Cargando…
Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example
BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmiss...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941367/ https://www.ncbi.nlm.nih.gov/pubmed/33750399 http://dx.doi.org/10.1186/s12976-021-00140-3 |
_version_ | 1783662139832008704 |
---|---|
author | Zhao, Shi Lou, Jingzhi Cao, Lirong Zheng, Hong Chong, Marc K. C. Chen, Zigui Zee, Benny C. Y. Chan, Paul K. S. Wang, Maggie H. |
author_facet | Zhao, Shi Lou, Jingzhi Cao, Lirong Zheng, Hong Chong, Marc K. C. Chen, Zigui Zee, Benny C. Y. Chan, Paul K. S. Wang, Maggie H. |
author_sort | Zhao, Shi |
collection | PubMed |
description | BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (R(t)). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and R(t). We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2 spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in R(t), which explains 61% of the R(t) variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and R(t). Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12976-021-00140-3. |
format | Online Article Text |
id | pubmed-7941367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79413672021-03-09 Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example Zhao, Shi Lou, Jingzhi Cao, Lirong Zheng, Hong Chong, Marc K. C. Chen, Zigui Zee, Benny C. Y. Chan, Paul K. S. Wang, Maggie H. Theor Biol Med Model Research BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (R(t)). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and R(t). We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2 spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in R(t), which explains 61% of the R(t) variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and R(t). Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12976-021-00140-3. BioMed Central 2021-03-09 /pmc/articles/PMC7941367/ /pubmed/33750399 http://dx.doi.org/10.1186/s12976-021-00140-3 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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 Zhao, Shi Lou, Jingzhi Cao, Lirong Zheng, Hong Chong, Marc K. C. Chen, Zigui Zee, Benny C. Y. Chan, Paul K. S. Wang, Maggie H. Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title | Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title_full | Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title_fullStr | Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title_full_unstemmed | Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title_short | Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example |
title_sort | modelling the association between covid-19 transmissibility and d614g substitution in sars-cov-2 spike protein: using the surveillance data in california as an example |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941367/ https://www.ncbi.nlm.nih.gov/pubmed/33750399 http://dx.doi.org/10.1186/s12976-021-00140-3 |
work_keys_str_mv | AT zhaoshi modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT loujingzhi modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT caolirong modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT zhenghong modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT chongmarckc modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT chenzigui modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT zeebennycy modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT chanpaulks modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample AT wangmaggieh modellingtheassociationbetweencovid19transmissibilityandd614gsubstitutioninsarscov2spikeproteinusingthesurveillancedataincaliforniaasanexample |