Table_1_Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease.XLSX

Alzheimer’s disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.

阿尔茨海默病（Alzheimer’s disease，简称 AD）乃一种渐进性疾病，导致患者行为不可逆转的变迁、情绪波动无常以及运动技能的丧失。此类症状使得阿尔茨海默病患者照护变得艰难甚至几近不可能。众多内部与外部病理因素可能影响甚至触发阿尔茨海默病的起始与进展。DNA 甲基化在阿尔茨海默病发病机制中扮演着至关重要的调控角色，而病理性的甲基化改变在不同阿尔茨海默病患者的大脑结构中可能存在差异。尽管已识别出与阿尔茨海默病起始与进展相关的多个位点，但大脑中阿尔茨海默病相关DNA甲基化的空间分布模式尚未明晰。基于不同结构脑区的系统性甲基化图谱，本研究应用多种机器学习算法对其进行了探究。首先，通过 Boruta 特征过滤方法对每个脑区的图谱进行分析，提取若干关键甲基化特征，并进一步采用最大相关性最小冗余方法进行深入分析，从而得到一个特征列表。随后，结合一些分类算法的增量特征选择方法，利用该列表识别出具有结构特异性的候选阿尔茨海默病相关甲基化位点，并建立一组定量规则，揭示DNA甲基化在各个脑区（即四个脑结构）对阿尔茨海默病发病机制的影响。此外，基于关键甲基化位点的某些高效分类器被提出，用于识别阿尔茨海默病样本。研究结果表明，不同脑结构的甲基化改变对阿尔茨海默病发病机制具有不同的贡献。本研究进一步阐释了阿尔茨海默病的复杂病理机制。