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Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa.

ABSTRACT: UNLABELLED:Antimicrobial-resistant bacteria pose a serious threat in the clinic. This is particularly true for opportunistic pathogens that possess high intrinsic resistance. Though many studies have focused on understanding the acquisition of bacterial resistance upon exposure to antimicrobials, the mechanisms controlling intrinsic resistance are not well understood. In this study, we subjected the model opportunistic superbug Pseudomonas aeruginosa to 14 antimicrobials under highly controlled conditions and assessed its response using expression- and fitness-based genomic approaches. Our results reveal that gene expression changes and mutant fitness in response to sub-MIC antimicrobials do not correlate on a genomewide scale, indicating that gene expression is not a good predictor of fitness determinants. In general, fewer fitness determinants were identified for antiseptics and disinfectants than for antibiotics. Analysis of gene expression and fitness data together allowed the prediction of antagonistic interactions between antimicrobials and insight into the molecular mechanisms controlling these interactions. IMPORTANCE:Infections involving multidrug-resistant pathogens are difficult to treat because the therapeutic options are limited. These infections impose a significant financial burden on infected patients and on health care systems. Despite years of antimicrobial resistance research, we lack a comprehensive understanding of the intrinsic mechanisms controlling antimicrobial resistance. This work uses two fine-scale genomic approaches to identify genetic loci important for antimicrobial resistance of the opportunistic pathogen Pseudomonas aeruginosa. Our results reveal that antibiotics have more resistance determinants than antiseptics/disinfectants and that gene expression upon exposure to antimicrobials is not a good predictor of these resistance determinants. In addition, we show that when used together, genomewide gene expression and fitness profiling can provide mechanistic insights into multidrug resistance mechanisms.

SUBMITTER: Murray JL

PROVIDER: S-EPMC4626858 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa.

Murray Justine L JL Kwon Taejoon T Marcotte Edward M EM Whiteley Marvin M

mBio 20151027 6

<h4>Unlabelled</h4>Antimicrobial-resistant bacteria pose a serious threat in the clinic. This is particularly true for opportunistic pathogens that possess high intrinsic resistance. Though many studies have focused on understanding the acquisition of bacterial resistance upon exposure to antimicrobials, the mechanisms controlling intrinsic resistance are not well understood. In this study, we subjected the model opportunistic superbug Pseudomonas aeruginosa to 14 antimicrobials under highly con ...[more]

PMID: 26507235

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Project description:AmpC is a group I, class C -lactamase present in most Enterobacteriaceae and in Pseudomonas aeruginosa and other nonfermenting gram-negative bacilli. The ?-lactam class of antibiotics is one of the most important structural classes of antibacterial compounds and act by inhibiting the bacterial D ,D - transpeptidases that are responsible for the final step of peptidoglycan cross-linking. Our main aim in the study is to screen possible inhibitors against AmpC / ? - lactamase (an enzyme responsible for antimicrobial activity in Pseudomonas aeruginosa), through virtual screening of 1364 NCI (National Cancer Institute) diversity set II compounds. Homology Model of AmpC / ? - lactamase was constructed using MODELLER and the Model was validated using PROCHECK and Verify 3D programs to obtain a stable structure, which was further used for virtual screening of NCI (National Cancer Institute) diversity set II compounds through molecular Docking studies using Autodock. The amino acid sequence of the ? - lactamase was also subjected to ScanProsite web server to find any pattern present in the sequence. After the prediction of 3-dimensional model of AmpC/ ?-lactamase, the possible Active sites of? - lactamase were determined using LIGSITE(csc) and CastP web servers simultaneously. The Docked complexes were validated and Enumerated based on the Autodock Scoring function to pick out the best inhibitor based on Autodock energy score. Thus from the entire 1364 NCI diversity set II compounds which were Docked, the best four docking solutions were selected (ZINC12670903, ZINC17465965, ZINC11681166 and ZINC13099024). Further the Complexes were analyzed through LIGPLOT for their interaction for the 4 best docked NCI diversity set II compounds. Thus from the Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds could be promising inhibitors for Pseudomonas aeruginosa using AmpC /? - lactamase as Drug target yet pharmacological studies have to confirm it.

Dataset Information

Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa.

Publications

Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa.

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