Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis

Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis | 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 Article Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis Xuedi Qiu, Fan Yang, Chenxi Li, Jian Wang, Yawen Yuan, Chao Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5300947/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Periodontitis is a prevalent chronic inflammatory disease characterized by immune cell dysregulation and tissue destruction. This study integrates single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and expression quantitative trait loci (eQTL) analyses to uncover immune cell subtypes, causal genes, and develop a predictive nomogram model for periodontitis. Methods We analyzed scRNA-seq data to identify differentially expressed genes (DEGs) and immune cell subtypes in periodontitis. MR analysis was conducted to determine causal relationships between immune cell gene expression and periodontitis risk, utilizing eQTL data. Gene ontology (GO) and pathway enrichment analyses were performed to understand functional implications. Additionally, CellChat trajectory analysis explored intercellular communication. A nomogram model was constructed to predict periodontitis risk based on immune-related DEGs. Results The integrated analysis identified 23 distinct immune cell clusters and seven hub genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) that were causally linked to periodontitis. Pathway enrichment analysis revealed their involvement in key immune regulatory mechanisms. A robust nomogram model based on these DEGs was developed and validated, demonstrating high predictive accuracy for periodontitis risk. Immune subtypes were further characterized, revealing distinct roles in disease progression. Conclusion This study highlights the crucial role of immune cell subpopulations and hub genes in the pathophysiology of periodontitis. The nomogram model offers a novel tool for predicting periodontitis risk and identifying potential therapeutic targets. These findings provide valuable insights into immune-related mechanisms and potential interventions for periodontitis. Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Immunology/Gene regulation in immune cells Immune cell Single-cell RNA sequencing Mendelian randomization Periodontitis Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Periodontitis is a chronic infectious and adaptive inflammatory disease, with a global prevalence of approximately 50%, making it the sixth most common disease worldwide 1 . The condition is characterized by gingival inflammation and periodontal destruction, often following the infiltration of immune cells into affected regions 2 , 3 . Mechanical irritation and dental plaque are major contributors to significant alterations in the oral microbiome and salivary pH, both of which can lead to periodontitis 4 , 5 , 6 . In contrast, periodontitis involves inflammation that causes irreversible loss of bone and connective tissue attached to the tooth root or surrounding bone. Immune dysbiosis plays a significant role in tissue destruction, particularly in inflammatory bone diseases. Factors such as bacterial endotoxins and inflammatory mediators induce osteoclast differentiation and proliferation, inhibit osteoblast activity, disrupt bone resorption formation balance, and leading to alveolar bone resorption 7 , 8 . Activated immune cells, particularly T and B cells, have been identified as key contributors to the progression of periodontitis 9 , 10 . Additionally, research indicated that periodontal soft tissues undergo notable morphological and functional changes during orthodontic treatment 11 . Researches elucidated the role of immune cells was critical for developing effective preventive and therapeutic strategies in periodontitis. The development of periodontitis is influenced by various factors, including genetics, environmental conditions, microbial infections, and lifestyle[13]. Despite significant advancements in the study of periodontitis, the specific etiology and pathogenesis at the molecular level remain insufficiently understood. Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity and function. It plays a crucial role in uncovering molecular mechanisms and identifying target cells. This technology also enables high-throughput sequencing of individual cells at genomic, transcriptomic, and epigenomic levels, revealing the heterogeneity among cells and the complexity of gene expression regulation 12 . scRNA-seq have been applied in research on tumors, microbiology, neurology, reproduction, immunology, as well as digestive and urinary systems, highlighting the critical role of single-cell sequencing in both basic and clinical research 13 . Furthermore, single-cell sequencing offers exceptional precision for clinical research and diagnosis, particularly in tumor immunity, neuroscience, embryonic development, and reproduction[16–18]. Recently, a comprehensive single-cell periodontitis map has been constructed using single-cell RNA sequencing, offering valuable insights into the molecular alterations in human dental pulp caused by periodontitis. However, scRNA-seq presents limitations in accurately classifying cells. To address these challenges, we integrated multiple datasets—including eQTL 14 , GWAS 15 , and scRNA-seq 16 —using machine learning to identify immune cell-related diagnostic features and clusters in periodontitis. This integrated model of immune response offers a novel approach for the diagnosis and treatment of periodontitis, providing valuable insights for personalized therapeutic strategies. Mendelian randomization (MR) is a novel statistical approach that examines causal relationships by leveraging genetic variants as unbiased “natural randomized trials”. These genetic variants are randomly allocated at conception and depend on parental genotypes. MR studies estimate the causal effects of exposures on outcomes while minimizing confounding factors and reverse causation 17 . This approach enables the classification and characterization of each cell at single-cell resolution, facilitating the discovery of biological pathways associated with transcriptomic signatures and phenotypic outcomes 18 . The combination of single-cell sequencing technology and Mendelian randomization offers a more comprehensive approach to studying caries from a molecular perspective 19 . In our study, key genes associated with periodontits were identified through single-cell sequencing, while Mendelian randomization was employed to further select genetic variants closely linked to the onset and progression of periodontitis. This integrated model of immune response offers a novel approach for the diagnosis and treatment of periodontitis, offering novel insights into personalized treatment approaches. Materials and methods Data collection : 1) We obtained dataset GSE174609 20 and GSE156993 21 . The GEO database ( https://www.ncbi.nlm.nih.gov/geo/info/datasets.html ), the GENE EXPRESSION OMNIBUS(GEO), is a gene expression database created and maintained by the National Center for Biotechnology Information, NCBI. GSE174609 includes single-cell data from eight samples—four from the control group and four from the disease group—downloaded from the NCBI, GEO public database. We retrieved the Series Matrix File of GSE156993, which contains expression profile data from 12 patients. The dataset was annotated using the GPL570 platform file, including 6 in the control group and 6 in the disease group. 2) Exposed data: Exposure data were sourced from the eQTLGen consortium ( https://www.eqtlgen.org ), a comprehensive project aimed at investigating the genetic architecture and its connection to complex traits in blood gene expression. In currently second phase, the eQTLGen project focuses on performing genome-wide meta-analyses of blood samples, and conducting 86 large-scale genome-wide meta-analyses in blood. 3) Outcome data: The FinnGen database focuses on genetic research within European populations, aim to studying unique genetic diseases and mutations prevalent in this group 22 . With data collected from a vast number of samples in various regions, it emphasizes the connection between genetic variation and disease. FinnGen is particularly significant for understanding the role of genetics in public health, especially in the realms of disease prevention and personalized medicine. This resource is invaluable to geneticists and epidemiologists aiming to better comprehend how genetic differences influence health and disease outcomes. In the context of periodontitis (finn-b-K11_PERIODON_CHRON), the dataset includes 3,046 cases and 195,395 controls Single cell RNA sequence analysis: The expression profile data were processed using the Seurat package, which filtered out low-quality cells and removed doublets through the DoubletFinder tool. Following this, the data were standardized and normalized, with subsequent PCA and UMAP analyses performed. UMAP analysis was used to reveal the positional relationships between clusters. These clusters were annotated based on information from existing literature and the CellMarker database, with specific annotations applied to cells related to periodontitis. To identify differentially expressed genes, the FindMarkers function was employed. This tool compares gene expression differences between immune cell subtypes, typically accepting samples from two or more groups and identifying genes with significant expression differences. Mendelian randomization analysis: Causal relationships between eQTL and disease were extracted from the screened outcome IDs. Single nucleotide polymorphisms (SNPs) associated with each gene, meeting the significance threshold (P < 1e-8) across the entire locus, were selected as potential instrumental variables (IVs). Linkage disequilibrium (LD) was calculated for each SNP, retaining only those with R² < 0.001 (clumping window size = 10,000 kb) and p < 5e-8. The analysis was conducted using the Inverse Variance Weighted (IVW) method, which aggregates Wald estimates for each SNP through meta-analysis, as well as MR Egger (which assumes instrument strength is independent of direct effects, InSIDE), the weighted median (allowing correct causality estimates in up to 50% of invalid IV cases), and the weighted mode (which offers improved detection of causal effects, lower bias, and reduced Type I error compared to MR Egger regression). For cases where only a single statistical method was available, the Wald ratio was used to assess the reliability of the causal relationships and estimate the overall impact of cis- and trans-region gene expression in peripheral blood on periodontitis lesions. The final causal relationships were verified using heterogeneity analysis (Cochran's IVW Q test) and genetic diversity testing. Heterogeneity test analysis: In this study, we employed the Mendelian heterogeneity test to assess the presence of statistical heterogeneity among the single nucleotide polymorphisms (SNPs) analyzed. The weighted sum of squares of effect sizes and standard errors for each SNP was calculated, generating a Q value, which follows a chi-square distribution with one degree of freedom less than the number of SNPs studied. A p-value greater than 0.05 for the Q value indicates insufficient evidence for heterogeneity in the effect sizes, suggesting that the SNPs' impact on disease risk is statistically consistent. GSEA enrichment analysis: Patients were categorized into high and low gene expression groups, and the differences in signaling pathways between these groups were assessed using Gene Set Enrichment Analysis (GSEA). The background gene set, annotated for subtype pathways, was sourced from the MsigDB database. Differential pathway expression between subtypes was evaluated, and significantly enriched gene sets (adjusted p-value < 0.05) were selected based on consistency scores. GSEA is commonly applied in research to closely link disease classification with biological significance. Construction Nomogram model Based on immune cell-related DEGs features, we used the “rms” package to construct the nomogram model to predict risk in periodontitis. In addition, “Calibration curve” and “clinical impact curve” were evaluated and verified the accuracy and efficiency of this model. Gene Set Variation Analysis (GSVA): GSVA is an unsupervised, non-parametric method used to evaluate gene set enrichment across the transcriptome. It transforms gene-level changes into pathway-level changes by calculating comprehensive scores for the gene sets of interest, allowing the determination of the biological function of each sample. In this study, gene sets were downloaded from the Molecular Signatures Database, and the GSVA algorithm was applied to score each gene set and evaluate differences. Regulatory network analysis of key genes: The 'RcisTarget' package in R was used to predict transcription factors based on motif analysis. The normalized enrichment score (NES) for each motif was calculated using the total number of motifs in the database. In