Joint Inference of Migration and Reassortment Patterns for Viruses with Segmented Genomes.

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.

Bayesian inference of reassortment networks reveals fitness benefits of reassortment in 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.

Landscape of Exhausted Virus-Specific CD8 T Cells in Chronic LCMV Infection.

A hallmark of chronic infections is the presence of exhausted CD8 T cells, characterized by a distinct transcriptional program compared with functional effector or memory cells, co-expression of multiple inhibitory receptors, and impaired effector function, mainly driven by recurrent T cell receptor engagement. In the context of chronic lymphocytic choriomeningitis virus (LCMV) infection in mice, most studies focused on studying splenic virus-specific CD8 T cells. Here, we provide a detailed characterization of exhausted CD8 T cells isolated from six different tissues during established LCMV infection, using single-cell RNA sequencing. Our data reveal that exhausted cells are heterogeneous, adopt organ-specific transcriptomic profiles, and can be divided into five main functional subpopulations: advanced exhaustion, effector-like, intermediate, proliferating, or memory-like. Adoptive transfer experiments showed that these phenotypes are plastic, suggesting that the tissue microenvironment has a major impact in shaping the phenotype and function of virus-specific CD8 T cells during chronic infection.