近日，西奈山伊坎医学院张斌教授及其团队的最新研究揭示了多尺度蛋白质组学模型揭示了驱动阿尔茨海默病发病机制的蛋白质网络。这一研究成果于2025年9月25日发表在国际顶尖学术期刊《细胞》上。
在这项研究中，该研究组通过整合AD易感大脑区域的大规模匹配蛋白质组学和遗传数据，开发了AD的多尺度蛋白质组学网络模型。这些模型揭示了详细的蛋白质相互作用结构，并确定了参与AD进展的推定关键驱动蛋白（kdp）。值得注意的是，网络分析揭示了AD相关的子网络，该子网络捕获了神经胶质细胞之间的相互作用。AHNAK是胶质神经元网络中的顶级KDP，在基于人类诱导多能干细胞（iPSC）的AD模型中得到了实验验证。这种对失调蛋白调控网络和KDP的系统鉴定为开发AD的创新治疗策略奠定了基础。
据了解，阿尔茨海默病（AD）是最常见的痴呆形式，其发病机制的分子机制仍然知之甚少。蛋白质组学为阐明阿尔茨海默病的发病机制提供了一种至关重要的方法，因为蛋白质表达的改变比遗传或转录组学水平的变化更直接地与表型结果相关。
附：英文原文
Title: Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis
Author: Erming Wang, Kaiwen Yu, Jiqing Cao, Minghui Wang, Pavel Katsel, Won-min Song, Zhen Wang, Yuxin Li, Xusheng Wang, Qian Wang, Peng Xu, Gefei Yu, Li Zhu, Jia Geng, Parnian Habibi, Lu Qian, Tony Tuck, Aiqun Li, Julia TCW, Panos Roussos, Kristen J. Brennand, Vahram Haroutunian, Erik C.B. Johnson, Nicholas T. Seyfried, Allan I. Levey, David A. Bennett, Junmin Peng, Dongming Cai, Bin Zhang
Issue&Volume: 2025-09-25
Abstract: The molecular mechanisms underlying the pathogenesis of Alzheimer’s disease (AD), the most common form of dementia, remain poorly understood. Proteomics offers a crucial approach to elucidating AD pathogenesis, as alterations in protein expression are more directly linked to phenotypic outcomes than changes at the genetic or transcriptomic level. In this study, we develop multiscale proteomic network models for AD by integrating large-scale matched proteomic and genetic data from brain regions vulnerable to the disease. These models reveal detailed protein interaction structures and identify putative key driver proteins (KDPs) involved in AD progression. Notably, the network analysis uncovers an AD-associated subnetwork that captures glia-neuron interactions. AHNAK, a top KDP in this glia-neuron network, is experimentally validated in human induced pluripotent stem cell (iPSC)-based models of AD. This systematic identification of dysregulated protein regulatory networks and KDPs lays down a foundation for developing innovative therapeutic strategies for AD.
DOI: 10.1016/j.cell.2025.08.038
Source: https://www.cell.com/cell/abstract/S0092-8674(25)01031-1