Identification of potential crucial gene network related to seasonal allergic rhinitis using microarray data

Genes related to allergen exposure in allergic rhinitis: a gene-chip-based study in a mouse model

BACKGROUND

The typical clinical symptoms of allergic rhinitis (AR) are known to be associated with allergen exposure; however, the underlying mechanisms are not fully understood. We wanted to gain a comprehensive view of the molecular mechanisms related to allergen exposure in a well-controlled mouse model of AR.

METHODS

An OVA-induced AR model was developed. Two hours and 4 weeks after the last OVA challenge, AR symptoms and local immune responses were assessed. At the same time, differentially expressed genes (DEG) in nasal mucosa were identified by gene expression microarray and further analyzed by bioinformatics methods. Verification of DEG was done by quantitative RT-PCR and immunohistochemistry.

RESULTS

The number of nasal rubbings and sneezes, serum OVA-specific IgE concentrations, and the number of neutrophils and eosinophils in the nasal mucosa were significantly increased at 2 h and decreased at 4 weeks after the last allergen challenge compared to controls. A total of 2119 DEG were identified, and their expression dynamics were clustered into 8 profiles. Enriched functions in Profile 5, which had a similar trend to clinical features, were mainly related to inflammatory and immune response to environmental factors, eosinophils and neutrophils chemotaxis, and cell migration. Gene co-expression Network for genes from profile 5 identified BCL3, NFKB2, SOCS3, and CD53 having a higher degree. Profile 6 showed persistence of inflammatory and immune response at 4 weeks after the last allergen challenge. Olfactory and coagulation functions were enriched mainly in profiles with downward trends.

CONCLUSIONS

A wide range of genes with sequential cooperative action were identified to be associated with allergen exposure in AR. BCL3 may be the most vital in symptoms manifestation. Moreover, some inflammatory responses persisted for a period after allergen exposure, supporting a new treatment strategy of targeting inflammation out of season. This study may contribute to a better understanding of AR pathogenesis and provide potential therapeutic targets for AR patients.

Identifying the Chinese Herbal Medicine Network and Core Formula for Allergic Rhinitis on a Real-World Database

MATERIALS AND METHODS

Patients with a primary diagnosis of AR (ICD-9-CM code: 477.9) in 2010 were included, and the National Health Insurance Research Database in Taiwan was used as the data source. Association rule mining and social network analysis were used to establish and explore the CHM network. Possible molecular pathways of the CHM network were summarized and compared with commonly used western medicine (WM) by conducting overrepresentation analysis in the Reactome pathway database. The potential proteins acted by CHMs were obtained from the CHM ingredient-protein databases, including STITCH, TCMSP, TCMID, and TCM@Taiwan.

RESULTS

There were 89,148 AR subjects found in 2010, and a total of 33,507 patients ever used CHM. On an average, 5.6 types of CHMs were utilized per prescription. Xin-Yi-Qing-Fei-Tang was used most frequently (25.5% of 222,279 prescriptions), while Xiao-Qing-Long-Tang with Xin-Yi-San was the most commonly prescribed CHM-CHM combination. Up to six distinctive clusters could be found among the CHM network, and core CHMs could be found for AR, such as Xiao-Qing-Long-Tang and Xin-Yi-Qing-Fei-Tang. A total of 140 molecular pathways were covered by the CHM network (2,432 ingredients from 31 kinds of CHMs), while 39 WMs covered 55 pathways. Among pathways responding to the immune system, WM mainly acted on cytokine signaling-related pathways, while CHM mostly acted on neutrophil/macrophage-related innate pathways and dendritic cell-related adaptive immunity pathways.

CONCLUSION

Our study demonstrated and analyzed the CHM network for AR. Core CHM for AR and possible molecular pathways were presented as well, and this information is crucial for researchers to select candidates for CHM-related studies.

Identification of Susceptibility Genes to Allergic Rhinitis by Gene Expression Data Sets

As an extremely prevalent disease worldwide, allergic rhinitis (AR) is a condition characterized by chronic inflammation of the nasal mucosa. To identify the finer molecular mechanisms associated with the AR susceptibility genes, differentially expressed genes (DEGs) in AR were investigated. The DEG expression and clinical data of the GSE19187 data set were used for weighted gene co‐expression network analysis (WGCNA). After the modules related to AR had been screened, the genes in the module were extracted for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, whereby the genes enriched in the KEGG pathway were regarded as the pathway‐genes. The DEGs in patients with AR were subsequently screened out from GSE19187, and the sensitive genes were identified in GSE18574 in connection with the allergen challenge. Two kinds of genes were compared with the pathway‐genes in order to screen the AR susceptibility genes. Receiver operating characteristic (ROC) curve was plotted to evaluate the capability of the susceptibility genes to distinguish the AR state. Based on the WGCNA in the GSE19187 data set, 10 co‐expression network modules were identified. The correlation analyses revealed that the yellow module was positively correlated with the disease state of AR. A total of 89 genes were found to be involved in the enrichment of the yellow module pathway. Four genes (CST1,SH2D1B,DPP4, and SLC5A5) were upregulated in AR and sensitive to allergen challenge, whose potentials were further confirmed by ROC curve. Taken together, CST1,SH2D1B,DPP4, and SLC5A5 are susceptibility genes to AR.

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.