Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
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Abstract Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. We here introduce a novel coalescent based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. Together, these provide the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.Keywords:
Reassortment
Viral phylodynamics
Coalescent theory
Multipartite
Rotaviruses of the species A (RVA) are of particular epidemiological importance and recognized as being worldwide dispersed. RVA causes the majority of infections in humans, as well as are highly prevalent among domestic animals. Several studies have shown that zoonotic transmission may occur from animals to humans and this may account for the current broad antigenic and genetic rotavirus diversity, representing a potential important evolutionary mechanism. The present study aims to identify the evolutionary origins of circulating RVA strains in the Amazon region, using phylodynamics methods to assess and understand the relationship between hosts, inter-specific gene transfer and the chronology of viral infection. In total 83 RVA-positive faecal samples of human origin were analyzed, all of which obtained within the official Rotavirus Epidemiological Surveillance Network in Brazil. The genome of 17 RVA samples were selected and sequenced. Eight RVA strains of zoonotic origins were identified. Of the five G4P[6] genotype samples were shown to be of porcine origin, and two samples were generated from reassortment events involving genotypes of human and porcine strains. One sample of G3P[3] genotype strain, identify as lineage III, have evolved from canine, feline or simian origin, including a reassortment event with RVA strains from lineage II. In addition, G12P[9] strains had their origins in chiroptera, cattle or felines. The present study included phylodynamic analyses in order to elucidate those otherwise unknown evolutionary patterns, mainly in regards to G3P[3] and G12P[9] genotypes. There was a particular focus on the occurrence of reassortment events and the evolutionary mechanism which underlie the emergence of these strains. These evolutionary events may help in the monitoring of emergent strains of RVA with zoonotic potential and vaccine-escape possibility.
Reassortment
Viral phylodynamics
Lineage (genetic)
Molecular Epidemiology
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The structured coalescent allows inferring migration patterns between viral subpopulations from genetic sequence data. However, these analyses typically assume that no genetic recombination process impacted the sequence evolution of pathogens. For segmented viruses, such as influenza, that can undergo reassortment this assumption is broken. Reassortment reshuffles the segments of different parent lineages upon a coinfection event, which means that the shared history of viruses has to be represented by a network instead of a tree. Therefore, full genome analyses of such viruses are complex or even impossible. Although this problem has been addressed for unstructured populations, it is still impossible to account for population structure, such as induced by different host populations, whereas also accounting for reassortment. We address this by extending the structured coalescent to account for reassortment and present a framework for investigating possible ties between reassortment and migration (host jump) events. This method can accurately estimate subpopulation dependent effective populations sizes, reassortment, and migration rates from simulated data. Additionally, we apply the new model to avian influenza A/H5N1 sequences, sampled from two avian host types, Anseriformes and Galliformes. We contrast our results with a structured coalescent without reassortment inference, which assumes independently evolving segments. This reveals that taking into account segment reassortment and using sequencing data from several viral segments for joint phylodynamic inference leads to different estimates for effective population sizes, migration, and clock rates. This new model is implemented as the Structured Coalescent with Reassortment package for BEAST 2.5 and is available at https://github.com/jugne/SCORE.
Reassortment
Coalescent theory
Viral phylodynamics
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Abstract Rift Valley Fever virus (RVFV) is a member of Bunyaviridae family that causes a febrile disease affecting mainly ruminants and occasionally humans in Africa, with symptoms that range from mid to severe. RVFV has a tri-segmented ssRNA genome that permits reassortment and could generate more virulent strains. In this study, we reveal the importance of reassortment for RVFV evolution using viral gene genealogy inference and phylodynamics. We uncovered seven events of reassortment that originated RVFV lineages with discordant origins among segments. Moreover, we also found that despite similar selection regimens, the three segments have distinct evolutionary dynamics; the longer segment L evolves at a significant lower rate. Episodes of discordance between population size estimates per segment also coincided with reassortment dating. Our results show that RVFV segments are decoupled enough to have distinct demographic histories and to evolve under different molecular rates.
