Wang E, Yu K, Cao J, Wang M, Katsel P, Song WM, Wang Z, Li Y, Wang X, Wang Q, Xu P, Yu G, Zhu L, Geng J, Habibi P, Qian L, Tuck T, Li A, Tcw J, Roussos P, Brennand KJ, Haroutunian V, Johnson EC, Seyfried NT, Levey AI, Bennett DA, Peng J, Cai D, Zhang B. Multiscale proteomic modeling reveals protein networks driving Alzheimer's disease pathogenesis. Cell. 2025 Oct 30;188(22):6186-6204.e13. Epub 2025 Sep 25 PubMed.
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University of California, Irvine
This study delivers a landmark multiscale proteomic network model for AD, integrating matched genetics, proteomics, and transcriptomics to reveal disease mechanisms. The authors identify a glia-neuron subnetwork most strongly associated with AD and predict 580 key driver proteins including AHNAK, MSN, PRDX6, and VIM. Differential protein signatures are robust across cohorts and overlap with mouse 5xFAD proteomes, underscoring biological relevance. Bayesian causal and co-expression analyses pinpoint astrocyte and microglia modules linked to immune and metabolic pathways central to AD.
Functional perturbation of AHNAK in APOE4/4 iPSC-derived astrocytes reduces p-tau and modestly lowers Aβ and ApoE levels, with neuron co-cultures showing increased spike activity. The study’s open datasets and code enhance reproducibility and lay a foundation for target discovery and therapeutic development.
Despite these strengths, the work falls short on in vivo validation, with the authors themselves noting that animal studies are still needed to define AHNAK’s role and test causal drivers. The reliance on postmortem human tissue and stringent differentially expressed protein cutoffs may under-detect changes in moderate stages, highlighting a need for longitudinal and functional studies.
Overall, this article advances AD systems biology while inviting future work that bridges its elegant networks to mechanistic interventions in animal models.
View all comments by Vivek SwarupUniversity of California, San Francisco
This impressive study from Wang et al. delivers a comprehensive proteomic dissection of the parahippocampal gyrus (PHG), a region affected early in AD. Deep tandem-mass-tag mass spectroscopic proteomic profiling identified hundreds of co-expressed protein modules in the PHG, including those linked to AD endophenotypes. Paired proteomic and genetic data powered a causal network model that identified 580 key driver proteins, with a multicellular M3 module bridging glial activation and neuronal loss. AHNAK, an astrocytic scaffold protein, emerged as a top driver and its knockdown in human iPSC-derived astrocytes (with 5xFAD neuron co-culture) reduced p-tau and rescued neuronal firing, providing a valuable functional validation of a network-derived prediction.
Looking ahead, the key driver list offers an exciting slate of potential therapeutic targets and perhaps equally important, protein-based biomarker development. Several questions have emerged, including which key drivers and top AD modules have biofluid readouts that could aid in staging and monitoring pharmacodynamic response? For example, we have observed biofluid alterations for a number of top hits from this study, including elevated cerebrospinal fluid abundance of PRDX6 in cerebrospinal fluid (Dammer et al., 2024) and decreased plasma abundance of AHNAK in AD vs. controls (Saloner et al., 2025). Leveraging paired biofluids and brain tissue samples will be crucial for clinical translation, echoing recent plasma–brain integration efforts (e.g., Afshar et al., 2025). Bridging these domains could accelerate target prioritization and enable in vivo monitoring of proteomic network changes.
References:
Afshar S, Dammer EB, Bian S, Bennett DA, Mohs R, Beauregard D, Dwyer J, Hales CM, Goldstein FC, Parker MW, Trammell AR, Watson CM, Golde TE, Seyfried NT, Roberts BR, Manzanares CM, Lah JJ, Levey AI, Johnson EC. Plasma proteomic associations with Alzheimer's disease endophenotypes. Nat Aging. 2025 Oct;5(10):2104-2124. Epub 2025 Sep 10 PubMed.
Dammer EB, Shantaraman A, Ping L, Duong DM, Gerasimov ES, Ravindran SP, Gudmundsdottir V, Frick EA, Gomez GT, Walker KA, Emilsson V, Jennings LL, Gudnason V, Western D, Cruchaga C, Lah JJ, Wingo TS, Wingo AP, Seyfried NT, Levey AI, Johnson EC. Proteomic analysis of Alzheimer's disease cerebrospinal fluid reveals alterations associated with APOE ε4 and atomoxetine treatment. Sci Transl Med. 2024 Jun 26;16(753):eadn3504. PubMed.
