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Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

The aim of our work here was to evaluate the immunogenicity of 60 mycobacterial antigens, some of which have not been previously assessed, notably a novel series of in vivo-expressed Mycobacterium tuberculosis (IVE-TB) antigens. We enrolled 505 subjects and separated them in individuals with and without latent tuberculosis infection (LTBI) vs. patients with active tuberculosis (TB). Following an overnight and 7 days stimulation of whole blood with purified recombinant M. tuberculosis antigens, interferon-γ (IFN-γ) levels were determined by ELISA. Several antigens could statistically significantly differentiate the groups of individuals. We obtained promising antigens from all studied antigen groups [dormancy survival regulon (DosR regulon) encoded antigens; resuscitation-promoting factors (Rpf) antigens; IVE-TB antigens; reactivation associated antigens]. Rv1733, which is a probable conserved transmembrane protein encoded in DosR regulon, turned out to be very immunogenic and able to discriminate between the three defined TB status, thus considered a candidate biomarker. Rv2389 and Rv2435n, belonging to Rpf family and IVE-TB group of antigens, respectively, also stood out as LTBI biomarkers. Although more studies are needed to support our findings, the combined use of these antigens would be an interesting approach to TB immunodiagnosis candidates.