Dataset Information

Exposure to Sub-inhibitory Concentrations of the Chemosensitizer 1-(1-Naphthylmethyl)-Piperazine Creates Membrane Destabilization in Multi-Drug Resistant Klebsiella pneumoniae.

ABSTRACT: Antimicrobial efflux is one of the important mechanisms causing multi-drug resistance (MDR) in bacteria. Chemosensitizers like 1-(1-naphthylmethyl)-piperazine (NMP) can inhibit an efflux pump and therefore can overcome MDR. However, secondary effects of NMP other than efflux pump inhibition are rarely investigated. Here, using phenotypic assays, phenotypic microarray and transcriptomic assays we show that NMP creates membrane destabilization in MDR Klebsiella pneumoniae MGH 78578 strain. The NMP mediated membrane destabilization activity was measured using ?-lactamase activity, membrane potential alteration studies, and transmission electron microscopy assays. Results from both ?-lactamase and membrane potential alteration studies shows that both outer and inner membranes are destabilized in NMP exposed K. pneumoniae MGH 78578 cells. Phenotypic Microarray and RNA-seq were further used to elucidate the metabolic and transcriptional signals underpinning membrane destabilization. Membrane destabilization happens as early as 15 min post-NMP treatment. Our RNA-seq data shows that many genes involved in envelope stress response were differentially regulated in the NMP treated cells. Up-regulation of genes encoding the envelope stress response and repair systems show the distortion in membrane homeostasis during survival in an environment containing sub-inhibitory concentration of NMP. In addition, the lsr operon encoding the production of autoinducer-2 responsible for biofilm production was down-regulated resulting in reduced biofilm formation in NMP treated cells, a phenotype confirmed by crystal violet-based assays. We postulate that the early membrane disruption leads to destabilization of inner membrane potential, impairing ATP production and consequently resulting in efflux pump inhibition.

SUBMITTER: Anes J

PROVIDER: S-EPMC6381021 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Exposure to Sub-inhibitory Concentrations of the Chemosensitizer 1-(1-Naphthylmethyl)-Piperazine Creates Membrane Destabilization in Multi-Drug Resistant <i>Klebsiella pneumoniae</i>.

Anes João J Sivasankaran Sathesh K SK Muthappa Dechamma M DM Fanning Séamus S Srikumar Shabarinath S

Frontiers in microbiology 20190213

Antimicrobial efflux is one of the important mechanisms causing multi-drug resistance (MDR) in bacteria. Chemosensitizers like 1-(1-naphthylmethyl)-piperazine (NMP) can inhibit an efflux pump and therefore can overcome MDR. However, secondary effects of NMP other than efflux pump inhibition are rarely investigated. Here, using phenotypic assays, phenotypic microarray and transcriptomic assays we show that NMP creates membrane destabilization in MDR <i>Klebsiella pneumoniae</i> MGH 78578 strain. ...[more]

PMID: 30814979

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Project description:Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Project description:Resistance to polymyxin antibiotics is increasing. Without new antibiotic classes, combination therapy is often required. We systematically investigated bacterial killing with polymyxin-based combinations against multidrug-resistant (including polymyxin-resistant), carbapenemase-producing Klebsiella pneumoniae Monotherapies and double- and triple-combination therapies were compared to identify the most efficacious treatment using static time-kill studies (24 h, six isolates), an in vitro pharmacokinetic/pharmacodynamic model (IVM; 48 h, two isolates), and the mouse thigh infection model (24 h, six isolates). In static time-kill studies, all monotherapies (polymyxin B, rifampin, amikacin, meropenem, or minocycline) were ineffective. Initial bacterial killing was enhanced with various polymyxin B-containing double combinations; however, substantial regrowth occurred in most cases by 24 h. Most polymyxin B-containing triple combinations provided greater and more sustained killing than double combinations. Standard dosage regimens of polymyxin B (2.5 mg/kg of body weight/day), rifampin (600 mg every 12 h), and amikacin (7.5 mg/kg every 12 h) were simulated in the IVM. Against isolate ATH 16, no viable bacteria were detected across 5 to 25 h with triple therapy, with regrowth to ∼2-log10 CFU/ml occurring at 48 h. Against isolate BD 32, rapid initial killing of ∼3.5-log10 CFU/ml at 5 h was followed by a slow decline to ∼2-log10 CFU/ml at 48 h. In infected mice, polymyxin B monotherapy (60 mg/kg/day) generally was ineffective. With triple therapy (polymyxin B at 60 mg/kg/day, rifampin at 120 mg/kg/day, and amikacin at 300 mg/kg/day), at 24 h there was an ∼1.7-log10 CFU/thigh reduction compared to the starting inoculum for all six isolates. Our results demonstrate that the polymyxin B-rifampin-amikacin combination significantly enhanced in vitro and in vivo bacterial killing, providing important information for the optimization of polymyxin-based combinations in patients.

Dataset Information

Exposure to Sub-inhibitory Concentrations of the Chemosensitizer 1-(1-Naphthylmethyl)-Piperazine Creates Membrane Destabilization in Multi-Drug Resistant Klebsiella pneumoniae.

Publications

Exposure to Sub-inhibitory Concentrations of the Chemosensitizer 1-(1-Naphthylmethyl)-Piperazine Creates Membrane Destabilization in Multi-Drug Resistant <i>Klebsiella pneumoniae</i>.

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