CALR-Regulated Lactylation Modifications in Periodontitis: Insights from Bulk and Single-Cell RNA Sequencing

CALR-Regulated Lactylation Modifications in Periodontitis: Insights from Bulk and Single-Cell RNA Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CALR-Regulated Lactylation Modifications in Periodontitis: Insights from Bulk and Single-Cell RNA Sequencing lu chen, lu wang, yuan zhou, jiahao lin, yongxi luo, cheng zeng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7867472/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Lactic acid accumulates in periodontal tissues during periodontitis, suggesting disrupted metabolism may contribute to disease progression. However, the role of lactic acid and its modifications, such as lactylation, remains unclear. Methods Gingival tissues were collected from healthy controls and periodontitis patients. Protein lactylation levels were evaluated through immunohistochemistry and molecular detection. A mouse periodontitis model was established with local lactate intervention, and bone resorption was quantified using micro-computed tomography (micro-CT). Periodontitis transcriptomic and single-cell sequencing data from the Gene Expression Omnibus (GEO) database were integrated to screen differentially expressed lactylation-related genes (DE-LRGs). The Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and support vector machine (SVM) algorithms were applied to identify core genes. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) was employed to analyze their association with immune infiltration. A CALR knockdown mouse model was constructed to validate gene function. Results Protein lactylation levels were significantly elevated in gingival tissues of periodontitis patients and positively correlated with inflammation severity. In mouse models, lactate intervention alleviated gingival inflammation and bone resorption. Through multi-omics analysis, CALR was identified as a core regulatory factor among key lactylation-related genes. CALR knockdown mice exhibited decreased lactate levels, aggravated inflammation, and significantly increased bone resorption, confirming its role in regulating the periodontal immune microenvironment through lactate metabolism. Conclusions Lactylation modification participates in immune regulation during periodontitis. The screened core LRGs, especially CALR , represent potential therapeutic targets. Periodontitis Lactylation Immune Response Bioinformatics Transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Periodontitis is a global public health issue characterized by chronic inflammation of the periodontal tissues and progressive bone resorption[1, 2]. Its prevalence remains high among adults and has become the leading cause of tooth loss in this population[3]. The pathological progression of periodontitis involves complex interactions between the host immune system and the oral microbiome, where the dysregulation of innate and adaptive immunity is a key mechanism driving tissue destruction[4, 5, 6]. Innate immune cells respond rapidly to pathogen invasion through pattern recognition receptors, while adaptive immune cells regulate the inflammatory process through specific responses[7, 8]. However, excessive activation or dysfunction of either immune component can lead to irreversible damage to the periodontal supporting structures. In recent years, the interplay between metabolic reprogramming and the immune microenvironment has emerged as a frontier in the study of inflammatory diseases[9, 10]. Lactate, traditionally regarded as a mere byproduct of glycolysis, has now been recognized as a key signaling molecule with roles in both epigenetic regulation and immune modulation[11]. In the tumor microenvironment, lactate promotes immune evasion by inhibiting T cell glycolytic activity[12, 13, 14]; in rheumatoid arthritis, lactate produced by synovial fibroblasts via lactate dehydrogenase A (LDHA) activates the HIF-1α/IL-1β axis, exacerbating joint destruction[15]. Notably, lactate can directly regulate gene transcription through histone lactylation. For instance, in a sepsis model, lactate-induced H3K18 lactylation enhances METTL3 promoter activity, upregulating the expression of the m6A methyltransferase and ultimately driving ferroptosis in alveolar epithelial cells[16, 17]. In the field of periodontitis, although mass spectrometry-based analyses have confirmed widespread protein lactylation in rat periodontal tissues[18], the specific characteristics and pathological significance of lactylation modifications in human periodontitis remain unclear. This study reveals a significant elevation of lysine lactylation on proteins in the gingival tissues of periodontitis patients, with modification patterns closely correlated with the severity of local inflammation. Using a mouse model of periodontitis, we demonstrate that exogenous lactate administration can partially alleviate gingival inflammation and bone resorption, suggesting a potential therapeutic role of lactate metabolism in periodontal tissue repair. By integrating single-cell transcriptomic sequencing, bulk RNA-seq data, and machine learning algorithms, we identified nine key lactylation-associated hub genes, including PPP1CB and CALR , which show strong correlations with the infiltration levels of immune subpopulations such as Type 2 T helper cells and CD56dim natural killer cells. Functional studies demonstrated that CALR deficiency perturbs local lactate metabolism and aggravated bone resorption. Thus, this work reveals for the first time the novel role of CALR in regulating periodontal bone resorption through modulation of lactate metabolism, implicating the "lactate- CALR " axis as critically involved in periodontal bone homeostasis. These findings provide a rational theoretical foundation for developing targeted therapeutic strategies against lactylation-driven pathology. Methods Collection of Clinical Samples ​​ Gingival tissues were collected from 12 participants (6 males, 6 females; mean age: 34.92 ± 12.85 years) at Nanfang Hospital. The study was approved by the Ethics Committee (NFEC-2025-031), with written informed consent obtained. Healthy controls met criteria: probing depth (PD) ≤ 3 mm, clinical attachment loss ≤ 2 mm, and normal gingival features (pink color, thin margins, no bleeding). Periodontitis patients had PD ≥ 5 mm, bleeding/suppuration post-therapy. Exclusions included pregnancy, systemic diseases, or antibiotic use within 3 months. Hematoxylin and Eosin (H&E) Staining ​​ Tissues were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned. After dewaxing via gradient xylene, sections were stained with hematoxylin (5 min), treated with 1% acid alcohol (2 sec), and counterstained with eosin (2 min). Slides were dehydrated, cleared in xylene, and mounted with neutral resin. Images were captured for histopathological analysis. Immunohistochemical (IHC) Staining​ ​ Human gingival tissues were fixed in 4% paraformaldehyde and embedded in paraffin. Immunohistochemical (IHC) staining was performed according to the manufacturer’s standard protocol. To prevent non-specific antibody binding, normal goat serum was used for blocking. Following antigen retrieval and blocking of non-specific antigens, tissue sections were incubated overnight at 4°C with a pan-Kla antibody diluted 1:100. Negative control sections were processed identically but without primary antibody. Regions of interest (ROI) were observed under a BX63 microscope (Olympus, South District, MA, USA). All images were semi-quantitatively analyzed using average optical density (AOD). Quantitative Real-Time PCR (qRT-PCR) ​​ Total RNA was extracted from gingival tissue samples using an RNA extraction kit (EZBioscience), isolating RNA from both healthy and periodontitis-affected gingival tissues. The extracted RNA was then reverse transcribed into cDNA using the Color Reverse Transcription Kit (EZBioscience, A0010CGQ). Quantitative real-time PCR (qRT-PCR) was performed using the QuantStudio™ Real-Time PCR Software (Thermo Fisher Scientific, V1.3) to assess the expression levels of inflammation and lactylation-related genes. Primer sequences were designed using the Primer-BLAST tool from the NCBI website, with detailed sequences provided in Supplementary Material 1. Western Blot Analysis​ ​ Gingival tissues were lysed in pre-chilled RIPA buffer. Protein concentrations were determined using the BCA assay. Equal amounts of protein were separated by 10% SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). Membranes were blocked with 5% non-fat milk, then incubated overnight at 4°C with primary antibodies against pan-Kla (PTM BioLab, Hangzhou, China) and β-actin (Proteintech, Wuhan, China). After washing, HRP-conjugated secondary antibodies were applied, and signals were detected using a chemiluminescence detection system. Establishment of Mouse Model of Periodontitis​ ​ 36 male C57BL/6J mice (7 weeks) were purchased from the Laboratory Animal Center of Southern Medical University. Mice were housed in SPF facilities with free access to food/water. The animal protocol was approved by the Institutional Ethics Committee (IACUC-LAC-20240527-009). After intraperitoneal anesthesia with avertin (100 mg/kg), bilateral maxillary second molars were ligated with 5 − 0 silk sutures. The ligation remained intact for 14 days, with daily checks for stability. Mice were randomized into three groups (n = 12/group): blank control (no intervention), simple ligation, and ligation + lactic acid (500 µM, 10 µL, every 2 days via 33G microsyringe). On day 14, animals were deeply anesthetized by intraperitoneal injection of Avertin (100 mg/kg), after confirmation of loss of consciousness, euthanasia was performed by cervical dislocation. The maxillary tissues were then harvested. Six samples per group underwent H&E/IHC staining (fixed in 4% paraformaldehyde, decalcified in EDTA), while others were analyzed by micro-CT. All procedures adhered to ethical guidelines, with no adverse events. Micro-CT Analysis The maxillary bone specimens were fixed in 4% paraformaldehyde solution for 24 hours, followed by dehydration through immersion in 75% ethanol. Micro-computed tomography (micro-CT) scanning was performed using a SkyScan 1276 system (Bruker, Belgium) with operational parameters set to 100 kV voltage, 200 µA current, and an isotropic voxel resolution of 10 µm. The alveolar bone region corresponding to the second maxillary molar was designated as the region of interest (ROI). Quantitative analysis of bone microstructural parameters, including bone volume fraction (BV/TV), trabecular number (Tb.N), and trabecular thickness (Tb.Th), was conducted using SkyScan CTAn software (v1.20.3.0). Data Sources ​​ The mRNA sequencing data on periodontitis, specifically from the GSE16134 and GSE173078 datasets, were sourced from GEO database ( https://www.ncbi.nlm.nih.gov/ ). The GSE16134 dataset includes 241 periodontitis patient samples and 69 healthy control samples, while the GSE173078 dataset contains 12 disease samples and 12 healthy control samples. DEGs were identified using the R package DESeq2 with specific filtering criteria. Specifically, we set the thresholds of |log2FoldChange| ≥ 0 and P-value < 0.05 for DEG selection in subsequent analyses. The single-cell RNA sequencing (scRNA-seq) data on periodontitis were sourced from the GSE164241 dataset, which is also available via the GEO database ( https://www.ncbi.nlm.nih.gov/ ). This study selected samples from 8 periodontitis cases and 13 normal tissue samples, forming the original dataset. Subsequent analysis was performed using the R package Seurat. In our study, we compiled a list of 336 lactylation-related genes, with detailed information provided in Supplementary Material 2. Single-Cell RNA Sequencing ( scRNA-seq) Analysis ​​ scRNA-seq data were filtered (nFeature_RNA > 5000, percent_mito 3%). Data were normalized and scaled using Seurat. Principal component analysis (PCA) was performed, followed by UMAP dimensionality reduction. Cells were clustered and annotated using marker genes. Lactylation scores were calculated via GSVA based on lactylation-related genes and visualized with the ggplot2 R package v4.2.2. Gene Set Variation Analysis (GSVA) ​​ HALLMARK, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathways were downloaded from MSigDB ( http://www.gsea-msigdb.org/gsea/index.jsp ). The R package "GSVA" was used to estimate pathway scores and evaluate the differences in pathways between high and low score groups. mRNA-seq Analysis​​ The mRNA data from two datasets (GSE16134 and GSE173078) were combined into a single file. Data normalization was performed using the R packages SVA ( https://bioconductor.org/packages/sva/ ) and preprocessCore to remove batch effects. To ensure the effectiveness of batch effect removal, PCA was conducted to visualize the data before and after batch effect removal. Subsequently, the limma package in R was used to establish criteria for the selection of DEGs. Specifically, DEGs were selected based on |log2FoldChange| > 0 and adjusted P-value < 0.05 for subsequent analysis. Machine Learning Identification of Core DE-LRGs ​​ By combining the DEGs identified from both single-cell RNA-seq and mRNA-seq data with the expression profiles of lactylation-related genes, DE-LRGs were identified. The selection criteria for LRGs were adj.P.Val < 0.05 and |log2 Fold Change| ≥ 0. LASSO is a multivariate linear regression method that adjusts model parameters to avoid overfitting and improve model generalization. LASSO regression was performed using the "glmnet" R package, and the results were filtered to select 15 genes. Random forest classifier and SVM analysis were implemented using the "randomForest" and "kernlab" R packages, with the top 10 genes retained. Finally, the intersection of LASSO regression, random forest, and SVM analysis results was used to identify core lactylation-related genes. The diagnostic value of these hub genes in periodontitis was assessed by calculating the area under the receiver operating characteristic curve (AUC) using the "pROC" R package. The interactions between core genes were analyzed using the "circlize" R package. Immune Cell Infiltration and Immune Relevance Analysis of Core DE-LRGs​ ​ Based on the principles of linear support vector regression, CIBERSORT identifies the cellular composition of complex tissues through gene expression profiles. The CIBERSORT algorithm was applied to analyze RNA-seq data from normal and periodontitis tissues to infer the relative proportions of immune infiltrating cells. Pearson correlation analysis was performed between DE-LRGs and immune cell content. Statistical significance was set at P < 0.05, and the results were visualized using a bubble chart. Establishment of CALR Knockdown Mouse Model of Periodontitis ​​ 36 male C57BL/6J mice (7 weeks) were divided into three groups (n = 12/group): Blank Control (no intervention), Simple Ligation (ligation-only), and CALR Knockdown (ligation + si- CALR ). Mice were anesthetized, and maxillary second molars were ligated with 5 − 0 silk sutures. For the CALR Knockdown group, si- CALR (1 µg/µL, 10 µL) was injected into buccal/palatal gingiva every 3 days for 14 days. On day 14, following euthanasia (as described previously), the maxillary tissues were harvested for micro-CT, H&E staining, IHC, and qRT-PCR. Statistical Analysis​ ​ Data processing and analysis were performed using GraphPad Prism 7.0 and R (version 4.2.2). Student's t-test was used to analyze differences between groups. All statistical P-values were based on two-tailed tests, and P < 0.05 was considered statistically significant. Results Elevated Protein Lactylation in Gingival Tissues of Periodontitis Patients H&E staining confirmed significant inflammatory cell infiltration, gingival epithelial hyperplasia, and structural disorganization in the periodontitis group (Fig. 1 A). IHC and WB analyses revealed that lysine lactylation levels were significantly higher in the periodontitis group compared to healthy controls(Figs. 1 B, C, E, F). WB detected lactylated proteins predominantly within the 10–30 kDa range. Additionally, mRNA expression of IL-6, IL-1β, and TNF-α was significantly upregulated in the periodontitis group (Fig. 1 D). Lactate Treatment Alleviates Gingival Inflammation and Bone Resorption in Mice In a murine periodontitis model (Fig. 2 A), the ligation group exhibited increased alveolar bone resorption (CEJ-ABC distance) and inflammatory infiltration compared to the control group, while the Ligature + Nala group effectively mitigated these phenotypes (Fig. 2 B). Micro-CT analysis demonstrated that Nala treatment significantly reduced ligation-induced bone loss, though it did not fully restore levels to those of the control group (Fig. 2 D). Bone histomorphometric analysis showed that the ligation group exhibited decreased BV/TV, Tb.Th, and Tb.N, along with increased Tb.Sp. Nala treatment partially reversed these bone parameter changes (Figs. 2 E-H). Local lactate administration significantly enhanced lysine lactylation levels (Fig. 2 C), confirming that lactate alleviates gingival inflammation and bone resorption via modulation of protein lactylation. Identification of Lactylation-Associated Genes Using scRNA-seq Data ScRNA-seq data (GSE164241) revealed 13 cell clusters via UMAP dimensionality reduction, annotated into nine core cell types, including fibroblasts and endothelial cells (Figs. 3 A-C). Differential gene analysis identified the top five genes specifically expressed in each cell type (Fig. 3 D). Pathway enrichment showed glycolysis and PI3K-AKT-mTOR pathways were active in epithelial/endothelial cells (Fig. 3 E), while lactylation modification correlated significantly with MYC target genes V1, p53 pathway, oxidative phosphorylation, and glycolysis (Fig. 3 F). Lactylation-associated gene expression was upregulated in all cell types except plasma cells, with fibroblasts and endothelial cells exhibiting the highest lactylation scores (Figs. 4 A-B). High-lactylation groups showed a higher proportion of fibroblasts/endothelial cells (Fig. 4 C) and significant enrichment of inflammatory and glycolytic pathways (Fig. 4 D). Identification of DE-LRGs Integration of periodontitis datasets (GSE16134 and GSE173078) after batch effect correction included 253 patients and 81 healthy controls (Supplementary Fig. 1). Differential analysis identified 4,225 upregulated and 5,658 downregulated genes in the periodontitis group (Fig. 5 A), visualized via heatmap for the top 20 genes (Fig. 5 B). scRNA-seq analysis retained 70,987 cells and 21,935 genes after quality control (Supplementary Fig. 2), identifying 220 upregulated and 511 downregulated genes (Fig. 5 C). Cross-analysis of transcriptomic, single-cell differential genes, and lactylation-related genes yielded 16 DE-LRGs: 10 downregulated (e.g., PTMA, PCNP, NPM1 ) and 6 upregulated (e.g., CDV3, BRD4, MSN ) (Figs. 5 D-E), with expression patterns visualized via heatmap (Fig. 5 F). Identification and Validation of Core Lactylation-Related Genes LASSO regression, random forest, and SVM algorithms identified nine core genes ( CALR, SOD1, BTF3, HMGN3, PPP1CB, FABP5, CALM1, PCNP, MSN ) (Figs. 6 A-D), with their interaction networks visualized via chord diagram (Fig. 6 E). ROC curve analysis confirmed high specificity of these genes for periodontitis diagnosis. qRT-PCR validation demonstrated that, except for SOD1 , the expression trends of the remaining eight genes in patient gingival tissues aligned with sequencing results, with CALR showing the most significant difference (Figs. 6 G-O). Immune Infiltration Analysis ssGSEA revealed significant alterations in infiltration levels of 20 immune cell types (except CD56bright/CD56dim NK cells and Th2 cells) in the high-risk periodontitis group (Fig. 6 P). DE-LRGs correlated closely with the immune microenvironment: upregulated genes CALR, CALM1 , and MSN showed positive correlations with naïve CD4 + T cells and resting NK cells, but negative correlations with follicular helper T cells, with CALR exhibiting the strongest immune regulatory associations (Fig. 6 Q), suggesting its pivotal role in periodontitis-related immune dysregulation. si- CALR Knockdown Exacerbates Gingival Inflammation and Bone Resorption in Mice CALR knockdown (si- CALR group) significantly reduced CALR expression compared to control and si-NC groups (Figs. 7 C-E), accompanied by decreased lactate levels and increased IL-18 (Figs. 7 F, H). Phenotypic analysis revealed aggravated inflammatory cell infiltration and alveolar bone resorption in the si- CALR group (Fig. 7 G), with micro-CT quantification confirming the most severe bone loss (Fig. 7 I). Bone parameter analysis demonstrated significantly reduced BV/TV, Tb.Th, and Tb.N, along with increased Tb.Sp in the si- CALR group (Figs. 7 J-M), indicating that CALR deficiency exacerbates gingival inflammation and bone destruction by suppressing lactate production. Discussion This study confirmed elevated levels of protein lactylation in the gingival tissues of periodontitis patients, demonstrating a positive correlation with the severity of inflammation and bone resorption. Animal experiments revealed that exogenous lactate intervention alleviated gingival inflammation and reduced bone loss by enhancing lactylation modification, suggesting that lactylation may play a dual regulatory role in both metabolic and immune processes in periodontitis. Different types of immune cells exhibit distinct functional roles in inflammatory responses, and their metabolic demands as well as the activation levels of lactate metabolic pathways may significantly influence disease progression[19, 20, 21]. Using single-cell transcriptomic data, this study preliminarily explored cell type-specific expression patterns of lactylation-related genes in the periodontal microenvironment. Fibroblasts and endothelial cells displayed relatively high lactylation features, and the coordinated activation of glycolysis-related genes (e.g., LDHA, SLC16A3) and the PI3K/AKT/mTOR signaling pathway aligned with previously reported metabolic reprogramming in stromal cells[22, 23], suggesting that the coupling of glycolysis and oxidative phosphorylation may provide energy support for extracellular matrix (ECM) remodeling during chronic inflammation. On the other hand, plasma cells showed significantly lower lactate metabolism levels compared to other immune cells, which may be related to their terminally differentiated functional specialization[24, 25]: pro-inflammatory myeloid cells tend to adopt Warburg-like metabolism to sustain inflammatory responses[26], whereas plasma cells rely more on endoplasmic reticulum biogenesis to support antibody secretion[27]. This metabolic heterogeneity implies that different immune cells may employ divergent adaptive strategies within the chronic inflammatory microenvironment, providing preliminary clues for understanding the role of immunometabolic regulation in the progression of periodontitis. To further investigate the potential functions of lactate metabolism-related genes in periodontitis, this study employed a multi-algorithm screening strategy and identified nine DE-LRGs, including CALR and SOD1 . These genes showed potential associations with immunometabolic processes in certain analyses. Diagnostic models suggested that this gene set has some ability to discriminate disease status, though experimental validation revealed inconsistencies with predictions for some genes. For instance, the expression trend of SOD1 in tissue samples did not fully align with bioinformatic predictions, possibly due to sample source heterogeneity, dynamic changes in