Shared Molecular Mechanisms between Atherosclerosis and ...

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Research Article Shared Molecular Mechanisms between Atherosclerosis and Periodontitis by Analyzing the Transcriptomic Alterations of Peripheral Blood Monocytes Wanchen Ning , 1 Yihong Ma , 2 Simin Li , 1 Xin Wang , 3 Hongying Pan , 4 Chenxuan Wei , 4 Shaochuan Zhang , 5 Dongying Bai , 6 Xiangqiong Liu , 7 Yupei Deng , 7 Aneesha Acharya , 8 George Pelekos , 9 Vuk Savkovic , 10 Hanluo Li , 10 Sebastian Gaus , 10 Rainer Haak , 11 Gerhard Schmalz , 11 Dirk Ziebolz , 11 Yanbo Ma , 6 and Yuzhen Xu 12 1 Stomatological Hospital, Southern Medical University, Guangzhou 510280, China 2 Department of Neurology, Graduate School of Medical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan 3 Department of Neurology, First Aliated Hospital of Harbin Medical University, Harbin 150001, China 4 School of Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI 48109, USA 5 Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan 6 College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471003, China 7 Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, 218 Anwaixiaoguanbeili Street, Chaoyang, Beijing 100029, China 8 Dr D Y Patil Dental College and Hospital, Dr D Y Patil Vidyapeeth, Pimpri, Pune, India 9 Faculty of Dentistry, University of Hong Kong, Hong KongChina 10 Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Liebigstr. 12, 04103 Leipzig, Germany 11 Department of Cariology, Endodontology and Periodontology, University of Leipzig, 04103 Leipzig, Germany 12 Department of Rehabilitation, The Second Aliated Hospital of Shandong First Medical University, Taian, Shandong Province 271000, China Correspondence should be addressed to Yanbo Ma; [email protected] and Yuzhen Xu; [email protected] Received 13 September 2021; Accepted 12 October 2021; Published 3 December 2021 Academic Editor: Min Tang Copyright © 2021 Wanchen Ning et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. This study investigated the nature of shared transcriptomic alterations in PBMs from periodontitis and atherosclerosis to unravel molecular mechanisms underpinning their association. Methods. Gene expression data from PBMs from patients with periodontitis and those with atherosclerosis were each downloaded from the GEO database. Dierentially expressed genes (DEGs) in periodontitis and atherosclerosis were identied through dierential gene expression analysis. The disease-related known genes related to periodontitis and atherosclerosis each were downloaded from the DisGeNET database. A Venn diagram was constructed to identify crosstalk genes from four categories: DEGs expressed in periodontitis, periodontitis-related known genes, DEGs expressed in atherosclerosis, and atherosclerosis-related known genes. A weighted gene coexpression network analysis (WGCNA) was performed to identify signicant coexpression modules, and then, coexpressed gene interaction networks belonging to each signicant module were constructed to identify the core crosstalk genes. Results. Functional enrichment analysis of signicant modules obtained by WGCNA analysis showed that several pathways might play the critical crosstalk role in linking both diseases, including bacterial invasion of epithelial cells, platelet activation, and Mitogen-Activated Protein Kinases (MAPK) signaling. By constructing the gene interaction network of signicant modules, the core crosstalk genes in each module were identied and included: for GSE23746 dataset, RASGRP2 in the blue module and VAMP7 and SNX3 in the green module, as well as HMGB1 and SUMO1 in the turquoise module were identied; for GSE61490 dataset, Hindawi Computational and Mathematical Methods in Medicine Volume 2021, Article ID 1498431, 28 pages https://doi.org/10.1155/2021/1498431

Transcript of Shared Molecular Mechanisms between Atherosclerosis and ...

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Research ArticleShared Molecular Mechanisms between Atherosclerosis andPeriodontitis by Analyzing the Transcriptomic Alterations ofPeripheral Blood Monocytes

Wanchen Ning ,1 Yihong Ma ,2 Simin Li ,1 Xin Wang ,3 Hongying Pan ,4

Chenxuan Wei ,4 Shaochuan Zhang ,5 Dongying Bai ,6 Xiangqiong Liu ,7

Yupei Deng ,7 Aneesha Acharya ,8 George Pelekos ,9 Vuk Savkovic ,10 Hanluo Li ,10

Sebastian Gaus ,10 Rainer Haak ,11 Gerhard Schmalz ,11 Dirk Ziebolz ,11

Yanbo Ma ,6 and Yuzhen Xu 12

1Stomatological Hospital, Southern Medical University, Guangzhou 510280, China2Department of Neurology, Graduate School of Medical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan3Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin 150001, China4School of Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI 48109, USA5Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine,Nagoya, Japan6College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471003, China7Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, 218 Anwaixiaoguanbeili Street,Chaoyang, Beijing 100029, China8Dr D Y Patil Dental College and Hospital, Dr D Y Patil Vidyapeeth, Pimpri, Pune, India9Faculty of Dentistry, University of Hong Kong, Hong KongChina10Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Liebigstr. 12, 04103 Leipzig, Germany11Department of Cariology, Endodontology and Periodontology, University of Leipzig, 04103 Leipzig, Germany12Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian,Shandong Province 271000, China

Correspondence should be addressed to Yanbo Ma; [email protected] and Yuzhen Xu; [email protected]

Received 13 September 2021; Accepted 12 October 2021; Published 3 December 2021

Academic Editor: Min Tang

Copyright © 2021 Wanchen Ning et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. This study investigated the nature of shared transcriptomic alterations in PBMs from periodontitis and atherosclerosisto unravel molecular mechanisms underpinning their association. Methods. Gene expression data from PBMs from patients withperiodontitis and those with atherosclerosis were each downloaded from the GEO database. Differentially expressed genes (DEGs)in periodontitis and atherosclerosis were identified through differential gene expression analysis. The disease-related known genesrelated to periodontitis and atherosclerosis each were downloaded from the DisGeNET database. A Venn diagram wasconstructed to identify crosstalk genes from four categories: DEGs expressed in periodontitis, periodontitis-related knowngenes, DEGs expressed in atherosclerosis, and atherosclerosis-related known genes. A weighted gene coexpression networkanalysis (WGCNA) was performed to identify significant coexpression modules, and then, coexpressed gene interactionnetworks belonging to each significant module were constructed to identify the core crosstalk genes. Results. Functionalenrichment analysis of significant modules obtained by WGCNA analysis showed that several pathways might play the criticalcrosstalk role in linking both diseases, including bacterial invasion of epithelial cells, platelet activation, and Mitogen-ActivatedProtein Kinases (MAPK) signaling. By constructing the gene interaction network of significant modules, the core crosstalkgenes in each module were identified and included: for GSE23746 dataset, RASGRP2 in the blue module and VAMP7 andSNX3 in the green module, as well as HMGB1 and SUMO1 in the turquoise module were identified; for GSE61490 dataset,

HindawiComputational and Mathematical Methods in MedicineVolume 2021, Article ID 1498431, 28 pageshttps://doi.org/10.1155/2021/1498431

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SEC61G, PSMB2, SELPLG, and FIBP in the turquoise module were identified. Conclusion. Exploration of available transcriptomicdatasets revealed core crosstalk genes (RASGRP2, VAMP7, SNX3, HMGB1, SUMO1, SEC61G, PSMB2, SELPLG, and FIBP) andsignificant pathways (bacterial invasion of epithelial cells, platelet activation, and MAPK signaling) as top candidate molecularlinkage mechanisms between atherosclerosis and periodontitis.

1. Introduction

Much epidemiological evidence has highlighted the causalrelationship between periodontitis and atherosclerosis, sug-gesting periodontitis an independent risk factor that contrib-utes to the development of atherosclerosis [1]. This causallinkage between the two diseases has been attributed totwo major mechanisms: a direct mechanism, whereby peri-odontal pathogens invade the vessel walls, and an indirectmechanism of systemic inflammatory responses that aretriggered by periodontitis leading to inflammatory mediatorenhancement [2]. These inflammatory mediators includeC-Reactive Protein (CRP), interleukin- (IL-) 6, IL-8, cyto-kines and chemokines, Matrix Metalloproteinases (MMPs),reactive oxygen species (ROS), nitric oxide, and thromboticand hemostatic markers (fibrinogen) [1].

Systemic inflammation constitutes a major linkage betweenperiodontitis and atherosclerosis, albeit both diseases also shareseveral common risk factors such as aging, smoking, alcoholabuse, race/ethnicity, educational and socioeconomic status,male sex, diabetes mellitus, overweight/obesity, and genetic sus-ceptibility [3]. Common genetic risk factors in periodontitis andatherosclerosis that may predispose certain individuals to sufferboth diseases have been identified [4, 5]. A twin study fromSweden using quantitative genetic analyses identified significantgenetic correlation between periodontitis and atherosclerosis[4]. Others, using genome-wide association (GWAS) identifiedthree genetic variants CDKN2B Antisense RNA 1 (CDKN2B-AS1), Plasminogen (PLG), and Vesicle- Associated MembraneProtein 3 (VAMP3) as significant to both diseases [5]. However,a broader understanding of shared genetic and molecular linksbetween these diseases is largely lacking. To the authors’ knowl-edge, comprehensive and integrated bioinformatic analyses ofdisease-related datasets in Gene Expression Omnibus (GEO)database [6] and disease-related known genes in the DisGeNETdatabase [7] to unravel potentially shared molecular mecha-nisms in this context have not been reported.

