Evolutionary epidemiology: preparing for an age of genomic plenty

A phylogenetic study of dengue virus in urban Vietnam shows long-term persistence of endemic strains

Dengue virus (DENV) causes repeated outbreaks of disease in endemic areas, with patterns of local transmission strongly influenced by seasonality, importation via human movement, immunity, and vector control efforts. An understanding of how each of these interacts to enable endemic transmission (continual circulation of local virus strains) is largely unknown. There are times of the year when no cases are reported, often for extended periods of time, perhaps wrongly implying the successful eradication of a local strain from that area. Individuals who presented at a clinic or hospital in four communes in Nha Trang, Vietnam, were initially tested for DENV antigen presence. Enrolled positive individuals then had their corresponding household members invited to participate, and those who enrolled were tested for DENV. The presence of viral nucleic acid in all samples was confirmed using quantitative polymerase chain reaction, and positive samples were then whole-genome sequenced using an amplicon and target enrichment library preparation techniques and Illumina MiSeq sequencing technology. Generated consensus genome sequences were then analysed using phylogenetic tree reconstruction to categorise sequences into clades with a common ancestor, enabling investigations of both viral clade persistence and introductions. Hypothetical introduction dates were additionally assessed using a molecular clock model that calculated the time to the most recent common ancestor (TMRCA). We obtained 511 DENV whole-genome sequences covering four serotypes and more than ten distinct viral clades. For five of these clades, we had sufficient data to show that the same viral lineage persisted for at least several months. We noted that some clades persisted longer than others during the sampling time, and by comparison with other published sequences from elsewhere in Vietnam and around the world, we saw that at least two different viral lineages were introduced into the population during the study period (April 2017-2019). Next, by inferring the TMRCA from the construction of molecular clock phylogenies, we predicted that two of the viral lineages had been present in the study population for over a decade. We observed five viral lineages co-circulating in Nha Trang from three DENV serotypes, with two likely to have remained as uninterrupted transmission chains for a decade. This suggests clade cryptic persistence in the area, even during periods of low reported incidence.

Contribution of phylogenetics to understanding the evolution and epidemiology of dengue virus

Dengue virus (DENV) is one of the most important arboviral pathogens in the tropics and subtropics, and nearly one‐third of the world's population is at risk of infection. The transmission of DENV involves a sylvatic cycle between nonhuman primates (NHP) and Aedes genus mosquitoes, and an endemic cycle between human hosts and predominantly Aedes aegypti. DENV belongs to the genus Flavivirus of the family Flaviviridae and consists of four antigenically distinct serotypes (DENV‐1‐4). Phylogenetic analyses of DENV have revealed its origin, epidemiology, and the drivers that determine its molecular evolution in nature. This review discusses how phylogenetic research has improved our understanding of DENV evolution and how it affects viral ecology and improved our ability to analyze and predict future DENV emergence.

First Detection of NADC34-like PRRSV as a Main Epidemic Strain on a Large Farm in China

The newly emerged sublineage 1.5 (NADC34-like) porcine reproductive and respiratory syndrome virus (PRRSV) has posed a direct threat to the Chinese pig industry since 2018. However, the prevalence and impact of NADC34-like PRRSV on Chinese pig farms is unclear. In the present study, we continuously monitored pathogens—including PRRSV, African swine fever virus (ASFV), classical swine fever virus (CSFV), pseudorabies virus (PRV), and porcine circovirus 2 (PCV2)—on a fattening pig farm with strict biosecurity practices located in Heilongjiang Province, China, from 2020 to 2021. The results showed that multiple types of PRRSV coexisted on a single pig farm. NADC30-like and NADC34-like PRRSVs were the predominant strains on this pig farm. Importantly, NADC34-like PRRSV—detected during the period of peak mortality—was one of the predominant strains on this pig farm. Sequence alignment suggested that these strains shared the same 100 aa deletion in the NSP2 protein as IA/2014/NADC34 isolated from the United States (U.S.) in 2014. Phylogenetic analysis based on open reading frame 5 (ORF5) showed that the genetic diversity of NADC34-like PRRSV on this farm was relatively singular, but it had a relatively high rate of evolution. Restriction fragment length polymorphism (RFLP) pattern analysis showed that almost all ORF5 RFLPs were 1-7-4, with one 1-4-4. In addition, two complete genomes of NADC34-like PRRSVs were sequenced. Recombination analysis and sequence alignment demonstrated that both viruses, with 98.9% nucleotide similarity, were non-recombinant viruses. This study reports the prevalence and characteristics of NADC34-like PRRSVs on a large-scale breeding farm in northern China for the first time. These results will help to reveal the impact of NADC34-like PRRSVs on Chinese pig farms, and provide a reference for the detection and further prevention and control of NADC34-like PRRSVs.

