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      A module for Rac temporal signal integration revealed with optogenetics

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          Abstract

          Dissecting the logic of individual signaling modules in complex networks can be challenging for cascades that exhibit feedback and redundancy. In this study, Graziano et al. take an optogenetics-based approach to identify and dissect a module that converts sustained PIP 3 production to transient Rac activation in the neutrophil chemotaxis signaling network.

          Abstract

          Sensory systems use adaptation to measure changes in signaling inputs rather than absolute levels of signaling inputs. Adaptation enables eukaryotic cells to directionally migrate over a large dynamic range of chemoattractant. Because of complex feedback interactions and redundancy, it has been difficult to define the portion or portions of eukaryotic chemotactic signaling networks that generate adaptation and identify the regulators of this process. In this study, we use a combination of optogenetic intracellular inputs, CRISPR-based knockouts, and pharmacological perturbations to probe the basis of neutrophil adaptation. We find that persistent, optogenetically driven phosphatidylinositol (3,4,5)-trisphosphate (PIP 3) production results in only transient activation of Rac, a hallmark feature of adaptive circuits. We further identify the guanine nucleotide exchange factor P-Rex1 as the primary PIP 3-stimulated Rac activator, whereas actin polymerization and the GTPase-activating protein ArhGAP15 are essential for proper Rac turnoff. This circuit is masked by feedback and redundancy when chemoattractant is used as the input, highlighting the value of probing signaling networks at intermediate nodes to deconvolve complex signaling cascades.

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          Most cited references66

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          Robustness in simple biochemical networks.

          Cells use complex networks of interacting molecular components to transfer and process information. These "computational devices of living cells" are responsible for many important cellular processes, including cell-cycle regulation and signal transduction. Here we address the issue of the sensitivity of the networks to variations in their biochemical parameters. We propose a mechanism for robust adaptation in simple signal transduction networks. We show that this mechanism applies in particular to bacterial chemotaxis. This is demonstrated within a quantitative model which explains, in a unified way, many aspects of chemotaxis, including proper responses to chemical gradients. The adaptation property is a consequence of the network's connectivity and does not require the 'fine-tuning' of parameters. We argue that the key properties of biochemical networks should be robust in order to ensure their proper functioning.
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            Membrane tension maintains cell polarity by confining signals to the leading edge during neutrophil migration.

            Little is known about how neutrophils and other cells establish a single zone of actin assembly during migration. A widespread assumption is that the leading edge prevents formation of additional fronts by generating long-range diffusible inhibitors or by sequestering essential polarity components. We use morphological perturbations, cell-severing experiments, and computational simulations to show that diffusion-based mechanisms are not sufficient for long-range inhibition by the pseudopod. Instead, plasma membrane tension could serve as a long-range inhibitor in neutrophils. We find that membrane tension doubles during leading-edge protrusion, and increasing tension is sufficient for long-range inhibition of actin assembly and Rac activation. Furthermore, reducing membrane tension causes uniform actin assembly. We suggest that tension, rather than diffusible molecules generated or sequestered at the leading edge, is the dominant source of long-range inhibition that constrains the spread of the existing front and prevents the formation of secondary fronts. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Signal processing in complex chemotaxis pathways.

              Bacteria use chemotaxis to migrate towards environments that are better for growth. Chemoreceptors detect changes in attractant levels and signal through two-component systems to control swimming direction. This basic pathway is conserved across all chemotactic bacteria and archaea; however, recent work combining systems biology and genome sequencing has started to elucidate the additional complexity of the process in many bacterial species. This article focuses on one of the best understood complex networks, which is found in Rhodobacter sphaeroides and integrates sensory data about the external environment and the metabolic state of the cell to produce a balanced response at the flagellar motor.
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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
                07 August 2017
                : 216
                : 8
                : 2515-2531
                Affiliations
                [1 ]Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA
                [2 ]Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA
                [3 ]The Babraham Institute, Babraham, England, UK
                Author notes
                Correspondence to Orion D. Weiner: orion.weiner@ 123456ucsf.edu
                Author information
                http://orcid.org/0000-0003-1081-908X
                http://orcid.org/0000-0001-8315-4926
                http://orcid.org/0000-0002-7394-6660
                http://orcid.org/0000-0002-1778-6543
                Article
                201604113
                10.1083/jcb.201604113
                5551696
                28687663
                de1299ed-8ffa-4949-9c33-4ff4ebb50cc4
                © 2017 Graziano 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
                : 26 April 2016
                : 23 November 2016
                : 18 May 2017
                Funding
                Funded by: Cancer Research Institute, DOI http://dx.doi.org/10.13039/100000884;
                Funded by: American Cancer Society, DOI http://dx.doi.org/10.13039/100000048;
                Funded by: American Heart Association, DOI http://dx.doi.org/10.13039/100000968;
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Award ID: GM118167
                Categories
                Research Articles
                Article
                48
                23

                Cell biology
                Cell biology

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