Abstract The structured coalescent allows inferring migration patterns between viral sub-populations 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 is complex or even impossible. While this problem has been addressed for unstructured populations, it is still impossible to account for population structure, such as induced by different host populations, while also accounting for reassortmentWe 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 sub-population 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 (SCoRe) package for BEAST 2.5 and is available at https://github.com/jugne/SCORE .
Abstract In 2016/2017, Washington State experienced a mumps outbreak despite high childhood vaccination rates, with cases more frequently detected among school-aged children and members of the Marshallese community. We sequenced 166 mumps virus genomes collected during outbreaks in Washington and other US states, and apply phylodynamic approaches to trace mumps introductions and transmission within Washington. We uncover that mumps was introduced into Washington at least 13 times, primarily from Arkansas, sparking multiple co-circulating transmission chains. Neither vaccination status nor age were strong determinants of transmission. Instead, the outbreak in Washington was overwhelmingly sustained by transmission within the Marshallese community. Our findings underscore the utility of genomic data to clarify epidemiologic factors driving transmission, and pinpoint contact networks as critical determinants of mumps transmission. These results imply that contact structures and historic disparities may leave populations at increased risk for respiratory virus disease even when a vaccine is effective and widely used.
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.
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. Here, we introduce a 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. This algorithm provides 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.
Abstract Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, it’s local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summary As shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.
Abstract Phylogenetics has traditionally inferred shared genetic histories of species over macroevolutionary time. With the expansion of cancer multi-region and single-cell sequencing, phylogenetic approaches have found new uses mapping the evolutionary histories of tumors as well. Recently developed “phylodynamic” methods that integrate phylogenetic and population dynamic modeling offer further promise - uncovering cancer growth dynamics from the topologies and branch lengths of tumor trees. Here, I discuss two recent efforts in this area: 1) Heterogeneous growth rates across a tumor become encoded in the branching patterns of phylogenetic trees built from multi-region sequencing. We introduce a cancer-specific extension to a class of multi-state birth-death models to infer these growth rate differences across space. We apply our model (SDevo) to a sample of hepatocellular carcinomas and infer that these tumor peripheries grow 2-6 times faster than their interiors. 2) Tumors experience differential growth rates over time as they acquire new driver mutations and invade their surrounding tissues, and these growth dynamics similarly leave characteristic signatures in their phylogenies. We employ Bayesian skyline models to dissect tumor growth dynamics from a dataset of diverse cancers and reveal variation in cancer growth rates over time. As cancer sequencing continues to proliferate, phylodynamic approaches are poised to reveal new quantitative insights into cancer growth dynamics across time and space. Citation Format: Maya Lewinsohn, Trevor Bedford, Nicola F. Müller, Alison Feder. Bayesian phylodynamics to quantify cancer growth across space and time [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA016.
ABSTRACT Background The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data is lacking. Methods We isolated, sequenced, and analyzed 383 influenza A(H1N1)pdm09 viral genomes isolated from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. Results The transmission dynamics of influenza A(H1N1)pdm09 virus in Kenya was characterized by: (i) multiple virus introductions into Kenya over the study period, although these were remarkably few, with only a few of those introductions instigating seasonal epidemics that then established local transmission clusters; (ii) persistence of transmission clusters over several epidemic seasons across the country; (iii) seasonal fluctuations in effective reproduction number ( R e ) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres; (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-11 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-17; and (v) virus migration from multiple geographical regions to multiple geographical destinations in Kenya. Conclusion Considerable influenza virus diversity circulates within Africa, as demonstrated in this report, including virus lineages that are unique to the region, which may be capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.
Abstract Model-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of H9N2 viruses isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China and Bangladesh. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries. Author summary What drives the geographic dispersal and genetic diversity of H9N2 avian influenza virus in Asia? We used two model-based approaches, DTA and MASCOT, to reconstruct the phylogeographic dynamics of the virus. Further, multiple potential predictors were used to inform the virus spread and population dynamics by GLMs. Here, we maximised the power of time-series predictors in Bayesian phylodynamic prediction. For the first time, we were able to quantify the contribution of both time-series and constant predictors to both migration rates and effective population sizes in a structured population. We identified a positive association of international poultry trade and national poultry production time-series with virus migration rates and effective population sizes respectively. We also identify geographic proximity as a strongly supported driver to virus migration rates and this points to the potential role of wild bird populations in virus dispersal across countries. Our study is a practical exemplar of the use of temporal information in predictors to model heterogeneous spatial diffusion and population dynamic processes and provides direction to H9N2 control efforts in Asia.
Abstract The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case-reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.
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