Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements
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41467_2017_Article_353.pdf
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Author(s)
Dunn, Denise E.
Avila-Pacheco, Julian
Greengard, Paul
Clish, Clary B.
Lo, Donald C.
Pirhaji, Leila
Milani, Pamela
Dalin, Simona
Wassie, Brook T.
Fenster, Robert
Date Issued
September 2017
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Pirhaji, Leila et al “Identifying Therapeutic Targets by Combining Transcriptional Data with Ordinal Clinical Measurements.” Nature Communications 8, 1 (September 2017): 623 © 2017 The Author(s)
Version
Final published version
Abstract
The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1038/s41467-017-00353-6
