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. 2024 Jan 25;9(5):5972–5984. doi: 10.1021/acsomega.3c09759

Combined Transcriptomic and Proteomic Profiling of the Mouse Anterior Cingulate Cortex Identifies Potential Therapeutic Targets for Pulpitis-Induced Pain

Xiaoning Kang 1, Jialin Si 1, Jing Zhang 1, Xia Yan 1, Zhuo Yu 1, Yaoyuan Zhang 1, Xinwei Li 1, Li-an Wu 1,*
PMCID: PMC10851247  PMID: 38343959

Abstract

graphic file with name ao3c09759_0009.jpg

Pulpitis is a common dental emergency that presents with intense pain; there is still no specific medicine to treat pulpitis-induced pain to date. Herein, differentially expressed genes in mouse anterior cingulate cortex (ACC) were investigated 7 days after pulp exposure via a combination of high-throughput transcriptomic and proteomic analyses. We screened 34 key genes associated with 8 critical pathways. Among these, genes (Elovl5, Ikbke, and Nbeal2) involved in immune or inflammatory responses exhibited exclusive regulation at the transcriptomic level, as confirmed by qRT-PCR. We also investigated the comprehensive expression profiles of genes (Erg1, Shank2, Bche, Serinf1, and Pax6) related to synaptic plasticity. Furthermore, the underlying mechanisms for pulpitis-induced pain through immune or inflammatory responses and synaptic plasticity were discussed. Taken together, our findings shed light on the mechanisms underlying pulpitis-induced pain, deepening our understanding of the molecular pathways and providing potential therapeutic and diagnostic targets.

Introduction

Pulpitis is characterized by acute and intense pain and often requires emergency dental care.1−4 Pulpitis often presents with pain without stimulation, frequently accompanied by neurological issues, such as sleeplessness, memory loss, and anxiety-like feelings that negatively impact patients’ quality of life.5−8 Currently, pulp chamber drainage, root canal therapy, and temporary drug relief methods are used to treat pulpitis.9 Despite significant scientific progress over the past decade, no rapid, specific, and effective pain medication has been developed for pulpitis-induced pain.10−12 Thus, it is imperative to investigate the underlying mechanisms to find effective treatments. The mechanism of pulpitis-induced pain has been largely investigated in the peripheral and trigeminal nucleus caudalis,3,13−16 with few reports on other central mechanisms. It has been reported that thalamic projections project upward to other brain regions such as the anterior cingulate cortex (ACC), the insular cortex, and the somatosensory cortex. Among them, ACC plays an important role in pain sensation and pain-related affective emotions. Neurons in ACC were activated in acute and chronic pain.17−19 However, whether ACC is involved in pulpitis-induced pain remains unknown.

Proteomics and transcriptomics represent high-throughput omic technologies that have been harnessed to explore new pathological mechanisms and potential therapeutic targets for many diseases.20−24 Specifically, a combination of transcriptomic and proteomic approaches25−29 can screen molecules that undergo significant changes at the transcriptional or protein level. This approach is not limited to individual genes or proteins and reflects their inter-regulatory relationships to a certain extent, providing systematic insights into the disease-related pathological changes.

In this study, we aimed to analyze changes in gene or protein expression in the ACC of pulpitis models by integrating high-throughput transcriptomics and proteomics. Our objective was to identify potential candidate molecules related to pain by screening differentially expressed molecules at mRNA and protein levels in ACC 7 days post pulpitis surgery. Finally, 8 key genes linked to immune inflammation and synaptic plasticity were identified through bioenrichment and qRT-PCR, potentially serving as biomarkers that offer insights into the mechanism of pulpitis-induced pain.

Materials and Methods

Experimental Animals and Study Design

All animal studies were approved by the Fourth Military Medical University Institutional Animal Care and Use Committee (SYXK2008-005). C57BL/6J mice (males, 8 weeks old) were housed at the Animal Research Center of Fourth Military Medical University under a controlled environment at 23 ± 2 °C, with 50–60% relative humidity, under cycles of 12 h light/12 h darkness. In addition to a standard pellet diet, the mice were provided with unlimited access to water.

