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Title: Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

Abstract

Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1392450
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Infectious Diseases; Journal Volume: 215; Journal Issue: suppl_1
Country of Publication:
United States
Language:
English
Subject:
Biolog; Klebsiella pneumoniae KPPR1; bacteria; flux balance analysis; gap-filling; metabolic model; resistance; transposon insertion sequencing

Citation Formats

Henry, Christopher S., Rotman, Ella, Lathem, Wyndham W., Tyo, Keith E. J., Hauser, Alan R., and Mandel, Mark J.. Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1. United States: N. p., 2017. Web. doi:10.1093/infdis/jiw465.
Henry, Christopher S., Rotman, Ella, Lathem, Wyndham W., Tyo, Keith E. J., Hauser, Alan R., & Mandel, Mark J.. Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1. United States. doi:10.1093/infdis/jiw465.
Henry, Christopher S., Rotman, Ella, Lathem, Wyndham W., Tyo, Keith E. J., Hauser, Alan R., and Mandel, Mark J.. Wed . "Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1". United States. doi:10.1093/infdis/jiw465.
@article{osti_1392450,
title = {Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1},
author = {Henry, Christopher S. and Rotman, Ella and Lathem, Wyndham W. and Tyo, Keith E. J. and Hauser, Alan R. and Mandel, Mark J.},
abstractNote = {Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.},
doi = {10.1093/infdis/jiw465},
journal = {Journal of Infectious Diseases},
number = suppl_1,
volume = 215,
place = {United States},
year = {Wed Feb 15 00:00:00 EST 2017},
month = {Wed Feb 15 00:00:00 EST 2017}
}
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