Over the past century, Influenza A virus (IAV) has caused four of the five reported pandemics, all of which originated from viruses possessing genome segments of avian origin. The recent spread of highly pathogenic avian influenza (HPAI) viruses, particularly the clade 2.3.4.4b A(H5N1) subtype, has led to an alarming increase in mammalian infections, raising concerns about the potential for future pandemics. In response to this, we developed FluMut, an open-source, cross-platform tool designed to identify molecular markers with potential impacts on H5N1 virus phenotypes. FluMut leverages an up-to-date database, FluMutDB, to rapidly analyze thousands of nucleotide sequences, identifying mutations associated with host adaptation, increased virulence, and antiviral resistance. The tool is available both as a command-line interface and a user-friendly graphical interface, making it accessible to researchers with varying levels of computational expertise. FluMut provides comprehensive outputs, including tables of detected markers, their biological effects, and corresponding literature references. This tool fills a critical gap in the genomic surveillance of HPAI H5N1, facilitating real-time monitoring of viral evolution and aiding in the identification of mutations that may signal increased pandemic potential. Future updates will extend FluMut’s capabilities to other influenza subtypes.

Avian influenza virus (AIV) currently causes a panzootic with extensive mortality in wild birds, poultry, and wild mammals, thus posing a major threat to global health and underscoring the need for efficient monitoring of its distribution and evolution. We here utilized a well-defined AIV strain to systematically investigate AIV genetic characterization through rapid, portable nanopore sequencing by comparing the latest DNA and RNA nanopore sequencing approaches and various computational pipelines for viral consensus sequence generation and phylogenetic analysis. We show that the latest direct RNA nanopore sequencing updates improve consensus sequence generation, but that the application of the latest DNA nanopore chemistry after reverse transcription and amplification outperforms, such native viral RNA sequencing by achieving higher sequencing accuracy and throughput. We additionally leveraged the direct RNA nanopore sequencing data for the detection of RNA modifications, such as N6-methyladenosine and pseudouridine, which play a role in viral immune evasion. Finally, we applied these sequencing approaches together with portable AIV diagnosis and quantification tools to environmental samples from a poultry farm, demonstrating the feasibility of nanopore sequencing for on-site non-invasive AIV monitoring in real-world outbreak scenarios.

Animal genomes are characterized by extensive variation in size. RNA viruses similarly exhibit substantial genomic diversity, with genome lengths ranging from 1.7 to ∼64 kb. Despite the myriad of novel viruses discovered by metagenomics, we know little of the factors that shape the evolution of the genome size in RNA viruses. We analyzed the variation in genome sizes across orders and families of animal RNA viruses. We found that RNA viruses can have highly variable genome sizes within and among orders, with the Nidovirales (including the Coronaviridae) exhibiting both significantly larger genomes and a greater range of genome sizes than other orders. In the Bunyavirales, Amarillovirales, Nidovirales, and Picornavirales, the genome sizes of invertebrate-associated RNA viruses were significantly larger than those that infect vertebrates, in contrast to their animal hosts in which vertebrates commonly have larger genomes than invertebrates. However, in the Mononegavirales, vertebrate viruses were significantly larger than those viruses associated with invertebrates. There were similarly complex associations between genome size and patterns of genome segmentation. In the Bunyavirales, Reovirales, and Nidovirales, viruses with segmented genomes, or that possessed a large number of segments, had significantly larger genome sizes than viruses with nonsegmented genomes or a small number of segments, while in Articulavirales, there were no significant differences in genome size among viruses possessing any number of genome segments. More broadly, our analysis revealed that taxonomic position (i.e. RNA virus order) had a greater impact on genome size than whether viruses infected vertebrates or invertebrates or their pattern of genome segmentation. Hence, the phylogenetic constraints on genome size are of sufficient magnitude to shape some other aspects of virus evolution.

Acute viral infections pose significant public health challenges. Since viral evolution, immune escape, and infection severity are influenced by how viruses spread between hosts, understanding transmission bottlenecks is crucial for predicting disease dynamics and developing effective control strategies. Transmission bottlenecks reduce viral population size and genetic diversity as the virus spreads to new hosts. Bottleneck size, defined as the number of viral individuals successfully establishing infection in a new host, varies across transmission events and can influence disease emergence and virus evolution. In this study, we introduce ViralBottleneck, an R package integrating six established methods for estimating transmission bottleneck size: the presence–absence method, Kullback–Leibler (KL) method, binomial method, two versions of the beta-binomial method, and the Wright–Fisher method. We demonstrate the package’s functionality using simulated datasets generated with SANTA-Sim under different scenarios with known bottleneck sizes. Our results reveal considerable variation in estimates across methods, highlighting the impact of methodological choice on bottleneck size estimation. The code and associated tutorial are available at https://github.com/BowenArchaman/ViralBottleneck.

