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      Targeting BCAA Catabolism to Treat Obesity-Associated Insulin Resistance

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          Abstract

          Recent studies implicate a strong association between elevated plasma branched-chain amino acids (BCAAs) and insulin resistance (IR). However, a causal relationship and whether interrupted BCAA homeostasis can serve as a therapeutic target for diabetes remain to be established experimentally. In this study, unbiased integrative pathway analyses identified a unique genetic link between obesity-associated IR and BCAA catabolic gene expression at the pathway level in human and mouse populations. In genetically obese ( ob/ob) mice, rate-limiting branched-chain α-keto acid (BCKA) dehydrogenase deficiency (i.e., BCAA and BCKA accumulation), a metabolic feature, accompanied the systemic suppression of BCAA catabolic genes. Restoring BCAA catabolic flux with a pharmacological inhibitor of BCKA dehydrogenase kinase (BCKDK) ( a suppressor of BCKA dehydrogenase) reduced the abundance of BCAA and BCKA and markedly attenuated IR in ob/ob mice. Similar outcomes were achieved by reducing protein (and thus BCAA) intake, whereas increasing BCAA intake did the opposite; this corroborates the pathogenic roles of BCAAs and BCKAs in IR in ob/ob mice. Like BCAAs, BCKAs also suppressed insulin signaling via activation of mammalian target of rapamycin complex 1. Finally, the small-molecule BCKDK inhibitor significantly attenuated IR in high-fat diet–induced obese mice. Collectively, these data demonstrate a pivotal causal role of a BCAA catabolic defect and elevated abundance of BCAAs and BCKAs in obesity-associated IR and provide proof-of-concept evidence for the therapeutic validity of manipulating BCAA metabolism for treating diabetes.

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          Most cited references42

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          Plasma amino acid levels and insulin secretion in obesity.

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            Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.

            A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
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              Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.

              Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Diabetes
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                September 2019
                14 August 2019
                : 68
                : 9
                : 1730-1746
                Affiliations
                [1] 1Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of the Chinese Ministry of Education, Hongqiao International Institute of Medicine, Shanghai Tongren Hospital/Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
                [2] 2Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX
                [3] 3Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA
                [4] 4Department of Endocrinology and Metabolism, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
                [5] 5Chemistry Center, National Institute of Biological Science, Beijing, China
                [6] 6Departments of Medicine, Microbiology, and Human Genetics, University of California at Los Angeles, Los Angeles, CA
                [7] 7Department of Clinical Nutrition, University of California at Los Angeles, Los Angeles, CA
                [8] 8Departments of Anesthesiology, Medicine, and Physiology, University of California at Los Angeles, Los Angeles, CA
                Author notes
                Corresponding author: Haipeng Sun, sun.haipeng@ 123456yahoo.com

                M.Z., J.S., C.-Y.W., and L.S. contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-1879-2136
                http://orcid.org/0000-0001-9013-0228
                http://orcid.org/0000-0002-3971-038X
                http://orcid.org/0000-0002-3640-8927
                http://orcid.org/0000-0003-2128-3209
                Article
                0927
                10.2337/db18-0927
                6702639
                31167878
                5b9c2c0a-9791-4090-a4fc-45827e19fcd3
                © 2019 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.

                History
                : 26 August 2018
                : 29 May 2019
                Page count
                Figures: 7, Tables: 0, Equations: 0, References: 60, Pages: 17
                Funding
                Funded by: National Heart, Lung, and Blood Institute, DOI http://dx.doi.org/10.13039/100000050;
                Award ID: HL080111
                Categories
                0704
                Metabolism

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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