Identification of hub genes and biological mechanisms associated with periodontitis and diabetes

doi:10.21203/rs.3.rs-3140878/v1

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Identification of hub genes and biological mechanisms associated with periodontitis and diabetes

https://doi.org/10.21203/rs.3.rs-3140878/v1

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Background

Periodontitis is independent associated with s diabetes (DM). However, the molecular mechanisms of complex interactions between DM and periodontitis are still unclear. This study was aim to explore the shared genes and common signatures of DM and periodontitis via bioinformatic analysis.

Methods

The series matrix files of GSE7014 of DM and GSE16134 of periodontitis were downloaded from Gene Expression Omnibus (GEO) database. Using R package to normalize the data and the utilizing limma package to identify the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes enrichment analysis of DEGs were performed via the “cluster profiler” package in R software. Protein-protein network was conducted to analyze the potential relationship among the proteins and CytoHubba, a plug-in for Cytoscape software, was used to identify the hub genes. GSE15932 for DM and GSE10334 for periodontitis were selected to be the validation datasets. Receiver Operating Characteristic Curves analysis was performed to obtain the area under the ROC curve. Correlations between hub genes and immune cells via immune infiltration analysis.

Results

There were 249 common DEGs were identified. LAPTM5, RAC2, LYN were identified as the hub genes, which were all up-regulated, of DM and periodontitis. The area under the curve of the 3 hub genes were all more than 0.7. The activation of B cells, and T cells as well as the phagocytosis could be the central role in the development of the both diseases.

Conclusions

LAPTM5, RAC2, LYN were defined as the hub genes of DM and periodontitis. The activation of B cells, and T cells as well as the phagocytosis could be the central role in the development of the both disease, which might the potential targets for diagnosis and treatment.

Periodontitis

diabetes

bioinformatics analysis

hub genes

immune process

Periodontitis, a chronic multifactor inflammatory disease, is corrected with dysbiotic plaque biofilms and characterized by progressive destruction of the tooth-supporting apparatus, with a high prevalence of 45%-50% overall [1]. A accumulated evidence supported that periodontitis is independent associated with several non-communicable diseases, including diabetes (DM) [2]. It inferred that DM could result in the negative effects on periodontal heath via immune function and inflammatory pathways, while periodontitis also has an adverse effect on glycemic control of DM patients due to the chronic inflammation and infection [3–5]. The information all suggested periodontitis and DM might have the common mechanisms.

With the gradually maturing of bioinformatics, numerous studies utilized this technology to excavate underlying mechanisms for variety of disease [6]. In this study, we firstly downloaded periodontitis and DM datasets from the gene expression omnibus (GEO) database and identified the differentially expressed genes (DEGs) by Limma. Then, investigating the common hub genes of the two diseases to elucidate the common mechanisms of periodontitis and DM.

Data downloading and preprocessing

GEO (https://www.ncbi.nlm.nih.gov/geo/) is a public functional genomics database from which we obtained periodontitis and DM datasets. Taking “periodontitis” or “diabetes” as the key words. The inclusion criteria: a. samples should include both diabetic and healthy control samples; b. the samples are Homo sapiens and the chip data type is gene expression profile. According to the criteria, there are two datasets were included in this study, GSE7014 [7] and GSE16134 [8]. The GSE7014 datasets contains the RNA-sequencing results of skeletal muscle biopsies from10 DM1, 20 DM2, and 6 healthy control samples. GSE16134 includes the RNA-sequencing results of gingival tissue phenotype from 241 periodontitis patients and 69 healthy control sample.

Identification of DEGs

The series matrix files of GSE7014 and GSE16134 were downloaded from GEO. Using R package to normalize the data and the utilizing limma package to identify the DEGs between periodontitis/DM and control samples. Genes meeting specific cutoff criteria of P < 0.05 and |logFC|>1.5 were defined as DEGs. The online Venn diagram tool was used to obtain their common DEGs (http://www.sangerbox.com/home.html).

Functional and pathway enrichment analysis

In order to analyze the function of the common DEGs, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes, including Biological Process (BP), Cellular Component (CC) Molecular Function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed via the “cluster profiler” package in R software [9]. “Homo Sapiens” was set as the species and the top 10 items were selected to draw the bubble map.

PPI network construction and identification of hub genes

The common DEGs were imported into STRING database (https://cn.string-db.org/) to obtain PPI network. Confidence scores were set to intermediate values (confidence scores > 0.4). CytoHubba, a plug-in for Cytoscape software, was used to explore the important nodes of biological network. In addition, MCODE, a plugin for Cytoscape, was used to filter the significant modules of core genes from the PPI network complex. The criteria were set as follows: Degree Cutoff = 2, Node Score Cutoff = 0.2, K-Core = 2, and Max. Depth = 100.

