Characterization of a novel non-steroidal glucocorticoid receptor agonist optimized for topical treatment.

Glucocorticoids (GCs) are commonly used topical treatments for skin diseases but are associated with both local and systemic side effects. In this study, we describe a selective non-steroidal glucocorticoid receptor (GR) agonist for topical use, LEO 134310, which is rapidly deactivated in the blood resulting in low systemic exposure and a higher therapeutic index in the TPA-induced skin inflammation mouse model compared with betamethasone valerate (BMV) and clobetasol propionate (CP). Selectivity of LEO 134310 for GR was confirmed within a panel of nuclear receptors, including the mineralocorticoid receptor (MR), which has been associated with induction of skin atrophy.

Identifying and mapping cell-type-specific chromatin programming of gene expression.

A problem of substantial interest is to systematically map variation in chromatin structure to gene-expression regulation across conditions, environments, or differentiated cell types. We developed and applied a quantitative framework for determining the existence, strength, and type of relationship between high-resolution chromatin structure in terms of DNaseI hypersensitivity and genome-wide gene-expression levels in 20 diverse human cell types. We show that ∼25% of genes show cell-type-specific expression explained by alterations in chromatin structure.

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.

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.

Asap: a framework for over-representation statistics for transcription factor binding sites.

Background: In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a common regulatory regime based on the occurrence of the modelled transcription factor binding sites. However there is little or no information available for guiding the end users choice of method.

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.