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Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples
Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075409/ https://www.ncbi.nlm.nih.gov/pubmed/37018331 http://dx.doi.org/10.1371/journal.ppat.1011265 |
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author | Terbot, John W. Johri, Parul Liphardt, Schuyler W. Soni, Vivak Pfeifer, Susanne P. Cooper, Brandon S. Good, Jeffrey M. Jensen, Jeffrey D. |
author_facet | Terbot, John W. Johri, Parul Liphardt, Schuyler W. Soni, Vivak Pfeifer, Susanne P. Cooper, Brandon S. Good, Jeffrey M. Jensen, Jeffrey D. |
author_sort | Terbot, John W. |
collection | PubMed |
description | Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model—mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization—and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis. |
format | Online Article Text |
id | pubmed-10075409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100754092023-04-06 Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples Terbot, John W. Johri, Parul Liphardt, Schuyler W. Soni, Vivak Pfeifer, Susanne P. Cooper, Brandon S. Good, Jeffrey M. Jensen, Jeffrey D. PLoS Pathog Review Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model—mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization—and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis. Public Library of Science 2023-04-05 /pmc/articles/PMC10075409/ /pubmed/37018331 http://dx.doi.org/10.1371/journal.ppat.1011265 Text en © 2023 Terbot et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Terbot, John W. Johri, Parul Liphardt, Schuyler W. Soni, Vivak Pfeifer, Susanne P. Cooper, Brandon S. Good, Jeffrey M. Jensen, Jeffrey D. Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title | Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title_full | Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title_fullStr | Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title_full_unstemmed | Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title_short | Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples |
title_sort | developing an appropriate evolutionary baseline model for the study of sars-cov-2 patient samples |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075409/ https://www.ncbi.nlm.nih.gov/pubmed/37018331 http://dx.doi.org/10.1371/journal.ppat.1011265 |
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