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Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution.


ABSTRACT: Many large national and transnational studies have been dedicated to the analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome, most of which focused on missense and nonsense mutations. However, approximately 30 per cent of the SARS-CoV-2 variants are synonymous, therefore changing the target codon without affecting the corresponding protein sequence. By performing a large-scale analysis of sequencing data generated from almost 400,000 SARS-CoV-2 samples, we show that silent mutations increasing the similarity of viral codons to the human ones tend to fixate in the viral genome overtime. This indicates that SARS-CoV-2 codon usage is adapting to the human host, likely improving its effectiveness in using the human aminoacyl-tRNA set through the accumulation of deceitfully neutral silent mutations. One-Sentence Summary. Synonymous SARS-CoV-2 mutations related to the activity of different mutational processes may positively impact viral evolution by increasing its adaptation to the human codon usage.

SUBMITTER: Ramazzotti D 

PROVIDER: S-EPMC8971538 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution.

Ramazzotti Daniele D   Angaroni Fabrizio F   Maspero Davide D   Mauri Mario M   D'Aliberti Deborah D   Fontana Diletta D   Antoniotti Marco M   Elli Elena Maria EM   Graudenzi Alex A   Piazza Rocco R  

Virus evolution 20220324 1


Many large national and transnational studies have been dedicated to the analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome, most of which focused on missense and nonsense mutations. However, approximately 30 per cent of the SARS-CoV-2 variants are synonymous, therefore changing the target codon without affecting the corresponding protein sequence. By performing a large-scale analysis of sequencing data generated from almost 400,000 SARS-CoV-2 samples, we show that s  ...[more]

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