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Feline coronaviruses (FCoVs) commonly cause mild enteric infections in felines worldwide (termed feline enteric coronavirus [FECV]), with around 12 per cent developing into deadly feline infectious peritonitis (FIP; feline infectious peritonitis virus [FIPV]). Genomic differences between FECV and FIPV have been reported, yet the putative genotypic basis of the highly pathogenic phenotype remains unclear. Here, we used state-of-the-art molecular evolutionary genetic statistical techniques to identify and compare differences in natural selection pressure between FECV and FIPV sequences, as well as to identify FIPV- and FECV-specific signals of positive selection. We analyzed full-length FCoV protein coding genes thought to contain mutations associated with FIPV (Spike, ORF3abc, and ORF7ab). We identified two sites exhibiting differences in natural selection pressure between FECV and FIPV: one within the S1/S2 furin cleavage site (FCS) and the other within the fusion domain of Spike. We also found fifteen sites subject to positive selection associated with FIPV within Spike, eleven of which have not previously been suggested as possibly relevant to FIP development. These sites fall within Spike protein subdomains that participate in host cell receptor interaction, immune evasion, tropism shifts, host cellular entry, and viral escape. There were fourteen sites (twelve novel sites) within Spike under positive selection associated with the FECV phenotype, almost exclusively within the S1/S2 FCS and adjacent to C domain, along with a signal of relaxed selection in FIPV relative to FECV, suggesting that furin cleavage functionality may not be needed for FIPV. Positive selection inferred in ORF7b was associated with the FECV phenotype and included twenty-four positively selected sites, while ORF7b had signals of relaxed selection in FIPV. We found evidence of positive selection in ORF3c in FCoV-wide analyses, but no specific association with the FIPV or FECV phenotype. We hypothesize that some combination of mutations in FECV may contribute to FIP development, and that it is unlikely to be one singular ‘switch’ mutational event. This work expands our understanding of the complexities of FIP development and provides insights into how evolutionary forces may alter pathogenesis in coronavirus genomes.

Background The vast majority of phylogenetic trees are inferred from molecular sequence data (nucleotides or amino acids) using time-reversible evolutionary models which assume that, for any pair of nucleotide or amino acid characters, the relative rate of X to Y substitution is the same as the relative rate of Y to X substitution. However, this reversibility assumption is unlikely to accurately reflect the actual underlying biochemical and/or evolutionary processes that lead to the fixation of substitutions. Here, we use empirical viral genome sequence data to reveal that evolutionary non-reversibility is pervasive among most groups of viruses. Specifically, we consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) in which Watson-Crick complementary substitutions occur at identical relative rates and which might therefor be most applicable to analyzing the evolution of genomes where both complementary strands are subject to the same mutational processes (such as might be expected for double-stranded (ds) RNA or dsDNA genomes); and (2) a 12-rate non-reversible model (NREV12) in which all relative substitution types are free to occur at different rates and which might therefore be applicable to analyzing the evolution of genomes where the complementary genome strands are subject to different mutational processes (such as might be expected for viruses with single-stranded (ss) RNA or ssDNA genomes). Results Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit to 21/31 dsRNA and 20/30 dsDNA datasets than did the general time reversible (GTR) and NREV6 models with NREV6 providing a better fit than NREV12 and GTR in only 5/30 dsDNA and 2/31 dsRNA datasets. As expected, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. Next, we used simulations to show that increasing degrees of strand-specific substitution bias decrease the accuracy of phylogenetic inference irrespective of whether GTR or NREV12 is used to describe mutational processes. However, in cases where strand-specific substitution biases are extreme (such as in SARS-CoV-2 and Torque teno sus virus datasets) NREV12 tends to yield more accurate phylogenetic trees than those obtained using GTR. Conclusion We show that NREV12 should, be seriously considered during the model selection phase of phylogenetic analyses involving viral genomic sequences.