Dental Pulp Exposure Procedure

We established the pulpitis model by exposing pulp.30,31 The mice were grouped into two groups. First, the mice were anesthetized with pentobarbital sodium (50 mg/kg) by intraperitoneal injection, positioned face up, and fixed to the operating board with adhesive tape. A custom-made elastic retractor was used to fix the tongue and incisor on the mice’s mouth, and the left maxillary first molars were drilled with a high-speed handpiece and Dia-Bur (with a #1/4 round tip). The pulp of the left maxillary first molar was exposed using a rotated dental burst without additional operations. Subsequently, they were returned to their cages. Anesthesia was administered to control (sham) mice before they were placed back in their respective cage.

Behavior Test

In the behavioral test, the withdrawal threshold (WT) of facial skin mechanical stimulation was performed with Von Frey filaments in two groups of mice (con [n = 8] and pulpitis [n = 8]) on days of −1, 1, 3, 5, 7, 9, 11, 13, and 15. The specifications and values represented by different Von Frey filaments are as follows: 1.65 (0.008 g), 2.36 (0.02 g), 2.44 (0.04 g), 2.83 (0.07 g), 3.22 (0.16 g), and 3.61 (0.4 g), respectively. Before assessing behavior, mice were placed in a porous metal mesh cage (5 × 5 × 12 cm) for about 30 min for the purpose of acclimatizing to the test environment. Each filament was applied 10 times with a pause of at least 5 s between the two applications. Mice were stimulated again by the next filament after a 3 min rest. The minimum filament value causing over 5 positive responses (rapid retraction and multiple head-shaking within 5 s in each stimulation) in 10 tests was taken as the pain threshold. Then, the responses of mouse facial skin to each Von Frey filament were scored.31

Dissection and Collection of ACC

Each mouse was anesthetized with pentobarbital sodium (ip, 50 mg/kg) and rapidly euthanized. Then, the ACC of the mouse was dissected depending on “Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates 4.0″, and the harvested tissues were stored in liquid nitrogen until processing.

Transcriptomic Analysis

Tissues were stored at −80 °C for batch RNA extractions to perform gene expression or RNA-Seq analysis. Total RNA was extracted from 6 samples. mRNA sequencing was conducted with a HiSeq2500 instrument at BGI-Shenzhen, China. Sequencing data were filtered with SOAPnuke (v1.5.2) by removing reads containing an appropriate sequencing adapter and a low-quality base ratio of more than 20%. After removing reads with an unknown base ratio of less than 5%, clean reads were obtained.32,33 The screening criteria for differentially expressed genes (DEGs) were |log2FC| > 0.263034 and P-value <0.05. All RNA-seq expression data were transformed to normal scores before analysis, unless otherwise stated. Rv4.0.0 software was employed for data analysis and figure generation. Pheat-map (V1.0.8) was used to generate heat maps according to the significant DEGs from all samples. The DEGs were analyzed for enrichment. Integrated gene IDs were used using the Visualization, Annotation, and Integrated Discovery Database version 6.8, while the RREVIGO resource was used to summarize and identify significantly enriched GO terms in the Gene Ontology database (https://www.geneontology.org/). It is widely acknowledged that genes often interact with each other and play roles in certain biological functions. Pathway-based analysis helps us to further understand the biological function of genes. KEGG is the major public pathway-related database (https://www.genome.jp/kegg/). Pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways in DEGs compared with the whole genome background. Then, the R package ggplot2 was applied to plot the fold change density of these gene sets. We displayed the genes of several key pathways through the network map by Cytoscape.

Proteomic Analysis

Protein Preparation

ACC samples were ground to a fine powder in liquid nitrogen and then lysed with lysis buffer (2 M thiourea, 7 M urea, 40 mM Tris–HCl, and 4% CHAPS, pH 8.5) containing 1 and 2 mM phenylmethylsulfonyl fluoride (final concentration). Five minutes later, 10 mM dithiothreitol was added to the samples. The supernatant was thoroughly mixed with a 5× volume of frozen acetone containing 10% (v/v) trichloroacetic acid and then incubated overnight at −20 °C. The supernatant was discarded after centrifugation. The suspension was sonicated and then centrifuged. The supernatant was transferred to another tube to reduce disulfide bonds in the supernatant protein, and 10 mM dithiothreitol (final concentration) was added and incubated. Cysteine was subsequently blocked by adding 55 mM iodoacetamide (final concentration) and incubated for 1 h in a dark chamber. Proteins were precipitated by mixing the supernatant. After centrifugation, the supernatant was discarded, dissolved, and sonicated. Finally, the samples were centrifuged at 30,000g for 15 min at 4 °C, and the supernatant was transferred to a new tube for quantification. Proteins in the supernatant were stored at −80 °C for further analysis.34

