Identification of abnormally methylated differentially expressed genes in chronic periodontitis by integrated bioinformatics analysis

Abstract

BACKGROUND:

DNA methylation plays a vital role as an epigenetic change that contributes to chronic periodontitis.

OBJECTIVE:

This study aimed to integrate two methylation datasets (GSE173081 and GSE59962) and two gene expression datasets (GSE10334 and GES16134) to identify abnormally methylated differentially expressed genes related to chronic periodontitis.

METHODS:

Differentially methylated genes were obtained. Functional enrichment analysis of DMGs was performed. The protein-protein interaction (PPI) network was constructed using STRING and Cytoscape software. Finally, the hub genes were selected from the PPI network by using CytoHubba.

RESULTS:

In total, 122 hypomethylated and highly expressed genes were enriched in the biological mechanisms that are involved in the differentiation of extracellular matrix organization, extracellular structure organization, and cell chemotaxis. The three selected hub genes of the PPI network were IL1B, KDR, and MMP9. A total of 122 hypermethylated and lowly expressed genes were identified, and biological processes, such as cornification, epidermis development, skin development, and keratinocyte differentiation were enriched. CDSN DSG1, and KRT2 were identified as the top 3 hub genes of the PPI network.

CONCLUSION:

Based on the comprehensive bioinformatics analysis, six hub genes (IL1B, KDR, MMP9, CDSN DSG1, and KRT2) were associated with chronic periodontitis. Our findings provide novel insights into the mechanisms underlying epigenetic changes in chronic periodontitis.

Keywords

Periodontitis methylation transcription bioinformatic analysis protein-protein interaction hub genes

1. Introduction

Chronic periodontitis (CP) is an infectious disease caused mainly by the bacterium P. orphyromonas gingivalis [1], causing periodontal tissue destruction, gingival recession, loss of periodontal attachment, tooth mobility, and even tooth loss [2]. A high prevalence of CP has been reported worldwide, with high incidence, complex etiology, recurrent pathological processes, and treatment challenges. CP is the leading cause of missing teeth in adults. Furthermore, it is linked to systemic diseases, such as diabetes, rheumatoid arthritis, atherosclerosis, adverse pregnancy outcomes, aspiration pneumonia, and oral cancer [3, 4, 5, 6, 7, 8]. Chronic inflammatory stimuli causes changes in epigenetic mechanisms, thus promoting the progression of periodontitis [9]. The molecular mechanism of CP remains unclear. Therefore, the pathogenesis of periodontitis should be studies to determine the novel molecular mechanisms of periodontal disease.

The epigenetic regulation of inflammatory and immunite responses, mainly DNA methylation and histone modifications, has emerged as a potential target for immunomodulation [10]. Considering that genetic, environmental, and lifestyle factors do not fully explain an individual’s susceptibility to periodontitis, researchers have become interested in epigenetics [11]. Epigenetic regulatory mechanisms are perturbed in periodontitis. Changes in the methylation status have been linked to periodontitis [12]. Microarray-based high-throughput analyses of DNA methylation in gingival biopsy samples obtained from periodontitis patients and healthy donors revealed dramatic gene variations associated with immune responses and molecular mechanisms [13]. Based on these findings, methylation status may be a potential prognostic biomarker for identifying periodontitis. DNA methylation in cytosines located in the CpG dinucleotide range or DNA methylation in promotor regions of genes is commonly inversely proportional to the transcriptional level of gene expression [14], and only a few exceptions have been reported [15]. The methylation profiles in CP versus healthy gingival tissue indicate the aberrant DNA methylation profiles of transcriptional enhancers and highlight preneoplastic DNA methylomes [16]. However, a vast amount of valuable information in the above methylation datasets remains unused. This information may contribute to our comprehensive understanding of the correlation between methylation and the etiology of periodontal disease.

