Identification of NLRP3 Inflammation-Related Gene Promoter Hypomethylation in Diabetic Retinopathy

Abstract

Purpose: To identify and validate key genes that could provide a new perspective for genetic marker screening of diabetic retinopathy (DR).

Methods: The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus. Differential expression analysis was conducted using the limma package, and then the functions of the differentially expressed genes (DEGs) were analyzed using the DAVID database, followed by protein–protein interaction (PPI) networks using Cytoscape software. We employed the Sequenom MassARRAY system to detect the promoter methylation levels of the candidate genes in peripheral blood mononuclear cells from 32 healthy individuals and 94 patients with type 2 diabetes mellitus (T2D; 64 with DR and 30 without DR) and in fibrovascular membranes (FVMs) from three proliferative DR patients and three controls with idiopathic epiretinal membranes. The mRNA levels of candidate genes were further confirmed via real-time polymerase chain reaction.

Results: A significant enrichment of 5906 DEGs was found in immune and inflammatory responses. TGFB1, CCL2, and TNFSF2 were identified as the top three core genes associated with NLRP3 inflammation in PPI networks. These genes have relatively low levels of promoter methylation, which have been validated in peripheral blood mononuclear cells and FVMs from DR patients, and the methylation levels were found to be negative correlated with the mRNA levels and HbA1c levels in T2D patients.

Conclusions: Overall, these data indicate that promoter hypomethylation of NLRP3, TGFB1, CCL2, and TNFSF2 may increase the risk of DR in the Chinese Han population, indicating that these genes might serve as potential targets for the detection and treatment of DR.

Diabetic retinopathy (DR), a microvascular disorder caused by chronic hyperglycemia, is a common diabetic complication. It is characterized by irreversible vision loss, which affects up to 80% diabetic patients for possibly 20 years or more.^1,2 At present, the pathogenesis of DR has not yet been fully elucidated. DR is believed to be a chronic inflammatory disorder closely linked with the immune inflammatory response, and inflammatory factors play an important role in its pathological processes.^3,4 It has been demonstrated that inflammatory reactions are critically involved in the pathogenesis of diabetes, and numerous inflammatory response factors, including tumor necrosis factor, C-reactive protein, and interleukins, are closely associated with the development of diabetes, as well as the complications of this disease.⁵ Vascular endothelial growth factor (VEGF) is currently considered to be the most potent proangiogenic factor in DR,⁶ although anti-VEGF drugs still cannot completely prevent the formation of pathological neovascularization and macular edema.⁷ Moreover, a comprehensive understanding of the underlying molecular mechanism for the pathogenesis of DR is still unclear. 

Gene expression profiling can be used for screening differentially expressed genes (DEGs) involved in DR. Here, we employed a bioinformatics method to comprehensively identify DR-related pathways. Aberrant DNA methylation has been found to be associated with inflammation in type 2 diabetes mellitus (T2D).⁸ The immune system possesses a specific gene expression signature determined by epigenetic regulation, including DNA methylation and histone modifications.⁹ As a main type of epigenetic modification, DNA methylation regulates the expression of target genes mainly by modulating interactions between DNA and proteins.¹⁰ Although alterations in DNA methylation have been examined in various tissues such as liver and adipose tissues of patients with T2D from a number of populations,^11,12 few studies on DNA methylation abnormality in DR have been conducted so far. In this study, we extracted and analyzed information from both microarray-based gene expression and methylation profiling conducted in DR to identify abnormally expressed or methylated genes and key signaling pathways related to this disorder. 

Materials and Methods

Database Information

Data extraction was performed on the gene expression (GSE 60436) and methylation (GSE 57362) profiles that are publicly available from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information.¹³ DR-related dataset GSE 60436 generated by Keijiro Ishikawa using the GPL 6684 platform (Illumina HumanWG-6 v3.0 expression beadchip) includes six proliferative DR (PDR) samples and three normal samples. Because the microarray-based gene methylation profiling utilized the GPL 13534 platform Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482), GSE 57362 includes nine PDR samples and eight normal samples (Supplementary Fig. S1 shows the workflow of the study). 

