美国圣路易斯华盛顿大学Elizabeth Herries课题组发现，脑脊液蛋白组学识别出常染色体显性阿尔茨海默病的早期变化。相关论文发表在2024年9月26日出版的《细胞》杂志上。
在这项关于常染色体显性阿尔茨海默病（ADAD）的高通量蛋白组学研究中，研究人员旨在识别脑脊液（CSF）中的早期生物标志物，以便进行疾病监测和治疗策略。
研究人员对286名突变携带者（MC）和177名非携带者（NC）的CSF蛋白进行了检查。所开发的多层回归模型区分了这两个组之间具有不同伪轨迹的蛋白质。研究人员使用独立的ADAD和散发性AD数据集验证了这些发现，并采用机器学习开发和验证预测模型。
该究识别出137种在MC和NC之间具有不同轨迹的蛋白质，其中八种在传统AD生物标志物之前发生变化。这些蛋白质分为三个阶段：早期阶段（应激反应、谷氨酸代谢、神经元线粒体损伤）、中期阶段（神经元死亡、凋亡）和晚期前症状阶段（小胶质细胞变化、细胞通信）。预测模型揭示了一组六种蛋白质，能够比传统生物标志物更有效地区分MC和NC。
附：英文原文
Title: CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease
Author: Yuanyuan Shen, Jigyasha Timsina, Gyujin Heo, Aleksandra Beric, Muhammad Ali, Ciyang Wang, Chengran Yang, Yueyao Wang, Daniel Western, Menghan Liu, Priyanka Gorijala, John Budde, Anh Do, Haiyan Liu, Brian Gordon, Jorge J. Llibre-Guerra, Nelly Joseph-Mathurin, Richard J. Perrin, Dario Maschi, Tony Wyss-Coray, Pau Pastor, Alan E. Renton, Ezequiel I. Surace, Erik C.B. Johnson, Allan I. Levey, Ignacio Alvarez, Johannes Levin, John M. Ringman, Ricardo Francisco Allegri, Nicholas Seyfried, Gregg S. Day, Qisi Wu, M. Victoria Fernández, Rawan Tarawneh, Eric McDade, John C. Morris, Randall J. Bateman, Alison Goate, James M. Noble, Gregory S. Day, Neill R. Graff-Radford, Jonathan Voglein, Ricardo Allegri, Patricio Chrem Mendez, Ezequiel Surace, Sarah B. Berman, Snezana Ikonomovic, Neelesh Nadkarni, Francisco Lopera, Laura Ramirez, David Aguillon, Yudy Leon, Claudia Ramos, Diana Alzate, Ana Baena, Natalia Londono, Sonia Moreno Mathias Jucker, Christoph Laske, Elke Kuder-Buletta, Susanne Graber-Sultan, Oliver Preische, Anna Hofmann, Takeshi Ikeuchi, Kensaku Kasuga, Yoshiki Niimi, Kenji Ishii, Michio Senda, Raquel Sanchez-Valle, Pedro Rosa-Neto, Nick Fox, Dave Cash, Jae-Hong Lee, Jee Hoon Roh, Meghan Riddle, William Menard, Courtney Bodge, Mustafa Surti, Leonel Tadao Takada, Martin Farlow, Jasmeer P. Chhatwal, V.J. Sanchez-Gonzalez, Maribel Orozco-Barajas, Alison Goate, Alan Renton, Bianca Esposito, Celeste M. Karch, Jacob Marsh, Carlos Cruchaga, Victoria Fernandez, Brian A. Gordon, Anne M. Fagan, Gina Jerome, Elizabeth Herries
Issue&Volume: 2024-09-26
Abstract: In this high-throughput proteomic study of autosomal dominant Alzheimer’s disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.
DOI: 10.1016/j.cell.2024.08.049
Source: https://www.cell.com/cell/abstract/S0092-8674(24)00978-4