Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis
Erming Wang, Kaiwen Yu, Jiqing Cao, Minghui Wang, Pavel Katsel, Won‐Min Song, Zhen Wang, Yuxin Li, Xusheng Wang, Qian Wang, Peng Xu, Gongqi Yu, Li Zhu, Jia Geng, Parnian Habibi, Qian Lü, Tony Tuck, Aiqun Li, Julia TCW, Panos Roussos, Kristen Brennand, Vahram Haroutunian, Erik C. B. Johnson, Nicholas T. Seyfried, Allan I. Levey, David A. Bennett, Junmin Peng, Dongming Cai, Bin Zhang
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.