Identification of novel key molecular signatures in the pathogenesis of experimental diabetic retinopathy. Issue 11 (6th September 2021)

• Record Type: Journal Article
• Authors: Liu, Caiying; Zhu, Tong; Zhang, Jieping; Wang, Juan; Gao, Furong; Ou, Qingjian; Jin, Caixia; Xu, Jing‐Ying; Zhang, Jingfa; Tian, Haibin; Xu, Guo‐Tong; Lu, Lixia
• Is Part Of: IUBMB life. Volume 73:Issue 11(2021)
• Page Start: 1307
• Page End: 1324
• Publication Date: 2021-09-06
• Subjects: bioinformatics -- diabetic retinopathy -- fatty acid metabolism; Biochemistry -- Periodicals; Molecular biology -- Periodicals; 572.8
• DOI: 10.1002/iub.2544
• Languages: English
• ISSNs: 1521-6543

Abstract

Abstract: Deep mining of the molecular mechanisms underlying diabetic retinopathy (DR) is critical for the development of novel therapeutic targets. This study aimed to identify key molecular signatures involved in experimental DR on the basis of integrated bioinformatics analysis. Four datasets consisting of 37 retinal samples were downloaded from the National Center of Biotechnology Information Gene Expression Omnibus. After batch‐effect adjustment, bioinformatics tools such as Networkanalyst, Enrichr, STRING, and Metascape were used to evaluate the differentially expressed genes (DEGs), perform enrichment analysis, and construct protein–protein interaction networks. The hub genes were identified using Cytoscape software. The DEGs of interest from the meta‐analysis were confirmed by quantitative reverse transcription‐polymerase chain reaction in diabetic rats and a high‐glucose‐treated retinal cell model, respectively. A total of 743 DEGs related to lens differentiation, insulin resistance, and high‐density lipoprotein (HDL) cholesterol metabolism were obtained using the meta‐analysis. Alterations of dynamic gene expression in the chloride ion channel, retinol metabolism, and fatty acid metabolism were involved in the course of DR in rats. Importantly, H3K27m3 modifications regulated the expression of most DEGs at the early stage of DR. Using an integrated bioinformatics approach, novel molecular signatures were obtained for different stages of DR progression, and the findings may represent distinct therapeutic strategies for DR patients.