addition to the motifs directly annotated by the source data, supplementary annotations were inferred by comparing motif similarities and gene sequences. The initial step involved calculating the area under the curve (AUC) for each motif-gene pair, using recovery curve calculations based on the gene set's ranking against the motifs. The NES for each motif was then derived from the AUC distribution of all motifs within the gene set. Immune infiltration analysis: The single-sample Gene Set Enrichment Analysis (ssGSEA) is a widely used technique for evaluating immune cell types within the microenvironment. It distinguishes 29 human immune cell phenotypes, including T cells, B cells, and NK cells. In this study, ssGSEA was used to quantify the proportions of these immune cell types in the expression profile, and perform correlation analysis between gene expression and immune cell content. Ligand-receptor interaction analysis (Cellcall): CellCall, a toolkit designed to infer intercellular communication networks and regulatory signals, was employed to integrate both intracellular and intercellular signals. It compiles ligand-receptor-transcription factor (L-R-TF) axis datasets based on KEGG pathways. Using prior knowledge of L-R-TF interactions. This approach enabled the inference of intercellular communication by analyzing specific ligand-receptor pairs and their downstream effects. Developmental trajectories of key cell subtypes: Studies at the single-cell level allow one to characterize complex physiological processes and the transcriptional regulation of highly heterogeneous cell populations. These studies have led to the discovery of genes that identify specific cell subtypes, genes that mark intermediate states of biological processes, and genes that are in transition states between two different cell fates. In many single-cell analyses, gene expression occurs asynchronously across individual cells, with each cell representing a specific moment in the transcriptional process. Monocle applies a pseudotime (pseudochronology) strategy, utilizing the asynchrony of gene expression to position cells along trajectories to place them on trajectories corresponding to biological processes such as cell differentiation. Statistical Analysis: Reliable Mendelian randomization (MR) analysis relies on three key assumptions: (1) the correlation assumption, where the instrumental variable is strongly associated with the exposure but not directly with the outcome; (2) the independence assumption, which requires the instrumental variable to be independent of confounding factors; and (3) the exclusivity hypothesis, where instrumental variables affect outcomes only through exposure. If the instrumental variable influences outcomes through other pathways, gene pleiotropy is assumed. All statistical analyses were conducted using R software (version 4.3.2), with a p-value of less than 0.05 considered statistically significant Results Single cell expression profile data This analysis utilized expression profiles from eight periodontal tissue remodeling-related tissue samples (S1 Fig. A). The data were processed using the Seurat package, where cells were filtered based on several criteria: total number of unique molecular identifiers (UMIs) per cell, the number of expressed genes, and the percentage of mitochondrial reads. Outliers were defined as those falling three median absolute deviations (MAD) from the median. Using the UMAP package, 18 distinct cellular clusters were identified within the periodontitis dataset (Fig. 1A). Low-expressing cells were filtered out based on violin and scatter plots using the criteria (nFeature_RNA > 200, percent.mt ≤ 3 MAD, nFeature_RNA ≤ 3 MAD, and nCount_RNA ≤ 3 MAD, percent.ribo ≤ 3 MAD). The DoubletFinder package was then employed to exclude doublets, resulting in the inclusion of a total of 66,209 cells for further analysis. Additionally, the 10 genes with the highest standard deviations were identified, with key hub genes such as PPBP, HBB, IGKC, IGLC2, IGLC3, HBA2, HBA1, S100A9, S100A8 , and LYPD2 being detected (Fig. 1E, S1 Fig. D). Cell subpopulation annotation for single cell data Immune cell characteristics in periodontitis were further explored by comparing transcriptomic differences across subpopulations. Cell subpopulations were annotated based on their distinct profiles. Through principal component analysis (PCA) for dimensionality reduction, a batch effect between samples was identified (S1 Fig. D), which was subsequently corrected using Harmony analysis (S1 Fig. E). The optimal number of principal components (PCs) determined by the ElbowPlot was 12 (S1 Fig. F). Ultimately, 23 subtypes were obtained via UMAP clustering. These subtypes were annotated to include CD4 + T cells, Naive CD4 + T cells, Naive CD8 + T cells, CD8 + T cells, Naive B cells, B cells, NK cells, Monocytes, Dendritic cells , and Progenitor cells (Fig. 1B). For each cell category, a bubble chart was generated to display classic markers for 10 immune cell types (Fig. 1C), as well as the proportion of these cells in disease and control samples (Fig. 1D). Differentially expressed genes (DEGs) for each immune cell subtype were identified, and marker genes were highlighted using the findMarkers function (Fig. 1E). The selection criteria for DEGs were set as |avg_log2FC| > 1.5 and p_val_adj < 0.05. The identified markers are detailed in the supplementary output (Output_markers.csv). Mendelian Randomization Analysis Mendelian randomization (MR) is a widely used causal inference approach that utilizes single nucleotide polymorphisms (SNPs) as instrumental variables to evaluate the causal effect of an exposure on an outcome 16 . In this study, SNPs associated with immune cell subtypes were used as instruments to assess their potential impact on periodontitis. Four MR methods were applied—inverse-variance weighted (IVW), MR-Egger, weighted median, and MR-PRESSO—to ensure the robustness of the causal estimates. The results from all four methods showed consistent effect estimates, indicating reliable associations. To further identify key genes influencing periodontitis, we used summary statistics from 198,441 periodontitis-related samples (controls: 195,395; cases: 3,046) in the FinnGen database (finn-b-K11_PERIODON_CHRON). The MR analysis revealed significant causal relationships for seven genes: ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1 , and VIM (Fig. 2A–G). These associations were determined to be statistically significant using the IVW method (p-value < 0.05). The effect estimates for each gene are as follows: NKG7 (OR = 0.821; 95% CI: 0.711–0.947; p = 0.007), ARL4C (OR = 0.861; 95% CI: 0.759–0.978; p = 0.021), CD79B (OR = 0.791; 95% CI: 0.634–0.985; p = 0.036), VIM (OR = 0.839; 95% CI: 0.706–0.998; p = 0.047), LRRC25 (OR = 0.914; 95% CI: 0.837–0.999; p = 0.046). These genes are potentially linked to a lower risk of periodontitis. In contrast, SLC11A1 (OR = 1.131; 95% CI: 1.036–1.234; p = 0.006) and ANXA1 (OR = 1.126; 95% CI: 1.037–1.222; p = 0.005) were found to be associated with an increased risk of periodontitis. Sensitivity analyses were performed to assess the reliability of these causal relationships. The results demonstrated that excluding any single SNP did not significantly affect the overall effect estimates, confirming the robustness of the causal relationships identified for the seven genes (Fig. 3 A–G). Overall, the MR analysis highlighted ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1 , and VIM as key genes with causal roles in periodontitis. These findings underscore the importance of these hub genes in the immune response and their potential as therapeutic targets for periodontitis. Signaling Pathways Involved in Key Genes To gain a deeper understanding of how key genes influence the progression of periodontitis, we investigated the signaling pathways enriched in the seven identified hub genes. Gene Set Enrichment Analysis (GSEA) was employed to identify the most significantly enriched pathways. For ANXA1 , the enriched pathways include Antigen Processing and Presentation, NOD-like Receptor Signaling Pathway, and Staphylococcus Aureus Infection, among others (Fig. 4A). ARL4C is enriched in the FoxO Signaling Pathway, Rap1 Signaling Pathway, Ras Signaling Pathway, and other relevant pathways (Fig. 4B). The enriched pathways for CD79B include the B-cell Receptor Signaling Pathway, Herpes Simplex Virus 1 Infection, and Primary Immunodeficiency (Fig. 4C). LRRC25 is associated with the B-cell Receptor Signaling Pathway, HIF-1 Signaling Pathway, and MAPK Signaling Pathways (Fig. 4D). NKG7 is enriched in pathways such as the FoxO Signaling Pathway, Rap1 Signaling Pathway, and Ras Signaling Pathway (Fig. 4E). SLC11A1 shows enrichment in the Adipocytokine Signaling Pathway, MAPK Signaling Pathway, and NOD-like Receptor Signaling Pathway (Fig. 4F). For VIM , the enriched pathways include the B-cell Receptor Signaling Pathway, Legionellosis, and Neutrophil Extracellular Trap Formation (Fig. 4G). We further analyzed eQTL data from 33,538 genes across 3,046 patient samples to identify periodontitis-related eQTLs and SNPs. The differential expression of the seven hub genes was confirmed with an F-statistic exceeding 10, indicating a strong association with periodontitis-related pathways. In parallel, Gene Set Variation Analysis (GSVA) was performed to assess pathway enrichment based on the expression levels of these hub genes: Highly expressed ANXA1 was enriched in pathways such as PI3K/AKT/mTOR Signaling, UV Response DN, and Heme Metabolism (Fig. 5A). ARL4C was enriched in mTORC1 Signaling, Androgen Response, and Heme Metabolism (Fig. 5B). CD79B was enriched in Hedgehog Signaling, Pancreas Beta Cells, and IL2/STAT5 Signaling (Fig. 5C). LRRC25 was linked to Hypoxia, Apoptosis, and the Reactive Oxygen Species (ROS) Pathway (Fig. 5D). NKG7 was associated with mTORC1 Signaling, Androgen Response, and Heme Metabolism (Fig. 5E). SLC11A1 was enriched in the ROS Pathway, IL6/JAK/STAT3 Signaling, and UV Response UP (Fig. 5F). VIM was enriched in Hedgehog Signaling, KRAS Signaling UP, and the ROS Pathway (Fig. 5G). These results suggest that the progression of periodontitis is likely influenced by the activation of these key signaling pathways, mediated by the hub genes identified. These findings provide insight into how the regulation of immune responses and cellular processes might contribute to disease progression and offer potential therapeutic targets. Key gene-related transcriptional regulatory network In this analysis, we focused on the transcriptional regulatory network of the seven identified hub genes, examining their regulation by shared transcription factors. Using these key genes as the gene set, we conducted enrichment analysis to identify the relevant transcription factors. Cumulative recovery curves were employed to evaluate the enrichment of transcription factor motifs, revealing significant regulatory patterns. Motif-TF annotation and selection analysis identified cisbp__M5167 as the motif with the highest normalized enrichment score (NES: 6.38), indicating its significant role in regulating these genes. This finding suggests that multiple transcription factors interact with this motif to control the expression of the seven key genes. Figure 6A illustrates the enriched motifs and their corresponding transcription factors, emphasizing the complexity of the regulatory network governing these critical genes in the context of periodontitis. Immune infiltration The periodontitis microenvironment is primarily composed of immune cells, the extracellular matrix, growth factors, inflammatory mediators, and distinct physical and chemical characteristics. These components play a crucial role in influencing disease progression, diagnosis, and treatment response. In this study, we further investigated the molecular mechanisms by which key genes contribute to periodontitis progression by analyzing their relationship with immune infiltration within the dataset. The analysis quantified the proportions of various immune cells in each patient and highlighted interactions between different immune cell types (Fig. 7A). Using CIBERSORT, we examined correlations among 29 immune cell types and visualized their interrelationships (Fig. 7B). The results showed a significantly higher proportion of T helper cells in patients with periodontitis compared to the control group (Fig. 7C), indicating their potential role in disease progression. We then explored the correlations between key genes and immune cells. Several key genes were strongly associated with specific immune cell populations. For instance, VIM exhibited a significant negative correlation with T follicular helper cells ( Tfh ), while ANXA1 showed a negative correlation with MHC_class_I . SLC11A1 was positively correlated with natural killer cells ( NK_cells ), but negatively correlated with B cells ( B_cells ). Additionally, ARL4C and NKG7 were positively correlated with cytolytic activity, while LRRC25 was positively associated with parainflammation but negatively correlated with B cells. CD79B