Affiliation:
1. College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, P.R. China
2. Department of
Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Korea
3. Zhejiang Provincial Key Laboratory of
Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Jiaxing, 314006, P.R. China
4. Skin Diseases
Research Center, Yangtze Delta Region Institute of Tsinghua University, Jiaxing, P.R. China
Abstract
Background:
Atopic dermatitis (AD), psoriasis (PS), and inflammatory acne (IA) are
well-known as inflammatory skin diseases. Studies of the transcriptome with altered expression
levels have reported a large number of dysregulated genes and gene clusters, particularly those involved
in inflammatory skin diseases.
Objective:
To identify genes commonly shared in AD, PS, and IA that are potential therapeutic targets,
we have identified consistently dysregulated genes and disease modules that overlap with
AD, PS, and IA.
Methods:
Microarray data from AD, PS, and IA patients were downloaded from Gene Expression
Omnibus (GEO), and identification of differentially expressed genes from microarrays of AD, PS,
and IA was conducted. Subsequently, gene ontology and gene set enrichment analysis, detection
of disease modules with known disease-associated genes, construction of the protein-protein interaction
(PPI) network, and PPI sub-mapping analysis of shared genes were performed. Finally, the
computational docking simulations between the selected target gene and inhibitors were conducted.
Results:
We identified 50 shared genes (36 up-regulated and 14 down-regulated) and disease modules
for each disease. Among the shared genes, 20 common genes in PPI network were detected
such as LCK, DLGAP5, SELL, CEP55, CDC20, RRM2, S100A7, S100A9, MCM10, AURKA,
CCNB1, CHEK1, BTC, IL1F7, AGTR1, HABP4, SERPINB13, RPS6KA4, GZMB, and TRIP13. Finally,
S100A9 was selected as the target gene for therapeutics. Docking simulations between
S100A9 and known inhibitors indicated several key binding residues, and based on this result, we
suggested several cannabinoids such as WIN-55212-2, JZL184, GP1a, Nabilone, Ajulemic acid,
and JWH-122 could be potential candidates for a clinical study for AD, PS, and IA via inhibition
of S100A9-related pathway.
Conclusion:
Overall, our approach may become an effective strategy for discovering new disease
candidate genes for inflammatory skin diseases with a reevaluation of clinical data.
Publisher
Bentham Science Publishers Ltd.