Dataset Information

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae.

ABSTRACT: Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ?90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

SUBMITTER: Nguyen M

PROVIDER: S-EPMC5765115 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae.

Nguyen Marcus M Brettin Thomas T Long S Wesley SW Musser James M JM Olsen Randall J RJ Olson Robert R Shukla Maulik M Stevens Rick L RL Xia Fangfang F Yoo Hyunseung H Davis James J JJ

Scientific reports 20180111 1

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machi ...[more]

PMID: 29323230

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Project description:Gram-negative bacteria possess stress responses to maintain the integrity of the cell envelope. Stress sensors monitor outer membrane permeability, envelope protein folding, and energization of the inner membrane. The systems used by Gram-negative bacteria to sense and combat stress resulting from disruption of the peptidoglycan layer are not well characterized. The peptidoglycan layer is a single molecule that completely surrounds the cell and ensures its structural integrity. During cell growth new peptidoglcyan subunits are incorporated into the peptidoglycan layer by a series of enzymes called the penicillin-binding proteins (PBPs). To explore how Gram-negative bacteria respond to peptidoglycan stress, global gene expression analysis was used to identify Escherichia coli stress responses activated following inhibition of specific PBPs by the β-lactam antibiotics mecillinam and cefsulodin. Inhibition of PBPs with different roles in peptidoglycan synthesis has different consequences for cell morphology and viability, suggesting that not all perturbations to the peptidoglycan layer generate equivalent stresses. We demonstrate that inhibition of different PBPs resulted in both shared and unique stress responses. The regulation of capsular synthesis (Rcs) phosphorelay was activated by inhibition of all of the PBPs tested. Furthermore, we show that activation of the Rcs phosphorelay increased survival in the presence of these antibiotics, independently of capsule synthesis. Both activation of the phosphorelay and survival required signal transduction via the outer membrane lipoprotein RcsF and the response regulator RcsB. We propose that the Rcs pathway responds to peptidoglycan damage and contributes to the intrinsic resistance of E. coli to β-lactam antibiotics. We used microarrays to identify changes in gene expression resulting from treatment of Escherichia coli with the β-lactam antibiotics cefsulodin, mecillinam, or the combination. Keywords: Dose response, stress response

Project description:Gram-negative bacteria possess stress responses to maintain the integrity of the cell envelope. Stress sensors monitor outer membrane permeability, envelope protein folding, and energization of the inner membrane. The systems used by Gram-negative bacteria to sense and combat stress resulting from disruption of the peptidoglycan layer are not well characterized. The peptidoglycan layer is a single molecule that completely surrounds the cell and ensures its structural integrity. During cell growth new peptidoglcyan subunits are incorporated into the peptidoglycan layer by a series of enzymes called the penicillin-binding proteins (PBPs). To explore how Gram-negative bacteria respond to peptidoglycan stress, global gene expression analysis was used to identify Escherichia coli stress responses activated following inhibition of specific PBPs by the Î²-lactam antibiotics mecillinam and cefsulodin. Inhibition of PBPs with different roles in peptidoglycan synthesis has different consequences for cell morphology and viability, suggesting that not all perturbations to the peptidoglycan layer generate equivalent stresses. We demonstrate that inhibition of different PBPs resulted in both shared and unique stress responses. The regulation of capsular synthesis (Rcs) phosphorelay was activated by inhibition of all of the PBPs tested. Furthermore, we show that activation of the Rcs phosphorelay increased survival in the presence of these antibiotics, independently of capsule synthesis. Both activation of the phosphorelay and survival required signal transduction via the outer membrane lipoprotein RcsF and the response regulator RcsB. We propose that the Rcs pathway responds to peptidoglycan damage and contributes to the intrinsic resistance of E. coli to Î²-lactam antibiotics. We used microarrays to identify changes in gene expression resulting from treatment of Escherichia coli with the Î²-lactam antibiotics cefsulodin, mecillinam, or the combination. Experiment Overall Design: E. coli was treated with two b-lactam antibiotics, cefsulodin and mecillinam, alone at the minimum inhibitory concentration to identify the expression changes in response to antibiotic-induced peptidoglycan stress. E. coli was grown to an OD of 0.2 at which time cefsulodin or mecillinam was added. Samples of the bacteria were collected at 5 and 10 minutes after treatment with cefsulodin and 60 minutes after treatment with mecillinam. Untreated cells were also collected at the same time points as controls. Three biological replicates were collected for all time points and samples, however, only one of the 5 minute untreated samples was hybridized to the arrays. In addition, a single sample from cells treated with the combination of cefsulodin and mecillinam was hybridized to allow for comparison with samples hybridized to Affymetrix antisense genechips.

Dataset Information

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae.

Publications

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae.

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