Saloner R, Casaletto KB, Rayaprolu S, Cornelis L, Chakrabarty P, Abisambra JF, Spina S, Grinberg LT, Seeley WW, Miller BL, Kramer JH, Rabinovici GD, Asken BM. Interrogating the plasma proteome of repetitive head impact exposure and chronic traumatic encephalopathy. Mol Neurodegener. 2025 Jun 16;20(1):71. PubMed.
View all comments by Rowan SalonerRush University Medical Center
Rush University Medical Center
Alzheimer’s disease is defined by the buildup of amyloid plaques and neurofibrillary tangles in the brain, but that’s just the tip of the iceberg. With recent single-cell and single-nucleus RNA-Seq studies, we are now seeing how AD impacts nearly every major brain cell type, creating a complex interplay (Green et al., 2024; Murdock et al., 2023; Mathys et al., 2023). Certain neuronal subpopulations are particularly vulnerable to AD, while others appear resilient to pathology. When it comes to glial cells, such as microglia and astrocytes, they are highly dynamic, constantly responding to their environment and communicating with neurons in ways we are only beginning to understand. Adding to that, proteomic studies of human brain tissue have identified modules associated with AD traits that are not observed at the RNA level, with AD risk loci converging in glia-enriched modules (Seyfried et al., 2017). Johnson et al. (2022), for example, compared RNA and protein co-expression modules and highlighted two key ones: a matrisome module and a MAPK/metabolism module (Johnson et al., 2022). Together, these studies highlight that certain AD-related mechanisms may be missed at the transcriptomic level, and that integrating analyses with proteomics can generate novel hypotheses.
This study by Wang et al. explores, for the first time, large-scale proteomics of the parahippocampal gyrus (PHG). Their analysis of nearly 200 brains revealed a high degree of reproducibility and specific effects of AD-associated proteins not seen in analysis of prefrontal cortex from independent cohorts or in AD mouse models. Importantly, by constructing co-expression networks, the authors identified well-known AD-related molecular signatures, such as the downregulation of neuronal genes and the upregulation of glia-enriched modules. They also showed that the top AD-associated PHG protein modules are strongly conserved across other protein and gene co-expression networks in AD. Using a Bayesian network model, the authors identified 580 key drivers, including proteins previously linked to AD, such as MSN and PRDX6, as well as many that have not been previously implicated or are understudied in the disease. From a systems biology perspective, the Bayesian causal network framework is particularly valuable, as it prioritizes proteins that may not be the most differentially expressed but exert regulatory control over disease‑associated modules. This approach could help shift the field from cataloging correlates of pathology to identifying tractable intervention points. Finally, they prioritized AHNAK, a top AD driver and hub gene in the leading glia/neuron module, which was experimentally validated in iPSC-based AD model systems.
This work is a valuable resource for AD research. First, it generated large-scale proteomic data from the parahippocampal gyrus (PHG); second, it provides a comprehensive comparative analysis across independent datasets and distinct AD phenotypes; and finally, its state-of-the-art network analyses revealed hundreds of protein modules and highlighted driver proteins. Its importance extends beyond the identified astrocytic hub AHNAK, as many other key driver proteins had not been previously reported in AD, offering a foundational framework for mechanistic discovery and therapeutic target prioritization in this disease.
References:
Green GS, Fujita M, Yang HS, Taga M, Cain A, McCabe C, Comandante-Lou N, White CC, Schmidtner AK, Zeng L, Sigalov A, Wang Y, Regev A, Klein HU, Menon V, Bennett DA, Habib N, De Jager PL. Cellular communities reveal trajectories of brain ageing and Alzheimer's disease. Nature. 2024 Sep;633(8030):634-645. Epub 2024 Aug 28 PubMed.
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Murdock MH, Tsai LH. Insights into Alzheimer's disease from single-cell genomic approaches. Nat Neurosci. 2023 Feb;26(2):181-195. Epub 2023 Jan 2 PubMed.
Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, Glass J, Gearing M, Thambisetty M, Troncoso JC, Geschwind DH, Lah JJ, Levey AI. A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. Cell Syst. 2017 Jan 25;4(1):60-72.e4. Epub 2016 Dec 15 PubMed.
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