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Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection
New coronavirus SARS-CoV-2 is capable to infect humans and cause a novel disease COVID-19. Aiming to understand a host genetic component of COVID-19, we focused on variants in genes encoding proteases and genes involved in innate immunity that could be important for susceptibility and resistance to...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410821/ https://www.ncbi.nlm.nih.gov/pubmed/32771700 http://dx.doi.org/10.1016/j.meegid.2020.104498 |
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author | Klaassen, Kristel Stankovic, Biljana Zukic, Branka Kotur, Nikola Gasic, Vladimir Pavlovic, Sonja Stojiljkovic, Maja |
author_facet | Klaassen, Kristel Stankovic, Biljana Zukic, Branka Kotur, Nikola Gasic, Vladimir Pavlovic, Sonja Stojiljkovic, Maja |
author_sort | Klaassen, Kristel |
collection | PubMed |
description | New coronavirus SARS-CoV-2 is capable to infect humans and cause a novel disease COVID-19. Aiming to understand a host genetic component of COVID-19, we focused on variants in genes encoding proteases and genes involved in innate immunity that could be important for susceptibility and resistance to SARS-CoV-2 infection. Analysis of sequence data of coding regions of FURIN, PLG, PRSS1, TMPRSS11a, MBL2 and OAS1 genes in 143 unrelated individuals from Serbian population identified 22 variants with potential functional effect. In silico analyses (PolyPhen-2, SIFT, MutPred2 and Swiss-Pdb Viewer) predicted that 10 variants could impact the structure and/or function of proteins. These protein-altering variants (p.Gly146Ser in FURIN; p.Arg261His and p.Ala494Val in PLG; p.Asn54Lys in PRSS1; p.Arg52Cys, p.Gly54Asp and p.Gly57Glu in MBL2; p.Arg47Gln, p.Ile99Val and p.Arg130His in OAS1) may have predictive value for inter-individual differences in the response to the SARS-CoV-2 infection. Next, we performed comparative population analysis for the same variants using extracted data from the 1000 Genomes project. Population genetic variability was assessed using delta MAF and Fst statistics. Our study pointed to 7 variants in PLG, TMPRSS11a, MBL2 and OAS1 genes with noticeable divergence in allelic frequencies between populations worldwide. Three of them, all in MBL2 gene, were predicted to be damaging, making them the most promising population-specific markers related to SARS-CoV-2 infection. Comparing allelic frequencies between Serbian and other populations, we found that the highest level of genetic divergence related to selected loci was observed with African, followed by East Asian, Central and South American and South Asian populations. When compared with European populations, the highest divergence was observed with Italian population. In conclusion, we identified 4 variants in genes encoding proteases (FURIN, PLG and PRSS1) and 6 in genes involved in the innate immunity (MBL2 and OAS1) that might be relevant for the host response to SARS-CoV-2 infection. |
format | Online Article Text |
id | pubmed-7410821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74108212020-08-07 Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection Klaassen, Kristel Stankovic, Biljana Zukic, Branka Kotur, Nikola Gasic, Vladimir Pavlovic, Sonja Stojiljkovic, Maja Infect Genet Evol Research Paper New coronavirus SARS-CoV-2 is capable to infect humans and cause a novel disease COVID-19. Aiming to understand a host genetic component of COVID-19, we focused on variants in genes encoding proteases and genes involved in innate immunity that could be important for susceptibility and resistance to SARS-CoV-2 infection. Analysis of sequence data of coding regions of FURIN, PLG, PRSS1, TMPRSS11a, MBL2 and OAS1 genes in 143 unrelated individuals from Serbian population identified 22 variants with potential functional effect. In silico analyses (PolyPhen-2, SIFT, MutPred2 and Swiss-Pdb Viewer) predicted that 10 variants could impact the structure and/or function of proteins. These protein-altering variants (p.Gly146Ser in FURIN; p.Arg261His and p.Ala494Val in PLG; p.Asn54Lys in PRSS1; p.Arg52Cys, p.Gly54Asp and p.Gly57Glu in MBL2; p.Arg47Gln, p.Ile99Val and p.Arg130His in OAS1) may have predictive value for inter-individual differences in the response to the SARS-CoV-2 infection. Next, we performed comparative population analysis for the same variants using extracted data from the 1000 Genomes project. Population genetic variability was assessed using delta MAF and Fst statistics. Our study pointed to 7 variants in PLG, TMPRSS11a, MBL2 and OAS1 genes with noticeable divergence in allelic frequencies between populations worldwide. Three of them, all in MBL2 gene, were predicted to be damaging, making them the most promising population-specific markers related to SARS-CoV-2 infection. Comparing allelic frequencies between Serbian and other populations, we found that the highest level of genetic divergence related to selected loci was observed with African, followed by East Asian, Central and South American and South Asian populations. When compared with European populations, the highest divergence was observed with Italian population. In conclusion, we identified 4 variants in genes encoding proteases (FURIN, PLG and PRSS1) and 6 in genes involved in the innate immunity (MBL2 and OAS1) that might be relevant for the host response to SARS-CoV-2 infection. Elsevier B.V. 2020-10 2020-08-07 /pmc/articles/PMC7410821/ /pubmed/32771700 http://dx.doi.org/10.1016/j.meegid.2020.104498 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Research Paper Klaassen, Kristel Stankovic, Biljana Zukic, Branka Kotur, Nikola Gasic, Vladimir Pavlovic, Sonja Stojiljkovic, Maja Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title | Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title_full | Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title_fullStr | Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title_full_unstemmed | Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title_short | Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection |
title_sort | functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to sars-cov-2 infection |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410821/ https://www.ncbi.nlm.nih.gov/pubmed/32771700 http://dx.doi.org/10.1016/j.meegid.2020.104498 |
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