Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy

Abstract

Background: Diabetic retinopathy is a diabetic microvascular complication. Pyroptosis, as a way of inflammatory death, plays an important role in the occurrence and development of diabetic retinopathy, but its underlying mechanism has not been fully elucidated. The purpose of this study is to identify the potential pyroptosis-related genes in diabetic retinopathy by bioinformatics analysis and validation in a diabetic retinopathy model and predict the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) interacting with them. Subsequently, the competing endogenous RNA (ceRNA) regulatory network is structured to explore their potential molecular mechanism.

Methods: We obtained mRNA expression profile dataset GSE60436 from the Gene Expression Omnibus (GEO) database and collected 51 pyroptosis-related genes from the PubMmed database. The differentially expressed pyroptosis-related genes were obtained by bioinformatics analysis with R software, and then eight key genes of interest were identified by correlation analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis. Then, the expression levels of these key pyroptosis-related genes were validated with quantitative real-time polymerase chain reaction (qRT-PCR) in human retinal endothelial cells with high glucose incubation, which was used as an in vitro model of diabetic retinopathy. Western blot was performed to measure the protein levels of gasdermin D (GSDMD), dasdermin E (GSDME) and cleaved caspase-3 in the cells. Moreover, the aforementioned genes were further confirmed with the validation set. Finally, the ceRNA regulatory network was structured, and the miRNAs and lncRNAs which interacted with CASP3, TLR4, and GBP2 were predicted.

Results: A total of 13 differentially expressed pyroptosis-related genes were screened from six proliferative diabetic retinopathy patients and three RNA samples from human retinas, including one downregulated gene and 12 upregulated genes. A correlation analysis showed that there was a correlation among these genes. Then, KEGG pathway and GO enrichment analyses were performed to explore the functional roles of these genes. The results showed that the mRNA of these genes was mainly related to inflammasome complex, interleukin-1 beta production, and NOD-like receptor signaling pathway. In addition, eight hub genes-CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD, and GBP1-were identified by PPI network analysis using Cytoscape software. High glucose increased the protein level of GSDMD and GSDME, as critical effectors of pyroptosis, in retinal vascular endothelial cells. Verified by qRT-PCR, the expression of all these eight hub genes in the in vitro model of diabetic retinopathy was consistent with the results of the bioinformatics analysis of mRNA chip. Among them, CASP4, GBP1, CASP3, TLR4, and GBP2 were further validated in the GSE179568 dataset. Finally, 20 miRNAs were predicted to target three key genes-CASP3, GBP2, and TLR4, and 22 lncRNAs were predicted to potentially bind to these 20 miRNAs. Then, we constructed a key ceRNA network that is expected to mediate cellular pyroptosis in diabetic retinopathy.

Conclusion: Through the data analysis of the GEO database by R software and verification by qRT-PCR and validation set, we successfully identified potential pyroptosis-related genes involved in the occurrence of diabetic retinopathy. The key ceRNA regulatory network associated with these genes was structured. These findings might improve the understanding of molecular mechanisms underlying pyroptosis in diabetic retinopathy.

Keywords: competing endogenous RNA regulatory network; diabetic retinopathy; expression profiling by array; inflammatory death; pyroptosis.

Figure 1

Design idea of this study. Downloaded GSE60436 dataset from the GEO database and 51 PRGs collected from the PubMed database. The R software was used to process the data, such as quality control, normalization, and background correction. A total of 13 differentially expressed PRGs in the dataset were identified by difference analysis, and GO and KEGG enrichment analyses were performed. At the same time, eight hub genes were identified by PPI analysis and then verified with qRT-PCR experiments and GSE179568. Finally, the specific mechanism of PRGs in DR was revealed by structuring a ceRNA regulatory network through multiMiR database and starbase database. GEO, Gene Expression Omnibus; PRGs, pyroptosis-related genes; PPI, protein–protein interaction; DR, diabetic retinopathy; qRT-PCR, quantitative real-time polymerase chain reaction; ceRNA, competing endogenous RNAs; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 2

