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      Skeletal muscle proteomes reveal downregulation of mitochondrial proteins in transition from prediabetes into type 2 diabetes

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          Summary

          Skeletal muscle insulin resistance is a central defect in the pathogenesis of type 2 diabetes (T2D). Here, we analyzed skeletal muscle proteome in 148 vastus lateralis muscle biopsies obtained from men covering all glucose tolerance phenotypes: normal, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and T2D. Skeletal muscle proteome was analyzed by a sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics technique. Our data indicate a downregulation in several proteins involved in mitochondrial electron transport or respiratory chain complex assembly already in IFG and IGT muscles, with most profound decreases observed in T2D. Additional phosphoproteomic analysis reveals altered phosphorylation in several signaling pathways in IFG, IGT, and T2D muscles, including those regulating glucose metabolic processes, and the structure of muscle cells. These data reveal several alterations present in skeletal muscle already in prediabetes and highlight impaired mitochondrial energy metabolism in the trajectory from prediabetes into T2D.

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          • •

            Skeletal muscle proteome from men with all stages of glucose tolerance was analyzed

          • •

            Phosphoproteomics reveal altered phosphorylation in IFG, IGT, and T2D muscles

          • •

            OXPHOS proteins are decreased in prediabetic muscles, with most decrease in T2D

          Abstract

          Molecular biology; Diabetology; Proteomics

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          Most cited references62

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)
          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Jürgen Cox, Matthias Mann (2008)
            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.
            Article 0 comments Cited 3015 times – based on 0 reviews     Review now
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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Da-Wei Huang, Brad T. Sherman, Richard A. Lempicki (2009)
              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
              Article 0 comments Cited 2467 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Heikki A. Koistinen
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 10 June 2021
                Publication date Collection: 23 July 2021
                Publication date (Electronic): 10 June 2021
                Volume: 24
                Issue: 7
                Electronic Location Identifier: 102712
                Affiliations
                [1 ]University of Helsinki, Molecular Systems Biology Research Group and Proteomics Unit, Institute of Biotechnology, 00014 Helsinki, Finland
                [2 ]University of Helsinki, Drug Research Program, Faculty of Pharmacy, 00014 Helsinki, Finland
                [3 ]University of Helsinki, Department of Medicine, Helsinki University Hospital, Haartmaninkatu 4, PO BOX 340, 00029 HUS, Helsinki, Finland
                [4 ]Minerva Foundation Institute for Medical Research, Tukholmankatu 8, 00290 Helsinki, Finland
                Author notes
                [∗ ]Corresponding author heikki.koistinen@ 123456helsinki.fi
                [5]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(21)00680-5 Publisher ID: 102712
                DOI: 10.1016/j.isci.2021.102712
                PMC ID: 8246593
                PubMed ID: 34235411
                SO-VID: 6ac656db-07b8-463d-9056-244a2cf68902
                Copyright © © 2021 The Author(s)
                License:

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

                History
                Date received : 2 December 2020
                Date revision received : 17 February 2021
                Date accepted : 8 June 2021
                Categories
                Subject: Article

                Keywords: molecular biology,diabetology,proteomics
                Data availability:
                Keywords: molecular biology, diabetology, proteomics

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