showed a positive correlation with immune checkpoints ( Check − point ) and a negative correlation with immature dendritic cells ( iDCs ) (Fig. 7D). These findings underscore the unique and cooperative roles of the identified immune cell subpopulations and suggest that immune infiltration plays a crucial role in the pathogenesis of periodontitis. The identified correlations provide further insight into how immune cells interact with key genes, offering potential targets for therapeutic intervention. Expression profiles of key genes in single-cell data and co-expression with osteoblast/osteoclast marker genes and co-expression with IL17 and TGFB This study investigated the expression patterns of the seven key genes ( ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1 , and VIM ) across various immune cell types in periodontitis. These included CD4 + T cells, Naive CD4 + T cells, Naive CD8 + T cells, CD8 + T cells, Naive B cells, B cells, NK cells, Monocytes, Dendritic cells , and Progenitor cells . The expression of these genes across the 10 identified immune cell subtypes was visualized, revealing their unique distributions and suggesting specific roles in immune regulation within the periodontal microenvironment (Fig. 8A). Next, we analyzed the co-expression of these seven key genes with osteoclast marker genes ( CTSK, RUNX2, ACP5, CALCR, DCSTAMP, MMP9 ) and osteoblast marker genes ( EBF2, CD44, OSCAR, SP7, TNFRSF11A, TBX3, RUNX2 ), using data from the CellMarker2.0 database. Co-expression patterns were visualized, demonstrating distinct associations of the hub genes with both osteoclast and osteoblast markers, which are critical for understanding bone remodeling in periodontitis (Fig. 8B). We further explored the co-expression relationships between the key genes and two key cytokines, IL17 and TGF-ß , which are known to play important roles in inflammation and immune response. Co-expression analysis revealed a strong negative correlation between IL17 and several of the hub genes, particularly ANXA1 (r = -0.021, p = 4.7e-06), CD79B (r = -0.04, p = 6.7e-06), and NKG7 (r = -0.017, p = 0.0024), suggesting that IL17 may negatively regulate the expression of these genes in resting cells (S2 Figs. A ~ G). Conversely, TGF-ß showed positive correlations with several key genes, including ANXA1 (r = 0.14, p < 2.2e-16), ARL4C (r = 0.29, p < 2.2e-16), NKG7 (r = 0.37, p < 2.2e-16), and SLC11A1 (r = 0.15, p < 2.2e-16), indicating a potential role for TGF-ß in promoting the expression of these genes in the periodontitis microenvironment. Negative correlations between TGF-ß and CD79B (r = -0.024, p = 0.043) and VIM (r = 0.054, p < 2.2e-16) were also observed, suggesting complex regulatory interactions between these genes and cytokines (S2 Figs. A ~ G). Together, these results provide insight into the regulation of key genes by IL17 and TGF-ß , as well as their roles in osteoclast and osteoblast activity in periodontitis. These findings emphasize the importance of immune-regulatory networks in the progression of the disease and identify potential targets for therapeutic intervention. Analysis of receptor-ligand relationship pairs of cell subpopulations from single-cell data and the developmental trajectories of key cell subtypes Using CellCall, we analyzed the communication patterns between immune cells and other cell types within the microenvironment of periodontitis, focusing on receptor-ligand interactions across multiple disease pathways. The analysis revealed a complex network of interactions, which were visualized through a bubble plot representing the number of ligand-receptor interactions (Fig. 9A). Additionally, a network plot depicted the interactions between immune cell subtypes (Fig. 9B). Dendritic cells exhibited the highest level of interactions, particularly with NK cells, monocytes, and CD8 + T cells. Monocytes, in turn, showed extensive interactions with NK cells, dendritic cells, CD8 + T cells, and CD4 + T cells, underscoring their central role in the immune response. To further explore the development and differentiation of novel T cell subtypes, we performed clustering analysis, identifying three distinct T cell subclusters. We then calculated the similarity between cells and constructed their differentiation trajectories over time using pseudotime analysis. This approach enabled us to visualize the developmental processes of these cell subtypes, illustrating gene expression patterns at various stages of cell differentiation. Cells were colored by pseudotime value, cell type, and state (distinguished by path branches), providing a comprehensive view of their differentiation pathways (Fig. 9C–D). By analyzing genes differentially expressed at key branch points along the developmental trajectory, we identified significant shifts in gene expression. These changes were visualized in a branch heat map, which highlights genes with substantial expression differences before and after the branching points (Fig. 9E). Finally, we tracked the expression changes of the key genes across the entire pseudotime course, revealing dynamic regulatory processes from the onset to the completion of cell differentiation (Fig. 9F). This analysis provides valuable insights into the cellular communication networks and the developmental trajectories of immune cell subpopulations involved in periodontitis. Discussion Periodontitis is a chronic inflammatory disease characterized by bone destruction and tooth loss, primarily caused by dysbiosis in the periodontal microbiota. This dysbiosis triggers an immune response and the release of cytokines, which contribute to alveolar bone resorption and progressive tooth loss 23 , 24 . The risk of periodontitis not only affects local tissues but also induces a systemic immune response. The understanding of periodontitis development has primarily focused on microbial colonization, often overlooking the intricate molecular mechanisms involved. However, recent studies have emphasized the critical role of immune signaling pathways in the onset and progression of periodontitis, revealing the complex interactions between host defense mechanisms and microbial invasion 25 ,(26),(27).. This shift in perspective highlights the importance of immune response in the pathogenesis of periodontitis, prompting a reassessment of prevention and treatment strategies. In contrast to traditional causes, recent studies have highlighted additional pathways and genes implicated in periodontitis, with a particular emphasis on immune-related mechanisms. Several researches reported hub genes related to immune cell infiltration in regulating immune response, highlighting how these variations can affect susceptibility to periodontitis 26 , 27 , 28 . Our findings revealed identification of genes such as ANXA1 and NKG7, which are linked to immune regulation and Corticosteroid-mediated anti-inflammatory response. These insights not only enhance our understanding of the genetic factors involved in dental caries but also indicate that the immune system's role in caries progression is more substantial than previously recognized. This discovery paves the way for further investigation into how genetic predisposition to altered immune responses may increase susceptibility to dental caries, thereby expanding the range of potential therapeutic targets. Further, we unveiled a diverse spectrum of immune cell types in periodontitis. The precise mechanisms underlying immune response regulation in periodontitis remain debated, especially in terms of treatment. In this study, we investigated the causal effects of key hub genes in periodontitis and their association with immune profiles. To our knowledge, this is the first study to integrate single-cell RNA sequencing (scRNA-seq), Genome-Wide Association Studies (GWAS), pathway analysis, CellCall, and Mendelian randomization (MR) to explore the molecular mechanisms of the immune response in periodontitis. Incorporating MR analysis, we identified several immune infiltration-associated genes that are also linked to genetic dimensions of susceptibility to periodontitis. These genes were essential for characterizing immune cell infiltration in periodontal remodeling. In particular, seven hub genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) were found to have causal interactions with immune cell subpopulations, as evidenced by Mendelian randomization analysis involving 198,441 cases. Importantly, memory T cells, especially CD4 + T cells, showed positive correlations with these hub genes. ANXA1 and NKG7 is particularly important in suppressing inflammatory responses by promoting the production of anti-inflammatory mediators and inhibiting the release of pro-inflammatory cytokines 29 . Previous studies have highlighted interaction between ANXA1 and NKG7. These genes can balance the inflammatory response. ANXA1 cooperates with ARL4C, NKG7 and SLC11A1 to enhance the response of immune cells by regulating cell signaling and cell motility 29 , 30 , 31 , 32 . At the same time, ANXA1 and VIM work together on cell migration and signaling, enabling immune cells to effectively locate to the inflammatory area 33 . The role of CD79B in periodontitis is reflected in promoting B cell activation, regulating inflammatory response and antibody production, which plays a protective role in chronic periodontal infection 34 . These results indicated a balance between immune response and tissue damage, providing insights into inflammation management and healing in periodontitis. These genes, due to their biological roles, not only provide a deeper understanding of the pathophysiology of periodontitis but also represent promising targets for future therapeutic and diagnostic innovations. Their involvement in immune regulation, inflammation, and tissue repair offers an immunological approach to combating periodontitis. The potential of these genes as biomarkers for susceptibility or disease progression also opens avenues for early intervention, underscoring the promise of precision medicine in oral health. As we continue to unravel the genetic and cellular mechanisms underlying periodontitis, the translation of these insights into clinical applications represents the next frontier in the pursuit of optimal oral health. Our study has certain limitations that should be considered. First, the statistical power of our analyses may be constrained by the sample size, which is particularly significant when assessing the impact of subtle genetic influences on complex traits, where larger sample sizes generally enhance the robustness of findings. Additionally, the accuracy of exposure measurement is crucial for the validity of Mendelian Randomization estimates. Any measurement error, particularly if differentially misclassified between comparison groups, could attenuate the observed associations, potentially leading to an underestimation of the true effects. Future studies should also explore the functional roles of the identified genes in caries development and assess their potential as biomarkers for early detection or targets for therapeutic intervention. Conclusion In conclusion, our scRNA-seq analysis of periodontitis identified seven hub genes that play critical roles in regulating immune cell subpopulations and signaling pathways. These genes, particularly in the context of CD4 + T cells, dendritic cells, and monocytes, were shown to have a causal effect on the progression of periodontitis. Our findings provide novel insights into the immune landscape of periodontitis and suggest potential targets for immunotherapy. The Mendelian randomization study further reinforced the importance of immune cell interactions and signaling pathways in disease development. The potential of these genes as intervention factors to prevent or mitigate periodontal disease depends on a deeper understanding of their interactions with oral environmental and microbial factors. Future research should focus on elucidating the mechanistic pathways by which these genes influence periodontal disease development, evaluating their potential as predictive markers for disease susceptibility, and exploring gene-based therapeutic approaches to enhance host defense against periodontal disease. These efforts will not only deepen our understanding of periodontal disease but also pave the way for precision medicine in dentistry, providing new hope for effective treatment strategies. Abbreviations scRNA-seq Single-cell RNA sequencing SNPs Single Nucleotide Polymorphisms GWAS genome-wide association studies MR Mendelian randomization GEO GENE EXPRESSION OMNIBUS NCBI National Center for Biotechnology Information UMAP Uniform Manifold Approximation and Projection IVs Instrumental variables LD linkage disequilibrium IVW Inverse Variance Weighted PLAU plasminogen activator, urokinase Declarations Clinical Trial Number: No applicable Ethics Statement This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Shihezi University School of Medicine (No: KJ-2024-295-02). Informed consent was obtained from all participants. Consent for Publication All authors consent to the publication of this manuscript in Journal of BMC Oral Health. Our manuscript adheres to the STROBE-MR guidelines. Availability of data and materials Data analysis in this study was derived from publicly available databases. These datas are 38919 available from TCGA (www.portal.gdc.cancer.gov/), GEO (www.ncbi.nlm.nih.gov/geo/) 390 QTLGen consortium (https://www.eqtlgen.org), FinnGene database, and genome-wide 391 association studies (GWAS) Catalog (https://www.ebi.ac.uk/gwas/). Code Availability: the 392 analysis code in R is available on request. If someone wants to request the data，please contact author ( [email protected] ) Funding Declaration No funding Competing interests No competing interests. Acknowledgements XDQ and FY: Writing – original draft, Writing – review & editing, Designing the research, Formal analysis. CXL: Software, Investigation, Resources, Methodology. JW: Software, Investigation, Software, Resources. YWY: Software, Resources, Validation, Investigation. CG: Writing – review & editing, Visualization, Validation, Formal analysis, Conceptualization, Project administration. Special thanks to single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and eQTL analyses for providing the technology that was essential for this study. All authors reviewed and approved the final manuscript. All authors reviewed and approved the final manuscript. References Li, Y., Jacox, L. A., Little, S. H. & Ko, C. C. Orthodontic tooth movement: The biology and clinical implications. Kaohsiung J. Med. Sci. 34 (4), 207–214. 