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BY 4.0 license Open Access Published by De Gruyter Open Access November 28, 2022

Identification of crucial salivary proteins/genes and pathways involved in pathogenesis of temporomandibular disorders

  • Ivan Talian EMAIL logo , Galina Laputková and Vladimíra Schwartzová
From the journal Open Chemistry

Abstract

Temporomandibular disorder (TMD) is a collective term for a group of conditions that lead to impairment of the function of the temporomandibular joint. The proteins/genes and signaling pathways associated with TMD are still poorly understood. The aim of this study was to identify key differentially expressed salivary proteins/genes (DEGs) associated with TMD progression using LC-MS/MS coupled with a bioinformatics approach. The protein–protein interaction network was obtained from the STRING database and the hub genes were identified using Cytoscape including cytoHubba and MCODE plug-ins. In addition, enrichment of gene ontology functions and the Reactome signaling pathway was performed. A total of 140 proteins/genes were differentially expressed. From cluster analysis, a set of 20 hub genes were significantly modulated: ALB, APOA1, B2M, C3, CAT, CLU, CTSD, ENO1, GSN, HBB, HP, HSPA8, LTF, LYZ, MMP9, S100A9, SERPINA1, TF, TPI1, and TXN. Two enriched signaling pathways, glycolysis and gluconeogenesis, and tryptophan signaling pathway involving the hub genes CAT, ENO1, and TPI1 have been identified. The rest of the hub genes were mainly enriched in the innate immune system and antimicrobial peptides signaling pathways. In summary, hub DEGs and the signaling pathways identified here have elucidated the molecular mechanisms of TMD pathogenesis.

1 Introduction

Temporomandibular disorder (TMD) is a cumulative term encompassing a group of conditions that cause impairment of the function of the temporomandibular joint (TMJ) and associated structures [1]. The overall incidence of TMD is about 31% in adults and 11% in children and adolescents [2].

A significant amount of research data has been collected in the field of TMD over the past decade [3,4,5]. To explain how biological, psychological, and social factors combine to predispose, perpetuate, or trigger painful TMD, although the exact pathophysiology remains unknown, several non-mutually exclusive mechanisms have been proposed. Studies on chronic pain and TMD may be further explored as potential diagnostic biomarkers or therapeutic targets suggest over neurological, endocrine, and inflammatory pathways. Some of these hypothetical mechanisms represent possible explanations for the development of painful and indolent comorbidities [5].

However, there is controversy over what should be defined as TMJ pathology given the multifactorial etiology and pathogenesis of TMD. Therefore, it is crucial to find and study the validated common biomarkers for prognosis and control of disease progression. Most previous studies on reported biomarkers of TMD have examined plasma or synovial fluid [6,7]. However, saliva offers a more accepted alternative to invasive blood testing or synovial fluid exploration because saliva sampling is non-invasive and causes less stress and discomfort to the patient. In addition, as a complex mixture of the secretions of the salivary glands with the blood plasma, saliva shares a large number of proteins with similar functions [8]. Clinical protein biomarkers are playing an increasingly important role in diagnosing different phases of TMD as they are extremely important for detecting symptoms in the early stages [9].

Simultaneously with sampling by non-invasive techniques, it is essential to use analytical methods based on instruments and techniques that allow information to be obtained reliably, quickly, and with relatively simple pre-treatment of the sample. Currently, proteomics coupled with advanced bioinformatics applications in clinical scientific research is widely used as essential tools in the field of molecular diagnostics to improve clinical estimates of prognosis and potential targets for intervention.

Currently, analysis by liquid chromatography with tandem mass spectrometry (LC-MS/MS) has proven its irreplaceable role in the quantitative detection of proteins, peptides, and their post-translational modifications [10,11].

To continue exploring the molecular mechanisms and pathogenesis of TMD [12], the present study aimed to investigate TMD using LC-MS/MS in conjunction with computational bioinformatics analysis of protein/gene expression to find central salivary proteins, which could serve as potential clinical markers of the disease. Differentially expressed proteins/genes (DEGs) between TMD and control samples from healthy subjects were identified. Functional enrichment and module analysis were performed in the protein–protein interaction network (PPI) using Cytoscape plug-ins. In addition, reactome enrichment analysis was used to identify signaling pathways potentially involved in the development of TMD.

2 Materials and methods

2.1 Reagents

Dithiothreitol (DTT), iodoacetamide (IAA), and urea were purchased from Bio-Rad (Bio-Rad Laboratories, Hercules, CA, USA). Formic acid (FA) and ammonium bicarbonate (AB) were purchased from AppliChem (AppliChem GmbH, Darmstadt, Germany), while acetonitrile (ACN) was purchased from Fluka (Honeywell Fluka, Charlotte, North Carolina, USA). Sequence-grade quality-modified trypsin was obtained from Promega (Promega, Madison, Wisconsin, USA). Microcon 0.5 mL centrifugal filter units, Molecular weight cutoff (MWCO) 30 kDa, were purchased from Merck (Merck, Millipore, Billerica, MA, USA). To inhibit the activity of salivary enzymes, a protease inhibitor cocktail (Roche, Basel, Switzerland) was used. For Coomassie dye-binding assays, the BioRad Quick Start Bradford Protein Assay (Bio-Rad Laboratories, Hercules, CA, USA) was used.

2.2 Patients, sampling, and handling procedures

All saliva samples were collected between January 1, 2021 and May 30, 2022 at the Stomatology and Maxillofacial Surgery Clinic, Louis Pasteur University Hospital, Košice, Slovakia. The study included 20 TMD patients (5 men, 15 women) aged between 21 and 75 years. The control group consisted of 20 healthy individuals (7 men and 13 women) aged between 25 and 64 years. Only the newly diagnosed TMD patients and control individuals without any medication were enrolled. Participants were asked to refrain from eating and oral hygiene procedures 2 h prior to saliva collection. To avoid interference from the circadian cycle, saliva samples were collected between 9 a.m. and 11 a.m.

Collection of the whole unstimulated saliva was performed as previously described by spitting into sterile 50 mL Falcon tubes kept on ice [12]. Approximately 3 mL of saliva was accumulated over 5–10 min. To inhibit the activity of salivary enzymes, the saliva was immediately treated with a protease inhibitor cocktail (1:20, v/v). The samples were then centrifuged at 12,000×g for 30 min at 4°C to remove bacteria, cell fragments, and food debris. The supernatant of each sample was aspirated and stored at −80°C until further use.

2.3 Salivary sample processing for LC-MS/MS analysis

The saliva sample was processed following the procedure reported in our previous study [13].

Microcon 0.5 mL MWCO 30 kDa centrifugal filter units were used for filter-added sample preparation. The filter was loaded with a sample of 50 μg salivary proteins and then centrifuged at 14,000×g for 10 min at 20°C until the top of the filter was dried.

Thereafter, the filter was washed with 200 μL of 8 mol L−1 urea in 25 mmol L−1 AB to remove low-weight material and then centrifuged at 14,000×g for 20 min at 20°C.

To reduce proteins, 200 μL of 50 mmol L−1 DTT in 8 mol L−1 urea and 25 mmol L−1 AB was added. The filter unit was incubated for 60 min at 37°C and then centrifuged at 14,000×g for 15 min at 20°C.

Alkylation of cysteine residues was performed by adding 100 μL of 50 mmol L−1 IAA in 8 mol L−1 urea and 25 mmol L−1 AB solution. After incubation in the dark for 45 min at 37°C, the sample was centrifuged at 14,000×g for 20 min at 20°C.

Thereafter, the filter was washed twice with 200 μL of 25 mmol L−1 AB followed by centrifugation at 14,000×g for 5 min each time at 20°C.

Proteolysis was performed by the addition of proteomic grade trypsin dissolved in 12.5 mmol L−1 AB at an enzyme-to-protein ratio of 1:30 (w/w). Proteins were digested overnight at 37°C in a thermoshaker. The digested peptides were collected in a clean centrifuge tube by centrifugation at 14,000×g for 15 min at 20°C, followed by two additional 200 μL washes with deionized water at 14,000×g for 5 min and 16 min at 20°C, and then vacuum-dried at 55°C. The peptides were resuspended in 50 μL of 3% (v/v) ACN containing 0.1% (v/v) aqueous FA. The samples were then homogenized for 5 min on a vortex, followed by 10 min in an ultrasonic bath at 100% ultrasonic amplitude.

