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      PGC-1α mediates a metabolic host defense response in human airway epithelium during rhinovirus infections

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          Abstract

          Human rhinoviruses (HRV) are common cold viruses associated with exacerbations of lower airways diseases. Although viral induced epithelial damage mediates inflammation, the molecular mechanisms responsible for airway epithelial damage and dysfunction remain undefined. Using experimental HRV infection studies in highly differentiated human bronchial epithelial cells grown at air-liquid interface (ALI), we examine the links between viral host defense, cellular metabolism, and epithelial barrier function. We observe that early HRV-C15 infection induces a transitory barrier-protective metabolic state characterized by glycolysis that ultimately becomes exhausted as the infection progresses and leads to cellular damage. Pharmacological promotion of glycolysis induces ROS-dependent upregulation of the mitochondrial metabolic regulator, peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α), thereby restoring epithelial barrier function, improving viral defense, and attenuating disease pathology. Therefore, PGC-1α regulates a metabolic pathway essential to host defense that can be therapeutically targeted to rescue airway epithelial barrier dysfunction and potentially prevent severe respiratory complications or secondary bacterial infections.

          Abstract

          Epithelial host defense to rhinovirus infections is enhanced by targeting the mitochondrial metabolic regulator, PGC-1a. Using metabolomics and proteomics, Michi et al show that human airway epithelial cells mount a barrier-protective early glycolysis-shift in response to rhinovirus, and that by targeting PGC-1a early in infection, epithelial barrier function, viral defense and pathology are improved.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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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
                dproud@ucalgary.ca
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 June 2021
                16 June 2021
                2021
                : 12
                : 3669
                Affiliations
                [1 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Physiology and Pharmacology, Cumming School of Medicine, , University of Calgary, ; Calgary, AB Canada
                [2 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Snyder Institute for Chronic Diseases, , University of Calgary, ; Calgary, AB Canada
                [3 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Critical Care Medicine, Cumming School of Medicine, , University of Calgary, ; Calgary, AB Canada
                [4 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, McCaig Institute for Bone and Joint Health, , University of Calgary, ; Calgary, AB Canada
                [5 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Institute of Parasitology, , McGill University, ; Ste-Anne-de-Bellevue, QC Canada
                [6 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Microbiology and Immunology, , McGill University, ; Montreal, QC Canada
                Author information
                http://orcid.org/0000-0001-7333-2610
                http://orcid.org/0000-0002-5887-9551
                http://orcid.org/0000-0002-3429-4188
                http://orcid.org/0000-0002-4296-2967
                http://orcid.org/0000-0001-9629-6595
                Article
                23925
                10.1038/s41467-021-23925-z
                8209127
                34135327
                8770402c-0300-47e9-a1f2-4863a0319b21
                © The Author(s) 2021

                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
                : 8 January 2021
                : 21 May 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000024, Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada);
                Award ID: PJT-159635
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000038, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada);
                Award ID: RGPIN-2018-03861
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cell signalling,viral pathogenesis,experimental models of disease
                Uncategorized
                cell signalling, viral pathogenesis, experimental models of disease

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