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. 2025 Dec 7;32(5):1399–1411. doi: 10.1111/odi.70167

Inflammatory Cytokine Changes in Herpes Simplex Virus Infected Periodontium: An In Vitro Proteomic Study

Yu Zhang 1, Ka‐lam Lo 1, Chun‐mei Wang 2, Xi‐ping Feng 1, Jing Ni 3,✉, Xi Chen 1,✉
PMCID: PMC13364949  PMID: 41353774

ABSTRACT

Objective

Periodontitis is the primary cause of tooth loss worldwide. This study aimed to identify herpes simplex virus (HSV) related biomarkers in periodontitis patients.

Methods

The present study used DIA‐based liquid chromatography–tandem mass spectrometry to analyze the proteome of human gingival fibroblasts (HGFs) from one healthy donor across early and late stages (12–72 h) of HSV‐1 infection.

Results

The study identified 890 differentially expressed proteins. At the early infection stage, there was a notable upregulation of proteins including interferon (IFN) regulatory factor 7, IFN‐stimulated genes 15, interleukin 6 (IL6), toll‐like receptor 2 (TLR2), and IFN‐induced protein (IFI), alongside a downregulation of matrix metalloproteinase 2 (MMP2). We observed the activation of pathways, including the complement and coagulation cascades, lysosome, nucleotide‐binding oligomerization domain‐like receptors, retinoic acid‐inducible gene I‐like receptors, and TLR signaling pathways. Conversely, at the late stage, IFIs, IL1, and MMP3 were significantly upregulated, while complement proteins were downregulated. Biomarkers such as TLR2 may underscore the host's antiviral defense response to HSV‐1 in the periodontal environment.

Conclusions

The present study identified several HGF proteins associated with periodontitis following HSV‐1 infection, providing an analytical framework for determining the host's anti‐viral defense response to antagonize HSV‐1 infection in periodontal tissues.

Keywords: cytokine, HSV‐1, infection, periodontitis

1. Introduction

Periodontitis is characterized as an inflammation of the periodontal tissues, leading to their progressive destruction and making it a major cause of tooth loss globally (Tonetti et al. 2018; Papapanou et al. 2018). Although bacteria (such as Porphyromonas gingivalis ) are the most predominant microorganisms associated with periodontal conditions, other microorganisms, such as archaea, fungi, and viruses, may also contribute to the etiopathogenesis of periodontitis. Herpes simplex virus (HSV) was first identified in 1973 in the sulcular epithelium (Zakay‐Rones et al. 1973). Since then, gingival crevicular fluid has been known as an infection reservoir even without established oral lesions (Slots 2015). In addition, HSV has been isolated from periodontitis patients, which suggests it is associated with periodontal lesions (Slots 2015). Various studies reported a high yield of HSV type 1 (HSV‐1) in saliva and subgingival plaque of periodontal patients at all stages and grades of periodontitis compared to healthy individuals (Slots 2015; Slots and Slots 2019; Arduino et al. 2022).

HSV‐1 is one of the most frequent pathogens affecting the oral‐facial tissue, infecting more than three and a half billion individuals globally (James et al. 2020). Similarly to herpesvirus systemic diseases, herpesvirus‐related periodontitis begins with rapid tissue breakdown followed by disease stability that may last for several years or even a lifetime, possibly interrupted by minor relapses (Slots 2017). After primary infection of the oral mucosa, HSV becomes latent in neurons (Denes et al. 2020) and reactivates periodically to align with the course of periodontitis (Zhu and Viejo‐Borbolla 2021). Early host antiviral responses are triggered by HSV‐1 evasion via innate immunological signals (Su et al. 2024). At the molecular level, HSV infection can lead to an increased expression of various host immunity factors by macrophages and monocytes, including interferon (IFN), tumor necrosis factor (TNF), and interleukin (IL). As a result, IL‐1α and TNFα may stimulate matrix metalloproteinases (MMPs) and diminish inhibitors of these enzymes, indeed mediating the alveolar bone destruction in the periodontium (Picolo et al. 2023). As the front line in resisting virus attack in the periodontium, how human gingival fibroblasts (HGFs) deal with virus invasion is not fully understood. A thorough assessment of the interaction between viral immune evasion strategies and host antiviral innate immunity may enhance our understanding of disease pathogenesis and develop immune‐therapeutic strategies for managing HSV‐1 infection‐associated periodontitis. The present study conducted a quantitative proteomic analysis of HGFs from the early to late HSV‐1 infection stages using DIA‐based liquid chromatography–tandem mass spectrometry (LC–MS/MS). This research aimed to identify host factor variations and provide information on the various cellular and molecular pathways activated in HGFs. These results enhanced the understanding of the pathological process post HSV‐1 infection in the periodontium and its associated role in pathogenic mechanisms.

2. Materials and Methods

The institutional ethical committee of the Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China, approved the research protocol (Ref No: SH9H‐2019‐T76‐2), conducted per the Declaration of Helsinki guidelines. All participants provided written informed consent. Five healthy individuals (two females and three males) with a mean age of 37.8 ± 6.0 undergoing gingivectomy during third molar extraction donated HGFs. The detailed inclusion and exclusion criteria are supplied in Table S1. Based on pre‐experiments using HGFs donated by five healthy subjects, HGFs from the same donor were used in this study.

2.1. Infection of HGFs With HSV‐1

To extract HGFs, the gingival tissues were gently rinsed with ethanol (80% v/v). The tissues were then incubated overnight in Dulbecco's modified Eagle's medium (DMEM; SH30243.01, Hyclone; +1% penicillin/streptomycin) containing dispase‐II (2.5 g/L; Solarbio, Beijing, China) and 10% fetal bovine serum (FBS, 10099141; Gibco). After cutting into small sections (1 mm3), the tissues were transferred to a T25 cell culture flask containing 1 mL of DMEM (2 mM l‐glutamine + 10% FBS + 1% non‐essential amino acids + 1% pen/strep). Approximately 2 weeks later, the fibroblasts were transferred to a T75 cell culture flask and cultured. The HGFs (passage 3–4) were used for HSV‐1 infection experiments. Cultures were routinely tested and confirmed negative for HSV, mycoplasma, bacteria, yeast, and fungi. The HGFs were then cultured in a humidified incubator at 37°C with 5% CO2. Once cell density reached 80%–90%, cells were passaged on a sterile workbench. Cells in their third to sixth logarithmic growth phase were used for experiments. Cells were infected with HSV‐1 strain F, supplied by Dr. Ling Zhao (Huazhong Agricultural University, Wuhan, China) at a multiplicity of infection (MOI) of 0.1. After 60 min, cells were washed three times with phosphate‐buffered saline (PBS) and incubated in 2% FBS‐supplemented DMEM for variable periods (12, 24, 36, 48, and 72 h) before collection. Subsequently, cultured cells were transferred into sterile 2 mL Eppendorf tubes and centrifuged for 10 min at 12,000 g, followed by the collection of the supernatant.

