Chromosome-scale assembly and whole-genome sequencing of 266 giant panda roundworms provide insights into their evolution, adaptation and potential drug targets

Abstract

Helminth diseases have long been a threat to the health of humans and animals. Roundworms are important organisms for studying parasitic mechanisms, disease transmission and prevention. The study of parasites in the giant panda is of importance for understanding how roundworms adapt to the host. Here, we report a high-quality chromosome-scale genome of Baylisascaris schroederi with a genome size of 253.60 Mb and 19,262 predicted protein-coding genes. We found that gene families related to epidermal chitin synthesis and environmental information processes in the roundworm genome have expanded significantly. Furthermore, we demonstrated unique genes involved in essential amino acid metabolism in the B. schroederi genome, inferred to be essential for the adaptation to the giant panda-specific diet. In addition, under different deworming pressures, we found that four resistance-related genes (glc-1, nrf-6, bre-4 and ced-7) were under strong positive selection in a captive population. Finally, 23 known drug targets and 47 potential drug target proteins (essential homologues linked to lethal phenotypes) were identified. The genome provides a unique reference for inferring the early evolution of roundworms and their adaptation to the host. Population genetic analysis and drug sensitivity prediction provide insights revealing the impact of deworming history on population genetic structure of importance for disease prevention.

Equus roundworms (Parascaris univalens) are undergoing rapid divergence while genes involved in metabolic as well as anthelminic resistance are under positive selection

“…The pairwise sequentially Markovian coalescent (PSMC) method [35] was used to evaluate the dynamic change of effective population size (Ne) of each population. We used 0.17 year per generation (g) and a mutation rate (μ) of 9 × 10-9 per generation per site to rescale the time to year [36,37]. More recent (within 1000 years) changes in effective population size of each population and separation time between different populations were further estimated by using the multiple sequentially Markovian coalescent (MSMC2) [38], which can much compensate for results from PSMC.…”