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Global landscape of SARS-CoV-2 mutations and conserved regions
BACKGROUND: At 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 ha...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958328/ https://www.ncbi.nlm.nih.gov/pubmed/36841805 http://dx.doi.org/10.1186/s12967-023-03996-w |
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author | Abbasian, Mohammad Hadi Mahmanzar, Mohammadamin Rahimian, Karim Mahdavi, Bahar Tokhanbigli, Samaneh Moradi, Bahman Sisakht, Mahsa Mollapour Deng, Youping |
author_facet | Abbasian, Mohammad Hadi Mahmanzar, Mohammadamin Rahimian, Karim Mahdavi, Bahar Tokhanbigli, Samaneh Moradi, Bahman Sisakht, Mahsa Mollapour Deng, Youping |
author_sort | Abbasian, Mohammad Hadi |
collection | PubMed |
description | BACKGROUND: At 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. METHODS: 10,287,271 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. RESULTS: Six 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. CONCLUSION: Therefore, 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03996-w. |
format | Online Article Text |
id | pubmed-9958328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99583282023-02-26 Global landscape of SARS-CoV-2 mutations and conserved regions Abbasian, Mohammad Hadi Mahmanzar, Mohammadamin Rahimian, Karim Mahdavi, Bahar Tokhanbigli, Samaneh Moradi, Bahman Sisakht, Mahsa Mollapour Deng, Youping J Transl Med Research BACKGROUND: At 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. METHODS: 10,287,271 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. RESULTS: Six 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. CONCLUSION: Therefore, 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03996-w. BioMed Central 2023-02-25 /pmc/articles/PMC9958328/ /pubmed/36841805 http://dx.doi.org/10.1186/s12967-023-03996-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Abbasian, Mohammad Hadi Mahmanzar, Mohammadamin Rahimian, Karim Mahdavi, Bahar Tokhanbigli, Samaneh Moradi, Bahman Sisakht, Mahsa Mollapour Deng, Youping Global landscape of SARS-CoV-2 mutations and conserved regions |
title | Global landscape of SARS-CoV-2 mutations and conserved regions |
title_full | Global landscape of SARS-CoV-2 mutations and conserved regions |
title_fullStr | Global landscape of SARS-CoV-2 mutations and conserved regions |
title_full_unstemmed | Global landscape of SARS-CoV-2 mutations and conserved regions |
title_short | Global landscape of SARS-CoV-2 mutations and conserved regions |
title_sort | global landscape of sars-cov-2 mutations and conserved regions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958328/ https://www.ncbi.nlm.nih.gov/pubmed/36841805 http://dx.doi.org/10.1186/s12967-023-03996-w |
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