Mice lacking Peroxisome Proliferator-Activated Receptor γ2 (PPARγ2) have unexpectedly normal glucose tolerance and mild insulin resistance. Mice lacking PPARγ2 were found to have elevated levels of Lipocalin prostaglandin D synthase (L-PGDS) expression in BAT and subcutaneous white adipose tissue (WAT). To determine if induction of L-PGDS was compensating for a lack of PPARγ2, we crossed L-PGDS KO mice to PPARγ2 KO mice to generate Double Knock Out mice (DKO). Using DKO mice we demonstrated a requirement of L-PGDS for maintenance of subcutaneous WAT (scWAT) function. In scWAT, DKO mice had reduced expression of thermogenic genes, the de novo lipogenic program and the lipases ATGL and HSL. Despite the reduction in markers of lipolysis in scWAT, DKO mice had a normal metabolic rate and elevated serum FFA levels compared to L-PGDS KO alone. Analysis of intra-abdominal white adipose tissue (epididymal WAT) showed elevated expression of mRNA and protein markers of lipolysis in DKO mice, suggesting that DKO mice may become more reliant on intra-abdominal WAT to supply lipid for oxidation. This switch in depot utilisation from subcutaneous to epididymal white adipose tissue was associated with a worsening of whole organism metabolic function, with DKO mice being glucose intolerant, and having elevated serum triglyceride levels compared to any other genotype. Overall, L-PGDS and PPARγ2 coordinate to regulate carbohydrate and lipid metabolism.

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.

The authors describe a new approach to studying cellular lipid profiles and propose a compensatory mechanism that may help maintain the normal membrane function of adipocytes in the context of obesity.

Identification of early mechanisms that may lead from obesity towards complications such as metabolic syndrome is of great interest. Here we performed lipidomic analyses of adipose tissue in twin pairs discordant for obesity but still metabolically compensated. In parallel we studied more evolved states of obesity by investigating a separated set of individuals considered to be morbidly obese. Despite lower dietary polyunsaturated fatty acid intake, the obese twin individuals had increased proportions of palmitoleic and arachidonic acids in their adipose tissue, including increased levels of ethanolamine plasmalogens containing arachidonic acid. Information gathered from these experimental groups was used for molecular dynamics simulations of lipid bilayers combined with dependency network analysis of combined clinical, lipidomics, and gene expression data. The simulations suggested that the observed lipid remodeling maintains the biophysical properties of lipid membranes, at the price, however, of increasing their vulnerability to inflammation. Conversely, in morbidly obese subjects, the proportion of plasmalogens containing arachidonic acid in the adipose tissue was markedly decreased. We also show by in vitro Elovl6 knockdown that the lipid network regulating the observed remodeling may be amenable to genetic modulation. Together, our novel approach suggests a physiological mechanism by which adaptation of adipocyte membranes to adipose tissue expansion associates with positive energy balance, potentially leading to higher vulnerability to inflammation in acquired obesity. Further studies will be needed to determine the cause of this effect.

Author Summary

Obesity is characterized by excess body fat, which is predominantly stored in the adipose tissue. When adipose tissue expands too much it stops storing lipid appropriately. The excess lipid accumulates in organs such as muscle, liver, and pancreas, causing metabolic disease. In this study, we aim to identify factors that cause adipose tissue to malfunction when it reaches its limit of expansion. We performed lipidomic analyses of human adipose tissue in twin pairs discordant for obesity—that is, one of the twins was lean and one was obese—but still metabolically healthy. We identified multiple changes in membrane phospholipids. Using computer modeling, we show that “lean” and “obese” membrane lipid compositions have the same physical properties despite their different compositions. We hypothesize that this represents allostasis—changes in lipid membrane composition in obesity occur to protect the physical properties of the membranes. However, protective changes cannot occur without a cost, and accordingly we demonstrate that switching to the “obese” lipid composition is associated with higher levels of adipose tissue inflammation. In a separate group of metabolically unhealthy obese individuals we investigated how the processes that regulate the “lean” and “obese” lipid profiles are changed. To determine how these lipid membrane changes are regulated we constructed an in silico network model that identified key control points and potential molecular players. We validated this network by performing genetic manipulations in cell models. Therapeutic targeting of this network may open new opportunities for the prevention or treatment of obesity-related metabolic complications.