Project description:Background Obesity is associated with changes in fat cell gene expression and metabolism. What drives these changes is not well understood. We aimed to explore fat cell epigenetics, i.e., DNA methylation, as one mediator of gene regulation, in obese women. The global DNA methylome for abdominal subcutaneous fat cells was compared between 15 obese case (BMI 41.4 ± 4.4 kg/m 2 , mean ± SD) and 14 never-obese control women (BMI 25.2 ± 2.5 kg/m 2 ). Global array-based transcriptome analysis was analyzed for subcutaneous white adipose tissue (WAT) from 11 obese and 9 never-obese women. Limma was used for statistical analysis. Results We identified 5529 differentially methylated DNA sites (DMS) for 2223 differentially expressed genes between obese cases and never-obese controls (false discovery rate <5 %). The 5529 DMS displayed a median difference in beta value of 0.09 (range 0.01 to 0.40) between groups. DMS were under-represented in CpG islands and in promoter regions, and over-represented in open sea-regions and gene bodies. The 2223 differentially expressed genes with DMS were over-represented in key fat cell pathways: 31 of 130 (25 %) genes linked to “adipogenesis” (adjusted P = 1.66 × 10 −11 ), 31 of 163 (19 %) genes linked to “insulin signaling” (adjusted P = 1.91 × 10 −9 ), and 18 of 67 (27 %) of genes linked to “lipolysis” (P = 6.1 × 10 −5 ). In most cases, gene expression and DMS displayed reciprocal changes in obese women. Furthermore, among 99 candidate genes in genetic loci associated with body fat distribution in genome-wide association studies (GWAS); 22 genes displayed differential expression accompanied by DMS in obese versus never-obese women (P = 0.0002), supporting the notion that a significant proportion of gene loci linked to fat distribution are epigenetically regulated. Conclusions Subcutaneous WAT from obese women is characterized by congruent changes in DNA methylation and expression of genes linked to generation, distribution, and metabolic function of fat cells. These alterations may contribute to obesity-associated metabolic disturbances such as insulin resistance in women. The global DNA methylome in abdominal subcutaneous fat cells was compared between 15 obese cases (BMI 41.4±4.4 kg/m2, mean ± SD) and 14 never-obese control women (BMI 25.2±2.5 kg/m2).

Project description:Background/Objectives: Obese subjects have increased number of enlarged fat cells which are reduced in size but not number in post-obesity. We performed DNA methylation profiling in fat cells with the aim of identifying differentially methylated DNA sites (DMS) linked to adipose hyperplasia (many small fat cells) in post-obesity. Subjects/Methods: Genome-wide DNA methylation was analyzed in abdominal subcutaneous fat cells from 16 women examined two years after gastric bypass surgery at a post-obese state (BMI 26±2 kg/m2, mean±s.d.) and 14 never-obese women (BMI 25±2 kg/m2). Gene expression was analyzed in subcutaneous adipose tissue from 9 women in each group. In a secondary analysis, we examined DNA methylation and expression of adipogenesis genes in 15 and 11 obese women, respectively. Results: The average degree of DNA methylation of all analyzed CpG-sites was lower in fat cells from post-obese as compared to never-obese women (P=0.014). 8,504 CpG sites were differentially methylated in fat cells from post-obese versus never-obese women (false discovery rate 1%). DMS were under-represented in CpG-islands and surrounding shores. The 8,504 DMS mapped to 3,717 unique genes; these genes were over-represented in cell differentiation pathways. Notably, 27% of genes linked to adipogenesis (i.e. 35 of 130) displayed DMS (adjusted P=10−8) in post-obese versus never-obese women. Next, we explored DNA methylation and expression of genes linked to adipogenesis in more detail in adipose tissue samples. DMS annotated to adipogenesis genes were not accompanied by differential gene expression in post-obese compared to never-obese women. In contrast, adipogenesis genes displayed differential DNA methylation accompanied by altered expression in obese women, Conclusions: Global CpG hypomethylation and overrepresentation of DMS in adipogenesis genes in fat cells may contribute to adipose hyperplasia in post-obese women. Post obese=16, Control group=14.

Project description:Background and Aims Small intestinal neuroendocrine tumours (SINETs) are the commonest malignancy of the small intestine; however underlying pathogenic mechanisms remain poorly characterised. Whole genome and exome sequencing has demonstrated that SINETs are mutationally quiet with the most frequent known mutation in the cyclin dependent kinase inhibitor 1B gene (CDKN1B) occurring in only ~8% of tumours, suggesting that alternative mechanisms may drive tumourigenesis. The aim of this study is to perform genome-wide molecular profiling of SINETs in order to identify pathogenic drivers based on molecular profiling. This study represents the largest unbiased integrated genomic, epigenomic, and transcriptomic analysis undertaken in this tumour type. Methods Here we present data from integrated molecular analysis of SINETs (n=97) including whole exome or targeted CDKN1B sequencing (n=29), HumanMethylation450 BeadChip (Illumina) array profiling (n=69), methylated DNA immunoprecipitation sequencing (n=16), copy number variance analysis (n=47) and Whole Genome-DASL (Illumina) expression array profiling (n=43). Results Based on molecular profiling SINETs can be classified in to three groups which demonstrate significantly different progression-free survival after resection of primary tumour (not reached at 10 years vs 56 months vs 21 months, p=0.04). Epimutations were found at a recurrence rate of up to 85% and 21 epigenetically dysregulated genes were identified, including CDX1 (86%), CELSR3 (84%), FBP1 (84%) and GIPR (74%). Conclusions This is the first comprehensive integrated molecular analysis of SINETs. We have demonstrated that these tumours are highly epigenetically dysregulated. Furthermore, we have identified novel molecular subtypes with significant impact on progression free survival. Background and Aims Small intestinal neuroendocrine tumours (SINETs) are the commonest malignancy of the small intestine; however underlying pathogenic mechanisms remain poorly characterised. Whole genome and exome sequencing has demonstrated that SINETs are mutationally quiet with the most frequent known mutation in the cyclin dependent kinase inhibitor 1B gene (CDKN1B) occurring in only ~8% of tumours, suggesting that alternative mechanisms may drive tumourigenesis. The aim of this study is to perform genome-wide molecular profiling of SINETs in order to identify pathogenic drivers based on molecular profiling. This study represents the largest unbiased integrated genomic, epigenomic, and transcriptomic analysis undertaken in this tumour type. Methods Here we present data from integrated molecular analysis of SINETs (n=97) including whole exome or targeted CDKN1B sequencing (n=29), HumanMethylation450 BeadChip (Illumina) array profiling (n=69), methylated DNA immunoprecipitation sequencing (n=16), copy number variance analysis (n=47) and Whole Genome-DASL (Illumina) expression array profiling (n=43). Results Based on molecular profiling SINETs can be classified in to three groups which demonstrate significantly different progression-free survival after resection of primary tumour (not reached at 10 years vs 56 months vs 21 months, p=0.04). Epimutations were found at a recurrence rate of up to 85% and 21 epigenetically dysregulated genes were identified, including CDX1 (86%), CELSR3 (84%), FBP1 (84%) and GIPR (74%). Conclusions This is the first comprehensive integrated molecular analysis of SINETs. We have demonstrated that these tumours are highly epigenetically dysregulated. Furthermore, we have identified novel molecular subtypes with significant impact on progression free survival. This study included 97 tumour samples from 85 individuals, this included both primary and metastatic tumour samples. 25 normal small intestinal samples were analysed.

Dataset Information

The epigenetic signature of systemic insulin resistance in obese women

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets