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Satellite meeting organised by Dr Oliver Pybus, Professor Christophe Fraser and Professor Andrew Rambaut
The dynamic interaction between genetically-variable infectious diseases and their hosts represents one of the most complex and intensively-studied phenomena in biology. This satellite meeting will provide a forum for discussion and exploration by researchers working on inter-disciplinary approaches in genomics, immunology, epidemiology and computing. We particularly encourage the participation of early-career researchers and those working on quantitative approaches.
Biographies of the organisers and speakers are available below. Audio recordings are freely available and the programme can be downloaded here.
This meeting was preceded by a related discussion meeting Next-generation molecular and evolutionary epidemiology of infectious disease 14 - 15 May 2012.
Dr Oliver Pybus, University of Oxford, UK
Dr Oliver Pybus is a Royal Society University Research Fellow at the Department of Zoology, University of Oxford, and Tutor for Biology at New College, Oxford. He is interested in understanding the evolutionary dynamics of pathogens, particularly viral infections of humans such as HIV, the Hepatitis C Virus, Influenza, and the flaviviruses. He hopes to explain how evolutionary and ecological processes - which occur on the same timescale for many pathogens - combine and interact in natural populations.
Professor Christophe Fraser, Imperial College London, UK
Christophe Fraser leads the Evolutionary Epidemiology group in the Department of Infectious Disease Epidemiology at Imperial College London. He first trained in theoretical particle physics in the 1990s, and converted to mathematical biology, training at Oxford University and then Imperial. He is supported by a Royal Society University Research Fellowship and has been Professor of Theoretical Epidemiology since 2009. The EvolutionaryEpidemiology group is interested in understanding the spread and evolution of major human pathogens, using a combination of mathematical and statistical modeling, and in working together with other biomedical scientists to provide a mechanistic and practical basis for this research. Current topics of major interest are: HIV virulence; HIV treatment as prevention; pneumococcal genomics; genomics in the clinic; antibiotic resistance. Christophe Fraser is also part of the MRC Centre for Outbreak Analysis and Modeling, and actively participated in the global response to SARS in 2003 and H1N1pdm in 2009.
Professor Andrew Rambaut University of Edinburgh UK
Andrew Rambaut is Professor of Molecular Evolution at the Institute of Evolutionary Biology, University of Edinburgh, where he also studied as an undergraduate. He received his DPhil from the University of Oxford in 1997. His research is centred on the molecular epidemiology and evolution of RNA viruses and the development of computational methods for understanding these.
Professor Christophe Fraser, Imperial College London, UKChair
Dr Xavier Didelot, University of Oxford, UKMicroevolution in Clostridium difficile genomes reveals limited hospital transmission
Xavier Didelot obtained his doctorate in Statistical Genetics from the University of Oxford in 2007. He spent three years as a research fellow at the University of Warwick before moving back to Oxford in 2010. He is best known as the author of ClonalFrame, a computer software that infers the relationships between a sample of bacteria while accounting for the disruptive effect of recombination. His primary research interests are in developing new methods that can be used to analyse whole microbial genome sequences. Such data is becoming increasingly available thanks to the advent of high-throughput sequencing technologies, and has great potential to lead to new insights into the evolution, ecology and epidemiology of many microbes.
The control of Clostridium difficile infection (CDI) is a major international healthcare priority, hindered by a limited understanding of transmission epidemiology for these bacteria. However, transmission studies of bacterial pathogens are rapidly being transformed by the advent of next generation sequencing. Here we sequence whole C.difficile genomes from 486 CDI cases arising over 4 years in Oxfordshire. We show that we can estimate the times back to common ancestors of bacterial lineages with sufficient resolution to distinguish whether direct transmission is plausible or not. Time depths were inferred using a within-host evolutionary rate that we estimated at 2.3 mutations per genome per year based on serially isolated genomes (with 95% credibility interval 1.6-3.0). The subset of plausible transmissions was found to be highly associated with pairs of patients sharing time and space in hospital. Conversely, the majority (81%) of pairs of genomes matched by conventional typing and isolated from patients within a month of each other were too distantly related to be direct transmissions. Our results suggest that nosocomial transmission between symptomatic CDI cases contributes far less to current rates of CDI acquisition than has been widely assumed, which clarifies the importance of future research into other transmission routes, for example from asymptomatic carriers. With the costs of DNA sequencing rapidly falling and its use becoming more and more widespread, genomics will revolutionize our understanding of the transmission of bacterial pathogens.
