A Computational Approach to Finding Novel Targets for Existing Drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects

Candidate genetic analysis of plasma high-density lipoprotein-cholesterol and severity of coronary atherosclerosis

<p>Abstract</p> <p>Background</p> <p>Plasma level of high-density lipoprotein-cholesterol (HDL-C), a heritable trait, is an important determinant of susceptibility to atherosclerosis. Non-synonymous and regulatory single nucleotide polymorphisms (SNPs) in genes implicated in HDL-C synthesis and metabolism are likely to influence plasma HDL-C, apolipoprotein A-I (apo A-I) levels and severity of coronary atherosclerosis.</p> <p>Methods</p> <p>We genotyped 784 unrelated Caucasian individuals from two sets of populations (Lipoprotein and Coronary Atherosclerosis Study- LCAS, N = 333 and TexGen, N = 451) for 94 SNPs in 42 candidate genes by 5' nuclease assays. We tested the distribution of the phenotypes by the Shapiro-Wilk normality test. We used Box-Cox regression to analyze associations of the non-normally distributed phenotypes (plasma HDL-C and apo A-I levels) with the genotypes. We included sex, age, body mass index (BMI), diabetes mellitus (DM), and cigarette smoking as covariates. We calculated the q values as indicators of the false positive discovery rate (FDR).</p> <p>Results</p> <p>Plasma HDL-C levels were associated with sex (higher in females), BMI (inversely), smoking (lower in smokers), DM (lower in those with DM) and SNPs in <it>APOA5, APOC2</it>, <it>CETP, LPL </it>and <it>LIPC </it>(each q ≤0.01). Likewise, plasma apo A-I levels, available in the LCAS subset, were associated with SNPs in <it>CETP</it>, <it>APOA5</it>, and <it>APOC2 </it>as well as with BMI, sex and age (all q values ≤0.03). The <it>APOA5 </it>variant S19W was also associated with minimal lumen diameter (MLD) of coronary atherosclerotic lesions, a quantitative index of severity of coronary atherosclerosis (q = 0.018); mean number of coronary artery occlusions (p = 0.034) at the baseline and progression of coronary atherosclerosis, as indicated by the loss of MLD.</p> <p>Conclusion</p> <p>Putatively functional variants of <it>APOA2</it>, <it>APOA5, APOC2</it>, <it>CETP, LPL</it>, <it>LIPC </it>and <it>SOAT2 </it>are independent genetic determinants of plasma HDL-C levels. The non-synonymous S19W SNP in <it>APOA5 </it>is also an independent determinant of plasma apo A-I level, severity of coronary atherosclerosis and its progression.</p

Multi-Ethnic Analysis of Lipid-Associated Loci: The NHLBI CARe Project

Background: Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities. Methodology/Principal Findings: We tested a set of $\sim$50,000 polymorphisms from $\sim$2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed. Conclusions/Significance: We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans

High fat diet modifies the association of lipoprotein lipase gene polymorphism with high density lipoprotein cholesterol in an Asian Indian population

Background Single nucleotide polymorphisms (SNPs) in lipoprotein lipase gene (LPL) have been shown to influence metabolism related to lipid phenotypes. Dietary factors have been shown to modify the association between LPL SNPs and lipids; however, to date, there are no studies in South Asians. Hence, we tested for the association of four common LPL SNPs with plasma lipids and examined the interactions between the SNPs and dietary factors on lipids in 1,845 Asian Indians. Methods The analysis was performed in 788 Type 2 diabetes cases and 1,057 controls randomly chosen from the cross-sectional Chennai Urban Rural Epidemiological Study. Serum triacylglycerol (TAG), serum total cholesterol, and high-density lipoprotein cholesterol (HDL-C) were measured using a Hitachi-912 autoanalyzer (Roche Diagnostics GmbH, Mannheim, Germany). Dietary intake was assessed using a semi-quantitative food frequency questionnaire. The SNPs (rs1121923, rs328, rs4922115 and rs285) were genotyped by polymerase chain reaction followed by restriction enzyme digestion and 20% of samples were sequenced to validate the genotypes obtained. Statistical Package for Social Sciences for Windows version 22.0 (SPSS, Chicago, IL) was used for statistical analysis. Results After correction for multiple testing and adjusting for potential confounders, SNPs rs328 and rs285 showed association with HDL-C (P = 0.0004) and serum TAG (P = 1×10−5), respectively. The interaction between SNP rs1121923 and fat intake (energy %) on HDL-C (P = 0.003) was also significant, where, among those who consumed a high fat diet (28.4 ± 2.5%), the T allele carriers (TT + XT) had significantly higher HDL-C concentrations (P = 0.0002) and 30% reduced risk of low HDL-C levels compared to the CC homozygotes. None of the interactions on other lipid traits were statistically significant. Conclusion Our findings suggest that individuals carrying T allele of the SNP rs1121923 have increased HDL-C levels when consuming a high fat diet compared to CC homozygotes. Our finding warrants confirmation in prospective studies and randomized controlled trials

Genetic variants associated with fasting blood lipids in the U.S. population: Third National Health and Nutrition Examination Survey

<p>Abstract</p> <p>Background</p> <p>The identification of genetic variants related to blood lipid levels within a large, population-based and nationally representative study might lead to a better understanding of the genetic contribution to serum lipid levels in the major race/ethnic groups in the U.S. population.</p> <p>Methods</p> <p>Using data from the second phase (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III), we examined associations between 22 polymorphisms in 13 candidate genes and four serum lipids: high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG). Univariate and multivariable linear regression and within-gene haplotype trend regression were used to test for genetic associations assuming an additive mode of inheritance for each of the three major race/ethnic groups in the United States (non-Hispanic white, non-Hispanic black, and Mexican American).</p> <p>Results</p> <p>Variants within <it>APOE </it>(rs7412, rs429358), <it>PON1 </it>(rs854560), <it>ITGB3 </it>(rs5918), and <it>NOS3 </it>(rs2070744) were found to be associated with one or more blood lipids in at least one race/ethnic group in crude and adjusted analyses. In non-Hispanic whites, no individual polymorphisms were associated with any lipid trait. However, the <it>PON1 </it>A-G haplotype was significantly associated with LDL-C and TC. In non-Hispanic blacks, <it>APOE </it>variant rs7412 and haplotype T-T were strongly associated with LDL-C and TC; whereas, rs5918 of <it>ITGB3 </it>was significantly associated with TG. Several variants and haplotypes of three genes were significantly related to lipids in Mexican Americans: <it>PON1 </it>in relation to HDL-C; <it>APOE </it>and <it>NOS3 </it>in relation to LDL-C; and <it>APOE </it>in relation to TC.</p> <p>Conclusions</p> <p>We report the significant associations of blood lipids with variants and haplotypes in <it>APOE</it>, <it>ITGB3, NOS3</it>, and <it>PON1 </it>in the three main race/ethnic groups in the U.S. population using a large, nationally representative and population-based sample survey. Results from our study contribute to a growing body of literature identifying key determinants of plasma lipoprotein concentrations and could provide insight into the biological mechanisms underlying serum lipid and cholesterol concentrations.</p

Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts.

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function