Blood DNA Methylation Biomarkers of Alzheimer's Disease

项目摘要 最近基于阵列的脑组织表观全基因组关联研究（EWAS）报告了差异DNA 已知和新发现的迟发性散发性阿尔茨海默病（LOAD）基因的甲基化， 强调了EWAS在揭示与LOAD发病机制相关的新基因和途径中的实用性。 作为研究供体脑样本的替代方法，在可获得的脑组织中进行DNA甲基化研究。 外周组织提供了改善LOAD诊断和预后的机会。最近，我们发现 血液中的差异甲基化位置（DMPs），以区分男性和女性， 多个基因中的769，190个位点中的477个。在这些DMP中，17个在使用临床观察的DMP之间共享 LOAD标记作为连续变量独立分析，包括Rey听觉言语学习测试 评分、脑脊液总tau（t-tau）和磷酸化tau 181（p-tau 181）水平以及t-tau/Aβ1-42（Aβ42）， p-tau 181/Aβ42和Aβ42/Aβ1-40（Aβ40）比值。在LOAD患者中，共有17种DMP中有12种是低血糖， B3 GALT 4（β-1，3-半乳糖基转移酶4）甲基化，该基因先前与上级中的LOAD相关。 颞回脑组织，和5个在ZADH 2（前列腺素还原酶3）中低甲基化，ZADH 2是一种新的LOAD- 相关基因这些数据共同加强了血液的使用，以确定与痴呆症相关的DMP， 来自LOAD，导致血液中DNA甲基化水平可用于鉴定新的 LOAD的诊断、预后和可修改的治疗靶点。使用基于全基因组的方法， 该提案建立在威斯康星州阿尔茨海默病研究中心（WADRC）现有的 生物流体和表型数据，以验证477个DMP，包括B3 GALT 4和ZADH 2中的位点，作为生物标志物 的LOAD，同时检查整个人类基因组的DNA甲基化水平（>25 百万个位点），具有进一步鉴定LOAD的新型DNA甲基化预测因子的潜力。另外这些 研究将通过检查第二批女性和男性参与者来扩展，这些参与者目前在 有和没有轻度认知障碍（MCI）的WADRC，以在治疗前鉴定DNA甲基化生物标志物。 加载开始。MCI是认知正常的老年人和LOAD之间的中间阶段。患者 MCI进展为痴呆症的风险增加。80%的MCI患者在治疗后转为LOAD。 平均6年。区分后来进展为LOAD的MCI患者与 MCI患者没有进展到LOAD，这为改善诊断和早期诊断提供了很大的机会。 对认知老化加速的患者进行干预。本提案的结果将提供 这是鉴定血液中DNA甲基化谱的基础，可预测 LOAD，检测与健康认知轨迹的偏差，识别可改变的风险因素和干预措施， 支持研究工作与表观遗传指标，整合遗传和获得的变量，影响 衰老

Project Summary Recent array-based epigenome-wide association studies (EWAS) of brain tissue report differential DNA methylation in known and newly recognized late-onset sporadic Alzheimer’s disease (LOAD) genes, thereby underscoring the utility of EWAS in disclosing novel genes and pathways associated with LOAD pathogenesis. As an alternative to the study of samples from donor brains, investigation of DNA methylation in accessible peripheral tissues offers the opportunity to improve LOAD diagnosis and prognosis. Recently, we’ve identified differentially methylated positions (DMPs) in blood that distinguish men and women with and without LOAD at 477 of 769,190 loci in a plurality of genes. Of these DMPs, 17 are shared between DMPs observed using clinical LOAD markers analyzed independently as continuous variables comprising Rey Auditory Verbal Learning Test scores, cerebrospinal fluid total tau (t-tau) and phosphorylated tau 181 (p-tau181) levels, and t-tau/Aβ1–42 (Aβ42), p-tau181/Aβ42, and Aβ42/Aβ1–40 (Aβ40) ratios. In patients with LOAD, 12 of the shared 17 DMPs are hypo- methylated in B3GALT4 (Beta-1,3-galatcosyltransferase 4), a gene previously associated with LOAD in superior temporal gyrus brain tissue, and 5 are hypo-methylated in ZADH2 (Prostaglandin reductase 3), a novel LOAD- associated gene. Together these data reinforce use of blood to identify DMPs associated with dementia that arises from LOAD, leading to the hypothesis that DNA methylation levels in blood may be used to identify novel diagnostic, prognostic, and modifiable therapeutic targets of LOAD. Using a whole-genome-based approach, this proposal builds upon the Wisconsin Alzheimer’s Disease Research Center’s (WADRC) existing banked biofluids and phenotypic data to validate the 477 DMPs, including sites in B3GALT4 and ZADH2, as biomarkers of LOAD, while at the same time examining DNA methylation levels across the entire human genome (>25 million loci), with the potential to further identify novel DNA methylation predictors of LOAD. In addition, these studies will be expanded by examining a second cohort of female and male participants presently enrolled in the WADRC with and without mild cognitive impairment (MCI) to identify DNA methylation biomarkers prior to the onset of LOAD. MCI is an intermediate stage between cognitively normal older adults and LOAD. Patients with MCI have an elevated risk of progressing to dementia. Eighty percent of MCI patients convert to LOAD after an average of 6 years. Blood biomarkers that distinguish patients with MCI who later progress to LOAD from those with MCI who do not progress to LOAD offer a substantial opportunity to improve the diagnosis and early intervention in patients with accelerated cognitive aging. Together, findings from the present proposal will provide the foundation for identifying DNA methylation profiles in blood that predict the expression and progression to LOAD, detect deviations from healthy cognitive trajectories, identify modifiable risk factors and interventions, and bolster research efforts with an epigenetic metric that integrates heritable and acquired variables that influence aging.