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      Pervasive Protein Thermal Stability Variation during the Cell Cycle

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          Summary

          Quantitative mass spectrometry has established proteome-wide regulation of protein abundance and post-translational modifications in various biological processes. Here, we used quantitative mass spectrometry to systematically analyze the thermal stability and solubility of proteins on a proteome-wide scale during the eukaryotic cell cycle. We demonstrate pervasive variation of these biophysical parameters with most changes occurring in mitosis and G1. Various cellular pathways and components vary in thermal stability, such as cell-cycle factors, polymerases, and chromatin remodelers. We demonstrate that protein thermal stability serves as a proxy for enzyme activity, DNA binding, and complex formation in situ. Strikingly, a large cohort of intrinsically disordered and mitotically phosphorylated proteins is stabilized and solubilized in mitosis, suggesting a fundamental remodeling of the biophysical environment of the mitotic cell. Our data represent a rich resource for cell, structural, and systems biologists interested in proteome regulation during biological transitions.

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          Highlights

          • Proteome-wide variation of in situ protein thermal stability and solubility

          • Thermal stability of RNA Pol II varies across the cell cycle and is DNA dependent

          • Thermal profiling across the cell cycle delineates protein subcomplexes

          • Intrinsically disordered proteins are less prone to aggregation during mitosis

          Abstract

          A proteome-wide assessment of thermal stability and solubility during the eukaryotic cell cycle demonstrates pervasive variation in mitosis and G1.

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          Most cited references50

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          Variance stabilization applied to microarray data calibration and to the quantification of differential expression.

          We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.
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            The endoplasmic reticulum: structure, function and response to cellular signaling

            The endoplasmic reticulum (ER) is a large, dynamic structure that serves many roles in the cell including calcium storage, protein synthesis and lipid metabolism. The diverse functions of the ER are performed by distinct domains; consisting of tubules, sheets and the nuclear envelope. Several proteins that contribute to the overall architecture and dynamics of the ER have been identified, but many questions remain as to how the ER changes shape in response to cellular cues, cell type, cell cycle state and during development of the organism. Here we discuss what is known about the dynamics of the ER, what questions remain, and how coordinated responses add to the layers of regulation in this dynamic organelle.
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              Tracking cancer drugs in living cells by thermal profiling of the proteome.

              The thermal stability of proteins can be used to assess ligand binding in living cells. We have generalized this concept by determining the thermal profiles of more than 7000 proteins in human cells by means of mass spectrometry. Monitoring the effects of small-molecule ligands on the profiles delineated more than 50 targets for the kinase inhibitor staurosporine. We identified the heme biosynthesis enzyme ferrochelatase as a target of kinase inhibitors and suggest that its inhibition causes the phototoxicity observed with vemurafenib and alectinib. Thermal shifts were also observed for downstream effectors of drug treatment. In live cells, dasatinib induced shifts in BCR-ABL pathway proteins, including CRK/CRKL. Thermal proteome profiling provides an unbiased measure of drug-target engagement and facilitates identification of markers for drug efficacy and toxicity. Copyright © 2014, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                31 May 2018
                31 May 2018
                : 173
                : 6
                : 1495-1507.e18
                Affiliations
                [1 ]Genome Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [2 ]Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [3 ]Proteomics Core Facility, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [4 ]Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
                [5 ]Molecular Medicine Partnership Unit, 69120 Heidelberg, Germany
                [6 ]Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
                [7 ]Cell Biology and Biophysics Unit, European Molecular Biology Laboratory 69117 Heidelberg, Germany
                Author notes
                []Corresponding author bork@ 123456embl.de
                [∗∗ ]Corresponding author martin.beck@ 123456embl.de
                [∗∗∗ ]Corresponding author mikhail.savitski@ 123456embl.de
                [8]

                These authors contributed equally

                [9]

                Lead Contact

                Article
                S0092-8674(18)30385-4
                10.1016/j.cell.2018.03.053
                5998384
                29706546
                60422ffe-3931-45ed-9990-d1e7155d981c
                © 2018 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 October 2017
                : 18 January 2018
                : 21 March 2018
                Categories
                Article

                Cell biology
                thermal proteome profiling,cell cycle,proteomics
                Cell biology
                thermal proteome profiling, cell cycle, proteomics

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