Determinants of Human Adipose Tissue Gene Expression: Impact of Diet, Sex, Metabolic Status, and Cis Genetic Regulation

Abstract

Weight control diets favorably affect parameters of the metabolic syndrome and delay the onset of diabetic complications. The adaptations occurring in adipose tissue (AT) are likely to have a profound impact on the whole body response as AT is a key target of dietary intervention. Identification of environmental and individual factors controlling AT adaptation is therefore essential. Here, expression of 271 transcripts, selected for regulation according to obesity and weight changes, was determined in 515 individuals before, after 8-week low-calorie diet-induced weight loss, and after 26-week ad libitum weight maintenance diets. For 175 genes, opposite regulation was observed during calorie restriction and weight maintenance phases, independently of variations in body weight. Metabolism and immunity genes showed inverse profiles. During the dietary intervention, network-based analyses revealed strong interconnection between expression of genes involved in de novo lipogenesis and components of the metabolic syndrome. Sex had a marked influence on AT expression of 88 transcripts, which persisted during the entire dietary intervention and after control for fat mass. In women, the influence of body mass index on expression of a subset of genes persisted during the dietary intervention. Twenty-two genes revealed a metabolic syndrome signature common to men and women. Genetic control of AT gene expression by cis signals was observed for 46 genes. Dietary intervention, sex, and cis genetic variants independently controlled AT gene expression. These analyses help understanding the relative importance of environmental and individual factors that control the expression of human AT genes and therefore may foster strategies aimed at improving AT function in metabolic diseases.

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

“…Total RNA was extracted from abdominal subcutaneous AT needle biopsies as previously described (23). Gene expression was then examined by using 100-nucleotide long paired-end RNA sequencing with an Illumina HiSeq 2000 of libraries prepared by using the Illumina TruSeq kit following the manufacturer's standard protocols.…”

“…Complementary DNA was prepared from 500 ng total RNA and processed by using the Biomark HD system with 96.96 Dynamic Array IFC (BioMark) and TaqMan assays (Applied Biosystems) following the protocol described by Viguerie et al (23). Raw data were obtained by using the Fluidigm software (Real-time PCR Analysis V4.1.1; BioMark) and were checked by using the graphical representation of plate layout.…”

Fatty acids from fat cell lipolysis do not activate an inflammatory response but are stored as triacylglycerols in adipose tissue macrophages

“…Measurements of mRNA levels Total RNA extraction and mRNA quantification was performed as previously described [21,22]. Values were normalised to the levels of 18S rRNA (Applied Biosystems, Foster City, CA, USA) for murine epididymal adipose tissue, proteasome 26S subunit ATPase 4 (Applied Biosystems) for THP-1 cells and LDL receptorrelated protein 10 (Applied Biosystems) for hMADS cells.…”

Inferring Networks from Multiple Samples with Consensus LASSO

“…It has to be noted that pairs of variables that were never selected over the 100 bootstrap samples have been removed from the histogram (it corresponded to approximately 70% of the 221×220 potential edges). Other shared edges are highly probable, such as the one between AZGP1 and GPD1L which are two known biomarkers of the metabolic syndrome [24]. However, quite interestingly, at least one condition specific edge is also expected: the genes PCK2 and CIDEA are the best biomarkers, among this set of genes, for the weight loss, and the main difference between the two conditions is indeed the weight loss [24].…”

“…As an application to a real biological data set, we analyzed gene expression data described in [24]. More precisely, the expression of 221 genes are used.…”