Dataset Information

Application of Mycobacterium smegmatis as a surrogate to evaluate drug leads against Mycobacterium tuberculosis.

ABSTRACT: Discovery of new anti-tuberculosis (TB) drugs is a time-consuming process due to the slow-growing nature of Mycobacterium tuberculosis (Mtb). A requirement of biosafety level 3 (BSL-3) facility for performing research associated with Mtb is another limitation for the development of TB drug discovery. In our screening of BSL-1 Mycobacterium spp. against a battery of TB drugs, M. smegmatis (ATCC607) exhibits good agreement with its drug susceptibility against the TB drugs under a low-nutrient culture medium (0.5% Tween 80 in Middlebrook 7H9 broth). M. smegmatis (ATCC607) enters its dormant form in 14 days under a nutrient-deficient condition (a PBS buffer), and shows resistance to a majority of TB drugs, but shows susceptibility to amikacin, capreomycin, ethambutol, and rifampicin (with high concentrations) whose activities against non-replicating (or dormant) Mtb were previously validated.

SUBMITTER: Lelovic N

PROVIDER: S-EPMC7554168 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Application of Mycobacterium smegmatis as a surrogate to evaluate drug leads against Mycobacterium tuberculosis.

Lelovic Nada N Mitachi Katsuhiko K Yang Junshu J Lemieux Maddie R MR Ji Yinduo Y Kurosu Michio M

The Journal of antibiotics 20200529 11

Discovery of new anti-tuberculosis (TB) drugs is a time-consuming process due to the slow-growing nature of Mycobacterium tuberculosis (Mtb). A requirement of biosafety level 3 (BSL-3) facility for performing research associated with Mtb is another limitation for the development of TB drug discovery. In our screening of BSL-1 Mycobacterium spp. against a battery of TB drugs, M. smegmatis (ATCC607) exhibits good agreement with its drug susceptibility against the TB drugs under a low-nutrient cult ...[more]

PMID: 32472054

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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/.

Project description:BackgroundTuberculosis (TB) is caused by Mycobacterium tuberculosis and represents one of the major challenges facing drug discovery initiatives worldwide. The considerable rise in bacterial drug resistance in recent years has led to the need of new drugs and drug regimens. Model systems are regularly used to speed-up the drug discovery process and circumvent biosafety issues associated with manipulating M. tuberculosis. These include the use of strains such as Mycobacterium smegmatis and Mycobacterium marinum that can be handled in biosafety level 2 facilities, making high-throughput screening feasible. However, each of these model species have their own limitations.ResultsWe report and describe the first complete genome sequence of Mycobacterium aurum ATCC23366, an environmental mycobacterium that can also grow in the gut of humans and animals as part of the microbiota. This species shows a comparable resistance profile to that of M. tuberculosis for several anti-TB drugs. The aims of this study were to (i) determine the drug resistance profile of a recently proposed model species, Mycobacterium aurum, strain ATCC23366, for anti-TB drug discovery as well as Mycobacterium smegmatis and Mycobacterium marinum (ii) sequence and annotate the complete genome sequence of this species obtained using Pacific Bioscience technology (iii) perform comparative genomics analyses of the various surrogate strains with M. tuberculosis (iv) discuss how the choice of the surrogate model used for drug screening can affect the drug discovery process.ConclusionsWe describe the complete genome sequence of M. aurum, a surrogate model for anti-tuberculosis drug discovery. Most of the genes already reported to be associated with drug resistance are shared between all the surrogate strains and M. tuberculosis. We consider that M. aurum might be used in high-throughput screening for tuberculosis drug discovery. We also highly recommend the use of different model species during the drug discovery screening process.

Dataset Information

Application of Mycobacterium smegmatis as a surrogate to evaluate drug leads against Mycobacterium tuberculosis.

Publications

Application of Mycobacterium smegmatis as a surrogate to evaluate drug leads against Mycobacterium tuberculosis.

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