10.1016/j.kjms.2018.01.007 (2018). Sanz, M. et al. Treatment of stage I–III periodontitis—The EFP S3 level clinical practice guideline. J. Clin. Periodontol. 47 (S22), 4–60. 10.1111/jcpe.13290 (2020). Klein, Y. et al. Immunorthodontics: in vivo gene expression of orthodontic tooth movement. Sci. Rep. 10 (1), 8172. 10.1038/s41598-020-65089-8 (2020). Meeran, N. A. 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Single-cell analysis of platelets from patients with periodontitis and diabetes. Res. Pract. Thromb. Haemost . 7 (2), 100099. 10.1016/j.rpth.2023.100099 (2023). Zeng, Y., Cao, S. & Chen, M. Integrated analysis and exploration of potential shared gene signatures between carotid atherosclerosis and periodontitis. BMC Med. Genomics . 15 (1), 227. 10.1186/s12920-022-01373-y (2022). Zhang, B. et al. Single-cell transcriptional profiling reveals immunomodulatory properties of stromal and epithelial cells in periodontal immune milieu with diabetes in rats. Int. Immunopharmacol. 123 , 110715. 10.1016/j.intimp.2023.110715 (2023). Perretti, M. & D’Acquisto, F. Annexin A1 and glucocorticoids as effectors of the resolution of inflammation. Nat. Rev. Immunol. 9 (1), 62–70. 10.1038/nri2470 (2009). NKG7 Enhances CD8 + T Cell Synapse Efficiency to Limit Inflammation. Accessed October 20. (2024). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.931630/full McHeyzer-Williams, M., Okitsu, S., Wang, N. & McHeyzer-Williams, L. Molecular programming of B cell memory. Nat. Rev. Immunol. 12 (1), 24–34. 10.1038/nri3128 (2012). Immunomodulation in the Treatment of Periodontitis: Progress and Perspectives. Accessed October 20. (2024). https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.781378/full Hyder, C. L., Pallari, H. M., Kochin, V. & Eriksson, J. E. Providing cellular signposts–post-translational modifications of intermediate filaments. FEBS Lett. 582 (14), 2140–2148. 10.1016/j.febslet.2008.04.064 (2008). Hajishengallis, G. & Korostoff, J. M. Revisiting the Page & Schroeder model: the good, the bad and the unknowns in the periodontal host response 40 years later. Periodontology 2000 . 75 (1), 116–151. 10.1111/prd.12181 (2017). Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Apr, 2025 Reviews received at journal 22 Mar, 2025 Reviews received at journal 10 Mar, 2025 Reviewers agreed at journal 22 Feb, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 14 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Editor invited by journal 12 Nov, 2024 Submission checks completed at journal 11 Nov, 2024 First submitted to journal 20 Oct, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5300947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379120495,"identity":"6371fb85-c34d-4405-b6cd-b61c24aa6fe0","order_by":0,"name":"Xuedi Qiu","email":"","orcid":"","institution":"First Affiliated Hospital of Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Xuedi","middleName":"","lastName":"Qiu","suffix":""},{"id":379120496,"identity":"fe8dad2c-3637-4554-a5a2-ce9eb5b3550f","order_by":1,"name":"Fan Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""},{"id":379120497,"identity":"1d8f9439-274f-46d6-bb22-a41ba44fbf7d","order_by":2,"name":"Chenxi Li","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Li","suffix":""},{"id":379120498,"identity":"dfb147be-79eb-458d-a6ee-e7fd943b54bd","order_by":3,"name":"Jian Wang","email":"","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":379120499,"identity":"43007300-c96f-4a14-a2e5-c1a85a597b56","order_by":4,"name":"Yawen Yuan","email":"","orcid":"","institution":"First Affiliated Hospital of Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Yuan","suffix":""},{"id":379120500,"identity":"d1ae4ae6-51db-4771-ac4e-d9574aa7550c","order_by":5,"name":"Chao Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACZiCu33+8+cCBDz9I0MLYcOZY4sGZPSRpuZFjfJiDjQjl8hHJBz8X1NgwM/ac+XCYgYdBnl/sAH4thjfSkqVnHEtjY2bv3XC4wILBcObsBAJaZuSYMfOwHeZh4zm74fAMHoYEg9tEafl3WIJHIucBUCMRWuQlgFp42w4bSEjkMBCnxYDnWbI0b19aggHPMQNgIEsQ9ot8OzDEeL7ZJBiwNz/+8OGHjTy/NCFbDqDyJfArB9vSQFjNKBgFo2AUjHQAAHMIRIMp3e7qAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Shihezi University","correspondingAuthor":true,"prefix":"","firstName":"Chao","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-10-21 03:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5300947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5300947/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19438-0","type":"published","date":"2025-10-13T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70180342,"identity":"3711ca83-d2b6-4f35-9d49-c875bcc5a1ee","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":994366,"visible":true,"origin":"","legend":"\u003cp\u003escRNA-seq atlas of cell cluster and DEGs analysis. (A) and (B) UMAP plot based on immune cell of cluster in periodontities single-cell dataset. (C) Bubble plot of ligand-receptor interactions between immune and DGEs. (D) Intersections between the 10 immune molecular subtypes and cell percent ratio in control and disease. (E) Differences in the expression and distribution of immune cell-associated genes.\u003c/p\u003e","description":"","filename":"figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/ccab7d1826c2dbb960bad365.jpg"},{"id":70180682,"identity":"078f7388-c92a-4664-bba6-7dd5b793ef1a","added_by":"auto","created_at":"2024-11-29 08:38:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1050051,"visible":true,"origin":"","legend":"\u003cp\u003eThe scatter plots demonstrate the associations between the seven candidate genes and periodontitis. (A) The causal effect of the ANXA1 gene on periodontitis is illustrated using four Mendelian randomization (MR) methods. (B) The causal effect of the ARL4C gene on periodontitis is shown through four MR methods. (C) The causal effect of the CD79B gene on periodontitis is depicted using four MR methods. (D) The causal effect of the LRRC25 gene on periodontitis is presented through four MR methods. (E) The causal effect of the NKG7 gene on periodontitis is displayed using four MR methods. (F) The causal effect of the SLC11A1 gene on periodontitis is illustrated using four MR methods. (G) The causal effect of the VIM gene on periodontitis is shown using four MR methods.\u003c/p\u003e","description":"","filename":"figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/2f097441b80f90744b92fb78.jpg"},{"id":70180345,"identity":"151b88df-6535-4809-99e9-eff25c42158b","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1824079,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plots demonstrating the association between 7 candidates and periodontitis. (A) The forest plots demonstrating the positive correlation between ANXA1 gene and periodontitis. (B) The forest plots demonstrating the negative correlation between ARL4C gene and periodontitis. (C) The forest plots demonstrating the negative correlation between CD79B gene and periodontitis. (D) The forest plots demonstrating the negative correlation between LRRC25 gene and periodontitis. (E) The forest plots demonstrating the negative correlation between NKG7 gene and periodontitis. (F) The forest plots demonstrating the rarely correlation between NKG7 gene and periodontitis. (G) The forest plots demonstrating the negative correlation between VIM gene and periodontitis\u003c/p\u003e","description":"","filename":"figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/e07a4358b69ce7bab5f0122a.jpg"},{"id":70180344,"identity":"27115924-06f2-47ed-afd0-3d58530c838d","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3711878,"visible":true,"origin":"","legend":"\u003cp\u003eGDEA analysis fo theintercation between DEGs and signal pathway based on the periodontities dataset. (A) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by ANXA1. (B) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by ARL4C. (C) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by CD79B. (D) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched byLRRC25. (E) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by NKG7. (F) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by SLC11A1. (G) Enrichment score and Zoomed-in circos plots of genetic presenting the pathways enriched by VIM.\u003c/p\u003e","description":"","filename":"figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/cac48b7f7b5b1bb0a3b7e0ac.jpg"},{"id":70180684,"identity":"bd7340de-7a40-4088-b74f-747f0a3d65ea","added_by":"auto","created_at":"2024-11-29 08:38:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1400124,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis of correlationship between DEGs and signal pathways in periodontitis. (A) GO analyses interaction network of ANXA1 (up- and down-related genes) in signal pathways. (B) GO analyses interaction network of ARL4C (up- and down-related genes) in signal pathways. (C) GO analyses interaction network of CD79B (up- and down-related genes) in signal pathways. (D) GO analyses interaction network of LRRC25 (up- and down-related genes) in signal pathways. (E) GO analyses interaction network of NKG7 (up- and down-related genes) in signal pathways. (F) GO analyses interaction network of SLC11A1 (up- and down-related genes) in signal pathways. (G) GO analyses interaction network of VIM (up- and down-related genes) in signal pathways.\u003c/p\u003e","description":"","filename":"figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/77c943b6e0af83d2175492e7.jpg"},{"id":70182115,"identity":"dfeb42c0-6c7b-4365-a6f4-c24e8fa2c9a1","added_by":"auto","created_at":"2024-11-29 08:46:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":546121,"visible":true,"origin":"","legend":"\u003cp\u003eTheNES of each motif analysis. (A) Enrichment score of motif analysis and correlation gene sequence.(B) The NES of each motif based on the AUC distribution.\u003c/p\u003e","description":"","filename":"figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/be1bdf28fc42a93799ff6adc.jpg"},{"id":70182114,"identity":"734e10b9-c3b4-4fc5-8756-bb814aab880d","added_by":"auto","created_at":"2024-11-29 08:46:32","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1259659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Infiltration Analysis.\u003c/strong\u003e (A) Panoramic mapping of the 22 immune cell types across high and low risk score stratifications. (B) Heatmap illustrating the interrelationships among immune cell populations. (C) Differential analysis of immune subpopulation abundance between periodontitis and control groups, using the Wilcoxon matched-pairs signed rank test (P \u0026lt; 0.05 considered significant). (D) Heatmap depicting the performance of seven gene sets across immune cell subpopulations.\u003c/p\u003e","description":"","filename":"figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/82c9d6c4fa50ed3fb875305e.jpg"},{"id":70180351,"identity":"9306e212-6c0c-4802-b750-eb827a250468","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1180097,"visible":true,"origin":"","legend":"\u003cp\u003escRNA-seq analysis for osteoblast/osteoclast marker genes.(A) Bubble plot showing the expression patterns of marker genes among different immune cell clusters. (B) UMAP clustering plot of the 7 genes in the periodontities samples.(C) Proportional plot showing the 7 genes distribution of immune cell in periodontitis.\u003c/p\u003e","description":"","filename":"figure8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/9c492f7c0f420e4df6d50136.jpg"},{"id":70180349,"identity":"efc09223-1a27-4ab4-acf5-df571f9b8754","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":431964,"visible":true,"origin":"","legend":"\u003cp\u003eDRSGs AUCell scores and Subtyping analysis.