Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Abstract

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.

Potential ferroptosis key genes in calcific aortic valve disease

“…Thus, it is suggested that inhibition targeting ferroptosis may be a possible strategy for regression of CAVD. In this study, ferroptosis ( Figure 2F ), ROS metabolic process ( Figures 3A,B ), and NAFLD (containing IL-6, BID, PRKAA2) signaling were identified as the activated biological processes or pathways involved in CAVD, and particularly, NAFLD was validated by WebGestalt, DAVID, and Metascape, which was consistent with the previous findings ( 6 , 21 ). Iron overload was observed in patients with NAFLD and the liver damage could be attenuated by iron removal ( 22 , 23 ).…”

Inflammatory serum factors from aortic valve stenosis patients modulate sex differences in valvular myofibroblast activation and osteoblast-like differentiation

“…In the future, exploring the combinatorial effects of annexin A2 and cystatin C on fibrosis and calcification in AVS may be fruitful, given the complete proteome in patient sera elicits patient-specific effects on myofibroblast activation. 21,48 Additionally, although porcine VICs are known to respond similarly to human VICs when treated with biochemical factors, 49 reproducing key experiments using human VICs would further enhance the clinical relevance of our findings. We further showed that annexin A2 and cystatin C modulate sex-specific VIC myofibroblast activation through p38 MAPK signaling.…”

“…51 VIC signaling networks are complex and predicting their effects on regulating the transitions from a quiescent fibroblast to myofibroblast or osteoblast-like phenotype remains an ongoing area of research. 48 Previous work has suggested that pro-inflammatory secreted factors mediate a quiescent VIC to myofibroblast transition, followed by proliferation, then termination in an osteoblast-like phenotype. 16 Our data suggest that annexin A2 and cystatin C increase both myofibroblast and osteoblast-like phenotypes.…”

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model

“…Research shows that genetic differences between males and females can also contribute to cardiovascular disease presentation and severity (70,71). Future studies could address this by integrating genomic or transcriptomic data into the model to account for additional differences due to biological sex in addition to gonadal hormones (72).…”