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

UNLABELLED

Accurate subspecies identification and drug susceptibility testing (DST) are essential for appropriate clinical management, particularly in patients infected with species (MABS). We developed a novel software, mlstverse, which uses multi-locus sequence typing to identify non-tuberculous mycobacteria (NTM) species, 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 evaluated the diagnostic accuracy of the system for subspecies identification and drug resistance prediction for clarithromycin (CAM) and amikacin (AMK). A total of 56 clinical isolates, previously identified as MABS by conventional methods, were analyzed. The mlstverse identified two isolates as and 54 isolates as MABS, with 28 identified as subsp. (MAB) and 26 as subsp. (MMA). All results obtained through mlstverse were fully consistent with the species/subspecies exhibiting the highest average nucleotide identity (ANI) values. In contrast to the identification by ANI values, the mlstverse system clearly distinguished between MABS subspecies, with the mean differences between the highest and second-highest MLST scores of 0.16 for MAB and 0.33 for MMA, 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 accurate subspecies identification and drug resistance prediction in MABS, supporting the potential of integrating portable next-generation sequencing technologies with real-time software analysis for improved diagnostic accuracy and treatment strategies in patients with NTM infections.

IMPORTANCE

Accurate subspecies identification and drug susceptibility testing (DST) are essential for appropriate clinical management of species (MABS) infections. We previously developed mlstverse, a novel software utilizing multi-locus sequence typing to identify non-tuberculous mycobacteria (NTM species, 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 evaluated the diagnostic accuracy of the system for subspecies identification and drug resistance prediction for clarithromycin (CAM) and amikacin (AMK). We showed that mlstverse can clearly distinguish MAB subspecies compared to ANI values and predicted drug susceptibility to CAM and AMK with high concordance to phenotypic DST. The mlstverse system provides a reliable method for accurate subspecies identification and drug resistance prediction in MABS, supporting the potential of integrating portable next-generation sequencing technologies with real-time software analysis for improved diagnostic accuracy and treatment strategies in patients with NTM infections.