On the influence of cell shape on dynamic reaction-diffusion polarization patterns

PLoS One. 2021 Mar 18;16(3):e0248293. doi: 10.1371/journal.pone.0248293. eCollection 2021.

Abstract

The distribution of signaling molecules following mechanical or chemical stimulation of a cell defines cell polarization, with regions of high active Cdc42 at the front and low active Cdc42 at the rear. As reaction-diffusion phenomena between signaling molecules, such as Rho GTPases, define the gradient dynamics, we hypothesize that the cell shape influences the maintenance of the "front-to-back" cell polarization patterns. We investigated the influence of cell shape on the Cdc42 patterns using an established computational polarization model. Our simulation results showed that not only cell shape but also Cdc42 and Rho-related (in)activation parameter values affected the distribution of active Cdc42. Despite an initial Cdc42 gradient, the in silico results showed that the maximal Cdc42 concentration shifts in the opposite direction, a phenomenon we propose to call "reverse polarization". Additional in silico analyses indicated that "reverse polarization" only occurred in a particular parameter value space that resulted in a balance between inactivation and activation of Rho GTPases. Future work should focus on a mathematical description of the underpinnings of reverse polarization, in combination with experimental validation using, for example, dedicated FRET-probes to spatiotemporally track Rho GTPase patterns in migrating cells. In summary, the findings of this study enhance our understanding of the role of cell shape in intracellular signaling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Polarity / physiology*
  • Cell Shape / physiology*
  • Computer Simulation
  • Diffusion
  • Models, Biological*
  • Signal Transduction / physiology
  • cdc42 GTP-Binding Protein / metabolism*

Substances

  • cdc42 GTP-Binding Protein

Grant support

AC and JDB kindly acknowledge the Dutch province of Limburg in the LINK (FCL67723) (“Limburg INvesteert in haar Kenniseconomie”) knowledge economy project. AC acknowledges a VENI grant (number 15075) from the Dutch Science Foundation (NWO). SV is supported by the European Union’s Horizon 2020 Programme (H2020-MSCA-ITN-2015; Grant agreement 676338). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.