Indazole-based ligands for estrogen-related receptor α as potential anti-diabetic agents.

Estrogen-related receptor α (ERRα) is an orphan nuclear receptor that has been functionally implicated in the regulation of energy homeostasis. Herein is described the development of indazole-based N-alkylthiazolidenediones, which function in biochemical assays as selective inverse agonists against this receptor. Series optimization provided several potent analogues that inhibited the recruitment of a co-activator peptide fragment in vitro (IC50s < 50 nM) and reduced fasted circulating insulin and triglyceride levels in a sub-chronic pre-diabetic rat model when administered orally (10 mg/kg). A multi-parametric optimization strategy led to the identification of 50 as an advanced lead, which was more extensively evaluated in additional diabetic models. Chronic oral administration of 50 in two murine models of obesity and insulin resistance improved glucose control and reduced circulating triglycerides with efficacies similar to that of rosiglitazone. Importantly, these effects were attained without the concomitant weight gain that is typically observed with the latter agent. Thus, these studies provide additional support for the development of such molecules for the potential treatment of metabolic diseases.

Identification of diaryl ether-based ligands for estrogen-related receptor α as potential antidiabetic agents.

Estrogen-related receptor α (ERRα) is an orphan nuclear receptor that has been functionally implicated in the regulation of energy homeostasis. Herein is described the development of diaryl ether based thiazolidenediones, which function as selective ligands against this receptor. Series optimization provided several potent analogues that inhibit the recruitment of a coactivator peptide fragment in in vitro biochemical assays (IC(50) < 150 nM) and cellular two-hybrid reporter assays against the ligand binding domain (IC(50) = 1-5 µM). A cocrystal structure of the ligand-binding domain of ERRα with lead compound 29 revealed the presence of a covalent interaction between the protein and ligand, which has been shown to be reversible. In diet-induced murine models of obesity and in an overt diabetic rat model, oral administration of 29 normalized insulin and circulating triglyceride levels, improved insulin sensitivity, and was body weight neutral. This provides the first demonstration of functional activities of an ERRα ligand in metabolic animal models.

Experimental validation of predicted mammalian erythroid cis-regulatory modules.

Multiple alignments of genome sequences are helpful guides to functional analysis, but predicting cis-regulatory modules (CRMs) accurately from such alignments remains an elusive goal. We predict CRMs for mammalian genes expressed in red blood cells by combining two properties gleaned from aligned, noncoding genome sequences: a positive regulatory potential (RP) score, which detects similarity to patterns in alignments distinctive for regulatory regions, and conservation of a binding site motif for the essential erythroid transcription factor GATA-1. Within eight target loci, we tested 75 noncoding segments by reporter gene assays in transiently transfected human K562 cells and/or after site-directed integration into murine erythroleukemia cells. Segments with a high RP score and a conserved exact match to the binding site consensus are validated at a good rate (50%-100%, with rates increasing at higher RP), whereas segments with lower RP scores or nonconsensus binding motifs tend to be inactive. Active DNA segments were shown to be occupied by GATA-1 protein by chromatin immunoprecipitation, whereas sites predicted to be inactive were not occupied. We verify four previously known erythroid CRMs and identify 28 novel ones. Thus, high RP in combination with another feature of a CRM, such as a conserved transcription factor binding site, is a good predictor of functional CRMs. Genome-wide predictions based on RP and a large set of well-defined transcription factor binding sites are available through servers at http://www.bx.psu.edu/.

Kullback-Leibler clustering of continuous wavelet transform measures of heart rate variability.

Power spectral analysis of beat-to-beat heart rate variability (HRV) has provided a useful means of understanding the interplay between autonomic and cardiovascular functionality. Despite their utility, commonly employed frequency-domain techniques are limited in their prerequisite for stationary signals and their inability to account for temporal changes in the power spectral and/or frequency properties of signals. The purpose of this study is to develop an algorithm that utilizes continuous wavelet transform (CWT) parameters as inputs to a Kohonen self-organizing map (SOM), providing a method of clustering subjects with similar wavelet transform signatures. Continuous interbeat-intervals were recorded (Portapres monitor at 200 Hz) during a perception of affect test in 79 African-American volunteers (ages 21-83), where after a 5-min baseline, participants evaluated emotional expressions in sentences and pictures of faces, followed by a 5-min recovery. Individual HRV biosignals from each session were pre-processed (artifact replacement and signal resampling at 2 Hz) and a CWT was applied (db9 wavelet basis function over 32 scales). Standard deviations of resulting wavelet coefficients at each scale were calculated, normalized, and used as inputs into a SOM with Kullback-Leibler divergence as the dissimilarity measure used for clustering. Differences in subject demographics between two final clusters were assessed via two-independent-groups t-tests or chi-square or Fisher's exact tests of contingency tables. Significant differences were found for age, initial systolic blood pressure, smoking status, and mean s.d. of coefficients in the high frequency band (0.15-0.4 Hz). These findings may have clinical significance and the developed algorithm provides an alternative means of analyzing HRV data originating from populations with complex covariates.