Peripheral blood monocytes (PBMs), as innate immuneeffector cells, play critical roles in the initiation and develop-ment of both periodontitis and atherosclerosis. In periodonti-tis, phenotypical alterations of PBMs compared to those fromperiodontal healthy subjects are recognized, showing a specificfunctional profile favoring T-helper- (Th-) 2 cell response overTh-1 cell response [8]. Using RNA-seq data (GSE41690), tran-scriptomic alterations in PBMs from periodontitis patientswere compared with PBMs of periodontal healthy subjects[9]. In case of atherosclerosis, PBMs are reported to be in anactivated state and respondmore strongly against lipopolysac-charide (LPS) stimulation, showing the increased expressionof inflammatory mediators [10]. A publicly available microar-ray dataset (GSE23746) pertains to transcriptomic aberrationsoccurring in the PBMs of atherosclerosis patients versus the

systemically healthy subjects. Regarding periodontitis-associated atherosclerosis, a previous animal study found thatligature-induced periodontitis could activate PBMs and resultin significant upregulation of proinflammatory genes includ-ing Tumor Necrosis Factor Alpha (TNFɑ) and IL-6 in PBMsto increase the adhesion of PBMs to the aortic endothelium,finally triggering the initiation of atherosclerosis [11]. Thesefindings suggest that PBMs might serve as key linkage cellsbetween periodontitis and atherosclerosis. Thus, this studyput forwarded to a hypothesis that the transcriptomic alter-ations in PBMs seen in periodontitis and atherosclerosis mayhave shared features.

In order to validate this hypothesis, the current study isaimed at investigating gene expression profiling data fromperiodontitis- and atherosclerosis-linked datasets, identify-ing the periodontitis- and atherosclerosis-related knowngenes in the DisGeNET database, and performing a seriesof comprehensive bioinformatics analyses to reveal keygenes, biological processes, and signaling pathways thatcould be regarded as molecular links between periodontitisand atherosclerosis. Such data can enable improved under-standing of the pathophysiological links between the twodiseases and uncover valuable targets for risk assessment ordrug development in this context.

2. Methods

2.1. Data Procurement. Gene expression datasets investigatingthe PBMs of periodontitis (GSE61490) and atherosclerosis(GSE23746) were each sourced and downloaded from theNCBI GEO [6]. GSE61490 investigated gene expression pat-terns of PBMs in patients with periodontitis versus periodontalhealthy controls (URL: https://www.ncbi.nlm. http://nih.gov/geo/query/acc.cgi?acc=GSE61490). GSE23746 investigatedaltered gene expression of PBMs among patients with athero-sclerosis compared to healthy controls (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23746). The diag-nosis of periodontitis should be defined in accordance with thecase definitions for periodontitis in the context of the 2017world workshop on the classification of periodontal disease:interdental CAL detectable at ≥2 nonadjacent teeth or buccalor oral CAL ≥ 3mm with pocketing > 3mm detectable at ≥2teeth [12].

2.2. Differential Expression Analysis. Differential expressionanalysis was performed for the GSE23746 dataset using thebioconductor package “limma” in R program [13, 14]. Thespecified cutoff criteria for differentially expressed genes(DEGs) were p value < 0.05 and ∣log FC ∣ ≥0. RNA seq datafrom GSE61490 was mapped to the human genome usingTopHat [15]. Qualification of the mapped reads and the dif-ferential expression analysis were conducted using Cufflinks[14], where genes with a p value < 0.05 and ∣log FC ∣ ≥0:5

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were selected as DEGs. By overlapping the DEGs dysregu-lated in the atherosclerosis-GSE23746 and DEGs dysregu-lated in the periodontitis-GSE61490 dataset, the Venndiagram was plotted using a specialized web tool (URL:http://bioinformatics.psb.ugent.be/webtools/Venn/). Theseoverlapped DEGs were dysregulated in both GSE23746 andGSE61490 dataset and thus were defined as candidate cross-talk genes linking atherosclerosis and periodontitis.

2.3. Functional Enrichment Analysis. The functional enrich-ment analyses were carried out in order to investigate theGene Ontology (GO) terms, particularly biological processes(BPs), and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways enriched by the candidate crosstalkgenes-overlapping DEGs. This analysis was performed withthe “ClusterProfiler” package in R program [16]. The func-tional terms with statistical significance p value< 0.05 wereselected as significant functions. The top 30 enriched BPsand KEEG pathways were visualized in bar plots.

2.4. Construction of Protein-Protein Interaction (PPI)Network. The PPI network was constructed using the candi-date crosstalk genes-overlapping DEGs dysregulated in boththe atherosclerosis-GSE23746 and periodontitis-GSE61490datasets. Experimentally validated PPI pairs were down-loaded from several databases including HPRD [17], BIO-GRID [18], DIP [19], MINT [20], mentha [21], PINA [22],InnateDB [23], and Instruct [24]. The PPI network was thenconstructed with the “Cytoscape” software [25], and the topo-logical features were analyzed using the “NetworkAnalyzer”plugin in Cytoscape to identify hub genes. The top 30 nodeswere selected based on descending order of degree.

2.5. Identification of Crosstalk Genes by Integrating GEODatasets and Disease-Related Known Genes in DisGeNETDatabase. The periodontitis-related and atherosclerosis-related known genes were, respectively, obtained from theDisGeNET database (version 6.0) [26]. By now, four groupsof genes were collected:

Group A: DEGs dysregulated in the atherosclerosis-GSE23746 dataset

Group B: DEGs dysregulated in the periodontitis-GSE61490 dataset

Group C: the periodontitis-related known genes (UMLSCUI: C0031099) obtained from DisGeNET database

Group D: the atherosclerosis-related known genes(UMLS CUI: C0004153) obtained from the DisGeNETdatabase

In order to visualize the overlap between these four setsof genes, a Venn diagram was plotted with the above men-tioned web tool.

The crosstalk genes were generated from this Venn dia-gram by the following two steps: Firstly, the intersections ofgene group belonging to periodontitis and gene groupbelonging to atherosclerosis were obtained: for example,intersection between group A and group B (A∩B), intersec-tion between group C and group D (C∩D), intersectionbetween group A and group C (A∩C), and intersectionbetween group B and group D (B∩D). It is important to

emphasize that the intersection between two gene groupsbelonging the same disease will not be regarded as crosstalkgenes, which means the intersection between group A andgroup D (A∩D) and intersection between group B and groupC (B ∩ D) will be included. Secondly, all these intersectiongenes (A∩B, C∩D, A∩C, and B∩D) were taken together as aunion, the genes of which was regarded as the crosstalk genes.

Afterward, the expression profile of periodontitis-relatedknown genes in the atherosclerosis-GSE23746 dataset, aswell as the expression profile of atherosclerosis-relatedknown genes in the periodontitis-GSE61490 dataset wasextracted and displayed using the pheatmap package in Rprogram [27].

2.6. Identification of Coexpression Modules. The weightedcorrelation network analysis (WGCNA) was constructedfor the genes in the atherosclerosis-GSE23746 dataset(adj.P.Val (p value)<0.05) and periodontitis-GSE61490 data-set (p value < 0.05), respectively. The “wgcna” package in Rwas applied to construct the coexpression network and fur-ther to identify the coexpression gene modules, accordingto previously described methodology for WGCNA analysis[28]. Briefly, an unsupervised coexpression relationshipwas initially built based on the adjacency matrix of connec-tion strengths using Pearson’s correlation coefficients forgene pairs. Then, the power β was calculated, using the“pickSoftThreshold” function. Based on the scale-free topol-ogy criterion, the optimum power β was selected to amplifythe strong connections between genes and penalize theweaker connections. In addition, the hybrid dynamic tree cut-tingmethodwas used to cut branches and cluster coexpressionmodules in the GSE23746 (cutHeight = 0:97, minSize = 20)and GSE61490 (cutHeight = 0:9, minSize = 20).

2.7. Identification of Significant Modules. The crosstalk genesof the above described coexpression modules were extractedand overlapped between any one coexpression module in theatherosclerosis-GSE23746 and peridoontitis-GSE61490. TheFisher’s exact probability test was used to validate the signif-icance of the overlapping across coexpression modules, anda heat map was plotted. The significant modules met the fol-lowing two criteria: first, the Fisher_p value between twomodules was less than 0.001, and grey unassigned modulewas not considered; second, the proportion was more than50% in at least either one module.

2.8. Biological Functions of Crosstalk Genes in the SignificantModules. The expression profiles of the crosstalk genes iden-tified in the different significant modules were obtained anddepicted using the “pheatmap” package in R. Next, func-tional enrichment analysis was performed to identify the sig-nificant BPs and KEGG pathways enriched by the abovecrosstalk genes using the “clusterProfiler” package in R [16].