2.4 LC-MS/MS analysis and protein identification

For LC-MS analysis, an AmaZon Speed ETD ion trap mass spectrometer (Bruker Daltonik, Germany) coupled to an Ultimate 3000 RSLC NCP system (Thermo Scientific, USA) was used; 2 µL of each sample was injected into an Acclaim PepMap 100 (Dionex, Thermo Scientific, USA) trap column, 100 µm × 2 cm, C18, 5 µm particles with water-ACN loading solvent at a ratio of 2:98, and v/v containing 0.1% FA at flow 8 µL min−1. The peptides were eluted and separated on a home-made capillary column 75 µm × 30 cm, packed with reverse phase C18, 3 µm particles (Magic C18 AQ, Michrom Bioresources, USA). Mobile phases consisted of 0.1% FA in water–ACN 98:2, v/v (A) and 0.1% FA in water-ACN 5:95, v/v (B) operated at a constant flow of 0.4 µL min−1. The gradient is shown in Table 1. Samples were measured in auto MS/MS mode, 10 precursors for 1 MS scan, and only 2+ and 3+ precursors were taken for fragmentation with active exclusion set to 0.5 min. The ICC target was set to 400,000 for MS and MS/MS scans; the maximum accumulation time was 0.050 s for MS and 0.1 s for MS/MS. The isolation window was set to 2.2 Da. The scan range was set to 300–1,300 Da. The search engine Mascot 2.4 (Matrix Science, UK) against the Swiss-Prot database was used to identify proteins. The parameters were determined as follows: taxonomy – Homo sapiens (human), variable modification – oxidation of methionine, fixed modification – carbamidomethylation of cysteine, MS tolerance – 0.6 Da, MS/MS tolerance – 0.6 Da, and the false discovery rate (FDR) threshold was set to 1%. All samples were analyzed in triplicate.

Table 1

Gradient profile used for peptide C18 separation

Time, min 0 5 83 85 100 108 123
%B 4 4 35 95 95 4 4

2.5 Data preprocessing and identification screening of DEGs

The MS data were preprocessed in ProlineStudio 2.1.2 (Profi Proteomics, France) to determine weighted spectral counts of identified proteins. These data were subsequently analyzed using Reactome analysis tools using the Camera algorithm [14] to identify DEGs by comparing the expression level of salivary proteins between TMD patients and healthy controls. The criteria for screening DEGs were defined with a cut-off value of p < 0.05 to identify an upregulated group and a downregulated group of proteins, respectively.

2.6 Gene ontology enrichment analysis

The UniProt mapping service (https://www.uniprot.org) was used to convert protein identifiers/accession numbers into gene identifiers. Downloaded matched genes were used for further analysis.

DEGs of salivary proteins identified by LC-MS/MS were subjected to hierarchical gene ontology (GO) overrepresentation tests using the BiNGO plug-in 3.0.5 [15] for Cytoscape 3.9.0 [16]. GO enrichment analysis for three categories of biological process, cell component, and molecular function was determined at a significance level of 0.05 (p-value < 0.05) using the Benjamini & Hochberg correction for multiple testing.

2.7 Protein–protein interaction network and module analysis

The STRING (Search Tool for the Retrieval of Interacting Genes) database of known and predicted PPIs implemented in Cytoscape 3.9.0 was used to generate the DEG network. PPIs from DEGs were selected with confidence (score) cut-off of >0.4. PPIs that were not in the STRING database were manually added to the network using the database https://thebiogrid.org/. Then, the network generated by the StringApp 1.7.0 [17,18] was loaded into Cytoscape to detect hub genes and significant modules using the cytoHubba 0.1 [19] and the MCODE 2.0.0 (Molecular Complex Detection) [20] plug-ins.

The 11 methods using the cytoHubba plug-in algorithms were used. Local-based methods included Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), Maximum Neighborhood Component (MNC), and Degree method (Deg) algorithms. The network was also analyzed using Global-based methods − Edge Percolated Component (EPC), BottleNeck (BN), EcCentricity (EC), Closeness (Clo), Radiality (Rad), Betweenness (BC), and Stress (Str) algorithms [19].

The MCODE plug-in was applied to search densely intraconnected regions – clusters – and the hub genes with a higher degree of connectivity in a PPI network complex. The criteria for selecting a cluster were as follows: degree cut-off = 2, node score cut-off = 0.2, max depth = 100, and k-score = 2.

2.8 Pathway enrichment analysis of common DEGs

The Reactome pathway database (http://www.reactome.org, release 79) [21] was used to perform enrichment analysis of DEGs to link information from large molecular data sets. The Reactome Camera workflow was adopted for quantitative pathway analysis. The MetScape plug-in (version 3.1.3, [22]) in the Cytoscape software was used for gene enrichment and cluster analysis of metabolic pathways.

3 Statements to methods

  1. Informed consent: All experiments were conducted in accordance with accepted ethical principles in medical research involving humans, including the World Medical Association Declaration of Helsinki. Each participant provided a signed informed consent form.

  2. Ethical approval: The study protocol was approved by the Ethics Committee of the Louis Pasteur University Hospital in Košice (Approval 2020/EK/06046).

4 Results

4.1 Identification of common up- and downregulated genes/proteins

Based on the in silico analysis by the Reactome analysis tool, a total of 140 genes were identified as DEGs, 91 of which were upregulated and 49 downregulated (Figure 1), and further analyzed. Table 2 shows the top ten upregulated genes/proteins and downregulated genes/proteins in the saliva of individuals with TMD compared to those in the control group.

Figure 1 
                  Volcano plot of differentially expressed proteins from samples from TMD patients and a healthy control group, visualizing the fold-change (x-axis) and statistical significance (y-axis). The red line corresponds to p = 0.05 and the blue line to p = 0.01.
Figure 1

Volcano plot of differentially expressed proteins from samples from TMD patients and a healthy control group, visualizing the fold-change (x-axis) and statistical significance (y-axis). The red line corresponds to p = 0.05 and the blue line to p = 0.01.

Table 2

Top ten proteins with significantly altered expression in saliva of TMD patients compared to healthy controls according to p-values

Identifier logFC AveExpr T p-Value Adj. p-val B
Upregulated proteins
A1AT_HUMAN 2.0325112 9.664072 7.513445 0.0000000 0.0000000 16.4144301
PGAM1_HUMAN 1.1034553 9.478124 5.370085 0.0000004 0.0000127 6.2432597
ILEU_HUMAN 0.9102551 11.428888 5.081849 0.0000013 0.0000365 4.9936232
CAP1_HUMAN 1.0358009 9.708594 4.943604 0.0000024 0.0000555 4.4703278
HSP7C_HUMAN 0.7677266 8.782142 4.744561 0.0000055 0.0001107 3.5781056
KPYM_HUMAN 1.0000916 11.298734 4.709385 0.0000064 0.0001122 3.4885306
TPIS_HUMAN 0.9720454 11.199395 4.581905 0.0000109 0.0001522 2.9968058
PRDX6_HUMAN 0.8335418 8.646942 4.488802 0.0000159 0.0002021 2.5433321
ENOB_HUMAN 0.7513593 10.106310 4.333581 0.0000296 0.0003449 2.1035469
SPB3_HUMAN 0.9695915 10.522049 3.984580 0.0001133 0.0012201 0.8343879
Downregulated proteins
AMY1A_HUMAN –0.8871388 17.137421 −5.902904 0.0000000 0.0000021 8.1074059
LYSC_HUMAN –1.2123838 12.467418 −5.612727 0.0000001 0.0000056 7.1930027
GDIA_HUMAN –0.2883159 7.677396 −4.590963 0.0000105 0.0001522 2.4559452
PERM_HUMAN –1.2213234 10.365379 −3.891195 0.0001602 0.0014161 0.5201779
HPTR_HUMAN –0.2550567 7.478796 −3.888422 0.0001618 0.0014161 −0.2410816
HV313_HUMAN –0.2306359 7.578379 −3.576688 0.0004933 0.0034534 −1.2524444
HV311_HUMAN –0.2396691 7.535566 −3.375296 0.0009788 0.0048938 −1.9122721
ZA2G_HUMAN –0.2325559 14.749377 −3.298321 0.0012623 0.0060938 −1.8842779
TIMP1_HUMAN –0.6896319 8.640103 −3.216423 0.0016470 0.0072057 −1.7963964
IGHA2_HUMAN –0.2172223 7.676157 −3.051676 0.0027721 0.0110883 −2.8157079

4.2 GO identification by gene/protein set enrichment analysis

To understand the representation of gene and gene product attributes in three non-overlapping areas of molecular biology, a gene ontology (GO) analysis of DEGs in TMD patients and the healthy control group was performed. A series of enrichment analyzes were performed to obtain detailed information on biological process, molecular function, and cell components mapping GO terms within specific gene/protein sets using the BiNGO plug-in for Cytoscape. GO enrichment revealed that the downregulated genes were involved in 102 GO terms, including 54 terms related to biological processes, 46 terms related to molecular functions, and 2 terms related to cell components. The upregulated genes were involved in 204 GO terms, including 137 terms associated with biological processes, 48 terms associated with molecular functions, and 59 terms associated with cell components. The top terms of GO enrichment related to DEGs are presented in Figure 2 and Table 3. The upregulated DEGs were mainly associated with the extracellular space and cytoplasm (Figure 2 and Table 3), and the central molecular function of these genes was found to bind to proteins and regulate enzyme activity. Among the upregulated DEGs, numerous genes were implicated in catabolic processes involving simple carbohydrates. The downregulated DEGs, which are also located in the extracellular area, seemed to be particularly involved in the defense and immune response of the organism.

