Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm

Mycobacterial infections pose a significant global health concern, requiring precise identification for effective treatment. However, diagnosing them is challenging due to inaccurate identifications and prolonged times. In this study, we aimed to develop a novel peptidome-based method using mycobacterial growth indicator tube (MGIT) cultures for faster and more accurate identification. We created the PEPtide Taxonomy/ORganism CHecking (PEP-TORCH), an algorithm that analyzes tryptic peptides identified by mass spectrometry to diagnose species and subspecies with predominance scores. PEP-TORCH demonstrated 100% accuracy in identifying mycobacterial species, subspecies, and co-infections in 81 individuals suspected of mycobacterial infections, eliminating the need for a sub-solid culture procedure, the gold standard in clinical practice. A notable strength of PEP-TORCH is its ability to provide information on species and subspecies simultaneously, a process conventionally achieved sequentially. This capability significantly expedites pathogen identification. Furthermore, a targeted proteomics method was validated in 63 clinical samples using the taxa-specific peptides selected by PEP-TORCH, making them suitable as biomarkers in more clinically friendly settings. This comprehensive identification approach holds promise for streamlining treatment strategies in clinical practice.