2.9. Identification of the Core Crosstalk Genes. The final stepwas aimed at identifying the core crosstalk genes. In orderto achieve this objective, the genetic interactions amongthe crosstalk genes were investigated for the different sig-nificant modules. The top 50 crosstalk genes were selectedfor each module, based on the descending order of the

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intramodular connectivity values. If there were less than50 crosstalk genes in a certain module, then all crosstalkgenes were used as the input. If there were more than50 crosstalk genes in a certain module, then top 50 cross-talk genes were taken as the input. Next, coexpression net-works for each significant module were visualized with the“Cytoscape” software [25], based on the all interaction pairsconsisting of the crosstalk genes. The crosstalk genes that weredysregulated in both periodontitis-GSE61490 dataset andatherosclerosis-GSE23746 dataset were regarded as the corecrosstalk genes.

3. Results

3.1. The Study Design of the Present Research. As shown inFigure 1, the present study included four major steps. Step 1was aimed at identifying the candidate crosstalk genes by over-lapping the DEGs dysregulated in both the periodontitis-GSE61490 dataset and atherosclerosis-GSE23746 dataset. Step2 was aimed at identifying the crosstalk genes by takingtogether all intersecting genes between pairs of four gene sets:DEGs dysregulated in periodontitis-GSE61490 dataset,DEGs dysregulated in the atherosclerosis-GSE23746 dataset,periodontitis-related known genes in DisGeNET database,and atherosclerosis-related known genes in DisGeNET data-base. In the step 3, aWGCNA analysis was performed to iden-tify coexpression modules in the GSE61490 dataset andGSE23746 dataset; of these, four significant coexpressionmodules with high correlation were identified. In step 4, byextracting the expression profile of the crosstalk genes(obtained in the step 2) in the significant modules, the cross-talk gene interaction networks were constructed for the fourdifferent significant modules.

3.2. 165 DEGs That Were Dysregulated in Both Periodontitisand Atherosclerosis. By performing differential expressionanalysis, a total of 1,170 DEGs consisting of 382 upregu-lated DEGs and 788 downregulated DEGs were identifiedto be involved in periodontitis, while a total of 2,621 DEGsconsisting of 1,413 upregulated DEGs and 1,208 downreg-ulated DEGs were identified as involved in atherosclerosis.As shown in the Venn diagram (Figure 2(a)), 165 DEGs werefound to be dysregulated in both periodontitis and atheroscle-rosis, by overlapping the DEGs identified in periodontitis-GSE46190 dataset and atherosclerosis-GSE23746 dataset.

3.3. PPI Network of the 165 Overlapping DEGs. Based on the165 DEGs overlapping between periodontitis-GSE46190dataset and atherosclerosis-GSE23746 dataset, a PPI net-work shown in Figure 2(b) was constructed and was con-sisted of 6,212 nodes and 11,202 edges. The topologicalcharacteristics of the top 30 nodes ranked in the descend-ing order of degree are displayed in Table 1. The purplenodes in Figure 2(b) represent the DEGs dysregulated inboth GSE46190 and GSE23746 datasets. Among these,the purple nodes with the greatest degrees were found tobe the gene Amyloid Beta Precursor Protein (APP), SmallUbiquitin Like Modifier 1 (SUMO1), GABA Type AReceptor Associated Protein Like 1 (GABARAPL1), SRC

Proto-Oncogene, Nonreceptor Tyrosine Kinase (SRC),Casein Kinase 2 Beta (CSNK2B), and OFD1 CentrioleAnd Centriolar Satellite Protein (OFD1) (Table 1). Thesegenes interacted with the greatest number of other DEGsin the PPI network, thus might play critical roles in thecrosstalk between both diseases.

3.4. Biological Processes and Signaling Pathways Enriched bythe 165 Overlapping DEGs. The 165 overlapping DEGs sharedbetween periodontitis-GSE46190 dataset and atherosclerosis-GSE23746 dataset were significantly enriched in several bio-logical processes, for example, cellular response to hydrogenperoxide and increased oxygen levels, neutrophil activationand degranulation, regulation of leukocyte activation, plateletdegranulation, positive regulation of chemokine biosyntheticprocess, and cellular response to antibiotic (Figure 2(c)). Inaddition, these overlapping DEGs were also significantlyenriched in multiple signaling pathways, including spliceo-some, platelet activation, bacterial invasion of epithelial cells,human T-cell leukemia virus 1 infection, vascular endothelialgrowth factor (VEGF) signaling pathway, and regulation ofactin cytoskeleton (Figure 2(d)).

3.5. Crosstalk Genes by Disease-Related Known Genes andDEGs Dysregulated in GEO Datasets. By mapping theperiodontitis-related known genes in DisGeNET databaseto the atherosclerosis-GSE23746 dataset, one group ofperiodontitis-known genes, including Apolipoprotein L1(APOL1), C-C Motif Chemokine Ligand 2 (CCL2), andInterferon Stimulated Exonuclease Gene 20 (ISG20), whilethe other group of periodontitis-known genes, includingHigh Mobility Group Box 1 (HMBG1), CCL5, and MMP8were downregulated, in the PBMs of atherosclerosis-GSE23746 dataset (Figure 3(a)). Likewise, the expression pro-files of atherosclerosis-related known genes in DisGeNETdatabase in the periodontitis-GSE46190 dataset are extractedand displayed in Figure 3(b). A group of atherosclerosis-known genes, such as HMGB1, TNF Superfamily Member(TNFSF) 13B, and IL-18, were upregulated, while anothergroup of atherosclerosis-known genes, such as CD14Molecule(CD14), Integrin Subunit Beta 2 (ITGB2), and IL1B weredownregulated, in the PBMs of periodontitis-GSE46190 data-set (Figure 3(b)).

Figure 3(c) depicts a Venn diagram of 354 crosstalkgenes linking atherosclerosis and periodontitis, marked inthe shaded emerald color. These 354 crosstalk genes repre-sent the union genes of four categories:

(i) 165 intersection genes between DEGs dysregulatedin atherosclerosis-GSE23746 dataset and DEGsdysregulated in periodontitis-GSE61490 dataset

(ii) 115 intersection genes between atherosclerosis-known genes in DisGeNET and periodontitis-related known genes in DisGeNET

(iii) 28 intersection genes between DEGs dysregulated inatherosclerosis-GSE23746 dataset and periodontitis-related known genes in DisGeNET

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(iv) 83 intersection genes between DEGs dysregulated inperiodontitis-GSE61490 dataset and atherosclerosis-related known genes in DisGeNET

These 354 genes were defined as the crosstalk genes link-ing both atherosclerosis and periodontitis and thus were usedfor the subsequent analysis. Furthermore, Figure 3(c) showsthat one gene (HMBG1) was not only a DEG dysregulatedin both periodontitis-GSE46190 dataset and atherosclerosis-GSE23746 dataset but also an atherosclerosis- andperiodontitis-related known gene in DisGeNET database.

3.6. Network Construction. The WGCNA construction pro-cesses for atherosclerosis-GSE23746 dataset and periodontitis-GSE61490 dataset are displayed in Figures 4 and 5, respectively.Figures 4(a) and 5(a) show powers for finding a network withscale-free topology properties, of which β = 9 (Figure 4(a))and β = 12 (Figure 5(a)) were identified for obtaining scale-free topology by the fit index greater than 0.8. The adjacencymatrix was produced through the adjacency function usingthe identified β value and gene expression matrix. Figure 4(b)shows a plot identifying scale free topology in expression datawhen the β was selected to be 9. In this Log-log plot, the distri-bution approximately follows a straight line, indicating anapproximately scale-free topology. Similarly, Figure 5(b) showsscale free topology in expression data when the β was selectedto be 12. Figures 4(c) and 5(c) show the gene significance acrossmodules (p = 3:4e − 19 and p = 9:4e − 24). Six coexpressionmodules of each dataset were constructed with various colors(e.g., blue, brown, green, grey, turquoise, and yellow), and the

gene number of each module is listed in Table 2. Among thesix modules identified in atherosclerosis-GSE23746 dataset,the green and brown modules were found to be with the high-est gene significance (Figure 4(c)). Among the six modulesidentified in periodontitis-GSE61490 dataset, the greenmodulewas found to show the highest gene significance (Figure 5(c)).Figures 4(d) and 5(d) show the clustering dendrograms basedon the topological overlap together with the assigned modulecolors. Figures 4(e) and 5(e) show the network heat map plotof (interconnectivity plot) of a gene network together withthe hierarchical clustering dendrograms and the six coexpres-sionmodules, separately. The heatmaps show the progressivelydarker red denotes, indicating high topological overlap andhigh coexpression interconnectedness. Figures 4(f) and 5(f)used the scatter plots to show the gene significance for weightversus module membership in the six coexpression modules,respectively. For the atherosclerosis-GSE23746 dataset, the bluemodule (p = 0:00015), brown module (p = 1:2e − 08), greenmodule (p = 0:018), and grey module (p = 2:7e − 106) exhib-ited the significant correlation (p < 0:05), indicating that hubgenes of these modules tend to be highly correlated with weight(Figure 4(f)). For the periodontitis-GSE61490 dataset, the bluemodule (p = 4e − 20), brown module (p = 1:8e − 07), greenmodule (p = 2:5e − 09), turquoise module (p = 7e − 14), andyellow module (p = 1:4e − 07) exhibited significant correlation(p < 0:05), indicating that hub genes of these modules tend tobe highly correlated with weight (Figure 5(f)).

