Isotopically Nonstationary Metabolic Flux Analysis Research Articles

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities.

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST-MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast-growing algae, and produced a synergistic multi-organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST-MFA to correct an oversimplified understanding of plant metabolism.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures.

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness.

Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional “shooting” methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types.

Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA).

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA.

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach.

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO2 fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle.

SummaryCyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.

Combining isotopically non-stationary metabolic flux analysis with proteomics to unravel the regulation of the Calvin-Benson-Bassham cycle in Synechocystis sp. PCC 6803

Photosynthetic microorganisms are increasingly being investigated as a sustainable alternative to existing bio-industrial processes, converting CO2 into desirable end products without the use of carbohydrate feedstock. The Calvin-Benson-Bassham (CBB) cycle is the main pathway of carbon fixation metabolism in photosynthetic organisms. In this study, we analyzed the metabolic fluxes in two strains of the unicellular cyanobacterium Synechocystis sp. PCC 6803 (Synechocystis) that overexpressed fructose-1,6/sedoheptulose-1,7-bisphosphatase (FBP/SBPase) and transketolase (TK), respectively. These two potential carbon flux control enzymes in the CBB cycle had previously been shown to improve biomass accumulation when overexpressed under air and low light (15 μmol m−2 s−1) conditions (Liang and Lindblad, 2016). We measured the growth rates of Synechocystis under atmospheric and high (3% v/v) CO2 conditions at 80 μmol m−2 s−1. Surprisingly, the cells overexpressing transketolase (tktA) demonstrated no significant increase in growth rates when CO2 was increased, suggesting an altered carbon flux distribution and a potential metabolic bottleneck in carbon fixation. Moreover, the tktA strain had an increased susceptibility to oxidative stress under high light as revealed by its chlorotic phenotype under high light conditions. In contrast, the fructose-1,6/sedoheptulose-1,7-bisphosphatase (70glpX) and wild-type cells demonstrated increases in growth rates as expected.To investigate the disparate phenotypical responses of these different Synechocystis strains, isotopically non-stationary metabolic flux analysis (INST-MFA) was used to estimate the carbon flux distribution of tktA, 70glpX, and a kanamycin-resistant control (Km), under atmospheric conditions. In addition, untargeted label-free proteomics, which can detect changes in relative enzymatic abundance, was employed to study the possible effects caused by overexpressing each enzyme. Fluxomic and proteomic results indicated a decrease in oxidative pentose phosphate pathway activity when either FBP/SBPase or TK were overexpressed, resulting in increased carbon fixation efficiency. These results are an example of the integration of multiple omic-level experimental techniques and can be used to guide future metabolic engineering efforts to improve performances and efficiencies.

Cyanobacterial carboxysome mutant analysis reveals the influence of enzyme compartmentalization on cellular metabolism and metabolic network rigidity

Cyanobacterial carboxysomes encapsulate carbonic anhydrase and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Genetic deletion of the major structural proteins encoded within the ccm operon in Synechococcus sp. PCC 7002 (ΔccmKLMN) disrupts carboxysome formation and significantly affects cellular physiology. Here we employed both metabolite pool size analysis and isotopically nonstationary metabolic flux analysis (INST-MFA) to examine metabolic regulation in cells lacking carboxysomes. Under high CO2 environments (1%), the ΔccmKLMN mutant could recover growth and had a similar central flux distribution as the control strain, with the exceptions of moderately decreased photosynthesis and elevated biomass protein content and photorespiration activity. Metabolite analyses indicated that the ΔccmKLMN strain had significantly larger pool sizes of pyruvate (>18 folds), UDPG (uridine diphosphate glucose), and aspartate as well as higher levels of secreted organic acids (e.g., malate and succinate). These results suggest that the ΔccmKLMN mutant is able to largely maintain a fluxome similar to the control strain by changing in intracellular metabolite concentrations and metabolite overflows under optimal growth conditions. When CO2 was insufficient (0.2%), provision of acetate moderately promoted mutant growth. Interestingly, the removal of microcompartments may loosen the flux network and promote RuBisCO side-reactions, facilitating redirection of central metabolites to competing pathways (i.e., pyruvate to heterologous lactate production). This study provides important insights into metabolic regulation via enzyme compartmentation and cyanobacterial compensatory responses.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

BackgroundSynechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973’s fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, 13C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.ResultsThe Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO2 conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.ConclusionsThis study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO2 assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.

Metabolic Flux Analysis of the Synechocystis sp. PCC 6803 ΔnrtABCD Mutant Reveals a Mechanism for Metabolic Adaptation to Nitrogen-Limited Conditions.

