Evidence map›Paper›PMID 40609069›Full record

ArticlePLoS computational biology2025

Understanding disease-associated metabolic changes in human colonic epithelial cells using the iColonEpithelium metabolic reconstruction.

Boyu Jiang, Nick Quinn-Bohmann, Christian Diener, Vignesh Bose Nathan, Yu Han-Hallett, Lavanya Reddivari, Sean M Gibbons, Priyanka Baloni

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Boyu JiangSchool of Health Sciences, Purdue University, West Lafayette, Indiana, United States of America.
Nick Quinn-BohmannInstitute for Systems Biology, Seattle, Washington, United States of America.
Christian DienerInstitute for Systems Biology, Seattle, Washington, United States of America.
Vignesh Bose NathanDepartment of Food Science, Purdue University, West Lafayette, Indiana, United States of America.
Yu Han-HallettBindley Bioscience Center, Purdue University, West Lafayette, Indiana, United States of America.
Lavanya ReddivariDepartment of Food Science, Purdue University, West Lafayette, Indiana, United States of America.
Sean M GibbonsInstitute for Systems Biology, Seattle, Washington, United States of America.
Priyanka BaloniSchool of Health Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0002-3382-4941

Funding

CyberGut: towards personalized human-microbiome metabolic modeling for precision health and nutritionR01DK133468 · NIDDK · INSTITUTE FOR SYSTEMS BIOLOGY · PI Sean Michael Gibbons · 2022 to 2026
$3.6M
NIDDK NIH HHS R01 DK133468
6 · The paper itself

Abstract

The colonic epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colonic epithelium, our present study focuses on the generation of the first cell-type-specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at the systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. iColonEpithelium is also capable of performing metabolic tasks specific to the colonic epithelium. A unique transport reaction compartment has been included to allow for the simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We used single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples to build disease-specific iColonEpithelium metabolic networks in order to predict metabolic signatures of colonocytes in both healthy and disease states. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, relative to healthy control, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and we show an initial proof-of-concept for how our tool can be leveraged to explore the metabolic interactions between host and gut microbiota.

Indexed as

ColonEpithelial CellsIntestinal MucosaModels, BiologicalColitis, UlcerativeComputational BiologyCrohn DiseaseGastrointestinal MicrobiomeHumansMetabolic Networks and Pathways

Identifiers

PMID40609069
PMCPMC12240309

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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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.