Dr Ed Feil, University of Bath, UKThe transmission and microevolution of MRSA as revealed by next-generation sequencing
Dr Feil’s interests lie in understanding the processes underpinning the evolution of bacterial pathogens over very short time scales. Much of his research career has been spent generating and interpreting multlocus sequence typing (MLST) data for numerous species. He developed the BURST algorithm to help visualise these datasets and this procedure was implemented by Brian Spratt’s group at Imperial College as eBURST.
More recently, he has also been working with the Pathogen Genomics Group at the Sanger Institute on the use of next-generation sequencing data to understand the micro-evolution and transmission of MRSA. This work was published in Science in 2010 (Vol.327 no. 5964 pp. 469-474). He is a PI in the CRCUK project, headed by Professor Sharon Peacock , looking at implementing this technology into routine epidemiological surveillance, in the FP7 Project TROCAR and in a consortium project funded by the insect pollinator initiative looking at the epidemiology of European Foul Brood in honeybees. Other interests include short-term purifying selection, mutation biases and the evolution of base composition.
The advent of next-generation sequencing technology is set to revolutionise our understanding of the intersection between transmission dynamics and micro-evolutionary processes in bacterial pathogens. Here I will review recent work in this area focussing on the methicillin resistant Staphylococcus aureus (MRSA). These studies illustrate the range of questions that can be addressed on differing epidemiological scales; from considering the global picture; down to a clustering on a national level; between hospitals within a single country; within a single hospital; and finally temporal divergence within a single host. I will consider key future questions relating to the extent to which key phenotypes (resistance / virulence) might be predicted from sequence data, and the relationships between patterns of mutation and recombination, the efficiency of purifying selection and epidemiological behaviour.
Dr Sarah Cobey, Harvard University, USAWhy is Asia ahead?
Sarah Cobey is a postdoctoral researcher at the Center for Communicable Disease Dynamics at the Harvard School of Public Health. She obtained her PhD in Ecology and Evolutionary Biology at the University of Michigan in 2009 and is currently a NIH Ruth Kirschstein NRSA fellow. Sarah is interested in how patterns of pathogen antigenic variation arise from the interplay of ecology, evolution, and immune-mediated competition. A major goal of her work is to anticipate how potential changes in host populations will affect pathogen dynamics. Sarah and her colleagues have used mathematical and statistical models to develop theories for the epidemiology and evolution of influenza viruses. More recently, she has investigated how immune responses modulate competition among serotypes of pneumococcus.
The most successful strains of influenza A (H3N2) tend to emerge from the tropics of East and Southeast Asia. In the absence of controlled trials, it is difficult to determine how the global ecology of influenza interacts with its antigenic evolution, and why some host populations might have a greater impact than others. I will review hypotheses of how influenza’s epidemiology could vary between regions. Using a toy model, I will show how each factor is expected to change the relative contributions of different host populations to the emergence of new variants. Predictions from this model will then be compared to measures of recent influenza evolution.
Dr Daniel Wilson, University of Oxford, UKModelling the growth and transmission of infectious disease by linking epidemiology and population genetics
Dr Daniel Wilson is an evolutionary biologist and researcher in statistical genetics working at the University of Oxford. His research centres on applications of population genetics, primarily to problems in pathogen biology and detecting evidence for adaptation in the genome. As part of the Modernising Medical Microbiology consortium, he is developing new ways to understand the transmission and evolution of pathogens such as Staphylococcus aureus and norovirus from whole genome sequencing data. Previously he has worked on the evolution and epidemiology of Campylobacter jejuni at Lancaster University and on methods to detect selection from patterns of polymorphism and divergence in the genome at the University of Chicago.
