Timing Is Critical for Effective Glucocorticoid Receptor Mediated Repression of the cAMP-Induced CRH Gene

Glucocorticoid negative feedback of the hypothalamus-pituitary-adrenal axis is mediated in part by direct repression of gene transcription in glucocorticoid receptor (GR) expressing cells. We have investigated the cross talk between the two main signaling pathways involved in activation and repression of corticotrophin releasing hormone (CRH) mRNA expression: cyclic AMP (cAMP) and GR. We report that in the At-T20 cell-line the glucocorticoid-mediated repression of the cAMP-induced human CRH proximal promoter activity depends on the relative timing of activation of both signaling pathways. Activation of the GR prior to or in conjunction with cAMP signaling results in an effective repression of the cAMP-induced transcription of the CRH gene. In contrast, activation of the GR 10 minutes after onset of cAMP treatment, results in a significant loss of GR-mediated repression. In addition, translocation of ligand-activated GR to the nucleus was found as early as 10 minutes after glucocorticoid treatment. Interestingly, while both signaling cascades counteract each other on the CRH proximal promoter, they synergize on a synthetic promoter containing ‘positive’ response elements. Since the order of activation of both signaling pathways may vary considerably in vivo, we conclude that a critical time-window exists for effective repression of the CRH gene by glucocorticoids

Knockdown of SF-1 and RNF31 Affects Components of Steroidogenesis, TGFβ, and Wnt/β-catenin Signaling in Adrenocortical Carcinoma Cells

The orphan nuclear receptor Steroidogenic Factor-1 (SF-1, NR5A1) is a critical regulator of development and homeostasis of the adrenal cortex and gonads. We recently showed that a complex containing E3 ubiquitin ligase RNF31 and the known SF-1 corepressor DAX-1 (NR0B1) interacts with SF-1 on target promoters and represses transcription of steroidogenic acute regulatory protein (StAR) and aromatase (CYP19) genes. To further evaluate the role of SF-1 in the adrenal cortex and the involvement of RNF31 in SF-1-dependent pathways, we performed genome-wide gene-expression analysis of adrenocortical NCI-H295R cells where SF-1 or RNF31 had been knocked down using RNA interference. We find RNF31 to be deeply connected to cholesterol metabolism and steroid hormone synthesis, strengthening its role as an SF-1 coregulator. We also find intriguing evidence of negative crosstalk between SF-1 and both transforming growth factor (TGF) β and Wnt/β-catenin signaling. This crosstalk could be of importance for adrenogonadal development, maintenance of adrenocortical progenitor cells and the development of adrenocortical carcinoma. Finally, the SF-1 gene profile can be used to distinguish malignant from benign adrenocortical tumors, a finding that implicates SF-1 in the development of malignant adrenocortical carcinoma

Differing Endoplasmic Reticulum Stress Response to Excess Lipogenesis versus Lipid Oversupply in Relation to Hepatic Steatosis and Insulin Resistance

Mitochondrial dysfunction and endoplasmic reticulum (ER) stress have been implicated in hepatic steatosis and insulin resistance. The present study investigated their roles in the development of hepatic steatosis and insulin resistance during de novo lipogenesis (DNL) compared to extrahepatic lipid oversupply. Male C57BL/6J mice were fed either a high fructose (HFru) or high fat (HFat) diet to induce DNL or lipid oversupply in/to the liver. Both HFru and HFat feeding increased hepatic triglyceride within 3 days (by 3.5 and 2.4 fold) and the steatosis remained persistent from 1 week onwards (p<0.01 vs Con). Glucose intolerance (iAUC increased by ∼60%) and blunted insulin-stimulated hepatic Akt and GSK3β phosphorylation (∼40–60%) were found in both feeding conditions (p<0.01 vs Con, assessed after 1 week). No impairment of mitochondrial function was found (oxidation capacity, expression of PGC1α, CPT1, respiratory complexes, enzymatic activity of citrate synthase & β-HAD). As expected, DNL was increased (∼60%) in HFru-fed mice and decreased (32%) in HFat-fed mice (all p<0.05). Interestingly, associated with the upregulated lipogenic enzymes (ACC, FAS and SCD1), two (PERK/eIF2α and IRE1/XBP1) of three ER stress pathways were significantly activated in HFru-fed mice. However, no significant ER stress was observed in HFat-fed mice during the development of hepatic steatosis. Our findings indicate that HFru and HFat diets can result in hepatic steatosis and insulin resistance without obvious mitochondrial defects via different lipid metabolic pathways. The fact that ER stress is apparent only with HFru feeding suggests that ER stress is involved in DNL per se rather than resulting from hepatic steatosis or insulin resistance

The PI3K regulatory subunits p55α and p50α regulate cell death in vivo.

The phosphatidylinositol 3-kinase (PI3K) regulatory subunits p55α and p50α are coordinately transcriptionally upregulated by signal transducer and activator of transcription 3 (Stat3) at the onset of mammary gland involution, a process that requires Stat3. Deletion of both p55α and p50α subunits in vivo abrogated mammary epithelial cell death during involution. This was associated also with reduced cytosolic levels and activity of the cysteine protease cathepsin L, which is implicated in lysosomal-mediated programmed cell death (LM-PCD) and is upregulated in involution. Furthermore, involution is delayed in cathepsin L-deficient mice suggesting that the p55α/p50α subunits mediate cell death in part by elevating the level of cathepsin L resulting in increased cytosolic activity. Surprisingly, we found that p55α/p50α localize to the nucleus where they bind to chromatin and regulate transcription of a subset of inflammatory/acute phase genes that are also Stat3 targets. Our findings reveal a novel role for these PI3K regulatory subunits as regulators of LM-PCD in vivo.We thank Maximilian Blanck and Ivan Ferrer-Vicens for immunofluorescence studies, Helen Skelton for tissue histology and the Watson lab members for helpful discussions. This work was funded by BBSRC and MRC grants (BB/D012937/1 and MR/J001023/1) awarded to CJW, SP is the recipient of a Marie Curie IEF fellowship (EU Marie Curie grant no. 273365), KN was supported by a BBSRC CASE PhD studentship, HR is funded by a Cambridge Cancer Center PhD studentship and PAK is the recipient of a Trinity College fellowship. TR was supported by the Excellence Initiative of the German Federal and State Governments (EXC 294), and the Deutche Forschungsgemeinschaft SFB 850 project B7.This is the accepted manuscript version of the paper. The final version is available online from Nature Publishing Group at http://www.nature.com/cdd/journal/vaop/ncurrent/full/cdd201459a.html

Epigenetic differences in monozygotic twins discordant for major depressive disorder

Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD