RESUMEN
It is commonly assumed that asexual lineages are short-lived evolutionarily, yet many asexual organisms can generate genetic and phenotypic variation, providing an avenue for further evolution. Previous work on the asexual plant pathogen Phytophthora ramorum NA1 revealed considerable genetic variation in the form of Structural Variants (SVs). To better understand how SVs arise and their significance to the California NA1 population, we studied the evolutionary histories of SVs and the forest conditions associated with their emergence. Ancestral state reconstruction suggests that SVs arose by somatic mutations among multiple independent lineages, rather than by recombination. We asked if this unusual phenomenon of parallel evolution between isolated populations is transmitted to extant lineages and found that SVs persist longer in a population if their genetic background had a lower mutation load. Genetic parallelism was also found in geographically distant demes where forest conditions such as host density, solar radiation, and temperature, were similar. Parallel SVs overlap with genes involved in pathogenicity such as RXLRs and have the potential to change the course of an epidemic. By combining genomics and environmental data, we identified an unexpected pattern of repeated evolution in an asexual population and identified environmental factors potentially driving this phenomenon.
RESUMEN
The NA1 clonal lineage of Phytophthora ramorum is responsible for sudden oak death, an epidemic that has devastated California coastal forest ecosystems. An NA1 isolate, Pr102, derived from coast live oak in California, was previously sequenced and reported with a 65-Mb assembly containing 12 Mb of gaps in 2,576 scaffolds. Here, we report an improved 70-Mb genome in 1,512 scaffolds with 6,752 bp of gaps after incorporating PacBio P5-C3 long reads. This assembly contains 19,494 gene models (average gene length of 2,515 bp) compared with 16,134 genes (average gene length of 1,673 bp) in the previous version. We predicted 29 new RXLR genes and 76 new paralogs of a total 392 RXLR genes from this assembly. We predicted 35 CRN genes compared with 19 in an earlier version with six paralogs. Our long non-coding RNA prediction identified 255 candidates. This new resource will be invaluable for future evolution studies on the invasive plant pathogen.
