Nuclear magnetic resonance and plant metabolic engineering.

Nuclear magnetic resonance (NMR) can be used to measure metabolite levels and metabolic fluxes, to probe the intracellular environment, and to follow transport and energetics nondestructively. NMR methods are therefore powerful aids to understanding plant metabolism and physiology. Both spectroscopy and imaging can help overcome the unique challenges that plants present to the metabolic engineer by detecting, identifying, quantifying, and localizing novel metabolites in vivo and in extracts; revealing the composition and physical state of cell wall and other polymers; allowing the identification of active pathways; providing quantitative measures of metabolic flux; and testing hypotheses about the effects of engineered traits on plant physiological function. The aim of this review is to highlight recent studies in which NMR has contributed to metabolic engineering of plants and to illustrate the unique characteristics of NMR measurements that give it the potential to make greater contributions in the future.     

Quantitative approaches for analysing fluxes through plant metabolic networks using NMR and stable isotope labelling

The quantitative analysis of metabolic networks is a prerequisite for understanding the integration and regulation of plant metabolism and for devising rational approaches for manipulating resource allocation in plants. The analysis of steady state stable isotope labelling experiments using nuclear magnetic resonance (NMR) spectroscopy has developed into a powerful method for determining these fluxes in micro-organisms and its application to heterotrophic plant metabolism is increasing. After an introductory discussion of the well known role of stable isotopes in pathway delineation, the review considers their application to metabolic flux analysis in plants. These applications are divided into two groups – small scale analyses of fluxes through particular pathways and large scale analyses of multiple fluxes through metabolic networks – and the problems caused by the complexity of intermediary metabolism in plants are discussed. It is concluded that metabolic flux analysis provides a powerful method for defining the metabolic phenotype of wild type, mutant and transgenic plants and that its development should be pursued.     

Simultaneous tracers and a unified model of positional and mass isotopomers for quantification of metabolic flux in liver

Computational models based on the metabolism of stable isotope tracers can yield valuable insight into the metabolic basis of disease. The complexity of these models is limited by the number of tracers and the ability to characterize tracer labeling in downstream metabolites. NMR spectroscopy is ideal for multiple tracer experiments since it precisely detects the position of tracer nuclei in molecules, but it lacks sensitivity for detecting low-concentration metabolites. GC-MS detects stable isotope mass enrichment in low-concentration metabolites, but lacks nuclei and positional specificity. We performed liver perfusions and in vivo infusions of ²H and ¹³C tracers, yielding complex glucose isotopomers that were assigned by NMR and fit to a newly developed metabolic model. Fluxes regressed from ²H and ¹³C NMR positional isotopomer enrichments served to validate GC-MS-based flux estimates obtained from the same experimental samples. NMR-derived fluxes were largely recapitulated by modeling the mass isotopomer distributions of six glucose fragment ions measured by GC-MS. Modest differences related to limited fragmentation coverage of glucose C1–C3 were identified, but fluxes such as gluconeogenesis, glycogenolysis, cataplerosis and TCA cycle flux were tightly correlated between the methods. Most importantly, modeling of GC-MS data could assign fluxes in primary mouse hepatocytes, an experiment that is impractical by ²H or ¹³C NMR.     

The population genetics of alleles affecting enzyme activity.

It is possible to predict the population genetics of allozymes by assuming that fitness is proportional to flux through a biochemical pathway. The model presented here extends previous work by incorporating two additional features of biological realism. Firstly, that more than one biochemical route may exist between any two metabolites. The major routes have been identified as the classical biochemical pathways but in the event of a mutation blocking a major route, minor routes become significant. These minor routes are named "bypass fluxes" and have profound effects on the population genetics of allozymes. Secondly, recent work has suggested that a metabolic cost is associated with enzyme synthesis; this will constitute an additional selective pressure on alleles which affect the amount of enzyme synthesized. The model generates a fitness curve which predicts the fitness associated with any level of enzyme activity. It can utilize data on null or near-null, structural or regulatory, mutations in the presence or absence of bypass fluxes. When data from natural populations of Drosophila are investigated, it is concluded that selection pressures acting on enzyme variants may be much higher than previously thought.     

Biological 1H NMR spectroscopy

Proton nuclear magnetic resonance spectroscopy (1H NMR) is a powerful analytical method used to identify and quantitate chemical compounds. In recent years, it has been used to study rates of metabolism in microbes, isolated perfused tissues, intact animals, and human beings. This review highlights some of the more recent biological applications of 1H NMR in the study of metabolic pathophysiology in animals and man. 1H NMR can rapidly analyze complex mixtures of metabolites found in body fluid and biopsy specimens. In vivo 1H NMR methods can measure intracellular pH, a wide variety of metabolites, tissue perfusion, and rates of metabolism of endogenous and exogenous compounds. Using 13C labeled compounds or magnetization transfer techniques metabolic fluxes may be measured in vivo during virtually all normal and abnormal physiological conditions.     

