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.

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.