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The tremendous majority of RNA genomes from pathogenic viruses analyzed and deposited in databases are consensus or "democratic" genomes. They represent the genomes most frequently found in the clinical samples of patients but do not account for the huge genetic diversity of coexisting genomes, which is better described as quasispecies. A viral quasispecies is defined as the dynamic distribution of nonidentical but closely related mutants, variants, recombinant, or reassortant viral genomes. Viral quasispecies have collective behavior and dynamics and are the subject of internal interactions that comprise interference, complementation, or cooperation. In the setting of SARS‐CoV‐2 infection, intrahost SARS‐CoV‐2 genetic diversity was recently notably reported for immunocompromised, chronically infected patients, for patients treated with monoclonal antibodies targeting the viral spike protein, and for different body compartments of a single patient. A question that deserves attention is whether such diversity is generated postinfection from a clonal genome in response to selection pressure or is already present at the time of infection as a quasispecies. In the present review, we summarize the data supporting that hosts are infected by a "wild bunch" of viruses rather than by multiple virions sharing the same genome. Each virion in the "wild bunch" may have different virulence and tissue tropisms. As the number of viruses replicated during host infections is huge, a viral quasispecies at any time of infection is wide and is also influenced by host‐specific selection pressure after infection, which accounts for the difficulty in deciphering and predicting the appearance of more fit variants and the evolution of epidemics of novel RNA viruses. [ABSTRACT FROM AUTHOR]

The nature and dynamics of mutations associated with the emergence, spread, and vanishing of SARS‐CoV‐2 variants causing successive waves are complex. We determined the kinetics of the most common French variant ("Marseille‐4") for 10 months since its onset in July 2020. Here, we analyzed and classified into subvariants and lineages 7453 genomes obtained by next‐generation sequencing. We identified two subvariants, Marseille‐4A, which contains 22 different lineages of at least 50 genomes, and Marseille‐4B. Their average lifetime was 4.1 ± 1.4 months, during which 4.1 ± 2.6 mutations accumulated. Growth rate was 0.079 ± 0.045, varying from 0.010 to 0.173. Most of the lineages exhibited a bell‐shaped distribution. Several beneficial mutations at unpredicted sites initiated a new outbreak, while the accumulation of other mutations resulted in more viral heterogenicity, increased diversity and vanishing of the lineages. Marseille‐4B emerged when the other Marseille‐4 lineages vanished. Its ORF8 gene was knocked out by a stop codon, as reported in SARS‐CoV‐2 of mink and in the Alpha variant. This subvariant was associated with increased hospitalization and death rates, suggesting that ORF8 is a nonvirulence gene. We speculate that the observed heterogenicity of a lineage may predict the end of the outbreak. [ABSTRACT FROM AUTHOR]

The tremendous majority of SARS-CoV-2 genomic data so far neglected intra-host genetic diversity. Here, we studied SARS-CoV-2 quasispecies based on data generated by next-generation sequencing (NGS) of complete genomes. SARS-CoV-2 raw NGS data had been generated for nasopharyngeal samples collected between March 2020 and February 2021 by the Illumina technology on a MiSeq instrument, without prior PCR amplification. To analyze viral quasispecies, we designed and implemented an in-house Excel file ("QuasiS") that can characterize intra-sample nucleotide diversity along the genomes using data of the mapping of NGS reads. We compared intra-sample genetic diversity and global genetic diversity available from Nextstrain. Hierarchical clustering of all samples based on the intra-sample genetic diversity was performed and visualized with the Morpheus web application. NGS mapping data from 110 SARS-CoV-2-positive respiratory samples characterized by a mean depth of 169 NGS reads/nucleotide position and for which consensus genomes that had been obtained were classified into 15 viral lineages were analyzed. Mean intra-sample nucleotide diversity was 0.21 ± 0.65%, and 5357 positions (17.9%) exhibited significant (>4%) diversity, in ≥2 genomes for 1730 (5.8%) of them. ORF10, spike, and N genes had the highest number of positions exhibiting diversity (0.56%, 0.34%, and 0.24%, respectively). Nine hot spots of intra-sample diversity were identified in the SARS-CoV-2 NSP6, NSP12, ORF8, and N genes. Hierarchical clustering delineated a set of six genomes of different lineages characterized by 920 positions exhibiting intra-sample diversity. In addition, 118 nucleotide positions (0.4%) exhibited diversity at both intra- and inter-patient levels. Overall, the present study illustrates that the SARS-CoV-2 consensus genome sequences are only an incomplete and imperfect representation of the entire viral population infecting a patient, and that quasispecies analysis may allow deciphering more accurately the viral evolutionary pathways. [ABSTRACT FROM AUTHOR]

Front Cover Caption: The cover image is based on the Review From viral democratic genomes to viral wild bunch of quasispecies by Philippe Colson et al., https://doi.org/10.1002/jmv.29209..By Philippe Colson; Wahiba Bader; Jacques Fantini; Pierre Dudouet; Anthony Levasseur; Pierre Pontarotti; Christian Devaux and Didier RaoultReported by Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

The evolution of SARS-CoV-2, the agent of COVID-19, has been remarkable for its high mutation potential, leading to the appearance of variants. Some mutations have never appeared in the published genomes, which represent consensus, or bona fide genomes. Here we tested the hypothesis that mutations that did not appear in consensus genomes were, in fact, as frequent as the mutations that appeared during the various epidemic episodes, but were not expressed because lethal. To identify these mutations, we analysed the genomes of 90 nasopharyngeal samples and the quasispecies determined by next-generation sequencing. Mutations observed in the quasispecies and not in the consensus genomes were considered to be lethal, what we called "outlaw" mutations. Among these mutations, we analysed the 21 most frequent. Eight of these "outlaws" were in the RNA polymerase and we were able to use a structural biology model and molecular dynamics simulations to demonstrate the functional incapacity of these mutated RNA polymerases. Three other mutations affected the spike, a major protein involved in the pathogenesis of COVID-19. Overall, by analysing the SARS-CoV-2 quasispecies obtained during sequencing, this method made it possible to identify "outlaws," showing areas that could potentially become the target of treatments.