Understanding the transmission of infectious disease is important for monitoring outbreaks, informing public health policy, and improving intervention strategies. Traditionally the fields of population genetics and epidemiology have been studied separately; however it is clear that using genetic information alongside epidemiological models has great potential for understanding the dynamics of infectious disease. Directly estimating epidemiological parameters such as transmission rates can be difficult, as it relies on comprehensive monitoring during an outbreak where relevant processes may be hidden or undetectable. However, genetic information provides an alternative window into the past. I will talk about a combined coalescent-based meta-population model for estimating the parameters of standard SI, SIS and SIR epidemiological models from genetic data. I will apply these models to a meta-analysis of Hepatitis C virus (HCV), with the aim of explaining differences in patterns of genetic diversity between populations in terms of the underlying epidemiological dynamics. I will look at differences between datasets in the growth rate of HCV and whether they are explained by subtype, host population size or prevalence of disease to understand the factors that drive global variation in Hepatitis C diversity.
Professor Marc Suchard, University of California, Los Angeles, USAChair
Marc Suchard, MD, PhD (Biomathematics) is helping to develop the nascent field of evolutionary medicine. This field harnesses the power of methods and theory from evolutionary biology to advance our understanding of human disease processes. Just as phylogenetic approaches have stimulated the field of evolution at large, they posses the potential to revolutionize evolutionary medicine, particularly in the study of rapidly evolving pathogens. To bridge the gap between phylogenetics and human-pathogen biology, Dr Suchard's interests focus on the development of novel reconstruction methods drawing heavily on statistical, mathematical and computation techniques. Some of his current projects involve jointly estimating alignments and phylogenies from molecular sequence data and mapping recombination hot-spots in the HIV genome.
Dr Tanja Stadler, ETH Zurich, SwitzerlandThe birth-death skyline plot and beyond
Tanja Stadler is a junior group leader at ETH Zurich (Switzerland). Her research focuses on developing phylogenetic methodology for inferring past evolutionary processes based on sequence data. She uses these methods for improving our understanding of both viral epidemics as well as species evolution.
Tanja obtained her undergraduate degree in Applied Mathematics from the Technical University of Munich (Germany), did her Master thesis with Mike Steel at the University of Canterbury (New Zealand), and received a PhD in 2008 from the Technical University of Munich, supervised by Anusch Taraz and Mike Steel. She did her postdoc in Sebastian Bonhoeffer's group at ETH Zurich.
Beyond reconstruction of ancestral relationships, phylogenetic trees can be used to infer the processes that generated them. We will introduce the "birth-death skyline plot" that explicitly estimates the rate of transmission, recovery / death and sampling, and allows all of these parameters to vary through time in a piecewise fashion. This model is a powerful exploratory method for understanding the processes driving phylogenetic diversity in measurably evolving populations such as RNA viruses. Being implemented in the software package Beast, the birth-death skyline plot is a direct replacement for the Bayesian skyline plot on viral phylogenies and more accurately models the different roles of incidence and prevalence in determining the phylogenetic diversity of an epidemic. The method is applied to HIV-1 sequence data from the UK as well as to an HCV dataset from Egypt, revealing interesting temporal changes of the basic reproductive number.
I will furthermore give an outlook on how we employ the birth-death skyline plot as well as other birth-death-based approaches to properly account for classical epidemiological dynamics with finite-size host populations (SIR dynamics). I will in particular show that it is possible to estimate the size of the host population using viral phylogenies and apply this method to an HIV-1 dataset from Switzerland.
Dr Thibaut Jombart, Imperial College London, UKExploring the genomic diversity of pathogen populations: a multivariate approach
Dr Thibaut Jombart is a biometrician working on statistical genetics of pathogen populations. He is currently working as a research associate at the MRC Center for Outbreak Analysis and Modelling, Imperial College, London, with Neil Ferguson, Christophe Fraser and Simon Cauchemez.
His work aims to develop novel statistical approaches for extracting information from genomic data in general, and from pathogen genomes in particular. He is also interested in using simulations to understand which and how biological processes shape the genetic diversity observed in biological populations. His other interests include multivariate analysis, spatial statistics, and the phylogenetic comparative method. He is developing or contributing to a number of computer packages for the R software, including adegenet (population genetics/genomics), adephylo (analysis of phylogenetic signal), geoGraph (spatial analysis), ade4 (multivariate statistics), phylobase (phylogenetics), and sedaR (spatial statistics).
Genetic sequence data are becoming increasingly available for a range of pathogens at a variety of spatial and temporal scales. These data can be exploited to inform infectious disease epidemiology in various ways, from the reconstruction of the historical spread of a disease worldwide to the near real-time genetic monitoring of local outbreaks. Here, we show how recent developments in multivariate methods can be used to investigate the genetic diversity of possibly large pathogen sequence datasets. This approach can identify clusters of genetically related infections and describe the spatio-temporal dynamics of the genetic diversity of pathogen populations. It can also be used to reveal alleles which most discriminate groups of pathogens, which can for instance be employed to detect host-specific genetic features.
