Here, we introduce a mass spectrometry-based analytical method and relevant technical details for dynamic cell respiration and CO2 labeling analysis. Such measurements can be utilized as additional information and constraints for model-based (13)C metabolic flux analysis. Dissolved dynamics of oxygen consumption and CO2 mass isotopomer evolution from (13)C-labeled tracer substrates through different cellular processes can be precisely measured on-line using a miniaturized reactor system equipped with a membrane-inlet mass spectrometer. The corresponding specific rates of physiologically relevant gases and CO2 mass isotopomers can be quantified within a short-term range based on the liquid-phase dynamics of dissolved fermentation gases.
Protein secretion in yeast is generally associated with a burden to cellular metabolism. To investigate this metabolic burden in Schizosaccharomyces pombe, we constructed a set of strains secreting the model protein maltase in different amounts. We quantified the influence of protein secretion on the metabolism applying (13)C-based metabolic flux analysis in chemostat cultures. Analysis of the macromolecular biomass composition revealed an increase in cellular lipid content at elevated levels of protein secretion and we observed altered metabolic fluxes in the pentose phosphate pathway, the TCA cycle, and around the pyruvate node including mitochondrial NADPH supply. Supplementing acetate to glucose or glycerol minimal media was found to improve protein secretion, accompanied by an increased cellular lipid content and carbon flux through the TCA cycle as well as increased mitochondrial NADPH production. Thus, systematic metabolic analyses can assist in identifying factors limiting protein secretion and in deriving strategies to overcome these limitations.
Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.
1,4-Butanediol (BDO) is an important commodity chemical used to manufacture over 2.5 million tons annually of valuable polymers, and it is currently produced exclusively through feedstocks derived from oil and natural gas. Herein we report what are to our knowledge the first direct biocatalytic routes to BDO from renewable carbohydrate feedstocks, leading to a strain of Escherichia coli capable of producing 18 g l(-1) of this highly reduced, non-natural chemical. A pathway-identification algorithm elucidated multiple pathways for the biosynthesis of BDO from common metabolic intermediates. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic operation of the oxidative tricarboxylic acid cycle, thereby generating reducing power to drive the BDO pathway. The organism produced BDO from glucose, xylose, sucrose and biomass-derived mixed sugar streams. This work demonstrates a systems-based metabolic engineering approach to strain design and development that can enable new bioprocesses for commodity chemicals that are not naturally produced by living cells.
Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil induced changes in metabolic fluxes in a murine atrial cell line (HL-1 cells). These cells were adapted to serum free conditions and incubated with 4 ?M verapamil and [U-¹³C?] glutamine. Specific extracellular metabolite uptake/production rates together with mass isotopomer fractions in alanine and glutamate were implemented into a metabolic network model to calculate metabolic flux distributions in the central metabolism. Verapamil decreased specific glucose consumption rate and glycolytic activity by 60%. Although the HL-1 cells show Warburg effect with high lactate production, verapamil treated cells completely stopped lactate production after 24 h while maintaining growth comparable to the untreated cells. Calculated fluxes in TCA cycle reactions as well as NADH/FADH? production rates were similar in both treated and untreated cells. This was confirmed by measurement of cell respiration. Reduction of lactate production seems to be the consequence of decreased glucose uptake due to verapamil. In case of tumors, this may have two fold effects; firstly depriving cancer cells of substrate for anaerobic glycolysis on which their growth is dependent; secondly changing pH of the tumor environment, as lactate secretion keeps the pH acidic and facilitates tumor growth. The results shown in this study may partly explain recent observations in which verapamil has been proposed to be a potential anticancer agent. Moreover, in biotechnological production using cell lines, verapamil may be used to reduce glucose uptake and lactate secretion thereby increasing protein production without introduction of genetic modifications and application of more complicated fed-batch processes.
Geobacter sulfurreducens is capable of coupling the complete oxidation of organic compounds to iron reduction. The metabolic response of G. sulfurreducens towards variations in electron donors (acetate, hydrogen) and acceptors (Fe(III), fumarate) was investigated via (13)C-based metabolic flux analysis. We examined the (13)C-labeling patterns of proteinogenic amino acids obtained from G. sulfurreducens cultured with (13)C-acetate.
(13)C-based metabolic flux analysis ((13)CMFA) is limited to smaller scale experiments due to very high costs of labeled substrates. We measured (13)C enrichment in proteinogenic amino acid hydrolyzates using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) from a series of parallel batch cultivations of Corynebacterium glutamicum utilizing mixtures of natural glucose and [1-(13)C] glucose, containing 0%, 0.5%, 1%, 2%, and 10% [1-(13)C] glucose. Decreasing the [1-(13)C] glucose content, kinetic isotope effects played an increasing role but could be corrected. From the corrected (13)C enrichments in vivo fluxes in the central metabolism were determined by numerical optimization. The obtained flux distribution was very similar to those obtained from parallel labeling experiments using conventional high labeling GC-MS method and to published results. The GC-C-IRMS-based method involving low labeling degree of expensive tracer substrate, e.g. 1%, is well suited for larger laboratory and industrial pilot scale fermentations.
Mass spectrometric (MS) isotopomer analysis has become a standard tool for investigating biological systems using stable isotopes. In particular, metabolic flux analysis uses mass isotopomers of metabolic products typically formed from (13)C-labeled substrates to quantitate intracellular pathway fluxes. In the current work, we describe a model-driven method of numerical bias estimation regarding MS isotopomer analysis. Correct bias estimation is crucial for measuring statistical qualities of measurements and obtaining reliable fluxes. The model we developed for bias estimation corrects a priori unknown systematic errors unique for each individual mass isotopomer peak. For validation, we carried out both computational simulations and experimental measurements. From stochastic simulations, it was observed that carbon mass isotopomer distributions and measurement noise can be determined much more precisely only if signals are corrected for possible systematic errors. By removing the estimated background signals, the residuals resulting from experimental measurement and model expectation became consistent with normality, experimental variability was reduced, and data consistency was improved. The method is useful for obtaining systematic error-free data from (13)C tracer experiments and can also be extended to other stable isotopes. As a result, the reliability of metabolic fluxes that are typically computed from mass isotopomer measurements is increased.
Attaining metabolic and isotopic balanced growth is one critical condition for physiological studies using isotope-labeled tracers, but is very difficult to obtain in batch culture due to the extensive metabolite exchange with the surrounding medium and related physiological changes. In the present study, we investigated metabolic and isotopic behavior of CHO cells in differently designed media. We observed that the assumption of balanced cell growth cannot be justified in batch culture of CHO cells directly using conventional, commercially available media. By systematically redesigning media composition and characterizing metabolic steady state based on mass balances and measurement of labeling dynamics, we achieved balanced cell growth for the main cellular substrates in CHO cells. This was done in a step-by-step analysis of growth and primary metabolism of CHO cells with the use of [U-13C]glucose feeding and adjusting concentrations of amino acids in the growth medium. The optimized media obtained at the end of the study provide balanced growth and isotopic steady state or at least asymptotic steady state. As a result, we established a platform to conduct isotope-based physiological studies of mammalian systems more reliably and therefore well suited for later use in metabolic profiling of mammalian systems such as 13C-labeled metabolic flux analysis.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.