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      Systemic and adipocyte transcriptional and metabolic dysregulation in idiopathic intracranial hypertension

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          Abstract

          BACKGROUND

          Idiopathic intracranial hypertension (IIH) is a condition predominantly affecting obese women of reproductive age. Recent evidence suggests that IIH is a disease of metabolic dysregulation, androgen excess, and an increased risk of cardiovascular morbidity. Here we evaluate systemic and adipose specific metabolic determinants of the IIH phenotype.

          METHODS

          In fasted, matched IIH ( n = 97) and control ( n = 43) patients, we assessed glucose and insulin homeostasis and leptin levels. Body composition was assessed along with an interrogation of adipose tissue function via nuclear magnetic resonance metabolomics and RNA sequencing in paired omental and subcutaneous biopsies in a case-control study.

          RESULTS

          We demonstrate an insulin- and leptin-resistant phenotype in IIH in excess of that driven by obesity. Adiposity in IIH is preferentially centripetal and is associated with increased disease activity and insulin resistance. IIH adipocytes appear transcriptionally and metabolically primed toward depot-specific lipogenesis.

          CONCLUSION

          These data show that IIH is a metabolic disorder in which adipose tissue dysfunction is a feature of the disease. Managing IIH as a metabolic disease could reduce disease morbidity and improve cardiovascular outcomes.

          FUNDING

          This study was supported by the UK NIHR (NIHR-CS-011-028), the UK Medical Research Council (MR/K015184/1), Diabetes UK, Wellcome Trust (104612/Z/14/Z), the Sir Jules Thorn Award, and the Midlands Neuroscience Teaching and Research Fund.

          Abstract

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          Most cited references56

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                Journal
                JCI Insight
                JCI Insight
                JCI Insight
                JCI Insight
                American Society for Clinical Investigation
                2379-3708
                24 May 2021
                24 May 2021
                24 May 2021
                : 6
                : 10
                : e145346
                Affiliations
                [1 ]Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.
                [2 ]Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom.
                [3 ]Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.
                [4 ]Department of Neurology, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Queen Elizabeth Hospital, Birmingham, United Kingdom.
                [5 ]School of Life Sciences, University of Warwick, Coventry, United Kingdom.
                [6 ]Upper GI Unit and Minimally Invasive Unit, Heartlands Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham United Kingdom.
                [7 ]Oxford Centre for Diabetes, Endocrinology & Metabolism, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Headington, Oxford, United Kingdom.
                [8 ]Birmingham Neuro-Ophthalmology, Ophthalmology Department, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.
                Author notes
                Address correspondence to: Alexandra Sinclair, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom. Email: a.b.sinclair@ 123456bham.ac.uk .
                Author information
                http://orcid.org/0000-0001-5066-8306
                http://orcid.org/0000-0003-4564-0282
                http://orcid.org/0000-0001-5622-8301
                http://orcid.org/0000-0001-8905-5734
                http://orcid.org/0000-0002-4030-9411
                http://orcid.org/0000-0002-2797-2569
                http://orcid.org/0000-0001-6785-9352
                http://orcid.org/0000-0001-5560-802X
                http://orcid.org/0000-0001-9396-7330
                http://orcid.org/0000-0003-0499-2732
                http://orcid.org/0000-0002-3170-8533
                http://orcid.org/0000-0002-6314-4437
                http://orcid.org/0000-0001-8901-6970
                http://orcid.org/0000-0001-5794-748X
                http://orcid.org/0000-0003-2777-5132
                Article
                145346
                10.1172/jci.insight.145346
                8262372
                33848268
                6cea9c93-bd33-49d2-b2f4-b998d8afb459
                © 2021 Westgate et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 October 2020
                : 7 April 2021
                Funding
                Funded by: National Institute of Health Research UK
                Award ID: NIHR-CS-011-028
                Funded by: Medical Research Council, https://doi.org/10.13039/501100000265;
                Award ID: MR/K015184/1
                Funded by: Midlands Neuroscience Teaching and Research Fund
                Award ID: N/A
                Categories
                Clinical Medicine

                neuroscience,ophthalmology,neurological disorders,obesity

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