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Investigating β-Lactam Drug Targets in Mycobacterium tuberculosis Using Chemical Probes.

ABSTRACT: Tuberculosis (TB), caused by the bacterial pathogen Mycobacterium tuberculosis (Mtb), infects 10 million people a year. An estimated 25% of humans harbor latent TB infections, an asymptomatic form of the disease. In both active and latent infections, Mtb relies on cell wall peptidoglycan for viability. In the current work, we synthesized fluorescent analogues of β-lactam antibiotics to study two classes of enzymes that maintain Mtb's peptidoglycan: penicillin-binding proteins (PBPs) and l,d-transpeptidases (LDTs). This set of activity-based probes included analogues of three classes of β-lactams: a monobactam (aztreonam-Cy5), a cephalosporin (cephalexin-Cy5), and a carbapenem (meropenem-Cy5). We used these probes to profile enzyme activity in protein gel-resolved lysates of Mtb. All three out-performed the commercial reagent Bocillin-FL, a penam. Meropenem-Cy5 was used to identify β-lactam targets by mass spectrometry, including PBPs, LDTs, and the β-lactamase BlaC. New probes were also used to compare PBP and LDT activity in two metabolic states: dormancy and active replication. We provide the first direct evidence that Mtb dynamically regulates the enzymes responsible for maintaining peptidoglycan in dormancy. Lastly, we profiled drug susceptibility in lysates and found that meropenem inhibits PBPs, LDTs, and BlaC.

SUBMITTER: Levine SR

PROVIDER: S-EPMC8096124 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.ResultsWe completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.ConclusionAlthough we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

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Investigating β-Lactam Drug Targets in Mycobacterium tuberculosis Using Chemical Probes.

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