bioRxivEvolutionary Biology
Extensive recombination-driven coronavirus diversification expands the pool of potential pandemic pathogens
S. A. Goldstein, J. Brown, B. S. Pedersen, A. R. Quinlan, N. C. Elde
Abstract
The ongoing SARS-CoV-2 pandemic is the third zoonotic coronavirus identified in the last twenty years. Enzootic and epizootic coronaviruses of diverse lineages also pose a significant threat to livestock, as most recently observed for virulent strains of porcine epidemic diarrhea virus (PEDV) and swine acute diarrhea-associated coronavirus (SADS-CoV). Unique to RNA viruses, coronaviruses encode a proofreading exonuclease (ExoN) that lowers point mutation rates to increase the viability of large RNA virus genomes, which comes with the cost of limiting virus adaptation via point mutation. This limitation can be overcome by high rates of recombination that facilitate rapid increases in genetic diversification. To compare dynamics of recombination between related sequences, we developed an open-source computational workflow (IDPlot) to measure nucleotide identity, locate recombination breakpoints, and infer phylogenetic relationships. We analyzed recombination dynamics among three groups of coronaviruses with noteworthy impacts on human health and agriculture: SARSr-CoV, Betacoronavirus-1, and SADSr-CoV. We found that all three groups undergo recombination with highly diverged viruses from sparsely sampled or undescribed lineages, which can disrupt the inference of phylogenetic relationships. In most cases, no parental origin of recombinant regions could be found in genetic databases, suggesting that much coronavirus diversity remains unknown. These patterns of recombination expand the genetic pool that may contribute to future zoonotic events. Our results also illustrate the limitations of current sampling approaches for anticipating zoonotic threats to human and animal health.
- organism: human
- organism: mouse
- organism: rat
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: phylogenetics / evolution
- research focus: host-pathogen & immune response
- approach: deep learning
- approach: statistical model
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