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Medium effects on minimum inhibitory concentrations of nylon-3 polymers against E. coli.

ABSTRACT: Minimum inhibitory concentrations (MICs) against E. coli were measured for three nylon-3 polymers using Luria-Bertani broth (LB), brain-heart infusion broth (BHI), and a chemically defined complete medium (EZRDM). The polymers differ in the ratio of hydrophobic to cationic subunits. The cationic homopolymer is inert against E. coli in BHI and LB, but becomes highly potent in EZRDM. A mixed hydrophobic/cationic polymer with a hydrophobic t-butylbenzoyl group at its N-terminus is effective in BHI, but becomes more effective in EZRDM. Supplementation of EZRDM with the tryptic digest of casein (often found in LB) recapitulates the LB and BHI behavior. Additional evidence suggests that polyanionic peptides present in LB and BHI may form electrostatic complexes with cationic polymers, decreasing activity by diminishing binding to the anionic lipopolysaccharide layer of E. coli. In contrast, two natural antimicrobial peptides show no medium effects. Thus, the use of a chemically defined medium helps to reveal factors that influence antimicrobial potency of cationic polymers and functional differences between these polymers and evolved antimicrobial peptides.

SUBMITTER: Choi H

PROVIDER: S-EPMC4143223 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Medium effects on minimum inhibitory concentrations of nylon-3 polymers against E. coli.

Choi Heejun H Chakraborty Saswata S Liu Runhui R Gellman Samuel H SH Weisshaar James C JC

PloS one 20140825 8

Minimum inhibitory concentrations (MICs) against E. coli were measured for three nylon-3 polymers using Luria-Bertani broth (LB), brain-heart infusion broth (BHI), and a chemically defined complete medium (EZRDM). The polymers differ in the ratio of hydrophobic to cationic subunits. The cationic homopolymer is inert against E. coli in BHI and LB, but becomes highly potent in EZRDM. A mixed hydrophobic/cationic polymer with a hydrophobic t-butylbenzoyl group at its N-terminus is effective in BHI, ...[more]

PMID: 25153714

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Project description:AimsWe investigated the antibacterial effect of seven essential oils (EOs) and one EO-containing liquid phytogenic solution marketed for poultry and pigs ('Product A') on chicken pathogens, as well as the relationship between minimum inhibitory concentration (MIC) in EOs and antibiotics commonly administered to chicken flocks in the Mekong Delta (Vietnam).Methods and resultsMicellar extracts from oregano (Origanum vulgare), cajeput (Melaleuca leucadendra), garlic (Allium sativum), black pepper (Piper nigrum), peppermint (Mentha × piperita L.), tea tree (Melaleuca alternifolia), cinnamon (Cinnamomum zeylanicum) EOs and Product A were investigated for their MIC against Avibacterium endocarditidis (N = 10), Pasteurella multocida (N = 7), Ornitobacterium rhinotracheale (ORT) (N = 10), Escherichia coli (N = 10) and Gallibacterium anatis (N = 10). Cinnamon EO had the lowest median MIC across strains (median 0.5 mg/ml [IQR, interquartile range 0.3-2.0 mg/ml]), followed by Product A (3.8 mg/ml [1.9-3.8 mg/ml]), oregano EO (30.4 mg/ml [7.6-60.8 mg/ml]) and garlic 63.1 mg/ml [3.9 to >505.0 mg/ml]. Peppermint, tea tree, cajeput and pepper EOs had all MIC ≥219 mg/ml. In addition, we determined the MIC of the 12 most commonly used antibiotics in chicken flocks in the area. After accounting for pathogen species, we found an independent, statistically significant (p < 0.05) positive correlation between MIC of 10 of 28 (35.7%) pairs of EOs. For 67/96 (69.8%) combinations of EOs and antibiotics, the MICs were correlated. Of all antibiotics, doxycycline was positively associated with the highest number of EOs (peppermint, tea tree, black pepper and cajeput, all p < 0.05). For cinnamon, the MICs were negatively correlated with the MICs of 11/12 antimicrobial tested (all except colistin).ConclusionsIncreases in MIC of antibiotics generally correlates with increased tolerance to EOs. For cinnamon EO, however, the opposite was observed.Significance and impact of the studyOur results suggest increased antibacterial effects of EOs on multi-drug resistant pathogens; cinnamon EO was particularly effective against bacterial poultry pathogens.

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.

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Medium effects on minimum inhibitory concentrations of nylon-3 polymers against E. coli.

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Medium effects on minimum inhibitory concentrations of nylon-3 polymers against E. coli.

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