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

Zoonotic tuberculosis (TB) caused by <i>Mycobacterium bovis</i> (Mbo) is a neglected disease that hinders efforts to eradicate human tuberculosis. Developing a rapid, high-throughput diagnostic test to distinguish Mbo from <i>Mycobacterium tuberculosis</i> (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 <i>Mycobacterium africanum</i> (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.