article: 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. 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. 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). 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. 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. NCT02152553.
  Glucocorticoids (GCs) have a key role in the metabolic, vascular, and immunological response to stress (Cain and Cidlowski, 2017; Oster et al. , 2017). GC secretion from the adrenal gland is under tight dynamic control by the hypothalamic–pituitary–adrenal axis and is regulated in a classic circadian pattern (Cain and Cidlowski, 2017; Oster et al. , 2017). Most actions of GCs are mediated by the ubiquitously expressed GC receptor (Cain and Cidlowski, 2017; Oster et al. , 2017). The tissue-specific effects of GCs are regulated by many local factors, including pre-receptor metabolism of GCs and the interaction of the GC receptor with tissue-specific transcription factors, or through non-genomic mechanisms (Cain and Cidlowski, 2017; Oster et al. , 2017). As a result of this complexity, circulating levels of cortisol relate poorly to tissue action of cortisol, and serum cortisol therefore has limited value as a biomarker for GC action (Karssen et al. , 2001). GCs are among the most commonly prescribed drugs, and GC treatment remains a cornerstone in the management of many rheumatic and inflammatory diseases despite the introduction of modern disease-modifying antirheumatic drugs and biological immunomodulatory treatment (Smolen et al. , 2017). GC replacement is essential for survival in patients with various forms of adrenal insufficiency (Johannsson et al. , 2015). However, metabolic and other side effects of GC treatment or replacement are common (Björnsdottir et al. , 2011; Fardet et al. , 2012), indicating that current methods to monitor their action and tailor their treatment are inadequate. Unphysiological GC exposure has been implicated in the etiology of several common diseases such as type 2 diabetes mellitus, hypertension, abdominal obesity, and cardiovascular disease (Ragnarsson et al. , 2019). Against this background, it is highly desirable to be able to measure and quantify GC action as this might be useful to refine current GC therapy. Biomarkers of GC action will also provide potential mechanistic understanding for the role of GCs in the etiology of many common diseases. Previous attempts to identify biomarkers using metabolomics have identified circulating metabolites associated with GC exposure (Alwashih et al. , 2017a; Alwashih et al. , 2017b). Integrated multi-omic analysis provides increased robustness over analysis of individual ‘omic data sets (Ideker et al. , 2011). In particular, the identification of groups within one ‘omic ‘layer’ with shared co-regulation within another ‘omic layer implies a functional relationship that can be used both to assess the mechanistical relevance and to support the identification of biomarkers (Karczewski and Snyder, 2018; Misra et al. , 2018). The aim of this exploratory study was to define multi-omic patterns derived from independent tissues related to GC action and to use these patterns to search for clinically applicable circulating biomarkers of GC action. Subjects with primary adrenal insufficiency, Addison’s disease, lack GC production from the adrenal cortex and can therefore be considered a human GC ‘knock-down’ model (Figure 1A). An experimental study design including subjects with Addison’s disease, standardizing for diurnal variation and food intake, allowed a within-individual comparison between physiological GC exposure and GC withdrawal (Figure 1B). A multi-omic analysis strategy combining data from gene expression in circulation (peripheral blood mononuclear cells [PBMCs]) and an important metabolic tissue, adipose tissue, integrated with circulating microRNAs (miRNAs) and metabolites was used to identify putative biomarkers. The strongest putative biomarkers were then replicated in independent study groups with different GC exposure.