iTRAQ Labeling and SCX Fractionation

Total protein (100 μg) was extracted from each sample solution and then digested with Trypsin Gold (Promega, Madison, WI, USA) at a protein:trypsin ratio of 30:1. After trypsin digestion, the peptides were digested with trypsin and dried by vacuum centrifugation. Peptides were reconstituted in 0.5 M triethylammonium bicarbonate. iTRAQ labels were accorded with the number of samples. Isobarically labeled peptides were pooled and dried by vacuum centrifugation. The elution peak was monitored at a wavelength of 214 nm, one component was collected per minute, and samples were combined according to the chromatographic elution peak plot to obtain 20 fractions and then freeze-dried.34,35

LC-ESI-MS/MS Analysis Based on Triple TOF 5600

Dried peptide samples were reconstituted with mobile phase A (2% acetonitrile and 0.1% FA) and centrifuged at 20,000g for 10 min, and then the supernatant was injected. The separation was carried out using a Thermo UltiMate 3000 UHPLC system. The nanoliter liquid phase separation end was directly connected to the mass spectrometer. The peptides separated by liquid phase chromatography were ionized by a nanoESI source and then passed to a Tandem Q-Exactive HF X mass spectrometer (Thermo Fisher Scientific, San Jose, CA) for DDA mode detection. The ion screening conditions for MS2 fragmentation were as follows: charge 2+ to 6+ and top 20 parent ions with the peak intensity exceeding 10,000. The ion fragmentation mode was HCD, and the fragment ions were detected in Orbitrap. The AGC was set to MS1 3E6 and MS2 1E5. Data were acquired using a Triple TOF 5600 system (AB SCIEX, Concord, ON, USA) with a Nanospray IIIsource (AB SCIEX) and a pull-quartz tip as the transmitter (New Targets, Woburn, MA, USA). Data were analyzed with Proteome Discoverer 2.3. The Mascot search algorithm (version 2.6.2, Matrix Science) was used for searching against the UniProt database. Following quantitative analysis, MaxQuant (v 1.5.4.1) data were imported into the Perseus software suite. GO and KEGG enrichment analyses were similar to the patterns of the transcriptome. Then, the String database was used to analyze the protein network (https://cn.string-db.org/). Detailed information on the Mascot search algorithm setting and the type of test used is provided in Supporting Information File 2.

Integrated Analyses of Proteomic and Transcriptomic Data

We integrated the transcriptomic and proteomic data sets described above to screen differentially expressed entities at the mRNA and protein levels.36−39 To ensure rigorous selection, we only included differentially expressed molecules (control vs pulpitis; P-value <0.05) in all four data sets (transcriptomics and proteomics, 7 days after pulpitis surgery).

Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

Whole RNA was extracted from mouse ACC using TRIzol reagent (Thermo Fisher Scientific, USA), followed by cDNA synthesis with PrimeScript RT Master Mix (TaKaRa, Japan) at 37 °C for 15 min and 85 °C for 15 s, and stored at −20 °C. Quantitative PCR was performed using SYBR Green MasterMix on an ABI 480 detection system (Applied Biosystems, USA). The conditions for PCR were as follows: 95 °C for 60 s, 95 °C for 5 s, 60 °C for 20 s, and 95 °C for 15 s for 45 cycles. Relative mRNA levels were analyzed, and all results were normalized to β-actin. The fold change was determined using the 2–ΔΔCt method (ΔΔCt = (ΔCt of genes of interest) – (ΔCt of β-actin)).40,41 The primer sequences used are listed in Table 1.

