Shen Yuanyuan, Ali Muhammad, Timsina Jigyasha, Wang Ciyang, Do Anh, Western Daniel, Liu Menghan, Gorijala Priyanka, Budde John, Liu Haiyan, Gordon Brian, McDade Eric, Morris John C, Llibre-Guerra Jorge J, Bateman Randall J, Joseph-Mathurin Nelly, Perrin Richard J, Maschi Dario, Wyss-Coray Tony, Pastor Pau, Goate Alison, Renton Alan E, Surace Ezequiel I, Johnson Erik C B, Levey Allan I, Alvarez Ignacio, Levin Johannes, Ringman John M, Allegri Ricardo Francisco, Seyfried Nicholas, Day Gregg S, Wu Qisi, Fernández M Victoria, Ibanez Laura, Sung Yun Ju, Cruchaga Carlos
medRxiv. 2024 Jan 13:2024.01.12.24301242. doi: 10.1101/2024.01.12.24301242.
To date, there is no high throughput proteomic study in the context of Autosomal Dominant Alzheimer's disease (ADAD). Here, we aimed to characterize early CSF proteome changes in ADAD and leverage them as potential biomarkers for disease monitoring and therapeutic strategies.
We utilized Somascan® 7K assay to quantify protein levels in the CSF from 291 mutation carriers (MCs) and 185 non-carriers (NCs). We employed a multi-layer regression model to identify proteins with different pseudo-trajectories between MCs and NCs. We replicated the results using publicly available ADAD datasets as well as proteomic data from sporadic Alzheimer's disease (sAD). To biologically contextualize the results, we performed network and pathway enrichment analyses. Machine learning was applied to create and validate predictive models.
We identified 125 proteins with significantly different pseudo-trajectories between MCs and NCs. Twelve proteins showed changes even before the traditional AD biomarkers (Aβ42, tau, ptau). These 125 proteins belong to three different modules that are associated with age at onset: 1) early stage module associated with stress response, glutamate metabolism, and mitochondria damage; 2) the middle stage module, enriched in neuronal death and apoptosis; and 3) the presymptomatic stage module was characterized by changes in microglia, and cell-to-cell communication processes, indicating an attempt of rebuilding and establishing new connections to maintain functionality. Machine learning identified a subset of nine proteins that can differentiate MCs from NCs better than traditional AD biomarkers (AUC>0.89).
Our findings comprehensively described early proteomic changes associated with ADAD and captured specific biological processes that happen in the early phases of the disease, fifteen to five years before clinical onset. We identified a small subset of proteins with the potentials to become therapy-monitoring biomarkers of ADAD MCs.
Proteomic data generation was supported by NIH: RF1AG044546.
迄今为止，尚未有针对常染色体显性阿尔茨海默病（ADAD）的高通量蛋白质组学研究。在此，我们旨在描述ADAD患者脑脊液蛋白质组的早期变化，并将其作为疾病监测和治疗策略的潜在生物标志物。
我们使用Somascan® 7K检测法对291名突变携带者（MCs）和185名非携带者（NCs）的脑脊液中的蛋白质水平进行定量。我们采用多层回归模型来识别MCs和NCs之间具有不同伪轨迹的蛋白质。我们使用公开可用的ADAD数据集以及散发性阿尔茨海默病（sAD）的蛋白质组学数据来重复这些结果。为了从生物学角度阐释结果，我们进行了网络和通路富集分析。应用机器学习来创建和验证预测模型。
我们识别出125种在MCs和NCs之间具有显著不同伪轨迹的蛋白质。甚至在传统的AD生物标志物（Aβ42、tau、磷酸化tau）出现变化之前，就有12种蛋白质显示出了变化。这125种蛋白质属于与发病年龄相关的三个不同模块：1）与应激反应、谷氨酸代谢和线粒体损伤相关的早期模块；2）富含神经元死亡和凋亡的中期模块；3）症状前阶段模块的特征是小胶质细胞和细胞间通讯过程的变化，表明有重建和建立新连接以维持功能的尝试。机器学习识别出一组九种蛋白质，它们比传统的AD生物标志物（曲线下面积>0.89）能更好地区分MCs和NCs。
我们的研究结果全面描述了与ADAD相关的早期蛋白质组学变化，并捕捉到了在疾病临床发病前15至5年的早期阶段发生的特定生物学过程。我们识别出一小部分有潜力成为ADAD MCs治疗监测生物标志物的蛋白质。
蛋白质组学数据的生成得到了美国国立卫生研究院（NIH）的支持：RF1AG044546。
