Anti-CRHBP polyclonal antibody

The purpose of this study was to examine molecular markers of the stress response at the pituitary and peripheral levels in animals that responded differently to chronic mild stress (CMS). Rats were subjected to 2-weeks CMS and symptoms of anhedonia was measured by the consumption of 1% sucrose solution. mRNA levels of CRH-family neuropeptides (Crh-corticotropin-releasing hormone, Ucn1-urocortin 1, Ucn2-urocortin 2, Ucn3-urocortin 3), CRH receptors (Crhr1-corticotropin-releasing hormone receptor 1, Crhr2-corticotropin-releasing hormone receptor 2) and Crhbp (corticotropin-releasing factor binding protein) in the pituitaries of rats were determined with real-time PCR. Plasma levels of ACTH ( adrenocorticotropin), CRH and urocortins were measured with ELISA assays. CMS procedure led to the development of anhedonia manifested by the decreased sucrose consumption (stress-reactive, SR, stress-susceptible group). Additionally, the group of animals not exhibiting any signs of anhedonia (stress non-reactive, SNR, stress-resilient group) and the group characterized by the increased sucrose consumption (stress invert-reactive group SIR) were selected. The significant increases in ACTH plasma level accompanied by the decreases in the pituitary gene expression of the Crh, Ucn2 and Ucn3 in both stress non-reactive and stress invert-reactive groups were observed. The only molecular change observed in stress-reactive group was the increase in UCN2 plasma level. The differentiated behavioral stress responses were reflected by gene expression changes in the pituitary. Alterations in the mRNA levels of Crh, Ucn2 and Ucn3 in the pituitary might confirm the paracrine and/or autocrine effects of these peptides in stress response. The opposite behavioral effect between SNR vs. SIR groups and the surprising similarity at gene expression and plasma ACTH levels in these two groups may suggest the discrepancy between molecular and behavioral stress responses; however, there results might indicate to similarity underlying different ways to cope with stress conditions. (C) 2014 Elsevier Inc. All rights reserved.

Transcriptomics technologies such as next-generation sequencing and microarray platforms provide exciting opportunities for improving diagnosis and treatment of complex diseases. Transcriptomics studies often share similar hypotheses, but are carried out on different platforms, in different conditions, and with different analysis approaches. These factors, in addition to small sample sizes, can result in a lack of reproducibility. A clear understanding and unified picture of many complex diseases are still elusive, highlighting an urgent need to effectively integrate multiple transcriptomic studies for disease signatures. We have integrated more than 3,000 high-quality transcriptomic datasets in oncology, immunology, neuroscience, cardiovascular and metabolic disease, and from both public and internal sources (DiseaseLand database). We established a systematic data integration and meta-analysis approach, which can be applied in multiple disease areas to create a unified picture of the disease signature and prioritize drug targets, pathways, and compounds. In this bipolar case study, we provided an illustrative example using our approach to combine a total of 30 genome-wide gene expression studies using postmortem human brain samples. First, the studies were integrated by extracting raw FASTQ or CEL files, then undergoing the same procedures for preprocessing, normalization, and statistical inference. Second, both p-value and effect size based meta-analysis algorithms were used to identify a total of 204 differentially expressed (DE) genes (FDR < 0.05) genes in the prefrontal cortex. Among these were BDNF, VGF, WFS1 , DUSP6, CRHBP, MAQA, and RELN, which have previously been implicated in bipolar disorder. Finally, pathway enrichment analysis revealed a role for GPCR, MAPK, immune, and Reelin pathways. Compound profiling analysis revealed MAPK and other inhibitors may modulate the DE genes. The ability to robustly combine and synthesize the information from multiple studies enables a more powerful understanding of this complex disease.