2-aminoimidazoles potentiate ß-lactam antimicrobial activity against Mycobacterium tuberculosis by reducing ß-lactamase secretion and increasing cell wall permeability
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ABSTRACT:
ORGANISM(S): Mycobacterium tuberculosis
PROVIDER: GSE95773 | GEO | 2017/07/28
SECONDARY ACCESSION(S): PRJNA378382
REPOSITORIES: GEO
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Project description:M. tuberculosis is intrinsically tolerant to many antibiotics largely due to the imperviousness of its unusual mycolic acid-containing cell wall to most antimicrobials. The emergence and increasingly widespread of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) revitalized keen interest in phage-inspired therapy. SWU1gp39 is a novel gene from mycobacteriophage SWU1 with unknown function. SWU1gp39 expressed in M. smegmatis conferred the host cell increased susceptibility to multiple antibiotics, including isoniazid, erythromycin, norfloxacin, ampicillin, ciprofloxacin, ofloxacin, rifampicin and vancomycin, and multiple environment stresses such as H2O2, heat shock, low pH and SDS. By using EtBr/Nile red uptake assays, WT-pAL-gp39 strain showed higher cell wall permeability than control strain WT-pAL. Moreover, the WT-pAL-gp39 strain produced more reactive oxygen species and reduced NAD(+)/NADH ratio. RNA-Seq transcriptomes of the WT-pAL-gp39 and WT-pAL revealed that the transcription of 867 genes was differentially regulated, including genes associated with lipid metabolism. Taken together, our results implicated that SWU1gp39, a novel gene from mycobacteriophage, disrupted the lipid metabolism of host and increased cell wall permeability, ultimately potentiated the efficacy of multiple antibiotics and stresses against mycobacteria.
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Project description:Inhibitory activities of monocyclic nitroimidazoles against Mycobacterium tuberculosis (Mtb) deazaflavin-dependent nitroreductase (DDN) were modeled by using docking, pharmacophore alignment and comparative molecular similarity indices analysis (CoMSIA) methods. A statistically significant model obtained from CoMSIA was established based on a training set using pharmacophore-based molecular alignment. The leave-one out cross-validation correlation coefficients q2 (CoMSIA) were 0.681. The CoMSIA model had a good correlation (r2(pred)/CoMSIA = 0.611) between the predicted and experimental activities against excluded test sets. The generated model suggests that electrostatic, hydrophobic and hydrogen bonding interactions all play important roles for interaction between ligands and receptors. The predicted cell wall permeability (logP(app)) for substrates with high inhibitory activity against Mtb were investigated. The distribution coefficient (logD) range was 2.41 < logD < 2.89 for the Mtb cell wall membrane permeability. The larger the polar surface area is, the better the permeability is. A larger radius of gyration (rgry) and a small fraction of rotatable bonds (f(rtob)) of these molecules leads to higher cell wall penetration ability. The information obtained from the in silico tools might be useful in the design of more potent compounds that are active against Mtb.
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Project description:The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC50) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.
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