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      Systems level expression correlation of Ras GTPase regulators

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          Abstract

          Background

          Proteins of the ubiquitously expressed core proteome are quantitatively correlated across multiple eukaryotic species. In addition, it was found that many protein paralogues exhibit expression anticorrelation, suggesting that the total level of protein with a given functionality must be kept constant.

          Methods

          We performed Spearman’s rank correlation analyses of gene expression levels for the RAS GTPase subfamily and their regulatory GEF and GAP proteins across tissues and across individuals for each tissue. A large set of published data for normal tissues from a wide range of species, human cancer tissues and human cell lines was analysed.

          Results

          We show that although the multidomain regulatory proteins of Ras GTPases exhibit considerable tissue and individual gene expression variability, their total amounts are balanced in normal tissues. In a given tissue, the sum of activating (GEFs) and deactivating (GAPs) domains of Ras GTPases can vary considerably, but each person has balanced GEF and GAP levels. This balance is impaired in cell lines and in cancer tissues for some individuals.

          Conclusions

          Our results are relevant for critical considerations of knock out experiments, where functionally related homologs may compensate for the down regulation of a protein.

          Electronic supplementary material

          The online version of this article (10.1186/s12964-018-0256-8) contains supplementary material, which is available to authorized users.

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          Most cited references 34

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          The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

          The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
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            The Genotype-Tissue Expression (GTEx) project.

            Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
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              Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.

                (2015)
              Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                besray@gmail.com
                christina.kiel@ucd.ie
                hannah.benisty@crg.eu
                a.campbell@beatson.gla.ac.uk
                k.pickering@beatson.gla.ac.uk
                nils.bluethgen@charite.de
                o.sansom@beatson.gla.ac.uk
                luis.serrano@crg.es , luis.serrano@crg.eu
                Journal
                Cell Commun Signal
                Cell Commun. Signal
                Cell Communication and Signaling : CCS
                BioMed Central (London )
                1478-811X
                15 August 2018
                15 August 2018
                2018
                : 16
                Affiliations
                [1 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Institute of Pathology, Charité - Universitätsmedizin Berlin, ; 10117 Berlin, Germany
                [2 ]ISNI 0000 0001 2248 7639, GRID grid.7468.d, Integrative Research Institute Life Sciences, , Humboldt Universität Berlin, ; 10115 Berlin, Germany
                [3 ]GRID grid.11478.3b, Centre for Genomic Regulation (CRG), Systems Biology Programme. The Barcelona Institute of Science and Technology, ; Dr. Aiguader 88, Barcelona, 08003 Spain
                [4 ]ISNI 0000 0001 0768 2743, GRID grid.7886.1, Present address: Systems Biology Ireland & Charles Institute of Dermatology & School of Medicine, , University College Dublin, ; Belfield, Dublin 4, Ireland
                [5 ]ISNI 0000 0000 8821 5196, GRID grid.23636.32, Cancer Research UK Beatson Institute, ; Garscube Estate, Switchback Road, Glasgow, G61 1BD UK
                [6 ]ISNI 0000 0001 2172 2676, GRID grid.5612.0, Universitat Pompeu Fabra (UPF), ; 08003 Barcelona, Spain
                [7 ]ISNI 0000 0000 9601 989X, GRID grid.425902.8, Institució Catalana de Recerca i Estudis Avançats (ICREA), ; Pg. Lluís Companys 23, 08010 Barcelona, Spain
                256
                10.1186/s12964-018-0256-8
                6094892
                30111366
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011259, FP7 International Cooperation;
                Award ID: FP7-HEALTH-F4-2011-278568
                Award ID: FP7-HEALTH-F4-2011-278568
                Award Recipient :
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                Research
                Custom metadata
                © The Author(s) 2018

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