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      DNA methylation changes in metabolic and immune-regulatory pathways in blood and lymph node CD4 + T cells in response to SIV infections

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          Abstract

          The molecular mechanisms underlying HIV-induced inflammation, which persists even during effective long-term treatment, remain incompletely defined. Here, we studied pathogenic and nonpathogenic simian immunodeficiency virus (SIV) infections in macaques and African green monkeys, respectively. We longitudinally analyzed genome-wide DNA methylation changes in CD4 + T cells from lymph node and blood, using arrays. DNA methylation changes after SIV infection were more pronounced in lymph nodes than blood and already detected in primary infection. Differentially methylated genes in pathogenic SIV infection were enriched for Th1-signaling (e.g., RUNX3, STAT4, NFKB1) and metabolic pathways (e.g., PRKCZ). In contrast, nonpathogenic SIVagm infection induced DNA methylation in genes coding for regulatory proteins such as LAG-3, arginase-2, interleukin-21 and interleukin-31. Between 15 and 18% of genes with DNA methylation changes were differentially expressed in CD4 + T cells in vivo. Selected identified sites were validated using bisulfite pyrosequencing in an independent cohort of uninfected, viremic and SIV controller macaques. Altered DNA methylation was confirmed in blood and lymph node CD4 + T cells in viremic macaques but was notably absent from SIV controller macaques. Our study identified key genes differentially methylated already in primary infection and in tissues that could contribute to the persisting metabolic disorders and inflammation in HIV-infected individuals despite effective treatment.

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          Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.

          Insulin resistance plays an important role in the pathophysiology of diabetes and is associated with obesity and other cardiovascular risk factors. The "gold standard" glucose clamp and minimal model analysis are two established methods for determining insulin sensitivity in vivo, but neither is easily implemented in large studies. Thus, it is of interest to develop a simple, accurate method for assessing insulin sensitivity that is useful for clinical investigations. We performed both hyperinsulinemic isoglycemic glucose clamp and insulin-modified frequently sampled iv glucose tolerance tests on 28 nonobese, 13 obese, and 15 type 2 diabetic subjects. We obtained correlations between indexes of insulin sensitivity from glucose clamp studies (SI(Clamp)) and minimal model analysis (SI(MM)) that were comparable to previous reports (r = 0.57). We performed a sensitivity analysis on our data and discovered that physiological steady state values [i.e. fasting insulin (I(0)) and glucose (G(0))] contain critical information about insulin sensitivity. We defined a quantitative insulin sensitivity check index (QUICKI = 1/[log(I(0)) + log(G(0))]) that has substantially better correlation with SI(Clamp) (r = 0.78) than the correlation we observed between SI(MM) and SI(Clamp). Moreover, we observed a comparable overall correlation between QUICKI and SI(Clamp) in a totally independent group of 21 obese and 14 nonobese subjects from another institution. We conclude that QUICKI is an index of insulin sensitivity obtained from a fasting blood sample that may be useful for clinical research.
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            INTERFEROME v2.0: an updated database of annotated interferon-regulated genes

            Interferome v2.0 (http://interferome.its.monash.edu.au/interferome/) is an update of an earlier version of the Interferome DB published in the 2009 NAR database edition. Vastly improved computational infrastructure now enables more complex and faster queries, and supports more data sets from types I, II and III interferon (IFN)-treated cells, mice or humans. Quantitative, MIAME compliant data are collected, subjected to thorough, standardized, quantitative and statistical analyses and then significant changes in gene expression are uploaded. Comprehensive manual collection of metadata in v2.0 allows flexible, detailed search capacity including the parameters: range of -fold change, IFN type, concentration and time, and cell/tissue type. There is no limit to the number of genes that can be used to search the database in a single query. Secondary analysis such as gene ontology, regulatory factors, chromosomal location or tissue expression plots of IFN-regulated genes (IRGs) can be performed in Interferome v2.0, or data can be downloaded in convenient text formats compatible with common secondary analysis programs. Given the importance of IFN to innate immune responses in infectious, inflammatory diseases and cancer, this upgrade of the Interferome to version 2.0 will facilitate the identification of gene signatures of importance in the pathogenesis of these diseases.
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              Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.

              Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.
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                Author and article information

                Contributors
                michaela.muller-trutwin@pasteur.fr
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                9 December 2020
                9 December 2020
                2020
                : 12
                : 188
                Affiliations
                [1 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, HIV Inflammation and Persistence Unit, , Institut Pasteur, ; 28 Rue Didot, 75015 Paris, France
                [2 ]GRID grid.508487.6, ISNI 0000 0004 7885 7602, Sorbonne Paris Cité, , Université Paris Diderot, ; Paris, France
                [3 ]GRID grid.457349.8, IDMIT Department/IBFJ, Immunology of Viral Infections and Autoimmune Diseases (IMVA), INSERM U1184, , CEA, Université Paris Sud, ; Fontenay-aux-Roses, France
                [4 ]GRID grid.418135.a, ISNI 0000 0004 0641 3404, Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, , CEA-Institut de Biologie François Jacob, ; Evry, France
                [5 ]GRID grid.508487.6, ISNI 0000 0004 7885 7602, UMR8104, CNRS, U1016, INSERM, Institut Cochin, , Université de Paris, ; 75014 Paris, France
                [6 ]GRID grid.418215.b, ISNI 0000 0000 8502 7018, Unit of Infection Models, , German Primate Center (DPZ), ; Göttingen, Germany
                [7 ]GRID grid.10419.3d, ISNI 0000000089452978, Present Address: Leiden University Medical Center, ; Leiden, The Netherlands
                Author information
                http://orcid.org/0000-0002-3854-2396
                Article
                971
                10.1186/s13148-020-00971-w
                7724887
                33298174
                18ac46b5-42ea-484c-ba18-6f87aab7ae01
                © The Author(s) 2020

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 September 2020
                : 5 November 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003323, Agence Nationale de Recherches sur le Sida et les Hépatites Virales;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Funded by: FundRef http://dx.doi.org/10.13039/100009060, Sidaction;
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                Genetics

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