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      Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance

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          Abstract

          The failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employ phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults causing insulin resistance, we observe a marked rewiring of the insulin signaling network. This includes both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying dysregulated phosphosites common to multiple insults reveals subnetworks containing non-canonical regulators of insulin action, such as MARK2/3, and causal drivers of insulin resistance. The presence of several bona fide GSK3 substrates among these phosphosites led us to establish a pipeline for identifying context-specific kinase substrates, revealing widespread dysregulation of GSK3 signaling. Pharmacological inhibition of GSK3 partially reverses insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that includes dysregulated MARK2/3 and GSK3 activity.

          Abstract

          The failure of metabolic tissues to respond to insulin is an early marker of type 2 diabetes. Here, the authors show, using global phosphoproteomics, that insulin resistance is caused by a marked rewiring of both canonical and non-canonical insulin signalling, and includes dysregulated GSK3 activity.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                djf72@medschl.cam.ac.uk
                david.james@sydney.edu.au
                sean.humphrey@mcri.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 February 2023
                18 February 2023
                2023
                : 14
                : 923
                Affiliations
                [1 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Charles Perkins Centre, School of Life and Environmental Sciences, , University of Sydney, ; Sydney, NSW 2006 Australia
                [2 ]GRID grid.5335.0, ISNI 0000000121885934, Metabolic Research Laboratories, Wellcome-Medical Research Council Institute of Metabolic Science, , University of Cambridge, ; Cambridge, CB2 0QQ UK
                [3 ]GRID grid.1049.c, ISNI 0000 0001 2294 1395, QIMR Berghofer Medical Research Institute, ; Brisbane, QL Australia
                [4 ]GRID grid.1024.7, ISNI 0000000089150953, Faculty of Health, School of Biomedical Sciences, , Queensland University of Technology, ; Brisbane, QL Australia
                [5 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Charles Perkins Centre, School of Mathematics and Statistics, , University of Sydney, ; Sydney, NSW 2006 Australia
                [6 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, , University of Sydney, ; Westmead, NSW 2145 Australia
                [7 ]GRID grid.417540.3, ISNI 0000 0000 2220 2544, Lilly Research Laboratories, Division of Eli Lilly and Company, ; Indianapolis, IN USA
                [8 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Sydney Medical School, , University of Sydney, ; Sydney, 2006 Australia
                [9 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Murdoch Children’s Research Institute, The Royal Children’s Hospital, ; Melbourne, VIC 3052 Australia
                Author information
                http://orcid.org/0000-0001-8241-2903
                http://orcid.org/0000-0001-6439-7893
                http://orcid.org/0000-0002-6609-6151
                http://orcid.org/0000-0003-1098-3138
                http://orcid.org/0000-0002-7946-3432
                http://orcid.org/0000-0001-5946-5257
                http://orcid.org/0000-0002-2666-9744
                Article
                36549
                10.1038/s41467-023-36549-2
                9938909
                36808134
                e30c1bed-7837-4b32-9cde-4eb6aa18c289
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 May 2022
                : 7 February 2023
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                proteomics,insulin signalling,cell signalling
                Uncategorized
                proteomics, insulin signalling, cell signalling

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