Authors

F Mc Kay¹; L Atkinson¹; C McCoy¹; N J Marks¹; A G Maule¹; A Mousley¹;  ¹ Queen's University Belfast, UK

Discussion

Nematode parasites impact health and agriculture worldwide, where increasing reports of anthelmintic failure underscore the need for novel discovery programmes.  The nematode neuropeptide system has been highlighted as a novel source of anthelmintic targets with neuropeptide G-protein coupled receptors (GPCRs) emerging as primary targets.  Nematode neuropeptide GPCRs receive input from neuropeptide ligands to control a range of muscle- and chemosensory-based activities that are pertinent to the biology and survival of nematodes.  Anthelmintic exploitation of neuropeptide GPCRs requires knowledge of neuropeptide and neuropeptide-GPCR complements across key nematode parasites, and an understanding of neuropeptide-receptor interactions. 

The increased availability and quality of ‘omics’ datasets in parasitic nematodes has enabled the identification of neuropeptides and neuropeptide GPCRs in key parasite species, however the complexity of the neuropeptidergic signalling system in nematode parasites makes identifying neuropeptide-GPCR interactions difficult.  Indeed, there are >250 neuropeptide ligands that have the potential to interact with ~150 neuropeptide GPCRs in parasite species.  The available neuropeptide-receptor interaction data, derived from Caenorhabditis elegans reverse pharmacology approaches, highlight a few receptor interactions that have been functionally corroborated in vivo.  However, interpretation of the available data is limited by the use of an incomplete ligand-library, and its relevance to ligand-receptor interactions in parasitic nematodes, where disparity exists in neuropeptide/receptor localisation for some targets.

This study provides data on the Neuropeptide-like protein (NLP) profile across 8 key nematode parasite species to complete the portfolio of putative interacting ligands [FMRFamide like peptide (FLP) and neuropeptide GPCR data are available for these species], and using an in silico bioinformatics approach, aims to predict ligand-receptor interactions that are possible in vivo.  These predictions are aided by analyses of RNA-Seq expression data for key lifestages, sexes, and tissue-types where interaction potential is greater for ligands and receptors that are expressed in the same lifestages, sexes, and tissues at the same time.