Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes

Objectives The early diagnosis and immunoregulatory mechanisms of active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remain unclear, and the role of metabolic genes in host-pathogen interactions requires further investigation. Methods Single-cell RNA sequencing (scRNA-seq) was applied to analyze peripheral blood mononuclear cells (PBMCs) from 7 individuals, including 2 healthy controls (HC), 2 LTBI patients, and 3 ATB patients. We identified T-cell-associated metabolic differentially expressed genes (TCM-DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM-RFE, and Boruta). These TCM-DEGs were then used to construct a diagnostic model and evaluate its clinical applicability. Results The analysis revealed significant immunological alterations in TB patients, characterized by markedly elevated monocyte/macrophage populations (p Conclusions Our findings reveal that TCM-DEGs critically regulate TB progression through immune-metabolic reprogramming and cell-cell communication networks. The developed diagnostic model and molecular subtyping strategy enable precise TB-LTBI differentiation and inform immunotherapy optimization.