3.7. Identification of Significant Modules. The module eigen-gene adjacency heat map indicated correlations between any

Step

4St

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Venn diagram of four groups

DEGs dysregulated in GSE61490Periodontitis-

GSE61490dataset

Atherosclerosis-GSE23746

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165 overlapping DEGs Candidate crosstalkgenes

Known genes inDisGe NET

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Atherosclerosis-related known genes obtained from DisGeNET

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Functional analysisPPI network

Six co-expression modules(blue, brown, green, grey,turquoise, yellow) in GSE61490

Six co-expression modules(blue, brown, green, grey,turquoise, yellow) in GSE23746

Overlapping between co-expression modules of eachdataset

Expression of crosstalkgenes in four significantco-expression modules

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Four significantco-expression moduleswith strong correlation:

Turquoise modules in GSE61490;

Blue, green, turquoisemodules in GSE23746

Core crosstalk genes

Intergration of GEO datasetsand disease-related knowngenes in DisGeNET

WGCNA analysis

WGCNA analysis

Figure 1: Work flowchart.

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GSE46190

GSE23746

1005 165 2456

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GSE61490_DEG

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nuclear−transcribed mRNA catabolic process, exonucleolyticcellular response to hydrogen peroxide

neutrophil degranulationcellular response to antibiotic

neutrophil activation involved in immune responseneutrophil activation

neutrophil mediated immunityprotein folding

platelet degranulationcellular response to drug

response to hydrogen peroxideregulation of cell cycle G2/M phase transition

response to increased oxygen levelsmulticellular organism growth

mRNA catabolic processpositive regulation of protein ubiquitination

aerobic respirationcellular response to reactive oxygen species

cellular response to metal ionpositive regulation of chemokine biosynthetic p rocess

pyrimidine ribonucleoside monophosphate biosynthetic p rocess

UMP biosynthetic processUMP metabolic process

pyrimidine ribonucleoside monophosphate metabolic p rocess

regulation of leukocyte activationprotein peptidyl−prolyl isomerization

histamine secretionresponse to topologically incorrect protein

cellular response to lipopolysaccharide RNA catabolic process

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Figure 2: Continued.

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of two modules belonging to the periodontitis-GSE61490dataset and atherosclerosis-GSE23746 dataset, respectively(Figure 6). As the grey module was unassigned, the turquoisemodule in the periodontitis-GSE61490 dataset was identifiedto have the strongest correlation with the turquoise modulein the atherosclerosis-GSE23746 dataset (p = 3:4e − 17)(Figure 6), with the greatest number of the overlappingcrosstalk genes (37 crosstalk genes) (Table 3). The secondstrongest correlation existed between the turquoise modulein the periodontitis-GSE61490 dataset and the blue modulein the atherosclerosis-GSE23746 dataset (p = 1:1e − 11)(Figure 6), with 17 overlapping crosstalk genes (Table 3).The third strongest correlation existed between the tur-quoise module in the periodontitis-GSE61490 dataset andthe green module in the atherosclerosis-GSE23746 dataset

(p = 1:9e − 06) (Figure 6), with 4 overlapping crosstalk genes(Table 3). Based on the above correlation values, the tur-quoise module was selected as the one significant modulein the periodontitis-GSE61490 dataset, while blue, green,and turquoise modules were selected as significant modulesin the athersclerosis-GSE23746 dataset.

3.8. Biological Functions of Crosstalk Genes in the SignificantModules. After extracting the expression profile of crosstalkgenes in the significant turquoise module identified in theperiodontitis-GSE61490 dataset (Figure 7(a)), the BPs andKEGG signaling pathways enriched by these crosstalk geneswere identified (shown in Figures 7(b) and 7(c), respec-tively). Figure 7(b) shows that organelle-related BPs (e.g.,membrane-bounded organelle, intracellular organelle, and

Spliceosome

Platelet activation

RNA degradation

Apelin signaling pathway

Mitophagy − animal

Endocytosis

Shigellosis

Focal adhesion

Viral carcinogenesis

Bacterial invasion of epithelial cells

Proteasome

GABAergic synapse

Kaposi sarcoma−associated herpesvirus infection

Pathogenic Escherichia coli infection

VEGF signaling pathway

Phagosome

Regulation of actin cytoskeleton

Human T−cell leukemia virus 1 infection

Tight junction

0.00 0.01 0.02 0.03 0.04 0.05

Pvalue

path

way

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−1⁎log10 (pvalue)

Count345

678

(d)

Figure 2: Identification of the overlapping DEGs shared between periodontitis-GSE46190 dataset and atherosclerosis-GSE23746 dataset. (a)The Venn diagram showing the overlapping 165 DEGs dysregulated in both periodontitis-GSE61490 and atherosclerosis-GSE23746datasets. (b) The protein-protein interaction (PPI) network that was constructed based on the 165 overlapping DEGs dysregulated inboth periodontitis and atherosclerosis. (c) The biological processes that were significantly enriched by the overlapping 165 DEGs. (d)The signaling pathways that were significantly enriched by the overlapping 165 DEGs.

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intracellular membrane-bounded organelle), metabolicprocess-related BPs (e.g., organic substance metabolic pro-cess, cellular metabolic process, and primary metabolicprocess) were mainly enriched in this module. Figure 7(c)shows that Ras, calcium, focal adhesion, FoxO, MAPK,oxidative phosphorylation, and metabolic pathways weremainly enriched in this module.

In addition, the expression profiles of crosstalk genes inthe three significant modules (turquoise, blue, and green)identified in the atherosclerosis-GSE23746 dataset are dis-played in Figures 8(a), 9(a), and 10(a), respectively. Theorganelle-related BPs (e.g., membrane-bounded organelle,intracellular organelle, intracellular membrane-boundedorganelle, organelle lumen, and intracellular organelle lumen)were enriched in all three modules (Figures 8(b), 9(b), and10(b)), while metabolic process-related BPs (e.g., organicsubstance metabolic process, cellular metabolic process, andprimary metabolic process) were mainly enriched in the tur-quoise (Figure 8(b)) and blue module (Figure 9(b)). As to

the KEGG signaling pathways, platelet activation, insulin,p53, bacterial invasion of epithelial cells, and oxidative phos-phorylation pathways were mainly enriched in the turquoisemodule (Figure 8(c)). The MAPK, AMP-activated proteinkinase (AMPK), leukocyte transendothelial migration, phos-phatidylinositol 3′-kinase- (PI3K-) Akt, insulin, and plateletactivation pathways were mainly enriched in the blue module(Figure 9(c)). Meanwhile, transport, metabolism, absorption,and biosynthesis-related signaling pathways (e.g., synapticvesicle cycle, mineral absorption, amino sugar and nucleotidesugar metabolism, SNARE interactions in vesicular transport,terpenoid backbone biosynthesis, endocytosis, and glycosami-noglycan biosynthesis-chondroitin sulfate/dermatan sulfate)were mainly enriched in the green module (Figure 10(c)).

3.9. Identification of the Core Crosstalk Genes in theSignificant Modules. The crosstalk gene-related gene interac-tion networks for the four significant modules are depictedin Figure 11, and the core crosstalk genes were thus

Table 1: The topological characteristics of the top 30 nodes with the greatest degree in the 165 overlapping DEG-based PPI network shownin Figure 2(b), ranked in the descending order of degree.

