Hub Genes Identification in a Murine Model of Allergic Rhinitis Based on Bioinformatics Analysis.

Le Chen,Yue Ma,Le Shi,Chunquan Zheng

doi:10.3389/fgene.2020.00970

Abstract

This study aimed to identify allergic rhinitis (AR)-related hub genes and functionally enriched pathways in a murine model. Dataset GSE52804 (including three normal controls and three AR mice) was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein–protein interaction (PPI) analyses of DEGs were performed to identify the hub genes in AR. The DEGs were classified into different modules by using the weighted gene co-expression network analysis (WGCNA). Moreover, to verify the potential hub genes, nasal mucosa tissues were obtained from murine AR models (n = 5) and controls (n = 5), and qRT-PCR and Western blot were performed. In this study, a total of 634 DEGs were identified. They were significantly enriched in 14 GO terms, such as integral component of membrane, plasma membrane, and G-protein-coupled receptor signaling pathway. Meanwhile, there were eight terms of KEGG pathways significantly enriched, such as Olfactory transduction, Cytokine–cytokine receptor interaction, and TNF signaling pathway. The top 10 hub genes (Rtp1, Rps27a, Penk, Cxcl2, Gng8, Gng3, Cxcl1, Cxcr2, Ccl9, and Anxa1) were identified by the PPI network. DEGs were classified into seven modules by WGCNA. According to qRT-PCR validation of the five genes of interest (Rtp1, Rps27a, Penk, Cxcl2, and Anxa1), the expression level of Rtp1 mRNA was significantly decreased in the AR group compared with the control group, while there are enhanced Rps27a, Penk, Cxcl2, and Anxa1 mRNA expressions in the AR mice group compared with the control group. Western blot was also performed to further explore the expression of Anxa1 in the protein level, and the results showed a similar expression trend.

Highlights

Allergic rhinitis (AR) is a common chronic hyperresponsive upper respiratory disease that affects all age groups (Wallace and Dykewicz, 2017)
In dataset GSE52804, a total of 634 Differentially expressed genes (DEGs) were identified between the allergic rhinitis (AR) group and the control group by R software (Supplementary Table S2), and the top 50 genes are shown in the heatmap (Figure 2A)
With the development of bioinformatics analysis and high-throughput microarray technology, microarray analyses have been widely used in many complicated diseases, but few related to AR

Summary

Introduction

Allergic rhinitis (AR) is a common chronic hyperresponsive upper respiratory disease that affects all age groups (Wallace and Dykewicz, 2017). Weighted gene co-expression network analysis (WGCNA) is a novel systematic biology approach to determine the correlations among genes. This method has been widely applied to investigate highly correlated gene expression modules across microarray samples (Wang et al, 2019) and to find the hub genes involved in many complicated diseases (Li et al, 2016; Riquelme Medina and Lubovac-Pilav, 2016; Ma et al, 2017; Chen et al, 2018; Shao et al, 2018; Tang et al, 2018; Xu et al, 2018). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein–protein interaction (PPI) analyses were applied

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Results

Conclusion