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Potential mechanism of action of meso-dihydroguaiaretic acid on Mycobacterium tuberculosis H37Rv.

ABSTRACT: The isolation and characterization of the lignan meso-dihydroguaiaretic acid (MDGA) from Larrea tridentata and its activity against Mycobacterial tuberculosis has been demonstrated, but no information regarding its mechanism of action has been documented. Therefore, in this study we carry out the gene expression from total RNA obtained from M. tuberculosis H37Rv treated with MDGA using microarray technology, which was validated by quantitative real time polymerase chain reaction. Results showed that the alpha subunit of coenzyme A transferase of M. tuberculosis H37Rv is present in both geraniol and 1-and 2-methylnaphthalene degradation pathways, which are targeted by MDGA. This assumption was supported by molecular docking which showed stable interaction between MDGA with the active site of the enzyme. We propose that inhibition of coenzyme A transferase of M. tuberculosis H37Rv results in the accumulation of geraniol and 1-and 2-methylnaphtalene inside bacteria, causing membrane destabilization and death of the pathogen. The natural product MDGA is thus an attractive template to develop new anti-tuberculosis drugs, because its target is different from those of known anti-tubercular agents.

SUBMITTER: Clemente-Soto AF

PROVIDER: S-EPMC6271217 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Potential mechanism of action of meso-dihydroguaiaretic acid on Mycobacterium tuberculosis H37Rv.

Clemente-Soto Aldo F AF Balderas-Rentería Isaías I Rivera Gildardo G Segura-Cabrera Aldo A Garza-González Elvira E del Rayo Camacho-Corona María M

Molecules (Basel, Switzerland) 20141202 12

The isolation and characterization of the lignan meso-dihydroguaiaretic acid (MDGA) from Larrea tridentata and its activity against Mycobacterial tuberculosis has been demonstrated, but no information regarding its mechanism of action has been documented. Therefore, in this study we carry out the gene expression from total RNA obtained from M. tuberculosis H37Rv treated with MDGA using microarray technology, which was validated by quantitative real time polymerase chain reaction. Results showed ...[more]

PMID: 25474289

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Project description:BackgroundMycobacterium 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.ResultsThis 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.ConclusionIn 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

Potential mechanism of action of meso-dihydroguaiaretic acid on Mycobacterium tuberculosis H37Rv.

Publications

Potential mechanism of action of meso-dihydroguaiaretic acid on Mycobacterium tuberculosis H37Rv.

Similar Datasets

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