UC San Diego

Cells such as myocytes and adventitial fibroblasts are responsive to mechanical cues in their local environment. In response to mechanical loads, a variety of mechano-transduction mechanisms and signaling pathways are activated to regulate their response to the altered conditions.

In order to define mechano-signaling networks and their role in cellular function and remodeling, we have adapted and refined previously published systems models of myocyte hypertrophy. Using uncertainty quantification, we first found that the model accuracy was robust to parameter changes over a wide range with model outputs being least sensitive to time constants and most affected by uncertainty in reaction weights. We also found epistemic uncertainty in the reaction logic of the model could greatly affect model accuracy while uncertainty in the validation data had a modest effect on model accuracy.

As a step forward toward understanding myocyte response to external loading, including direction-dependent pathways, we extended this previous network model to include the transcriptional regulatory networks controlling gene expression as well as protein translation, and introduce a mass-action method to model quantitative gene expression. By incorporating RNA-sequencing data, this new approach displayed high accuracy with 69% agreement overall and 72% agreement for predicted differentially expressed genes in response to longitudinal stretch. We further found that the difference between transverse and longitudinal stretch responses in cardiomyocytes could be related to the sensitivity of directional mechanotransduction, with the sensitivity of longitudinal stretch being greater than transverse. Upon analyzing genes regulated by multiple TFs, we found that expression of these genes didn’t monotonically change with the number of TFs, which indicates TF regulation effects may saturate faster when multiple TFs coregulate gene expression. Moreover, we identified AT1 and ET1 receptors as main regulators of the stretch induced responses through receptor inhibition simulations and subsequent experiments.

A similar approach was used to study mechanical signaling and remodeling responses in PAAFs. In the current work, we have modified an existing systems model of cardiac fibroblast signaling to PAAFs and the cellular regulation of profibrotic signaling by combining both in-vitro and in-silico models of cell signaling in response to altered mechanical conditions. A UQ analysis on this model highlighted parameters to be optimized and network modules to be elucidated with more experiments. The signaling model in PAAFs and the subsequent experiments identified that both stretch and increased substrate stiffness regulated profibrotic genes, while no interaction effect was found between stretch and stiffness for several key genes studied. In addition, the activation of fibronectin expression by stretch in PAAFs may be angiotensin-independent when the cells are adhered on stiff but not soft substrates.

While these signaling network models can help distinguish regulators and their sensitivity to different mechanical stimuli, it is not known how these regulators participate in gene regulation of in-vivo hypertrophy. In the future, these signaling network models can be used to identify key regulators of hypertrophy-related heart failure and tissue fibrosis and provide support for drug discovery.