Discrimination between Mycobacterium tuberculosis and Mycobacterium bovis using Fourier Transform Infrared Spectroscopy

Zoonotic tuberculosis (TB) caused by Mycobacterium bovis (Mb) is a neglected disease that hinders efforts to eradicate human tuberculosis. Developing a rapid, high-throughput diagnostic test to distinguish Mb 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 Mb and Mtb. Two bacterial inactivation protocols— paraformaldehyde and boiling—were tested using Mtb and BCG strains grown in liquid culture. While both methods allowed FT-IRS analysis, boiling was preferred due to its ease of use and efficiency in biomass recovery. Subsequently, Mtb and Mb isolates were analyzed using FT-IRS, and the resulting spectra were used to construct a sample classifier employing machine learning algorithms. Linear Discriminant Analysis (LDA) and a UPGMA dendrogram demonstrated clear separations between Mtb and Mb ecotypes. Additionally, a classifier built and internally validated using artificial neural networks achieved 99% accuracy in distinguishing Mb and Mtb. Further FT-IRS analysis of few available Mycobacterium africanum (Maf) strains demonstrated its capacity to differentiate Maf from Mtb and Mb, 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 Mb and Mtb strains, with promising applications for other tuberculous mycobacteria such as Maf.