Inference and interpretation of evolutionary processes, in particular of the types and targets of natural selection affecting coding sequences, are critically influenced by the assumptions built into statistical models and tests. If certain aspects of the substitution process (even when they are not of direct interest) are presumed absent or are modeled with too crude of a simplification, estimates of key model parameters can become biased, often systematically, and lead to poor statistical performance. Previous work established that failing to accommodate multi-nucleotide (or multi-hit, MH) substitutions strongly biases dN/dS-based inference towards false positive inferences of diversifying episodic selection, as does failing to model variation in the rate of synonymous substitution (SRV) among sites. Here we develop an integrated analytical framework and software tools to simultaneously incorporate these sources of evolutionary complexity into selection analyses. We found that both MH and SRV are ubiquitous in empirical alignments, and incorporating them has a strong effect on whether or not positive selection is detected, (1.4-fold reduction) and on the distributions of inferred evolutionary rates. With simulation studies, we show that this effect is not attributable to reduced statistical power caused by using a more complex model. After a detailed examination of 21 benchmark alignments and a new high-resolution analysis showing which parts of the alignment provide support for positive selection, we show that MH substitutions occurring along shorter branches in the tree explain a significant fraction of discrepant results in selection detection. Our results add to the growing body of literature which examines decadesold modeling assumptions (including MH) and finds them to be problematic for comparative genomic data analysis. Because multi-nucleotide substitutions have a significant impact on natural selection detection even at the level of an entire gene, we recommend that selection analyses of this type consider their inclusion as a matter of routine. To facilitate this procedure, we developed, implemented, and benchmarked a simple and well-performing model testing selection detection framework able to screen an alignment for positive selection with two biologically important confounding processes: site-to-site synonymous rate variation, and multi-nucleotide instantaneous substitutions.

Whether freshwater fish colonize remote islands following tectonic or transoceanic dispersal remains an evolutionary puzzle. Integrating dating of known tectonic events with phylogenomics and current species distribution, we find that killifish species distribution is not explained by species dispersal by tectonic drift only. Investigating the colonization of a nonannual killifish (golden panchax, Pachypanchax playfairii) on the Seychelle islands, we found genetic support for transoceanic dispersal and experimentally discovered an adaptation to complete tolerance to seawater. At the macroevolutionary scale, despite their long-lasting isolation, nonannual golden panchax show stronger genome-wide purifying selection than annual killifishes from continental Africa. However, progressive decline in effective population size over a more recent timescale has probably led to the segregation of slightly deleterious mutations across golden panchax populations, which represents a potential threat for species preservation in the long term.

The mammalian gene Brain-Derived Neurotrophic Factor (BDNF) is an essential mediator of brain assembly, development, and maturation which has been implicated in a variety of brain disorders such as neurodevelopmental disorders (e.g. autism spectrum disorder), neuropsychiatric disorders (e.g. depression, PTSD, schizophrenia), and neurodegenerative disorders (e.g. Parkinson’s). Loss of BDNF during early development is embryonic lethal, and depletion of BDNF during adolescence or adulthood can result in disease-related neuropathology across a broad range of model organisms. In order to better understand the role of BDNF in disease, we seek to provide an evolutionary context to BDNF’s role within the brain by elucidating the molecular and genetic comparative history of BDNF across species. We conduct sequence alignment and phylogenetic reconstruction of the BDNF gene across a diverse selection of over 160 mammalian species spanning ∼177 million years of evolution. Selective evolutionary change was examined via several independent computational models of codon evolution including FEL, MEME, and BGM. We report strict purifying selection in the main functional domain of BDNF (NGF domain, essentially comprising the mature BDNF protein). Specifically, we discover 6 sites in our homologous alignment which are under episodic selection in the early regulatory region of BDNF (i.e. the prodomain) and 23 pairs of coevolving sites that are a part of complex spatial relationships that are distributed across the entire BDNF gene. Thus, we propose that our discovery of both local and distal sites of co-evolution within the pro- and mature-domains of BDNF that likely reflect the evolutionary fine-tuning of BDNF’s unique and complex regulatory capacities whilst also retaining it’s core yet diverse ontogenic functionality within the central nervous system. This discovery consequently supports the idea that the BDNF prodomain is more prone to change than the mature domain, however the fact that this region has also been subject to negative purifying selection also highlights genetic sensitivity and thus partially explains the prodomain’s disease relevance (e.g. Val66Met and other variants) to numerous neuropsychiatric disorders.HIGHLIGHTSWe extracted coding sequences for Brain-Derived Neurotrophic Factor (BDNF) from over 160 mammalian genomes that span approximately ∼177 million years of evolution.We observe strict purifying selection in the main functional domain (NGF) of the BDNF gene in mammals.We observe novel results with 6 sites in our homologous alignment which are under episodic selection in the early regulatory region of BDNF (i.e. the prodomain).We observe 23 pairs of coevolving sites within BDNF. Many of which are a part of complex spatial relationships and are distributed across the entire BDNF gene.These data define exactly how “BDNF is highly conserved” by defining exactly where and how the mammalian BDNF has evolved, confirming the widespread belief that the BDNF prodomain is more prone to change than the mature BDNF protein.

