Summary of Study ST001186
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000798. The data can be accessed directly via it's Project DOI: 10.21228/M8J399 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001186 |
| Study Title | Untargeted metabolomics on control and compound-treated STHdhQ111 cells and control STHdhQ7 cells |
| Study Summary | Cells expressing mutant huntingtin were treated in triplicate with serum-free DMEM with vehicle (Q111SST) or serum-free DMEM with one of 14 protective compounds for 24 hours. Wild type cells were also treated with serum-free DMEM with vehicle (Q7SST) as an additional control for 24 hours. We examined the compounds' metabolomic effects on the cells using untargeted mass spectrometry, which measured lipids and polar metabolites. |
| Institute | Massachusetts Institute of Technology |
| Laboratory | Fraenkel Lab |
| Last Name | Patel-Murray |
| First Name | Natasha |
| Address | 77 Massachusetts Avenue, Building 16 Room 244 |
| nlpm@mit.edu | |
| Phone | 6179490941 |
| Submit Date | 2019-05-24 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2020-01-22 |
| Release Version | 1 |
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Project:
| Project ID: | PR000798 |
| Project DOI: | doi: 10.21228/M8J399 |
| Project Title: | A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules |
| Project Summary: | High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. |
| Institute: | Massachusetts Institute of Technology |
| Laboratory: | Fraenkel Lab |
| Last Name: | Patel-Murray |
| First Name: | Natasha |
| Address: | 77 Massachusetts Avenue |
| Email: | nlpm@mit.edu |
| Phone: | 6179490941 |