Understanding the viral diversity harboured by wildlife is essential for effective mapping and prevention of future zoonotic outbreaks. Bats, in particular, are recognized as natural reservoirs for several high-impact zoonotic viral pathogens, including coronaviruses responsible for Severe Acute Respiratory Syndrome (SARS), the rabies virus, diverse paramyxoviruses, Marburg, Ebola, Nipah, and Hendra viruses. However, a large extent of bat viruses remains unexplored, especially in highly biodiverse regions of the Neotropics such as Brazilian ecosystems. We used a meta-transcriptomic approach to characterize new virus genomes found in blood, oral, and anal samples collected from cave- and noncave bats from Northeast Brazil, Caatinga, and Atlantic Forest biomes. From a total of 19 coronavirus-positive bats, we have assembled two complete genomes of a new Betacoronavirus subgenus, named Ambecovirus (American betacoronavirus). The subgenus herein described is phylogenetically placed between the Sarbeco-/Hibeco-/Nobecovirus and the Merbeco-/Embecovirus clades, being basal to the former. While the conserved S2 region of the spike protein retained hallmark domains, including HR1 and HR2, the S1/S2 cleavage site and the furin cleavage site, the S1 region consistently displayed only the N-terminal domain. The receptor-binding domain from the C-terminal domai (CTD) region could not be identified due to high dissimilarity relative to known congeners. The detection of Ambercovirus in sympatric Pteronotus gymnonotus and Carollia perspicillata bats suggests potential interspecies transmission. Longitudinal sampling confirmed persistent Ambecovirus infection in P. gymnonotus over multiple years and virus dispersion at a minimum distance of 270 km between caves. The present study confirms that viral diversity in neotropical hosts remains largely unknown, not just in Brazil but likely in the other countries of the region, supporting the need for a systematic approach to virome exploration and analysis followed by in vitro experimentation to assess zoonotic potential.

Advancements in high-throughput sequencing and associated bioinformatics methods have significantly expanded the RNA virus repertoire, including novel viruses with highly divergent genomes encoding “orphan” proteins that apparently lack homologous sequences. This absence of homologs in routine sequence similarity search complicates their taxonomic classification and raises a fundamental question: Do these orphan viral genomes represent bona ide viruses? In 2022, an orphan viral genome encoding a large polyprotein was identified in alfalfa (Medicago sativa) and thrips (Frankliniella occidentalis), and named Snake River alfalfa virus (SRAV). SRAV was initially proposed as an uncommon flavi-like virus identified in a plant host distantly related to family Flaviviridae. Subsequently, another research group showed its common occurrence in alfalfa but challenged its taxonomic position, suggesting it belongs to the family Endornaviridae. In this study, a large-scale analysis of 77 publicly available small RNA datasets indicates that SRAV could be detected across various tissues and cultivars of alfalfa, and has a broad geographical distribution. Moreover, profiles of the SRAV-derived small interfering RNAs (vsiRNAs) exhibited typical characteristics of viruses in plant hosts. The evolutionary analysis suggests that SRAV represents a unique class of plant-hosted flavi-like viruses with an unusual genome organization and evolutionary status, distinct from previously identified flavi-like viruses documented to infect plants. The latter shows a close evolutionary relationship to flavi-like viruses primarily found in plant-feeding invertebrates and lacks evidence of triggering host RNA interference (RNAi) responses so far. Moreover, mining the transcriptome shotgun assembly (TSA) database identified two novel viral sequences with a similar genome organization and evolutionary status to SRAV. In summary, our study resolves the disagreement regarding the taxonomic status of SRAV and suggests the potential existence of two distinct clades of plant-hosted flavi-like viruses with independent evolutionary origins. Furthermore, our research provides the first evidence of plant-hosted flavi-like viruses triggering the host’s RNAi antiviral response. The widespread occurrence of SRAV underscores its potential ecological significance in alfalfa, a crop of substantial economic importance.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40%. Feature selection revealed key phage λ and Escherchia coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method's success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.

Endogenous viral elements (EVEs) are remnants of viral genetic material endogenized into the host genome. They have, in the last decades, attracted attention for their role as potential contributors to pathogenesis, drivers of selective advantage for the host, and genomic remnants of ancient viruses. EVEs have a nuanced and complex influence on both host health and evolution, and can offer insights on the deep evolutionary history of viruses. As an emerging field of research, several factors limit a comprehensive understanding of EVEs: they are currently underestimated and periodically overlooked in studies of the host genome, transcriptome, and virome. The absence of standardized guidelines for ensuring EVE-related data availability and accessibility following the FAIR (‘findable, accessible, interoperable, and reusable’) principles obstructs our ability to gather and connect information. Here, we discuss challenges to the availability and accessibility of EVE-related data and propose potential solutions. We identified the biological and research focus imbalance between different types of EVEs, and their overall biological complexity as genomic loci with viral ancestry, as potential challenges that can be addressed with the development of a user-oriented identification tool. In addition, reports of EVE identification are scattered between different subfields under different keywords, and EVE sequences and associated data are not properly gathered in databases. While developing an open and dedicated database might be ideal, targeted improvements of generalist databases might provide a pragmatic solution to EVE data and metadata accessibility. The implementation of these solutions, as well as the collective effort by the EVE scientific community in discussing and setting guidelines, is now drastically needed to lead the development of EVE research and offer insights into host–virus interactions and their evolutionary history.

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis, multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Deep mutational scanning experiments aid in the surveillance and forecasting of viral evolution by providing prospective measurements of mutational effects on viral traits, but epistatic shifts in the impacts of mutations can hinder viral forecasting when measurements were made in outdated strain backgrounds. Here, we report measurements of the impact of all single amino acid mutations on ACE2-binding affinity and protein folding and expression in the SARS-CoV-2 Omicron BA.2.86 spike receptor-binding domain. As with other SARS-CoV-2 variants, we find a plastic and evolvable basis for receptor binding, with many mutations at the ACE2 interface maintaining or even improving ACE2-binding affinity. Despite its large genetic divergence, mutational effects in BA.2.86 have not diverged greatly from those measured in its Omicron BA.2 ancestor. However, we do identify strong positive epistasis among subsequent mutations that have accrued in BA.2.86 descendants. Specifically, the Q493E mutation that decreased ACE2-binding affinity in all previous SARS-CoV-2 backgrounds is reversed in sign to enhance human ACE2-binding affinity when coupled with L455S and F456L in the currently emerging KP.3 variant. Our results point to a modest degree of epistatic drift in mutational effects during recent SARS-CoV-2 evolution but highlight how these small epistatic shifts can have important consequences for the emergence of new SARS-CoV-2 variants.