Table 1. Sequences of the Primers Used for Quantitative Real-Time PCR.
gene species primer type 5′–3′ sequence
β-actin mouse forward 5′-GTCCCTCACCCTCCCAAAAG-3′
β-actin mouse reverse 5′-GCTGCCTCAACACCTCAACCC-3′
Elovl5 mouse forward 5′-TGCAGCTTGCTTCTGTTCCC-3′
Elovl5 mouse reverse 5′-TTTGACTCTTGTATCTCGGGGG-3′
Ikbke mouse forward 5′-CCACTTGGAGTGCAGGAAGA-3′
Ikbke mouse reverse 5′-GCTGGCTGAGTTGAAGACCT-3′
Nbeal2 mouse forward 5′-TTGCAGGAGGAAAGGCGAAT-3′
Nbeal2 mouse reverse 5′-CAGGGTACGCAGTTCTGGTC-3′
Egr1 mouse forward 5′-TGAGCACCTGACCACAGAGTC-3′
Egr1 mouse reverse 5′-TGAAAAGGGGTTCAGGCCAC-3′
Shank2 mouse forward 5′-TTGGCTGTGATGATGAGCGT-3′
Shank2 mouse reverse 5′-ATGGGGGTATCGGCTTTTGC-3′
Bche mouse forward 5′-GGCCAGAGCCAGATCAAGTT-3′
Bche mouse reverse 5′-GAGTCTGCATGTTGATTTCGGA-3′
Serpinf1 mouse forward 5′- CGATCTGTACCGCCTGAGAT-3′
Serpinf1 mouse reverse 5′-TCGATGTTCAGCTCCCAGAGA-3′
Pax6 mouse forward 5′-GCCACCAGACTCACCTGAC-3′
Pax6 mouse forward 5′-CACTCCGCTGTGACTGTTCT-3′
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Data Normalization and Statistics

All statistical analyses were performed using GraphPad Prism 9 (GraphPad, San Diego, CA). Repeated measure analysis of variance was used for statistical analysis at different time points between groups. Statistical significance was evaluated by Student’s t test between two groups for the expression of mRNA. The samples were distributed in a completely random order, and the overall sample size was 12 cases. Rank transformation was conducted for analysis to minimize the influence of outliers, which inevitably appear in large-scale data. We included only samples for which mRNA and protein data were available. All data were represented as the mean ± SEM. Differences were considered significant at *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Results

Establishment of the Pulpitis Model

The mouse pulpitis model was successfully established by pulp exposure, inducing pain-related behavior. The behavioral evaluation was performed 1 day before and 1, 3, 5, 7, 9, 11, 13, and 15 days after the operation (Figure 1A). The mechanical pain threshold was detected by using Von Frey filaments. We found that the pain threshold of the dental pulp fluctuated over time in the pulpitis group compared with the control group, specifically the significant different time points that began on day 1 and reached a nadir on day 7 (P < 0.05) (Figure 1B), which was the same as the results of our pulpitis model in rats.31

Figure 1.

Figure 1

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Profiles of the mouse pulpitis model. (A) Time course of the experiments. (B) Pain-related behavior of control and pulpitis groups. *P < 0.05 compared with the control group.

Pulpitis Pain-Related Changes in RNA-Seq Expression

The raw data were filtered, the sequencing error rate was checked, and the GC content distribution was verified to obtain clean reads for subsequent analysis. All mRNA underwent RNA sequencing to quantify the expression of 17,906 genes (Supporting Information File 1). When |log2FC| was greater than 0.263034, 1155 DEGs were identified based on a P-value <0.05, including 336 upregulated and 819 downregulated DEGs. A volcano plot of DEGs was generated based on screening parameters |log2FC| > 0.263034 and P-value <0.05, and genes with no significant changes were labeled as gray dots (Figure 2A). After normalizing the expression data, we generated a heat map from the DEG data (Figure 2B). GO and KEGG enrichment analyses were conducted, and the significance threshold was set to P-value <0.05.

Figure 2.

Figure 2

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Analysis of DEGs. (A) Volcano plots of DEGs. Screening parameters were |log2FC| > 0.263034 and P-value <0.05, 336 upregulated genes (red) and 819 downregulated genes (blue) were identified, and genes with no significant changes were labeled as gray dots. (B) The heat map showed the relationship between the samples and the differentially expressed RNAs. The differential expression in the control and pulpitis groups was clearly distinguishable. Red represents higher expression; blue represents lower expression. (C) GO enrichment analysis of DEGs and statistical plots of the significantly enriched pathways. (D) KEGG pathway analysis of DEGs. The count represents the number of DEGs. (E) Network plot of the relationship between the GO term and key pathways associated with pulpitis-induced pain.

