Diabetic retinopathy is a type of microvascular complication featured by dysfunction in the retinal microvascular, and it can lead to impaired vision and loss of vision in diabetic patients.1 The incidence of diabetic retinopathy tends to increase annually in China with the increased aging population.2 The death of endothelial cells and pericytes are the main features at the early stage of diabetic retinopathy. With the progression of diabetic retinopathy, the increased vascular leakage will lead to diabetic macular edema, which may cause vision loss.3 The main treatments for diabetic retinopathy include intraocular injection of anti-neovascularization drug, panretinal photocoagulation and vitrectomy.1 There is growing evidence indicating that the main causative contributors to diabetic retinopathy include oxidative stress, cell apoptosis, inflammation and autophagy;3 whereas the molecular mechanisms underlying diabetic retinopathy progression remain unclear. In this regard, further understanding into the pathophysiology of diabetic retinopathy is of great importance for developing novel therapies for diabetic retinopathy.
With the development of high-throughput technologies, discovery of novel genes associated with specific diseases has been greatly accelerated.4 Recently, several key regulators in the pathophysiology of diabetic retinopathy have been uncovered via different high-throughput technologies. Lam et al identified that runt-related transcription factor 1 was involved in aberrant retinal angiogenesis by using transcriptomic analysis.5 Berdasco et al performed the genome-scale DNA methylation profiling using samples from normal human eye and five ocular-related diseases, and the studies found that three key genes including ETS proto-oncogene 1 and PR domain containing 16 participated in neuro-vascularization during diabetic retinopathy.6 With the aid of bioinformatics tool, analysis of public available datasets has revealed new mediators in the regulation of diabetic retinopathy progression. You et al performed the analysis of weighted genes in diabetic retinopathy from GSE19122 datasets and proposed that metastasis associated with lung adenocarcinoma transcript 1 might play important roles in diabetic retinopathy.7 Ishikawa et al performed microarray analysis (GSE60436) and found that extracellular matrix-related molecules, such as periostin, tenascin C, tumor growth factor beta, and angiogenic factors, have important roles in promoting the development of preretinal fibrovascular membranes associated with diabetic retinopathy.8 Lam et al performed the transcriptomic analysis and their results suggested that the preferential selection of inflammatory and angiogenic pathways using this gene list is highly consistent with diabetic retinopathy pathogenesis, which involves leaky and aberrant vessel growth.5 Platania et al performed Gene Expression Omnibus (GEO) datasets with an enrichment-information approach, which gave as output a series of complex gene-pathway and drug-gene networks. Analysis of these networks identified genes and biological pathways related to inflammation, fibrosis and G protein-coupled receptors that are potentially involved in the development of the disease.9
In the present study, we analyzed the differentially expressed genes (DEGs) from GSE60436 and GSE94019 datasets; further comprehensive bioinformatics analysis was performed to decipher potential hub genes in the pathophysiology of diabetic retinopathy. These hub genes expression levels were validated in an in vitro cellular diabetic retinopathy model. The mechanistic studies were further undertaken to uncover the potential role of serpin family H member 1 (SERPINH1) in the pathophysiology of diabetic retinopathy.
Materials and Methods
Collection of Microarray Data, Data Preprocessing and DEGs Screening
The GEO datasets including GSE60436 (3 normal retinal tissues and 6 retinal tissues with diabetic retinopathy) and GSE94019 (3 normal retinal tissues and 9 retinal tissues with diabetic retinopathy) were retrieved from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) for GSE94019 were extracted using the Geo RNA-seq experiments Interactive Navigator tool;10 for GSE60634, the data were processed with log2 transforming by “Limma” R package and normalized by median normalization. Then, we also used the “Limma” R package to screen the DEGs. A |logFC| 1.5 and false discovery rate < 0.05 were chosen as an optimum fold change cutoff value for the identification of DEGs.
Functional Analysis of DEGs
Gene Ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) database were undertaken to classify the functionalities of these DEGs.11–13 A ontology-based tool, g:Profiler (https://biit.cs.ut.ee/gprofiler/gost), was used to perform Gene Ontology (GO) enrichment, and KEGG pathway, Reactome pathway, WikiPathway and miRNA pathway analysis for the DEGs.14
Protein–Protein Interaction (PPI) Network Analysis
The PPI network analysis was performed using the Search Tool for the Retrieval of Interacting Genes (STRING).15 The interactions between DEGs were evaluated using STRING and genes with a combined score 0.7 were defined as hub DEGs. Subsequently, Cytoscape (version 3.6.1) was used to generate PPI network of hub DEGs that were identified. Molecular complex detection (MCODE) and cytoHubba, the Cytoscape plugins, were used with the default parameters to identify subset modules.
Human retinal endothelial cells (HRECs) were purchased from ScienCell (Carlsbad, USA). Cells were kept in the endothelial cell medium supplied with 5% fetal bovine serum (FBS; Thermo Fisher Scientific, Waltham, USA) and 1% endothelial cell growth supplement (ScienCell) as suggested by the manufacturer. The HRECs were kept in a humidified atmosphere with 5% CO2 at 37°C.
Hyperglycemia Treatment and Cell Transfections
HRECs were seeded in the 6-well plates with a density of 2×105 cells/well and were treated with 25 mM glucose (high glucose group), 5.5 mM glucose (normal glucose group) or 19.5 mM mannitol together with 5.5 mM glucose (osmotic group) for 48 h under normoxic conditions. The culture medium was refreshed every 24 h during the culturing process.
The SERPINH1 siRNA and scrambled siRNA were obtained from RiboBio (Guangzhou, China). MiR-29b mimics, miR-29b inhibitor and the corresponding negative controls (NCs: mimics NC and inhibitor NC) were also purchased from RiboBio. For SERPINH1-overexpressing vector, pcDNA-SERPINH1 and its NC (pcDNA) were Sangon Biotech Co., Ltd. (Shanghai, China). The HRECs were transfected with siRNAs, miRNAs or plasmids by using the Lipofectamine 3000 reagent (Invitrogen, Carlsbad, USA).
The 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxy-methoxyphenyl)-2-(4-sulfophenyl)-2H–tetrazolium (MTS) kit (Beyotime, Beijing, China) was used to evaluate the cell viability of HRECs by following by the manufacturer’s protocol. HRECs cells were seeded in the 96-well plates at a density of 2×104 cells/well. After different treatments for 48 h, the cells were incubated with 20 μL of MTS for 1.5 h at 37 °C. After that, the cell proliferative index was evaluated by detecting the absorbance at 490 nm.
Quantitative Real-Time PCR
Total RNA was extracted from HERCs with different treatments for 48 h using the TRIzol reagent (Invitrogen, Carlsbad, USA) according to the manufacturer’s protocol, and RNA concentration and purity were measured on a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific). A total of 500 ng RNA was reversely transcribed using a Perfect real-time RT reagent kit (Takara Bio, Beijing, China). The real-time PCR reactions were performed on a LightCycler 480 (Roche Diagnostics, Basel, Switzerland). Gene expression was detected using 2−ΔΔCt method. U6 and GAPDH were used as the internal controls for miRNA and mRNA expression, respectively. The sequences for the primers are listed in Supplemental Table S1.
The HREC proliferation was accessed using a 5-ethynyl-2ʹ-deoxyuridine (EdU) detection kit (Beyotime, Beijing, China). Briefly, HRECs with different treatments were incubated with 50 μM EdU for 2 h at 37°C. After that, HRECs were fixed with 4% paraformaldehyde for 15 min at room temperature followed by EdU staining at room temperature for 30 min in the dark. After that, the cells were incubated with 5 μg/mL Hoechst 33,342 dye for 30 min at room temperature for 20 min. The cell proliferation was assessed by percentage of EdU-positive…
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