Methylation-based epigenetic studies and gene integration analysis of preeclampsia

Introduction

Preeclampsia (PE) is a common and serious complication of pregnancy and the Leading cause of maternal and infant mortality worldwide (1). The pathogenesis of PE is unclear, the important factors are immune imbalance, placental superficial implantation, oxidative stress, thrombosis, and genetic factors (2). Clinical symptoms include hypertension and obvious proteinuria, vascular endothelial dysfunction, and systemic inflammatory response (3). In addition, endocrine, genetic, environmental elements, and immune response are intimately associated with the formation and progression of PE (4). Furthermore, environmental factors may cause epigenetic changes, which are strongly associated with PE (5).

Epigenetics describes conditions where the DNA sequence remains unchanged, but changes in gene expression result in phenotypic changes, including histone modification, chromatin remodeling, genomic imprinting, random chromosome inactivation, and chemical modification of DNA and RNA (6). Genetic methylation is an important molecular genetic mechanism, involved in regulating the function of trophoblasts and closely linked to the pathogenesis of PE. Genome-wide abnormal methylation content, methylation of PE related genes, and so forth, may promote the onset of PE (7). A study has confirmed that aberrant methylation of CpG islands in the human genome is associated with related gene silencing. During trophoblast differentiation, methylation and unmethylation of CpG islands in the promoter region of some genes can change the state of gene expression, which in turn affects the physiological function of trophoblasts to participate in the pathogenesis of PE (8).

Placental abnormalities are a prominent trigger for the development and progression of PE (9). Extensive studies have indicated that the pathogenesis of PE is related to DNA methylation of genes. DNA methylation is an epigenetic modification of the genome, which is linked to the regulation of various cellular activities (10) and acts as a regulator in the regulation of the genetic phenotype (11). The release of vasoactive factors, pro-inflammatory cytokines, and syncytial debris from the placenta into the maternal circulation can cause systemic endothelial dysfunction and ultimately induce PE (12). Epigenetic modulation has recently been indicated as having a critical role in the physiological regulation of the placenta (13). Therefore, the evaluation of the association between gene transcription and DNA methylation modifications is vital to reveal the epigenetic regulatory mechanisms of PE. Hence, in this study, an integrated analysis of DNA methylation and gene expression data of PE was performed, and then key modules and hub genes were screened out using the weighted gene co-expression network analysis (WGCNA) approach. Finally, 6 candidate genes (PLXNB1, PMCH, PPARG, GOPC, CD79A, MME) were identified as potential diagnostic and therapeutic targets for PE. We present the following article in accordance with the MDAR reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-5556/rc).

Methods

Sample collection

During March 2021–December 2021, 20 cases with PE and 20 normal pregnant women who had undergone cesarean section were recruited from the Department of Obstetrics, Second Hospital of Hebei Medical University (Shijiazhuang, Hebei, China). The diagnosis of PE was according to the relevant guidelines of the American College of Obstetricians and Gynecologists (ACOG) Practice Bulletin 2019. Table 1 shows the baseline characteristics of all participants. All pregnant women enrolled in the study were excluded from multiple pregnancies, primary hypertension, congenital malformations, chromosomal abnormalities, and suspected signs of perinatal infection. Placental tissue was obtained within 10 minutes after cesarean sections. We collected placentas located near the root of the umbilical cord to avoid infection, hemorrhage, and calcification. The tissues obtained were cut into 1 cm³ size, after which they were washed with phosphate-buffered saline (PBS) and wrapped with gauze to absorb the surface fluid. Then, we cut them into pieces and immediately after adding 1 mL of trizol reagent, they were frozen at −80 ℃. We then selected 3 cases and 3 paired controls as samples and tested them according to the quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. The other part was fixed with 4% paraformaldehyde for immunohistochemistry (IHC). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University (No. 2018-R270) and informed consent was taken from all the patients.

Methylation assay

The data for each sample was run repeatedly to determine the random location of each sample on the plate by establishing a plate maps. All samples were routed in a particular bunch. Illumina Human Methylation-27 Assay (Illumina, San Diego, CA, USA) was used to assess the methylation of CpG sites.

DNA methylation analysis

Differential methylation sites were screened using R-COHCAP packages. Δβ is the value of differentially methylated sites. β-free methylation sites with >20% distribution were first screened in the samples. Next, differentially methylated sites and regions were obtained with Δβ >0.2, FDR <0.05, and then genomic functional enrichment of differentially methylated sites was analyzed using Fisher’s precision assays (dominance ratio ≠1, P<0.05).