Figure 2 
                  Gene ontology analysis of DEGs of three ontologies: biological process, molecular function, and cell component. The x-axis represents the number of up/downregulated genes/proteins. The y-axis represents the GO term.
Figure 2

Gene ontology analysis of DEGs of three ontologies: biological process, molecular function, and cell component. The x-axis represents the number of up/downregulated genes/proteins. The y-axis represents the GO term.

Table 3

Go enrichment analysis of the top ten upregulated and downregulated GO terms according to their p-value between TMD patients and the healthy control group

GO ID GO identifier p-value Corrected p-value Genes
Upregulated cell component
5615 Extracellular space 3.49 × 10−13 6.67 × 10−11 A2M, ALB, AMY2A, APOA1, B2M, C3, CTSD, DEFA1, DEFA1B, FGA, FGB, FGG, GC, GPI, GSN, HPX, LGALS3BP, MIF, ORM1, PSAP, SERPINA1, SERPINB1, SFN
44421 Extracellular region part 1.07 × 10−11 1.02 × 10−9 A2M, ALB, ALDOA, AMY2A, APOA1, B2M, C3, CTSD, DEFA1, DEFA1B, FGA, FGB, FGG, GC, GPI, GSN, HPX, LGALS3BP, MIF, ORM1, PSAP, SERPINA1, SERPINB1, SFN
5737 Cytoplasm 2.32 × 10−10 1.48 × 10−8 A2M, ACTB, ACTC1, ACTN1, ACTN4, ALB, ALDOA, APOA1, ARHGDIB, B2M, CAP1, CAT, CFL1, CORO1A, CSTB, CTSD, DSG1, DSG3, ENO1, ENO3, EZR, FABP5, FGA, FGB, FGG, GAPDH, GDI2, GPI, GSN, GSTP1, HSPA1L, HSPA8, LCP1, LTA4H, MIF, MSN, P4HB, PFN1, PGAM1, PGD, PGK1, PKM, PLS1, PPIA, PRDX6, PSAP, PYGL, SERPINA1, SERPINB1, SERPINB3, SFN, SPRR1A, SPRR1B, SPRR3, TALDO1, TF, TPI1, TXN, YWHAZ
5576 Extracellular region 1.43 × 10−9 6.84 × 10−8 A2M, ALB, ALDOA, AMY2A, APOA1, B2M, C3, CTSD, DEFA1, DEFA1B, FGA, FGB, FGG, GC, GPI, GSN, HP, HPX, LGALS3BP, MIF, MUC5B, ORM1, P4HB, PPIA, PSAP, SERPINA1, SERPINB1, SFN, TF, TXN,
31093 Platelet alpha granule lumen 7.86 × 10−9 2.99 × 10−7 A2M, ALB, FGA, FGB, FGG, SERPINA1
60205 Cytoplasmic membrane-bounded vesicle lumen 9.40 × 10−9 2.99 × 10−7 A2M, ALB, FGA, FGB, FGG, SERPINA1
30863 Cortical cytoskeleton 1.32 × 10−8 3.16 × 10−7 ACTB, ACTN4, CAP1, CFL1, CORO1A, EZR
31983 Vesicle lumen 1.32 × 10−8 3.16 × 10−7 A2M, ALB, FGA, FGB, FGG, SERPINA1
31988 Membrane-bounded vesicle 1.82 × 10−8 3.86 × 10−7 A2M, ALB, ALDOA, APOA1, ARHGDIB, CORO1A, CTSD, FGA, FGB, FGG, HSPA8, P4HB, PRDX6, SERPINA1, TF, YWHAZ
15629 Actin cytoskeleton 2.57 × 10−8 4.91 × 10−7 ACTC1, ACTN1, ACTN4, ALDOA, CAP1, CFL1, CORO1A, EZR, GSN, LCP1, PFN1
Upregulated molecular function
3779 Actin binding 2.25 × 10−9 5.78 × 10−7 ACTN1, ACTN4, ALDOA, CAP1, CFL1, CORO1A, EZR, GC, GSN, LCP1, MSN, PFN1, PLS1
8092 Cytoskeletal protein binding 7.59 × 10−9 9.76 × 10−7 ACTB, ACTC1, ACTN1, ACTN4, ALDOA, CAP1, CFL1, CORO1A, EZR, GC, GSN, LCP1, MSN, PFN1, PLS1
5515 Protein binding 1.46 × 10−8 1.25 × 10−6 A2M, ACTB, ACTC1, ACTN1, ACTN4, ALB, ALDOA, APOA1, ARHGDIB, B2M, C3, CAP1, CAT, CFL1, CORO1A, CSTB, DSG1, ENO1, ENO3, EZR, FABP5, FGA, FGB, FGG, GAPDH, GC, GDI2, GPI, GSN, GSTP1, HP, HSPA8, LCP1, LGALS3BP, LTA4H, LYPD3, MIF, MSN, MUC5B, ORM1, P4HB, PFN1, PGAM1, PGD, PKM, PLS1, PPIA, PYGL, SERPINA1, SERPINB3, SFN, SPRR1A, SPRR1B, SPRR3, TAGLN2, TALDO1, TF, TPI1, TXN, YWHAZ
51015 Actin filament binding 2.35 × 10−6 1.51 × 10−4 ACTN4, CORO1A, EZR, LCP1, PLS1
30414 Peptidase inhibitor activity 6.95 × 10−6 3.57 × 10−4 A2M, C3, CSTB, MUC5B, SERPINA1, SERPINB1, SERPINB3
61134 Peptidase regulator activity 1.72 × 10−5 7.35 × 10−4 A2M, C3, CSTB, MUC5B, SERPINA1, SERPINB1, SERPINB3
16853 Isomerase activity 2.90 × 10−5 9.52 × 10−4 GPI, MIF, P4HB, PGAM1, PPIA, TPI1
16860 Intramolecular oxidoreductase activity 2.99 × 10−5 9.52 × 10−4 GPI, MIF, P4HB, TPI1
43498 Cell surface binding 3.65 × 10−5 9.52 × 10−4 FGA, FGB, FGG, MIF
43499 Eukaryotic cell surface binding 4.07 × 10−5 9.52 × 10−4 FGA, FGB, FGG
Upregulated biological process
6007 Glucose catabolic process 5.72 × 10−14 5.00 × 10−11 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, TALDO1, TPI1
16052 Carbohydrate catabolic process 1.01 × 10−13 5.00 × 10−11 ALDOA, AMY2A, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, PYGL, TALDO1, TPI1
44275 Cellular carbohydrate catabolic process 1.33 × 10−13 5.00 × 10−11 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, PYGL, TALDO1, TPI1
19320 Hexose catabolic process 3.61 × 10−13 1.02 × 10−10 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, TALDO1, TPI1
46365 Monosaccharide catabolic process 5.89 × 10−13 1.33 × 10−10 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, TALDO1, TPI1
46164 Alcohol catabolic process 3.36 × 10−12 6.32 × 10−10 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, TALDO1, TPI1
6096 Glycolysis 2.00 × 10−11 3.22 × 10−9 ALDOA, ENO1, ENO3, GAPDH, GPI, PGK1, PKM, TPI1
6006 Glucose metabolic process 9.40 × 10−11 1.33 × 10−8 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, PYGL, TALDO1, TPI1
19318 Hexose metabolic process 1.19 × 10−9 1.50 × 10−7 ALDOA, ENO1, ENO3, GAPDH, GPI, PGD, PGK1, PKM, PYGL, TALDO1, TPI1
7015 Actin filament organization 3.12 × 10−9 3.52 × 10−7 ACTC1, ACTN4, ALDOA, CFL1, CORO1A, EZR, GSN, LCP1
Downregulated cell component
5576 Extracellular region 4.84 × 10−13 4.46 × 10−11 AZGP1, CA6, CST3, DMBT1, HPR, JCHAIN, LCN1, LCN2, LPO, LTF, LYZ, MMP9, MPO, MUC7, PGLYRP1, PIGR, PIP, S100A8, S100A9, SLPI, TCN1, TIMP1
5833 Hemoglobin complex 1.62 × 10−6 7.45 × 10−5 HBA1, HBA2, HBB, HBD
Downregulated molecular function
5506 Iron ion binding 3.32 × 10−7 2.65 × 10−5 HBA1, HBA2, HBB, HBD, LCN2, LPO, LTF, MPO
46906 Tetrapyrrole binding 4.21 × 10−7 2.65 × 10−5 HBA1, HBA2, HBB, HBD, LPO, MPO, TCN1
5344 Oxygen transporter activity 3.00 × 10−6 1.26 × 10−4 HBA1, HBA2, HBB, HBD
20037 Heme binding 7.61 × 10−6 2.40 × 10−4 HBA1, HBA2, HBB, HBD, LPO, MPO
30492 Hemoglobin binding 2.99 × 10−5 7.52 × 10−4 HBB, HPR
48306 Calcium-dependent protein binding 5.06 × 10−5 1.06 × 10−3 DMBT1, S100A11, S100P
19825 Oxygen binding 1.15 × 10−4 2.07 × 10−3 HBA1, HBA2, HBB, HBD
4866 Endopeptidase inhibitor activity 3.20 × 10−4 4.59 × 10−3 CST3, LCN1, SLPI, TIMP1
61135 Endopeptidase regulator activity 3.28 × 10−4 4.59 × 10−3 CST3, LCN1, SLPI, TIMP1
4252 Serine-type endopeptidase activity 4.01 × 10−4 4.59 × 10−3 HPR, KLK1, LTF, PRTN3
Downregulated biological process
6952 Defense response 3.56 × 10−8 1.36 × 10−5 CST3, DMBT1, KRT1, LCN2, LTF, LYZ, MPO, PGLYRP1, S100A12, S100A8, S100A9
42742 Defense response to bacterium 5.23 × 10−6 9.99 × 10−4 DMBT1, LTF, LYZ, PGLYRP1, S100A12
2376 Immune system process 1.92 × 10−5 2.44 × 10−3 AZGP1, DMBT1, JCHAIN, KRT1, LCN2, LTF, MMP9, PGLYRP1, S100A9, TIMP1
15671 Oxygen transport 6.78 × 10−5 6.47 × 10−3 HBA1, HBA2, HBB
9617 Response to bacterium 1.27 × 10−4 9.68 × 10−3 DMBT1, LTF, LYZ, PGLYRP1, S100A12
6950 response to stress 1.84 × 10−4 1.17 × 10−2 CST3, DMBT1, KRT1, LCN2, LPO, LTF, LYZ, MPO, PGLYRP1, S100A12, S100A8, S100A9
15669 Gas transport 2.47 × 10−4 1.35 × 10−2 HBA1, HBA2, HBB
6955 Immune response 2.91 × 10−4 1.39 × 10−2 AZGP1, DMBT1, JCHAIN, KRT1, LCN2, LTF, PGLYRP1
45087 Innate immune response 4.31 × 10−4 1.78 × 10−2 DMBT1, KRT1, LCN2, PGLYRP1
6954 Inflammatory response 5.32 × 10−4 1.78 × 10−2 KRT1, LYZ, S100A12, S100A8, S100A9
41 Transition metal ion transport 5.38 × 10−4 1.78 × 10−2 LCN2, LTF, TCN1

