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An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.
Brief Bioinform. 2022 01 17; 23(1)BB

Abstract

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

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Authors+Show Affiliations

Lin CX
School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. Hunan Provincial Key Lab of Bioinformatics, Central South University, Changsha, Hunan 410083, P. R. China.
Li HD
School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. Hunan Provincial Key Lab of Bioinformatics, Central South University, Changsha, Hunan 410083, P. R. China.
Deng C
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. Hunan Provincial Key Lab of Bioinformatics, Central South University, Changsha, Hunan 410083, P. R. China.
Liu W
School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. Hunan Provincial Key Lab of Bioinformatics, Central South University, Changsha, Hunan 410083, P. R. China.
Erhardt S
Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Wu FX
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada.
Zhao XM
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Guan Y
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States.
Wang J
Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Wang D
Department of Biostatistics and Medical Informatics and Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Hu B
Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China.
Wang J
School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. Hunan Provincial Key Lab of Bioinformatics, Central South University, Changsha, Hunan 410083, P. R. China.

MeSH

AgingAlzheimer DiseaseBrainGene Expression ProfilingGene Regulatory NetworksGenetic Predisposition to DiseaseHumansLongitudinal StudiesMemoryProteomicsRNA-SeqTranscriptome

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

34953465

Citation

Lin, Cui-Xiang, et al. "An Integrated Brain-specific Network Identifies Genes Associated With Neuropathologic and Clinical Traits of Alzheimer's Disease." Briefings in Bioinformatics, vol. 23, no. 1, 2022.
Lin CX, Li HD, Deng C, et al. An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease. Brief Bioinform. 2022;23(1).
Lin, C. X., Li, H. D., Deng, C., Liu, W., Erhardt, S., Wu, F. X., Zhao, X. M., Guan, Y., Wang, J., Wang, D., Hu, B., & Wang, J. (2022). An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease. Briefings in Bioinformatics, 23(1). https://doi.org/10.1093/bib/bbab522
Lin CX, et al. An Integrated Brain-specific Network Identifies Genes Associated With Neuropathologic and Clinical Traits of Alzheimer's Disease. Brief Bioinform. 2022 01 17;23(1) PubMed PMID: 34953465.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease. AU - Lin,Cui-Xiang, AU - Li,Hong-Dong, AU - Deng,Chao, AU - Liu,Weisheng, AU - Erhardt,Shannon, AU - Wu,Fang-Xiang, AU - Zhao,Xing-Ming, AU - Guan,Yuanfang, AU - Wang,Jun, AU - Wang,Daifeng, AU - Hu,Bin, AU - Wang,Jianxin, PY - 2021/08/06/received PY - 2021/10/26/revised PY - 2021/11/13/accepted PY - 2021/12/26/pubmed PY - 2022/3/12/medline PY - 2021/12/25/entrez KW - brain gene network KW - disease gene prediction KW - multi-omics JF - Briefings in bioinformatics JO - Brief Bioinform VL - 23 IS - 1 N2 - Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD. SN - 1477-4054 UR - https://www.unboundmedicine.com/medline/citation/34953465/An_integrated_brain_specific_network_identifies_genes_associated_with_neuropathologic_and_clinical_traits_of_Alzheimer's_disease_ DB - PRIME DP - Unbound Medicine ER -