Project 2: Joint Transcriptomic and Epigenomic Studies for Male Osteoporosis

骨质疏松症是最常见的代谢性骨病，主要表现为骨密度降低 (BMD，--面积BMD（aBMD），除非另有说明）。男性骨质疏松症是一个主要的，但最被忽视的公众 健康问题。外周血单核细胞（PBM）可以作为破骨细胞，骨吸收细胞， 并产生对破骨细胞分化、活化和凋亡重要的细胞因子，因此代表了一种主要的 骨代谢系统细胞。micro-RNA（miRNAs）介导的基因表达修饰是重要的 转录组动力学是人类疾病的基础，并参与体外破骨细胞生成。下一个-G RNA- seq具有无与伦比的能力来全面表征转录组，特别是揭示新颖的 miRNAs。因此，我们的假设是：PBMs中mRNA和miRNA表达谱的变化是男性 BMD和骨质量/强度变化，并且可以通过最先进的Next-G RNA-seq进行最有力的识别。 通过临床核心，我们将招募200名高加索人和100名非裔美国人（AA）并进行临床表型分析。 男性，年龄20-30岁，150例（100例白人和50例AA）高BMD和150例（100例白人和50例AA）低BMD。 还将评估骨质量/强度（通过定量CT [QCT]和有限元分析[FEA]测量） 在每个主题上。一半的白种人（50名高BMD受试者对50名低BMD受试者）将作为“发现队列”， 另一半是“复制队列”。这些相同的主题都将用于项目2和3。 在目标1中，我们将全面鉴定对男性骨质疏松症重要的mRNA。我们将使用PBM总数 “发现队列”的RNA进行基于RNA-seq的转录组研究，以鉴别差异性mRNA 在高与低BMD受试者中表达（DEx）。我们将确定前10个DEX基因，并在 发现队列（队列内技术验证）、复制队列（跨队列生物学验证）、 AA队列（跨种族验证）和另一组独立的86名白人女性（46名高vs. 40名低 BMD）受试者（来自我们的SCOR，用于跨性别验证）。不同性别受试者的计算机模拟复制和/或 将在现有的功能基因组学数据集中进行种族分析。 在目标2中，我们将鉴定/验证高与低BMD受试者中的DEx miRNAs以及前DEx miRNAs的靶基因。 miRNAs。我们将确定前15个DEx miRNAs，并为每个miRNAs确定前5个潜在的靶mRNA 通过相关性和生物信息学分析，也验证了它们在上述中的“靶向”关系 提到的群组/数据集以及使用基于内切酶的功能测定。 将测试以上鉴定的显著mRNA/miRNA对于QTC和FEA测量的显著性。 本项目产生的数据将用于更高级的分析，如eQTL/mQTL分析， 基因网络分析、因果关系分析和其他综合分析，例如，1）获得功能性见解， 在项目1和3中鉴定的遗传变异和DNA甲基化标记;和2）寻找 通过项目1中收集的数据在DNA和mRNA水平上对子信号进行组合分析， 2; 3）鉴定各种表观遗传机制如miRNA和DNA甲基化的相互作用。

Osteoporosis is the most common metabolic bone disease mainly characterized by low bone mineral density (BMD,--areal BMD (aBMD) unless otherwise specified). Male osteoporosis is a major but most neglected public health problem. Peripheral blood monocytes (PBMs) may act as precursors of osteoclasts, the bone resorption cells, and produce cytokines important for osteoclast differentiation, activation, and apoptosis, and thus represent a major systemic cell for bone metabolism. micro-RNA (miRNAs)-mediated gene expression modifications are important transcriptomic dynamics underlying human diseases, and are involved in osteoclastogenesis in vitro. Next-G RNA- seq has an unparalleled power to comprehensively characterize transcriptome, in particular, revealing novel miRNAs. Therefore, our Hypothesis is: Changes in mRNA and miRNA expression profiles in PBMs underlie male BMD and bone quality/strength variation and can be identified most powerfully by the cutting edge Next-G RNA-seq. Through the Clinical Core, we will recruit and clinically phenotype 200 Caucasian and 100 African American (AA) men, aged 20-30, 150 (100 Caucasians and 50 AA) with high and 150 (100 Caucasians and 50 AA) with low BMD. Bone quality/strength (measured by quantitative CT [QCT] and finite element analyses [FEA]) will also be assessed on each subject. Half of the Caucasians (50 high vs. 50 low BMD subjects) will serve as a “discovery cohort” and the other half as a “replication cohort”. These same subjects will all be used in Proj 2 & 3. In Aim 1, we will comprehensively identify mRNAs important to male osteoporosis. We will use the PBMs total RNAs of the “discovery cohort” to perform RNA-seq-based transcriptome studies to identify mRNAs differentially expressed (DEx) in high vs. low BMD subjects. We will identify the top 10 DEx genes and validate them in the discovery cohort (within-cohort technical validation), the replication cohort (across-cohort biological validation), the AA cohort (across-ethnicity validation), and another independent set of 86 Caucasian female (46 high vs. 40 low BMD) subjects (from our SCOR, for across-sex validation). In silico replication in subjects of different sexes and/or ethnicities will be performed in existing functional genomics datasets. In Aim 2, we will identify/validate DEx miRNAs in high vs. low BMD subjects and the target genes of the top DEx miRNAs. We will identify the top 15 DEx miRNAs, and for each of which, identify top 5 potential target mRNAs through correlation and bioinformatics analyses and also validate their “targeting” relationship in the above mentioned cohorts/datasets as well as using luciferase-based functional assays. The significant mRNA/miRNA identified above will be tested for their significance for QTC and FEA measures. The data generated in this project will be used for more advanced analyses, such as eQTL/mQTL analyses, gene network analyses, causality analyses and other integrative analyses , e.g., to 1) gain functional insights into the genetic variants and DNA methylation marks identified in Proj 1 & 3; and 2) search for consistent signals of important risk genes through combined analyses of sub-signals at DNA and mRNA levels by data collected in Proj 1 & 2; 3) identify interactions of various epigenetic mechanisms such as miRNA and DNA methylation.