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Integrated Metabolomic and Transcriptomic Analyses Reveal Alterations in the Serotonergic Synapse Pathway and a Robust Diagnostic Model in Ulcerative Colitis.

Overview

  1. Formula-Pattern Research Center, Jiangxi University of Chinese Medicine, Nanchang 330004, China
  2. Department of Postgraduate, Jiangxi University of Chinese Medicine, Nanchang 330004, China
  3. Department of Anorectal Surgery, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang 330006, China
  4. School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang 330004, China
Journal: Metabolites, volume 16, issue 4, article 263
Dates: received 22 February 2026; accepted 9 April 2026; published online 14 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/metabo16040263 · PMID 42042908 · PMCID PMC13117237 · OpenAlex W7154277130
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), cellular / molecular (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: ulcerative colitis, metabolomic, transcriptomic, serotonergic synapse pathway, diagnostic model
Topic: Inflammatory Bowel Disease (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Qihuang Scholar Cultivation Project (NATCM Personnel & Education Letter [2025] No. 182); Jiangxi Provincial Natural Science Foundation (No. 20252BAC250103, No. 20224BAB206099)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

Objectives: To overcome the limitations of invasive diagnostic approaches for ulcerative colitis (UC) diagnosis, this study integrates liquid chromatography–mass spectrometry (LC–MS)-based serum metabolomics with mucosal transcriptomics to elucidate the interplay between systemic metabolic perturbations and neuroendocrine signaling in UC pathogenesis. Methods: Serum metabolites and mucosal differentially expressed genes (DEGs) were identified through multi-omics profiling. Key neurotransmitter receptor-related genes (NRRGs) were prioritized using three machine learning algorithms: LASSO, Random Forest, and SVM-RFE. A three-gene diagnostic nomogram was developed and rigorously validated across multiple independent cohorts (GSE48958, GSE73661) using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results: The integrated analysis revealed 334 dysregulated metabolites and 3093 DEGs, both converging on the serotonergic synapse pathway. Specific molecular alterations were uncovered, including tryptophan depletion linked to the downregulation of SLC6A4, concomitant with abnormal serotonin accumulation and PTGS2-mediated inflammatory responses. The three-gene signature, HTR3C, RPS6KA6, and NETO2, formed a highly robust diagnostic model, achieving an area under the ROC curve (AUC) exceeding 0.96 in both the training cohort and external validation sets. Conclusions: This multi-omics study delineates a neuroimmune mechanism in UC centered on dysregulation of the serotonergic synapse. The resulting three-gene nomogram identifies a candidate biomarker signature that demonstrates strong discriminative potential; however, given the exceptionally high performance metrics, these findings should be interpreted as a preliminary diagnostic framework rather than a clinically validated tool, and its efficacy relative to standard markers like CRP or fecal calprotectin requires further investigation in prospective real-world cohorts. Nonetheless, this study provides critical mechanistic insights into gut–brain axis dysfunction in UC.

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

Code

The paper links to its data, not to its authors' code: see the Data section.

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Data

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Data Availability Statement

The datasets presented in this study are available in online repositories. The names of the repository/repositories and accession number(s) are provided in the article/Supplementary Materials. Additional data supporting the conclusions of this study will be made available by the authors 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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 2 funders, 50 references.

Cite

This paper

Wang, H., Wu, H., Fu, Y., Lv, X., Li, C., Jin, Y., Ge, W., & Wu, Z. (2026). Integrated Metabolomic and Transcriptomic Analyses Reveal Alterations in the Serotonergic Synapse Pathway and a Robust Diagnostic Model in Ulcerative Colitis. Metabolites, 16(4), 263. https://doi.org/10.3390/metabo16040263

BibTeX

@article{wang2026integrated,
author = {Wang, Haiyan and Wu, Hanlin and Fu, Yuzhen and Lv, Xuhan and Li, Chao and Jin, Yan and Ge, Wei and Wu, Zenan},
title = {{Integrated Metabolomic and Transcriptomic Analyses Reveal Alterations in the Serotonergic Synapse Pathway and a Robust Diagnostic Model in Ulcerative Colitis}},
journal = {Metabolites},
year = {2026},
month = apr,
volume = {16},
number = {4},
pages = {263},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2218-1989},
doi = {10.3390/metabo16040263},
url = {https://doi.org/10.3390/metabo16040263},
pmid = {42042908},
pmcid = {PMC13117237}
}

RIS

TY - JOUR
AU - Wang, Haiyan
AU - Wu, Hanlin
AU - Fu, Yuzhen
AU - Lv, Xuhan
AU - Li, Chao
AU - Jin, Yan
AU - Ge, Wei
AU - Wu, Zenan
TI - Integrated Metabolomic and Transcriptomic Analyses Reveal Alterations in the Serotonergic Synapse Pathway and a Robust Diagnostic Model in Ulcerative Colitis
T2 - Metabolites
J2 - Metabolites
PY - 2026
DA - 2026/04/14
VL - 16
IS - 4
SP - 263
SN - 2218-1989
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/metabo16040263
UR - https://doi.org/10.3390/metabo16040263
LA - en
ER -

CSL-JSON

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