DataSheet_1_Phylum-Spanning Neuropeptide GPCR Identification and Prioritization: Shaping Drug Target Discovery Pipelines for Nematode Parasite Control.pdf

Nematode parasites undermine human health and global food security. The frontline anthelmintic portfolio used to treat parasitic nematodes is threatened by the escalation of anthelmintic resistance, resulting in a demand for new drug targets for parasite control. Nematode neuropeptide signalling pathways represent an attractive source of novel drug targets which currently remain unexploited. The complexity of the nematode neuropeptidergic system challenges the discovery of new targets for parasite control, however recent advances in parasite ‘omics’ offers an opportunity for the in silico identification and prioritization of targets to seed anthelmintic discovery pipelines. In this study we employed Hidden Markov Model-based searches to identify ~1059 Caenorhabditis elegans neuropeptide G-protein coupled receptor (Ce-NP-GPCR) encoding gene homologs in the predicted protein datasets of 10 key parasitic nematodes that span several phylogenetic clades and lifestyles. We show that, whilst parasitic nematodes possess a reduced complement of Ce-NP-GPCRs, several receptors are broadly conserved across nematode species. To prioritize the most appealing parasitic nematode NP-GPCR anthelmintic targets, we developed a novel in silico nematode parasite drug target prioritization pipeline that incorporates pan-phylum NP-GPCR conservation, C. elegans-derived reverse genetics phenotype, and parasite life-stage specific expression datasets. Several NP-GPCRs emerge as the most attractive anthelmintic targets for broad spectrum nematode parasite control. Our analyses have also identified the most appropriate targets for species- and life stage- directed chemotherapies; in this context we have identified several NP-GPCRs with macrofilaricidal potential. These data focus functional validation efforts towards the most appealing NP-GPCR targets and, in addition, the prioritization strategy employed here provides a blueprint for parasitic nematode target selection beyond NP-GPCRs.

寄生线虫会损害人类健康与全球粮食安全。当前用于治疗寄生线虫的一线抗蠕虫药物组合，正面临抗药性不断升级的威胁，这催生了对新型寄生虫防控药物靶点的需求。线虫神经肽信号通路是极具潜力的新型药物靶点来源，但目前尚未得到充分开发。线虫神经肽能系统的复杂性给新型寄生虫防控靶点的发现带来了挑战，但近年来寄生虫组学研究的进展，为通过虚拟识别与筛选靶点以推动抗蠕虫药物研发流程提供了契机。本研究采用基于隐马尔可夫模型（Hidden Markov Model）的搜索方法，在10种涵盖多个系统发育分支与生活史类型的重要寄生线虫的预测蛋白质数据集中，识别出约1059个编码秀丽隐杆线虫（Caenorhabditis elegans）神经肽G蛋白偶联受体（Ce-NP-GPCR）的基因同源序列。研究结果表明，尽管寄生线虫的Ce-NP-GPCR基因数量有所缩减，但部分受体在各类线虫物种中仍具有广泛的保守性。为筛选出最具开发价值的寄生线虫NP-GPCR类抗蠕虫药物靶点，本研究开发了一套全新的虚拟线虫寄生虫药物靶点筛选流程，该流程整合了线虫门NP-GPCR保守性分析、秀丽隐杆线虫来源的反向遗传学表型数据，以及寄生虫发育阶段特异性表达数据集。部分NP-GPCRs成为了可实现广谱线虫寄生虫防控的极具潜力的抗蠕虫药物靶点。本研究分析还确定了适用于物种特异性与发育阶段靶向化疗的最优靶点；在此框架下，我们识别出数个具有杀丝虫成虫潜力的NP-GPCRs。本研究数据将后续功能验证工作聚焦于最具开发价值的NP-GPCR靶点，此外，本次采用的靶点筛选策略，也为NP-GPCR以外的寄生线虫药物靶点筛选提供了参考范式。