4.3 Protein–protein interaction network and network cluster analysis

A PPI was constructed based on 140 identified DEGs with combined scores greater than 0.4. The network formed from the corresponding 142 genes and 2333 edges is shown in Figure 3. Topological metrics of the network is summarized in Table 4.

Figure 3 
                  Identification of DEGs and construction of the TMD-related PPI. According to the node fill color mapping, the red color denotes the upregulated genes, and the green color stands for the downregulated genes; edges represented by lines are gray. The color intensity corresponds to the fold-change.
Figure 3

Identification of DEGs and construction of the TMD-related PPI. According to the node fill color mapping, the red color denotes the upregulated genes, and the green color stands for the downregulated genes; edges represented by lines are gray. The color intensity corresponds to the fold-change.

Table 4

Topological parameters of the network

Topological parameters Values
Number of nodes 142
Number of edges 2333
Clustering coefficient 0.601
Characteristic path length 1.933
Network diameter 4
Network density 0.250
Average number of neighbors 34.0

The complex network topology was examined with the cytoHubba plug-in integrated into Cytoscape3.9.0. The highly interconnected hub genes have been filtered out of the main network of DEGs. The network was analyzed with each of the 11 algorithms embodied in cytoHubba. Then, 22 genes found in the intersection of at least four methods were sequentially ordered as follows: ACTB, ALB, APOA1, B2M, CAT, CLU, CTSD, ENO1, GAPDH, GSN, HBB, HP, HSPA8, LYZ, MMP9, S100A9, SERPINA1, TF, TXN, C3, LTF, and TPI1 (Table 5).

Table 5

22 Major hub genes (in red) identified by different algorithms of the cytoHubba plug-in

MCN DMNC MNC Degree EPC BottleNeck EcCentricity Closeness Radiality Betweenness Stress
ACTB CST3 ACTB ACTB ACTB A2M ACTC1 ACTB ACTB ACTB ACTB
ALB ENO3 ALB ALB ALB ACTB ALB ALB ALB ALB ALB
ALDOA FGA APOA1 APOA1 ALDOA ACTC1 B2M APOA1 APOA1 AZGP1 AZGP1
CAT FGG B2M B2M APOA1 ALB CLU B2M B2M B2M B2M
CFL1 GC CAT CAT B2M B2M FGA CAT C3 C3 C3
ENO1 GPI CLU CLU CAT C3 FGB CLU CAT CAT CAT
GAPDH GSTP1 CTSD CTSD CLU CAT GSTP1 CTSD CLU ENO1 ENO1
HSPA1A HPX ENO1 ENO1 CTSD ENO1 HBA1 ENO1 CTSD GAPDH GAPDH
HSPA5 HSPA1A GAPDH GAPDH ENO1 FABP5 HBD GAPDH ENO1 GSN GSN
HSPA8 HSPA1L GSN GSN GAPDH GAPDH KLK1 GSN GAPDH HBB HBB
LDHB HSPA5 HP HP GSN HBB LCP1 HBB GSN HSPA5 IGHA1
P4HB P4HB HSPA8 HSPA8 HBB HPR LDHA HP HBB IGHA1 IGHG2
PGAM1 PGAM1 LTF LTF HP IGHA1 LDHB HSPA8 HP IGHG2 IGHG4
PGK1 PGK1 LYZ LYZ HSPA8 IGHG2 LGALS3BP LTF HSPA8 IGHG4 IGHM
PKM PKM MMP9 MMP9 MMP9 IGHM PGK1 LYZ LTF IGHM IGHV3-23
PPIA PPIA S100A9 S100A9 PFN1 IGHV3-23 PPIA MMP9 MMP9 IGHV3-23 IGHV4-34
PRDX6 PRDX6 SERPINA1 SERPINA1 S100A9 KRT1 PSAP S100A9 S100A9 IGHV4-34 LYZ
TKT TALDO1 TF TF SERPINA1 LYZ S100A8 SERPINA1 SERPINA1 LYZ S100A9
TPI1 TKT TPI1 TPI1 TPI1 SLPI TXN TF TF TF TF
TXN YWHAZ TXN TXN TXN TXN YWHAZ TXN TXN TXN TXN

Figure 4 shows the network of key genes with high binding degree identified by the MNC algorithm of the cytoHubba plug-in in the set of DEGs.

Figure 4 
                  Module analysis of the protein-protein interaction by the cytoHubba plug-in. The PPI hub gene module of the top 20 genes ranked by MNC algorithm.
Figure 4

Module analysis of the protein-protein interaction by the cytoHubba plug-in. The PPI hub gene module of the top 20 genes ranked by MNC algorithm.

In addition to the previous clustering method, the MCODE plug-in in Cytoscape 3.9.0 was used to identify the most densely connected regions. The MCODE application enabled the identification of 10 intraconnected regions/clusters, and the nine hub genes – seeds, with high-level connectivity in a PPI network complex of DEGs (Figure 5 and Table 6).