Differentially expressed PRGs in PDR and normal samples. (A) Volcano plot of 3,188 differentially expressed genes in the GSE60436 dataset. It contains 1,640 significantly upregulated genes, represented by red dots, and 1,548 significantly downregulated genes, represented by blue dots, whereas gray dots represent stably expressed genes. (B) Heatmap of 13 differentially expressed PRGs in the GSE60436 dataset. It contains one significantly downregulated gene and 12 significantly upregulated genes. The green bars represent control specimens from human retinas, denoted by “CON”, and the red bars represent specimens from PDR patients, denoted by “DR”. (C) Boxplot of 13 differentially expressed PRGs in FVM tissues of PRD patients and in RNA samples from human retinas. The blue bars represent control specimens from normal individuals, denoted by “CON”, and the yellow bars represent specimens from PDR patients, denoted by “DR”. Adjusted P-value <0.05 and |log₂ fold change| >0.5. (D, E) Correlation analysis of 13 differentially expressed PRGs. There was a strong correlation among the 12 upregulated genes. PRGs, pyroptosis-related genes; PDR, proliferative diabetic retinopathy; FVM, fibrovascular membrane.

Figure 3

GO enrichment analysis of 13 differentially expressed PRGs. (A) Bar plot of enriched GO terms. (B) Bubble plot of enriched GO terms. (C) Chordal graph of enriched GO terms. (D) Eight diagrams of enriched GO terms. It contains three aspects—BPs, CCs, and MFs—and shows the specific genes involved in each GO term. GO, Gene Ontology; PRGs, pyroptosis-related genes; BPs, biological processes; CCs, cellular components; MFs, molecular functions.

Figure 4

Further analysis of the results obtained by GO enrichment analysis. (A) Relationship between the pathways obtained by GO enrichment analysis. (B) Common genes among the three most prominent pathways. Each path is represented by lines of different colors. (C) Heatmap-like functional classification. The enrichment relationships of differentially expressed PRGs in the eight most significant pathways are shown. GO, Gene Ontology; PRGs, pyroptosis-related genes.

Figure 5

KEGG pathway analysis of 13 differentially expressed PRGs. According to the adjusted P-value, a total of 67 pathways were reported. KEGG, Kyoto Encyclopedia of Genes and Genomes; PRGs, pyroptosis-related genes.

Figure 6

Construction of PPI network and identification of hub genes. (A) The PPI network of 13 differentially expressed PRGs was constructed by using String database. It contains 13 nodes and 35 edges. The average node degree is 5.38, and the PPI enrichment P-value is less than 1.0e-16. (B) First eight hub genes of the PPI network. First eight genes with the highest degree identified by Cytoscape software and CytoHubba. These genes are ranked in descending order from red to yellow. PPI, protein–protein interaction; PRGs, pyroptosis-related genes.

Figure 7

qRT-PCR experiment to verify the expression of PRGs of interest in the in vitro model. HRECs exposed to high glucose for 48 h were used as DR model, and HRECs cultured on normal culture medium were used as control. The P-values were calculated using a two-sided unpaired Student’s t-test. *P < 0.05; **P < 0.01. qRT-PCR, quantitative real-time polymerase chain reaction; PRGs, pyroptosis-related genes; HRECs, human retinal endothelial cells; DR, diabetic retinopathy.

Figure 8

Validation of PRGs in the GSE179568 dataset. (A) Heatmap of 15 differentially expressed PRGs in the GSE60436 dataset. (B) Box plot of 15 differentially expressed PRGs in the RNV membranes of PRD patients and in the epiretinal membranes of macular pucker and macular hole samples. Adjusted P-value <0.05 and |log₂ fold change| >1.0. PRGs, pyroptosis-related genes; RNV, retinal neovascularization; PDR, proliferative diabetic retinopathy.

Figure 9

Western blot analysis of caspase-3 and gasdermin proteins in in vitro models. (A) Cleaved caspase-3 (17 kDa) was tested. GAPDH (36 kDa) was used as a control. (B) GSDMD (31 kDa) was examined. GAPDH was used as a control. (C) GSDME (55 kDa) was examined. GAPDH was used as a control. The experiments were repeated independently at least three times. *P < 0.05. CON, control group; HG, high-glucose group; GSDMD, gasdermin D; GSDME, gasdermin E.

Figure 10

ceRNA-regulating networks. The yellow diamond represents the protein coding genes, the purple circle represents miRNAs, and the green rectangle represents lncRNAs. The black lines indicate the interaction of lncRNA–miRNA–mRNA. ceRNA, competing endogenous RNAs; miRNAs, microRNAs; lncRNAs, long non-coding RNAs.