Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements

Leila Pirhaji, Pamela Milani, Simona Dalin, Brook T. Wassie, Denise E. Dunn, Robert J. Fenster, Julian Avila-Pacheco, Paul Greengard, Clary B. Clish, Myriam Heiman, Donald C. Lo and Ernest Fraenkel ()
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Leila Pirhaji: Massachusetts Institute of Technology
Pamela Milani: Massachusetts Institute of Technology
Simona Dalin: Massachusetts Institute of Technology
Brook T. Wassie: Massachusetts Institute of Technology
Denise E. Dunn: Duke University Medical Center
Robert J. Fenster: Picower Institute for Learning and Memory
Julian Avila-Pacheco: Broad Institute
Paul Greengard: The Rockefeller University
Clary B. Clish: Broad Institute
Myriam Heiman: Picower Institute for Learning and Memory
Donald C. Lo: Duke University Medical Center
Ernest Fraenkel: Massachusetts Institute of Technology

Nature Communications, 2017, vol. 8, issue 1, 1-13

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00353-6

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