Figure 5 
                  Top two cluster networks generated from the complex PPI network by the Molecular Complex Detection (MCODE) plug-in in Cytoscape. The nodes are represented by circles (green – unclustered, red – cluster 1, and pink – cluster 2) and edges as lines (gray).
Figure 5

Top two cluster networks generated from the complex PPI network by the Molecular Complex Detection (MCODE) plug-in in Cytoscape. The nodes are represented by circles (green – unclustered, red – cluster 1, and pink – cluster 2) and edges as lines (gray).

Table 6

Clusters generated from DEGs network using the Molecular Complex Detection (MCODE) plug-in in Cytoscape

Cluster Score (Density*#Nodes) Nodes Edges Node IDs
1 22.047 44 474 EZR, CLU, FGA, GDI2, FABP5, HBA1, FGB, ACTN1, MSN, MIF, FGG, SFN, S100A12, TIMP1, GSN, CTSD, HP, HBB, S100A8, AZGP1, MPO, HBA2, HPR, CAT, HSPA8, ACTC1, HSPA5, YWHAZ, HPX, *LYZ, MMP9, LCN2, ALB, PIGR, ORM1, PRTN3, SERPINB1, SERPINA1, ACTN4, ENO1, TF, SLPI, LGALS3BP, S100A11
2 19.2 31 289 ALDOA, GPI, HSPA1L, LTF, A2M, GSTP1, B2M, LDHA, CST3, CFL1, LDHB, PGK1, PGD, PGAM1, APOA1, GC, PPIA, TALDO1, TAGLN2, C3, PFN1, *HSPA1A, TXN, TKT, ENO3, P4HB, HSPA6, S100A9, PRDX6, PKM, TPI1
3 4.667 7 14 IGKV3-11, IGKC, *IGHV3-23, IGLV3-21, IGHV3-7, IGHG3, IGKV4-1
4 4 12 22 MUC5B, LCP1, HBD, DEFA1, ARHGDIB, *GAPDH, PIP, PGLYRP1, PSAP, ACTB, LCN1, PYGL
5 4 4 6 *CAP1, COTL1, PLS1, CORO1A
6 4 5 8 IGHA1, *IGKV3-20, IGHG4, IGHM, IGLV1-47
7 3.5 5 7 CA6, SERPINB3, *DMBT1, MUC7, AMY1A
8 3 3 3 BPIFB1, BPIFA2, BPIFB2
9 3 3 3 DSG3, *SPRR3, DSC2
10 3 3 3 *DSG1, SPRR1A, SPRR1B

*seed.

By the conjunction of the results of the two top-scoring modules of the MCODE clustering (cluster 1 and cluster 2) with the results of the cytoHubba analysis, a total of 20 hub genes including ALB, APOA1, B2M, C3, CAT, CLU, CTSD, ENO1, GSN, HBB, HP, HSPA8, LTF, LYZ, MMP9, S100A9, SERPINA1, TF, TPI1, and TXN were selected.

4.4 Pathway enrichment analysis

To map the signaling pathways of TMD, the associated genes of all down- and upregulated proteins were uploaded to the Reactome pathways database. As seen in Table 7 and Figure 6, the downregulated proteins were significantly enriched in the top five different signaling pathways including the R-HSA-6803157 antimicrobial peptides pathway, while the upregulated proteins were enriched in several different signaling pathways, including R-HSA-70171 glycolysis, R-HSA-70263 gluconeogenesis, and R-HSA-71387 metabolism of carbohydrates.

Table 7

Pathway enrichment analysis of top five significantly up- and downregulated Reactome signaling pathways according to their p-value between TMD patients and the healthy control group

Reactome pathway ID Name FDR p-value Number of genes Averaged fold change
Upregulated
R-HSA-70171 Glycolysis 0.03028 0.00019 9 0.74174
R-HSA-70326 Glucose metabolism 0.03028 0.00019 9 0.74174
R-HSA-71387 Metabolism of carbohydrates 0.03028 0.00022 13 0.64465
R-HSA-70263 Gluconeogenesis 0.05830 0.00077 8 0.70944
R-HSA-1430728 Metabolism 0.16152 0.00286 25 0.44195
R-HSA-199977 ER to golgi anterograde transport 0.29497 0.00848 1 2.03251
Downregulated
R-HSA-2168880 Scavenging of heme from plasma 0.03028 0.00033 26 –0.10295
R-HSA-2173782 Binding and uptake of ligands by scavenger receptors 0.03028 0.00033 26 –0.10295
R-HSA-6803157 Antimicrobial peptides 0.16152 0.00263 12 –0.24929
R-HSA-1222556 ROS and RNS production in phagocytes 0.45861 0.01522 2 –0.87347
R-HSA-8941413 Events associated with phagocytolytic activity of PMN cells 0.45861 0.01522 2 –0.87347
Figure 6 
                  Volcano plot of differentially expressed Reactome pathways from TMD patient samples and a healthy control group, visualizing the fold-change (x-axis) and statistical significance (y-axis). The red line corresponds to p = 0.05, and the blue line p = 0.01.
Figure 6

Volcano plot of differentially expressed Reactome pathways from TMD patient samples and a healthy control group, visualizing the fold-change (x-axis) and statistical significance (y-axis). The red line corresponds to p = 0.05, and the blue line p = 0.01.

From cluster analysis, we identified a set of 20 hub genes that were significantly modulated in TMD (Figure 4). These genes were further analyzed by MetScape. Two enriched metabolic pathways, glycolysis, and gluconeogenesis, and tryptophan metabolism pathways were identified, including the upregulated hub genes CAT, ENO1, and TPI1.

The rest of the hub genes were mainly enriched in the innate immune system and antimicrobial peptides taking into account the number of entities found. Among the signaling pathways, the p-value of the neutrophil degranulation pathway was the lowest mapped on 13 Reactome entities including upregulated B2M, C3, CTSD, GSN, HP, HSPA8, SERPINA1, TF and downregulated LTF, MMP9, S100A9, HBB, and LYZ.

5 Discussion

According to recent research, multiple proteins/peptides involved in the regulation of specific processes in the body are involved in the occurrence of TMD caused by various factors [23,24,25,26,27,28]. Several research articles have shown that proteomic analysis of TMD is associated with biomarkers that can be used clinically either as diagnostic or as prognostic indicators or as potential therapeutic targets [29,30]. Although numerous studies have been conducted on synovial fluid, plasma, synovial cells, or articular disc involved in TMD, proteomic techniques combined with network-based approaches can provide deeper insight and a promising framework for modeling and interpreting complex protein–protein interactions involved in a group of joint anatomical structures or the human organism as a complex system.

In our study, the up- and downregulated salivary proteins associated with TMD were analyzed using gene ontology enrichment analysis, PPI network construction, and identification of the essential protein from the core network by module analysis.

Based on GO enrichment analysis, upregulated DEGs have been implicated in various biological functions, the most highly represented terms in GO biological process being glucose catabolic process (GO:0006007) and carbohydrate catabolic process (GO:0016052). Simultaneously, the two major enriched Reactome pathways were found: glycolysis (R-HSA-70171) and glucose metabolism (R-HSA-70326) from upregulated DEGs. The priority of glucose metabolism was also confirmed using a MetScape analysis of 20 hub genes significantly modulated in TMD, which revealed several hub genes as part of the glycolysis and gluconeogenesis pathway in the metabolic pathway database.

Not only can TMD be caused by injury to the TMJs or surrounding tissues, but systemic inflammatory polyarthritis, osteoarthritis (OA), and arthritis are not uncommon when the TMJ is involved. Meta-analysis showed a higher prevalence of changes in the TMJ bone structures of RA patients, including erosion, flattening, sclerosis, and osteophytes, diagnosed by cone-beam computed tomography [31].

In arthritis, fibroblast-like synoviocytes, a non-immune cell type of the articular synovium, contribute to joint destruction by producing cytokines, chemokines, and matrix-degrading enzymes. Their intracellular metabolism and metabolic changes due to the inflammation-induced tissue response accompanied by changes in the concentrations of essential nutrients, including glucose, are intensively discussed. Upregulated glucose along with bioactive signaling molecules is likely involved in activating fibroblast-like synoviocytes and mediating immune responses and joint degradation [32]. If upregulated glucose metabolism in fibroblast-like synoviocytes can trigger inflammation and joint damage, inhibition of glycolysis can directly modulate synoviocyte-mediated inflammatory functions and reduce arthritis severity [33].

