Evidence map›Paper›PMID 39707393›Full record

ArticleJournal of translational medicine2024

Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.

Guorong Chen, Hongying Qi, Li Jiang, Shijie Sun, Junhai Zhang, Jiali Yu, Fang Liu, Yanli Zhang, Shiyu Du

Abstract read
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Guorong Chen *Department of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Hongying Qi *Department of Spleen and Stomach Diseases of Traditional Chinese Medicine, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Li Jiang *Department of Endocrinology, Aviation General Hospital, Beijing, 100025, China.
Shijie SunDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Junhai ZhangDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Jiali YuDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Fang LiuDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China.
Yanli ZhangDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China. 13521234067@163.com.
Shiyu DuDepartment of Gastroenterology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Beijing, 100029, China. dushiyu1975@126.com.ORCID 0000-0002-5482-2240

Funding

China Health and Medical Development Foundation 2022-HX-6
6 · The paper itself

Abstract

backgroundUlcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic disruptions. Tryptophan metabolism has been believed as a significant factor in UC pathogenesis, with specific metabolites influencing immune modulation and gut microbiota interactions. However, the precise regulatory mechanisms and key genes involved remain unclear.

methodsAUCell, Ucell, and other functional enrichment algorithms were utilized to determine the activation patterns of tryptophan metabolism at the UC cell level. Differential analysis identified key genes associated with tryptophan metabolism. Five machine learning algorithms, including Random Forest, Boruta algorithm, LASSO, SVM-RFE, and GBM were integrated to identify and categorize disease-specific characteristic genes.

resultsWe observed significant heterogeneity in tryptophan metabolism activity across cell types in UC, with the highest activity levels in macrophages and fibroblasts. Among the key tryptophan metabolism-related genes, CTSS, S100A11, and TUBB were predominantly expressed in macrophages and significantly upregulated in UC, highlighting their involvement in immune dysregulation and inflammation. Cross-analysis with bulk RNA data confirmed the consistent upregulation of these genes in UC samples, highly indicating their relevance in UC pathology and potential as targets for therapeutic intervention.

conclusionsThis study is the first to reveal the heterogeneity of tryptophan metabolism at the single-cell level in UC, with macrophages emerging as key contributors to inflammatory processes. The identification of CTSS, S100A11, and TUBB as key regulators of tryptophan metabolism in UC underscores their potential as biomarkers and therapeutic targets.

Indexed as

Colitis, UlcerativeMachine LearningRNA-SeqSingle-Cell AnalysisTryptophanAlgorithmsHumansSingle-Cell Gene Expression AnalysisTryptophanMacrophageTryptophan metabolismUlcerative colitis

Identifiers

PMID39707393
PMCPMC11662780

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.