{"title":"人类牙龈单细胞图谱揭示了与 NET 相关的中性粒细胞亚群对牙周免疫的调节作用。","authors":"Wei Qiu, Ruiming Guo, Hongwen Yu, Xiaoxin Chen, Zehao Chen, Dian Ding, Jindou Zhong, Yumeng Yang, Fuchun Fang","doi":"10.1016/j.jare.2024.07.028","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Exaggerated neutrophil recruitment and activation are the major features of pathological alterations in periodontitis, in which neutrophil extracellular traps (NETs) are considered to be responsible for inflammatory periodontal lesions. Despite the critical role of NETs in the development and progression of periodontitis, their specific functions and mechanisms remain unclear.</p><p><strong>Objectives: </strong>To demonstrate the important functions and specific mechanisms of NETs involved in periodontal immunopathology.</p><p><strong>Methods: </strong>We performed single-cell RNA sequencing on gingival tissues from both healthy individuals and patients diagnosed with periodontitis. High-dimensional weighted gene co-expression network analysis and pseudotime analysis were then applied to characterize the heterogeneity of neutrophils. Animal models of periodontitis were treated with NETs inhibitors to investigate the effects of NETs in severe periodontitis. Additionally, we established a periodontitis prediction model based on NETs-related genes using six types of machine learning methods. Cell-cell communication analysis was used to identify ligand-receptor pairs among the major cell groups within the immune microenvironment.</p><p><strong>Results: </strong>We constructed a single-cell atlas of the periodontal microenvironment and obtained nine major cell populations. We further identified a NETs-related subgroup (NrNeu) in neutrophils. An in vivo inhibition experiment confirmed the involvement of NETs in gingival inflammatory infiltration and alveolar bone absorption in severe periodontitis. We further screened three key NETs-related genes (PTGS2, MME and SLC2A3) and verified that they have the potential to predict periodontitis. Moreover, our findings revealed that gingival fibroblasts had the most interactions with NrNeu and that they might facilitate the production of NETs through the MIF-CD74/CXCR4 axis in periodontitis.</p><p><strong>Conclusion: </strong>This study highlights the pathogenic role of NETs in periodontal immunity and elucidates the specific regulatory relationship by which gingival fibroblasts activate NETs, which provides new insights into the clinical diagnosis and treatment of periodontitis.</p>","PeriodicalId":94063,"journal":{"name":"Journal of advanced research","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single-cell atlas of human gingiva unveils a NETs-related neutrophil subpopulation regulating periodontal immunity.\",\"authors\":\"Wei Qiu, Ruiming Guo, Hongwen Yu, Xiaoxin Chen, Zehao Chen, Dian Ding, Jindou Zhong, Yumeng Yang, Fuchun Fang\",\"doi\":\"10.1016/j.jare.2024.07.028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Exaggerated neutrophil recruitment and activation are the major features of pathological alterations in periodontitis, in which neutrophil extracellular traps (NETs) are considered to be responsible for inflammatory periodontal lesions. Despite the critical role of NETs in the development and progression of periodontitis, their specific functions and mechanisms remain unclear.</p><p><strong>Objectives: </strong>To demonstrate the important functions and specific mechanisms of NETs involved in periodontal immunopathology.</p><p><strong>Methods: </strong>We performed single-cell RNA sequencing on gingival tissues from both healthy individuals and patients diagnosed with periodontitis. High-dimensional weighted gene co-expression network analysis and pseudotime analysis were then applied to characterize the heterogeneity of neutrophils. Animal models of periodontitis were treated with NETs inhibitors to investigate the effects of NETs in severe periodontitis. Additionally, we established a periodontitis prediction model based on NETs-related genes using six types of machine learning methods. Cell-cell communication analysis was used to identify ligand-receptor pairs among the major cell groups within the immune microenvironment.</p><p><strong>Results: </strong>We constructed a single-cell atlas of the periodontal microenvironment and obtained nine major cell populations. We further identified a NETs-related subgroup (NrNeu) in neutrophils. An in vivo inhibition experiment confirmed the involvement of NETs in gingival inflammatory infiltration and alveolar bone absorption in severe periodontitis. We further screened three key NETs-related genes (PTGS2, MME and SLC2A3) and verified that they have the potential to predict periodontitis. Moreover, our findings revealed that gingival fibroblasts had the most interactions with NrNeu and that they might facilitate the production of NETs through the MIF-CD74/CXCR4 axis in periodontitis.</p><p><strong>Conclusion: </strong>This study highlights the pathogenic role of NETs in periodontal immunity and elucidates the specific regulatory relationship by which gingival fibroblasts activate NETs, which provides new insights into the clinical diagnosis and treatment of periodontitis.</p>\",\"PeriodicalId\":94063,\"journal\":{\"name\":\"Journal of advanced research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of advanced research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jare.2024.07.028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of advanced research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jare.2024.07.028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Single-cell atlas of human gingiva unveils a NETs-related neutrophil subpopulation regulating periodontal immunity.
Introduction: Exaggerated neutrophil recruitment and activation are the major features of pathological alterations in periodontitis, in which neutrophil extracellular traps (NETs) are considered to be responsible for inflammatory periodontal lesions. Despite the critical role of NETs in the development and progression of periodontitis, their specific functions and mechanisms remain unclear.
Objectives: To demonstrate the important functions and specific mechanisms of NETs involved in periodontal immunopathology.
Methods: We performed single-cell RNA sequencing on gingival tissues from both healthy individuals and patients diagnosed with periodontitis. High-dimensional weighted gene co-expression network analysis and pseudotime analysis were then applied to characterize the heterogeneity of neutrophils. Animal models of periodontitis were treated with NETs inhibitors to investigate the effects of NETs in severe periodontitis. Additionally, we established a periodontitis prediction model based on NETs-related genes using six types of machine learning methods. Cell-cell communication analysis was used to identify ligand-receptor pairs among the major cell groups within the immune microenvironment.
Results: We constructed a single-cell atlas of the periodontal microenvironment and obtained nine major cell populations. We further identified a NETs-related subgroup (NrNeu) in neutrophils. An in vivo inhibition experiment confirmed the involvement of NETs in gingival inflammatory infiltration and alveolar bone absorption in severe periodontitis. We further screened three key NETs-related genes (PTGS2, MME and SLC2A3) and verified that they have the potential to predict periodontitis. Moreover, our findings revealed that gingival fibroblasts had the most interactions with NrNeu and that they might facilitate the production of NETs through the MIF-CD74/CXCR4 axis in periodontitis.
Conclusion: This study highlights the pathogenic role of NETs in periodontal immunity and elucidates the specific regulatory relationship by which gingival fibroblasts activate NETs, which provides new insights into the clinical diagnosis and treatment of periodontitis.