2.2. Immunofluorescence Assay

The cells used for the immunofluorescence assay were rinsed with PBS before fixing in cold methanol at −20°C for 10 min. Then, these cells were treated with Tween‐20 (0.1%) in PBS for 5 min to permeabilize cells at room temperature. Afterward, cells were blocked using PBS containing bovine serum albumin (5%) (60 min, 37°C) and incubated for an hour (37°C) with the mouse anti‐HSV1 gD monoclonal antibody (Abcam) with PBS (1:50 dilution) and 1% bovine serum albumin. Subsequently, these cells were rinsed with PBS thrice before incubation in the dark for 1 h using the fluorescently labeled secondary antibody. Lastly, the nuclei were analyzed under a fluorescent microscope with an objective lens (Carl Zeiss, Oberkochen, Germany) after staining with 4′,6‐diamidino‐2‐phenylindole (C1002; Beyotime Biotechnology, China).

2.3. Total Protein Extraction, Digestion, and High pH RP‐UPLC Separation

To inhibit protease activity, the samples were treated with a protein lysis buffer (100 mM Tris/HCl pH 7.6, 4% [w/v] SDS, 0.1 M DTT) (Wisniewski et al. 2009), which contained suitable protease inhibitors. The mixture was treated thrice (40 s each) by the high‐flux tissue grinding machine. The mixture was vortex mixed (5–10 s after every 5 min) for 30 min while incubated on ice, followed by 30 min centrifugation (4°C, 16,000 g). The supernatant was collected and analyzed to determine the protein concentration using the Bicinchoninic acid (BCA) method and Protein Assay Kit (Thermo Scientific).

For protein quantification, 100 μg protein was re‐suspended in triethylammonium bicarbonate buffer (final concentration: 100 mM) and reduced for 60 min at 37°C using the Tris (2‐carboxyethyl) phosphine (10 mM) and alkylated in darkness for 40 min at room temperature using the iodoacetamide (40 mM). The mixture was centrifuged for 20 min (at 4°C, 10,000 g), and the pellet was collected and re‐suspended in 100 μL triethylammonium bicarbonate buffer to obtain a final concentration of 100 mM. Furthermore, trypsin was added (trypsin‐to‐protein mass ratio of 1:50) and incubated overnight at 37°C. From each sample pool, an equal amount of the trypsin‐digested peptides was vacuum‐dried and re‐suspended using the UPLC loading buffer (Phase A: pH 10, 2% acetonitrile; Phase B: pH 10, 80% acetonitrile). Vanquish Flex binary UHPLC chromatography (Thermo, USA) and Acquity UPLC BEH C18 Column (1.7 μm, 2.1 mm × 150 mm; Waters, USA) were used to fractionate the peptides mixture and enhance the proteomic depth, respectively.

2.4. Liquid Chromatography‐Mass Spectrometry Analysis

The spectrometry loading buffer, consisting of 2% ACN with 0.1% formic acid and an appropriate iRT peptide, was used to redissolve peptides and calibrate retention time. The samples were analyzed using an online nano‐flow liquid chromatography–tandem mass spectrometry EASY‐nLC system (Thermo, USA) connected to a Q Exactive HF‐X quadrupole orbitrap mass spectrometer (Thermo, USA) via a nanoelectrospray ion source. The C18‐reversed phase column (25 cm × 75 μm; Thermo, USA) was equilibrated with solvent A (0.1% formic acid, 2% ACN) and solvent B (0.1% formic acid, 80% ACN). Peptides were eluted using the following gradient profile: 0–70 min, 5%–23% solvent B; 70–90 min, 23%–29% solvent B; 90–100 min, 29%–38% solvent B; 100–102 min, 38%–48% solvent B; 102–103 min, 48%–100% solvent B; and 103–120 min, maintained at 100% solvent B. The flow rate was set at 300 nL/min to isolate the tryptic peptides. The system employed a data‐dependent acquisition mode to facilitate automatic switching between full‐scan MS and MS/MS acquisition. At a resolution of 60 K, the Orbitrap acquired a survey of full‐scan MS spectra (m/z 300–1500). The automatic gain control (AGC) target was set at 3e6, with a maximum fill time of 50 ms. Following this, the higher‐energy collisional dissociation fragmented the top 20 most intense precursor ions. The maximum fill time for MS/MS was automated, with a resolution set at 30,000 and AGC at 3e6. Additionally, the system utilized data‐independent acquisition (DIA) mode to support comprehensive spectral acquisition. This involved acquiring a full‐scan MS spectra survey, followed by fragmentation of all precursor ions using higher‐energy collision dissociation within 40 variable isolation windows, each overlapping by 1 m/z. The MS proteomics data was deposited to the ProteomeXchange Consortium through the PRIDE (Perez‐Riverol et al. 2022; Deutsch et al. 2023), which is a partner repository (dataset identifier: PXD053087).

2.5. Bioinformatics Data Analysis

The ProteomeDiscoverer (v2.4; Thermo Scientific) was utilized to investigate the data‐dependent acquisition files against the UniProt database using the following MS/MS search criteria: Mass tolerance (MS, 10 ppm; MS/MS, 0.05 Da), trypsin as an enzyme allowing two missed cleavages, carbamido methylation of cysteine for fixed modification, and oxidation while acetyl for dynamic modifications. Protein identifications were performed at the peptide level using the high‐confidence peptides at a target false discovery rate (FDR) threshold of 1%, which was computed using the Benjamini–Hochberg method. Proteins having at least one unique peptide were considered for protein identification. In addition, Spectronaut (v14; Biognosys AG) was used to investigate the DIA data with the default settings and dynamic iRT set for the retention time prediction type (Bruderer et al. 2016). Based on gradient stability and iRT calibration, Spectronaut may establish an ideal extraction window. The FDR cutoff (1%) was set at the precursor and protein levels (Gibbs et al. 2025; Li et al. 2022). The intensities of the six most intense peptides were considered for quantification. Differentially expressed proteins (DEPs) were identified using the fold change thresholds set at ≥ 2.0 or ≤ 0.50 (Barrozo et al. 2025) and p < 0.05. The data were presented as mean ± SEM using the bar plots. Student's t‐test or one‐way ANOVA was applied to depict the p‐values as denoted in the figure legends. The annotation and visualization database was used to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation. GO and KEGG enrichment analyses were performed using DEPs. The String (v12.0) was used to perform the protein–protein interaction analysis. For the Gene set enrichment analysis (GSEA, v4.3.2), Gene set files were acquired from the GSEA website (https://www.gsea‐msigdb.org/gsea). The relative expression changes of the proteomics data, mfuzz (v2.6.0), were used to cluster DEPs into significant discrete clusters.

3. Results

3.1. Overall Proteomic Characteristics of HSV‐1 Infected HGFs

HSV‐1 infected HGFs (multiplicity of infection, MOI = 0.1) were monitored using a confocal immunofluorescent microscope at different post‐infection intervals (12, 24, 36, 48, and 72 h) (Figure 1A). A total of 6643 proteins were obtained. A Venn diagram depicted the distribution of each group's unique and common proteins with a relative expression level > 1. The analysis showed 6567, 6618, 6627, 6626, 6618, and 6619 proteins from early to late HSV‐1 infection stages (mock infection, 12, 24, 36, 48, 72 h post‐infection), of which 6519 proteins were common in all groups (Figure 1B). In addition, a unique protein was identified in the mock and 24 h groups. Principal component analysis (PCA) revealed that these cells were separated by hours of post‐infection (hpi) (Figure 1C). Moreover, unsupervised clustering showed that the biological replicates within the same group had a high correlation (Figure S1, Pearson's r > 0.95).

FIGURE 1.

FIGURE 1

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Flowchart for the study and global proteomic characteristics of HSV‐1 infected HGFs. (A) Schematic representation of the experimental design and workflow of proteomic analysis. HSV‐1 infected human gingival fibroblasts (MOI = 0.1) at 12, 24, 36, 48, and 72 h post‐infection intervals. Cellular labeling was accomplished using precise antibodies. 4′,6‐diamidino‐2‐phenylindole labeling was used for cellular nuclei, while the fluorescent signals were monitored using a confocal immunofluorescent microscope (scale bars = 100 μm). This figure was created with BioRender.com. (B) Venn diagram depicting the protein distributions at various post‐infection intervals (12, 24, 36, 48, and 72 h). (C) Principal components analysis on the common set of detected proteins at various post‐infection intervals (12, 24, 36, 48, and 72 h). (D, E) GO (top 10 in each) and KEGG (top 20) annotations for the overall function profiles. (F) Pfam annotation of detected proteins. (G) Subcellular location analysis of detected proteins.

We conducted protein functional annotation to gain insights into the functions and pathways of the identified proteins. GO analysis categorized the differentially expressed proteins based on their activity, localization, and function, encompassing cellular components (such as organelles and membranes), biological processes (including regulation of biological and organic substance metabolic processes), and molecular functions (such as binding of proteins and organic cyclic compounds) (Figure 1D). Figure 1E illustrates the KEGG analysis of the top 20 pathways, highlighting the roles of these proteins in viral infections and the immune system, relevant to human diseases and organismal systems.

Additionally, we utilized the EggNOG database for further functional annotation of proteins (Figure S2), which predominantly classified the proteins in categories related to intracellular trafficking, vesicular transport, secretion, and protein turnover. To refine our understanding of these proteins, we performed Pfam annotation to ascertain functional domain information (Figure 1F) and conducted subcellular location analysis (Figure 1G). This analysis revealed that among the detected proteins, 3707 were localized in the cytoplasm, 1028 in the nucleus, 597 in the mitochondria, and 331 in the endoplasmic reticulum.

3.2. Protein Variations in HSV‐1 Infected HGFs

To identify the specific biomarkers in HSV‐1 infected treated HGFs at various time points, we conducted a comparative analysis of proteins in each infection stage group. The DEPs satisfied the following conditions: |log2FoldChange| ≥ 2 and p < 0.05. DEP statistics among different groups are shown in Figure 2A. A total of 890 proteins were identified in all comparisons. Based on the DEPs matrix, the upset diagram was used to analyze the co‐DEPs and specifically DEPs among groups compared to non‐infected cells (Figure 2B). A total of 108 proteins were upregulated, and 61 proteins were downregulated in all stages post‐infection compared to mock infection. There were 135 upregulated proteins, which included IFN‐induced protein with tetratricopeptide repeats family members (IFIT), IFN regulatory factor 7 (IRF7), ubiquitin‐like protein IFN‐stimulated gene factor 15 (ISG15), interleukin‐6 (IL6), C‐X‐C motif chemokine 10 (CXCL10), C‐C motif chemokine 2 (CCL2), toll‐like receptor 2 (TLR2), IFN‐inducible protein 6 (IFI6), interleukin‐7 receptor (IL7R), tumor necrosis factor (TNF) ligand superfamily member 10 (TNFSF10), and 182 downregulated proteins including matrix metallopeptidase 2 (MMP2), CXCL3, E3 ubiquitin‐protein ligase RNF115, guanylate‐binding protein 4, complement C5 and C7 in the 12 hpi group compared to mock infection cells (Figure 2C). To more intuitively observe the DEPs after HSV‐1 infection at 12 hpi, the STRING database was used to construct a PPI network and evaluate the interactions between upregulated and downregulated DEPs (Figure 2D,E). Meanwhile, 280 proteins were upregulated while 278 proteins were downregulated for 48 hpi versus controls, including fibroblast growth factor 2 (FGF2), IL6, IFI6, IFI27, IFITs, histone H3, signal transducer and activator of transcription 1 (STAT1), CCLs, and TLRs. The numbers were 321 and 235 proteins for 72 hpi versus controls, which included enhanced vascular endothelial growth factor (VEGF) and MMP3, indicating that HSV‐1 significantly changed the protein expression in HGFs (Figure 2F–I).

FIGURE 2.

FIGURE 2

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Protein variations in HSV‐1 infected HGFs. The protein abundances were evaluated for significant elevation or depletion using Student's t‐test between two groups with the standard of |log2FoldChange| ≥ 2 and p‐value < 0.05. (A) Overview of differentially expressed proteins (DEPs) in the present study. (B) The co‐DEPs and specific DEPs in various infection time points versus non‐infected cells were depicted using the upset plot. (C) The DEPs at 12 h post‐infection versus non‐infected cells were depicted using the volcano plot. (D, E) PPI networks of the upregulated (D) and downregulated (E) DEPs at 12 h post‐infection versus non‐infected cells using the STRING database. (F–I) The DEPs at different post‐infection intervals (24, 36, 48, and 72 h) versus non‐infected cells.

3.3. Functional Variations in HSV‐1 Infected HGFs

To elucidate the main biological functions of DEPs at various stages of HSV‐1 infection in HGFs, we conducted functional annotation and enrichment analysis using the GO and KEGG databases. At 12 hpi, GO enrichment analysis identified significant enrichment in innate immune response, defense response to other organisms, and keratinization (Figure 3A,B). We utilized a KEGG enrichment string diagram to illustrate the correspondence between the DEPs and KEGG pathways at 12 hpi compared to non‐infected cells (Figure 3C). The diagram details the pathways where upregulated and downregulated DEPs are particularly prominent (Figure 3D).

FIGURE 3.

FIGURE 3

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Functional variations in HSV‐1 infected HGFs. (A, B) Bubble chart (A) and directed acyclic graph (B) for GO enrichment analysis for the DEPs at 12 h post‐infection versus non‐infected cells. BP, biological process; CC, cellular component; MF, molecular function. In a directed acyclic graph, each box represents a GO term; the colored boxes represent GO terms with significant enrichment in the gene set; the black arrow represents “is‐a” relationship, and the inclusion relationship is represented along the direction of the arrow; the blue arrow for a “part‐of” relationship, the red arrow for a negatively regulation relationship, the green arrow for a positively regulation relationship. (C) String diagram for KEGG enrichment analysis for the DEPs at 12 h post‐infection versus non‐infected cells. (D) The enriched KEGG pathways of upregulated and downregulated DEPs 12 h post‐infection versus non‐infected cells. Enrichment ratio = number of enriched proteins in this pathway/total number of annotated proteins. (E) String diagram for KEGG enrichment analysis for the DEPs at 72 versus 12 h post‐infection. (F) The enriched KEGG pathways of upregulated and downregulated DEPs at 72 versus 12 h post‐infection. (G) Gene signatures enriched in 12 h post‐infection versus non‐infected cells based on proteomics dataset. (H–M) GSEA enrichment plot and heatmaps depicting protein expression of the (H) “complement and coagulation cascades”, (I) “lysosome”, (J) “NLR signaling pathway”, (K) “cytokine‐cytokine receptor interaction”, (L) “RLR signaling pathway”, and (M) “TLR signaling pathway” signature in 12 h post‐infection versus non‐infected HGFs. NES, normalized enrichment score. *p < 0.05, **p < 0.01, ***p < 0.001.

