MicroRNAs as effectors of variable human drug metabolism

描述(申请人提供)：药物基因组学的承诺--第一时间为每个人确定正确的药物剂量--在一定程度上还没有完全实现 因为我们对药物反应可变性背后的机制缺乏完整的了解。最近的研究表明，microRNAs(MiRs)-转录后调节基因表达的非编码小RNA-可能是药物代谢和处置中个体间差异的重要决定因素。在这里，我们建议使用各种新的方法来识别与药物代谢酶变异性有关的MIR。应用的重点将是UDP-葡萄糖醛酸基转移酶(UGT)1A酶，这些酶对于消除许多治疗指数较窄的临床重要药物至关重要。重要的是，所有9种UGT1A酶(总共18种人类UGT)都是由剪接的mRNAs编码的，这些mRNA共享一个相同的3‘-非翻译区(UTR)。虽然我们有证据表明这些酶是由转录后机制共同调节的，但关于miRs在UGT1A表达中的作用几乎一无所知。我们的主要假设是，特定的miRs通过保守的UGT1A-3‘-UTRmRNA区域和/或通过调节调控转录因子水平间接调节UGT1A基因的表达。为了验证这一假设，本文提出了以下具体目标。在目标1中，将通过多种独立的方法，包括生物信息学、功能基因组学筛选和全基因组(miR-转录组)关联研究，确定与UGT1A葡萄糖醛酸化作用相关的候选miRs。我们提出的新的功能基因组学筛选能够从所有已知的人类MIR的慢病毒文库中快速识别抑制荧光标记的MIR 3‘-UTR报告的MIR。这种方法应该在我们的领域之外有广泛的应用，从而实现一种经济有效的快速方法来描述“miR相互作用组”。将评估参考的UGT1A-3‘-UTR等位基因以及与体内胆红素葡萄糖醛酸化作用降低相关的主要变异(*1b)。在人类肝库样本中过表达的具有低UGT1A活性和翻译效率表型的MIR也将通过miR转录组学(阵列和下一代小RNA-SEQ)进行鉴定。在目标2中，我们将通过候选miRs在人类模型细胞系和原代肝细胞中的过度表达(Lenti-miRs)和敲除(Lenti-antagomir)来证实候选miRs对UGT1A葡萄糖醛酸化反应的功能影响。在目标3中，我们将通过构建UGT1A-3‘UTR荧光素酶报告基因，通过研究miR在模型细胞和原代人肝细胞中的过度表达和敲除对特定候选转录因子蛋白(PXR和HNF1)以及整个蛋白质组的影响(iTRAQ LC-MS/MS方法)，来确定功能性miRs对UGT1A的调节机制。这项工作最终将导致开发经过验证的预测生物标记物(血液中miR相关的多态性和/或miR水平)，这些标志物将被纳入目前正在开发的药物遗传算法中，以指导低治疗指数药物的剂量。

DESCRIPTION (provided by applicant): The promise of pharmacogenomics - identification of the right dose of the right drug the first time for everyone - has yet to be fully realized in part because we lack a complete understanding of the mechanisms underlying drug response variability. Recent studies suggest that microRNAs (miRs) - small noncoding RNAs that regulate gene expression post-transcriptionally - could be an important determinant of interindividual variability in drug metabolism and disposition. Here we propose to use a variety of novel approaches to identify miRs that are responsible for drug metabolizing enzyme variability. The focus of the application will be the UDP- glucuronosyltransferase (UGT) 1A enzymes that are critical to the elimination of many clinically important drugs with a narrow therapeutic index. Importantly, al 9 UGT1A enzymes (out of 18 total human UGTs) are encoded by spliced mRNAs that share an identical 3'-untranslated region (UTR). Although we have evidence that these enzymes are co-regulated by a post-transcriptional mechanism, virtually nothing is known regarding the role of miRs in UGT1A expression. Our overarching hypothesis is that specific miRs regulate UGT1A gene expression through the conserved UGT1A-3'-UTR mRNA region and/or indirectly by modulating regulatory transcription factor levels. To test this hypothesis, th following specific aims are proposed. In Aim 1 candidate miRs associated with UGT1A glucuronidation will be identified by multiple independent approaches, including bioinformatics, functional genomics screen, and whole genome (miR-transcriptome) association studies. The novel functional genomics screen we propose enables rapid identification of miRs that suppress a fluorescent- tagged miR 3'-UTR reporter from a pooled lentivirus library of all known human miRs. This method should have broad application outside of our field enabling a cost-effective rapid approach to describing of the "miR interactome". The reference UGT1A-3'-UTR allele will be evaluated as well as the major variant (*1b) associated with decreased bilirubin glucuronidation in vivo. miRs that are overexpressed in human liver bank samples with a low UGT1A activity and translational efficiency phenotype will also be identified by miR transcriptomics (arrays and Next gen small RNA-seq). In Aim 2 we wil confirm the functional impact of candidate miRs on UGT1A glucuronidation through overexpression (lenti-miRs) and knockdown (lenti- antagomiRs) of candidate miRs in human model cell lines and primary hepatocytes. In Aim 3 we will determine the mechanism of UGT1A regulation by functional miRs through use of UGT1A-3'UTR luciferase-reporter constructs and through studying effects of miR overexpression and knockdown in model cell lines and primary human hepatocytes on specific candidate transcription factor proteins (PXR and HNF1) as well as the entire proteome (iTRAQ LC-MS/MS method). This work will ultimately lead to development of validated predictive biomarkers (miR-associated polymorphisms and/or miR levels in blood) that would be incorporated into the pharmacogenetic algorithms currently being developed to guide dosing of low therapeutic index drugs.