  Similarity network fusion (SNF) was used to demonstrate overall similarity between subjects across and between ‘omic layers, prior to analysis (Appendix 1 and Appendix 1—figure 1). Differential gene expression was associated with GC response in both PBMC and adipose tissue (Appendix 1). Differential expression of metabolites and miRNA was identified in blood in relation to GC response (Appendix 1). Differentially expressed ‘omic elements (DEOEs) are presented in Table 2 and Supplementary file 1a–d. All DEOEs were used for integrated analysis, and false discovery rate (FDR) -corrected DEOEs were used for all other analyses (Table 2). DEOEs from the PBMC and adipose tissue transcriptomes were shown to have limited overlap in response to GC but were enriched for shared pathways, revealing an overlap that indicated shared mechanism in relation to GC exposure (Appendix 2 and Appendix 2—figure 3). We assessed the impact of differential expression on the entire interactome to aid in the identification of similar GC-related function. Interactome network models were generated using differentially expressed genes (DEGs) from both the PBMC transcriptome and the adipose tissue transcriptome. These were shown to be consistent with one another (Appendix 2 and Appendix 2—figures 1 and 2) despite the limited overlap of DEGs. GC-responsive genes were shown to have higher connectivity in the human interactome than expected by chance, demonstrated using 10,000 permutations of this network model (Appendix 2). Hypernetworks are network structures where edges are not restricted to defining a relationship between two nodes but may be shared between many nodes. As such, these structures can be used to describe complex relationships that link multiple elements. Hypernetworks also allow for the same pair of nodes to be connected by multiple edges. This means that relationships between nodes can be ranked by the number of edges shared between them. Hypernetworks allow for the summary of correlation matrices, compressing the high-dimensional relationships between data points (transcripts/miRNA/metabolites) into a single metric of similarity. Hypernetworks facilitate integration of ‘omic data and can be used to define strongly associated elements. Elements with large numbers of shared edges are more similar and likely to be of functional relevance; clustering allows refinement of large ‘omic data sets to highly associated elements (Figure 3A, B). Hypernetworks are robust to random error and act to filter out false-positive correlations as these will not have a uniform pattern of correlation across all ‘omic elements. To assess similarity, we defined the correlation coefficient between each differentially expressed ‘omic measurement and assessed as' present' in the network model those correlations with an r-value >| 1. 5 | standard deviations (sd). Edges were defined as PBMC transcripts with shared correlations, for example, two PBMC transcripts that are both correlated with the same three metabolites are connected by three edges. We summarized the shared correlations as a measure of similarity between each pair of GC-responsive PBMC transcripts, counting correlations across the other ‘omic data sets (Figure 3—figure supplement 1). The greatest number of correlations shared was between PBMC and adipose tissue transcriptome (525 genes, Figure 3C), reinforcing the observation that, while the gene-level overlap of differential expression was limited, common pathways are active in both tissues related to GC action, which involve similar networks of co-expressed genes. The rank order of the number of correlations shared with the GC-responsive PBMC transcriptome was adipose tissue transcriptome > plasma miRNAome > serum metabolome, and this was confirmed both by comparison of the heat maps (Figure 3—figure supplement 1) and by a Venn diagram (Figure 3D). The Venn diagram also reveals a strong correspondence between the serum metabolome and both PBMC and adipose tissue transcriptomes. Robustness testing was performed in which hypernetworks were generated to model dissimilarity based on the absence of correlations with PBMC transcripts. Any genes that were highlighted by these hypernetworks were removed from the downstream predictive analysis. Using this approach, we defined 271 of 965 PBMC transcripts with maximum predictive potential. This set of genes perfectly classified the HC- and saline-treated groups using partial least squares discriminant analysis (PLS-DA) (Figure 4A). We identified variables of importance using Random Forest and modeled the background experimental noise using permutation analysis (BORUTA) (Figure 4B). This identified a set of 59 genes as variables of importance with fold changes in the same directions in both transcriptomic data sets that perfectly classified HC from saline treatment (Supplementary file 1e). Nine of these genes were significantly differentially expressed in both PBMC and adipose tissue transcriptomes (Figure 4C), and, of these nine genes, six were associated with GC response via gene ontology (IL18RAP, JAK2, MTSS1, RIN2, KIF1B, and BCL9L) (Figure 4D). The gene set (n = 59) that we identified, which classified both PBMC and adipose tissue transcriptomes in relation to GC exposure, was validated (area under the curve [AUC] 0. 