As shown in Figure 2C, DEGs were significantly enriched for the following GO terms associated with biological processes, including the regulation of the MAPK cascade, immune response, metal ion transport, and learning or memory. In the meantime, it was worth noting that many DEGs were involved in glial cell differentiation, synaptic signaling, dopamine metabolic process and rhythmic process, and so on. DEGs were mainly accumulated in cells, cell parts, membranes, and protein-containing complexes when enriched into the cell component. At the same time, it also has some DEGs involved in vesicle and organelle fractions. Finally, GO terms significantly enriched in molecular function categories included binding and catalytic activity. Interestingly, we also determined that downregulated DEGs (e.g., Egr1, Per1, Per2, Rorb, Sik1, Cav1, Shank2, Shank3, Pax6, Serpinf1, Gfap, Bche, and Cyp2d22) in pulpitis were primarily involved in the immune system process and response to stimulus, while the upregulated DEGs (e.g., 5-HT1A, 5-HT4, Usp2, Rbm3, Hprt, and Pnkd) were predominantly involved in the cell part, membrane part, binding, transcription factor activity protein binding, and cell process. Subsequently, KEGG pathway enrichment analysis revealed that the DEGs were primarily associated with the PI3K-Akt signaling pathway, Rap1 signaling pathway, focal adhesion, phagosome, regulation of actin cytoskeleton, and ABC transporters (Figure 2D). Possible key signaling pathways were represented by network analysis using ClusterProfiler, and the selected genes (represented by black characters in Figure 2E) mainly involved in the rhythmic process, learning or memory, dopamine metabolic process, and immune response were likely the results of changes of synaptic signaling in ACC.

Pulpitis Pain-Related Changes in Protein Expression

We used iTRAQ-coupled liquid chromatography–tandem mass spectrometry/mass spectrometry (LC-MS/MS) to analyze protein fragments. We acquired untargeted proteomic data and 7096 quantified proteins (Supporting Information File 2). A total of 278 differentially expressed proteins (DEPs) were identified, and the Clusters of Orthologous Groups of proteins (COGs) database was used to predict the potential functions of these proteins. A total of 111 proteins were upregulated and 167 were downregulated in the pulpitis group compared with the control group. A volcano plot of DEPs was generated based on the screening parameters |log2FC| > 0.263034 and P-value <0.05, and genes with no significant changes were labeled as gray dots (Figure 3A). After normalizing the expression data and screening the DEPs, we constructed a heat map (Figure 3B).

Figure 3.

Figure 3

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Analysis of DEPs. (A) Volcano plots of DEPs. Screening parameters were |log2FC| > 0.263034 and P-value <0.05, 111 upregulated genes (red dots) and 167 downregulated genes (blue dots) were identified, and genes with no significant changes were labeled as gray dots. (B) The heat map showed the relationship between the samples and the differentially expressed RNAs. The differential expression in the control and pulpitis groups was clearly distinguishable. Red represents higher expression; blue represents lower expression. (C) GO enrichment analysis of DEPs and statistical plots of significantly enriched pathways. (D) KEGG pathway analysis of DEPs. The count represents the number of DEPs. (E) Network plot of the relationship between the GO term and key pathways associated with pulpitis-induced pain.

Subsequently, GO annotation and KEGG pathway enrichment analyses of these DEPs were also be performed. Correspondingly, Bnip3, Gfap, Sh3bp4, Mmp1a, and Mstn were significantly upregulated, while Rbm3, Mt1, Mt3, Nr1d2, Slit2, Sirt2, and Trim13 were downregulated. To identify the most significantly altered pathways, we identified that a total of 269 GO terms were significantly enriched in the ACC of the pulpitis model, especially biological processes, including glial cell differentiation, cellular response to cytokine stimulus, response to wounding, regulation of axon extension, and rhythmic process. Other functional clusters were significantly altered, including the cytoskeleton and vesicle. We further performed GO analysis of protein expression changes to elucidate the relationship between individual proteins and biological processes (Figure 3C). More proteins related to the response to cytokines were related to positive regulation of chemokine (C-X-C motif) ligand 2 production, astrocyte development, cellular response to mechanical stimulus, and melanocortin receptor binding, which were significantly changed in the ACC of pulpitis mice.

Furthermore, the KEGG pathway enrichment analysis revealed that the DEPs were primarily associated with protein processing in vitamin digestion and absorption, alanine, and ECM–receptor interaction (Figure 3D). Interestingly, several signaling pathways were associated with metabolism and immunity, response to axon injury, particularly lipid metabolism, and innate immunity. Possible key signaling pathways were represented by network analysis using ClusterProfiler, and the filtered key genes (represented by black characters in Figure 3E) were mainly involved in the rhythmic process, cellular response to vitamin D, regulation of axon extension, cellular response to cytokine stimulus, and inflammatory response.