While useful for exploring pathogen genetic data at large scales, this approach may be less relevant at smaller scales where the overall genetic diversity remains relatively low. This is typically the case in disease outbreaks, where clear-cut genetic clusters might be difficult to identify, but where sequence data may still contain relevant information about transmission pathways. We show how a simple graph approach can be used for reconstructing transmission trees (“who infected whom”) in the case of densely sampled outbreaks. We conclude on how such approaches may be improved by integrating simultaneously genetic and epidemiological information for the reconstruction of disease outbreaks.
Professor Alexei Drummond, University of Auckland, New ZealandBayesian inference of epidemiological parameters using birth-death tree priors
Professor Drummond’s research interests are centered on probabilistic models of molecular evolution and population genetics, especially the application of coalescent theory to (i) estimate species trees and (ii) provide inferences about rapidly evolving viruses. Recently he has taken a renewed interest in the emerging field of phylogenetic epidemiology and is actively involved in the development of methods that enable estimation of fundamental epidemiology parameters directly from molecular sequence data. Professor Drummond is best known for two software packages: the open-source BEAST package for Bayesian phylogenetics (co-authored with Andrew Rambaut, Marc Suchard and many others) and the Geneious software package, developed by Biomatters Ltd (a company he is a Director and co-founder of). Alexei was recently awarded a 5-year Rutherford Discovery Fellowship by the Royal Society of New Zealand to develop a new version of the BEAST software package along with extensions to its evolutionary models.
A general piecewise-constant birth-death-sampling tree prior is described that acts as a kernel for the construction of a class of epidemiological tree priors that are parameterized by fundamental epidemiological parameters like R0, and the infectious interval. This class of priors enables Bayesian inference of epidemiological parameters directly from appropriately sampled molecular sequence data. I will review recent work on this family of tree priors and describe efforts to extend the family both in terms of the observational process (handling sampling heterogeneity) and in terms of the spatial dynamics (handling population structure via multiple demes). Examples of the method will be provided for Dengue-4 and HIV-1.
Dr Marijn van Ballegooigen, RIVM, The NetherlandsNext generation molecular epidemiology in public health settings
Marijn van Ballegooijen works as a mathematical modeler at the Dutch National Institute for Public Health and the Environment (RIVM). The goal of his work is to use molecular and epidemiological information of infectious disease to inform policy decisions. Molecular data of infectious disease, such as DNA sequences, are increasingly available, sometimes as part of routine surveillance, or resulting from outbreak investigation. He develops statistical methods, dynamic transmission models and phylogenetic techniques. Van Ballegooijen: “We want to understand what molecular epidemiological information can tell us about disease transmission.”
The focus of his works has been on hepatitis B and C; infectious diseases of significant public health impact for which detailed molecular and epidemiological data are available. Other areas of work are Borrelia and avian influenza. He obtained his PhD from the University of Amsterdam in 2006, working on ecology and evolution of infectious disease, with a focus on evolutionary dilemmas and spatial pattern formation.
The quantity and quality at which molecular data of infectious diseases is routinely collected in public health surveillance and outbreak investigations is rapidly increasing. This has naturally led to the question how these data can be used to inform policy makers. In this presentation I would like to present two recent studies that attempt to address this question.
The first case is the monitoring of a vaccination program against hepatitis B based on sequence data. Hepatitis B is caused by a sexually transmittable virus that can cause liver failure years after initial infection. The Netherlands introduced risk group vaccination against hepatitis B in 2002. Because initial infections are often asymptomatic, routine surveillance is sensitive to observation bias. In this case, however, surveillance and coalescent reconstructions of effective population size show a matching trend, suggesting the vaccination program is effective.
The second case is the reconstruction of an outbreak of avian influenza in poultry farms. Analysis of molecular sequences obtained from (nearly) all farms, combined with geographic and temporal information enables a probabilistic reconstruction of individual farm to farm transmissions. This detailed reconstruction of the transmission tree makes it possible to estimate the relative transmission risk of different farm types and even enables the estimation of the role of wind in farm to farm transmission. This information makes it possible to design better intervention strategies.