In the early stages of infection, several pathways were notably activated, including the nucleotide‐binding oligomerization domain (NOD)‐like receptors (NLRs) signaling with 33 upregulated DEPs, nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB) signaling with 26 upregulated DEPs, TNF signaling with 25 upregulated DEPs, retinoic acid‐inducible gene‐I‐like receptors (RLRs) signaling with 17 upregulated DEPs, and the HSV‐1 infection pathway with 39 upregulated DEPs.

At later stages of infection, there was a marked enrichment of upregulated DEPs in the complement and coagulation cascades with 60 DEPs, phagosome with 93 DEPs, and HSV‐1 infection pathway with 90 DEPs at 72 hpi compared to 12 hpi (Figure 3E,F). For instance, MHC class I antigen proteins (A0A6B7FTP4, A0A455JVW4, and E2D5L6), whose expression continuously rose after the viral infection, were enriched in the HSV‐1 infection pathway. Moreover, we conducted GSEA for functional annotations based on proteomic data in 12 h post‐infection versus non‐infected HGFs (Figure 3G). In comparison to mock infection, prominent variations in protein expression were reflective of numerous pathways associated with inflammation, which included “complement and coagulation cascades” (normalized enrichment score (NES) = −2.15), “lysosome” (NES = −1.46), “cytokine‐cytokine receptor interaction” (NES = 1.79), “NLR signaling pathway” (NES = 1.97), “RLR signaling pathway” (NES = 1.73) and “TLR signaling pathway” (NES = 1.71) (Figure 3H–M; Table S2). Functional variations at other time points were presented in Figures S3 and S4. Consistent with these results, GO enrichment analysis demonstrated enhanced enrichment of DEPs in negative regulation of the immune system process and defense response at a late stage (Figure S5).

3.4. Stage‐Specific Clusters of Proteins in HSV‐1 Infected HGFs

To more intuitively present the changes in relative expression within the proteomics data, the mFuzz clustering method was employed to analyze 890 DEPs with time series characteristics. These proteins were organized into 10 significant discrete clusters (Figure 4A). After correlating their expression patterns with HSV‐1 infection times, DEPs in cluster_1, which includes 116 proteins such as IRF7, TLR2, IL7R, TNFSF10, guanylate‐binding protein 1 (GBP1), and CXCL10, demonstrated rapid and pronounced upregulation that continuously increased over time following virus infection. Moreover, most of these proteins were localized in the cytoplasm (Figure 4B).

FIGURE 4.

FIGURE 4

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Identification of specific clusters of proteins in HSV‐1 infected HGFs. (A) Illustration of relative expression changes to the proteomics data by clustering 890 DEPs using mFuzz into significant discrete clusters. (B) Subcellular location analysis of proteins in cluster_1. (C) KEGG enrichment analysis of proteins in cluster_1. (D) PPI networks of proteins in cluster_1 using the STRING database.

Pathway enrichment analyses of the 116 DEPs in cluster_1 revealed that 32 proteins were involved in signal transduction pathways. These included the NF‐kappa B signaling pathway with 11 DEPs, the TNF signaling pathway with 8 DEPs, the PI3K‐Akt signaling pathway with 6 DEPs, the MAPK signaling pathway with 4 DEPs, and the JAK–STAT signaling pathway with 3 DEPs. Additionally, 15 proteins were categorized under signaling molecules and interactions, encompassing the cytokine‐cytokine receptor interaction with 12 DEPs and viral protein interactions with cytokine and cytokine receptors with 8 DEPs (Figure 4C).

A PPI network was constructed to evaluate the interactions between proteins in cluster 1. TLR2, IL16, IL7R, TNFSF10, IFI44, and CXCL10 interacted with each other (Figure 4D). While at the initial stage of HSV‐1 infection, proteins in clusters 2 and 9 (such as MMP3 and IFI27) were remarkably downregulated, and subsequently upregulated with time and held steady at the later stage. Clusters 7 and 8 (such as C3) depicted a strong and continuous rising trend, which became flat and held steady in the late period of the infection. The PPI networks of these clusters were constructed (Figures S6–S9).

3.5. HSV‐1‐Associated Host Antiviral Signaling Pathway Alterations in HGFs

The host antiviral immunity members, involving IFNs, TNFs, and ILs, can control immunocyte subpopulations to facilitate antiviral immune response to handle the intruder in virus‐infected host cells (Stefan et al. 2020). On the other hand, viruses developed various strategies against immunity response during invasion (Kong et al. 2020). To antagonize the signal transduction, HSV‐1 indirectly or directly targets the adaptors in the antiviral innate immunity, such as RLRs and TLRs signaling pathways. To explore this, we carried out the HSV‐1‐associated changes of DEPs at various infection stages using manually constructed pathway maps according to map05168 (HSV‐1 infection) in the KEGG database. The summarized variations in protein abundance are presented in a modified KEGG pathway representation (Figure 5; Figure S10).

FIGURE 5.

FIGURE 5

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HSV‐1‐associated host antiviral signaling pathway alterations in HGFs. The pathway modules were modified from the KEGG pathway maps, according to map05168 (HSV‐1 infection), and these were created with BioRender.com. The protein abundances were evaluated for significant elevation or depletion by Student's t‐test between two groups, and standard of |log2FoldChange| ≥ 2, as well as p‐value < 0.05. *p < 0.05, **p < 0.01, ***p < 0.001.

In the case of the RLR signaling pathway, RIG‐I and ISG15 exhibited an instant, significant increase in HGFs and maintained high expression levels after the HSV‐1 infection. Interestingly, the downstream adaptor protein mitochondrial antiviral signal proteins (MAVS), and IκB kinase (IKK) exhibited no significant variations during post‐viral infection, while the NF‐κB was upregulated. The HSV‐1 infection reduced TNF receptor‐associated factor‐2 (TRAF2) and TANK binding kinase‐1 (TBK1). Meanwhile, IRF7 exhibited a significant increase in HGFs after the HSV‐1 infection. However, the expression level was slightly decreased with the infection time. In the TLR signaling pathway, the present study revealed that the transmembrane protein TLR2 remarkably increased after HSV‐1 infection, and reached its peak at 36 h. The downstream protein myeloid differentiation factor 88 (MyD88) was significantly upregulated post‐infection until 48 h. In addition, IL6 remarkably increased after HSV‐1 infection, which remained at an elevated level for up to 72 h. Meanwhile, TRAF6 showed no significant change after the virus infection. In contrast, the downstream protein phosphatidylinositol‐3‐kinase (PI3K) of TLR2, as well as AKT in the PI3K‐AKT‐mammalian target of the rapamycin pathway, were remarkably decreased after the viral infection and then slightly increased at the late stage.

