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 [35]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 [36]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: pyroptosis, inflammatory death, diabetic retinopathy,
   competing endogenous RNA regulatory network, expression profiling by
   array

Introduction

   Diabetic retinopathy is an irreversible blindness disease characterized
   by retinal microvascular dysfunction ([37]1). As one of the most
   prominent microvascular complications in advanced diabetes mellitus
   (DM), its incidence has been increasing worldwide and become a global
   public health problem ([38]2, [39]3). The occurrence of diabetic
   retinopathy is closely related to the duration of disease and blood
   glucose level. Chronic exposure to hyperglycemia and other risk factors
   will lead to pathological retinal changes, such as breakdown of the
   retinal–blood barrier and aberrant retinal angiogenesis, which cause
   vision function impairment or even vision loss. It not only affects the
   quality of life in diabetic patients but also imposes a significant
   financial burden on the society ([40]4). At present, the therapeutic
   options for diabetic retinopathy depend on the severity of the disease.
   It includes retinal laser photocoagulation, intravitreal injection of
   anti-VEGF drugs ([41]5) or implantation of dexamethasone
   sustained-release agent (Ozurdex), and vitreoretinal surgery ([42]6).
   However, some of the patients still suffer from a continuous decline of
   visual acuity after the aforementioned treatments due to progression of
   diabetic retinopathy. Therefore, elucidating the pathogenesis of
   diabetic retinopathy and providing treatment according to it might
   offer better therapeutic effects.

   Pyroptosis is a new way of programmed cell death mediated by gasdermin
   ([43]7). Differently from other ways of cell death, such as apoptosis
   and necrosis, pyroptosis is characterized by dependence on inflammatory
   caspase and the release of a large number of pro-inflammatory factors
   ([44]8). According to the activation mechanism, pyroptosis can be
   divided into two signal pathways. The canonical pathway is
   characterized by the direct activation of caspase-1. It can cleave the
   N-terminal sequence of gasdermin D (GSDMD) which forms pores on the
   cellular membrane, followed by cytoplasmic swelling and membrane
   rupture. Meanwhile, activated caspase-1 can promote the release of
   inflammatory factors such as IL-18 and IL-1β to induce inflammatory
   response ([45]9–[46]11). In the non-classical pathway, inflammatory
   stimulators, such as lipopolysaccharide (LPS), can directly activate
   caspase-4/5/11 and other members of the caspase family. It can process
   GSDMD and indirectly activate caspase-1 to induce pyroptosis ([47]12,
   [48]13). Under physiological conditions, pyroptosis is crucial for the
   maintenance of innate immunity and prevention of tumor occurrence
   ([49]14), but accumulated dates have shown that abnormal pyroptosis is
   associated with cardiovascular diseases ([50]15), nervous system
   disorders ([51]16), and diabetic complications such as diabetic
   cardiomyopathy and diabetic retinopathy ([52]17, [53]18). However,
   there is a little information concerning pyroptosis in diabetic
   retinopathy. Further exploration of the role of pyroptosis in diabetic
   retinopathy and uncovering the molecular mechanisms underlying it can
   provide a novel insight into the pathogenesis of diabetic retinopathy.

   Ishikawa K et al. generated the dataset [54]GSE60436 ([55]19). They
   analyzed the gene profile expressed in the fibrovascular membranes
   (FVMs) associated with proliferative diabetic retinopathy by detecting
   the gene expression of the FVMs from patients with proliferative
   diabetic retinopathy. In this study, the [56]GSE60436 dataset was used
   to explore potential pyroptosis-related genes associated with diabetic
   retinopathy. Through correlation analysis, Gene Ontology (GO)
   enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway analysis, and protein–protein interaction (PPI) network
   analysis of differentially expressed genes (DEGs), we identified the
   pyroptosis-related genes that participate in diabetic retinopathy and
   verified them by the independent dataset [57]GSE179568 and quantitative
   real-time polymerase chain reaction (qRT-PCR) in a diabetic model.
   Finally, we predicted the targeting microRNAs (miRNAs) and long
   non-coding RNAs (lncRNAs) that are correlated with the validated key
   pyroptosis-related genes and structured a competing endogenous RNA
   (ceRNA) regulatory network. It will lay the groundwork for revealing
   the mechanisms of pyroptosis in diabetic retinopathy and generate
   therapeutic insights ([58]Figure 1).

