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In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches

BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: We utilize genetic data from 144 sequences of...

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Autores principales: Oulas, Anastasis, Richter, Jan, Zanti, Maria, Tomazou, Marios, Michailidou, Kyriaki, Christodoulou, Kyproula, Christodoulou, Christina, Spyrou, George M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590444/
https://www.ncbi.nlm.nih.gov/pubmed/34773976
http://dx.doi.org/10.1186/s12863-021-01007-9
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author Oulas, Anastasis
Richter, Jan
Zanti, Maria
Tomazou, Marios
Michailidou, Kyriaki
Christodoulou, Kyproula
Christodoulou, Christina
Spyrou, George M.
author_facet Oulas, Anastasis
Richter, Jan
Zanti, Maria
Tomazou, Marios
Michailidou, Kyriaki
Christodoulou, Kyproula
Christodoulou, Christina
Spyrou, George M.
author_sort Oulas, Anastasis
collection PubMed
description BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. RESULTS: We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. CONCLUSIONS: Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-021-01007-9.
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spelling pubmed-85904442021-11-15 In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches Oulas, Anastasis Richter, Jan Zanti, Maria Tomazou, Marios Michailidou, Kyriaki Christodoulou, Kyproula Christodoulou, Christina Spyrou, George M. BMC Genom Data Research BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. RESULTS: We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. CONCLUSIONS: Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-021-01007-9. BioMed Central 2021-11-13 /pmc/articles/PMC8590444/ /pubmed/34773976 http://dx.doi.org/10.1186/s12863-021-01007-9 Text en © The Author(s) 2021 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
Oulas, Anastasis
Richter, Jan
Zanti, Maria
Tomazou, Marios
Michailidou, Kyriaki
Christodoulou, Kyproula
Christodoulou, Christina
Spyrou, George M.
In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title_full In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title_fullStr In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title_full_unstemmed In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title_short In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
title_sort in depth analysis of cyprus-specific mutations of sars-cov-2 strains using computational approaches
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590444/
https://www.ncbi.nlm.nih.gov/pubmed/34773976
http://dx.doi.org/10.1186/s12863-021-01007-9
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