the microenvironment, or masking of cell subset-specific expression by bulk tissue analysis. Despite discrepancies in individual genes, the overall data indicate that DE-LRGs may play a role in the development of periodontitis. This set of lactylation-related genes provides candidate directions for future research, though their specific functions and clinical applicability require further validation. The initiation and progression of periodontitis involve complex immune changes[28], in which dysregulation of lactate metabolism may play an important regulatory role. To explore potential links between lactate metabolism and immune responses, this study applied single-sample gene set enrichment analysis (ssGSEA) to characterize immune cell infiltration in periodontitis-affected gingival tissues. Results showed that DE-LRG expression was positively correlated with the infiltration of Th2 cells and CD56dim NK cells, suggesting their potential involvement in immune regulation by promoting the secretion of reparative cytokines (e.g., IL-4, IL-13) and enhancing cytotoxic function[29]. In contrast, a negative correlation was observed with the infiltration of innate immune cells such as monocytes and macrophages, implying that lactate metabolism may suppress excessive activation of innate immunity[30]. This bidirectional correlation pattern suggests that lactate metabolic dysregulation may be associated with a shift in the periodontal microenvironment from an innate immunity-dominated pro-inflammatory state to a chronic state involving adaptive immunity. CALR may serve as an important regulatory node, as its expression is associated not only with adaptive immune responses[31, 32]but also with the regulation of innate immune cell function. Previous studies have indicated that CALR can act as an "eat-me" signal to promote the phagocytic clearance of apoptotic cells by macrophages, supporting its potential role as an immune bridge in inflammatory microenvironments[33]. In a mouse periodontitis model, CALR knockdown led to aggravated inflammation, increased bone resorption, and reduced local lactate levels. This observation appears inconsistent with the traditional view emphasizing the pro-inflammatory properties of lactate. However, emerging studies suggest that under specific pathological conditions (e.g., hypoxia or immunosuppressive states), lactate may exert anti-inflammatory and pro-repair functions[19, 34]. Our results indicate that in the chronic inflammatory environment of periodontitis, lactate may exert a potentially protective regulatory effect through CALR -mediated metabolic-immune interactions, beyond its role as a metabolic byproduct. The concurrent decrease in lactate levels and increase in bone resorption following CALR knockdown suggest that CALR may be involved in maintaining local metabolic homeostasis. Existing evidence indicates that lactate can influence bone remodeling by modulating osteoclast activity and local pH balance[35, 36], highlighting its dual identity as both a metabolite and a signaling molecule. This study proposes that CALR may influence bone homeostasis through the regulation of lactate metabolism, providing preliminary experimental evidence for a potential link between CALR , lactate metabolism, and bone stability in periodontitis. By integrating scRNA-seq derived DEGs, bulk RNA-seq DEGs, and lactylation-related genes, this study identified a set of hub lactylation-related genes that may play roles in periodontitis. Preliminary exploration of their correlations with the immune microenvironment provided initial evidence supporting the involvement of lactate accumulation in the metabolic-immune crosstalk in periodontitis. Analyses also indicated an association between elevated lactate levels in inflamed gingival tissues and periodontitis, suggesting that lactylation-related genes may represent potential targets for further research. Additionally, CALR was found to potentially influence the immune microenvironment via lactate metabolism, supporting the putative role of lactate metabolic dysregulation in immune modulation. However, this study has several limitations: Due to inherent constraints of bioinformatic methodologies, the functional roles of hub DE-LRGs in disease pathogenesis and their therapeutic relevance require further experimental validation. Moreover, as the analysis relied on public database information, it was not possible to fully adjust for confounding factors such as age, sex, ethnicity, and comorbidities. Future animal studies and clinical investigations are needed to validate these findings. Conclusion This study reveals that lactylation modification in periodontitis influences inflammation and bone resorption via metabolic-immune dual regulatory mechanisms. Single-cell analysis highlights cell-type-specific metabolic reprogramming, with 9 lactate-related genes exhibiting diagnostic potential (AUC > 0.7). CALR , identified as a hub gene, regulates lactate metabolism to maintain bone homeostasis, challenging the traditional pro-inflammatory perception of lactate. These findings provide novel strategies for targeting lactate metabolic networks in therapeutic interventions. Abbreviations Abbreviation Full name ABC Alveolar Bone Crest AOD Average Optical Density AUC Area Under the Curve BV/TV Bone Volume/Total Volume CALR Calreticulin CEJ Cemento-Enamel Junction CIBERSORT Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts DEGs Differentially Expressed Genes DE-LRGs Differentially Expressed Lactylation-Related Genes ECM Extracellular Matrix GEO Gene Expression Omnibus GSVA Gene Set Variation Analysis H&E Hematoxylin and Eosin IHC Immunohistochemistry KEGG Kyoto Encyclopedia of Genes and Genomes Kla Lysine lactylation LASSO Least Absolute Shrinkage and Selection Operator LDHA Lactate Dehydrogenase A micro-CT Micro-Computed Tomography PCA Principal Component Analysis PD P robing depth qRT-PCR Quantitative Real-Time Polymerase Chain Reaction ROC Receiver Operating Characteristic ROI Region of Interest scRNA-seq Single-Cell RNA Sequencing ssGSEA Single-Sample Gene Set Enrichment Analysis SVM Support Vector Machine Tb.N Trabecular Number Tb.Sp Trabecular Separation Tb.Th Trabecular Thickness UMAP Uniform Manifold Approximation and Projection WB Western Blot Declarations Ethics approval and consent to participate All procedures were conducted in compliance with ARRIVE 2.0 guidelines. Single-sex (male) design was justified by eliminating estrogen interference in bone metabolism.Written informed consent was obtained from patients or their guardians prior to study enrollment. This research was reviewed and approved by the Ethics Committee of Southern Medical University (Approval No.NFEC-2025-031) and conducted in strict accordance with the World Medical Association Declaration of Helsinki . Animal studies were approved by the Animal Welfare and Use Committee of Southern Medical University Nanfang Hospital (Approval No.IACUC-LAC-20240527-009) and complied with the guidelines established by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC). Consent for publication By submitting my article I agree to pay the APC in full if my article is accepted for publication. Availability of data and materials All supporting data for this study are fully preserved in the main text and supplementary materials. Key datasets (GSE16134，GSE173078 and GSE164241) were derived from the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/), and raw data are publicly accessible via the repository platform (dataset query URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134/ GSE173078/ GSE164241). Competing Interests All authors hereby declare that there are no potential conflicts of interest related to the authorship or publication of this article. The funding agencies had no involvement in the study design, data collection, statistical analysis, interpretation of results, manuscript preparation, or decision to publish the findings, and did not exert any influence on the research conclusions. Funding This study was supported by the President Foundation of Nanfang Hospital, Southern Medical University (2024A035) and Guangzhou Municipal Science and Technology Project (2024A04J5188). Authors' contributions Lu Chen, Lu Wang, and Yuan Zhou led the research design and implementation, managed data collection and analysis, and participated in drafting the manuscript. Zhao Chen and Huiyong Xu jointly oversaw the overall project coordination, provided critical interpretation of results, revised the manuscript through multiple iterations, and approved the final version for submission. Jiahao Linand Yongxi Luo were responsible for experimental data collection and analysis, contributed to result interpretation, and participated in manuscript revisions. Cheng Zeng, Xinmiao Luo and Qingxia Zhao handled experimental data acquisition and preliminary analysis. All authors jointly reviewed the final manuscript and assume collective responsibility for the scientific rigor of the study design, reliability of the data, and academic integrity of the entire work. Acknowledgements Special thanks to the research team that generously shared the single-cell RNA sequencing database (GSE16134，GSE173078 and GSE164241), which provided critical data support for this research. The authors extend their sincere gratitude to all collaborating institutions and scholars for their invaluable assistance. References Belluci MM, de Molon RS, Rossa CJ, Tetradis S, Giro G, Cerri PS, et al. Severe magnesium deficiency compromises systemic bone mineral density and aggravates inflammatory bone resorption. J Nutr Biochem (2020) 77:108301. doi: 10.1016/j.jnutbio.2019.108301. Noriega Muro ST, Cucina A. Periodontitis and alveolar resorption in human skeletal remains: The relationship between quantitative alveolar bone loss, occlusal wear, antemortem tooth loss, dental calculus and age at death in a low socioeconomic status, modern forensic human collection from Yucatan. Int J Paleopathol (2024) 45:7-17. doi: 10.1016/j.ijpp.2024.02.001. 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Wilson RP, McGettigan SE, Dang VD, Kumar A, Cancro MP, Nikbakht N, et al. IgM plasma cells reside in healthy skin and accumulate with chronic inflammation. J Invest Dermatol (2019) 139(12):2477-87. doi: 10.1016/j.jid.2019.05.009. Sun K, Shen Y, Xiao X, Xu H, Zhang Q, Li M. Crosstalk between lactate and tumor-associated immune cells: Clinical relevance and insight. Front Oncol (2024) 14:1506849. doi: 10.3389/fonc.2024.1506849. Gossez M, Vigneron C, Vandermoeten A, Lepage M, Courcol L, Coudereau R, et al. PD-L1(+) plasma cells suppress T lymphocyte responses in patients with sepsis and mouse sepsis models. Nat Commun (2025) 16(1):3030. doi: 10.1038/s41467-025-57706-9. Fucikova J, Spisek R, Kroemer G, Galluzzi L. Calreticulin and cancer. Cell Res (2021) 31(1):5-16. doi: 10.1038/s41422-020-0383-9. Guilbaud E, Kroemer G, Galluzzi L. Calreticulin exposure orchestrates innate immunosurveillance. Cancer Cell (2023) 41(6):1014-16. doi: 10.1016/j.ccell.2023.04.015. Xiao L, Zhang L, Guo C, Xin Q, Gu X, Jiang C, et al. "Find Me" and "Eat Me" signals: Tools to drive phagocytic processes for modulating antitumor immunity. Cancer Commun (Lond) (2024) 44(7):791-832. doi: 10.1002/cac2.12579. Banuelos A, Baez M, Zhang A, Yılmaz L, Kasberg W, Volk R, et al. Macrophages release neuraminidase and cleaved calreticulin for programmed cell removal. Proc Natl Acad Sci U S a (2025) 122(21):e1868323174. doi: 10.1073/pnas.2426644122. Manosalva C, Quiroga J, Hidalgo AI, Alarcón P, Anseoleaga N, Hidalgo MA, et al. Role of Lactate in Inflammatory Processes: Friend or Foe. Front Immunol (2021) 12:808799. doi: 10.3389/fimmu.2021.808799. Li F, Bao S, Sun X, Ma J, Ma X. Extracellular acidification stimulates OGR1 to modify osteoclast differentiation and activity through the