Name DegreeAverage shortest

path lengthBetweennesscentrality

Closenesscentrality

Clusteringcoefficient

Topologicalcoefficient

APP 2209 2.0703 0.5313 0.4830 0.0002 0.0011

SUMO1 802 2.3347 0.1769 0.4283 0.0009 0.0021

GABARAPL1 534 2.6731 0.0912 0.3741 0.0013 0.0043

SRC 488 2.7686 0.0958 0.3612 0.0005 0.0057

CSNK2B 294 2.7594 0.0525 0.3624 0.0016 0.0063

OFD1 258 2.8469 0.0482 0.3513 0.0016 0.0074

CDK4 215 2.5037 0.0339 0.3994 0.0049 0.0059

AP2M1 214 2.8266 0.0328 0.3538 0.0036 0.0089

TUBA1C 192 2.7066 0.0236 0.3695 0.0081 0.0088

NUDC 160 2.8917 0.0286 0.3458 0.0017 0.0120

ACTN1 150 2.6568 0.0242 0.3764 0.0094 0.0097

RPL17 150 2.8182 0.0129 0.3548 0.0056 0.0110

SHFM1 137 2.9457 0.0170 0.3395 0.0013 0.0224

ITGB1 134 2.9170 0.0188 0.3428 0.0028 0.0158

U2AF1 131 2.9573 0.0120 0.3381 0.0000 0.0321

UBC 129 2.0214 0.1225 0.4947 0.0109 0.0139

PDLIM7 128 2.8821 0.0179 0.3470 0.0066 0.0144

TAGLN2 127 2.5288 0.0183 0.3954 0.0126 0.0099

NR4A1 125 2.8661 0.0173 0.3489 0.0028 0.0120

PSMB5 119 2.8568 0.0100 0.3500 0.0182 0.0152

HMGB1 114 2.9565 0.0152 0.3382 0.0000 0.0287

FKBP8 100 2.9373 0.0091 0.3404 0.0160 0.0235

SSBP1 99 2.8495 0.0104 0.3509 0.0099 0.0160

PSMB2 97 2.8640 0.0078 0.3492 0.0253 0.0168

SNX3 91 2.8637 0.0109 0.3492 0.0085 0.0160

PTPN12 82 2.6034 0.0141 0.3841 0.0057 0.0133

UNC45A 82 2.9257 0.0076 0.3418 0.0066 0.0226

TPM4 79 2.9301 0.0068 0.3413 0.0110 0.0252

NMI 79 2.9721 0.0125 0.3365 0.0003 0.0204

EIF2S2 77 2.5806 0.0110 0.3875 0.0116 0.0152

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GSM

1506101

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APOL1CCL2ISG20EGR1UTS2KLF2TESCPTNSPP1MIFNME2AIF1IL1RNNDUFA2EIF3KSELPLGTNFSF12TP53LPLMMRN1PTPRFSRCATN1SEMA4DSLC9A1CETPPDGFBSERPINE1ECE1F11RLMNAFLNAGP6VWFMYH9TGFB1PROS1THBS1PCSK6PTGS1MTSS1NR4A1PLA2G6SLC2A6AGERLCATJAM3ZGLP1TTRPF4DHCR7PLTPGJA4ABCB6ITGA2BITGB5HDAC5ILKMMP8MPOARG1CHI3L1F13A1SPARCPPBPCLUGPX1LDLRAP1APPSELPORMDL3ST3GAL4DAPHMGB1LGMNSLC6A4MYLKTGFB1I1CCL5HSPB1RBP4GSTO1PDGFA

Sample

Regulate

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Figure 3: Continued.

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CD14ITGB2ANP32BPTGES3TNFSF13BIL6CXCL2CCL20IL17FCSF1MMEVAMP3MST1F2RL1ERCC2PTGES2OSMPTGS2IL1BCXCL8TNFHMGB1ADGRE1DLDIL18NOD2IFI16INPP5D

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Atherosclerosis-DisgeNETPeriodontitis-DisgeNET

GSE61490GSE23746818

129

2301

12

155

1HMGBI

863

923

8

100

89

14

1

11

(c)

Figure 3: Identification of crosstalk genes linking atherosclerosis and periodontitis. (a) Heat map showing the expression pattern of DisGeNET-periodontitis-related known genes mapped to the atherosclerosis-GSE23746 dataset. (b) Heat map showing the expression pattern of theDisGeNET-atherosclerosis-related known genes mapped to the periodontitis-GSE46190 dataset. (c) Venn diagram to identify crosstalk geneslinking atherosclerosis and periodontitis, via intersecting any two groups of genes belonging to the four groups (i.e., DEGs dysregulated inatherosclerosis-GSE23746 dataset, DEGs dysregulated in periodontitis-GSE61490 dataset, atherosclerosis-related known genes in DisGeNET,and periodontitis-related known genes in DisGeNET). The 354 crosstalk genes identified by the Venn diagram were displayed in the sharedemerald color.

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data set I, power= 9 scale R^2= 0.79 , slope= −1.21

Log10 (k)

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k))

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Blue Brown Green Grey Turquoise Yell ow

Module significance p = 3.4e−19

colorhdata One

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ataO

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Figure 4: Continued.

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hclust (*, "average")as.dist(dissTOMdataOne)

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(d)

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(e)

Figure 4: Continued.

12 Computational and Mathematical Methods in Medicine

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identified. Considering the three significant modules of theatherosclerosis-GSE23746 dataset, two core crosstalk genesFormin Homology 2 Domain Containing 1 (FHOD1) andRAS Guanyl Releasing Protein 2 (RASGRP2) were identifiedin the blue module (Figure 11(a)), as well as four core crosstalkgenes VAMP7, Sorting Nexin 3 (SNX3), Cell Cycle Progres-sion 1 (CCPG1), and PEST Proteolytic Signal ContainingNuclear Protein (PCNP) were identified in the green module(Figure 11(b)), while two core crosstalk genes (HMGB1 andSUMO1) were identified in the turquoise module. Besides,18 core crosstalk genes were identified in the turquoise moduleof the periodontitis-GSE61490 dataset (Figure 11(d)), includ-ing ATPase H+ Transporting V0 Subunit D1 (ATP6V0D1),Split Hand/Foot Malformation (Ectrodactyly) Type 1(SHFM1/SEM1), Ubiquinol-Cytochrome C Reductase CoreProtein 1 (UQCRC1), Chromosome 14 Open Reading Frame166 (C14orf166), C9orf78, LSM3 Homolog, U6 Small Nuclear

RNA And MRNADegradation Associated (LSM3), G ProteinSubunit Gamma 5 (GNG5), SEC61 Translocon SubunitGamma (SEC61G), Eukaryotic Translation Initiation Factor5A (EIF5A), Proteasome 20S Subunit Beta 2 (PSMB2), IMPU3 Small Nucleolar Ribonucleoprotein 3 (IMP3), Catenin BetaLike 1 (CTNNBL1), Selectin P Ligand (SELPLG), FGF1 Intra-cellular Binding Protein (FIBP), R3H Domain And Coiled-Coil Containing 1 (R3HCC1), N-Acetylneuraminate Synthase(NANS), SYF2 Pre-MRNA Splicing Factor (SYF2), and Eukary-otic Translation Initiation Factor 2 Subunit Beta (EIF2S2).

4. Discussion

This study integrated transcriptomes from periodontitis andatherosclerosis and derived crosstalk genes, modules, andenriched biological processes and pathways relevant toperiodontitis-atherosclerosis linkage, thereby highlighting

5 10 15 20 25 30 352

4

6

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set I, blue cor = 0.22, p = 0.00015

Intramodular k

Gen

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set I, brown cor=0.72, p=1.2e−08

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set I, yellow cor=0.19, p=0.31

Intramodular kG

ene S

igni

fican

ce

(f)

Figure 4: Identification of significant modules in atherosclerosis-GSE23746 dataset by performing WCGNA analysis. (a) Scale-free fit indexfor different powers (β) and mean connectivity analysis for various soft threshold powers (β). (b) Log-log plot of the whole-networkconnectivity distribution showing the quality of relationship between connectivity (k) and PðKÞ, when power β was selected to be 9. X-axis: the logarithm of whole network connectivity; Y-axis: the logarithm of the corresponding frequency distribution. (c) Bar plot ofmean gene significance (p = 3:4e − 19) across six coexpression modules constructed with various colors (e.g., blue, brown, green, grey,turquoise, and yellow). The higher the mean gene significance in a module, the more significantly related the module is to the clinicaltrait of interest. (d) Gene dendrogram obtained by average linkage hierarchical clustering. The color row underneath the dendrogramshows the module assignment determined by the Dynamic Tree Cut. (e) Network heat map plot of topological overlap in the genenetwork. In the heat map, each row and column correspond to a gene, light color denotes low topological overlap, and progressivelydarker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram andmodule assignment are shown along the left and top. (f) The scatter plots of gene significance versus intramodular k in the differentmodules of atherosclerosis-GSE23746 dataset.

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(e)

Figure 5: Continued.

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key molecular mechanisms that putatively link these twodiseases. The core crosstalk genes identified in four signifi-cant modules (e.g., RASGRP2, VAMP7, SNX3, HMGB1,SUMO1, FIBP, PSMB2, SELPLG, and SEC61G) were dis-

cussed by describing their plausible mechanisms in linkingthe two diseases.

RASGRP2, a core crosstalk gene identified in the bluemodule of the atherosclerosis-GSE23746 dataset, is a bloodvessel-related gene that can activate platelets by modulatingthe affinity and avidity of integrins and contribute to the for-mation of thrombi [29]. Thrombi formation involves proco-agulant and proinflammatory serine proteases that lead tothe atherosclerotic process by influencing the expression ofcytokines and chemokines [30], and RASGRP2 might playa significant role in the initiating atherosclerosis. In addition,RASGRP2 can activate Ras-proximate-1/Ras-related pro-tein 1 (RAP1) and further the proinflammatory processby increasing the production of proinflammatory cyto-kines, particularly IL-6 and affecting the Nuclear Factor-Kappa B (NF-ĸB) pathway [31]. By promoting thrombiand inflammation, RASGRP2 might play a significant rolein the pathogenesis of periodontitis-triggered systemicinflammation-initiated atherosclerosis.