Bioinformatics involves computer-based tools for the collection, collation, and analysis of data derived from DNA, RNA, and proteins to identify molecular and biological mechanisms underlying the disease occurrence. Bioinformatics analysis can be employed to identify potential disease-related genes, mRNA, and lncRNA and provide a theoretical basis for the selection of treatment options for some major diseases, such as cardiovascular diseases, cerebrovascular diseases, tumors, and infectious diseases. Considering that bioinformatics databases contain various disease data, a secondary analysis based on previous studies can be carried out to determine potential disease-related genes by screening for differential gene expression, functional enrichment analysis, and PPI network. in the present study, four bioinformatics tools were used to analyze the abnormal expression of methylated genes in CP. Gene Ontology (GO) describes the functions of gene products from all organisms by using a set of dynamic vocabularies. It is subdivided into three ontologies, namely, molecular function, cellular component, and biological processes of genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) integrates genomic, chemical, and systemic functional information and compares the gene catalog from a fully sequenced genome with higher-level cells, species, and ecosystem-level systemic functional associations. STRING is a database of known and predicted protein-protein interactions (PPIs) containing experimental data, results mined from PubMed, synthesized data from other databases, and predictive results based on bioinformatics methods. Cytoscape was used to create biomolecular interaction networks and integrate them with high-throughput gene expression data and other molecular state information. This software is mainly used for the large-scale analysis of PPIs, DNA-protein interactions, and genetic interactions to visualize the results.

Published gene expression and methylation datasets from patients with CP were used to analyze aberrantly methylated genes. This study provides a comprehensive molecular and signaling pathway for differentially methylated genes (DMGs) and advances our understanding of the molecular mechanisms of epigenetic changes in chronic periodontitis. These potential genes can be used as biomarkers for the accurate diagnosis and treatment of CP.

2. Methods

2.1 Affymetrix microarray data

Gene methylation profiling datasets GSE173081 [14] and GSE59962 [16] and gene expression profiling datasets GSE10334 [17] and GES16134 [18] were obtained from the gene expression omnibus (GEO, Home – GEO – NCBI (nih.gov)). A summary of the individual studies is shown in Table 1. This study was approved by the Ethics Committee of Liaocheng People’s Hospital.

Table 1
Details for GEO periodontitis datasets

Study type	GEO	Platform	Tissue	Samples (H:P)	Year
DNA methylation	GSE173081	GPL21145	Gingival	12:12	2021
	GSE59962	GPL13534	Gingival	12:10	2015
mRNA transcription	GSE10334	GPL570	Gingival	64:183	2008
	GSE16134	GPL570	Gingival	69:241	2009

Table 2

The hub gene and their functions

Category	Gene symbol	Description	Function
Hypomethylated/	IL1B	Interleukin-1 beta	Potent proinflammatory cytokine; promotes Th17 differentiation of T-cells.
high-expressed genes	KDR	Kinase insert domain receptor	Regulation of angiogenesis, vascular development; Promotes proliferation, survival, migration and differentiation of endothelial cells.
	MMP9	Matrix metallopro-teinase-9	Local proteolysis of the extracellular matrix and in leukocyte migration.
Hypermethylated/	CDSN	Corneodesmosin	Important for the epidermal barrier integrity.
low-expressed genes	DSG1	Desmoglein-1	Component of intercellular desmosome junctions; Involved in the interaction of plaque proteins and intermediate filaments mediating cell-cell adhesion.
	KRT2	Keratin 2	Associated with keratinocyte activation, proliferation and keratinization.

2.2 Data acquisition and processing

We identified DMGs and differentially expressed genes (DEGs) by analyzing the raw submitter-supplied data of microarrays by using GEO2R online software. Among the parameters, $|$ logFC $|$ $>$ 1 and $p<$ 0.05 were used as the cut-off standards to obtain DEGs, and $|t|>$ 2 and $P<$ 0.05 were used as cut-off standards for determination of DMGs. Eventually, a Venn diagram was used to identify hypermethylated, lowly expressed genes and hypomethylated highly expressed genes.