Recombination contributes to the genetic diversity found in coronaviruses and is known to be a prominent mechanism whereby they evolve. It is apparent, both from controlled experiments and in genome sequences sampled from nature, that patterns of recombination in coronaviruses are non-random and that this is likely attributable to a combination of sequence features that favour the occurrence of recombination break points at specific genomic sites, and selection disfavouring the survival of recombinants within which favourable intra-genome interactions have been disrupted. Here we leverage available whole-genome sequence data for six coronavirus subgenera to identify specific patterns of recombination that are conserved between multiple subgenera and then identify the likely factors that underlie these conserved patterns. Specifically, we confirm the non-randomness of recombination break points across all six tested coronavirus subgenera, locate conserved recombination hot- and cold-spots, and determine that the locations of transcriptional regulatory sequences are likely major determinants of conserved recombination break-point hotspot locations. We find that while the locations of recombination break points are not uniformly associated with degrees of nucleotide sequence conservation, they display significant tendencies in multiple coronavirus subgenera to occur in low guanine-cytosine content genome regions, in non-coding regions, at the edges of genes, and at sites within the Spike gene that are predicted to be minimally disruptive of Spike protein folding. While it is apparent that sequence features such as transcriptional regulatory sequences are likely major determinants of where the template-switching events that yield recombination break points most commonly occur, it is evident that selection against misfolded recombinant proteins also strongly impacts observable recombination break-point distributions in coronavirus genomes sampled from nature.

Unmitigated spread in Brazil Despite an extensive network of primary care availability, Brazil has suffered profoundly during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Using daily data from state health offices, Castro et al. analyzed the pattern of spread of COVID-19 cases and deaths in the country from February to October 2020. Clusters of deaths before cases became apparent indicated unmitigated spread. SARS-CoV-2 circulated undetected in Brazil for more than a month as it spread north from Sã o Paulo. In Manaus, transmission reached unprecedented levels after a momentary respite in mid-2020. Faria et al. tracked the evolution of a new, more aggressive lineage called P.1, which has 17 mutations, including three (K417T, E484K, and N501Y) in the spike protein. After a period of accelerated evolution, this variant emerged in Brazil during November 2020. Coupled with the emergence of P.1, disease spread was accelerated by stark local inequalities and political upheaval, which compromised a prompt federal response. Science , abh1558 and abh2644, this issue p. 821 and p. 815

Continued uncontrolled transmission of SARS-CoV-2 in many parts of the world is creating conditions for substantial evolutionary changes to the virus1,2. Here we describe a newly arisen lineage of SARS-CoV-2 (designated 501Y.V2; also known as B.1.351 or 20H) that is defined by eight mutations in the spike protein, including three substitutions (K417N, E484K and N501Y) at residues in its receptor-binding domain that may have functional importance3,4,5. This lineage was identified in South Africa after the first wave of the epidemic in a severely affected metropolitan area (Nelson Mandela Bay) that is located on the coast of the Eastern Cape province. This lineage spread rapidly, and became dominant in Eastern Cape, Western Cape and KwaZulu–Natal provinces within weeks. Although the full import of the mutations is yet to be determined, the genomic data—which show rapid expansion and displacement of other lineages in several regions—suggest that this lineage is associated with a selection advantage that most plausibly results from increased transmissibility or immune escape6,7,8.

Background: With the approval of the HIV-1 capsid inhibitor, lenacapavir, capsid sequencing will be required for managing lenacapavir-experienced individuals with detectable viremia. Successful sequence interpretation will require examining new capsid sequences in the context of previously published sequence data. Methods: We analyzed published HIV-1 group M capsid sequences from 21,012 capsid-inhibitor naïve individuals to characterize amino acid variability at each position and influence of subtype and cytotoxic T lymphocyte (CTL) selection pressure. We determined the distributions of usual mutations, defined as amino acid differences from the group M consensus, with a prevalence ≥ 0.1%. Co-evolving mutations were identified using a phylogenetically-informed Bayesian graphical model method. Results: 162 (70.1%) positions had no usual mutations (45.9%) or only conservative usual mutations with a positive BLOSUM62 score (24.2%). Variability correlated independently with subtype-specific amino acid occurrence (Spearman rho = 0.83; p < 1 × 10−9) and the number of times positions were reported to contain an HLA-associated polymorphism, an indicator of CTL pressure (rho = 0.43; p = 0.0002). Conclusions: Knowing the distribution of usual capsid mutations is essential for sequence quality control. Comparing capsid sequences from lenacapavir-treated and lenacapavir-naïve individuals will enable the identification of additional mutations potentially associated with lenacapavir therapy.

