High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatm Show more
High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202. From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values. This analysis included 1181 patients. At P less than 1.5×10, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait. Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials' datasets for genetic associations. Show less
To orchestrate the genomic response to cellular stress signals, p53 recognizes and binds to DNA containing specific and well-characterized p53-responsive elements (REs). Differences in RE sequences ca Show more
To orchestrate the genomic response to cellular stress signals, p53 recognizes and binds to DNA containing specific and well-characterized p53-responsive elements (REs). Differences in RE sequences can strongly affect the p53 transactivation capacity and occur even between closely related species. Therefore, the identification and characterization of a species-specific p53 Binding sistes (BS) consensus sequence and of the associated target genes may help to provide new insights into the evolution of the p53 regulatory networks across different species. Although p53 functions were studied in a wide range of species, little is known about the p53-mediated transcriptional signature in Danio rerio. Here, we designed and biochemically validated a computational approach to identify novel p53 target genes in Danio rerio genome. Screening all the Danio rerio genome by pattern-matching-based analysis, we found p53 RE-like patterns proximal to 979 annotated Danio rerio genes. Prioritization analysis identified a subset of 134 candidate pattern-related genes, 31 of which have been investigated in further biochemical assays. Our study identified runx1, axin1, traf4a, hspa8, col4a5, necab2, and dnajc9 genes as novel direct p53 targets and 12 additional p53-controlled genes in Danio rerio genome. The proposed combinatorial approach resulted to be highly sensitive and robust for identifying new p53 target genes also in additional animal species. Show less