Figure 1.

   [59]Figure 1
   [60]Open in a new tab

   Design idea of this study. Downloaded [61]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 [62]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.

Materials and methods

Microarray data and the exploration of PRGs

   The [63]GSE60436 microarray dataset deposited by Ishikawa K et al. was
   downloaded from the Gene Expression Omnibus (GEO) database
   ([64]https://www.ncbi.nlm.nih.gov/geo/). The dataset contains three
   active FVMs and three inactive FVMs, which were obtained from
   proliferative diabetic retinopathy patients, and the RNAs from human
   retinas were purchased and were extracted from normal retinas pooled
   from 99 Caucasians. All samples were analyzed by [65]GPL6884 platform
   (IlluminaHumanWG-6v3.0 expression beadchip), and R software (version
   4.1.0) was used for data quality control, normalization, background
   correction, and subsequent analysis. The RNA-seq-based dataset
   [66]GSE179568 was also obtained from GEO, including seven human retinal
   neovascularization (RNV) membranes, 10 macular pucker, and seven
   macular hole samples as controls. The search for pyroptosis-related
   genes was mainly carried out in the PubMed database
   ([67]https://pubmed.ncbi.nlm.nih.gov/). Finally, 51 genes were
   collected ([68]11, [69]20–[70]26).

Differential expression analysis and correlation analysis

   We used “limma” package in R software to screen the differential genes
   in FVM samples of proliferative diabetic retinopathy patients and
   control groups. Genes with an adjusted P-value <0.05 and |log[2] fold
   change| >0.5 were considered as differentially expressed genes, and one
   downregulated pyroptosis-related gene and 12 upregulated
   pyroptosis-related genes were identified. With P-value <0.05 and
   |log[2]fold change| >1.0 as the threshold, 15 upregulated
   pyroptosis-related genes were obtained by analyzing the verification
   set [71]GSE179568 with the same method. The correlation analysis of
   DEGs was carried out by using Spearman correlation in the “corrplot”
   package.

Functional and pathway enrichment analysis

   In order to evaluate the function of differentially expressed
   pyroptosis-related genes, we used GO enrichment analysis and KEGG
   pathway analysis in R software for functional and pathway enrichment
   analysis and visualization. The adjusted P-value <0.05 was taken as the
   standard with statistical significance.

Construction of PPI network

   The STRING database ([72]https://string-db.org/) was used to construct
   the PPI network of DEG-encoded proteins ([73]27), the threshold was set
   to a combined score ≥0.4, and the file in tsv format was downloaded.
   Then, Cytoscape software (version 3.9.0) was used to visualize the PPI
   network, and cytoHubba was used to excavate eight hub genes.

Construction of ceRNA-regulating network

   The “multiMiR” package in R software combines 14 databases, including
   the mirTarbase database
   ([74]https://maayanlab.cloud/Harmonizome/resource/MiRTarBase) with
   experimental methods to verify the relationship, and uses this tool to
   predict the miRNAs of the pyroptosis-related genes of interest. All the
   lncRNA–miRNA interaction data were obtained in the starbase database
   ([75]https://starbase.sysu.edu.cn/), and the target lncRNA was screened
   according to clipExpNum >7. Finally, visualization was carried out in
   Cytoscape software.

Cell culture

   Human retinal endothelial cells (HRECs) (CSC; Kirkland, WA, USA) were
   cultured in endothelial Complete Classic Medium (CSC; Kirkland, WA,
   USA) containing 10% fetal bovine serum (Gibco, USA). The culture medium
   was changed every 2 days. When the cells were 85–90% confluent, it was
   passaged at a ratio of 1:3. The in vitro diabetic model was induced by
   culturing HRECs with high-glucose medium containing 30 mM anhydrous
   glucose (Solarbio, Beijing, China) for 48 h, and the cells cultured in
   normal medium containing 5 mM glucose were used as control under the
   same conditions. In order to ensure the accuracy and stability of the
   results, 8–10 generations of cells were selected for follow-up study.