Data Processing

Identification of DEGs in the patient and control groups was carried out by using a limma package-based empirical Bayes method within R software (R Foundation for Statistical Computing, Vienna, Austria).¹⁴ A false discovery rate (FDR) < 0.05 and log fold change (logFC) > 1 were defined as the cutoff criteria for this identification. Upregulated and downregulated genes from the gene expression profiling datasets (GSE 60436) were identified, and hypo- and hypermethylated genes from GSE 57362 were confirmed. R statistical software and the pheatmap R package were used for bidirectional hierarchical clustering. 

Functional and Pathway Enrichment Analyses

DAVID 6.7 (Database for Annotation, Visualization, and Integrated Discovery)¹⁵ was employed to conduct Gene Ontology (GO)¹⁶ functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway¹⁷ analyses of DR-associated DEGs. GO analysis involved categories of cellular components, molecular function, and biological processes. KEGG analysis predicted the enriched pathways for those DEGs. FDR < 0.01 was defined as the cutoff point. 

Construction and Analysis of the Protein–Protein Interaction Network

PPI network analysis was performed to identify DR-related core genes, as well as the gene modules. STRING,¹⁸ an online database search tool for the retrieval of interacting genes, was used to carry out the PPI analysis on hypomethylated/downregulated and hypermethylated/upregulated differentially methylated genes (DMGs). The score for interaction was set at 0.4. We then utilized Molecular Complex Detection (MCODE) in Cytoscape software to screen modules within the PPI network. The screening criteria were MCODE score > 3 and node score cutoff = 3. 

MassARRAY-Based Validation of Proteomics Data

This study recruited a total of 94 T2D patients who were admitted into the Zhongshan ophthalmic center from June 1, 2019, to August 1, 2020. The patients included 30 cases without retinopathy (NDR), 31cases with non-proliferative DR (NPDR), and 33 cases with PDR. A total of 32 age- and sex-matched healthy individuals were included as the normal controls. At the time of recruitment, all patients with T2D were on an appropriate diet and receiving glucose-lowering medication, such as insulin (n = 28; 29.8%), oral hypoglycemic agents (n = 36; 38.3%), or the insulin-sensitizer agent metformin (n = 30; 31.9%), alone or in combination. 

The 2002 American Diabetes Association standards were used to confirm the diagnosis of T2D.¹⁹ Patients with infectious diseases, uncontrolled hypertension, or other diabetic complications such as nephropathy (defined as patients with stage 3 chronic kidney disease, macroalbuminuria, or proteinuria and those undergoing hemodialysis) were excluded. Chronic kidney disease stages were based on the National Kidney Foundation Disease Outcomes Quality Initiative clinical practice guidelines. Patients were also excluded if they had been subjected to intraocular procedures, intravitreal treatments, photocoagulation in the prior 3 months, uveitis, trauma, vitreous hemorrhage, retinal detachment, or immunosuppressive drug administration. DR was assessed by fluorescein fundus angiography (FF450 Fundus Camera; Carl Zeiss Meditec AG, Jena, Germany). Body mass index (BMI) was determined using the formula weight (kg)/height (m²). Based on the Diabetic Retinopathy Disease Severity Scale, diabetics were placed into three groups: no DR, non-proliferative DR, or proliferative DR.²⁰ 

We evaluated the methylation of candidate genes in peripheral blood mononuclear cells (PBMCs) obtained from DR patients and biopsies from PDR patients. PBMCs were prepared as described previously.²¹ During pars plana vitrectomy, fibrovascular membranes (FVMs) were surgically removed via membrane peeling from three eyes of PDR patients (age, 57.33 ± 1.53 years; one male, two females; duration of diabetes, 12.00 ± 3.46 years; fasting plasma glucose [FPG], 10.83 ± 1.53; glycated hemoglobin [HbA1c], 10.67 ± 1.15%). As controls, epiretinal membrane (ERM) resection was performed in three patients with idiopathic ERMs (age, 59.67 ± 3.51 years; two males, one female; FPG:5.67 ± 0.57; HbA1c, 4.97 ± 0.50). The variations in ages and gender among groups were not significant. In the laboratory, FVMs and ERMs were snap-frozen within 1 hour of removal in optimal cutting temperature compound and kept at –80°C. Written informed consent was obtained from all participants, and this study was granted ethics approval from the Zhongshan ophthalmic center. 

