add the two raster files together. The overlap of the two species is presented in Fig. 6. The deer range accounted for 81.9% of the total landscape of the lower 48 states. In contrast, the area where B. anthracis and white-tailed deer overlap accounts for only 35.9% of the total deer range. This suggests that the GARP modeling process is not biasing the B. anthracis distribution to the larger extent of its host species. Likewise, a comparison of Figs. 5 and 6 shows that, had only the deer distribution been used as a proxy for B. anthracis, it would have excluded the areas successfully predicted (and validated) across Nevada and California, suggesting that the deer distribution was not limiting the B. anthracis model. This suggests that the modeled geographic potential of

Figure 6. The known distribution of white-tailed deer (Odocoileus virginianus) for the contiguous United States is shown in gray. In total, this represents 81.9% of the total U.S. landscape. The black shading indicates the portion of white-tailed deer distribution that overlaps with the potential distribution of Bacillus anthracis (~35.9% of the total deer landscape), illustrating the drastically reduced portion of the landscape where the pathogen and host species might naturally overlap.

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B. anthracis is not simply capturing the distribution of the host species and more likely illustrates environments that support spore survival. This also supports the suggestion by Blackburn et al. [4] that these B. anthracis models likely indicate surveillance priorities for deer that are meaningful (i.e., the area of overlap in Fig. 6). 5.2. Modeling Scenario 2: Projecting the Distribution of B. anthracis to Unknown Landscapes Although the specific timing and location of the introduction of anthrax into the United States is still not fully understood [76], recent genetic data suggest a relationship between the North American sublineage and the dominant European subgroup, which supports a European introduction [77]. There are data on the distribution of anthrax in Canada [9, 21] and the contiguous United States [4]; however, comparable data on anthrax outbreaks are especially lacking for Mexico despite historical reports of anthrax as far back as 1923 [78]. Although Mexico participates in country-level livestock reporting to the Organisation Mondiale de la Santé Animale on an annual basis, the specific geographic distribution of B. anthracis throughout the country remains unknown, under-reported, and poorly understood [79]. With the exception of limited reports from the state of Zacatecas in 1981 and 1983 [80], a report from ProMED Mail [81], and two municipalities in the state of Nuevo Leon, even aggregated data at regional levels are lacking in Mexico’s reporting efforts [82]. The border area between Mexico and the United States is already known to be susceptible to anthrax, with the disease being endemic in western and central Texas [3] and predicted to occur along the Arizona and California borders (see Fig. 5). In Texas, the greatest numbers of cases are associated with wild populations of white-tailed deer, O. virginianus [12]. These deer have the potential to move freely throughout the cross-border Tamaulipan/Mexquital ecoregion. Given the shared U.S./Mexico border and the high likelihood of international crossborder transmission, it is critical to determine the distribution of anthrax in Mexico. To develop model results for Mexico, I modeled the distribution of B. anthracis for the contiguous United States using Modeling Scenario 1 above. I elected to use the GARP modeling system specifically because it allows the user to develop a model for an area with known occurrence data and then project those model rule sets onto the geography and environmental layers of another region (see Fig. 2). This is important because it prevents Mexican localities from being included explicitly as pseudoabsences, as would be the case if both countries were modeled in a single experiment using only U.S. outbreak data. 5.2.1. Model Results The projected distribution of B. anthracis in Mexico (Fig. 7B) suggests that the disease is present along the U.S. border near central and western Texas, central Arizona, and south central California. In each of these locations, the distribution in Mexico is predicted to be a southward continuation of predicted areas in the United States Beyond the border regions, the prediction reaches south into central Mexico as far as the state of Puebla. Although limited data were available to validate the model results for Mexico, the model accurately predicted the area surrounding a farm with laboratory-confirmed

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livestock cases in the year 2000 (Hugh-Jones, unpublished data), the region of Nuevo Leon described in Siefert et al. [82], and much of the state of Zacatecas [80]. In this latter account, no specific data are provided on the region of the state that reported the outbreaks. The only published report not predicted by five or more models in the best subset is the south central state of Michoacan reported on ProMED-mail [81]. As with the reports from Zacatecas, there is limited information available on the specific region within the state where the outbreaks occurred.

Figure 7. A. GARP prediction for the distribution of Bacillus anthracis in the U.S. based on outbreak data from 1957 to 2005. Open circles represent training data used for model building; gray circles represent the independent post hoc data for calculating accuracy metrics. Includes the projection onto Mexico. B. Close-up of the predicted distribution of B. antrhacis in Mexico. Star indicates the location of a laboratory-confirmed anthrax outbreak in 2000. Gray-outlined states indicate limited data from the literature that identify areas with known outbreak histories. Grayscale ramp indicates model agreement from the ten-best model subset. A five-model threshold is used to visualize predicted presence to balance between overfitting and overpredicting of the model set. This five-model threshold for mapping limits the between-model variability resulting from stochastic effects of the modeling process.

The predicted distribution of anthrax in Mexico suggests that B. anthracis is present in adjoining areas where the United States has reported outbreaks in recent years [3, 4]. For example, the northern region of Coahuila De Zaragoza and Nuevo Leon [82]) joins with the southwestern Texas border, where wide-spread epidemics in white-tailed deer, farmed exotic wildlife, and livestock have been frequent [12]. Both sides of the border are predicted to sustain the disease. This suggests that anthrax control efforts in the United States alone are not sufficient to prevent future outbreaks or protect either Mexican or U.S. livestock or wildlife interests.

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5.3. Modeling Scenario 3: Predicting the Distribution of Bacillus anthracis in the United States in 2050 Climate change has become an important topic in the ecology community and the field of spatial epidemiology. This is evident in a recent Scientific and Technical Review by the Organisation Mondiale de la Santé Animale [83] dedicated to the topic of climate change and animal diseases, complete with several article on the effect of climate change on disease distributions. As a preliminary effort to evaluate the potential changes in the geographic distribution of B. anthracis in the contiguous United States, I developed a model of the organism under current climatic conditions and future climatic conditions using the HADCM3 B2 scenario. A large body of literature supports that species have the potential to conserve ecological niches over evolutionary time scales [84–86]. Although it would be ideal to develop the U.S. models of B. anthracis by genetic lineage [4], the modeling efforts of Blackburn et al. [4] and illustrated here suggest that B. anthracis has established a natural ecology in the United States, and the modeling results show a high degree of predictive accuracy, including the ability to exclude some spurious data in the modeling phase. Blackburn et al. [4] show that GARP was sensitive to an outbreak in Oklahoma in 1957 that Van Ness [87] reports was likely from road maintenance that disturbed soils or more likely a food-borne outbreak in animal feed [4]. Given the conservative nature of the GARP rule sets across models for B. anthracis and that anthrax remains a reccurring disease on the American landscape despite control and vaccination efforts [6], I feel it is safe to assume B. anthracis will have niche requirements o similar to today’s in 2050. 5.3.1. Model Results Similar to the efforts for projecting the Mexican distribution, I used the current U.S. environmental variables and point data from recent outbreaks to build and test a model set to project onto the future climate data set. Accuracy metrics for this current-day model indicate models with low omission and high AUC scores (Table 3). Figure 8A shows the distribution of B. anthracis using the current climatic conditions from the HADCM3 data set. Although pixel sizes were quite large relative to the U.S. model presented in scenario 1, the overall geographic distribution is fairly similar, with a dominant south-to-north corridor through western Texas into the Dakotas and Minnesota and westward to the Snake River Drainage. A second east-to-west corridor exists across southern New Mexico and Arizona, with a disjunct portion of habitat in the California Central Valley. Figure 8B illustrates the potential distribution of B. anthracis in 2050. Although the south-to-north corridor is still visible, there is an apparent gap in northern Texas, with more loss of spore-promoting habitat in southernmost Texas. To illustrate this better, I recoded presence and absence for both the current-day prediction and the future projection using the classification. I selected a threshold of six or more model agreement from the best subset of each time period and recoded all values of six or more to a score of 1 to define presence in the current-day models and a score of 4 in the future models. In the current-day model, values of five or less were scored as a zero;

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Figure 8. A. The potential geographic distribution of Bacillus anthracis in the contiguous United States based on a nine-variable ecological niche modeling experiment based on current climatic conditions from the HADCM3 B2 future climate scenario. B. The potential geographic distribution of B. anthracis in 2050 using the future climate data from the HADCM3 B2 climate scenario. A threshold of six models or better was selected to visualize these analyses.

in the future model, agreement of five or less was scored as a 1. I used raster algebra in the GIS to subtract the future scenario from the present scenario to evaluate areas of predicted overlap, habitat loss, and habitat expansion over the next 50 years. This provides four possible scores. A score of –1 indicates that both time periods predict those pixels as absent. A score of –4 indicates a habitat expansion, where B. anthracis is predicted in areas not predicted under the current-day model. A score of 0 indicates that B. anthracis is present in the current-day model and that habitat that supports spores has been lost. A score of –3 indicates that the species is predicted present under both climate scenarios. Figure 9 illustrates the differences in the two time periods. 5.3.2. Regional Changes Although the HADCM3 is based on a global temperature increase of 2.1–3.9°C, changes in the species’ distribution shown in Fig. 9 are regional. Overall, 50.7% of the predicted distribution of B. anthracis for the present day does not change in 2050. Another 39.6% of the landscape was not predicted as suitable in either of the predictions. There was a 6.09% loss of suitable environment from these experiments from present day to the future, with only a 3.57% expansion of habitat in the future data

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set, for a total loss of 2.52% of the landscape no longer suitable for B. anthracis survival. Because these projections cannot be validated with field data for another 47 years, speculation must be tempered with caution. However, there are some interesting spatial patterns on the map in Fig. 9.

Figure 9. Spatial patterns of habitat expansion and loss between the current time period and 2050 for Bacillus anthracis from a GARP modeling experiment. Expanded habitat is present in 2050 but not in the current time period. No change indicates predicted presence in both the current and future model outputs. Not suitable indicates absence of B. anthracis habitat in both modeling experiments. Habitat loss indicates habitat that was predicted as present in the current scenario and excluded from the future scenario.

There is minimal geographic change between the two time periods in the Dakotas region. In contrast, much of the southern and southwestern parts of Texas are reduced from the present day to 2050. This shows a geographic change in the environment and the potential extirpation of B. anthracis from the southern portion of its U.S. range. This is interesting because the largest number of outbreaks and large numbers of individual animals have been reported in Texas [4]. At the relatively low latitude of southern Texas, it is likely that the increase in temperature and solar radiation may increase soil temperature beyond the physiological limits of the spores. It is interesting to consider what might happen in the northern end of the predicted range, where any spatial expansion would be minimal. A number of studies on climate change in the Midwestern states have documented increases in winter air temperature [88], early arrival of spring temperatures [89], and earlier greenup periods in spring [90]. In the case of B. anthracis across the southern states, this scenario could suggest a reduction of the geographic range and a subsequent reduction in outbreaks as the

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environment becomes unsuitable for spore survival. In the northern states, the models suggest a minimum amount of spatial expansion or contraction on the landscape, but earlier springs with longer growing seasons and warm periods might increase the number of outbreaks or the length of anthrax season. Isard et al. [88] also notes colder soil temperatures during the relatively shorter winters, but, given the frequently recurring outbreaks of anthrax in wood bison at much higher latitude [21], it is difficult to imagine that cooler soil temperatures would have a drastic effect on spore survival. 6. Conclusions and Future Directions This chapter attempts to present a number of spatial analytical techniques within a larger theoretical framework of how to employ these techniques to advance spatial epidemiology and disease ecology. The techniques presented here are relatively easy to execute in the software applications introduced in the pages of this chapter. However, it is necessary to understand the limitations and underlying ecological or statistical theory that supports the results of an analysis. As an example, the body of literature on ESDA is only in its second decade. There are a number of research opportunities in determining the usefulness of several local measures of autocorrelation and their application to realworld epidemiological data sets such as those presented here looking at fly distributions and clusters or anthrax outbreaks. Likewise, the definition of the ecological niche is in no way static. Although those definitions first posed by Johnson [49], Grinnell [50], Hutchinson [51, 52], and MacArthur [53] and updated through a large body of literature (see Chase and Leibold [55]) have been circulating in the literature for nearly a century, there is a great deal of work to be done to better define niche variability, niche seasonality and niche competition and on how to incorporate more information on biotic interactions into the spatial-modeling process reviewed here, particularly as it pertains to pathogens and disease transmission. Acknowledgements Many of the ideas presented here were born from long conversations with Dr. M.E. Hugh-Jones, Dr. A. Curtis, and my graduate students. Portions of an earlier draft of the Mexico modeling work were co-authored by K. McNyset, A. Curtis, M. Mitchell, and M.E. Hugh-Jones and presented at an international meeting in Mexico in 2007. Dr. J. Carroll assisted with the map algebra that lead to Fig. 9. Partial funding for research in Kazakhstan was provided by the U.S. Defense Threat Reduction Agency under the project KZ-1 and administered by the U.S. Civilian Research and Development Foundation. References 1.

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54. Morrison, M.L., Hall, L.S. Standard terminology: toward a common language to advance ecological understanding and application. In: Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A., Samson, F.B., editors. Predicting species occurrences: issues of accuracy and scale. Washington, DC: Island Press; 2002. pp. 43–52. 55. Chase, J.M., Leibold, M.A. Ecological niches: linking classical and contemporary approaches. Chicago: University of Chicago Press; 2003. 56. Peterson, A.T. 2008. Biogeography of diseases: a framework for analysis. Naturwissenschaften 95:483– 491. 57. Peterson, A.T. 2006. Ecologic niche modeling and spatial patterns of disease transmission. Emerg. Infect. Dis. 12:1822–1826. 58. Stockwell, D., Peters, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geo. Inform. Sci. 13:143–158. 59. Rogers, D.J. Satellites, space, time and the African trypanosomiases. In: Hay, S.I., Randolph, S.E., Rogers, D.J., editors. Remote sensing and geographical information systems in epidemiology. London: Academic Press; 2000. 60. Phillips, S.J., Anderson, R.P., Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190:231–259. 61. Adjemian, J.C.Z., Girvetz, E.H., Beckett, L., Foley, J.E. 2006. Analysis of genetic algorithm for rule-set prodution (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersinia pestis, in California. J. Med. Entomol. 43:93–103. 62. Ron, R.S. 2005. Predicting the distribution of the amphibian pathogen Batrachochytrium dendrobatidis in the New World. Biotropica 37:209–221. 63. Stockwell D.R.B., Peterson A.T. 2002. Effects of sample size on accuracy of species distribution models. Ecol. Model. 148:1–13. 64. Anderson, R.P., Lew, D., Peterson, A.T. 2003. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol. Model. 162:211–232. 65. Kluza, D.A., McNyset, K.M. 2005. Ecological niche modeling of aquatic invasion species. Aquat. Invad. 16:1–7. 66. McNyset, K.M. 2005. Use of ecological niche modelling to predict distributions of freshwater fish species in Kansas. Ecol. Freshwater Fish 14:243–255. 67. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones P.G., Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25:1965–1978. 68. Hay, S.I., Tatem, A.J., Graham, A.J., Goetz, S.J., Rogers, D.J. Global environmental data for mapping infectious disease distribution. In: Hay, S., Graham, A.J., Rogers, D.J., editors. Global mapping of infectious diseases: methods, examples, and emerging application. London: Academic Press; 2006. 69. Nakicenovic, N., Swart, R., editors. Emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press; 2000. 70. Peterson, A.T., Martínez-Meyer, E., González-Salazar, C., Hall, P. 2004. Modeled climate change effects on distributions of Canadian butterfly species. Can. J. Zool. 82:851–858. 71. Wiley, E.O., McNyset, K.M., Peterson, A.T., Robins, C.R., Stewart, A.M. 2003. Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography 16:120–127. 72. Centor, R.M. 1991. Signal detectability: the use of ROC curves and their analyses. Med. Decis. Mak. 11:102–106. 73. Zweig, M.H., Campbell, G. 1993. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39:561–577. 74. Hanley, J.A., McNeil, B.J. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36. 75. Patterson, B.D., Ceballos, G., Sechrest, W., Tognelli, M.F., Brooks, T., Luna, L., Ortega, P., Salazar, I., Young, B.E. Digital distribution maps of the mammals of the western hemisphere, version 3.0. Arlington, VA: NatureServe; 2007. 76. Stein, C.D. 1945. The history and distribution of anthrax in livestock in the United States. Vet. Med. 40:340–349. 77. Van Ert, M.N., Easterday, W.R., Huynh, L.Y., Okinaka, R.T., Hugh-Jones, M.E., Ravel, J., Zanecki, S.R., Pearson, T., Simonson, T., Uren, J.M., Kachur, S.M., Leadem-Dougherty, R.R., Rhoton, S.D., Zinser, G., Farlow, J., Coker, P.R., Smith, K.L., Wang, B., Kenefic, L.J., Fraser-Liggett, C.M., Wagner, D.M., Keim, P. 2007. Global genetic population structure of Bacillus anthracis. PLoS ONE 2:e461.

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78. Machado, M.A. 1976. An industry in limbo: the Mexican cattle industry 1920–1924. Ag. His. 50:615–625. 79. Hugh-Jones, M. 1999. 1996–97 global anthrax report. J. Appl. Microbiol. 87:189–191. 80. Fragoso Uribe, R., Villicana Fuentes, H. 1984. Antrax en dos communidades de Zacatecas, Mexico. Bol. Oficina Sanit. Panam. 97:526–533. 81. Anthrax-cattle, human, livestock, Mexico (Michoacan). ProMED-mail, June 22, 2003. 20030622.1543. Available at http://www.promedmail.org. Accessed August 27, 2007. 82. Siefert, H.S., Bader, K., Cyplik, J., González Salinas, J., Roth, F., Salinas Meléndez, J.A., Sukop, U. 1996. Environment, incidence, aetiology, epizootiology and immunoprophylaxis of soil-borne diseases in north-east Mexico. Zentralbl. Veterinarmed B. 43:593–606. 83. de la Rocque, S., Hendrickx, G., Morand, S., editors. 2008. Climate change: impact on epidemiology and control in animal diseases. Revue Scientifique et Technique, OIE, 27(2). 84. Holt, R.D., Gaines, M.S. 1992. Analysis and adaptation in heterogeneous landscapes: implications for the evolution of fundamental niches. Evolution. Ecol. 6:433–337. 85. Peterson, A.T., Soberon, J., Sanchez-Cordero, V. 1999. Conservatism of ecological niches in evolutionary time. Science 285:1265–1267. 86. Peterson, A.T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. Q. Rev. Biol. 78:419–433. 87. Van Ness, G.B. 1959. Soil relationship in the Oklahoma-Kansas anthrax outbreak of 1957. J. Soil Water Conserv. 14:70–71. 88. Isard, S.A., Schaetzl, R.J., Andresen, J.A. 2007. Soils cool as climate warms in the Great Lakes region: 1951–2000. Ann. Assoc. Am. Geog. 97:467–476. 89. Strode, P.K. 2003. Implications of climate change for North American wood warblers (Parulidae). Global Change Biol. 9:1137–1144. 90. Bradley, N.L., Leopold, A.C., Ross, J., Huffaker, W. 1999. Phenological changes reflect climate change in Wisconsin. Proc. Natl. Acad. Sci. U S A 96:9701–9704.

Section II Molecular Analysis and Tools

Applications of Paleomicrobiology to the Understanding of Emerging and Re-emerging Infectious Diseases Gérard ABOUDHARAM1,2, Michel DRANCOURT1, and Didier RAOULT1 1 Unité des Rickettsies, Faculté de Médecine, Université de la Méditerranée, Marseille, France 2 Unité d’Odontologie Conservatrice, Faculté d’Odontologie, Université de la Méditerranée, Marseille, France

Abstract. Advanced molecular methods are being applied to the analysis of microbial infections in centuries-old human populations. As evidenced through work using modern animal models of infection, and if extracted under proper conditions, dental pulp can be a good source for well-preserved microbial DNA deposited inside teeth during historical bacteremias. The use of frozen and fixed tissues, bones and teeth as sampling substrates is also discussed. The methods described in this paper, including “suicide PCR”, were used to analyze samples from the remains of human victims of a historical plague outbreak in France. Isolates from this historical outbreak were assigned to specific biotypes of Yersinia pestis based upon an analysis of intergenic spacer DNA.

