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Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium
BACKGROUND: Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6257948/ https://www.ncbi.nlm.nih.gov/pubmed/30482220 http://dx.doi.org/10.1186/s13071-018-3197-6 |
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author | Stroehlein, Andreas J. Gasser, Robin B. Hall, Ross S. Young, Neil D. |
author_facet | Stroehlein, Andreas J. Gasser, Robin B. Hall, Ross S. Young, Neil D. |
author_sort | Stroehlein, Andreas J. |
collection | PubMed |
description | BACKGROUND: Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to this anthelmintic, such that research towards alternative anti-schistosomal drugs is warranted. In this context, a number of studies have employed computational approaches to prioritise proteins for investigation as drug targets, based on extensive genomic, transcriptomic and small-molecule data now available. METHODS: Here, we established a customisable, online application for the prioritisation of drug targets and applied it, for the first time, to the entire inferred proteome of S. haematobium. This application enables selection of weighted and ranked proteins representing potential drug targets, and integrates transcriptional data, orthology and gene essentiality information as well as drug-drug target associations and chemical properties of predicted ligands. RESULTS: Using this application, we defined 25 potential drug targets in S. haematobium that associated with approved drugs, and 3402 targets that (although they could not be linked to any compounds) are conserved among a range of socioeconomically important flatworm species and might represent targets for new trematocides. CONCLUSIONS: The online application developed here represents an interactive, customisable, expandable and reproducible drug target ranking and prioritisation approach that should be useful for the prediction of drug targets in schistosomes and other species of parasitic worms in the future. We have demonstrated the utility of this online application by predicting potential drug targets in S. haematobium that can now be evaluated using functional genomics tools and/or small molecules, to establish whether they are indeed essential for parasite survival, and to assist in the discovery of novel anti-schistosomal compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-018-3197-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6257948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62579482018-11-29 Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium Stroehlein, Andreas J. Gasser, Robin B. Hall, Ross S. Young, Neil D. Parasit Vectors Research BACKGROUND: Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to this anthelmintic, such that research towards alternative anti-schistosomal drugs is warranted. In this context, a number of studies have employed computational approaches to prioritise proteins for investigation as drug targets, based on extensive genomic, transcriptomic and small-molecule data now available. METHODS: Here, we established a customisable, online application for the prioritisation of drug targets and applied it, for the first time, to the entire inferred proteome of S. haematobium. This application enables selection of weighted and ranked proteins representing potential drug targets, and integrates transcriptional data, orthology and gene essentiality information as well as drug-drug target associations and chemical properties of predicted ligands. RESULTS: Using this application, we defined 25 potential drug targets in S. haematobium that associated with approved drugs, and 3402 targets that (although they could not be linked to any compounds) are conserved among a range of socioeconomically important flatworm species and might represent targets for new trematocides. CONCLUSIONS: The online application developed here represents an interactive, customisable, expandable and reproducible drug target ranking and prioritisation approach that should be useful for the prediction of drug targets in schistosomes and other species of parasitic worms in the future. We have demonstrated the utility of this online application by predicting potential drug targets in S. haematobium that can now be evaluated using functional genomics tools and/or small molecules, to establish whether they are indeed essential for parasite survival, and to assist in the discovery of novel anti-schistosomal compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-018-3197-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-27 /pmc/articles/PMC6257948/ /pubmed/30482220 http://dx.doi.org/10.1186/s13071-018-3197-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Stroehlein, Andreas J. Gasser, Robin B. Hall, Ross S. Young, Neil D. Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title | Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title_full | Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title_fullStr | Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title_full_unstemmed | Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title_short | Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium |
title_sort | interactive online application for the prediction, ranking and prioritisation of drug targets in schistosoma haematobium |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6257948/ https://www.ncbi.nlm.nih.gov/pubmed/30482220 http://dx.doi.org/10.1186/s13071-018-3197-6 |
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