Search bioRxiv⌕ 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

The Almond Expression Atlas: A comprehensive multi-tissue transcriptomic resource for almond gene expression

Background: Improving crop adaptation and enhancing agronomic and quality traits requires a deeper understanding of gene expression regulation, for which expression atlases are valuable tools. Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important tree nut crops worldwide. However, an integrated transcriptomic resource for this species is still lacking. Available data remain fragmented across independent studies, limiting their comparability and reuse. Results: Here, we present the first almond gene expression atlas: a comprehensive multi-tissue transcriptomic resource integrating publicly available RNA sequencing data. A total of 142 high-quality untreated and control samples from 17 independent projects were curated through a metadata standardization process and analyzed using a unified bioinformatics pipeline. Standardized expression profiles were generated for 25,225 annotated genes in the Prunus dulcis Texas v2.0 reference genome. The atlas integrates two complementary expression approaches: a median TPM approach capturing quantitative expression levels and a rank-based strategy implemented using the Bgee expression calling method, designed to improve comparability across heterogeneous datasets. This design enables the identification of tissue-specific candidate genes and the exploration of expression patterns across tissues and developmental stages. Its applicability is demonstrated through two use cases, highlighting its ability to identify biologically meaningful gene sets associated with bud dormancy transitions and to prioritize candidate reference genes for RT-qPCR normalization in almond. Conclusions: The Almond Expression Atlas v1.0 is publicly available through FruitDBase (fruitdbase.csic.es), providing an interactive platform for gene expression exploration and visualization. This resource supports functional genomics studies and facilitates candidate gene discovery in almond, while ensuring data traceability and promoting data reusability.

bioinformatics↗

Leakage-Controlled Benchmarking of Variant Effect Predictors and Development of Gradient-Boosting Consensus Model

Single amino acid mutations are among the most common forms of genetic variation, with effects ranging from negligible to severe disruption of protein function. Accurate quantitative evaluation of protein variants therefore represents a key challenge in medicine and protein engineering. Computational variant effect predictors (VEPs) are widely used to prioritize missense variants, yet their fair comparison is hindered by data circularity, benchmarks restricted to human clinical annotations, and limited availability of code and training data, all of which can inflate apparent performance. Here, we reviewed 18 recent and 3 older, widely adopted variant effect predictors for reproducible benchmarking and assembled a rigorously curated, leakage-controlled Benchmark dataset by integrating clinically annotated human variants from ClinVar and Humsavar as well as cross-species variants with functional annotations. The evaluation on this dataset revealed a narrow overall performance range for the benchmarked tools (AUC 0.631-0.758) and a consistent drop from clinical to functional data. Surprisingly, several tools published two decades ago demonstrated comparable performance to mid-ranked modern predictors. Building on these results, we developed a gradient-boosting consensus model, PredictSNPprot, which achieved an AUC of 0.82 on a held-out test set, outperforming the original PredictSNP meta-predictor and the best individual state-of-the-art tool by 13.6 and 5.7 percentage points, respectively. The Benchmark dataset, including the predictions from the evaluated tools, is available at https://doi.org/10.5281/zenodo.23597130 and PredictSNPprot consensus predictor is available at https://github.com/loschmidt/PredictSNPprot.

bioinformatics↗

MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Single-molecule localisation microscopy (SMLM) is a powerful tool in molecular and cellular biology and part of the broad umbrella of super-resolution microscopy. A distinct feature of SMLM, is its capacity for deep quantification, beyond just spatial information, to copy number, stoichiometry, molecular interaction and dynamics in living cells. The hardware required is increasingly straightforward to implement. However, complexity remains in the image analysis where much remains fragmented across specialised software tools. Here, we present MaCADEMIA (Matlab Computational Analysis of Diffusion, Emitter Molecular Intensity and Abundance), a simple yet powerful toolbox of MATLAB functions and associated documentation to enable straightforward extraction of quantitative single-molecule information. MaCADEMIA performs ensemble particle detection achieving localisation accuracies down to 0.5 pixels Root mean squared error (RMSE) across wide signal-to-noise (SNR) ranges on standardised particle tracking benchmarks, consistently matching or exceeding the performance of 14 leading methods. Our integrated workflow enables molecular counting and stoichiometry determination through stepwise photobleaching analysis, mobility measurement via mean squared displacement, colocalisaton analysis and absolute protein copy number quantification using three-dimensional cellular models.

bioinformatics↗