Biomarker Genes Discovery of Alzheimer's Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia

被引:3
|
作者
Gao, Wenliang [1 ]
Kong, Wei [1 ]
Wang, Shuaiqun [1 ]
Wen, Gen [2 ]
Yu, Yaling [2 ,3 ]
机构
[1] Shanghai Maritime Univ, Coll Informat Engn, 1550 Haigang Ave, Shanghai 201306, Peoples R China
[2] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Dept Orthoped Surg, Shanghai 200233, Peoples R China
[3] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Inst Microsurg Extrem, Shanghai 200233, Peoples R China
基金
中国国家自然科学基金; 上海市自然科学基金;
关键词
SCENIC; multi-omics; gene regulatory network; microglia; prognosis; immunotherapy; BETA;
D O I
10.3390/brainsci12091196
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer's disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC). This enables snRNA-seq data and bulkRNA-seq data to obtain data on the deeper intermolecular regulatory relationships, related genes, and the molecular mechanisms of immune-cell action. In our approach, not only were central transcription factors (TF) STAT3, CEBPB, SPI1, and regulatory mechanisms identified more accurately than with single-omics but also immunotherapy targeting central TFs to drugs was found to be significantly different between patients. Thus, in addition to providing new insights into the potential regulatory mechanisms and pathogenic genes of AD microglia, this approach can assist clinicians in making the most rational treatment plans for patients with different risks; it also has significant implications for identifying AD immunotherapy targets and targeting microglia-associated immune drugs.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Microbial biomarker discovery in Parkinson's disease through a network-based approach
    Zhao, Zhe
    Chen, Jing
    Zhao, Danhua
    Chen, Baoyu
    Wang, Qi
    Li, Yuan
    Chen, Junyi
    Bai, Chaobo
    Guo, Xintong
    Hu, Nan
    Zhang, Bingwei
    Zhao, Rongsheng
    Yuan, Junliang
    NPJ PARKINSONS DISEASE, 2024, 10 (01)
  • [32] Dynamic Regulatory Network Reconstruction for Alzheimer's Disease Based on Matrix Decomposition Techniques
    Kong, Wei
    Mou, Xiaoyang
    Zhi, Xing
    Zhang, Xin
    Yang, Yang
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2014, 2014
  • [33] The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics
    Dong, Yun
    Song, Xun
    Wang, Xiao
    Wang, Shaoxiang
    He, Zhendan
    CNS NEUROSCIENCE & THERAPEUTICS, 2024, 30 (11)
  • [34] Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil
    Luís Rita
    Natalie R. Neumann
    Ivan Laponogov
    Guadalupe Gonzalez
    Dennis Veselkov
    Domenico Pratico
    Reza Aalizadeh
    Nikolaos S. Thomaidis
    David C. Thompson
    Vasilis Vasiliou
    Kirill Veselkov
    Human Genomics, 17
  • [35] An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery
    Wei, Ting
    Fa, Botao
    Luo, Chengwen
    Johnston, Luke
    Zhang, Yue
    Yu, Zhangsheng
    FRONTIERS IN GENETICS, 2021, 11
  • [36] NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction
    Zhu, Bencong
    Zhang, Zhen
    Leung, Suet Yi
    Fan, Xiaodan
    BRIEFINGS IN BIOINFORMATICS, 2024, 25 (05)
  • [37] Unraveling the gene regulatory networks that drive microglia and immune cell activation in Alzheimer's disease at single-cell resolution
    Scheyltjens, I.
    Kancheva, D.
    Van Hove, H.
    De Vlaminck, K.
    Antunes, A. R. P.
    Martens, L.
    Vandamme, N.
    De Prijck, S.
    Aerts, J.
    Saeys, Y.
    Reumers, J.
    Moechars, D.
    Van Ginderachter, J. A.
    Movahedi, K.
    GLIA, 2019, 67 : E600 - E600
  • [38] Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil
    Rita, Luis
    Neumann, Natalie R.
    Laponogov, Ivan
    Gonzalez, Guadalupe
    Veselkov, Dennis
    Pratico, Domenico
    Aalizadeh, Reza
    Thomaidis, Nikolaos S.
    Thompson, David C.
    Vasiliou, Vasilis
    Veselkov, Kirill
    HUMAN GENOMICS, 2023, 17 (01)
  • [39] Exploring the Efficacy and Target Genes of Atractylodes Macrocephala Koidz Against Alzheimer's Disease Based on Multi-Omics, Computational Chemistry, and Experimental Verification
    Zheng, Yuanteng
    Gao, Xin
    Tang, Jiyang
    Gao, Li
    Cui, Xiaotong
    Liu, Kechun
    Zhang, Xiujun
    Jin, Meng
    CURRENT ISSUES IN MOLECULAR BIOLOGY, 2025, 47 (02)
  • [40] Integrative multi-omics analysis reveals the critical role of the PBXIP1 gene in Alzheimer's disease
    Zhang, Jingyun
    Sun, Xiaoyi
    Jia, Xueqing
    Sun, Binggui
    Xu, Shijun
    Zhang, Weiping
    Liu, Zuyun
    AGING CELL, 2024, 23 (02)