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Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.

RNA polymerase II (RNAPII) pausing/termination shortly after initiation is a hallmark of gene regulation. Here, we show that negative elongation factor (NELF) interacts with Integrator complex subunits (INTScom), RNAPII and Spt5. The interaction between NELF and INTScom subunits is RNA and DNA independent. Using both human immunodeficiency virus type 1 promoter and genome-wide analyses, we demonstrate that Integrator subunits specifically control NELF-mediated RNAPII pause/release at coding genes. The strength of RNAPII pausing is determined by the nature of the NELF-associated INTScom subunits. Interestingly, in addition to controlling RNAPII pause-release INTS11 catalytic subunit of the INTScom is required for RNAPII processivity. Finally, INTScom target genes are enriched in human immunodeficiency virus type 1 transactivation response element/NELF binding element and in a 3' box sequence required for small nuclear RNA biogenesis. Revealing these unexpected functions of INTScom in regulating RNAPII pause-release and completion of mRNA synthesis of NELF-target genes will contribute to our understanding of the gene expression cycle.

SUMMARY Eukaryotic chromosomes are partitioned into topologically associating domains (TADs) that are demarcated by distinct insulator-binding proteins (IBPs) in Drosophila. Whether IBPs regulate specific long-range contacts and how this may impact gene expression remains unclear. Here we identify ‘indirect peaks’ of multiple IBPs, that represent their distant sites of interactions through long-range contacts. Indirect peaks depend on protein-protein interactions among multiple IBPs and their common co-factors, including CP190, as confirmed by high-resolution analyses of long-range contacts. Mutant IBPs unable to interact with CP190 impair long-range contacts as well as the expression of hundreds of distant genes that are specifically flanked by indirect peaks. Regulation of distant genes strongly correlates with RNAPII pausing, highlighting how this key transcriptional stage may trap insulator-based long-range interactions. Our data illustrate how indirect peaks may decipher gene regulatory networks through specific long-range interactions.

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Background: In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.

Apelin, an adipocyte-secreted factor upregulated by insulin, is increased in adipose tissue (AT) and plasma with obesity. Apelin was recently identified as a new player in the control of glucose homeostasis. However, the regulation of apelin and APJ (apelin receptor) expression in skeletal muscle in relation to insulin resistance or type 2 diabetes is not known. Thus we studied apelin and APJ expression in AT and muscle in different mice models of obesity and in type 2 diabetic patients. In insulin-resistant high-fat (HF)-fed mice, apelin and APJ expression were increased in AT compared with control. This was not the case in AT of highly insulin-resistant db/ db mice. In skeletal muscle, apelin expression was similar in control and HF-fed mice and decreased in db/ db mice. APJ expression was decreased in both HF-fed and db/ db mice. Control subjects and type 2 diabetic patients were subjected to a hyperinsulinemic-euglycemic clamp, and tissues biopsies were obtained before and at the end of the clamp. There was no significant difference in basal apelin and APJ expression in AT and muscle between control and diabetic patients. However, apelin plasma levels were significantly increased in diabetic patients. During the clamp, hyperinsulinemia increased apelin and APJ expression in AT of control but not in diabetic subjects. In muscle, only APJ mRNA levels were increased in control but also in diabetic patients. Taken together, these data show that apelin and APJ expression in mice and humans is regulated in a tissue-dependent manner and according to the severity of insulin resistance.

Changes in lifestyle are suspected to have strongly influenced the current obesity epidemic. Based on recent experimental, clinical, and epidemiological work, it has been proposed that some food contaminants may exert damaging effects on endocrine and metabolic functions, thereby promoting obesity and associated metabolic diseases such as nonalcoholic fatty liver disease (NAFLD). In this work, we investigated the effect of one suspicious food contaminant, bisphenol A (BPA), in vivo. We used a transcriptomic approach in male CD1 mice exposed for 28 days to different doses of BPA (0, 5, 50, 500, and 5,000 lg/kg/day) through food contamination. Data analysis revealed a specific impact of low doses of BPA on the hepatic transcriptome, more particularly on genes involved in lipid synthesis. Strikingly, the effect of BPA on the expression of de novo lipogenesis followed a nonmonotonic dose-response curve, with more important effects at lower doses than at the higher dose. In addition to lipogenic enzymes (Acc, Fasn, Scd1), the expression of transcription factors such as liver X Receptor, the sterol regulatory element binding protein-1c, and the carbohydrate responsive element binding protein that govern the expression of lipogenic genes also followed a nonmonotonic dose-response curve in response to BPA. Consistent with an increased fatty acid biosynthesis, determination of fat in the liver showed an accumulation of cholesteryl esters and of triglycerides. Conclusion: Our work suggests that exposure to low BPA doses may influence de novo fatty acid synthesis through increased expression of lipogenic genes, thereby contributing to hepatic steatosis. Exposure to such contaminants should be carefully examined in the etiology of metabolic diseases such as NAFLD and nonalcoholic steatohepatitis. (HEPATOLOGY 2012;55:395-407) C hanges in diet and lifestyle are leading causes for the emergence of the metabolic diseases associated with obesity. Recently, the hypothesis that a number of food contaminants acting as endocrine-disrupting chemicals may influence metabolic diseases has been proposed. 1 Bisphenol A (BPA) is an endocrine disruptor highly prevalent in our environment. It is used as the monomer of polycarbonate plastics and epoxy resins. 2 The human population is widely exposed to low levels of BPA, primarily by way of the diet by migration from food and beverage containers. 2 93% of urine samples Abbreviations: Acc, acetyl-CoA carboxylase; BPA, bisphenol A; ER, estrogen receptor; FA, fatty acid; Fasn, fatty acid synthase; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PPAR, peroxisome proliferator-activated receptor; Scd1, stearoyl-CoA desaturase-1.From the