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Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation.

Overview

Authors: Wenhui Wang1, Meizhen Sun2
  1. Department of Neurology, Children’s Hospital of Shanxi, Shanxi Maternal and Child Health Hospital, The Affiliated Children’s Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
  2. Department of Neurology, First Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
Journal: Frontiers in neuroscience, volume 20, article 1913179
Dates: received 18 June 2026; accepted 7 July 2026; published online 17 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1913179 · PMID 42677124 · PMCID PMC13528210 · OpenAlex W7203612561
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), human (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Spectral & time-frequency
Keywords: artificial intelligence, astrocyte, drug repositioning, epilepsy, tryptophan metabolism
Topic: Epilepsy research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

Background: Epilepsy (EP) is a prevalent neurological disorder with complex etiology, often involving metabolic dysregulation. Emerging evidence highlights the role of tryptophan metabolism (TM) and astrocyte dysfunction in EP pathogenesis. This study aimed to decode the TM and astrocyte (TA)-related molecular signature and identify a central therapeutic target for EP.

Methods: By integrating seven hippocampal bulk profiles (GSE28674, GSE256068, GSE57585, GSE163296, GSE63808, GSE90886, and GSE134697) from patients with EP using integrative bioinformatics pipelines, including limma, xCell, WGCNA, machine learning, and consensus clustering, we identified a TA-associated diagnostic signature and a molecular stratification model for EP. Next, the TA-associated hub gene was identified by SHAP analysis, and its molecular patterns in astrocytes were characterized using single-cell hippocampal data from patients with EP (GSE190452). In addition, the active-learning framework DrugReflector and molecular docking were used to identify a potential therapeutic agent targeting the TA-associated hub gene in the integrated GSE63808 and GSE90886 dataset. Finally, hippocampal tissue from patients with EP was used to examine expression of the TA-associated hub gene.

Results: Four TA-associated shared DEGs were identified: CAT, TXNDC2, RBM27, and GPT. These four DEGs showed predictive value for EP. RBM27 was identified as the central pathogenic factor; it was upregulated and predominantly expressed in astrocytes. Drug prediction identified BRD-K04111260 as a promising compound targeting RBM27 for the treatment of EP.

Conclusion: This study establishes a novel TA-associated molecular axis in EP pathogenesis, identifying RBM27 as a critical hub gene with strong diagnostic potential and therapeutic promise.

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

Code

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Data

Datasets cited

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 44 references.

Cite

This paper

Wang, W., & Sun, M. (2026). Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation. Frontiers in neuroscience, 20, 1913179. https://doi.org/10.3389/fnins.2026.1913179

BibTeX

@article{wang2026decoding,
author = {Wang, Wenhui and Sun, Meizhen},
title = {{Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation}},
journal = {Frontiers in neuroscience},
year = {2026},
month = aug,
volume = {20},
pages = {1913179},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1913179},
url = {https://doi.org/10.3389/fnins.2026.1913179},
pmid = {42677124},
pmcid = {PMC13528210}
}

RIS

TY - JOUR
AU - Wang, Wenhui
AU - Sun, Meizhen
TI - Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/08/17
VL - 20
SP - 1913179
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1913179
UR - https://doi.org/10.3389/fnins.2026.1913179
LA - en
ER -

CSL-JSON

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