bioRxivEvolutionary Biology
Evolutionary epidemiology of a zoonosis
G. I. Corsi, S. Tichkule, A. R. Sannella, P. Vatta, F. Asnicar, N. Segata, A. R. Jex, C. v. Oosterhout, S. M. Caccio
Abstract
Cryptosporidium parvum is a global zoonoses and a major cause of diarrhoea in humans and ruminants. The parasites life cycle comprises an obligatory sexual phase, during which genetic exchanges can occur between previously isolated lineages. Here, we compare 32 whole genome sequences from human- and ruminant-derived parasite isolates collected across Europe, Egypt and China. We identify three strongly supported clusters that comprise a mix of isolates from different host species, geographic origins, and subtypes. We show that: (1) recombination occurs between ruminant isolates into human isolates; (2) these recombinant regions can be passed on to other human subtypes through gene flow and population admixture; (3) there have been multiple genetic exchanges, and all are likely recent; (4) putative virulence genes are significantly enriched within these genetic exchanges, and (5) this results in an increase in their nucleotide diversity. We carefully dissect the phylogenetic sequence of two genetic exchanges, illustrating the long-term evolutionary consequences of these events. Our results suggest that increased globalisation and close human-animal contacts increase the opportunity for genetic exchanges between previously isolated parasite lineages, resulting in spillover and spillback events. We discuss how this can provide a novel substrate for natural selection at genes involved in host-parasite interactions, thereby potentially altering the dynamic coevolutionary equilibrium in the Red Queens arms race. Data SummaryAll raw and processed sequencing data generated and analysed during the current study have been submitted to the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/bioproject/), under BioProjects PRJNA634014 and PRJNA633764.
- organism: human
- organism: rat
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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