A longitudinal methylome study to detect biomarkers predicting MDD trajectories

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，其终生患病率近15%。基因研究对重度抑郁症的效果不如对其他精神疾病的效果好。DNA甲基化研究是一个特别有希望的补充。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序列变异不能解释发病年龄的变异性或以加重和缓解为典型的重度抑郁症的动态病程。DNA甲基化研究可能会显示出年龄依赖性的变化，并且在大脑有丝分裂后的组织中是动态的。第三，甲基化研究的转化潜力是深远的，甲基化位点是药理学干预的极好可修改目标，并且甲基化是稳定的，可以在血液中成本有效地测量，它们可以潜在地用于临床环境。我们的首要目标是在现有的外周血液样本中识别与临床MDD轨迹相关的甲基化标记物，超过六年的时间。虽然血液中的甲基化标记不会直接影响重度抑郁症，但影响轨迹的因素（例如压力）也可能影响血液中的甲基化标记。由于这些甲基化变化的痕迹可能在细胞分裂期间保留下来，我们的研究也可以间接地揭示因果机制。人（非干细胞）DNA的甲基化发生在CpG位点。由于缺乏生物学知识来确定良好的候选CpG位点，我们将使用下一代测序技术筛选人类基因组中与MDD持久性相关的bb1028万个CpG，然后使用不同的技术在独立样本中复制最重要的发现。具体来说，我们将使用从基线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1500个甲基组进行测序：1)无重度抑郁症的对照组，2)基线时患有重度抑郁症并完全缓解的病例，以及3)慢性重度抑郁症患者。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，例如这些样本已经可用的全基因组转录组数据。使用平行纵向组设计，将使用不同的技术在1500个独立样本中复制50个最有希望的站点。这项研究的成功完成将产生可复制的MDD疾病轨迹的甲基化特征，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。

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.