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      AKR1D1 knockout mice develop a sex-dependent metabolic phenotype

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          Abstract

          Steroid 5β-reductase (AKR1D1) plays important role in hepatic bile acid synthesis and glucocorticoid clearance. Bile acids and glucocorticoids are potent metabolic regulators, but whether AKR1D1 controls metabolic phenotype in vivo is unknown. Akr1d1 –/– mice were generated on a C57BL/6 background. Liquid chromatography/mass spectrometry, metabolomic and transcriptomic approaches were used to determine effects on glucocorticoid and bile acid homeostasis. Metabolic phenotypes including body weight and composition, lipid homeostasis, glucose tolerance and insulin tolerance were evaluated. Molecular changes were assessed by RNA-Seq and Western blotting. Male Akr1d1 –/– mice were challenged with a high fat diet (60% kcal from fat) for 20 weeks. Akr1d1 –/– mice had a sex-specific metabolic phenotype. At 30 weeks of age, male, but not female, Akr1d1 –/– mice were more insulin tolerant and had reduced lipid accumulation in the liver and adipose tissue yet had hypertriglyceridemia and increased intramuscular triacylglycerol. This phenotype was associated with sexually dimorphic changes in bile acid metabolism and composition but without overt effects on circulating glucocorticoid levels or glucocorticoid-regulated gene expression in the liver. Male Akr1d1 –/– mice were not protected against diet-induced obesity and insulin resistance. In conclusion, this study shows that AKR1D1 controls bile acid homeostasis in vivo and that altering its activity can affect insulin tolerance and lipid homeostasis in a sex-dependent manner.

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          Most cited references54

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            FactoMineR: AnRPackage for Multivariate Analysis

              • Record: found
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              • Article: not found

              Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

              High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.

                Author and article information

                Journal
                J Endocrinol
                J Endocrinol
                JOE
                The Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0022-0795
                1479-6805
                23 March 2022
                01 June 2022
                : 253
                : 3
                : 97-113
                Affiliations
                [1 ]Oxford Centre for Diabetes , Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Oxford, UK
                [2 ]Department of Biological and Medical Sciences , Oxford Brookes University, Oxford, UK
                [3 ]Institute of Metabolism and Systems Research , University of Birmingham, Birmingham, UK
                [4 ]Swiss Centre for Applied Human Toxicology and Division of Molecular and Systems Toxicology , Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
                [5 ]Department of Pharmaceutical Sciences , Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
                [6 ]Mammalian Genetics Unit , Medical Research Council Harwell, Oxford, UK
                [7 ]Center of Excellence in Environmental Toxicology , Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
                [8 ]Department of Internal Medicine and Clinical Nutrition , Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
                [9 ]Institute of Biomedicine , Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
                Author notes
                Correspondence should be addressed to J W Tomlinson: jeremy.tomlinson@ 123456ocdem.ox.ac.uk

                *These authors contributed equally

                Author information
                http://orcid.org/0000-0001-5983-0306
                http://orcid.org/0000-0002-8789-8436
                http://orcid.org/0000-0002-3937-1066
                http://orcid.org/0000-0002-6820-2712
                http://orcid.org/0000-0002-3170-8533
                Article
                JOE-21-0280
                10.1530/JOE-21-0280
                9086936
                35318963
                01122403-980a-473b-8977-667f3779dd00
                © The authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 11 February 2022
                : 23 March 2022
                Categories
                Research

                Endocrinology & Diabetes
                steroid,bile acid,cortisol,cholic acid,chenodeoxycholic acid,non-alcoholic fatty liver disease,metabolic syndrome

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