Next Generation Sequencing and Bioinformatics Methodologies for Infectious Disease Research and Public Health: Approaches, Applications, and Considerations for Development of Laboratory Capacity

Next generation sequencing (NGS) combined with bioinformatics has successfully been used in a vast array of analyses for infectious disease research of public health relevance. For instance, NGS and bioinformatics approaches have been used to identify outbreak origins, track transmissions, investigate epidemic dynamics, determine etiological agents of a disease, and discover novel human pathogens. However, implementation of high-quality NGS and bioinformatics in research and public health laboratories can be challenging. These challenges mainly include the choice of the sequencing platform and the sequencing approach, the choice of bioinformatics methodologies, access to the appropriate computation and information technology infrastructure, and recruiting and retaining personnel with the specialized skills and experience in this field. In this review, we summarize the most common NGS and bioinformatics workflows in the context of infectious disease genomic surveillance and pathogen discovery, and highlight the main challenges and considerations for setting up an NGS and bioinformatics-focused infectious disease research public health laboratory. We describe the most commonly used sequencing platforms and review their strengths and weaknesses. We review sequencing approaches that have been used for various pathogens and study questions, as well as the most common difficulties associated with these approaches that should be considered when implementing in a public health or research setting. In addition, we provide a review of some common bioinformatics tools and procedures used for pathogen discovery and genome assembly, along with the most common challenges and solutions. Finally, we summarize the bioinformatics of advanced viral, bacterial, and parasite pathogen characterization, including types of study questions that can be answered when utilizing NGS and bioinformatics.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

Genomic Epidemiology as a Public Health Tool to Combat Mosquito-Borne Virus Outbreaks

Next-generation sequencing technologies, exponential increases in the availability of virus genomic data, and ongoing advances in phylogenomic methods have made genomic epidemiology an increasingly powerful tool for public health response to a range of mosquito-borne virus outbreaks. In this review, we offer a brief primer on the scope and methods of phylogenomic analyses that can answer key epidemiological questions during mosquito-borne virus public health emergencies. We then focus on case examples of outbreaks, including those caused by dengue, Zika, yellow fever, West Nile, and chikungunya viruses, to demonstrate the utility of genomic epidemiology to support the prevention and control of mosquito-borne virus threats. We extend these case studies with operational perspectives on how to best incorporate genomic epidemiology into structured surveillance and response programs for mosquito-borne virus control. Many tools for genomic epidemiology already exist, but so do technical and nontechnical challenges to advancing their use. Frameworks to support the rapid sharing of multidimensional data and increased cross-sector partnerships, networks, and collaborations can support advancement on all scales, from research and development to implementation by public health agencies.

Nextstrain: real-time tracking of pathogen evolution

Summary

Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike.

Availability and implementation

All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

OutbreakFinder: a visualization tool for rapid detection of bacterial strain clusters based on optimized multidimensional scaling

With the evolution of next generation sequencing (NGS) technologies, whole-genome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering. OutbreakFinder can be downloaded for free at https://github.com/skypes/Newton-method-MDS.

Microbial evolutionary medicine: from theory to clinical practice

Medicine and clinical microbiology have traditionally attempted to identify and eliminate the agents that cause disease. However, this traditional approach is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are non-communicable chronic diseases, often driven by altered immunity and inflammation, and communicable infections from agents which harbour antibiotic resistance. This Review focuses on the so-called evolutionary medicine framework, to study how microbial communities influence human health. The evolutionary medicine framework aims to predict and manipulate microbial effects on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics, and clinical expertise. We focus on the potential of evolutionary medicine to address three key challenges: detecting microbial transmission, predicting antimicrobial resistance, and understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.

