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Force-dependent focal adhesion assembly and disassembly: A computational study

Cells interact with the extracellular matrix (ECM) via cell–ECM adhesions. These physical interactions are transduced into biochemical signals inside the cell which influence cell behaviour. Although cell–ECM interactions have been studied extensively, it is not completely understood how immature (n...

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Detalles Bibliográficos
Autores principales: Honasoge, Kailas Shankar, Karagöz, Zeynep, Goult, Benjamin T., Wolfenson, Haguy, LaPointe, Vanessa L. S., Carlier, Aurélie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584152/
https://www.ncbi.nlm.nih.gov/pubmed/37801464
http://dx.doi.org/10.1371/journal.pcbi.1011500
Descripción
Sumario:Cells interact with the extracellular matrix (ECM) via cell–ECM adhesions. These physical interactions are transduced into biochemical signals inside the cell which influence cell behaviour. Although cell–ECM interactions have been studied extensively, it is not completely understood how immature (nascent) adhesions develop into mature (focal) adhesions and how mechanical forces influence this process. Given the small size, dynamic nature and short lifetimes of nascent adhesions, studying them using conventional microscopic and experimental techniques is challenging. Computational modelling provides a valuable resource for simulating and exploring various “what if?” scenarios in silico and identifying key molecular components and mechanisms for further investigation. Here, we present a simplified mechano-chemical model based on ordinary differential equations with three major proteins involved in adhesions: integrins, talin and vinculin. Additionally, we incorporate a hypothetical signal molecule that influences adhesion (dis)assembly rates. We find that assembly and disassembly rates need to vary dynamically to limit maturation of nascent adhesions. The model predicts biphasic variation of actin retrograde velocity and maturation fraction with substrate stiffness, with maturation fractions between 18–35%, optimal stiffness of ∼1 pN/nm, and a mechanosensitive range of 1-100 pN/nm, all corresponding to key experimental findings. Sensitivity analyses show robustness of outcomes to small changes in parameter values, allowing model tuning to reflect specific cell types and signaling cascades. The model proposes that signal-dependent disassembly rate variations play an underappreciated role in maturation fraction regulation, which should be investigated further. We also provide predictions on the changes in traction force generation under increased/decreased vinculin concentrations, complementing previous vinculin overexpression/knockout experiments in different cell types. In summary, this work proposes a model framework to robustly simulate the mechanochemical processes underlying adhesion maturation and maintenance, thereby enhancing our fundamental knowledge of cell–ECM interactions.