There is relatively new evidence for an altered metabolism toward glycolysis in synovial tissue cells, a hallmark of inflamed joints in patients with rheumatoid arthritis (RA). The observed metabolic pattern was similar in RA and OA in the acute stage of the disease, where acute inflammation predominates [34]. According to the study [35], T helper cells are key effector cells that dysregulate the glucose metabolism of synovial fibroblasts, driving them towards a glycolytic and pro-inflammatory phenotype in RA. Similarly, associations between dysregulated glucose metabolism in TMJ synovial fibroblasts and OA were investigated. It was pointed out that the expression level of the critical glycolytic enzyme lactate dehydrogenase A in synovial tissue and synovial fibroblasts of TMJ osteoarthritis was dramatically higher than in the reference group [36].

There is no doubt that glucose pathways control cartilage development, although the details of the process are not yet well understood. The authors of a systematic review of the role of glucose metabolism in cartilage development even suggest that dysregulated glucose metabolism is the driving force behind cartilage growth abnormalities [37]. The main aspects of TMJ OA include the death of chondrocytes responsible for maintaining and remodeling the structural and functional integrity of the cartilage extracellular matrix. Disruption of glucose transport and the glycolytic pathway is thought to affect chondrocytes derived from osteoarthritic cartilage since glucose is essential for the maintenance of chondrocyte metabolism and is a precursor to key matrix components. Alterations in metabolism include, for example, increased anaerobic glycolysis and molecular pathways associated with the regulation of bioactive lipids [38].

The oral/gut microbiome has recently emerged as an important factor in human health. Although the identification of significant bacterial growth in tissues (i.e., condylar head) of the TMJ of patients with advanced OA has failed [39], many clinical studies have suggested an association between the human oral microbiota with either RA or OA [40,41]. Oral bacteria can enter the bloodstream via the vasculature, either directly through relatively permeable epithelial pockets or as internalized particles of immune cells, and then migrate throughout the body [42,43,44]. The authors of the study [44] found that the oral microbiota in RA and OA has higher microbial diversity than in healthy subjects, with an increased frequency of Streptococcus and Haemophilus in the diseased subjects. Functional analysis revealed significantly upregulated lipopolysaccharide biosynthesis, lipopolysaccharide biosynthesis proteins, and glycolysis/gluconeogenesis KEGG pathways in patients with RA, OA compared to healthy subjects [45].

GO Analysis of biological process terms revealed that downregulated genes in TMD were involved in several processes, mainly related to defense response (GO:0006952), defense response to bacterium (GO:0042742), and immune system process (GO:0002376).

The functions of the innate immune system represent a primary defense strategy against joint inflammation triggered by a series of events leading to further and persistent joint damage [46]. Considering that the joint is a complex structure, interrelationships between synovial cells, macrophages, and other immune cells appear to control enzymatic activity in cartilage, which in turn acts on the synovial membrane, further stimulating degradation and destruction [47].

The hypothesis of the crucial role of synovial macrophages, which function as immune cells, in the symptomatology and structural progression of OA has been extensively discussed by the authors of a recent study [48]. Activated macrophages are regulated by mTOR, NF-κB, JNK, PI3K/Akt, and other signaling pathways. Simultaneously with autocrine signaling, paracrine communication between macrophages and chondrocytes leads to the secretion of inflammatory cytokines, growth factors, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases resulting in cartilage impairment. At the same time, the transition of the macrophage phenotype into the inflammatory joint environment can play a crucial role in the condition of the articular cartilage and the course of the disease [49].

In RA, disorders of the body’s immune system play a similar role, leading to the proliferation of articular synovial tissue, joint pannus formation, and cartilage destruction. The pathogenic mechanism of immune system disorders of RA bodies includes autoreactive CD4+ T cells, pathogenic B cells, M1 macrophages, inflammatory cytokines, chemokines, and an increase in autoantibodies [50]. Monocytes/macrophages and T cells are the two key cellular components of the joint synovium of RA patients. While monocytes/macrophages can stimulate the differentiation of T cells into inflammatory phenotypes, different T cell subtypes can induce the differentiation of osteoblasts and production of inflammatory cytokines [51].

Reactome pathways enriched in TMD, including scavenging of heme from plasma and binding and uptake of ligands by scavenger receptors, indicated an impaired phagocytic process. Furthermore, downregulated innate immune processes involved in antimicrobial peptides, ROS and RNS production in phagocytes, and events associated with phagocytolytic activity of PMN cells pathways support the concept that the host-immune response may be an important factor affecting joint inflammation and the progression of TMD. Recent evidence summarizes examples of beneficial ROS in immune homeostasis, infection, and acute inflammatory diseases and indirectly confirms that their decrease can result in the compromise of host protective immunity [52].

5.1 Upregulated hub proteins/genes involved in the hub protein network

Beta-2-microglobulin (B2M − protein-encoding gene) is an LMW protein (17 kDa) synthesized in all nucleated cells including immunocompetent cells such as macrophages, active T and B lymphocytes, and salivary gland epithelial cells [53]. Beta-2-microglobulin associates with the major histocompatibility complex I-related gene protein and forms the light chain subunit of the class l human leukocyte antigen on the surface of all nucleated cells [54].

It is well documented that the pathologically high serum concentration of beta-2-microglobulin, which leads to protein aggregation, has the amyloidogenic potential [55,56,57,58]. Amyloid deposition in skeletal joints, bones, and muscles leads to microarchitectural deterioration of bone tissue and movement impairment [57]. In addition, along with transferrin, beta-2-microglobulin is a known regulator of cartilage regeneration and differentiation. There is evidence that beta-2-microglobulin is highly expressed in cartilage and synovial fluid of patients with OA compared to control [59]. Since OA may be localized in the TMJ, elevated beta-2-microglobulin levels may also impair condylar chondrocyte function, which could contribute to TMD pathogenesis [60]. At the same time, beta-2-microglobulin is a recognized indicator of inflammatory disease activity [61]. The author of the study points to the association of immune system, inflammation, and metabolic deregulation with the onset symptoms of OA with elevated beta-2-microglobulin in chondrocytes. The positive correlations between serum and synovial fluid protein levels in RA patients have also been reported [62,63].

In addition, beta-2-microglobulin, including modified beta-2-microglobulin, is of significant importance in innate defenses with antimicrobial activity against a variety of microorganisms, including Streptococci [64]. Therefore, in the future, it would be interesting to investigate the connection between the occurrence of TMD and possible changes that have occurred in the microbial composition of the oral cavity or the microbiome as such.

Complement C3 (C3 − protein-encoding gene) is an essential protein of the immune system. This protein affects innate and adaptive immunity and plays a crucial role in immunosurveillance and homeostasis in the human body. It can cause the system to malfunction if it becomes dysregulated or over-activated, such as in several inflammatory diseases.

The complement system is believed to be actively involved in the pathogenesis of OA. It is produced and activated in the OA joint with the involvement of cartilage, bone, and synovium [65,66]. The complement system promotes inflammation of the joint tissue, which manifests itself in the degradation of the cartilage and the release of components into the synovial fluid [67]. Activation of complement C3 as part of the complement system can be triggered in cartilage cultured by interleukin-1 alpha, as the study authors have shown [68]. In addition, a mass spectrometry study of the synovial fluid showed that several other pro-inflammatory cytokines, namely, interleukin-6, interleukin-8, and interleukin-18, play a significant role in the etiology and progression of OA [69], which is present in 10–17% of patients with TMJ pain [70].

Cathepsin D (CTSD − protein-coding gene) is a lysosomal proteinase. Cathepsin D released from lysosomes is involved in the digestion of surrounding tissues and also plays a role in the processing of protein antigens for presentation to the immune system [71].

The mechanism of the degenerative processes in the joint triggered by catabolic enzymes has not yet been clarified. However, there is some work confirming the involvement of cathepsin D concomitantly with interleukin-1 in the modulation of terminal complement complex formation in cultured human disc tissue [72] or local changes within the osteoarthritic joint [73].

Gelsolin (GSN − protein-coding gene) is a calcium-regulated, actin-modulating protein that can promote assembly and disassembly of actin filaments. Gelsolin is involved in cell formation, metabolism, and wound healing processes. The protein has been found to be associated with joint homeostasis in various types of arthritis and has positive anti-inflammatory and chondroprotective effects [74,75].

In the proteomic analysis of mouse interleukin-1 alpha and retinoic acid, which stimulated cartilage degradation and protein release, the authors of the study identified gelsolin as one of the potential biomarkers for cartilage deterioration. A particularly novel finding was that the modified expression of gelsolin in cartilage leads to reduced concentrations of gelsolin in explant media [76].

