Dataset Information

Using NMR metabolomics to investigate tricarboxylic acid cycle-dependent signal transduction in Staphylococcus epidermidis.

ABSTRACT: Staphylococcus epidermidis is a skin-resident bacterium and a major cause of biomaterial-associated infections. The transition from residing on the skin to residing on an implanted biomaterial is accompanied by regulatory changes that facilitate bacterial survival in the new environment. These regulatory changes are dependent upon the ability of bacteria to "sense" environmental changes. In S. epidermidis, disparate environmental signals can affect synthesis of the biofilm matrix polysaccharide intercellular adhesin (PIA). Previously, we demonstrated that PIA biosynthesis is regulated by tricarboxylic acid (TCA) cycle activity. The observations that very different environmental signals result in a common phenotype (i.e. increased PIA synthesis) and that TCA cycle activity regulates PIA biosynthesis led us to hypothesize that S. epidermidis is "sensing" disparate environmental signals through the modulation of TCA cycle activity. In this study, we used NMR metabolomics to demonstrate that divergent environmental signals are transduced into common metabolomic changes that are "sensed" by metabolite-responsive regulators, such as CcpA, to affect PIA biosynthesis. These data clarify one mechanism by which very different environmental signals cause common phenotypic changes. In addition, due to the frequency of the TCA cycle in diverse genera of bacteria and the intrinsic properties of TCA cycle enzymes, it is likely the TCA cycle acts as a signal transduction pathway in many bacteria.

SUBMITTER: Sadykov MR

PROVIDER: S-EPMC2978590 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Using NMR metabolomics to investigate tricarboxylic acid cycle-dependent signal transduction in Staphylococcus epidermidis.

Sadykov Marat R MR Zhang Bo B Halouska Steven S Nelson Jennifer L JL Kreimer Lauren W LW Zhu Yefei Y Powers Robert R Somerville Greg A GA

The Journal of biological chemistry 20100922 47

Staphylococcus epidermidis is a skin-resident bacterium and a major cause of biomaterial-associated infections. The transition from residing on the skin to residing on an implanted biomaterial is accompanied by regulatory changes that facilitate bacterial survival in the new environment. These regulatory changes are dependent upon the ability of bacteria to "sense" environmental changes. In S. epidermidis, disparate environmental signals can affect synthesis of the biofilm matrix polysaccharide ...[more]

PMID: 20861019

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Project description:BackgroundThe aerobic energy metabolism of cardiac muscle cells is of major importance for the contractile function of the heart. Because energy metabolism is very heterogeneously distributed in heart tissue, especially during coronary disease, a method to quantify metabolic fluxes in small tissue samples is desirable. Taking tissue biopsies after infusion of substrates labeled with stable carbon isotopes makes this possible in animal experiments. However, the appreciable noise level in NMR spectra of extracted tissue samples makes computational estimation of metabolic fluxes challenging and a good method to define confidence regions was not yet available.ResultsHere we present a computational analysis method for nuclear magnetic resonance (NMR) measurements of tricarboxylic acid (TCA) cycle metabolites. The method was validated using measurements on extracts of single tissue biopsies taken from porcine heart in vivo. Isotopic enrichment of glutamate was measured by NMR spectroscopy in tissue samples taken at a single time point after the timed infusion of 13C labeled substrates for the TCA cycle. The NMR intensities for glutamate were analyzed with a computational model describing carbon transitions in the TCA cycle and carbon exchange with amino acids. The model dynamics depended on five flux parameters, which were optimized to fit the NMR measurements. To determine confidence regions for the estimated fluxes, we used the Metropolis-Hastings algorithm for Markov chain Monte Carlo (MCMC) sampling to generate extensive ensembles of feasible flux combinations that describe the data within measurement precision limits. To validate our method, we compared myocardial oxygen consumption calculated from the TCA cycle flux with in vivo blood gas measurements for 38 hearts under several experimental conditions, e.g. during coronary artery narrowing.ConclusionsDespite the appreciable NMR noise level, the oxygen consumption in the tissue samples, estimated from the NMR spectra, correlates with blood-gas oxygen uptake measurements for the whole heart. The MCMC method provides confidence regions for the estimated metabolic fluxes in single cardiac biopsies, taking the quantified measurement noise level and the nonlinear dependencies between parameters fully into account.

Dataset Information

Using NMR metabolomics to investigate tricarboxylic acid cycle-dependent signal transduction in Staphylococcus epidermidis.

Publications

Using NMR metabolomics to investigate tricarboxylic acid cycle-dependent signal transduction in Staphylococcus epidermidis.

Similar Datasets

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