Kitani Akihiro, Matsui Yusuke
Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Institute for Glyco-core Research (iGCORE), Nagoya University, 461-8673 Nagoya, Aichi, Japan.
bioRxiv. 2024 Oct 28:2024.06.14.599092. doi: 10.1101/2024.06.14.599092.
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.
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).
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 gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis.
These findings indicate 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).
尽管淀粉样β蛋白和tau蛋白之间的相互作用与阿尔茨海默病（AD）有关，但其促进疾病进展的具体机制尚未完全明确。此外，尽管深度学习在各个生物医学领域的应用日益广泛，但其在整合网络以分析AD研究中的疾病机制方面的应用仍然有限。在本研究中，我们采用了基于深度学习的网络整合方法BIONIC，来整合蛋白质组学和蛋白质-蛋白质相互作用数据，旨在揭示调节Aβ-tau相互作用对轻度认知障碍（MCI）和早期AD影响的因素。
使用基于深度学习的模型将ROSMAP队列的蛋白质组学数据与蛋白质-蛋白质相互作用（PPI）数据进行整合。对组织病理学和基因表达数据进行线性回归分析，并使用互信息来检测调节因子。使用Benjamini-Hochberg校正确定统计学显著性（p < 0.05）。
我们的结果表明星形胶质细胞和GPNMB+小胶质细胞调节Aβ-tau相互作用。基于对组织病理学和基因表达数据的线性回归，GFAP和IBA1水平以及基因表达在非痴呆病例中对tau与Aβ的相互作用有正向贡献，重复了网络分析的结果。
这些发现表明GPNMB+小胶质细胞在早期AD中调节Aβ-tau相互作用，因此是一个新的治疗靶点。为便于进一步研究，我们已将整合网络作为可视化工具提供给科学界（网址：https://igcore.cloud/GerOmics/AlzPPMap）。
