Key result
A computational model of valve myofibroblast signaling accurately predicted patient-specific fibrotic gene expression responses to the ETAR inhibitor bosentan with an area under the curve of 0.81.
Why the study?
Pathogenic extracellular matrix remodeling in aortic valve stenosis is driven by complex myofibroblast signaling networks influenced by patient-specific extracellular factors.
A computational network model successfully predicted personalized fibrotic gene expression and drug responses in valvular myofibroblasts using AVS patient sera, demonstrating potential for personalized drug screening.
Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually 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, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera 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.
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Rogers et al. (2022) studied Aortic valve stenosis (n=8). Bosentan (ETAR inhibitor) and computational modeling vs. Without bosentan was evaluated on Predictive accuracy of patient-specific drug responses (responder vs nonresponder) to ETAR inhibition (AUC 0.81). A computational model of valve myofibroblast signaling accurately predicted patient-specific fibrotic gene expression responses to the ETAR inhibitor bosentan with an area under the curve of 0.81.
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