摘要
背景：DNA甲基化等DNA改变可能是重度抑郁症（MDD）发病的关键分子事件。我们试图阐明DNA甲基化图谱与MDD患者症状异质性之间的相关性：我们使用 Infinium MethylationEPIC BeadChip 对 MDD 患者和对照组的血液样本进行了全基因组 DNA 甲基化分析：我们分析了 283 份血液样本，其中 141 份来自初始队列（69 名 MDD 患者，72 名对照组），142 份来自第二个验证队列（67 名 MDD 患者，75 名对照组）。在对年龄、性别和血细胞异质性进行调整后，2699 个 CpG 位点的 DNA 甲基化状态在初始队列和第二验证队列中的 MDD 患者和对照组之间存在差异。根据这 2699 个 CpG 位点的 DNA 甲基化状态对患者进行的层次聚类显示，这与 GRID-Hamilton 抑郁评分量表（GRID-HAMD）项目（抑郁情绪、负罪感、早期失眠、中期失眠、工作和活动、精神焦虑、食欲不振、一般躯体症状和总分）的得分有显著相关性，这表明基于血液 DNA 甲基化检测进行严重程度诊断是可行的。通路过度代表性分析表明，DNA甲基化状态与表观遗传学聚类相关的基因在涉及各种细胞功能（尤其是神经发育）的分子通路中累积。就PLEKHD1、STK10和FOXK1而言，DNA甲基化水平与临床蛋白质组肿瘤分析联盟数据库中的表达水平成反比。在两个队列中，PLEKHD1、STK10和FOXK1的DNA低甲基化与较高的GRID-HAMD评分相关：局限性：尽管我们利用包括283名参与者在内的2个队列进行了标记探索，但由于MDD分子机制的异质性，可能需要更大的队列来建立具有充分诊断影响的标准：这些研究结果表明，特定基因的DNA甲基化状态可能与MDD症状的严重程度相关，对血液样本进行全基因组DNA甲基化分析将有助于明确与症状异质性相关的DNA甲基化特征。Background: Alterations in DNA, such as DNA methylation, may be key molecular events involved in the development of major depressive disorder (MDD). We sought to clarify correlations between DNA methylation profiles and symptom heterogeneity among patients with MDD.
Methods: We conducted a genome-wide DNA methylation analysis of blood samples from patients with MDD and controls, using the Infinium MethylationEPIC BeadChip.
Results: We analyzed 283 blood samples, including 141 from an initial cohort (69 patients with MDD, 72 controls) and 142 from a second validation cohort (67 patients with MDD, 75 controls). After adjustment for age, sex, and blood cell heterogeneity, DNA methylation status at 2699 CpG sites tended to differ between patients with MDD and controls in both the initial and second cohorts. Hierarchical clustering of patients based on DNA methylation status at these 2699 CpG sites revealed a significant correlation with scores for GRID-Hamilton Depression Rating Scale (GRID-HAMD) items (depressed mood, guilt, early insomnia, middle insomnia, work and activities, psychic anxiety, loss of appetite, general somatic symptoms, and total score), suggesting the feasibility of severity diagnostics based on blood DNA methylation testing. Pathway over-representation analysis revealed that genes whose DNA methylation status was correlated with epigenetic clustering were accumulated in molecular pathways involved in various cellular functions, especially nerve development. For PLEKHD1, STK10, and FOXK1, DNA methylation levels were inversely correlated with expression levels in the Clinical Proteomic Tumor Analysis Consortium database. DNA hypomethylation of PLEKHD1, STK10, and FOXK1 was correlated with higher GRID-HAMD scores in both cohorts.
Limitations: Although we performed marker exploration using 2 cohorts including 283 participants, the heterogeneity of the molecular mechanisms operating in MDD might necessitate a larger cohort for establishment of criteria with sufficient diagnostic impact.
Conclusion: These findings indicate that the DNA methylation status of specific genes may correlate with the severity of MDD symptoms, and that genome-wide DNA methylation analysis of blood samples would be useful for clarifying the DNA methylation profiles related to symptom heterogeneity.
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