Genomics Proteomics & BioinformaticsGenomicsPeer-reviewed journal article
Genome of the giant panda roundworm illuminates its host shift and parasitic adaptation
Yue XieiD, Sen WangiD, Shuangyang WuiD, Shenghan GaoiD, Qingshu MengiD, Chengdong WangiD, Jingchao LaniD, Li LuoiD, Xuan ZhouiD, Jing XuiD, Xiaobin GuiD, Ran HeiD, Zijiang YangiD, Xuerong PengiD, Songnian HuiD, Guangyou YangiD
Abstract
Baylisascaris schroederi, a bamboo-feeding giant panda (Ailuropoda melanoleuca)-specific roundworm (ascaridoid) parasite, is the causative agent of baylisascariasis, which represents a leading reason for the mortality of wild giant panda populations and therefore poses a significant threat to giant panda conservation. Here we present a 293-Mb chromosome-level genome assembly of B. schroederi to inform its biology, including host adaptations. Comparative genomics revealed an evolutionary trajectory accompanied by host-shift events in ascaridoid parasite lineages after host separations, suggesting their potential transmissions and fast adaptations to hosts. Genomic and anatomical lines of evidence, including expansion and positive selection of genes related to cuticle and basal metabolisms, indicates that B. schroederi undergoes specific adaptations to survive in the sharp-edged bamboo enriched gut of giant panda by structurally increasing its cuticle thickness and efficiently utilizing host nutrients during gut parasitism. Also, we characterized the secretome and predicted potential drug and vaccine targets for new interventions. Overall, this genome resource provides new insights into the host adaptation of B. schroederi to giant panda as well as the host-shift events in ascaridoid parasite lineages. These findings also add to our knowledge on the unique biology of the giant panda roundworm and aid the development of much-needed novel strategies for the control of baylisascariasis and thus the protection of giant panda populations.
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 17
- FWCI
- 4.00
- Field percentile
- top 6%
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