Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction.

TitleConstraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction.
Publication TypeJournal Article
Year of Publication2009
AuthorsRaghunathan A, Reed JL, Shin S, Palsson BØ, Daefler S
JournalBMC systems biology
Volume3
Pagination38
PubMed Date2009
ISSN1752-0509
KeywordsAnimals, Cell Line, Gene Deletion, Gene Expression Profiling, Genes, Bacterial, Genes, Essential, Genes, Lethal, Host-Pathogen Interactions, Humans, Metabolic Networks and Pathways, Mice, Models, Biological, Oligonucleotide Array Sequence Analysis, Proteomics, Pseudogenes, Reproducibility of Results, Salmonella Infections, Salmonella typhimurium
Abstract

BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction.

RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection.

CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains.

Alternate JournalBMC Syst Biol
PubMed ID19356237

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