SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method.

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

A framework for automated scalable designation of viral pathogen lineages from genomic data.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.

Optimizing ancestral trait reconstruction of large HIV Subtype C datasets through multiple-trait subsampling.

Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of human immunodeficiency virus Type 1 Subtype C using a comprehensive subsampling strategy and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that a most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by all available traits, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the trait risk group. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatio-temporal patterns of infectious diseases.

Identifying SARS-CoV-2 regional introductions and transmission clusters in real time.

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Evolution of endemic and sylvatic lineages of dengue virus.

Recent disease outbreaks have raised awareness of tropical pathogens, especially mosquito-borne viruses. Dengue virus (DENV) is a widely studied mammalian pathogen transmitted by various species of mosquito in the genus Aedes, especially Aedes aegypti and Aedes albopictus. The prevailing view of the research community is that endemic viral lineages that cause epidemics of DENV in humans have emerged over time from sylvatic viral lineages, which persist in wild, non-human primates. These notions have been examined by researchers through phylogenetic analyses of the envelope gene (E) from the four serotypes of DENV (serotypes DENV-1 to DENV-4). In these previous reports, researchers used visual inspection of a phylogeny in order to assert that sylvatic lineages lead to endemic clades. In making this assertion, these researchers also reasserted the model of periodic sylvatic to endemic disease outbreaks. Since that study, there has been a significant increase in data both in terms of metadata (e.g., place and host of isolation) and genetic sequences of DENV. Here, we re-examine the model of sylvatic to endemic shifts in viral lineages through a phylogenetic tree search and character evolution study of metadata on the tree. We built a dataset of nucleotide sequences for 188 isolates of DENV that have metadata on sylvatic or endemic sampling along with three orthologous sequences from West Nile virus as the outgroup for the phylogenetic analysis. In contrast to previous research, we find that there are several shifts from endemic to sylvatic lineages as well as sylvatic to endemic lineages, indicating a much more dynamic model of evolution. We propose that a model that allows oscillation between sylvatic and endemic hosts better captures the dynamics of DENV transmission.

Analysis of Hepatitis B Virus Genotype D in Greenland Suggests the Presence of a Novel Quasi-Subgenotype.

A disproportionate number of Greenland's Inuit population are chronically infected with Hepatitis B virus (HBV; 5-10%). HBV genotypes B and D are most prevalent in the circumpolar Arctic. Here, we report 39 novel HBV/D sequences from individuals residing in southwestern Greenland. We performed phylodynamic analyses with ancient HBV DNA calibrators to investigate the origin and relationship of these taxa to other HBV sequences. We inferred a substitution rate of 1.4 × 10-5 [95% HPD 8.8 × 10-6, 2.0 × 10-5] and a time to the most recent common ancestor of 629 CE [95% HPD 37-1138 CE]. The Greenland taxa form a sister clade to HBV/D2 sequences, specifically New Caledonian and Indigenous Taiwanese sequences. The Greenland sequences share amino acid signatures with subgenotypes D1 and D2 and ~97% sequence identity. Our results suggest the classification of these novel sequences does not fit within the current nomenclature. Thus, we propose these taxa be considered a novel quasi-subgenotype.

StrainHub: a phylogenetic tool to construct pathogen transmission networks.

SUMMARY: In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g. host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in metadata, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis. AVAILABILITY AND IMPLEMENTATION: strainhub.io and https://github.com/abschneider/StrainHub.

Molecular evolution of Zika virus as it crossed the Pacific to the Americas.

Zika virus was previously considered to cause only a benign infection in humans. Studies of recent outbreaks of Zika virus in the Pacific, South America, Mexico and the Caribbean have associated the virus with severe neuropathology. Viral evolution may be one factor contributing to an apparent change in Zika disease as it spread from Southeast Asia across the Pacific to the Americas. To address this possibility, we have employed computational tools to compare the phylogeny, geography, immunology and RNA structure of Zika virus isolates from Africa, Asia, the Pacific and the Americas. In doing so, we compare and contrast methods and results for tree search and rooting of Zika virus phylogenies. In some phylogenetic analyses we find support for the hypothesis that there is a deep common ancestor between African and Asian clades (the "Asia/Africa" hypothesis). In other phylogenetic analyses, we find that Asian lineages are descendent from African lineages (the "out of Africa" hypothesis). In addition, we identify and evaluate key mutations in viral envelope protein coding and untranslated terminal RNA regions. We find stepwise mutations that have altered both immunological motif sets and regulatory sequence elements. Both of these sets of changes distinguish viruses found in Africa from those in the emergent Asia-Pacific-Americas lineage. These findings support the working hypothesis that mutations acquired by Zika virus in the Pacific and Americas contribute to changes in pathology. These results can inform experiments required to elucidate the role of viral genetic evolution in changes in neuropathology, including microcephaly and other neurological and skeletomuscular issues in infants, and Guillain-Barré syndrome in adults.