1. Introduction Dental pulp and dentin together form an embryologic, histological, and functional entity termed the pulpo-dental organ. Within the pulpo-dental organ, dental pulp is a specialized tissue that occupies a central position within the tooth and embodies the sensory functions, the dentinogenesis, and the defense of the dental organ. The vascularization of this organ is significant, proportionally comparable with that of the human brain [1] of terminal type. After a short passage by a precapillary structure, the capillaries form a fine network distributed to the periphery of the pulpar cavity. The venous return consists of four to six postcapillary veinlets, whose diameter varies from 15 to 25 µm, then by collecting veinlets (25–50 µm). Close to the apical opening, their diameter is less than 50 µm. A transitory bacteriemia can occur in an asymptomatic individual, and the bacteria responsible for this bacteriemia can colonize dental pulp. Studies carried out in the past on animal models have shown the possibility for micro-organisms circulating in the blood to colonize dental pulp [2–6]. This phenomenon, however, has been much debated and has long remained highly controversial [7].

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2. Is It Possible to Find Microbial DNA in Dental Pulp? Efforts to isolate molecular signatures of microbial pathogens in dental pulp have produced mixed results. Glick and colleagues detected HIV proviral DNA in dental pulp [8, 9], whereas Blanquet-Grossard and colleagues failed to find evidence of prions in teeth [10]. In contrast, the forensic use of signatures obtained from teeth, and pulp in particular, is well-established. For example, teeth were used to carry out blood grouping [11, 12]. It has been several years since the introduction of the tooth as a source of DNA opened up new prospects in the field of forensic medicine [13]. For this type of analysis, however, it is necessary to obtain a quantity of at least 50–100 ng. Dental pulp as a source of DNA during polymerase chain reaction (PCR) testing, taking into account the excellent results obtained, has proven useful for cases in which one has a small quantity of pulpar DNA [14]. The temperatures to which teeth can be subjected do not affect the possibility of using them as a possible source of DNA [15, 16]. The quantities of DNA extracted from pulp are small but nevertheless of great quality because of the preservation of conjunctive tissues inside the pulpar cavity. Given the published evidence, the tooth represents a potential source of DNA and constitutes a sample matrix invaluable in forensic medicine. Our laboratory has been at the forefront of efforts to use the tooth in pathogenesis research. In particular, we are pioneering the use of bacterial DNA within total pulpar DNA for the characterization of pathogens. 3. The Molecular Model: Bacteriemia, Guinea Pig, and Coxiella burnetii Among the innovations we have introduced is the use of the guinea pig as an animal model. Through the use of this new animal model we aimed to remove the ambiguity that remained in distinguishing between bacteremia and pulpar colonization. Because blood circulates in the vasculature of pulp, bacteria present in the blood of a bacteriemics animal could be detected in pulp. In our experimental study guinea pigs were infected with a noncommensal, strictly intracellular bacterium of the oral flora of the guinea pig, which was detected using a specific molecular marker. The results demonstrated unambiguously the presence of colonizing bacteria in pulp. The molecular technique of detection (PCR), with its great sensitivity and its specificity in the choice of targets, was an original element compared with other models. The results observed with the two molecular targets for the model with Coxiella burnetii differed. Assays for the detection of a fragment of the insertion sequence IS1111 [17] showed a greater sensitivity than did those detecting a fragment of the sod gene. The difference was explained by the fact that the detection of bacteria in pulp can depend on the sensitivity of the PCR and the number of copies of the target – the first target is present in a single copy per genome whereas IS1111 is present in multiple copies. Thus, the choice of molecular target used takes on great importance. The presence of bacterial DNA was detected by PCR both 15 and 20 days after the infection, whereas, the presence of culturable bacteria in spleen and blood was detectable only after 5 and 10 days after infection. From these results we concluded that either the bacteria reached the dental pulp only after 15–20 days and they were no longer culturable after 10 days

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in blood and spleens, or the system of detection used (culture) was not sufficiently sensitive to be able to detect the bacteria after 5 and 10 days. However, because the tests of detection in blood culture and pulp (PCR) were different, and although they were concordant with the data in the literature and the laboratory, it was not possible to draw more conclusions from the difference in the results [18]. 4. Application to the Diagnosis of Bacteriemias The description of old infectious diseases always was made starting from old texts, iconography, or anthropological data. These anthropological data were established, most of the time, starting from observations of the human remains coming from mass graves discovered fortuitously. However, if the old texts are easier to interpret than the charts, they raise the problem of their conservation and access to the original texts. It is very difficult to give an objective interpretation based on such descriptions. Semiology is not sufficiently reliable to make it possible to make an exact diagnosis and to establish an analytical classification for ancient old infectious diseases. In addition, it should be noted that the anthropological and historical analyses allowed an analogical description of old infectious diseases, generating various etiological and epidemiological assumptions to account for the same observations, and this often led to controversies. 5. Various Tissues Used for Objective Analysis 5.1. Frozen Tissues Frozen human tissues represent an ideal situation because these samples allow the insulation and the culture of the pathogen and its immunological and molecular characterization, but this situation is obviously exceptional. The man discovered in the ice in the Tyrol gave rise to research [19] that made it possible to show that his muscles were rich in bacterial DNA. Nevertheless, taking into account the number of detected bacterial species and their variety (Sphingomonas, Afipia, Curtobacterium, Microbacterium, Agromyces, and others), the question of external contaminations can be mentioned. 5.2. Fixed Tissues Fixed tissues represent samples of good quality for molecular detection but do not allow the use of a culture medium. These tissues are rarer and more difficult to use if one takes contamination into account. Nevertheless, Mycobacterium tuberculosis could be detected starting from soft tissues from a Peruvian mummy dating back 1,000 years [20], and syphilis could be detected starting from Italian samples dating from 16th century [21]. The presence of Carrion’s disease (Bartonella bacilliformis) was detected in mummified soft tissues from mummies associated with human sacrifice among the ancient Huaris of southern Peru [22].

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5.3. Bones Bones are the most abundant remains, but it is difficult to use molecular techniques in their analysis because of the difficulty in sample preparation, including the washing of samples and the possibility of their contamination during preparation. DNA must be extracted from bone before it can be analyzed. This is a difficult requirement, because the extraction is preceded by a stage of decalcification using EDTA as a chelator. EDTA must then be removed from the sample by extensive washing, because EDTA is an inhibitor of DNA polymerases that require magnesium as a co-factor [23]. Once sample DNA has been extracted, however, PCR amplification allows genomic material to be obtained in sufficient quantity to enable its characterization, and it eliminates the problems of the contamination of samples and the specificity of the amplicons obtained. 5.4. Teeth Teeth constitute a target organ of quality because of their preservation in time, but all the possibilities that they offer for the detection of bacterial DNA have not been exploited, whereas, as noted above, the use of dental pulp as sample material in forensic medicine is well-established. Because blood infections carry bacteria to all parts of the vasculature in systemic diseases, teeth potentially contain bacterial DNA. This fact was the motivation for the work that is discussed in detail below. 6. Detection of Yersinia pestis in Dental Pulp The provision of remains coming from two mass graves identified as containing victims of the “great plague” gave us the opportunity to apply the techniques developed through the use of the animal models described above. The origin of the first mass grave located in the gardens of the monastery of the Observance [24] did not leave any doubt after compilation and comparison of the anthropological data with the historical data and the records of the monastery, which was used as a hospital at that time. The second mass grave was located close to the site of Fédons. A study of contemporary records, aiming to specify the statute and the origin of the places better, made it possible to retain the assumption of an epidemic disease. This archival study led to strong presumptions, according to which, a cemetery and an infirmary were set up on this site to face an epidemic of plague [25]. The first part of this work consisted in developing a technique for the recovery of dental pulp and the extraction of the pulpar DNA, which permitted a second phase to amplify specific gene fragments of Yersinia pestis. The selection of teeth is a significant stage. The teeth on which research is carried out must answer criteria of selection: young teeth having belonged to a child or an adolescent and, preferably, single-root teeth with the apex almost closed; unerupted teeth are preferred for their absence of contact with any external elements. The technique for the removal of pulp consisted in the preparation of a preliminary fracture line and the fracture itself along the largest axis of the tooth, followed by the recovery of the powdery organic remains that line the pulp cavity using an excavator. Pulpar DNA extraction using the classical gave the best results; we chose this method after testing several other protocols for DNA extraction.

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The second part of the work was the search for the DNA of Y. pestis. The selected molecular targets were twofold. First, various fragments of small sizes of the plasmidborne pla gene were tested (250–300 nt). It is specific to Y. pestis, and it is present in multiple copies per genome. The fact that it is present in several copies per bacterium represented a significant advantage, taking into account the possible degradation of the DNA. The second gene tested was a fragment of 133 bp of the rpoB gene [26] specific to Y. pestis. This second gene is present as a single copy per bacterial chromosome and is used in routine diagnosis in the laboratory. The results confirmed the cause of death of the individuals because the various fragments of genes tested were found. The results also confirmed the colonization of dental pulp via the hematogen ducts in a bacteriemia and that the tooth represented a choice tool for this type of investigation [27]. 7. Development of a New Protocol of Amplification to Prevent the Risk of Contamination: “Suicide PCR” In preceding work, for the first time, we established a paleomicrobiological diagnosis of plague in human remains dating from the 16th and 18th centuries, thus establishing the diagnosis of an old, septicemic disease [27]. In addition to the difficulties relating to the fact that old DNA is fragile and fragmented and that one finds only a small number of copies of the required molecular target, other technical difficulties, primarily related to problems of contamination of old DNA, appeared. Only the most stringent laboratory hygiene made it possible to avoid these contaminations; the more sensitive the techniques of detection, the greater the risk of contamination [28]. These problems of contamination can be related to the handling by the operator or the organization of the laboratory. To mitigate such disadvantages, the laboratory is organized so as to avoid cross contaminations and false-positive detections [29]. The circulation of the samples and the amplicons should never cross, taking into account the ease of spread of the latter and the number of copies at the end of a reaction of PCR. DNA extractions and PCR reactions are carried out in different, physically separated sites. Exposure of working surfaces to ultraviolet irradiation also makes it possible to reduce the risk of contamination. To take into account the difficulties encountered in preceding work and to establish a diagnosis that can in no case be debatable, samples of human material dated according to anthropological and historical criteria from the 14th century were analyzed in a novel way. Moreover, to make a diagnosis could be of considerable historical interest because it is reported that, from 1,347 to 1,351, the Black Death killed nearly 30 million Europeans. Although for a long time historians put forth the assumption that it was indeed the plague, this assumption had not been objectively confirms with experimental evidence. Moreover, taking into account the spread of the epidemic, doubts remained as to the responsibility of Y. pestis. Given the symptoms described in historical accounts, a diagnosis of hemorrhagic fever could also reasonably be made. Only a paleomicrobiological diagnosis could eliminate or confirm Y. pestis as the etiological agent responsible for the Black Death. In this work, we showed, thanks to the strategy of “suicide amplification,” that the Black Death of 1,348 was indeed caused by Y. pestis [30]. This procedure was performed using sample preparation methods and protocols that were developed specifically for this project. The absence of contamination being able to be

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related to the presence of DNA in an aerosol provided confidence in the accuracy of our results. PCR was carried out in the absence of any positive controls, which themselves could have been the source of contaminating DNA. This original approach demanded a theoretical calculation of the conditions of amplification. However, while the results generated are indisputably unaffected by contamination from positive control DNA, the correct amplification of target sequences can be confirmed only by the sequencing the resulting amplicons. In “suicide PCR”,, a single primer set is used in only one PCR reaction ever in the analytical laboratory. This single-use strategy avoids the possibility that previously amplified DNA will contaminate future PCR reactions. The success of this strategy has implications beyond the characterization of Y. pestis in ancient human samples. “Suicide PCR” now is applied routinely for the molecular diagnosis of contemporary infectious diseases. 8. Genotyping of Yersinia pestis Based on the conventionally determined geographical origins of Y. pestis and the historical writings that indicate the geographical origin of plague pandemics, it was suggested that each pandemic came from a different biovar: the biovar Antiqua of East Africa caused the first pandemic, and the biovar Medievalis of Central Asia caused the second. The bacteria related to the third pandemic are biovar Orientalis. In this study, we evaluated this assumption for the first time by detecting biovars in old human remains. Starting from the two available genomes of Y. pestis – strain CO92 and Kim – bioinformatic analysis made it possible to locate eight intergenic spacers, enabling the differentiation of the three biotypes of Y. pestis. This multispacer typing then was applied to 35 contemporary strains from different geographical origins to establish the genotyping of these strains. With the use of this multispacer typing, three groups were determined, representing the three biovars. This multispacer typing was used to test the dental pulp from the remains of several individuals. These remains dated from the Justinian plague and the Black Death. The analysis showed that the samples attributed to the Justinian plague indeed were contaminated by Y. pestis and that the first two pandemics, attributed historically to the Antiqua and Medievalis biotypes, could in fact be attributed to the Orientalis biovar [31]. 9. Conclusions and Prospects This study showed that DNA signatures of pathogens can be found in ancient dental pulp. Because of its exceptional ability to preserve DNA, dental pulp constitutes an extremely interesting sample matrix for the research of old pathogens. Dental pulp and the tools we have developed for its analysis therefore comprise an important new set of research tools in microbiology and paleomicrobiology. Future courses of research include the use of protocols of repair of DNA to collect more data on ancient pathogens and the use of universal molecular targets to be able to answer several etiological questions.

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References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.

Kim, S. 1985. Regulation of pulpal blood flow. J. Dent. Res. 64:590–596. Robinson, H.B.G., Billing, L.R. 1941. The anachoretic effect in pulpitis. J. Am. Dent. Assoc. 28:268– 282. Burke, G.W., Knighton, T.H. 1960. The localization of microorganisms in inflamed dental pulps of rats following bacteremia. J. Dent. Res. 39:205–214. Delivanis, P.D., Snowden, R.B., Doyle, R.J. 1981. Localisation of blood-borne bacteria in instrumented unfilled root canals. Oral Surg. Oral Med. Oral Pathol. 52:430–432. Tziafas, D. 1989. Experimental bacterial anachoresis in dog dental pulps capped with calcium hydroxyde. J. Endodont. 15:591–595. Hoshino, E., Ando, N., Sato, M., Kota, K. 1992. Bacterial invasion of non exposed dental pulp. Int. Endod. J 25:2–5. Kettreing, J.D., Torabinejad, M. Microbiology and immunology. In S. Cohen, R. Burns (eds.) Pathways of the Pulp. 6th ed. St. Louis: Mosby-Year Book Inc.; 1994. Glick, M., Trope, M., Pliskin, M.E. 1989. Detection of HIV in the dental pulp of a patient with AIDS. J. Am. Dent. Assoc. 119:649–650. Glick, M., Trope, M., Bagasra, O., Pliskin, M.E. 1991. Human immunodeficiency virus infection of fibroblasts of dental pulp in seropositive patients. Oral Surg. Oral Med. Oral Pathol. 71:733–736. Blanquet-Grossard, F., Sazdovitch, V., Jean, A., Deslys, J.P., Dormont, D., Hauw, J.J., Marion, D., Brown, P., Cesbron, J.Y. 2000. Prion protein is not detectable in dental pulp from patients with Creutzfeldt-Jakob disease. J. Dent. Res. 79:700. Yamada, Y., Yamamoto, K., Yoshii, T., Ishiyama, I. 1989. Analysis of DNA from tooth and application to forensic dental medicine. Nippon Hoigaku Zashi 43:420–423. Smeets, B., Van de Vorde, H., Hooft, P. 1991. ABO bloodgrouping on tooth material. Forensic Sci. Int. 50:277–284. Ohira, H., Yamada, Y. 1999. Advantages of dental mitochondrial DNA for detection and classification of the sequence variation using restriction fragment length polymorphisms. Am. J. Forensic Med. Pathol. 20:261–268. Pillay, U., Kramer, B. 1997. A simple method for the determination of sex from the pulp of freshly extracted human teeth utilising the polymerase reaction. J. Dent. Assoc. S. Afr. 52:673–677. Myers, S.L., Williams, J.M., Hodges, J.S. 1999. Effects of extreme heat on teeth with implications for histologic processing. J. Forensic Sci. 44:805–809. Murakami, H., Yamamoto, Y., Yoshitome, K., Ono, T., Okamoto, O., Shigeta, Y., Doi, Y., Miyaishi, S., Ishizu, H. 2000. Forensic study of sex determination using PCR on teeth samples. Acta Med. Okayama 54:21–32. Willems, H., Thiele, D., Frölich-Ritter, R., Kraus, H. 1994. Detection of Coxiella burnetii in cow’0s milk using the polymerase chain reaction (PCR). J. Vet. Med. 41:580–587. Aboudharam, G., La Scola, B., Raoult, D., Drancourt, M. 2000. Detection of Coxiella burnetii in dental pulp during experimental bacteremia. Microb. Pathog. 28:249–254. Rollo, F., Luciani, S., Canapa, A., Marota, I. 2000. Analysis of bacterial DNA in skin and muscle of the Tyrolean iceman offers new insight into the mummification process. Am. J. Phys. Anthropol. 111:211– 219. Salo, W.K., Aufterheide, A.C., Buikstra, J., Holcomb, T.A. 1994. Identification of Mycobacterium tuberculosis DNA in a pre-Columbian Peruvian mummy. Proc. Natl. Acad. Sci. U S A 91:2091–2094. Fornaciari, G., Castagna, M., Tognetti, A., Tornaboni, D., Bruno, J. 1989. Syphilis in a renaissance Italian mummy. Lancet 2:614. Allison, M.J., Pezzia, A., Gerszten, E., Mendoza, D. 1974. A case of Carrion’s disease associated with human sacrifice from Huari culture of southern Peru. Am. J. Phys. Anthrop. 41:295–300. Al-Soud, W.A., Radstrom, P. 2001. Purification and characterization of PCR-inhibitory components in blood cells. J. Clin. Microbiol. 39:485–493. Dutour, O., Signoli, M., Georgeon, E., Da Silva, J. .1994. Le charnier de la grande peste de Marseille (rue Leca) Données de la fouille de la partie centrale et premiers résultats anthropologiques. Préhistoire Anthropologie Méditerranéennes 3:191–203.

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25. Reynaud, P., Maurin, M., Ducout, G., Moretti, J.P., Thiebaux, R. Lot 32: Lambecs/Les Fedons (Bouches du Rhône), TGV Ligne 5-secteur 1: Avignon-Marseille. Rapport d’évaluation. Aix en Provence: AFAN; 1996. 26. Mollet, C., Drancourt, M., Raoult, D. 1997. rpoB sequence analysis as a novel basis for bacterial identification. Mol. Microbiol. 26:1005–1011. 27. Drancourt, M., Aboudharam, G., Signoli, M., Dutour, O., Raoult, D. 1998. Detection of 400-year-old Yersinia pestis DNA in human dental pulp: an approach to the diagnosis of ancient septicemia. Proc. Natl. Acad. Sci. U S A 95:12637–12640. 28. Lisby, G. 1999. Application of nucleic acid amplification in clinical microbiology. Mol. Biotechnol. 12:75–99. 29. Vaneechoutte, M., Van Eldere, J. 1997. The possibilities and limitations of nucleic acid amplification technology in diagnostic microbiology. J. Med. Microbiol. 46:188–194. 30. Raoult, D., Aboudharam, G., Crubezy, E., Larrouy, G., Ludes, B., Drancourt, M. 2000. Molecular identification by “suicide PCR” of Yersinia pestis as the agent of medieval black death. Proc. Natl. Acad. Sci. U S A 97:12800–12803. 31. Drancourt, M., Roux, V., Dang, L.V., Tran-Hung, L., Castex, D., Chenal-Francisque, V., Ogata, H., Fournier, P.E., Crubézy, E., Raoult, D. 2004. Genotyping, Orientalis-like Yersinia pestis, and plague pandemics. Emerg. Infect. Dis. 10:1585–1592.

Characterization of a Putative Hemagglutinin Gene in the Caprine Model for Brucellosis Quinesha L. PERRY1, Sue D. HAGIUS2, Joel V. WALKER2, Lauren DUHON1, and Philip H. ELZER1,2 1 Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana 2 Department of Veterinary Science, Louisiana State University Agricultural Center, Baton Rouge, Louisiana Abstract. With the completion of the genomic sequences of Brucella melitensis 16M and B. abortus 2308 and the vaccine strain RB51, a putative hemagglutinin gene was identified that is present in 16M and absent in B. abortus. The possibility of this hemagglutinin being a potential host specificity factor was evaluated via expression in trans in B. abortus 2308-QAE and RB51-QAE. Using the caprine brucellosis model, colonization and pathogenesis studies were performed to evaluate the strains.