DNA Isolation

The QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) was used to extract the genomic DNA from PBMCs. Total genomic DNA was isolated from FVMs and control membranes using a Qiagen genomic DNA purification kit. Quantitative and qualitative analyses of all DNA samples were carried out using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). 

Quantitative Analysis of DNA Methylation

The primers were designed to amplify the region containing the majority of the CpGs in the candidate genes. The chosen amplicons were mapped onto the promoter regions of the target genes. DNA sequences of each target gene promoter region were determined based on the University of California, Santa Cruz, website (http://www.genome.ucsc.edu). We designed the PCR primers for target genes through the EpiDesigner online system (in the public domain at http://www.epidesigner.com) on the basis of the obtained DNA sequences. For transcription, each reverse primer was tagged by an additional T7 promoter sequence, and a 10-mer tag was added to every forward primer. The specific primer sequences of each target gene are shown in Table 2. The Sequenom MassARRAY platform (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [MALDI-TOF MS]; Agena Bioscience, San Diego, CA, USA) was used to collect the mass spectra, and the methylation ratios were calculated using Sequenom EpiTYPER software. 

Bisulfate conversion of genomic DNA (µg) was performed using the EZ DNA Methylation Kit (Zymo Research, Irvine, CA, USA) as instructed. The Sequenom MassARRAY MALDI-TOF system was utilized to conduct quantitative methylation analysis of bisulfite-treated genomic DNA. Briefly, bisulfite-treated DNA was amplified as follows: 95°C for 4 minutes, followed by 45 cycles of 95°C for 20 seconds, 56°C for 30 seconds, 72°C for 60 seconds, and a final incubation at 72°C for 3 minutes. Shrimp alkaline phosphatase was used to dephosphorylate the unincorporated dNTPs. Subsequently, the transcription and cleavage were conducted by incubation with RNase A and T7 RNA and DNA polymerase at 37°C for 3 hours. The cleavage reactants were desalted using the clean resin and dispensed on a 384 SpectroCHIP (Agena Bioscience). MassARRAY MALDI-TOF-MS and EpiTYPER software (version 1.2) were used to collect and analyze the mass spectra, respectively. Each bisulfite-treated sample was tested in duplicate. Poor-quality data for the quantitative methylation of each CpG unit were excluded. 

Real-Time PCR

Total RNA was extracted from the PBMCs using a TRIzol Plus RNA Purification Kit (Thermo Fisher Scientific), and then subjected to reverse transcription to generate cDNA using a reverse transcriptase kit (Takara Biotechnology, Dalian, China). The Applied Biosystems 7500 System with SYBR-Green (Qiagen) was utilized to conduct real-time PCR. The PCR primer sequences of the target genes and the internal reference gene glyceraldehyde-3-phosphate dehydrogenase (GADPH) were as follows: 

• • GADPH—forward, 5′-ATCACCATCTTCCAGGAGCG-3′; reverse, GGGCAGAGATGATGACCCTT-3′
• • TGFB1—forward, 5′-GGATACCAACTATTGCTTCAGCTCC-3′; reverse, 5′-AGGCTCCAAATATAGGGGCAGGGTC-3′
• • CCL2—forward, 5′-CAGCCAGATGCAATCAATGCC-3′; reverse, 5′-TGGAATCCTGAACCCACTTCT-3′
• • TNFSF2—forward, 5′-CACTAAGAATTCAAACTGGGGC-3′; reverse, 5′-GAGGAAGGCCTAAGGTCCAC-3′

Quantitative real-time PCR (qPCR) was conducted with a LightCycler CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) according to the manufacturer's instructions. All of the qPCR reactions were performed in triplicate. The signal was collected at the endpoint of every cycle. Following amplification, melting-curves analyses of PCR products were acquired on the SYBR channel using a ramping rate of 1°C/60 seconds from 60° to 95°C. Relative mRNA expression was measured using the ∆∆Ct method. 

