Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts
Anders R Nelson, Steven L Christiansen, Kristen M Naegle, Jeffrey J Saucerman

Fibroblasts are crucial regulators of extracellular matrix deposition following cardiac injuries. These cells exhibit highly plastic responses in phenotype during fibrosis as a result of ecological stimuli. Here, we test whether and just how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which might help identify treating cardiac fibrosis. We conducted a higher content microscopy screen of human cardiac fibroblasts given 13 clinically relevant drugs poor TGF|? and/or IL-1|?, calculating phenotype across 137 single-cell features. We used the phenotypic data from your high-content imaging to coach a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce F-actin set up and F-actin stress fiber formation, correspondingly. Validating the LogiMML model conjecture that PI3K partly mediates the results of Src inhibition, we discovered that PI3K inhibition reduces F-actin fiber formation and procollagen I production in human cardiac fibroblasts. Within this study, we set up a modeling approach mixing the strengths of logic-based network models and regularized regression models, apply this method to calculate mechanisms that mediate the differential results of drugs on fibroblasts, revealing Src inhibition acting via PI3K like a potential therapy for cardiac fibrosis.

Significance: Cardiac fibrosis is really a dysregulation from the normal wound healing response, leading to excessive scarring and cardiac disorder. As cardiac fibroblasts mainly regulate this method, we explored how candidate anti-fibrotic drugs affect the fibroblast phenotype. We identify some 137 phenotypic features that change as a result of prescription drugs. Utilizing a new computational modeling approach termed logic-based mechanistic machine learning, we expect how pirfenidone and Src inhibition modify the regulating the phenotypic features F-actin set up and F-actin stress fiber formation. We reveal that inhibition of PI3K reduces F-actin fiber formation and procollagen I production in human cardiac fibroblasts, supporting a job for PI3K like a mechanism through which Src inhibition may suppress fibrosis.