During the course of infection, human immunodeficiency virus (HIV) maintains a stably integrated reservoir of replication-competent proviruses within the host genome that are unaffected by antiretroviral therapy. Curative advancements rely heavily on targeting the reservoir, though determinants of its evolutionary origins remain ill-supported through current strategies and are often limited by sample variety. Here, we describe a single-cell deoxyribonucleic acid sequencing (scDNA-seq) method, optimized for sequencing of proviral and host DNA from a treatment-interrupted HIV animal model. We report its benefits for improving viral reservoir resolution to support critical evolutionary events otherwise considered unreliable using traditional viral envelope gene signal alone, as well as comparative advantages to existing near-full-length genome sequencing methods. Given the variety of proviral characteristics that may influence viral rebound, scDNA-seq holds immense value in its ability to streamline many of the present-day applications available in viral reservoir studies, such as integration status and putative replication competency.

Among the 30 nonsynonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (1) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (2) interactions of Spike with ACE2 receptors, and (3) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron overall previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected. ispartof: MOLECULAR BIOLOGY AND EVOLUTION vol:39 issue:4 ispartof: location:United States status: published

SARS-CoV-2 Omicron variants contain many mutations in its spike receptor binding domain, the target of all authorized monoclonal antibodies (mAbs). Determining the extent to which Omicron variants reduced mAb susceptibility is critical to preventing and treating COVID-19. We systematically reviewed PubMed and three preprint servers, last updated February 22, 2022, of the in vitro activity of authorized mAbs against the Omicron variants. Thirty-three studies were eligible including 33 containing Omicron BA.1 susceptibility data and five that also contained Omicron BA.2 susceptibility data. The first two authorized mAb combinations, bamlanivimab/etesevimab and casirivimab/imdevimab, were inactive against the Omicron BA.1 and BA.2 variants. In 24 studies, sotrovimab (third authorized mAb) displayed a median 4.1-fold (IQR: 2.4-7.6) reduced activity against Omicron BA.1 and, in four studies, a median 26-fold (IQR:16-35) reduced activity against Omicron BA.2. In 18 studies, cilgavimab and tixagevimab independently displayed median reductions in activity of >300-fold against Omicron BA.1, while in ten studies, the cilgavimab/tixagevimab combination (fourth authorized mAb preparation) displayed a median 63-fold (IQR: 26-145) reduced activity against Omicron BA.1. In two studies, cilgavimab was approximately 100-fold more susceptible to BA.2 than to BA.1. In two studies, bebtelovimab, the most recently authorized mAb, was fully active against both the Omicron variants. Disparate results between assays were common as evidenced by a median 42-fold range (IQR: 25-625) in IC50 between assays for the eight authorized individual mAbs and three authorized mAb combinations. Highly disparate results between published assays indicates a need for improved mAb susceptibility test standardization or inter-assay calibration.

A canine coronavirus (CCoV) has now been reported from two independent human samples from Malaysia (respiratory, collected in 2017–2018; CCoV-HuPn-2018) and Haiti (urine, collected in 2017); these two viruses were nearly genetically identical. In an effort to identify any novel adaptations associated with this apparent shift in tropism we carried out detailed evolutionary analyses of the spike gene of this virus in the context of related Alphacoronavirus 1 species. The spike 0-domain retains homology to CCoV2b (enteric infections) and Transmissible Gastroenteritis Virus (TGEV; enteric and respiratory). This domain is subject to relaxed selection pressure and an increased rate of molecular evolution. It contains unique amino acid substitutions, including within a region important for sialic acid binding and pathogenesis in TGEV. Overall, the spike gene is extensively recombinant, with a feline coronavirus type II strain serving a prominent role in the recombinant history of the virus. Molecular divergence time for a segment of the gene where temporal signal could be determined, was estimated at around 60 years ago. We hypothesize that the virus had an enteric origin, but that it may be losing that particular tropism, possibly because of mutations in the sialic acid binding region of the spike 0-domain.