70–0. 96) by further testing in five other previous studies of GC action by other research groups in cellular models (Table 3). Further robustness of the random forest observations was provided by demonstrating that the minimal depth at which the variables of importance became active in prediction was small (Figure 4—figure supplement 1). We further examined interactions between the circulating ‘omics data associated with GC exposure (Figure 3D). All of the circulating ‘omics data was combined to form a correlation matrix and hierarchical clustering used to identify ‘omic data points with similar correlation (Figure 5—figure supplement 1). Eleven clusters including transcriptomic, miRNAomic, and metabolomic data were identified, and these clusters were shown to have enrichment within the interactome network model (Supplementary file 1f and Appendix 2). We then quantified the number of correlations between all the circulating ‘omic data associated with GC exposure (n = 336) using a hypernetwork. This approach was used to define a group of highly connected multi-omic elements with a relationship to GC exposure (Figure 5A). A hypernetwork model of the core group of 139 highly connected elements was generated (Figure 5B). DCK was the only gene shared with the GC-dependent adipose tissue transcriptome that also had predictive value (highlighted with a red square in Figure 5B). Deletion of the DCK gene region has been shown to be associated with increased sensitivity to GCs (Malani et al. , 2017), an observation in alignment with the reduction in expression we found in both PBMC and adipose tissue transcriptomes in association with GC exposure (Figure 4C). The hypernetwork model (Figure 5B) also highlighted a range of related miRNAs and metabolites. A hierarchical model of modules within the network was assessed using the measure of network centrality (Figure 5C). These modules revealed multi-omic relationships and demonstrated that miR-122-5p was the only miRNA present in higher order modules as measured by network centrality. miR-122-5p was correlated with cortisol exposure and the expression of FKBP5, a regulator of GC sensitivity (cluster 11 in Figure 5—figure supplement 1 and Supplementary file 1f). Targeted replication of the plasma miR-122-5p fold change from the experimental study in subjects with Addison’s disease using an independent RNA separation procedure showed a marked down-regulation of miR-122-5p by increased GC exposure (p=0. 009) (Figure 6). Two subjects did not show this miR-122-5p response, one man (disease duration 2 years, body mass index [BMI] 23. 8 kg/m2; hydrocortisone 20 mg daily, fludrocortisone 0. 1 mg daily) and one woman (disease duration 23 years; BMI 28. 1 kg/m2; hydrocortisone 30 mg daily, fludrocortisone 0. 2 mg daily) who both experienced mild mental fatigue during GC withdrawal. Based on (i) the functional association of a circulating miRNA with gene expression and metabolomics, and (ii) the correlation between the PBMC transcriptome and plasma miRNAome (Figure 3D), a targeted replication of the plasma miRNA findings was conducted using an independent RNA separation procedure. Twelve miRNAs were re-analyzed in the current study and in three other independent studies including subjects with different GC exposures: (i) in 60 subjects with rheumatoid arthritis with and without tertiary adrenal insufficiency after a short-term stop in their GC treatment (low vs. physiological GC exposure, respectively) (Borresen et al. , 2017); (ii) in 20 subjects with Addison’s disease receiving HC replacement therapy and in 20 matched healthy control subjects (low vs. physiological GC exposure, respectively) (Bergthorsdottir et al. , 2017); and (iii) acute low, medium, and excessive GC exposure in 20 healthy subjects (Stimson et al. , 2017). From this analysis, miR-122-5p was significantly associated with different GC exposure in all studies (Figure 7A– D). The expression of miR-122-5p was higher in subjects with rheumatoid arthritis and reduced GC exposure due to tertiary adrenal insufficiency (Figure 7A), and subjects with Addison’s disease had higher expression of miR-122-5p than healthy matched controls (Figure 7B). In the experimental study in healthy subjects, the expression of miR-122-5p was increased both after low and excessive high GC exposure compared to medium GC exposure at both high and low insulin levels (Figure 7C, D, respectively). The other 11 miRNAs (including miR-425-3p) did not show a relationship with GC exposure in the three replication studies.
