Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements
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 a...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
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Nature Publishing Group
2017
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| Online Access: | http://hdl.handle.net/1721.1/112189 https://orcid.org/0000-0001-6246-276X https://orcid.org/0000-0003-0250-0474 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6365-8673 https://orcid.org/0000-0001-9249-8181 |
| _version_ | 1826200602665811968 |
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| author | 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 Heiman, Myriam Fraenkel, Ernest |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering 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 Heiman, Myriam Fraenkel, Ernest |
| author_sort | Dunn, Denise E. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:39:00Z |
| format | Article |
| id | mit-1721.1/112189 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:39:00Z |
| publishDate | 2017 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1121892022-09-27T21:00:21Z Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements 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 Heiman, Myriam Fraenkel, Ernest Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Pirhaji, Leila Milani, Pamela Dalin, Simona Wassie, Brook T. Fenster, Robert Heiman, Myriam Fraenkel, Ernest 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. 2017-11-14T19:12:48Z 2017-11-14T19:12:48Z 2017-09 2016-01 2017-11-13T17:46:38Z Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/112189 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) https://orcid.org/0000-0001-6246-276X https://orcid.org/0000-0003-0250-0474 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6365-8673 https://orcid.org/0000-0001-9249-8181 http://dx.doi.org/10.1038/s41467-017-00353-6 Nature Communications Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | 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 Heiman, Myriam Fraenkel, Ernest Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title | Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title_full | Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title_fullStr | Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title_full_unstemmed | Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title_short | Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| title_sort | identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements |
| url | http://hdl.handle.net/1721.1/112189 https://orcid.org/0000-0001-6246-276X https://orcid.org/0000-0003-0250-0474 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6365-8673 https://orcid.org/0000-0001-9249-8181 |
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