期刊
SCIENCE
卷 371, 期 6530, 页码 693-+出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.abd0724
关键词
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资金
- Winslow Family
- L.K. Whittier Foundation
- Roddenberry Foundation
- Younger Family Fund
- California Institute of Regenerative Medicine [DISC2-09098]
- NHLBI/NIH [R01 HL057181, P01 HL146366, R01 HL150100, R01 HL127240, T32-HL007544]
- National Center for Research Resources [U01-HL098179, U01-HL100406, C06-RR018928]
- Russian Science Foundation [18-14-00152]
- American Heart Association
- Roddenberry Fellowship
- Winslow Fellowship
- UCSF Discovery Fellowship
- UCSF DSCB [NIH-T32-HD007470]
- UCSF Medical Scientist Training Program [NIH-T32-GM007618]
- Boston Combined Residency Program in Pediatrics
- Harvard Medical School Genetics Residency Program
- Uehara Memorial Foundation
- Howard Hughes Medical Institute Fellowship of the Damon Runyon Cancer Research Foundation [DRG-2206-14]
- Russian Science Foundation [18-14-00152] Funding Source: Russian Science Foundation
By using a machine-learning approach, small molecules that can broadly correct dysregulated gene networks in a human heart disease model were identified, with the most efficacious candidate successfully preventing and treating AV disease in a mouse model.
Mapping the gene-regulatory networks dysregulated in human disease would allow the design of networkcorrecting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs atmost, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.
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