Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Bundy, Jacob G.; Papp, Balázs; Harmston, Rebecca; Browne, R. A.; Clayson, Edward M.; Burton, Nicola; Reece, Richard J.; Oliver, Stephen G.; Brindle, Kevin M.

doi:10.1101/gr.5662207

Cited by 67 publications

(50 citation statements)

References 37 publications

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“…The metabonomics of the urine from animal feeding experiments are very useful for evaluating the safety of GM crops. Metabonomics has been widely used in drug toxicity analysis, disease diagnosis, functional genomics, and the environmental sciences (35)(36)(37)(38). The NMR technique, coupled with PR methods, can provide valuable information on external factors that may induce biochemical perturbations caused by urine analysis (8,39).…”

Section: Discussionmentioning

confidence: 99%

Safety assessment of transgenic Bacillus thuringiensis rice T1c‐19 in Sprague–Dawley rats from metabonomics and bacterial profile perspectives

Cao

et al. 2012

IUBMB Life

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SummaryBacillus thuringiensis rice is facing commercialization as the main food source in the near future. The unintended effects of genetically modified (GM) organisms are the most important barriers to their promotion. We aimed to establish a new in vivo evaluation model for genetically modified foods by using metabonomics and bacterial profile approaches. T1c-19 rice flour or its transgenic parent MH63 was used at 70% wt/wt to produce diets that were fed to rats for 90 days. Urine metabolite changes were detected using 1 H NMR. Denaturing gradient gel electrophoresis and real-time polymerase chain reaction (RT-PCR) were used to detect the bacterial profiles between the two groups. The metabonomics was analyzed for metabolite changes in rat urine, when compared with the non-GM rice group, where rats were fed a GM rice diet. Several metabolites correlated with rat age and sex but not with GM rice diet. Significant biological differences were not identified between the GM rice diet and the non-GM rice diet. The bacteria related to rat urine metabolites were also discussed. The results from metabonomics and bacterial profile analyses were comparable with the results attained using the traditional method. Because metabonomics and bacterial profiling offer noninvasive, dynamic approaches for monitoring food safety, they provide a novel process for assessing the safety of GM foods.

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Section: Discussionmentioning

confidence: 99%

Safety assessment of transgenic Bacillus thuringiensis rice T1c‐19 in Sprague–Dawley rats from metabonomics and bacterial profile perspectives

Cao

et al. 2012

IUBMB Life

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“…In contrast, exometabolome or supernatant profiling ("metabolic footprinting") is simple, and extracellular metabolites can exhibit very large changes in pool size (1,27,40,45). These multiple advantages mean that exometabolome analysis has already been used for a number of diverse applications, such as phenotyping of both single-gene deletion mutants and isolates from natural populations, although thus far mostly for fungi rather than bacteria (1,2,9,25,40,48).…”

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confidence: 99%

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

Behrends

Ebbels

Williams

et al. 2009

Appl Environ Microbiol

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Untargeted profiling of small-molecule metabolites from microbial culture supernatants (metabolic footprinting) has great potential as a phenotyping tool. We used time-resolved metabolic footprinting to compare one Escherichia coli and three Pseudomonas aeruginosa strains growing on complex media and show that considering metabolite changes over the whole course of growth provides much more information than analyses based on data from a single time point. Most strikingly, there was pronounced selectivity in metabolite uptake, even when the bacteria were growing apparently exponentially, with certain groups of metabolites not taken up until others had been entirely depleted from the medium. In addition, metabolite excretion showed some complex patterns. Fitting nonlinear equations (four-parameter sigmoids) to individual metabolite data allowed us to model these changes for metabolite uptake and visualize them by back-projecting the curve-fit parameters onto the original growth curves. These "uptake window" plots clearly demonstrated strain differences, with the uptake of some compounds being reversed in order between different strains. Comparison of an undefined rich medium with a defined complex medium designed to mimic cystic fibrosis sputum showed many differences, both qualitative and quantitative, with a greater proportion of excreted to utilized metabolites in the defined medium. Extending the strain comparison to a more closely related set of isolates showed that it was possible to discriminate two species of the Burkholderia cepacia complex based on uptake dynamics alone. We believe time-resolved metabolic footprinting could be a valuable tool for many questions in bacteriology, including isolate comparisons, phenotyping deletion mutants, and as a functional complement to taxonomic classifications.

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“…Detecting this kind of dependencies is important for the identification of knock-out targets, because some reactions may be easier to manipulate than others [18]. In addition, dependency information can also be used to check the consistency of metabolic network reconstructions [7] or to find co-regulated reactions [33].…”

Section: Introductionmentioning

confidence: 99%

Generic flux coupling analysis

Reimers

Goldstein

Bockmayr

2015

Mathematical Biosciences

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a b s t r a c tFlux coupling analysis (FCA) has become a useful tool for aiding metabolic reconstructions and guiding genetic manipulations. Originally, it was introduced for constraint-based models of metabolic networks that are based on the steady-state assumption. Recently, we have shown that the steady-state assumption can be replaced by a weaker lattice-theoretic property related to the supports of metabolic fluxes. In this paper, we further extend our approach and develop an efficient algorithm for generic flux coupling analysis that works with any kind of qualitative pathway model. We illustrate our method by thermodynamic flux coupling analysis (tFCA), which allows studying steady-state metabolic models with loop-law thermodynamic constraints. These models do not satisfy the lattice-theoretic properties required in our previous work. For a selection of genome-scale metabolic network reconstructions, we discuss both theoretically and practically, how thermodynamic constraints strengthen the coupling results that can be obtained with classical FCA.A prototype implementation of tFCA is available at http://hoverboard.io/L4FC.

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Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Cited by 67 publications

References 37 publications

Safety assessment of transgenic Bacillus thuringiensis rice T1c‐19 in Sprague–Dawley rats from metabonomics and bacterial profile perspectives

Safety assessment of transgenic Bacillus thuringiensis rice T1c‐19 in Sprague–Dawley rats from metabonomics and bacterial profile perspectives

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

Generic flux coupling analysis

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