Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8686
Authors: | Schmidt, Hanno Mauer, Katharina Glaser, Manuel Sayyaf Dezfuli, Bahram Hellmann, Sören Lukas Silva Gomes, Ana Lúcia Butter, Falk Wade, Rebecca C. Hankeln, Thomas Herlyn, Holger |
Title: | Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
Online publication date: | 3-Feb-2023 |
Year of first publication: | 2022 |
Language: | english |
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. |
DDC: | 610 Medizin 610 Medical sciences |
Institution: | Johannes Gutenberg-Universität Mainz |
Department: | FB 04 Medizin |
Place: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-8686 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | BMC genomics 23 |
Pages or article number: | 677 |
Publisher: | BioMed Central |
Publisher place: | London |
Issue date: | 2022 |
ISSN: | 1471-2164 |
Publisher DOI: | 10.1186/s12864-022-08882-1 |
Appears in collections: | DFG-491381577-G |
Files in This Item:
File | Description | Size | Format | ||
---|---|---|---|---|---|
identification_of_antiparasit-20230127112543132.pdf | 1.98 MB | Adobe PDF | View/Open |