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SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 Are Correlated with the Development of Atopic Eczema

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SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 Are Correlated with the Development of Atopic Eczema European Review for Medical and Pharmacological Sciences 2021; 25: 1436-1446 SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 are correlated with the development of atopic eczema Y.-W. YU, Y.-F. LI, M. JIANG, J.-J. ZHAO Department of Dermatology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China Abstract. – OBJECTIVE: Atopic eczema (AE) Introduction is a chronic relapsing inflammatory skin dis- ease. This study aims to identify key genes re- lated to the development of AE. Atopic eczema (AE, also known as eczema or MATERIALS AND METHODS: The GSE6012 atopic dermatitis) is a non-contagious, inflam- dataset was obtained from the Gene Expres- matory, relapsing, and itchy skin disorder1,2. AE sion Omnibus (GEO) database. The limma patients often have scaly and dry skin spanning package was used to analyze differentially ex- almost their entire body, along with intensely pressed genes (DEGs). Then, the weighted itchy red lesions that are at high risk of viral, gene co-expression network analysis (WGC- bacterial, and fungal colonization3-5. AE affects NA) package was utilized to generate weight- ed correlation networks of up-and downregu- 2-10% of adults and 15-30% of children in de- lated genes. Additionally, the WGCNA package veloped countries, and the rate has approxi- was used for enrichment analyses to explore mately tripled in the United States over the past the underlying functions of DEGs in modules 30-40 years6-8. (weighted correlation sub-networks) signifi- Recently, several studies have investigated cantly associated with AE. the mechanisms of AE development. FLG null RESULTS: A total of 515 DEGs were identified between lesional and non-lesional skin sam- alleles predispose patients to a type of eczema ples. For the upregulated genes, the blue mod- that persists from early infancy to adulthood ule was found to have a significant positive cor- and can act as an indicator of poor prognosis relation with AE. Importantly, small proline-rich in AE patients9. The chromosomal region con- protein 2C (SPRR2C) and defensin, beta 4A taining the low-affinity Fc receptor for the IgE (DEFB4A) exhibited higher |log fold change (FC) (FCER2) gene has a regulatory function in atop- values and were the key nodes of the network. ic disorders10. Mutations of nucleotide-binding Moreover, KEGG pathway analysis revealed that the upregulated genes in the blue module were oligomerization domain protein 1 (NOD1) are 11 primarily involved in cytokine-cytokine receptor closely related with atopy susceptibility . The interaction. Additionally, for the downregulated C-1237T promoter polymorphism of toll-like re- genes, the brown module was found to have a ceptor 9 (TLR9) may play a role in AE suscep- significant positive correlation with AE. Further, tibility, particularly in patients with an intrinsic WNT inhibitory factor 1 (WIF1), cryptochrome 2 AE variant12. As the predominant aquaporin in (CRY2), and keratin 19 (KRT19) had higher |log FC| values and were key nodes of the network. human skin, aquaporin 3 (AQP3) expression is CONCLUSIONS: SPRR2C, DEFB4A, WIF1, upregulated and has altered cellular distribution CRY2, KRT19 and cytokine-cytokine receptor in- in eczema, which may result in water loss13. The teraction might be correlated with the develop- levels of reduced soluble cluster of differentia- ment of AE. tion 14 (sCD14) in the fetal and neonatal gastro- intestinal tract are related to the development of Key Words: eczema or atopy, and thus sCD14 may be used in Atopic eczema, Differentially expressed genes, 14 Weighted correlation network, Functional and path- disease treatment . However, despite the above way enrichment analyses. findings, the detailed mechanisms underlying AE remain to be elucidated. 1436 Corresponding Author: Jingjun Zhao, MD; e-mail: [email protected] SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 are correlated with the development of atopic eczema Using the data of Mobini et al15, we further eral times to include no more than 3,600 DEGs. A screened differentially expressed genes (DEGs) correlation coefficient of no less than 0.8 was set in AE. Additionally, weighted correlation net- as the weighting coefficient. Finally, the correlation works were constructed separately for the up- and matrix was converted to a topological matrix. downregulated genes. The potential functions of Hierarchical clustering was performed sepa- DEGs in modules (weighted correlation sub-net- rately for the up- and downregulated genes using works) that were significantly correlated with AE a hybrid dynamic shear tree method, and different were analyzed by Gene Ontology (GO) and path- branches