Host Circulating Immunometabolism-Associated Biomarkers for Early Diagnosis of Active Tuberculosis: Multi-Omics Screening with Experimental Validation.

Background: Accurate diagnosis of active tuberculosis (TB) remains challenging when facing with no clinical symptom and negative pathogen tests. Metabolic reprogramming is the main characteristic of Methods: Datasets including healthy donors (HCs) and active TB patients were obtained from the Gene Expression Omnibus database. Machine learning methods were used to identify the metabolism-related hub genes. Correlation analysis between gene expression and immune cell infiltration was performed using the CIBERSORT algorithm. Single-cell RNA-seq analysis was performed to explore the expression of hub genes in the different immune cells. Results: In this study, we first obtained 41 differentially expressed metabolism-related genes in active TB patients compared to HCs through bulk transcriptomic analysis. Four metabolism-related hub genes (GCH1, GK, MTHFD2, and SLC7A6) were identified using machine learning algorithms for the diagnosis of active TB with high accuracy and verified using external datasets. A nomogram was constructed to comprehensively predict the risk of active TB. Mechanistically, protein-protein interactions and gene set enrichment analysis revealed that four hub genes affected pteridine and lipid metabolism and were associated with the innate immune pathways. Immune cell infiltration and single-cell sequencing analyses showed that GCH1, GK, and MTHFD2 were mainly expressed in M1 macrophages and were significantly upregulated after Conclusion: Our results demonstrate that changes in immunometabolism-related genes are associated with TB pathogenesis and could serve as biomarkers for the evaluation of active TB.