Statistical Analysis

In addition to all of the bioinformatics analyses mentioned above, all evaluations were conducted with the SPSS Statistics 22 for Windows (IBM, Armonk, NY, USA). Group variations between diabetes patients and controls were evaluated by one-way analysis of variance (ANOVA) or nonparametric Kruskal–Wallis tests according to normality assumptions and homogeneity of variances with a post hoc Scheffe test. Variations among all groups were additionally examined by Mann–Whitney U tests or Student's t-tests. Relationships among study parameters were examined via Spearman's correlation test. The associations between PBMC methylation levels of aberrant genes and demographic factors in T2D patients were estimated with multivariate logistic regression models. Graphs were drawn using Prism 5 (GraphPad Software, Inc., San Diego, CA, USA). For each test, P < 0.05 was considered statistically significant. 

Results

Identification of DEGs in PDR

We utilized the expression profiling dataset GSE 60436 to screen and detect DEGs in the PDR and the control cases. The analysis revealed a total of 5906 DEGs comprised of 2337 upregulated and 3569 downregulated genes for the PDR cases. Thereafter, the 5906 DEGs and six samples were clustered, and DEGs could well differentiate the PDR samples from the normal controls (Fig. 1A). 

Figure 1.

 

Correlation cluster diagrams for the modules of GSE 60436. (A) Clustering heat maps for DEGs. Color changing from blue to red indicates that the expression values range from low to high. For the analysis of DEGs in the gene expression dataset GSE 60436, a total of 5906 significant DEGs were identified. (B) Gene enrichment and functional categories, where blue represents high gene expression in PDR and red represents high gene expression in normal tissue. (C) Pathway enrichment analysis. The circle represents the signal pathway, the green line is the pathway enriched by highly expressed DEGs in PDR cases, and the blue line is the pathway enriched by DEGs expressed at a low level in PDR cases. The red color depth in the circle represents the P value, such that the darker color represents smaller P values. (D) Differentially expressed genes in the NF-κB signaling pathway. Green represents high gene expression in PDR, and red represents low gene expression in PDR.

GO Enrichment Study

We employed DAVID to perform the GO enrichment analysis on the previously detected DEGs to identify functional alterations during PDR progression. As shown in Figure 1B, upregulated DEGs were found to be highly enriched in gene ontology categories related to immune function such as immune response, cytokine regulation, and defensive inflammatory response. 

KEGG Pathway Enrichment Analysis

We next conducted a DAVID-based KEGG pathway analysis on the identified DEGs. As depicted in Figure 1C, among all DEGs, the upregulated genes were remarkably correlated with inflammatory disease and cytokine regulation pathways. According to the KEGG analysis, the highly expressed genes were markedly enriched in the nuclear factor kappa B (NF-κB) pathway. A total of 32 DEGs were found to be implicated in the NF-κB pathway; among them, only five genes were significantly downregulated in PDR, whereas the other 27 genes were significantly highly expressed (Fig. 1D). Given that the NF-κB pathway is closely related to the inflammatory response, the highly expressed NF-κB pathway suggests a close correlation between DR and inflammatory response. 

Identification of Differentially Methylated CpG Sites in PDR

We obtained the methylation data on PDR from dataset GSE 57362 in the GEO database. GSE 57362 contains 265 samples, of which 17 are related to DR. Among all 17 of these DR-related samples, nine were PDR and eight were normal. In this study, we identified a total of 173,451 differentially methylated sites from the GSE 57362. Heat maps were used to illustrate the CpG sites with differential methylation in the microarray data of methylation (Figs. 2A–2D). 

Figure 2.

 

Identification of DMGs. (A) Distribution of DMGs relative to CpG islands (CGIs) including island, shore, shelf, and open sea. (B) Distribution of DMGs across the gene regions TSS200, TSS1500, 5′ UTR, first exon, gene bodies, 3′ UTR, and intergenic region. (C) Distribution of DMGs in various functional genomic regions (promoter region, CGIs, and CGI promoter). (D) Heat map of top 50 DMGs in the GSE 57362 microarray. Yellow indicates hypermethylated genes; blue indicates hypomethylated genes. (E–G) The PPI network and modules of common DMGs in the PDR and normal control groups: SRGN module (E), MMP7 module (F), and ENG module (G).