Ca2+‑calcineurin‑NFATc1 pathway. Exp Ther Med (2025) 29(2):28. doi: 10.3892/etm.2024.12778. Wu J, Hu M, Jiang H, Ma J, Xie C, Zhang Z, et al. Endothelial Cell-Derived lactate triggers bone mesenchymal stem cell histone lactylation to attenuate osteoporosis. Adv Sci (Weinh) (2023) 10(31):e2301300. doi: 10.1002/advs.202301300. Additional Declarations No competing interests reported. Supplementary Files sup.1.tiff Sup.1. Integration of gene expression datasets (GSE173037 and GSE16134) via merging, batch correction ("limma" and "sva"), and normalization ("preprocessCore") to prioritize biological variation. (A) Pre-merge PCA: GSE173037 (12 disease/12 healthy) and GSE16134 (241 disease/69 healthy); (B)Post-merge PCA (17,506 genes, 334 samples) with reduced batch effects ("FactoMineR" and "factoextra")；(C)Pre-normalization outliers (range: -300–400)；(D)Post-normalization tightened distribution (biologically plausible range). sup.2.tiff Sup.2. Single-cell data were integrated and processed via quality filtering , batch correction ( harmony ), normalization ( Seurat ), and clustering to prioritize biological variation. (A-B) Gene/RNA count quality control:(A)Pre-filtering distribution (raw data); (B)Post-filtering (retained: 70,987 cells, 21,935 genes); (C-D)Gene expression proportion quality control:(C) Pre-filtering outliers ( percent.mito ≥15%, percent.ribo ≤3%, percent.hb ≥0.1%)；(D) Post-filtering tightened distribution ( percent.mito 3%, percent.hb <0.1%). supplementarymaterial1.docx Supplementaryfigurelegends.docx supplementarymaterial2.xlsx animalethic.pdf humanethic.pdf actinmerge.tif KLAMERGE.tif Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 16 Feb, 2026 Editor invited by journal 23 Jan, 2026 Editor assigned by journal 12 Nov, 2025 Submission checks completed at journal 12 Nov, 2025 First submitted to journal 12 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7867472","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593425909,"identity":"da998edd-c330-4bb6-9b38-b7007bccc73b","order_by":0,"name":"lu chen","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"lu","middleName":"","lastName":"chen","suffix":""},{"id":593425910,"identity":"651e9b84-3404-4240-a338-0c4b930a4c8d","order_by":1,"name":"lu wang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"lu","middleName":"","lastName":"wang","suffix":""},{"id":593425911,"identity":"bf8aa60a-9291-4c41-b371-d18a6dcc9fc1","order_by":2,"name":"yuan zhou","email":"","orcid":"","institution":"Guangzhou Eighth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"yuan","middleName":"","lastName":"zhou","suffix":""},{"id":593425912,"identity":"e850cab9-e1b2-4f23-870b-a11507edcc4f","order_by":3,"name":"jiahao lin","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"jiahao","middleName":"","lastName":"lin","suffix":""},{"id":593425913,"identity":"82557bd5-7ec6-460e-85f7-eda7627fcb9b","order_by":4,"name":"yongxi luo","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"yongxi","middleName":"","lastName":"luo","suffix":""},{"id":593425914,"identity":"1b44ac64-2cd9-461e-87ea-a8cb21ee7f13","order_by":5,"name":"cheng zeng","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"cheng","middleName":"","lastName":"zeng","suffix":""},{"id":593425915,"identity":"1cab73be-948a-4d22-921b-13b1f684e6dd","order_by":6,"name":"xinmiao luo","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"xinmiao","middleName":"","lastName":"luo","suffix":""},{"id":593425916,"identity":"db68d9a4-8af0-48f2-90c6-45eb733394ad","order_by":7,"name":"qingxia zhao","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"qingxia","middleName":"","lastName":"zhao","suffix":""},{"id":593425917,"identity":"fa787428-1117-44b7-b14a-ef585e98f8d1","order_by":8,"name":"zhao chen","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"zhao","middleName":"","lastName":"chen","suffix":""},{"id":593425918,"identity":"affa1ac1-d933-4df1-8ccb-6ff4bec71fcc","order_by":9,"name":"huiyong xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACHgaGBBDN3tgAFUkgVgvPQVK0gIEEXCUBLQY3co9JPGyzyZOPfNzA+DPnMAM/e44Bw88duLVIzshLk0hsSys2vJ3YwMy77TCDZM8bA8beM7i18EvkmAG1HE7cOBuohRGoxeBGjgEzYxtuLWxwLTMPAh0G1GJPSAvclvkSjA0MIIcZSBDQAnS5sUXCubTEDTyJDYd5t6XzSJx5VnCwF48Wg+M5hjd/lNkkzm8//vDhz23WcvztyRsf/MSjBQwY2YB6DzAwABEkmg4Q0AAEfxgY5BsIKxsFo2AUjIIRCgCTEVFwTlPuLwAAAABJRU5ErkJggg==","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"huiyong","middleName":"","lastName":"xu","suffix":""}],"badges":[],"createdAt":"2025-10-15 11:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7867472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7867472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103166154,"identity":"95be1bf2-6da7-42d1-9e37-03bb54168255","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6401366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLevels of gingival tissue protein lactylation in human health and periodontitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) H\u0026amp;E staining was used to observe the histological differences in gingival samples from healthy and chronic periodontitis patients. Red arrows indicate inflammatory cells; (B) IHC staining was used to observe the expression levels of Pan-Kla in gingival samples from healthy and chronic periodontitis patients. Red arrows indicate the positive areas labeled by Pan-Kla; (C) Quantification of Pan-Kla expression levels in gingival samples from healthy and chronic periodontitis patients, as determined by IHC staining; (D) mRNA expression levels of IL-6, IL-1β, and TNF-α in gingival samples from healthy and chronic periodontitis patients were measured by qRT-PCR; (E) Expression levels of Pan-Kla in gingival samples from healthy and chronic periodontitis patients were detected by WB; (F) Quantification of Pan-Kla expression levels in gingival samples from healthy and chronic periodontitis patients, as determined by WB. β-actin as an internal control, normalized to the corresponding density analysis is shown in (E). The data are shown as the mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. AOD, average optical density.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/bf8133ea60462575032eda4b.png"},{"id":103166168,"identity":"41aa11ca-118f-4400-a2cb-35034e2b28d9","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38415951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLactic acid treatment alleviated gingival inflammation and bone resorption in mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic diagram of the periodontitis mouse model and local injection of lactic acid; (B) H\u0026amp;E staining was used to observe histological differences in mouse periodontal tissue. The yellow arrows indicate the network structure formed by gingival inflammation. The yellow line represents the distance from the cemento-enamel junction to the alveolar bone crest (CEJ-ABC distance); (C) IHC staining was used to observe the expression levels of Pan-Kla in mouse periodontal tissue. The red arrows indicate the positive areas labeled by Pan-Kla; (D) Micro-CT analysis of alveolar bone loss in the mouse maxilla. Bone loss is represented by the area between the light yellow lines; (E-H) Quantitative micro-CT analysis of bone volume and microstructure in the mouse maxilla. The data are shown as the mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. AOD, average optical density.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/538863963f520974224d7e5c.png"},{"id":103504897,"identity":"3f1b4bb5-c671-46d0-9e8b-0969d3581a70","added_by":"auto","created_at":"2026-02-26 13:22:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9397463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA-Seq analysis of protein lactylation modifications in gingival tissue during periodontitis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Single-cell RNA sequence analysis Clustered tree diagram of unit grouping at different resolutions; (B) The cells were divided into 14 independent clusters by UMAP; (C)Number of cells in each cluster and (D) each cluster differentially expressed genes (top5); (E) Each cell type was scored according to the HALLMARK pathway, and the heatmap showed the HALLMARK score of each cell; (F) The GSVA R package was used to score lactylation modifications in each cell, and the correlation between lactylation scores and HALLMARK pathway scores was calculated.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/7140397acc77bae38a1e8e56.png"},{"id":103504868,"identity":"6b000fd8-29d8-48e8-a59d-26b0f444cb91","added_by":"auto","created_at":"2026-02-26 13:21:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7042496,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLactylation score analysis based on scRNA-seq.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The GSVA R package was used to score the Lactylation gene set for different cell types. (B) UMAP plot showing the Lactylation gene set scores for different cell types (grouped into high and low expression levels based on the median). (C) The number and proportion of cell types between the high and low groups of the Lactylation gene set scores. (D) HALLMARK pathway scores between the high and low groups of the Lactylation gene set scores.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/5153d8529b3544de3176c2a1.png"},{"id":103505212,"identity":"cd9317fe-aa08-4d75-886a-5a6598612fb4","added_by":"auto","created_at":"2026-02-26 13:27:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2613690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of lactylation-related differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot showing differentially expressed genes from bulk RNA-seq, with a threshold of P \u0026lt; 0.05; (B) Heatmap displaying the top 20 differentially expressed genes from bulk RNA-seq; (C) Volcano plot showing differentially expressed genes from scRNA-seq, with a threshold of P \u0026lt; 0.05; (D) Venn diagram illustrating the upregulated shared differentially expressed genes between the bulk RNA-seq differentially expressed genes, scRNA-seq differentially expressed genes, and lactylation-related genes; (E) Venn diagram illustrating the downregulated shared differentially expressed genes between the bulk RNA-seq differentially expressed genes, scRNA-seq differentially expressed genes, and lactylation-related genes; (F) Heatmap showing 16 shared lactylation-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/4224c6b2933503ee193e8d52.png"},{"id":103504620,"identity":"d1961a80-c2b1-4d64-b61f-30b98be10082","added_by":"auto","created_at":"2026-02-26 13:20:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7946649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and validation of the 9 hub lactylation-related genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) 15 genes were selected using the LASSO regression method; (B) 10 genes were selected using the random forest algorithm; (C) 10 genes were selected using the SVM support vector machine method; (D) The intersection of the results from the three methods yielded 9 hub genes; (E) Correlation analysis of the 9 hub genes; (F) ROC curve analysis of the 9 genes. qRT-PCR was used to measure the mRNA levels of (G) \u003cem\u003eSOD1\u003c/em\u003e, (H) \u003cem\u003eBTF3\u003c/em\u003e, (I)\u003cem\u003e HMGN3\u003c/em\u003e, (J) \u003cem\u003ePPP1CB\u003c/em\u003e, (K) \u003cem\u003eFABP5\u003c/em\u003e, (L) \u003cem\u003eCALR\u003c/em\u003e, (M) \u003cem\u003eCALM1\u003c/em\u003e, (N) \u003cem\u003ePCNP\u003c/em\u003e, and (O) \u003cem\u003eMSN\u003c/em\u003e in gingival samples from healthy and chronic periodontitis patients; (P) Immune cell infiltration analysis based on bulk RNA-seq; (Q) Correlation analysis between the 9 hub genes and immune cells.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/b4fe5402040e0533bac1ea3b.png"},{"id":103166167,"identity":"4b116570-708a-41d8-ab0d-e3aa688d5e5c","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":31022621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe knockdown of CALR exacerbated gingival inflammation and bone resorption in mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Schematic diagram of the periodontitis mouse model and local injection of si-\u003cem\u003eCALR\u003c/em\u003e. qRT-PCR was used to detect the mRNA levels of (C) TNF-a, (D) IL-18, and (E) \u003cem\u003eCALR\u003c/em\u003ein the mouse maxilla; (F) Measurement of lactic acid levels in the mouse maxilla; (G) H\u0026amp;E staining was used to observe the maxilla of mice. The green line represents the CEJ-ABC distance. The brown line indicates the detached junctional epithelium. Regions of interest were identified in the gingival tissue area (g4-g6) and the bone tissue area (g7-g9); (H) IF staining showed the expression of \u003cem\u003eCALR\u003c/em\u003e and the inflammatory marker IL-18 in the mouse maxilla; (I) Micro-CT showed 3D reconstruction images of the mouse maxilla; (J-M) Quantitative micro-CT analysis of the mouse maxilla. The data are shown as the mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. AOD, average optical density.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/d166d261ca710c6208ee806c.png"},{"id":104783929,"identity":"d06a4878-ede2-4061-8e3a-33a8e1a4de7c","added_by":"auto","created_at":"2026-03-17 08:04:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":95687298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/e836b29b-e709-4710-8b94-34eb1c2545d1.pdf"},{"id":103504775,"identity":"d85efaf3-6b38-472c-ba8a-a53d43cccc37","added_by":"auto","created_at":"2026-02-26 13:21:22","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":256636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSup.1. Integration of gene expression datasets (GSE173037 and GSE16134) via merging, batch correction (\"limma\" and \"sva\"), and normalization (\"preprocessCore\") to prioritize biological variation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Pre-merge PCA: GSE173037 (12 disease/12 healthy) and GSE16134 (241 disease/69 healthy); (B)Post-merge PCA (17,506 genes, 334 samples) with reduced batch effects (\"FactoMineR\" and \"factoextra\")；(C)Pre-normalization outliers (range: -300–400)；(D)Post-normalization tightened distribution (biologically plausible range).\u003c/p\u003e","description":"","filename":"sup.1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/4bc00068595cc584583234da.tiff"},{"id":103505056,"identity":"9b1d5bd7-c93e-4e21-97c6-cbc8858eb38d","added_by":"auto","created_at":"2026-02-26 13:22:43","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSup.2. Single-cell data were integrated and processed via quality filtering , batch correction (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eharmony\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), normalization (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSeurat\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), and clustering to prioritize biological variation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) \u003cstrong\u003eGene/RNA count quality control:\u003c/strong\u003e(A)Pre-filtering distribution (raw data); (B)Post-filtering (retained: 70,987 cells, 21,935 genes); (C-D)\u003cstrong\u003eGene expression proportion quality control:\u003c/strong\u003e(C) Pre-filtering outliers (\u003cem\u003epercent.mito\u003c/em\u003e ≥15%, \u003cem\u003epercent.ribo\u003c/em\u003e ≤3%, \u003cem\u003epercent.hb\u003c/em\u003e ≥0.1%)；(D) Post-filtering tightened distribution \u0026nbsp;(\u003cem\u003epercent.mito\u003c/em\u003e \u0026lt;15%, \u003cem\u003epercent.ribo\u003c/em\u003e \u0026gt;3%, \u003cem\u003epercent.hb\u003c/em\u003e \u0026lt;0.1%).\u003c/p\u003e","description":"","filename":"sup.2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/5ad51633c2bbea1d63a0b737.tiff"},{"id":103166160,"identity":"87b9e80c-f5f0-47a7-91e8-8cd037feeeac","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15335,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/dc651661a70973cb6b8656ba.docx"},{"id":103504364,"identity":"d8f3ec98-75eb-4f19-a11c-2accc47bed74","added_by":"auto","created_at":"2026-02-26 13:19:30","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13909,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/4d8f9de8bf71253795ff3a53.docx"},{"id":103166157,"identity":"2f1c0220-ed3a-433c-b68f-b7ad0fb5b8ec","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14424,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/fc0b11558946559016b13709.xlsx"},{"id":103505153,"identity":"64be581a-489c-42aa-bdc4-570b70c9b70d","added_by":"auto","created_at":"2026-02-26 13:25:23","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":352281,"visible":true,"origin":"","legend":"","description":"","filename":"animalethic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/4c04ba5df90f265b583840a2.pdf"},{"id":103166165,"identity":"d37acc15-3861-43a2-a5d4-e9cb14dcca08","added_by":"auto","created_at":"2026-02-22 12:37:29","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1069167,"visible":true,"origin":"","legend":"","description":"","filename":"humanethic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/fc453341b4d01f2dac5b7b47.pdf"},{"id":103504848,"identity":"45160eb3-45c2-413e-9044-7df3cb150c65","added_by":"auto","created_at":"2026-02-26 13:21:44","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1473456,"visible":true,"origin":"","legend":"","description":"","filename":"actinmerge.tif","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/b0c8e23d8a027e00580aa9ce.tif"},{"id":103505641,"identity":"09d976a6-3477-4e26-8993-0deb38a5706c","added_by":"auto","created_at":"2026-02-26 13:32:21","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1473456,"visible":true,"origin":"","legend":"","description":"","filename":"KLAMERGE.tif","url":"https://assets-eu.researchsquare.com/files/rs-7867472/v1/3eb224ec0f9d9451ee31f7a1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"CALR-Regulated Lactylation Modifications in Periodontitis: Insights from Bulk and Single-Cell RNA Sequencing","fulltext":[{"header":"Background","content":"\u003cp\u003ePeriodontitis is a global public health issue characterized by chronic inflammation of the periodontal tissues and progressive bone resorption[1, 2]. Its prevalence remains high among adults and has become the leading cause of tooth loss in this population[3]. The pathological progression of periodontitis involves complex interactions between the host immune system and the oral microbiome, where the dysregulation of innate and adaptive immunity is a key mechanism driving tissue destruction[4, 5, 6]. Innate immune cells respond rapidly to pathogen invasion through pattern recognition receptors, while adaptive immune cells regulate the inflammatory process through specific responses[7, 8]. However, excessive activation or dysfunction of either immune component can lead to irreversible damage to the periodontal supporting structures.\u003c/p\u003e \u003cp\u003eIn recent years, the interplay between metabolic reprogramming and the immune microenvironment has emerged as a frontier in the study of inflammatory diseases[9, 10]. Lactate, traditionally regarded as a mere byproduct of glycolysis, has now been recognized as a key signaling molecule with roles in both epigenetic regulation and immune modulation[11]. In the tumor microenvironment, lactate promotes immune evasion by inhibiting T cell glycolytic activity[12, 13, 14]; in rheumatoid arthritis, lactate produced by synovial fibroblasts via lactate dehydrogenase A (LDHA) activates the HIF-1α/IL-1β axis, exacerbating joint destruction[15]. Notably, lactate can directly regulate gene transcription through histone lactylation. For instance, in a sepsis model, lactate-induced H3K18 lactylation enhances METTL3 promoter activity, upregulating the expression of the m6A methyltransferase and ultimately driving ferroptosis in alveolar epithelial cells[16, 17]. In the field of periodontitis, although mass spectrometry-based analyses have confirmed widespread protein lactylation in rat periodontal tissues[18], the specific characteristics and pathological significance of lactylation modifications in human periodontitis remain unclear.\u003c/p\u003e \u003cp\u003eThis study reveals a significant elevation of lysine lactylation on proteins in the gingival tissues of periodontitis patients, with modification patterns closely correlated with the severity of local inflammation. Using a mouse model of periodontitis, we demonstrate that exogenous lactate administration can partially alleviate gingival inflammation and bone resorption, suggesting a potential therapeutic role of lactate metabolism in periodontal tissue repair. By integrating single-cell transcriptomic sequencing, bulk RNA-seq data, and machine learning algorithms, we identified nine key lactylation-associated hub genes, including \u003cem\u003ePPP1CB\u003c/em\u003e and \u003cem\u003eCALR\u003c/em\u003e, which show strong correlations with the infiltration levels of immune subpopulations such as Type 2 T helper cells and CD56dim natural killer cells. Functional studies demonstrated that \u003cem\u003eCALR\u003c/em\u003e deficiency perturbs local lactate metabolism and aggravated bone resorption. Thus, this work reveals for the first time the novel role of \u003cem\u003eCALR\u003c/em\u003e in regulating periodontal bone resorption through modulation of lactate metabolism, implicating the \"lactate-\u003cem\u003eCALR\u003c/em\u003e\" axis as critically involved in periodontal bone homeostasis. These findings provide a rational theoretical foundation for developing targeted therapeutic strategies against lactylation-driven pathology.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eCollection of Clinical Samples\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eGingival tissues were collected from 12 participants (6 males, 6 females; mean age: 34.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.85 years) at Nanfang Hospital. The study was approved by the Ethics Committee (NFEC-2025-031), with written informed consent obtained. Healthy controls met criteria: probing depth (PD)\u0026thinsp;\u0026le;\u0026thinsp;3 mm, clinical attachment loss\u0026thinsp;\u0026le;\u0026thinsp;2 mm, and normal gingival features (pink color, thin margins, no bleeding). Periodontitis patients had PD\u0026thinsp;\u0026ge;\u0026thinsp;5 mm, bleeding/suppuration post-therapy. Exclusions included pregnancy, systemic diseases, or antibiotic use within 3 months.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHematoxylin and Eosin (H\u0026amp;E) Staining\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eTissues were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned. After dewaxing via gradient xylene, sections were stained with hematoxylin (5 min), treated with 1% acid alcohol (2 sec), and counterstained with eosin (2 min). Slides were dehydrated, cleared in xylene, and mounted with neutral resin. Images were captured for histopathological analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemical (IHC) Staining​\u003c/b\u003e​\u003c/p\u003e \u003cp\u003eHuman gingival tissues were fixed in 4% paraformaldehyde and embedded in paraffin. Immunohistochemical (IHC) staining was performed according to the manufacturer\u0026rsquo;s standard protocol. To prevent non-specific antibody binding, normal goat serum was used for blocking. Following antigen retrieval and blocking of non-specific antigens, tissue sections were incubated overnight at 4\u0026deg;C with a pan-Kla antibody diluted 1:100. Negative control sections were processed identically but without primary antibody. Regions of interest (ROI) were observed under a BX63 microscope (Olympus, South District, MA, USA). All images were semi-quantitatively analyzed using average optical density (AOD).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantitative Real-Time PCR (qRT-PCR)\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eTotal RNA was extracted from gingival tissue samples using an RNA extraction kit (EZBioscience), isolating RNA from both healthy and periodontitis-affected gingival tissues. The extracted RNA was then reverse transcribed into cDNA using the Color Reverse Transcription Kit (EZBioscience, A0010CGQ). Quantitative real-time PCR (qRT-PCR) was performed using the QuantStudio\u0026trade; Real-Time PCR Software (Thermo Fisher Scientific, V1.3) to assess the expression levels of inflammation and lactylation-related genes. Primer sequences were designed using the Primer-BLAST tool from the NCBI website, with detailed sequences provided in Supplementary Material 1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWestern Blot Analysis​\u003c/b\u003e​\u003c/p\u003e \u003cp\u003eGingival tissues were lysed in pre-chilled RIPA buffer. Protein concentrations were determined using the BCA assay. Equal amounts of protein were separated by 10% SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). Membranes were blocked with 5% non-fat milk, then incubated overnight at 4\u0026deg;C with primary antibodies against pan-Kla (PTM BioLab, Hangzhou, China) and β-actin (Proteintech, Wuhan, China). After washing, HRP-conjugated secondary antibodies were applied, and signals were detected using a chemiluminescence detection system.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstablishment of Mouse Model of Periodontitis​\u003c/b\u003e​\u003c/p\u003e \u003cp\u003e36 male C57BL/6J mice (7 weeks) were purchased from the Laboratory Animal Center of Southern Medical University. Mice were housed in SPF facilities with free access to food/water. The animal protocol was approved by the Institutional Ethics Committee (IACUC-LAC-20240527-009). After intraperitoneal anesthesia with avertin (100 mg/kg), bilateral maxillary second molars were ligated with 5\u0026thinsp;\u0026minus;\u0026thinsp;0 silk sutures. The ligation remained intact for 14 days, with daily checks for stability. Mice were randomized into three groups (n\u0026thinsp;=\u0026thinsp;12/group): blank control (no intervention), simple ligation, and ligation\u0026thinsp;+\u0026thinsp;lactic acid (500 \u0026micro;M, 10 \u0026micro;L, every 2 days via 33G microsyringe). On day 14, animals were deeply anesthetized by intraperitoneal injection of Avertin (100 mg/kg), after confirmation of loss of consciousness, euthanasia was performed by cervical dislocation. The maxillary tissues were then harvested. Six samples per group underwent H\u0026amp;E/IHC staining (fixed in 4% paraformaldehyde, decalcified in EDTA), while others were analyzed by micro-CT. All procedures adhered to ethical guidelines, with no adverse events.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicro-CT Analysis\u003c/h2\u003e \u003cp\u003eThe maxillary bone specimens were fixed in 4% paraformaldehyde solution for 24 hours, followed by dehydration through immersion in 75% ethanol. Micro-computed tomography (micro-CT) scanning was performed using a SkyScan 1276 system (Bruker, Belgium) with operational parameters set to 100 kV voltage, 200 \u0026micro;A current, and an isotropic voxel resolution of 10 \u0026micro;m. The alveolar bone region corresponding to the second maxillary molar was designated as the region of interest (ROI). Quantitative analysis of bone microstructural parameters, including bone volume fraction (BV/TV), trabecular number (Tb.N), and trabecular thickness (Tb.Th), was conducted using SkyScan CTAn software (v1.20.3.0).\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Sources\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eThe mRNA sequencing data on periodontitis, specifically from the GSE16134 and GSE173078 datasets, were sourced from GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE16134 dataset includes 241 periodontitis patient samples and 69 healthy control samples, while the GSE173078 dataset contains 12 disease samples and 12 healthy control samples. DEGs were identified using the R package DESeq2 with specific filtering criteria. Specifically, we set the thresholds of |log2FoldChange| \u0026ge; 0 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for DEG selection in subsequent analyses. The single-cell RNA sequencing (scRNA-seq) data on periodontitis were sourced from the GSE164241 dataset, which is also available via the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This study selected samples from 8 periodontitis cases and 13 normal tissue samples, forming the original dataset. Subsequent analysis was performed using the R package Seurat. In our study, we compiled a list of 336 lactylation-related genes, with detailed information provided in Supplementary Material 2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-Cell RNA Sequencing\u003c/b\u003e (\u003cb\u003escRNA-seq) Analysis\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003escRNA-seq data were filtered (nFeature_RNA\u0026thinsp;\u0026gt;\u0026thinsp;5000, percent_mito\u0026thinsp;\u0026lt;\u0026thinsp;15%, percent_ribo\u0026thinsp;\u0026gt;\u0026thinsp;3%). Data were normalized and scaled using Seurat. Principal component analysis (PCA) was performed, followed by UMAP dimensionality reduction. Cells were clustered and annotated using marker genes. Lactylation scores were calculated via GSVA based on lactylation-related genes and visualized with the ggplot2 R package v4.2.2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Set Variation Analysis (GSVA)\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eHALLMARK, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathways were downloaded from MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The R package \"GSVA\" was used to estimate pathway scores and evaluate the differences in pathways between high and low score groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003emRNA-seq Analysis​​\u003c/h3\u003e\n\u003cp\u003eThe mRNA data from two datasets (GSE16134 and GSE173078) were combined into a single file. Data normalization was performed using the R packages SVA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/sva/\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/sva/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and preprocessCore to remove batch effects. To ensure the effectiveness of batch effect removal, PCA was conducted to visualize the data before and after batch effect removal. Subsequently, the limma package in R was used to establish criteria for the selection of DEGs. Specifically, DEGs were selected based on |log2FoldChange| \u0026gt; 0 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMachine Learning Identification of Core DE-LRGs\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003eBy combining the DEGs identified from both single-cell RNA-seq and mRNA-seq data with the expression profiles of lactylation-related genes, DE-LRGs were identified. The selection criteria for LRGs were adj.P.Val\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 Fold Change| \u0026ge; 0. LASSO is a multivariate linear regression method that adjusts model parameters to avoid overfitting and improve model generalization. LASSO regression was performed using the \"glmnet\" R package, and the results were filtered to select 15 genes. Random forest classifier and SVM analysis were implemented using the \"randomForest\" and \"kernlab\" R packages, with the top 10 genes retained. Finally, the intersection of LASSO regression, random forest, and SVM analysis results was used to identify core lactylation-related genes. The diagnostic value of these hub genes in periodontitis was assessed by calculating the area under the receiver operating characteristic curve (AUC) using the \"pROC\" R package. The interactions between core genes were analyzed using the \"circlize\" R package.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune Cell Infiltration and Immune Relevance Analysis of Core DE-LRGs​\u003c/b\u003e​\u003c/p\u003e \u003cp\u003eBased on the principles of linear support vector regression, CIBERSORT identifies the cellular composition of complex tissues through gene expression profiles. The CIBERSORT algorithm was applied to analyze RNA-seq data from normal and periodontitis tissues to infer the relative proportions of immune infiltrating cells. Pearson correlation analysis was performed between DE-LRGs and immune cell content. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the results were visualized using a bubble chart.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstablishment of\u003c/b\u003e \u003cb\u003eCALR\u003c/b\u003e \u003cb\u003eKnockdown Mouse Model of Periodontitis\u003c/b\u003e​​\u003c/p\u003e \u003cp\u003e36 male C57BL/6J mice (7 weeks) were divided into three groups (n\u0026thinsp;=\u0026thinsp;12/group): Blank Control (no intervention), Simple Ligation (ligation-only), and \u003cem\u003eCALR\u003c/em\u003e Knockdown (ligation\u0026thinsp;+\u0026thinsp;si-\u003cem\u003eCALR\u003c/em\u003e). Mice were anesthetized, and maxillary second molars were ligated with 5\u0026thinsp;\u0026minus;\u0026thinsp;0 silk sutures. For the \u003cem\u003eCALR\u003c/em\u003e Knockdown group, si-\u003cem\u003eCALR\u003c/em\u003e (1 \u0026micro;g/\u0026micro;L, 10 \u0026micro;L) was injected into buccal/palatal gingiva every 3 days for 14 days. On day 14, following euthanasia (as described previously), the maxillary tissues were harvested for micro-CT, H\u0026amp;E staining, IHC, and qRT-PCR.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis​\u003c/b\u003e​\u003c/p\u003e \u003cp\u003eData processing and analysis were performed using GraphPad Prism 7.0 and R (version 4.2.2). Student's t-test was used to analyze differences between groups. All statistical P-values were based on two-tailed tests, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eElevated Protein Lactylation in Gingival Tissues of Periodontitis Patients\u003c/h2\u003e \u003cp\u003eH\u0026amp;E staining confirmed significant inflammatory cell infiltration, gingival epithelial hyperplasia, and structural disorganization in the periodontitis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). IHC and WB analyses revealed that lysine lactylation levels were significantly higher in the periodontitis group compared to healthy controls(Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C, E, F). WB detected lactylated proteins predominantly within the 10\u0026ndash;30 kDa range. Additionally, mRNA expression of IL-6, IL-1β, and TNF-α was significantly upregulated in the periodontitis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLactate Treatment Alleviates Gingival Inflammation and Bone Resorption in Mice\u003c/h3\u003e\n\u003cp\u003eIn a murine periodontitis model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), the ligation group exhibited increased alveolar bone resorption (CEJ-ABC distance) and inflammatory infiltration compared to the control group, while the Ligature\u0026thinsp;+\u0026thinsp;Nala group effectively mitigated these phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Micro-CT analysis demonstrated that Nala treatment significantly reduced ligation-induced bone loss, though it did not fully restore levels to those of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Bone histomorphometric analysis showed that the ligation group exhibited decreased BV/TV, Tb.Th, and Tb.N, along with increased Tb.Sp. Nala treatment partially reversed these bone parameter changes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-H). Local lactate administration significantly enhanced lysine lactylation levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), confirming that lactate alleviates gingival inflammation and bone resorption via modulation of protein lactylation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Lactylation-Associated Genes Using scRNA-seq Data\u003c/h2\u003e \u003cp\u003eScRNA-seq data (GSE164241) revealed 13 cell clusters via UMAP dimensionality reduction, annotated into nine core cell types, including fibroblasts and endothelial cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Differential gene analysis identified the top five genes specifically expressed in each cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Pathway enrichment showed glycolysis and PI3K-AKT-mTOR pathways were active in epithelial/endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), while lactylation modification correlated significantly with MYC target genes V1, p53 pathway, oxidative phosphorylation, and glycolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Lactylation-associated gene expression was upregulated in all cell types except plasma cells, with fibroblasts and endothelial cells exhibiting the highest lactylation scores (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). High-lactylation groups showed a higher proportion of fibroblasts/endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and significant enrichment of inflammatory and glycolytic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of DE-LRGs\u003c/h3\u003e\n\u003cp\u003eIntegration of periodontitis datasets (GSE16134 and GSE173078) after batch effect correction included 253 patients and 81 healthy controls (Supplementary Fig.