The IFNAR‐JAK–STAT and NLR signaling pathways play crucial roles in restricting the systemic spread of HSV‐1 through type‐I interferons (IFN‐I). Specifically, JAK1 and STAT2 exhibited a declining trend in expression following viral infection. In contrast, STAT1 gradually increased post‐infection, continuing up to 72 h. IRF9 showed a notable response pattern: an immediate and significant increase in expression in HGFs following HSV‐1 infection, a decrease until 24 h, and an increase at later stages. Intriguingly, the expression of the cytosolic DNA sensor, stimulator of interferon genes (STING), displayed a trend roughly opposite to that of IRF9. Expression of the cytosolic DNA sensor IFI16 rapidly decreased within the first 12 h post‐infection, surged significantly at 24 h, dipped again at 36 h, and then consistently increased until 72 h.

The analysis of the antigen processing signaling pathway revealed that TAP2 expression notably and continuously increased with infection time, indicating a strengthened host defense mechanism in response to HSV‐1 infection over time (Figure 5).

4. Discussion

HSV‐1 has existed for millennia in human beings, and the battle between the host and the virus has never ceased. The present study analyzed the association between HSV‐1 infection and specific host cell proteins and their functional pathways in HGFs. It provided information on the host's antiviral defense response to antagonize HSV‐1 infections in the HGFs from the periodontal environment.

At the early stage of HSV‐1 infection, significant upregulation was observed in proteins such as IRF7, ISG15, IL6, TLR2, TNFSF10, and IFIT. Concurrently, pathways including NF‐κB, NLR, TNF, RLR, and those directly related to HSV‐1 infection were prominently activated. This activation during the primary infection stages reflects the complex interplay of host‐virus interactions that elicit a robust host‐immune response to limit and restrict viral proliferation. However, HSV‐1 has evolved sophisticated mechanisms to evade and disrupt host immune surveillance. This capability enables the virus to bypass the initial immune defenses and establish and maintain latent infections, showcasing its potent immune evasion properties. The host's ability to recognize and respond to viral infections hinges on the expression of pathogen recognition receptors (PRRs). These receptors detect viral pathogen‐associated molecular patterns (PAMPs), triggering a cascade of immune responses. The activation of PRRs leads to the induction of IRF family members and NF‐κB, critical transcription factors that modulate the expression of IFN‐I. The production of IFN‐I, in turn, triggers the expression of ISGs, which play crucial roles in antiviral defense, further illustrating the dynamic and reciprocal nature of host‐pathogen interactions during HSV‐1 infection (Ma and He 2014; Full and Ensser 2019).

After engaging with HSV‐1 viral ligands, receptors for TLRs and other cytosolic conduct signals to downstream adaptor proteins and encourage immune responses. Sufficient evidence has indicated that TLRs, whose signal transduction relies on adaptor protein MyD88 (such as TLR 2 in this study) or toll‐IL‐1‐receptor domain‐containing adaptor inducing IFN‐beta (TRIF) (such as TLR3 in this study), act as PRRs to identify HSV‐1 PAMPs (Bowie and Unterholzner 2008). These TLRs can sense HSV‐1 and transduce signals through MyD88 and TRIF, activating the IRFs and NF‐κB (Jahanban‐Esfahlan et al. 2019). At present, TLR2, TLR3, TLR4, TLR7, and TLR9 are demonstrated to contribute to the identification of HSV‐1 and induce IFN‐I at the time of viral entry and replication (Liu et al. 2013; Villalba et al. 2012). Furthermore, TLR2 and TLR4 predominate in periodontal tissues (Hatakeyama et al. 2003; Cekici et al. 2014). We observed the significant activation of TLR2 in HGFs. In the case of HSV‐1, TLR2 identifies glycoproteins such as gB and gH/gL (Leoni et al. 2012; Cai et al. 2012). Upon HSV‐1 infection, TLR2 facilitated priming of IFN‐I production and induced pro‐inflammatory cytokines in neonatal mice's brains and microglial cells (Aravalli et al. 2005). These changes may lead to an overzealous neuroimmune response in the central nervous system, exacerbating the mortality caused by viral encephalitis. These facts prompt that TLR2 may induce inflammatory reactions and degenerate periodontal tissues after HSV‐1 infection. Santos and colleagues reported that TLR2 from gingival and periodontal ligament fibroblasts can regulate the secretion of IL‐6 and IL‐8, which contribute to periodontal pathogenesis (Morandini et al. 2013). Therefore, future investigations through knocking down TLR2 may better explore the participation of downstream molecules of the TLR pathway in HSV‐1 related HGFs immune‐inflammatory responses. A thorough analysis of the molecular processes that enable HGFs to fine‐tune their cytokine expression would help to clarify the unique characteristics and distinctions of each one in pathogenic processes affecting gingiva and periodontal ligament.

Regarding periodontitis pathogenesis, TNF‐α has multiple functions, including the recruitment of cells to inflammatory and tissue degeneration (Algate et al. 2016). In addition, TNF‐α enhances selectins expression in endothelial cells, which ultimately stimulates the secretion of chemokines and facilitates the recruitment of leukocytes to inflammatory tissues. On the other hand, TNF‐α upregulates the production of various biomolecules, including collagenases, IL‐1β, IL‐6, MMPs, and RANKL in gingival epithelial cells, which ultimately enhances the degradation of extracellular matrix and bone resorption (Fujihara et al. 2014), apoptosis in fibroblasts, and inhibition of osteoblast differentiation (Cekici et al. 2014; Meng et al. 2018). Furthermore, TNF‐α potentiates infiltration of P. gingivalis into human gingival epithelial cells (Kato et al. 2014), which may explain why co‐infection of HSV‐1 and periodontal pathogens accelerates periodontal tissue destruction.

Another crucial category of PRRs includes the RLRs, such as RIG‐I and MDA5, which act as cytoplasmic sensors detected by PAMPs during viral infection (Meylan et al. 2006). Their activated recruitment domains exhibit contact with MAVS, subsequently engaging with TNF receptor‐associated factor 3 (TRAF3) and recruiting IKK‐related kinases, such as IKKε and TBK1. IKKε and TBK1 have the ability to phosphorylate IRF3/7 (Fitzgerald et al. 2003; Kawai et al. 2005; Meylan et al. 2005; Seth et al. 2005). The phosphorylated and dimerized IRF3/7 can translocate into the nucleus to activate IFN‐α/β (Totura et al. 2015; Fitzgerald and Kagan 2020; Sun et al. 2010). These receptors rapidly increase expression immediately following HSV‐1 infection in HGFs and maintain elevated levels for 72 h. RLRs are adept at recognizing dsRNA intermediates produced during HSV‐1 replication, serving as nucleic acid ligands for RLRs to initiate the production of IFN‐I (Melchjorsen 2012; Melchjorsen et al. 2010).