RNA Extraction and qRT-PCR

   According to the manufacturer’s plan, the total RNA of the experimental
   group and the control group was extracted by TRIzol reagent (Ambion,
   Carlsbad, CA). The concentration and the purity of RNA were measured by
   NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific). Then, cDNA
   was reverse-transcribed from total RNA using UEIris II RT-PCR System
   (Suzhou Yuheng Biological Co., Ltd.) in strict accordance with the
   manufacturer’s instructions, and qRT-PCR was performed using 2× SYBR
   Green qPCR Master Mix (Suzhou Yuheng Biological Co., Ltd.). The
   sequence of the qRT-PCR primers is shown in [76]Supplementary Table S1.
   Gene expression was detected by the 2^−ΔΔCt method. β-actin mRNA was
   selected as the internal control.

Western blot analysis

   HRECs were lysed with RIPA lysis buffer (Sangon Biotech, Shanghai,
   China) containing protease inhibitors and phosphatase inhibitors, and
   the protein concentrations were determined using the BCA kit (Thermo
   Fisher Scientific, Inc.). The samples were separated by 12% sodium
   dodecyl sulfate-polyacrylamide gel electrophoresis at 140 V and
   transferred to polyvinylidene membranes. Subsequently, the membranes
   were placed in 5% blocking buffer (bovine serum albumin) to block for
   1 h, incubated with primary antibodies against CASP3 (Abcam, MA, USA),
   GSDMD (Abcam, MA, USA), GSDME (Abcam, MA, USA), and GAPDH (Abcam, MA,
   USA) at 4˚C overnight, followed by horseradish peroxidase-labeled
   secondary antibodies (Abcam, MA, USA) for 1 h at room temperature, and
   finally developed with an enhanced chemiluminescence kit for
   visualization. The collected images were examined using ImageJ software
   (version 6.0; Media Cybernetics, Inc.).

Statistical analysis

   Each in vitro experiment was repeated at least three times. Statistical
   analysis was performed using GraphPad Prism software (version 8.2.1).
   On the basis of the homogeneity of the variance test, Student’s t-test
   was performed, and P <0.05 was considered significant and statistically
   significant.

Results

Differential expression of pyroptosis-related genes in the FVMs of
proliferative diabetic retinopathy patients and the retina of normal
individuals

   After quality control, normalization, and background correction of the
   data in the [77]GSE60436 dataset, 3,188 genes with significant changes
   were obtained by using adjusted P-value <0.05 and |log[2] fold change|
   >0.5 as thresholds, including 1,640 upregulated genes and 1,548
   downregulated genes as shown in the volcano plot ([78]Figure 2A). Due
   to the lack of a systematic database of pyroptosis-related genes, we
   collected 51 pyroptosis-related genes from PubMed database, including
   reported pyroptosis core components, genes with pyroptosis function or
   genes that regulate the expression of pyroptosis-related genes, and
   inflammasome that play an important role in cell pyroptosis, which were
   listed in [79]Table 1 ([80]11, [81]20–[82]26). One downregulated
   pyroptosis-related gene CTSG and 12 upregulated pyroptosis-related
   genes—CASP4, TNFRSF21, PYCARD, CASP1, CASP3, GBP1, TLR4, CARD8, NLRP3,
   GBP2, GBP5, and GZMA—were screened by intersecting these
   pyroptosis-related genes with genes in the [83]GSE60436 database. These
   genes are annotated in the volcano plot ([84]Figure 2A) and organized
   in [85]Table 2. The expression of these genes was plotted as heatmap
   and box plot ([86]Figures 2B, C). At the same time, we discussed the
   correlation of these genes, and the results showed that there was a
   correlation among the 13 pyroptosis-related genes ([87]Figures 2D, E).
   Then, we further analyzed the differentially expressed genes between
   active FVMs and inactive FVMs according to the same thresholds and
   found an upregulated gene GZMB, indicating that GZMB may play an
   important regulatory role in different states of proliferative diabetic
   retinopathy.

Figure 2.

   [88]Figure 2
   [89]Open in a new tab

   Differentially expressed PRGs in PDR and normal samples. (A) Volcano
   plot of 3,188 differentially expressed genes in the [90]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 [91]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 |log[2] 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.

Table 1.

   Compilation of pyroptosis-related genes.
   Gene function Gene symbol Gene name
   Pyroptosis core components GSDMA gasdermin A
   GSDMB gasdermin B
   GSDMC gasdermin C
   GSDMD gasdermin D
   GSDME (DFNA5) gasdermin E (DFNA5)
   IL-1β interleukin 1 beta
   IL18 interleukin 18
   Genes with pyroptosis function or regulate the expression of PRGs CASP1
   caspase-1
   CASP3 caspase-3
   CASP4 caspase-4
   CASP5 caspase-5
   CASP6 caspase-6
   CASP8 caspase-8
   GZMA granzyme A
   GZMB granzyme B
   HMGB1 high mobility group box 1
   GBP1 guanylate binding protein 1
   GBP2 guanylate binding protein 2
   GBP3 guanylate binding protein 3
   GBP4 guanylate binding protein 4
   GBP5 guanylate binding protein 5
   DDX3X DEAD-box helicase 3 X-linked
   NAIP NLR family apoptosis inhibitory protein
   SERPINB1 serpin family B member 1
   C-FLIP cellular FADD-like interleukin-1β converting enzyme inhibitory
   protein TNF receptor superfamily member 21
   TNFRSF21 interferon regulatory factor 2
   IRF2 Z-DNA binding protein 1
   ZBP1 perforin 1
   PRF1 DExH-box helicase 9
   DHX9 guanylate binding protein 5
   DFNB59 (PJVK) DFNB59 (pejvakin)
   CTSG cathepsin G
   APIP APAF1 interacting protein
   Inflammasome AIM2 absent in melanoma 2
   NLRC4 NLR family CARD domain containing 4
   NLRP1 NLR family pyrin domain containing 1
   NLRP2 NLR family pyrin domain containing 2
   NLRP3 NLR family pyrin domain containing 3
   NLRP6 NLR family pyrin domain containing 6
   NLRP7 NLR family pyrin domain containing 7
   NLRP9 NLR family pyrin domain containing 9
   NLRP12 NLR family pyrin domain containing 12
   NLRX1 NLR family member X1
   NOD2 nucleotide binding oligomerization domain containing 2
   TLR4 toll like receptor 4
   MEFV (Pyrin) MEFV innate immuity regulator (Pyrin)
   PYDC1 pyrin domain containing 1
   PYCARD PYD and CARD domain containing
   CARD8 caspase recruitment domain family member 8
   CARD16 caspase recruitment domain family member 16
   CARD18 caspase recruitment domain family member 18
   [92]Open in a new tab

   51 pyroptosis-related genes were collected from the PubMed database
   ([93]11, [94]20–[95]26).

Table 2.

   The 13 differentially expressed pyroptosis-related genes in diabetic
   retinopathy samples compared to normal samples.
   Gene symbol    logFC    Changes   P-value    Adjusted P-value Gene id
   CTSG        -2.04863800 Down    3.474745e-12   3.535901e-09    1511
   CASP4       1.18248927  Up      3.190990e-09   5.237341e-07     837
   TNFRSF21    0.97602261  Up      1.671004e-07   8.553391e-06    27242
   PYCARD      0.77897161  Up      8.722862e-07   2.709519e-05    29108
   CASP1       1.23521922  Up      8.987948e-07   2.748238e-05     834
   CASP3       1.08640598  Up      1.156639e-06   3.351356e-05     836
   GBP1        1.64253837  Up      3.128202e-06   6.727089e-05    2633
   TLR4        0.71026963  Up      3.722308e-05   4.113837e-04    7099
   CARD8       0.91154823  Up      7.318641e-05   6.827511e-04    22900
   NLRP3       0.72061228  Up      6.323034e-04   3.673396e-03   114548
   GBP2        0.67696609  Up      6.970046e-04   3.974848e-03    2634
   GBP5        0.96463926  Up      2.668633e-03   1.181312e-02   115362
   GZMA        1.16170618  Up      5.368367e-03   2.079660e-02    3001
   [96]Open in a new tab

   PRGs, pyroptosis-related genes; DR, diabetic retinopathy; FC, fold
   change.

Functional and pathway enrichment analysis of differentially expressed
pyroptosis-related genes

   The enrichment analysis was performed on the differentially expressed
   pyroptosis-related genes to further understand their potential
   molecular functions and signaling pathways. The GO analysis showed that
   383 GO terms were significantly enriched, including 331 biological
   processes, five cellular components, and 47 molecular functions,
   involving interleukin-1 beta production, inflammasome complex, and CARD
   domain binding, indicating that these pyroptosis-related genes play a
   role in diabetic retinopathy through pyroptosis and inflammatory
   response ([97]Figures 3A–D). Subsequently, we analyzed the relationship
   between these pathways, and the result is shown in [98]Figure 4A. Then,
   we further analyzed the common genes of the first three pathways to
   clarify the relationship between the differentially expressed
   pyroptosis-related genes and these pathways ([99]Figure 4B). The
   heatmap-like functional classification showed the specific enrichment
   of these genes in the first eight pathways ([100]Figure 4C). At the
   same time, 67 significantly enriched pathways were obtained by KEGG
   analysis, indicating that these differentially expressed
   pyroptosis-related genes may play a key role in NOD-like receptor
   signaling pathway and other pathways ([101]Figure 5). The most
   significant pathways obtained by GO analysis and KEGG analysis were
   shown in [102]Table 3.

Figure 3.

   [103]Figure 3
   [104]Open in a new tab

   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.

   [105]Figure 4
   [106]Open in a new tab

   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.

   [107]Figure 5
   [108]Open in a new tab

   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.

Table 3.

   Top 7 GO terms and top 3 KEGG pathway analyses for the differentially
   expressed PRGs.
   Pathway id Category Description Count Genes P-value
   GO:0061702 CC inflammasome complex 5 CASP4, PYCARD, CASP1, CARD8, NLRP3
   3.36E-13
   GO:0034612 BP cysteine-type endopeptidase activity
   involved in apoptotic process 8 CASP4, TNFRSF21, PYCARD, CASP1, CASP3,
   GBP1, CARD8, GBP2 7.52E-12
   GO:0032611 BP interleukin-1 beta production 6 PYCARD, CASP1, TLR4,
   CARD8, NLRP3, GBP5 5.09E-11
   GO:0032612 BP interleukin-1 production 6 PYCARD, CASP1, TLR4, CARD8,
   NLRP3, GBP5 1.30E-10
   GO:0097153 MF cysteine-type endopeptidase activity involved in
   apoptotic process 4 CASP4, PYCARD, CASP1, CASP3 2.06E-10
   GO:0032731 BP positive regulation of interleukin-1 beta production 5
   PYCARD, CASP1, TLR4, CARD8, NLRP3 2.43E-10
   GO:0071356 BP cellular response to tumor necrosis factor 7 CASP4,
   TNFRSF21, , CASP1, GBP1, CARD8, GBP2 3.46E-10
   hsa04621 KEGG NOD-like receptor signaling pathway 9 CASP4, PYCARD,
   CASP1, GBP1, TLR4, CARD8, NLRP3, GBP2, GBP5 8.37E-13
   hsa05133 KEGG Pertussis 5 PYCARD, CASP1, CASP3, TLR4, NLRP3 7.53E-08
   hsa05130 KEGG Pathogenic Escherichia coli infection 6 CASP4, PYCARD,
   CASP1, CASP3, TLR4, NLRP3 2.77E-07
   [109]Open in a new tab

   *GO, Gene Ontology; KEGG, Kyoto Encyclopedia ofGenes and Genomes; PRGs,
   pyroptosis-related genes.

Construction of PPI network

   The PPI network is composed of individual proteins through the
   interaction between each other, which is of great significance for
   understanding protein function and interaction ([110]27). In order to
   further explore the differentially expressed pyroptosis-related genes,
   we carried out PPI analysis. Firstly, the network files sorted out in R
   software are uploaded to String database, which is a network tool for
   exploring known and predicted protein–protein interactions ([111]28). A
   PPI network with 13 nodes and 35 sides is obtained, and the average
   node degree is 5.38 ([112]Figure 6A). Then, we used the cytoHubba in
   CytoScape software for further analysis, sequenced these differentially
   expressed pyroptosis-related genes by MCC mode, and screened the first
   eight hub genes, namely: CASP1, NLRP3, CASP4, PYCARD, TLR4, CASP3,
   GBP2, and GBP1 ([113]Figure 6B, [114]Table 4).

Figure 6.

   [115]Figure 6
   [116]Open in a new tab

   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.

Table 4.

   PPI network of the top 8 hub genes.
   Rank Gene symbol Score Gene id Gene name
   1 CASP1 196 834 caspase-1
   2 NLRP3 192 114548 NLR family pyrin domain containing 3 caspase-4
   3 CASP4 168 837 PYD and CARD domain containing toll like receptor 4
   4 PYCARD 144 29108 NLR family pyrin domain containing 3 caspase-4
   5 TLR4 122 7099 PYD and CARD domain containing toll like receptor 4
   6 CASP3 121 836 caspase-3
   7 GBP2 48 2634 guanylate binding protein 2
   8 GBP1 48 2633 guanylate binding protein 1
   [117]Open in a new tab

   PPI, protein-protein interaction.

