Michael R Garvin1, Erica T Prates1, Mirko Pavicic1, Piet Jones1,2, B Kirtley Amos1,3, Armin Geiger1,2, Manesh B Shah1, Jared Streich1, Joao Gabriel Felipe Machado Gazolla1, David Kainer1, Ashley Cliff1,2, Jonathon Romero1,2, Nathan Keith4, James B Brown4, Daniel Jacobson5,6,7. 1. Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA. 2. The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA. 3. Department of Horticulture, N-318 Ag Sciences Center, University of Kentucky, Lexington, KY, USA. 4. Lawrence Berkeley National Laboratory, Environmental Genomics & Systems Biology, Berkeley, CA, USA. 5. Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA. jacobsonda@ornl.gov. 6. The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA. jacobsonda@ornl.gov. 7. Department of Psychology, University of Tennessee Knoxville, Knoxville, TN, USA. jacobsonda@ornl.gov.
Abstract
BACKGROUND: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.
<span class="abstract_title">BACKGROUND: A mechanistic understanding of the emical">spread of <emical">span class="Species">SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.
Entities:
Keywords:
Adaptive mutation; COVID-19; Coronavirus; Local adaptation; Molecular evolution; SARS-CoV-2
Authors: Marne C Hagemeijer; Iryna Monastyrska; Janice Griffith; Peter van der Sluijs; Jarno Voortman; Paul M van Bergen en Henegouwen; Annelotte M Vonk; Peter J M Rottier; Fulvio Reggiori; Cornelis A M de Haan Journal: Virology Date: 2014-05-13 Impact factor: 3.616
Authors: Erica T Prates; Michael R Garvin; Piet Jones; J Izaak Miller; Kyle A Sullivan; Ashley Cliff; Joao Gabriel Felipe Machado Gazolla; Manesh B Shah; Angelica M Walker; Matthew Lane; Christopher T Rentsch; Amy Justice; Mirko Pavicic; Jonathon Romero; Daniel Jacobson Journal: Methods Mol Biol Date: 2022
Authors: Ahmed M Almehdi; Ghalia Khoder; Aminah S Alchakee; Azizeh T Alsayyid; Nadin H Sarg; Sameh S M Soliman Journal: Infection Date: 2021-08-02 Impact factor: 3.553