Model diagnostics and refinement for phylodynamic models

Phylodynamic modelling, which studies the joint dynamics of epidemiological and evolutionary processes, has made significant progress in recent years due to increasingly available genomic data and advances in statistical modelling. These advances have greatly improved our understanding of transmission dynamics of many important pathogens. Nevertheless, there remains a lack of effective, targetted diagnostic tools for systematically detecting model mis-specification. Development of such tools is essential for model criticism, refinement, and calibration. The idea of utilising latent residuals for model assessment has already been exploited in general spatio-temporal epidemiological settings. Specifically, by proposing appropriately designed non-centered, re-parameterizations of a given epidemiological process, one can construct latent residuals with known sampling distributions which can be used to quantify evidence of model mis-specification. In this paper, we extend this idea to formulate a novel model-diagnostic framework for phylodynamic models. Using simulated examples, we show that our framework may effectively detect a particular form of mis-specification in a phylodynamic model, particularly in the event of superspreading. We also exemplify our approach by applying the framework to a dataset describing a local foot-and-mouth (FMD) outbreak in the UK, eliciting strong evidence against the assumption of no within-host-diversity in the outbreak. We further demonstrate that our framework can facilitate model calibration in real-life scenarios, by proposing a within-host-diversity model which appears to offer a better fit to data than one that assumes no within-host-diversity of FMD virus.

Integrated modelling of conventional epidemiological data and modern genomic data (i.e. phylodynamics) has made significant progress in recent years, due to the ever-increasing availability of genomic data and development of statistical methods. However, there is a lack of tools for carrying out effective diagnostics for phylodynamic models. We propose a novel model diagnostic framework that involves a latent residual process which is a priori independent of model assumptions and which can be used to quantify, and reveal the nature of, model inadequacy. Our results suggest that our framework may systematically detect deviation from a particular model assumption and greatly facilitate subsequent model calibration.

Porcine circovirus 2 (PCV-2) genetic variability under natural infection scenario reveals a complex network of viral quasispecies

Porcine circovirus 2 (PCV-2) is a virus characterized by a high evolutionary rate, promoting the potential emergence of different genotypes and strains. Despite the likely relevance in the emergence of new PCV-2 variants, the subtle evolutionary patterns of PCV-2 at the individual-host level or over short transmission chains are still largely unknown. This study aimed to analyze the within-host genetic variability of PCV-2 subpopulations to unravel the forces driving PCV-2 evolution. A longitudinal weekly sampling was conducted on individual animals located in three farms after the first PCV-2 detection. The analysis of polymorphisms evaluated throughout the full PCV-2 genome demonstrated the presence of several single nucleotide polymorphisms (SNPs) especially in the genome region encoding for the capsid gene. The global haplotype reconstruction allowed inferring the virus transmission network over time, suggesting a relevant within-farm circulation. Evidences of co-infection and recombination involving multiple PCV-2 genotypes were found after mixing with pigs originating from other sources. The present study demonstrates the remarkable within-host genetic variability of PCV-2 quasispecies, suggesting the role of the natural selection induced by the host immune response in driving PCV-2 evolution. Moreover, the effect of pig management in multiple genotype coinfections occurrence and recombination likelihood was demonstrated.

Understanding dengue virus evolution to support epidemic surveillance and counter-measure development

Dengue virus (DENV) causes a profound burden of morbidity and mortality, and its global burden is rising due to the co-circulation of four divergent DENV serotypes in the ecological context of globalization, travel, climate change, urbanization, and expansion of the geographic range of the Ae.aegypti and Ae.albopictus vectors. Understanding DENV evolution offers valuable opportunities to enhance surveillance and response to DENV epidemics via advances in RNA virus sequencing, bioinformatics, phylogenetic and other computational biology methods. Here we provide a scoping overview of the evolution and molecular epidemiology of DENV and the range of ways that evolutionary analyses can be applied as a public health tool against this arboviral pathogen.

Inferring epidemiologic dynamics from viral evolution: 2014-2015 Eurasian/North American highly pathogenic avian influenza viruses exceed transmission threshold, R0 = 1, in wild birds and poultry in North America

