A rapid increase in the amount of available pathogen genetic data, which is ongoing and likely to continue for the foreseeable future, presents new opportunities and challenges in molecular epidemiology, and in the emerging field of “phylodynamics”, which seeks to unify the study of the evolutionary and epidemiological dynamics of pathogen populations. This thesis explores some of these challenges and opportunities, with a focus on pathogens infecting livestock and poultry. I conducted analyses of sequences from two serotypes of foot-and-mouth-disease virus (FMDV) in order to investigate the global population dynamics of the virus. For serotype SAT 2, the amount of publicly available genomic data is still small enough that all of it could be included in a single analysis. A particular focus was the origins of historical outbreaks occurring in North Africa and the Middle East, outside the endemic area for the serotype. The results suggested sources for these in countries just south of the Sahara, and that the viruses responsible for three outbreaks occurring in 2012 were the result of separate introductions. For serotype O, including every available sequence was not feasible and the data had to be sub-sampled. Little research has been conducted on how to design a sampling strategy for sequence analysis of pathogens, an issue of increasing importance, so a simulation study was conducted to identify one. This suggested that, when reconstructing the temporal and spatial dynamics of a structured population of pathogens or infected individuals, it is preferable to stratify by subpopulation and by time period. The type O analysis itself showed that the south-east Asian topotype moves between countries according to cattle trade networks, but that geographic proximity is also important for strains from southern Asia and the Middle East. With genetic data available at an epidemiological resolution that was previously inconceivable, there are opportunities for new types of inference. For example, if we can acquire a sequence from all or most infected cases in an epidemic, they can inform inference of who infected who, complementing traditional contact-tracing approaches. I introduce a novel phylodynamic method for the simultaneous reconstruction of phylogeny and transmission tree for an epidemic in a situation where every infected host or premises can be identified and a sequence acquired from most of them. The performance of this method was demonstrated using simulated data, and then it was applied to reconstruct both trees from the 2003 H7N7 avian influenza outbreak in the Netherlands.

Influenza A virus (AIV) has an extremely high rate of mutation. Frequent exchanges of gene segments between different AIV (reassortment) have been responsible for major pandemics in recent human history. The presence of a wild bird reservoir maintains the threat of incursion of AIV into domestic birds, humans and other animals. In this thesis, I addressed unanswered questions of how diverse AIV subtypes (classified according to antigenicity of the two surface proteins, haemagglutinin and neuraminidase) evolve and interact among different bird populations in different parts of the world, using Bayesian phylogenetic methods with large datasets of full genome sequences. Firstly, I explored the reassortment patterns of AIV internal segments among different subtypes by quantifying evolutionary parameters including reassortment rate, evolutionary rate and selective constraint in time-resolved Bayesian tree phylogenies. A major conclusion was that reassortment rate is negatively associated with selective constraint and that infection of wild rather than domestic birds was associated with a higher reassortment rate. Secondly, I described the spatial transmission pattern of AIV in China. Clustering of related viruses in particular geographic areas and economic zones was identified from the viral phylogeographic diffusion networks. The results indicated that Central China and the Pearl River Delta are two main sources of viral out flow; while the East Coast, especially the Yangtze River delta, is the major recipient area. Simultaneously, by applying a general linear model, the predictors that have the strongest impact on viral spatial diffusion were identified, including economic (agricultural) activity, climate, and ecology. Thirdly, I determined the genetic and phylogeographic origin of a recent H7N3 highly pathogenic avian influenza outbreak in Mexico. Location, subtype, avian host species and pathogenicity were modelled as discrete traits and jointly analysed using all eight viral gene segments. The results indicated that the outbreak AIV is a novel reassortant carried by wild waterfowl from different migration flyways in North America during the time period studied. Importantly, I concluded that Mexico, and Central America in general, might be a potential hotspot for AIV reassortment events, a possibility which to date has not attracted widespread attention. Overall, the work carried out in this thesis described the evolutionary dynamics of AIV from which important conclusions regarding its epidemiological impact in both Eurasia and North America can be drawn.

Submitted by Ana Maria Fiscina Sampaio (fiscina@bahia.fiocruz.br) on 2012-07-23T21:39:24Z No. of bitstreams: 1 Luciane Amorim Santos Uso de ferramentas de bioinformática....pdf: 5329323 bytes, checksum: b477ef604e538000e5f8d84e1188ae91 (MD5) / Made available in DSpace on 2012-07-23T21:39:24Z (GMT). No. of bitstreams: 1 Luciane Amorim Santos Uso de ferramentas de bioinformática....pdf: 5329323 bytes, checksum: b477ef604e538000e5f8d84e1188ae91 (MD5) Previous issue date: 2010 / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, Bahia, Brasil / As ferramentas de bioinformática tem sido amplamente utilizada para o melhor entendimento de diversos microorganismos. Neste trabalho foram realizados três estudos utilizando estas ferramentas para avaliar diferentes questões biológicas. No primeiro estudo realizou-se uma caracterização molecular de 57 sequências do gene pol, provenientes de pacientes infectados pelo HIV-1 de Salvador, Bahia, Brasil. Para identificar os subtipos e formas recombinantes do HIV-1 circulante na cidade de Salvador foi realizado análises filogenéticas, e através do algoritmo do banco de dados Stanford HIV resistance as mutações associadas à resistência aos ARVs foram detectadas. Entre as 57 sequências analisadas foram identificados neste estudo 45 (77,2%) pertencem ao subtipo B, 11 (21,0%) recombinantes BF e uma (1,8%) do subtipo F1. Além disto, uma alta frequência de eventos de recombinação entre os subtipos B e F foram detectados com 5 padrões de recombinação, duas intergênicas e três intragênicas, mostrando uma alta diversidade. As mutações encontradas com uma maior prevalência foram: I54V (PI) em 7,0%; M184V (NRTI) em 14,0% e K103N (NNRTI) em 10,5% das sequências analisadas. Estes resultados contribuem para traçar o perfil da epidemiologia molecular e diversidade do HIV-1 em Salvador. O segundo estudo avaliou a filodinâmica do HIV-1 em pares de mãe e filho infectados, e em diferentes fases da infecção, três pares na fase aguda e um na fase crônica, e que apresentavam sequências de diferentes tempos. Para este fim foi realizado inferências filogenéticas bayesianas, onde a hipótese do relógio molecular e de diferentes crescimentos populacional foram testadas. Não foi possível observar uma diferença entre a dinâmica da população viral da mãe e a encontrada no filho. Porém, quando observamos o crescimento populacional e o tamanho da população efetiva, ao longo do tempo, sequências provenientes de pares em fase crônica da infecção tem um crescimento mais constante, enquanto as sequências dos pares na fase aguda da infecção se observa uma dinâmica das populações virais, provavelmente devido à pressão do sistema imune e a não adaptação destes vírus. No terceiro estudo, 104 sequências do genoma completo do WNV, disponíveis no Genebank, foram estudadas para identificar a região genômica que apresenta máximo poder interpretativo para inferir relações temporais e geográficas entre as cepas do vírus. Alinhamentos de cada gene foram submetidos à avaliação do sinal filogenético através do programa TREEPUZZEL. As regiões NS3 e NS4 apresentaram um sinal filogenético acima de 70%, sendo as regiões mais indicadas para construção filogenética. Além disto, árvores bayesianas foram inferidas utilizando as regiões NS3, NS5 e E, onde os clados das árvores NS3 e NS5 apresentaram um maior suporte e estrutural temporal geográfica, diferente da região E. Estes achados mostram que os genes NS3 e NS5 são os mais indicados para análises filogenéticas. Neste trabalho foi demonstrando o uso de ferramentas de bioinformática para a melhor caracterização da diversidade, epidemiologia molecular, dinâmica populacional e determinação das relações temporal e geográfica dos vírus. / The bioinformatics tools have been widely used for better understanding of several microorganisms. Here three studies were performed using these tools to answer different biological questions. In the first study, it was conducted the molecular characterization of 57 HIV-1 pol gene sequences from infected patients from Salvador, Bahia, Brazil. To identify the HIV-1 subtypes and recombinants forms, phylogenetics analyses were performed and the Stanford HIV resistance Database were used to analyze the antiretroviral susceptibility. Among all analyzed sequences, 45 of them were (77.2%) subtype B, 11 (21.0%) were BF recombinant and one sequence was (1.8%) subtype F1. Furthermore, a high frequency of recombination events between subtypes B and F was detected with five different patterns: two intergenic and three intragenic. The mutations found with higher prevalence were: I54V (PI) in 7.0%; M184V (NRTI) in 14.0% and K103N (NNRTI) in 10.5% of the analyzed sequences. These results contribute for the knowledge of the molecular epidemiology and diversity of HIV-1 in Salvador. The second study have evaluated the HIV-1 phylodynamics in mother and child infected pairs in different stages of infections: three pairs acutely infected and one chronically infected. Phylogenetic inference was performed using the Bayesian framework were the molecular clock and different population growth models hypothesis were tested. We did not find any difference of the population dynamics between mother and child. However, when observing the population growth and the effective population size through time, the chronically infected pair sequences showed a constant growth, while the acutely infected pair sequences showed a more dynamic population growth, probably due to the immune system selective pressure. In the third study, 104 WNV full genome sequences were selected from Genbank, to identify the best genomic region, which could provide the maximal interpretative power to infer temporal and geographic relationships among the virus strains. The phylogenetic signal was evaluated using the TREEPUZZEL program. The results showed that the NS3 and NS5 regions are the best ones to infer phylogeny since their phylogenetic signal was higher than 70%. Furthermore, Bayesian trees were constructed using the NS3, NS5 and E regions, and the NS3 and NS5 tree clades showed a higher support and a temporal geographic structure, different from the E region. These findings show that the NS3 and NS5 genes are the most informative genes for phylogenetic analyses. These studies demonstrated the use of bioinformatics tools for the better characterization of the virus diversity, molecular epidemiology, and population dynamics.