PPI Module Analysis

PPI network analysis was performed using the STRING database. Screening of the database via MCODE in the Cytoscape software identified three modules. KOBAS 3.0 was then used to analyze the modules. The analysis revealed that SRGN in module 1, MMP7 in module 2, and ENG in module 3 were functionally related to inflammation. As shown in Figures 2E to 2G, transforming growth factor beta-1 (TGFB1), C-C motif chemokine 2 (CCL2), and tumor necrosis factor ligand superfamily member 2 (TNFSF2) were chosen as the core genes, which are defined as genes associated with NOD-like receptor (NLRP3) inflammasome. The mRNA expression of NLRP3, TGFB1, CCL2, and TNFSF2 was found to be significantly increased in PDR patients compared to healthy control (Fig. 3). 

Figure 3.

 

The mRNA expression of NLRP3, TGFB1, CCL2, and TNFSF2 was found to be significantly increased in PDR patients compared to healthy controls based on GSE 60436 datasets.

Differential Expression and Differential Methylation Levels of NLRP3

NLRP3 is an important gene involved in the formation and activation of inflammatory bodies and is one of the targets for controlling inflammation. We found that the NLRP3 gene was highly expressed in the PDR group (fold change = 3.55). A lower methylation level in NLRP3 gene promoter was observed in the PDR cases compared with the normal samples, and the methylation levels in the gene body and 3′ UTR regions were high (Fig. 4). It is known that methylation of the promoter region inhibits gene expression. As shown in Figure 3, the methylation levels were found to be consistent with transcriptome levels. 

Figure 4.

 

Methylation level of the NLRP3 gene region. FVMs represent cases of PDR. This figure shows the methylation profiles in PDR patients and healthy controls of the NLRP3 gene across gene regions TSS200, TSS1500, 5′ UTR, first exon, gene bodies, 3′ UTR, and intergenic region.

Detection of Promoter Hypomethylation of NLRP3, TGFB1, CCL2, and TNFSF2 in DR Patients

To assess whether epigenetic variations in NLRP3, TGFB1, CCL2, and TNFSF2 were related to DR, we measured the promoter methylation levels of these genes using MassARRAY spectrometry in PBMCs and compared the methylation levels among DR cases of varying severity, NDR cases, and healthy individuals. Demographic information and the clinical parameters for the NDR or DR patients and controls are summarized in Table 1. There was no significant variation among groups in age (P = 0.181) or gender (P = 0.546). BMI distribution was significantly higher in patients with T2D than in healthy controls (P = 0.004). The mean extent of diabetes was significantly longer in the PDR and NPDR groups than in the NDR group (P < 0.001). HbA1c values ranging from 4.27% to 6.07% were considered normal. HbA1c and fasting glucose levels were also found to be significantly elevated in the PDR, NPDR, and NDR groups compared to the control group (all P < 0.001). 

Table 1.

 

Clinical and Biochemical Characteristics of Type 2 Diabetic Patients and Healthy Control Subjects

 

Table 2.

 

Primer Sequence for Amplifying Target Genes

 