Validation of pyroptosis-related genes

   To ensure the reliability of the results of the [118]GSE60436 dataset
   analysis, we use qRT-PCR experiments and RNA-seq-based dataset
   [119]GSE179568 for validation. Firstly, the expression levels of the
   eight key differentially expressed pyroptosis-related genes were
   further identified by qRT-PCR in the in vitro model of diabetic
   retinopathy. HRECs were cultured in high glucose medium (30 mmol/L
   anhydrous glucose) or normal-glucose medium. High-glucose-exposed HRECs
   were used as an in vitro model of diabetic retinopathy. Similar to the
   results of mRNA chip in the [120]GSE60436 dataset, high glucose
   treatment significantly increased the mRNA expression levels of CASP3,
   TLR4, GBP2, CASP4, and GBP1 in HRECs. In addition, the expression
   levels of NLRP3, CASP1, and PYCARD were also increased ([121]Figure 7).
   These results were in accordance with the bioinformatics analysis.
   Then, as described previously, we analyzed the 51 pyroptosis-related
   genes in the verification set with |log[2] fold change| >1.0 as the
   threshold, and the trend of five pyroptosis-related genes was
   consistent with that of the hub genes analyzed by the [122]GSE60436
   dataset, including CASP4, GBP1, CASP3, TLR4, and GBP2 ([123]Figure 8).
   In general, the results between the training set and the validation set
   are relatively consistent. Next, we detected the expression of
   caspase-3 and gasdermin proteins, the key executor of pyroptosis, in
   the in vitro model of diabetic retinopathy by western blotting. It
   showed that the protein levels of cleaved caspase-3, GSDMD, and
   gasdermin E (GSDME) were increased in human retinal endothelial cells
   after high glucose incubation, suggesting the occurrence of pyroptosis
   ([124]Figure 9).

Figure 7.

   [125]Figure 7
   [126]Open in a new tab

   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.

   [127]Figure 8
   [128]Open in a new tab

   Validation of PRGs in the [129]GSE179568 dataset. (A) Heatmap of 15
   differentially expressed PRGs in the [130]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 |log[2] fold change| >1.0.
   PRGs, pyroptosis-related genes; RNV, retinal neovascularization; PDR,
   proliferative diabetic retinopathy.

Figure 9.

   [131]Figure 9
   [132]Open in a new tab

   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.

Construction of ceRNA-regulating network

   In order to further explore the interaction between lncRNA, miRNA, and
   mRNA in diabetic retinopathy, we structured a ceRNA regulatory network.
   MultiMiR is a new miRNA–target interaction R package and database,
   which combines human and mouse records from 14 databases and includes
   mirTarbase databases with experimental methods to verify the
   interactions ([133]29). Through this database, not only the miRNA that
   interacts with mRNA but also the targeted mRNA of miRNA can be
   predicted. We used the luciferase reporter assay as the screening
   criterion and used this tool to predict 20 miRNAs, including
   hsa-let-7a-5p, hsa-miR-433-3p, and so on ([134]Table 5). Then, based on
   these miRNAs, we obtained 22 targeted lncRNAs through the starbase
   database. Finally, the three mRNAs, 20 miRNAs, and 22 lncRNAs were used
   to structure the ceRNA regulatory network, and the visualization was
   carried out in the CytoScape software ([135]Figure 10).

Table 5.

   miRNAs and specic targeted mRNAs in ceRNA regulatory network.
   mRNA miRNA
   CASP3 hsa-miR-421, hsa-let-7g-5p, hsa-miR-30e-5p, hsa-let-7c-5p,
   hsa-let-7a-5p,
   hsa-miR-363-3p, hsa-miR-30c-5p, hsa-miR-138-5p, hsa-miR-98-5p,
   hsa-miR-224-5p, hsa-miR-155-5p, hsa-miR-30d-5p, hsa-miR-582-5p,
   hsa-miR-885-5p
   TLR4 hsa-let-7i-5p, hsa-miR-146b-5p, hsa-miR-26b-5p, hsa-let-7b-5p,
   hsa-miR-146a-5p
   GBP2 hsa-miR-433-3p
   [136]Open in a new tab

   miRNAs, microRNAs; ceRNA, competing endogenous RNAs.

