Predictive Network Analysis Identifies JMJD6 and Other Novel Key Drivers in Alzheimer Disease
Julie P. MerchantiD, Kuixi ZhuiD, Marc HenrioniD, Syed Shujaat Ali ZaidiiD, Branden Lau, Sara Moein, Melissa L. AlampreseiD, Richard V. PearseiD, David Alan BennettiD, Nilüfer Ertekin‐TaneriD, Tracy L. Young‐PearseiD, Rui R. ChangiD
Abstract
Despite decades of genetic studies on late onset Alzheimers disease (LOAD), the molecular mechanisms of Alzheimers disease (AD) remain unclear. Furthermore, different cell types in the central nervous system (CNS) play distinct roles in the onset and progression of AD pathology. To better comprehend the complex etiology of AD, we used an integrative approach to build robust predictive (causal) network models which were cross-validated over multiple large human multi-omics datasets in AD. We employed a published method to delineate bulk-tissue gene expression into single cell-type gene expression and integrated clinical and pathologic traits of AD, single nucleotide variation, and deconvoluted gene expression for the construction of predictive network models for each cell type in AD. With these predictive causal models, we are able to identify and prioritize robust key drivers of the AD-associated network state. In this study, we focused on neuron-specific network models and prioritized 19 predicted key drivers modulating AD pathology. These targets were validated via shRNA knockdown in human induced pluripotent stem cell (iPSC) derived neurons (iNs), in which 10 out of the 19 neuron-related targets (JMJD6, NSF, NUDT2, YWHAZ, RBM4, DCAF12, NDRG4, STXBP1, ATP1B1, and FIBP) significantly modulated levels of amyloid-beta and/or phosphorylated tau peptides in the postmitotic iNs. Most notably, knockdown of JMJD6 significantly altered the neurotoxic ratios of A{beta}42 to 40 and p231-tau to total tau, indicating its potential therapeutic relevance to both amyloid and tau pathology in AD. Molecular validation by RNA sequencing (RNAseq) in iNs further confirmed the network structure, showing significant enrichment in differentially expressed genes after knockdown of the validated targets. Interestingly, our network model predicts that these 10 key drivers are upstream regulators of REST and VGF, two recently identified key regulators of AD pathogenesis.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease
Chirag Gupta, Jielin Xu, … Daifeng Wang·PLoS Computational BiologyPeer-reviewed·Jul 18, 2022
- Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimers Disease
Kátia de Paiva Lopes, Ricardo A. Vialle, … David Bennett·Acta Neuropathologica CommunicationsPeer-reviewed·Nov 4, 2025
- Molecular Networks and Key Regulators of the Dysregulated Neuronal System in Alzheimer’s Disease
Minghui Wang, Aiqun Li, … Bin Zhang·bioRxiv·Oct 1, 2019
- Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease
Jielin Xu, Pengyue Zhang, … Feixiong Cheng·Genome ResearchPeer-reviewed·Feb 24, 2021
- A molecular network of the aging brain implicates INPPL1 and PLXNB1 in Alzheimer’s disease
Sara Mostafavi, Chris Gaiteri, … Philip L. De Jager·Nature NeurosciencePeer-reviewed·May 21, 2018
- Mapping cross-domain drivers of Alzheimer's disease risk through integrated network analysis
Gregory A. Cary, Stephen Keegan, … Gregory W. Carter·Alzheimer s & DementiaPeer-reviewed·Jun 1, 2026