Snapshot of the evolution and mutation patterns of SARS-CoV-2 Article Swipe
Jin Zhao
,
Jiumeng Sun
,
Wanting He
,
Xiang Ji
,
Qi Gao
,
Xiaofeng Zhai
,
Marc A. Suchard
,
Samuel L. Hong
,
Guy Baele
,
Shuo Su
,
Jiyong Zhou
,
Michael Veit
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.07.04.187435
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.07.04.187435
The COVID-19 pandemic is the most important public health threat in recent history. Here we study how its causal agent, SARS-CoV-2, has diversified genetically since its first emergence in December 2019. We have created a pipeline combining both phylogenetic and structural analysis to identify possible human-adaptation related mutations in a data set consisting of 4,894 SARS-CoV-2 complete genome sequences. Although the phylogenetic diversity of SARS-CoV-2 is low, the whole genome phylogenetic tree can be divided into five clusters/clades based on the tree topology and clustering of specific mutations, but its branches exhibit low genetic distance and bootstrap support values. We also identified 11 residues that are high-frequency substitutions, with four of them currently showing some signal for potential positive selection. These fast-evolving sites are in the non-structural proteins nsp2, nsp5 (3CL-protease), nsp6, nsp12 (polymerase) and nsp13 (helicase), in accessory proteins (ORF3a, ORF8) and in the structural proteins N and S. Temporal and spatial analysis of these potentially adaptive mutations revealed that the incidence of some of these sites was declining after having reached an (often local) peak, whereas the frequency of other sites is continually increasing and now exhibit a worldwide distribution. Structural analysis revealed that the mutations are located on the surface of the proteins that modulate biochemical properties. We speculate that this improves binding to cellular proteins and hence represents fine-tuning of adaptation to human cells. Our study has implications for the design of biochemical and clinical experiments to assess whether important properties of SARS-CoV-2 have changed during the epidemic.
Related Topics
Concepts
Snapshot (computer storage)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Coronavirus disease 2019 (COVID-19)
Mutation
2019-20 coronavirus outbreak
Geography
Biology
Genetics
Virology
Computer science
Medicine
Infectious disease (medical specialty)
Outbreak
Disease
Gene
Operating system
Pathology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.07.04.187435
- https://www.biorxiv.org/content/biorxiv/early/2020/07/05/2020.07.04.187435.full.pdf
- OA Status
- green
- Cited By
- 26
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3039177096
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3039177096Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.07.04.187435Digital Object Identifier
- Title
-
Snapshot of the evolution and mutation patterns of SARS-CoV-2Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-05Full publication date if available
- Authors
-
Jin Zhao, Jiumeng Sun, Wanting He, Xiang Ji, Qi Gao, Xiaofeng Zhai, Marc A. Suchard, Samuel L. Hong, Guy Baele, Shuo Su, Jiyong Zhou, Michael VeitList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.07.04.187435Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/07/05/2020.07.04.187435.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/07/05/2020.07.04.187435.full.pdfDirect OA link when available
- Concepts
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Snapshot (computer storage), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19), Mutation, 2019-20 coronavirus outbreak, Geography, Biology, Genetics, Virology, Computer science, Medicine, Infectious disease (medical specialty), Outbreak, Disease, Gene, Operating system, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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26Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 3, 2022: 5, 2021: 9, 2020: 7Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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