Identification of differentially expressed genes (DEGs) between PE and normal

DEGs were screened using the R-DESeq2 package. The degree of methylation of each gene was inferred by analyzing changes in its expression levels at different times and locations. Genes with >20% distribution in the sample were first screened (count =0). These genes are involved in transcriptional regulation, signaling, and so on. Secondly, DEGs were identified using |log2FC|≥1 and P<0.05.

Construction of WGCNA

First, the WGCNA program in R (http://www.r-project.org/) was used to screen important modules and genes in PE at a threshold of β=5. The correlation between the modules and PE were explored by |correlation coefficients|>0.2 and P<0.05. The enrichment analysis was followed by Fisher’s Exact test (P<0.05) to analyze the correlation between the module and PE/normal, and the relationship between the modules and DEGs. Finally, visualization using Cytoscape software (https://cytoscape.org/), the threshold >0.1. The criteria for selecting HUB genes from the optimal modules was |correlation. module module| (|Cor.MM|) >0.8.

Identification of hub genes in the turquoise module

To explore key genes associated with PE, we introduced DEGs from the top 100 in the modules most associated with PE traits into the STRING online database (http://string-db.org) for protein-protein interaction (PPI) analysis. Important candidate subsites were identified in this dataset by predicting PPIs and function, and these potential mechanisms of action were speculated in combination with other experimental evidence. The visualization network was created by Cytoscape (version 3.5; https://cytoscape.org/).

Correlation analysis of DNA methylation regions and DEGs

Correlation analysis of DNA methylation genes and gene expression profiles was performed, focusing on the association between different methylation regions and differences in corresponding gene expression. First, CpG islands of differentially methylated genes were integrated with DEGs in an up-regulation/downregulation relationship. Next, single nucleotide polymorphisms were detected at each differentially methylated site. Thirdly, correlation between differentially methylated regions (DMRs) and DEGs was analyzed by P<0.05 and |correlation| (|COR|) >0.5. Finally, DEGs with significant negative correlation to DMRs were identified by P<0.05.

qRT-PCR confirmation

Twenty cases of PE and normal pregnant women were selected according to the diagnostic criteria of PE. Retrospective qRT-PCR was performed on PE and normal placental tissue samples. We used RNA-easy^TM Isolation Reagent (Vazyme, Beijing, China) to isolate total RNA from cells. The RNA samples were reliably amplified and labeled to generate complementary DNA (cDNA) with Hiscript^® III RT SuperMix (Vazyme, China) based on CFX96^TM Real-Time System (Bio-Rad, Hercules, CA, USA). ChamQ Universal SYBR qPCR Master Mix (Vazyme, China) was used for qRT-PCR following the instructions. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and actin beta (ACTB) were used as internal references. Gene expression levels were measured using the 2^-ΔΔCt method. All experiments were performed in triplicate with three technical replicates. All primers designed by Sangon Biotech Co., Ltd. (Shanghai, China) are shown in Table 2.

Immunohistochemical (IHC) analysis

The paraffin embedded sections were stained with IHC. Placenta samples were treated with 3% hydrogen peroxide for 30 minutes after dewaxing, hydration, and antigen recovery to block endogenous peroxidase activity. Afterwards, the slides were incubated with primary antibody overnight at 4 ℃ (PLXNB1, 1:500 dilution, Rabbit pAb, 23795-1-AP, Proteintech^®; PMCH, 1:500 dilution, Rabbit pAb, 18725R, Bioss; PPARG, 1:300 dilution, Rabbit pAb, 340844, ZEN BIO; GOPC, 1:200 dilution, Rabbit pAb, 383119, ZEN BIO; CD79A, 1:50 dilution, Rabbit mAb, R23860, ZEN BIO; MME 1:200 dilution, Mouse mAb, 250203, ZEN BIO). The slides were washed by PBS and reheated 30min for the next day. Following the addition of secondary antibody, slices were incubated with diaminobenzidine (DAB) color developer for 5 minutes, washed in PBS, counterstained in hematoxylin, continuously dehydrated, and the images were recorded by two senior pathologists in a double blind manner. All sections were observed in 5 visual fields under an inverted fluorescence microscope. Brown showed positive expression. All experiments were performed in triplicate with three technical replicates.

