Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial

Abstract

Background:Glucocorticoids are among the most commonly prescribed drugs, but there is no biomarker that can quantify their action. The aim of the study was to identify and validate circulating biomarkers of glucocorticoid action.Methods:In a randomized, crossover, single-blind, discovery study, 10 subjects with primary adrenal insufficiency (and no other endocrinopathies) were admitted at the in-patient clinic and studied during physiological glucocorticoid exposure and withdrawal. A randomization plan before the first intervention was used. Besides mild physical and/or mental fatigue and salt craving, no serious adverse events were observed. The transcriptome in peripheral blood mononuclear cells and adipose tissue, plasma miRNAomic, and serum metabolomics were compared between the interventions using integrated multi-omic analysis.Results:We identified a transcriptomic profile derived from two tissues and a multi-omic cluster, both predictive of glucocorticoid exposure. A microRNA (miR-122-5p) that was correlated with genes and metabolites regulated by glucocorticoid exposure was identified (p=0.009) and replicated in independent studies with varying glucocorticoid exposure (0.01 ≤ p≤0.05).Conclusions:We have generated results that construct the basis for successful discovery of biomarker(s) to measure effects of glucocorticoids, allowing strategies to individualize and optimize glucocorticoid therapy, and shedding light on disease etiology related to unphysiological glucocorticoid exposure, such as in cardiovascular disease and obesity.Funding:The Swedish Research Council (Grant 2015-02561 and 2019-01112); The Swedish federal government under the LUA/ALF agreement (Grant ALFGBG-719531); The Swedish Endocrinology Association; The Gothenburg Medical Society; Wellcome Trust; The Medical Research Council, UK; The Chief Scientist Office, UK; The Eva Madura’s Foundation; The Research Foundation of Copenhagen University Hospital; and The Danish Rheumatism Association.Clinical trial number:NCT02152553.

Wnt-Signaling Regulated by Glucocorticoid-Induced miRNAs

“…We found that the ratio of transcriptome regulated by GC depended on tissue type. Our results were in line with previous findings that in skin a relatively smaller gene set (6.3%) while in adipose tissue, brain and peripheral blood mononuclear cells a larger gene set (10–20%) were regulated by GC, [ 21 , 58 , 65 , 66 ]. Interestingly, in the liver GC induced vast transcriptional responses, with more than 30% of genes being regulated [ 59 ].…”

“…Interestingly, in a recent article, miR-122-5p was identified as a possible link between nonphysiological (externally administered) GCs and the effects caused by GC administration. Additionally, the measurement of miR-122-5p expression can be a potential marker to monitor appropriate glucocorticoid activity and to avoid overtreatment [ 21 ].…”

Untargeted Plasma Metabolomics Unravels a Metabolic Signature for Tissue Sensitivity to Glucocorticoids in Healthy Subjects: Its Implications in Dietary Planning for a Healthy Lifestyle

“…This phenomenon has been associated with decreased expression of the hormone-stimulated enzymes regulating the fat mobilization process [30] and has been related to insulin resistance and obesity. Fatty acids related to triglyceride metabolism (e.g., palmitic, stearic, linoleic) have been associated with glucocorticoid action in two recent untargeted metabolomic studies of the dose-dependent effect of glucocorticoid treatment of glucocorticoid-dependent disorders [31,32].…”

Transientgrb10aKnockdown Permanently Alters Growth, Cardiometabolic Phenotype and the Transcriptome inDanio rerio

“…Hypernetwork analysis was performed to investigate higher order interactions between target genes, a general model of which is outlined in Supplementary Figure 1 30 . All analyses were performed in R (version 3.4.2).…”

“…This hypernetwork adjacency matrix represents the higher order interactions between pairs of genes in a manner not captured by traditional transcriptomic approaches. As a measurement of co-ordination, hypernetworks have been suggested to model functional relationships 30 . Coordination between age-related genes and the rest of transcriptome was investigated by interrogating the incidence matrix of the hypernetwork and identifying a subset of edges and nodes which could form a complete subgraph.…”