Correlation Analysis of mRNA and Protein Expression

From the omic data of samples of 7 day postpulpitis and control groups, we integrated the transcriptomic and proteomic data sets described above to screen differentially expressed entities at the mRNA and protein levels. To ensure rigorous selection, we only included differentially expressed molecules (control vs pulpitis; P-value <0.05 in all four data sets (transcriptomics and proteomics, 7 days after pulpitis surgery)). This comprehensive analysis yielded 6794 genes at 7 days after pulpitis surgery, among which 496 were regulated at the transcription or protein level (Supporting Information File 3; Figure 4A). Eighteen genes were differentially expressed during transcriptomics and proteomics (Figure 4B,C). A significant positive correlation was found between mRNA and protein (r = 0.0239, P-value <0.04.91). Based on our Venn plot in Figure 4B, we identified genes whose expression levels were not consistent in mRNA and protein expression. As shown in Figure 4C, 10 downregulated proteins and 8 upregulated genes were identified. A volcano plot of DEGs was generated based on the screening parameters |log2FC| > 0.263034 and P-value <0.05, while genes with no significant changes were labeled as gray dots (Figure 4D). Elovl5, Ikbke, Serinc5, and Nbeal2 were downregulated at both mRNA and protein levels. After normalizing the transcriptomic and proteomic data, we constructed a heat map based on the 18 screened key genes (Figure 4E).

Figure 4.

Figure 4

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Correlation of expression during proteotranscriptomic profiling between control and pulpitis groups. (A) Venn diagram of the gene set identified both in the transcriptome and proteome. (B) Venn diagram of gene sets of mRNA–protein pairs. (C) Nine-quadrant diagram of mRNA–protein pairs. (D) Volcano plot of proteotranscriptomic results. Screening parameters were |log2FC| > 0.263034 and P-value <0.05. Left: RNA; right: protein. (E) The heat map showed the relationship between the samples and the differentially expressed RNAs and proteins.

Moreover, when the variations in protein levels correlated with changes in the corresponding transcripts in the pulpitis group, the distribution of the corresponding ratios of mRNA/protein was analyzed. GO annotation and KEGG pathway enrichment analyses were performed for mRNAs and proteins exhibiting the same expression trends. In the first, seventh, and ninth quadrants, significantly enriched GO terms in the cellular component category associated with pulpitis-induced pain were the cytoplasmic part, dendritic tree, cytoplasm, intracellular organelle part, and bounding membrane of organelle. At the same time, the significantly enriched signaling pathways included biological processes, response to cytokine, response to cold, regulation of autophagy, and interleukin-17-mediated signaling pathway. The signaling pathways enriched into molecular functions included IκB kinase activity, neurotransmitter uptake, vesicle organization, regulation of lipid storage, megakaryocyte differentiation, and glial cell proliferation (Figure 5). KEGG analysis showed that these genes were associated with response to fatty acid elongation, mucin-type O-glycan biosynthesis, biosynthesis of unsaturated fatty acids, human papillomavirus infection, other types of O-glycan biosynthesis, and coronavirus disease, COVID-19 (Figure 6).

Figure 5.

Figure 5

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GO term analysis of pathway clusters of quadrants 1, 7, and 9 was performed. (A) The first lap indicated the top 20 GO terms, and the number of genes corresponds to the outer lap. The second lap indicated the number of genes in the genome background and P-value for the enrichment of the genes for the specified biological process and cellular component. The third lap indicated the ratio of the genes (purple) of quadrants 1, 7, and 9. The fourth lap indicated the enrichment factor of each GO term. GO, gene ontology. (B) Presentation of the above information content.

Figure 6.

Figure 6

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KEGG term analysis of pathway clusters of quadrants 1, 7, and 9 was performed. (A) The first lap indicated the top 20 KEGG terms, and the number of genes corresponds to the outer lap. The second lap indicated the number of genes in the genome background and P-value for the enrichment of the genes for the specified biological process. The third lap indicated the ratio of the genes (purple) of quadrants 1, 7, and 9. The fourth lap indicated the enrichment factor of each KEGG term. KEGG, gene ontology. (B) Presentation of the above information content.

Putative Mechanisms of Pulpitis-Induced Pain

Drawing from the previous literature and sequencing analyses, we identified the most promising signaling pathways and key molecules (Figure 7A). Additionally, the heat map in Figure 7B illustrates the expressions of potential key molecules in both transcriptomic and proteomic data.

Figure 7.

Figure 7

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Putative mechanisms. (A) Potential signaling pathways and key molecules. (B) The heat map of the key molecules showed expression in both the transcriptome and proteome.