As shown in Figure 5, markedly lower methylation levels in CpG-3 and CpG-6 of NLRP3 were present in PDR and NPDR patients compared with the healthy individuals (CpG-3, P = 0.004 vs. P = 0.047; CpG-6, P = 0.001 vs. P = 0.024). When compared with NDR patients, PDR patients also presented significantly decreased methylation levels (CpG-3, P = 0.005; CpG-6, P = 0.049) (Table 3). Likewise, compared with the control cases, PDR and NPDR patients displayed a significant decrease in the methylation levels of CpG-5, CpG-9, and CpG-10 sites in the CCL2 target region (CpG-5, P < 0.001 vs. P = 0.040; CpG-9, P = 0.026 vs. P = 0.021; CpG-10, P < 0.001 vs. P = 0.018) (Fig. 5). The methylation levels of the CpG-5 and CpG-10 sites in the CCL2 also presented the same trend in PDR patients when compared with the NDR group (CpG-5, P = 0.005; CpG-10, P < 0.001). Also, a significant reduction in the methylation levels of CpG-4, 5, 6 and CpG-9 units in TNFSF2 (CpG-4, 5, 6, P < 0.001 vs. P = 0.019; CpG-9, P = 0.015 vs. P = 0.029), as well as CpG-1 and CpG-5units in TGFB1 (CpG-1, P = 0.001 vs. P = 0.002; CpG-5, P = 0.004 vs. P = 0.023), was detected in PDR and NPDR patients compared with the healthy individuals. At the same time, hypomethylation of CpG-4, 5, 6 units in TNFSF2 (P = 0.012) and CpG-1 units in TGFB1 (P = 0.004) were also found in PDR patients compared to the NDR group. The methylation level of CpG-1 units in TGFB1 in NPDR patients was also found to be significantly decreased when compared to NDR patients (P = 0.005). 

Figure 5.

 

NLRP3, CCL2, TNFSF2, and TGFB1 methylation levels in PBMCs from PDR patients (n = 33), NPDR patients (n = 31), NDR patients (n = 30), and healthy controls (n = 32). Methylation levels of CpG-3 and CpG-6 of NLRP3 (A, B); CpG-5, CpG-9, and CpG-10 of CCL2 (C–E); CpG-4, 5, 6 and CpG-9 of TNFSF2 (F, G); and CpG-1 and CpG-6 of TGFB1 (H, I) were all significantly decreased in DR patients compared to healthy controls. Group variations between diabetes patients and controls were evaluated by one-way ANOVA with a post hoc Scheffe test. Data represent mean ± SEM. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

Table 3.

 

Methylation Levels for Each CG Unit of NLRP3, CCL2, TNFSF2, and TGFB1 in PBMCs of DR Patients and Healthy Controls

 

To further validated the DNA methylation profiles of these genes, independent membrane tissue samples from PDR patients and control group were collected. Although the sample sizes were relatively small, in general the methylation levels of NLRP3, TGFB1, CCL2, and TNFSF2 were decreased in FVMs from the PDR group, similar to those measured for PBMCs (Fig. 6). 

Figure 6.

 

Methylation profile of CpG sites for NLRP3,CCL2,TNFSF2 and TGFB1 genes in fibrovascular membranes obtained from three PDR patients and epiretinal membranes from three idiopathic epiretinal membrane patients during surgery. The color of the circles is related to the percent of methylation in each CpG site.

Promoter Methylation Was Correlated with mRNA Levels of NLRP3, TGFB1, CCL2, and TNFSF2

To determine whether the promoter methylation level was associated with gene expression, we analyzed the transcriptional expression of NLRP3, TGFB1, CCL2, and TNFSF2 in PBMCs. As shown in Figure 7, the mRNA levels of NLRP3, TGFB1, CCL2, and TNFSF2 were markedly upregulated in PDR and NPDR patients compared to the NDR patients and healthy individuals (all P < 0.05). We next investigated whether the methylation levels of the changed CpG sites in NLRP3, TGFB1, CCL2, and TNFSF2 were associated with gene expression at mRNA levels in the entire studied population. As depicted in Figure 8, the analysis revealed a negative correlation between DNA methylation and mRNA expression in all tested genes (P < 0.05). 

Figure 7.

 

The mRNA expression levels of NLRP3, CCL2, TNFSF2, and TGFB1 in PBMCs from PDR patients (n = 33), NPDR patients (n = 31), NDR patients (n = 30), and healthy controls (n = 32). NLRP3, CCL2, TNFSF2, and TGFB1 mRNA expression was markedly increased in DR patients compared to healthy controls. Group variations between diabetes patients and controls were evaluated by one-way ANOVA with a post hoc Scheffe test. Data represent mean ± SEM. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

Figure 8.

 

Correlation between DNA methylation and respective mRNA expression levels in the entire studied population. The DNA methylation levels of CpG-3 and CpG-6 of NLRP3 (A, B); CpG-5, CpG-9, and CpG-10 of CCL2 (C–E); CpG-1 and CpG-6 of TGFB1 (F, G); and CpG 4,5,6 and CpG 9 of TNFSF2 (H, I) were negatively correlated with their mRNA expression (n = 126).