A number of evolutionary hypotheses can be tested by comparing selective pressures among sets of branches in a phylogenetic tree. When the question of interest is to identify specific sites within genes that may be evolving differently, a common approach is to perform separate analyses on subsets of sequences and compare parameter estimates in a post hoc fashion. This approach is statistically suboptimal and not always applicable. Here, we develop a simple extension of a popular fixed effects likelihood method in the context of codon-based evolutionary phylogenetic maximum likelihood testing, Contrast-FEL. It is suitable for identifying individual alignment sites where any among the K≥2 sets of branches in a phylogenetic tree have detectably different ω ratios, indicative of different selective regimes. Using extensive simulations, we show that Contrast-FEL delivers good power, exceeding 90% for sufficiently large differences, while maintaining tight control over false positive rates, when the model is correctly specified. We conclude by applying Contrast-FEL to data from five previously published studies spanning a diverse range of organisms and focusing on different evolutionary questions.

Small heat shock proteins (sHSPs) emerged early in evolution and occur in all domains of life and nearly in all species, including humans. Mutations in four sHSPs (HspB1, HspB3, HspB5, HspB8) are associated with neuromuscular disorders. The aim of this study is to investigate the evolutionary forces shaping these sHSPs during vertebrate evolution. We performed comparative evolutionary analyses on a set of orthologous sHSP sequences, based on the ratio of non-synonymous: synonymous substitution rates for each codon. We found that these sHSPs had been historically exposed to different degrees of purifying selection, decreasing in this order: HspB8 > HspB1, HspB5 > HspB3. Within each sHSP, regions with different degrees of purifying selection can be discerned, resulting in characteristic selective pressure profiles. The conserved α-crystallin domains were exposed to the most stringent purifying selection compared to the flanking regions, supporting a 'dimorphic pattern' of evolution. Thus, during vertebrate evolution the different sequence partitions were exposed to different and measurable degrees of selective pressures. Among the disease-associated mutations, most are missense mutations primarily in HspB1 and to a lesser extent in the other sHSPs. Our data provide an explanation for this disparate incidence. Contrary to the expectation, most missense mutations cause dominant disease phenotypes. Theoretical considerations support a connection between the historic exposure of these sHSP genes to a high degree of purifying selection and the unusual prevalence of genetic dominance of the associated disease phenotypes. Our study puts the genetics of inheritable sHSP-borne diseases into the context of vertebrate evolution.

SARS-CoV-2 evolution threatens vaccine- and natural infection-derived immunity as well as the efficacy of therapeutic antibodies. To improve public health preparedness, we sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. We tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network-based protein sequence modeling, and identified primary biological drivers of SARS-CoV-2 intra-pandemic evolution. We found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. We retroactively identified with high accuracy (area under the receiver operator characteristic curve, AUROC=0.92-0.97) mutations that will spread, at up to four months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure wherein epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. We validate this result against Omicron, showing elevated predictive scores for its component mutations prior to emergence, and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.

The lack of an identifiable intermediate host species for the proximal animal ancestor of SARS-CoV-2, and the large geographical distance between Wuhan and where the closest evolutionary related coronaviruses circulating in horseshoe bats (members of the Sarbecovirus subgenus) have been identified, is fueling speculation on the natural origins of SARS-CoV-2. We performed a comprehensive phylogenetic study on SARS-CoV-2 and all the related bat and pangolin sarbecoviruses sampled so far. Determining the likely recombination events reveals a highly reticulate evolutionary history within this group of coronaviruses. Distribution of the inferred recombination events is nonrandom with evidence that Spike, the main target for humoral immunity, is beside a recombination hotspot likely driving antigenic shift events in the ancestry of bat sarbecoviruses. Coupled with the geographic ranges of their hosts and the sampling locations, across southern China, and into Southeast Asia, we confirm that horseshoe bats, Rhinolophus, are the likely reservoir species for the SARS-CoV-2 progenitor. By tracing the recombinant sequence patterns, we conclude that there has been relatively recent geographic movement and cocirculation of these viruses’ ancestors, extending across their bat host ranges in China and Southeast Asia over the last 100 years. We confirm that a direct proximal ancestor to SARS-CoV-2 has not yet been sampled, since the closest known relatives collected in Yunnan shared a common ancestor with SARS-CoV-2 approximately 40 years ago. Our analysis highlights the need for dramatically more wildlife sampling to: 1) pinpoint the exact origins of SARS-CoV-2’s animal progenitor, 2) the intermediate species that facilitated transmission from bats to humans (if there is one), and 3) survey the extent of the diversity in the related sarbecoviruses’ phylogeny that present high risk for future spillovers.