Highly pathogenic avian influenza virus (HPAIV) is a multihost pathogen with lineages that pose health risks for domestic birds, wild birds, and humans. One mechanism of intercontinental HPAIV spread is through wild bird reservoirs, and wild birds were the likely sources of a Eurasian (EA) lineage HPAIV into North America in 2014. The introduction resulted in several reassortment events with North American (NA) lineage low‐pathogenic avian influenza viruses and the reassortant EA/NA H5N2 went on to cause one of the largest HPAIV poultry outbreaks in North America. We evaluated three hypotheses about novel HPAIV introduced into wild and domestic bird hosts: (i) transmission of novel HPAIVs in wild birds was restricted by mechanisms associated with highly pathogenic phenotypes; (ii) the HPAIV poultry outbreak was not self‐sustaining and required viral input from wild birds; and (iii) reassortment of the EA H5N8 generated reassortant EA/NA AIVs with a fitness advantage over fully Eurasian lineages in North American wild birds. We used a time‐rooted phylodynamic model that explicitly incorporated viral population dynamics with evolutionary dynamics to estimate the basic reproductive number (R₀) and viral migration among host types in domestic and wild birds, as well as between the EA H5N8 and EA/NA H5N2 in wild birds. We did not find evidence to support hypothesis (i) or (ii) as our estimates of the transmission parameters suggested that the HPAIV outbreak met or exceeded the threshold for persistence in wild birds (R₀ > 1) and poultry (R₀ ≈ 1) with minimal estimated transmission among host types. There was also no evidence to support hypothesis (iii) because R₀ values were similar among EA H5N8 and EA/NA H5N2 in wild birds. Our results suggest that this novel HPAIV and reassortments did not encounter any transmission barriers sufficient to prevent persistence when introduced to wild or domestic birds.

Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States

The US swine industry has been impaired over the last 25 years by the far-reaching financial losses caused by the porcine reproductive and respiratory syndrome (PRRS). Here, we explored the relations between the spatial risk of PRRS outbreaks and its phylodynamic history in the U.S during 1998–2016 using ORF5 sequences collected from swine farms in the Midwest region. We used maximum entropy and Bayesian phylodynamic models to generate risk maps for PRRS outbreaks and reconstructed the evolutionary history of three selected phylogenetic clades (A, B and C). High-risk areas for PRRS were best-predicted by pig density and climate seasonality and included Minnesota, Iowa and South Dakota. Phylodynamic models demonstrated that the geographical spread of the three clades followed a heterogeneous spatial diffusion process. Furthermore, PRRS viruses were characterized by typical seasonality in their population size. However, endemic strains were characterized by a substantially slower population growth and evolutionary rates, as well as smaller spatial dispersal rates when compared to emerging strains. We demonstrated the prospects of combining inferences derived from two unique analytical methods to inform decisions related to risk-based interventions of an important pathogen affecting one of the largest food animal industries in the world.

Phylodynamic applications in 21st century global infectious disease research

BACKGROUND

Phylodynamics, the study of the interaction between epidemiological and pathogen evolutionary processes within and among populations, was originally defined in the context of rapidly evolving viruses and used to characterize transmission dynamics. The concept of phylodynamics has evolved since the early 21st century, extending its reach to slower-evolving pathogens, including bacteria and fungi, and to the identification of influential factors in disease spread and pathogen population dynamics.

RESULTS

The phylodynamic approach has now become a fundamental building block for the development of comparative phylogenetic tools capable of incorporating epidemiological surveillance data with molecular sequences into a single statistical framework. These innovative tools have greatly enhanced scientific investigations of the temporal and geographical origins, evolutionary history, and ecological risk factors associated with the growth and spread of viruses such as human immunodeficiency virus (HIV), Zika, and dengue and bacteria such as Methicillin-resistant Staphylococcus aureus.

CONCLUSIONS

Capitalizing on an extensive review of the literature, we discuss the evolution of the field of infectious disease epidemiology and recent accomplishments, highlighting the advancements in phylodynamics, as well as the challenges and limitations currently facing researchers studying emerging pathogen epidemics across the globe.

Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.

Host age modulates within-host parasite competition

In many host populations, one of the most striking differences among hosts is their age. While parasite prevalence differences in relation to host age are well known, little is known on how host age impacts ecological and evolutionary dynamics of diseases. Using two clones of the water flea Daphnia magna and two clones of its bacterial parasite Pasteuria ramosa, we examined how host age at exposure influences within-host parasite competition and virulence. We found that multiply-exposed hosts were more susceptible to infection and suffered higher mortality than singly-exposed hosts. Hosts oldest at exposure were least often infected and vice versa. Furthermore, we found that in young multiply-exposed hosts competition was weak, allowing coexistence and transmission of both parasite clones, whereas in older multiply-exposed hosts competitive exclusion was observed. Thus, age-dependent parasite exposure and host demography (age structure) could together play an important role in mediating parasite evolution. At the individual level, our results demonstrate a previously unnoticed interaction of the host's immune system with host age, suggesting that the specificity of immune function changes as hosts mature. Therefore, evolutionary models of parasite virulence might benefit from incorporating age-dependent epidemiological parameters.