Haptoglobin (HP − protein-coding gene) belongs to the family of acute-phase plasma proteins that play a role in modulating many aspects of the acute-phase response and also exhibit antibacterial activity. An increase in haptoglobin plasma levels has been found to be associated with carcinogenesis, infection, and chronic inflammation or autoimmune inflammatory rheumatic disease [77,78].

However, there is not much data correlating the risk of joint defects with haptoglobin expression in cartilage, synovial fluid, or plasma. One of the few studies states that porcine articular cartilage homogenates with increased haptoglobin concentrations have higher (p < 0.05) zymographic activities of the active form of MMP-2, but there is no association with degenerative processes in the cartilage [79].

HSPA8 protein (HSPA8 − protein-coding gene) is a cognate protein of the HSP70 family that is upregulated during cellular stress states such as inflammation, infection, and cancer [80]. HSPA8 plays a key role in the presentation of peptide antigens to CD4+ T cells, which regulates T- and B-cell activation and antibody secretion by plasma cells [81]. According to the researchers [82], HSPA8 is persistently expressed in the nucleus pulposus tissues of the human intervertebral disc and its expression correlates with the degree of disc degeneration. On the other hand, the upregulation of heat shock protein 70 may also contribute to the robustness and active matrix production of articular cartilage chondrons [83].

Using in-gel-based label-free ESI-LC-MS/MS analysis, bacteria-responsive host-defense proteins/pathways leading to inflammation were identified and compared between non-degenerated and degenerated discs. Stress-responsive and antioxidant proteins − HSPA8 protein, peroxiredoxins-1,2,6, catalase representing the host response to inflammaging – have been specifically expressed in degenerated discs [84]. HSPA8 has also been identified as one of the key hub genes correlating with RA onset and progression [85].

Alpha-1-antitrypsin (SERPINA1 − protein-coding gene) is a member of the serine protease inhibitor superfamily. A high transcript-level expression of SERPINA1 gene has been identified predominantly in superficial cartilage zones, as well as high levels of the alpha‐1‐antitrypsin protein have been found in synovial fluid [86,87].

Alpha-1-antitrypsin is an acute phase reactant that protects tissues from enzymes released during inflammation with highest affinity for neutrophil elastase [88,89]. Authors of the studies [74,90] also presented the properties of alpha‐1‐antitrypsin in the context of cartilage protection, joint inflammation, and manifestations of associated pain. In ex vivo analyzes of arthritic joints, they showed that alpha‐1‐antitrypsin promoted transcription of collagen alpha-1(II) chain, aggrecan core protein, and transcription factor SOX-9, as well as downregulated MMP-13, disintegrin, and metalloproteinase with thrombospondin motifs 5.

On the other hand, the concentration of alpha-1-antitrypsin activity inversely correlates with the levels of the residual neutrophil elastase − proteoglycan-degrading enzyme that has historically been associated with inflammatory arthritis [91]. Bioinformatic analyzes showed that OA cartilage exhibited intense alpha-1-antitrypsin staining in the most damaged areas of the cartilage due to the infiltration of greater amounts of alpha-1-antitrypsin from synovial fluid after degradation of the proteoglycan matrix [92].

Catalase (CAT − protein-coding gene) is a key antioxidant enzyme in the metabolism of H2O2 and reactive nitrogen species. The subcellular localization of catalase is mainly peroxisomal, but a cytosolic catalase can bind cytosolic proteins or be localized to the cytoplasmic membrane involved in the protection of important cellular elements (i.e., proteins and chromosomes) from oxidative damage [93].

Oxidative stress due to an imbalance between pro-oxidants and antioxidants can activate antioxidant defenses, leading to differential expression of some genes involved in inflammatory pathways [94]. The hypothesis that oxidative stress may be involved in the pathogenesis of joint diseases and lead to TMD was the subject of a recent authors’ study examining oxidative stress biomarkers in the saliva of TMD patients. Although they had significantly higher malondialdehyde levels, there were no significant differences in TMD salivary catalase levels compared to healthy controls [95]. Likewise, no correlation was observed between oxidative stress markers, including catalase, in the TMD subjects and the control group [96]. On the other hand, there was a decrease in catalase activity in the oral fluid of children [97].

Enolase 1 (ENO1 − protein-coding gene) is a multifunctional enzyme expressed in the cytoplasm of prokaryotic and eukaryotic cells and on the surface of several cell types, where it acts as a plasminogen receptor [98,99]. Enolase 1 mediates activation of plasmin, degradation of the extracellular matrix, and induces a specific humoral and cellular immune response [100]. The enzyme has been detected in the synovial fluid of inflamed joints [101] and in the serum [102] of RA patients. Enolase 1 stimulates the production of pro-inflammatory mediators from monocytes and macrophages isolated from RA patients via the p38-MAPK and NF-kappa-B signaling pathways [103,104].

Triosephosphate isomerase 1 (TPI1 − protein-coding gene) is a key enzyme in the glycolysis and gluconeogenesis signaling pathway that is found in almost all cell types. Furthermore, it has been shown that triosephosphate isomerase 1 is an RA-related metabolic regulator capable of catalyzing the interconversion of dihydroxyacetone phosphate and D-glyceraldehyde-3-phosphate and balancing glycolysis and gluconeogenesis. Overexpression of triosephosphate isomerase in macrophages from inflamed joints facilitates macrophage polarization to the pro-inflammatory M1 phenotype by promoting glycolysis [105].

The enzyme was also identified by SILAC and nLC-MS/MS as one of the chondrogenesis-regulated proteins in mesenchymal stromal cells of osteoarthritic human bone marrow [106].

The intercellular communication between chondrocytes and osteoblasts plays a new and crucial role in the metabolic homeostasis of the bone-cartilage unit. The experiments on a non-contact co-culture model show that osteoblasts trigger increased ATP generation in chondrocytes via an energetic shift represented by enhanced glycolysis and an impaired mitochondrial tricarboxylic acid cycle. Intensified glycolysis is also reflected in the upregulation of glycolytic enzymes, including triosephosphate isomerase 1 [107].

Thioredoxin (TXN − protein-coding gene) is a component of the thioredoxin system, which forms redox-dependent signaling pathways critical to fundamental cellular processes, including metabolism, the redox processes associated with oxidative stress, and the regulation of nitrosative stress [108].

Salivary oxidative stress profiles between patients with myogenic TMD and healthy subjects were analyzed to reveal associations between total antioxidant capacity and total oxidative status with clinical features of the disease. While total antioxidant capacity scores were significantly higher, total oxidative status scores decreased in myogenic TMD patients compared to those in controls [109]. The results confirmed data from the authors’ previous proteomic studies of the whole stimulated saliva from TMD myalgia patients. Statistical analysis of the quantitative proteomics data revealed 20 differentially regulated proteins, including the antioxidant proteins thioredoxin and S100-A8 [110].

The truncated form of thioredoxin is a potent mitogenic cytokine. Pro-inflammatory cytokines interleukin-1 beta and tumor necrosis factor or H2O2-induced increase in the truncated form of thioredoxin released from synoviocytes in RA patients may implicate a link between inflammation and the immune system in RA [111].

Albumin (ALB − protein-coding gene) Bovine serum albumin is widely used as an agent capable of inducing TMJ inflammation in model systems [112,113]. By comparing essential proteins in the serum of TMD patients and healthy controls, a standard blood sample analysis revealed that TMD patients had, among other things, significantly higher serum levels of albumin [114]. Similarly, a remarkable association of serum albumin with TMD was found in patients on chronic hemodialysis, in whom increased risk factors for the development of TMD were observed [115]. Furthermore, the analysis of the redox states of albumin in the serum and in the synovial fluid of patients with TMD showed that the oxidized fraction of albumin in the synovial fluid is expressed significantly higher than that of healthy subjects. On this basis, the authors of the study hypothesized that synovial albumin might play a scavenging role against intra-articular oxidative stress [116].

Apolipoprotein A-I (APOA1−protein-coding gene) is the major protein component of high-density lipoprotein in plasma [117]. Apolipoprotein A-I, possibly derived from adipose tissue, may be involved in the pathologic process within the joint [118]. Pro-inflammatory properties of apolipoprotein A-I have been confirmed in vitro in human joint cells isolated from cartilage and synovial membrane of OA patients. A dysregulated lipid profile in the synovial fluid of OA patients has been correlated with apolipoprotein A-I-triggered release of inflammatory parameters such as interleukin-6, MMP-1, and MMP-3 [119].

