Conserved recombination patterns across coronavirus subgenera

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 nonrandom and that this is likely attributable to a combination of sequence features that favour the occurrence of recombination breakpoints 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 breakpoints 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 breakpoint hot-spot locations. We find that while the locations of recombination breakpoints 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 breakpoints most commonly occur, it is evident that selection against misfolded recombinant proteins also strongly impacts observable recombination breakpoint distributions in coronavirus genomes sampled from nature.

Grantor

Computational Biology Division

Year dc:date.issued

2022

Author dc:creator

• de Klerk, Arne

Advisor dc:contributor.advisor

• Martin, Darrin Patrick

• Computational Biology

Handle dc:identifier.uri

http://hdl.handle.net/11427/37216

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/37216