Discrimination between Mycobacterium tuberculosis and Mycobacterium bovis using Fourier transform infrared...

Zoonotic tuberculosis (TB) caused by Mycobacterium bovis (Mbo) is a neglected disease that hinders efforts to eradicate human tuberculosis. Developing a rapid, high-throughput diagnostic test to distinguish Mbo from Mycobacterium tuberculosis (Mtb) isolates could enhance global zoonotic TB diagnostics and surveillance. This study aimed to evaluate the ability of Fourier Transform Infrared Spectroscopy (FT-IRS), using the IR Biotyper® system, to differentiate clinical isolates of Mbo and Mtb. Two bacterial inactivation protocols, paraformaldehyde and heat, were tested using Mtb and Bacillus Calmette-Guérin (BCG) strains grown in liquid culture. While both methods allowed FT-IRS analysis, heat inactivation was preferred due to its ease of use and efficiency in biomass recovery. Subsequently, Mtb and Mbo isolates were analyzed using FT-IRS based on polysaccharides, proteins/carbohydrates, and lipids spectral regions, and the resulting spectra were used to construct sample classifiers employing machine learning algorithms. Linear Discriminant Analysis and a UPGMA dendrogram demonstrated clear separations between Mtb and Mbo, particularly for the polysaccharide spectra. Additionally, a classifier built and internally validated using artificial neural networks for the polysaccharide spectra achieved 99% accuracy in distinguishing Mbo and Mtb. Further FT-IRS analysis of few available Mycobacterium africanum (Maf) strains demonstrated its capacity to differentiate Maf from Mtb and Mbo, expanding its utility in regions where Maf is endemic. This is the first study to apply FT-IRS to distinguish tuberculous mycobacteria. FT-IRS proved to be a highly effective, rapid, and accurate diagnostic tool for differentiating the Mbo and Mtb strains evaluated in this study, with promising applications for other tuberculous mycobacteria such as Maf.