In all, we validated hitherto undocumented potential central nervous system targets using mRNA-Seq and iTRAQ data in ACC of pulpitis-induced pain models. Moreover, bioinformatic analyses revealed the enrichment of several key genes. Among thousands of regulatory entities, 4 key molecules, Elovl5, Ikbke, Serin5, and Nbeal2, were significantly downregulated in terms of mRNA and protein levels. Moreover, an additional 30 differentially expressed key molecules were significantly upregulated or downregulated. Furthermore, regulation of autophagy, immune or inflammation response, response to 5-HT, melanogenesis, dopamine metabolic process, circadian rhythm, synaptic plasticity, learning or memory, and response to cold were screened for providing a new direction and basis for further elucidating pulpitis-related pain via the mRNA or protein network.

Expression Validation of the Eight Key Genes Involved in Immune Inflammation and Synaptic Plasticity by qRT-PCR

To better characterize the expression levels of the key genes in normal and pulpitis tissues, 10 normal ACC samples and 10 pulpitis ACC samples were collected. As shown in Figure 8, compared to normal ACC samples, the relative mRNA expression levels of Elovl5, Ikbke, Nbeal2, Egr1, Shank2, Bche, Serpinf1, and Pax1 were significantly decreased in pulpitis samples (P < 0.01), consistent with the results of high-throughput transcriptomics and proteomics.

Figure 8.

Figure 8

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Validation of the expression levels of the eight key genes between control samples (n = 10) and pulpitis samples (n = 10) by qRT-PCR analysis. (A) Quantification for Elovl5, Ikbke, and Nbeal2. (B) Quantification for Egr1, Shank2, Bche, Serpinf1, and Pax1. All data were presented as the mean ± SEM. Significant differences were statistically defined as P-value <0.05.

Discussion

Currently, the mechanisms underlying pulpitis-induced pain were primarily at the peripheral level, with limited research on central mechanisms.3,13−16,42 While it is known that ACC was activated during pain,17−19,43,44 its involvement in pulpitis-induced pain remained unclear. To address these gaps, we conducted transcriptomic and proteomic analyses and verified the key DEGs and DEPs by qRT-PCR in the ACC of pulpitis models.

First, we conducted a screening of the DEGs that were significantly expressed in the leukocyte transendothelial migration and B cell receptor signaling pathway; phagosome played an important role in host innate immunity, and the PI3K-Akt signaling pathway and Rap1 signaling pathway were essential in regulating host adaptive immunity.45−47 Our findings revealed that Egr1, Shank2, Pax6, Serpinf1, and Bche were markedly downregulated. Subsequently, we discovered that the DEGs were also associated with synaptic plasticity and learning or memory; meanwhile, our findings revealed that the downregulated DEGs in pulpitis were primarily involved in the immune system process and response to stimulus, while the upregulated DEGs were predominantly involved in the cell part, membrane part, binding, transcription factor activity protein binding, and cell process. Our verified results of qRT-PCR also showed that the DEGs involved in the pathway of immune response and synaptic plasticity were significantly downregulated, which was consistent with previous reports.43,48−50 This suggested that pulpitis may evade the host’s immunity in the brain by inhibiting the host immune system and host apoptosis of infection and may also utilize autophagy51,52 and ATP to promote metabolic rate to control the body’s immune system.53−55 We hypothesized that these signaling pathways may occur in conjunction with synaptic plasticity changes in the brain to regulate the body’s immune system. To unravel the mechanism of pulpitis pain, further in-depth proteomic studies were warranted to substantiate our results. Meanwhile, what is interesting to us is that pathways about cellular response to cytokine stimuli,56 axonal extension regulation,57,58 and response to axon injury58,59 were also significantly enriched, which had been reported to be connected with pain. Meanwhile, as is reported, impaired autophagy also reduced the protective effect of astrocytes on neurons against ROS-induced stress.60−62 Neuroinflammation and reactive oxygen species (ROS) levels have been reported to increase or decrease following inhibition or activation of autophagy in the literature.60 Interestingly, DEPs were associated with astrocyte differentiation pathways; therefore, according to our proteomic results, we speculated that pulpitis-induced pain may be caused by immune evasion, which was consistent with previous transcriptomic results. Therefore, the integration of transcriptomic and proteomic data sets was required to further elucidate the mechanism of pulpitis-induced pain. Finally, transcriptomic and proteomic correlation data showed that our prediction was consistent with the role of immune inflammation in pulpitis-induced pain. Elovl5, Ikbke, Serinc5, and Nbeal2 were significantly downregulated at both mRNA and protein levels. Also, the expression of Elovl5, Ikbke, and Serinc5 was verified to be dramatically downregulated after pulpitis at mRNA levels by qRT-PCR. Elovl is widely distributed in neurons and glial cells of the brain with 7 subtypes (Elovl1–Elovl7), and Elovl5 is thought to be associated with a variety of pathological conditions, including neurogenesis, neuronal survival, synaptic activity, and regulation of brain inflammation. Ikbke (nuclear factor κ-B kinase subunit epsilon) is a member of the nonclassical IKK family that plays an important role in regulating NF-κb-mediated inflammatory responses, activating immune cells, tumorigenesis, and inflammatory pain. The literature reports that under inflammatory conditions, IKKε regulates the pathogenesis of neuralgia by activating NF-κB. Nbeal2 has been shown to play an important role in immunity, and its deletion leads to altered development or function of neutrophils and intracellular granules of natural killer (NK) cell. Nbeal2 is downregulated at the transcriptional and protein levels, but the exact mechanism is unknown. Therefore, the above 4 genes may play a key role in the stability of pulpitis through inflammatory response, immune response, neuronal autophagy, and sheathization.We hypothesized that the immune inflammatory response may interact with a series of signaling pathways to play an important role in pulpitis-induced pain.

