ArticlePLoS computational biology2017
A curated genome-scale metabolic model of Bordetella pertussis metabolism.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A new class of binding-protein dependent solute transporter exemplified by the TAXI-GltS system from Bordetella pertussis.Communications biology · 2025Article
- Deep longitudinal multi-omics analysis ofFrontiers in microbiology · 2023Article
- Integrating proteomic data with metabolic modeling provides insight into key pathways ofFrontiers in microbiology · 2023Article
- Trophic preferences of the pathogen Ralstonia solanacearum and consequences on its growth in xylem sap.MicrobiologyOpen · 2022Article
- An inducible transposon mutagenesis approach for the intracellular human pathogenWellcome open research · 2021Article
- Genome-Scale Investigation of the Metabolic Determinants Generating Bacterial Fastidious Growth.mSystems · 2020Article
- Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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