Recombination is an evolutionary process by which many pathogens generate diversity and acquire novel functions. Although a common occurrence during coronavirus replication, recombination can only be detected when two genetically distinct viruses contemporaneously infect the same host. Here, we identify an instance of SARS-CoV-2 superinfection, whereby an individual was simultaneously infected with two distinct viral variants: Alpha (B.1.1.7) and Epsilon (B.1.429). This superinfection was first noted when an Alpha genome sequence failed to exhibit the classic S gene target failure behavior used to track this variant. Full genome sequencing from four independent extracts revealed that Alpha variant alleles comprised between 70-80% of the genomes, whereas the Epsilon variant alleles comprised between 20-30% of the sample. Further investigation revealed the presence of numerous recombinant haplotypes spanning the genome, specifically in the spike, nucleocapsid, and ORF 8 coding regions. These findings support the potential for recombination to reshape SARS-CoV-2 genetic diversity.

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Recombination is an evolutionary process by which many pathogens generate diversity and acquire novel functions. Although a common occurrence during coronavirus replication, detection of recombination is only feasible when genetically distinct viruses contemporaneously infect the same host. Here, we identify an instance of SARS-CoV-2 superinfection, whereby an individual was infected with two distinct viral variants: Alpha (B.1.1.7) and Epsilon (B.1.429). This superinfection was first noted when an Alpha genome sequence failed to exhibit the classic S gene target failure behavior used to track this variant. Full genome sequencing from four independent extracts reveals that Alpha variant alleles comprise around 75% of the genomes, whereas the Epsilon variant alleles comprise around 20% of the sample. Further investigation reveals the presence of numerous recombinant haplotypes spanning the genome, specifically in the spike, nucleocapsid, and ORF 8 coding regions. These findings support the potential for recombination to reshape SARS-CoV-2 genetic diversity.

Two years after the start of the COVID-19 pandemic, key questions about the emergence of its aetiological agent (SARS-CoV-2) remain a matter of considerable debate. Identifying when SARS-CoV-2 began spreading among people is one of those questions. Although the current canonically accepted timeline hypothesises viral emergence in Wuhan, China, in November or December 2019, a growing body of diverse studies provides evidence that the virus may have been spreading worldwide weeks, or even months, prior to that time. However, the hypothesis of earlier SARS-CoV-2 circulation is often dismissed with prejudicial scepticism and experimental studies pointing to early origins are frequently and speculatively attributed to false-positive tests. In this paper, we critically review current evidence that SARS-CoV-2 had been circulating prior to December of 2019, and emphasise how, despite some scientific limitations, this hypothesis should no longer be ignored and considered sufficient to warrant further larger-scale studies to determine its veracity.

SummaryThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants respectively1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Although most laboratories are capable of employing established protocols to perform full-genome SARS-CoV-2 sequencing, many are unable to assess sequence quality, select appropriate mutation-detection thresholds, or report on the potential clinical significance of mutations in the targets of antiviral therapy METHODS: We describe the technical aspects and benchmark the performance of Sierra SARS-CoV-2, a program designed to perform these functions on user-submitted FASTQ and FASTA sequence files and lists of Spike mutations. Sierra SARS-CoV-2 indicates which sequences contain an unexpectedly large number of unusual mutations and which mutations are associated with reduced susceptibility to clinical stage mAbs, the RdRP inhibitor remdesivir, or the Mpro inhibitor nirmatrelvir RESULTS: To assess the performance of Sierra SARS-CoV-2 on FASTQ files, we applied it to 600 representative FASTQ sequences and compared the results to the COVID-19 EDGE program. To assess its performance on FASTA files, we applied it to nearly one million representative FASTA sequences and compared the results to the GISAID mutation annotation. To assess its performance on mutations lists, we applied it to 13,578 distinct Spike RBD mutation patterns and showed that exactly or partially matching annotations were available for 88% of patterns CONCLUSION: Sierra SARS-CoV-2 leverages previously published data to improve the quality control of submitted viral genomic data and to provide functional annotation on the impact of mutations in the targets of antiviral SARS-CoV-2 therapy. The program can be found at https://covdb.stanford.edu/sierra/sars2/ and its source code at https://github.com/hivdb/sierra-sars2.

