Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen
Rare diseases: drug re-discovery Canadian researchers use three different approaches to identify drugs that could be repurposed to treat rare genetic diseases. Prompted by the growing gap between known rare diseases and therapies and postulating that diseases might be treated with drugs that modulat...
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Nature Portfolio
2017
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oai:doaj.org-article:3d2784f19fee4e1ebd89fab035bb9d112021-12-02T12:34:18ZMining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen10.1038/s41525-017-0018-32056-7944https://doaj.org/article/3d2784f19fee4e1ebd89fab035bb9d112017-04-01T00:00:00Zhttps://doi.org/10.1038/s41525-017-0018-3https://doaj.org/toc/2056-7944Rare diseases: drug re-discovery Canadian researchers use three different approaches to identify drugs that could be repurposed to treat rare genetic diseases. Prompted by the growing gap between known rare diseases and therapies and postulating that diseases might be treated with drugs that modulate levels of disease-related proteins, Alex Mackenzie at the University of Ottawa and colleagues, studied the impact of 300 clinically approved drugs on 75 rare disease associated genes. Analyses of Connectivity Map gene expression data, published texts and a cell-based screening assay revealed thousands of putative and potentially clinically beneficial interactions. Although just 5–10% altered protein expression, the different approaches were complementary rather than redundant methodologies identifying potentially useful starting points on the path to rare disease therapy.A. J. MearsS. C. SchockJ. HadwenS. PutosD. DymentK. M. BoycottAlex MacKenzieNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 2, Iss 1, Pp 1-8 (2017) |
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DOAJ |
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Medicine R Genetics QH426-470 |
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Medicine R Genetics QH426-470 A. J. Mears S. C. Schock J. Hadwen S. Putos D. Dyment K. M. Boycott Alex MacKenzie Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| description |
Rare diseases: drug re-discovery Canadian researchers use three different approaches to identify drugs that could be repurposed to treat rare genetic diseases. Prompted by the growing gap between known rare diseases and therapies and postulating that diseases might be treated with drugs that modulate levels of disease-related proteins, Alex Mackenzie at the University of Ottawa and colleagues, studied the impact of 300 clinically approved drugs on 75 rare disease associated genes. Analyses of Connectivity Map gene expression data, published texts and a cell-based screening assay revealed thousands of putative and potentially clinically beneficial interactions. Although just 5–10% altered protein expression, the different approaches were complementary rather than redundant methodologies identifying potentially useful starting points on the path to rare disease therapy. |
| format |
article |
| author |
A. J. Mears S. C. Schock J. Hadwen S. Putos D. Dyment K. M. Boycott Alex MacKenzie |
| author_facet |
A. J. Mears S. C. Schock J. Hadwen S. Putos D. Dyment K. M. Boycott Alex MacKenzie |
| author_sort |
A. J. Mears |
| title |
Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| title_short |
Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| title_full |
Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| title_fullStr |
Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| title_full_unstemmed |
Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| title_sort |
mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/3d2784f19fee4e1ebd89fab035bb9d11 |
| work_keys_str_mv |
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