Prospects for new tuberculosis treatment in Africa

Growth and translation inhibition through sequence-specific RNA binding by Mycobacterium tuberculosis VapC toxin

The Mycobacterium tuberculosis genome harbors an unusually large number of toxin-antitoxin (TA) modules. Curiously, over half of these are VapBC (virulence-associated protein) family members. Nonetheless, the cellular target, precise mode of action, and physiological role of the VapC toxins in this important pathogen remain unclear. To better understand the function of this toxin family, we studied the features and biochemical properties of a prototype M. tuberculosis VapBC TA system, vapBC-mt4 (Rv0596c-Rv0595c). VapC-mt4 expression resulted in growth arrest, a hallmark of all TA toxins, in Escherichia coli, Mycobacterium smegmatis, and M. tuberculosis. Its expression led to translation inhibition accompanied by a gradual decrease in the steady-state levels of several mRNAs. VapC-mt4 exhibited sequence-specific endoribonuclease activity on mRNA templates at ACGC and AC(A/U)GC sequences. However, the cleavage activity of VapC-mt4 was comparatively weak relative to the TA toxin MazF-mt1 (Rv2801c). Unlike other TA toxins, translation inhibition and growth arrest preceded mRNA cleavage, suggesting that the RNA binding property of VapC-mt4, not RNA cleavage, initiates toxicity. In support of this hypothesis, expression of VapC-mt4 led to an increase in the recovery of total RNA with time in contrast to TA toxins that inhibit translation via direct mRNA cleavage. Additionally, VapC-mt4 exhibited stable, sequence-specific RNA binding in an electrophoretic mobility shift assay. Finally, VapC-mt4 inhibited protein synthesis in a cell-free system without cleaving the corresponding mRNA. Therefore, the activity of VapC-mt4 is mechanistically distinct from other TA toxins because it appears to primarily inhibit translation through selective, stable binding to RNA.

In vitro activity of a new isothiazoloquinolone, ACH-702, against Mycobacterium tuberculosis and other mycobacteria

In this work, we describe the activity of ACH-702 against clinical isolates of Mycobacterium tuberculosis and six different nontuberculous mycobacteria. The MIC(50) and MIC(90) of both susceptible and drug-resistant M. tuberculosis strains tested were 0.0625 and 0.125 microg/ml, respectively. The MIC(50) and MIC(90) values for Mycobacterium fortuitum isolates were 0.0625 microg/ml in both cases; Mycobacterium avium complex isolates showed MIC(50) and MIC(90) values of 0.25 and 4 microg/ml, respectively.

Rv1106c from Mycobacterium tuberculosis is a 3beta-hydroxysteroid dehydrogenase

New approaches are required to combat Mycobacterium tuberculosis (Mtb), especially the multi-drug resistant and extremely drug resistant organisms (MDR-TB and XDR-TB). There are many reports that mycobacteria oxidize 3beta-hydroxysterols to 3-ketosteroids, but the enzymes responsible for this activity have not been identified in mycobacterial species. In this work, the Rv1106c gene that is annotated as a 3beta-hydroxysteroid dehydrogenase in Mtb has been cloned and heterologously expressed. The purified enzyme was kinetically characterized and found to have a pH optimum between 8.5 and 9.5. The enzyme, which is a member of the short chain dehydrogenase superfamily, uses NAD+ as a cofactor and oxidizes cholesterol, pregnenolone, and dehydroepiandrosterone to their respective 3-keto-4-ene products. The enzyme forms a ternary complex with NAD+ binding before the sterol. The enzyme shows no substrate preference for dehydroepiandrosterone versus pregnenolone with second-order rate constants (kcat/Km) of 3.2 +/- 0.4 and 3.9 +/- 0.9 microM-1 min-1, respectively, at pH 8.5, 150 mM NaCl, 30 mM MgCl2, and saturating NAD+. Trilostane is a competitive inhibitor of dehydroepiandrosterone with a Ki of 197 +/- 8 microM. The expression of the 3beta-hydroxysteroid dehydrogenase in Mtb is intracellular. Disruption of the 3beta-hydroxysteroid dehydrogenase gene in Mtb abrogates mycobacterial cholesterol oxidation activity. These data are consistent with the Rv1106c gene being the one responsible for 3beta-hydroxysterol oxidation in Mtb.

Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum

BACKGROUND

We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis.

METHODS

We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data.

FINDINGS

Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%.

INTERPRETATION

The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.