Validation of Hub Gene Expression

The expression levels of the identified hub genes were validated in GSE10334 for periodontitis and GSE54675 for DM. GSE10334 includes 183 periodontitis and 64 healthy control gingival tissue samples. GSE54675 contains 8 DM patients and 9 healthy control peripheral blood samples. Rank sum test was used to comparison of the two databases. P < 0.05 was considered the significant.

Receiver Operating Characteristic Curves

Receiver Operating Characteristic Curves (ROC) analysis was performed by using the R software package pROC (version 1.17.0.1) to obtain the area under the ROC curve.

Immune Infiltration Analysis

The expression levels of immune cells for each sample of GSE7014 and GSE16134 were quantified using the ssGSEA algorithm from the GSVA R package. The ssGSEA score was used to quantify the infiltration of immune cells in DM or periodontitis tissues and determine the level of immune infiltration in each sample of datasets. Correlations between 3 hub genes and 22 immune cells were determined using Spearman correlation analysis to reveal the relationship between hub genes and immune cells.

Identification of Common DEGs

GSE7014 dataset contained 6106 DEGs between DM and control group samples and GSE16134 included 733 DEGs in periodontitis patients compared to control group (Figure. 1A and B). Venn diagrams was showed there were 249 common DEGs were identified.

GO and KEGG Enrichment analysis

The common DEGs were used to analyze the biological information by performing GO and KEGG analysis. The results showed that in the terms of BP, the genes were mainly involved with immune system process, immune system process, immune response, regulation of immune system process, etc., in the terms of CC, the genes enriched in extracellular region, vesicle, endomembrane system, etc., in the terms of MF, the genes were mainly enriched in signaling receptor binding, protein dimerization activity, protein homodimerization activity, etc (Figrue.2 A-C). Besides, KEGG enrichment analysis result indicated that Natural killer cell mediated cytotoxicity, Leukocyte transendothelial migration, B cell receptor signaling pathway (Figure.2D).

PPI network analysis of the common DEGs and identification of Hub Genes

In order to explore the potential relationship among the proteins coded by these common DEGs, PPI network was performed, which containing 243 nodes, 540 edges and PPI enrichment p-value < 1.0e-16 (Supplementary. Figure 1). There were mainly 3 models were built from PPI network. Model 1 contains 19 nodes, 68 edges, and the cluster score was 7.556; Model 2 included 7 nodes, 19 edges, and the cluster score was 6.333; Model 3 included 6 nodes, 12 edges, and the cluster score was 4.8 (Figure.3A). Then, Degre, EPC, MNC and MCC were used to predict the top 20 important hub genes (Figure.3B). It could be seen in Figure.3C, there were 15 candidate hub genes: PLEK, CD53, BTK, HCLS1, LAPTM5, HCK, RAC2, FCER1G, ITGAL, CCR1, C1QB, C1QA, LYN, PLCG2, C1QC.

Validation of Hub genes

GSE15932 for DM and GSE10334 for periodontitis were selected to be the validation datasets. As is shown in Figure.4A and B, there were only 3 genes were significant difference in both DM and periodontitis groups, compared with control group. To be more precisely, LAPTM5, RAC2, LYN were obviously up-regulated in DM (GSE15932: DM vs Control: LAPTM5: 4433.82 ± 381.63 vs 3808.95 ± 441.00; RAC2: 2312.99 ± 292.12 vs 1896.33 ± 318.39; LYN: 2498.27 ± 271.41 vs 2157.13 ± 302.93) and periodontitis group (GSE10334: Periodontitis vs Control: LAPTM5: 9.30 ± 0.58 vs 8.63 ± 0.63; RAC2: 8.17 ± 0.60 vs 7.11 ± 0.72; LYN: 7.92 ± 0.49 vs 7.14 ± 0.59; P < 0.05).

Efficacy Evaluation and PPI Construction of Hub Genes

We performed ROC analysis using the R software package pROC (version 1.17.0.1) to obtain the AUC. It could be seen in Figure.5A-D, the AUC values of the 3 hub genes were all more than 0.7 in GSE7014, GSE16134, GSE25932 and GSE10334. The results suggested that LAPTM5, RAC2, LYN were the potential diagnostic markers of periodontitis and DM.

The 3 hub genes were analyzed by GeneMANIA, along with the 19 genes their interact with, to further predict the interact protein network and pathway. The results showed that phagocytosis, phagocytic vesicle, B cell activation, regulation of B cell activation and regulation of T cell activation (Figure.6).

The analysis of immune infiltration

According to the analysis of results of GeneMANIA, 22 immune cells were chosen to further explore the relationship with the three hub genes. In GSE7014 (Figure.7A), the levels of Macrophages (r = 0.83, P = 4.46684E-10), Macrophages_M1 (r = 0.7, P = 1.58489E-06) were significantly association with LAPTM5. In GES16134, LAPTM5, RAC2, LYN were correction with the levels of B cells, CD4+, macrophages_M1 and Memory_B cells (r > 0.5, P < 0.05).

