Global landscape of SARS-CoV-2 mutations and conserved regions

Journal of Translational Medicine

Research

Mahsa Mollapour Sisakht¹  Karim Rahimian²  Bahman Moradi³  Bahar Mahdavi⁴  Mohammad Hadi Abbasian⁵  Mohammadamin Mahmanzar⁶  Youping Deng⁶  Samaneh Tokhanbigli⁷ 

[1] Department of Biochemistry, Erasmus University Medical Center, 2040, 3000 CA, Rotterdam, The Netherlands;Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran;Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran;Department of Computer Science, Tarbiat Modares University, Tehran, Iran;Department of Medical Genetics, National Institute for Genetic Engineering and Biotechnology, Tehran, Iran;Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, 96813, Honolulu, HI, USA;Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, Australia;

关键词: SARS-CoV-2;    COVID-19;    Emerging variants;    Genome;    Amino Acid;    Vaccines;   

DOI  :  10.1186/s12967-023-03996-w

 received in 2022-12-06, accepted in 2023-02-15,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAt the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.Methods10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.ResultsSix mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively.ConclusionTherefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.

【 授权许可】

CC BY   
© The Author(s) 2023

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