A curated genome-scale metabolic model of Bordetella pertussis metabolism

Nick Fyson, Jerry King, Thomas Belcher, Andrew Preston, Caroline Colijn

Research output: Contribution to journalArticle

1 Citation (Scopus)
102 Downloads (Pure)

Abstract

The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.
Original languageEnglish
Article numbere1005639
JournalPlos Computational Biology
Volume13
Issue number7
DOIs
Publication statusPublished - 17 Jul 2017

Fingerprint

Bordetella pertussis
Metabolism
Genome
genome
Genes
metabolism
Growth
Biomass
prediction
Prediction
Gene
whooping cough
Predict
Fumarates
Model
Vaccines
Whooping Cough
gene
Vaccine
biomass

Cite this

A curated genome-scale metabolic model of Bordetella pertussis metabolism. / Fyson, Nick; King, Jerry; Belcher, Thomas; Preston, Andrew; Colijn, Caroline.

In: Plos Computational Biology, Vol. 13, No. 7, e1005639, 17.07.2017.

Research output: Contribution to journalArticle

Fyson, Nick ; King, Jerry ; Belcher, Thomas ; Preston, Andrew ; Colijn, Caroline. / A curated genome-scale metabolic model of Bordetella pertussis metabolism. In: Plos Computational Biology. 2017 ; Vol. 13, No. 7.
@article{ddc41d6db71d48079a27e43cd359f750,
title = "A curated genome-scale metabolic model of Bordetella pertussis metabolism",
abstract = "The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83{\%} and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.",
author = "Nick Fyson and Jerry King and Thomas Belcher and Andrew Preston and Caroline Colijn",
year = "2017",
month = "7",
day = "17",
doi = "10.1371/journal.pcbi.1005639",
language = "English",
volume = "13",
journal = "Plos Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "7",

}

TY - JOUR

T1 - A curated genome-scale metabolic model of Bordetella pertussis metabolism

AU - Fyson, Nick

AU - King, Jerry

AU - Belcher, Thomas

AU - Preston, Andrew

AU - Colijn, Caroline

PY - 2017/7/17

Y1 - 2017/7/17

N2 - The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.

AB - The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.

UR - http://dx.doi.org/10.1371/journal.pcbi.1005639

U2 - 10.1371/journal.pcbi.1005639

DO - 10.1371/journal.pcbi.1005639

M3 - Article

VL - 13

JO - Plos Computational Biology

JF - Plos Computational Biology

SN - 1553-734X

IS - 7

M1 - e1005639

ER -