Evaluation of mlstverse system for accurate subspecies identification and drug resistance prediction in Mycobacterium abscessus species

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is an increasing global health concern, with Mycobacterium abscessus species (MABS) contributing to refractory infections. Accurate subspecies identification and drug susceptibility testing (DST) are essential for appropriate clinical management, particularly in NTM-PD patients with MABS. We developed a novel software, mlstverse, which uses multi-locus sequence typing (MLST) to identify NTM species and subspecies, demonstrating rapid and accurate diagnostic performance. However, these studies included only a limited number of MABS samples. In this study, we focused on MABS and validated the diagnostic accuracy of the system for subspecies identification and drug resistance prediction for clarithromycin (CAM) and amikacin (AMK), key agents in the treatment of MABS treatment, using an adequately sized sample set. A total of 56 clinical isolates, previously identified as MABS by DNA-DNA hybridization or MALDI-TOF MS, were analyzed. The mlstverse system effectively discriminated between MABS subspecies, with the mean differences between the highest and second-highest MLST scores of 0.16 for Mycobacterium abscessus subsp. abscessus and 0.33 for M. abscessus subsp. massiliense, respectively. The system predicted drug susceptibility to CAM and AMK with high concordance to phenotypic DST (CAM: 98.1%, AMK: 100%). These results suggest that mlstverse provides a reliable method for comprehensive subspecies identification and simultaneous drug resistance prediction, supporting the potential of integrating portable next-generation sequencing technologies with real-time software analysis for improved diagnostic accuracy and treatment strategies in NTM-PD.