Besides TLRs and RLRs, the PRRs have two other subfamilies (STING and NLRs), which contribute to identifying HSV‐1 through the host's innate immune reaction (Phelan et al. 2020; Johnson et al. 2013). Since HSV‐1 is a dsDNA virus, the host can recognize HSV‐1 through abundant DNA sensors and related proteins upstream of STING, such as IFI16 (Maruzuru et al. 2018), which displayed an ascent trend after an initial decline in this study. The IFI16 identifies HSV‐1 genomic DNA that leads to its acetylation, a necessary requirement for cytoplasmic reorganization and recruitment of STING to trigger NF‐κB and IRF3. After that, the induction of IL‐6 and IFN‐β confines viral replication and begins an inflammatory response (Ansari et al. 2015; Zheng 2018). We demonstrated the activation of the IFI16‐mediated DNA‐sensing pathway with HSV‐1 infection. Notably, Chandran and colleagues have pointed out that IFI16 mediated IFN‐β induction at the initial stage of HSV‐1 infection. During the post‐infection stage, viral proteins could target IFI16 for prompt degradation based on the E3 ubiquitin ligase activity (Johnson et al. 2013). This is consistent with the IFI16 expression variations observed in the present study.

Further research is required to fully understand the countermeasures that HSV‐1 adapts to escape IFI16‐mediated antiviral responses. Other periodontitis‐related chemokines, such as CCL2, CXCL1, and CXCL8, were continuously upregulated upon HSV‐1 infection. CXCL8 promotes chemotaxis through neutrophils and lymphocyte migration to the infection site and accelerates phagocytosis in pathological processes during the development of periodontal lesions (Plemmenos et al. 2021). Cytokines and chemokines are messages between cells. Together, chemotactic cytokines effectively contribute to bone remodeling by driving osteoblast migration and activation in the pathological mechanism of periodontitis. In addition, viruses' etiologic significance in periodontal disorders is controversial (Emecen‐Huja et al. 2020). The correlation between viral particles and periodontal diseases may only indicate an elevated concentration of blood and virus‐infected cells. Further evidence of active viral infection, including elevated levels of viral particles and/or the identification of cells synthesizing viral ribonucleic acid or protein, would substantiate the involvement of viruses in the etiology of periodontal disorders. Herpesviruses‐associated periodontitis may mark a turning point in the research and practice of periodontology, offering a new approach to managing periodontitis and possibly related medical conditions. Using such proteomic techniques will considerably increase our knowledge of the remodeling of the cellular proteome under various situations relevant to HSV‐1 infection. However, since herpesvirus‐bacteria interaction is implicated in the onset of rapidly advancing periodontitis (Slots and Slots 2019), we did not consider the bacterial biofilm in this study. Several mechanisms have been described for a pathogenic viral‐bacterial synergy that could negatively affect the periodontium (Teles et al. 2022). HSV‐1 can potentially promote the pathogenicity and virulence of bacteria. Severe periodontitis may result from the synergistic cooperation of active herpesviruses and bacterial infections (Chen et al. 2020). HSV‐1 damage to epithelial cells can disclose bacterial attachment sites on the basement membrane. Moreover, P. gingivalis and other periodontal bacteria generate butyric acid and other short‐chain fatty acid metabolic end products, which can activate latent herpesvirus (Makino et al. 2018). Further research is required to determine how herpesvirus‐bacteria interaction (such as P. gingivalis and HSV‐1) precludes the development of severe periodontitis. The clinical impact and subsequent consequences of turning a latent herpesvirus infection into an active one provide an intriguing new area of periodontal research.

5. Conclusions

This research has provided crucial insights into the intricate interactions between cellular processes and host factors at the molecular level, which govern the spatiotemporal regulation of the host's immune defense against HSV‐1 infection in periodontal cells. These findings are instrumental in identifying potential immune‐therapeutic strategies for managing HSV‐1‐associated periodontitis. Further research is required to determine how the herpesvirus‐bacteria interaction precludes the development of periodontitis.

Author Contributions

Yu Zhang: conceptualization, investigation, funding acquisition, writing – original draft, writing – review and editing, visualization, methodology, software, formal analysis, project administration, data curation. Ka‐lam Lo: writing – original draft, writing – review and editing, visualization, formal analysis, software, data curation. Chun‐mei Wang: validation, formal analysis, investigation, data curation, writing – original draft, writing – review and editing. Xi‐ping Feng: methodology, data curation, writing – review and editing, writing – original draft. Jing Ni: resources, supervision, writing – original draft, writing – review and editing, conceptualization. Xi Chen: resources, supervision, writing – review and editing, writing – original draft, conceptualization.

Funding

The study was funded by the Shanghai Municipal Health Commission (Grant number: 20234Y0103) and the Shanghai Ninth People's Hospital Physicians in Clinical Research Training Program (Grant number: 2022hbyjxys‐zy).

Ethics Statement

The institutional ethical committee of the Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China, approved the research protocol (Ref No: SH9H‐2019‐T76‐2), conducted per the Declaration of Helsinki guidelines.

Consent

Informed consent was obtained from all individual participants included in the study.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: odi70167‐sup‐0001‐DataS1.docx.

ODI-32-1399-s001.docx (8.5MB, docx)

Acknowledgments

We are grateful to all the participants in the study.

Contributor Information

Jing Ni, Email: natalie1229_9@126.com.

Xi Chen, Email: chenxi9h@126.com.

Data Availability Statement

All relevant data is presented in the article and the Supporting Information. The datasets generated during the current study are available in the ProteomeXchange Consortium via the PRIDE (Perez‐Riverol et al. 2022; Deutsch et al. 2023) partner repository at http://www.ebi.ac.uk/pride, dataset identifier: PXD053087.

References

  1. Algate, K. , Haynes D. R., Bartold P. M., Crotti T. N., and Cantley M. D.. 2016. “The Effects of Tumour Necrosis Factor‐Alpha on Bone Cells Involved in Periodontal Alveolar Bone Loss; Osteoclasts, Osteoblasts and Osteocytes.” Journal of Periodontal Research 51: 549–566. [DOI] [PubMed] [Google Scholar]
  2. Ansari, M. A. , Dutta S., Veettil M. V., et al. 2015. “Herpesvirus Genome Recognition Induced Acetylation of Nuclear IFI16 Is Essential for Its Cytoplasmic Translocation, Inflammasome and IFN‐Beta Responses.” PLoS Pathogens 11: e1005019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aravalli, R. N. , Hu S., Rowen T. N., Palmquist J. M., and Lokensgard J. R.. 2005. “Cutting Edge: TLR2‐Mediated Proinflammatory Cytokine and Chemokine Production by Microglial Cells in Response to Herpes Simplex Virus.” Journal of Immunology 175: 4189–4193. [DOI] [PubMed] [Google Scholar]
  4. Arduino, P. G. , Cabras M., Lodi G., and Petti S.. 2022. “Herpes Simplex Virus Type 1 in Subgingival Plaque and Periodontal Diseases. Meta‐Analysis of Observational Studies.” Journal of Periodontal Research 57: 256–268. [DOI] [PubMed] [Google Scholar]
  5. Barrozo, E. R. , Racusin D. A., Jochum M. D., et al. 2025. “Discrete Placental Gene Expression Signatures Accompany Diabetic Disease Classifications During Pregnancy.” American Journal of Obstetrics and Gynecology 232, no. 3: 326.e1–326.e15. [DOI] [PubMed] [Google Scholar]
  6. Bowie, A. G. , and Unterholzner L.. 2008. “Viral Evasion and Subversion of Pattern‐Recognition Receptor Signalling.” Nature Reviews. Immunology 8: 911–922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bruderer, R. , Bernhardt O. M., Gandhi T., and Reiter L.. 2016. “High‐Precision iRT Prediction in the Targeted Analysis of Data‐Independent Acquisition and Its Impact on Identification and Quantitation.” Proteomics 16: 2246–2256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cai, M. S. , Li M. L., and Zheng C. F.. 2012. “Herpesviral Infection and Toll‐Like Receptor 2.” Protein & Cell 3: 590–601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cekici, A. , Kantarci A., Hasturk H., and Van Dyke T. E.. 2014. “Inflammatory and Immune Pathways in the Pathogenesis of Periodontal Disease.” Periodontology 2000 64: 57–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chen, C. , Feng P., and Slots J.. 2020. “Herpesvirus‐Bacteria Synergistic Interaction in Periodontitis.” Periodontology 2000 82: 42–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Denes, C. E. , Everett R. D., and Diefenbach R. J.. 2020. “Tour de Herpes: Cycling Through the Life and Biology of HSV‐1.” Methods in Molecular Biology 2060: 1–30. [DOI] [PubMed] [Google Scholar]
  12. Deutsch, E. W. , Bandeira N., Perez‐Riverol Y., et al. 2023. “The ProteomeXchange Consortium at 10 Years: 2023 Update.” Nucleic Acids Research 51: D1539–D1548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Emecen‐Huja, P. , Danaher R. J., Dawson D. R. 3rd, et al. 2020. “Relationship Between Herpesviruses and Periodontal Disease Progression.” Journal of Clinical Periodontology 47: 442–450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fitzgerald, K. A. , and Kagan J. C.. 2020. “Toll‐Like Receptors and the Control of Immunity.” Cell 180: 1044–1066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fitzgerald, K. A. , McWhirter S. M., Faia K. L., et al. 2003. “IKKepsilon and TBK1 Are Essential Components of the IRF3 Signaling Pathway.” Nature Immunology 4: 491–496. [DOI] [PubMed] [Google Scholar]
  16. Fujihara, R. , Usui M., Yamamoto G., et al. 2014. “Tumor Necrosis Factor‐Alpha Enhances RANKL Expression in Gingival Epithelial Cells via Protein Kinase A Signaling.” Journal of Periodontal Research 49: 508–517. [DOI] [PubMed] [Google Scholar]
  17. Full, F. , and Ensser A.. 2019. “Early Nuclear Events After Herpesviral Infection.” Journal of Clinical Medicine 8: 1408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gibbs, D. C. , McCrary M. R., Moreno C. S., et al. 2025. “Epidermal Growth Factor Dampens Pro‐Inflammatory Gene Expression Induced by Interferon‐Gamma in Global Transcriptome Analysis of Keratinocytes.” BMC Genomics 26: 122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hatakeyama, J. , Tamai R., Sugiyama A., Akashi S., Sugawara S., and Takada H.. 2003. “Contrasting Responses of Human Gingival and Periodontal Ligament Fibroblasts to Bacterial Cell‐Surface Components Through the CD14/Toll‐Like Receptor System.” Oral Microbiology and Immunology 18: 14–23. [DOI] [PubMed] [Google Scholar]
  20. Jahanban‐Esfahlan, R. , Seidi K., Majidinia M., et al. 2019. “Toll‐Like Receptors as Novel Therapeutic Targets for Herpes Simplex Virus Infection.” Reviews in Medical Virology 29: e2048. [DOI] [PubMed] [Google Scholar]
  21. James, C. , Harfouche M., Welton N. J., et al. 2020. “Herpes Simplex Virus: Global Infection Prevalence and Incidence Estimates, 2016.” Bulletin of the World Health Organization 98: 315–329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Johnson, K. E. , Chikoti L., and Chandran B.. 2013. “Herpes Simplex Virus 1 Infection Induces Activation and Subsequent Inhibition of the IFI16 and NLRP3 Inflammasomes.” Journal of Virology 87: 5005–5018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kato, Y. , Hagiwara M., Ishihara Y., et al. 2014. “TNF‐Alpha Augmented Porphyromonas gingivalis Invasion in Human Gingival Epithelial Cells Through Rab5 and ICAM‐1.” BMC Microbiology 14: 229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kawai, T. , Takahashi K., Sato S., et al. 2005. “IPS‐1, an Adaptor Triggering RIG‐I‐ and Mda5‐Mediated Type I Interferon Induction.” Nature Immunology 6: 981–988. [DOI] [PubMed] [Google Scholar]
  25. Kong, N. , Shan T., Wang H., et al. 2020. “BST2 Suppresses Porcine Epidemic Diarrhea Virus Replication by Targeting and Degrading Virus Nucleocapsid Protein With Selective Autophagy.” Autophagy 16: 1737–1752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Leoni, V. , Gianni T., Salvioli S., and Campadelli‐Fiume G.. 2012. “Herpes Simplex Virus Glycoproteins gH/gL and gB Bind Toll‐Like Receptor 2, and Soluble gH/gL Is Sufficient to Activate NF‐kappaB.” Journal of Virology 86: 6555–6562. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Li, Y. , He Q., Guo H., et al. 2022. “MSSort‐DIA(XMBD): A Deep Learning Classification Tool of the Peptide Precursors Quantified by OpenSWATH.” Journal of Proteomics 259: 104542. [DOI] [PubMed] [Google Scholar]
  28. Liu, Z. , Guan Y., Sun X., et al. 2013. “HSV‐1 Activates NF‐kappaB in Mouse Astrocytes and Increases TNF‐Alpha and IL‐6 Expression via Toll‐Like Receptor 3.” Neurological Research 35: 755–762. [DOI] [PubMed] [Google Scholar]
