Methylation-driven mechanisms of allergic rhinitis during pollen and non-pollen seasons using integrated bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38699230", "kind": "science-record", "revision": 4, "content_hash": "sha256:bbefb5af94e9b9f353419a3f7c60c6926c655e7d14d5edbe1c851ad58ebc2243", "created_at": "2026-11-01T19:20:39Z", "updated_at": "2026-11-01T22:11:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Methylation-driven mechanisms of allergic rhinitis during pollen and non-pollen seasons using integrated bioinformatics analysis.", "doi": "10.3389/fgene.2024.1242974", "pmid": "38699230", "authors": ["Pengcheng Sun", "Yi Wang", "Xing Liu", "Zhuqing Li", "Diankun Cui", "Qianru Li", "Qi Wang", "Ji Wang"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Allergic rhinitis (AR) is a widespread allergic airway disease that results from a complex interplay between genetic and environmental factors and affects approximately 10%-40% of the global population. Pollen is a common allergen, and exposure to pollen can cause epigenetic changes. However, the mechanism underlying pollen-induced DNA methylation changes and their potential effects on the allergic march are still unclear. The purpose of this study was to explore the methylation-driven mechanisms of AR during the pollen and non-pollen seasons using bioinformatics analysis and to investigate their relationship with asthma.\nMethods: We downloaded DNA methylation and gene expression data from the GEO database (GSE50387: GSE50222, GSE50101) and identified differentially methylated positions (DMPs) and differentially expressed genes (DEGs) during the pollen and non-pollen seasons using the CHAMP and limma packages. Through correlation analysis, we identified methylation-driven genes and performed pathway enrichment analysis to annotate their functions. We incorporated external data on AR combined with asthma (GSE101720) for analysis to identify key CpGs that promote the transformation of AR to asthma. We also utilized external data on olive pollen allergy (GSE54522) for analysis to validate the methylation-driven genes. Weighted correlation network analysis (WGCNA) was employed to identify gene modules significantly correlated with pollen allergy. We extracted genes related to the key methylation-driven gene ZNF667-AS1 from the significant module and performed pathway intelligent clustering using KOBAS-i. We also utilized gene set enrichment analysis to explore the potential function of ZNF667-AS1.\nResults: We identified 20 and 24 CpG-Gene pairings during the pollen and non-pollen seasons. After incorporating external data from GSE101720, we found that ZNF667-AS1 is a key gene that may facilitate the transformation of AR into asthma during the pollen season. This finding was further validated in another external dataset, GSE54522, which is associated with pollen allergy. WGCNA identified 17 modules, among which the blue module showed significant correlation with allergies. ZNF667-AS1 was located in the blue module. We performed pathway analysis on the genes correlated with ZNF667-AS1 extracted from the blue module and identified a prominent cluster of pathways in the KOBAS-i results, including Toll-like receptor (TLR) family, MyD88, MAPK, and oxidative stress. Gene set enrichment analysis around cg05508084 (paired with ZNF667-AS1) also indicated its potential involvement in initiating and modulating allergic inflammation from the perspective of TLR and MAPK signaling.\nConclusion: We identified methylation-driven genes and their related pathways during the pollen and non-pollen seasons in patients with AR and identified key CpGs that promote the transformation of AR into asthma due to pollen exposure. This study provides new insights into the underlying molecular mechanisms of the transformation of AR to asthma.", "claims": [{"id": "claim-method", "statement": "Then we calculated the Spearman correlation coefficient between the methylation β values at CpG sites and the gene expression values.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38699230", "locator": "sections[6] \"Integrated analysis of DNA methylation and gene expression data\" paragraph 2", "quote": "Then we calculated the Spearman correlation coefficient between the methylation β values at CpG sites and the gene expression values."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38699230", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 20 and 24 methylation-driven CpG–gene pairings during the pollen and non-pollen seasons, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38699230", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified 20 and 24 CpG-Gene pairings during the pollen and non-pollen seasons."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 20 and 24 methylation-driven CpG–gene pairings during the pollen and non-pollen seasons, respectively.", "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:38699230"], "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:GSE101720"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE101720"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38699230", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1242974", "sha256": 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