13C labeling experiments at metabolic nonstationary conditions: An exploratory study

<p>Abstract</p> <p>Background</p> <p>Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of ¹³C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.</p> <p>Results</p> <p>In this contribution, the idea of increasing the information content of the dynamic experiment by adding ¹³C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.</p> <p>Conclusion</p> <p>It was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without ¹³C-labeled substrate.</p

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p

Pteridium aquilinum: The most important fern in the world

Pteridium aquilinum also called bracken fern is one of the most common weed plants in the world. Its propagation is only limited by extreme kinds of temperature or humidity. Due to changing agricultural factors; farm animals are sometimes forced to consume huge amounts of bracken fern. Humans also are in contact with it either directly, by eating the fronds and rhizomes or indirectly by consuming bracken fern infested milk. As a result, farm animals show a series of known syndromes due to the toxic substances of bracken fern. These include; thamine deficiency of monogastric animals, acute haemorrhagic syndrome together with bone marrow aplasia and upper alimentary ulceration, retinal atrophy in sheep and two neoplastic disease syndromes. These two syndromes consist of neoplasia of the urinary bladder leading to haematuria and a syndrome involving upper alimentary carcinomata. The main reason for these neoplastic formations is the nor- sesquiterpenoid type of a glycosid called ptaquiloside. Also other carcinogens and mutagens seem to play a role especially in association with papilloma viruses. Epidemiological analyses showed that the risk of cancer in humans is increased by direct or indirect consumption of bracken fern. Ptaquilosides are forming a DNA-Adduct which interacts with genetic material, especially adenin which then subsequently causes abnormal mutations. Experimental trials on mice determined the first steps in the control of the immunotoxic effect induced by ptaquilosides and proved that selenium has a preventive role as well as a reversible effect

In vivo quantification of parallel and bidirectional fluxes in the anaplerosis of Corynebacterium glutamicum

The C(3)-C(4) metabolite interconversion at the anaplerotic node in many microorganisms involves a complex set of reactions. C(3) carboxylation to oxaloacetate can originate from phosphoenolpyruvate and pyruvate, and at the same time multiple C(4)-decarboxylating enzymes may be present. The functions of such parallel reactions are not yet fully understood. Using a (13)C NMR-based strategy, we here quantify the individual fluxes at the anaplerotic node of Corynebacterium glutamicum, which is an example of a bacterium possessing multiple carboxylation and decarboxylation reactions. C. glutamicum was grown with a (13)C-labeled glucose isotopomer mixture as the main carbon source and (13)C-labeled lactate as a cosubstrate. 58 isotopomers as well as 15 positional labels of biomass compounds were quantified. Applying a generally applicable mathematical model to include metabolite mass and carbon labeling balances, it is shown that pyruvate carboxylase contributed 91 +/- 7% to C(3) carboxylation. The total in vivo carboxylation rate of 1.28 +/- 0.14 mmol/g dry weight/h exceeds the demand of carboxylated metabolites for biosyntheses 3-fold. Excess oxaloacetate was recycled to phosphoenolpyruvate by phosphoenolpyruvate carboxykinase. This shows that the reactions at the anaplerotic node might serve additional purposes other than only providing C(4) metabolites for biosynthesis

"How to explain the huge differences in rebound estimates : A meta-regression analysis of the literature" - Replication files

Replication files for "How to explain the huge differences in rebound estimates : A meta-regression analysis of the literature " accepted for publication in "The Energy Journal". Abstract: Rebound effects are commonly defined as the relative gap between the potential and realized savings in resource use following efficiency improvements or sufficiency changes. While a considerable number of studies quantify rebound effects, empirical estimates vary widely. Reliable information on the magnitude of rebound effects is therefore still lacking, yet needed so that, for example, policies for the sustainable use of natural resources can be adjusted accordingly. Here, we present the first meta-regression analysis of microeconomic rebound effects at the household level, using 43 studies with 1118 estimates to determine average rebound effects and to explain heterogeneous empirical findings. We find that the total microeconomic rebound is, on average, about 41-52%. The variance can be explained by differences in the type of data used, the scenario setup and the specifics of the rebound estimation in the primary studies. Furthermore, we find only small absolute transfer errors, indicating a good predictability of rebound effects using our meta-regression model