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      GDF15 promotes weight loss by enhancing energy expenditure in muscle

      research-article
      1 , 2 , 1 , 2 , 3 , 3 , 1 , 2 , 4 , 5 , 6 , 1 , 2 , 7 , 8 , 9 , 1 , 2 , 1 , 2 , 10 , 11 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 12 , 12 , 1 , 2 , 13 , 1 , 13 , 14 , 15 , 1 , 16 , 10 , 11 , 9 , 8 , 17 , 18 , 19 , 4 , 20 , 3 , 5 , 21 , 1 , 2 , 22 ,
      Nature
      Nature Publishing Group UK
      Obesity, Diabetes, Fat metabolism, Recombinant protein therapy

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          Abstract

          Caloric restriction that promotes weight loss is an effective strategy for treating non-alcoholic fatty liver disease and improving insulin sensitivity in people with type 2 diabetes 1 . Despite its effectiveness, in most individuals, weight loss is usually not maintained partly due to physiological adaptations that suppress energy expenditure, a process known as adaptive thermogenesis, the mechanistic underpinnings of which are unclear 2, 3 . Treatment of rodents fed a high-fat diet with recombinant growth differentiating factor 15 (GDF15) reduces obesity and improves glycaemic control through glial-cell-derived neurotrophic factor family receptor α-like (GFRAL)-dependent suppression of food intake 47 . Here we find that, in addition to suppressing appetite, GDF15 counteracts compensatory reductions in energy expenditure, eliciting greater weight loss and reductions in non-alcoholic fatty liver disease (NAFLD) compared to caloric restriction alone. This effect of GDF15 to maintain energy expenditure during calorie restriction requires a GFRAL–β-adrenergic-dependent signalling axis that increases fatty acid oxidation and calcium futile cycling in the skeletal muscle of mice. These data indicate that therapeutic targeting of the GDF15–GFRAL pathway may be useful for maintaining energy expenditure in skeletal muscle during caloric restriction.

          Abstract

          GDF15 treatment in mice counteracts compensatory reductions in energy expenditure, resulting in greater weight loss and reductions in non-alcoholic fatty liver disease compared to caloric restriction alone.

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          Most cited references91

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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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            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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              MultiQC: summarize analysis results for multiple tools and samples in a single report

              Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. Results: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info Contact: phil.ewels@scilifelab.se
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                Author and article information

                Contributors
                gsteinberg@mcmaster.ca
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                28 June 2023
                28 June 2023
                2023
                : 619
                : 7968
                : 143-150
                Affiliations
                [1 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Centre for Metabolism, Obesity and Diabetes Research, , McMaster University, ; Hamilton, Ontario Canada
                [2 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Division of Endocrinology and Metabolism, Department of Medicine, , McMaster University, ; Hamilton, Ontario Canada
                [3 ]GRID grid.34429.38, ISNI 0000 0004 1936 8198, Department of Human Health and Nutritional Sciences, , University of Guelph, ; Guelph, Ontario Canada
                [4 ]GRID grid.86715.3d, ISNI 0000 0000 9064 6198, Department of Pharmacology-Physiology, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, , Université de Sherbrooke, ; Sherbrooke, Quebec Canada
                [5 ]GRID grid.425956.9, ISNI 0000 0004 0391 2646, Global Obesity and Liver Disease Research, Global Drug Discovery, , Novo Nordisk, ; Maaloev, Denmark
                [6 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Biomedical Sciences, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [7 ]GRID grid.411615.6, ISNI 0000 0000 9938 1755, Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, , Beijing Technology and Business University, ; Beijing, China
                [8 ]GRID grid.9227.e, ISNI 0000000119573309, Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, , Chinese Academy of Sciences, ; Shenzhen, China
                [9 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Pathology and Molecular Medicine, , McMaster University, ; Hamilton, Ontario Canada
                [10 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, , University of Ottawa, ; Ottawa, Ontario Canada
                [11 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, Ottawa Institute of Systems Biology, , University of Ottawa, ; Ottawa, Ontario Canada
                [12 ]GRID grid.46078.3d, ISNI 0000 0000 8644 1405, Department of Kinesiology and Health Sciences, , University of Waterloo, ; Waterloo, Ontario Canada
                [13 ]GRID grid.413615.4, ISNI 0000 0004 0408 1354, Population Health Research Institute, , Hamilton Health Sciences and McMaster University, ; Hamilton, Ontario Canada
                [14 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Thrombosis and Atherosclerosis Research Institute, , McMaster University, Hamilton Health Sciences, ; Hamilton, Ontario Canada
                [15 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Health Research Methods, Evidence, and Impact, , McMaster University, ; Hamilton, Ontario Canada
                [16 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Oncology, , McMaster University, ; Hamilton, Ontario Canada
                [17 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, , Chinese Academy of Sciences, ; Beijing, China
                [18 ]GRID grid.7107.1, ISNI 0000 0004 1936 7291, School of Biological Sciences, , University of Aberdeen, ; Aberdeen, UK
                [19 ]CAS Center for Excellence in Animal Evolution and Genetics (CCEAEG), Kunming, China
                [20 ]GRID grid.86715.3d, ISNI 0000 0000 9064 6198, Division of Neurology, Department of Medicine, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, , Université de Sherbrooke, ; Sherbrooke, Quebec Canada
                [21 ]GRID grid.452762.0, ISNI 0000 0004 5913 0299, Bio Innovation Hub Transformational Research Unit, , Novo Nordisk, ; Boston, MA USA
                [22 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Biochemistry and Biomedical Sciences, , McMaster University, ; Hamilton, Ontario Canada
                Author information
                http://orcid.org/0000-0002-6195-4428
                http://orcid.org/0000-0003-4787-340X
                http://orcid.org/0000-0003-0472-4615
                http://orcid.org/0000-0002-3618-4492
                http://orcid.org/0000-0002-4732-5473
                http://orcid.org/0000-0002-3895-6767
                http://orcid.org/0000-0001-8072-2836
                http://orcid.org/0000-0002-6795-4760
                http://orcid.org/0000-0003-3864-5886
                http://orcid.org/0000-0002-2457-1823
                http://orcid.org/0000-0001-6805-0985
                http://orcid.org/0000-0002-9648-0971
                http://orcid.org/0000-0001-5425-8275
                Article
                6249
                10.1038/s41586-023-06249-4
                10322716
                37380764
                d1890846-46d4-4c0c-a040-c82fe7b76a9a
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 July 2022
                : 23 May 2023
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                © Springer Nature Limited 2023

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                obesity,diabetes,fat metabolism,recombinant protein therapy
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                obesity, diabetes, fat metabolism, recombinant protein therapy

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