Experimental design principles for isotopically instationary 13C labeling experiments

13C metabolic flux analysis (MFA) is a well-established tool in Metabolic Engineering that found numerous applications in recent years. However, one strong limitation of the current method is the requirement of an-at least approximate-isotopic stationary state at sampling time. This requirement leads to a principle lower limit for the duration of a 13C labeling experiment. A new methodological development is based on repeated sampling during the instationary transient of the 13C labeling dynamics. The statistical and computational treatment of such instationary experiments is a completely new terrain. The computational effort is very high because large differential equations have to be solved and, moreover, the intracellular pool sizes play a significant role. For this reason, the present contribution works out principles and strategies for the experimental design of instationary experiments based on a simple example network. Hereby, the potential of isotopically instationary experiments is investigated in detail. Various statistical results on instationary flux identifiability are presented and possible pitfalls of experimental design are discussed. Finally, a framework for almost optimal experimental design of isotopically instationary experiments is proposed which provides a practical guideline for the analysis of large-scale networks.     

Photobioreactor design for isotopic non‐stationary 13C‐metabolic flux analysis (INST 13C‐MFA) under photoautotrophic conditions

Adaptive metabolic behavior of photoautotrophic microorganisms toward genetic and environmental perturbations can be interpreted in a quantitative depiction of carbon flow through a biochemical reaction network using isotopic non‐stationary ¹³C‐metabolic flux analysis (INST ¹³C‐MFA). To evaluate ¹³C‐metabolic flux maps for Chlamydomonas reinhardtii, an original experimental framework was designed allowing rapid, reliable collection of high‐quality isotopomer data against time. It involved (i) a short‐time ¹³C labeling injection device based on mixing control in a torus‐shaped photobioreactor with plug‐flow hydrodynamics allowing a sudden step‐change in the ¹³C proportion in the substrate feed and (ii) a rapid sampling procedure using an automatic fast filtration method coupled to a manual rapid liquid nitrogen quenching step. ¹³C‐substrate labeling enrichment was controlled through the total dissolved inorganic carbon concentration in the pulsed solution. First results were obtained from steady‐state continuous culture measurements allowing the characterization of the kinetics of label incorporation into light‐limited growing cells cultivated in a photobioreactor operating at the maximal biomass productivity for an incident photon flux density of 200 µmol m^?2 s^?1. ¹³C label incorporation was measured for 21 intracellular metabolites using IC‐MS/MS in 58 samples collected across a labeling experiment duration of 7 min. The fastest labeling rate was observed for 2/3‐phosphoglycerate with an apparent isotopic stationary state reached after 300 s. The labeling rate was consistent with the optimized mixing time of about 4.9 s inside the reactor and the shortest reliable sampling period assessed at 5 s. Biotechnol. Bioeng. 2012; 109: 3030–3040. © 2012 Wiley Periodicals, Inc.     

Cumulative bondomers: a new concept in flux analysis from 2D [13C,1H] COSY NMR data

A well-established way of determining metabolic fluxes is to measure 2D [(13)C,(1)H] COSY NMR spectra of components of biomass grown on uniformly (13)C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the (13)C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C-C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [(13)C,(1)H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [(13)C,(1)H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.     

Metabolic flux analysis for succinic acid production by recombinant Escherichia coli with amplified malic enzyme activity

A pfl ldhA double mutant Escherichia coli strain NZN111 was used to produce succinic acid by overexpressing the E. coli malic enzyme. Escherichia coli strain NZN111 harboring pTrcML produced 6 and 8 g/L of succinic acid from 20 g/L of glucose in flask culture at 37 degrees C and 30 degrees C, respectively. When NZN111(pTrcML) was cultured at 30 degrees C with intermittent glucose feeding the final succinic acid concentration obtained was 9.5 g/L and the ratio of succinic acid to acetic acid was 13:1. This system could not be analyzed by conventional metabolic flux analysis techniques, since some pyruvate and succinic acid were accumulated intracellularly. Therefore, a new flux analysis method was proposed by introducing intracellular pyruvate and succinic acid pools. By this new method the concentrations of intracellular metabolites were successfully predicted and the differences between the measured and calculated reaction rates could be considerably reduced.     

Metabolic flux analysis using 13C peptide label measurements

¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady‐state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady‐state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable ‘single‐sample’ spatially and temporally resolved steady‐state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC–MS measurement‐based approach. Deconvolution of PMDs of the storage protein β–conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC–MS‐derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.     