  29. Ma, Y. , and He B.. 2014. “Recognition of Herpes Simplex Viruses: Toll‐Like Receptors and Beyond.” Journal of Molecular Biology 426: 1133–1147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Makino, K. , Takeichi O., Imai K., et al. 2018. “ Porphyromonas endodontalis Reactivates Latent Epstein‐Barr Virus.” International Endodontic Journal 51: 1410–1419. [DOI] [PubMed] [Google Scholar]
  31. Maruzuru, Y. , Ichinohe T., Sato R., et al. 2018. “Herpes Simplex Virus 1 VP22 Inhibits AIM2‐Dependent Inflammasome Activation to Enable Efficient Viral Replication.” Cell Host & Microbe 23: 254–265.e7. [DOI] [PubMed] [Google Scholar]
  32. Melchjorsen, J. 2012. “Sensing Herpes: More Than Toll.” Reviews in Medical Virology 22: 106–121. [DOI] [PubMed] [Google Scholar]
  33. Melchjorsen, J. , Rintahaka J., Soby S., et al. 2010. “Early Innate Recognition of Herpes Simplex Virus in Human Primary Macrophages Is Mediated via the MDA5/MAVS‐Dependent and MDA5/MAVS/RNA Polymerase III‐Independent Pathways.” Journal of Virology 84: 11350–11358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Meng, T. , Zhou Y., Li J., et al. 2018. “Azithromycin Promotes the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells After Stimulation With TNF‐Alpha.” Stem Cells International 2018: 7961962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Meylan, E. , Curran J., Hofmann K., et al. 2005. “Cardif Is an Adaptor Protein in the RIG‐I Antiviral Pathway and Is Targeted by Hepatitis C Virus.” Nature 437: 1167–1172. [DOI] [PubMed] [Google Scholar]
  36. Meylan, E. , Tschopp J., and Karin M.. 2006. “Intracellular Pattern Recognition Receptors in the Host Response.” Nature 442: 39–44. [DOI] [PubMed] [Google Scholar]
  37. Morandini, A. C. , Chaves Souza P. P., Ramos‐Junior E. S., et al. 2013. “Toll‐Like Receptor 2 Knockdown Modulates Interleukin (IL)‐6 and IL‐8 but Not Stromal Derived Factor‐1 (SDF‐1/CXCL12) in Human Periodontal Ligament and Gingival Fibroblasts.” Journal of Periodontology 84: 535–544. [DOI] [PubMed] [Google Scholar]
  38. Papapanou, P. N. , Sanz M., Buduneli N., et al. 2018. “Periodontitis: Consensus Report of Workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri‐Implant Diseases and Conditions.” Journal of Clinical Periodontology 45, no. Suppl 20: S162–S170. [DOI] [PubMed] [Google Scholar]
  39. Perez‐Riverol, Y. , Bai J., Bandla C., et al. 2022. “The PRIDE Database Resources in 2022: A Hub for Mass Spectrometry‐Based Proteomics Evidences.” Nucleic Acids Research 50: D543–D552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Phelan, T. , Little M. A., and Brady G.. 2020. “Targeting of the cGAS‐STING System by DNA Viruses.” Biochemical Pharmacology 174: 113831. [DOI] [PubMed] [Google Scholar]
  41. Picolo, M. , de Araujo Nobre M. A., Salvado F., and Barroso H.. 2023. “Association of Herpesvirus and Periodontitis: A Clinical and Laboratorial Case‐Control Study.” European Journal of Dentistry 17: 1300–1308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Plemmenos, G. , Evangeliou E., Polizogopoulos N., Chalazias A., Deligianni M., and Piperi C.. 2021. “Central Regulatory Role of Cytokines in Periodontitis and Targeting Options.” Current Medicinal Chemistry 28: 3032–3058. [DOI] [PubMed] [Google Scholar]
  43. Seth, R. B. , Sun L., Ea C. K., and Chen Z. J.. 2005. “Identification and Characterization of MAVS, a Mitochondrial Antiviral Signaling Protein That Activates NF‐kappaB and IRF 3.” Cell 122: 669–682. [DOI] [PubMed] [Google Scholar]
  44. Slots, J. 2015. “Periodontal Herpesviruses: Prevalence, Pathogenicity, Systemic Risk.” Periodontology 2000 69: 28–45. [DOI] [PubMed] [Google Scholar]
  45. Slots, J. 2017. “Periodontitis: Facts, Fallacies and the Future.” Periodontology 2000 75: 7–23. [DOI] [PubMed] [Google Scholar]
  46. Slots, J. , and Slots H.. 2019. “Periodontal Herpesvirus Morbidity and Treatment.” Periodontology 2000 79: 210–220. [DOI] [PubMed] [Google Scholar]
  47. Stefan, K. L. , Kim M. V., Iwasaki A., and Kasper D. L.. 2020. “Commensal Microbiota Modulation of Natural Resistance to Virus Infection.” Cell 183: 1312–1324.e10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Su, D. , Han L., Shi C., et al. 2024. “An Updated Review of HSV‐1 Infection‐Associated Diseases and Treatment, Vaccine Development, and Vector Therapy Application.” Virulence 15: 2425744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sun, L. , Liu S., and Chen Z. J.. 2010. “SnapShot: Pathways of Antiviral Innate Immunity.” Cell 140: 436–436.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Teles, F. , Collman R. G., Mominkhan D., and Wang Y.. 2022. “Viruses, Periodontitis, and Comorbidities.” Periodontology 2000 89: 190–206. [DOI] [PubMed] [Google Scholar]
  51. Tonetti, M. S. , Greenwell H., and Kornman K. S.. 2018. “Staging and Grading of Periodontitis: Framework and Proposal of a New Classification and Case Definition.” Journal of Periodontology 89, no. Suppl 1: S159–S172. [DOI] [PubMed] [Google Scholar]
  52. Totura, A. L. , Whitmore A., Agnihothram S., et al. 2015. “Toll‐Like Receptor 3 Signaling via TRIF Contributes to a Protective Innate Immune Response to Severe Acute Respiratory Syndrome Coronavirus Infection.” MBio 6: e00638‐15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Villalba, M. , Hott M., Martin C., et al. 2012. “Herpes Simplex Virus Type 1 Induces Simultaneous Activation of Toll‐Like Receptors 2 and 4 and Expression of the Endogenous Ligand Serum Amyloid A in Astrocytes.” Medical Microbiology and Immunology 201: 371–379. [DOI] [PubMed] [Google Scholar]
  54. Wisniewski, J. R. , Zougman A., Nagaraj N., and Mann M.. 2009. “Universal Sample Preparation Method for Proteome Analysis.” Nature Methods 6: 359–362. [DOI] [PubMed] [Google Scholar]
  55. Zakay‐Rones, Z. , Ehrlich J., Hochman N., and Levy R.. 1973. “The Sulcular Epithelium as a Reservoir for Herpes Simplex Virus in Man.” Journal of Periodontology 44: 779–781. [DOI] [PubMed] [Google Scholar]
  56. Zheng, C. 2018. “Evasion of Cytosolic DNA‐Stimulated Innate Immune Responses by Herpes Simplex Virus 1.” Journal of Virology 92: e00099‐17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhu, S. , and Viejo‐Borbolla A.. 2021. “Pathogenesis and Virulence of Herpes Simplex Virus.” Virulence 12: 2670–2702. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: odi70167‐sup‐0001‐DataS1.docx.

ODI-32-1399-s001.docx (8.5MB, docx)

Data Availability Statement

All relevant data is presented in the article and the Supporting Information. The datasets generated during the current study are available in the ProteomeXchange Consortium via the PRIDE (Perez‐Riverol et al. 2022; Deutsch et al. 2023) partner repository at http://www.ebi.ac.uk/pride, dataset identifier: PXD053087.

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