  In a clinical experimental study designed to identify biomarkers of GC action, we succeeded in generating two profoundly different states of GC exposure within the physiological range in the same individual. The novelty of this study is the identification of pathways related to GC response and putative biomarkers of GC action in gene expression, metabolome, and miRNAs derived from integrated multi-omic analysis in two independent tissues. We identified a transcriptomic profile that was under similar GC regulation in both PBMC and adipose tissue transcriptomes, which was then validated by comparison to a range of previously published data by other research groups from cellular assays. We also identified a circulating miRNA, miR-122-5p, which was correlated with the circulating transcriptome and metabolome findings, suggesting for the first time a functional role in GC action. Moreover, the association between the expression of miR-122-5p and GC exposure was replicated in three independent study groups. In order to identify putative biomarkers of GC action in humans, a clinical study was considered to be the most appropriate experimental setting. Addison’s disease or primary adrenal insufficiency is a rare disorder, but a unique clinical model for GC biomarker discovery due to absent or very low endogenous GC production (Gan et al. , 2014; Sævik et al. , 2020). Subjects with Addison’s disease were studied in a random order during physiological GC exposure and GC withdrawal. During GC exposure, infusion of HC delivered in isotonic saline via an infusion pump using a circadian pattern and saline alone (using the same volume and infusion pattern as during HC infusion) was administered during the GC withdrawal in order to prevent a state of sodium and fluid deficiency. This study design therefore allowed a within-individual comparison accounting for circadian rhythm and food intake. The marked difference in serum and urinary cortisol and cortisone, and the similar serum electrolytes, glucose, body weight, and blood pressure between the two interventions support the experimental success of the study design and strongly indicate that confounders related to metabolic changes or other secondary events related to the GC exposure or GC withdrawal were not influencing the output of the study. The measurable but very low concentrations of serum cortisol and cortisone throughout the GC withdrawal may be explained by a residual adrenal steroid secretion in some subjects (Gan et al. , 2014; Sævik et al. , 2020) and/or due to conversion of cortisone to cortisol in the liver and adipose tissue (Stimson et al. , 2014). Network models of ‘omic data can be used as a framework to assess the potential utility of biomarkers (Stevens et al. , 2014). In this study, we have used a hypernetwork model of GC action based on differential gene expression in PBMCs as a basis to integrate adipose tissue transcriptome, plasma miRNA, and serum metabolomic data. Hypernetwork analysis leverages the power inherent in large data sets to assess interactions between ‘omic elements in a manner that is robust to false positives (Battiston et al. , 2020). The associated interactome network derived from the PBMC transcriptome was shown to contain a number of genes with previously known GC-dependent binding of NR3C1 (the GC receptor) to regulatory elements, evidence that supports the specificity of the study design (Davis et al. , 2018; Casper et al. , 2018). Gene ontology analysis of the differential gene expression identified a range of pathways classically associated with GC action including GC-receptor signaling, immunoregulatory pathways such as those involving NF-κB, metabolic pathways, and cell cycle pathways. The plasma miRNA and serum metabolomic data was shown to map to the interactome network model of GC action, and this was taken as support for this data being putative circulating biomarkers functionally related to GC action. Differential expression induced by GC treatment in both PBMCs and adipose tissue was indirectly associated with similar downstream elements by gene ontology analysis. These genes were not directly implicated with GC response, so, while the exact mechanisms may be different in each tissue, effects are coordinated through the same elements. Integration of the multi-omic data including both PBMC and adipose tissue transcriptomes was performed in order to increase the robustness of putative markers that could reflect action in other tissues such as adipose tissue, which is an important target organ for the metabolic actions of GCs. The 59 genes that behaved similarly in PBMC and adipose tissue were then validated in a range of studies examining GC response in different cellular systems. These included primary cell culture on keratinocytes (Stojadinovic et al. , 2007) and lens epithelial cells (Gupta et al. , 2005), along with PBMCs (Carlet et al. , 2010) and cancer cells [both lymphoblastic leukemia (Carlet et al. , 2010) and osteosarcoma (Lu et al. , 2007; Jewell et al. , 2012) ]. The set of nine genes co-regulated in relation to GC exposure and GC withdrawal in both PBMC and adipose tissue transcriptomes can therefore be considered as putative markers of GC response. These could be used as a gene set to interrogate GC action in other experimental settings. All the miRNA findings in this study are novel. While emerging experimental evidence indicates impact on regulation of GC action at several points by miRNAs (Clayton et al. , 2018), this is the first time that miRNAs are shown to be globally correlated to GC action in humans. Both the hypernetwork analysis and the interactome network model implied the functional significance of some miRNAs, particularly miR-122-5p. In our hypernetwork model, the expression of miR-122-5p was correlated with clusters of genes that were centrally coordinated by expression of both RNF157 and TBXAS1, the former suggested to be a key regulator of both PI3K and MAPK signaling pathways, commonly perturbated in cancer and metabolic disorders (Dogan et al. , 2017). Expression of TBXAS1 is pharmacogenomic linked to inhaled GC exposure in asthma (Dahlin et al. , 2020). miR-122 is precursor transcript of mature miRNAs, including miR-122-5p (Carthew and Sontheimer, 2009; Bartel, 2004). miR-122 is expressed in the liver in humans (Tsai et al. , 2009; GTEx Consortium, 2015; GTEx Consortium, 2013) and mice (Tsai et al. , 2009). Hepatocyte nuclear factor HNF4A (Li et al. , 2011; Xu et al. , 2010), along with HNF3A (FOXOA1), HNF3B (FOXOA2), and HNF1A (Xu et al. , 2010; Coulouarn et al. , 2009), has been shown to be a key regulator of miR-122 expression in human cells. Down-regulation of miR-122 in murine models has been associated with non-alcoholic fatty liver disease (Alisi et al. , 2011) and diabetes mellitus (Guay et al. , 2011), and in humans, miR-122-5p has also been associated with fatty liver disease (Raitoharju et al. , 2016). miR-122-5p may be a functional link between unphysiological GC exposure and metabolic and cardiovascular disease. Increased exposure to GCs impairs glucose tolerance and may induce type 2 diabetes (Hackett et al. , 2014). Indeed, reduced miR-122-5p expression has been seen in animal models of diabetes, and the reduction of this miRNA in response to increased GC exposure may suggest that miR-122-5p is a functional link between GC action and metabolism. In support of these findings are observations showing that miR‐122-5p regulate insulin sensitivity in murine hepatic cells by targeting the insulin‐like growth factor (IGF) 1 receptor (Dong et al. , 2019). Recent human studies have also suggested that miR-122-5p is an indicator of the metabolic syndrome, with reduced expression in response to weight loss in overweight/obese subjects (Hess et al. , 2020). miR-122-5p has also been suggested as a biomarker of coronary artery stenosis and plaque instability (Wang et al. , 2019; Singh et al. , 2020; Ling et al. , 2020). As unphysiological GC exposure has been associated with obesity, diabetes, and cardiovascular disease (Walker, 2007), it is possible that miR-122-5p is reflecting different GC exposure in these disorders. The subjects with Addison’s disease in our clinical experimental study had no other comorbidities previously known to be associated with miR-122-5p expression, and therefore the presence of such confounders in our miR-122-5p finding seems to be unlike. Specific miRNAs circulating in a stable, cell-free form in plasma or serum may serve as biomarkers in some diseases (Kroh et al. , 2010), and, in our integrated analysis, they seem to be a realistic and clinically useful marker of GC action. We therefore focused on the replication of the miRNA findings from the discovery study. For this purpose, we performed a targeted analysis of 12 putative miRNAs and analyzed them in 120 subjects from independent study groups with different GC exposure in terms of dose, duration of exposure, and route of administration. The rationale for selecting these groups was that their GC exposure mostly remained within the normal physiological range. Despite the experimental differences between these studies, and the fact that these studies were not designed to study miRNA biomarkers of GC action, miR-122-5p was down-regulated by increased GC exposure in all of them. One exception was when short-term excessively high GC exposure was studied in afternoon samples in 20 subjects. There is no clear explanation for this, except the possibility that high non-physiological GC exposure has other secondary effects that may affect the levels of miR-122-5p. The network analysis also identified putative metabolomic markers of GC action. GCs have a key role in metabolic regulation of stress by mobilizing energy through glucose, protein, and lipid metabolism. Previous studies have found an association between different GC doses and levels of branched-chain amino acids, fatty acids, some acyl carnitines, and tryptophan and its metabolites (Alwashih et al. , 2017a; Sorgdrager et al. , 2018). In our study, the amino acid tyrosine and the pyrimidine base uracil had a central position in the hypernetwork, which defined a group of highly connected multi-omic relationships within physiological GC exposure. Some of the other metabolomic data from our study was also in line with previous metabolomic studies in patients with adrenal insufficiency (Alwashih et al. , 2017b; Sorgdrager et al. , 2018). Excessive exposure to GCs in healthy subjects has, on the other hand, shown a strong, immediately and long-lasting impact on numerous biological pathways in the metabolome that may be either direct or indirect through the metabolic and cardiovascular action of pharmacological doses of GCs (Bordag et al. , 2015). There are some study limitations that need to be acknowledged. The low number of subjects included in the clinical experimental study could have reduced the power to detect a putative marker in individual ‘omic data sets, but this limitation was compensated for by the crossover study design and the integration of multi-omic layers. Another limitation is that we have only studied markers collected in the morning during physiologically peak cortisol exposure. However, the strengths of our study are the experimental study design, consideration of diurnal variation in GC action and impact of food intake, and the within-individual comparison, which minimizes confounders, as well as the fact that the putative markers that we have replicated are associated with known GC-responsive genes in two different tissues, suggesting their functional importance in GC action. Moreover, the integration of multi-omic layers allows for the reduction of background noise (Huang et al. , 2017) and forms the basis for a detailed model of GC action. Hypernetwork summaries of correlation networks are recognized as providing signatures of mechanism (Pearcy et al. , 2016; Johnson, 2011; Butte et al. , 2000; Oldham et al. , 2006) and, as such, are useful to assess both function and define markers of direct action. In this clinical biomarker discovery study, we identified genes, miRNA, and metabolites that are differently expressed during GC exposure and GC withdrawal in subjects with Addison’s disease. The multi-omic data showed a high degree of coherence, and network analysis identified transcriptomics and metabolites that were closely correlated. The final outcome of the study is identification of a miRNA that is regulated by GC exposure and correlated with genes and metabolites that are also regulated by GCs in this study, indicating its functional relevance. The replication of this miRNA in three independent study groups increases the likelihood that the discovered miRNA, miR-122-5p, could become a biomarker of GC action to be used in clinical settings.
