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      An excitable Rho GTPase signaling network generates dynamic subcellular contraction patterns

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          Abstract

          A signaling network is revealed that combines positive and negative feedback to control the activity of the small GTPase Rho in adherent cells. This network generates spontaneous pulses of Rho activity and actomyosin contraction that are modulated by extracellular elasticity.

          Abstract

          Rho GTPase-based signaling networks control cellular dynamics by coordinating protrusions and retractions in space and time. Here, we reveal a signaling network that generates pulses and propagating waves of cell contractions. These dynamic patterns emerge via self-organization from an activator–inhibitor network, in which the small GTPase Rho amplifies its activity by recruiting its activator, the guanine nucleotide exchange factor GEF-H1. Rho also inhibits itself by local recruitment of actomyosin and the associated RhoGAP Myo9b. This network structure enables spontaneous, self-limiting patterns of subcellular contractility that can explore mechanical cues in the extracellular environment. Indeed, actomyosin pulse frequency in cells is altered by matrix elasticity, showing that coupling of contractility pulses to environmental deformations modulates network dynamics. Thus, our study reveals a mechanism that integrates intracellular biochemical and extracellular mechanical signals into subcellular activity patterns to control cellular contractility dynamics.

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          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
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            Non-muscle myosin II takes centre stage in cell adhesion and migration.

            Non-muscle myosin II (NM II) is an actin-binding protein that has actin cross-linking and contractile properties and is regulated by the phosphorylation of its light and heavy chains. The three mammalian NM II isoforms have both overlapping and unique properties. Owing to its position downstream of convergent signalling pathways, NM II is central in the control of cell adhesion, cell migration and tissue architecture. Recent insight into the role of NM II in these processes has been gained from loss-of-function and mutant approaches, methods that quantitatively measure actin and adhesion dynamics and the discovery of NM II mutations that cause monogenic diseases.
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              Coordination of Rho GTPase activities during cell protrusion

              The GTPases Rac1, RhoA and Cdc42 act in concert to control cytoskeleton dynamics1-3. Recent biosensor studies have shown that all three GTPases are activated at the front of migrating cells4-7 and biochemical evidence suggests that they may regulate one another: Cdc42 can activate Rac18, and Rac1 and RhoA are mutually inhibitory9-12. However, their spatiotemporal coordination, at the seconds and single micron dimensions typical of individual protrusion events, remains unknown. Here, we examine GTPase coordination both through simultaneous visualization of two GTPase biosensors and using a “computational multiplexing” approach capable of defining the relationships between multiple protein activities visualized in separate experiments. We found that RhoA is activated at the cell edge synchronous with edge advancement, whereas Cdc42 and Rac1 are activated 2 μm behind the edge with a delay of 40 sec. This indicates that Rac1 and RhoA operate antagonistically through spatial separation and precise timing, and that RhoA plays a role in the initial events of protrusion, while Rac1 and Cdc42 activate pathways implicated in reinforcement and stabilization of newly expanded protrusions.
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                Author and article information

                Journal
                J Cell Biol
                J. Cell Biol
                jcb
                jcb
                The Journal of Cell Biology
                The Rockefeller University Press
                0021-9525
                1540-8140
                4 December 2017
                : 216
                : 12
                : 4271-4285
                Affiliations
                [1 ]Department of Molecular Cell Biology, Center for Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
                [2 ]Department of Systemic Cell Biology, Max Planck Institute of Molecular Physiology and Fakultät für Chemie und Chemische Biologie, TU Dortmund University, Dortmund, Germany
                [3 ]Institute of Complex Systems, Forschungszentrum Jülich, Jülich, Germany
                Author notes
                Correspondence to Perihan Nalbant: perihan.nalbant@ 123456uni-due.de ;
                [*]

                M. Graessl and J. Koch contributed equally to this paper.

                [**]

                L. Dehmelt and P. Nalbant contributed equally to this paper.

                Author information
                http://orcid.org/0000-0002-3803-8835
                http://orcid.org/0000-0002-5649-5933
                Article
                201706052
                10.1083/jcb.201706052
                5716289
                29055010
                ffb11a40-bef6-4bf9-9ccf-e791359cb432
                © 2017 Graessl et al.

                This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).

                History
                : 10 June 2017
                : 25 August 2017
                : 08 September 2017
                Funding
                Funded by: MERCUR
                Award ID: Pr-2012-0022
                Funded by: Bundesministerium für Bildung und Forschung, DOI https://doi.org/10.13039/501100002347;
                Funded by: 0315258
                Funded by: Deutsche Forschungsgemeinschaft, DOI https://doi.org/10.13039/501100001659;
                Award ID: SPP 1462/2
                Funded by: Marie Skłodowska-Curie Innovative Training Network InCeM
                Award ID: H2020-MSCA-ITN-2014
                Award ID: 642866
                Funded by: Deutsche Forschungsgemeinschaft, DOI https://doi.org/10.13039/501100001659;
                Categories
                Research Articles
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                Cell biology
                Cell biology

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