Readdressing the genetic diversity and taxonomy of the Mesoniviridae family, as well as its relationships with other nidoviruses and putative mesonivirus-like viral sequences

Copyright © 2022 Elsevier B.V. All rights reserved.Research on the recently established Mesoniviridae family (Order Nidovirales), RNA genome insect-specific viruses, has been steadily growing in the last decade. However, after the last detailed phylogenetic characterization of mesoniviruses in 2014, numerous new sequences, even in organisms other than mosquitos, have been identified and characterized. In this study, we analyzed nucleotide and protein sequences of mesoniviruses with a wide range of molecular tools including genetic distance, Shannon entropy, selective pressure analysis, polymorphism identification, principal coordinate analysis, likelihood mapping and phylodynamic reconstruction. We also sought to revaluate new mesoniviruses sequence positions within the family, proposing a taxonomic revision. The different sub-lineages of mosquito mesoniviruses sequences presented low sequence diversity and entropy, with incongruences to the existing taxonomy being found after an extensive phylogenetic characterization. High sequence discrepancy and differences in genome organization were found between mosquito mesoniviruses and other mesoniviruses, so their future classification, as other meso-like viruses that are found in other organisms, should be approached with caution. No evidence of frequent recombination was found, and mesonivirus genomes seem to evolve under strong purifying selection. Insufficient data by root-to-tip analysis did not yet allow for an adequate phylogeographic reconstruction.publishersversionpublishe

Genetic lineage characterization and spatiotemporal dynamics of the recently established Brevihamaparvovirus genus

Copyright © 2022 Elsevier B.V. All rights reserved.The analysis of the viruses allocated to the recently established Brevihamaparvovirus genus (Parvoviridae family), which includes all previously known brevidensoviruses, has not yet been carried out on an extensive basis. As a result, no detailed genetic lineage characterization has ever been performed for this group of insect-specific viruses. Using a wide range of molecular tools, we have explored this taxon by calculating Shannon entropy values, intra- and inter-taxon genetic distances, analysed sequence polymorphisms, and evaluated selective pressures acting on the viral genome. While the calculated Brevihamaparvovirus mutation rates were within the range of those of other parvoviruses, their genomes look to be under strong purifying selection, and are also characterized by low diversity and entropy. Furthermore, even though recombination events are quite common among parvoviruses, no evidence of recombination (either intra or intergenic) was found in the Brevihamaparvoviruses sequences analyzed. An extended taxonomic analysis and reevaluation of existing Brevihamaparvoviruses sequences, many still unclassified, was performed using cut-off values defining NS1 identity between viral sequences from the Parvovirus family. Two existing genetic lineages, Dipteran Brevihamaparvovirus 1 and Dipteran Brevihamaparvovirus 2, were rearranged and the creation of a new one, Dipteran Brevihamaparvovirus 3, was suggested. Finally, despite the uncertainties associated with both the time estimates of the most recent common ancestors, which could span from twenty thousand years before the current era to way earlier (in the last century), and the dispersal routes proposed for Brevihamaparvoviruses sequences by phylodynamic reconstruction, the analyses here presented could help define how future studies should be conducted as more isolates continue to be identified in the future, and contribute to eliminating possible analytical biases.publishersversionpublishe

Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore-Washington metropolitan area.

The early COVID-19 pandemic was characterized by rapid global spread. In Maryland and Washington, DC, United States, more than 2500 cases were reported within 3 weeks of the first COVID-19 detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2 - the virus that causes COVID-19 - in the region. We analyzed 620 samples collected from the Johns Hopkins Health System during March 11-31, 2020, comprising 28.6% of the total cases in Maryland and Washington, DC. From these samples, we generated 114 complete viral genomes. Analysis of these genomes alongside a subsampling of over 1000 previously published sequences showed that the diversity in this region rivaled global SARS-CoV-2 genetic diversity at that time and that the sequences belong to all of the major globally circulating lineages, suggesting multiple introductions into the region. We also analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and found that clinically severe cases had viral genomes belonging to all major viral lineages. We conclude that efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and the interconnectedness of the region as a whole

Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable.

There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1), analyzed 160 SARS-CoV-2 full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asian (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the “ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia”. There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in Figure 1 of the paper, is only 16 or 17 mutations in length. Indeed, the network seems to be mis-rooted because (see Supplementary Figure 4) a virus from Wuhan from week 0 (24th December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1-9 (presumably from many places outside China), which makes no evolutionary (2), nor epidemiological sense (3).N

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

The Evolutionary Dynamics of Influenza A Viruses Circulating in Mallards in Duck Hunting Preserves in Maryland, USA

Duck hunting preserves (DHP) have resident populations of farm-raised mallard ducks, which create potential foci for the evolution of novel influenza A viruses (IAVs). Through an eleven-year (2003–2013) IAV surveillance project in seven DHPs in Maryland, USA, we frequently identified IAVs in the resident, free-flying mallard ducks (5.8% of cloacal samples were IAV-positive). The IAV population had high genetic diversity, including 12 HA subtypes and 9 NA subtypes. By sequencing the complete genomes of 290 viruses, we determined that genetically diverse IAVs were introduced annually into DHP ducks, predominantly from wild birds in the Anatidae family that inhabit the Atlantic and Mississippi flyways. The relatively low viral gene flow observed out of DHPs suggests that raised mallards do not sustain long-term viral persistence nor do they serve as important sources of new viruses in wild birds. Overall, our findings indicate that DHPs offer reliable samples of the diversity of IAV subtypes, and could serve as regional sentinel sites that mimic the viral diversity found in local wild duck populations, which would provide a cost-efficient strategy for long-term IAV monitoring. Such monitoring could allow for early identification and characterization of viruses that threaten bird species of high economic and environmental interest

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

Hamiltonian zigzag accelerates large-scale inference for conditional dependencies between complex biological traits

Inferring dependencies between complex biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The phylogenetic multivariate probit model uses a latent variable framework to accommodate binary and continuous traits, but integrating many latent variables requires many computationally expensive draws from a high-dimensional truncated normal. The state-of-the-art approach, which combines the bouncy particle sampler (BPS) with dynamically programmed gradient evaluations, breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. We develop an inference scheme that combines the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and joint updates for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedups now allow us to tackle larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution.Comment: 36 pages, 5 figures, 3 table

Accelerating Bayesian inference of dependency between mixed-type biological traits.

Inferring dependencies between mixed-type biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variable framework, and utilizes an efficient bouncy particle sampler (BPS) to tackle the computational bottleneck-integrating many latent variables from a high-dimensional truncated normal distribution. This approach breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. Here, we propose an inference pipeline for phylogenetic probit models that greatly outperforms BPS. The novelty lies in 1) a combination of the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and 2) a joint sampling scheme for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedup now enables us to tackle even larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution