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Turning the respiratory flexibility of Mycobacterium tuberculosis against itself.

ABSTRACT: The Mycobacterium tuberculosis (Mtb) electron transport chain (ETC) has received significant attention as a drug target, however its vulnerability may be affected by its flexibility in response to disruption. Here we determine the effect of the ETC inhibitors bedaquiline, Q203 and clofazimine on the Mtb ETC, and the value of the ETC as a drug target, by measuring Mtb's respiration using extracellular flux technology. We find that Mtb's ETC rapidly reroutes around inhibition by these drugs and increases total respiration to maintain ATP levels. Rerouting is possible because Mtb rapidly switches between terminal oxidases, and, unlike eukaryotes, is not susceptible to back pressure. Increased ETC activity potentiates clofazimine's production of reactive oxygen species, causing rapid killing in vitro and in a macrophage model. Our results indicate that combination therapy targeting the ETC can be exploited to enhance killing of Mtb.

SUBMITTER: Lamprecht DA

PROVIDER: S-EPMC4987515 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Turning the respiratory flexibility of Mycobacterium tuberculosis against itself.

Lamprecht Dirk A DA Finin Peter M PM Rahman Md Aejazur MA Cumming Bridgette M BM Russell Shannon L SL Jonnala Surendranadha R SR Adamson John H JH Steyn Adrie J C AJ

Nature communications 20160810

The Mycobacterium tuberculosis (Mtb) electron transport chain (ETC) has received significant attention as a drug target, however its vulnerability may be affected by its flexibility in response to disruption. Here we determine the effect of the ETC inhibitors bedaquiline, Q203 and clofazimine on the Mtb ETC, and the value of the ETC as a drug target, by measuring Mtb's respiration using extracellular flux technology. We find that Mtb's ETC rapidly reroutes around inhibition by these drugs and in ...[more]

PMID: 27506290

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Project description:BACKGROUND: Mycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains. RESULTS: This study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach. CONCLUSION: In this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Dataset Information

Turning the respiratory flexibility of Mycobacterium tuberculosis against itself.

Publications

Turning the respiratory flexibility of Mycobacterium tuberculosis against itself.

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