DM and periodontitis are both the common, chronic disease [10]. The epidemiological data confirmed that DM was the main risk factor of periodontitis and the susceptibility to periodontitis increases about threefold in people with DM [11]. However, the molecular mechanisms of complex interactions between DM and periodontitis are still unclear. This study was the first research to explore the shared genes and common signatures of DM and periodontitis via bioinformatic analysis. LAPTM5, RAC2 and LYN were determined the hub genes, which were all up-regulated in both DM and periodontitis. Besides, they were associated with phagocytosis, B cell activation and T cell activation.

LAPTM5, a negative regulator of T and B cell receptor and a positive modulator of macrophages, is preferentially expressed in immune cells [12, 13]. T and B cells are the main immunosuppressive cells that maintain immune homeostasis [14]. B cells IgG could protect the host from periodontopathogen infection [15], and CD4 T-cell subset also plays a significant role in periodontitis [16]. Interestingly, healthy controls exhibit a higher CD40 and B cells than DM patients [17]. Because B cells proceed anti-inflammatory cytokines IL-10 to abolish the self- or islet-reactive peptides [17]. A reporter indicated that LAPTM5 was the positive modulator of inflammatory signaling pathways and hence cytokine secretion in macrophages [18]. It's worth noting that DM patients, characterized with hyperglycemia could polarize macrophage toward M1 macrophages to induce inflammatory response, which might take responsibility for aggravated periodontal damage in periodontitis under diabetic condition [19]. Our research determined LAPTM5 as the hub gene, which participate the phagocytosis, B cell activation and T cell activation and play an important in the development of periodontitis and DM.

RAC2 is the member of Ras superfamily of small guanosine triphosphate-metabolizing proteins and the key factor of B cell development and T cell activation [20–22]. The deficiency of RAC2 significant prevent the T-cell and B-cell development [23, 24]. Our results showed that RAC2 was up-regulated in periodontitis and DM groups, which was consistent with the previous reporters [25, 26]. Thiago et al indicated that in diabetic mice, the decreased activation of RAC2 protect the vascular injury in DM [27]. In addition, RAC2 also participated the biological process of insulin-secreting INS-1 cells against cytokine-induced apoptosis [28]. Corneliu et al found the more sever bone loss at the alveolar bone to the periodontal ligament interface in RAC2-null mice compared with WT mice. It explained as the activation of inflammatory macrophages, B and T cell synthesize prostaglandin E2 to generate pro-osteoclastic environments and the absent of RAC2 influence the osteoclast maturation and function [29]. RAC2 was identified the hub gene of DM and periodontitis which involving the activation of B cells and T cells.

LYN gene could encode tyrosine protein kinase Lyn (LynK) which were expressed in B cells, adipose tissue, hematopoietic cells et al [30]. LynK has emerged as one of the most attractive therapeutic targets for DM, which was upregulated under diabetic stress [31, 32]. Continuous intake of green tea could reduce the risk of DM via lowing of fasting blood glucose levels and "Benifuuki" green tea has been shown to the suppression of LynK of cellular protein kinas to prevent the release of histamine [33]. Similar to our results, Li et al. also found that LYN was the identified as the key gene of periodontitis [34]. However, it needed more data to support the role of LYN in the development of periodontitis.

Our study exists some limitations: a. the three hub genes LAPTM5, RAC2, LYN have not been validated by laboratory experiments, although their role in DM and periodontitis have been reflected in some clinical and animal research; b. the results also needed further verified in the patients with DM and periodontitis, which will be conducted when the datasets exist.

This study identified LAPTM5, RAC2, LYN, as the hub genes of DM and periodontitis and the activation of B cells, and T cells as well as the phagocytosis could be the central role in the development of the both disease, which might the potential targets for diagnosis and treatment.

Author Contributions

RL and ZYL collected the data and write the manuscript. HJC and BB analyzed the data. RL and XLL contributed to the study design. All authors contributed to the article and approved the submitted version.

Acknowledge

We acknowledge GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Data Availability Statement

The data used to support the findings of this study are included within the article and supplementary information file.

Funding

This research was supported by Taizhou Science and Technology planning Project (NO. 20ywb67).

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.

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No competing interests reported.

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Identification of hub genes and biological mechanisms associated with periodontitis and diabetes

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Data downloading and preprocessing

Identification of DEGs

Functional and pathway enrichment analysis

PPI network construction and identification of hub genes

Validation of Hub Gene Expression

Receiver Operating Characteristic Curves

Immune Infiltration Analysis

Results

Identification of Common DEGs

GO and KEGG Enrichment analysis

PPI network analysis of the common DEGs and identification of Hub Genes

Validation of Hub genes

Efficacy Evaluation and PPI Construction of Hub Genes

The analysis of immune infiltration

Discussion

Conclusion

Declarations

Author Contributions

Acknowledge

Data Availability Statement

Funding

Conflict of Interest

References

Additional Declarations

Supplementary Files

Status:

Version 1