(A) Intercation of signal pathways and immune cell subpopulations.(B) Ligand-receptor interaction network between immune cell subpopulatons.(C) Differentiation trajectory plot of immune cell subpopulatons. (D) Differentiation timeline plot of differential immune cells.(E) Heatmap of log interaction scores between immune cell subpopulatons.(F) Differentiation trajectory plot of DEGs.\u003c/p\u003e","description":"","filename":"figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/77defc68affff3d076d7ace3.jpg"},{"id":93955978,"identity":"fd4cf633-3c75-46fc-bc38-a64648e41e5d","added_by":"auto","created_at":"2025-10-20 16:08:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13504901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/848b5279-5198-48f5-bf11-3c9a56a25d69.pdf"},{"id":70180350,"identity":"1c955381-236b-40f3-ad0f-16d26cc36999","added_by":"auto","created_at":"2024-11-29 08:30:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3640042,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5300947/v1/7e91f1518453af3d94b04f52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeriodontitis is a chronic infectious and adaptive inflammatory disease, with a global prevalence of approximately 50%, making it the sixth most common disease worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The condition is characterized by gingival inflammation and periodontal destruction, often following the infiltration of immune cells into affected regions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Mechanical irritation and dental plaque are major contributors to significant alterations in the oral microbiome and salivary pH, both of which can lead to periodontitis \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, periodontitis involves inflammation that causes irreversible loss of bone and connective tissue attached to the tooth root or surrounding bone. Immune dysbiosis plays a significant role in tissue destruction, particularly in inflammatory bone diseases. Factors such as bacterial endotoxins and inflammatory mediators induce osteoclast differentiation and proliferation, inhibit osteoblast activity, disrupt bone resorption formation balance, and leading to alveolar bone resorption\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Activated immune cells, particularly T and B cells, have been identified as key contributors to the progression of periodontitis \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, research indicated that periodontal soft tissues undergo notable morphological and functional changes during orthodontic treatment \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Researches elucidated the role of immune cells was critical for developing effective preventive and therapeutic strategies in periodontitis.\u003c/p\u003e \u003cp\u003eThe development of periodontitis is influenced by various factors, including genetics, environmental conditions, microbial infections, and lifestyle[13]. Despite significant advancements in the study of periodontitis, the specific etiology and pathogenesis at the molecular level remain insufficiently understood. Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity and function. It plays a crucial role in uncovering molecular mechanisms and identifying target cells. This technology also enables high-throughput sequencing of individual cells at genomic, transcriptomic, and epigenomic levels, revealing the heterogeneity among cells and the complexity of gene expression regulation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. scRNA-seq have been applied in research on tumors, microbiology, neurology, reproduction, immunology, as well as digestive and urinary systems, highlighting the critical role of single-cell sequencing in both basic and clinical research\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, single-cell sequencing offers exceptional precision for clinical research and diagnosis, particularly in tumor immunity, neuroscience, embryonic development, and reproduction[16\u0026ndash;18]. Recently, a comprehensive single-cell periodontitis map has been constructed using single-cell RNA sequencing, offering valuable insights into the molecular alterations in human dental pulp caused by periodontitis. However, scRNA-seq presents limitations in accurately classifying cells. To address these challenges, we integrated multiple datasets\u0026mdash;including eQTL\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, GWAS\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and scRNA-seq \u003csup\u003e16\u003c/sup\u003e \u0026mdash;using machine learning to identify immune cell-related diagnostic features and clusters in periodontitis. This integrated model of immune response offers a novel approach for the diagnosis and treatment of periodontitis, providing valuable insights for personalized therapeutic strategies.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a novel statistical approach that examines causal relationships by leveraging genetic variants as unbiased \u0026ldquo;natural randomized trials\u0026rdquo;. These genetic variants are randomly allocated at conception and depend on parental genotypes. MR studies estimate the causal effects of exposures on outcomes while minimizing confounding factors and reverse causation \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This approach enables the classification and characterization of each cell at single-cell resolution, facilitating the discovery of biological pathways associated with transcriptomic signatures and phenotypic outcomes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe combination of single-cell sequencing technology and Mendelian randomization offers a more comprehensive approach to studying caries from a molecular perspective\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In our study, key genes associated with periodontits were identified through single-cell sequencing, while Mendelian randomization was employed to further select genetic variants closely linked to the onset and progression of periodontitis. This integrated model of immune response offers a novel approach for the diagnosis and treatment of periodontitis, offering novel insights into personalized treatment approaches.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eData collection\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e1) We obtained dataset GSE174609 \u003csup\u003e20\u003c/sup\u003e and GSE156993 \u003csup\u003e21\u003c/sup\u003e. The GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/info/datasets.html\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/info/datasets.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the GENE EXPRESSION OMNIBUS(GEO), is a gene expression database created and maintained by the National Center for Biotechnology Information, NCBI. GSE174609 includes single-cell data from eight samples\u0026mdash;four from the control group and four from the disease group\u0026mdash;downloaded from the NCBI, GEO public database. We retrieved the Series Matrix File of GSE156993, which contains expression profile data from 12 patients. The dataset was annotated using the GPL570 platform file, including 6 in the control group and 6 in the disease group.\u003c/p\u003e \u003cp\u003e2) Exposed data: Exposure data were sourced from the eQTLGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a comprehensive project aimed at investigating the genetic architecture and its connection to complex traits in blood gene expression. In currently second phase, the eQTLGen project focuses on performing genome-wide meta-analyses of blood samples, and conducting 86 large-scale genome-wide meta-analyses in blood.\u003c/p\u003e \u003cp\u003e3) Outcome data: The FinnGen database focuses on genetic research within European populations, aim to studying unique genetic diseases and mutations prevalent in this group\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. With data collected from a vast number of samples in various regions, it emphasizes the connection between genetic variation and disease. FinnGen is particularly significant for understanding the role of genetics in public health, especially in the realms of disease prevention and personalized medicine. This resource is invaluable to geneticists and epidemiologists aiming to better comprehend how genetic differences influence health and disease outcomes. In the context of periodontitis (finn-b-K11_PERIODON_CHRON), the dataset includes 3,046 cases and 195,395 controls\u003c/p\u003e\n\u003ch3\u003eSingle cell RNA sequence analysis:\u003c/h3\u003e\n\u003cp\u003eThe expression profile data were processed using the Seurat package, which filtered out low-quality cells and removed doublets through the DoubletFinder tool. Following this, the data were standardized and normalized, with subsequent PCA and UMAP analyses performed. UMAP analysis was used to reveal the positional relationships between clusters. These clusters were annotated based on information from existing literature and the CellMarker database, with specific annotations applied to cells related to periodontitis. To identify differentially expressed genes, the FindMarkers function was employed. This tool compares gene expression differences between immune cell subtypes, typically accepting samples from two or more groups and identifying genes with significant expression differences.\u003c/p\u003e\n\u003ch3\u003eMendelian randomization analysis:\u003c/h3\u003e\n\u003cp\u003eCausal relationships between eQTL and disease were extracted from the screened outcome IDs. Single nucleotide polymorphisms (SNPs) associated with each gene, meeting the significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;1e-8) across the entire locus, were selected as potential instrumental variables (IVs). Linkage disequilibrium (LD) was calculated for each SNP, retaining only those with R\u0026sup2; \u0026lt; 0.001 (clumping window size\u0026thinsp;=\u0026thinsp;10,000 kb) and p\u0026thinsp;\u0026lt;\u0026thinsp;5e-8. The analysis was conducted using the Inverse Variance Weighted (IVW) method, which aggregates Wald estimates for each SNP through meta-analysis, as well as MR Egger (which assumes instrument strength is independent of direct effects, InSIDE), the weighted median (allowing correct causality estimates in up to 50% of invalid IV cases), and the weighted mode (which offers improved detection of causal effects, lower bias, and reduced Type I error compared to MR Egger regression). For cases where only a single statistical method was available, the Wald ratio was used to assess the reliability of the causal relationships and estimate the overall impact of cis- and trans-region gene expression in peripheral blood on periodontitis lesions. The final causal relationships were verified using heterogeneity analysis (Cochran's IVW Q test) and genetic diversity testing.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity test analysis:\u003c/h2\u003e \u003cp\u003eIn this study, we employed the Mendelian heterogeneity test to assess the presence of statistical heterogeneity among the single nucleotide polymorphisms (SNPs) analyzed. The weighted sum of squares of effect sizes and standard errors for each SNP was calculated, generating a Q value, which follows a chi-square distribution with one degree of freedom less than the number of SNPs studied. A p-value greater than 0.05 for the Q value indicates insufficient evidence for heterogeneity in the effect sizes, suggesting that the SNPs' impact on disease risk is statistically consistent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGSEA enrichment analysis:\u003c/h2\u003e \u003cp\u003ePatients were categorized into high and low gene expression groups, and the differences in signaling pathways between these groups were assessed using Gene Set Enrichment Analysis (GSEA). The background gene set, annotated for subtype pathways, was sourced from the MsigDB database. Differential pathway expression between subtypes was evaluated, and significantly enriched gene sets (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected based on consistency scores. GSEA is commonly applied in research to closely link disease classification with biological significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction Nomogram model\u003c/h2\u003e \u003cp\u003eBased on immune cell-related DEGs features, we used the \u0026ldquo;rms\u0026rdquo; package to construct the nomogram model to predict risk in periodontitis. In addition, \u0026ldquo;Calibration curve\u0026rdquo; and \u0026ldquo;clinical impact curve\u0026rdquo; were evaluated and verified the accuracy and efficiency of this model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Variation Analysis (GSVA):\u003c/h2\u003e \u003cp\u003eGSVA is an unsupervised, non-parametric method used to evaluate gene set enrichment across the transcriptome. It transforms gene-level changes into pathway-level changes by calculating comprehensive scores for the gene sets of interest, allowing the determination of the biological function of each sample. In this study, gene