Leveraging Variant-perturbed Gene Regulation to Support Precision Medicine in COPD

摘要/摘要 微核糖核酸(MiRNAs)对正常的肺发育和肺健康至关重要，并且一直是 涉及肺部疾病，包括慢性阻塞性肺疾病(COPD)。此外，最近 研究已经确定miRNAs是COPD的潜在治疗靶点。MiRNAs在 基因调控。MiRNAs在涉及其他生物分子的复杂调控结构中发挥作用， 包括转录因子、蛋白质、信使RNA(MRNAs)和其他表观遗传机制，所有这些 这可能受到基因变异的影响。一些遗传变异已经被鉴定为 通过全基因组关联研究对慢性阻塞性肺疾病的重要性。然而，大量的计算 方法上的挑战阻碍了将遗传变异与改变的分子机制联系起来，以及 确定能够有效地针对这些机制产生临床影响的治疗干预措施。 了解基因调控的复杂结构，包括由miRNAs和miRNAs的调控，以及它是如何在 疾病或对基因变异的反应，对于开发有效的、以精确为基础的医学至关重要 慢阻肺的治疗策略。 在这个项目中，我们假设了基于网络方法，这些方法模拟了生物的集体反应 分子，有可能确定COPD的新治疗策略。我们的目标是利用 连通性图分析中调控网络模型做出的预测，以确定潜在的治疗方法 慢性阻塞性肺病的干预措施。为了实现这一目标，我们将首先开发一种网络方法来模拟监管 通过和使用现有的COPDgene中血液样本的mRNA和miRNA表达数据进行miRNAs的分析。我们 还将生成来自肺组织研究联盟(LTRC)的肺组织中的miRNA表达数据。 我们将使用这些数据来评估与COPD相关的miRNAs的变化，并量化两者之间的关联 MiRNAs和基因变体。将发展共表达、调控和遗传关联网络，以 支持与COPD相关的多OMICS预测。网络分析结果将在以下背景下进行分析 来自连通性地图的药物反应概况，以确定潜在的治疗策略。这样做的结果是 项目将更深入地了解miRNAs在COPD中的调节作用，以及疾病特异性miRNA是如何 在慢性阻塞性肺疾病GWAS变种的背景下，调节被改变，并且对新的治疗策略的预测 慢性阻塞性肺疾病（慢阻肺）。该项目开发的方法也将支持发展精确医学的更广泛目标 慢性阻塞性肺病的治疗策略。

Summary/Abstract Micro-ribonucleic acids (miRNAs) are crucial for normal lung development, lung health, and have been implicated in lung diseases, including Chronic Obstructive Pulmonary Disease (COPD). In addition, recent studies have identified miRNAs as potential therapeutic targets for COPD. miRNAs play an important role in gene regulation. miRNAs act within a complex regulatory structure that involves other biological molecules, including transcription factors, proteins, messenger RNAs (mRNAs), and other epigenetic mechanisms, all of which may be under the influence of genetic variants. A number of genetic variants have been identified as important for COPD through Genome-Wide Association Studies (GWAS). However, substantial computational and methodological challenges impede both linking genetic variants to altered molecular mechanisms and identifying therapeutic interventions that can effectively target these mechanisms for clinical impact. Understanding the complex structure of gene regulation, including by and of miRNAs, and how it is altered in disease or in response to a genetic variant, is critical for developing effective, precision-medicine based therapeutic strategies in COPD. In this project, we hypothesize that network-based methods, which model the collective response of biological molecules, have the potential to identify novel therapeutic strategies for COPD. Our goal is to leverage the predictions made by regulatory network models in a Connectivity Map analysis to identify potential therapeutic interventions for COPD. To accomplish this goal, we will first develop a network approach to model regulation by and of miRNAs using existing mRNA and miRNA expression data from blood samples in COPDGene. We will also generate miRNA expression data in lung tissue from the Lung Tissue Research Consortium (LTRC). We will use these data to assess changes in miRNAs related to COPD and to quantify associations between miRNAs and genetic variants. Co-expression, regulatory, and genetic-association networks will be developed to support multi-Omics predictions relevant to COPD. Network analysis results will be analyzed in the context of drug response profiles from the Connectivity Map to identify potential treatment strategies. The outcome of this project will be a deeper understanding of the regulatory role of miRNAs in COPD, how disease-specific miRNA regulation is altered in the context of COPD GWAS variants, and predictions for novel therapeutic strategies for COPD. The methods developed in the project will also support the broader goal of developing precision-medicine strategies for COPD.