Statistical analysis

The average beta significance between 0 and 1 was created for each CpG site, indicating the ratio of methylated cytosine residues to the number of cytosine residues. Student’s t-test was applied to compare the mean methylation between the PE group and the control group. The significant difference in methylation was defined as P<0.05 and beta values ≥0.02.

Results

A total of 830 DEGs and 1,828 differentially methylated sites were identified. The results of the WGCNA analysis showed that the royal blue module was the optimal module. Besides, the hub genes (PLXNB1, PMCH, PPARG, GOPC, CD79A, MME) in this module were also differentially methylated genes and DEGs. Studies revealed that PPARG, CD79A, and PLXNB1 may be diagnostic gene markers for PE. Furthermore, low expression of PPARG was closely connected with the development of PE. The results of the IHC analysis demonstrated that the expression level of PLXNB1, PMCH, GOPC, CD79A, and MME were increased in PE tissues. The PCR results showed that PLXNB1, PMCH, GOPC, CD79a, and MME were upregulated, and PPARG was downregulated. The results of both experiments were in line with the bioinformatics results.

DNA methylation analysis

For the analysis of differential methylation in PE, 1,828 differentially methylated sites were identified, including 1,035 hypermethylated sites and 793 hypomethylated sites, the heatmaps of which are shown in Figure 1A. Figure 1B demonstrates the heatmap of the top 200 differentially methylated sites in the histological diagnosis. A total of 1,340 differentially methylated CpG islands were identified, including 1,037 hypermethylated regions and 303 hypomethylated regions, using Δβ>0.2 and FDR <0.05 as the criteria.

Figure 1 DNA methylation analysis. (A) The heat map of the differentially methylated sites. (B) The heatmap of the top 200 differentially methylated sites in terms of histological diagnosis.

Identification of DEGs between PE and normal samples

According to the screening criteria of |log2FC|≥1 and P<0.05, we gleaned 830 DEGs, including 409 up-regulated genes and 421 down-regulated genes. The top 100 DEGs were screened out with a heatmap (Figure 2).

Figure 2 Identification of DEGs between preeclampsia and normal. The top 100 DEGs were screened out with a heat map. DEGs, differentially expressed genes.

Construction of WGCNA

Based on the WGCNA co-expression network analysis, The total number of modules we obtained was 49 (Figure 3). To investigate the characterization of those modules, we carried out correlation analysis between modules and PE/normal condition cases. We found that 8 modules were markedly associated with PE/normal status, and turquoise was the most connected with PE/normal condition.

Figure 3 Construction of WGCNA. WGCNA co-expression network identified key modules. WGCNA, weighted gene co-expression network analysis.

Identification of hub genes in the turquoise module

To investigate the hub genes involved in PE, we introduced the top 100 DEGs into the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) online database (http://string-db.org) for PPI analysis, and networks were created by Cytoscape (version 3.5; https://cytoscape.org/). As shown in Figure 4A-4F, the top 6 hub DEGs were identified in the turquoise module, namely PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME.

Figure 4 Identification of hub genes in the turquoise module. (A-F) The top 6 hub DEGs were identified in the turquoise module. DEGs, differentially expressed genes.

Correlation analysis of DNA methylation regions and DEGs

A total of 117 DMRs-DEGS association combinations were obtained, including 67 combinations of hypermethylated and down-regulated DEGs pairs and 35 combinations of hypomethylated and up-regulated genes. Among them, PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME were both DMRs and DEGs.

qRT-PCR confirmation

We used qRT-PCR to explore whether PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME were involved in the progression of PE. The messenger RNA (mRNA) expression of PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME was examined using qRT-PCR verification. The results exhibited that PLXNB1, PMCH, GOPC, CD79A, and MME were up-regulated and PPARG down-regulated (Figure 5), which was consistent with our findings in bioinformatics.

Figure 5 qRT-PCR Confirmation. qRT-PCR was used to evaluate the mRNA expression of PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME. *, P<0.05, **, P<0.01. qRT-PCR, quantitative reverse transcription polymerase chain reaction; mRNA, messenger RNA; NC, normal contrast; PE, preeclampsia.

