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：
8729012
负责人：
EDWIN VAN DEN OORD
金额：
$ 56.42万
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 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8729012
关键词：
Accounting Affect Age of Onset Algorithms Anxiety Autistic Disorder Bioinformatics Biological Biological Markers 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 Gene Expression Profile Genetic Goals Human Human Genome Individual Differences Intervention Knowledge Left Length Life Light Major Depressive Disorder Measures Mental Depression Mental disorders Methylation Mitotic Modeling Netherlands Pathway interactions Pharmacologic Substance Prevalence Prevention Procedures Reading Research Sampling Schizophrenia Sex Characteristics Site Staging Stress Technology Testing Time Tissues Variant Work age related base burden of illness case control clinical remission data integration data reduction design disorder subtype follow-up genome wide association study genome-wide improved methylome next generation sequencing outcome forecast peripheral blood public health relevance pyrosequencing

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，终身患病率接近15％。遗传研究对MDD的起作用不如其他精神疾病。 DNA甲基化研究是一个特别有希望的补体。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，甲基化研究可以改善疾病的理解，因为它们可以解释一系列临床疾病特征。例如，DNA序列变体无法解释发作年龄或MDD的动态过程的变异性，而MDD的动态过程则由恶化和恢复。 DNA甲基化研究可能会随着甲基化水平的年龄依赖性变化，并且在大脑的有丝分裂后组织是动态的。第三，甲基化研究的翻译潜力是深层的甲基化位点是药理学干预措施的极好的可修改靶标，并且由于甲基化稳定，并且可以在临床环境中可能使用它们的血液成本高效地测量。我们的总体目标是在六年时间内与临床MDD轨迹相关的现有外围血液样本中的甲基化标记。尽管血液中的甲基化标记不会直接影响MDD，但影响轨迹（例如压力）的因素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以在细胞分裂过程中保留，因此间接的研究也可以阐明因果机制。人（非茎细胞）DNA的甲基化发生在CpG位点。由于缺乏生物学知识来识别良好的候选CPG站点，我们将使用下一代测序来筛选人类基因组中> 2800万个CPG，以与MDD的持久性相关联，然后使用另一种技术在独立样品中复制最佳发现。具体而言，我们将使用基线时从同一受试者中收集的DNA序列为1,500个甲基组，并在荷兰抑郁症和焦虑症研究的三个组中进行了六年后：1）没有MDD的对照，2）2）基线时MDD的病例，然后完全汇总，然后完全汇总，3）患有长期MDD的病例。为了提高统计能力并选择最有意义的甲基化标记，我们将整合其他数据，例如全基因组转录组数据，这些数据已经可用于这些样品。使用平行的纵向3组设计，使用不同的技术将在1,500个独立样本中复制50个最有前途的站点。拟议研究的成功完成将产生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，这些算法最终可以在诊所中用于改善预防，治疗和诊断。