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author:("nevski, fragi")
1.  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.
doi:10.1186/1472-6769-8-3
PMCID: PMC2532679  PMID: 18721464
2.  Variation in GYS1 Interacts with Exercise and Gender to Predict Cardiovascular Mortality 
PLoS ONE  2007;2(3):e285.
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, ε2/ε3/ε4) 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/ε4), 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.
doi:10.1371/journal.pone.0000285
PMCID: PMC1805686  PMID: 17356695
3.  Authors' Reply 
PLoS Medicine  2006;3(2):e127.
doi:10.1371/journal.pmed.0030127
PMCID: PMC1388071
4.  Genetic Prediction of Future Type 2 Diabetes 
PLoS Medicine  2005;2(12):e345.
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 ≥ 5.6 mmol/l and body mass index ≥ 30 kg/m2, 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/m2.
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.
In a large prospective study, Lyssenko and colleagues show that variants in the PPARG and CAPN10 genes can help predict whether a person will develop Type 2 diabetes.
doi:10.1371/journal.pmed.0020345
PMCID: PMC1274281  PMID: 17570749

Results 1-4 (4)