sets were downloaded from the Molecular Signatures Database, and the GSVA algorithm was applied to score each gene set and evaluate differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory network analysis of key genes:\u003c/h2\u003e \u003cp\u003eThe 'RcisTarget' package in R was used to predict transcription factors based on motif analysis. The normalized enrichment score (NES) for each motif was calculated using the total number of motifs in the database. In addition to the motifs directly annotated by the source data, supplementary annotations were inferred by comparing motif similarities and gene sequences. The initial step involved calculating the area under the curve (AUC) for each motif-gene pair, using recovery curve calculations based on the gene set's ranking against the motifs. The NES for each motif was then derived from the AUC distribution of all motifs within the gene set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis:\u003c/h2\u003e \u003cp\u003eThe single-sample Gene Set Enrichment Analysis (ssGSEA) is a widely used technique for evaluating immune cell types within the microenvironment. It distinguishes 29 human immune cell phenotypes, including T cells, B cells, and NK cells. In this study, ssGSEA was used to quantify the proportions of these immune cell types in the expression profile, and perform correlation analysis between gene expression and immune cell content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLigand-receptor interaction analysis (Cellcall):\u003c/h2\u003e \u003cp\u003eCellCall, a toolkit designed to infer intercellular communication networks and regulatory signals, was employed to integrate both intracellular and intercellular signals. It compiles ligand-receptor-transcription factor (L-R-TF) axis datasets based on KEGG pathways. Using prior knowledge of L-R-TF interactions. This approach enabled the inference of intercellular communication by analyzing specific ligand-receptor pairs and their downstream effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopmental trajectories of key cell subtypes:\u003c/h2\u003e \u003cp\u003eStudies at the single-cell level allow one to characterize complex physiological processes and the transcriptional regulation of highly heterogeneous cell populations. These studies have led to the discovery of genes that identify specific cell subtypes, genes that mark intermediate states of biological processes, and genes that are in transition states between two different cell fates. In many single-cell analyses, gene expression occurs asynchronously across individual cells, with each cell representing a specific moment in the transcriptional process. Monocle applies a pseudotime (pseudochronology) strategy, utilizing the asynchrony of gene expression to position cells along trajectories to place them on trajectories corresponding to biological processes such as cell differentiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eReliable Mendelian randomization (MR) analysis relies on three key assumptions: (1) the correlation assumption, where the instrumental variable is strongly associated with the exposure but not directly with the outcome; (2) the independence assumption, which requires the instrumental variable to be independent of confounding factors; and (3) the exclusivity hypothesis, where instrumental variables affect outcomes only through exposure. If the instrumental variable influences outcomes through other pathways, gene pleiotropy is assumed. All statistical analyses were conducted using R software (version 4.3.2), with a p-value of less than 0.05 considered statistically significant\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell expression profile data\u003c/h2\u003e \u003cp\u003eThis analysis utilized expression profiles from eight periodontal tissue remodeling-related tissue samples (S1 Fig. A). The data were processed using the Seurat package, where cells were filtered based on several criteria: total number of unique molecular identifiers (UMIs) per cell, the number of expressed genes, and the percentage of mitochondrial reads. Outliers were defined as those falling three median absolute deviations (MAD) from the median. Using the UMAP package, 18 distinct cellular clusters were identified within the periodontitis dataset (Fig.\u0026nbsp;1A). Low-expressing cells were filtered out based on violin and scatter plots using the criteria (nFeature_RNA\u0026thinsp;\u0026gt;\u0026thinsp;200, percent.mt\u0026thinsp;\u0026le;\u0026thinsp;3\u003cem\u003eMAD, nFeature_RNA\u0026thinsp;\u0026le;\u0026thinsp;3\u003c/em\u003eMAD, and nCount_RNA\u0026thinsp;\u0026le;\u0026thinsp;3\u003cem\u003eMAD, percent.ribo\u0026thinsp;\u0026le;\u0026thinsp;3\u003c/em\u003eMAD). The DoubletFinder package was then employed to exclude doublets, resulting in the inclusion of a total of 66,209 cells for further analysis. Additionally, the 10 genes with the highest standard deviations were identified, with key hub genes such as \u003cem\u003ePPBP, HBB, IGKC, IGLC2, IGLC3, HBA2, HBA1, S100A9, S100A8\u003c/em\u003e, and \u003cem\u003eLYPD2\u003c/em\u003e being detected (Fig.\u0026nbsp;1E, S1 Fig. D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell subpopulation annotation for single cell data\u003c/h2\u003e \u003cp\u003eImmune cell characteristics in periodontitis were further explored by comparing transcriptomic differences across subpopulations. Cell subpopulations were annotated based on their distinct profiles. Through principal component analysis (PCA) for dimensionality reduction, a batch effect between samples was identified (S1 Fig. D), which was subsequently corrected using Harmony analysis (S1 Fig. E). The optimal number of principal components (PCs) determined by the ElbowPlot was 12 (S1 Fig. F). Ultimately, 23 subtypes were obtained via UMAP clustering. These subtypes were annotated to include \u003cem\u003eCD4\u0026thinsp;+\u0026thinsp;T cells, Naive CD4\u0026thinsp;+\u0026thinsp;T cells, Naive CD8\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Naive B cells, B cells, NK cells, Monocytes, Dendritic cells\u003c/em\u003e, and \u003cem\u003eProgenitor cells\u003c/em\u003e (Fig.\u0026nbsp;1B).\u003c/p\u003e \u003cp\u003eFor each cell category, a bubble chart was generated to display classic markers for 10 immune cell types (Fig.\u0026nbsp;1C), as well as the proportion of these cells in disease and control samples (Fig.\u0026nbsp;1D). Differentially expressed genes (DEGs) for each immune cell subtype were identified, and marker genes were highlighted using the findMarkers function (Fig.\u0026nbsp;1E). The selection criteria for DEGs were set as |avg_log2FC| \u0026gt; 1.5 and p_val_adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The identified markers are detailed in the supplementary output (Output_markers.csv).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization Analysis\u003c/h2\u003e \u003cp\u003eMendelian randomization (MR) is a widely used causal inference approach that utilizes single nucleotide polymorphisms (SNPs) as instrumental variables to evaluate the causal effect of an exposure on an outcome\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In this study, SNPs associated with immune cell subtypes were used as instruments to assess their potential impact on periodontitis. Four MR methods were applied\u0026mdash;inverse-variance weighted (IVW), MR-Egger, weighted median, and MR-PRESSO\u0026mdash;to ensure the robustness of the causal estimates. The results from all four methods showed consistent effect estimates, indicating reliable associations. To further identify key genes influencing periodontitis, we used summary statistics from 198,441 periodontitis-related samples (controls: 195,395; cases: 3,046) in the FinnGen database (finn-b-K11_PERIODON_CHRON). The MR analysis revealed significant causal relationships for seven genes: \u003cem\u003eANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e (Fig.\u0026nbsp;2A\u0026ndash;G). These associations were determined to be statistically significant using the IVW method (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe effect estimates for each gene are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNKG7\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.821; 95% CI: 0.711\u0026ndash;0.947; p\u0026thinsp;=\u0026thinsp;0.007),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eARL4C\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.861; 95% CI: 0.759\u0026ndash;0.978; p\u0026thinsp;=\u0026thinsp;0.021),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCD79B\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.791; 95% CI: 0.634\u0026ndash;0.985; p\u0026thinsp;=\u0026thinsp;0.036),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eVIM\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.839; 95% CI: 0.706\u0026ndash;0.998; p\u0026thinsp;=\u0026thinsp;0.047),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLRRC25\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.914; 95% CI: 0.837\u0026ndash;0.999; p\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese genes are potentially linked to a lower risk of periodontitis. In contrast, SLC11A1 (OR\u0026thinsp;=\u0026thinsp;1.131; 95% CI: 1.036\u0026ndash;1.234; p\u0026thinsp;=\u0026thinsp;0.006) and ANXA1 (OR\u0026thinsp;=\u0026thinsp;1.126; 95% CI: 1.037\u0026ndash;1.222; p\u0026thinsp;=\u0026thinsp;0.005) were found to be associated with an increased risk of periodontitis. Sensitivity analyses were performed to assess the reliability of these causal relationships. The results demonstrated that excluding any single SNP did not significantly affect the overall effect estimates, confirming the robustness of the causal relationships identified for the seven genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;G).\u003c/p\u003e \u003cp\u003eOverall, the MR analysis highlighted \u003cem\u003eANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e as key genes with causal roles in periodontitis. These findings underscore the importance of these hub genes in the immune response and their potential as therapeutic targets for periodontitis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSignaling Pathways Involved in Key Genes\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of how key genes influence the progression of periodontitis, we investigated the signaling pathways enriched in the seven identified hub genes. Gene Set Enrichment Analysis (GSEA) was employed to identify the most significantly enriched pathways.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor \u003cem\u003eANXA1\u003c/em\u003e, the enriched pathways include Antigen Processing and Presentation, NOD-like Receptor Signaling Pathway, and Staphylococcus Aureus Infection, among others (Fig.\u0026nbsp;4A).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eARL4C\u003c/em\u003e is enriched in the FoxO Signaling Pathway, Rap1 Signaling Pathway, Ras Signaling Pathway, and other relevant pathways (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe enriched pathways for \u003cem\u003eCD79B\u003c/em\u003e include the B-cell Receptor Signaling Pathway, Herpes Simplex Virus 1 Infection, and Primary Immunodeficiency (Fig.\u0026nbsp;4C).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLRRC25\u003c/em\u003e is associated with the B-cell Receptor Signaling Pathway, HIF-1 Signaling Pathway, and MAPK Signaling Pathways (Fig.\u0026nbsp;4D).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNKG7\u003c/em\u003e is enriched in pathways such as the FoxO Signaling Pathway, Rap1 Signaling Pathway, and Ras Signaling Pathway (Fig.\u0026nbsp;4E).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSLC11A1\u003c/em\u003e shows enrichment in the Adipocytokine Signaling Pathway, MAPK Signaling Pathway, and NOD-like Receptor Signaling Pathway (Fig.\u0026nbsp;4F).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor \u003cem\u003eVIM\u003c/em\u003e, the enriched pathways include the B-cell Receptor Signaling Pathway, Legionellosis, and Neutrophil Extracellular Trap Formation (Fig.\u0026nbsp;4G).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed eQTL data from 33,538 genes across 3,046 patient samples to identify periodontitis-related eQTLs and SNPs. The differential expression of the seven hub genes was confirmed with an F-statistic exceeding 10, indicating a strong association with periodontitis-related pathways. In parallel, Gene Set Variation Analysis (GSVA) was performed to assess pathway enrichment based on the expression levels of these hub genes:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHighly expressed \u003cem\u003eANXA1\u003c/em\u003e was enriched in pathways such as PI3K/AKT/mTOR Signaling, UV Response DN, and Heme Metabolism (Fig.\u0026nbsp;5A).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eARL4C\u003c/em\u003e was enriched in mTORC1 Signaling, Androgen Response, and Heme Metabolism (Fig.\u0026nbsp;5B).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCD79B\u003c/em\u003e was enriched in Hedgehog Signaling, Pancreas Beta Cells, and IL2/STAT5 Signaling (Fig.\u0026nbsp;5C).