Genome-wide DNA methylation profiles of lymphoblastoid cell lines from sex-matched sibling pairs discordant for autism diagnosis. Homo sapiens

The goals of this study were to: 1) investigate changes in DNA methylation in lymphoblastoid cell lines (LCL) from individuals with a specific subtype of ASD and their respective non-autistic siblings, 2) relate the differentially methylated genes to ASD-relevant pathways and functions, and 3) explore the potential for class determination (case-control) biomarkers based on methylation signatures. Overall design: DNA from LCL of 21 sibling pairs (42 samples in all) were analyzed for methylated regions using the MeDIP-chip protocol. Each sample was represented by MeDIP-enriched DNA and input DNA which were each fragmented, labeled and separately hybridized to Affymetrix Human Promoter 1.0R GeneChips (84 arrays in all) following the Affymetrix protocols. Data from the scanned genechips were first analyzed for methylation-enriched regions using Partek's workflow for tiling arrays, which resulted in MAT scores representing methylation-enriched (positive MAT scores) and depleted (negative MAT scores) across the genome as well as determination of average probe intensities across the promoter regions. The intensities were then imported into Multi-experiment View (MeV) microarray analysis software for further analyses, These additional data analyses to identify differentially methylated genes for class prediction included Significance Analysis of Microarrays (SAM), Pavlidis Template Matching (PTM), Uncorrelated Shrunken Centroids (USC), and Support Vector Machines (SVM) which were all incorporated into the MeV software package.

本研究的目标为：1）探究特定亚型自闭症谱系障碍（Autism Spectrum Disorder, ASD）患者及其非自闭症同胞的淋巴母细胞系（lymphoblastoid cell lines, LCL）中DNA甲基化的变化情况；2）将差异甲基化基因与自闭症相关通路及功能进行关联分析；3）探索基于甲基化特征开展分类判定（病例-对照）的生物标志物潜力。
总体实验设计：本研究对21对同胞共42份样本的淋巴母细胞系DNA采用甲基化DNA免疫沉淀芯片（MeDIP-chip）实验方案进行甲基化区域检测。每份样本分别制备甲基化富集DNA与输入DNA，经片段化、标记后，按照Affymetrix官方实验流程分别杂交至Affymetrix人类启动子1.0R基因芯片（总计84张芯片）。扫描得到的基因芯片数据首先采用Partek的平铺阵列分析流程进行甲基化富集区域鉴定，所得MAT评分可反映全基因组范围内的甲基化富集（MAT评分为正值）与缺失（MAT评分为负值）情况，并可计算启动子区域内探针的平均信号强度。随后将信号强度数据导入多实验视图（Multi-experiment View, MeV）微阵列分析软件进行后续分析。用于识别差异甲基化基因以实现分类预测的额外分析方法包括：微阵列显著性分析（Significance Analysis of Microarrays, SAM）、帕夫利迪斯模板匹配（Pavlidis Template Matching, PTM）、非相关收缩质心法（Uncorrelated Shrunken Centroids, USC）以及支持向量机（Support Vector Machines, SVM），上述方法均已整合至MeV软件包中。