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 < 0.001) accompanied by reduced T and NK cell counts. Notably, LTBI cases demonstrated an intermediate CD4+/CD8+ T-cell ratio, indicative of dynamic immune homeostasis. The TB cohort exhibited increased inflammatory T-cell populations, while CD8+ T-cell-mediated MHC-I and BTLA signaling pathways were identified as key regulators of immune clearance and modulation. Transcriptomic profiling identified five metabolically significant differentially expressed genes (FHIT, MAN1C1, SLC4C7, NT5E, AKR1C3; p < 0.05) that effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (TB). The machine learning-driven diagnostic framework demonstrated remarkable consistency across independent validation cohorts (GSE39940, GSE39939), exhibiting AUC values spanning 0.867-0.873. Molecular subtyping analysis delineated two distinct TB phenotypes: an immune-activated M1 macrophage-dominant subtype and a CD8 + T-cell infiltrated immunophenotype. Clinical validation substantiated the differential expression patterns of T-cell-related metabolic differentially expressed genes (TCM-DEGs; p < 0.05), while the nomogram predictive model achieved exceptional discriminative capacity (C-index = 0.944), demonstrating superior clinical applicability through decision curve analysis.

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.