Figure 10.

   [137]Figure 10
   [138]Open in a new tab

   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.

Discussion

   It has been demonstrated that leukostasis, infiltration of neutrophil
   and macrophage, and activation of complements and microglial cells were
   found in the retinas of patients or animal model with diabetic
   retinopathy. Meanwhile, elevated levels of inflammatory cytokines and
   chemokines were detected in vitreous or aqueous humor from diabetic
   patients, indicating a critical role of inflammation in diabetic
   retinopathy ([139]30, [140]31). Pyroptosis, known as cell inflammatory
   death, is accompanied by the release of a large number of
   pro-inflammatory factors, which is closely related to the pathogenesis
   of various chronic inflammatory diseases ([141]8). Diabetic
   retinopathy is currently considered a chronic inflammatory disease.
   Thus, the involvement of pyroptosis-induced inflammation in diabetic
   retinopathy draws more and more attention. However, the
   molecular mechanisms underlying pyroptosis in diabetic retinopathy are
   unclear and need further investigations ([142]32).

   Bioinformatics methods have been widely used for the exploration of key
   pathogenic factors and potential therapeutic targets in diabetic
   retinopathy ([143]33–[144]35). To the best of our knowledge,
   bioinformatics analysis of pyroptosis-related genes in diabetic
   retinopathy has not been reported. In the current study, 13
   differentially expressed pyroptosis-related genes in diabetic
   retinopathy were identified by bioinformatics for the first time, and
   eight of the most critical genes were identified by PPI analysis. At
   the same time, we performed GO analysis and KEGG analysis on these 13
   differentially expressed pyroptosis-related genes to clarify the
   enrichment relationship between these genes and related pathways. The
   results showed that these genes were enriched in inflammasome complex,
   interleukin-1 beta production, and other pathways, confirming the role
   of pyroptosis in diabetic retinopathy.

   Based on the results of the bioinformatics analysis, we identified
   eight most interesting pyroptosis-related genes according to PPI
   analysis and enrichment analysis. HRECs were incubated
   with high-glucose medium to mimic diabetic retinopathy in vitro.
   qRT-PCR was used for the verification of selected pyroptosis-related
   genes in a diabetic retinopathy model. The results showed that the
   change of mRNA level of CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD,
   and GBP1 was consistent with that of the bioinformatics analysis of the
   mRNA chip. Among them, CASP3, TLR4, GBP2, CASP4, and GBP1 are verified
   in the validation set. We also showed that the cleaved CASP3 was
   upregulated in the in vitro model of diabetic retinopathy
   ([145]Figure 9A). Several reports have shown that hyperglycemia can
   trigger the retinal inflammatory responses by abnormal metabolic
   pathways. It is accompanied by the activation of inflammasomes such as
   NLRP3 and TLR4, which play an important role in cell pyroptosis
   ([146]36–[147]38). Meanwhile, it has been reported that these potential
   pyroptosis-related genes mediated pyroptosis mainly due to the release
   of inflammatory cytokines such as IL-1β and IL-18 and lactate
   dehydrogenase, and the activation of GSDMD promotes pore formation in
   cells. Pyroptosis is associated with the damage of retinal vascular
   endothelial cells, retinal pericytes, and retinal pigment epithelium
   (RPE) cells in diabetic retinopathy ([148]39–[149]44). In the current
   study, both GSDMD and GSDME were increased in the high-glucose-treated
   retinal vascular endothelial cells, which is consistent with previous
   findings, indicating that hyperglycemia can trigger pyroptosis in
   retinal vascular endothelial cells. Destruction of retinal vascular
   endothelial cells by hyperglycemia leads to blood–retinal barrier
   damage and occlusion of the retinal capillary, which cause macular
   edema and pathological retinal neovascularization ([150]45, [151]46).
   Rescuing the disrupted retinal vascular endothelial cells is the
   therapeutic strategy for diabetic retinopathy. Therefore, further
   elucidating the function of the aforementioned pyroptosis-related genes
   in retinal vascular endothelial cells will provide a deep insight into
   the mechanism underlying diabetic retinopathy.