13C‐metabolic flux analysis for batch culture of Escherichia coli and its pyk and pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites

Since most bio‐production processes are conducted in a batch or fed‐batch manner, the evaluation of metabolism with respect to time is highly desirable. Toward this aim, we applied ¹³C‐metabolic flux analysis to nonstationary conditions by measuring the mass isotopomer distribution of intracellular metabolites. We performed our analysis on batch cultures of wild‐type Escherichia coli, as well as on Pyk and Pgi mutants, obtained the fluxes and metabolite concentrations as a function of time. Our results for the wild‐type indicated that the TCA cycle flux tended to increase during growth on glucose. Following glucose exhaustion, cells controlled the branch ratio between the glyoxylate pathway and the TCA cycle, depending on the availability of acetate. In the Pyk mutant, the concentrations of glycolytic intermediates changed drastically over time due to the dumping and feedback inhibition caused by PEP accumulation. Nevertheless, the flux distribution and free amino acid concentrations changed little. The growth rate and the fluxes remained constant in the Pgi mutant and the glucose‐6‐phosphate dehydrogenase reaction was the rate‐limiting step. The measured fluxes were compared with those predicted by flux balance analysis using maximization of biomass yield or ATP production. Our findings indicate that the objective function of biosynthesis became less important as time proceeds on glucose in the wild‐type, while it remained highly important in the Pyk mutant. Furthermore, ATP production was the primary objective function in the Pgi mutant. This study demonstrates how cells adjust their metabolism in response to environmental changes and/or genetic perturbations in the batch cultivation. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

基于代谢流量分布信息理解大肠杆菌中异柠檬酸裂解酶调节因子的代谢调控作用

基因的表达受不同的转录调节因子调节。大肠杆菌中的异柠檬酸裂解酶调节因子(IclR)能够抑制编码乙醛酸支路酶的aceBAK操纵子的表达。本研究基于代谢物的13C同位体物质分布来定量解析代谢反应,主要研究了iclR基因在大肠杆菌生理和代谢中的作用。大肠杆菌iclR基因缺失突变株的生长速率、糖耗速率和乙酸的产量相对于原始菌株都有所降低,但菌体得率略有增加。通过代谢途径的流量比率分析发现基因缺失株的乙醛酸支路得到了激活,33%的异柠檬酸流经了乙醛酸支路;戊糖磷酸途径的流量变小,使得CO2的生成量减少。同时,乙醛酸支路激活,但草酰乙酸形成磷酸烯醇式丙酮酸的流量基本不变,说明磷酸烯醇式丙酮酸-乙醛酸循环没有激活,没有过多的碳原子在磷酸烯醇式丙酮酸羧化激酶反应中以CO2形式排出,从而确保了菌体得率。葡萄糖利用速率的降低、乙酰辅酶A的代谢效率提高等使得iclR基因敲除菌的乙酸分泌较原始菌株有所降低。     

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.     

Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli

The use of parallel labeling experiments for ¹³C metabolic flux analysis (¹³C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-¹³C]glucose and mixtures of [1-¹³C]glucose and [U-¹³C]glucose, four novel tracers were applied in this study: [2,3-¹³C]glucose, [4,5,6-¹³C]glucose, [2,3,4,5,6-¹³C]glucose and a mixture of [1-¹³C]glucose and [4,5,6-¹³C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-¹³C]glucose+20% [U-¹³C]glucose, while [4,5,6-¹³C]glucose and [5-¹³C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.     

13C Metabolic Flux Analysis of Escherichia coli Engineered for Gamma‐Aminobutyrate Production

Escherichia coli is engineered for γ‐aminobutyrate (GABA) production in glucose minimal medium. For this, overexpression of mutant glutamate decarboxylase (GadB) and mutant glutamate/GABA antiporter (GadC), as well as deletion of GABA transaminase (GabT), are accomplished. In addition, the carbon flux to the tricarboxylic acid cycle is engineered by the overexpression of gltA, ppc, or both. The overexpression of citrate synthase (CS), encoded by gltA, increases GABA productivity, as expected. Meanwhile, the overexpression of phosphoenolpyruvate carboxylase (PPC) causes a decrease in the rate of glucose uptake, resulting in a decrease in GABA production. The phenotypes of the strains are characterized by ¹³C metabolic flux analysis (¹³C MFA). The results reveal that CS overexpression increases glycolysis and anaplerotic reaction rates, as well as the citrate synthesis rate, while PPC overexpression causes little changes in metabolic fluxes, but reduces glucose uptake rate. The engineered strain produces 1.2 g L^?1 of GABA from glucose. Thus, by using ¹³C MFA, important information is obtained for designing metabolically engineered strains for efficient GABA production.     