The current state of molecular data collected for public health typically copes with missing data, biased sampling and small scale outbreaks. Scientific methods that can adequately deal with these shortcomings may offer the best opportunities for public health settings.
Professor Andrew Rambaut University of Edinburgh UKChair
Dr Roman Biek, University of Glasgow, UKCombining whole genome sequencing and network models to understand the epidemiology of bovine TB in the UK
Roman Biek currently holds a lectureship position at the University of Glasgow. His research aims to understand how infectious organisms spread and persist in animal populations, and how the ecological and evolutionary dynamics of pathogens are linked to those of their hosts and the physical environment. This research program is primarily pursued through the use of molecular markers and genetic inference, combined with field based investigations and epidemiological models. Apart from addressing basic questions in disease ecology and evolution, he is interested in providing solutions to applied problems in animal and human health. Such problems commonly arise since many of the pathogens he currently studies are shared between wildlife and domestic species or are transmissible to humans. While the majority of his work has been on RNA viruses (e.g. rabies, Ebola, oncogenic retroviruses in sheep, FIV, bluetongue), recent projects are also considering more slowly evolving pathogens (Mycobacterium bovis, Borrelia), facilitated by sequencing technologies that now permit studying molecular epidemiology based on full bacterial genomes.
Quantifying transmission dynamics of pathogens infecting multiple host species can pose significant research challenges, especially when the sampling process is biased towards certain types of host. This is exemplified by Mycobacterium bovis, the bacterium causing bovine TB (bTB) in cattle. In the UK, badgers are considered an important wildlife reservoir for bTB, which is thought to prevent the successful eradication of the disease from cattle. However, despite considerable research effort, the epidemiological role badgers play in maintaining and spreading bTB to cattle is still poorly understood. Here, we show how whole genome sequencing (WGS) technology can be combined with high-resolution data on contact networks of cattle to shed new light onto this problem. Focussing on a small cluster of infected cattle and badger samples from Northern Ireland, we provide the first direct genetic evidence of M bovis persistence on farms over multiple outbreaks with a continued, ongoing interaction with local badgers. In addition to providing novel insights into bTB epidemiology, even at extremely local scales, our study suggests that WGS based on more extensive sampling will allow quantification of the extent and direction of M bovis transmission between cattle and badgers, especially in situations where detailed demographic and contact data for cattle are also available.
Dr Rebecca Gray, University of Oxford, UKIncorporating geographic information systems data into phylogenetic analysis
Rebecca received her PhD in Molecular Anthropology from the University of Florida in 2008 and completed a post-doctoral fellowship in the UF College of Medicine in 2011. She is currently an MRC Research Fellow in the Department of Zoology at the University of Oxford working with Oliver Pybus on molecular evolution of diseases.
As a molecular anthropologist, Rebecca’s broad interest focuses on the complexity of interactions between humans and infectious diseases. Her research spans multiple levels of analysis, including within-host evolution of HIV and Hep-C, to behavior of pathogens in global epidemics. She is interested in using tools of molecular evolution and GIS to uncover spatial patterns that can lead to greater understanding of pathogen behaviour.
Geographic information systems data (GIS) has been a valuable tool to correlate the spread of infectious diseases with environmental variables. Independently, molecular epidemiology relies upon pathogen genetic mutations that segregate in space and time, which are used in increasingly sophisticated evolutionary models to infer migration paths, rates, and population demography. Clearly a comprehensive approach that incorporates both GIS and evolutionary analyses would allow for rigorous hypothesis testing and greater understanding of the forces governing disease movements. I willdiscuss the advantages of using GIS in molecular epidemiological studies aswell as some of the current computational and theoretical challenges. I will present some recent work on West Nile Virus and rabies virus in which we have used information gained from the phylogeny on migration patterns within thecontext of GIS.
Dr Trevor Bedford, University of Edinburgh, UKAntigenic flux in the influenza virus population
Dr Trevor Bedford is a Newton International Fellow at the University of Edinburgh working in the field of evolutionary dynamics. Previously, Dr Bedford studied population genetics at Harvard University and disease dynamics at the University of Michigan. His work integrates population genetics, phylogenetics and epidemiological modeling to understand patterns of genetic and antigenic evolution in the human influenza virus. Additionally, he has studied the geographic circulation of virus populations. This work has a strong statistical and computational basis, using sequence data to arrive at an understanding of hidden underlying processes. Such an understanding of evolutionary and epidemiological processes contributes to successful surveillance and control strategies.
