TreeKnit: Inferring ancestral reassortment graphs of influenza viruses

The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the "first-infection tree," a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Genetic characterization and pathogenicity in a mouse model of newly isolated bat-originated mammalian orthoreovirus in South Korea

Mammalian orthoreoviruses (MRVs) infect a wide range of hosts, including humans, livestock, and wildlife. In the present study, we isolated a novel Mammalian orthoreovirus from the intestine of a microbat (Myotis aurascens) and investigated its biological and pathological characteristics. Phylogenetic analysis indicated that the new isolate was serotype 2, sharing the segments with those from different hosts. Our results showed that it can infect a wide range of cell lines from different mammalian species, including human, swine, and non-human primate cell lines. Additionally, media containing trypsin, yeast extract, and tryptose phosphate broth promoted virus propagation in primate cell lines and most human cell lines, but not in A549 and porcine cell lines. Mice infected with this strain via the intranasal route, but not via the oral route, exhibited weight loss and respiratory distress. The virus is distributed in a broad range of organs and causes lung damage. In vitro and in vivo experiments also suggested that the new virus could be a neurotropic infectious strain that can infect a neuroblastoma cell line and replicate in the brains of infected mice. Additionally, it caused a delayed immune response, as indicated by the high expression levels of cytokines and chemokines only at 14 days post-infection (dpi). These data provide an important understanding of the genetics and pathogenicity of mammalian orthoreoviruses in bats at risk of spillover infections.IMPORTANCEMammalian orthoreoviruses (MRVs) have a broad range of hosts and can cause serious respiratory and gastroenteritis diseases in humans and livestock. Some strains infect the central nervous system, causing severe encephalitis. In this study, we identified BatMRV2/SNU1/Korea/2021, a reassortment of MRV serotype 2, isolated from bats with broad tissue tropism, including the neurological system. In addition, it has been shown to cause respiratory syndrome in mouse models. The given data will provide more evidence of the risk of mammalian orthoreovirus transmission from wildlife to various animal species and the sources of spillover infections.

HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.

Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to new viral variants. Large-scale genome rearrangements, such as reassortment, have the potential to quickly generate new phenotypes, making the understanding of viral reassortment important to both evolutionary biology and public health research. In this paper, we argue that reassortment cannot be reliably inferred from incongruities between segment phylogenies using the established remove-and-rejoin or coalescent approaches. We instead show that reassortment must be considered in the context of a broader population process that includes the dynamics of the infected hosts. Using illustrative examples and simulation we identify four types of evolutionary events that are difficult or impossible to reconstruct with incongruence-based methods. Further, we show that these specific situations are very common and will likely occur even in small samples. Finally, we argue that existing methods can be augmented or modified to account for all the problematic situations that we identify in this paper. Robust assessment of the role of reassortment in viral evolution is difficult, and we hope to provide conceptual clarity on some important methodological issues that can arise in the development of the next generation of tools for studying reassortment.

Mobile Genetic Element Flexibility as an Underlying Principle to Bacterial Evolution

Mobile genetic elements are key to the evolution of bacteria and traits that affect host and ecosystem health. Here, we use a framework of a hierarchical and modular system that scales from genes to populations to synthesize recent findings on mobile genetic elements (MGEs) of bacteria. Doing so highlights the role that emergent properties of flexibility, robustness, and genetic capacitance of MGEs have on the evolution of bacteria. Some of their traits can be stored, shared, and diversified across different MGEs, taxa of bacteria, and time. Collectively, these properties contribute to maintaining functionality against perturbations while allowing changes to accumulate in order to diversify and give rise to new traits. These properties of MGEs have long challenged our abilities to study them. Implementation of new technologies and strategies allows for MGEs to be analyzed in new and powerful ways.