Chemogenetic fingerprinting by analysis of cellular growth dynamics.

BACKGROUND: A fundamental goal in chemical biology is the elucidation of on- and off-target effects of drugs and biocides. To this aim chemogenetic screens that quantify drug induced changes in cellular fitness, typically taken as changes in composite growth, is commonly applied. RESULTS: Using the model organism Saccharomyces cerevisiae we here report that resolving cellular growth dynamics into its individual components, growth lag, growth rate and growth efficiency, increases the predictive power of chemogenetic screens. Both in terms of drug-drug and gene-drug interactions did the individual growth variables capture distinct and only partially overlapping aspects of cell physiology. In fact, the impact on cellular growth dynamics represented functionally distinct chemical fingerprints. DISCUSSION: Our findings suggest that the resolution and quantification of all facets of growth increases the informational and interpretational output of chemogenetic screening. Hence, by facilitating a physiologically more complete analysis of gene-drug and drug-drug interactions the here reported results may simplify the assignment of mode-of-action to orphan bioactive compounds.

Polymorphisms associated with cholesterol and risk of cardiovascular events.

BACKGROUND: Common single-nucleotide polymorphisms (SNPs) that are associated with blood low-density lipoprotein (LDL) or high-density lipoprotein (HDL) cholesterol modestly affect lipid levels. We tested the hypothesis that a combination of such SNPs contributes to the risk of cardiovascular disease. METHODS: We studied SNPs at nine loci in 5414 subjects from the cardiovascular cohort of the Malmö Diet and Cancer Study. We first validated the association between SNPs and either LDL or HDL cholesterol and subsequently created a genotype score on the basis of the number of unfavorable alleles. We used Cox proportional-hazards models to determine the time to the first cardiovascular event in relation to the genotype score. RESULTS: All nine SNPs showed replication of an association with levels of either LDL or HDL cholesterol. With increasing genotype scores, the level of LDL cholesterol increased from 152 mg to 171 mg per deciliter (3.9 to 4.4 mmol per liter), whereas HDL cholesterol decreased from 60 mg to 51 mg per deciliter (1.6 to 1.3 mmol per liter). During follow-up (median, 10.6 years), 238 subjects had a first cardiovascular event. The genotype score was associated with incident cardiovascular disease in models adjusted for covariates including baseline lipid levels (P<0.001). The use of the genotype score did not improve the clinical risk prediction, as assessed by the C statistic. However, there was a significant improvement in risk classification with the use of models that included the genotype score, as compared with those that did not include the genotype score. CONCLUSIONS: A genotype score of nine validated SNPs that are associated with modulation in levels of LDL or HDL cholesterol was an independent risk factor for incident cardiovascular disease. The score did not improve risk discrimination but did modestly improve clinical risk reclassification for individual subjects beyond standard clinical factors.

Variation in GYS1 interacts with exercise and gender to predict cardiovascular mortality.

BACKGROUND: The muscle glycogen synthase gene (GYS1) has been associated with type 2 diabetes (T2D), the metabolic syndrome (MetS), male myocardial infarction and a defective increase in muscle glycogen synthase protein in response to exercise. We addressed the questions whether polymorphism in GYS1 can predict cardiovascular (CV) mortality in a high-risk population, if this risk is influenced by gender or physical activity, and if the association is independent of genetic variation in nearby apolipoprotein E gene (APOE). METHODOLOGY/PRINCIPAL FINDINGS: Polymorphisms in GYS1 (XbaIC>T) and APOE (-219G>T, epsilon2/epsilon3/epsilon4) were genotyped in 4,654 subjects participating in the Botnia T2D-family study and followed for a median of eight years. Mortality analyses were performed using Cox proportional-hazards regression. During the follow-up period, 749 individuals died, 409 due to CV causes. In males the GYS1 XbaI T-allele (hazard ratio (HR) 1.9 [1.2-2.9]), T2D (2.5 [1.7-3.8]), earlier CV events (1.7 [1.2-2.5]), physical inactivity (1.9 [1.2-2.9]) and smoking (1.5 [1.0-2.3]) predicted CV mortality. The GYS1 XbaI T-allele predicted CV mortality particularly in physically active males (HR 1.7 [1.3-2.0]). Association of GYS1 with CV mortality was independent of APOE (219TT/epsilon4), which by its own exerted an effect on CV mortality risk in females (2.9 [1.9-4.4]). Other independent predictors of CV mortality in females were fasting plasma glucose (1.2 [1.1-1.2]), high body mass index (BMI) (1.0 [1.0-1.1]), hypertension (1.9 [1.2-3.1]), earlier CV events (1.9 [1.3-2.8]) and physical inactivity (1.9 [1.2-2.8]). CONCLUSIONS/SIGNIFICANCE: Polymorphisms in GYS1 and APOE predict CV mortality in T2D families in a gender-specific fashion and independently of each other. Physical exercise seems to unmask the effect associated with the GYS1 polymorphism, rendering carriers of the variant allele less susceptible to the protective effect of exercise on the risk of CV death, which finding could be compatible with a previous demonstration of defective increase in the glycogen synthase protein in carriers of this polymorphism.

