Abstract

Recombination contributes to the genetic diversity found in coronaviruses and is known to be a prominent mechanism whereby they evolve. It is apparent, both from controlled experiments and in genome sequences sampled from nature, that patterns of recombination in coronaviruses are non-random and that this is likely attributable to a combination of sequence features that favour the occurrence of recombination break points at specific genomic sites, and selection disfavouring the survival of recombinants within which favourable intra-genome interactions have been disrupted. Here we leverage available whole-genome sequence data for six coronavirus subgenera to identify specific patterns of recombination that are conserved between multiple subgenera and then identify the likely factors that underlie these conserved patterns. Specifically, we confirm the non-randomness of recombination break points across all six tested coronavirus subgenera, locate conserved recombination hot- and cold-spots, and determine that the locations of transcriptional regulatory sequences are likely major determinants of conserved recombination break-point hotspot locations. We find that while the locations of recombination break points are not uniformly associated with degrees of nucleotide sequence conservation, they display significant tendencies in multiple coronavirus subgenera to occur in low guanine-cytosine content genome regions, in non-coding regions, at the edges of genes, and at sites within the Spike gene that are predicted to be minimally disruptive of Spike protein folding. While it is apparent that sequence features such as transcriptional regulatory sequences are likely major determinants of where the template-switching events that yield recombination break points most commonly occur, it is evident that selection against misfolded recombinant proteins also strongly impacts observable recombination break-point distributions in coronavirus genomes sampled from nature.

Details

Title
Conserved recombination patterns across coronavirus subgenera
Author
Arné de Klerk 1 ; Swanepoel, Phillip 1 ; Lourens, Rentia 2 ; Zondo, Mpumelelo 1 ; Abodunran, Isaac 1 ; Lytras, Spyros 3 ; MacLean, Oscar A 3 ; Robertson, David 3 ; Kosakovsky Pond, Sergei L 4 ; Zehr, Jordan D 4 ; Kumar, Venkatesh 5 ; Stanhope, Michael J 6 ; Harkins, Gordon 7 ; Murrell, Ben 5 ; Martin, Darren P 1 

 Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town , Cape Town 7701, South Africa 
 Division of Neurosurgery, Neuroscience Institute, Department of Surgery, University of Cape Town , Cape Town, 7701, South Africa 
 MRC-University of Glasgow Centre for Virus Research, University of Glasgow , Glasgow G61 1QH, UK 
 Department of Biology, Temple University, Institute for Genomics and Evolutionary Medicine , Philadelphia, PA 19122, USA 
 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet , Stockholm, 14186, Sweden 
 Department of Population and Ecosystem Health, College of Veterinary Medicine, Cornell University , Ithaca, NY, 14853, USA 
 South African National Bioinformatics Institute, University of the Western Cape , Cape Town, 7535, South Africa 
Publication year
2022
Publication date
2022
Publisher
Oxford University Press
e-ISSN
20571577
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171772023
Copyright
© The Author(s) 2022. Published by Oxford University Press. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.