Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model
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 re...
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Johannes Gutenberg-Universität Mainz
2022
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ftunivmainzpubl:oai:openscience.ub.uni-mainz.de:20.500.12030/8702 2023-05-15T16:08:42+02:00 Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model 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 2022 https://openscience.ub.uni-mainz.de/handle/20.500.12030/8702 https://hdl.handle.net/20.500.12030/8702 https://doi.org/10.25358/openscience-8686 eng eng Johannes Gutenberg-Universität Mainz http://doi.org/10.25358/openscience-8686 https://openscience.ub.uni-mainz.de/handle/20.500.12030/8702 1471-2164 CC BY https://creativecommons.org/licenses/by/4.0/ openAccess CC-BY BMC genomics. 23. -. 2022. -. -. 677 ddc:610 Zeitschriftenaufsatz publishedVersion Text doc-type:article 2022 ftunivmainzpubl https://doi.org/20.500.12030/8702 https://doi.org/10.25358/openscience-8686 2023-02-05T23:38:02Z 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 ... Article in Journal/Newspaper European eel Gutenberg Open Science (Open-Science-Repository of the Johannes Gutenberg-University Mainz) |
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Gutenberg Open Science (Open-Science-Repository of the Johannes Gutenberg-University Mainz) |
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language |
English |
topic |
ddc:610 |
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ddc:610 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 Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
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ddc:610 |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
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 |
author_facet |
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 |
author_sort |
Schmidt, Hanno |
title |
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
title_short |
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
title_full |
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
title_fullStr |
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
title_full_unstemmed |
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
title_sort |
identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model |
publisher |
Johannes Gutenberg-Universität Mainz |
publishDate |
2022 |
url |
https://openscience.ub.uni-mainz.de/handle/20.500.12030/8702 https://hdl.handle.net/20.500.12030/8702 https://doi.org/10.25358/openscience-8686 |
genre |
European eel |
genre_facet |
European eel |
op_source |
BMC genomics. 23. -. 2022. -. -. 677 |
op_relation |
http://doi.org/10.25358/openscience-8686 https://openscience.ub.uni-mainz.de/handle/20.500.12030/8702 1471-2164 |
op_rights |
CC BY https://creativecommons.org/licenses/by/4.0/ openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/20.500.12030/8702 https://doi.org/10.25358/openscience-8686 |
_version_ |
1766404716294569984 |