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      ARHGEF17 is an essential spindle assembly checkpoint factor that targets Mps1 to kinetochores

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          Abstract

          The spindle assembly checkpoint (SAC) ensures genome stability during cell division. Here, a new essential SAC factor, ARHGEF17, is characterized by quantitative imaging, biochemical, and biophysical experiments, which show that it targets the checkpoint kinase Mps1 to kinetochores.

          Abstract

          To prevent genome instability, mitotic exit is delayed until all chromosomes are properly attached to the mitotic spindle by the spindle assembly checkpoint (SAC). In this study, we characterized the function of ARHGEF17, identified in a genome-wide RNA interference screen for human mitosis genes. Through a series of quantitative imaging, biochemical, and biophysical experiments, we showed that ARHGEF17 is essential for SAC activity, because it is the major targeting factor that controls localization of the checkpoint kinase Mps1 to the kinetochore. This mitotic function is mediated by direct interaction of the central domain of ARHGEF17 with Mps1, which is autoregulated by the activity of Mps1 kinase, for which ARHGEF17 is a substrate. This mitosis-specific role is independent of ARHGEF17’s RhoGEF activity in interphase. Our study thus assigns a new mitotic function to ARHGEF17 and reveals the molecular mechanism for a key step in SAC establishment.

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          The spindle-assembly checkpoint in space and time.

          In eukaryotes, the spindle-assembly checkpoint (SAC) is a ubiquitous safety device that ensures the fidelity of chromosome segregation in mitosis. The SAC prevents chromosome mis-segregation and aneuploidy, and its dysfunction is implicated in tumorigenesis. Recent molecular analyses have begun to shed light on the complex interaction of the checkpoint proteins with kinetochores--structures that mediate the binding of spindle microtubules to chromosomes in mitosis. These studies are finally starting to reveal the mechanisms of checkpoint activation and silencing during mitotic progression.
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            Feature point tracking and trajectory analysis for video imaging in cell biology.

            This paper presents a computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology. The tracking process requires no a priori mathematical modeling of the motion, it is self-initializing, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. The efficiency of the algorithm is validated on synthetic video data where it is compared to existing methods and its accuracy and precision are assessed for a wide range of signal-to-noise ratios. The algorithm is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. Its applicability is demonstrated in three case studies involving transport of low-density lipoproteins in endosomes, motion of fluorescently labeled Adenovirus-2 particles along microtubules, and tracking of quantum dots on the plasma membrane of live cells. The present automated tracking process enables the quantification of dispersive processes in cell biology using techniques such as moment scaling spectra.
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              Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes.

              Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.
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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
                14 March 2016
                : 212
                : 6
                : 647-659
                Affiliations
                [1 ]Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [2 ]Centre for Molecular and Cellular Imaging, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [3 ]Department of Mechanistic Cell Biology, Max Planck Institute of Molecular Physiology, 44227 Dortmund, Germany
                [4 ]Molecular and Cellular Biology, Research Institute of Molecular Pathology, 1030 Vienna, Austria
                Author notes
                Correspondence to Jan Ellenberg: jan.ellenberg@ 123456embl.de

                R. Mahen's present address is Photonics Group, Dept. of Physics, Imperial College London, London SW7 2AZ, England, UK.

                T. Walter’s present address is Centre for Computational Biology, Mines ParisTech, Fontainebleau 77300, France; Institut Curie, Paris 75248, France; and U900, Institut National de la Santé et de la Recherche Médicale, Paris 75248, France.

                T.S. Kitajima’s present address is RIKEN Center for Developmental Biology, Kobe 650-0047, Japan.

                Article
                201408089
                10.1083/jcb.201408089
                4792069
                26953350
                8709511a-2160-4f67-8b2e-f853f1278381
                © 2016 Isokane 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 3.0 Unported license, as described at http://creativecommons.org/licenses/by-nc-sa/3.0/).

                History
                : 21 August 2014
                : 11 February 2016
                Funding
                Funded by: European Commission http://dx.doi.org/10.13039/501100000780
                Award ID: 241548
                Award ID: 258068
                Funded by: Alexander von Humboldt-Stiftung http://dx.doi.org/10.13039/100005156
                Award ID: 7000247787
                Funded by: Wellcome Trust http://dx.doi.org/10.13039/100004440
                Award ID: 100090/Z/12/Z
                Funded by: European Molecular Biology Organization http://dx.doi.org/10.13039/100004410
                Award ID: ALTF 416-2012
                Award ID: GA-2010-2767416
                Categories
                Research Articles
                Article

                Cell biology
                Cell biology

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