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLRRC25\u003c/em\u003e was linked to Hypoxia, Apoptosis, and the Reactive Oxygen Species (ROS) Pathway (Fig.\u0026nbsp;5D).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNKG7\u003c/em\u003e was associated with mTORC1 Signaling, Androgen Response, and Heme Metabolism (Fig.\u0026nbsp;5E).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSLC11A1\u003c/em\u003e was enriched in the ROS Pathway, IL6/JAK/STAT3 Signaling, and UV Response UP (Fig.\u0026nbsp;5F).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eVIM\u003c/em\u003e was enriched in Hedgehog Signaling, KRAS Signaling UP, and the ROS Pathway (Fig.\u0026nbsp;5G).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese results suggest that the progression of periodontitis is likely influenced by the activation of these key signaling pathways, mediated by the hub genes identified. These findings provide insight into how the regulation of immune responses and cellular processes might contribute to disease progression and offer potential therapeutic targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKey gene-related transcriptional regulatory network\u003c/h2\u003e \u003cp\u003eIn this analysis, we focused on the transcriptional regulatory network of the seven identified hub genes, examining their regulation by shared transcription factors. Using these key genes as the gene set, we conducted enrichment analysis to identify the relevant transcription factors. Cumulative recovery curves were employed to evaluate the enrichment of transcription factor motifs, revealing significant regulatory patterns.\u003c/p\u003e \u003cp\u003eMotif-TF annotation and selection analysis identified \u003cb\u003ecisbp__M5167\u003c/b\u003e as the motif with the highest normalized enrichment score (NES: 6.38), indicating its significant role in regulating these genes. This finding suggests that multiple transcription factors interact with this motif to control the expression of the seven key genes. Figure\u0026nbsp;6A illustrates the enriched motifs and their corresponding transcription factors, emphasizing the complexity of the regulatory network governing these critical genes in the context of periodontitis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration\u003c/h2\u003e \u003cp\u003eThe periodontitis microenvironment is primarily composed of immune cells, the extracellular matrix, growth factors, inflammatory mediators, and distinct physical and chemical characteristics. These components play a crucial role in influencing disease progression, diagnosis, and treatment response. In this study, we further investigated the molecular mechanisms by which key genes contribute to periodontitis progression by analyzing their relationship with immune infiltration within the dataset. The analysis quantified the proportions of various immune cells in each patient and highlighted interactions between different immune cell types (Fig.\u0026nbsp;7A). Using CIBERSORT, we examined correlations among 29 immune cell types and visualized their interrelationships (Fig.\u0026nbsp;7B). The results showed a significantly higher proportion of T helper cells in patients with periodontitis compared to the control group (Fig.\u0026nbsp;7C), indicating their potential role in disease progression.\u003c/p\u003e \u003cp\u003eWe then explored the correlations between key genes and immune cells. Several key genes were strongly associated with specific immune cell populations. For instance, \u003cem\u003eVIM\u003c/em\u003e exhibited a significant negative correlation with T follicular helper cells (\u003cem\u003eTfh\u003c/em\u003e), while \u003cem\u003eANXA1\u003c/em\u003e showed a negative correlation with \u003cem\u003eMHC_class_I\u003c/em\u003e. \u003cem\u003eSLC11A1\u003c/em\u003e was positively correlated with natural killer cells (\u003cem\u003eNK_cells\u003c/em\u003e), but negatively correlated with B cells (\u003cem\u003eB_cells\u003c/em\u003e). Additionally, \u003cem\u003eARL4C\u003c/em\u003e and \u003cem\u003eNKG7\u003c/em\u003e were positively correlated with cytolytic activity, while \u003cem\u003eLRRC25\u003c/em\u003e was positively associated with parainflammation but negatively correlated with B cells. \u003cem\u003eCD79B\u003c/em\u003e showed a positive correlation with immune checkpoints (\u003cem\u003eCheck\u0026thinsp;\u0026minus;\u0026thinsp;point\u003c/em\u003e) and a negative correlation with immature dendritic cells (\u003cem\u003eiDCs\u003c/em\u003e) (Fig.\u0026nbsp;7D). These findings underscore the unique and cooperative roles of the identified immune cell subpopulations and suggest that immune infiltration plays a crucial role in the pathogenesis of periodontitis. The identified correlations provide further insight into how immune cells interact with key genes, offering potential targets for therapeutic intervention.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression profiles of key genes in single-cell data and co-expression with osteoblast/osteoclast marker genes and co-expression with IL17 and TGFB\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study investigated the expression patterns of the seven key genes (\u003cem\u003eANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e) across various immune cell types in periodontitis. These included \u003cem\u003eCD4\u0026thinsp;+\u0026thinsp;T cells, Naive CD4\u0026thinsp;+\u0026thinsp;T cells, Naive CD8\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Naive B cells, B cells, NK cells, Monocytes, Dendritic cells\u003c/em\u003e, and \u003cem\u003eProgenitor cells\u003c/em\u003e. The expression of these genes across the 10 identified immune cell subtypes was visualized, revealing their unique distributions and suggesting specific roles in immune regulation within the periodontal microenvironment (Fig.\u0026nbsp;8A). Next, we analyzed the co-expression of these seven key genes with osteoclast marker genes (\u003cem\u003eCTSK, RUNX2, ACP5, CALCR, DCSTAMP, MMP9\u003c/em\u003e) and osteoblast marker genes (\u003cem\u003eEBF2, CD44, OSCAR, SP7, TNFRSF11A, TBX3, RUNX2\u003c/em\u003e), using data from the CellMarker2.0 database. Co-expression patterns were visualized, demonstrating distinct associations of the hub genes with both osteoclast and osteoblast markers, which are critical for understanding bone remodeling in periodontitis (Fig.\u0026nbsp;8B).\u003c/p\u003e \u003cp\u003eWe further explored the co-expression relationships between the key genes and two key cytokines, \u003cem\u003eIL17\u003c/em\u003e and \u003cem\u003eTGF-\u0026szlig;\u003c/em\u003e, which are known to play important roles in inflammation and immune response. Co-expression analysis revealed a strong negative correlation between \u003cem\u003eIL17\u003c/em\u003e and several of the hub genes, particularly \u003cem\u003eANXA1\u003c/em\u003e (r = -0.021, p\u0026thinsp;=\u0026thinsp;4.7e-06), \u003cem\u003eCD79B\u003c/em\u003e (r = -0.04, p\u0026thinsp;=\u0026thinsp;6.7e-06), and \u003cem\u003eNKG7\u003c/em\u003e (r = -0.017, p\u0026thinsp;=\u0026thinsp;0.0024), suggesting that \u003cem\u003eIL17\u003c/em\u003e may negatively regulate the expression of these genes in resting cells (S2 Figs. A\u0026thinsp;~\u0026thinsp;G). Conversely, \u003cem\u003eTGF-\u0026szlig;\u003c/em\u003e showed positive correlations with several key genes, including \u003cem\u003eANXA1\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), \u003cem\u003eARL4C\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), \u003cem\u003eNKG7\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), and \u003cem\u003eSLC11A1\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), indicating a potential role for \u003cem\u003eTGF-\u0026szlig;\u003c/em\u003e in promoting the expression of these genes in the periodontitis microenvironment. Negative correlations between \u003cem\u003eTGF-\u0026szlig;\u003c/em\u003e and \u003cem\u003eCD79B\u003c/em\u003e (r = -0.024, p\u0026thinsp;=\u0026thinsp;0.043) and \u003cem\u003eVIM\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.054, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) were also observed, suggesting complex regulatory interactions between these genes and cytokines (S2 Figs. A\u0026thinsp;~\u0026thinsp;G).\u003c/p\u003e \u003cp\u003eTogether, these results provide insight into the regulation of key genes by \u003cem\u003eIL17\u003c/em\u003e and \u003cem\u003eTGF-\u0026szlig;\u003c/em\u003e, as well as their roles in osteoclast and osteoblast activity in periodontitis. These findings emphasize the importance of immune-regulatory networks in the progression of the disease and identify potential targets for therapeutic intervention.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of receptor-ligand relationship pairs of cell subpopulations from single-cell data and the developmental trajectories of key cell subtypes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing CellCall, we analyzed the communication patterns between immune cells and other cell types within the microenvironment of periodontitis, focusing on receptor-ligand interactions across multiple disease pathways. The analysis revealed a complex network of interactions, which were visualized through a bubble plot representing the number of ligand-receptor interactions (Fig.\u0026nbsp;9A). Additionally, a network plot depicted the interactions between immune cell subtypes (Fig.\u0026nbsp;9B). Dendritic cells exhibited the highest level of interactions, particularly with NK cells, monocytes, and CD8\u0026thinsp;+\u0026thinsp;T cells. Monocytes, in turn, showed extensive interactions with NK cells, dendritic cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and CD4\u0026thinsp;+\u0026thinsp;T cells, underscoring their central role in the immune response.\u003c/p\u003e \u003cp\u003eTo further explore the development and differentiation of novel T cell subtypes, we performed clustering analysis, identifying three distinct T cell subclusters. We then calculated the similarity between cells and constructed their differentiation trajectories over time using pseudotime analysis. This approach enabled us to visualize the developmental processes of these cell subtypes, illustrating gene expression patterns at various stages of cell differentiation. Cells were colored by pseudotime value, cell type, and state (distinguished by path branches), providing a comprehensive view of their differentiation pathways (Fig.\u0026nbsp;9C\u0026ndash;D).\u003c/p\u003e \u003cp\u003eBy analyzing genes differentially expressed at key branch points along the developmental trajectory, we identified significant shifts in gene expression. These changes were visualized in a branch heat map, which highlights genes with substantial expression differences before and after the branching points (Fig.\u0026nbsp;9E). Finally, we tracked the expression changes of the key genes across the entire pseudotime course, revealing dynamic regulatory processes from the onset to the completion of cell differentiation (Fig.\u0026nbsp;9F). This analysis provides valuable insights into the cellular communication networks and the developmental trajectories of immune cell subpopulations involved in periodontitis.