1. Introduction Brucella species are short, nonmotile, nonsporulating, nonencapsulated, gram-negative aerobic rods. They are facultative intracellular pathogens of animals and humans [1, 33, 34, 39, 41]. The Brucella genus is highly homogeneous, with all members showing greater than 90% homology in DNA–DNA pairing studies [2, 3], and little is known about Brucella virulence. The genus Brucella consists of six species, each with a preference for a primary host and varying degrees of pathogenicity. B. melitensis primarily infects goats and is the most pathogenic for humans; B. abortus infects cattle. Brucella LPS has important cell surface properties, yet there is no evidence showing its role in invasion [4]. Other outer membrane proteins also may play a role in the organisms’ virulence [5, 6, 40]. An organism’s ability to adhere to a mucosal surface is a crucial first step in the pathogenesis of many pathogens [7]. Initial attachment of the brucellae to epithelial cells is mostly unknown. With the completion of Brucella genomes, specifically B. melitensis 16M and B. abortus 2308, studies have been done and are currently underway to detect and characterize novel genes that may be involved in Brucella pathogenicity [8, 9]. Of particular note is a putative hemagglutinin gene found within the B. melitensis 16M genome that is absent in B. abortus [9, 10]. The gene is present in B. suis and B. canis but with minor nucleotide substitutions. There are two copies of the gene in B. ovis [11].

K.P. O’Connell et al. (eds.), Emerging and Endemic Pathogens, DOI 10.1007/978-90-481-9637-1_9, © Springer Science + Business Media B.V. 2010

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A study done by del C Rocha-Gracia and colleagues [5] explored the possibility of hemagglutinins on the cell surface of brucellae serving as adhesins to eukaryotic cells through the ability of B. abortus and B. melitensis to hemagglutinate human and animal (rabbit, hamster, guinea pig, rat, mouse, sheep, and dog) erythrocytes and attempted to identify a receptor moiety involved in that reaction. All Brucella strains (B. abortus 2308, B. abortus S19, B. abortus 02, and B. melitensis 03) tested showed hemagglutination with the red blood cells from the various sources, with B. melitensis 03 showing the highest hemagglutination titers against all red blood cells and B. abortus 2308 the lowest titer. This study evaluated the host specificity of Region E, a putative ~2.0-kilobase hemagglutinin gene using the completed genome of B. melitensis 16M [8]. Experiments using variants of B. abortus 2308 and RB51 expressing Region E in trans were carried out in the caprine brucellosis model to provide insight into possible vaccine development. 2. Materials and Methods 2.1. Bacterial Strains Virulent B. abortus strain 2308, vaccine strain RB51, and B. melitensis strain 16M were used in these studies to create B. abortus 2308-QAE, RB51-QAE. B. abortus 2308, RB51 and B. melitensis 16M were grown on Schaedler Brucella Agar (SBA) (Difco Laboratories, Detroit, MI) and B. abortus 2308-QAE, RB51-QAE were grown on SBA containing 100 μg/mL ampicillin or 45 μg/mL kanamycin, respectively. Plates were incubated at 37°C in a 5% CO2 atmosphere for 2–3 days. Inoculation doses of B. abortus 2308, RB51, B. melitensis 16M, and B. abortus 2308-QAE, RB51-QAE were made as previously described [12]. Viability counts on SBA plates, SBA plates with ampicillin (100 μg/mL), and SBA plates with kanamycin (45 μg/mL) using serial dilutions were done to validate the concentration of the inoculation doses the day of use. 2.2. Creation of B. abortus 2308 and RB51 Variants A 4,950-bp plasmid called pBBR1MCS-4 [13] was digested using EcoR V (New England Biolabs, Beverly, MA). Region E, an ~2.0-kilobase PCR-amplified putative hemagglutinin gene, was generated from B. melitensis 16M genomic DNA using the primers ORF-944F (5'-GAATTGGCGACCTGACTGAGGA-3') and ORF-944R (5'CTCACGGCTGTTCTCCTTTAACA-3') (the Institute of Molecular Biology and Medicine at the University of Scranton, Scranton, PA). PCR-amplified Region E was ligated into the EcoR V-linearized, gel-purified pBBR1MCS-4 plasmid using the FastLink™ DNA Ligation Kit for Blunt End Ligation (Epicentre Biotechnologies, Madison, WI) to create pQAE. The ligation mixture then was used to transform One Shot® Chemically Competent Cells (Invitrogen Corporation, Carlsbad, CA) according to the manufacturer’s directions. Successful transformants were cultured and their plasmids isolated using the Qiagen Buffer System (Qiagen, Inc., Valencia, CA). The isolated plasmid DNA was electroporated into B. abortus 2308 and RB51 as previously described [12], creating B. abortus 2308-QAE and RB51-QAE.

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2.3. Confirmation of B. abortus 2308 and RB51 Variants Expression of pQAE in trans in B. abortus 2308 and RB51 was achieved by the introduction and maintenance of the low copy number plasmid in the cell. The new plasmid containing Region E from B. melitensis (pQAE) was electroporated into B. abortus 2308 or RB51 and screened for successful transformation using SBA plates supplemented with 100 µg/mL ampicillin. The new variant of B. abortus 2308 was named B. abortus 2308-QAE, and RB51 was named RB51-QAE. Presence of the gene was confirmed via PCR amplification of the putative hemagglutinin using the Region E primers and restriction enzyme digestion of pQAE. 2.4. Standard Identification Tests Potential variant/mutant colonies were isolated for Brucella typing using techniques commonly performed to differentiate Brucella species from other gram-negative organisms. Standard biochemical tests were performed, including urease, oxidase, and catalase, along with observing colony morphology and growth rate [14]. Suspected variants/mutants along with their parental strains also were tested for sensitivity to dyes: azure A, basic fuchsine, crystal violet, pyronin, safranin, and thionin, according to the manufacturer’s protocol (Key Scientific Products, Round Rock, TX). 2.5. Goats For all animal studies, male or female Angora or Spanish goats were obtained from commercial herds or from the Louisiana State University (LSU) herd (LSUniversity Agricultural Center, Baton Rouge, LA). All animals were housed throughout the study at the Ben Hur Large Animal Isolation Facility, a restricted-access United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Veterinary Services – and Centers for Disease Control and Prevention (CDC) – approved facility. All animals were cared for in accordance with the LSU AgCenter Animal Care and Use Committee guidelines. For the colonization studies, 30 male or nonpregnant female goats divided into five equal groups were inoculated conjunctivally with either 1 × 109 colony forming units (cfu) of B. abortus 2308, RB51, B. abortus 2308-QAE, RB51-QAE, or B. melitensis 16M [15]. At predetermined time points, the goats were euthanized by captive-bolt and exsanguination. Two animals from each group were sacrificed on days 7, 14, and 21. The following tissues were collected and examined bacteriologically: parotid, prescapular, internal iliac, inguinal, and supramammary lymph nodes; liver; and spleen. Results were recorded as colony-forming-units per gram (cfu/g) of tissue. For the pathogenesis studies, dams were bred with Brucella-negative billies, and their pregnancies later were confirmed via ultrasound examination. Goats in late gestation were exposed conjunctivally to either the virulent parental strains B. melitensis 16M or B. abortus 2308 or the variant B. abortus 2308-QAE with 1 × 107 cfu. Pregnancies were monitored until delivery, and kids were recorded as aborted/weak or live/healthy.

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Live kids were euthanized by CO2 asphyxiation, and lung tissue and abomasal fluid were collected on all kids born or aborted. A month following the last birth or abortion, all dams were euthanized by captive-bolt and exsanguination. The following tissues were collected: parotid, prescapular, internal iliac, and supramammary lymph nodes; liver; spleen; and mammary gland. All tissues collected were stored at –20°C until cultured for bacteriological analysis. 2.6. Serological Analysis All animal sera samples were brucellosis card tested and evaluated by Western blot [16] before any experimentation to confirm the absence of Brucella-specific antibodies. Necropsy samples were tested similarly. For Western immunoblot analysis, cell lysates of B. abortus 2308, RB51, B. melitensis 16M, B. abortus 2308-QAE, and RB51-QAE were prepared by sonication and dilution in Laemmli sample buffer [17]. Cell lysates were separated by polyacrylamide gel electrophoresis (SDS-PAGE) using 12% TrisHCl Ready Gels (BioRad Laboratories, Inc., Hercules, CA) and transferred to a nitrocellulose membrane (Osmotics, Livermore, CA). After blocking with 1% skim milk, individual blots were incubated in a 1:40 dilution of test serum on a shaker at room temperature overnight. After incubation, blots were washed with tris buffered saline (TBS)-Tween and TBS and incubated on a shaker for 45 min at room temperature in a 1:800 dilution of rabbit anti-goat IgG peroxidase conjugate (Sigma-Aldrich Co., St. Louis, MO). Blots were developed using 4-chloro-1 napthol tablets (Sigma-Aldrich Co.) in a TBS-methanol-3% hydrogen peroxide solution. Reactions were stopped by the addition of dH2O. 2.7. Bacteriological Analysis Tissue samples were thawed, weighed, homogenized in sterile phosphate buffered saline, and plated on SBA plates supplemented with 5% bovine blood and Brucella Selective Supplement (Oxoid Ltd., Basingstoke, Hampshire, England) [18]. After a 14day incubation period at 37°C in a 5% CO2 atmosphere, the total number of colonies present on each plate was counted and cfu/g of tissue calculated. The limit of detection for our laboratory using this system is 13 cfu/g or mL. Brucella species were identified by colony morphology, growth rate, and biochemical tests [14]. B. abortus 2308-QAE and RB51-QAE were differentiated from B. abortus 2308 and RB51 based on their ability to grow on SBA plates containing 100 µg/mL ampicillin. 2.8. Statistics Numbers of colonized dams, colonized kids, and abortions in the pathogenesis study were compared between two groups at a time using a Fisher exact probability test, with P < 0.05 being considered significant [19]. Statistical analysis was performed with Sigma Plot statistical software (Sigma Stat Statistical Software 1.0, Jandel Scientific, San Rafael, CA).

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3. Results 3.1. Dye-Sensitivity Analyses Dye-sensitivity analysis of B. abortus 2308-QAE and RB51-QAE was typical of that usually seen with B. abortus 2308 and RB51. 3.2. Colonization of B. abortus 2308-QAE and RB51-QAE A short-term colonization study was performed to see whether B. abortus 2308-QAE or RB51-QAE could colonize nonpregnant goats. At predetermined time points, two animals from each group were sacrificed; and the following tissues were collected for bacterial culture: parotid, prescapular, internal iliac, inguinal, and supramammary lymph nodes; liver; and spleen. Results were recorded as cfu/g of tissue (Table 1). There were no significant differences between the experimental groups and the controls. Both modified strains were capable of colonizing the caprine hosts at levels comparable with the parental strains. TABLE 1. Colonization of Nonpregnant Goats Inoculated with Brucella abortus 2308, B. abortus 2308-QAE, Brucella melitensis 16M, RB51, or RB51-QAE Strain Brucella abortus 2308

7 Days 1 × 104

14 Days 2.7 × 104

21 Days 1.4 × 104

B. abortus 2308-QAE Brucella melitensis 16M RB51 RB51-QAE

3 × 104 5.4 × 104 1.5 × 102 1.0 × 103

2.3 × 105 8.6 × 104 1.0 × 101 3.2 × 103

6.8 × 104 3.2 × 104 1.2 × 102 1.2 × 103

Calculated in mean cfu/g of tissue.

All resulting colonies were evaluated to verify their Brucella origin via oxidase, catalase, and urease tests. All colonies were confirmed to be Brucella and grew on the appropriate antibiotic-supplemented media. Serological analysis of the colonization goats on days 7, 14, and 21 via Brucella card test and Western immunoblot analysis revealed that all animals given 2308 or 16M on days 14 and 21 were seropositive. RB51 animals were seronegative on the card test. 3.3. Pathogenesis of B. abortus 2308-QAE To assess the pathogenicity of the experimental strains in the ruminant host, pregnant goats in late gestation were exposed to conjunctivally one of the three strains of Brucella. Study results are presented in Table 2. Goats inoculated with B. abortus 2308 displayed a 27% abortion rate as compared with goats infected with B. abortus 2308QAE, which exhibited a 67% abortion rate (P < 0.05). Additionally, 78% of the animals inoculated with B. melitensis 16M aborted.

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TABLE 2. Colonization and Abortion Rates of Pregnant Goats Inoculated with Brucella abortus 2308, B. abortus 2308-QAE, or Brucella melitensis 16M Brucella abortus 2308 Dam–kid pair* bacterial colonization rate Abortion rate (aborted fetuses/weak kids)

5/11 (45%)**

Brucella abortus 2308-QAE 9/12 (75%)

Brucella melitensis 16M 11/14 (79%)

27%**

67%

78%

Dam tissues homogenized and plated included: parotid, prescapular, and supramammary lymph nodes; liver; spleen; and internal iliac and mammary gland. Kid tissues homogenized included: lung and abomasal fluid. *One positive dam or kid constituted a positive dam–kid pair. ** P ≤ 0.05.

Dam and kid culture results were analyzed in pairs. Either a positive kid or dam within a pair was recorded as a culture-positive pair (Table 2). The parotid, prescapular, internal iliac, and supramammary lymph nodes; liver; spleen; and mammary glands were taken from the adults. Lung and abomasal fluid were taken from the fetuses or kids. All tissues were homogenized and plated for bacterial growth. Results were recorded as cfu/g of tissue. Bacteriologically, 5 of 11 dam–kid pairs (45%) infected with B. abortus 2308 were culture-positive. Nine of 12 goats (75%) inoculated with B. abortus 2308-QAE were found to be culture-positive. B. melitensis 16M-infected goats resulted in 11 of 14 (79%) animals being culture-positive (Table 2). All emerging colonies were evaluated via biochemical tests, and all colonies were confirmed to be Brucella and grew on the appropriate antibiotic-supplemented media. 4. Discussion An important step in understanding the molecular basis of pathogenesis is the identification of genes causing disease. Opportunities to determine the possible virulence genes in the Brucella species increased with the complete characterization of genomes within the genus. Various techniques have been used to evaluate potential virulence genes [20]. Numerous techniques also have been used to identify numerous Brucella virulence and survival genes [21–26]. Many experiments have been conducted using deletion mutants generated by gene replacement via homologous recombination to identify gene function [12, 27–30]. Edmonds and colleagues [31, 32] describe a B. melitensis 16M OMP 25 deletion mutant created via gene replacement that colonized fewer pregnant goats and kids than did the virulent B. melitensis 16M strain without resulting in abortions. Experiments such as these will lead to the discovery of potentially efficacious vaccine candidates. In a recent study, del C Rocha-Gracia and colleagues [5] investigated the ability of various strains of B. abortus and B. melitensis to hemagglutinate human and animal red blood cells. They identified a 29-kd surface protein (SP29) that is associated with the hemagglutination of all of the Brucella strains tested with human (A+ and B+) and animal (rabbit, hamster, rat, and mouse) erythrocytes.

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The results of this study suggest that the manipulation of a B. melitensis 16M putative hemagglutinin gene – Region E – would play a role in the host specificity of the organism. The absence of this hemagglutinin gene in B. abortus 2308 raised the question of whether or not the gene has an effect on the colonization and pathogenesis of either B. melitensis 16M or B. abortus 2308 in the caprine model. It was proposed that the expression of Region E in trans in B. abortus 2308 would cause increased colonization. The caprine model was used to test the host specificity of the B. abortus 2308 and RB51 Region E variants based on colonization of pregnant goats, nonpregnant females, and fetuses/kids and delivery status of the fetuses/kids [15]. B. abortus 2308-QAE and RB51-QAE were evaluated for their ability to colonize the expected reticuloendothelial organs and cause abortions in goats. Animals inoculated conjunctively usually are colonized in the parotid lymph node within the first 3 days postinfection, with the organism disseminating to the liver and spleen by 7 days postinfection. The supramammary and internal iliac lymph nodes should show signs of colonization by 14 days postinfection [15]. B. abortus 2308-QAE displayed colonization results typical of virulent Brucella species in that the parotid lymph node was colonized by 7 days postinoculation. However, B. abortus 2308-QAE infection resulted in a greater number of cfu/g than did its parental B. abortus 2308 strain in animals given the same dosage of infectious organisms – 1×109 cfu (Table 1). This also held true for the RB51-QAE strain compared with RB51. Serologically, all animals infected with B. abortus 2308-QAE tested positive for the presence of Brucella antibodies in their sera on the brucellosis card test and by Western immunoblot analysis, which used cell lysates from B. abortus 2308, B. abortus 2308-QAE, and B. melitensis 16M. Western blot analysis results were indicative of what is seen typically when the sera from animals exposed to smooth Brucella species are analyzed. RB51 animals all remained negative on the brucellosis card test. B. abortus 2308-QAE, the B. abortus 2308 variant, and RB51-QAE also were capable of infecting and colonizing the animal with no sign of attenuation. There was a slight increase in the number of cfu/g of tissues colonized by the organism in comparison with its virulent parental strain 2308 or vaccine strain RB51. Typically, pathogenesis studies in the goat model reveal 90–100% dam–kid pair colonization and a 70–100% abortion rate with B. melitensis 16M-infected animals. In contrast, studies with animals infected with B. abortus 2308 usually display a 50–70% dam–kid pair colonization and a 30–50% abortion rate. The pathogenicity of mutants or variants is measured by comparing their colonization and abortion rates with those of their virulent parental strains [15]. In this pathogenesis study, animals infected with B. abortus 2308-QAE aborted (67%) and were colonized (75%) at rates similar to the 70–100% abortion and 90– 100% colonization rates of B. melitensis 16M (Table 2). Statistically, these B. abortus 2308-QAE rates were significantly different from the B. abortus 2308 parental strain rates observed (P ≤ 0.05). Results showed a 45% colonization and a 27% abortion rate in B. abortus 2308-infected pregnant goats, which is consistent with the reported colonization and abortion rates [15]. The addition and expression of Region E in trans in B. abortus 2308 caused a significant increase in pathogenicity in infected pregnant goats (P ≤ 0.05). However, B. abortus 2308-QAE appeared no more pathogenic than did B. melitensis 16M.

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All animals infected in the pathogenesis study were found to have a positive serologic response typical of a smooth Brucella-strain infection. Region E is reported to be a putative hemagglutinin and may be a virulence factor for B. melitensis 16M. It also may be evaluated as a possible host specificity factor for B. melitensis 16M in goats. Further studies should be conducted to determine the possibility of Region E’s being a host specificity factor by evaluating its effects in various animals. Additionally, the immunologic response elicited by Region E must be assessed. Studies also may be done to exploit the stability of pQAE in other Brucella species or strains to be tested as possible vaccine candidates given the increased colonization rate of pQAE-containing B. abortus 2308. The colonization data comparing RB51 and RB51-QAE also support further work using pQAE. Placing this plasmid into a rough Brucella strain or current vaccine may be effective in developing an efficacious vaccine that spans the Brucella genus to help eradicate the worldwide problem of brucellosis for both man and animals. Acknowledgements We thank the Institute of Molecular Biology and Medicine at the University of Scranton, Scranton, Pennsylvania, for the Region E sequence and PCR Region E primers-sequences. This paper was supported with USDA Animal Health and Louisiana State University Agricultural Center research funds. References 1. 2.