Association Among Methylation Levels of NLRP3, TGFB1, CCL2, and TNFSF2 and Demographic Factors in PBMCs from T2D Patients

As shown in Table 4, in logistic regression models that tested the association between the degree of PBMC methylation and demographic factors in T2D patients, we observed that the methylation rates for CpG-3 (β = –0.006, 95% confidence interval [CI] = –0.011 to –0.001, P = 0.015) and CpG-6 (β = –0.002, 95% CI = –0.005 to 0.000, P = 0.035) in NLRP3; CpG-5 (β = –0.009, 95% CI = –0.014 to –0.004, P = 0.001) and CpG-10 (β = –0.004, 95% CI = –0.007 to –0.001, P = 0.011) in CCL2; CpG-4, 5, 6 (β = –0.009, 95% CI = –0.017 to 0.000, P = 0.039) in TNFSF2; and CpG-1 (β = –0.002, 95% CI = –0.003 to 0.000, P = 0.030) and CpG-5 (β = −0.020, 95% CI = –0.036 to –0.004, P = 0.012) in TGFB1 were all negatively correlated with HbA1c levels. 

Table 4.

 

Associations Among Aberrant CG Units of NLRP3, CCL2, TNFSF2, and TGFB1 in PBMCs of Type 2 Diabetic Patients With or Without DR and Clinical Parameters

 

Discussion

Here, we carried out a bioinformatics study to identify host genes and aberrant DNA methylation genes associated with PDR and then validated the findings in PBMCs from another cohort with varying severity of DR, based on high-resolution bisulfite sequencing. This study revealed that the inflammatory response-related genes were significantly more highly expressed in DR patients, especially PDR patients. Compared with the healthy controls, DR patients displayed a significant decrease in the promoter methylation levels of NLRP3, a key gene involved in the formation and activation of inflammatory bodies. Genes associated with inflammatory bodies (TGFB1, CCL2, and TNFSF2) were also found to be highly expressed in the DR cases. Among all of the DEGs, genes highly expressed in DR were remarkably enriched in pathways associated with immune responses, as well as inflammatory responses. These data suggest a close relationship between DR and inflammatory bodies. 

We examined the gene expression profiling prior to the GO and KEGG analysis. In this case, we showed that while up-/downregulated DEGs were highly enriched in immune responses and inflammatory responses, the NF-κB pathway was identified as being a highly enriched pathway for the upregulated DEGs. It has been reported that NF-κB signaling plays a critical role NLPR3 inflammasome activation in DR.²² Moreover, toll-like receptor and NF-κB signaling induced the first signal in inflammasome activation and then facilitated the transcriptional expression of proIL-1β, proIL-18, and NLPR3.²³ 

Aberrant methylation is an important molecular mechanism underlying DR initiation and development.²⁴ Understanding the aberrantly methylated/expressed key genes in DR would provide novel insights into its diagnosis, treatment, and prognosis. Increasing evidence demonstrates that aberrant epigenetic regulation is implicated in diabetes, as well as the related complications of diabetic nephropathy and DR.^25,26 Here, we found that compared with the normal controls in Chinese population, diabetic patients with DR displayed a reduction in promoter methylation levels of NLRP3 in PBMCs, suggestive of an abnormal expression of inflammatory corpuscles in the patients. PBMCs appear to be suitable for studies of the mechanisms of immune dysfunction in T2D, as they are easily accessible and have been shown to reflect the responses of blood glucose modifications and oxidative stress at the gene and protein expression levels.²⁷ In agreement with our findings, monocytes from patients in Germany with chronic nonbacterial osteomyelitis, an autoinflammatory bone disorder, had reduced methylation levels in the NLRP3 genes compared to healthy controls.²⁸ The methylation levels of NLRP3 in FVMs from PDR in our study also showed the same trend. It is known that NLRP3 gene polymorphisms are linked to T2D; however, the NLRP3 methylation profile in T2D remains to be determined.²⁹ The promoter region of NLRP3 has been shown to harbor two CpG islands and the binding sites for multiple transcription factors, indicative of a role of DNA methylation in gene transcriptional regulation. It has been reported that a lower level of total methylation in NLRP3 promoter was associated with a 17.78-fold increase in risk for micro- and macrovascular complications in T2D.³⁰ As discussed earlier, the CpG sites in the gene promoter region were usually adjacent to transcription factor binding sites. As such, hypomethylation in the promoter may cause increased gene expression.³¹ In the cultured smooth muscle cells, demethylation was found to be correlated with enhanced NLRP3 transcription.³² Although the cause of this hypomethylation is currently unclear, the aberrant methylation can be a potential target for the intervention of NLRP3 inflammatory bodies in DR. Therefore, intervention in the activation of inflammatory bodies and decreased expression of the NLRP3 gene may provide a new therapeutic option for DR. 

