lncRNAs, Linking Genetic Susceptibility to Molecular Phenotype in IPF

我们提出的研究的总体目标是了解非蛋白质编码的RNA有多长 (LncRNAs)影响常见的特发性肺纤维化(IPF)风险变异对 转录谱和IPF的临床严重程度。在过去的5年里，我们发现：1)收益- 功能MUC5B启动子突变rs35705950是IPF发生的最强危险因素(2-11)； 表观遗传机制影响MUC5B(12)的表达，MUC5B似乎参与了 IPF的发病机制(2，13-15)；3)IPF是一种复杂的遗传病，具有罕见和常见的变异 促进了这种疾病的发展(16)，包括DNA甲基化的显著变化(17)和 转录亚型(18)。我们最近对10个IPF风险基因座(5)进行了测序，这些基因座在大量的 IPF患者(N=3,642)和对照组(N=4,442)，并发现了10种常见的代表 只有这些IPF风险基因座中的共同独立信号和合计至少占IPF风险的40% IPF(16)。然而，对于IPF或Low的分子/临床异质性还没有明确的解释。 MUC5B启动子变异体(2)或其他常见的IPF风险变异体(19-22)在这一罕见病例中的外显性 疾病(23、24)。新的发现表明，lncRNA可以通过以下方式改变常见风险变体的影响 影响导致IPF的分子机制，进而解释IPF的病理生理表型 对IPF的影响。总而言之，这些观察使我们假设IPF的病因和严重性将是最好的 通过一种综合的方法来理解，这种方法考虑了遗传因素，并反过来影响了 编码-非编码转录组。因此，我们假设lncRNAs调节普通ipf的作用。 转录谱上的风险变异驱动IPF的发展和临床严重程度。在目标1中， 我们将通过比较肺组织中的lncRNAs，大致确定在IPF肺组织中调控失调的lncRNAs。 IPF患者(N=450例)和正常对照(N=300例)的组织。我们将使用这些数据来区分 表达lncRNAs和mRNAs，探索受lncRNAs调控的mRNAs网络。在目标2中，我们将 首先确定IPF相关常见遗传变异对正常肺组织lncRNAs的顺式和反式影响 组织，然后进入IPF肺。这将使我们能够磨练在IPF中不受调控的候选LncRNAs 是由常见的IPF风险变量驱动的。在目标3中，我们将使用大规模平行报告分析(MPRA)来 识别并在功能上验证10个关键IPF风险基因座内改变编码和表达的调控变量 非编码候选RNA，与IPF的临床和/或病理亚型相关。最终的结果 将加深对新基因、调控途径和网络以及分子的理解 IPF的发病机制和临床严重程度有关。

The overall goal of our proposed research is to understand how long non-protein-coding RNAs (lncRNAs) impact the effect of common idiopathic pulmonary fibrosis (IPF) risk variants on transcriptional profiles and the clinical severity of IPF. In the past 5 years, we have found that: 1) a gain-of- function MUC5B promoter variant rs35705950 is the strongest risk factor for the development of IPF (2-11); 2) epigenetic mechanisms affect the expression of MUC5B (12) and MUC5B appears to be involved in the pathogenesis of IPF (2, 13-15); and 3) IPF is a complex genetic disease with rare and common variants contributing to the development of this disease (16), including pronounced changes in DNA methylation (17) and transcriptional subtypes (18). We have recently sequenced across 10 IPF risk loci (5) in a large number of patients with IPF (N=3,642) and controls (N=4,442), and have identified 10 common variants that represent the only common independent signals in these IPF risk loci and in aggregate account for at least 40% of the risk of IPF (16). However, there is no clear explanation for the molecular/clinical heterogeneity of IPF or the low penetrance of either the MUC5B promoter variant (2) or the other common IPF risk variants (19-22) in this rare disease (23, 24). Emerging findings suggest that lncRNAs could alter the impact of common risk variants by influencing the molecular machinery that leads to IPF, and in turn account for the pathophysiologic phenotypes of IPF. In aggregate, these observations lead us to postulate that the etiology and severity of IPF will be best understood through an integrated approach that accounts for inherited factors and in turn their influence on the coding-noncoding transcriptome. Thus, we hypothesize that lncRNAs regulate the effect of common IPF risk variants on transcriptional profiles that drive the development and clinical severity of IPF. In Aim 1, we will broadly identify lncRNAs that are dysregulated in IPF lung tissue by comparing lncRNAs from the lung tissue of cases of IPF (N=450) to unaffected controls (N=300). We will use these data to identify differentially expressed lncRNAs and mRNAs, and explore networks of mRNAs regulated by lncRNAs. In Aim 2, we will determine cis- and trans-effects of IPF-associated common genetic variants on lncRNAs first in normal lung tissue and then in the IPF lung. This will enable us to hone in on candidate lncRNAs dysregulated in IPF that are driven by common IPF risk variants. In Aim 3, we will use a massively parallel reporter assay (MPRA) to identify and functionally validate regulatory variants within 10 key IPF risk loci that alter expression of coding and noncoding candidate RNAs and are associated with clinical and/or pathological subtypes of IPF. The end-result will be an enhanced understanding of the novel genes, regulatory pathways and networks, and molecular mechanisms involved in the etiology and clinical severity of IPF.