Owing to rapid mutation, the evolution of the influenza virus occurs on a human timescale; rather than being forced to infer past evolutionary events, we can observe them in near real-time. While individuals develop long-lasting immunity to particular influenza strains after infection, antigenic mutations to the influenza virus genome result in proteins that are recognized to a lesser degree by the human immune system, leaving individuals susceptible to future infection. Mutations are only transiently advantageous; the virus population must keep evolving antigenically to stay ahead of developing human immunity. This talk focuses the process of antigenic innovation and the spread of novel strains through the human population. In this case, we have serological data from the hemagglutination inhibition (HI) assay comparing the level of cross-reactivity between different strains of influenza, as well as sequence data across strains. Here, we use a probabilistic framework called Bayesian multidimensional scaling (BMDS) to find a single consistent representation of antigenic distances between viruses by placing strains on a two-dimensional map. We integrate sequence evolution by treating BMDS location as a continuous diffusion across the phylogenetic tree. In this context, we examine the process of antigenic drift and investigate historical choices in vaccine strain by the World Health Organization.
Dr Katrina Lythgoe, Imperial College London, UKMultiscale evolutionary dynamics of HIV
Dr Katrina Lythgoe is interested in applying ecological and evolutionary theory to better predict the evolutionary dynamics of infectious disease in humans and other species, with the ultimate aim of informing public health decisions. Her current research is focused on the within- and between-host evolution of HIV and in particular on the consequences of population structure on the evolutionary dynamics of the virus. She is a member of the Evolutionary Epidemiology Group within the Department of Infectious Disease Epidemiology, Imperial College London and currently holds a Wellcome Trust Re-Entry Fellowship. Before joining the group at Imperial, Dr Lythgoe was the Editor of Trends in Ecology of Evolution (TREE) for seven years.
Through the use of next-generation sequencing, evidence is growing that ancestral HIV-1 genotypes (i.e. the viral genotypes observed during early infection) are, at least sometimes, preferentially transmitted over the majority virus circulating in a donor at the time of transmission. This ancestral virus probably persists at a low frequency within hosts due to the cycling of virus through very long-lived memory CD4+ T-Cells, a process that we call ‘store and retrieve’. We show how incorporating the store and retrieve process into our models can help explain two puzzling phenomena: (1) the fact that HIV-1 appears to evolve much faster within individuals than it does at the epidemic level and (2) the low levels of resistance found in developed countries despite the widespread use of antiretroviral drugs. The preferential transmission of ancestral virus needs to be properly integrated into evolutionary models if we are to accurately predict the evolution of immune escape, drug resistance and virulence in HIV-1 at the population level. Moreover, early infection viruses should be the major target for vaccine design, since these are the viral strains primarily involved in transmission.
Dr Oliver Pybus, University of Oxford, UKChair
Professor Claus Wilke, University of Texas, USAIntegrating sequence variation and protein structure to identify sites under positive or negative selection
Claus O Wilke is an Associate Professor in the Section of Integrative Biology at The University of Texas at Austin. He is also a member of the Center for Computational Biology and Bioinformatics and the Institute for Cell and Molecular Biology at The University of Texas at Austin. Claus O Wilke received his PhD in theoretical physics from the Ruhr-University Bochum, Germany, in 1999. From 2000 to 2004, he was a postdoc at the California Institute of Technology, working on viral evolution and artificial life. After his postdoc, he spent a year as a Research Assistant Professor at the Keck Graduate Institute of Applied Life Sciences, Claremont, before joining The University of Texas in the fall of 2005. His current research interests are in molecular evolution, structural biology, and biostatistics.
Claus O Wilke is the author of approximately 100 scientific papers. He serves as Associate Editor for PLoS Computational Biology and PLoS Pathogens, and as Section Editor for BMC Evolutionary Biology. In 2011, Wilke was recognized as a Leading Texas Innovator by The Academy of Medicine, Engineering, and Science of Texas.
We present a novel method to identify sites under positive or negative selection in protein-coding genes. Our method combines a traditional Goldman-Yang model of coding-sequence evolution with information obtained from the 3d structure of the evolving protein, specifically the relative solvent accessibility (RSA) of individual residues. We allow individual sites to fall into different evolutionary-rate classes, and we model the RSA-dependence of rate classes via linear functions. We demonstrate that our RSA-dependent model provides a significantly better fit to molecular sequence data than a traditional, RSA-independent model. We further show that our model provides a natural, RSA-dependent neutral baseline for the evolutionary rate ratio omega=dN/dS, and that sites that deviate from this neutral baseline can be considered to be positively or negatively selected. We apply our method to the influenza proteins haemagglutinin and neuraminidase. For haemagglutinin, our method recovers positively selected sites in known antibody binding regions or near the sialic-acid binding site. For neuraminidase, which has no sites with omega>1, our method recovers positively selected sites involved with tamiflu resistance and negatively selected sites that participate in important stabilizing hydrogen bonds.
Dr Samuel Alizon, CNRS, FranceWithin-host and between-host evolutionary rates across the HIV-1 genome
Dr Samuel Alizon is tenure full-time researcher (Chargé de Recherche) at the CNRS since 2010, working in the laboratory Infectious diseases and vectors:ecology, genetics, evolution and control (MIVEGEC) in Montpellier France. Previous to this, he was a Coleman post-doctoral fellow at Queen's University in Kingston, Ontario (2006-2008) and an ETH fellow in Zürich (2008-2010).
He trained in ecology and evolution (PhD Paris 6 University in 2006) with a strong focus on host-parasite interactions.
He is especially interested in virulence evolution, within-host evolution and in linking pathogen genetic data to clinical data.
HIV evolves rapidly over the course of an infection due to its short generation times and to the selective pressure exerted by the host’s immune response. The virus is therefore is subject to multi-level selective pressures: at the within-host level, natural selection favours virus strains that grow rapidly inside the host, whereas at the between-host level it favours strains that spread rapidly in the host population. HIV within-host evolutionary rates have been suggested to be approximately 10 times higher than its between-host evolutionary rates. However, this conclusion is based on few analyses of a short portion of the virus envelope gene and it has been shown for instance for HCV that the a difference in evolutionary rates can be restricted to small genomic region. Here, we study in detail these evolutionary rates across the HIV genome using longitudinal data collected in two hosts, one of which is a long-term non-progressor. Our results provide the first large-scale overview of the differences in the HIV rates of molecular evolution at the within- and between-host levels. This work has implications for the understanding of the role of the transmission bottleneck in the evolutionary dynamics of HIV.
Coauthor: Christophe Fraser
Dr James Lloyd-Smith, University of California, Los Angeles, USAToward realistic models for the evolutionary emergence of novel pathogens
James Lloyd-Smith earned his PhD in Biophysics from the University of California, Berkeley in 2005 for his study of disease transmission dynamics in heterogeneous populations, and conducted postdoctoral studies at the Center for Infectious Disease Dynamics at the Pennsylvania State University. In 2009 he joined the faculty in Ecology and Evolutionary Biology at University of California, Los Angeles where he holds the De Logi Chair in Biological Sciences. Dr. Lloyd-Smith investigates the ecological and evolutionary dynamics of emerging infections, with emphasis on zoonotic pathogens and the dynamics of cross-species disease transmission. Current projects focus on integrating diverse data streams to understand the spread of zoonoses including monkeypox and leptospirosis, and development of new theoretical approaches to study evolutionary emergence of pathogens.
Over the past decade, a nascent body of theory has explored the process by which novel pathogen strains can emerge by evolutionary adaptation in response to new environments (such as new host species). These models have clarified basic principles, but their depiction of pathogen evolution has been simplistic and there has been almost no connection to empirical research. In this talk I will present several new models aiming to address these shortcomings. First I will show how consideration of more complex genotype spaces, motivated by empirical research, can overturn the standard finding that higher mutation rates lead to greater probability of emergence. Next I will introduce a cross-scale model for pathogen emergence, which accounts for selection acting at within-host and population scales, and show how cross-scale conflicts in selection can prevent emergence of a nearby, fitter genotype. Finally, time permitting, I will present a phylodynamic analysis of the emergence of transmissible defective dengue viruses in Southeast Asia in 2001, and discuss common principles and lessons for pathogen emergence research in general.
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