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Context and ObjectiveCirculating cortisol fluctuates diurnally under the control of the “master” circadian CLOCK, while the peripheral “slave” counterpart of the latter regulates the transcriptional activity of the glucocorticoid receptor (GR) at local glucocorticoid target tissues through acetylation. In this manuscript, we studied the effect of CLOCK-mediated GR acetylation on the sensitivity of peripheral tissues to glucocorticoids in humans.Design and ParticipantsWe examined GR acetylation and mRNA expression of GR, CLOCK-related and glucocorticoid-responsive genes in peripheral blood mononuclear cells (PBMCs) obtained at 8 am and 8 pm from 10 healthy subjects, as well as in PBMCs obtained in the morning and cultured for 24 hours with exposure to 3-hour hydrocortisone pulses every 6 hours. We used EBV-transformed lymphocytes (EBVLs) as non-synchronized controls.ResultsGR acetylation was higher in the morning than in the evening in PBMCs, mirroring the fluctuations of circulating cortisol in reverse phase. All known glucocorticoid-responsive genes tested responded as expected to hydrocortisone in non-synchronized EBVLs, however, some of these genes did not show the expected diurnal mRNA fluctuations in PBMCs in vivo. Instead, their mRNA oscillated in a Clock- and a GR acetylation-dependent fashion in naturally synchronized PBMCs cultured ex vivo in the absence of the endogenous glucocorticoid, suggesting that circulating cortisol might prevent circadian GR acetylation-dependent effects in some glucocorticoid-responsive genes in vivo.ConclusionsPeripheral CLOCK-mediated circadian acetylation of the human GR may function as a target-tissue, gene-specific counter regulatory mechanism to the actions of diurnally fluctuating cortisol, effectively decreasing tissue sensitivity to glucocorticoids in the morning and increasing it at night.

BackgroundMicroRNAs (miRNAs) in circulation have emerged as promising biomarkers. In this study, we aimed to identify a circulating miRNA signature for osteoarthritis (OA) patients and in combination with bioinformatics analysis to evaluate the utility of selected differentially expressed miRNAs in the serum as potential OA biomarkers.MethodsSerum samples were collected from 12 primary OA patients, and 12 healthy individuals were screened using the Agilent Human miRNA Microarray platform interrogating 2549 miRNAs. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the deregulated miRNAs. Expression levels of selected miRNAs were validated by quantitative real-time PCR (qRT-PCR) in all serum and in articular cartilage samples from OA patients (n = 12) and healthy individuals (n = 7). Bioinformatics analysis was used to investigate the involved pathways and target genes for the above miRNAs.ResultsWe identified 279 differentially expressed miRNAs in the serum of OA patients compared to controls. Two hundred and five miRNAs (73.5%) were upregulated and 74 (26.5%) downregulated. ROC analysis revealed that 77 miRNAs had area under the curve (AUC) > 0.8 and p < 0.05. Bioinformatics analysis in the 77 miRNAs revealed that their target genes were involved in multiple signaling pathways associated with OA, among which FoxO, mTOR, Wnt, pI3K/akt, TGF-β signaling pathways, ECM-receptor interaction, and fatty acid biosynthesis. qRT-PCR validation in seven selected out of the 77 miRNAs revealed 3 significantly downregulated miRNAs (hsa-miR-33b-3p, hsa-miR-671-3p, and hsa-miR-140-3p) in the serum of OA patients, which were in silico predicted to be enriched in pathways involved in metabolic processes. Target-gene analysis of hsa-miR-140-3p, hsa-miR-33b-3p, and hsa-miR-671-3p revealed that InsR and IGFR1 were common targets of all three miRNAs, highlighting their involvement in regulation of metabolic processes that contribute to OA pathology. Hsa-miR-140-3p and hsa-miR-671-3p expression levels were consistently downregulated in articular cartilage of OA patients compared to healthy individuals.ConclusionsA serum miRNA signature was established for the first time using high density resolution miR-arrays in OA patients. We identified a three-miRNA signature, hsa-miR-140-3p, hsa-miR-671-3p, and hsa-miR-33b-3p, in the serum of OA patients, predicted to regulate metabolic processes, which could serve as a potential biomarker for the evaluation of OA risk and progression.Electronic supplementary materialThe online version of this article (10.1186/s13148-017-0428-1) contains supplementary material, which is available to authorized users.

BackgroundCurrent diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays.PurposeOur goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays.Methodology/Principal FindingsBC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways.Conclusions/SignificanceThe identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers.