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. 2021 Apr 1;2021:6633563. doi: 10.1155/2021/6633563

Shared Molecular Mechanisms between Alzheimer's Disease and Periodontitis Revealed by Transcriptomic Analysis

Jieqi Jin 1, Mengkai Guang 2, Anthony Chukwunonso Ogbuehi 3, Simin Li 4, Kai Zhang 2, Yihong Ma 5, Aneesha Acharya 6, Bihan Guo 7, Zongwu Peng 7, Xiangqiong Liu 8, Yupei Deng 8, Zhaobi Fang 9, Xiongjie Zhu 9, Shiting Hua 9, Cong Li 9, Rainer Haak 4, Dirk Ziebolz 4, Gerhard Schmalz 4, Lei Liu 10, Baohua Xu 2,✉, Xiaofeng Huang 1
PMCID: PMC8032519  PMID: 33869630

Abstract

Objective

To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer's disease (AD).

Background

Periodontitis, a common oral infectious disease, is associated with Alzheimer's disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported.

Methods

Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes.

Results

Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD.

Conclusions

Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD.

1. Introduction

An association between periodontitis and Alzheimer's disease (AD) has been demonstrated, and periodontitis reportedly confers risk for the incidence and progression of Alzheimer's disease (AD) [1, 2]. Alzheimer's disease is a neurodegenerative disease characterized by the formation of amyloid-β peptide (AβP) plaques and intraneuronal neurofibrillary tangles (NFTs), which drives neuroinflammation in the brain [3]. Periodontitis, a chronic, immunoinflammatory disease affecting supporting structures of teeth, is multifactorial in nature, driven by polymicrobial dysbiosis and unfavorable shifts in the plaque biofilm composition, which disrupts the host-microbial homeostasis [4, 5]. Inflammation is considered the key connecting link between both these diseases [6]. Two purported mechanistic links have been highlighted. First, proinflammatory mediators including specific cytokines or chemokines in the periodontal milieu that enter systemic circulation impose a systemic inflammatory burden, propagating inflammatory responses by microglial cells in the brain [6]. Second, periodontal pathogens may directly enter the brain via blood circulation or peripheral nerves, as evidenced by the discovery of the keystone periodontal pathogen Porphyromonas gingivalis (Pg) in AD patients' brains [7, 8].

At the same time, genetic susceptibility and gene dysregulation have been identified in the context of both AD and periodontitis. In AD, frequently reported dysregulated genes include amyloid-beta precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), apolipoproteins, and lipid homeostasis, genes involved in endocytosis, and membrane-spanning 4 (MS4) family [9]. In periodontitis, aberrant genes highlighted include interleukin-1, interleukin-6, interleukin-10, transforming growth factor-beta (TGF-β), tumor necrosis factor-α (TNF-α), interferon-gamma (IFN-γ), and matrix metalloproteinases (MMPs) among others [10]. It is plausible that gene dysregulation in periodontitis could contribute to its association with AD, and crosstalk genes may biologically link AD and periodontitis serving as either shared susceptibility factors or molecular links.

Here, we designed a bioinformatic study of existing experimental datasets to understand putative molecular links between periodontitis and Alzheimer's disease by identifying crosstalk genes, transcription factors, and signaling pathways involved in both disorders. The molecular mechanisms identified through this approach could suggest potential therapeutic targets particularly relevant to drug development and personalized medicine approaches.

2. Materials and Methods

2.1. Study Design

Figure 1 depicts a flowchart outlining the study workflow. DEGs dysregulated in periodontitis- and AD-related genes were obtained from the GEO database and DisGeNET database, respectively. Crosstalk genes linking periodontitis and AD were identified as the AD-related genes that overlapped with significantly up- or downregulated DEGs in periodontitis. Thereafter, feature selection from the crosstalk genes was performed using a conventional recursive feature elimination (RFE) algorithm and the Boruta algorithm. The crosstalk genes obtained by feature selection were used to construct two networks to identify the transcription factors and the differentially expressed pathways that target these crosstalk genes. In the next step, “core” crosstalk genes were identified as the crosstalk genes obtained by feature selection that were overlapping with chronic periodontitis-related genes in the DisGeNET database.

Figure 1.

Figure 1

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Flowchart depicting study workflow.

2.2. Procurement of Periodontitis-Related Datasets

Sample-matched whole-genome gene expression datasets from periodontitis were sourced and downloaded from the NCBI Gene Expression Omnibus (GEO). The eligibility criteria for these datasets were as follows: datasets that included established periodontitis samples as the experimental group and healthy gingival samples as the control group, where periodontitis was defined based in accordance with the case definition presented in the 2017 World Workshop: (1) interdental CAL detectable at ≥2 nonadjacent teeth or (2) buccal or oral CAL ≥ 3 mm with pocketing >3 mm detectable at ≥2 teeth [11].

2.3. Differential Gene Expression Analysis

Differential gene expression analysis of periodontitis-related datasets was carried out using the Linear Models for Microarray (limma) package [12] in the R project (version 3.0.1, http://www.r-project.org/) [13]. Three such datasets, GSE23586, GSE16134, and GSE10334, were sourced and analyzed. Genes with p value < 0.05 and ∣logFC (fold change) | ≥1 were regarded as significant differentially expressed genes (DEGs). For another dataset, GSE79705, the screening range of DEGs was broadened by extending the thresholds and settings to p value < 0.05 and ∣logFC | >0 as DEGs.

Next, a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) was drawn to identify shared genes within the DEGs identified from the four datasets. The common up/downregulated DEGs in four datasets were used for the following analyses, and DEGs that were not common to all the datasets were excluded.

2.4. Functional Enrichment Analysis

Functional enrichment analysis of up/downregulated periodontitis-related DEGs was performed with DAVID (Database for Annotation, Visualization and Integrated Discovery, v6.8) [14, 15]. p < 0.05 was set as the threshold. GO (Gene Ontology) and pathway enrichment analysis of the identified DEGs was performed [16, 17].

2.5. Construction of the Protein-Protein Interaction Network of Periodontitis-Related DEGs

280,826 PPI (protein-protein interaction) pairs including 19,610 genes were downloaded from HPRD (Human Protein Reference Database) [18], BioGRID (Biological General Repository for Interaction Datasets) [19], DIP (Database of Interacting Proteins) [20], MINT (Molecular INTeraction database) [21], PINA (Protein Interaction Network Analysis) [22], InnateDB (a knowledge resource for innate immunity interactions and pathways) [23], and INstruct (3D protein interactome networks with structural resolution) [24]. The Cytoscape platform was used to visualize the network derived from PPI pairs of DEGs and conduct a topological analysis [25].

2.6. Identification of Crosstalk Genes and Construction of the Crosstalk Gene-Related PPI Network

AD-related genes were downloaded from the DisGeNET database [26]. The AD-related genes that overlapped with the up- and downregulated periodontitis-related DEGs were identified. These overlapping genes were regarded as “crosstalk” genes linking AD and periodontitis and further used for constructing a crosstalk gene-related PPI network. The crosstalk gene-related PPI network consisted of four types of nodes, namely, (1) DEGs dysregulated in periodontitis (not related to AD), (2) crosstalk genes or AD-related genes which were also DEGs dysregulated in periodontitis, (3) AD-related genes (not dysregulated in periodontitis), and (4) other genes (neither related to AD nor dysregulated in periodontitis).

2.7. Feature Selection from Crosstalk Genes

Since GSE16134 had the largest sample size among the periodontitis-related datasets, it was used as the test set. The other three datasets (GSE23586, GSE10334, and GSE79705) were used as validation sets. Firstly, expression values of the crosstalk genes (identified in the previous step) from GSE16134 were used as input for the Boruta algorithm in the R project [27] and the conventional recursive feature elimination (RFE) algorithm [28] and feature selection was performed. Each gene was regarded as a feature.

2.8. Support Vector Machine (SVM) Modeling Using Feature-Selected Crosstalk Genes

The expression values in GSE16134 and GSE10334 were scale-standardized. Next, it was examined if the crosstalk genes obtained by feature selection were found in the four periodontitis-related datasets (i.e., GSE23586, GSE16134, GSE10334, and GSE79705). The gene expression values of these feature selection-obtained crosstalk genes were extracted from these datasets. If the number of expression profile genes in a certain dataset after extraction was lower than the number of feature selection-obtained crosstalk genes, the expression values of the missing genes were considered missing values and represented by the NA symbol. The missing values were processed by using the DMwR package [29] in R, and the K-Nearest Neighbors (KNN) algorithm was used to impute these missing values (NA) in that dataset. By imputing these missing values, all the four periodontitis-related datasets presented all the feature selection-obtained crosstalk genes. Thereafter, the scikit-learn package [30] was used to perform a grid search, and the best hyperparameters of a support vector machine (SVM) model were found by using 5-fold cross-validation (CV) [31]. A SVM classifier model was established by using data from GSE16134 as the training set and test set, where the samples of the GSE16134 dataset were split into 60% : 40% for the training set and test set, respectively. Data from the other three datasets (GSE23586, GSE10334, and GSE79705) were used as the validation set. The decision function method was used to obtain the score for each sample. Next, receiver operating characteristic (ROC) curves for the four datasets were generated by using the pROC package and displayed using the ggplot2 package in R.

2.9. Targeting Relationships between Transcription Factors (TFs) and Crosstalk Genes

Transcription factor- (TF-) target gene regulation pairs were obtained and downloaded from TRRUST [32], cGRNB [33], HTRIdb [34], ORTI [35], and TRANSFAC [36] databases. The TF-target gene interaction pairs corresponding to the feature selection-obtained crosstalk genes were extracted and used for constructing a TF-target gene interaction network with visualization using Cytoscape software [37].

2.10. Pathway Analysis of the Crosstalk Genes

Human data describing relationships between signaling pathways and genes were downloaded from the KEGG database, and all pathways related to the feature selection-obtained crosstalk genes were extracted. The expression levels of these crosstalk gene-related pathways were plotted as a heatmap, and differential expression analysis was performed and applied to the four periodontitis-related datasets to identify the DEPs (differentially expressed pathways) using the R package limma. For the three datasets (GSE23586, GSE16134, and GSE79705), the pathways with p value < 0.05 and ∣logFC | ≥1 were regarded as differentially expressed pathways (DEPs), while for the dataset GSE79705, the pathways with p value < 0.05 and ∣log FC | >0 were regarded as DEPs.

2.11. Classification Performance of the Core Crosstalk Genes

The genes related to periodontitis were downloaded from the DisGeNET database [26]. The overlap between the periodontitis-related genes obtained from the DisGeNET database and the feature selection-obtained crosstalk genes was analyzed, and the overlapping genes were termed core crosstalk genes. The corresponding expression values of these overlapping genes in the four periodontitis-related datasets were obtained, and ROC curves were drawn.

3. Results

3.1. Included Periodontitis-Related Datasets

Four datasets pertaining to periodontitis (i.e., GSE23586, GSE16134, GSE10334, and GSE79705) were included and analyzed. Table 1 provides key details regarding the included datasets.

Table 1.

Details of the included periodontitis-related GEO datasets.

Included four datasets Type of periodontitis Experimental platform Number of examined genes Number of inflamed gingival tissue samples Number of healthy control samples Number of total samples
GSE23586 3 patients with severe chronic periodontitis GPL570 23,518 3 3 6
GSE16134 120 patients (65 with chronic periodontitis and 55 with aggressive periodontitis) GPL570 24,441 241 69 310
GSE10334 63 with chronic periodontitis and 27 with aggressive periodontitis GPL570 24,441 183 64 247
GSE79705 Generalized aggressive periodontitis (GAgP): n = 4; chronic periodontitis (CP): n = 4 GPL18734 19,305 8 4 12
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3.2. Identification of Periodontitis-Related DEGs and Their Functions

Table 2 shows the number of up- and downregulated DEGs that were identified in each of the four datasets. Figure 2 depicts a Venn diagram showing the overlap of DEGs identified from the four datasets. A total of 816 DEGs including 405 upregulated genes and 411 downregulated genes were used for the following analyses. Figure 3 shows the biological processes and signaling pathways in which the up- and downregulated DEGs were significantly enriched.

Table 2.

The number of up/downregulated DEGs identified in the four periodontitis-related datasets GSE23586, GSE16134, GSE10334, and GSE79705 at thresholds set for defining DEGs.

Data Number of upregulated DEGs Number of downregulated DEGs Number of total DEGs ∣log2 FC∣ p value Expression scale (if the data normalization was performed)
GSE23586 7 69 76 >1 <0.05 True
GSE16134 188 48 236 >1 <0.05 False
GSE10334 152 42 194 >1 <0.05 False
GSE79705 219 291 510 >0 <0.05 True
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Figure 2.

Figure 2

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Venn diagram showing the overlap between DEGs identified in the four periodontitis-related datasets.

Figure 3.

Figure 3

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Functional enrichment analysis of up- and downregulated periodontitis-related DEGs. (a) The functional terms (the biological processes (A) and KEGG pathways (B)) enriched by upregulated periodontitis-related DEGs. (b) The functional terms (the biological processes (A) and KEGG pathways (B)) enriched by downregulated periodontitis-related DEGs.

3.3. The Hub Genes Identified by the Periodontitis-Related PPI Network

Figure 4 depicts a PPI network based on the periodontitis-related DEGs, and Table 3 shows the topological characteristics of the top 30 nodes in the PPI network. As seen in Table 3, several genes with the highest degree were identified as hub genes. These upregulated DEGs included SMAD3, TRIM27, VIM, YWHAH, and FOS, and downregulated DEGs included MYC, HSPB1, DDB1, RPS3, KAT5, SMARCA4, RPL13, and PLCG1.

Figure 4.

Figure 4

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Protein-protein interaction (PPI) network associated with periodontitis. Red nodes represent the DEGs which were upregulated in periodontitis, green nodes represent the DEGs which were downregulated in periodontitis, and blue nodes represent other genes which were not DEGs but interacted with DEGs.

Table 3.

Topological characteristics of the top 30 nodes in the periodontitis-related PPI network, ranked in descending order of degree.

Node Label Degree Average shortest path length Betweenness centrality Closeness centrality Clustering coefficient Topological coefficient
MYC DEG_down 1027 2.432727 0.177217 0.411061 0.001896 0.002805
HSPB1 DEG_down 433 2.638061 0.059908 0.379066 0.00278 0.005213
SMAD3 DEG_up 396 2.667636 0.052167 0.374864 0.004283 0.005425
UBC 395 2.112 0.241673 0.473485 0.004524 0.007004
TRIM27 DEG_up 334 2.864727 0.046792 0.349073 0.000791 0.006306
VIM DEG_up 320 2.705212 0.037607 0.369657 0.005251 0.007619
YWHAH DEG_up 282 2.838788 0.034115 0.352263 0.001868 0.00865
DDB1 DEG_down 276 2.76497 0.029677 0.361668 0.003531 0.008012
RPS3 DEG_down 275 2.844848 0.015036 0.351513 0.019323 0.014779
KAT5 DEG_down 245 2.717697 0.028224 0.367959 0.00716 0.007732
SMARCA4 DEG_down 242 2.715758 0.021646 0.368221 0.010116 0.008636
RPL13 DEG_down 234 2.755152 0.015773 0.362956 0.024541 0.011238
PLCG1 DEG_down 227 2.885091 0.030011 0.34661 0.002144 0.009614
FOS DEG_up 226 2.880727 0.024538 0.347135 0.003265 0.009974
AP2M1 DEG_down 214 2.850182 0.025582 0.350855 0.007635 0.010848
SF3B3 DEG_down 203 2.765576 0.015188 0.361588 0.014583 0.010661
TP53RK DEG_up 200 2.871394 0.013972 0.348263 0.01608 0.012798
MMS19 DEG_down 195 2.806909 0.022335 0.356264 0.004705 0.008763
RPS7 DEG_down 193 2.88303 0.008126 0.346857 0.028335 0.016367
MEPCE DEG_down 189 2.905212 0.0145 0.344209 0.005066 0.012337
TUBB4B DEG_down 173 2.880364 0.008167 0.347178 0.007595 0.019412
CDH1 DEG_down 171 2.888485 0.016186 0.346202 0.005848 0.011775
EZR DEG_down 170 2.906667 0.016295 0.344037 0.004107 0.01554
DDX41 DEG_down 168 2.950061 0.009545 0.338976 0.007057 0.017509
NUDC DEG_up 160 2.943758 0.016886 0.339702 0.00283 0.013954
SF3A2 DEG_down 156 2.949818 0.009075 0.339004 0.009926 0.01593
RPL27 DEG_down 147 2.912727 0.003949 0.343321 0.043985 0.020057
NIFK DEG_up 145 3.107879 0.011111 0.321763 0.010345 0.016053
SRPRB DEG_up 143 2.945333 0.017545 0.33952 0.001674 0.015983
ATP1A1 DEG_up 140 2.816606 0.011269 0.355037 0.009147 0.013032
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3.4. Crosstalk Genes Bridging Alzheimer's Disease and Periodontitis

In total, 51 upregulated crosstalk genes and 41 downregulated crosstalk genes were identified and are listed in Table 4. The crosstalk gene-related PPI network as shown in Figure 5 consisted of 3496 nodes and 5141 edges. The topological characteristics of the top 30 nodes in this network are presented in Table 5. Hub crosstalk genes with the highest degree included MYC, HSPB1, VIM, KAT5, RPL13, FOS, and CDH1.

Table 4.

The 51 crosstalk genes that were upregulated and 41 crosstalk genes which were downregulated in periodontitis.

Regulation pattern in periodontitis Crosstalk genes linking periodontitis and Alzheimer's disease
Upregulated AMIGO1 (adhesion molecule with Ig-like domain 1; gene ID: 57463)
ARG2 (arginase 2; gene ID: 384)
BDKRB2 (bradykinin receptor B2; gene ID: 624)
BRI3 (brain protein I3; gene ID: 25798)
C1D (C1D nuclear receptor corepressor; gene ID: 10438)
C3 (complement C3; gene ID: 718)
C4A (complement C4A (Rodgers blood group); gene ID: 720)
C4B (complement C4B; gene ID: 721)
C4B_2 (complement component 4B (Chido blood group), copy 2; gene ID: 100293534)
CASP7 (caspase 7; gene ID: 840)
CD177 (CD177 molecule; gene ID: 57126)
CD38 (CD38 molecule; gene ID: 952)
CHCHD10 (coiled-coil-helix-coiled-coil-helix domain containing 10; gene ID: 400916)
COX8A (cytochrome c oxidase subunit 8A; gene ID: 1351)
CSF3 (colony-stimulating factor 3; gene ID: 1440)
CTGF (cellular communication network factor 2; gene ID: 1490)
CTSS (cathepsin S; gene ID: 1520)
CXCL1 (C-X-C motif chemokine ligand 1; gene ID: 2919)
CXCL12 (C-X-C motif chemokine ligand 12; gene ID: 6387)
CXCL8 (C-X-C motif chemokine ligand 8; gene ID: 3576)
CXCR4 (C-X-C motif chemokine receptor 4; gene ID: 7842)
ENPP2 (ectonucleotide pyrophosphatase/phosphodiesterase 2; gene ID: 5168)
FCGR3A (Fc fragment of IgG receptor IIIa; gene ID: 2214)
FCGR3B (Fc fragment of IgG receptor IIIb; gene ID: 2215)
FHL2 (four and a half LIM domains 2; gene ID: 2274)
FOS (Fos proto-oncogene, AP-1 transcription factor subunit; gene ID: 2353)
FOXO1 (forkhead box O1; gene ID: 2308)
GSTK1 (glutathione S-transferase kappa 1; gene ID: 373156)
GSTO1 (glutathione S-transferase omega 1; gene ID: 9446)
HCLS1 (hematopoietic cell-specific Lyn substrate 1; gene ID: 3059)
IGFBP7 (insulin-like growth factor binding protein 7; gene ID: 3490)
IL1B (interleukin-1 beta; gene ID: 3553)
LYZ (lysozyme; gene ID: 4069)
MAK16 (MAK16 homolog; gene ID: 84549)
MEF2C (myocyte-specific enhancer factor 2C; gene ID: 4208)
MME (membrane metalloendopeptidase; gene ID: 4311)
MMP1 (matrix metallopeptidase 1; gene ID: 4312)
MMP3 (matrix metallopeptidase 3; gene ID: 4314)
MS4A1 (membrane-spanning 4-domains A1; gene ID: 931)
MSRA (methionine sulfoxide reductase A; gene ID: 4482)
MZB1 (marginal zone B and B1 cell-specific protein; gene ID: 51237)
NDUFB8 (NADH:ubiquinone oxidoreductase subunit B8; gene ID: 4714)
PECAM1 (platelet endothelial cell adhesion molecule 1; gene ID: 5175)
PLAT (plasminogen activator, tissue type; gene ID: 5327)
PSENEN (presenilin enhancer, gamma-secretase subunit; gene ID: 55851)
SEL1L (SEL1L adaptor subunit of ERAD E3 ubiquitin ligase; gene ID: 6400)
SERPINI1 (serpin family I member 1; gene ID: 5274)
ST6GAL1 (ST6 beta-galactoside alpha-2,6-sialyltransferase 1; gene ID: 6480)
VCAN (versican; gene ID: 1462)
VIM (vimentin; gene ID: 7431)
WARS2 (tryptophanyl tRNA synthetase 2, mitochondrial; gene ID: 10352)
Downregulated ABCA12 (ATP binding cassette subfamily A member 12; gene ID: 26154)
ACE (angiotensin I converting enzyme; gene ID: 1636)
ADRB1 (adrenoceptor beta 1; gene ID: 153)
BCL2L2 (BCL2-like 2; gene ID: 599)
BRSK1 (BR serine/threonine kinase 1; gene ID: 84446)
CALML5 (calmodulin-like 5; gene ID: 51806)
CALR (calreticulin; gene ID: 811)
CD36 (CD36 molecule; gene ID: 948)
CDH1 (cadherin 1; gene ID: 999)
CDK5R1 (cyclin-dependent kinase 5 regulatory subunit 1; gene ID: 8851)
CTDSP2 (CTD small phosphatase 2; gene ID: 10106)
DNM1 (dynamin 1; gene ID: 1759)
DSG1 (desmoglein 1; gene ID: 1828)
FLG (filaggrin; gene ID: 2312)
GJA1 (gap junction protein alpha 1; gene ID: 2697)
GRHL3 (grainyhead-like transcription factor 3; gene ID: 57822)
GRIN3B (glutamate ionotropic receptor NMDA type subunit 3B; gene ID: 116444)
HM13 (histocompatibility minor 13; gene ID: 81502)
HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase; gene ID: 3156)
HSPB1 (heat shock protein family B (small) member 1; gene ID: 3315)
KAT5 (lysine acetyltransferase 5; gene ID: 10524)
MAP3K12 (mitogen-activated protein kinase kinase kinase 12; gene ID: 7786)
MED12 (mediator complex subunit 12; gene ID: 9968)
MFN2 (mitofusin 2; gene ID: 9927)
MYC (MYC proto-oncogene, bHLH transcription factor; gene ID: 4609)
NEFL (neurofilament light; gene ID: 4747)
NES (exportin 1; gene ID: 7514)
NGF (nerve growth factor; gene ID: 4803)
NOS3 (nitric oxide synthase 3; gene ID: 4846)
NPTXR (neuronal pentraxin receptor; gene ID: 23467)
PLXNA3 (plexin A3; gene ID: 55558)
PPIL2 (peptidylprolyl isomerase-like 2; gene ID: 23759)
PTGS1 (prostaglandin-endoperoxide synthase 1; gene ID: 5742)
RPL13 (ribosomal protein L13; gene ID: 6137)
SCIMP (SLP adaptor and CSK interacting membrane protein; gene ID: 388325)
SPPL2B (signal peptide peptidase-like 2B; gene ID: 56928)
TIAF1 (TGFB1-induced antiapoptotic factor 1; gene ID: 9220)
TP73 (tumor protein P73; gene ID: 7161)
TYRP1 (tyrosinase-related protein 1; gene ID: 7306)
USF2 (upstream transcription factor 2, C-Fos interacting; gene ID: 7392)
VEGFA (vascular endothelial growth factor A; gene ID: 7422)
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Figure 5.

Figure 5

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The crosstalk gene-related PPI network.

Table 5.

Topological characteristics of the top 30 nodes in the crosstalk gene-related PPI network, ranked in descending order of degree.

Node Label Degree Average shortest path length Betweenness centrality Closeness centrality Clustering coefficient Topological coefficient
MYC DEG_AD_down 1027 2.127525 0.466698 0.47003 5.03E − 04 0.001842
HSPB1 DEG_AD_down 433 2.36959 0.184598 0.422014 0.001636 0.003351
VIM DEG_AD_up 320 2.663301 0.116611 0.375474 0.001763 0.005783
KAT5 DEG_AD_down 245 2.566359 0.089476 0.389657 0.003011 0.005621
RPL13 DEG_AD_down 234 2.589729 0.089708 0.386141 0.001394 0.005672
FOS DEG_AD_up 226 2.883151 0.083642 0.346843 1.97E − 04 0.017606
CDH1 DEG_AD_down 171 2.93191 0.058428 0.341075 0 0.025911
TP73 DEG_AD_down 137 2.61483 0.046781 0.382434 0.006333 0.009173
FHL2 DEG_AD_up 136 2.641085 0.057192 0.378632 0.004031 0.00889
CALR DEG_AD_down 135 2.812464 0.046142 0.35556 0.002764 0.010949
GSTK1 DEG_AD_up 111 2.988459 0.035152 0.334621 0 0.026289
MME DEG_AD_up 104 2.836988 0.040386 0.352487 0.001307 0.011694
MED12 DEG_AD_down 91 2.695615 0.030682 0.370973 0.006105 0.012517
CASP7 DEG_AD_up 90 2.901039 0.027131 0.344704 0.005243 0.015603
DNM1 DEG_AD_down 84 2.960185 0.029086 0.337817 0.001147 0.023716
FOXO1 DEG_AD_up 67 2.977784 0.017403 0.33582 0.004071 0.026586
CXCR4 DEG_AD_up 65 2.998557 0.020851 0.333494 0.003846 0.028808
NOS3 DEG_AD_down 62 2.963647 0.018786 0.337422 0.002644 0.027957
NDUFB8 DEG_AD_up 56 3.042412 0.02136 0.328687 0 0.027447
UBC Other gene 53 2.084824 0.149265 0.479657 0.015965 0.027336
NES DEG_AD_down 53 2.920946 0.017012 0.342355 0.007983 0.022954
CDK5R1 DEG_AD_down 50 3.0176 0.0133 0.331389 0.002449 0.037222
NEFL DEG_AD_down 49 2.913445 0.014886 0.343236 0.008503 0.024673
C3 DEG_AD_up 43 3.043278 0.01705 0.328593 0 0.042101
SEL1L DEG_AD_up 42 3.036065 0.013066 0.329374 0 0.045635
DSG1 DEG_AD_down 41 3.046163 0.00857 0.328282 0 0.068464
GJA1 DEG_AD_down 41 3.035199 0.015703 0.329468 0 0.04037
MEF2C DEG_AD_up 41 3.039238 0.008676 0.32903 0 0.068892
LYZ DEG_AD_up 40 3.042123 0.008578 0.328718 0 0.065086
GSTO1 DEG_AD_up 38 2.820254 0.010905 0.354578 0.019915 0.030356
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3.5. Crosstalk Genes Obtained by Feature Selection

A total of 48 crosstalk genes were selected by using the Boruta algorithm (Figure 6(a)). In addition, 62 crosstalk genes were selected by using the RFE algorithm (Figure 6(b)). All 48 genes obtained by using the Boruta algorithm were included in the 62 genes obtained by the RFE algorithm, indicating that this 48-gene set was representative of the characteristics of all 92 crosstalk genes.

Figure 6.

Figure 6

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48 crosstalk genes selected by using the Boruta algorithm (a) and 62 crosstalk genes selected by using the RFE algorithm (b).

3.6. Classification Accuracy Using the Feature Selection-Obtained Crosstalk Genes

The 48 genes obtained by SVM feature selection were not shown in all of the four periodontitis-related datasets. The gene expression profiles of GSE16134 and GSE10334 included all of the 48 crosstalk genes, whereas GSE23586 included 44 crosstalk genes and GSE79705 included 45 crosstalk genes. The classification performance of these 48 crosstalk genes for the four datasets is shown in Table 6. For the test set GSE16134 and the validation set GSE10334, the accuracy performance was high at 91.94% and 88.26%, respectively. By comparison, the performance for the other two datasets GSE23586 and GSE79705 was low at 50% and 66.66%, respectively.

Table 6.

Classification performance of the 48 feature selection-obtained crosstalk genes in the four periodontitis-related datasets.

GSE16134 GSE10334 GSE23586 GSE79705
Samples: test set Samples: SVM model predicts accurately Accuracy Samples: validation set Samples: SVM model predicts accurately Accuracy Samples: validation set Samples: SVM model predicts accurately Accuracy Samples: validation set Samples: SVM model predicts accurately Accuracy
Case 93 92 91.94% 183 177 88.26% 3 3 50% 8 8 66.66%
Control 31 22 64 41 3 0 4 0
Total 124 114 247 218 6 3 12 8
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3.7. ROC Curves for the Four Periodontitis-Related Datasets

As shown in Figure 7, the AUC (area under the curve) values for the GSE16134 test set and the GSE10334 validation set were high at 95.77% and 90.53%, respectively, congruent with the results in Table 6. It was thus inferred that the classifier performance was adequate only when the sample sizes of the validation sets were similar to those of the training and test datasets, and therefore, poor performance was noted for GSE23586 and GSE79705 having much lower sample numbers.

Figure 7.

Figure 7

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The ROC curves of four periodontitis-related datasets.

3.8. The Identification of Transcription Factors Regulating the Crosstalk Genes

As shown in Figure 8, the TF-crosstalk gene target network consisted of 388 nodes and 1178 edges. Several transcription factors which were also DEGs played critical roles by regulating the most number of crosstalk genes, for example, FOS, MEF2C, and USF2 (Table 7).

Figure 8.

Figure 8

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The transcription factor-crosstalk gene target network.

Table 7.

Topological characteristics of the top 30 nodes in the TF-crosstalk gene target network, ranking in descending order.

Node Label Degree Average shortest path length Betweenness centrality Closeness centrality Clustering coefficient Topological coefficient
FOS DEG_up&TF 89 2.178295 0.206101 0.459075 0.020429 0.03825
VEGFA DEG_down 77 2.211886 0.197219 0.452103 0.010936 0.038456
MEF2C DEG_up&TF 76 2.328165 0.159945 0.429523 0.004211 0.044258
CDH1 DEG_down 69 2.540052 0.159392 0.393693 0 0.086634
CXCR4 DEG_up 47 2.596899 0.075105 0.385075 9.25E − 04 0.083836
MME DEG_up 44 2.664083 0.04365 0.375364 0 0.131313
C4A DEG_up 38 2.661499 0.029742 0.375728 0.01138 0.118617
VIM DEG_up 38 2.521964 0.060293 0.396516 0.012802 0.065789
CSF3 DEG_up 36 2.550388 0.031734 0.392097 0.019048 0.075137
C4B DEG_up 34 2.679587 0.018887 0.373192 0.012478 0.122037
VCAN DEG_up 34 2.726098 0.030567 0.366825 0 0.143717
IL1B DEG_up 33 2.741602 0.05746 0.36475 0 0.134602
BRSK1 DEG_down 30 2.741602 0.030404 0.36475 0 0.153788
SERPINI1 DEG_up 29 2.793282 0.019014 0.358002 0 0.127586
USF2 DEG_down&TF 27 2.591731 0.027575 0.385842 0.045584 0.081607
GRHL3 DEG_down 27 2.775194 0.027067 0.360335 0 0.150732
HSPB1 DEG_down 26 2.780362 0.017981 0.359665 0 0.155678
MMP3 DEG_up 26 2.607235 0.024377 0.383548 0.036923 0.074984
CTGF DEG_up 25 2.788114 0.020669 0.358665 0 0.161905
HMGCR DEG_down 25 2.767442 0.041119 0.361345 0 0.16
GATA2 TF 24 2.095607 0.052687 0.477189 0.018116 0.104678
PLAT DEG_up 24 2.762274 0.028075 0.362021 0 0.173148
ETS1 TF 22 2.273902 0.03767 0.439773 0.012987 0.092022
NPTXR DEG_down 22 2.826873 0.020478 0.353748 0 0.154545
AR TF 21 2.24031 0.040698 0.446367 0.02381 0.105465
YBX1 TF 21 2.315245 0.037801 0.43192 0.004762 0.098928
BCL2L2 DEG_down 21 2.813953 0.018179 0.355372 0 0.144048
FCGR3A DEG_up 21 2.81137 0.020521 0.355699 0 0.192799
ABCA12 DEG_down 20 2.79845 0.010961 0.357341 0 0.2
SP1 TF 20 2.191214 0.037362 0.456368 0.026316 0.109167
CDK5R1 DEG_down 20 2.824289 0.017878 0.354071 0 0.156098
FLG DEG_down 20 2.653747 0.012281 0.376826 0.042105 0.093043
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3.9. Signaling Pathways Enriched in the Crosstalk Genes

From the 48 feature selection-obtained crosstalk genes, 37 crosstalk genes were found among gene-pathway interaction pair data in the KEGG database. 137 KEGG pathways corresponded to these 37 crosstalk genes. Figure 9 shows the expression values of these 137 pathways in the four periodontitis-related datasets.

Figure 9.

Figure 9

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Heatmaps depicting 137 signaling pathways enriched in the crosstalk genes in the four periodontitis-related datasets.

The numbers of crosstalk gene-related DEPs obtained from each of the four periodontitis-related datasets are listed in Table 8. The interaction relationships between crosstalk genes and DEPs are depicted in Figure 10, showing that several DEPs were dysregulated in at least two datasets, including cytokine-related pathways (cytokine-cytokine receptor interaction, chemokine, and IL-17), immune cell-related pathways (T cell receptor, B cell receptor, Th1 and Th2 cell differentiation, Th17 cell differentiation, natural killer cell-mediated cytotoxicity, and osteoclast differentiation), JAK-STAT signaling, NOD-like receptor signaling, MAPK signaling, Toll-like receptor signaling, NF-kappa B signaling, and C-type lectin receptor signaling.

Table 8.

The number of crosstalk gene-related DEPs within the four periodontitis-related datasets.

Data Sample DEP DEP DEP Log FC p value
Up Down Total Abs
GSE23586 6 1 12 13 >1 <0.05
GSE16134 310 84 2 86 >1 <0.05
GSE10334 247 77 2 79 >1 <0.05
GSE79705 12 4 4 8 >0 <0.05
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Figure 10.

Figure 10

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The crosstalk gene-differentially expressed pathway interaction network.

3.10. The Identification of Core Crosstalk Genes

Among the 48 feature selection-obtained crosstalk genes and periodontitis-related genes in the DisGeNET database, 12 common genes were identified as core crosstalk genes including C3, C4A, C4B, CXCL12, FCGR3A, FCGR3B, HSPB1, IL1B, MME, MMP3, PLAT, and VEGFA (Table 9). Among these 12 genes, 6 genes, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3, were found associated with chronic periodontitis.

Table 9.

The 12 overlapping genes between 48 feature selection-obtained crosstalk genes and periodontitis-related genes in the DisGeNET database. Among the 12 genes, 6 genes, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3, were associated with chronic periodontitis.

