Direct identification of Mycobacterium species from liquid medium (MGIT) using FastPrep-2 bead beating system and MALDI-TOF mass spectrometry technology: a comparison with solid media and PCR-based method

Abstract

Background: Mycobacterial infections present significant global health challenges. Traditional diagnostic methods are inadequate for the identification of Mycobacterium species, highlighting the need for more efficient techniques. Matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) technology offers potential advantages in rapid and accurate pathogen identification. Objective: This study evaluates the accuracy and precision of the MALDI-TOF using the FastPrep-2 bead beating system and VITEK MS version 3.2 for identifying Mycobacterium species directly from Mycobacteria Growth Indicator Tube (MGIT), liquid medium, compared to MALDI-TOF (VITEK MS) based on the traditional solid medium (Lowenstein-Jensen). We also compared the result of the MALDI-TOF from MGIT to M tuberculosis results by PCR-based method. Methods: The study included 16 mycobacterium tuberculosis (MTB) and 37 nontuberculous mycobacteria (NTM). Isolates were grown in LJ solid medium and MGIT liquid medium, and lysed using the FastPrep-2 bead beating system. Identification was performed using VITEK MS version 3.2 from liquid. Results: NTM comprised 70 % (37/53) of the total isolates evaluated. Of these, 92 % (34/37) were successfully identified using VITEK MS from MGIT liquid medium. Overall, the method achieved 88.6 % accuracy for identifying Mycobacterium species from liquid medium, compared to 96.2 % from solid medium. The agreement between both methods was moderate (Kappa = 0.470, p < 0.001). MTB isolates were identified with 100 % accuracy, and the approach demonstrated excellent reproducibility with 100 % intra-assay and inter-day consistency. Conclusion: Using VITEK MS version 3.2 to directly identify MTB and NTM from MGIT liquid medium provides a rapid, cost-effective, and reliable method for identifying Mycobacterium species. Further optimization and database expansion are recommended to enhance accuracy for rare and less common mycobacterial species.