Using combinatorial BMP signals to predict and control cardiac differentiation.
Description: Around half of cardiovascular disease-caused deaths are due to ischemic heart disease which leads to death of cardiomyocytes and heart failure. While existing treatments for heart failure can effectively delay disease progression, regenerative treatments promise to restore function to a damaged heart. A major challenge in realizing the goals of regenerative medicine is achieving reproducible, predictable, and specific differentiation of stem cells into desired cell types. Bone Morphogenetic Protein (BMP) signaling is one of the most important signals used to control differentiation. In fact, addition of the single BMP4 ligand (along with inhibition of Wnt signaling) is typically among the first steps in directed differentiation to cardiac cell fates, mimicking the early role that BMP ligands play in directing mesodermal differentiation during development. However, while the use of BMP4 is effective at producing cardiac cell types, it differs from the way in which BMP ligands are actually deployed in vivo, exhibits strong dosage sensitivity, and fails to leverage the full cell fate control capabilities that this complex pathway could provide. In particular, in vivo, multiple BMP ligands function in combinations, interacting in a many-to-many fashion with multiple BMP receptors on target cells to specifically control the level of signaling in different cell types. BMP signaling increasingly appears to operate through a contextual, combinatorial code involving multiple ligands and receptors. Deciphering this code would allow us to exploit natural mechanisms of combinatorial signaling to achieve more precise control of cardiac differentiation. Here, we seek to develop a predictive, quantitative model of combinatorial ligand-receptor interactions in the BMP pathway and use it to more precisely control cardiac differentiation.
Funder: American Heart Association
Amount: $156,600
Funding type: fellowship
Funder scheme: Postdoctoral Fellowship
Start date: 2025-01-01
End date: 2026-12-31
Investigators: Maire Gavagan, Maire Gavagan
Institution: California Institute of Technology
Subfield: Molecular Biology
Domain: Life Sciences
Funded outputs count: 0
Funder award ID: 25POST1376774
OpenAlex ID: G6578675929