A major contributor to heart valve disease is the excessive buildup of scar tissue in the valve, which can interfere with the valve’s ability to open and close and can ultimately lead to heart failure. The control of this scar tissue remodeling is very difficult due, on the one hand, to a complex cellular regulatory system and, on the other hand, to great variability between different patients. We have constructed a computer model of the cellular biochemical network that regulates valve remodeling, which allows virtual predictions of valve healing given patient-specific biochemical levels. With this model, we performed personalized drug screens to predict each patient’s response to particular therapies, and follow-up cell culture experiments validated our predictions with over 80% accuracy.
Patients with aortic valve stenosis (AVS) exhibit pathogenic stiffening of the valve leaflets due to excessive remodeling of the extracellular matrix (ECM). Many microenvironmental signals influence the pathogenic expression of ECM remodeling genes in tissue-resident valve myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a hand-curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale logical differential equation model in which 11 biochemical and biomechanical signals were transduced through a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis reactions. -related genes. Correlations were found between gene expression predicted by the personalized model and echocardiography data from AVS patients, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Additionally, global network perturbation analyzes revealed signaling molecules with the most influence on network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), as well as the downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Finally, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valve interstitial cells cultured with sera from AVS patients and treated with or without bosentan. , a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions, including patient stratification and personalized drug screening.
- Accepted January 12, 2022.
Author contributions: research designed by JDR, BAA, KSA and WJR; JDR and BAA conducted research; JDR and KMW provided new analytical reagents/tools; JDR, BAA, KMW, KSA and WJR analyzed the data; JDR, BAA, KMW and WJR wrote the article; and KSA edited the document.
Reviewers: KM, University of Wisconsin-Madison; and KY, Cincinnati Children’s Hospital Medical Center.
The authors declare no competing interests.
This article contains additional information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2117323119/-/DCSupplemental.
- Copyright © 2022 the author(s). Published by PNAS.