Our quantitative proteomic and transcriptomic analyses showed that the pulpitis group had more DEGs than DEPs compared to the control group, while only a few DEGs encode DEPs. This phenomenon may be explained by previous reports that transcript levels do not always coincide with trends in protein abundance. Correlation analysis showed that the overexpression of some proteins was negatively correlated with the expression levels of the corresponding genes. One possible explanation for the low correlation between transcription levels and protein expression is that transcription levels fluctuate faster than protein translation and modification processes. Our results also demonstrated that the number of downregulated genes was greater than that of upregulated genes at the transcriptional and protein levels. Since the tissue’s proteome may exhibit undetectable variability in its transcriptome, comparative validation of the proteome and transcriptome of ACC brain tissues after pulpitis modeling has huge prospects. It is highly conceivable that genes involved in the acute and chronic phases of pulpitis may not be consistent.

Conclusions

This study aimed to investigate hitherto unreported targets within the central nervous system. Specifically, we observed a significant decrease in the expression levels of key genes (Elovl5, Ikbke, Nbeal2, Erg1, Shank2, Bche, Serinf1, and Pax6) in pulpitis samples, as confirmed through qRT-PCR analysis. These findings imply that pulpitis-induced pain may be initiated by immune or inflammatory responses, possibly mediated by alterations in synaptic plasticity within ACC. Collectively, our data provide a new direction and basis for further elucidating pulpitis-induced pain through mRNA or protein networks.

Acknowledgments

We sincerely appreciate Professor Jing Hang of the Fourth Military Medical University for modifying the manuscript and Pengju Han of Sichuan University for his guidance in the process of bioinformatic data analysis.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c09759.

  • Quantification of the expression of 17,906 genes after RNA sequencing (XLS)

  • Detailed information on the Mascot search algorithm setting and the type of test used; untargeted proteomic data and 7096 quantified proteins (XLS)

  • Transcriptomic and proteomic association analysis yielded 6794 genes at 7 days after pulpitis surgery (XLS)

Author Contributions

+ X.K., J.S., and J.Z. contributed equally to this work as cofirst authors. X.K. and J.S. wrote the paper and analyzed the data. J.Z. analyzed data. X.Y. established animal models. Z.Y. and Y.Z. participated in statistical analysis. X.L. revised the manuscript. L.-a.W. conceived and designed the study and provided technical and data support. All authors approved the final version of the paper.

This work was supported by grants from the National Natural Science Foundation of China (81771095 and 82071235), Key R&D Program of Shanxi Province (2021KWZ-26), State Key Laboratory of Military Stomatology (2020ZA01), and Shaanxi Provincial Health Research Innovation Ability Improvement Plan Team Support Project (2023TD-01).

The authors declare no competing financial interest.

Supplementary Material

ao3c09759_si_001.xls (27.3MB, xls)
ao3c09759_si_002.xls (15.9MB, xls)
ao3c09759_si_003.xls (9.6MB, xls)

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Associated Data

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

Supplementary Materials

ao3c09759_si_001.xls (27.3MB, xls)
ao3c09759_si_002.xls (15.9MB, xls)
ao3c09759_si_003.xls (9.6MB, xls)

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