5.2 Downregulated proteins/genes involved in hub protein network

Clusterin (CLU − protein-coding gene) is present in all fluids of the organism except the intracellular matrix; it is also produced in the articular cartilage and synovium [120]. Numerous functions of clusterin include neuroprotection, cardioprotection, and pain modulation. The protein is also associated with inflammation [121]. While the intracellular form of clusterin can suppress stress-induced apoptosis, the secreted form of the protein has cytoprotective functions in osteoarticular tissues [122]. Suitable sources of the secreted form of clusterin are synovial and systemic fluids [123].

Serotransferrin (TF − protein-coding gene) is an iron-binding protein that primarily transports iron from storage sites to regions of iron metabolism. The protein belongs to the acute phase proteins with antimicrobial activity, capable of preventing the adhesion of gram-positive and gram-negative bacteria to surfaces. Transferrin levels in serum drop during, e.g., infection and inflammation [124].

As a traditional acute-phase protein, differentially expressed serotransferrin can indicate an elevated inflammatory status. Serum protein profiles in RA examined using two-dimensional differential gel electrophoresis and mass spectrometry identified several differentially expressed serum proteins between patients with RA and healthy controls. Among them, serotransferrin was significantly downregulated in RA patients [125]. Similarly, serotransferrin was downregulated in early OA sets compared to the control, as identified by a quantitative proteomic 18O labeling approach [126].

Lactotransferrin (LTF − protein-coding gene) is an iron-binding multifunctional cationic glycoprotein. As a key element of host defense, lactotransferrin maintains various biological functions as it is an anti-inflammatory, antibacterial, antiviral, antioxidant, antitumor, and immunomodulatory agent [127,128]. Numerous research data related to such functions of lactotransferrin as the modulation of regenerative processes in bone or cartilage [128]. Lactotransferrin has the potential to inhibit chondrocyte apoptosis, which is responsible for cartilage degeneration [129], and to regulate chondrocyte metabolism [130]. When released into the synovial fluid, lactotransferrin allegedly facilitates cartilage remodeling by promoting the degradation of cartilage material [131].

Matrix metalloproteinase-9 (MMP-9 − protein-coding gene)

MMPs form a family of calcium-dependent and zinc-dependent endopeptidases. Increased proteolytic activity of MMP, particularly MMP-9, induces degradation of organic matrix components of bone and erosion of cartilage and is closely associated with the inflammatory response [131]. Overexpressed MMP-7 and MMP-9, which are involved in extracellular matrix homeostasis and articular disc remodeling, were found in the synovial tissue of patients with anterior disc displacement of the TMJ without reduction using an immunohistochemical approach [131]. Recent advances in understanding tissue-degrading MMP-9 levels indicated that the upregulated enzyme can be found in the serum, synovial fluid, cartilage, and synovial and subchondral bone tissues of patients with OA or RA, in contrast to controls [7,132,133,134,135,136].

TMDs are defined as musculoskeletal conditions involving acute or chronic orofacial pain that can be coupled with inflammatory processes in the TMJ. The authors of the current study investigated how the production and activity of MMPs, including MMP-9, are regulated by the limbic system during persistent orofacial pain-induced temporomandibular inflammation [137]. Normally, MMPs are downregulated in the tissue by the four specific metalloproteinase inhibitors [131]. Interestingly, in our experiments, the MMP-9 expression found in the saliva of TMD patients was slightly reduced compared to healthy controls, but the level of metalloproteinase inhibitor 1 expression in the TMD saliva samples was also reduced.

Protein S100-A9 (S100A9 − protein-coding gene) is a calcium- and zinc-binding protein of the S100 protein family involved in various biological processes. Its differential expression is associated with both intra- and extracellular environments [138].

Protein S100-A9 is predominantly expressed by neutrophils, monocytes, and activated macrophages involved in pathological processes including inflammation. The actively released protein S100-A9 modulates the inflammatory response by stimulating leukocyte recruitment and inducing cytokine secretion [139,140]. Protein S100-A9 molecules are supposed to act as damage-associated molecular patterns; however, the mechanism of their action has not yet been fully elucidated [141].

Protein S100-A9 is known to affect joint integrity. Stimulation of osteoarthritic chondrocytes with protein S100-A9 (and S100-A8) induced significant upregulation of catabolic markers (MMP-1, MMP-3, MMP-9 and MMP-13, interleukin-6, interleukin-8, and monocyte chemoattractant protein-1) and downregulation of anabolic markers (aggrecan core protein and collagen alpha 1 type II), which can thus stimulate cartilage degradation [142]. Upregulated proteins of most S100 family proteins are closely related to RA pathogenesis [143] and are a marker of disease activity in RA and systemic juvenile idiopathic arthritis [144,145].

Nevertheless, in our experiments, all proteins from the S100 protein family including S100-A9 found in the saliva of TMD patients were downregulated compared to healthy control.

Hemoglobin subunit beta (HBB − protein-coding gene) is an essential component of the hemoglobin α2β2 heterotetramer, which is particularly known as the iron-containing protein essential for O2 transport in mammals [146]. Furthermore, the antimicrobial activity of a group of peptides produced by proteolytic cleavage of hemoglobin and acting as a second line of defense in enhancing the innate immune system of organisms has been observed [147]. As established by in silico analysis, the abundance of peptides derived from antimicrobial regions of hemoglobin subunit beta can contribute to an antimicrobial environment of infected wounds [148]. Human hemoglobin-derived peptides inhibit the growth of microbial invaders and reduce the biological activity of endotoxins through lipopolysaccharide binding [149].

A transcriptomic profile of the masticatory muscles of TMD patients with migraine compared to controls revealed a predominant downregulation of genes involved in the myogenetic process. Among them, a downregulation of hemoglobin subunit beta was registered [150]. The researchers concluded that the muscle regeneration process may be slowed down in TMD patients with migraine due to a decrease in muscle strength that occurs in the presence of anemia [151].

Lysozyme C (LYZ − protein-coding gene) is a 14 kDa protein expressed in mucosal secretions (tears, saliva, and mucus) and tissues. Lysozyme, whose natural substrate is the bacterial cell wall peptidoglycan, is an important component of innate immunity. Simultaneously with its direct antimicrobial role, lysozyme modulates the host’s immune response to infection [152]. At the same time, compared to the control group, a reduced activity of lysozyme and antimicrobial protection, as well as an increase in the degree of contamination with pathogenic and conditionally pathogenic microflora, was observed [97,153]. The downregulation of lysozyme in oral fluid suggests the possibility of a host immune response to infection rather than a direct effect on the joint [154].

Candidate biomarkers for the diagnosis of TMD were discovered and analyzed by quantitative proteomic analysis. A computational bioinformatic approach enabled the determination of multiple hub proteins/genes ALB, APOA1, B2M, C3, CAT, CLU, CTSD, ENO1, GSN, HBB, HP, HSPA8, LTF, LYZ, MMP9, S100A9, SERPINA1, TF, TPI1, and TXN from the set of differentially expressed proteins in TMD compared to healthy control. In addition, the construction of PPIs enabled a deeper understanding of the underlying molecular mechanisms of TMD and offered the opportunity to improve therapeutic strategies in TMD. Our analysis provided important information on signaling pathway changes involved in TMD progression, including upregulated glycolysis, gluconeogenesis, and metabolism of carbohydrates, as well as downregulated innate immune system and antimicrobial peptide signaling pathways. The results suggest that the identified hub proteins could be biomarkers for TMD after experiments were completed to determine the full extent of metabolic networks and colonization of oral bacteria in salivary secretion and synovial fluid in TMD.

Abbreviations

CAN

acetonitrile

AB

ammonium bicarbonate

DEG

differentially expressed gene

DTT

dithiothreitol

FA

formic acid

GO

gene ontology

IAA

iodoacetamide

LC-MS/MS

liquid chromatography with tandem mass spectrometry

MMP

matrix metalloproteinase

OA

osteoarthritis

PPI

protein–protein interaction network

RA

rheumatoid arthritis

TMD

temporomandibular disorder

TMJ

temporomandibular joint


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Acknowledgment

This work was supported by the French Proteomic Infrastructure (ProFI) ANR-10-INBS-08-03.

  1. Funding information: This research was conducted with support from the Scientific Grants Agency of the Slovak Republic under grant number 1/0196/20.

  2. Author contributions: I. T. − conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, software, supervision, validation, writing – review & editing. G. L. − conceptualization, formal analysis, investigation, methodology, visualization, writing – original draft, writing – review & editing. V. Sch. − conceptualization, resources

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-09-26
Revised: 2022-10-20
Accepted: 2022-11-03
Published Online: 2022-11-28

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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