A recent study reported the occurrence of Canine Coronavirus (CCoV) in nasopharyngeal swabs from a small number of patients hospitalized with pneumonia during a 2017-18 period in Sarawak, Malaysia. Because the genome sequence for one of these isolates is available, we conducted comparative evolutionary analyses of the spike gene of this strain (CCoV-HuPn-2018), with other available Alphacoronavirus 1 spike sequences. The most N-terminus subdomain (0-domain) of the CCoV-HuPn-2018 spike protein has sequence similarity to Transmissible Gastroenteritis Virus (TGEV) and CCoV2b strains, but not to other members of the type II Alphacoronaviruses (i.e., CCoV2a and Feline CoV2-FCoV2). This 0-domain in CCoV-HuPn-2018 has evidence for relaxed selection pressure, an increased rate of molecular evolution, and a number of unique amino acid substitutions relative to CCoV2b and TGEV sequences. A region of the 0-domain determined to be key to sialic acid binding and pathogenesis in TGEV had clear differences in amino acid sequences in CCoV-HuPn-2018 relative to both CCoV2b (enteric) and TGEV (enteric and respiratory). The 0-domain of CCoV-HuPn-2018 also had several sites inferred to be under positive diversifying selection, including sites within the signal peptide. Downstream of the 0-domain, FCoV2 shared sequence similarity to the CCoV2b and TGEV sequences, with analyses of this larger alignment identifying positively selected sites in the putative Receptor Binding Domain (RBD) and Connector Domain (CD). Recombination analyses strongly implicated a particular FCoV2 strain in the recombinant history of CCoV-HuPn-2018 with molecular divergence times estimated at around 60 years ago. We hypothesize that CCoV-HuPn-2018 had an enteric origin, but that it has lost that particular tropism, because of mutations in the sialic acid binding region of the spike 0-domain. As selection pressure on this region was reduced, the virus evolved a respiratory tropism, analogous to other Alphacoronavirus 1, such as Porcine Respiratory Coronavirus (PRCV), that have lost this region entirely. We also suggest that signals of positive selection in the signal peptide as well as other changes in the 0-domain of CCoV-HuPn-2018 could represent an adaptive role in this new host and that this could be in part due to the different spatial distribution of the N-linked glycan repertoire for this strain.

The past several months have witnessed the emergence of SARS-CoV-2 variants with novel spike protein mutations that are influencing the epidemiological and clinical aspects of the COVID-19 pandemic. These variants can increase rates of virus transmission and/or increase the risk of reinfection and reduce the protection afforded by neutralizing monoclonal antibodies and vaccination. These variants can therefore enable SARS-CoV-2 to continue its spread in the face of rising population immunity while maintaining or increasing its replication fitness. The identification of four rapidly expanding virus lineages since December 2020, designated variants of concern, has ushered in a new stage of the pandemic. The four variants of concern, the Alpha variant (originally identified in the UK), the Beta variant (originally identified in South Africa), the Gamma variant (originally identified in Brazil) and the Delta variant (originally identified in India), share several mutations with one another as well as with an increasing number of other recently identified SARS-CoV-2 variants. Collectively, these SARS-CoV-2 variants complicate the COVID-19 research agenda and necessitate additional avenues of laboratory, epidemiological and clinical research., In this Review, the authors describe our latest understanding of the emergence and properties of SARS-CoV-2 genetic variants, particularly those designated as WHO (World Health Organization) ‘variants of concern’. They focus on the consequences of these variants for antibody-mediated virus neutralization, with important implications for reinfection risk and for vaccine effectiveness., Key points The past several months have witnessed the emergence of four SARS-CoV-2 variants of concern (Alpha, Beta, Gamma and Delta) associated with increased transmissibility, increased risk of reinfection and/or reduced vaccine efficacy.Many additional SARS-CoV-2 variants sharing mutations and biological features with these variants are also increasingly being identified.The increasing number of SARS-CoV-2 variants share a repertoire of mutations that is enabling the virus to spread despite rising population immunity while maintaining or increasing its replication fitness.Whereas most emerging mutations reduce the protective effects of neutralizing antibodies generated by infection and vaccination, several recently identified mutations appear to antagonize the innate immune response to initial infection.The emergence of SARS-CoV-2 variants requires an expanded research agenda to improve our understanding of emerging SARS-CoV-2 mutations and the correlates of protective immunity against variants with these mutations.