\u0026nbsp;1). Differential analysis identified 4,225 upregulated and 5,658 downregulated genes in the periodontitis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), visualized via heatmap for the top 20 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). scRNA-seq analysis retained 70,987 cells and 21,935 genes after quality control (Supplementary Fig.\u0026nbsp;2), identifying 220 upregulated and 511 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Cross-analysis of transcriptomic, single-cell differential genes, and lactylation-related genes yielded 16 DE-LRGs: 10 downregulated (e.g., \u003cem\u003ePTMA, PCNP, NPM1\u003c/em\u003e) and 6 upregulated (e.g., \u003cem\u003eCDV3, BRD4, MSN\u003c/em\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E), with expression patterns visualized via heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification and Validation of Core Lactylation-Related Genes\u003c/h3\u003e\n\u003cp\u003eLASSO regression, random forest, and SVM algorithms identified nine core genes (\u003cem\u003eCALR, SOD1, BTF3, HMGN3, PPP1CB, FABP5, CALM1, PCNP, MSN\u003c/em\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D), with their interaction networks visualized via chord diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). ROC curve analysis confirmed high specificity of these genes for periodontitis diagnosis. qRT-PCR validation demonstrated that, except for \u003cem\u003eSOD1\u003c/em\u003e, the expression trends of the remaining eight genes in patient gingival tissues aligned with sequencing results, with \u003cem\u003eCALR\u003c/em\u003e showing the most significant difference (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-O).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration Analysis\u003c/h2\u003e \u003cp\u003essGSEA revealed significant alterations in infiltration levels of 20 immune cell types (except CD56bright/CD56dim NK cells and Th2 cells) in the high-risk periodontitis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eP). DE-LRGs correlated closely with the immune microenvironment: upregulated genes \u003cem\u003eCALR, CALM1\u003c/em\u003e, and \u003cem\u003eMSN\u003c/em\u003e showed positive correlations with na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells and resting NK cells, but negative correlations with follicular helper T cells, with \u003cem\u003eCALR\u003c/em\u003e exhibiting the strongest immune regulatory associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eQ), suggesting its pivotal role in periodontitis-related immune dysregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003esi-\u003cem\u003eCALR\u003c/em\u003e Knockdown Exacerbates Gingival Inflammation and Bone Resorption in Mice\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCALR\u003c/em\u003e knockdown (si-\u003cem\u003eCALR\u003c/em\u003e group) significantly reduced \u003cem\u003eCALR\u003c/em\u003e expression compared to control and si-NC groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-E), accompanied by decreased lactate levels and increased IL-18 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, H). Phenotypic analysis revealed aggravated inflammatory cell infiltration and alveolar bone resorption in the si-\u003cem\u003eCALR\u003c/em\u003e group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG), with micro-CT quantification confirming the most severe bone loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). Bone parameter analysis demonstrated significantly reduced BV/TV, Tb.Th, and Tb.N, along with increased Tb.Sp in the si-\u003cem\u003eCALR\u003c/em\u003e group (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ-M), indicating that \u003cem\u003eCALR\u003c/em\u003e deficiency exacerbates gingival inflammation and bone destruction by suppressing lactate production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study confirmed elevated levels of protein lactylation in the gingival tissues of periodontitis patients, demonstrating a positive correlation with the severity of inflammation and bone resorption. Animal experiments revealed that exogenous lactate intervention alleviated gingival inflammation and reduced bone loss by enhancing lactylation modification, suggesting that lactylation may play a dual regulatory role in both metabolic and immune processes in periodontitis.\u003c/p\u003e \u003cp\u003eDifferent types of immune cells exhibit distinct functional roles in inflammatory responses, and their metabolic demands as well as the activation levels of lactate metabolic pathways may significantly influence disease progression[19, 20, 21]. Using single-cell transcriptomic data, this study preliminarily explored cell type-specific expression patterns of lactylation-related genes in the periodontal microenvironment. Fibroblasts and endothelial cells displayed relatively high lactylation features, and the coordinated activation of glycolysis-related genes (e.g., LDHA, SLC16A3) and the PI3K/AKT/mTOR signaling pathway aligned with previously reported metabolic reprogramming in stromal cells[22, 23], suggesting that the coupling of glycolysis and oxidative phosphorylation may provide energy support for extracellular matrix (ECM) remodeling during chronic inflammation. On the other hand, plasma cells showed significantly lower lactate metabolism levels compared to other immune cells, which may be related to their terminally differentiated functional specialization[24, 25]: pro-inflammatory myeloid cells tend to adopt Warburg-like metabolism to sustain inflammatory responses[26], whereas plasma cells rely more on endoplasmic reticulum biogenesis to support antibody secretion[27]. This metabolic heterogeneity implies that different immune cells may employ divergent adaptive strategies within the chronic inflammatory microenvironment, providing preliminary clues for understanding the role of immunometabolic regulation in the progression of periodontitis.\u003c/p\u003e \u003cp\u003eTo further investigate the potential functions of lactate metabolism-related genes in periodontitis, this study employed a multi-algorithm screening strategy and identified nine DE-LRGs, including \u003cem\u003eCALR\u003c/em\u003e and \u003cem\u003eSOD1\u003c/em\u003e. These genes showed potential associations with immunometabolic processes in certain analyses. Diagnostic models suggested that this gene set has some ability to discriminate disease status, though experimental validation revealed inconsistencies with predictions for some genes. For instance, the expression trend of \u003cem\u003eSOD1\u003c/em\u003e in tissue samples did not fully align with bioinformatic predictions, possibly due to sample source heterogeneity, dynamic changes in the microenvironment, or masking of cell subset-specific expression by bulk tissue analysis. Despite discrepancies in individual genes, the overall data indicate that DE-LRGs may play a role in the development of periodontitis. This set of lactylation-related genes provides candidate directions for future research, though their specific functions and clinical applicability require further validation.\u003c/p\u003e \u003cp\u003eThe initiation and progression of periodontitis involve complex immune changes[28], in which dysregulation of lactate metabolism may play an important regulatory role. To explore potential links between lactate metabolism and immune responses, this study applied single-sample gene set enrichment analysis (ssGSEA) to characterize immune cell infiltration in periodontitis-affected gingival tissues. Results showed that DE-LRG expression was positively correlated with the infiltration of Th2 cells and CD56dim NK cells, suggesting their potential involvement in immune regulation by promoting the secretion of reparative cytokines (e.g., IL-4, IL-13) and enhancing cytotoxic function[29]. In contrast, a negative correlation was observed with the infiltration of innate immune cells such as monocytes and macrophages, implying that lactate metabolism may suppress excessive activation of innate immunity[30]. This bidirectional correlation pattern suggests that lactate metabolic dysregulation may be associated with a shift in the periodontal microenvironment from an innate immunity-dominated pro-inflammatory state to a chronic state involving adaptive immunity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCALR\u003c/em\u003e may serve as an important regulatory node, as its expression is associated not only with adaptive immune responses[31, 32]but also with the regulation of innate immune cell function. Previous studies have indicated that \u003cem\u003eCALR\u003c/em\u003e can act as an \"eat-me\" signal to promote the phagocytic clearance of apoptotic cells by macrophages, supporting its potential role as an immune bridge in inflammatory microenvironments[33]. In a mouse periodontitis model, \u003cem\u003eCALR\u003c/em\u003e knockdown led to aggravated inflammation, increased bone resorption, and reduced local lactate levels. This observation appears inconsistent with the traditional view emphasizing the pro-inflammatory properties of lactate.\u003c/p\u003e \u003cp\u003eHowever, emerging studies suggest that under specific pathological conditions (e.g., hypoxia or immunosuppressive states), lactate may exert anti-inflammatory and pro-repair functions[19, 34]. Our results indicate that in the chronic inflammatory environment of periodontitis, lactate may exert a potentially protective regulatory effect through \u003cem\u003eCALR\u003c/em\u003e-mediated metabolic-immune interactions, beyond its role as a metabolic byproduct. The concurrent decrease in lactate levels and increase in bone resorption following \u003cem\u003eCALR\u003c/em\u003e knockdown suggest that \u003cem\u003eCALR\u003c/em\u003e may be involved in maintaining local metabolic homeostasis. Existing evidence indicates that lactate can influence bone remodeling by modulating osteoclast activity and local pH balance[35, 36], highlighting its dual identity as both a metabolite and a signaling molecule. This study proposes that \u003cem\u003eCALR\u003c/em\u003e may influence bone homeostasis through the regulation of lactate metabolism, providing preliminary experimental evidence for a potential link between \u003cem\u003eCALR\u003c/em\u003e, lactate metabolism, and bone stability in periodontitis.