Association of genetic vatiation within UBL5 with phenotypes of metabolic syndrome

The BEACON gene was initially identified using the differential display polymerase chain reaction on hypothalamic mRNA samples collected from lean and obese Psammomys obesus, a polygenic animal model of obesity. Hypothalamic BEACON gene expression was positively correlated with percentage of body fat, and intracerebroventricular infusion of the Beacon protein resulted in a dose-dependent increase in food intake and body weight. The human homolog of BEACON, UBL5, is located on chromosome 19p in a region previously linked to quantitative traits related to obesity. Our previous studies showed a statistically significant association between UBL5 sequence variation and several obesity- and diabetes-related quantitative physiological measures in Asian Indian and Micronesian cohorts. Here we undertake a replication study in a Mexican American cohort where the original linkage signal was first detected. We exhaustively resequenced the complete gene plus the putative promoter region for genetic variation in 55 individuals and identified five single nucleotide polymorphisms (SNPs), one of which was novel. These SNPs were genotyped in a Mexican American cohort of 900 individuals from 40 families. Using a quantitative trait linkage disequilibrium test, we found significant associations between UBL5 genetic variants and waist-to-hip ratio (p = 0.027), and the circulating concentrations of insulin (p = 0.018) and total cholesterol (p = 0.023) in fasted individuals. These data are consistent with our earlier published studies and further support a functional role for the UBL5 gene in influencing physiological traits that underpin the development of metabolic syndrome.

Control and simulation of robotic swarms in heterogeneous environments

Simulation provides a low cost method of initial testing of control for robotic swarms. The expansion of robotic swarms to heterogeneous environments drives the need to model cooperative operation in those environments. The Autonomous Control Engineering center at The University of Texas at San Antonio is investigating methods of simulation techniques and simulation environments. This paper presents results from adapting simulation tools for diverse environments.

Chemerin is associated with metabolic syndrome phenotypes in a Mexican-American population

Context: Chemerin is a novel adipokine previously associated with metabolic syndrome phenotypes in a small sample of subjects from Mauritius. Objective: The aim of the study was to determine whether plasma chemerin levels were associated with metabolic syndrome phenotypes in a larger sample from a second, unrelated human population. Design, Setting, Patients, and Intervention: Plasma samples were obtained from the San Antonio Family Heart Study (SAFHS), a large family-based genetic epidemiological study including 1431 Mexican-American individuals. Individuals were randomly sampled without regard to phenotype or disease status. This sample is well-characterized for a variety of phenotypes related to the metabolic syndrome. Main Outcomes: Plasma chemerin levels were measured by sandwich ELISA. Linear regression and correlation analyses were used to determine associations between plasma chemerin levels and metabolic syndrome phenotypes. Results: Circulating chemerin levels were significantly higher in nondiabetic subjects with body mass index (BMI) greater than 30 kg/m2 compared with those with a BMI below 25 kg/m2 (P < 0.0001). Plasma chemerin levels were significantly associated with metabolic syndrome-related parameters, including BMI (P < 0.0001), fasting serum insulin (P < 0.0001), triglycerides (P < 0.0001), and high-density lipoprotein cholesterol (P = 0.00014), independent of age and sex in nondiabetic subjects. Conclusion: Circulating chemerin levels were associated with metabolic syndrome phenotypes in a second, unrelated human population. This replicated result using a large human sample suggests that chemerin may be involved in the development of the metabolic syndrome.

A gene expression signature for insulin resistance

Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its aetiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a Gene Expression Signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made ‘insulin resistant’ by treatment with tumour necrosis factor-alpha (TNFα) and then reversed with aspirin and troglitazone (‘re-sensitized’). The GES consisted of five genes whose expression levels best discriminated between the insulin resistant and insulin re-sensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3- L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed using aspirin and troglitazone. This screen identified both known and new insulin sensitizing compounds including non-steroidal anti inflammatory agents, β-adrenergic antagonists, beta-lactams and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study (n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels, P < 0.001). These findings show that GES technology can be used for both the discovery of insulin sensitizing compounds and the characterization of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalized medicine approach to type 2 diabetes.