Dataset Information

Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605)

ABSTRACT: Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605) This model is described in the article: Systems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. Constraints-based model of Francisella tularensis. Raghunathan A, Shin S, Daefler S. BMC Syst Biol 2010; 4: 118 Abstract: BACKGROUND: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. RESULTS: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. CONCLUSIONS: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. This model is hosted on BioModels Database and identified by: MODEL1507180003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Nicolas Le Novère

PROVIDER: MODEL1507180003 | BioModels | 2015-07-30

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL1507180003?filename=MODEL1507180003-biopax2.owl	Owl
	MODEL1507180003?filename=MODEL1507180003-biopax3.owl	Owl
	MODEL1507180003?filename=MODEL1507180003.m	Other
	MODEL1507180003?filename=MODEL1507180003.pdf	Pdf
	MODEL1507180003?filename=MODEL1507180003.png	Other

Items per page:

1 - 5 of 11

Publications

Systems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. Constraints-based model of Francisella tularensis.

Raghunathan Anu A Shin Sookil S Daefler Simon S

BMC systems biology 20100823

<h4>Background</h4>Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell.<h4>Results</h4>We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach d ...[more]

PMID: 20731870

Similar Datasets

Project description:Anaplasma phagocytophilum is an emerging zoonotic pathogen that causes human granulocytic anaplasmosis. These intracellular bacteria establish infection by affecting cell function in both the vertebrate host and the tick vector, Ixodes scapularis. Previous studies have characterized the tick transcriptome and proteome in response to A. phagocytophilum infection. However, in the post-genomic era, the integration of omics datasets through a systems biology approach allows network-based analyses to describe the complexity and functionality of biological systems such as host-pathogen interactions and the discovery of new targets for prevention and control of infectious diseases. This study reports for the first time a systems biology integration of metabolomics, transcriptomics and proteomics data to characterize essential metabolic pathways involved in the response of tick cells to A. phagocytophilum infection. The results showed that infection affected protein processing in endoplasmic reticulum and glucose metabolic pathways in tick cells. These results supported tick-Anaplasma co-evolution by providing new evidence of how tick cells limit pathogen infection, while the pathogen benefits from the tick cell response to establish infection. The results suggested that A. phagocytophilum induces protein misfolding to limit the tick cell response and facilitate infection, but requires protein degradation to prevent ER stress and cell apoptosis to survive in infected cells. Additionally, A. phagocytophilum may benefit from the tick cellâs ability to limit bacterial infection through PEPCK inhibition leading to decreased glucose metabolism, which also results in the inhibition of cell apoptosis that increases infection of tick cells. These results support the use of this experimental approach to systematically identify tick cell pathways and molecular mechanisms involved in tick-pathogen interactions Two samples with two replicates each were analyzed. Samples included Ixodes scapularis ISE6 cells uninfected (control) and infected with Anaplasma phagocytophilum human NY18 isolate.