Meta-analysis of primary target genes of peroxisome proliferator-activated receptors

A combined experimental and in silico approach identifies Peroxisome Proliferator Activated Receptor (PPAR) binding sites and six novel target genes in the human genome

Nine newly identified individuals refine the phenotype associated with MYT1L mutations

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Variations in the Ghrelin receptor gene associate with obesity and glucose metabolism in individuals with impaired glucose tolerance

Peer reviewe

Distinct HDACs regulate the transcriptional response of human cyclin-dependent kinase inhibitor genes to trichostatin A and 1α,25-dihydroxyvitamin D3

The anti-proliferative effects of histone deacetylase (HDAC) inhibitors and 1α,25-dihydroxyvitamin D3 [1α,25(OH)2D3] converge via the interaction of un-liganded vitamin D receptor (VDR) with co-repressors recruiting multiprotein complexes containing HDACs and via the induction of cyclin-dependent kinase inhibitor (CDKI) genes of the INK4 and Cip/Kip family. We investigated the effects of the HDAC inhibitor Trichostatin A (TSA) and 1α,25(OH)2D3 on the proliferation and CDKI gene expression in malignant and non-malignant mammary epithelial cell lines. TSA induced the INK4-family genes p18 and p19, whereas the Cip/Kip family gene p21 was stimulated by 1α,25(OH)2D3. Chromatin immunoprecipitation and RNA inhibition assays showed that the co-repressor NCoR1 and some HDAC family members complexed un-liganded VDR and repressed the basal level of CDKI genes, but their role in regulating CDKI gene expression by TSA and 1α,25(OH)2D3 were contrary. HDAC3 and HDAC7 attenuated 1α,25(OH)2D3-dependent induction of the p21 gene, for which NCoR1 is essential. In contrast, TSA-mediated induction of the p18 gene was dependent on HDAC3 and HDAC4, but was opposed by NCoR1 and un-liganded VDR. This suggests that the attenuation of the response to TSA by NCoR1 or that to 1α,25(OH)2D3 by HDACs can be overcome by their combined application achieving maximal induction of anti-proliferative target genes

Profiling of promoter occupancy by PPARα in human hepatoma cells via ChIP-chip analysis

The transcription factor peroxisome proliferator-activated receptor α (PPARα) is an important regulator of hepatic lipid metabolism. While PPARα is known to activate transcription of numerous genes, no comprehensive picture of PPARα binding to endogenous genes has yet been reported. To fill this gap, we performed Chromatin immunoprecipitation (ChIP)-chip in combination with transcriptional profiling on HepG2 human hepatoma cells treated with the PPARα agonist GW7647. We found that GW7647 increased PPARα binding to 4220 binding regions. GW7647-induced binding regions showed a bias around the transcription start site and most contained a predicted PPAR binding motif. Several genes known to be regulated by PPARα, such as ACOX1, SULT2A1, ACADL, CD36, IGFBP1 and G0S2, showed GW7647-induced PPARα binding to their promoter. A GW7647-induced PPARα-binding region was also assigned to SREBP-targets HMGCS1, HMGCR, FDFT1, SC4MOL, and LPIN1, expression of which was induced by GW7647, suggesting cross-talk between PPARα and SREBP signaling. Our data furthermore demonstrate interaction between PPARα and STAT transcription factors in PPARα-mediated transcriptional repression, and suggest interaction between PPARα and TBP, and PPARα and C/EBPα in PPARα-mediated transcriptional activation. Overall, our analysis leads to important new insights into the mechanisms and impact of transcriptional regulation by PPARα in human liver and highlight the importance of cross-talk with other transcription factors

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

Association of genomic regions of PPAR target genes with PPARs and their partner proteins

Chromatin was extracted from HEK293 cells that had been treated with solvent (DMSO) or for 120 minutes with 100 nM GW7647. The association of PPARα, RXRα and pPol II was monitored by ChIP assays with respective antibodies on genomic regions of the eight PPAR target genes that are close to the TSS, upstream of the TSS and downstream of the TSS; for location see Figure 3 and Table 2. Since the gene is not expressed in HEK293 cells, the data for its four genomic regions were obtained using chromatin derived from HepG2 cells. Real-time quantitative PCR was performed on chromatin templates and the fold change of the antibody-precipitated template in relation to an IgG-precipitated specificity control template was calculated. PPARα shows specific association with 15 of the 23 tested regions and the relative association with these regions is shown. Columns represent means of at least three experiments and bars indicate standard deviations. Two-tailed Student's -tests were performed to determine the significance of association in reference to IgG controls (*< 0.05, **< 0.01, ***< 0.001).<p><b>Copyright information:</b></p><p>Taken from "Meta-analysis of primary target genes of peroxisome proliferator-activated receptors"</p><p>http://genomebiology.com/2007/8/7/R147</p><p>Genome Biology 2007;8(7):R147-R147.</p><p>Published online 25 Jul 2007</p><p>PMCID:PMC2323243.</p><p></p

SOM analysis of established primary PPAR target genes, clusters III and IV

The genes were sorted by SOM analysis with respect to overall PPRE pattern similarity and their evolutionary conservation into cluster III and cluster IV. For more details, see the Figure 6 legend.<p><b>Copyright information:</b></p><p>Taken from "Meta-analysis of primary target genes of peroxisome proliferator-activated receptors"</p><p>http://genomebiology.com/2007/8/7/R147</p><p>Genome Biology 2007;8(7):R147-R147.</p><p>Published online 25 Jul 2007</p><p>PMCID:PMC2323243.</p><p></p