Corbel, M.J. 1997. Recent advances in brucellosis. J. Med. Microbiol. 46:101–103. Verger, J.-M., Grimont, F., Grimont, P.A.D., Grayon, M. 1985. Brucella, a monospecific genus as shown by deoxyribonucleic acid hybridization. Int. J. Syst. Bactiol. 35:292–295. 3. Vizcaino, N., Cloeckaert, A., Verger, J.-M., Grayon, M., Fernandez-Lago, L. 2000. DNA polymorphism in the genus Brucella. Microbes Infect. 2:1089–1100. 4. Aragon, V., Diaz, R., Moreno, E., Moriyon, I. 1996. Characterization of Brucella abortus and Brucella melitensis native haptens as outer membrane O-type polysaccharides independent from the smooth lipopolysaccharide. J. Bacteriol. 178:1070–1079. 5. del C Rocha-Gracia, R., Castaneda-Roldan, E.I., Giono-Cerezo, S., Giron, J.A. 2002. Brucella sp. bind to sialic acid residues on human and animal red blood cells. FEMS Microbiol. Lett. 213:219–224. 6. Corbel M.J., Morgan, W.J.B. Genus Brucella Meyer and Shaw 1920, 173 AL. In: Holt, J.G., editor. Bergey’s Manual of Systematic Bacteriology, 2nd ed. Baltimore: Williams & Wilkins, 1984. pp. 377–388. 7. Finlay, B.B., Falkow, S. 1997. Common themes in microbial pathogenicity: revisited. Microbiol. Mol. Biol. Rev. 61:136–169. 8. DelVecchio, V.G., Kapatral, V. Redkar, R.J., Patra, G., Mujer, C., Los, T., Ivanova, N., Anderson, I., Bhattacharyya, A., Lykidis, A., Reznik, G., Jablonski, L., Larsen, N., D’Souza, M., Bernal, A., Mazur, M., Goltsman, E. Selkov, E. Elzer, P.H., Hagius, S. O’Callaghan, D., Letesson, J., Haselkorn, R., Kyrpides, N., Overbeek, R. 2002. The genome sequence of the facultative intracellular pathogen Brucella melitensis. Proc. Natl. Acad. Sci. U S A 99:443–448. 9. Del Vecchio, V.G., Kapatral, V., Elzer, P., Patra, G., Mujer, C.V. 2002. The genome of Brucella melitensis. Vet. Microbiol. 90:587–592. 10. Del Vecchio, V.G., Wagner, M.A., Eschenbrenner, M., Horn, T.A., Kraycer, J.A., Estock, F., Elzer, P., Mujer, C.V. 2002. Brucella proteomes – a review. Vet. Microbiol. 90:592–603. 11. Paulsen, I.T., Seshadri, R., Nelson, K.E., Eisen, J.A., Heidelberg, J.F., Read, T.D., Dodson, R.J., Umayam, L., Brinkac, L.M., Beanan, M.J., Daugherty, S.C., Deboy, R.T., Durkin, A.S., Kolonay, J.F., Madupu, R., Nelson, W.C., Ayodeji, B., Kraul, M., Shetty, J., Malek, J., Van Aken, S.E., Riedmuller,

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S., Tettelin, H., Gill, S.R., White, O., Salzberg, S.L., Hoover, D.L., Lindler, L.E., Halling, S.M., Boyle, S.M., Fraser, C.M. 2002. The Brucella suis genome reveals fundamental similarities between animal and plant pathogens and symbionts. Proc. Natl. Acad. Sci. U S A 99:13148–13153. Elzer, P.H., Phillips, R.W., Kovach, M.E., Peterson, K.M., Roop, R.M. II. 1994. Characterization and genetic complementation of a Brucella abortus high temperature requirement A (htrA) deletion mutant. Infect. Immun. 62:4135–4139. Kovach, M.E., Elzer, P.H., Hill, D.S., Robertson, G.T., Farris, M.A., Roop, R.M. II, Peterson, K.M. 1995. Four new derivatives of the broad-host-range cloning vector pBBR1MCS, carrying different antibiotic-resistance cassettes. Gene 166:175–176. Alton, G.G., Jones, L.M., Angus, R.D., Verger, J.M. Techniques for the brucellosis laboratory. Nouzilly, France: Institut National de la Recherche Agronomique; 1988. Elzer, P.H., Hagius, S.D., Davis, D.S., DelVecchio, V.G., Enright, F.M. 2002. Characterization of the caprine model for ruminant brucellosis. Vet. Microbiol. 90:425–431. Edmonds, M.D., Schurig, G.G., Samartino, L.E, Hoyt, P.G., Walker, J.V., Hagius, S.D., Elzer, P.H. 1999. Biosafety of Brucella abortus strain RB51 for vaccination of mature bulls and pregnant heifers. Am. J. Vet. Res. 60:722–725. Edmonds, M.D., Ward, F.M., O’Hara, T.M., Elzer, P.H. 1999. Use of western immunoblot analysis for testing moose serum for Brucella suis biovar 4 specific antibodies. J. Wild. Dis. 35:591–595. Farrell, I.D. 1974. The development of a new selective medium for the isolation of Brucella abortus form contaminated sources. Res. Vet. Sci. 16:280–286. Snedecor, G.W., Cochran, W.G. In: Statistical methods. Ames, IA: Iowa State University Press; 1989. pp. 83–106. Hensel, M., Holden, D.W. 1996. Molecular genetic approaches for the study of virulence in both pathogenic bacteria and fungi. Microbiology 142:1049–1058. Lestrate, P., Delrue, R.-M., Danese, I., Didembourg, C., taminiau, B., Mertens, P., De Bolle, X., Tibor, A., Tang, C.M., Letesson, J.-J. 2000. Identification and characterization of in vivo attenuated mutants of Brucella melitensis. Mol. Microbiol. 38:543–551. Hong, P.C., Tsolis, R.M., Ficht, T.A. 2000. Idendification of genes requires for chronic persistence of Brucella abortus in mice. Infect. Immun. 68:4102–4107. Kahl-McDonagh, M.M., Ficht, T.A. 2006. Evaluation of protection afforded by Brucella abortus and Brucella melitensis unmarked deletion mutants exhibiting different rates of clearance in BALB/c mice. Infect. Immun. 74:4048–4057. Zygmunt, M.S., Hagius, S.D., Walker, J.V., Elzer, P.H. 2006. Identification of Brucella melitensis 16M genes required for bacterial survival in the caprine host. Microbes Infect. 8:2849–2854. Wu, Q., Pei, J., Turse, C., Ficht, T.A. 2006. Mariner mutagenesis of Brucella melitensis reveals genes with previously uncharacterized roles in virulence and survival. BMC Microbiol. 6:102. O’Callaghan, D., Cazevieille, C., Allardet-Serven, A., Boschiroli, M.L., Bourg, G., Foulongne, V., Frutos, P., Kulakov, Y., Ramuz, M. 1999. A homologue of the Agrobacterium tumefaciens VirB and Bordetella pertussis Ptl type IV secretion systems is essential for intracellular survival of Brucella suis. Mol. Microbiol. 33:1210–1220. Halling, S.M., Detilleux, P.G., Tatum, F.M., Judge, B.A., Mayfield, J.E. 1991. Deletion of the BCSP31 gene of Brucella abortus by replacement. Infect. Immun. 59:3863–3868. Drazek, E. S., Houng, H.-S.H., Crawford, R.M., Hadfield, T.L., Hover, D.L., Warren, R. 1995. Deletion of purE attenuates Brucella melitensis 16M for growth in human monocyte-derived macrophages. Infect. Immun. 63:3297–3301. Gee J.M., Kovach, M.E., Grippe, V.K., Hagius, S.D., Walker, J.V., Elzer, P.H., Roop, R.M. II. 2004. Role of catalase in the virulence of Brucella melitensis in pregnant goats. Vet. Microbiol. 102:111–115. Gee J.M., Valderas, M.W., Kovach, M.E., Grippe, V.K., Robertson, G.T., Ng, W.L., Richardson, J.M., Winkler, M.E., Roop, R.M. II. 2005. The Brucella abortus Cu,Zn superoxide dismutase is required for optimal resistance to oxidative killing by murine macrophages and wild-type virulence in experimentally infected mice. Infect Immun. 73:2873–2880. Edmonds, M D., Cloeckaert, A., Elzer, P.H. 2002. Brucella species lacking the major outer membrane protein Omp25 are attenuated in mice and protect against Brucella melitensis and Brucella ovis. 88:205–221. Edmonds, M.D., Cloeckaert, A., Hagius, S.D., Samartino, L.E., Fulton, W.T., Walker, J.V., Enright, F.M., Booth, N.J., Elzer, P.H. 2002. Pathogenicity and protective activity in pregnant goats of a Brucella melitensis Deltaomp25 deletion mutant. Res. Vet. Sci. 72:235–239.

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Pathoadaptation of Especially Dangerous Pathogens Alexander RAKIN Max von Pettenkofer-Institut, Munich, Germany Abstract. Gene acquisition by lateral gene transfer is considered to play the main role in the evolution of bacteria, including those that are especially dangerous. However, gene gain alone cannot explain the high virulence potential of especially dangerous pathogens. Negative selection (pathoadaptive mutations) plays an important role in pathogens’ evolution. Moreover, point mutations might drastically change the mode of behavior of the pathogen or its host range. All of these mechanisms must be taken into account to understand the emergence and peculiarities of especially dangerous pathogens.

The fate of an infection depends on the ability of the pathogen to overcome all environmental challenges and the hostile defenses of the host. The genetic diversity of the infecting population is a prerequisite for selection of the fittest clones that are best suited to the rapid changes taking place in their uncertain environment and have the best chances to multiply efficiently and to survive. Population diversity is achieved by random DNA mutations and rearrangements in bacterial cells in addition to external DNA acquisitions. This diversity supplies bacteria with the tools necessary for fitness alterations and pathoadaptation. Pathoadaptation thus can be described as a complex set of reactions of pathogenic bacteria in response to challenges in the environment, including those taking place in their virulence niches (hosts, vectors). Any bacterial species can be described by its pan-genome, which is composed of a core genome shared by all strains, a dispensable genome containing genes present in only some strains, and a set of strain-specific genes [1–3]. The core genes are responsible for the basic aspects of bacterial biology, and the dispensable and strainspecific parts code for nonessential functions but may confer certain selective advantages. Virulence-associated genes make up part of the variable genome. However, genes of the two dispensable groups are represented mostly by hypothetical, phage-related, or mobile elements-related sequences that mainly contribute to genome plasticity. Species can have an open or a closed pan-genome. An open pan-genome is typical of those species that colonize multiple environments and have multiple ways of exchanging genetic information. These include representatives of Streptococcus, Helicobacter, Salmonellae, and Escherichia. Species such as Bacillus anthracis, Yersinia pestis, Mycobacterium tuberculosis, and Chlamydia trachomatis are more conserved and live in isolated niches with limited access to the global microbial gene pool. Such species, with a low capacity to acquire foreign genes, have a closed pan-genome. The set of dispensable genes is limited in these species.

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1. Effect of Gain of Function on Pathogen Fitness Bacteria do not depend solely on the occasional random mutation to improve their fitness; they have evolved mechanisms for acquiring genes from other bacteria. Thus, a bacterium can improve its pathogenic capacities in quantum leaps, i.e., acquisition by horizontal gene transfer (HGT) of a cluster of virulence genes that already have proved their usefulness in other bacteria [4]. Gene acquisition by HGT supplies pathogens with the ability to infect previously inaccessible hosts. HGT is regarded as the main source of bacterial innovations – a means of rapid emergence of novel pathogens. HGT can be realized as a result of the acquisition of: • • •

Plasmids that encode virulence determinants (e.g., virulence plasmids in B. anthracis and Y. pestis) Bacteriophage genomes that are sources of genes encoding traits that allow the pathogen to colonize and compete successfully within a virulence niche (e.g., Vibrio cholerae prophage encoding the cholera toxin) Pathogenicity islands, which are large clusters of virulence-associated genes transferred together as single genetic and functional entities (e.g., the highpathogenicity island in Y. pestis)

Y. pestis, the agent of the Black Death, is a recently evolved pathotype of enteropathogenic Yersinia pseudotuberculosis with two additional plasmids (pPCP [also pYP] and pFra [also pMT]) evidently acquired by HGT. The small, 10-kb pPCP plasmid encodes a plasminogen activator, which is required for tissue invasion [5]. The 100-kb pFra plasmid codes for a phospholipase D necessary for survival in the flea [6] and a capsular fraction I, most probably not required for lethality in mice [7] or humans [8]. Two cryptic plasmids were identified recently in Y. pestis isolates of Chinese origin. A 5,919 bp pYC plasmid was found in 230 Y. pestis strains isolated from humans and rats in the Yunnan province in China. Two genes encoding proteins involved in repair of DNA damage, DinJ1 and DinJ2, were identified on this plasmid [9]. No additional virulence-associated genes were found. Another 21,742 bp pCRY plasmid harbors a putative type 4 secretion system, but the plasmid seems to be restricted to a single isolate of Y. pestis strain 91001 isolate [10]. The Y. pestis 91001 strain belongs to a newly proposed biovar, Microtus, that is of low virulence or avirulent to humans. A similar group of rhamnose-positive Y. pestis isolates (designated Pestoides; another nomenclature based on isolation locus is used in the former Soviet Union) that are of low virulence to guinea pigs in comparison with the strains of the established epidemic biovars (Antiqua, Mediaevalis, and Orientalis) were described in the 1960s [11, 12]. To uncover Pestoides strain-specific markers, we performed suppression subtractive hybridization with a Y. pestis Yokohama Antiqua strain and a Pestoides strain (Y. pestis G8786) isolated in the high-mountainous plague locus in the Caucasus [13]. Several Pestoides-specific suppression subtractive hybridization fragments reside on a pFra-like plasmid and demonstrate high similarity to genes implicated in conjugative transfer (such as traN and traH of the F-plasmid; Fig. 1). One cannot exclude that trans complementation of the tra genes missing from this replicon might restore the transmissive character of the pFra-like virulence plasmid. It is also interesting to note that the presence of these two Pestoides-specific regions on the pG8786 plasmid

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might serve as additional markers to differentiate closely related rhamnose-positive Y. pestis isolates. Region 1 is present in the pFra plasmid in the Y. pestis 91001 strain of Chinese origin [10]; the second region seems to be a “dispensable” marker for the strains of the high-mountainous plague locus in the Caucasus.

Figure 1. pFra-like plasmid from Yersinia pestis strain 8786 isolated in the high-mountainous plague locus in the Caucasus. Insertions of two strain-specific regions into the pFra backbone are shown by black bars in the inner circle.

Another Y. pestis acquisition, a filamentous YpfPhi phage similar to CUS-1 found in highly pathogenic Escherichia coli O18:K1:H7, was proposed to be responsible for Y. pestis’ high pandemic potential [14]. Because this prophage is absent from the closely related Y. pseudotuberculosis, it has been suggested that the YpfPhi prophage might participate in plague microbe evolution toward a deadly pathogen. However, this prophage is present only in the Y. pestis biovar Orientalis epidemic strains. Furthermore, we were able to detect sequences with 99% identity to YpfPhi phage in a distantly

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related Yersinia enterocolitica Y11 O:3 draft genome sequence (Fig. 2). This implies that Yersinia might have been infected multiple times with this filamentous bacteriophage that hardly plays a significant role in the evolution of the modern plague bacillus.

Y. enter O:3 +

+

+ + +

+

Figure 2. The filamentous YpfPhi prophage in Yersinia pestis CO92. Two DNA fragments of the Yersinia enterocolitica O:3 draft genome sequence demonstrate >99% identity to Y. pestis YpfPhi prophage (identical genes are marked with +).

Taken together, of 21 genomic islands that are supposed to be present in Y. pestis, 18 also are shared by Y. pseudotuberculosis. The remaining Y. pestis-specific islands are predicted to encode putative bacteriophages. Based on these data, the authors [15] doubted the significance of HGT into the chromosome of Y. pestis. Moreover, hybridization against a larger collection of Y. pseudotuberculosis O:1 strains has reduced the number of Y. pestis-specific chromosomal regions to two putative prophages (Table 1) [16]. Whether the presence of these putative phage sequences is enough to supply Y. pestis with a hypervirulent phenotype is not yet known. Moreover, one of the two putative prophages (YPO2084, YPO2093) has suffered sequential deletions in Y. pestis strains of different origins (Fig. 3); therefore, it is uncertain whether it plays a significant role in Y. pestis pathogenesis. TABLE 1. Two Yersinia pestis-Specific Chromosomal Regions Gene ID YPO0387 YPO0388 YPO0390 YPO0391 YPO0392 YPO0393 YPO0394 YPO0396 YPO2084 YPO2087 YPO2088 YPO2090 YPO2091 YPO2093

Length (bp) 2,064 1,317 468 1,326 723 1,236 288 1,413 3,246 261 645 606 390 564

Function Hypothetical protein Conserved hypothetical protein Hypothetical protein Modification methylase Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Putative phage integrase Putative phage excisionase Putative methyltransferase Putative phage protein Putative phage antitermination protein Putative phage protein

Chromosomally located genes that are unique to Y. pestis and make up the dispensable part of its genome are laterally acquired mobile genomic elements. Until now, there has been no direct evidence of their effect on pathogenicity. Perhaps further analysis of a larger collection of Yersinia strains might further reduce the number of Y. pestis-specific chromosomal fragments.

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Figure 3. Sequential deletions of a putative prophage YPO2084-YPO2134 in Yersinia pestis. Windows show the position of identical sequences. The large DNA fragment carrying the putative prophage has suffered an inversion in Y. pestis KIM. Figure was generated by Mauve, free/open-source software available from http://gel.ahabs.wisc.edu/mauve/

2. Contribution of Loss of Function to Pathogen Evolution Massive gene acquisition is perhaps not the only way to obtain novel virulence features. Loss of function contributes equally to the evolution of pathogens [17, 18]. However, it is necessary to distinguish genome reduction (neutral genetic drift in which deleted genes are no longer required for a pathogen’s survival) from pathoadaptive mutations (negative selection in which deleted or down-regulated genes are no longer compatible with the new bacterial lifestyle). In reductive evolution, commitment to an intracellular or vector-borne lifestyle for certain pathogens results in loss of genes not essential to life within the host [19]. Mycobacterium leprae, Coxiella burnetii, Rickettsiae, and Y. pestis are examples of genome reduction. Not only have these organisms deleted genes that are no longer required for their survival, their restricted lifestyle limits opportunities for new gene acquisition from other microorganisms. Y. pestis presents an example of significant genome decay. Initially, 149 pseudogenes were identified in the Y. pestis CO92 genome [20]. Later, Lerat and Ochman [21] increased the pseudogene quantity to 188 in Y. pestis CO92, 207 in Y. pestis KIM, 149 in Y. pestis 91001, and 124 in Y. pseudotuberculosis. Thus, 337 genes are thought to be lost or inactivated in Y. pestis CO 92. Whether these genes really play a role in the pathogenicity of enteropathogenic Yersinia still has to be proven. Adaptation via gene loss complements the pathway of pathogen evolution by “gain of function” mutation and gene acquisition. Genes required for fitness in one niche actually may inhibit fitness in another environment. Newly acquired virulence traits, along with the preexisting genes, continue to undergo selection after the pathogen has

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acquired the ability to colonize a new niche. Such “pathoadaptative mutations” are genetic modifications that enhance the fitness of the pathogen in the novel (hostassociated) environment, and these might eliminate or down-regulate expression of any gene that is incompatible with growth in the new niche “antivirulence” gene [22]. Thus, the antivirulence gene is the gene whose expression is incompatible with the virulence of the pathogen in its niche. Adaptation can be achieved by deletion of the antivirulence gene, generating “black holes” in the pathogen genome by point mutations within the gene or suppression of gene expression [22]. It is well known that Shigella and E. coli share many biochemical traits. However, some properties clearly differentiate them. One of these is lysine decarboxylase (LDC) activity, encoded by the cadA gene in E. coli. Whereas LDC is expressed in most of E. coli isolates, no strains of Shigella express LDC activity [23]. On the other hand, the virulence plasmid-encoded Shigella enterotoxins proved to be inhibited by cadaverine, which is the product of the decarboxylation of lysine. Cadaverine also was found to block the ability of an LDC-expressing Shigella flexneri to elicit transepithelial migration of polymorphonuclear neutrophils. Thus, attenuation of virulence phenotypes is linked to expression of LDC (and subsequent production of cadaverine), which has been proven in an S. flexneri 2a strain supplemented with a E. coli cadA gene. Thus, cadA represents an example of an “anti-virulence” gene. The rscA gene, established as a negative biofilm regulator, might serve as the example of the antivirulence gene in Y. pestis [24]. Y. pseudotuberculosis, in contrast to Y. pestis, does not produce biofilms in insects. rscA is a negative regulator of biofilm formation and a part of the phosphorelay signaling system. It is functional in Y. pseudotuberculosis, but it is silent in Y. pestis. However, a functional Y. pseudotuberculosis rscA allele strongly represses biofilm formation in the flea. This is an example of a “negative” selection rather than a neutral genetic drift. A common enteropathogenic yersinias adhesin, yadA, in fact was the first antivirulence gene described in Y. pestis [25]. It has been shown that the yadA gene in Y. pestis has suffered a point mutation leading to a frameshift and its inactivation. Moreover, yadA complementation in Y. pestis with a wild type allele leads to its virulence attenuation, which proves an antivirulence character of the yadA gene in Y. pestis. B. anthracis, the agent of anthrax, is genetically very closely related to other members of the Bacillus cereus group (B. cereus and Bacillus thuringiensis species). The main feature thought to distinguish these organisms is the acquisition of two virulence plasmids, one of which codes for anthrax toxin. Nevertheless, elimination of certain chromosomally encoded traits seems to be of importance for anthrax evolution. The arginine deiminase gene cluster present in the chromosome of B. cereus appears to be entirely deleted from B. anthracis [26]. The selective pressure for loss of this gene cluster may have been that ammonium production by arginine deiminase is unfavorable for B. anthracis because ammonium inhibits receptor-mediated uptake of the lethal anthrax toxin. Comparison of the sequenced genomes of two other especially dangerous pathogens, Burkholderia mallei and Burkholderia pseudomallei, did not reveal operons or biochemical systems unique to B. mallei [27]. Thus, there is no evidence of gain of function acquired through HGT, which may increase the virulence or fitness of B. mallei in

