Probing SARS-CoV-2 sequence diversity of Pakistani isolates.
Ontology highlight
ABSTRACT:
SUBMITTER: Rehman Z
PROVIDER: S-EPMC8035043 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20 th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states. GNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID ) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed. Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.
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Project description:Tracking temporal and spatial genomic changes and evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are among the most urgent research topics worldwide, which help to elucidate the coronavirus disease 2019 (COVID-19) pathogenesis and the effect of deleterious variants. Our current study concentrates genetic diversity of SARS-CoV-2 variants in Uzbekistan and their associations with COVID-19 severity. Thirty-nine whole genome sequences (WGS) of SARS-CoV-2 isolated from PCR-positive patients from Tashkent, Uzbekistan for the period of July-August 2021, were generated and further subjected to further genomic analysis. Genome-wide annotations of clinical isolates from our study have revealed a total of 223 nucleotide-level variations including SNPs and 34 deletions at different positions throughout the entire genome of SARS-CoV-2. These changes included two novel mutations at the Nonstructural protein (Nsp) 13: A85P and Nsp12: Y479N, which were unreported previously. There were two groups of co-occurred substitution patterns: the missense mutations in the Spike (S): D614G, Open Reading Frame (ORF) 1b: P314L, Nsp3: F924, 5`UTR:C241T; Nsp3:P2046L and Nsp3:P2287S, and the synonymous mutations in the Nsp4:D2907 (C8986T), Nsp6:T3646A and Nsp14:A1918V regions, respectively. The "Nextstrain" clustered the largest number of SARS-CoV-2 strains into the Delta clade (n = 32; 82%), followed by two Alpha-originated (n = 4; 10,3%) and 20A (n = 3; 7,7%) clades. Geographically the Delta clade sample sequences were grouped into several clusters with the SARS-CoV genotypes from Russia, Denmark, USA, Egypt and Bangladesh. Phylogenetically, the Delta isolates in our study belong to the two main subclades 21A (56%) and 21J (44%). We found that females were more affected by 21A, whereas males by 21J variant (χ2 = 4.57; p ≤ 0.05, n = 32). The amino acid substitution ORF7a:P45L in the Delta isolates found to be significantly associated with disease severity. In conclusion, this study evidenced that Identified novel substitutions Nsp13: A85P and Nsp12: Y479N, have a destabilizing effect, while missense substitution ORF7a: P45L significantly associated with disease severity.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that was first reported in Wuhan, China in December of 2019 and has since caused a global pandemic resulting in millions of deaths and tens of millions of patients testing positive for infection. Analysis of different viral strains has identified a D614G change in the spike protein that is correlated with the virus becoming more transmissible. While studies have shown G614 viruses to be more transmissible, the effects of this mutation on the host response, especially on the cellular level, are yet to be fully elucidated. In this experiment we infected NHBE cells with the Washington (D614) strain or the New York (G614) strains of SARS-CoV-2. We generated RNA sequencing data at three different time points to improve our understanding of how the intracellular host response differs between infections with these two strains. We analyzed these data with a bioinformatics pipeline that identifies differentially expressed genes, enriched Gene Ontology terms and dysregulated signaling pathways. We detected over 2,000 differentially expressed genes, over 600 Gene Ontology terms, and 29 affected pathways between the two treatments. Many of these entities play a role in immune signaling and response. When comparing the different strains and different time points we found more overall similarities between matched time points than across different time points with the same strain. When specifically comparing the affected pathways, we saw that the 24hr time point of the New York strain was more similar to the 12hr time point of the Washington strain with a large number of pathways related to translation being inhibited in both strains at these time points. These results suggest that D614G substitution in the spike protein, combined with other nonsynonymous changes in the viral gene products cause distinct responses in infected host cells, especially relating to how quickly translation is dysregulated after infection. These observed differences in the intracellular host response to infection could play a role in driving the increase in pathogenicity and mortality seen in the New York outbreaks versus the Washington outbreaks at the beginning of the SARS-CoV-2 pandemic.
2022-09-06 | GSE207923 | GEO
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