  Plasma cortisol and cortisone were analyzed using liquid chromatography-mass spectrometry (LC-MS), and urinary-free cortisol and cortisone were analyzed using gas chromatography-mass spectrometry (GC-MS) at the Mass Spectrometry Core Laboratory, Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK. PBMCs were isolated on-site from whole blood using a gradient-based separation procedure and Ficoll-Paque PREMIUM (GE Healthcare). A microarray gene expression analysis using Affymetrix Human Gene 2. 0 ST arrays in both PBMC and adipose tissue was performed at the Array and Analysis Facility, Science for Life Laboratory at Uppsala Biomedical Center (BMC), Sweden. The untargeted miRNA analysis in plasma was performed at Exiqon Services, Denmark. The targeted miRNA analyses in plasma (including the replication samples) were performed at Exiqon Services, Denmark, at a later date than the untargeted analysis. The 14 miRNAs included in the analysis based on the findings from the untargeted analysis were miR-425-3p, miR-186-5p, miR-15b-5p, miR-95-3p, miR-16-1-3p, miR-576-5p, miR-122-5p, miR-200a-3p, miR-193b-3p, miR-424-5p, miR-574-3p, miR-148a-3p, miR-18a-5p, and let-7g-5p. Metabolic profiling of serum by GC-MS and LC-MS was performed at the Swedish Metabolomics Center in Umeå, Sweden. Preprocessing of ‘omics data sets was carried out in the following ways. PBMC and adipose tissue transcriptomes were normalized using robust multichip average (RMA) via the R package oligo (Carvalho and Irizarry, 2010), which corrects for background variation, quantile normalizes, and summarizes features to gene-probe set level (Figure 3—figure supplement 2). GC-MS and LC-MS metabolomic data sets were analyzed using the R package MetaboanalystR (Chong and Xia, 2018), which filters variables based on ranked interquartile range, normalizes metabolites to sample median, and log transforms the resultant intensities (Figure 3—figure supplement 3). Qlucore Omics Explorer (version 3. 3, Lund, Sweden) was used to scale and mean center miRome data. How all these analyses were performed is described in detail in Appendix 3. Principal component analysis (PCA) was performed to provide further quality control and define the relationship of variance between samples, allowing structure within the data set to be defined (Qlucore Omics Explorer 3. 3). Quality control of transcriptomic data was performed using PCA with cross-validation and data consistency was confirmed. No outliers were identified. Differential gene expression was determined by a paired t-test comparing the two interventions. Network analysis of DEGs was performed using Advaita Bio’s iPathwayGuide (https: //www. advaitabio. com/ipathwayguide); gene ontology performed using this software analysis tool implements the ‘Impact Analysis’ approach that takes into consideration the direction and type of all signals on a pathway, and the position, role, and type of every gene (Ahsan and Drăghici, 2017). Gene ontologies were associated with differentially regulated gene lists (Ingenuity Pathway Analysis [IPA], Qiagen, Redwood City, CA). miRNAs were paired with genes that were theoretically regulated by specific miRNAs using IPA. The databases used for this mapping were TarBase (Vlachos et al. , 2015), miRecords (Xiao et al. , 2009), and peer-reviewed biomedical literature, as well as predicted miR–mRNA interactions from TargetScan (Agarwal et al. , 2015). The Encyclopedia of DNA Elements (ENCODE) data (Rosenbloom et al. , 2013) was used to map genes in the interactome network model of GC action that had been previously shown to have dexamethasone dose-dependent DNA binding of NR3C1, the GC receptor gene. Causal network analysis (CNA) allows the identification and prioritization of regulatory system elements within transcriptomic models. CNA was performed within IPA (Krämer et al. , 2014). CNA identifies upstream molecules, up to three steps distant, that potentially control the expression of the genes in the data set (Krämer et al. , 2014). A prediction of the activation state for each regulatory factor (master regulator), based on the direction of change, was calculated (Z-score) using the gene expression patterns of the transcription factor and its downstream genes. An absolute Z-score of ≥|1. 4| and a corrected p-value<0. 05 (Fisher’s exact test) were used to compare the regulators identified. Lists of DEGs were used to generate network models of protein interactions in Cytoscape 2. 8. 3 (Smoot et al. , 2011) by inference using the BioGRID (3. 4. 137) database (Chatr-Aryamontri et al. , 2015). The Cytoscape plug-in Moduland (Kovács et al. , 2010; Szalay-Beko et al. , 2012) was applied to identify overlapping modules, an approach that models complex modular architecture within the human interactome (Chang et al. , 2013) by accounting for the non-discrete nature of network modules (Kovács et al. , 2010). Modular hierarchy was determined using a centrality score and further assessed using hierarchical network layouts (summarizing the underlying network topology). The central module cores (metanode of the 10 most central elements) was determined and used as a basis to integrate the miRNA and metabolomic data. Transcriptomic and metabolomic data were combined to form a single network model using the Metscape (Karnovsky et al. , 2012) plug-in for Cytoscape. Differential ‘omic data was compared and clustered in a correlation matrix using the corrplot plug-in (Murdoch and Chow, 1996) for R (R Development Core Team, 2020). Subject-level similarity network fusion (SNF) (Wang et al. , 2014) was performed on ‘omic data as a test for similarity. To perform SNF, the SNFTool R-package was used (Wang et al. , 2014). First, Euclidean distances were calculated between gene probe sets, and these were then combined using a nonlinear nearest neighbor method over 20 iterations. The fused data was subjected to spectral clustering and presented as a heat map. We modeled the dynamics of potentially relevant PBMC and adipose tissue transcripts, miRNAs, and metabolites by assessing their activity as measured by the number of shared correlations against the background of all ‘omic elements called present after data processing. A matrix (m rows and n columns) was generated of correlation distances (r-values) between the significantly differentially expressed multi-omic data (forming m rows) and all ‘omic data called present (forming n columns). The r-values were normally distributed. A similarity matrix was defined by dichotomizing the correlation distance based on an r-value threshold of ≥| 1. 5 |sd (if sd of | r |≥1. 5, then value = 1; if sd of | r |<1. 5, then value = 0); the new matrix was termed M and represents the incidence matrix of the hypernetwork. An element of M, mij, where i and j are elements of m and n respectively, is defined as follows: mij={ 1, &| r |≥1. 5 sd0, &| r |<1. 5 sd To generate the hypernetwork, we multiplied M by the transpose of M, MT (Johnson, 2011; Ha et al. , 2020), the elements of the resulting square matrix (sM, an m×m matrix) are the number of correlations shared by each pair of interacting ‘omic elements; this is also the number of edges connecting each pair of nodes. sM was clustered using hierarchical clustering to identify the group of highly connected ‘omic elements. The dichotomization parameters were shown to correspond to maximum signal window in the data using chi-squared distance metric (Figure 3—figure supplement 4). The chi-squared distance (X 2) was defined asΧ2=∑i=1N (mi−me) 2mewhere N is the order of the matrix sM, i is the i' th element, mi, of sM, and me is the expected value of an element of sM. The expected value of an element of sM was calculated at any chosen dichotomization threshold by dividing the total number of correlations by the order of the matrix. Differential expression analysis was performed to refine genes for hypernetwork analysis. This approach serves to identify potentially relevant ‘omic elements. FDR-corrected p-values for all elements selected for hypernetwork integration are presented in Supplementary file 1g. We identified 4426 DEGs in PBMCs, 3520 adipose tissue DEGs, 38 metabolites (17 LC-MS, 21 GC-MS), and 12 miRNAs below an uncorrected p-value of 0. 05. Data was analyzed across nine matching samples (normalized log2 score was inverted between GC exposure and GC withdrawal, i. e. , +1 and –1, respectively). A hypernetwork is inherently robust as individual correlations are not considered significant; rather hypernetworks model higher order interactions between nodes (‘omic elements) based on large numbers of shared edges (correlations). This approach only highlights ‘omic elements that are supported by the majority of the data and, as such, is robust to a wide range of r-value thresholds as well as small sample sizes. Further, robustness of the hypernetwork observations was determined using a dissimilarity matrix derived from the original similarity matrix (i. e. , the complement of the similarity matrix). The elements assessed as dissimilar were