0 5 10 15 20 25

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(f)

Figure 5: Identification of interesting modules in the periodontitis-GSE61490 dataset by performing WCGNA analysis. (a) Scale-free ftindex for different powers (β) and mean connectivity analysis for various soft thresholding powers (β). (b) Log-log plot of the whole-networkconnectivity distribution showing the quality of relationship between connectivity (k) and PðKÞ, when power β was selected to be 12. X-axis:the logarithm of whole network connectivity; Y-axis: the logarithm of the corresponding frequency distribution. (c) Bar plot of mean genesignificance (p = 9:4e − 24) across six coexpression modules constructed with various colors (e.g., blue, brown, green, grey, turquoise, andyellow). The higher the mean gene significance in a module, the more significantly related the module is to the clinical trait of interest. (d)Gene dendrogram obtained by average linkage hierarchical clustering. The color row underneath the dendrogram shows the moduleassignment determined by the Dynamic Tree Cut. (e) Network heat map plot of topological overlap in the gene network. In the heat map,each row and column correspond to a gene, light color denotes low topological overlap, and progressively darker red denotes highertopological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shownalong the left and top. (f) The scatter plots of gene significance versus intramodular k in the different modules of periodontitis-GSE61490 dataset.

Table 2: The number of crosstalk genes in different coexpressionmodules of periodontitis-GSE61490 dataset and atherosclerosis-GSE3746 dataset.

Coexpressionmodules

Number of genes in theperiodontitis-

GSE61490 dataset

Number of genes in theatherosclerosis-

GSE23746 dataset

Blue 56 293

Brown 49 47

Green 20 30

Grey 366 1749

Turquoise 665 471

Yellow 21 31

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Of the four core crosstalk genes identified in the greenmodule of the atherosclerosis-GSE23746 dataset, VAMP7is related to another member of VAMP gene family-VAMP3 identified in a GWAS study as a shared susceptibil-ity gene between periodontitis and atherosclerosis. VAMP7links exocytosis and actin reorganization and thus playinga critical role in platelet activation and platelet granulerelease [32]. Platelet activation leads to the release of media-tors, such as platelet endothelial cell adhesion molecule 1(PECAM1), RANTES (also known as CCL5), and C-X-Cmotif chemokine 5 (CXCL5 or ENA78) within platelet gran-ules, promoting cell adhesion, coagulation, proteolysis, andenhanced synthesis of cytokines and chemokines, all ofwhich accelerate the formation of atherosclerotic plaque[33]. VAMP7 has also implicated in regulating inflamma-tion. VAMP7 was able to promote the secretion of proin-flammatory cytokine IL12 in dendritic cells [34] and alsodemonstrated as required for optimal macrophage phagocy-tosis [35], where TNFɑ plays a central regulating role [36].Another crosstalk gene-SNX3 is reported as a retromer ofpromoting Signal Transducer And Activator Of Transcrip-tion 3 (STAT3) in cardiovascular diseases [37]. In addition,SNX3 has been suggested as a retromer for Wnt secretionand Wntless (Wls) trafficking [38] in atherosclerosis [39].Considering the functions of STAT3 [40], Wnt, and Wls sig-

naling in inflammatory bone loss of periodontitis [41, 42],SNX3 can be speculated as an essential regulator in peri-odontitis, despite no direct experimental evidence.

Two core crosstalk genes, HMGB1 and SUMO1, wereidentified in the turquoise module of the atherosclerosis-GSE23746 dataset. Substantial evidence supports HMGB1 asa potential therapeutic target in both periodontitis and athero-sclerosis, owing to its interaction with Pattern RecognitionReceptors (PRR), such as Receptor of Advanced GlycationEnd-Products (RAGE) and Toll-Like Receptors (TLRs), aswell as its activation of inflammatory cytokines IL1B, IL-6,and TNFɑ [43–45]. HGMB1 was found to be upregulatedupon periodontal infection, while anti-HMGB1 antibodycould suppress the progression of periodontitis through inhi-bition of inflammatory cytokines, indicating the vital role ofHGMB1 in the initiation and progression of periodontitis[46, 47]. In addition, HGMB1 was found to activate peripheralimmunity and trigger inflammation in atherosclerosis pro-gression after stroke through binding RAGE and inducingcytokine production in immune cells (monocytes and lym-phocytes) [48, 49]. SUMO1, a member of SUMO, is character-ized by dynamic and reversible SUMOylation (SUMOconjugation) [50] in multiple cellular activities through modi-fying posttranslational proteins. SUMO1 is suggested toinhibit NF-ĸB signaling through modification of IĸBɑ(Inhibitor of ĸB) in atherosclerosis [51]. Also, SUMO1 wasable to inhibit prolyl-isomerase-1 (Pin1) [52], and downregu-lation of Pin1 could play a protective role in atherosclerosis[53]. An anti-inflammatory effect of PIN1 inhibition wasobserved in the periodontal ligament cells induced by nico-tine and LPS via blockade of NF-ĸB signaling, implicatingPin1 in periodontitis [51, 54]. SUMO1 may thus regulatethe periodontitis-atherosclerosis linkage through Pin1 andNF-ĸB modification [51].

Blue: 0

Brown: 16

Green: 7

Grey: 44

Turquoise: 171

Yellow: 2 0

20

40

60

Perio

dont

itis-

GSE

6149

0 da

tase

t 0(1)

0(1)0

(1)

0(1)0

(1)0

(1)0

(1)

0(1)

0(1)

0(1)0

(1)0

(1)0

(1)0

(1)0

(1)0

(1)0

(1)0

(1)0

(1)0

(1)

0(1)0

(1)0

(1)

0(1)

0(1)0

(1)1

(5.5e –01)17

(1.1e –11)

15(5.5e –03)

1(0.339)

2(0.016)

4(1.9e –06)

4(7.9e –03)

6(1.5e –06)

79(1.8e –04)

37(3.4e –17)

Blue: 21

Brown: 10

Green: 4

Grey: 11

5

Yellow: 1

Turquoise: 40

Atherosclerosis-GSE23746 dataset

Figure 6: The module eigengene adjacency heat map shows the correlations between coexpression modules in GSE61490 and GSE23746datasets. The horizontal axis represents the six coexpression modules in the atherosclerosis-GSE23746 dataset, and the vertical axisrepresents the six coexpression modules in the periodontitis-GSE61490 dataset. Colors correspond to correlations, in which darker redcolor represents high correlation, and lighter red means low correlation. The number of overlapped crosstalk genes and p values isalso labeled.

Table 3: The correlations between significant modules.

GSE61490 GSE23746 Fisher (p value)Overlapping crosstalk

genes (number)

Turquoise Turquoise 3.4e-17 37

Turquoise Blue 1.1e-11 17

Turquoise Green 1.9e-06 4

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1506

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HNRNPABMRFAP1HNRNPA1L2SRSF9TP53DBIPHBRPL28SSBP1BRI3FUCA1HNRNPA1RPS24SYF2TRMT112TOMM6C14orf166SHFM1EXOSC6LSM2COMMD6LSM3C9orf78DCPSEIF2S2MESDC1POLR3KSLC15A3SEC61GLSM7NDUFB11NOP10EIF5AR3HCC1CDK4NHP2L1AAMPFBLATP6V0D1EIF3KSS18L2GNG5NANSHM13ERP29MYEOV2PPIBIMP3SELPLGCTNNBL1PSMB2MRPL47TACO1PSMC3RNF181DNAJC7CISD3C2orf18EIF4EBP1FIBPPRR13CSNK2BCTSZATP5HUQCRC1

(a)

500

50

70

90

110

130

300400

Count

CellCell part

IntracellularIntracellular part

Binding

Intracellular membrane−bounded organelle

Membrane−bounded organelleOrganelle

Cytoplasm

Intracellular organelle

Cellular process

Metabolic processOrganic substance metabolic process

Cellular metabolic processPrimary metabolic process

Biological regulationRegulation of biological process

Cellular component organization or biogenesisCellular component organization

Macromolecule metabolic processLocalization

MembraneRegulation of cellular process

Single-organism processSingle-organism cellular process

Cytoplasmic partProtein binding

Organelle partIntracellular organelle part

Nucleus

60 80−Log10 (Pvalue)

100 120

−1 ⁎ log10 (pvalue)

(b)

Long-term depressionVascular smooth muscle contraction

Ras signaling pathwayGABAergic synapse

Tyrosine metabolismRegulation of actin cytoskeleton

Vibrio cholerae infectionFc gamma R-mediated phagocytosis

Platelet activationCalcium signaling pathway

Focal adhesionFoxO signaling pathway

Cardiac muscle contractionEndocytosis

HTLV-I infectionCell cycleLysosome

Viral carcinogenesisTight junction

RibosomeProteasome

MAPK signaling pathwayOxidative phosphorylation

Non-alcoholic fatty liver disease (NAFLD)Alzheimer’s disease

RNA degradationHuntington’s disease

Parkinson’s diseaseMetabolic pathways

Spliseosome

Path

way

0.0000 0.0005 0.0010 0.0015 0.0020Pvalue

1020

30

4050

Count

5.0

7.5

10.0

12.5

−1 ⁎ log10 (pvalue)

(c)

Figure 7: The expression pattern of genes in the significant turquoise module of periodontitis-GSE61490 dataset. (a) Heat map showing theexpression profiles of genes in the turquoise module of periodontitis-GSE61490 dataset. (b) The biological processes significantly enrichedby the genes in the turquoise module of periodontitis-GSE61490 dataset. (c) The signaling pathways significantly enriched by the genes inthe turquoise module of periodontitis-GSE61490 dataset.

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(a)

Cellular macromolecule metabolic process

Macromolecular complex

Cellular metabolic process

Macromolecule metabolic process

Primary metabolic process

Cytoplasmic part

Biological regulationRegulation of biological process

Single−organism cellular process

Metabolic process

Organic substance metabolic process

Single−organism process

Organelle part

Intracellular organelle lumenOrganelle lumen

Intracellular organelle part

Nucleus

Nuclear part

Intracellular membrane−bounded organelle

Cytoplasm

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Cellular process

Protein binding

Binding

Intracellular partIntracellular

Cell part

Biol

ogic

al p

roce

ss

Count200

300

350400250

Cell

60 80 100 120

−log10 (Pvalue)

60

80

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(b)

Figure 8: Continued.

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The turquoise module of the periodontitis-GSE61490dataset identified the greatest number (18) of core crosstalkgenes as compared with the other three significant modules.Among these 18 core crosstalk genes, four genes (FIBP,PSMB2, SELPLG, and SEC61G) are supported by experi-mental evidence. FIBP encodes an intracellular protein thatbinds selectively to Acidic Fibroblast Growth Factor (aFGF,also named as FGF1), a cytokine that can regulate theinflammatory response [55]. FGF1 was found to promotethe phenotypic transition of vascular smooth muscle cellsfrom a contractile phenotype to a secretory phenotype,further resulting in increased expression of proinflammatorychemokines (CXCL9, CXCL10, and CXCL11) [56]. Theseoverproduced chemokines play significant roles in facilitatingleukocyte infiltration and atherosclerotic plaque formation[56]. Evidence related to FGF1 effects on inflammatoryresponse in periodontitis is not available to our knowledge.

However, another member of the FGF family-FGF2 caninhibit periodontal inflammation by inhibiting CD40 signal-ing and decreasing the expression of proinflammatory cyto-kines (IL6 and TNFɑ) [57]. PSMB2 plays a critical role inreleasing peptides by regulating the ubiquitin-proteasomepathway in various cellular processes [41]. The Ubiquitin–Proteasome System (UPS) can activate NF-ĸB [41], which isa master regulator of inflammatory and immune responsesthrough regulation of cytokines (ILl, IL6, and TNFɑ) and celladhesion molecules (Intercellular AdhesionMolecule (ICAM)1, Vascular Cell Adhesion Molecule (VCAM) 1, E-selectin) inperiodontitis and atherosclerosis [42, 58]. In addition,proteasome downregulation has the potential to attenuateatherosclerotic inflammation, further resulting in plaquedestabilization [58]. Thus, the involvement of PSMB2 in theperiodontitis-atherosclerosis linkage seems plausible. In caseof SELPLG, the several studies have focused on its role

8

6

4

0.0000 0.0005 0.0010 0.0015

Count102030

−1 ⁎ log10 (pvalue)

TuberculosisPa

thw

ay

Renal cell carcinomaLongevity regulating pathway

Rheumatoid arthritisPlarelet activation

Pathogenic Escherichia coli infectionFanconi anemia pathway

Herpers simplex infectionCollecting suct acid secretion

Biosynthesis of amino acidsInsulin signaling pathway

Regulation of actin cytoskeletonp53 signaling pathway

Alzheimer’s diseaseLong-term potentiation

ShigellosisHuntington’s disease

PhagosomeSalmonella infection

Bacterial invation of epithelial cellsRibosome

Oxidative phosphorylationmRNA surveillance pathway

Synaptic vesicle cycleProtein processing in endoplasmic reticulum

RNA transportMetabolic pathways

EndocytosisSpliceosome

Pvalue

(c)

Figure 8: The expression pattern of genes in the significant turquoise module of atherosclerosis-GSE23746 dataset. (a) Heat map showingthe expression profiles of genes in the turquoise module of atherosclerosis-GSE23746 dataset. (b) The biological processes significantlyenriched by the genes in the turquoise module of periodontitis-GSE23746 dataset. (c) The signaling pathways significantly enriched bythe genes in the turquoise module of atherosclerosis-GSE23746 dataset.

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GSM

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(a)

Regulation of cellular processCellular response to stimulus

Cellular macromolecule metabolic processMacromolecular complex

Cellular metabolic processResponse to stimulus

Macromolecule metabolic processPrimary metabolic process

Cytoplasmic partBiological regulation

Regulation of biological processSingle−organism cellular process

MembraneMetabolic process

Organic substance metabolic processSingle−organism process

Organelle partIntracellular organelle part

Intracellular membrane−bounded organelleCytoplasm

Intracellular organelleMembrane−bounded organelle

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BindingIntracellular part

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30 40 50 60 70

−log10(Pvalue)

Biol

ogic

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40

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60

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Count150200250

Cell

(b)

Figure 9: Continued.

21Computational and Mathematical Methods in Medicine

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mediating the pathogenesis of atherosclerosis. SELPLG wasfound to promote the development of atherosclerosis viamechanisms of regulating glucose metabolism, lipid metabo-lism, amino acid, and phospholipid metabolism, activatingand recruiting leukocytes, and promoting the adhesive inter-actions between endothelial cells and leukocytes [59, 60].Another study showed that SELPLG could significantly pro-mote systemic inflammatory responses by upregulating theexpression levels of proinflammatory cytokines (TNFɑ andIL6) [61]. However, whether periodontitis-triggered systemicinflammation can lead to the upregulation of SELPLG hasnot been investigated. Considering SEC61G, the Sec61 com-plex has been demonstrated to be the central component ofthe protein translocation apparatus of the endoplasmic reticu-lum (ER) membrane [62]. Increasing evidence shows that theER stress signaling pathway is involved in pathological condi-tions including periodontitis and atherosclerosis. ER signalingreportedly contributes to the progression of periodontitis by

two ways: the induction of apoptosis and upregulating inflam-matory response by activating the proinflammatory NF-ĸBpathway [63]. ER stress is implicated in the pathogenesis ofatherosclerosis, and prolonged ER stress is a significantcontributing factor in the proatherogenic progression of theatherosclerotic lesions by inducing apoptosis of lesional mac-rophages and enhancing oxidative stress-mediated damageof vascular cells [64].

In line with previous studies, several inflammatory andimmune signaling pathways, such as the bacterial invasionof epithelial cells, platelet activation, and MAPK signalingpathway, were found primarily enriched by the crosstalkgenes in the significant modules. Bacterial invasion of epi-thelial cells could be an essential link between periodontitisand atherosclerosis, as periodontal pathogen burden maybe contributed to atherosclerosis progression [1]. Addition-ally, platelet activation might link atherosclerosis withperiodontitis [65]. The platelets activated by periodontal

EndocytosisRegulation of actin cytoskeleton

Metabolic pathwaysPlatelet activation

LysosomeTuberculosis

Epstein−Barr virus infectionProteoglycans in cancer

Protein processing in endoplasmic reticulumSalmonella infection

LegionellosisFc gamma R−mediated phagocytosis

MeaslesOther types of O−glycan biosynthesis

Phospholipase D signaling pathwayHTLV−I infection

PhagosomeInsulin resistance

PI3K−Akt signaling pathwayPertussis

Leukocyte transendothelial migrationToxoplasmosis

Neurotrophin signaling pathwayAMPK signaling pathway

Glycosaminoglycan degradationHerpes simplex infection

MalariaN−Glycan biosynthesis

Huntington's diseaseApoptosis

MAPK signaling pathway

0e+00 2e−04 4e−04 6e−04

Pvalue

Path

way

4

5

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−1 ⁎ log10(pvalue)

Count1020

(c)

Figure 9: The expression pattern of genes in the significant blue module of atherosclerosis-GSE23746 dataset. (a) Heat map showing theexpression profiles of genes in the blue module of atherosclerosis-GSE23746 dataset. (b) The biological processes significantly enrichedby the genes in the blue module of periodontitis-GSE23746 dataset. (c) The signaling pathways significantly enriched by the genes in theblue module of atherosclerosis-GSE23746 dataset.

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ogic

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Count1015

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7

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11

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−1 ⁎ log10(pvalue)

(b)

Figure 10: Continued.