The programmed frameshift element (PFE) rerouting translation from ORF1a to ORF1b is essential for the propagation of coronaviruses. The combination of genomic features that make up PFE—the overlap between the two reading frames, a slippery sequence, as well as an ensemble of complex secondary structure elements—places severe constraints on this region as most possible nucleotide substitution may disrupt one or more of these elements. The vast amount of SARS-CoV-2 sequencing data generated within the past year provides an opportunity to assess the evolutionary dynamics of PFE in great detail. Here, we performed a comparative analysis of all available coronaviral genomic data available to date. We show that the overlap between ORF1a and ORF1b evolved as a set of discrete 7, 16, 22, 25, and 31 nucleotide stretches with a well-defined phylogenetic specificity. We further examined sequencing data from over 1,500,000 complete genomes and 55,000 raw read data sets to demonstrate exceptional conservation and detect signatures of selection within the PFE region.

Humanity is currently facing the challenge of two devastating pandemics caused by two very different RNA viruses: HIV-1, which has been with us for decades, and SARS-CoV-2, which has swept the world in the course of a single year. The same evolutionary strategies that drive HIV-1 evolution are at play in SARS-CoV-2. Single nucleotide mutations, multi-base insertions and deletions, recombination, and variation in surface glycans all generate the variability that, guided by natural selection, enables both HIV-1’s extraordinary diversity and SARS-CoV-2’s slower pace of mutation accumulation. Even though SARS-CoV-2 diversity is more limited, recently emergent SARS-CoV-2 variants carry Spike mutations that have important phenotypic consequences in terms of both antibody resistance and enhanced infectivity. We review and compare how these mutational patterns manifest in these two distinct viruses to provide the variability that fuels their evolution by natural selection. Fil: Fischer, Will. Los Alamos National Laboratory; Estados Unidos. New Mexico Consortium; México Fil: Giorgi, Elena E.. New Mexico Consortium; México. Los Alamos National Laboratory; Estados Unidos Fil: Chakraborty, Srirupa. Center For Nonlinear Studies; Estados Unidos. Los Alamos National Laboratory; Estados Unidos Fil: Nguyen, Kien. Los Alamos National Laboratory; Estados Unidos Fil: Bhattacharya, Tanmoy. Los Alamos National Laboratory; Estados Unidos Fil: Theiler, James. Los Alamos National Laboratory; Estados Unidos Fil: Goloboff, Pablo Augusto. American Museum of Natural History; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; Argentina Fil: Yoon, Hyejin. Los Alamos National Laboratory; Estados Unidos Fil: Abfalterer, Werner. Los Alamos National Laboratory; Estados Unidos Fil: Foley, Brian T.. Los Alamos National Laboratory; Estados Unidos Fil: Tegally, Houriiyah. University Of Kwazulu-natal; Sudáfrica Fil: San, James Emmanuel. University Of Kwazulu-natal; Sudáfrica Fil: de Oliveira, Tulio. University of KwaZulu-Natal; Sudáfrica Fil: Gnanakaran, Sandrasegaram. Los Alamos National Laboratory; Estados Unidos Fil: Korber, Bette. Los Alamos National Laboratory; Estados Unidos. New Mexico Consortium; México

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, whilst the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), whilst a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1 and SARS-CoV-2), we developed a methodological pipeline to classify shared non-synonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection, and draw upon protein structure data to identify potential biological implications. We find 30 candidate mutations, from which four [codon sites 18121 (nsp14/residue 28), 21623 (spike/21), 21635 (spike/25) and 23948 (spike/796); SARS-CoV-2 genome numbering] further display evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity.

Significance The ratio of the nonsynonymous codon-substitution rate to that for synonymous codons ( d N / d S ) is widely used to estimate the strength and direction of selection. However, many synonymous changes are under selection, particularly in genes with high expression levels. We developed a model to include both neutral and selected synonymous substitutions and applied it to a large order of bacteria. The analysis revealed that the conventional estimate of dS is usually well below the strictly neutral rate (80% on average). It follows that conventional dN/dS estimates that are less than 1 substantially underestimate the level of negative selection and that cases of apparent positive selection ( dN/dS > 1), as well as other applications of dN/dS , should be reconsidered.

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data.Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org.