Applications of Bayesian Phylodynamic Methods in a Recent U.S. Porcine Reproductive and Respiratory Syndrome Virus Outbreak

Classical phylogenetic methods such as neighbor-joining or maximum likelihood trees, provide limited inferences about the evolution of important pathogens and ignore important evolutionary parameters and uncertainties, which in turn limits decision making related to surveillance, control, and prevention resources. Bayesian phylodynamic models have recently been used to test research hypotheses related to evolution of infectious agents. However, few studies have attempted to model the evolutionary dynamics of porcine reproductive and respiratory syndrome virus (PRRSV) and, to the authors' knowledge, no attempt has been made to use large volumes of routinely collected data, sometimes referred to as big data, in the context of animal disease surveillance. The objective of this study was to explore and discuss the applications of Bayesian phylodynamic methods for modeling the evolution and spread of a notable 1-7-4 RFLP-type PRRSV between 2014 and 2015. A convenience sample of 288 ORF5 sequences was collected from 5 swine production systems in the United States between September 2003 and March 2015. Using coalescence and discrete trait phylodynamic models, we were able to infer population growth and demographic history of the virus, identified the most likely ancestral system (root state posterior probability = 0.95) and revealed significant dispersal routes (Bayes factor > 6) of viral exchange among systems. Results indicate that currently circulating viruses are evolving rapidly, and show a higher level of relative genetic diversity over time, when compared to earlier relatives. Biological soundness of model results is supported by the finding that sow farms were responsible for PRRSV spread within the systems. Such results cannot be obtained by traditional phylogenetic methods, and therefore, our results provide a methodological framework for molecular epidemiological modeling of new PRRSV outbreaks and demonstrate the prospects of phylodynamic models to inform decision-making processes for routine surveillance and, ultimately, to support prevention and control of food animal disease at local and regional scales.

Novel analytic tools for the study of porcine reproductive and respiratory syndrome virus (PRRSv) in endemic settings: lessons learned in the U.S

Since its emergence in the late 1980's, the porcine reproductive and respiratory syndrome virus (PRRSv) has posed a significant challenge to the pig industry worldwide. Since then, a number of epidemiological tools have been created to support control and eventual elimination of the disease at the farm and regional levels. Still, many aspects of the disease dynamics are yet-to-be elucidated, such as what are the economically optimal control strategies at the farm and regional level, what is the role that the voluntary regional control programs may play, how to optimize the use of molecular tools for surveillance and monitoring in infected settings, what is the full impact of the disease in a farm, or what is the relative contribution of alternative transmission routes on the occurrence of PRRSv outbreaks. Here, we summarize a number of projects demonstrating the use of novel analytical tools in the assessment of PRRSv epidemiology in the United States. Results presented demonstrate how quantitative analysis of routinely collected data may help in understanding regional epidemiology of PRRSv and to quantify its full impact, and how the integration of phylodynamic methods as a standard tool for molecular surveillance of PRRSv might help to inform control and prevention strategies in high-risk epidemiological situations. Ultimately, these tools will help to support PRRSv control at farm and regional levels in endemically infected settings.

Biannual birth pulses allow filoviruses to persist in bat populations

Filoviruses Ebolavirus (EBOV) and Marburgvirus (MARV) cause haemorrhagic fevers with high mortality rates, posing significant threats to public health. To understand transmission into human populations, filovirus dynamics within reservoir host populations must be understood. Studies have directly linked filoviruses to bats, but the mechanisms allowing viral persistence within bat populations are poorly understood. Theory suggests seasonal birthing may decrease the probability of pathogen persistence within populations, but data suggest MARV may persist within colonies of seasonally breeding Egyptian fruit bats, Rousettus aegyptiacus. I synthesize available filovirus and bat data in a stochastic compartmental model to explore fundamental questions relating to filovirus ecology: can filoviruses persist within isolated bat colonies; do critical community sizes exist; and how do host-pathogen relationships affect spillover transmission potential? Synchronous annual breeding and shorter incubation periods did not allow filovirus persistence, whereas bi-annual breeding and longer incubation periods, such as reported for Egyptian fruit bats and EBOV in experimental studies, allowed persistence in colony sizes often found in nature. Serological data support the findings, with bats from species with two annual birth pulses more likely to be seropositive (odds ratio (OR) 4.4, 95% confidence interval (CI) 2.5-8.7) than those with one, suggesting that biannual birthing is necessary for filovirus persistence.