This study showed that the DR patients displayed significantly lower promoter methylation levels of the NLRP3-related genes TGFB1, CCL2, and TNFSF2 compared to the healthy individuals. In the PDR patients, the difference was still significant compared with the NDR group. Moreover, the methylation level of each unit was negatively correlated with mRNA expression. Together, these data provide evidence that promoter hypomethylation of these genes is involved in DR development, especially in PDR. 

Recently, it has been shown that TGFB1 induced the activation of NLRP3 in DR, serving as a molecular platform for the maturation and secretion of IL-1β and IL-18.^33,34 Likewise, TGFB1 induced a reactive oxygen species-dependent activation of NLRP3 inflammasome in diabetic nephropathy.³⁵ In T2D, high glucose and TGFB1 could induce reactive oxygen species,^36,37 leading to the generation of a potential ligand of NLRP3. 

Chemokine CCL2 and its receptor CCR2 were found to be critically involved in the attraction of monocytes and relevant cells that is essential for developing inflammatory responses.^38,39 Further investigation demonstrated that CCL2 was highly expressed in DR patients, and NLRP3 was associated with a decrease in inflammation-induced expression of chemokines, including CCL2.^40–42 As a proinflammatory cytokine primarily generated by activated macrophages, TNF regulates the immune response.⁴³ Numerous inflammatory diseases including diabetes can be attributed to TNF dysregulation. A genome-wide analysis of DNA methylation in blood samples from 28 PDR cases identified a total of 349 methylated CpG sites located in 233 genes, including TNF.⁴⁴ TNF has been shown to act as the mediator for liver inflammation, and can be activated via constitutive activation of NLRP3 inflammasome in myeloid derived cells.^45,46 

Finally, our data also suggest a negative correlations between HbA1c levels and NLRP3, TGFB1, CCL2, and TNFSF2 methylation levels in DR patients. Previous studies have observed negative correlations between HbA1c levels and NLRP3, TGFB1, CCL2, and TNFSF2 protein expression in animal models,^47–49 indicating that glycemic control plays an important role in the immune response of diabetic subjects. 

In conclusion, this study demonstrated that hypomethylation in the promoters of NLRP3, TGFB1, CCL2, and TNFSF2 not only were associated with DR, especially in PDR but also were negatively correlated with the mRNA expression of those genes. These findings may provide insight into how these factors are involved in DR pathogenesis, thus providing a new perspective for genetic marker screening of DR. At present, the cause of this hypomethylation is still unclear, but this abnormality in methylation could become an important target for the intervention of inflammatory bodies in DR, and intervening in the activation and decreased expression of the NLRP3 gene could be therapeutically beneficial for patients with DR. Overall, the findings in this study must be validated further in more databases, and the clinical potential of those core genes in DR remains to be further assessed. 

Acknowledgments

Supported by Grants from the National Natural Science Foundation of China (81970813), Natural Science Foundation of Guangdong Province (2018A030313635), and Guangzhou Municipal Science and Technology Project (201904010062). 

Disclosure: H. Chen, None; X. Zhang, None; N. Liao, None; Y. Ji, None; L. Mi, None; Y. Gan, None; Y. Su, None; F. Wen, None