Comparative Atlas of SARS-CoV-2 Substitution Mutations: A Focus on Iranian Strains Amidst Global Trends.


Journal

Viruses
ISSN: 1999-4915
Titre abrégé: Viruses
Pays: Switzerland
ID NLM: 101509722

Informations de publication

Date de publication:
20 Aug 2024
Historique:
received: 13 07 2024
revised: 12 08 2024
accepted: 17 08 2024
medline: 1 9 2024
pubmed: 31 8 2024
entrez: 29 8 2024
Statut: epublish

Résumé

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19. we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference. Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran. real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments.

Sections du résumé

BACKGROUND BACKGROUND
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19.
METHODS METHODS
we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference.
RESULTS RESULTS
Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran.
CONCLUSIONS CONCLUSIONS
real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments.

Identifiants

pubmed: 39205305
pii: v16081331
doi: 10.3390/v16081331
pii:
doi:

Substances chimiques

Spike Glycoprotein, Coronavirus 0

Types de publication

Journal Article Comparative Study

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Mohammad Hadi Abbasian (MH)

Department of Medical Genetics, National Institute for Genetic Engineering and Biotechnology, Tehran 1497716316, Iran.

Karim Rahimian (K)

Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14174, Iran.

Mohammadamin Mahmanzar (M)

Department of Bioinformatics, Kish International Campus University of Tehran, Kish 7941639982, Iran.
Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.

Saleha Bayat (S)

Department of Biology & Research Center for Animal Development Applied Biology, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran.

Donna Lee Kuehu (DL)

Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.

Mahsa Mollapour Sisakht (MM)

Faculty of Pharmacy, Biotechnology Research Center, Tehran University of Medical Sciences, Tehran 1936893813, Iran.

Bahman Moradi (B)

Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran.

Youping Deng (Y)

Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.

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Classifications MeSH