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the host. Rather, the 6.0 Mbp B. mallei genome seems to be a subset of the 7.2 Mbp B. pseudomallei genome, suggesting that the significant increase in virulence and narrowing of the host range observed in B. mallei most probably is associated with loss or inactivation of multiple ancestral loci (i.e., different antivirulence genes). The antivirulence nature of an operon encoding arabinose catabolic enzymes has been demonstrated for B. pseudomallei [27]. This operon is also present in nonpathogenic Burkholderia thailandensis, but it is absent from the pathogen’s genome. Reintroduction of this operon into B. pseudomallei restores its ability to grow on arabinose but significantly reduces virulence in hamsters. As a result, expressing arabinose utilization genes in B. pseudomallei results in down-regulation of the type 3 secretion system genes required for virulence in hamsters. Thus, the arabinose utilization locus harbors genes antivirulent for pathogenic Burkholderia. 3. Point Mutations with Significant Effects on Pathogen Fitness Not only gene exchange but also single point mutations can affect the fate of the pathogen significantly. Some point mutations make a pathogen more virulent than the parent strain or greatly affect its host range; this results in “hypervirulent” phenotypes [28]. Others modify the expression of virulence-associated markers to achieve better survival and multiplication in the host. Certain amino acid substitutions might dramatically change the structure and function of the initial polypeptide, which affects the host range of the pathogen. A single amino-acid substitution in the Venezuelan equine encephalitis virus envelope glycoprotein changes its host range to horses and people instead of small mammals [29]. Two single substitutions in the listerial invasion protein InlA increase its binding affinity by four orders of magnitude and extend its binding specificity to formerly incompatible murine receptor E-cadherin [30]. Point mutations also can modify certain virulence traits to achieve maximal systemic dissemination of the pathogen. Macrophages infected by pathogenic species of Yersinia typically undergo apoptosis owing to the activity of a protein (YopJ in Y. pseudotuberculosis and Y. pestis, YopP in Y. enterocolitica) secreted by the Type 3 system. YopP demonstrates greater capacity for secretion than does YopJ, and this correlates with its enhanced cytotoxicity. In contrast, Y. pseudotuberculosis infection of bone marrow-derived dendritic cells does not lead to increased cell death [31]. However, apoptosis was enhanced in dendritic cells infected by Y. pseudotuberculosis in which YopJ was replaced by YopP. Mutations leading to two amino acid changes were responsible for their differential secretion, translocation, and consequent cytotoxicity. Thus, intermediate levels of YopJ-mediated cytotoxicity are necessary for maximal systemic virulence of this bacterial pathogen. Aspartase deficiency in Y. pestis is another example of a substantial effect of a single base transversion (in this case a missense mutation) that nevertheless results in 99.99% reduction in enzyme activity [32]. AspA activity catalyzes the deamination of L-aspartate to form fumarate, a component of the tricarboxylic acid cycle. Comparison of aspA in Y. pestis and closely related Y. pseudotuberculosis defines only a single base transversion (G.C-T.A) at a.a. position 363. This causes exchange of valine (GUG) in

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the active enzyme of Y. pseudotuberculosis for leucine (UUG, missense mutation) in Y. pestis. This is the only change of one aliphatic acid for another that leads to 99.99% reduction in enzyme activity. Genetic diversity of highly pathogenic Francisella tularensis type A is correlated with its geographic distribution, including a major population subdivision referred to as A.I and A.II. The biological significance of the A.I–A.II genetic differentiation is unknown, though there are suggestive ecologic and epidemiologic correlations. The complete sequence of an A.II strain (WY96-3418) was determined and compared with the genome of Schu S4 (A.I ) [33]. Although extensive genomic variation exists between WY96 and Schu S4, there is only one whole gene difference. This one gene difference is a hypothetical protein of unknown function. In contrast, there are 3,367 single nucleotide polymorphisms, 1,015 small indels, seven IS element differences, and 31 large chromosomal rearrangements, including both inversions and translocations. Other potential virulence-associated genes (231) varied at 559 nucleotide positions, including 357 nonsynonymous changes. It is likely that many of the 281 non-conservative amino-acid changes in the 112 virulence-related genes in WY96 play a role in the virulence attenuation observed in the F. tularensis A.II strains when compared with the A.I strains. The present organization of especially dangerous pathogens has been established in a process of infinite interactions with their hosts and various environmental factors. The high fluidity of bacterial genomes and genetic diversity of bacterial populations, in combination with gene acquisition, supply pathogens with new means of rapid response to unfavorable conditions. Multiple possibilities make pathogens fitted to the existing and always-changing environments to support their multiplication and survival as a part of existing ecosystems [34]. Still, this is an everlasting battle for survival. References 1.

2. 3. 4. 5. 6. 7.

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11. 12. 13. 14. 15.

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Detection of Pathogens Via High-Throughput Sequencing Akbar S. KHAN CB Directorate, Defense Threat Reduction Agency, Fort Belvoir, Virginia Abstract. Recent advances in DNA sequencing technology have allowed the rapid sequencing of pathogen genomes for detection and forensic purposes. DNA sequencing emerged in 1977 with the chemical method of Maxam and Gilbert, followed by the biochemical dideoxy method of Sanger, Nicken, and Coulson. The Sanger method revitalized the sequencing industry with the completion of the sequence of the first draft of the human genome published in 2001, but the refinement and analysis of the human genome sequence and understanding the biology of different aspects of the human genome will continue for the unforeseeable future. During the past 5 years, new “massively parallel” sequencing methods coupled with automation are greatly increasing sequencing capacity and making it possible to collect large amounts of data in a day or two to be analyzed for functional significance for detection, diagnostic, and forensic use. This review article will focus on how to analyze the different sequencing methodologies available in the market and their application in the clinical microbiology laboratory for detection, diagnostic, and forensic use and, therefore, the management of infectious disease outbreaks.

1. Introduction Sequencing technologies have evolved since Sanger in 1977 [1] first introduced dideoxy shotgun sequencing and assembly as a unique new methodology for sequencing the entire genome [2]. In the beginning, the set of methodologies applicable to small genomic sequences and individual human genes such as the genome of the bacteriophage lambda [3], SV40 [4], human mitochondria [5], and Epstein–Barr virus [6] was expensive and required a great deal of manual labor to sequence and assemble the entire sequence from different sequence reads generated by these methods. In 1986 at California Institute of Technology, Leroy Hood’s group, in collaboration with Applied Biosystems (ABI; Foster City, CA), worked and published on the automation of DNA sequencing [7]. This initial report showed that sequencing data could be collected directly to a computer without autoradiography and manually reading the sequencing gels. Sanger’s dideoxy method was chosen, and a sequencing primer was end-labeled fluorescently using four different dyes. A differently labeled primer was used in each of the four dideoxy-sequencing reactions, and, after completion of the reactions, they were combined and electrophoresed in a single polyacrylamide gel well lane. A detector at the bottom of the gel observed the DNA by fluorescence, and the

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four dyes were distinguished by their four different fluorescent colors. The resulting fluorescence data were recorded continuously and stored by a computer over the course of a typical 13-h run, and programs developed by ABI automatically interpreted the data to produce an actual sequence from collected fluorescent data output [8]. By 1987, ABI had released an automated fluorescent sequencer, which was adopted rapidly by different groups to sequence different parts of human genome such as single-pass sequencing of human brain cDNAs [9], two bacterial genomes (Haemophilus influenzae [10] and Mycoplasma genitalium [11]), completion of human chromosome 22 [12], and then completion of the whole human genome [13, 14]. 2. Next-Generation Sequencing Technology In the past 5 years or so, new methods have emerged that have proved to be superior to dideoxy sequencing and have resulted in sequencing instruments capable of sequencing the whole genome without tedious cloning and purification of DNA fragments. The main feature of these technologies is that they use a “massively parallel” system, meaning that the number of sequence reads from a single experiment is vastly greater than the 96 capillary–based Sanger sequencer. 2.1. Sequencing by Synthesis I: Pyrosequencing The first of the massively parallel methods to become commercially available was developed by 454 Life Sciences, a Roche company (Branford, CT) [15], and uses pyrosequencing techniques [16, 17]. This system allows shotgun sequencing of whole genomes without cloning and purification of DNA clones in Escherichia coli or any host cell. Briefly, a DNA fragment first is randomly sheared and ligated to linkers sequences, which permits individual molecules captured on the surface of the beads to be amplified in emulsion PCR [16]. A very large collection of such beads is arrayed in the 1.6 million pico-titer plate, and sequencing is carried out using primed synthesis by DNA polymerase. The amount of incorporation of the four deoxyribonucleotides (dNTPs) is monitored by luminometric detection of pyrophosphate released, which is recorded by a charge-coupled device (CCD) camera coupled to the fiber-optics array; these data get converted into sequence by the software system. The second-generation 454 Genome Sequencer FLX reportedly is able to produce 100 Mb of sequence with 99.5% accuracy for individual reads; the average read is more than 250 bases in length. 2.2. Sequencing by Synthesis II: Solexa The second technology is the Solexa technology [18, 19], which differs from 454 technology in that it uses chain-terminating nucleotides. This method sequences clusters of DNA molecules amplified from individual fragments attached randomly on the surface of a flow cell. Owing to very high densities of clusters that can be analyzed, the machine can produce 1 billion bases (1 Gb) of 30–40 base sequence read in a single run.

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2.3. Sequencing by Ligation ABI’ next machine, the Supported Oligonucleotide Ligation and Detection (SOLiD) system, is based on hybridization-ligation chemistry [20]. The sample preparation aspect of this technology, including library preparation and clonal amplification of the target DNA by emulsion PCR on beads, is very similar to the 454 processes in principle. However, the size of the beads used for emulsion PCR (1 vs 26 μm) and the array format (random versus ordered) are different. The sequence generation is performed through the repeated cycles of hybridization of a mixture of sequencing primers and fluorescently labeled probes, followed by ligation of the sequencing primers and the probes, then the detection of the fluorescent signals on the probes, which encode the bases that are being incorporated. The read length here is short – about 25–35 bases – and it can generate approximately 2–3 Gb of sequence per run. These new technologies will dominate for the next few years. 3. Detection of Microbes By Sequencing Sequencing microbes with these massively parallel systems is possible and evident – 454 technology has been applied to sequence and identify a new class of arenaviruses from human transplant patients [21]. Similarly, these sequencing technologies can be applied easily to sequence the genomes of microbes relevant to the Department of Defense. In addition, these technologies will be applied for forensic purposes in future outbreaks of infectious disease to detect and identify the infectious disease of interest as illustrated in Fig. 1.

Figure 1. High-throughput sequencing of the microbe.

4. Metagenomics A very small fraction of the microbes found in nature have been grown in culture, and, therefore, we lack a comprehensive view and understanding of the genetic diversity to be found in our environment. An approach to this problem has emerged called metagenomics or environmental genomics [22, 23]. Direct sequencing of environmental

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DNA samples is possible with these massively parallel sequencing systems. It is clear that the metagenomic approach is becoming a major tool for understanding the genetic diversity of our environment, which will identify a vast array of new genes and their role in the environment. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

14. 15. 16. 17. 18.

Sanger, F., Nicklen, S., Coulson, A.R. 1977. DNA sequencing with chain-terminating inhibitors. Proc. Natl. Acd. Sci. U S A 74:5463–5467. Sanger, F., Coulson, A.R., Barrell, B.G., Smith, A.J., Roe, B.A. 1980. Cloning in single-stranded bacteriophage as an aid to rapid DNA sequencing. J. Mol. Biol. 143:161–178. Sanger, F., Coulson, A.R., Hong, G.F., Hill, D.F., Petersen, G.B. 1982. Nucleotide sequence of bacteriophage lambda DNA. J. Mol. Biol. 162:729–773. Fiers, W., Contreras, R., Haegeman, G., Rogiers, R., Van de Voorde, A., Van Heuverswyn, H., Van Herreweghe, J., Volckaert, G., Ysebaert, M. 1978. Complete nucleotide sequence of SV40 DNA. Nature 273:113–120. Anderson, S., Bankier, A.T., Barrell, B.G., de Bruijn, M.H., Coulson, A.R., Drouin, J., Eperon, I.C., Nierlich, D.P., Roe, B.A., Sanger, F., Schreier, P.H., Smith, A.J., Staden, R., Young, I.G. 1981. Sequence and organization of the human mitochondrial genome. Nature 290:457–465. Baer, R., Bankier, A.T., Biggin, M.D., Deininger, P.L., Farrell, P.J., Gibson, T.J., Hatfull, G., Hudson, G.S., Satchwell, S.C., Séguin, C., Tuffnell P.S., Barrell, B.G. 1984. DNA sequence and expression of the B95-8 Epstein-Barr virus genome. Nature 310:207–211. Smith, L.M., Sanders, J.Z., Kaiser, R.J., Hughes, P., Dodd, C., Connell, C.R., Heiner, C., Kent, S.B., Hood, L.E. 1986. Fluorescence detection in automated DNA sequence analysis. Nature 321:674–679. Connell, C., Fung, S., Heiner, C., Bridgham, J., Chakerian, V., Heron, E., Jones, B., Menchen, S., Mordan, W., Raff, M., Recknor, M., Smith, L., Springer, J., Woo, S., Hunkapiller, M. 1987. Automated DNA sequence analysis. Biotechniques 5:342–348. Khan, A.S., Wilcox, A.S., Polymeropoulos, M.H., Hopkins, J.A., Stevens, T.J., Robinson, M., Orpana, A. Sikela, J.M. 1992. Single pass sequencing and physical and genetic mapping of human brain cDNAs. Nature Genet. 3:180–185. Fleischmann, R.D., Adams, M.D., White, O., Clayton, R.A., Kirkness, E.F., Keravage, A.R., Bult, C.J., Tomb, J.F., Dougherty, B.A., Merrick, J.M., McKenney, K., Sutton, G., et al. 1995. Whole-genome sequencing and assembly of Haemophilus influenzae Rd. Science 269:496–512. Fraser, C.M., Gocayne, J.D., White, O., Adams, M.D., Clayton, R.A., Fleishmann, R.D., Bult, C. J., Kerlavage, A.R., Sutton, G., Kelley, J.M., Fritchman, R.D., Weidman, J.F., et al. 1995. The minimal gene complement of Mycoplasma genitalium. Science 270:397–403. Dunham, I., Hunt, A.R., Collins, J.E., Bruskiewich, R., Beare, D.M., Clamp, M., Smink, L.J., Ainscough, R., Almeida, J.P., Babbage, A., Bagguley, C., Bailey, J., Barlow, K., Bates, et al. 1999. The DNA sequence of human chromosome 22. Nature 402:489–495. Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A., International Human Genome Sequencing Consortium, et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:860–921. Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amanatides, P., Ballew, R.M., Huson, D.H., et al. 2001. The sequence of the human genome. Science 291:1304–1351. Margulies, M., Egholm, M., Altman, W.E., Attiya, S. Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z., Dewell, S.B., Du, L., Fierro, J.M., Gomes, X.V., et al. 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380. Nyrén, P., Pettersson, B., Uhlén, M. 1993. Solid phase DNA minisequencing by enzymatic luminometric inorganic pyrophosphate detection assay. Anal. Biochem. 208:171–175. Ronaghi, M., Karamohammed, S., Pettersson, B., Uhlén, M., Nyrén, P. 1996. Real-time DNA sequencing using detection of pyrophosphate release. Anal. Biochem. 242:84–89. Bennett, S. 2004. Solexa Ltd. Pharmacogenomics 5:433–438.

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19. Bennett, S.T., Barnes, C., Cox, A., Davies, L., Brown, C. 2005. Toward the 1000 dollars human genome. Pharmacogenomics 6:373–382. 20. Shendure, J., Porreca, G.J., Reppas, N.B., Lin, X., McCutcheon, J.P., Rosenbaum, A.M., Wang, M.D., Zhang, K., Mitra, R.D., Church, G.M. 2005. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309:1728–1732. 21. Palacios, G. Druce, J., Du, L., Tran, T., Birch, C., Briese, T., Conlan, S., Quan, P.L., Hui, J., Marshall, J., Simons, J.F., Egholm, M., Paddock, C.D., Shieh, W.J., Goldsmith, C.S., Zaki, S.R., Catton, M., Lipkin, W.I. 2008. A new arenavirus in a cluster of fatal transplant-associated diseases. N. Engl. J. Med. 358:991–998. 22. Tringe, S.G., Rubin, E.M. 2005. Metagenomics: DNA sequencing of environmental samples. 2005. Nat. Rev. Genet. 6:805–814. 23. Tringe, S.G., von Mering, C., Kobayashi, A., Salamov, A.A., Chen, K., Chang, H.W., Podar, M., Short, J.M., Mathur, E.J., Detter, J.C , Bork, P., Hugenholtz, P., Rubin, E.M. 2005. Comparative metagenomics of microbial communities. Science 308:554–557.

Environmental Influences on the Relative Stability of Baculoviruses and Vaccinia Virus: A Review Gary D. OUELLETTE1, Patricia E. BUCKLEY2, and Kevin P. O’CONNELL2 1 SAIC, c/o U.S. Army ECBC, APG, Maryland 2 U.S. Army ECBC, AMSRD-ECB-RT-BM, APG, Maryland Abstract. The environmental fate of viruses is a topic of recent interest because the transport and fate of viruses in the environment may impact human and animal health. In studying the transport of pathogenic viruses in the environment some workers have used non-pathogenic surrogate or “simulant” viruses as tracer organisms for safety reasons. In an effort to identify simulants for orthopoxviruses, the use of baculoviruses has been proposed. Like poxviruses, they are also large, ds-DNA viruses. Unlike poxviruses, however, they are generally regarded as harmless to plants and animals outside their narrow insect host range and have been broadly disseminated for decades in organic agriculture as natural insecticides. The use of baculoviruses as simulants for the development of decontaminants requires an understanding of the relative resistance of both poxviruses and baculoviruses to environmental stressors, so that their relative, inherent rates of environmental degradation can be accounted for in determining whether a candidate decontamination regime is effective. To this end, we review here what is known about the susceptibility of baculoviruses and poxviruses to environmental stressors (temperature, UV light, moisture and pH) and the influence of their physical environments (soil, phyllosphere, or aquatic surroundings).

1. Introduction The environmental fate of viruses is a topic of recent interest because of the growing understanding that the transport and fate of viruses in the environment, whether naturally occurring (such as enteroviruses moving through groundwater) or intentional (such as the use of a virus as a biological weapon) may impact human and animal health [10, 17, 24, 33]. Overall, virus viability and persistence can vary greatly between environment, virus type and virucidal agent. For example, in 1759, graveside mourners in Somerset, England were exposed to an exhumed coffin of an unknown smallpox victim and then contracted smallpox, presumably still viable after being buried for 30 years [1]. In contrast, Vaccinia virus (VACV), another orthopoxvirus, prepared in suspension will lose viability after 15 h exposed at 50°C [118]. Hence, understanding basic viral properties and the extent to which viruses lose viability in response to environmental stressors or virucidal substances and treatments is an integral to realizing the broader goals of the biodefense community.

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In studying the transport of pathogenic viruses in the environment some workers have used non-pathogenic surrogate or “simulant” viruses as tracer organisms for safety reasons. For example, bacteriophage MS2 has been used as a simulant for enteroviruses in studying the movement of the latter in groundwater [33]. An understanding of the natural persistence of a simulant virus is crucial in using the simulant in transport studies because differences between the rate of degradation of the simulant and the simulated pathogenic virus can skew the results of such work. Similarly, an understanding of the natural environmental stability of pathogenic viruses is required when devising methods for inactivating them (collectively, decontamination methods) that act faster than natural degradative processes. In an effort to better understand the mobility, persistence, and detection of poxviruses in the environment, Garnier and colleagues [30] have proposed the use of granuloviruses as simulants because, like poxviruses, they are also large, ds-DNA viruses. Unlike poxviruses, however, they are generally regarded as harmless to plants and animals outside their narrow insect host range and have been broadly disseminated for decades in organic agriculture as natural insecticides. Members of the Baculoviridae are pathogens of insect pests, and baculoviruses have been used for decades as part of “natural” integrated pest management (IPM) schemes for the control of such pests. Their safety has been extensively documented in an extensive literature, and several baculoviruses have been approved for use by the U. S. Environmental Protection Agency (EPA). Baculoviruses, furthermore, physically resemble poxviruses somewhat in many ways: their virions tend to have particle sizes similar to poxviruses, their genomes are composed of double stranded DNA and are also of comparable size. As implied above, there are no known mammalian hosts of baculoviruses, and in some cases, their molecular biology has been described in great detail. A granulovirus (Cydia pomonella granulovirus, or CpGV) has been proposed as a viral simulant [30]. This, and potentially other baculoviruses possess the attractive characteristics for use as a viral simulant set forth above; in particular, CpGV is currently registered with the EPA, and the form of CpGV that is used as a pest control agent is commercially available. The use of baculoviruses as simulants for the development of decontaminants will require an understanding of the relative resistance of both poxviruses and baculoviruses to environmental stressors, so that their relative, inherent rates of environmental degradation can be accounted for in determining whether a candidate decontamination regime is effective. To this end, we have researched the literature to determine what is known about the susceptibility of baculoviruses and poxviruses to environmental stressors (temperature, UV light, moisture and pH) and the influence of their physical environments (soil, phyllosphere, or aquatic surroundings). We summarize here what is known about the susceptibility of baculoviruses to virucidal substances and conditions and relate this information to similar work published for poxvirus (Vaccinia). We excerpt and summarize data for viability and persistence for these viruses exposed to various conditions and environments influencing virus survival and persistence.

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2. Taxonomy and Biology 2.1. Baculovirus Overview Baculoviruses (Family: Baculoviridae) are double stranded DNA viruses characterized by a circular DNA genome and the presence of a large occlusion body or protein matrix [88]. Baculoviruses are primarily pathogenic to arthropods and are distributed worldwide. Baculovirus-based insecticides are highly specific to the larval stage of their hosts and safe to humans and the environment; characteristics which make them particularly attractive as pest control agents [18]. The current taxonomy divides the family into two distinct genera based upon structural properties: Granulovirus (GV) and Nuclear Polyhedrosis Virus (NPV) [118]. Baculoviruses are identified as potential candidates for viral simulant research because they physically resemble pox viruses with similar virion particle sizes (50–100 × 400 nm) and double-stranded DNA genomes. 2.1.1. Cydia pomonella Granulosis Virus (CpGV) CpGV is a granulovirus described from coddling moth larvae (Cydia pomonella L.) collected in Mexican apple and pear orchards [110]. The viral occlusions are formed in the nucleus of the cell with the capsule size averaging 393 × 207 μm [110]. Each granule contains only one virion [59] and the occlusion matrix protein is comprised largely of granulin [109]. The genome is double-stranded DNA composed of 123.5 kilobase pairs [77]. Over the past two decades, CpGV has been adopted for use in agricultural systems as an agent highly pathogenic to the coddling moth yet harmless to non-target organisms [105]. It is currently available commercially in Europe and North America under several brand names, Cyd-X (Certis USA, L.LC.), Virosoft (BioTEPP Inc.), Carpovirosine (Sumitomo Corp.), Madex (Andermatt Biocontrol), and Granupom (Hoerst) [76]. 2.1.2. Helicoverpa zea Single Nucleopolyhedrosis Virus (HzSNPV) HzSNPV is a nucleopolyhedrovirus (NPV) highly effective as a biocontrol agent against the corn earworm, Helicoverpa zea (Boddie). Like other NPVs, the virus forms multi-sided occlusion bodies composed of a matrix protein known as polyhedron. The viral occlusions are often referred to as polyhedral inclusion bodies (PIB) and contain randomly occluded viral particles [59, 112]. Physically, the baculovirus possesses a circular, double-stranded DNA genome comprised of 120 kilobase pairs (molecular weight of about 78 × 106) [74]. HzSNPV has been widely used as a microbial insecticide since the mid-1970s as a result of its specificity towards the corn earworm and biological safety to humans and non-target organisms [14]. It is currently registered in the USA and sold under the trade name Gemstar (Certis USA, L.L.C.).

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2.2. Pox Virus The poxviruses (Family: Poxviridae) are large enveloped DNA viruses which includes the causal agent to smallpox (VARV). In 1980, the World Health Organization (WHO), after a comprehensive vaccination program, announced the eradication of smallpox. This program utilized live vaccinia (VACV), a similar virus from the same genus, Orthopoxvirus. A VACV infection is mild and typically asymptomatic in healthy individuals. Immune response generated from a VACV infection protects the person against a lethal smallpox infection. Because of this characteristic, VACV was primarily employed for vaccination against smallpox via inoculation [39]. VACV is comprised of a linear, double-stranded DNA genome approximately 190 kb in length [80], a size comparable to both VARV and the baculoviruses. Because of these close physical and chemical similarities with VARV, VACV is typically employed as a surrogate virus for smallpox research. Due to a paucity of information on VARV, this review identifies results from previous VACV studies to demonstrate how VARV would hypothetically respond under similar study conditions. 3. Overview of Factors Influencing Virus Viability Numerous physical, chemical and biological factors influence virus persistence and activity. Some of the primary physical factors involved in the persistence of viruses outside living hosts include temperature, ultraviolet light, moisture (or relative humidity) and pH. These factors may interact with each other, and their effect can be additionally altered by virucidal chemicals, formulation additives, and a variety of substrates, including soil, fabrics and plant foliage. Overall, studies on virus hardiness and persistence are quite different for VACV and baculoviruses. Research on VACV has focused mainly in three areas, (1) virus persistence, (2) potency of stored vaccine preparations as well as, (3) disinfection measures and virucidal materials to interrupt the chain of infection for the prevention of disease spread in the Healthcare and Food industry [94]. While, comparatively, baculovirus studies are agricultural based focusing on improving bio-insecticide performance [27]. Baculovirus research has targeted both physical and biotic factors of viruses such as virulence; virus stability and persistence in field applications; virus dispersal; and viral transmission. Among physical factors of the environment, baculovirus studies have focused on the effects of sunlight, temperature, pH and moisture as well as the use of additives to prolong the hardiness of a virus. 3.1. Ultraviolet Radiation The literature concerning the responses of viruses to ultraviolet radiation largely concerns baculoviruses [45]. However, ultraviolet radiation is a destructive abiotic factor affecting the persistence and viability of many viruses including poxviruses [26, 104]. Over the years, a considerable variety of approaches have been undertaken in the study of the extent to which UV radiation can adversely affect viruses and have

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included natural sunlight; monochromators; and, germicidal lamps. Table 1 summarizes some of the published data on the effect of ultraviolet radiation on virus persistence. 3.1.1. VACV The extent to which viral pathogens of humans persist in the environment to reach other hosts is of primary public health interest and concern. Hence, numerous studies have focused on germicidal ultraviolet cell (UVC) light (254-nm) for airborne VACV inactivation and the control of microbial infection spread [62, 82, 89]. As evidenced from studies, VACV is readily inactivated by ultraviolet light [39]. VACV released as an aerosol, devoid of UV light, is capable of survival up to 24 h [39]. In an earlier study [62] reported VACV is highly susceptible to UVC with greater than 99% viral inactivation with short exposure times. McDevitt et al. [82] found similar results, demonstrating VACV susceptibility to UVC. In 1965, Rauth [89] investigated the UVC sensitivity in the range 225–302 nm for 12 medically important viruses including VACV and postulated the susceptibility of vaccinia to UV inactivation is the result of its relative large size and adsorption cross-section compared with those other viruses [89]. In a comparison of UV spectra effects on VACV [103], greater deactivation was observed in the range between 280 and 300 nm. These results are comparative to those found with baculoviruses. TABLE 1. Effect of Ultraviolet Radiation on Virus Viability Virus Helicoverpa spp. NPV Helicoverpa spp. NPV Helicoverpa spp. NPV HzSNPV HzSNPV LfNPV OpNPV OpNPV OpNPV OpNPV PrGV VAC VAC VAC VAC VAC VAC VAC VAC

UV wavelength (nm) 257

Exposure time

Reference

2h

Loss of activity (estimated %) >50

307.5

2h

>50

11

364

2h

<10

11

254 254 and 365 366 290 300 310 320 254 and 365 254 254 254 254 254 254 254 200–1,100

5 min 4h 100 h – – – – 4h 0.3–0.6 s 7.6 s 7.6 s 7.6 s 7.6 s 7.6 s 7.6 s 300 μs

100 90 <10 32.7–53.5 0–74.2 0–82 0–51.3 95 99.9 99* 90* 90* 90* 90* 80* 100

36 51 84 34 34 34 34 51 62 82 82 82 82 82 82 93

11

The stability of viruses exposed to ionizing radiations has also received attention. Exposure to x-rays causes an exponential loss of VACV activity [81]. In further investigation of the effect on VACV inactivation using low-voltage electrons [81],

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doses of energy (1,000 V) penetrating the virus as far as 330 A only resulted in slight virus inactivation. However, as the energy was increased to 1,500 V, with electron penetration occurring between 500 and 700 A, a rapid loss of virus activity was observed. 3.1.2. Baculovirus Numerous studies have investigated effects of UV light inactivation of baculoviruses [12, 32, 34, 70, 84]. Generally, the virucidal effects of UV radiation is rapid and has been partly attributed to the adverse affects on DNA or virus-associated structural proteins [42]. In the field, baculovirus activity can be completely lost in less than 24 h, but the mean half-life varies between 2 and 5 days [60, 85]. One field study suggested CpGV half life, under natural light, ranges between 48 and 72 h [70]. A field application study [32] showed CpGV virus activity was reduced in half after 3 days exposure to natural light with some activity persisting up to 4–8 weeks. Bullock and others [11, 122] reported HzSNPV retained little activity 2 days after application on cotton. Similar findings were observed on soybean foliage [52] and on corn silk [53]. Studies have suggested field applications of baculoviruses are not completely inactivated at a constant rate. Bi-segmented inactivation curves for CpGV have been observed, with 99% initially inactivated with a small proportion persisting much longer [19, 70]. After a 3 h exposure to natural sunlight, a 0.05% suspension of Pieris brassicae Granulovirus (PbGV) rapidly inactivated on cabbage leaves with complete inactivation after 12–19 h [21]. The inactivation of baculoviruses under UV lamps and simulated sunlight is more rapid than demonstrated by direct sunlight. Ignoffo et al. [51] estimated a half life of 2 h for Pieris rapae Granulovirus (PrGV) and Helicoverpa spp. NPV exposed to UV lamps. In support of these findings, the same group [54] estimated the half life of both occluded and nonoccluded HzSNPV virions was 5.8 h and 6.3 h respectively under simulated sunlight (320–380 nm). Approximately 70% of virus was inactivated within the first 4 h of exposure. Virus stock suspension from Trichoplusia ni Nuclear Polyhedrosis Virus (TnNPV) and HzSNPV were exposed to UV light (254 nm) emitted from a Spectroline lamp for varying times and distances [36]. TnNPV was found more stable with significant to complete virus inactivation by exposures at 2–6 in between 4 and 10 min; whereas, similar inactivation for HzSNPV was reported at 2–8 in for 1-5 min. In a more recent study [93] the effects of high intensity broad spectrum pulsed light from a PureBright® system on virus inactivation were investigated. PureBright® uses wavelengths ranging from 200 to 1,100 nm and short flashes (300 μs) at an intensity 1,000 times that of conventional light. VACV was completely inactivated at total fluences >1.5 (J/cm2). The subject of UV inactivation cannot be left without considering the possibility of interactions with other abiotic factors attributing to variations in persistence. Using simulated sunlight, the interaction of UVA and UVB (290–400 nm) was observed [55] with pH (at 3, 6 and 9), water, and temperature (at 10, 22, 35 and 50°C) on the inactivation of HzSNPV. No interactions were observed with either temperature or pH; however HzSNPV was more sensitive when exposed to water than dry virus. McLeod

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et al. [83] reported a significant interaction between the effects of temperature and UV radiation on virus inactivation. Inactivation of HzSNPV exposed to sunlight at 45°C was extensive in comparison to virus exposed at 30°C. There is considerable evidence that the ultraviolet portion of sunlight inactivates viruses, specifically, UVB (280–320 nm) and UVC (185–280 nm) because wavelengths in the range from 200 to 280 nm are highly absorbed by nucleic acids [12, 60, 103, 104]. Since wavelengths shorter than 290 nm do not penetrate to the earth’s surface, virus inactivation in the field are due to wavelengths greater than 290 nm [34]. Bullock et al. [12] reported that 2 h exposure to wavelengths of 257 nm and 307.5 nm significantly inactivated Helicoverpa spp. NPV virus while exposure to a wavelength of 364 nm and a broad-band mixture of visible and infrared light did not affect activity in bioassays. In a related study by Griego et al. [34], monochromatic UV radiation at wavelengths 290, 300, 310 and 320 nm inactivated Orgyia pseudotsugata Nuclear Polyhedrosis Virus (OpNPV). Similarly, it has been observed [19] that virus inactivation decreased as the UV wavelengths progressively increased within the range 250–330 nm, with the greatest inactivation found between 250 and 270 nm for PbGV. Ultraviolet radiation exposure at 366 nm for periods up to 100 h was demonstrated to be only slightly virucidal to Lambdina fiscellaria Nuclear Polyhedrosis Virus (LfNPV) [84]. Studies have demonstrated that deposits of impure viral preparations are not inactivated as quickly as pure preparations when exposed to ultraviolet or natural sunlight [20, 22, 34, 59]. It is suggested that dark color and protein content of crude suspensions prolong virus activity [59]. Griego et al. [34] observed crude virus suspensions preserve virus better than highly purified inclusion bodies. Similarly, crude preparations of PbNPV remained active after 120 h exposure to UV light (253 nm) as dry film [20]. Viral activity of PbGV persisted for 7 days, while held in darkness, with the incorporation of hemolymph from host insects [22]. 3.2. Temperature The deleterious affects of temperature on virus persistence is significant in both storage and in the environment, but is comparably less important than solar radiation [60]. Inactivation by heat is a nonselective process and affects various functions associated with the virus. Virucidal effects of temperature operate through several mechanisms, including protein denaturation, RNA damage, and influence on microbial or enzymatic activity [38, 104]. Storage data for VACV and baculoviruses have been collected under a variety of conditions with viruses stored in suspensions, as PIBs, as dried powders, at room temperature, refrigerated, and in some cases frozen. Generally, studies have shown as temperatures are lowered, virus inactivation is greatly prolonged. Table 2 summaries data on the effect of temperature on viral storage. 3.2.1. VACV The thermostability of VACV was a major concern for the development of efficient vaccination programs [39]. In general, poxviruses show high environmental stability compared with many other viruses [91, 117]. VACV stored at low temperatures have been noted to resist deterioration for considerable periods of time. It has been reported

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that VACV has retained virulence for periods exceeding 15 years frozen in buffer at – 20°C [91]. Alternatively, while dried, VACV can be stored for >35 weeks at 4°C without any loss in viability. However, dried VACV embedded in rabbit dermal scabs inactivated after a 1 h incubation at 90°C in saline solution [95]. TABLE 2. Effect of Storage Temperatures on Virus Stability Virus HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV HzSNPV PbGV PbGV PbGV PbGV TnNPV TnNPV TnNPV VAC VAC VAC VAC VAC

Temperature ( C) 75 80 88 5 50 5 37 50 37.7 60 71.1 82.2 93.3 20–22 20–22 20–22 20–22 80 82 88 4 –20 50 55 60

Exposure time 10 min 10 min 10 min 25 years 100 days 30 days 60 days 120 days 2h 2h 15 min 2h 15 min 2 days <1 h 7 days 14 days 10 min 10 min 10 min 245 days 15 years 15 h 2.5 h 15 min

Loss of activity (estimated %) ca. 40 100 100 Still active 100 ca. 50 ca. 50 ca. 50 <10 <10 >50 >95 >95 ~50 10–15 >80 >95 ca. 70 ca. 97 100 0 0 100 100 100

Reference 36 36 36 45 45 97 97 97 106 106 106 106 106 23 23 23 23 36 36 36 91 91 118 118 118

When aerosolized, VACV has been demonstrated much less persistent. Aerosolized VACV was almost completely inactivated within 6 h at 31–33°C and 80% humidity [39]. As the temperature decreases to 10°C–11°C and humidity is decreased to 20%, the survival rate of virions increases; after 24 h, only 33% of the virus was reported inactivated [39]. When frozen in buffer at –20°C, a titer reduction of only 3 log-steps is reported within 15 years [91]. Freeze-dried material persisted at 45°C for a period of 6 years with little signs of inactivation [28]. Kaplan [66] investigated the persistence of VACV and determined inactivation curves at temperature ranges between 50°C–60°C. The results from this study demonstrated a rapid fall of infectivity followed by a complete inactivation at a much slower rate. VACV can be inactivated by pasteurization at 60°C in a variety of protein solutions [90]. Approximately a 2 log10 inactivation of VACV was observed after 1 h pasteurization in alpha1-proteinase inhibitor solution; within 3 h of exposure, no active VACV was detectable. Similarly, in human plasma protein solution, VACV was reduced by 4 log10 within 30 min, and by 2 h virus activity was undetectable.

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Earlier work demonstrated the effects of metal ions on heat inactivation of VACV. The stability of VACV suspensions was enhanced by the addition of 2M-Na+ [116]; virus was protected up to 4 h at 50°C and 24 h at 37°C. Similarly, the reaction velocity of inactivation in suspensions of VACV decreased as the concentration of Na2HPO4 increased [67]. Further investigating revealed in the presence of metal ions, specifically a mixture of 100 mM-Na+ and 1 mM-Mg2+, VACV was significantly more stable at 55°C and 60°C when compared with suspensions without ions. 3.2.2. Baculovirus There appears to be much variation in the stability of baculoviruses where storage temperature is concerned. In general, baculoviruses are quite stable at normal room temperatures. Temperatures below 10°C have shown to increase the persistence of HzSNPV [45]. Stored at 5°C, HzSNPV inclusions were still active after 25 years storage. Shapiro and Ignoffo [97] reported HzSNPV in water suspension was still present after 120 and 225 days storage at 5°C, 37°C and 50°C. While a 10 min exposure at 80°C completely inactivated HzSNPV. The study concluded that virus should still be active after 500 days storage at 5°C [97]. Alternatively, purified Pieris brassicae GV, in a dry form, lost a significant amount of activity after 2 days at 20°C [23]. Research has generally indicated that baculoviruses rapidly inactivate at temperatures above 60°C but withstand maximum temperatures normally encountered in field environments for short periods [59]. Studies investigating the effects of temperature on baculovirus viability have focused largely on ambient temperature ranges experienced during growing seasons [45, 59]. At temperatures approaching 50°C, the persistence of HzSNPV significantly decreased to less than 100 days [45]. Gudauskas and Canerday [36] reported TnNPV was inactivated by approximately 70% at 80°C and 97% at 82°C after heating for 10 min. Comparatively, HzSNPV was inactivated by approximately 40% and 100% at 75°C and 80°C respectively. Both viruses were totally inactivated at 88°C after 10 min. Studies have shown Heliothis virescens NPV withstanding temperatures of 60°C for 2 h and still remaining active at 93.3°C for 30 min [106]. As mentioned above, the pernicious affects of temperatures target various functions of viruses. Temperature has been observed to inhibit replication in some baculoviruses, such as Anticarsa gemmatalis Nuclear Polyhedrosis Virus (AgNPV) (10°C and 40°C), TnNPV (39°C), and PrGV (36°C) [63, 85]. One explanation is that a similarity between the virus and insect host development temperature is expected; as temperatures rise higher than normally associated with the virus-insect relationship, virus replication becomes adversely affected. Also in this regard, studies have suggested, temperature affects the integrity of viral proteins; specifically, higher temperatures breakdown and degrade proteins. The effect of temperature on inclusion body protein was observed to play an important role for the stability of baculoviruses in storage [59]. 3.3. Moisture and Relative Humidity The effect of moisture and relative humidity (RH) on virus persistence in the environment chiefly varies with virus type. As one would expect, viruses with higher lipid content tend to be more viable and persistent at a lower RH [104]. Both VACV

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and baculoviruses are generally sensitive to conditions of moisture and RH; the stability of these viruses is often improved through desiccation [22]. 3.3.1. VACV Studies have demonstrated VACV is relatively stable in dry environs, but when held in a high humidity atmosphere, it rapidly inactivates [100]. In scabs, RH was shown to be an important factor limiting the viability of VARV [43, 78]. At ambient room temperature and RH between 85% and 90%, no viable virus was detected after 8 weeks. When the RH was lowered (<10%), the viability was prolonged to 12 weeks. Similarly, other studies have revealed that VACV was less stable in high humidity environments (78% RH) [100], and that the survival of aerosolized vaccinia in the presence of UVC is significantly influenced by RH [82]. 3.3.2. Baculovirus Surface moisture may influence the susceptibility of baculovirus deposits to inactivation by ultraviolet light. Consistent with the studies on VACV, it was observed that PbGV inactivated more rapidly in wet virus films exposed to UV radiation than in dry films [19], as in the cases of TnNPV [58] and HzSNPV [55]. These findings suggest that surface moisture favors the inactivation of baculoviruses exposed to UV radiation. Surprisingly, there exists little research on the effects of rainfall on baculovirus persistence. Heavy rain produces little effect on HzSNPV [46]. Similar reports have been made for PbGV and Smithiavirus pityocampae [13, 20]. Generally, rainfall is considered an important dispersal mechanism for NPV in the habitat [9, 57, 123]. Virus inclusion bodies from cadavers of Psedoplusia includens (Walker) and Anticarsa gemmatalis (Hübner) were easily washed and dispersed from soybean foliage by sprinkler irrigation [123]. Rain is a primary agent responsible for spreading sawfly baculoviruses throughout trees [9]. In contrast, HzSNPV is not easily dislodged from cotton leaves after a natural rain [11], and PbGV persisted on cabbage exposed to either simulated rainfall or detergent-water rinse [20]. 3.4. pH The stability of viruses either in suspension, aerosols or on surfaces can also be adversely affected by pH. In general, the inclusion body protein or envelope in which baculovirus and VACV are embedded provides the virus particle with some degree of protection against the deleterious effects of chemicals. Both pox and baculoviruses are stable at pH near neutrality, and, from pH 4–8, these viruses have good short-term persistence [3, 48, 91, 125]. 3.4.1. VACV One physical characteristic of Poxviruses is the possession of low lipid content [91]. As a result, poxviruses tend to be less sensitive to the extreme pH ranges found in organic solvents and disinfectants compared with other enveloped viruses. VACV is most

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stable between the range of pH 4.5 and pH 10 with immediate virus inactivation below pH 2.5 or above pH 11.5 [6]. 3.4.2. Baculovirus The virucidal effects of pH have been studied more extensively for baculoviruses. Ignoffo and Garcia [48] observed a significant reduction in virus activity for inclusion bodies exposed at pH 1.2 and pH 12.4. Similar results were reported for HzSNPV at pH 2 and pH 12 [36]. Virus inactivation was recorded at 50% and 88% after 30 min and 24 h respectively, suspended at pH 2 in 1.0 M phosphate buffer. Comparatively, virus suspended at pH 12, in 1.0 M phosphate buffer, was inactivated by 40% and 92% after 30 min and 24 h respectively. Similarly, TnNPV in soil exposed to low pH (4.83–5.22) is rapidly inactivated over a month [113]. The effects of alkalis on baculoviruses are important for virus stability in the field habitat. Strongly alkaline pH (>10.0) is reported to dissolve the inclusion body protein of baculoviruses, releasing the virion [68, 97]. Prolonged (10–120 min.) exposure of Bombyx mori Nuclear Polyhedrosis Virus (BmNPV) to an alkaline solution results in the disruption of the virus envelope, subsequently releasing the virion [68]. Similarly, studies have shown HzSNPV inactivation in solutions of pH 9.0 or above, with rapid inactivation occurring as the pH approaches 10.0 [3, 48, 125]. Slightly alkaline conditions (pH 7.8) have been employed to liberate virions in Trichoplusia ni Granulovirus (TnGV) [108]. The alkaline pH (8.0–10.0) on cotton leaf surfaces are reported to inactivate HzSNPV [125]. HzSNPV inactivates in dew from cotton leaves whose pH ranged between 8.2 and 9.1 [3]. HzSNPV inactivated more rapidly on cotton leaves than on soybean or tomato whose leaf surfaces are close to pH 7.0 [125]. In support of these findings, exposure to pH 9.3 cotton dew resulted in a substantial loss of viral activity [83]. However, no significant difference has been observed between the three hosts when cotton leaves were protected from the sun suggesting an interaction with UV radiation [124]. One study reported improved virus persistence when the virus was buffered to a neutral pH [27]. In contrast, virus in a neutral phosphate buffer failed to increase persistence on cotton leaves [125]. 4. Environmental and Substrate Influences on Baculovirus and VACV Survival 4.1. Soil The stability and persistence of baculoviruses in the soil differ substantially partly because of exposure of viruses to different environmental conditions. Overall, there are few studies on the stability of viruses in the soil. GV and NPV viruses from several insects persisting in soil up to several months, even during winter months [111]. In a similar study, TnNPV persisted in soil for periods exceeding a year [57]. As expected, the pH of soil plays a vital role in viral inactivation in soil. The effect on viral persistance of a range of soil pH (4.83–7.17) over 3 month intervals was examined [113] and at a lower pH (4.83, 5.02, and 5.22), the virus rapidly inactivated.

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After 9 months, virus exposed to lower pH soils were mostly inactivated. In contrast, virus in soils closer to pH 7 persisted up to 12 months; though there was a significant decrease in recovery of active virus [113]. 4.2. The Phyllosphere Persistence of viruses on foliage is influenced primarily by their exposure to sunlight. Field studies on the impact of UV radiation have focused on the estimation of physical loss of baculovirus occlusion body deposits over time from the test plant surfaces. As discussed previously, virus inactivation by UV radiation is regarded as the most important abiotic factor affecting baculovirus stability. On plants, the effect of sunlight is greatly influenced by two main factors, (1) crop architecture, and (2) the position of virus on the plant. Studies have found HzSNPV on the adaxial surface of cotton leaves in the field have become inactive after 3 days [11]. Ignoffo et al. [50] made similar observations. HzSNPV on corn silks was reported to be active for up to 24 days [53]. A combination of UV light and the alkaline pH of cotton leaf surfaces inactivated HzSNPV [124, 125]. The decline in activity of baculovirus deposits on trees is typically rapid, but the rate of inactivation is usually slower than on agricultural crops [61]. In pine trees, NPV on the lower canopy position persisted significantly longer as a result of decreased UV exposure [71]. Comparably, Neodiprion sertifer Nuclear Polyhedrosis Virus (NsNPV) can persist for periods up to 2 years on pine foliage [69]. A thorough search of literature revealed no studies on the survival or persistence of orthopoxviruses on plant materials. 4.3. Textiles Research involving virus persistence on fabrics have received much attention over the years [99, 100]; specifically the considerable importance of the potential dissemination of viruses by various fomites such as clothing and other textiles. Vaccinia persisted up to 14 weeks on wool fabrics with low humidity, but persisted for a shorter period on the cotton fabrics [99]. VARV in scabs survives 3–4 months in raw cotton at 58% RH [78]. To our knowledge, there are no published studies on the persistence of baculoviruses on textiles. 4.4. Effect of Insect Metabolism on Viruses Few studies have focused on the effect of viral inactivation by insect metabolism. One study investigated the infectivity of Helicoverpa punctigera NPV through the gut of a predator insect [7]. Active viral inclusion bodies were collected from the excreta of the predatory, Nabis tasmanicus Remame up to 4 days after with no loss of virus activity. Similarly, Bartzokas et al. [5] studied the effect of formaldehyde fumigation on VACV infected cockroaches to determine the limits of survivability. The study found VACV ingested before formaldehyde fumigation remains viable inside the cockroaches gut and active virus may be excreted up to 5 days later.

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5. Virucidal Chemicals 5.1. VACV The effects of virucidal chemicals on VACV inactivation has been studied for several decades. Research investigating chemical disinfectants on surfaces for the prevention of disease spread has focused mainly on the healthcare and food industry [94]. Discussed below are specific details, from studies, on the known or expected activities of major classes of chemical virucides against VACV. 5.1.1. Aldehydes Numerous studies have demonstrated formulations based on formaldehyde and glutaraldehyde as highly effective at inactivating VACV. In the interest of learning the effects of formaldehyde vapor disinfection on smallpox, a study was conducted using VACV as a surrogate virus [35]. VACV, embedded in scabs, was exposed to a recommended dose of formaldehyde vapor (5g/m3) for 6 h at 75% RH in hospital rooms. Up to 85% virus inactivation at the recommended dose; however, by doubling the dose, the virus inactivation increased up to 95%. In suspension assays complete VACV inactivation was observed within 150–200 h in tests combining 0.006 M formaldehyde, 0.02 M glycine and virus [2]. Paraformaldehyde, in concentrations of either 0.3%, 1.5% or 7.5%, rapidly inactivated recombinant VACV in baby hamster kidney cells [41]. At concentrations of 1.5% or 7.5%, VACV inactivated in less than 10 min; however, at the lower concentration (0.3%) virus persisted up to 50 min. Glutaraldehyde is known to have virucidal activity against VACV as well as against other viruses. Sidwell et al. [102] reported laundering bedpads with glutaraldehyde, in combination with a detergent, inactivated VACV and lowered virus titers to below detectable levels. Bachrach and Rosenkovitch [4] compared the effect of aldehydes on the inactivation of VACV by a plaque assay. Oxidized spermine was reported more affective than glutaraldehyde or formaldehyde at inactivating a VACV suspension at 37°C for 10 h. Oxidized spermine, at a concentration of 0.82 mM, completely inactivated the virus. Schümann and Grossgebauer [95] reported complete inactivation of VACV embedded in rabbit dermal scabs with 90 min exposures to suspensions of either 2% glutaraldehyde or 2% formaldehyde. 5.1.2. Amphotericin B Methyl Ester Jordan and Seet studied the antiviral effects of Amphotericin B Methyl Ester (AME), an antimicrobial agent, on several viruses including VACV in a plaque reduction assay [64]. The concentration of AME resulting in a 50% inactivation of the plaque forming units, after a 60 min exposure at 35°C, was reported to be 5.0 μg/mL. The authors suggested lipid components in the host cell membrane which incorporate into the viral envelope may serve as a susceptible site for AME.

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5.1.3. Ascorbic Acid Early experiments on the viability of VACV in the presence of various reducing agents revealed ascorbic acid was strongly virucidal [73]. Turner [114] reported quantities of ascorbic acid, as little as 62.5 μg/mL, completely inactivated VACV exposed for 2 h at 37°C; under similar conditions, 100 μg/mL inactivated VACV in the span of 15 min. Moreover, in the presence of Cu2+ (5 μg/mL), a catalytic effect was observed; the rate at which VACV inactivated significantly increased when exposed to ascorbic acid. 5.1.4. Dithiothreitol (Reducing Agent) A study on the effects of bond reducing chemicals on viruses [16] investigated the susceptibility of virus inactivation to dithiothreitol (DTT), a disulfide bond reducing agent. DTT was found to inactivate VACV after a 3-h incubation at 37°C. The authors suggested that the lipoprotein envelope present around poxviruses accounted for VACV susceptibility to DTT. 5.1.5. Ethylene Oxide Many chemicals have been considered as virus inactivating agents for the sterilization of potential fomites. One agent, ethylene oxide, (CH2)2O, possesses antimicrobial properties against a suite of fungi, bacteria and viruses. Sidwell et al. [101] investigated the virucidal effects of ethylene oxide gas, on VACV, applied using Steri-Vac sterilizer following two cycle regimes: 29°C for 180 min and 60°C for 48 min. Under both experimental conditions, VACV was inactivated with virus titer reduced to less than detectable limits. Similar findings were observed by other investigators [40, 72]. 5.1.6. Quaternary Ammonium Another investigation reported fabrics impregnated with quaternary ammonium compounds expressed virucidal effects and significantly inactivated the VACV [99]. A mixture of virucidal compounds comprised of quaternary ammonium (benzalkonium chloride), a detergent (Triton X100) and citric acid inactivated VACV after a 20 min incubation [86]. 5.1.7. Sanitary Alcohols In recognition of the need for broad spectrum hand disinfectants, new formulations of sanitary rubs have been tested for virucidal activity. Enveloped viruses including VACV were exposed to three common alcohol-based sanitary rubs containing at least 75% alcohol, at exposure times of 15, 30 and 60 s. Significant viral inactivation (≥4 log 10 steps) after 15 s with infectivity reduced to below detectable levels [65]. In an earlier study, Schümann and Grossgebauer [95] observed complete disinfection of VACV from hands using 70% isopropyl-alcohol with a 2–5 min exposure. Similarly, VACV embedded in rabbit dermal scabs were completely inactivated after 3 h exposures in

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suspensions of 60% n-propyl-alcohol, 70% isopropyl-alcohol, or 80% ethyl-alcohol. A formulation consisting of 0.2% paracetic acid (PAA) and 80% ethanol (v/v) completely inactivated VACV after a 30 s exposure [119]. In addition, Kramer et al. [75] studied the virucidal effects from an alcohol based formulation including 55% ethanol (w/w) with 10% (w/w) propan-1-ol, 5.9% (w/w) propan-1.2-diol, 5.7% (w/w) butan-1.3-diol and 0.75% phosphoric acid. In suspension tests, VACV inactivated after a 30 s exposure. 5.1.8. Solvent/Detergent Treatment Treatments including combinations of solvents/detergents have been widely used in the healthcare industry to inactivate viruses within plasma or blood products. Cocktails of VACV (10% v/v) and Tri-(n-butyl)-phosphate (TNBP)/Tween 80 in solutions of antihaemophilic factor incubated at 28°C for 6 h reduced virus titer by 3 log10 [90] The observed resistance of VACV to TNBP/Tween 80 treatment supported earlier findings [92]. Comparatively, combinations of VACV (10% v/v) and TNBP/cholate in solutions of intravenous immunoglobulin completely inactivated virus >1 h [90]. The healthcare industry also commonly includes treatments using caprylic acid salts in plasma products. Suspensions of VACV (10% v/v) in Gamunex® intermediate solution containing 10% sodium caprylate resulted in complete virus inactivation within 3 min when incubated at 22°C in 20 mM caprylate [90]. 5.1.9. Surface Disinfectants The virucidal activity of monopercitric acid (MPCA) was determined by suspension tests against both nonenveloped and enveloped viruses including VACV [120]. The study showed that viruses were 99.9% inactivated by a 0.5% concentration within 30 s demonstrating MPCA as a suitable candidate for a disinfectant. Sugimoto and Toyoshima [107] reported on the inactivation of VACV by Nα-Cocoyl-L-Arginine Ethyl Ester, DLPyroglutamic Acid Salt after a 30 min exposure at room temperature. At all tested concentrations (0.025%, 0.05%, 0.1%, and 0.25%), there was greater than 90% inactivation of VACV. Ferrier et al. [29] studied the virucidal effects of a non-corrosive commercial disinfectant whose composition combines a suite of viral inactivating agents including quaternary ammonium, aldehydes, alcohol and detergent. The disinfectant Sanytex® was assessed in the presence of protein in both suspension and surface tests against VACV. Suspension assays showed 1% concentrations of Sanytex effectively inactivating (log reduction >4) VACV in the presence of 3 mg/mL protein. In parallel, the authors tested the virucidal activity of sodium hypochlorite in suspension assay and reported 0.525% concentration of active chlorine inactivating VACV (log reduction >4) with 10 mg/mL protein present. In surface tests, Sanytex was less effective requiring higher concentrations and longer exposure times for viral reduction. The virus was effectively inactivated after 15 min exposure by 10% detergent concentration and after 10 min by a 30% concentration of detergent containing 10 mg/mL protein concentration. Overall, the study demonstrated Sanytex as a highly effective disinfectant for use in laboratories and clinical facilities.

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In evaluating the virucidal efficacy of three surface disinfectants (quaternary ammonium, phenolic, and iodophor) on a simulated hard-environmental surface with a 10 min exposure at 20°C, it was observed that the quaternary ammonium compound was the most effective at inactivating VACV (ca. 4.5 Log10 reduction) [31]. Both iodophor and phenolic, however, produced similar results with an inactivation of 3.9 Log10 and 3.7 Log10 respectively. As illustrated above, various disinfectants are used for inactivation of pox viruses in laboratory, clinical and hospital facilities. However, these are generally not suitable for the home environment, posing both health and environmental hazards. Butcher and Ulaeto [15] assessed common household disinfectants on VACV and reported that a household, chloroxylenol-based disinfectant completely inactivated VACV at ambient room temperature. 5.1.10. Miscellaneous With recent concerns over the use of smallpox virus as a biological weapon, broad poxinhibitory agents have been investigated. ST-246 (4-trifluoromethyl-N-(3,3a,4,4a,5,5a,6, 6a-octahydro-1,3-dioxo-4,6-ethenocycloprop[f]isoindol-2(1H)-yl)-benzamide) was identified as a potential chemotherapeutic agent for use against smallpox virus [121]. In cell culture, ST-246 was demonstrated a specific inhibitor of poxvirus replication in two separate VACV assays: (1), in a cytopathic effect assay, an EC50 for inhibition was recorded at 0.01 μM, and; (2), a lack of extracellular virus formation in a virus yield assay after exposure to 5 μM ST-246. Similarly, plaque formation was completely inhibited in cowpox virus exposed to ST-246. In orally administered ST-246 assays, formation of VACV induced tail lesions was inhibited in mice infected with live VACV via tail vein. The same study also identified ST-246 as a broad virucidal chemical against several orthopoxviruses. In recent studies, 4(3H)-Quinazolinone derivatives have been reported to possess both antimicrobial and antiallergic properties [25]. In a cell culture study, 6-Bromo-2phenyl-3-[(4-amino-5-(4-chlorophenyl)-6-ethylpyrimidin-2-yl]-4(3H)-quinazolinone exhibited antiviral activity against VACV in E6SM cell culture at a concentration of 1.92 μg/mL. 5.2. Baculovirus The effects of antiviral agents and disinfectants on baculoviruses have chiefly focused on their use in the laboratory for sterilizing insect diet preparations, or decontaminating work areas for the prevention of virus transmission during experiments [59, 115]. Earlier studies demonstrated antiviral activity from formaldehyde incorporated into diets [59, 49, 115]. Formalin (0.04%) reduced viral activity when added to suspensions of HzSNPV and TnNPV or incorporated into insect diets containing viruses [59]. Comparably, formalin solution (10%) has also been used to surface-sterilize eggs to prevent baculovirus infections [87]. Sodium hypochlorite at various concentrations has also been used effectively as an egg surface sterilant [44, 47, 115]. Ignoffo and Dutky [47] reported treatments of 0.5%, and 0.05% sodium hypochlorite for 1–30 min completely inactivated TnNPV. The cations strontium, ferrous and ferric chloride are also inhibitory to virus activity of the Lymantria dispar Nuclear Polyhedrosis Virus (LdNPV) [96].

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Several reports have identified virucidal chemicals operating by denaturing the polyhedral protein matrix of viruses. Sodium carbonate (0.005M Na2CO3) induced significant swelling of HzSNPV polyhedra after incubation [3]; based on these findings, it was concluded viral polyhedra would dissolve over time. Consistent with these observations, the inclusion body protein of insects was reported to dissolve several minutes after being exposed to 0.01M Na2CO3 [59]. Sodium carbonate solutions dissolved inclusion bodies from HzSNPV and deactivated 99.9% of the virus [97]. Furthermore, magnesium (0.003 M MgSO4) reduced infectivity by five- to sixfold and a sevenfold decrease of infectivity was reported for β-mercaptoethanol (8 M; pH 7.6). 6. Discussion and Conclusion The ability to draw reference correlations, based on repeatable studies, between baculovirus simulants and poxviruses is a requirement for the practical use of baculoviruses for testing decontamination regimes for pathogenic viruses. Overall, the research focus on virion persistence for each group has had orthogonal goals. Poxvirus research has focused entirely on virus elimination (save for the viability and efficacy of vaccines stored and transported inside vials). On the other hand, baculovirus researchers have embraced the goal, in some instances, of increasing the persistance of virions to extend their usefulness as natural insecticides in agriculture. These divergent research foci, and the diversity of methods and conditions employed, have resulted in many datasets that are difficult to compare with one another. However, some common themes can be gleaned from a broad examination of the data. We discuss below six broad physical and biochemical commonalities shared between VACV and baculoviruses. These similarities suggest that one or more baculoviruses may serve as effective simulants for poxviruses for purposes of studying virus decontamination and detection. •

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Effect of pH. In general, pox and baculoviruses are viable under similar pH ranges. Near pH 7, both VACV and baculoviruses remain stable, and from pH 4–8, these viruses show good short-term persistence [3, 48, 91]. Both VACV and baculoviruses rapidly inactivate below pH 2 or above pH 11. Significant reduction in virus activity was reported for HzSNPV at pH 1.2 [48] with 88% virus inactivation after 24 h exposure at pH 2 [36] and immediate inactivation of VACV was observed at pH 2.5 [6]. Strong alkaline pH (>10) conditions are documented to dissolve the inclusion body protein in baculoviruses and inactivate the viruses [68, 97]. Pox viruses are reported less sensitive to extreme pH ranges due to a low lipid content [91]; however, like baculoviruses, exposure to alkaline ranges above pH 11, results in rapid virus inactivation [6]. Effect of moisture or humidity. Based on a paucity of research, the most constructive generalization is to state VACV and baculoviruses are susceptible to inactivation under similar conditions of high moisture and RH. A greater loss of VACV viability was observed in aerosols at 80% RH when compared to lower RH conditions <50% [37]. Similar results have been reported by Sidwell et al. [100]. Furthermore, McDevitt et al. [82] demonstrated the survival of aerosolized VACV in the presence of UVC is significantly influenced by relative humidity. As the moisture content (RH) increases, VACV becomes more susceptible to UVC inactivation. Comparably,

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under conditions with relatively high moisture content, baculoviruses showed increased susceptibility to UV radiation. PbGV inactivated more rapidly in wet virus films exposed to UV radiation than in dry films [19], as reported for TnNPV [58] and, for HzSNPV [55, 56]. Overall, studies have demonstrated under dry environs, VACV and baculoviruses respond similarly and remain relatively stable. Effect of temperature. Thermal sensitivities have been shown to be similar for both VACV and baculoviruses. As discussed earlier, viral persistence may be affected by elevated temperatures through several mechanisms which include protein denaturation, RNA damage, and influence on enzymatic activity or DNA replication. The effects of temperature on the persistence of both pox and baculovirus groups has been examined in liquid and dried forms under a number of different conditions. Through these studies, a common trend arose for both virus groups; as expected, virus inactivation rates increased with increasing temperature. Temperatures exceeding 60°C result in a rapid rate of virus inactivation. Virus inactivation curves [66, 118] demonstrated a significant loss of VACV activity at temperature ranges between 50°C and 60°C. In a liquid medium (CAM suspension), VACV completely inactivated at 60°C after 2 h; at 70°C after 30 min; and at temperature ranges of 80°C and above after 10 min [98]. Though thermal stability studies have produced diverse results, baculoviruses, like poxviruses, are inactivated by relatively short exposures to high temperatures. Studies have demonstrated that baculoviruses were completely inactivated by exposure as suspensions or dried to temperatures of 70– 80°C for 10 min [8, 59, 79]. Conversely, research has shown lower temperatures (<5°C) generally enhance virus survival and most viruses can be stored frozen to maintain their infectivity for long periods of time. Studies have documented VACV still viable after 15 years stored at –20°C [91]; comparably, HzSNPV remained active after 25 years stored at 5°C [45]. The slight differences in the effects of temperatures between the virus groups (e.g., pox and baculovirus) may be due to differences of the virus type, the temperatures to which they are stored/frozen, the rate of freezing and thawing and the medium in which they are stored/frozen. Effect of chemical decontaminants. Exposure to formalin and sodium hypochlorite inactivated both VACV and a large proportion of baculoviruses [35, 59, 115]. Formaldehyde vapor, as well as in suspension, completely inactivated VACV [35, 95]. In line with these findings, formaldehyde has been successfully used as surface disinfectants for insect diets against baculovirus contamination [87]. Comparably, sodium hypochlorite has been used effectively as a disinfectant of baculoviruses and VACV for work areas in laboratories, as well as healthcare and food industry facilities [115]. Effect of UV radiation. In general, studies have demonstrated both pox and baculoviruses are readily inactivated by ultraviolet radiation. The majority of studies on UV radiation inactivation of VACV have focused primarily on germicidal ultraviolet cell light (254 nm) for the control of microbial infection spread; exposure to this wavelength results in rapid inactivation of VACV (<10 s). Similarly, suspensions of HzSNPV and TnNPV rapidly inactivated when exposed to these wavelengths with a complete loss of activity in <10 min [36]. Bullock et al. [11] reported similar findings at 2 h exposure to wavelengths of 257 nm. These studies suggest common physical characteristics shared between the pox and baculoviruses; exposed

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to a germicidal UV wavelength (254 nm), both virus groups are quickly inactivated. Furthermore, studies [89, 103] suggest VACV is susceptible to UV ranges between 280 and 300 nm. However, a thorough search of literature revealed few studies to make direct comparisons at higher wavelengths. Additional work is required to identify and compare different UV wavelengths on the stability of both pox and baculoviruses. Effect of passage through insects. Insect metabolism did not appreciably reduce VACV or baculovirus activity. Overall, the effect of natural biological activity on the survival of viruses is poorly documented. However, data show VACV and Helicoverpa punctigera NPV respond similarly when passed through insect digestive tracks. Results from these experiments have found no difference between virus inactivation rates [5, 7]; virus particles excreted up to 4–5 days after insect ingestion remained active. The prolonged persistence of these viruses attests to a relatively comparable effect of insect metabolism on viral inactivation.

Significantly, the literature review identified several substances and treatment conditions which make VACV susceptible to inactivation that in turn can be similarly tested on the baculovirus simulants, and suggests a near-term strategy for identifying baculovirus species that might serve as useful simulants. Virions of CpGV and HzSNPV, for example, are baculoviruses with single genome copies per particle (as poxviruses do), and might be good candidates for initial studies. The resulting experimental data can then be compared to published data, and side-by-side experiments can be designed and executed to validate the initial findings. Final results can then be used to further determine and conclude on the potential of baculoviruses as components to a broader biologica defense research program in which simulants might be used meaningfully to discover new decontamination methods and substances, new detection methods, and so on. To further the goal of validating a given baculovirus as a simulant for the purpose of determining new decontamination methods, a researcher might consider the decontamination or deactivation conditions reviewed above as a springboard for future studies. Such studies, based on this literature, might include examinations of the following phenomena and methods: • • • • • • • • • •

Antiviral effects of Amphotericin B Methyl Ester by a plaque reduction assay [64]). Toxicity of glutaraldehyde and oxidized spermine using a plaque reduction assay [4]. Antiviral effects of 6-Bromo-2-phenyl-3-[(4-amino-5-(4-chlorophenyl)-6ethylpyrimidin-2-yl]-4(3H)-quinazolinone in cell culture assay [25]. Virucidal effects of an alcohol based formulations including 55% ethanol (w/w) with 10% (w/w) propan-1-ol, 5.9% (w/w) propan-1.2-diol, 5.7% (w/w) butan-1.3-diol and 0.75% phosphoric acid in a suspension test [75]. Toxicity of 0.5% monopercitric acid (MPCA) by suspension test [119]. Virucidal effects of alcohol-based hand rubs by suspension test [65]. Evaluation of virucidal effects of three surface disinfectants (quaternary ammonium, phenolic, and iodophor) on a simulated hard environmental surface [31]. Virus inactivation by solvent/detergent treatments of TNBP/Tween 80 or TNBP/ Cholate in suspension tests [90]. Virucidal effects of ascorbic acid by suspension test [114]. Sensitivity of aerosolized virus at predetermined temperature and humidity ranges [39].

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Sensitivity of aerosolized virus exposed to UVC (254 nm) under the influence of relative humidity [82]. Determination of virus persistence on various surfaces including cotton and wool fabrics [100]; and plant materials [11].

In conclusion, the rate of inactivation and the extent of virus survival varies depending upon virus type (pox or baculovirus), temperature, UV light exposure as well as other environmental and abiotic conditions. Overall, a lack of comparative studies exist between pox and baculoviruses which stems from the employment of these virus groups for different purposes. Baculoviruses such as CpGV and HzSNPV (in particular, where data are available) have demonstrated a broad similarity to VACV in their sensitivity to environmental stressors, and the published data support the proposition that baculoviruses may function as simulants for poxviruses in decontamination science. However, additional quantitative information is required for a direct comparison of VACV and baculoviruses on the survival, persistence and inactivation rates in various environmental conditions such as wet states (as aerosols or under high RH), when subjected to drying and desiccation, when deposited on various surfaces (textiles, soil or plant materials), when aerosolized, when subjected to chemical treatments such as virucides, disinfectants and other germicidal treatment chemicals (e.g., aldehydes, alkaline material treatments), and when subjected to physical process such as, UV irradiation, and extreme changes in temperature. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

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Biological Abbreviations and Acronyms AfMNPV AgNPV BmNPV CpGV GV HzSNPV IPM LdNPV LfNPV NPV NsNPV OB OpNPV PAA PbGV PIB PrGV TNBP TnGV TnNPV VACV VARV WHO

Anagrapha falcifera Nuclear Polyhedrosis Virus Anticarsa gemmatalis Nuclear Polyhedrosis Virus Bombyx mori Nuclear Polyhedrosis Virus Cydia pomonella Granulovirus Granulovirus Helicoverpa zea Nuclear Polyhedrosis Virus Integrated Pest Management Lymantria dispar Nuclear Polyhedrosis Virus Lambdina fiscellaria Nuclear Polyhedrosis Virus Nuclear Polyhedrosis Virus Neodiprion sertifer Nuclear Polyhedrosis Virus Occlusion bodies Orgyia pseudotsugata Nuclear Polyhedrosis Virus Paracetic acid Pieris brassicae Granulovirus Polyhedral inclusion bodies Pieris rapae Granulovirus Tri-(n-butyl)-phosphate Trichoplusia ni Granulovirus Trichoplusia ni Nuclear Polyhedrosis Virus Vaccinia virus Variola virus World Health Organization

Subject Index multiple-locus variable number tandem repeat analysis, 42 “hearths” of disease, 53 “suicide PCR”, 113

Brucella melitensis 16M, 7 Brucellosis, 32, 52 Burkholderia mallei, 135, 138 Burkholderia pseudomallei, 135, 138 Burkholderia thailandensis, 135

A C Afipia, 110 Agromyces, 110 ALERT, 6, 58, 65, 66, 69, 70 amikacin, 31 Anagrapha falcifera Nuclear Polyhedrosis Virus, 173 anthrax, 28, 32, 53, 54, 59, 74, 75, 76, 77, 78, 82, 83, 84, 85, 86, 88, 91, 96, 97, 98, 101, 102, 103, 104, 105, 106, 135 antibiotics, 30 Anticarsa gemmatalis Nuclear Polyhedrosis Virus, 173 anti-plague (AP) laboratory, 36 area under the curve, 91, 94 arginine deiminase, 135 Asian psyllid, 23 aspA, 136 Azerbaijan, 39, 46, 52 B Bacillus anthracis, 6, 14, 32, 33, 38, 74, 75, 76, 78, 80, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 102, 103, 105, 128, 138 bacteriemia, 108, 112 bacteriophage lambda, 140, 143 Baculovirus, 161 Baculoviruses, 145 Bangladesh, 20 Bartonella bacilliformis, 110 biological weapons, 59 Bison bison athabascae, 77 blood, 67, 77, 108, 109, 111, 114, 115, 117, 119, 122, 124 Bones, 110 Brucella, 7, 32, 52, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127 Brucella abortus, 7, 53, 120, 121, 124, 125, 126, 127 Brucella melitensis, 7, 32, 53, 116, 120, 121, 124, 125, 126, 127

cadA, 134, 138 cadaverine, 134 Callopsylla caspia, 38 caprine brucellosis, 116, 117 cefotaxim, 31 Celebes, 20 Centers for Disease Control and Prevention, 57, 72, 73, 89, 118 Ceratophyllus caspius, 55 Ceratophyllus consimilis, 55 Ceratophyllus iranus, 54 Ceratophyllus laeviceps, 37, 54 Cervidae, 77 chain-terminating nucleotides, 142 Chlamydia trachomatis, 129 cholera, 17, 18, 19, 20, 21, 22, 25, 26, 28, 31, 33, 55, 56, 57, 59, 129 Cholera, 17, 55, 59, 64 cholera toxin, 18 Ciflox, 31 Citrus greening, 5, 17, 23, 25 Civilian Research and Development Foundation, 11, 102 colonization, 109, 112, 116, 119, 120, 121, 123, 124 common vole, 38 Commonwealth of Independent States, 30 corticosteroids, 31 Coxiella burnetii, 109, 114, 115, 133 CpGV, 146 Ctenophthalmus teres, 38 CTX phage, 19 Curtobacterium, 110 Cydia pomonella granulovirus, 146 Cydia pomonella Granulovirus, 173 D Dagestan, 46 Dedoplistskaro, 46

151

152

SUBJECT INDEX

Defense Threat Reduction Agency, 12, 13, 39, 102, 140 deletion mutants, 122, 126 Dental pulp, 108, 114 dentin, 108 Department of Defense, 39, 142 detection, 6, 18, 58, 60, 61, 62, 63, 65, 69, 70, 71, 72, 73, 109, 110, 111, 112, 114, 120, 140, 141, 142, 144 Diaphorina citri, 5 DNA, 6, 7, 8, 20, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 124, 126, 128, 129, 132, 133, 137, 138, 140, 141, 142, 143, 144 DNA polymerase, 141 Doxycycline, 31 E early warning, 58 ecologic niches, 18 ecological niche modeling (ENM), 74 enterocolitica, 136 enterotoxin, 18 environment, 171 EpiData, 68 epidemic-prone diseases, 58 EpiInfo, 68 Epstein-Barr virus, 140, 143 Escherichia, 128, 131, 138, 141 exploratory spatial data analysis, 83 extracellular polysaccharide, 19

goats, 32, 52, 116, 118, 119, 120, 121, 122, 123, 124, 126 H HADCM3, 90, 91, 94, 98, 99, 100 HADCM3 data set, 98 Haemophilus influenzae, 141, 144 Helicobacter, 128 Helicoverpa zea Nuclear Polyhedrosis Virus, 173 hemagglutinin, 7, 116, 117, 118, 123, 124 horizontal gene transfer, 129 I immunoglobulin erythrocyte diagnosticum, 32 India, 20 influenza, 6, 58, 59, 60, 63, 64, 65, 69, 71 influenza A/H5, 43 insertion sequence, 42 Integrated Pest Management, 173 International Health Regulations, 58, 60, 62, 70, 71 Intetrix, 31 IS100, 46, 47 IS1111, 109 J Jarisch-Herxher reaction, 30

F

K

F. tularensis Live Vaccine Strain, 47 F. tularensis mediasiatica, 32 fleas, 28, 29, 35, 37, 54, 103, 105 fluorescent sequencing, 140 fluorescently labeled probes, 142 F-plasmid, 130 Francisella tularensis, 32, 38, 39, 136, 139 Francisella tularensis holarctica, 32

Kaffa, 34 Kazakh Scientific Centre for Quarantine and Zoonotic Diseases, 28, 31 Kazakhstan, 6, 28, 81, 85 Kirgizia, 46 KSCQZD, 32 Kyzylmay, 31 L

G GARP, 87, 88, 92, 93, 95, 96, 97, 98, 100, 104, 105 gene replacement, 116, 122 genetic algorithm for rule-set prediction, 87 gentamycin, 31 geographic information systems, 74, 103 Georgia, 6, 34 Giorgi Eliava, 36

Lambdina fiscellaria Nuclear Polyhedrosis Virus, 173 landscape ecology, 78 Landscape Ecology, 75 Liberobacter asiaticum, 23 ligation, 118, 142 Lymantria dispar Nuclear Polyhedrosis Virus, 173 lysine decarboxylase, 134

SUBJECT INDEX M Macedonia, 6 meningoencephalitis, 30 Meriones erythrourus, 37 Meriones libicus erythrourus, 36 metagenomics, 143, 144 Mexico, 14, 74, 78, 80, 87, 89, 90, 92, 93, 94, 96, 97, 99, 102, 104, 105 Microbacterium, 110 microbes, 140, 142, 143 Microtus arvalis, 38 MLST, 19, 22 MLVA, 28, 32 model, 21, 22, 74, 75, 76, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 100, 109, 116, 117, 123, 125, 127 multiple-locus variable number tandem repeat analysis, 48 Mycobacterium leprae, 133 Mycobacterium tuberculosis, 110, 115, 129 Mycoplasma genitalium, 141, 144 N Nairovirus species, 79 National Center for Disease Control and Public Health (NCDC), 38 Neodiprion sertifer Nuclear Polyhedrosis Virus, 173 next-generation sequencing, 7 Ninotsminda, 45 Nosopsillus consimilis, 38 Nuclear Polyhedrosis Virus, 173 O O-antigen, 20 Occlusion bodies, 173 Odocoileus virginianus, 77, 95 Orgyia pseudotsugata Nuclear Polyhedrosis Virus, 173 oxacillin, 31 P Paracetic acid, 173 pathoadaptation, 128 pathogen, 7 PCR, 6, 7, 8, 109, 111, 112, 113, 114, 115, 117, 118, 124, 141, 142 Peru, 21, 26, 103, 110, 115 Pestoides, 130, 137 PFGE, 47

153

pFra, 129, 130, 131, 137 Pieris brassicae Granulovirus, 173 Pieris rapae Granulovirus, 173 plague, 29, 30, 54, 64 Plague, 34, 59 plasmids, 118, 129, 135 point mutations, 128, 134, 135 Polyhedral inclusion bodies, 173 polymerase chain reaction, 108, 114 polyphytoleum, 31 primers, 117, 118, 124, 142 proviral DNA, 108 pseudotuberculosis, 129, 131, 132, 134, 136, 137, 138 pulpo-dental organ, 108 pulsed-field gel electrophoresis, 42 Pulsed-field gel electrophoresis, 44 pyrosequencing, 141 R real-time RT-PCR, 43 receiver operating characteristic, 91, 105 Region E, 7, 117, 118, 123, 124 Republic of Macedonia, 58 reservoir species, 28 restriction endonuclease, 44 rha-locus, 29 rifampicin, 31 rpoB, 112, 115 rscA, 134 Russian empire, 55 S Salmonellae, 128 Schu S4, 136 sequencing, 7, 8, 113, 140, 141, 142, 143, 144 Shigella, 134, 138 simulant, 146 sod gene, 109 Solexa, 142, 144 spatial epidemiology, 82, 98, 101 Sphingomonas, 110 STATSGO data set, 91 Streptococcus, 128, 137 surveillance, 6, 18, 28, 29, 33, 34, 35, 36, 38, 39, 40, 41, 58, 60, 61, 62, 63, 65, 67, 68, 69, 70, 71, 72, 73, 78, 79, 95, 103 Surveillance, 5, 58 SV40, 140, 143 Syndromic Surveillance, 61

154

SUBJECT INDEX

T Tabanidae, 77 Teeth, 111 tra genes, 130 traH, 130 traN, 130 transnational organized crime, 59 Tri-(n-butyl)-phosphate, 173 Trichoplusia ni Granulovirus, 174 Trichoplusia ni Nuclear Polyhedrosis Virus, 174 Trypanosoma cruzi, 94 Tularemia, 32

VHF telemetry, 77 Vibrio cholerae, 5, 18, 31, 38, 56 virulence, 7, 18, 19, 28, 29, 54, 55, 116, 122, 124, 125, 126, 127, 128, 129, 133, 134, 135, 136, 137, 138 VNTR, 32 W Western blot, 119, 123 World Health Organization, 11, 18, 25, 41, 58, 59, 70, 71, 174 X

U

Xenopsylla conformis, 37, 54

United States, 14, 17, 35, 72, 74, 75, 77, 78, 83, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 102, 105, 118 University of Maryland, Baltimore, 39 UPGMA, 32 US Army Edgewood Chemical Biological Center (ECBC), 39 UV light, 151

Y

V V. cholerae, 18, 19, 20, 21, 22, 26, 31, 56 V. cholerae El Tor, 56 Vaccinia, 145, 174 Variable-Number Tandem Repeats, 49 Variola, 174 vectors, 38

Y. pestis 91001, 129, 134 Y. pestis CO 92, 134 Y. pestis KIM, 133, 134 yadA, 134 Yersinia enterocolitica, 126, 131, 132 Yersinia pestis, 6, 17, 29, 33, 34, 38, 39, 41, 54, 79, 103, 105, 111, 113, 115, 128, 131, 132, 133, 137, 138 Yersinia pseudotuberculosis, 30 YopJ, 136, 138 YopP, 136 Z Zoonotic pathogens, 17