Metabolic flux redirection during nitrogen-limited growth was investigated in the Synechocystis sp. PCC 6803 glucose-tolerant (GT) strain under photoautotrophic conditions by isotopically non-stationary metabolic flux analysis (INST-MFA). A ΔnrtABCD mutant of Synechocystis sp. PCC 6803 was constructed to reproduce phenotypes arising during nitrogen starvation. The ΔnrtABCD mutant and the wild-type GT strain were cultured under photoautotrophic conditions by a photobioreactor. Intracellular metabolites were labeled over a time course using NaH13CO3 as a carbon source. Based on these data, the metabolic flux distributions in the wild-type and ΔnrtABCD cells were estimated by INST-MFA. The wild-type GT and ΔnrtABCD strains displayed similar distribution patterns, although the absolute levels of metabolic flux were lower in ΔnrtABCD. Furthermore, the relative flux levels for glycogen metabolism, anaplerotic reactions and the oxidative pentose phosphate pathway were increased in ΔnrtABCD. This was probably due to the increased expression of enzyme genes that respond to nitrogen depletion. Additionally, we found that the ratio of ATP/NADPH demand increased slightly in the ΔnrtABCD mutant. These results indicated that futile ATP consumption increases under nitrogen-limited conditions because the Calvin-Benson cycle and the oxidative pentose phosphate pathway form a metabolic futile cycle that consumes ATP without CO2 fixation and NADPH regeneration.

Metabolic Flux Analysis of the Synechocystis sp. PCC 6803 ΔnrtABCD Mutant Reveals a Mechanism for Metabolic Adaptation to Nitrogen-Limited Conditions.

Metabolic flux redirection during nitrogen-limited growth was investigated in the Synechocystis sp. PCC 6803 glucose-tolerant (GT) strain under photoautotrophic conditions by isotopically non-stationary metabolic flux analysis (INST-MFA). A ΔnrtABCD mutant of Synechocystis sp. PCC 6803 was constructed to reproduce phenotypes arising during nitrogen starvation. The ΔnrtABCD mutant and the wild-type GT strain were cultured under photoautotrophic conditions by a photobioreactor. Intracellular metabolites were labeled over a time course using NaH13CO3 as a carbon source. Based on these data, the metabolic flux distributions in the wild-type and ΔnrtABCD cells were estimated by INST-MFA. The wild-type GT and ΔnrtABCD strains displayed similar distribution patterns, although the absolute levels of metabolic flux were lower in ΔnrtABCD. Furthermore, the relative flux levels for glycogen metabolism, anaplerotic reactions and the oxidative pentose phosphate pathway were increased in ΔnrtABCD. This was probably due to the increased expression of enzyme genes that respond to nitrogen depletion. Additionally, we found that the ratio of ATP/NADPH demand increased slightly in the ΔnrtABCD mutant. These results indicated that futile ATP consumption increases under nitrogen-limited conditions because the Calvin-Benson cycle and the oxidative pentose phosphate pathway form a metabolic futile cycle that consumes ATP without CO2 fixation and NADPH regeneration.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants.

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary 13C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO2 over a diurnal period implying that the metabolic steady state is limited to less than 12h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient 13CO2 labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

13 C flux analysis of cyanobacterial metabolism.

(13)C metabolic flux analysis (MFA) has made important contributions to our understanding of the physiology of model strains of E. coli and yeast, and it has been widely used to guide metabolic engineering efforts in these microorganisms. Recent advancements in (13)C MFA methodology combined with publicly available software tools are creating new opportunities to extend this approach to examine less characterized microbes. In particular, growing interest in the use of cyanobacteria as industrial hosts for photosynthetic production of biofuels and biochemicals has led to a critical need to better understand how cyanobacterial metabolic fluxes are regulated in response to changes in growth conditions or introduction of heterologous pathways. In this contribution, we review several prior studies that have applied isotopic steady-state (13)C MFA to examine heterotrophic or mixotrophic growth of cyanobacteria, as well as recent studies that have pioneered the use of isotopically nonstationary MFA (INST-MFA) to study autotrophic cultures. We also provide recommendations for the design and analysis of INST-MFA experiments in cyanobacteria, based on our previous experience and a series of simulation studies used to assess the selection of measurements and sample time points. We anticipate that this emerging knowledgebase of prior (13)C MFA studies, optimized experimental protocols, and public software tools will catalyze increasing use of (13)C MFA techniques by the cyanobacteria research community.