RT Journal Article
SR Electronic
T1 Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease
JF Science
JO Science
FD American Association for the Advancement of Science
SP eabd0724
DO 10.1126/science.abd0724
A1 Theodoris, Christina V.
A1 Zhou, Ping
A1 Liu, Lei
A1 Zhang, Yu
A1 Nishino, Tomohiro
A1 Huang, Yu
A1 Kostina, Aleksandra
A1 Ranade, Sanjeev S.
A1 Gifford, Casey A.
A1 Uspenskiy, Vladimir
A1 Malaschicheva, Anna
A1 Ding, Sheng
A1 Srivastava, Deepak
YR 2020
UL http://science.sciencemag.org/content/early/2020/12/09/science.abd0724.abstract
AB Mapping the gene regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, 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. Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary aortic valve cells and was sufficient to prevent and treat aortic valve 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.