2.3 Functional and KEGG pathway enrichment analysis

The selected hypermethylated, lowly expressed genes or hypomethylated, highly expressed genes were subjected to Gene Ontology (GO) analysis, (molecular function, biological process, and cell composition) and KEGG pathway enrichment analysis by using the online analysis website DAVID (DAVID Functional Annotation Bioinformatics Microarray Analysis (ncifcrf.gov)). $P<$ 0.05 and false discovery rate (FDR) $<$ 5% were considered statistically significant.

2.4 PPI network integration, modules analysis, and selection of hub gene

The STRING (STRING: functional protein association networks (string-db.org)) database was used to construct the PPI network of hypomethylated, highly expressed genes and hypermethylated lowly expressed genes. An interaction score of 0.4 was considered the cut-off criterion, and the PPI was visualized. Molecular Complex Detection (MCODE) in Cytoscape software was used to screen modules with an MCODE score $>$ 3, and the number of nodes was $>$ 4 in the PPI network. The number of each protein node was calculated. Six genes were screened out as the pivot genes by using the CytoHubba app in Cytoscape software by taking the set of six calculation methods.

3. Results

3.1 Identification of abnormally methylated and differentially expressed genes in CP

The flow chart of our study is shown in Fig. 1. For DEGs of the gene expression microarray, 331 overlapping highly expressed genes (337 in GSE10334 and 438 in GSE16134) and 233 overlapping lowly expressed genes (279 in GSE10334 and 250 in GSE16134) were obtained. For DMGs of the gene methylation microarray, 11,140 overlapping hypermethylated genes (16,372 in GSE59962 and 12,969 in GSE173081) and 9,079 overlapping hypomethylated genes (14,140 in GSE59962 and 10,355 in GSE173081) were obtained. A total of 122 hypomethylated, highly expressed genes were obtained by conjugating 9,079 hypomethylated genes and 331 highly expressed genes (Fig. 2A). A total of 122 hypermethylated, lowly expressed genes were obtained by conjugating 11,140 hypermethylated genes and 233 lowly expressed genes (Fig. 2B). Cluster analysis was performed on the top 20 hypomethylated, highly expressed genes and the top 20 hypomethylated, lowly-expressed genes in GSE173081 (Fig. 3A) and GSE59962 (Fig. 3B). Cluster heatmap results are shown in Fig. 3.

Figure 1.

Flowchart of the experimental design of the study.

Figure 2.

Identification of aberrantly methylated and differentially expressed genes by using Funrich software. (A) Hypomethylated genes/highly expressed genes; (B) Hypermethylated/lowly expressed genes. (Different colors indicate different datasets.)

Figure 3.

Cluster heat map of top 20 differentially expressed/hypomethylated, -highly expressed genes and top 20 differentially expressed/hypermethylated, -lowly expressed genes in methylation datasets. (A) Heat map in GSE173081. (B) Heat map in GSE59962.

3.2 GO analysis of DEGs

In this study, 122 hypomethylated, highly expressed genes and 122 hypermethylated, lowly expressed genes were analyzed using GO. Results showed that hypomethylated, highly expressed genes were primarily associated with biological processes, including extracellular matrix organization, cell chemotaxis, and myeloid leukocyte migration. Enriched cellular component annotations included endoplasmic reticulum lumen, collagen containing extracellular matrix, uropod, cell trailing edge, and low-density lipoprotein particle. Molecular function annotations included CXCR chemokine receptor binding, chemokine activity, molecular activity, cytokine activity, chemokine receptor binding, and metallopeptidase activity (Fig. 4A). Hypermethylated, lowly expressed genes were highly associated with biological processes, including cornification, epidermis development, skin development, and keratinocyte differentiation. Enriched cellular component annotations included cornified envelope, desmosome, keratin filament, intermediate filament, intermediate filament cytoskeleton, and dendrite terminus. Molecular function annotations included fatty acid synthase activity, gamma catenin binding, structural constituent of skin epidermis, monooxygenase activity, steroid binding, and sterol binding (Fig. 4B). These screened pathways demonstrated that DEGs and DMGs play a crucial role in microbial infections in periodontitis lesions and the defense and repair of periodontal tissues.