   Recently, increasing data demonstrated that lncRNA- and miRNA-mediated
   pyroptosis is involved in the occurrence of diabetic retinopathy. miRNA
   miR-590-3p, which has been proved to play an active role in
   inflammation ([152]47), could directly target NLRP1 and NOX4 and
   inhibit pyroptosis in diabetic retinopathy through the
   NOX4/ROS/TXNIP/NLRP3 pathway ([153]42). METTL3, as the key
   methyltransferase for m^6A RNA methylation, reduced the pyroptosis
   level in a diabetic model by targeting the miR-25-3p/PTEN/Akt signal
   cascade with the assistance of DGCR8 ([154]43). CircZNF532 could also
   regulate pyroptosis in RPE cells from diabetic retinopathy patients by
   targeting the miR-20b-5p/STAT3 axis ([155]44). Another study found that
   lncRNA MIAT was significantly upregulated in human retinal pericytes
   and promoted human retinal pericyte pyroptosis by regulating miR-342-3p
   targeting CASP1 ([156]41). Therefore, we constructed the
   ceRNA-regulating network to figure out the reciprocal interaction
   between lncRNA and miRNA and its function in regulating
   pyroptosis-related gene expression. By using multiMiR database, we
   predicted 20 miRNAs to be involved in the modulation of three essential
   pyroptosis-related genes, such as CASP3, TLR4, and GBP2. Subsequently,
   we found a possible interaction between 22 lncRNAs and these 20 miRNAs.
   Based on these findings, we created the ceRNA-regulating network
   through the aforementioned 22 lncRNAs, 20 miRNAs, and three target
   mRNAs. Among these genes, TLR4 as a pattern recognition receptor is
   very important for the activation of NLRP3 inflammatory bodies
   ([157]48). After activation, inflammatory bodies can bind to
   pro-caspase-1 through the binding protein ASC, activate caspase-1, and
   induce cell pyroptosis through the classical pathway. At the same time,
   in the non-classical pathway, although caspase-4/5/11 can be directly
   activated by LPS without the participation of receptors, it still needs
   to be completed with the assistance of caspase-1 activated by NLRP3.
   Both classical and non-classical pathways will induce pyroptosis by
   cleaving GSDMD ([158]49, [159]50). GBP2 plays a dual role in the
   occurrence of pyroptosis. It not only promotes the activation of AIM2
   inflammatory cytokines but also controls the recruitment of caspase-4
   ([160]51, [161]52). In recent years, accumulated evidence have shown
   that caspase-3, which is usually regarded as a marker of apoptosis, can
   play an important role in pyroptosis by cleaving GSDME, which is
   another new pathway of pyroptosis that is different from the classical
   and non-classical pathways ([162]8, [163]53, [164]54). In this study,
   we confirmed that high glucose could upregulate the expression of
   caspase-3 and GSDME in the diabetic model, suggesting that
   CASP3-mediated pyroptosis is involved in the development of diabetic
   retinopathy, which has not been explored. Further studies are required
   to uncover it.

Conclusions

   In this study, the key pyroptosis-related genes in diabetic
   retinopathy, such as CASP3, GBP2, and TLR4, were preliminarily
   identified by bioinformatics analysis and confirmed by [165]GSE179568
   and qRT-PCR in a diabetic model. lncRNAs and miRNAs that are associated
   with these genes were further predicted ([166]Figure 10). It provides a
   novel insight into the molecular mechanism of pyroptosis in the
   pathogenesis of diabetic retinopathy.

Data availability statement

   The dataset presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found in the article/[167] Supplementary Material .

Author contributions

   NW and LD analyzed the data and drafted the manuscript. DL, XX, QZ, and
   GZ edited and provided comments to improve the manuscript. SX designed
   this experiment and reviewed and revised the manuscripts. All the
   authors contributed to the article and approved the final manuscript.

Funding

   This study was financially supported by the National Natural Science
   Foundation of China (no. 81974137 to SX), the National Natural Science
   Foundation of China (no. 82070966 to LD), the Natural Science
   Foundation of Hunan Province (no. 2019JJ40507 to SX), and the Science
   and Technology Innovation Program of Hunan Province (no. 2021RC3026 to
   LD).

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [168]https://www.frontiersin.org/articles/10.3389/fendo.2022.918605/ful
   l#supplementary-material
   [169]Click here for additional data file.^ (452.2KB, docx)
   [170]Click here for additional data file.^ (20.4KB, xlsx)

References