Single-cell sequencing data identifies differentially expressed genes and immune cell subtypes in periodontitis patients

Abstract

BACKGROUND: Periodontitis is a chronic inflammatory disease. Previous research has predominantly focused on specific immune cells or cytokines. Therefore, systematically elucidating its immune mechanisms and discovering novel therapeutic targets hold significant implications. OBJECTIVE: To analyze the expression profiles of periodontitis-associated immune cell subpopulations and identify key differentially expressed genes with a causal relationship to the disease, thereby exploring potential molecular mechanisms and key genes involved in periodontitis and immune cell dynamics. METHODS: Single-cell RNA sequencing data from the GEO database were used to analyze immune cell subset heterogeneity and identify differentially expressed genes. Mendelian randomization analysis was performed using expression quantitative trait loci data to infer causal relationships between immune cell gene expression and periodontitis risk. Pathway enrichment and immune infiltration analyses were performed on the identified causal genes to reveal the associations between differentially expressed genes and immune cells with the development and progression of periodontitis. CellChat trajectory analysis was used to explore intercellular communication. To validate key findings, gingival tissue samples were collected from 20 patients with periodontitis diagnosed by the Department of Stomatology at The First Affiliated Hospital of Shihezi University (periodontitis group) and 20 healthy gingival tissue samples from patients undergoing orthodontic or impacted tooth extraction (control group). RT-qPCR and immunohistochemistry were used to detect the expression of key genes. RESULTS AND CONCLUSION: Comprehensive analysis identified 23 immune cell clusters in periodontitis and three key genes with significant causal relationships to periodontitis risk: annexin A1 (ANXA1), solute carrier family 11 member 1 (SLC11A1), and vimentin (VIM). Pathway enrichment analysis revealed their involvement in key immune regulatory mechanisms. Further analyses of immune subtype receptor-ligand interactions and key cell subtype trajectories characterized the distinct roles of ANXA1, SLC11A1, and VIM in disease progression. Compared with healthy controls, the mRNA expression levels of ANXA1, SLC11A1, and VIM were upregulated in periodontitis tissues (P < 0.05). This study reveals the key roles of immune cell subpopulations in periodontitis and validates causal genes (ANXA1, SLC11A1, VIM) associated with the disease.

1. Introduction

Periodontal disease is a chronic, non-specific, infectious, and complex inflammatory disease [1]. Activated immune cells, particularly T cells and B cells, are considered key factors in the progression of periodontitis [2-3]. Studies have shown that the role of immune cells is crucial for developing effective prevention and treatment strategies for periodontitis [4]. The development of periodontitis is influenced by multiple factors, including genetics, environmental conditions, microbial infection, and lifestyle. Despite significant advances in periodontitis research, the specific etiology and pathogenesis at the molecular level remain incompletely understood.

Single-cell RNA sequencing plays a vital role in revealing molecular mechanisms and identifying target cells, as it can uncover heterogeneity among cells and the complexity of gene expression regulation [5]. Previous studies have shown that differentially expressed genes in monocytes are associated with periodontitis [6]. However, these results rarely reflect the combination of immune cells and key genes. Mendelian randomization analysis can utilize genetic variants as tools in a 'natural random experiment' to test causality. This approach enables classification and characterization of each cell at single-cell resolution, thereby uncovering biological pathways related to transcriptomic features and phenotypic outcomes [7].

By combining Mendelian randomization with bioinformatics analysis, new perspectives can be brought to related research [8]. This study integrates multiple datasets, including single-cell RNA sequencing data and expression quantitative trait loci data, to explore the correlation and causal association between key genes and immune cells in periodontitis by combining single-cell sequencing and Mendelian randomization. Through single-cell sequencing, immune cell-related diagnostic features and clusters in periodontitis are identified, and Mendelian randomization is further used to screen genetic variants associated with the onset and progression of periodontitis, enhancing the precision of disease cell-specific mechanism research. Validation is performed via RT-qPCR and immunohistochemistry, providing a new approach for the diagnosis and treatment of periodontitis and offering new insights for personalized therapy.