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003ePeriodontitis is a chronic inflammatory disease characterized by bone destruction and tooth loss, primarily caused by dysbiosis in the periodontal microbiota. This dysbiosis triggers an immune response and the release of cytokines, which contribute to alveolar bone resorption and progressive tooth loss \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The risk of periodontitis not only affects local tissues but also induces a systemic immune response. The understanding of periodontitis development has primarily focused on microbial colonization, often overlooking the intricate molecular mechanisms involved. However, recent studies have emphasized the critical role of immune signaling pathways in the onset and progression of periodontitis, revealing the complex interactions between host defense mechanisms and microbial invasion\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e,(26),(27).. This shift in perspective highlights the importance of immune response in the pathogenesis of periodontitis, prompting a reassessment of prevention and treatment strategies. In contrast to traditional causes, recent studies have highlighted additional pathways and genes implicated in periodontitis, with a particular emphasis on immune-related mechanisms. Several researches reported hub genes related to immune cell infiltration in regulating immune response, highlighting how these variations can affect susceptibility to periodontitis\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings revealed identification of genes such as ANXA1 and NKG7, which are linked to immune regulation and Corticosteroid-mediated anti-inflammatory response. These insights not only enhance our understanding of the genetic factors involved in dental caries but also indicate that the immune system's role in caries progression is more substantial than previously recognized. This discovery paves the way for further investigation into how genetic predisposition to altered immune responses may increase susceptibility to dental caries, thereby expanding the range of potential therapeutic targets. Further, we unveiled a diverse spectrum of immune cell types in periodontitis. The precise mechanisms underlying immune response regulation in periodontitis remain debated, especially in terms of treatment. In this study, we investigated the causal effects of key hub genes in periodontitis and their association with immune profiles. To our knowledge, this is the first study to integrate single-cell RNA sequencing (scRNA-seq), Genome-Wide Association Studies (GWAS), pathway analysis, CellCall, and Mendelian randomization (MR) to explore the molecular mechanisms of the immune response in periodontitis. Incorporating MR analysis, we identified several immune infiltration-associated genes that are also linked to genetic dimensions of susceptibility to periodontitis. These genes were essential for characterizing immune cell infiltration in periodontal remodeling. In particular, seven hub genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) were found to have causal interactions with immune cell subpopulations, as evidenced by Mendelian randomization analysis involving 198,441 cases. Importantly, memory T cells, especially CD4\u0026thinsp;+\u0026thinsp;T cells, showed positive correlations with these hub genes. ANXA1 and NKG7 is particularly important in suppressing inflammatory responses by promoting the production of anti-inflammatory mediators and inhibiting the release of pro-inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Previous studies have highlighted interaction between ANXA1 and NKG7. These genes can balance the inflammatory response. ANXA1 cooperates with ARL4C, NKG7 and SLC11A1 to enhance the response of immune cells by regulating cell signaling and cell motility\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. At the same time, ANXA1 and VIM work together on cell migration and signaling, enabling immune cells to effectively locate to the inflammatory area\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The role of CD79B in periodontitis is reflected in promoting B cell activation, regulating inflammatory response and antibody production, which plays a protective role in chronic periodontal infection\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These results indicated a balance between immune response and tissue damage, providing insights into inflammation management and healing in periodontitis.\u003c/p\u003e \u003cp\u003eThese genes, due to their biological roles, not only provide a deeper understanding of the pathophysiology of periodontitis but also represent promising targets for future therapeutic and diagnostic innovations. Their involvement in immune regulation, inflammation, and tissue repair offers an immunological approach to combating periodontitis. The potential of these genes as biomarkers for susceptibility or disease progression also opens avenues for early intervention, underscoring the promise of precision medicine in oral health. As we continue to unravel the genetic and cellular mechanisms underlying periodontitis, the translation of these insights into clinical applications represents the next frontier in the pursuit of optimal oral health.\u003c/p\u003e \u003cp\u003eOur study has certain limitations that should be considered. First, the statistical power of our analyses may be constrained by the sample size, which is particularly significant when assessing the impact of subtle genetic influences on complex traits, where larger sample sizes generally enhance the robustness of findings. Additionally, the accuracy of exposure measurement is crucial for the validity of Mendelian Randomization estimates. Any measurement error, particularly if differentially misclassified between comparison groups, could attenuate the observed associations, potentially leading to an underestimation of the true effects. Future studies should also explore the functional roles of the identified genes in caries development and assess their potential as biomarkers for early detection or targets for therapeutic intervention.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our scRNA-seq analysis of periodontitis identified seven hub genes that play critical roles in regulating immune cell subpopulations and signaling pathways. These genes, particularly in the context of CD4\u0026thinsp;+\u0026thinsp;T cells, dendritic cells, and monocytes, were shown to have a causal effect on the progression of periodontitis. Our findings provide novel insights into the immune landscape of periodontitis and suggest potential targets for immunotherapy. The Mendelian randomization study further reinforced the importance of immune cell interactions and signaling pathways in disease development. The potential of these genes as intervention factors to prevent or mitigate periodontal disease depends on a deeper understanding of their interactions with oral environmental and microbial factors. Future research should focus on elucidating the mechanistic pathways by which these genes influence periodontal disease development, evaluating their potential as predictive markers for disease susceptibility, and exploring gene-based therapeutic approaches to enhance host defense against periodontal disease. These efforts will not only deepen our understanding of periodontal disease but also pave the way for precision medicine in dentistry, providing new hope for effective treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003escRNA-seq\u0026nbsp;\u003c/strong\u003e\u0026nbsp; Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single Nucleotide Polymorphisms\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; genome-wide association studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mendelian randomization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; GENE EXPRESSION OMNIBUS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCBI\u0026nbsp;\u003c/strong\u003eNational Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uniform Manifold Approximation and Projection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVs\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Instrumental variables\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLD\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;linkage disequilibrium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVW\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Inverse Variance Weighted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLAU\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;plasminogen activator, urokinase\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Shihezi University School of Medicine (No: KJ-2024-295-02). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript in Journal of BMC Oral Health. Our manuscript adheres to the STROBE-MR guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e Data analysis in this study was derived from publicly available databases. These datas are 38919 available from TCGA (www.portal.gdc.cancer.gov/), GEO (www.ncbi.nlm.nih.gov/geo/) 390 QTLGen consortium (https://www.eqtlgen.org), FinnGene database, and genome-wide 391 association studies (GWAS) Catalog (https://www.ebi.ac.uk/gwas/). Code Availability: the 392 analysis code in R is available on request. If someone wants to request the data，please contact author ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXDQ and FY: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Designing the research, Formal analysis. CXL: Software, Investigation, Resources, Methodology. JW: Software, Investigation, Software, Resources. YWY: Software, Resources, Validation, Investigation. CG: Writing \u0026ndash; review \u0026amp; editing, Visualization, Validation, Formal analysis, Conceptualization, Project administration. Special thanks to single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and eQTL analyses for providing the technology that was essential for this study. All authors reviewed and approved the final manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi, Y., Jacox, L. A., Little, S. H. \u0026amp; Ko, C. C. Orthodontic tooth movement: The biology and clinical implications. \u003cem\u003eKaohsiung J. Med. Sci.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (4), 207\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.kjms.2018.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.kjms.2018.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanz, M. et al. 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Revisiting the Page \u0026amp; Schroeder model: the good, the bad and the unknowns in the periodontal host response 40 years later. \u003cem\u003ePeriodontology 2000\u003c/em\u003e. \u003cb\u003e75\u003c/b\u003e (1), 116\u0026ndash;151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/prd.12181\u003c/span\u003e\u003cspan address=\"10.1111/prd.12181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immune cell, Single-cell RNA sequencing, Mendelian randomization, Periodontitis, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5300947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5300947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePeriodontitis is a prevalent chronic inflammatory disease characterized by immune cell dysregulation and tissue destruction. This study integrates single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and expression quantitative trait loci (eQTL) analyses to uncover immune cell subtypes, causal genes, and develop a predictive nomogram model for periodontitis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed scRNA-seq data to identify differentially expressed genes (DEGs) and immune cell subtypes in periodontitis. MR analysis was conducted to determine causal relationships between immune cell gene expression and periodontitis risk, utilizing eQTL data. Gene ontology (GO) and pathway enrichment analyses were performed to understand functional implications. Additionally, CellChat trajectory analysis explored intercellular communication. A nomogram model was constructed to predict periodontitis risk based on immune-related DEGs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe integrated analysis identified 23 distinct immune cell clusters and seven hub genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) that were causally linked to periodontitis. Pathway enrichment analysis revealed their involvement in key immune regulatory mechanisms. A robust nomogram model based on these DEGs was developed and validated, demonstrating high predictive accuracy for periodontitis risk. Immune subtypes were further characterized, revealing distinct roles in disease progression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the crucial role of immune cell subpopulations and hub genes in the pathophysiology of periodontitis. The nomogram model offers a novel tool for predicting periodontitis risk and identifying potential therapeutic targets. These findings provide valuable insights into immune-related mechanisms and potential interventions for periodontitis.\u003c/p\u003e","manuscriptTitle":"Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 08:30:27","doi":"10.21203/rs.3.rs-5300947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-02T09:32:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-22T13:27:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-10T21:26:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172372663363797306478356005557192621540","date":"2025-02-22T20:05:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193721426313648961811849516159641470725","date":"2025-02-08T11:08:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162517128860634719164874685114844775600","date":"2024-11-17T19:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-14T09:42:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T08:26:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-13T04:52:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T04:07:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-21T03:46:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13551b3f-b4ac-4581-b02e-e27743eca96e","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40368746,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":40368747,"name":"Biological sciences/Immunology"},{"id":40368748,"name":"Biological sciences/Immunology/Gene regulation in immune cells"}],"tags":[],"updatedAt":"2025-10-20T16:00:51+00:00","versionOfRecord":{"articleIdentity":"rs-5300947","link":"https://doi.org/10.1038/s41598-025-19438-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-13 15:57:24","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2024-11-29 08:30:27","video":"","vorDoi":"10.1038/s41598-025-19438-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-19438-0","workflowStages":[]},"version":"v1","identity":"rs-5300947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5300947","identity":"rs-5300947","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}