Gene symbol Gene ID Disease ID Disease name
C3 718 C0031099 Periodontitis
C4A 720 C0266929 Chronic periodontitis
C4B 721 C0266929 Chronic periodontitis
CXCL12 6387 C0266929 Chronic periodontitis
FCGR3A 2214 C0266929 Chronic periodontitis
FCGR3A 2214 C0031099 Periodontitis
FCGR3A 2214 C0031106 Periodontitis, juvenile
FCGR3A 2214 C0399447 Early-onset periodontitis
FCGR3A 2214 C0031030 Periapical periodontitis
FCGR3B 2215 C0399447 Early-onset periodontitis
FCGR3B 2215 C0031106 Periodontitis, juvenile
HSPB1 3315 C0031030 Periapical periodontitis
IL1B 3553 C1719494 Periodontitis, localized aggressive
IL1B 3553 C1719495 Aggressive periodontitis, generalized
IL1B 3553 C0399447 Early-onset periodontitis
IL1B 3553 C0001342 Acute periodontitis
IL1B 3553 C0031106 Periodontitis, juvenile
IL1B 3553 C0031030 Periapical periodontitis
IL1B 3553 C4025886 Severe periodontitis
IL1B 3553 C0031099 Periodontitis
IL1B 3553 C0266929 Chronic periodontitis
MME 4311 C0031099 Periodontitis
MMP3 4314 C0031106 Periodontitis, juvenile
MMP3 4314 C0001342 Acute periodontitis
MMP3 4314 C0266929 Chronic periodontitis
MMP3 4314 C0031099 Periodontitis
PLAT 5327 C1719495 Aggressive periodontitis, generalized
VEGFA 7422 C0031099 Periodontitis
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Figure 11 shows ROC curves for the 6 chronic periodontitis-related genes for each of the four datasets. An AUC value of more than 80% was presented by three genes, C4A, C4B, and CXCL12, for the dataset GSE10334, 5 genes, C4A, C4B, CXCL12, FCGR3A, and IL1B, for GSE16134, 3 genes, CXCL12, IL1B, and MMP3, for GSE23586, and all 6 genes for GSE79705. The 3 genes, C4A, C4B, and CXCL1, had the highest classification accuracy when the datasets with small samples (GSE23586 and GSE79705) were not considered.

Figure 11.

Figure 11

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The ROC curves for 6 chronic periodontitis-related genes, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3, in the four periodontitis-related datasets.

4. Discussion

The present study addressed shared genetic mechanisms and molecular links between periodontitis and Alzheimer's diseases by identifying gene expression, signaling pathways, and TFs that were most robustly associated with both these diseases. These findings are largely substantiated by preexisting experimental data.

Six genes C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were identified as the most significant crosstalk genes linking chronic periodontitis and Alzheimer's disease. C4B and C4A, respectively, encode the basic and acidic forms of the complement factor 4, and C4 gene deficiency has been noted to predispose the development of severe chronic periodontitis [38]. In AD, the expression levels of C4 mRNA were shown to be 3.27-fold increased in temporal cortex samples as compared to controls [39]. The CXCL12 (C-X-C motif chemokine ligand 12) gene is the ligand of the C-X-C motif chemokine receptor 4 (CXCR4). CXCL12 expression in the gingival crevicular fluid of periodontitis patients was shown to be significantly higher than that of healthy subjects, suggesting that it might play a role in enhancing neutrophil migration and further the progression of periodontitis [40]. A decreased level of CXCL12 in Alzheimer's disease has been documented as affecting cognitive function, impairing learning and memory [41]. FCGR3A (Fc fragment of IgG receptor IIIa) encodes a receptor for the Fc portion of immunoglobulin G, and FCGR3A polymorphisms are shown to confer susceptibility to periodontitis in Caucasians [42]. In AD, the Fc gamma receptor (FcγR) was recently found to exacerbate neurodegeneration [43]. Cytokines are considered a primary link between chronic periodontitis and Alzheimer's disease as they can enter systemic circulation through periodontal pockets [6]. The classical proinflammatory cytokine, IL1B, is elevated in periodontitis and can induce resorption of alveolar bone [44]. In AD, IL1B gene polymorphisms are linked to disease susceptibility [45]. MMP3 (matrix metalloproteinase 3) is implicated in the progression of chronic periodontitis and can degrade the periodontal tissue matrix [46]. Elevated brain levels of MMP3 have been associated with the duration of Alzheimer's disease, and it has been found to increase the activity of MMP9, thereby indirectly promoting aggregation and cerebral accumulation of tau deposits [47].

More interestingly, the six genes discussed in the last paragraph were also found to be the molecular crosstalks in linking the peripheral immune system and central nervous system (CNS). It has been well demonstrated that the periodontal disease-evoked peripheral systemic host immune response can aggravate the progression of neuroinflammation and neurodegeneration in Alzheimer's disease by switching the microglia from the primed phenotype to an aggressive proinflammatory phenotype [48, 49]. The immune-inflammatory mediators (e.g., cytokines and chemokines) abundantly expressed during periodontal inflammation can circulate into the bloodstream and travel into the brain by crossing the blood-brain barrier (BBB) and impact the function of CNS [50]. Therefore, the crosstalk between the peripheral immune system and the CNS might be an important mechanism underlying periodontitis, increasing the risk of AD. This paragraph will provide a description regarding the potential role of the six crosstalk genes in linking periodontal disease and AD, especially by means of neuroimmune interaction. For example, the complement components C4A and C4B highly expressed in periodontal disease were found to modulate T cell immune response by stimulating the activation and migration of T cells [51–53]. The migration of T cells enhanced by C4A and C4B might allow T cells to traffic across the BBB and enter the brain. For another example, the chemokine and its receptor-composed system CXCL12/CXCR4-7 system were found to be a significant player of the neuroimmune interface [54]. On the one hand, the chemokine CXCL12 mediated the immune-inflammatory response in CNS by recruiting lymphocytes and macrophages [55]. On the other hand, CXCL12 can lead to neurotoxicity and neurodegenerescence by activating the neuronal survival-associated G protein-activated inward rectifier K(+) (GIRK) [54]. FCGR3A (also named CD16) is essential for the antibody-dependent cellular cytotoxicity (ADCC) mediated by natural killer (NK) cells [56]. The increased cytotoxic activity of NK cells was found to cause the dysregulation of protein kinase C and further led to the cognitive deficits in Alzheimer's disease, indicating the contribution of immunological factors to the dysfunction of CNS [57]. IL1B, which was upregulated in periodontitis and transported through the vascular circulation into the brain, was found to play promoting roles in neuroinflammation by enhancing the expression of leukocyte chemotactic chemokines, cell surface adhesion molecules, cyclooxygenases, and MMPs within the brain parenchyma [58]. Likewise, MMP3 abundantly produced in periodontitis was also found to be associated with neuroinflammation via activating microglial cells, as well as participating in the BBB breakdown through the proteolysis of fibronectin and type IV collagen [59]. Taken together, the six crosstalk genes identified in the present research were well evidenced to be involved in periodontitis-triggered peripheral systemic host immune response caused CNS dysfunction in Alzheimer's disease.

Three transcription factors, FOS, MEF2C, and USF2, were identified as related to the regulation of the crosstalk genes and were also found to be dysregulated in chronic periodontitis. The proto-oncogene FOS (also named C-Fos) was found to be involved in the transcriptional regulation of collagenase and cell proliferation genes in periodontal gingival fibroblasts [60]. In AD, FOS is reported to initiate amyloid-β-mediated apoptosis and found to be increased in the hippocampal regions of AD patients [61]. Myocyte-specific enhancer factor 2C (MEF2C) was identified as a critical transcription factor involved in the coexpression network of chronic periodontitis [62]. Genome-wide association studies (GWAS) have shown the linkage between mutation of MEF2C and aging-associated late-onset Alzheimer's disease [63, 64]. Experimentally, a lack of MEF2C expression was shown to exaggerate microglial response and negatively affect brain function [65]. The USF2 (upstream transcription factor 2, C-Fos interacting) transcription factor is reported to enhance osteogenic differentiation of periodontal ligament cells (PDLCs) [66]; however, its involvement in periodontal inflammation has not been reported. In the context of AD, the USF2 gene was shown to regulate the expression of genes Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2, which drive the neuropathological mechanisms [67]. However, a study of Japanese participants found that the single nucleotide polymorphisms of the USF2 gene were not significantly related to the onset of AD [68].

Differentially expressed pathways were identified from the crosstalk gene-pathway network, and several pathways including JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity were found as the most robust differentially expressed pathways in at least two periodontitis datasets. Overall, experimental evidence supports these as linkage mechanisms between periodontitis and AD. The activation of the JAK-STAT pathway induced by the Porphyromonas gingivalis lipopolysaccharide (LPS) and nicotine was shown to increase the expression of cyclooxygenase-2 (COX-2), prostaglandin E2 (PGE2), and proinflammatory cytokines in osteoblasts, thus further accelerating periodontitis progression [69, 70]. In AD, the inhibitor of the JAK-STAK pathway is reported as a therapeutic target, and blocking this pathway can protect against neuroinflammation and dopaminergic neurodegeneration [71]. The MAPK pathway is the upstream signaling intermediate to many inflammatory cytokines such as TNF-α, IL-1β, IL-6, and prostaglandin E2 [72], and the blockage of this pathway could be beneficial for treating inflammatory diseases like chronic periodontitis and AD [73, 74]. The activation of MAPK signaling is noted to promote the production of MMPs and RANKL, leading to osteoclastogenesis and the acceleration of alveolar bone loss [73]. MAPK signaling is also implicated in multiple aspects of the neuropathology of AD, such as promoting neuroinflammation, amyloid-beta toxicity and aggregation, autophagy, and apoptosis [75]. The overexpression of NF-κB signaling plays a pivotal role in periodontitis-associated bone destruction by promoting the differentiation and activation of osteoclasts [76]. The blockade of NF-κB signaling is found to trigger detrimental neural alterations including neuroinflammation, activation of microglia, oxidative stress-related complications, and apoptosis [77]. Natural killer (NK) cells are important regulators of innate and adaptive immunity and are closely linked to the regulation of cytotoxicity [78]. Experimental data shows NK cells can directly recognize the Fusobacterium nucleatum pathogen, leading to alveolar bone resorption and periodontitis [79]. The overactivity of NK is also purported to play a driving role in the progression of AD by producing a series of proinflammatory cytokines [80].

The findings of this in silico analysis must be considered in light of the strengths and limitations of this work. By using a machine learning-based feature selection method as the core technique, the most putatively robust crosstalk genes could be identified. Furthermore, functional molecular links were also analyzed in terms of differentially expressed pathways. The integrated analysis of multiple periodontitis-related GEO datasets enabled a larger sample size for improved accuracy of our computational prediction in the present study. The major limitation of the current approach is that no experimental validation of the identified pivotal genetic molecular linkage candidates was performed. This work has multiple implications for future research. Experimental and clinical studies focused on these candidates could be valuable from the perspectives of identification of shared susceptibility, exaggerating pathogenic mechanisms, biomarkers, and therapeutic targets relevant to precision medicine and drug development or repurposing.

5. Conclusion

Bioinformatic analysis integrating experimental transcriptomic data from Alzheimer's disease and periodontitis revealed the most robust potentially shared molecular linkages. Six crosstalk genes, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3, three transcription factors, FOS, MEF2C, and USF2, and several pathways, JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity, emerged as top candidate shared molecular linkage entities and merit future research in experimental and clinical studies.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Ethical Approval

As this study only applied bioinformatic techniques based on computational analyses of publicly available datasets, therefore, this study did not require ethical approval.

Consent

Consent for publication is not applicable in this study because no individual person's data was used.

Conflicts of Interest

The authors declare no potential conflict of interest with respect to the authorship and publication of this paper.

Authors' Contributions

Lei Liu (Email: law1984@126.com), Baohua Xu (Email: zrkqxbh@163.com), and Xiaofeng Huang (Email: huangxf1998@163.com) contributed equally to this work as the corresponding author. Jieqi Jin (Email: jjq007@126.com), Mengkai Guang (Email: tygmk@qq.com), and Anthony Chukwunonso Ogbuehi (Email: treasurerite@yahoo.com) contributed equally as the first author. Dirk Ziebolz (Email: dirk.ziebolz@medizin.uni-leipzig.de) and Gerhard Schmalz (Email: gerhard.schmalz@medizin.uni-leipzig.de) contributed equally as the senior author.

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Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

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