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...

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Published inPLoS pathogens Vol. 19; no. 4; p. e1011265
Main Authors Terbot, 2nd, John W, Johri, Parul, Liphardt, Schuyler W, Soni, Vivak, Pfeifer, Susanne P, Cooper, Brandon S, Good, Jeffrey M, Jensen, Jeffrey D
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.04.2023
Public Library of Science (PLoS)
Subjects
Analysis
Biology and life sciences
Coronaviruses
COVID-19
Estimates
Evolution
Evolutionary biology
Genetic diversity
Genetic variation
Genomes
Genomics
Global health
Health aspects
Human populations
Humans
Infections
Mathematical models
Medicine and health sciences
Methods
Mutation
Mutation rates
Public health
Recombination
Review
SARS-CoV-2
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Statistical analysis
Viral diseases
Viruses
United States
United States > US
Montana
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Abstract 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.
AbstractList 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.
Audience Academic
Author Cooper, Brandon S
Jensen, Jeffrey D
Good, Jeffrey M
Terbot, 2nd, John W
Soni, Vivak
Johri, Parul
Liphardt, Schuyler W
Pfeifer, Susanne P
AuthorAffiliation University of Alberta, CANADA
1 University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
2 Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
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Copyright Copyright: © 2023 Terbot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023 Terbot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2023 Terbot et al 2023 Terbot et al
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– notice: 2023 Terbot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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  publication-title: BioRxiv
  contributor:
    fullname: TP Peacock
– volume: 70
  start-page: 2470
  year: 2016
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  article-title: An experimental evaluation of drug-induced mutational meltdown as an antiviral treatment strategy
  publication-title: Evolution
  doi: 10.1111/evo.13041
  contributor:
    fullname: C Bank
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Snippet 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...
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StartPage e1011265
SubjectTerms Analysis
Biology and life sciences
Coronaviruses
COVID-19
Estimates
Evolution
Evolutionary biology
Genetic diversity
Genetic variation
Genomes
Genomics
Global health
Health aspects
Human populations
Humans
Infections
Mathematical models
Medicine and health sciences
Methods
Mutation
Mutation rates
Public health
Recombination
Review
SARS-CoV-2
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Statistical analysis
Viral diseases
Viruses
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Title Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples
URI https://www.ncbi.nlm.nih.gov/pubmed/37018331
https://www.proquest.com/docview/2814441447
https://search.proquest.com/docview/2796161429
https://pubmed.ncbi.nlm.nih.gov/PMC10075409
https://doaj.org/article/b14b00d7f6f8449fa6db36baf00ae770
http://dx.doi.org/10.1371/journal.ppat.1011265
Volume 19
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