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

Abstract

Accurate subspecies identification and drug susceptibility testing (DST) are essential for appropriate clinical management of Mycobacterium abscessus 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.