bioRxiv Science⌕ Search

bioRxiv · 10.1101/207316

Profiling G protein-coupled receptors of Fasciola hepatica identifies orphan rhodopsins unique to phylum Platyhelminthes

Abstract

G protein-coupled receptors (GPCRs) are established drug targets. Despite their considerable appeal as targets for next-generation anthelmintics, poor understanding of their diversity and function in parasitic helminths has thwarted progress towards GPCR-targeted anti-parasite drugs. This study facilitates GPCR research in the liver fluke, Fasciola hepatica, by generating the first profile of GPCRs from the F. hepatica genome. Our dataset describes 146 high confidence GPCRs, representing the largest cohort of GPCRs, and the most complete set of in silico ligand-receptor predictions, yet reported in any parasitic helminth. All GPCRs fall within the established GRAFS nomenclature; comprising three glutamate, 135 rhodopsin, two adhesion, five frizzled and one smoothened GPCR. Stringent annotation pipelines identified 18 highly diverged rhodopsins in F. hepatica that maintained core rhodopsin signatures, but lacked significant similarity with non-flatworm sequences, providing a new sub-group of potential flukicide targets. These facilitated identification of a larger cohort of 76 related sequences from available flatworm genomes, representing new members of existing groups of flatworm-specific rhodopsins. These receptors imply flatworm specific GPCR functions, and/or co-evolution with unique flatworm ligands, and could facilitate development of exquisitely selective anthelminthics. Ligand binding domain sequence conservation relative to deorphanised rhodopsins enabled high confidence ligand-receptor matching of seventeen receptors activated by acetylcholine, neuropeptide F/Y, octopamine or serotonin. RNA-Seq analyses showed expression of 101 GPCRs across various developmental stages, with the majority expressed most highly in the pathogenic intra-mammalian juvenile parasites. These data identify a broad complement of GPCRs in F. hepatica, including rhodopsins likely to have key functions in neuromuscular control and sensory perception, as well as frizzled and adhesion families implicated, in other species, in growth, development and reproduction. This catalogue of liver fluke GPCRs provides a platform for new avenues into our understanding of flatworm biology and anthelmintic discovery.\n\nAuthor SummaryFasciola spp. liver fluke are important veterinary pathogens with impacts on human and animal health, and food security, around the world. Liver fluke have developed resistance to most of the drugs used to treat them (flukicides). Since no vaccines exist, we need to develop new flukicides as a matter of urgency. Most anthelmintic drugs used to treat parasitic worm infections operate by impeding the functioning of their nerve and muscle. In flatworms, most nervous signals are received by a type of receptor called a G protein-coupled receptor (GPCR). Since GPCRs control important parasite functions (e.g. movement, egg-laying, feeding), they represent appealing targets for new flukicides, but have not yet been targeted as such. This work exploited the F. hepatica genome to determine the quantity and diversity of GPCRs in liver fluke. We found more GPCRs in the Fasciola genome than have been reported in any other parasitic worm. These findings provide a foundation that for researchers to determine the functions of these receptors, and which molecules/ligands they are activated by. These data will pave the way to exploring the potential of F. hepatica GPCRs as targets for new flukicides.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

McVeigh, P., McCammick, E., McCusker, P., Wells, D., Hodgkinson, J., Paterson, S., Mousley, A., Marks, N. J., Maule, A. G.. 2017-10-22. Profiling G protein-coupled receptors of Fasciola hepatica identifies orphan rhodopsins unique to phylum Platyhelminthes. https://doi.org/10.1101/207316

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

scRM: A single-cell foundation model trained on scRNA-seq reads

To our knowledge, scRM is the first single-cell foundation model trained on sequencing reads, demonstrating that raw data pretraining is not only feasible but competitive at a fraction of the computational cost. We anticipate that scaling this framework and exploring alternative architectures will unlock biological information inaccessible to count models and extend the dataset-specific pretraining it already makes possible. The source code is available at https://github.com/altos-labs/scRM.

bioinformatics↗

Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Changes in protein abundance incompletely describe how molecular perturbations reshape cellular organization. Here we introduce DIFFRACT, a framework that integrates quantitative proteomics with protein-protein interaction networks to identify reorganization through changes in local fractal dimension. Molecular associations define effective distances between connected proteins, allowing the geometry of their surrounding network neighborhoods to be compared across conditions. Analyses across four organisms (E. coli, A. thaliana, C. elegans, and H. sapiens) and three interaction resources (STRING, BioGRID, and IntAct) establish heterogeneous local scaling, while controlled perturbations demonstrate sensitivity to regional changes and invariance to uniform distance rescaling. Applied to RAVER1-deficient A549 cells, DIFFRACT identifies components associated with established RNA-regulatory functions and candidate roles in tRNA biology, mitochondrial organization and ciliogenesis. Selected RNA-regulatory and ciliary themes are not significantly enriched by abundance-ranked gene set analysis, supporting complementary information from network geometry. Selected functional annotations also recur in HEK293T cells. Guided by the ciliary predictions, we show that RAVER1 depletion increases the proportion of ciliated A549 cells 2.57-fold and increases cilium length. DIFFRACT thus connects changes in molecular network geometry to experimentally testable biological responses, extending the interpretation of proteomic perturbations beyond differential abundance.

bioinformatics↗

Peripheral Exercise Transcriptomes Do Not Converge on Glutamatergic or Neuroinflammatory Signatures: A Cross-Platform Meta-Analysis

Executive dysfunction following physical overtraining has been linked to glutamatergic dysregulation in the lateral prefrontal cortex (LPFC), but whether this central mechanism is reflected in peripheral gene expression is unknown. This study tested whether peripheral transcriptomic responses to exercise converge on the glutamatergic or neuroinflammatory pathways implicated in LPFC dysfunction. Three publicly available GEO datasets spanning skeletal muscle, whole blood, and circulating neutrophils were analyzed across four exercise groups using limma, with a discovery threshold of nominal p < 0.05 and absolute log2 fold change above 0.5. Differential expression analysis identified 7,311 unique differentially expressed genes (DEGs) across the four contrasts, but overlap between datasets was limited: 7 genes were shared between skeletal muscle and blood, 38 between skeletal muscle and the GSE43856 contrasts, and 90 between blood and the GSE43856 contrasts, with none significant across all three studies. Gene Ontology and KEGG enrichment of the DEG union identified ribonucleoprotein complex biogenesis, ribosome biogenesis, protein folding, and T cell activation as the dominant biological processes; no glutamatergic or neuroinflammatory terms reached significance. Cross-tissue concordance between skeletal muscle and blood was absent (r = 0). Effect sizes among the genes driving the top enriched pathways were dominated by ribosomal proteins, heat shock proteins, and translation initiation factors rather than glutamate receptors or transporters. These findings indicate that peripheral transcriptomic responses to exercise are tissue-specific and dominated by proteostasis, translational, and immune programs rather than glutamatergic signaling. Direct assessment of central nervous system tissue or fluid-based glutamatergic biomarkers may be required to link overtraining-induced fatigue to LPFC dysfunction.

bioinformatics↗