Key result
A large-scale computational model accurately predicted patient-specific myofibroblast gene expression signatures and stratified patient responses to the ET1 receptor inhibitor bosentan with an area-under-the-curve of 0.81.
Why the study?
In aortic valve stenosis, pathogenic extracellular matrix remodeling is driven by complex myofibroblast signaling networks influenced by patient-specific extracellular cues, necessitating computational approaches to predict personalized gene expression and drug responses.
Does a personalized computational network model accurately predict myofibroblast gene expression and drug responses in cells cultured with AVS patient sera?
Does a personalized computational network model accurately predict myofibroblast gene expression and drug responses in cells cultured with AVS patient sera?
A personalized computational network model can accurately predict patient-specific fibrotic gene expression and drug responses in valvular myofibroblasts, offering a potential tool for personalized drug screening in aortic valve stenosis.
Patient-specific myofibroblast network modeling in AVS may inform targeted therapies; leaves open clinical translation pending validation.
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 are 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 VICs 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. (2021) studied Aortic valve stenosis (n=8). Patient-specific sera and Bosentan (ETAR inhibitor) vs. Un-treated / Default model was evaluated on Predictive accuracy of patient-specific drug responses (responder vs. non-responder status) (AUC 0.81). A large-scale computational model accurately predicted patient-specific myofibroblast gene expression signatures and stratified patient responses to the ET1 receptor inhibitor bosentan with an area-under-the-curve of 0.81.
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