Blood DNA Methylation Biomarkers of Alzheimer's Disease

项目摘要 最近基于阵列的脑组织表观基因组广泛关联研究报告差异DNA 已知和新发现的晚发性散发性阿尔茨海默病(LOAD)基因甲基化，从而 强调了EWASs在揭示与LOAD发病相关的新基因和途径方面的效用。 作为对供体大脑样本研究的替代，Access中DNA甲基化的调查 外周组织提供了改善负荷诊断和预后的机会。最近，我们确认了 血液中区分男性和女性的差异甲基化位置(DMP) 在多个基因的769,190个座位中有477个。在这些DMP中，有17个在使用临床观察的DMP之间共享 独立分析作为连续变量的负荷标记构成Rey听觉言语学习测验 评分，脑脊液总tau(t-tau)和磷酸化tau181(p-tau181)水平，t-tau/Aβ1-42(Aβ42)， P-tau181/Aβ42和Aβ42/Aβ1-40(Aβ40)比率。在有LOAD的患者中，共有的17个DMP中有12个是低血糖的。 B3GALT4(β-1，3-半乳糖基转移酶4)的甲基化，这是一个以前与上级负荷相关的基因 其中5例在ZADH2(前列腺素还原酶3)中发生低甲基化，这是一种新的负载- 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 产生于负载，导致假设血液中的DNA甲基化水平可能被用来识别新的 LOAD的诊断、预后和可修改的治疗目标。使用基于全基因组的方法， 这项建议建立在威斯康星州阿尔茨海默病研究中心(WADRC)现有的银行基础上 生物流体和表型数据，以验证477个DMP，包括B3GALT4和ZADH2上的位点，作为生物标志物 同时检查整个人类基因组的DNA甲基化水平(&gt；25 百万个基因座)，有可能进一步识别新的DNA甲基化负荷预测因子。此外，这些 研究范围将扩大，检查目前在 WADRC合并和不合并轻度认知障碍(MCI)以确定DNA甲基化生物标记物 开始加载。MCI是介于认知正常的老年人和LOAD之间的中间阶段。患有疾病的患者 MCI进展为痴呆症的风险增加。80%的MCI患者在术后转为负荷 平均6年。血液生物标记物用于区分进展型MCI患者与非MCI患者 对于没有进展到负荷的MCI，提供了一个实质性的机会来提高诊断和早期诊断 对认知老化加速患者的干预。综上所述，本提案的结论将提供 识别血液中DNA甲基化特征以预测其表达和进展的基础 负荷，检测与健康认知轨迹的偏差，确定可修改的风险因素和干预措施，以及 使用表观遗传指标支持研究工作，该指标整合了影响 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.