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both ¹H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR–NMR, LCMS–LCMS) and inter-method (NMR–LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

13C tracer experiments and metabolite balancing for metabolic flux analysis: comparing two approaches

Conventional metabolic flux analysis uses the information gained from determination of measurable fluxes and a steady-state assumption for intracellular metabolites to calculate the metabolic fluxes in a given metabolic network. The determination of intracellular fluxes depends heavily on the correctness of the assumed stoichiometry including the presence of all reactions with a noticeable impact on the model metabolite balances. Determination of fluxes in complex metabolic networks often requires the inclusion of NADH and NADPH balances, which are subject to controversial debate. Transhydrogenation reactions that transfer reduction equivalents from NADH to NADPH or vice versa can usually not be included in the stoichiometric model, because they result in singularities in the stoichiometric matrix. However, it is the NADPH balance that, to a large extent, determines the calculated flux through the pentose phosphate pathway. Hence, wrong assumptions on the presence or activity of transhydrogenation reactions will result in wrong estimations of the intracellular flux distribution. Using 13C tracer experiments and NMR analysis, flux analysis can be performed on the basis of only well established stoichiometric equations and measurements of the labeling state of intracellular metabolites. Neither NADH/NADPH balancing nor assumptions on energy yields need to be included to determine the intracellular fluxes. Because metabolite balancing methods and the use of 13C labeling measurements are two different approaches to the determination of intracellular fluxes, both methods can be used to verify each other or to discuss the origin and significance of deviations in the results. Flux analysis based entirely on metabolite balancing and flux analysis, including labeling information, have been performed independently for a wild-type strain of Aspergillus oryzae producing alpha-amylase. Two different nitrogen sources, NH4+ and NO3-, have been used to investigate the influence of the NADPH requirements on the intracellular flux distribution. The two different approaches to the calculation of fluxes are compared and deviations in the results are discussed. Copyright 1998 John Wiley & Sons, Inc.     

Nuclear magnetic resonance micro-imaging in the investigation of plant cell metabolism

Micro-imaging based on nuclear magnetic resonance offers the possibility to map metabolites in plant tissues non-invasively. Major metabolites such as sucrose and amino acids can be observed with high spatial resolution. Stable isotope tracers, such as (13)C-labelled metabolites can be used to measure the in vivo conversion rates in a metabolic network. This review summarizes the different nuclear magnetic resonance micro-imaging techniques that are available to obtain spatially resolved information on metabolites in plants. A short general introduction into NMR imaging techniques is provided. Particular emphasis is given to the difficulties encountered when NMR micro-imaging is applied to plant systems.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance (NMR)-based metabonomics study platform, Automics.To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction (OSC). Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event. We also found that the major contributing metabolites were some specific amino acids (i.e., threonine and alanine), which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance （NMR）-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction （OSC）. Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event, We also found that the major contributing metabolites were some specific amino acids （i.e., threonine and alanine）, which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

NMR-based stable isotope resolved metabolomics in systems biochemistry

An important goal of metabolomics is to characterize the changes in metabolic networks in cells or various tissues of an organism in response to external perturbations or pathologies. The profiling of metabolites and their steady state concentrations does not directly provide information regarding the architecture and fluxes through metabolic networks. This requires tracer approaches. NMR is especially powerful as it can be used not only to identify and quantify metabolites in an unfractionated mixture such as biofluids or crude cell/tissue extracts, but also determine the positional isotopomer distributions of metabolites derived from a precursor enriched in stable isotopes such as (13)C and (15)N via metabolic transformations. In this article we demonstrate the application of a variety of 2-D NMR editing experiments to define the positional isotopomers of compounds present in polar and non-polar extracts of human lung cancer cells grown in either [U-(13)C]-glucose or [U-(13)C,(15)N]-glutamine as source tracers. The information provided by such experiments enabled unambiguous reconstruction of metabolic pathways, which is the foundation for further metabolic flux modeling.     

Similar Metabolic Changes Induced by HIPVs Exposure as Herbivore in Ammopiptanthus mongolicus

Herbivore-induced plant volatiles (HIPVs) are important compounds to prim neighboring undamaged plants; however, the mechanism for this priming process remains unclear. To reveal metabolic changes in plants exposed to HIPVs, metabolism of leaves and roots of Ammopiptanthus mongolicus seedlings exposed to HIPVs released from conspecific plants infested with larvae of Orgyia ericae were analyzed together with control and infested seedlings using nuclear magnetic resonance (NMR)-based metabolic technology and multi variate data analysis. Results presented showed that HIPVs exposure led to similar but specific metabolic changes compared with those induced by infestation in both leaves and roots. Furthermore, both HIPVs exposure and herbivore attack resulted in metabolic changes involving a series of primary and secondary metabolites in both leaves and roots. Taken together, these results suggested that priming of yet-damaged plants may be achieved by reconfiguring metabolic pathways in leaves and roots to make similar concentrations for all metabolites as those in seedlings infested. Therefore, we propose that improved readiness of defense induction of primed plants toward subsequent herbivore attack may be based on the similar metabolic profiling induced by HIPVs exposure as those caused by herbivore.     

NMR-based plant metabolomics: where do we stand, where do we go?

NMR-based metabolomics is an important tool for studying biological systems and has been applied in various organisms, including animals, plants and microbes. NMR is able to provide a 'holistic view' of the metabolites under certain conditions, and thus is advantageous for metabolomic studies. To maximize the use of the information obtained, it is also important to create a platform to measure, store and share data. Public databases for storing and sharing information are still lacking for NMR-based metabolomic analysis in plants. Such databases are urgently needed to make metabolic profiling a real omics technology. In addition, to understand metabolic processes in depth, single-cell analysis and the turnover of metabolites in pathways (fluxomics) should be measured.