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system

¹³C-Metabolic flux analysis (¹³C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by ¹³C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for ¹³C-MFA. The best single tracers were doubly ¹³C-labeled glucose tracers, including [1,6-¹³C]glucose, [5,6-¹³C]glucose and [1,2-¹³C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-¹³C]glucose and [1,2-¹³C]glucose. Combined analysis of [1,6-¹³C]glucose and [1,2-¹³C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-¹³C]glucose +20% [U-¹³C]glucose.     

A priori analysis of metabolic flux identifiability from (13)C-labeling data

The (13)C-labeling technique was introduced in the field of metabolic engineering as a tool for determining fluxes that could not be found using the 'classical' method of flux balancing. An a priori flux identifiability analysis is required in order to determine whether a (13)C-labeling experiment allows the identification of all the fluxes. In this article, we propose a method for identifiability analysis that is based on the recently introduced 'cumomer' concept. The method improves upon previous identifiability methods in that it provides a way of systematically reducing the metabolic network on the basis of structural elements that constitute a network and to use the implicit function theorem to analytically determine whether the fluxes in the reduced network are theoretically identifiable for various types of real measurement data. Application of the method to a realistic flux identification problem shows both the potential of the method in yielding new, interesting conclusions regarding the identifiability and its practical limitations that are caused by the fact that symbolic calculations grow fast with the dimension of the studied system.     

13C代谢流量分析发展30年

13C代谢流量分析(13C metabolic flux analysis,13C-MFA),是通过标记实验分析蛋白氨基酸或胞内代谢物同位素标记异构体的分布情况,从而准确定量胞内反应速率.该技术在系统理解细胞代谢特性、指导代谢工程改造和揭示病理生理学等方面起着重要作用,引起研究者的广泛重视.文中重点综述了代谢流分析30...     

New tools for mass isotopomer data evaluation in (13)C flux analysis: mass isotope correction, data consistency checking, and precursor relationships

13C metabolic flux analysis (MFA) is based on carbon-labeling experiments where a specifically (13)C labeled substrate is fed. The labeled carbon atoms distribute over the metabolic network and the label enrichment of certain metabolic pools is measured by using different methods. Recently, MS methods have been dramatically improved-large and precise datasets are now available. MS data has to be preprocessed and corrected for natural stable mass isotopes. In this article we present (1). a new elegant method to correct MS measurement data for natural stable mass isotopes by infinite dimensional matrix calculus and (2). we statistically analyze and discuss a reconstruction of labeling pattern in metabolic precursors from biosynthesis molecules. Moreover, we establish a new method for consistency checking of MS spectra that can be applied for automatic error recognition in high-throughput flux analysis procedures. Preprocessing the measurement data changes their statistical properties which have to be considered in the subsequent parameter fitting process for (13)C MFA. We show that correcting for stable mass isotopes leads to rather small correlations. On the other hand, a direct reconstruction of a precursor labeling pattern from an aromatic amino acid measurement turns out to be critical. Reasonable results are only obtained if additional, independent information about the labeling of at least one precursor is available. A versatile MatLab tool for the rapid correction and consistency checking of MS spectra is presented. Practical examples for the described methods are also given.     

The YfiD protein contributes to the pyruvate formate-lyase flux in an Escherichia coli arcA mutant strain

The product of yfiD gene is similar to pyruvate formate-lyase (PFL) activase and it has been reported to activate PFL by replacing the glycyl radical domain. To quantitate the effect of YfiD on the cell metabolism in microaerobic cultures, glucose-limited chemostat cultures were conducted with Escherichia coli yfiD mutant and yfiDarcA mutant strains. The microaerobic condition was controlled by purging the culture media with 2.5% O(2) in N(2). The intracellular metabolic flux distributions in these cultures were estimated based on C-13 labeling experiments. By comparing with the flux distributions in wild-type E. coli and the arcA mutant, it was shown that YfiD contributes to about 18% of the PFL flux in the arcA mutant, but it did not contribute to the PFL flux in wild-type E. coli. It was also shown that the cell used both PFL and pyruvate dehydrogenase (PDH) to supplement the acetyl-coenzyme A (AcCoA) pool under microaerobic conditions. The flux through PDH was about 22-30% of the total flux toward AcCoA in the wild-type, the yfiD mutant and yfiDarcA mutant strains. Relatively higher lactate production was seen in the yfiDarcA mutant than the other strains, which was due to the lower total flux through PFL and PDH toward AcCoA in this strain.