23Computational and Mathematical Methods in Medicine

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pathogen burden can further promote the production ofproinflammatory cytokines, thus accentuating risk foratherosclerosis and increased platelet activation inducedprocoagulant, and inflammatory state in periodontitis is rec-ognized [65–68]. Furthermore, MAPK signaling has beenimplicated in atherosclerosis and periodontitis, given its rolein promoting inflammatory responses. The activation ofMAPK signaling axis was shown to contribute to the severityof periodontitis by promoting bone destruction and peri-odontal inflammation via upregulating IL1β, IL6, TNFɑ,MMP13, and Receptor Activator of Nuclear Factor Kappa-B Ligand (RANKL) [69]. The LPS-activated MAPK wasshown to be a key driver of atherosclerosis by playing proa-poptotic, proinflammatory, and antiproliferative roles viaactivating its downstream targets-MAPK-Activated ProteinKinase 2 (MAPKAPK2/MK2) and the Heat Shock Protein27 (HSP27) [70]. Of note, some biological processes, such

as cellular response to reactive oxygen species and increasedoxygen levels, were identified in the enrichment analysis,indicating oxidative stress as a shared pathology in peri-odontitis and atherosclerosis [71].

It is important to highlight why PBMs were selected forinvestigation in the current research. The innate immuneeffector role of PBMs in the pathogenesis of both diseases isone reason for our focus. Another reason is that the geneexpression is typically tissue-specific; thus, a disease may causea certain gene to be upregulated in one tissue but downregu-lated in another type of tissue. Thus, comparing the geneticalterations of distinct tissue (gingival tissue, atheroscleroticplaques)may be less biologically meaningful. The gene expres-sion alterations in PBMs in periodontitis and atherosclerosiswere therefore selected for analysis in the current study.

This study bears some potential limitations. The presentwork included the publicly available microarray/RNA-

Cell cycle

Glycosaminoglycan biosynthesis − chondroitin sulfate / dermatan sulfate

Endocytosis

Terpenoid backbone biosynthesis

Collecting duct acid secretion

SNARE interactions in vesicular transport

Prion diseases

Amino sugar and nucleotide sugar metabolism

Vibrio cholerae infection

Mineral absorption

Colorectal cancer

Synaptic vesicle cycle

Pancreatic cancer

Epithelial cell signaling in helicobacter pylori infection

0.01 0.02 0.03 0.04 0.05

Pvalue

Path

way

Count1.001.251.50

1.752.00

1.5

1.8

2.1

2.4

−1⁎log10 (pvalue)

(c)

Figure 10: The expression pattern of genes in the interested green module of atherosclerosis-GSE23746 dataset. (a) Heat map showing theexpression profiles of genes in the green module of atherosclerosis-GSE23746 dataset. (b) The biological processes significantly enriched bythe genes in the green module of periodontitis-GSE23746 dataset. (c) The signaling pathways significantly enriched by the genes in the greenmodule of atherosclerosis-GSE23746 dataset.

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sequencing data for periodontitis vs. control and atheroscle-rosis vs. control cases that originated from different popula-tions. The lack of data from patients with both periodontitisand atherosclerosis is the greatest limitation of this study,and the future research should validate these findings in

cohorts with both diseases. Within the limitations of thisstudy, the genetic crosstalk markers identified can beconsidered as the preliminary basis for new hypotheseswhich can direct targeted experimentation and validationin future research.

(a)

DYNLT3

CCPG1

SNX3

VAMP7

STAG2ZFR

GHITMPLEKHF2

DSE

SNX10

TMX1PPP6R3

UBLCP1 DEKTMED5

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SMAD4

CYB5R4

KIDINS220VBP1 CYBRD1

ATP6V1A

COPS4

ZMPSTE24

CCDC59PRNP

CLINT1 PAPD4

GSE23746_Cross_Talk_Gene GSE61490_Cross_Talk_Gene

Non-Cross_talk_GeneGSE23746&GSE61490_Cross_Talk_Gene

(b)

(c)

ATP6V0D1PRR13

COMMD6 EIF3KCTSZ

HNRNPABUQCRC1

MYEOV2

TRMT112 TACO1FUCA1C9orf78

C14orf166

SRSF9

LSM2FIBP EIF4EBP1

NHP2L1

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RNF181 R3HCC1EIF5A

LSM7

MRFAP1NANS

NDUFB11

HNRNPA1 C2orf18 DBILSM3 SYF2

AAMPHNRNPA1L2

BRI3

SEC61GMESDC1EIF2S2

TP53

RPL28TOMM6

ATP5HGNG5

PPIBIMP3

CTNNBL1PSMB2

SHFM1

SS18L2

GSE23746_Cross_Talk_Gene GSE61490_Cross_Talk_Gene

Non-Cross_talk_GeneGSE23746&GSE61490_Cross_Talk_Gene

(d)

Figure 11: The crosstalk gene-related gene interaction networks of the four significant modules: (a) crosstalk gene-related network of thegreen module in the atherosclerosis-GSE23746 dataset; (b) crosstalk gene-related network of the turquoise module in the atherosclerosis-GSE23746 dataset; (c) crosstalk gene-related network of the turquoise module in the atherosclerosis-GSE23746 dataset; (d) crosstalkgene-related network of the turquoise module in the periodontitis-GSE41690 dataset. Round nodes with yellow color: crosstalk genesdysregulated in the atherosclerosis-GSE23746 dataset; round nodes with rose red color: crosstalk genes dysregulated in the periodontitis-GSE61490 dataset; diamond nodes with the rose red border: crosstalk genes dysregulted in both atherosclerosis-GSE23746 dataset andperiodontitis-GSE61490 dataset; round nodes with the sky blue color: noncrosstalk genes which interact with the crosstalk genes.

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Taken together, the most important finding in our studywas the identification of putative significant crosstalk genesinvolved in shared molecular mechanisms between athero-sclerosis and periodontitis. Although the experimentalvalidation of these purported linkage genes was not per-formed, these findings were broadly supported by existingexperimental evidence. Therefore, these findings from min-ing experimental transcriptomes could be considered aswell-supported hypotheses, for directing research in thecontext of pathophysiology of CP-atherosclerosis linkage.Simultaneously, these data also provide new directions forfuture translational research for drug development and riskmitigation for both diseases.

5. Conclusion

Exploration of available transcriptomic datasets revealedcrosstalk genes (e.g., RASGRP2, VAMP7, SNX3, HMGB1,SUMO1, FIBP, PSMB2, SELPLG, and SEC61G) and signifi-cant pathways (bacterial invasion of epithelial cells, plateletactivation, and VEGF signaling pathway) as the top candi-date molecular linkage mechanisms between atherosclerosisand periodontitis.

Data Availability

The datasets used and/or analyzed during the current study areavailable from the corresponding author on reasonable request.

Ethical Approval

As this study only applied bioinformatics techniques basedon computational analyses, all of the data from peripheralblood monocytes were obtained from the public datasets,and original samples were not analyzed. Therefore, thisstudy does not require ethical approval.

Consent

Consent for publication is not applicable in this studybecause no individual person’s data was used.

Disclosure

This article acknowledges the European Stroke Organisa-tion and World Stroke Organization (ESO-WSO 2020)for publishing this abstract in their conference proceed-ings. The citation of this conference paper is as below:Yihong Ma, Simin Li, Xin Wang, Vuk Savkovic, HanluoLi, Shaochuan Zhang, Sebastian Gaus, Rainer Haak, DirkZiebolz, Gerhard Schmalz. “Shared Molecular Mechanismsbetween Atherosclerosis and Chronic PeriodontitisRevealed by Transcriptomic Analysis.” International Jour-nal of Stroke. International Journal of Stroke. Volume 15Issue 1_suppl, November 2020. The joint European StrokeOrganisation and World Stroke Organization (ESO-WSO2020) Virtual Conference. 02421/#219. AS33. Genetics,“Omics and Biomarkers”. 06-11-2020 8 : 00AM-8 : 30AM.URL: https://journals.sagepub.com/toc/wsoa/15/1_suppl.

Conflicts of Interest

The authors declare no potential conflict of interests withrespect to the authorship and/or publication of this paper.

Authors’ Contributions

Dr. Wanchen Ning ([email protected]), Dr.Yihong Ma ([email protected]), and Dr. Simin Li([email protected]) contributed equally as the firstauthor. Dr. Gerhard Schmlaz ([email protected]) and Prof. Dr. Dirk Ziebolz ([email protected]) contributed equally as the seniorauthor. Dr. Yanbo Ma ([email protected]) and Dr.Yuzhen Xu ([email protected]) contributed equally asthe corresponding author.

Acknowledgments

This work was partly supported by the Science Foundation forDistinguished Professor of Henan University of Science andTechnology (Grant Number 13510001 to Dr. Yanbo Ma)and PhD Research Startup Foundation of Henan Universityof Science and Technology (Grant Number 13480082 to Dr.Dongying Bai). Apart from this, we also appreciate the fund-ing (Grant No. PY2021002) by the Science Research Cultiva-tion Program of Stomatological Hospital, Southern MedicalUniversity, which was provided to support the postdocresearch of Dr. med. dent. Wanchen Ning (email: [email protected]), as well as the funding (Grant No.PY2020004) provided by the same affiliation, which wasprovided to support the postdoc research of Dr. rer. med.Simin Li (Email: [email protected]).

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