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A neuron-distributed DEK integrates ac4C modification and neuroinflammation in pathogenesis of Parkinson disease: evidence from Mendelian randomization, multi-omics and <i>in vitro</i> validation.

Overview

Authors: Yuan Li1
  1. West China Hospital, West China School of Nursing, Sichuan University, Chengdu, Sichuan, China
Journal: Frontiers in neuroscience, volume 20, article 1950353
Dates: received 10 August 2026; accepted 19 August 2026; published online 1 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1950353 · PMID 42745872 · PMCID PMC13574935 · OpenAlex W7204889728
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: genetics / omics (modality), human (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: ac4C modification, DEK, machine learning, multi-omics, neuroinflammation, Parkinson’s disease
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Background: Epi-transcriptomic modifications, particularly N4-acetylcytidine (ac4C), and chronic neuroinflammation have emerged as pivotal players in the pathogenesis of Parkinson’s disease (PD). However, the specific molecular co-expression patterns linking ac4C RNA modification to neuronal inflammatory responses remains largely uncharted.

Objective: This study aimed to decode the ac4C-neuroinflammation (AN)-associated molecular patterns in PD and to identify a neuron-specific central pathogenic and therapeutic factor.

Methods: We integrated multi-omics analyses by using peripheral blood bulk transcriptomes (GSE18838, GSE49126, GSE22491, GSE6613, and GSE57475) and GWAS data from PD patients for identification of AN-related risk genes. Next, consensus clustering and 3 machine learning algorithms (LASSO, RF, and SVM-RFE) were applied for patient stratification, hub gene identification, and diagnostic modeling. Single-cell transcriptomic profiling of PD patients (GSE140231) was leveraged to map the cellular distribution and mechanistic roles of the hub gene within the substantia nigra (SN). An AI-driven active learning framework and the CTD database were utilized to screen therapeutic candidates targeting the hub gene, with binding affinities validated via molecular docking. In vitro experiments finally validated the expression of hub gene.

Results: We pinpointed 7 AN-associated risk DEGs for PD patients, including HSP90AA1, DEK, LEF1, IRF2, BCL2L1, CFL1, and BCR, which effectively stratified PD patients into 2 distinct immune-molecular subgroups. DEK can be considered as neuron-distributed and up-regulated AN-associated central pathogenic factor for PD patients. The DrugReflector active learning framework identified BRD-K57589644 as a computationally prioritized compound warranting further investigation.

Conclusion: This study establishes a novel AN-associated molecular patterns in PD, identifying DEK as a computationally identified neuron-specific factor associated with ac4C modification and neuroinflammatory cascades for PD patients.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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The paper's code and data availability statement is in the Data section.

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Data

Data links

Data availability statement

All multi-omics data supporting this study are publicly accessible via three repositories: Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), IEU Open GWAS database (https://gwas.mrcieu.ac.uk/), GeneCards (https://www.genecards.org/). Analyzed datasets include GSE18838, GSE49126, GSE22491, GSE6613, GSE57475, GSE140231, and GWAS summary statistics (ieu-b-7). Analytical R scripts are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 1 author, 6 keywords, 37 references.

Cite

This paper

Li, Y. (2026). A neuron-distributed DEK integrates ac4C modification and neuroinflammation in pathogenesis of Parkinson disease: evidence from Mendelian randomization, multi-omics and <i>in vitro</i> validation. Frontiers in neuroscience, 20, 1950353. https://doi.org/10.3389/fnins.2026.1950353

BibTeX

@article{li2026neuron,
author = {Li, Yuan},
title = {{A neuron-distributed DEK integrates ac4C modification and neuroinflammation in pathogenesis of Parkinson disease: evidence from Mendelian randomization, multi-omics and \<i\>in vitro\</i\> validation}},
journal = {Frontiers in neuroscience},
year = {2026},
month = sep,
volume = {20},
pages = {1950353},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1950353},
url = {https://doi.org/10.3389/fnins.2026.1950353},
pmid = {42745872},
pmcid = {PMC13574935}
}

RIS

TY - JOUR
AU - Li, Yuan
TI - A neuron-distributed DEK integrates ac4C modification and neuroinflammation in pathogenesis of Parkinson disease: evidence from Mendelian randomization, multi-omics and <i>in vitro</i> validation
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/09/01
VL - 20
SP - 1950353
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1950353
UR - https://doi.org/10.3389/fnins.2026.1950353
LA - en
ER -

CSL-JSON

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