Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA.
Department of Mathematics & Computer Science, Albion College, Albion, MI, USA.
Brain Res. 2019 Dec 1;1724:146457. doi: 10.1016/j.brainres.2019.146457. Epub 2019 Sep 12.
A great diversity of factors contribute to the pathogenesis of autism and autism spectrum disorder (ASD). Early detection is known to correlate with improved long term outcomes. There is therefore intense scientific interest in the pathogenesis of and early prediction of autism. Recent reports suggest that epigenetic alterations may play a vital role in disease pathophysiology. We conducted an epigenome-wide analysis of newborn leucocyte (blood spot) DNA in autism as defined at the time of sample collection. Our goal was to investigate the epigenetic basis of autism and identification of early biomarkers for disease prediction. Infinium HumanMethylation450 BeadChip assay was performed to measure DNA methylation level in 14 autism cases and 10 controls. The accuracy of cytosine methylation for autism detection using six different Machine Learning/Artificial Intelligence (AI) approaches including Deep-Learning (DL) was determined. Ingenuity Pathway Analysis (IPA) was further used to interrogate autism pathogenesis by identifying over-represented biological pathways. We found highly significant dysregulation of CpG methylation in 230 loci (249 genes). DL yielded an AUC (95% CI) = 1.00 (0.80-1.00) with 97.5% sensitivity and 100.0% specificity for autism detection. Epigenetic dysregulation was identified in several important candidate genes including some previously linked to autism development e.g.: EIF4E, FYN, SHANK1, VIM, LMX1B, GABRB1, SDHAP3 and PACS2. We observed significant enrichment of molecular pathways involved in neuroinflammation signaling, synaptic long term potentiation, serotonin degradation, mTOR signaling and signaling by Rho-Family GTPases. Our findings suggest significant epigenetic role in autism development and epigenetic markers appeared highly accurate for newborn prediction.
多种因素导致自闭症和自闭症谱系障碍（ASD）的发病机制。众所周知，早期发现与改善长期预后相关。因此，科学界对自闭症的发病机制和早期预测非常感兴趣。最近的报告表明，表观遗传改变可能在疾病病理生理学中发挥重要作用。我们对自闭症患者的新生儿白细胞（血斑）DNA 进行了全基因组表观遗传分析，自闭症的定义是在样本采集时确定的。我们的目标是研究自闭症的表观遗传基础，并确定用于疾病预测的早期生物标志物。我们使用 Infinium HumanMethylation450 BeadChip assay 来测量 14 例自闭症病例和 10 例对照的 DNA 甲基化水平。使用包括深度学习 (DL) 在内的六种不同的机器学习/人工智能 (AI) 方法来确定用于自闭症检测的胞嘧啶甲基化的准确性。进一步使用 IPA 来通过识别过度表达的生物学途径来探讨自闭症的发病机制。我们发现 230 个基因座（249 个基因）的 CpG 甲基化高度失调。DL 产生 AUC（95%CI）= 1.00（0.80-1.00），自闭症检测的灵敏度为 97.5%，特异性为 100.0%。在几个重要的候选基因中发现了表观遗传失调，包括一些先前与自闭症发展相关的基因，例如：EIF4E、FYN、SHANK1、VIM、LMX1B、GABRB1、SDHAP3 和 PACS2。我们观察到参与神经炎症信号、突触长时程增强、血清素降解、mTOR 信号和 Rho 家族 GTPase 信号的分子途径显著富集。我们的研究结果表明，在自闭症的发展中存在显著的表观遗传作用，并且表观遗传标记对于新生儿的预测似乎非常准确。
