A longitudinal methylome study to detect biomarkers predicting MDD trajectories

纵向甲基化组研究检测预测 MDD 轨迹的生物标志物

基本信息

批准号：
9313328
负责人：
EDWIN VAN DEN OORD
金额：
$ 37.14万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9313328
关键词：
Affect Age of Onset Algorithms Anxiety Autistic Disorder Autopsy Bioinformatics Biological Bipolar Disorder Blood Blood specimen Brain Cell division Cells Chronic Clinic Clinical Complement Cost Measures DNA DNA Methylation DNA Sequence Data Development Diagnosis Disease Disease remission Equation Etiology Event Gene Expression Genetic Genetic study Goals Human Human Genome Individual Differences Intervention Knowledge Length Life Light Major Depressive Disorder Measures Mental Depression Mental disorders Methylation Mitotic Modeling Netherlands Pathway Analysis Pathway interactions Pharmacologic Substance Pharmacology Prevalence Prevention Procedures Research Sampling Schizophrenia Sex Characteristics Site Solid Stress Technology Testing Time Tissues Ultrasonics Variant Work age related burden of illness case control clinical remission data integration data reduction design disorder subtype follow up assessment genetic approach genome wide association study genome-wide improved methylation biomarker methylome next generation sequencing outcome forecast peripheral blood prediction algorithm predictive marker public health relevance pyrosequencing transcriptome

项目摘要

DESCRIPTION (provided by applicant): Major depressive disorder (MDD) is a leading cause of the global disease burden with a life time prevalence of almost 15%. Genetic studies have not worked as well for MDD as for other psychiatric conditions. DNA methylation studies are a particularly promising complement. First, methylation markers may have better predictive power as methylation is directly related to gene expression. Second, methylation studies may improve disease understanding as they can account for a range of clinical disease features. For example, DNA sequence variants cannot explain the variability in age of onset or the dynamic course of MDD that is typified by exacerbations and remissions. DNA methylation studies potentially can as methylation levels show age-dependent changes and are dynamic in post-mitotic tissues in the brain. Third, the translational potential of methylation studies is profound Methylation sites are excellent modifiable targets for pharmacological interventions and as methylation is stable and can be measured cost-effectively in blood they can potentially be used in clinical settings. Our overarching goal is to identify methylation markers in existing periphera blood samples associated with clinical MDD trajectories over a six year time period. Although methylation marks in blood will not directly impact MDD, factors that affect trajectories (e.g. stress) may also affect methylation signatures in blood. As traces of these methylation changes may be preserved during cell division, indirectly our studies can also shed light on causal mechanisms. Methylation of human (non-stem cell) DNA occurs at CpG sites. As the biological knowledge is lacking to identify good candidate CpG sites, we will use next-generation sequencing to screen the >28 million CpGs in the human genome for their association with the persistence of MDD, and then replicate the top findings in independent samples using a different technology. Specifically, we will sequence 1,500 methylomes using DNA collected from the same subjects at baseline and after six years from three groups from the Netherlands Study of Depression and Anxiety: 1) controls with no MDD, 2) cases with MDD at baseline and then fully remit, and 3) cases with chronic MDD. To improve statistical power and to select the biologically most meaningful methylation markers, we will integrate other data such as genome-wide transcriptome data that is already available for these samples. Using a parallel longitudinal 3 group design, the 50 most promising sites will be replicated in 1,500 independent samples using a different technology. Successful completion of the proposed research will yield replicable methylation signatures of MDD disease trajectories with which we will start generating prediction algorithms that could eventually be used in the clinic to improve prevention, treatment, and diagnosis.

描述（由申请人提供）：重度抑郁症 (MDD) 是全球疾病负担的主要原因，其一生患病率接近 15%。基因研究对于重度抑郁症的治疗效果不如其他精神疾病。 DNA 甲基化研究是一个特别有前途的补充。首先，甲基化标记可能具有更好的预测能力，因为甲基化与基因表达直接相关。其次，甲基化研究可以提高对疾病的理解，因为它们可以解释一系列临床疾病特征。例如，DNA 序列变异无法解释发病年龄的变异性或 MDD 的动态过程（以恶化和缓解为代表）。 DNA 甲基化研究可能可以发挥作用，因为甲基化水平显示出年龄依赖性变化，并且在大脑有丝分裂后组织中呈动态变化。第三，甲基化研究的转化潜力是深远的甲基化位点是药物干预的极好的可修改目标，并且由于甲基化是稳定的并且可以在血液中经济有效地测量，因此它们有可能用于临床环境。我们的首要目标是确定现有外周血样本中与六年期间临床 MDD 轨迹相关的甲基化标记物。虽然血液中的甲基化标记不会直接影响MDD，但影响轨迹的因素（例如压力）也可能影响血液中的甲基化特征。由于这些甲基化变化的痕迹可能在细胞分裂过程中保留下来，因此我们的研究也可以间接揭示因果机制。人类（非干细胞）DNA 的甲基化发生在 CpG 位点。由于缺乏识别良好候选 CpG 位点的生物学知识，我们将使用新一代测序来筛选人类基因组中超过 2800 万个 CpG，以确定它们与 MDD 持续存在的关联，然后使用以下方法在独立样本中复制最重要的发现：不同的技术。具体来说，我们将使用在基线时和六年后从荷兰抑郁和焦虑研究的三组中收集的相同受试者的 DNA 对 1,500 个甲基化组进行测序：1) 无 MDD 的对照，2) 基线时患有 MDD 的病例，然后完全缓解和 3) 慢性 MDD 病例。为了提高统计能力并选择生物学上最有意义的甲基化标记，我们将整合其他数据，例如这些样本已有的全基因组转录组数据。使用平行纵向 3 组设计，将使用不同的技术将 50 个最有希望的位点复制到 1,500 个独立样本中。成功完成拟议的研究将产生 MDD 疾病轨迹的可复制甲基化特征，我们将开始生成预测算法，最终可用于临床以改善预防、治疗和诊断。