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pmid:39935835

Vitamin A family suppresses periodontitis by restoring mitochondrial metabolic reprogramming in macrophages through JAK-STAT pathway.

Zishuo Cheng · Shun Huang · Qiya Tang · Danlan Zhang · Lan Huang

Frontiers in genetics · 2025 doi:10.3389/fgene.2025.1505933

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title
Vitamin A family suppresses periodontitis by restoring mitochondrial metabolic reprogramming in macrophages through JAK-STAT pathway.
doi
10.3389/fgene.2025.1505933
pmid
39935835
authors array · 5 items
[0]
Zishuo Cheng
[1]
Shun Huang
[2]
Qiya Tang
[3]
Danlan Zhang
[4]
Lan Huang
journal
Frontiers in genetics
year
2025
abstract
Objective: Mitochondrial metabolic reprogramming in macrophages is crucial in the development and progression of inflammation. Given vitamin A's antioxidant properties and its therapeutic effects on inflammation, this study aims to elucidate how vitamin A influences mitochondrial metabolic reprogramming in inflammatory states, specifically in periodontitis, through genetic bioinformatics and experimental methods. Method: The study utilized the GSE16134 dataset from the Gene Expression Omnibus (GEO) database, focusing on human periodontitis. Vitamin A-targeted genes (ATGs) were identified and analyzed using CIBERSORT to explore their role in inflammation. Cluster analysis revealed two phenotypes associated with ATGs, showing differential expression of genes like COX1, IL-1β, and STAT3, and immune activation patterns. Weighted Gene Co-expression Network Analysis (WGCNA) identified 145 markers correlated with ATG-guided phenotypes and inflammation. Machine learning models, combined with Gene Set Variation Analysis (GSVA), identified five key genes (RGS1, ACAT2, KDR, TUBB2A, TDO2) linked to periodontitis. Cell Type-Specific Enrichment Analysis (CSEA) highlighted macrophages as critical in metabolic reprogramming, validated by external datasets with an AUC of 0.856 in GSE10334 and 0.750 in GSE1730678. Experimental validation showed vitamin A's role in suppressing endoplasmic reticulum stress and altering mitochondrial dynamics, as well as metabolic reprogramming influencing inflammation via the STAT3 pathway in RAW 264.7 cells. Results: The study identified 13 differentially expressed ATGs in periodontitis, showing strong correlations with inflammation, particularly in plasma cells, macrophages, dendritic cells, neutrophils, and mast cells. Two ATG-guided phenotypes were identified, differing in gene expression and immune activation. WGCNA and machine learning models identified 145 markers and five key genes associated with periodontitis. GSVA and CSEA analyses highlighted the JAK-STAT pathway and macrophage involvement in metabolic reprogramming. Experimental data confirmed vitamin A's effects on mitochondrial dynamics and metabolic reprogramming through the STAT3 pathway. Conclusion: The study demonstrates that vitamin A's therapeutic effect on periodontitis is mediated through JAK-STAT pathway-guided mitochondrial metabolic reprogramming in macrophages. It identifies two genetic and immune-related phenotypes and five genetic identifiers associated with periodontitis risk.
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To identify co-expression modules, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package (version 1.70.3) (Langfelder and Horvath, 2008).
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To identify co-expression modules, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package (version 1.70.3) (Langfelder and Horvath, 2008).
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The data presented in the study are deposited in the Gene Expression Omnibus repository, accession numbers GSE16134, GSE173078, GSE10334.
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Homo sapiens
title
Bacterial Correlates of Gingival Gene Expression
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2026-11-01T15:39:54Z
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null
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20301
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Homo sapiens
title
Differential mRNA transcription in gingival tissues in periodontal health and disease [RNA-seq]
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2026-11-01T18:06:43Z
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identifier_resolution_only_no_download_no_licence_read
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To identify co-expression modules, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package (version 1.70.3) (Langfelder and Horvath, 2008).
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data object · 6 fields
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statements array · 1 item
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The data presented in the study are deposited in the Gene Expression Omnibus repository, accession numbers GSE16134, GSE173078, GSE10334.
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The paper names GSE16134, GSE10334, GSE173078 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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minimax
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minimax
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39935835", "kind": "science-record", "revision": 3, "content_hash": "sha256:d47aa3481cfae41513333b1ccab6738c37cce90e2ed706b0997c23a72cec55ed", "created_at": "2026-11-01T20:30:12Z", "updated_at": "2026-11-01T22:13:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Vitamin A family suppresses periodontitis by restoring mitochondrial metabolic reprogramming in macrophages through JAK-STAT pathway.", "doi": "10.3389/fgene.2025.1505933", "pmid": "39935835", "authors": ["Zishuo Cheng", "Shun Huang", "Qiya Tang", "Danlan Zhang", "Lan Huang"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Objective: Mitochondrial metabolic reprogramming in macrophages is crucial in the development and progression of inflammation. Given vitamin A's antioxidant properties and its therapeutic effects on inflammation, this study aims to elucidate how vitamin A influences mitochondrial metabolic reprogramming in inflammatory states, specifically in periodontitis, through genetic bioinformatics and experimental methods.\nMethod: The study utilized the GSE16134 dataset from the Gene Expression Omnibus (GEO) database, focusing on human periodontitis. Vitamin A-targeted genes (ATGs) were identified and analyzed using CIBERSORT to explore their role in inflammation. Cluster analysis revealed two phenotypes associated with ATGs, showing differential expression of genes like COX1, IL-1β, and STAT3, and immune activation patterns. Weighted Gene Co-expression Network Analysis (WGCNA) identified 145 markers correlated with ATG-guided phenotypes and inflammation. Machine learning models, combined with Gene Set Variation Analysis (GSVA), identified five key genes (RGS1, ACAT2, KDR, TUBB2A, TDO2) linked to periodontitis. Cell Type-Specific Enrichment Analysis (CSEA) highlighted macrophages as critical in metabolic reprogramming, validated by external datasets with an AUC of 0.856 in GSE10334 and 0.750 in GSE1730678. Experimental validation showed vitamin A's role in suppressing endoplasmic reticulum stress and altering mitochondrial dynamics, as well as metabolic reprogramming influencing inflammation via the STAT3 pathway in RAW 264.7 cells.\nResults: The study identified 13 differentially expressed ATGs in periodontitis, showing strong correlations with inflammation, particularly in plasma cells, macrophages, dendritic cells, neutrophils, and mast cells. Two ATG-guided phenotypes were identified, differing in gene expression and immune activation. WGCNA and machine learning models identified 145 markers and five key genes associated with periodontitis. GSVA and CSEA analyses highlighted the JAK-STAT pathway and macrophage involvement in metabolic reprogramming. Experimental data confirmed vitamin A's effects on mitochondrial dynamics and metabolic reprogramming through the STAT3 pathway.\nConclusion: The study demonstrates that vitamin A's therapeutic effect on periodontitis is mediated through JAK-STAT pathway-guided mitochondrial metabolic reprogramming in macrophages. It identifies two genetic and immune-related phenotypes and five genetic identifiers associated with periodontitis risk.", "claims": [{"id": "claim-method", "statement": "To identify co-expression modules, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package (version 1.70.3) (Langfelder and Horvath, 2008).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39935835", "locator": "sections[7] \"2.5 Weighted gene co-expression network analysis (WGCNA)\" paragraph 2", "quote": "To identify co-expression modules, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package (version 1.70.3) (Langfelder and Horvath, 2008)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data presented in the study are deposited in the Gene Expression Omnibus repository, accession numbers GSE16134, GSE173078, GSE10334.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39935835", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The data presented in the study are deposited in the Gene Expression Omnibus repository, accession numbers GSE16134, GSE173078, GSE10334."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39935835"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10334"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10334"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39935835", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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