IHC analysis for PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME

We performed IHC to validate the roles of PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME in the placental tissues obtained from patients with PE. In PE placenta tissues, the number and degree of positive cells staining for the PLXNB1, PMCH, GOPC, CD79A, and MME genes were appreciably enhanced in PE tissues compared with normal tissues (Figure 6). Furthermore, the expression of PLXNB1, PMCH, GOPC, CD79A, and MME genes was positively correlated with the occurrence and development of PE. The results of IHC were consistent with qRT-PCR.

Figure 6 (A) Immunohistochemistry was used to validate the expression of CD79A, GOPC, and MME in the placental tissues. (B) Immunohistochemistry was used to validate the expression of PLXNB1, PMCH, and PPARG in the placental tissues. *, P<0.05, **, P<0.01, ***, P<0.001. NC, normal contrast; PE, preeclampsia.

Discussion

PE is a complication of pregnancy that can lead to the death of both the fetus and mother (14). It is often accompanied by renal insufficiency, abnormal liver function, neurological symptoms, and thrombocytopenia (15). PE can cause serious fetal growth restriction, and long-term cardiovascular diseases such as hypertension in pregnant women, resulting in maternal and neonatal mortality, which has become one of the leading causes of death worldwide (16).

In the current study, we identified differential methylation in 6 genes: PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME were closely related to the development of PE. In addition, PLXNB1, PMCH, GOPC, CD79A, and MME were enhanced whereas PPARG was reduced and hypomethylated in PE tissues.

PPARG belongs to the nuclear receptor superfamily of ligand-activated transcription factors, which exert their function by regulating the binding of gene fragments to response elements (17). PPARG regulates transcription by forming a heterodimer with RXR, a member of the secondary nuclear receptor family (18,19). The binding of PPARG to natural ligands (fatty acids and prostaglandins) may activate PPARG/RXR heterodimers, and while the latter is coupled with peroxisome proliferator response components, they will remain inactive (20). Furthermore, ligand binding induces a conformational alteration of the PPARG/RXR heterodimer, triggering the release of co-repressors and the recruitment of co-activators (21).

CD79A is a membrane protein that is conserved in a wide range of species. It is expressed at the primary stage of B-cell growth and its continued expression is observed until the final stage of B-cell maturation (22). Generally, CD79A consists of a disulfide-linked heterodimer that polymerizes with CD79B and assembles non-covalently with membrane-bound IgM to form the B-cell signaling complex receptor (BCR) (23). The role of dimeric CD79a/b is to transfer the signal generated by the binding of antigen to the BCR into the cell and initiate B cell activation (24).

Plexin-B1 (PLXNB1) is a transmembrane receptor for semaphoring 4D (25). Earlier findings have shown that PLXNB1 is implicated in various cellular processes (26). Moreover, several studies have shown that PLXNB1 plays a crucial role in the development of cancer (27,28). Nevertheless, to date, the biological role of PLXNB1 in PE remains unknown.

At present, the research on DNA methylation of PE is mainly focused on the following aspects: genome-wide methylation, methylation of PE-related genes, methylation related to stress response, and methylation of imprinted genes. Blair et al. use genome-wide expression profiling revealed a negative correlation between abnormal gene expression, such as angiogenic factor endothelial PAS protein 1 (EPAS1), and Fms related tyrosine kinase 1 (FLT1), and methylation level (29). Syncytin-1, a PE-related gene, is a functional fusion protein that mediates the fusion of trophoblasts to syncytiotrophoblast cells. In the placenta of PE, the mean methylation level of the HERVW promoter region of the Syncytin-1 gene was higher than that of the normal pregnancy group (30). Hogg et al. found the hypermethylation at CpG sites of stress-related genes such as the glucocorticoid receptor (NR31C1) and corticotropin-releasing hormone-binding protein (CRHBP) in PE may be regulated by DNA methylation (31).

As the detection of DNA methylation continues to advance, we can screen from methylation changes of specific genes in PE for novel indicators of biological diagnostics, and enrich the pathogenesis in the future.

Conclusions

In the present study, PLXNB1, PMCH, PPARG, GOPC, CD79A, and MME were revealed as genes that were both differentially methylated and differentially expressed. The IHC analysis revealed that PLXNB1, PMCH, GOPC, CD79A, and MME gene expression levels were elevated in PE tissues, whereas the gene expression of PPARG was reduced, which was consistent with the bioinformatics results. Hence, these identified molecular indicators could be invaluable in diagnosing and curing PE.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-5556/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-5556/dss

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-5556/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University (No. 2018-R270). Informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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