Genetic variation in the 3'-UTR of CYP1A2, CYP2B6, CYP2D6, CYP3A4, NR1I2, and UGT2B7: potential effects on regulation by microRNA and pharmacogenomics relevance

Abstract

Introduction: Pharmacogenomics research has concentrated on variation in genes coding for drug metabolizing enzymes, transporters and nuclear receptors. However, variation affecting microRNA could also play a role in drug response. This project set out to investigate potential microRNA target sites in 11 genes and the extent of variation in the 3'-UTR of six selected genes; CYP1A2, CYP2B6, CYP2D6, CYP3A4, NR1I2, and UGT2B7.

Methods: Fifteen microRNA target prediction algorithms were used to identify microRNAs predicted to regulate 11 genes. The 3'-UTR of the 6 genes which topped the list of potential microRNA targets was sequenced in 30 black South Africans. In addition, genetic variants within these genes were investigated for interference with mRNA-microRNA interactions. Potential effects of observed variants were determined using in silico prediction tools.

Results: The 11 genes coding for DMEs, transporters and nuclear receptors were predicted to be targets of microRNAs with CYP2B6, NR1I2 (PXR), CYP3A4, and CYP1A2, interacting with the most microRNAs. The majority of identified genetic variants were predicted to interfere with microRNA regulation. For example, the variant, rs1054190C in NR1I2 was predicted to result in the presence of a binding site for the microRNA miR-1250-5p, while the variant rs1054191G was predicted to result in the absence of a recognition site for miR-371b-3p, miR-4258 and miR-4707-3p. Fifteen of the seventeen, novel variants occurred within microRNA target sequences.

Conclusion: The 3'-UTR harbors variation that is likely to influence regulation of specific genes by microRNA. In silico prediction followed by functional validation could aid in decoding the contribution of variation in the 3'-UTR, to some unexplained heritability that affects drug response. Understanding the specific role of each microRNA may lead to identification of markers for targeted therapy and therefore improve personalized drug treatment.

Keywords: bioinformatics prediction; drug metabolizing enzymes; miRSNPs; microRNA; pharmacogenomics; polymorphisms.