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，以低骨密度为主要特征 (BMD，--面积骨密度(ABMD)，除非另有说明)。男性骨质疏松症是一个主要但最被忽视的人群 健康问题。外周血单核细胞可能是破骨细胞、骨吸收细胞、 并产生对破骨细胞分化、激活和凋亡至关重要的细胞因子，因此代表着一种主要的 骨骼代谢的系统细胞。微RNA(MiRNAs)介导的基因表达修饰是重要的 人类疾病背后的转录动力学，并参与体外破骨细胞的形成。Next-G RNA- SEQ在全面描述转录组，特别是揭示小说的特征方面具有无与伦比的能力 MiRNAs。因此，我们的假设是：pBM中mrna和miRNA表达谱的变化是男性的基础。 BMD和骨质量/强度的变化，最能被尖端的Next-G RNA-seq所识别。 通过临床核心，我们将招募200名高加索人和100名非洲裔美国人(AA)并进行临床表型 男性，20-30岁，150人(100名高加索人，50名AA)高BMD，150名(100名高加索人，50名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。一半的高加索人(50名高BMD受试者与50名低BMD受试者)将作为“发现队列”，而 另一半则是“复制队列”。这些相同的主题都将在项目2和3中使用。 在目标1中，我们将全面识别与男性骨质疏松症有关的mRNAs。我们将使用PBMS合计 “发现队列”中的RNA进行基于RNA序列的转录组研究，以区别识别mRNA 在高骨密度受试者和低骨密度受试者中表达(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(跨队列生物验证)、 AA队列(跨种族验证)，以及另一组独立的86名高加索女性(高46人对低40人 BMD)受试者(来自我们的SCOR，用于跨性别验证)。在不同性别和/或受试者的电子复制中 种族将在现有的功能基因组数据集中进行。 在目标2中，我们将在高BMD和低BMD受试者中识别/验证Dex miRNAs和top Dex的靶基因 MiRNAs。我们将确定前15个Dex miRNAs，并针对每个mRNAs确定前5个潜在目标mRNAs 通过相关分析和生物信息学分析，也验证了它们在上述中的“靶向”关系 提到的队列/数据集以及使用基于荧光素酶的功能分析。 上面确定的有意义的mRNA/miRNA将被测试它们对QTC和FEA测量的意义。 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、因果分析和其他综合分析，例如：1)获得对 项目1和3中确定的遗传变异和DNA甲基化标记；和2)寻找一致的信号 通过在项目1中收集的数据在DNA和mRNA水平上联合分析重要的风险基因 -2；3)确定各种表观遗传机制的相互作用，如miRNA和DNA甲基化。

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.