Accumulating evidence has shown that mechanical properties (e.g., elasticity, viscoelasticity, and viscoplasticity) of the extracellular matrix (ECM) play important roles in regulating cell behaviors, such as cell spreading, proliferation, and differentiation. It is of great importance to study the behaviors of cells responding to the mechanical properties of the ECM and its potential molecular mechanism. But how do cells sense and respond to mechanical properties of the ECM? We answer this question by studying one mechanosensitive pathway that begins with cell adhesions on the cell-ECM interface and continues through a cascade of chemical reactions that leads to nuclear translocation of the Yes-associated protein and transcriptional coactivator with PDZ-binding motif (YAP/TAZ).
In the cover image for the January 3 issue of Biophysical Journal, a single cell is placed on a two-dimensional substrate and connected to the substrate by some cell adhesions (blue ellipsoids on the gray substrate). Inside the cell, we use some "colored lights" (blue and purple lines) to represent the general mechano-signal transmission from cell adhesions to the nucleus for different types of cells. In these colored lights, we can find some proteins that are moving in these lights from adhesion to nucleus. The yellow proteins in lights represent typical bio-signals (such as Rho or Rho-associated protein kinase [ROCK]) that are activated by focal adhesion kinase (FAK) molecules on the membrane. The red proteins represent the YAP/TAZ molecules, which can enter the nucleus because of the Rho- or ROCK-mediated intracellular F-actin rearrangement.
To quantitatively describe the mechanosensing process in many experiments, we have developed a stochastic computational model with a single treatment accounting for the above mechanosensitive pathway between matrix mechanics, adhesion dynamics, and integrin-FAK-actomyosin-YAP/TAZ signaling, unlike previous computational models, which mainly focus on the adhesion dynamics on different types of substrates. We combine intracellular signal dynamics and cell adhesion dynamics and describe them with a unified stochastic simulation algorithm framework. Overall, through our study and this cover, we endeavor to explain a fundamental problem of how cells respond to their mechanical environments and how they translate these into intracellular signals. We also hope that such a model can serve as a computational tool that can capture the essential biophysics of substrate-to-nucleus signal transduction and can identify potential inhibitors or agonists that can be used as mechanotherapeutics.
— Bo Cheng, Moxiao Li, Wanting Wan, Hui Guo, Guy M. Genin, Min Lin, and Feng Xu