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      Dimethyl fumarate reduces hepatocyte senescence following paracetamol exposure

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          Summary

          Liver disease is a major cause of premature death. Oxidative stress in the liver represents a key disease driver. Compounds, such as dimethyl fumarate (DMF), can activate the antioxidant response and are used clinically to treat disease. In this study, we tested the protective properties of DMF before or after paracetamol exposure. Following DMF administration, Nrf2 nuclear translocation was tracked at the single-cell level and target gene transactivation confirmed. Next, the protective properties of DMF were examined following paracetamol exposure. Transcriptomic and biochemical analysis revealed that DMF rescue was underpinned by reduced Nf-kB and TGF-β signaling and cell senescence. Following on from these studies, we employed a Zebrafish model to study paracetamol exposure in vivo. We combined a genetically modified Zebrafish model, expressing green fluorescent protein exclusively in the liver, with automated microscopy. Pre-treatment with DMF, prior to paracetamol exposure, led to reduced liver damage in Zebrafish demonstrating protective properties.

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          Highlights

          • Dimethyl fumarate protects hepatocyte-like cells from paracetamol-induced stress

          • Nrf2 activation and TGF-β inhibition are main drivers of cell protection in vitro

          • DMF pre-treatment of zebrafish prevents paracetamol-induced liver injury in vivo

          Abstract

          Molecular biology; Toxicology ; Cell biology; Transcriptomics.

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          Most cited references64

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                19 May 2021
                25 June 2021
                19 May 2021
                : 24
                : 6
                : 102552
                Affiliations
                [1 ]Centre for Regenerative Medicine, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
                [2 ]Centre for Inflammation Research, University of Edinburgh, 47 Little France Drive, Edinburgh EH16 4TJ, UK
                [3 ]Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, 47 Little France Drive, Edinburgh, UK
                [4 ]Novo Nordisk Research Centre Oxford (NNRCO), Novo Nordisk Ltd, Innovation Building - Old Road Campus Roosevelt Drive, OX3 7FZ Oxford, UK
                [5 ]Novo Nordisk Ltd, Novo Nordisk Park 1, 2760 Måløv, Denmark
                Author notes
                []Corresponding author davehay@ 123456talktalk.net
                [6]

                Lead contact

                Article
                S2589-0042(21)00520-4 102552
                10.1016/j.isci.2021.102552
                8188562
                34151225
                df2a9113-17c3-47b6-8627-bd00e75c8cd2
                © 2021 The Author(s)

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

                History
                : 17 February 2021
                : 7 April 2021
                : 14 May 2021
                Categories
                Article

                molecular biology,toxicology,cell biology,transcriptomics

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