{"title":"CD4+ Effector Memory T Cells Related Marker Gene Signatures in Osteoporosis and Aging: Insight From Single-Cell Analysis and Mendelian Randomization.","authors":"Xiangwen Shi, Linmeng Tang, Mingjun Li, Yipeng Wu, Yongqing Xu","doi":"10.2174/0113862073353509241205065221","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>With the accelerated aging of the population, aging has emerged as a major risk factor for osteoporosis (OP). This study aims to investigate the relationship and shared molecular mechanisms between OP and aging through various genetic approaches.</p><p><strong>Methods: </strong>Single-cell data from the peripheral blood of osteoporosis patients, aging individuals, and healthy controls were integrated to analyze characteristic changes in cell subpopulations. Differentially expressed genes (DEGs) were then identified within core subpopulations, and Mendelian Randomization (MR) analysis was employed to explore potential causal links between key genes and OP. Additionally, an OP model was established in rats, and mRNA levels of key genes were measured using RT-qPCR.</p><p><strong>Results: </strong>Through the integration, filtering, and analysis of scRNA-seq data, an increased proportion of CD4+ effector memory T (CD4+ TEM) cells were identified in OP and aging samples, marking them as a core subpopulation. Differential expression analysis identified 49 DEGs, and further analysis through Mendelian Randomization (MR) identified three key genes (KLRB1, NR4A2, and S100A4) significantly associated with OP. Notably, the upregulation of KLRB1 and S100A4 may enhance the interactions within T cells and with other cell subgroups. At the same time, the downregulation of NR4A2 could impede communication between T cells and other cell subpopulations. The RT-qPCR results indicated that NR4A2 was significantly downregulated in the OP group.</p><p><strong>Conclusion: </strong>This study conducted a comprehensive analysis of the potential link between OP and aging, identifying CD4+ TEM cells as the core cell subgroup in OP and aging samples. It further revealed the causal relationship between KLRB1, NR4A2, and S100A4 and the occurrence of OP. The upregulation of KLRB1 and S100A4 may contribute to OP pathogenesis by promoting interactions between CD4+ TEM cells and other cell subgroups, providing new insights for molecular targeting and immunotherapy of OP.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Combinatorial chemistry & high throughput screening","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0113862073353509241205065221","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: With the accelerated aging of the population, aging has emerged as a major risk factor for osteoporosis (OP). This study aims to investigate the relationship and shared molecular mechanisms between OP and aging through various genetic approaches.
Methods: Single-cell data from the peripheral blood of osteoporosis patients, aging individuals, and healthy controls were integrated to analyze characteristic changes in cell subpopulations. Differentially expressed genes (DEGs) were then identified within core subpopulations, and Mendelian Randomization (MR) analysis was employed to explore potential causal links between key genes and OP. Additionally, an OP model was established in rats, and mRNA levels of key genes were measured using RT-qPCR.
Results: Through the integration, filtering, and analysis of scRNA-seq data, an increased proportion of CD4+ effector memory T (CD4+ TEM) cells were identified in OP and aging samples, marking them as a core subpopulation. Differential expression analysis identified 49 DEGs, and further analysis through Mendelian Randomization (MR) identified three key genes (KLRB1, NR4A2, and S100A4) significantly associated with OP. Notably, the upregulation of KLRB1 and S100A4 may enhance the interactions within T cells and with other cell subgroups. At the same time, the downregulation of NR4A2 could impede communication between T cells and other cell subpopulations. The RT-qPCR results indicated that NR4A2 was significantly downregulated in the OP group.
Conclusion: This study conducted a comprehensive analysis of the potential link between OP and aging, identifying CD4+ TEM cells as the core cell subgroup in OP and aging samples. It further revealed the causal relationship between KLRB1, NR4A2, and S100A4 and the occurrence of OP. The upregulation of KLRB1 and S100A4 may contribute to OP pathogenesis by promoting interactions between CD4+ TEM cells and other cell subgroups, providing new insights for molecular targeting and immunotherapy of OP.
期刊介绍:
Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal:
Target identification and validation
Assay design, development, miniaturization and comparison
High throughput/high content/in silico screening and associated technologies
Label-free detection technologies and applications
Stem cell technologies
Biomarkers
ADMET/PK/PD methodologies and screening
Probe discovery and development, hit to lead optimization
Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries)
Chemical library design and chemical diversity
Chemo/bio-informatics, data mining
Compound management
Pharmacognosy
Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products)
Natural Product Analytical Studies
Bipharmaceutical studies of Natural products
Drug repurposing
Data management and statistical analysis
Laboratory automation, robotics, microfluidics, signal detection technologies
Current & Future Institutional Research Profile
Technology transfer, legal and licensing issues
Patents.