of the clustering tree represented differ- way enrichment analyses. We expected to iden- ent gene modules. The minimum number of genes tify genes associated with AE development and involved in each gene module was set to 30. Sub- provide novel therapeutic targets for AE. This sequently, the feature vector was calculated for study may contribute new insights into the chang- each module and cluster analysis was performed es in gene expression in AE, potentially revealing on the modules. Modules that clustered closely the underlying mechanisms of AE. were merged into new modules. Cytoscape19 was utilized to visualize the weighted correlation net- work. Correlation analysis between gene modules Materials and Methods and AE was carried out using the correlation co- efficient method and network significance meth- Microarray Data od, respectively. The GSE6012 dataset was obtained from the Gene Expression Omnibus (GEO, http://www. Functional and Pathway Enrichment ncbi.nlm.nih.gov/geo/) database. This dataset was Analyses generated using a GPL96 [HG-U133A] Affymetrix Using the WGCNA package in R, GO20, en- Human Genome U133A Array. The GSE6012 data- richment analysis was performed on the DEGs in set included 10 skin biopsies from patients with AE modules significantly correlated with AE. Addi- and 10 skin biopsies from healthy controls. tionally, the culsterProfiler package in R was used to perform Kyoto Encyclopedia of Genes and Data Preprocessing and Differentially Genomes (KEGG)21 pathway enrichment analy- Gene Expression Analysis sis separately for up- and downregulated genes After the GSE6012 dataset was download- in the modules most significantly correlated with ed, the microarray data were preprocessed ac- AE. GO and KEGG pathway enrichment analy- cording to the following steps. First, probe names ses were performed on the top 30 DEGs in key were converted to gene names. Next, for genes modules. The cut-off criterion was p-value < 0.05. mapped with multiple probes, the final gene ex- pression value was determined as the average value of each probe. Expression values were log2 Results transformed and normalized by the preproces- sCore package16 in R. The distributions of gene Data Preprocessing and Identification expression values before and after normalization of DEGs are displayed in a box plot. The limma package17 Following data preprocessing, 12,927 genes in R was utilized to analyze the DEGs between were mapped to the probes. Gene expression lesional and non-lesional skin samples with the profiles prior to and following normalization are cutoff being adjusted p-value < 0.05 and |log fold shown in Figure 1. The results indicate that the change (FC)| > 1. median probe intensity was similar across all con- ditions, demonstrating the consistency of the tech- Weighted Correlation Network nical quality of the data following normalization. Construction A total of 515 significantly DEGs, between le- The weighted gene co-expression network sional and non-lesional skin samples, including analysis (WGCNA) package18 in R was utilized 286 upregulated and 229 downregulated genes, to generate weighted correlation networks for the were screened with the criteria of adjusted p-val- up- and downregulated genes. Briefly, gene clus- ue < 0.05 and |log FC| > 1. The heatmap and vol- tering was performed using the expression matrix cano plot of DEGs are shown in Figure 2. The two of the DEGs. Next, DEGs were further screened by groups of samples can be significantly separated, removing outliers. The criteria were adjusted sev- indicating the reliability of the analysis. 1437 Y.-W. Yu, Y.-F. Li, M. Jiang, J.-J. Zhao Weighted Correlation Network method (Figure 3d) revealed that the blue module Construction had the highest Module Significance (MS) value According to the gene clustering tree (Figure (that is, the average value of gene significance, 3a), a total of 256 upregulated genes were used MS value = 0.62) when compared with other mod- to construct a weighted correlation network. The ules. This was in accordance with the result of the weighting coefficient was set to 7 (Figure 3b). A correlation coefficient method. total of five modules (namely the red module, blue Based on the gene clustering tree (Figure 4a), module, grey module, black module, and green a total of 225 downregulated genes were used to module) were screened for the upregulated genes construct a weighted correlation network. The (Figure 3c). The correlation coefficient method weighting coefficient was set to 5 (Figure 4b). A (Table IA) revealed that the blue module had a total of four modules (namely the brown module, significant positive correlation (correlation coef- blue module, grey module and yellow module) ficient = 0.94) with AE. The network significance were screened for the significantly downregulat- Figure 1. Normalized expression value data. The box in the black line indicates the median of
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