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      The human genetic variant rs6190 unveils Foxc1 and Arid5a as novel pro-metabolic targets of the glucocorticoid receptor in muscle

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          Abstract

          Genetic variations in the glucocorticoid receptor (GR) gene NR3C1 can impact metabolism. The single nucleotide polymorphism (SNP) rs6190 (p.R23K) has been associated in humans with enhanced metabolic health, but the SNP mechanism of action remains completely unknown. We generated a transgenic knock-in mice genocopying this polymorphism to elucidate how the mutant GR impacts metabolism. Compared to non-mutant littermates, mutant mice showed increased muscle insulin sensitivity and strength on regular chow and high-fat diet, blunting the diet-induced adverse effects on weight gain and exercise intolerance. Overlay of RNA-seq and ChIP-seq profiling in skeletal muscle revealed increased transactivation of Foxc1 and Arid5A genes by the mutant GR. Using adeno-associated viruses for in vivo overexpression in muscle, we found that Foxc1 was sufficient to transcriptionally activate the insulin response pathway genes Insr and Irs1. In parallel, Arid5a was sufficient to transcriptionally repress the lipid uptake genes Cd36 and Fabp4, reducing muscle triacylglycerol accumulation. Collectively, our findings identify a muscle-autonomous epigenetic mechanism of action for the rs6190 SNP effect on metabolic homeostasis, while leveraging a human nuclear receptor coding variant to unveil Foxc1 and Arid5a as novel epigenetic regulators of muscle metabolism.

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          Analyzing real-time PCR data by the comparative CT method

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            Animal models of necrotizing enterocolitis: review of the literature and state of the art

            Abstract Necrotizing enterocolitis (NEC) remains the leading cause of gastrointestinal surgical emergency in preterm neonates. Over the last five decades, a variety of experimental models have been developed to study the pathophysiology of this disease and to test the effectiveness of novel therapeutic strategies. Experimental NEC is mainly modeled in neonatal rats, mice and piglets. In this review, we focus on these experimental models and discuss the major advantages and disadvantages of each. We also briefly discuss other models that are not as widely used but have contributed to our current knowledge of NEC.
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              Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

              The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                31 March 2024
                : 2024.03.28.586997
                Affiliations
                [1 ]Molecular Cardiovascular Biology, Heart Institute, Cincinnati Children’s Hospital Medical Center and Dept. Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
                [2 ]Stem Cell Laboratory, Department of Pathophysiology and Transplantation, Dino Ferrari Centre, University of Milan, Italy
                [3 ]Systems Biology and Physiology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
                Author notes
                [* ] Corresponding Author: Mattia Quattrocelli, PhD, Molecular Cardiovascular Biology, Heart Institute, Cincinnati Children’s Hospital Medical Center, 240 Albert Sabin Way T4.676, Cincinnati, OH 45229. mattia.quattrocelli@ 123456cchmc.org
                Author information
                http://orcid.org/0000-0001-7011-1740
                Article
                10.1101/2024.03.28.586997
                10996618
                38585940
                7055a3fb-2c61-412d-a696-de05f92d08c0

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                Funded by: NIH
                Award ID: R01HL166356-01
                Award ID: R03DK130908-01A1
                Award ID: R01AG078174-01
                Funded by: RIP, CCRF Endowed Scholarship, HI Translational Funds (CCHMC)
                Categories
                Article

                glucocorticoid receptor,rs6190,muscle metabolism,insulin sensitivity,glucose tolerance,fatty acid uptake

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