Viral sequence data are increasingly being used to estimate evolutionary and epidemiological parameters to understand the dynamics of viral diseases. This thesis focuses on developing novel and improved computational methods for high-throughput analysis of large viral sequence datasets. I have developed a novel computational pipeline, Pipelign, to detect potentially unrelated sequences from groups of viral sequences during sequence alignment. Pipelign detected a large number of unrelated and mis-annotated sequences from several viral sequence datasets collected from GenBank. I subsequently developed ANVIL, a machine learning-based recombination detection and subtyping framework for pathogen sequences. ANVIL's performance was benchmarked using two large HIV datasets collected from the Los Alamos HIV Sequence Database and the UK HIV Drug Resistance Database, as well as on simulated data. Finally, I present a computational pipeline named Phlow, for rapid phylodynamic inference of heterochronous pathogen sequence data. Phlow is implemented with specialised and published analysis tools to infer important phylodynamic parameters from large datasets. Phlow was run with three empirical viral datasets and their outputs were compared with published results. These results show that Phlow is suitable for high-throughput exploratory phylodynamic analysis of large viral datasets. When combined, these three novel computational tools offer a comprehensive system for large scale viral sequence analysis addressing three important aspects: 1) establishing accurate evolutionary history, 2) recombination detection and subtyping, and 3) inferring phylodynamic history from heterochronous sequence datasets.

Viral sequence data has great potential for answering questions about the epidemiological dynamics and evolution of viruses. Classical approaches have sought amino acid changes that alter pathogenesis or transmissibility by influencing a virus's ability to enter or replicate within cells. However, this approach rarely recognises the fundamental impact of heterogeneous host contact structures and existing immunological responses on viral transmission. This thesis draws heavily on ecological and immunological concepts to explore the epidemiological dynamics, diversity and evolution of viruses using molecular sequence data. A number of different research approaches and study systems are used in this thesis. I begin by describing a novel polyomavirus in a European badger, and apply phylogenetic techniques to analyze the evolutionary history of the Polyomaviridae. I subsequently describe a large metaviromic study in a population of wild mute swans, for which host demographic data are available. I describe nine new viral species and test whether age and season are associated with differences in abundance and prevalence of different viral taxonomic groups. The study highlights the potential of metaviromics for investigating viral epidemiological dynamics in natural populations. Influenza A viruses of avian origin (AIV) threaten human and animal health. Using phylogeographic methods, I reconstruct the spatial spread of an H5N8 virus at a regional scale, and investigate how bird density and migration shaped this dispersal. Despite the importance of acquisition of humoral immunity to different strains throughout the lifespan of wild birds for epidemiological dynamics, this topic is poorly understood. I assess the accumulation of immune responses to AIV with age in mute swans. I consider how ecological factors, including age-structured immunity, might have affected the epidemiology of an H5N8 outbreak in the population.