Phylodynamics of H5N1 Highly Pathogenic Avian Influenza in Europe, 2005-2010: Potential for Molecular Surveillance of New Outbreaks

Previous Bayesian phylogeographic studies of H5N1 highly pathogenic avian influenza viruses (HPAIVs) explored the origin and spread of the epidemic from China into Russia, indicating that HPAIV circulated in Russia prior to its detection there in 2005. In this study, we extend this research to explore the evolution and spread of HPAIV within Europe during the 2005-2010 epidemic, using all available sequences of the hemagglutinin (HA) and neuraminidase (NA) gene regions that were collected in Europe and Russia during the outbreak. We use discrete-trait phylodynamic models within a Bayesian statistical framework to explore the evolution of HPAIV. Our results indicate that the genetic diversity and effective population size of HPAIV peaked between mid-2005 and early 2006, followed by drastic decline in 2007, which coincides with the end of the epidemic in Europe. Our results also suggest that domestic birds were the most likely source of the spread of the virus from Russia into Europe. Additionally, estimates of viral dispersal routes indicate that Russia, Romania, and Germany were key epicenters of these outbreaks. Our study quantifies the dynamics of a major European HPAIV pandemic and substantiates the ability of phylodynamic models to improve molecular surveillance of novel AIVs.

Deciphering the origins and tracking the evolution of cholera epidemics with whole-genome-based molecular epidemiology

The devastating Haitian cholera outbreak that began in October 2010 is the first known cholera epidemic in this island nation. Epidemiological and genomic data have provided strong evidence that United Nations security forces from Nepal introduced toxigenic Vibrio cholerae O1, the cause of epidemic cholera, to Haiti shortly before the outbreak arose. However, some have contended that indigenous V. cholerae contributed to the outbreak. In a recent paper (mBio4:e00398-13, 2013), L. S. Katz et al. explored the nature and rate of changes in this ancient pathogen’s genome during an outbreak, based on whole-genome sequencing of 23 Haitian V. cholerae clinical isolates obtained over a 20-month period. Notably, they detected point mutations, deletions, and inversions but found no insertion of horizontally transmitted DNA, arguing strongly against the idea that autochthonous V. cholerae donated DNA to the outbreak strain. Furthermore, they found that Haitian epidemic V. cholerae isolates were virtually untransformable. Comparative genomic analyses revealed that the Haitian isolates were nearly identical to isolates from Nepal and that the Nepalese-Haitian isolates were distinguishable from isolates circulating elsewhere in the world. Reconstruction of the phylogeny of the Haitian isolates was consistent with a single introduction of V. cholerae to Haiti sometime between late July and late October 2010, dates remarkably concordant with epidemiological observations. In aggregate, this paper provides additional compelling evidence that the V. cholerae strain responsible for the Haitian cholera epidemic came from Nepal and illustrates the power of whole-genome-based analyses for epidemiology, pathogen evolution, and forensics.

Molecular evolution in court: analysis of a large hepatitis C virus outbreak from an evolving source

BACKGROUND

Molecular phylogenetic analyses are used increasingly in the epidemiological investigation of outbreaks and transmission cases involving rapidly evolving RNA viruses. Here, we present the results of such an analysis that contributed to the conviction of an anesthetist as being responsible for the infection of 275 of his patients with hepatitis C virus.

RESULTS

We obtained sequences of the NS5B and E1-E2 regions in the viral genome for 322 patients suspected to have been infected by the doctor, and for 44 local, unrelated controls. The analysis of 4,184 cloned sequences of the E1-E2 region allowed us to exclude 47 patients from the outbreak. A subset of patients had known dates of infection. We used these data to calibrate a relaxed molecular clock and to determine a rough estimate of the time of infection for each patient. A similar analysis led to an estimate for the time of infection of the source. The date turned out to be 10 years before the detection of the outbreak. The number of patients infected was small at first, but it increased substantially in the months before the detection of the outbreak.

CONCLUSIONS

We have developed a procedure to integrate molecular phylogenetic reconstructions of rapidly evolving viral populations into a forensic setting adequate for molecular epidemiological analysis of outbreaks and transmission events. We applied this procedure to a large outbreak of hepatitis C virus caused by a single source and the results obtained played a key role in the trial that led to the conviction of the suspected source.