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

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. Methods: 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. Results: 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. Conclusion: 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.

Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods.

The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components of Guominkang (GMK) were obtained from 6 databases and activate components were identified by SwissADME website. Utilizing the SwissTarget Prediction, PharmMapper, SymMap, and HERB, the targets of GMK were predicted and subsequently validated using gene chip data from our team previous study. Differentially expressed proteins (DEPs) related to the allergic constitution were also extracted based on a previous study. Pathway enrichment analysis was performed using KOBAS-i on the GWAS, RRA, WGCNA modules, DEPs, and GMK targets. P values from multi-omics datasets were combined by meta-analysis, and Bonferroni correction was applied. The significant pathways were further validated using Gene Set Enrichment Analysis (GSEA) with intervention data of GMK. The GWAS results yielded 172 genes. Four datasets AR1, AA1, AD1, and AD2 were acquired from GSE75011, GSE125916, and GSE184237. The RRA algorithm identified 19 upregulated and 20 downregulated genes. WGCNA identified 5 significant modules, with the blue and turquoise modules displaying a moderate correlation with allergies. By performing network pharmacology analysis, we identified 127 active ingredients of GMK and predicted 618 targets. Validation using gene chip data confirmed 107 GMK targets. Single-omics pathway analysis was conducted using KOBAS-i, and 39 significant pathways were identified across multiple omics datasets. GSEA analysis using GMK intervention data identified 11 of 39 significant pathways as the final key pathways associated with the allergic constitution. Through multi-omics integrated pathway analysis, we identified 11 critical pathways of allergic constitution, including TH1 and TH2 cell differentiation, TLR cascade, and TH17 cell differentiation. Identifying these pathways suggests that the observed alterations at the pathway level may play significant roles in the molecular characteristics of the allergic constitution.

Traditional Chinese Medicine Constitution Identification Based on Objective Facial and Tongue Features: A Delphi Study and a Diagnostic Nomogram for Blood Stasis Constitution.

Objective: The aim of this study was to systematically summarize and form an expert consensus on the theoretical experience of tongue and facial features for the identification of nine types of traditional Chinese medicine (TCM) constitution. Additionally, we sought to explore the feasibility of TCM constitution identification through objective tongue and facial features. Methods: We used Delphi method to investigate the opinions of experts on facial and tongue feature items for identifying TCM constitution. We developed and validated a diagnostic nomogram for blood stasis constitution (BSC) based on objective facial and tongue features to demonstrate the reliability of expert consultation. Results: Eleven experts participated in two rounds of expert consultation. The recovery rates of the two rounds of expert consultation were 100.0% and 90.9%. After the first round, 39 items were screened out from 147 initial items, and 2 items were supplemented by experts. In the second round, 7 items were eliminated, leaving 34 items for 8 types of TCM constitution. The coefficient of variation in the first round was 0.11-0.49 for the 147 items and 0.11-0.29 for the included items. The coefficient of variation in the second round was 0.10-0.27 for the 41 items and 0.10-0.20 for the included items. The W value was 0.548 (P < 0.001) in the first round and 0.240 (P < 0.001) in the second round. Based on expert consultation, we selected BSC as an example and developed and validated a diagnostic nomogram consisting of six indicators: sex, hair volume, lip color-dark purple, susceptibility-facial pigmentation/chloasma/ecchymosis, zygomatic texture-red blood streaks, and sublingual vein-varicose and dark purple. The nomogram showed good discrimination (AUC: 0.917 [95% confidence interval [CI], 0.877-0.956] for the primary dataset, 0.902 [95% CI, 0.828-0.976] for the validation dataset) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: This is the first study to systematically summarize the existing knowledge and clinical experience to form an expert consensus on the tongue and facial features of nine types of TCM constitution. Our results will provide important prior knowledge and expert experience for future constitution identification research. Based on expert consultation, this study presents a nomogram for BSC that incorporates objective facial and tongue features, which can be conveniently used to facilitate the individualized identification of BSC.