Predictors of and longitudinal changes in insulin sensitivity and secretion preceding onset of type 2 diabetes.

Identification of individuals at high risk of developing type 2 diabetes is a prerequisite for prevention of the disease. We therefore studied risk factors predicting type 2 diabetes in the Botnia Study in Western Finland. A total of 2,115 nondiabetic individuals were prospectively followed with repeated oral glucose tolerance tests. After a median follow-up of 6 years, 127 (6%) subjects developed diabetes. A family history of diabetes (hazard ratio [HR] 2.2, P = 0.008), BMI (HR for comparison of values below or above the median 2.1, P < 0.001), waist-to-height index (2.3, P < 0.001), insulin resistance (2.1, P = 0.0004), and beta-cell function adjusted for insulin resistance (2.7, P < 0.0001) predicted diabetes. Marked deterioration in beta-cell function with modest changes in insulin sensitivity was observed during the transition to diabetes. The combination of FPG > or =5.6 mmol/l, BMI > or =30 kg/m(2), and family history of diabetes was a strong predictor of diabetes (3.7, P < 0.0001). Of note, using FPG > or =6.1 mmol/l or 2-h glucose > or =7.8 mmol/l did not significantly improve prediction of type 2 diabetes. In conclusion, a marked deterioration in beta-cell function precedes the onset of type 2 diabetes. These individuals can be identified early by knowledge of FPG, BMI, and family history of diabetes.

Genetic prediction of future type 2 diabetes.

BACKGROUND: Type 2 diabetes (T2D) is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease. A number of common variants have been associated with T2D but our knowledge of their ability to predict T2D prospectively is limited. METHODS AND FINDINGS: By using a Cox proportional hazard model, common variants in the PPARG (P12A), CAPN10 (SNP43 and 44), KCNJ11 (E23K), UCP2 (-866G>A), and IRS1 (G972R) genes were studied for their ability to predict T2D in 2,293 individuals participating in the Botnia study in Finland. After a median follow-up of 6 y, 132 (6%) persons developed T2D. The hazard ratio for risk of developing T2D was 1.7 (95% confidence interval [CI] 1.1-2.7) for the PPARG PP genotype, 1.5 (95% CI 1.0-2.2) for the CAPN10 SNP44 TT genotype, and 2.6 (95% CI 1.5-4.5) for the combination of PPARG and CAPN10 risk genotypes. In individuals with fasting plasma glucose > or = 5.6 mmol/l and body mass index > or = 30 kg/m(2), the hazard ratio increased to 21.2 (95% CI 8.7-51.4) for the combination of the PPARG PP and CAPN10 SNP43/44 GG/TT genotypes as compared to those with the low-risk genotypes with normal fasting plasma glucose and body mass index < 30 kg/m(2). CONCLUSION: We demonstrate in a large prospective study that variants in the PPARG and CAPN10 genes predict future T2D. Genetic testing might become a future approach to identify individuals at risk of developing T2D.