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      Nrf2 activation reprograms macrophage intermediary metabolism and suppresses the type I interferon response

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          Summary

          To overcome oxidative, inflammatory, and metabolic stress, cells have evolved cytoprotective protein networks controlled by nuclear factor-erythroid 2 p45-related factor 2 (Nrf2) and its negative regulator, Kelch-like ECH associated protein 1 (Keap1). Here, using high-resolution mass spectrometry we characterize the proteomes of macrophages with altered Nrf2 status revealing significant differences among the genotypes in metabolism and redox homeostasis, which were validated with respirometry and metabolomics. Nrf2 affected the proteome following lipopolysaccharide (LPS) stimulation, with alterations in redox, carbohydrate and lipid metabolism, and innate immunity. Notably, Nrf2 activation promoted mitochondrial fusion. The Keap1 inhibitor, 4-octyl itaconate remodeled the inflammatory macrophage proteome, increasing redox and suppressing type I interferon (IFN) response. Similarly, pharmacologic or genetic Nrf2 activation inhibited the transcription of IFN-β and its downstream effector IFIT2 during LPS stimulation. These data suggest that Nrf2 activation facilitates metabolic reprogramming and mitochondrial adaptation, and finetunes the innate immune response in macrophages.

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          Highlights

          • High-resolution proteome and metabolome of macrophages with altered Nrf2 status

          • Nrf2 regulates macrophage intermediary metabolism and mitochondrial adaptation

          • Genetic Nrf2 activation with Keap1-KD suppresses IFN-β and the type I IFN response

          • Nrf2 activation with electrophilic Keap1 modifiers suppresses the type I IFN response

          Abstract

          Biochemistry; Immunology; Proteomics

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          Most cited references53

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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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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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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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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                30 January 2022
                18 February 2022
                30 January 2022
                : 25
                : 2
                : 103827
                Affiliations
                [1 ]School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
                [2 ]Medical Research Council Cancer Unit, University of Cambridge, Cambridge, UK
                [3 ]Division of Cellular Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arrott Drive, Dundee, Scotland, UK
                [4 ]Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK
                [5 ]Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, Scotland, UK
                [6 ]Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, Scotland, UK
                [7 ]Department of Chemistry and Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, NY, USA
                [8 ]School of Chemistry, University of Glasgow, Glasgow, UK
                [9 ]Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
                [10 ]Department of Pharmacology and Molecular Sciences and Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                Author notes
                []Corresponding author a.dinkovakostova@ 123456dundee.ac.uk
                [11]

                These authors contributed equally

                [12]

                Lead contact

                Article
                S2589-0042(22)00097-9 103827
                10.1016/j.isci.2022.103827
                8844662
                35198887
                93dcebe9-ee0c-490f-9ecc-6ccf4ccb777e
                © 2022 The Author(s)

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

                History
                : 26 August 2021
                : 17 January 2022
                : 22 January 2022
                Categories
                Article

                biochemistry,immunology,proteomics
                biochemistry, immunology, proteomics

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