\u003c/p\u003e \u003cp\u003eBy integrating scRNA-seq derived DEGs, bulk RNA-seq DEGs, and lactylation-related genes, this study identified a set of hub lactylation-related genes that may play roles in periodontitis. Preliminary exploration of their correlations with the immune microenvironment provided initial evidence supporting the involvement of lactate accumulation in the metabolic-immune crosstalk in periodontitis. Analyses also indicated an association between elevated lactate levels in inflamed gingival tissues and periodontitis, suggesting that lactylation-related genes may represent potential targets for further research. Additionally, \u003cem\u003eCALR\u003c/em\u003e was found to potentially influence the immune microenvironment via lactate metabolism, supporting the putative role of lactate metabolic dysregulation in immune modulation. However, this study has several limitations: Due to inherent constraints of bioinformatic methodologies, the functional roles of hub DE-LRGs in disease pathogenesis and their therapeutic relevance require further experimental validation. Moreover, as the analysis relied on public database information, it was not possible to fully adjust for confounding factors such as age, sex, ethnicity, and comorbidities. Future animal studies and clinical investigations are needed to validate these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals that lactylation modification in periodontitis influences inflammation and bone resorption via metabolic-immune dual regulatory mechanisms. Single-cell analysis highlights cell-type-specific metabolic reprogramming, with 9 lactate-related genes exhibiting diagnostic potential (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7). \u003cem\u003eCALR\u003c/em\u003e, identified as a hub gene, regulates lactate metabolism to maintain bone homeostasis, challenging the traditional pro-inflammatory perception of lactate. These findings provide novel strategies for targeting lactate metabolic networks in therapeutic interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eABC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlveolar Bone Crest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage Optical Density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBV/TV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBone Volume/Total Volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCALR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalreticulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCemento-Enamel Junction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIBERSORT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCell-type Identification By Estimating Relative Subsets Of RNA Transcripts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDE-LRGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferentially Expressed Lactylation-Related Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtracellular Matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u0026amp;E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHematoxylin and Eosin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLysine lactylation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLASSO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLactate Dehydrogenase A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emicro-CT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMicro-Computed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003erobing depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuantitative Real-Time Polymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRegion of Interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003escRNA-seq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle-Cell RNA Sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003essGSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle-Sample Gene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupport Vector Machine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTb.N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrabecular Number\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTb.Sp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrabecular Separation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTb.Th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrabecular Thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUMAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWestern Blot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in compliance with ARRIVE 2.0 guidelines. Single-sex (male) design was justified by eliminating estrogen interference in bone metabolism.Written informed consent was obtained from patients or their guardians prior to study enrollment. This research was reviewed and approved by the Ethics Committee of Southern Medical University (Approval No.NFEC-2025-031) and conducted in strict accordance with \u003cem\u003ethe\u003c/em\u003e \u003cem\u003eWorld Medical Association Declaration of Helsinki\u003c/em\u003e. Animal studies were approved by the Animal Welfare and Use Committee of Southern Medical University Nanfang Hospital (Approval\u0026nbsp;No.IACUC-LAC-20240527-009) and complied with the guidelines established by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy submitting my article I agree to pay the APC in full if my article is accepted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll supporting data for this study are fully preserved in the main text and supplementary materials. Key datasets (GSE16134，GSE173078 and \u0026nbsp;GSE164241) were derived from the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/), and raw data are publicly accessible via the repository platform (dataset query URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134/ GSE173078/ GSE164241).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors hereby declare that there are no potential conflicts of interest related to the authorship or publication of this article. The funding agencies had no involvement in the study design, data collection, statistical analysis, interpretation of results, manuscript preparation, or decision to publish the findings, and did not exert any influence on the research conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the President Foundation of Nanfang Hospital, Southern Medical University (2024A035) and Guangzhou Municipal Science and Technology Project (2024A04J5188).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLu Chen, Lu Wang, and Yuan Zhou led the research design and implementation, managed data collection and analysis, and participated in drafting the manuscript. Zhao Chen and Huiyong Xu jointly oversaw the overall project coordination, provided critical interpretation of results, revised the manuscript through multiple iterations, and approved the final version for submission. Jiahao Linand Yongxi Luo were responsible for experimental data collection and analysis, contributed to result interpretation, and participated in manuscript revisions. Cheng Zeng, Xinmiao Luo and Qingxia Zhao handled experimental data acquisition and preliminary analysis. All authors jointly reviewed the final manuscript and assume collective responsibility for the scientific rigor of the study design, reliability of the data, and academic integrity of the entire work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks to the research team that generously shared the single-cell RNA sequencing database (GSE16134，GSE173078 and GSE164241), which provided critical data support for this research. The authors extend their sincere gratitude to all collaborating institutions and scholars for their invaluable assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBelluci MM, de Molon RS, Rossa CJ, Tetradis S, Giro G, Cerri PS, et al. Severe magnesium deficiency compromises systemic bone mineral density and aggravates inflammatory bone resorption. \u003cem\u003eJ Nutr Biochem\u003c/em\u003e (2020) 77:108301. doi: 10.1016/j.jnutbio.2019.108301.\u003c/li\u003e\n\u003cli\u003eNoriega Muro ST, Cucina A. 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Endothelial Cell-Derived lactate triggers bone mesenchymal stem cell histone lactylation to attenuate osteoporosis. \u003cem\u003eAdv Sci (Weinh)\u003c/em\u003e (2023) 10(31):e2301300. doi: 10.1002/advs.202301300.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Periodontitis, Lactylation, Immune Response, Bioinformatics, Transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-7867472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7867472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLactic acid accumulates in periodontal tissues during periodontitis, suggesting disrupted metabolism may contribute to disease progression. However, the role of lactic acid and its modifications, such as lactylation, remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGingival tissues were collected from healthy controls and periodontitis patients. Protein lactylation levels were evaluated through immunohistochemistry and molecular detection. A mouse periodontitis model was established with local lactate intervention, and bone resorption was quantified using micro-computed tomography (micro-CT). Periodontitis transcriptomic and single-cell sequencing data from the Gene Expression Omnibus (GEO) database were integrated to screen differentially expressed lactylation-related genes (DE-LRGs). The Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and support vector machine (SVM) algorithms were applied to identify core genes. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) was employed to analyze their association with immune infiltration. A \u003cem\u003eCALR\u003c/em\u003e knockdown mouse model was constructed to validate gene function.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eProtein lactylation levels were significantly elevated in gingival tissues of periodontitis patients and positively correlated with inflammation severity. In mouse models, lactate intervention alleviated gingival inflammation and bone resorption. Through multi-omics analysis, \u003cem\u003eCALR\u003c/em\u003e was identified as a core regulatory factor among key lactylation-related genes. \u003cem\u003eCALR\u003c/em\u003e knockdown mice exhibited decreased lactate levels, aggravated inflammation, and significantly increased bone resorption, confirming its role in regulating the periodontal immune microenvironment through lactate metabolism.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLactylation modification participates in immune regulation during periodontitis. The screened core LRGs, especially \u003cem\u003eCALR\u003c/em\u003e, represent potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"CALR-Regulated Lactylation Modifications in Periodontitis: Insights from Bulk and Single-Cell RNA Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:37:24","doi":"10.21203/rs.3.rs-7867472/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-17T12:56:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332125642183076577336021238094009467526","date":"2026-02-17T10:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T13:21:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T07:40:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T16:59:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-12T08:54:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-11-12T08:50:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"808cb077-511c-43ef-8f5a-a2680889fe6a","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T12:37:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:37:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7867472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7867472","identity":"rs-7867472","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}