ArticleCommunications biology2023
Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Multi-Omics Integration and Causal Inference Identify HSD17B1 as a Potential Nobiletin Target Linking Neurosteroid Metabolism to Alzheimer's Disease.International journal of molecular sciences · 2026Article
- The JMJD family histone demethylases: structure, mechanism of action, diseases and therapeutic targets.Molecular biomedicine · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Pathomic immune biomarkers define recurrence risk in early-stage melanoma.The oncologist · 2025Article
- Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.Nature communications · 2025Article
- RNA dysregulation in neurodegenerative diseases.The EMBO journal · 2025Review
- Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
- Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders.Pharmaceuticals (Basel, Switzerland) · 2024Review
- JMJD6 Autoantibodies as a Potential Biomarker for Inflammation-Related Diseases.International journal of molecular sciences · 2024Article
- Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.bioRxiv : the preprint server for biology · 2024Article
- The ROSMAP project: aging and neurodegenerative diseases through omic sciences.Frontiers in neuroinformatics · 2024Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.