Figure 4.

Gene ontology annotation and pathway enriched analysis and demonstration of aberrantly methylated and differentially expressed genes. (A) GO analysis of hypomethylated, highly expressed genes. (B) GO of hypermethylated, lowly expressed genes. (C) KEGG of hypomethylated, highly expressed genes. (D) KEGG of hypermethylated, lowly expressed genes. The high enriched score means that the genes were found more frequently in the particular ontology. GO, Gene ontology. KEGG, Kyoto Encyclopedia of Gene and Genome.

3.3 KEGG pathway analysis of DEGs

The pathway analysis revealed that hypomethylated, highly expressed genes were substantially associated with rheumatoid arthritis, leishmaniasis, cytokine-cytokine receptor interaction, protein digestion and absorption, cell adhesion molecules (CAMs), amoebiasis, renin-angiotensin system, regulation of actin cytoskeleton, staphylococcus aureus infection, and leukocyte trans-endothelial migration (Fig. 4C). Hypermethylated, lowly expressed genes were highly associated with phagosome, pathogenic Escherichia coli infections, and arachidonic acid metabolism (Fig. 4D). Therefore, the activation of these signaling pathways plays an important role in anti-inflammatory responses, immune responses, and resistance to bacterial infections.

3.4 Construction of PPI networks, module analysis, and selection of hub genes

Figure 5.

PPI network and modules of aberrantly methylated and differentially expressed genes. (A) PPI network of hypomethylated, highly expressed genes and (B) PPI network of hypermethylated, lowly expressed genes. (C) Module of hypomethylated, highly expressed genes and (D) Module of hypermethylated, lowly expressed genes.

The PPI network of hypomethylated highly expressed genes is shown in Fig. 5A, and the corresponding modules are shown in Fig. 5C. The top three hub genes include IL1B, KDR, and MMP9. Figure 5B shows the PPI network of hypermethylated, lowly expressed genes, and Fig. 5D shows the corresponding modules. The top three hub genes were CDSN, DSG1, and KRT2.

4. Discussion

Understanding the potential mechanisms for chronic periodontitis aids in the diagnosis, treatment, and prognosis evaluation of chronic periodontitis. In the present study, 122 hypermethylated, lowly expressed genes and 122 hypomethylated, highly expressed genes were identified. The functional enrichment of these genes proves that aberrant methylation affected specific pathways and hub genes. Our findings may provide new insights into the mechanisms underlying the pathogenesis of CP.

GO enrichment analysis demonstrated that hypermethylated, lowly expressed genes were involved mainly in the extracellular matrix organization, cell chemotaxis, myeloid leukocyte migration, response to ATP, and response to fluid shear stress. However, hypomethylated, highly expressed genes were involved in cornification, epidermis development, skin development, keratinocyte differentiation, and multicellular organismal water homeostasis. Single-cell sequencing technology results have shown that inflammatory cytokines, chemokines, proteases and growth factors trigger polymorphic cell differentiation in the progression of chronic periodontitis [19]. A case-control study shows that the severity of periodontitis is directly related to the expression of epithelial-mesenchymal transition process markers (TGF- $\beta$ and vimentin) [20]. The IL-1 $\beta$ $+$ 3954 gene shows significant differences in clinical parameters between cases and controls [21]. The KEGG pathway of hypomethylated, -highly expressed genes are mainly involved in cytokine cytokine receptor interaction, protein digestion and absorption, CAMs, and the renin-angiotensin system. Therefore, the activation of signaling pathways plays an important role in anti-inflammatory responses, immune responses, and resistance to bacterial infections. Unexpectedly, hypermethylated, lowly expressed genes were only related to phagosomes, pathogenic E. coli infection, and arachidonic acid metabolism. Phagocytosis is the process of ingestion and elimination of invasive pathogenic microorganisms by phagocytes [22]. $\alpha$ -hemolysin (HlyA) from E. coli is an important virulence factor for aggressive periodontitis [23]. The cyclooxygenase system catalyzes the conversion of arachidonic acid into prostaglandins, and lipoxygenases regulate the production of leukotrienes and lipid peroxides, playing a major role in the inflammation-related metabolic network [24].

A PPI network was constructed to explore the functional associations between hypomethylated, highly expressed genes. The following hub genes were selected: IL1B, KDR, and MMP9. The levels of IL-1 $\beta$ in GCF and gingival tissue of patients with periodontitis were remarkably increased and were closely related to the severity of periodontal tissue damage [25]. Patients with periodontitis showed significantly lower CD133 ${}^{+}$ /KDR ${}^{+}$ levels than healthy controls. Higher levels of CD133 ${}^{+}$ /KDR ${}^{+}$ EPC in patients with periodontitis were associated with more severe periodontitis and tooth loss [26]. Salivary MMP-9 is a sensitive marker of periodontal inflammation during orthodontic treatment, while MMP-8 and MMP-9 are valuable diagnostic tools for the treatment of periodontitis [27]. Hypermethylated, lowly expressed genes such as CDSN, DSG1, and KRT2 were the most prominent hub genes. Transcriptome sequencing of gingival biopsy samples from patients with CP identified differentially expressed genes, of which KRT2 was one of the top 10 down-regulated genes [28]. Differential gene expression analysis revealed significantly lower levels of epithelial adhesion molecules in patients with periodontitis. Immunostaining showed increased secretion of DSG1 and OCLN in periodontitis [29]. This finding is consistent with our study results and indirectly proves the reliability of our research.

In the present study, many aberrantly methylated DEGs, which could be associated with various signaling pathways involved in the occurrence and development of CP, were observed. These new findings may provide insights into the pathogenesis of CP. Moreover, the six most modified central genes, namely, IL1B, KDR, MMP9, CDSN, DSG1, and KRT2, were identified as potential candidates for optimal hypermethylation biomarkers for the screening, diagnosis, and prognosis of CP. Furthermore, our study also provides a basis for exploring new therapeutic targets for CP. However, the inference of effects between identified methylated genes and their predictivity for CP needs further experimental verification.

5. Conclusion

In the present study, the combined analysis of available gene expression microarrays and gene methylation microarrays was carried out to improve the reliability and precision of screening results. Moreover, a comprehensive bioinformatics analysis of aberrantly methylated genes that may be involved in the development of chronic periodontitis was carried out, and identified six hub genes (IL1B, KDR, MMP9, CDSN, DSG1, and KRT2) were identified. These selected genes may provide new insights into the pathogenesis of CP. However, functional studies on targeted genes are necessary to validate the causal relationship between epigenetic and transcriptional perturbations in chronic periodontitis pathobiology.

Availability of data and materials

The data and materials used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Author contributions

DW and KL contributed equally to this work, DW and KL were responsible for the study concept and design, JZ, JM and YL were involved in the acquisition of data. The analysis and interpretation of data was performed by XT and CW. Drafting of the manuscript was performed by XT. All authors contributed to the article and approved the final manuscript.

Funding

This work was supported by grant ZR2019PH050 from the Natural Science Foundation of Shandong Province and Shandong Medical and Health Science and Technology Development Plan (No. 2018WSA15026). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and writing of the manuscript.

Footnotes

Acknowledgments

This work has benefited from GEO. The authors thank the GEO network for its generous sharing of large amounts of data.

Conflict of interest

The authors declare that they have no conflict of interest.

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Identification of abnormally methylated differentially expressed genes in chronic periodontitis by integrated bioinformatics analysis

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Affymetrix microarray data

Table 1 Details for GEO periodontitis datasets

2.3 Functional and KEGG pathway enrichment analysis

2.4 PPI network integration, modules analysis, and selection of hub gene

3. Results

3.1 Identification of abnormally methylated and differentially expressed genes in CP

3.4 Construction of PPI networks, module analysis, and selection of hub genes

5. Conclusion

Availability of data and materials

Author contributions

Funding

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Details for GEO periodontitis datasets