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Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease
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Abstract
BACKGROUND: Although interactions between amyloid-beta and tau proteins have been implicated in Alzheimer's disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein-protein interaction data, with an aim to uncover factors that moderate the effects of the Aβ-tau interaction on mild cognitive impairment (MCI) and early-stage AD. METHODS: Proteomic data from the ROSMAP cohort were integrated with protein-protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini-Hochberg correction (p < 0.05). RESULTS: Our results suggested that astrocytes and GPNMB + microglia moderate the Aβ-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis. CONCLUSIONS: These findings suggest that GPNMB + microglia moderate the Aβ-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap ).
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AI-based quantitative modeling and integrative network analysis have been applied to assess autophagy and UPS-related pathways in AD models.
“To investigate these proteostatic disruptions, AI-based methods such as quantitative modeling and integrative network analysis have been applied to assess autophagy and UPS-related pathways in AD models52,53.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [53]
The Results (‘Integration of protein co-expression and physical PPI networks’ and ‘Stress response factors moderate Aβ-tau interactions’; page not provided) describe deep-learning-based network analysis of human AD brain data and identify ubiquitin–proteasome-system factors as potential moderators. The passages do not show an assessment of autophagy pathways or an analysis in AD models; the study used postmortem patient data. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI-driven multi-omics approaches have shown considerable promise across key domains, including early diagnosis, mechanistic elucidation, biomarker discovery, therapeutic development, and patient stratification.
“These integrative approaches have shown considerable promise across key domains, including early diagnosis, mechanistic elucidation, biomarker discovery, therapeutic development, and patient stratification (Fig. 4, Table 325,53,58,226,237,251,253,255,261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 276,278,283,287,289,319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330,”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [53]
The Background and Discussion (pages unavailable) support mechanistic elucidation: the paper uses deep-learning network integration to investigate Aβ–tau interactions in early-stage Alzheimer’s disease. The Discussion also suggests that its findings could inform future therapeutic strategies. It does not establish promise for early diagnosis, biomarker discovery, or patient stratification, so it supports only part of the cited list. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe BIONIC deep learning model integrating ROS/MAP proteomic data with PPI networks highlighted the roles of GPNMB-positive microglia and astrocytic markers like IBA1 and GFAP in mediating the amyloid-tau cascade.
“Deep learning models such as BIONIC have facilitated mechanistic dissection by integrating ROS/MAP proteomic data with PPI networks. This analysis highlighted the roles of GPNMB-positive microglia and astrocytic markers like IBA1 and GFAP in mediating the amyloid–tau cascade.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [53]
The Results (Integration of protein co-expression and physical PPI networks) describe using BIONIC to integrate ROSMAP proteomic data with a PPI network, and the Discussion identifies GPNMB-positive microglia and astrocytes as possible moderators of the Aβ–tau interaction. However, the Results (Histopathological data analysis) explicitly identify IBA1 as a microglial marker and GFAP as an astrocyte marker. The paper describes moderation and statistical interactions, not established mediation of the cascade. (LLM, conf=0.88)
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Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97
Interneuron transcriptomics reveals pathologic markers of Alzheimer's disease progression
Chen, K. S., Noureldein, M. H., Rigan, D. M., Hayes, J. M., Loi, K. J., Hur, J., Savelieff, M. G., & Feldman, E. L. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.107012 · Cited by 9
The Role of Glycoprotein Non‐Metastatic Melanoma Protein B (GPNMB) in Tumors and Other Diseases
Wu, Z., Sun, N., Qu, M., Kang, X., Zhang, Y., & Qi, X. (2025) · MedComm – Oncology · doi:10.1002/mog2.70033 · Cited by 6
Moderating effects of plasma glial fibrillary acidic protein along the Alzheimer's disease continuum
Lee, S. Y., Diaz, V. E., Emanuel, O. M., Matusz, E. F., Webb, J. D., Chan, B., Lago, A. L., Wang, W., DeSimone, J. C., Rojas, J. C., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70626 · Cited by 4
Microglial transcriptional profiles of a transgenic rat model closely model Alzheimer's disease
Finno, C. J., Ghosh, S., Rodriguez, V., Valenzuela, A. E., Andrew, P. M., Park, H. H., Grodzki, A. C., Nasim, N., Roberts, K. A., Durbin-Johnson, B., et al. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251410206 · Cited by 1
VSMC DDR1 Mechanosensation Aggravates Atherosclerosis via Paracrine Signaling
Zhou, S., Chu, X., Zhang, J., Zhang, J., Xu, Y., Fan, L., Liang, Z., Shen, Y., Yao, W., Chen, Z., et al. (2026) · Circulation Research · doi:10.1161/circresaha.126.328540
Metadata
- Authors
- Kitani, A., Matsui, Y., & Initiative, F. T. A. D. N.
- Affiliations
- Nagoya University (JP)
- Corresponding author
- Matsui, Yusuke
- Venue
- Alzheimer's Research & Therapy
- Published
- 2025-04-02
- Volume
- 17
- Issue
- 1
- Pages
- 70-70
- ISSN
- 1758-9193 (ISSN-L)
- PMC
- PMC11967117
- OpenAlex
- W4409126526
- Cited by
- 6
- References
- 75
- Citations by year
- 2026: 2 · 2025: 4
- FWCI
- 3.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alzheimer s Research & Therapy (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Human Glycome Atlas Project
- Japan Society for the Promotion of Science (JP20H04282)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Alzheimer's disease, amyloid-beta, tau protein, moderators, GPNMB, microglia, astrocytes, glial fibrillary acidic protein, Iba1, mild cognitive impairment, early Alzheimer's disease, proteomics, protein-protein interaction network, network integration, therapeutic target
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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