Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness.
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ABSTRACT:
SUBMITTER: Obermeyer F
PROVIDER: S-EPMC9161372 | biostudies-literature | 2022 Jun
REPOSITORIES: biostudies-literature
Publications
Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness.
Obermeyer Fritz F Jankowiak Martin M Barkas Nikolaos N Schaffner Stephen F SF Pyle Jesse D JD Yurkovetskiy Leonid L Bosso Matteo M Park Daniel J DJ Babadi Mehrtash M MacInnis Bronwyn L BL Luban Jeremy J Luban Jeremy J Sabeti Pardis C PC Lemieux Jacob E JE
Science (New York, N.Y.) 20220524 6599
Repeated emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR<sub>0</sub>, a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR<sub>0</sub> to all publicl ...[more]
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Project description:Repeated emergence of SARS-CoV-2 variants with increased fitness necessitates rapid detection and characterization of new lineages. To address this need, we developed PyR 0 , a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR 0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR 0 forecasts growth of new lineages from their mutational profile, identifies viral lineages of concern as they emerge, and prioritizes mutations of biological and public health concern for functional characterization.One sentence summaryA Bayesian hierarchical model of all SARS-CoV-2 viral genomes predicts lineage fitness and identifies associated mutations.
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Project description:Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino-acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.
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Project description:Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here, we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.
| S-EPMC10506532 | biostudies-literature
Project description:The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.
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| S-EPMC7959702 | biostudies-literature
Project description:Over 10,000 viral genome sequences of the SARS-CoV-2virus have been made readily available during the ongoing coronavirus pandemic since the initial genome sequence of the virus was released on the open access Virological website (http://virological.org/) early on January 11. We utilize the published data on the single stranded RNAs of 11,132 SARS-CoV-2 patients in the GISAID database, which contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Among many important research questions which are currently being investigated, one aspect pertains to the genetic characterization/classification of the virus. We analyze data on the nucleotide sequencing of the virus and geographic information of a subset of 7640 SARS-CoV-2 patients without missing entries that are available in the GISAID database. Instead of modeling the mutation rate, applying phylogenetic tree approaches, and so forth, we here utilize a model-free clustering approach that compares the viruses at a genome-wide level. We apply principal component analysis to a similarity matrix that compares all pairs of these SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index. Our analysis results of the SARS-CoV-2 genome data illustrates the geographic and chronological progression of the virus, starting from the first cases that were observed in China to the current wave of cases in Europe and North America. This is in line with a phylogenetic analysis which we use to contrast our results. We also observe that, based on their sequence data, the SARS-CoV-2 viruses cluster in distinct genetic subgroups. It is the subject of ongoing research to examine whether the genetic subgroup could be related to diseases outcome and its potential implications for vaccine development.
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Project description:The evolutional process of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) development remains inconclusive. This study compared the genome sequences of severe acute respiratory syndrome coronavirus (SARS-CoV), bat coronavirus RaTG13, and SARS-CoV-2. In total, the genomes of SARS-CoV-2 and RaTG13 were 77.9% and 77.7% identical to the genome of SARS-CoV, respectively. A total of 3.6% (1,068 bases) of the SARS-CoV-2 genome was derived from insertion and/or deletion (indel) mutations, and 18.6% (5,548 bases) was from point mutations from the genome of SARS-CoV. At least 35 indel sites were confirmed in the genome of SARS-CoV-2, in which 17 were with ≥10 consecutive bases long. Ten of these relatively long indels were located in the spike (S) gene, five in nonstructural protein 3 (Nsp3) gene of open reading frame (ORF) 1a, and one in ORF8 and noncoding region. Seventeen (48.6%) of the 35 indels were based on insertion-and-deletion mutations with exchanged gene sequences of 7-325 consecutive bases. Almost the complete ORF8 gene was replaced by a single 325 consecutive base-long indel. The distribution of these indels was roughly in accordance with the distribution of the rate of point mutation rate around the indels. The genome sequence of SARS-CoV-2 was 96.0% identical to that of RaTG13. There was no long insertion-and-deletion mutation between the genomes of RaTG13 and SARS-CoV-2. The findings of the uneven distribution of multiple indels and the presence of multiple long insertion-and-deletion mutations with exchanged consecutive base sequences in the viral genome may provide insights into SARS-CoV-2 development. IMPORTANCE The developmental mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains inconclusive. This study compared the base sequence one-by-one between severe acute respiratory syndrome coronavirus (SARS-CoV) or bat coronavirus RaTG13 and SARS-CoV-2. The genomes of SARS-CoV-2 and RaTG13 were 77.9% and 77.7% identical to the genome of SARS-CoV, respectively. Seventeen of the 35 sites with insertion and/or deletion mutations between SARS-CoV-2 and SARS-CoV were based on insertion-and-deletion mutations with the replacement of 7-325 consecutive bases. Most of these long insertion-and-deletion sites were concentrated in the nonstructural protein 3 (Nsp3) gene of open reading frame (ORF) 1a, S1 domain of the spike protein, and ORF8 genes. Such long insertion-and-deletion mutations were not observed between the genomes of RaTG13 and SARS-CoV-2. The presence of multiple long insertion-and-deletion mutations in the genome of SARS-CoV-2 and their uneven distributions may provide further insights into the development of the virus.
| S-EPMC9241832 | biostudies-literature
Project description:Abstract SARS-CoV-2 continues to evolve, causing waves of the pandemic. Up to May 2022, 10 million genome sequences have accumulated, which are classified into five major variants of concern. With the growing number of sequenced genomes, analysis of the big dataset has become increasingly challenging. Here we developed systematic approaches based on sets of correlated single nucleotide variations (SNVs) for comprehensive subtyping and pattern recognition of transmission dynamics. The approach outperformed single-SNV and spike-centric scans. Moreover, the derived subtypes elucidate the relationship of signature SNVs and transmission dynamics. We found that different subtypes of the same variant, including Delta and Omicron exhibited distinct temporal trajectories. For example, some Delta and Omicron subtypes did not spread rapidly, while others did. We identified sets of characteristic SNVs that appeared to enhance transmission or decrease efficacy of antibodies for some subtypes. We also identified a set of SNVs that appeared to suppress transmission or increase viral sensitivity to antibodies. For the Omicron variant, the dominant type in the world, we identified the subtypes with enhanced and suppressed transmission in an analysis of eight million genomes as of March 2022 and further confirmed the findings in a later analysis of ten million genomes as of May 2022. While the “enhancer” SNVs exhibited an enriched presence on the spike protein, the “suppressor” SNVs are mainly elsewhere. Disruption of the SNV correlation largely destroyed the enhancer-suppressor phenomena. These results suggest the importance of fine subtyping of variants, and point to potential complex interactions among SNVs.
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Project description:The second wave of SARS-CoV-2 has hit India hard and though the vaccination drive has started, moderate number of COVID affected patients is still present in the country, thereby leading to the analysis of the evolving virus strains. In this regard, multiple sequence alignment of 17271 Indian SARS-CoV-2 sequences is performed using MAFFT followed by their phylogenetic analysis using Nextstrain. Subsequently, mutation points as SNPs are identified by Nextstrain. Thereafter, from the aligned sequences temporal and spatial analysis are carried out to identify top 10 hotspot mutations in the coding regions based on entropy. Finally, to judge the functional characteristics of all the non-synonymous hotspot mutations, their changes in proteins are evaluated as biological functions considering the sequences by using PolyPhen-2 while I-Mutant 2.0 evaluates their structural stability. For both temporal and spatial analysis, there are 21 non-synonymous hotspot mutations which are unstable and damaging.
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