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      Proteomic Analysis of Cardiac Adaptation to Exercise by High Resolution Mass Spectrometry

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          Abstract

          Regular exercise has many health benefits, among which is a significant reduction of cardiovascular risk. Although many beneficial effects of exercise are well described, the exact mechanisms by which exercise confers cardiovascular benefits are yet to be fully understood. In the current study, we have used high resolution mass spectrometry to determine the proteomic responses of the heart to exercise training in mice. The impact of exercise-induced oxidative stress on modifications of cardiomyocyte proteins with lipid peroxidation biomarker 4-hydroxynonenal (4-HNE) was examined as well. Fourteen male mice were randomized into the control (sedentary) group and the exercise group that was subjected to a swim exercise training program for 5 days a week for 5 months. Proteins were isolated from the left ventricular tissue, fractionated and digested for shotgun proteomics. Peptides were separated by nanoliquid chromatography and analyzed on an Orbitrap Fusion mass spectrometer using high-energy collision–induced dissociation and electron transfer dissociation fragmentation. We identified distinct ventricular protein signatures established in response to exercise training. Comparative proteomics identified 23 proteins that were upregulated and 37 proteins that were downregulated with exercise, in addition to 65 proteins that were identified only in ventricular tissue samples of exercised mice. Most of the proteins specific to exercised mice are involved in respiratory electron transport and/or implicated in glutathione conjugation. Additionally, 10 proteins were found to be modified with 4-HNE. This study provides new data on the effects of exercise on the cardiac proteome and contributes to our understanding of the molecular mechanisms underlying the beneficial effects of exercise on the heart.

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          The PRIDE database and related tools and resources in 2019: improving support for quantification data

          Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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              Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0)

              PANTHER Classification System ( www.pantherdb.org ) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools to enable biologists to analyze large-scale genome-wide experimental data. The current system (PANTHER v.14.0) covers 131 complete genomes organized into gene families and subfamilies; evolutionary relationships between genes are represented in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models, or HMMs). The families and subfamilies are annotated with Gene Ontology terms and sequences are assigned to PANTHER pathways. A suite of tools has been built to allow users to browse and query gene functions, and analyze large-scale experimental data with a number of statistical tests. PANTHER is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. Since the protocol to use this tool (v8.0) was originally published in 2013, there have been significant improvements and updates in the areas of data quality, data coverage, statistical algorithms and user experience. This Protocol Update will provide a detailed description of how to analyze genome-wide experimental data in the PANTHER Classification System.
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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                01 September 2021
                2021
                : 8
                : 723858
                Affiliations
                [ 1 ]Division of Medicine, University College London, London, United Kingdom
                [ 2 ]Qatar Analytics and BioResearch Lab, Anti Doping Lab Qatar, Doha, Qatar
                [ 3 ]Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
                [ 4 ]Centre for Cardiovascular and Metabolic Neuroscience, Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
                [ 5 ]Department of Clinical and Movement Neuroscience, UCL Institute of Neurology, London, United Kingdom
                [ 6 ]Division of Molecular Medicine, Rudjer Boskovic Institute, Zagreb, Croatia
                Author notes

                Edited by: Amelia Palermo, University of California, Los Angeles, United States

                Reviewed by: Edward Lau, University of Colorado, United States

                Ornella Cominetti, Nestle Institute of Health Sciences (NIHS), Switzerland

                *Correspondence: Morana Jaganjac, morana.jaganjac@ 123456irb.hr

                This article was submitted to Metabolomics, a section of the journal Frontiers in Molecular Biosciences

                Article
                723858
                10.3389/fmolb.2021.723858
                8440823
                34540898
                d5a0d093-6eb2-4ad9-b460-232954230140
                Copyright © 2021 Al-Menhali, Anderson, Gourine, Abramov, D’Souza and Jaganjac.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 June 2021
                : 05 August 2021
                Funding
                Funded by: Anti-Doping Laboratory Qatar 10.13039/501100007141
                Funded by: British Heart Foundation 10.13039/501100000274
                Categories
                Molecular Biosciences
                Brief Research Report

                exercise,left ventricle,proteomics,oxidative stress,4-hydroxynonenal

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