Metabolomics

Dataset Information

0

Salmonella Modulates Metabolism during Growth under Conditions that Induce Expression of Virulence Genes


ABSTRACT: Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.

INSTRUMENT(S): 5975C Series GC/MSD (Agilent)

SUBMITTER: Tom Metz 

PROVIDER: MTBLS35 | MetaboLights | 2013-05-02

REPOSITORIES: MetaboLights

Dataset's files

Source:
altmetric image

Publications


Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown  ...[more]

Similar Datasets

2014-06-25 | ST000085 | MetabolomicsWorkbench
2024-10-20 | GSE266444 | GEO
2024-10-20 | GSE266443 | GEO
| MSV000090243 | MassIVE
2023-02-07 | MSV000091219 | MassIVE
2012-08-02 | E-GEOD-39785 | biostudies-arrayexpress
2015-09-04 | E-GEOD-72691 | biostudies-arrayexpress
2015-08-17 | E-MTAB-3765 | biostudies-arrayexpress
2013-09-19 | E-MTAB-1848 | biostudies-arrayexpress
2015-07-30 | MODEL1507180058 | BioModels