tetano
Editor, Senior Moderator
J Transl Med
. 2023 Feb 25;21(1):152.
doi: 10.1186/s12967-023-03996-w.
Global landscape of SARS-CoV-2 mutations and conserved regions
Mohammad Hadi Abbasian[SUP] #[/SUP][SUP] 1 [/SUP], Mohammadamin Mahmanzar[SUP] #[/SUP][SUP] 2 [/SUP], Karim Rahimian[SUP] #[/SUP][SUP] 3 [/SUP], Bahar Mahdavi[SUP] 4 [/SUP], Samaneh Tokhanbigli[SUP] 5 [/SUP], Bahman Moradi[SUP] 6 [/SUP], Mahsa Mollapour Sisakht[SUP] 7 [/SUP], Youping Deng[SUP] 8 [/SUP]
Affiliations
Abstract
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.
Keywords: Amino Acid; COVID-19; Emerging variants; Genome; SARS-CoV-2; Vaccines.
. 2023 Feb 25;21(1):152.
doi: 10.1186/s12967-023-03996-w.
Global landscape of SARS-CoV-2 mutations and conserved regions
Mohammad Hadi Abbasian[SUP] #[/SUP][SUP] 1 [/SUP], Mohammadamin Mahmanzar[SUP] #[/SUP][SUP] 2 [/SUP], Karim Rahimian[SUP] #[/SUP][SUP] 3 [/SUP], Bahar Mahdavi[SUP] 4 [/SUP], Samaneh Tokhanbigli[SUP] 5 [/SUP], Bahman Moradi[SUP] 6 [/SUP], Mahsa Mollapour Sisakht[SUP] 7 [/SUP], Youping Deng[SUP] 8 [/SUP]
Affiliations
- PMID: 36841805
- DOI: 10.1186/s12967-023-03996-w
Abstract
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.
Keywords: Amino Acid; COVID-19; Emerging variants; Genome; SARS-CoV-2; Vaccines.