subtracted from those defined as similar. Elements within the M×MT output of the dissimilarity analysis that were also similar were eliminated from further predictive analysis. The BORUTA R package (Kursa, 2014; Kursa and Rudnicki, 2010) was used for feature selection of transcriptomic data with predictive value. Random Forest (Breiman, 2001) was implemented in R using 5000 trees to determine the predictive value expressed as the area under the curve of the receiver operating characteristic. Unsupervised analysis of metabolomic and transcriptomic data to assess how GC exposure grouped the study subjects was performed using Orthogonal Projections to Latent Structures Discriminant Analysis in SIMCA 13. 0 (Sartorius) or PLS-DA MixOmics plug-in (Rohart et al. , 2017) for R. For quantitative variables with normal distribution, we performed independent samples t-test. Mann–Whitney U-test was performed for non-normally distributed variables. Chi-squared test or Fisher’s exact test, as appropriate, was used for categorical variables. Wilcoxon rank test was used for detecting differences between the two interventions in quantitative non-normally distributed variables. All statistical tests were two-sided, and p<0. 05 was considered to be statistically significant. Further robustness for ‘omic data analysis was provided by considering the findings as clusters of co-expressed findings (Cleary et al. , 2017). Statistical analyses were performed using SPSS (Statistical Package for Social Science) program, version 24 software for Mac. Unless otherwise stated, all other statistical analyses were performed in R version 4. 0. 2 for Windows. Figures were plotted using ggplot2 (Wickham, 2016), gplots (Warnes et al. , 2020), ggpubr (Kassambara, 2020), and reshape2 (Wickham, 2007).
summary: Several diseases, including asthma, arthritis, some skin conditions, and cancer, are treated with medications called glucocorticoids, which are synthetic versions of human hormones. These drugs are also used to treat people with a condition call adrenal insufficiency who do not produce enough of an important hormone called cortisol. Use of glucocorticoids is very common, the proportion of people in a given country taking them can range from 0. 5% to 21% of the population depending on the duration of the treatment. But, like any medication, glucocorticoids have both benefits and risks: people who take glucocorticoids for a long time have an increased risk of diabetes, obesity, cardiovascular disease, and death. Because of the risks associated with taking glucocorticoids, it is very important for physicians to tailor the dose to each patient’s needs. Doing this can be tricky, because the levels of glucocorticoids in a patient’s blood are not a good indicator of the medication’s activity in the body. A test that can accurately measure the glucocorticoid activity could help physicians personalize treatment and reduce harmful side effects. As a first step towards developing such a test, Chantzichristos et al. identified a potential way to measure glucocorticoid activity in patient’s blood. In the experiments, blood samples were collected from ten patients with adrenal insufficiency both when they were on no medication, and when they were taking a glucocorticoid to replace their missing hormones. Next, the blood samples were analyzed to determine which genes were turned on and off in each patient with and without the medication. They also compared small molecules in the blood called metabolites and tiny pieces of genetic material called microRNAs that turn genes on and off. The experiments revealed networks of genes, metabolites, and microRNAs that are associated with glucocorticoid activity, and one microRNA called miR-122-5p stood out as a potential way to measure glucocorticoid activity. To verify this microRNA’s usefulness, Chantzichristos et al. looked at levels of miR-122-5p in people participating in three other studies and confirmed that it was a good indicator of the glucocorticoid activity. More research is needed to confirm Chantzichristos et al. ’s findings and to develop a test that can be used by physicians to measure glucocorticoid activity. The microRNA identified, miR-122-5p, has been previously linked to diabetes, so studying it further may also help scientists understand how taking glucocorticoids may increase the risk of developing diabetes and related diseases.
keywords: medicine
  computational and systems biology
title: Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial