Persistent organic pollutant exposure leads to insulin resistance syndrome.

BACKGROUND: The incidence of the insulin resistance syndrome has increased at an alarming rate worldwide, creating a serious challenge to public health care in the 21st century. Recently, epidemiological studies have associated the prevalence of type 2 diabetes with elevated body burdens of persistent organic pollutants (POPs). However, experimental evidence demonstrating a causal link between POPs and the development of insulin resistance is lacking. OBJECTIVE: We investigated whether exposure to POPs contributes to insulin resistance and metabolic disorders. METHODS: Sprague-Dawley rats were exposed for 28 days to lipophilic POPs through the consumption of a high-fat diet containing either refined or crude fish oil obtained from farmed Atlantic salmon. In addition, differentiated adipocytes were exposed to several POP mixtures that mimicked the relative abundance of organic pollutants present in crude salmon oil. We measured body weight, whole-body insulin sensitivity, POP accumulation, lipid and glucose homeostasis, and gene expression and we performed microarray analysis. RESULTS: Adult male rats exposed to crude, but not refined, salmon oil developed insulin resistance, abdominal obesity, and hepatosteatosis. The contribution of POPs to insulin resistance was confirmed in cultured adipocytes where POPs, especially organochlorine pesticides, led to robust inhibition of insulin action. Moreover, POPs induced down-regulation of insulin-induced gene-1 (Insig-1) and Lpin1, two master regulators of lipid homeostasis. CONCLUSION: Our findings provide evidence that exposure to POPs commonly present in food chains leads to insulin resistance and associated metabolic disorders.

Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE.

Finding and characterizing mRNAs, their transcription start sites (TSS), and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS than previously believed, and deeper sequencing is necessary to detect all active promoters in a given tissue. Here, we present a new method for high-throughput sequencing of 5' cDNA tags-DeepCAGE: merging the Cap Analysis of Gene Expression method with ultra-high-throughput sequence technology. We apply DeepCAGE to characterize 1.4 million sequenced TSS from mouse hippocampus and reveal a wealth of novel core promoters that are preferentially used in hippocampus: This is the most comprehensive promoter data set for any tissue to date. Using these data, we present evidence indicating a key role for the Arnt2 transcription factor in hippocampus gene regulation. DeepCAGE can also detect promoters used only in a small subset of cells within the complex tissue.

Modeling promoter grammars with evolving hidden Markov models.

MOTIVATION: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation. RESULTS: With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk of overfitting and generally improves performance. We apply this method to identify TFBSs and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar. AVAILABILITY: The software and the datasets are available from http://modem.ucsd.edu/won/eHMM.tar.gz

The genome landscape of ERalpha- and ERbeta-binding DNA regions.

In this article, we have applied the ChIP-on-chip approach to pursue a large scale identification of ERalpha- and ERbeta-binding DNA regions in intact chromatin. We show that there is a high degree of overlap between the regions identified as bound by ERalpha and ERbeta, respectively, but there are also regions that are bound by ERalpha only in the presence of ERbeta, as well as regions that are selectively bound by either receptor. Analysis of bound regions shows that regions bound by ERalpha have distinct properties in terms of genome landscape, sequence features, and conservation compared with regions that are bound by ERbeta. ERbeta-bound regions are, as a group, located more closely to transcription start sites. ERalpha- and ERbeta-bound regions differ in sequence properties, with ERalpha-bound regions having an overrepresentation of TA-rich motifs including forkhead binding sites and ERbeta-bound regions having a predominance of classical estrogen response elements (EREs) and GC-rich motifs. Differences in the properties of ER bound regions might explain some of the differences in gene expression programs and physiological effects shown by the respective estrogen receptors.