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      Identification of glucocorticoid-related molecular signature by whole blood methylome analysis

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          Abstract

          Objective

          Cushing’s syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing’s syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing’s syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers.

          Design

          We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing’s syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation.

          Methods

          Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features.

          Results

          Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing’s syndrome correction. In Cushing’s syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts.

          Conclusions

          Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays.

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          Most cited references50

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

                Author and article information

                Journal
                Eur J Endocrinol
                Eur J Endocrinol
                EJE
                European Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0804-4643
                1479-683X
                16 December 2021
                01 February 2022
                : 186
                : 2
                : 297-308
                Affiliations
                [1 ]Université de Paris , Institut Cochin, INSERM U1016, CNRS UMR8104, Paris, France
                [2 ]ARAMIS Project-Team , Inria Paris, France
                [3 ]CMAP , UMR 7641, CNRS, École polytechnique, I.P. Paris, France
                [4 ]Sorbonne Université , Inserm, UMS Pass, Plateforme Post-génomique de la Pitié-Salpêtrière, P3S, Paris, France
                [5 ]Medizinische Klinik und Poliklinik IV , Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany
                [6 ]Assistance Publique-Hôpitaux de Paris , Hôpital Cochin, Service d’Hormonologie, Paris, France
                [7 ]Assistance Publique-Hôpitaux de Paris , Hôpital Européen Georges Pompidou, Centre d’Investigations Cliniques 9201, Paris, France
                [8 ]Université de Paris , PARCC, INSERM, Paris, France
                [9 ]Assistance Publique-Hôpitaux de Paris , Hôpital Européen Georges Pompidou, Unité Hypertension Artérielle, Paris, France
                [10 ]UOC Endocrinologia , Dipartimento di Medicina DIMED, Azienda Ospedaliera-Università di Padova, Padua, Italy
                [11 ]Clinica dell’Ipertensione Arteriosa , Department of Medicine-DIMED, University of Padua, Padua, Italy
                [12 ]Division of Internal Medicine and Hypertension Unit , Department of Medical Sciences, University of Turin, Turin, Italy
                [13 ]CRC , UMR S1138, Université de Paris, INSERM, Sorbonne Université, Paris, France
                [14 ]Assistance Publique-Hôpitaux de Paris , Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France
                [15 ]Klinik für Endokrinologie , Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich, Zürich, Switzerland
                [16 ]Assistance Publique-Hôpitaux de Paris , Hôpital Cochin, Service d’Endocrinologie, Center for Rare Adrenal Diseases, Paris, France
                Author notes
                Correspondence should be addressed to R Armignacco or G Assié; Email: roberta.armignacco@ 123456inserm.fr or guillaume.assie@ 123456aphp.fr
                Author information
                http://orcid.org/0000-0002-7963-0931
                http://orcid.org/0000-0001-7826-3984
                Article
                EJE-21-0907
                10.1530/EJE-21-0907
                8789024
                34914631
                7d148bb3-6eca-4d4e-9d1b-cf96174c2d00
                © The authors

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

                History
                : 01 September 2021
                : 16 December 2021
                Categories
                Clinical Study

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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