Aguado, B. A. | bioRxiv Science

bioRxiv2024

Y chromosome linked UTY modulates sex differences in valvular fibroblast methylation in response to nanoscale extracellular matrix cues

Aortic valve stenosis (AVS) is a progressive disease wherein males more often develop valve calcification relative to females that develop valve fibrosis. Valvular interstitial cells (VICs) aberrantly activate to myofibroblasts during AVS, driving the fibrotic valve phenotype in females. Myofibroblasts further differentiate into osteoblast-like cells and produce calcium nanoparticles, driving valve calcification in males. We hypothesized the lysine demethylase UTY (ubiquitously transcribed tetratricopeptide repeat containing, Y-linked) decreases methylation uniquely in male VICs responding to nanoscale extracellular matrix cues to promote an osteoblast-like cell phenotype. Here, we describe a hydrogel biomaterial cell culture platform to interrogate how nanoscale cues modulate sex-specific methylation states in VICs activating to myofibroblasts and osteoblast-like cells. We found UTY modulates the osteoblast-like cell phenotype in response to nanoscale cues uniquely in male VICs. Overall, we reveal a novel role of UTY in the regulation of calcification processes in males during AVS progression.

bioengineering↗

bioRxiv2021

Network Modeling Predicts Personalized Gene Expression and Drug Responses in Valve Myofibroblasts Cultured with Patient Sera

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 {beta} (TGF{beta}) 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.

systems biology↗