<p>In this dissertation, I present a general statistical framework for phylodynamic inference that can be used to estimate epidemiological parameters and reconstruct disease dynamics from pathogen genealogies. This framework can be used to fit a broad class of epidemiological models, including nonlinear stochastic models, to genealogies by relating the population dynamics of a pathogen to its genealogy using coalescent theory. By combining Markov chain Monte Carlo and particle filtering methods, efficient Bayesian inference of all parameters and unobserved latent variables is possible even when analytical likelihood expressions are not available under the epidemiological model. Through extensive simulations, I show that this method can be used to reliably estimate epidemiological parameters of interest as well as reconstruct past disease dynamics from genealogies, or jointly from genealogies and other common sources of epidemiological data like time series. I then extend this basic framework to include different types of host population structure, including models with spatial structure, multiple-hosts or vectors, and different stages of infection. The later is demonstrated by using a multistage model of HIV infection to estimate stage-specific transmission rates and incidence from HIV sequence data collected in Detroit, Michigan. Finally, to demonstrate how the approach can be used more generally, I consider the case of dengue virus in southern Vietnam. I show how earlier phylodynamic inference methods fail to reliably reconstruct the dynamics of dengue observed in hospitalization data, but by deriving coalescent models that take into consideration ecological complexities like seasonality, vector dynamics and spatial structure, accurate dynamics can be reconstructed from genealogies. In sum, by extending phylodynamics to include more ecologically realistic and mechanistic models, this framework can provide more accurate estimates and give deeper insight into the processes driving infectious disease dynamics.</p> / Dissertation

One of the primary objectives of infectious disease research is uncovering the direct link that exists between viral population dynamics and molecular evolution. For RNA viruses in particular, evolution occurs at such a rapid pace that epidemiological processes become ingrained into gene sequences. Conceptually, this link is easy to make: as RNA viruses spread throughout a population, they evolve with each new host infection. However, developing a quantitative understanding of this connection is difficult. Thus, the emerging discipline of phylodynamics is centered on reconciling epidemiology and phylogenetics using genetic analysis. Here, we present two research studies that draw on phylodynamic principles in order to characterize the progression and evolution of the Ebola virus and the human immunodefficiency virus (HIV). In the first study, the interplay between selection and epistasis in the Ebola virus genome is elucidated through the ancestral reconstruction of a critical region in the Ebola virus glycoprotein. Hence, we provide a novel mechanistic account of the structural changes that led up to the 2014 Ebola virus outbreak. The second study applies an approximate Bayesian computation (ABC) approach to the inference of epidemiological parameters. First, we demonstrate the accuracy of this approach with simulated data. Then, we infer the dynamics of the Swiss HIV-1 epidemic, illustrating the applicability of this statistical method to the public health sector. Altogether, this thesis unravels some of the complex dynamics that shape epidemic progression, and provides potential avenues for facilitating viral surveillance efforts.

No description available.

During human immunodeficiency virus 1 (HIV-1) infection, the viral population is in constant battle with the host immune system. The cytotoxic T-lymphocyte (CTL) response, a branch of the adaptive immune response, is implicated in viral control and can drive viral evolution in the infected host population. Endogenous viral peptides, or ‘epitopes’, are presented to CTLs by human leukocyte antigen (HLA) class I molecules on the surface of infected cells where they may be identified as non-self. Mutations in or proximal to a viral epitope can result in ‘escape’ from CTLs targeting that epitope. The repertoire of epitopes which may be presented is dependent upon host class I HLA types. As such, reversion may occur after transmission due to changes in viral fitness and selection in the context of a new HLA background. Thus, parameters describing the dynamics of CTL escape and reversion are key to understanding how CTL responses within individuals relate to HIV-1 sequence evolution in the infected host population. Escape and reversion can be studied directly using biological assays and longitudinal viral sequence data, or indirectly by considering viral sequences across multiple hosts. Indirect approaches include tree based methods which detect associations between host HLA and viral sequence but do not estimate rates of escape and reversion, and ordinary differential equation (ODE) models which estimate these rates but do not consider the dependency structure inherent in viral sequence data. We introduce two models which estimate escape and reversion rates whilst accounting for the shared ancestry of viral sequence data. For our first model, we lay out an integrated Bayesian approach which combines genealogical inference and an existing epidemiological model to inform escape and reversion rate estimates. Using this model, we find evidence for correlation between escape rate estimates across widely separated geographical regions. We also observe a non-linear negative correlation between in vitro replicative capacity and escape rate. Both findings suggest that epistasis does not play a strong role in the escape process. Although our first model worked well, it had some key limitations which we address in our second method. Notably, by making a series of approximations, we are able account for recombination and analyse very large datasets which would be computationally infeasible under the first model. We verify our second approach through extensive simulations, and use the method to estimate both drug and HLA associated selection along portions of the HIV-1 genome. We test the results of the model using existing knowledge, and determine a collection of putative selected sites which warrant further investigation. Finally, we find evidence to support the notion that the CTL response played a role in HIV-1 subtype diversification.