Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model
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Abstract:
Abstract
Background
With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (
Pomphorhynchus laevis, Neoechinorhynchus agilis
,
Neoechinorhynchus buttnerae
) from four fish species (common barbel, European eel, thinlip mullet, tambaqui).
Results
The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel.
Conclusions
The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL
tinyurl.com/yx72rda7
.
SEEK ID: https://publications.h-its.org/publications/1513
DOI: 10.1186/s12864-022-08882-1
Research Groups: Molecular and Cellular Modeling
Publication type: Journal
Journal: BMC Genomics
Citation: BMC Genomics 23(1),677
Date Published: 1st Dec 2022
Registered Mode: by DOI
Submitter
Citation
Schmidt, H., Mauer, K., Glaser, M., Dezfuli, B. S., Hellmann, S. L., Silva Gomes, A. L., Butter, F., Wade, R. C., Hankeln, T., & Herlyn, H. (2022). Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model. In BMC Genomics (Vol. 23, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1186/s12864-022-08882-1
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Created: 4th Oct 2022 at 07:43
Last updated: 5th Mar 2024 at 21:24
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