A novel selective 11b-hydroxysteroid dehydrogenase type 1 inhibitor prevents human adipogenesis.

Glucocorticoid excess increases fat mass, preferentially within omental depots; yet circulating cortisol concentrations are normal in most patients with metabolic syndrome (MS). At a pre-receptor level, 11b-hydroxysteroid dehydrogenase type 1 (11b-HSD1) activates cortisol from cortisone locally within adipose tissue, and inhibition of 11b-HSD1 in liver and adipose tissue has been proposed as a novel therapy to treat MS by reducing hepatic glucose output and adiposity. Using a transformed human subcutaneous preadipocyte cell line (Chub-S7) and human primary preadipocytes, we have defined the role of glucocorticoids and 11b-HSD1 in regulating adipose tissue differentiation. Human cells were differentiated with 1.0 mM cortisol (F), or cortisone (E) with or without 100 nM of a highly selective 11b-HSD1 inhibitor PF-877423. 11b-HSD1 mRNA expression increased across adipocyte differentiation (P!0.001, nZ4), which was paralleled by an increase in 11b-HSD1 oxo-reductase activity (from nil on day 0 to 5.9G1.9 pmol/mg per h on day 16,P!0.01, nZ7). Cortisone enhanced adipocyte differentiation; fatty acid-binding protein 4 expression increased 312-fold (P!0.001) and glycerol-3-phosphate dehydrogenase 47-fold (P!0.001) versus controls. This was abolished by co-incubation with PF-877423. In addition, cellular lipid content decreased significantly. These findings were confirmed in the primary cultures of human subcutaneous preadipocytes. The increase in 11b-HSD1 mRNA expression and activity is essential for the induction of human adipogenesis. Blocking adipogenesis with a novel and specific 11b-HSD1 inhibitor may represent a novel approach to treat obesity in patients with MS

A novel selective 11β-hydroxysteroid dehydrogenase type 1 inhibitor prevents human adipogenesis

Glucocorticoid excess increases fat mass, preferentially within omental depots; yet circulating cortisol concentrations are normal in most patients with metabolic syndrome (MS). At a pre-receptor level, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) activates cortisol from cortisone locally within adipose tissue, and inhibition of 11β-HSD1 in liver and adipose tissue has been proposed as a novel therapy to treat MS by reducing hepatic glucose output and adiposity. Using a transformed human subcutaneous preadipocyte cell line (Chub-S7) and human primary preadipocytes, we have defined the role of glucocorticoids and 11β-HSD1 in regulating adipose tissue differentiation. Human cells were differentiated with 1·0 μM cortisol (F), or cortisone (E) with or without 100 nM of a highly selective 11β-HSD1 inhibitor PF-877423. 11β-HSD1 mRNA expression increased across adipocyte differentiation (P<0·001, n=4), which was paralleled by an increase in 11β-HSD1 oxo-reductase activity (from nil on day 0 to 5·9±1.9 pmol/mg per h on day 16, P<0·01, n=7). Cortisone enhanced adipocyte differentiation; fatty acid-binding protein 4 expression increased 312-fold (P<0·001) and glycerol-3-phosphate dehydrogenase 47-fold (P<0·001) versus controls. This was abolished by co-incubation with PF-877423. In addition, cellular lipid content decreased significantly. These findings were confirmed in the primary cultures of human subcutaneous preadipocytes. The increase in 11β-HSD1 mRNA expression and activity is essential for the induction of human adipogenesis. Blocking adipogenesis with a novel and specific 11β-HSD1 inhibitor may represent a novel approach to treat obesity in patients with MS

Decoding complex patterns of genomic rearrangement in hepatocellular carcinoma

published_or_final_versio

Functional genomics in chickens:development of integrated-systems microarrays for transcriptional profiling and discovery of regulatory pathways

The genetic networks that govern the differentiation and growth of major tissues of economic importance in the chicken are largely unknown. Under a functional genomics project, our consortium has generated 30 609 expressed sequence tags (ESTs) and developed several chicken DNA microarrays, which represent the Chicken Metabolic/Somatic (10 K) and Neuroendocrine/Reproductive (8 K) Systems (http://udgenome.ags.udel.edu/cogburn/). One of the major challenges facing functional genomics is the development of mathematical models to reconstruct functional gene networks and regulatory pathways from vast volumes of microarray data. In initial studies with liver-specific microarrays (3.1 K), we have examined gene expression profiles in liver during the peri-hatch transition and during a strong metabolic perturbation—fasting and re-feeding—in divergently selected broiler chickens (fast vs. slow-growth lines). The expression of many genes controlling metabolic pathways is dramatically altered by these perturbations. Our analysis has revealed a large number of clusters of functionally related genes (mainly metabolic enzymes and transcription factors) that control major metabolic pathways. Currently, we are conducting transcriptional profiling studies of multiple tissues during development of two sets of divergently selected broiler chickens (fast vs. slow growing and fat vs. lean lines). Transcriptional profiling across multiple tissues should permit construction of a detailed genetic blueprint that illustrates the developmental events and hierarchy of genes that govern growth and development of chickens. This review will briefly describe the recent acquisition of chicken genomic resources (ESTs and microarrays) and our consortium's efforts to help launch the new era of functional genomics in the chicken

Comparative biomarker analysis of PALOMA-2/3 trials for palbociclib.

While cyclin-dependent kinase 4/6 (CDK4/6) inhibitors, including palbociclib, combined with endocrine therapy (ET), are becoming the standard-of-care for hormone receptor-positive/human epidermal growth factor receptor 2‒negative metastatic breast cancer, further mechanistic insights are needed to maximize benefit from the treatment regimen. Herein, we conducted a systematic comparative analysis of gene expression/progression-free survival relationship from two phase 3 trials (PALOMA-2 [first-line] and PALOMA-3 [≥second-line]). In the ET-only arm, there was no inter-therapy line correlation. However, adding palbociclib resulted in concordant biomarkers independent of initial ET responsiveness, with shared sensitivity genes enriched in estrogen response and resistance genes over-represented by mTORC1 signaling and G2/M checkpoint. Biomarker patterns from the combination arm resembled patterns observed in ET in advanced treatment-naive patients, especially patients likely to be endocrine-responsive. Our findings suggest palbociclib may recondition endocrine-resistant tumors to ET, and may guide optimal therapeutic sequencing by partnering CDK4/6 inhibitors with different ETs. Pfizer (NCT01740427; NCT01942135)

The effect of tightly-bound water molecules on scaffold diversity in computer-aided de novo ligand design of CDK2 inhibitors

We have determined the effects that tightly bound water molecules have on the de novo design of cyclin-dependent kinase-2 (CDK2) ligands. In particular, we have analyzed the impact of a specific structural water molecule on the chemical diversity and binding mode of ligands generated through a de novo structure-based ligand generation method in the binding site of CDK2. The tightly bound water molecule modifies the size and shape of the binding site and we have found that it also imposed constraints on the observed binding modes of the generated ligands. This in turn had the indirect effect of reducing the chemical diversity of the underlying molecular scaffolds that were able to bind to the enzyme satisfactorily

Specificity quantification of biomolecular recognition and its implication for drug discovery

Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the “native” protein-ligand complex discriminating against “non-native” binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and “native” binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery

Statistical method on nonrandom clustering with application to somatic mutations in cancer

<p>Abstract</p> <p>Background</p> <p>Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.</p> <p>Results</p> <p>We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and <it>β</it>-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.</p> <p>Conclusions</p> <p>Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.</p