Reassortment
Viral phylodynamics
Phlebovirus
Rift Valley Fever
Evolutionary Dynamics
Balancing selection
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Reassortment
Viral phylodynamics
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Coalescent methods are widely used to infer the demographic history of populations from gene genealogies. These approaches—often referred to as phylodynamic methods—have proven especially useful for reconstructing the dynamics of rapidly evolving viral pathogens. Yet, population dynamics inferred from viral genealogies often differ widely from those observed from other sources of epidemiological data, such as hospitalization records. We demonstrate how a modeling framework that allows for the direct fitting of mechanistic epidemiological models to genealogies can be used to test different hypotheses about what ecological factors cause phylodynamic inferences to differ from observed dynamics. We use this framework to test different hypotheses about why dengue serotype 1 (DENV-1) population dynamics in southern Vietnam inferred using existing phylodynamic methods differ from hospitalization data. Specifically, we consider how factors such as seasonality, vector dynamics, and spatial structure can affect inferences drawn from genealogies. The coalescent models we derive to take into account vector dynamics and spatial structure reveal that these ecological complexities can substantially affect coalescent rates among lineages. We show that incorporating these additional ecological complexities into coalescent models can also greatly improve estimates of historical population dynamics and lead to new insights into the factors shaping viral genealogies.
Coalescent theory
Viral phylodynamics
Demographic history
Evolutionary Dynamics
Effective population size
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One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R 0 close to 1, but substantial differences remain for large R 0 . In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.
Coalescent theory
Viral phylodynamics
Evolutionary Dynamics
Birth–death process
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Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.
Coalescent theory
Viral phylodynamics
Sexual contact
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The H1N1 subtype of influenza A virus has caused substantial morbidity and mortality in humans, first documented in the global pandemic of 1918 and continuing to the present day. Despite this disease burden, the evolutionary history of the A/H1N1 virus is not well understood, particularly whether there is a virological basis for several notable epidemics of unusual severity in the 1940s and 1950s. Using a data set of 71 representative complete genome sequences sampled between 1918 and 2006, we show that segmental reassortment has played an important role in the genomic evolution of A/H1N1 since 1918. Specifically, we demonstrate that an A/H1N1 isolate from the 1947 epidemic acquired novel PB2 and HA genes through intra-subtype reassortment, which may explain the abrupt antigenic evolution of this virus. Similarly, the 1951 influenza epidemic may also have been associated with reassortant A/H1N1 viruses. Intra-subtype reassortment therefore appears to be a more important process in the evolution and epidemiology of H1N1 influenza A virus than previously realized.
Reassortment
Viral phylodynamics
Antigenic shift
Pandemic
Viral evolution
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Abstract Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.
Coalescent theory
Viral phylodynamics
Sexual contact
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The principle of the present study was to determine the evolution of pandemic novel influenza A/H1N1 2009 virus (NIV) by phylogenetic, comparative and statistical analyses. The phylogenetic trees of eight genomic segments illustrate that, so far, the sequences of the NIVs (outbreak group A) are relatively homogeneous and derived by the event of multiple genetic reassortment of Eurasian and North American swine, avian and human viruses (group B). It implies that some of the influenza viruses in group B had higher potential to evolve and getting the ability to transmit from human-to-human after animal-to-human cross-species transmission. The second analysis shows that NIV had attempted a little evolutionary change among humans and before introduction into human it had long evolutionary history. Statistical analysis shows that viruses from both outbreak and nearest group have homologous genes in their genomes which might be reflecting the phylogenetic relationship of strains, and also the presence of unique mutations between groups A-B may associate with increased virulence of NIVs. Both phylogenetic and cluster analyses confirm that the gene exchange takes place between viruses originated from different species and it could be generated NIV with unpredictable pandemic potential. Hence, we conclude that an extensive study should be made to recognize, which reassortment groups are closely related to NIVs, and to determine the sites in the genes of NIV under greatest or least selection pressure, which will ultimately be important in the effective design of vaccine and drugs for 'swine flu'.
Reassortment
Pandemic
Viral phylodynamics
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