Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms

Abstract

Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI−TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072–0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI−TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.

Cutaneous Mycobacterium Abscessus Infection Following Plastic Surgery: Three Case Reports

“…However, a combination of MALDI-TOF MS and machine learning has shown a high accuracy in identifying MABC subspecies, although the geographic origin of the strains can impact the protein spectra. 44 , 45 …”

“…However, a combination of MALDI-TOF MS and machine learning has shown a high accuracy in identifying MABC subspecies, although the geographic origin of the strains can impact the protein spectra. 44,45 In addition to identifying subspecies, it is necessary to genotype macrolide susceptibility to guide precise treatment for clinical M. abscessus infections. The real-time multiplex assay allows for both the distinguishing of MABC subspecies and the determination of its susceptibility to macrolides.…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning technologies can be implemented for rapid differentiation of resistant Mycobacterium abscessus complex subspecies from macrolide-susceptible subspecies [117]. These technologies have been used with other methods to investigate mechanisms of resistance in Pneumocystis jirovecii [118].…”

Automatic antibiotic resistance prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra

“…Other approaches, intended for high-dimensional data, such as [67], proposed using sparse SVMs to classify the intestinal bacterial composition. Posterior research, such as [68], used an RF to identify subspecies of Mycobacterium abscessus based on their MALDI-TOFs. However, other authors have proposed using different spectroscopy methods to identify pathogenic bacteria.…”