PPAR gamma 2 prevents lipotoxicity by controlling adipose tissue expandability and peripheral lipid metabolism.

Peroxisome proliferator activated receptor gamma 2 (PPARg2) is the nutritionally regulated isoform of PPARg. Ablation of PPARg2 in the ob/ob background, PPARg2(-/-) Lep(ob)/Lep(ob) (POKO mouse), resulted in decreased fat mass, severe insulin resistance, beta-cell failure, and dyslipidaemia. Our results indicate that the PPARg2 isoform plays an important role, mediating adipose tissue expansion in response to positive energy balance. Lipidomic analyses suggest that PPARg2 plays an important antilipotoxic role when induced ectopically in liver and muscle by facilitating deposition of fat as relatively harmless triacylglycerol species and thus preventing accumulation of reactive lipid species. Our data also indicate that PPARg2 may be required for the beta-cell hypertrophic adaptive response to insulin resistance. In summary, the PPARg2 isoform prevents lipotoxicity by (a) promoting adipose tissue expansion, (b) increasing the lipid-buffering capacity of peripheral organs, and (c) facilitating the adaptive proliferative response of beta-cells to insulin resistance.

Pathways to the analysis of microarray data.

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.

Transcript and metabolite analysis of the effects of tamoxifen in rat liver reveals inhibition of fatty acid synthesis in the presence of hepatic steatosis.

Nonalcoholic steatohepatitis (NASH) is a common feature of the metabolic syndrome and toxic reactions to pharmacological drugs. Tamoxifen, (TMX) a widely used anti-breast cancer drug, can induce NASH and changes in plasma cholesterol levels through mechanisms that are unclear. We studied primary actions of TMX using a short-term treatment (5 days) that induces microvesicular hepatic steatosis and marked hypercholesterolemia in male rats. Using a combined approach of gene expression profiling and NMR-based metabolite analysis, we found that TMX-treated livers have increased saturated fatty acid content despite changes in gene expression, indicating decreased de novo lipogenesis and increased fatty acid oxidation. Our results show that TMX predominantly down-regulates FAS expression and activity as indicated by the accumulation of malonyl-CoA, a known inhibitor of mitochondrial beta-oxidation. In the face of a continued supply of exogenous free fatty acids, the blockade of fatty acid oxidation produced by elevated malonyl-CoA is likely to be the major factor leading to steatosis. Use of a combination of metabolomic and transcriptomic analysis has allowed us to identify mechanisms underlying important metabolic side effects of a widely prescribed drug. Given the broader importance of hepatic steatosis, the novel molecular mechanism revealed in this study should be examined in other forms of steatosis and nonalcoholic steatohepatitis.

Analysing microarray data using modular regulation analysis.

MOTIVATION: Microarray experiments measure complex changes in the abundance of many mRNAs under different conditions. Current analysis methods cannot distinguish between direct and indirect effects on expression, or calculate the relative importance of mRNAs in effecting responses. RESULTS: Application of modular regulation analysis to microarray data reveals and quantifies which mRNA changes are important for cellular responses. The mRNAs are clustered, and then we calculate how perturbations alter each cluster and how strongly those clusters affect an output response. The product of these values quantifies how an input changes a response through each cluster. Two published datasets are analysed. Two mRNA clusters transmit most of the response of yeast doubling time to galactose; one contains mainly galactose metabolic genes, and the other a regulatory gene. Analysis of the response of yeast relative fitness to 2-deoxy-D-glucose reveals that control is distributed between several mRNA clusters, but experimental error limits statistical significance.

Control analysis of DNA microarray expression data.

DNA microarrays produce large amounts of data. Complex changes in gene expression are revealed; sometimes thousands of mRNAs change between experiments. Here we apply modular regulation analysis to microarray data to reveal and quantify the mRNA changes that are important for cellular responses. The mRNAs are sorted into clusters. How strongly a perturbation alters each cluster is multiplied by how strongly each cluster affects an output, to obtain coefficients that describe how much of the change in the output is transmitted through each mRNA cluster. An example published dataset is analysed to reveal that the response ('relative fitness') of yeast to 2-deoxy-D-glucose is not transmitted by a single mRNA cluster, but instead many clusters contribute to the overall response. The method is applicable to microarray, transcriptome, proteome and metabolome data.

Simplifying metabolic complexity.

A complete description of the regulation of metabolism in even a single cell will be very hard to achieve, enormous and indigestible. However, there are two powerful ways to simplify the complexity. Firstly, related processes and intermediates can be grouped into a small number of modules, and the regulation of the simplified system can be studied. Secondly, control analysis can be used. With these simplifications to illuminate the important regulatory features, even a full description could be made intellectually and experimentally accessible without distorting the essential regulatory features. Modular control analysis is powerful because it can quantify the relative importance of different flows of regulatory information through any metabolic, physiological, signalling or transcriptional network. It can answer global questions about the importance of different pathways mediating any change to a system. It has been used to analyse how cadmium, a poison with multiple effects, changes oxidative phosphorylation in isolated mitochondria, and to quantify the regulation of energy metabolism in hepatocytes. It has been used to measure how energy metabolism is regulated during mitogen stimulation of thymocytes, quantifying the relative importance of different signalling pathways and how each pathway contributes to the activation of energy metabolism. Recently, we have applied modular control analysis to modern DNA microarray expression profiling to measure the importance of different groups of mRNA transcripts in mediating physiological responses.