Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease
Open accessPublished version of a preprint
Abstract
Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.
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A recent study identified TFs contributing to disruption of gene regulatory networks in AD.
“In fact, a recent study89 identified TFs contributing to disruption of gene regulatory networks in AD, demonstrated their ability to predict AD cognitive phenotypes, and used them to prioritize candidate drugs that could be repurposed for AD.”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [89]
TFs can predict AD cognitive phenotypes.
“In fact, a recent study89 identified TFs contributing to disruption of gene regulatory networks in AD, demonstrated their ability to predict AD cognitive phenotypes, and used them to prioritize candidate drugs that could be repurposed for AD.”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [89]
TFs were used to prioritize candidate drugs that could be repurposed for AD.
“In fact, a recent study89 identified TFs contributing to disruption of gene regulatory networks in AD, demonstrated their ability to predict AD cognitive phenotypes, and used them to prioritize candidate drugs that could be repurposed for AD.”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [89]
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References 109 · Cited by 33
Research works printed but not linked: 6
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- Authors
- Gupta, C., Xu, J., Jin, T., Khullar, S., Liu, X., Alatkar, S., Cheng, F., & Wang, D.
- Affiliations
- University of Wisconsin–Madison (US)
- Cleveland Clinic (US)
- Corresponding author
- Wang, Daifeng
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-07-18
- Volume
- 18
- Issue
- 7
- Pages
- e1010287
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC9333448
- OpenAlex
- W4285728858
- Cited by
- 33
- References
- 109
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 8 · 2023: 7 · 2022: 1
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-07-28)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01AG067025, R21CA237955, R03NS123969, U01MH116492, R01AG066707, U01AG073323, 3R01AG066707-02S1, 1R56AG074001-01)
- National Science Foundation (2144475)
- University of Wisconsin–Madison
- National Institute of Child Health and Human Development (NIH P50HD105353)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Alzheimer's disease, gene regulatory networks, cell type-specific gene regulation, transcription factors, network motifs, feed-forward loop, microglia, excitatory neurons, inhibitory neurons, oligodendrocytes, network modules, drug repurposing, cognitive impairment prediction, single-cell multi-omics
- MeSH terms
- Alzheimer Disease, Biology, Humans, Phenotype, Gene Expression Profiling, Gene Regulatory Networks, Drug Repositioning
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