13CFLUX2--high-performance software suite for (13)C-metabolic flux analysis

Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale

BACKGROUND

A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network.

RESULT

Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling.

CONCLUSION

This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Separation of 13C- and 15N-Isotopologues of Amino Acids with a Primary Amine without Mass Resolution by Means of O-Phthalaldehyde Derivatization and Collision Induced Dissociation

Computational and experimental advances of recent years have culminated in establishing ¹³C-Metabolic Flux Analysis (¹³C-MFA) as a routine methodology to unravel the fluxome. As the acronym suggests, ¹³C-MFA has relied on the relative abundance of ¹³C-isotopes in metabolites for flux inference, most commonly measured by mass spectrometry. In this manuscript we expand the scope of labeling measurements to the case of simultaneous ¹³C- and ¹⁵N-labeling of amino acids. Analytically, the separation of isotopologues of this metabolite class can only be achieved at resolving power beyond 65,000. In this manuscript we harvest an overlooked property of the collision induced dissociation of amino acid adducts to discern ¹³C- and ¹⁵N- isotopologues of amino acids with a primary amine without separating them in the m/z domain.

High-resolution 13C metabolic flux analysis

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. ¹³C metabolic flux analysis (¹³C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for ¹³C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with ¹³C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for ¹³C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis

Motivation

The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging.

Results

Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference.

Supplementary information

Supplementary data are available at Bioinformatics online.

High extracellular lactate causes reductive carboxylation in breast tissue cell lines grown under normoxic conditions

In cancer tumors, lactate accumulation was initially attributed to high glucose consumption associated with the Warburg Effect. Now it is evident that lactate can also serve as an energy source in cancer cell metabolism. Additionally, lactate has been shown to promote metastasis, generate gene expression patterns in cancer cells consistent with "cancer stem cell" phenotypes, and result in treatment resistant tumors. Therefore, the goal of this work was to quantify the impact of lactate on metabolism in three breast cell lines (one normal and two breast cancer cell lines-MCF 10A, MCF7, and MDA-MB-231), in order to better understand the role lactate may have in different disease cell types. Parallel labeling metabolic flux analysis (13C-MFA) was used to quantify the intracellular fluxes under normal and high extracellular lactate culture conditions. Additionally, high extracellular lactate cultures were labelled in parallel with [U-13C] lactate, which provided qualitative information regarding the lactate uptake and metabolism. The 13C-MFA model, which incorporated the measured extracellular fluxes and the parallel labeling mass isotopomer distributions (MIDs) for five glycolysis, four tricarboxylic acid cycle (TCA), and three intracellular amino acid metabolites, predicted lower glycolysis fluxes in the high lactate cultures. All three cell lines experienced reductive carboxylation of glutamine to citrate in the TCA cycle as a result of high extracellular lactate. Reductive carboxylation previously has been observed under hypoxia and other mitochondrial stresses, whereas these cultures were grown aerobically. In addition, this is the first study to investigate the intracellular metabolic responses of different stages of breast cancer progression to high lactate exposure. These results provide insight into the role lactate accumulation has on metabolic reaction distributions in the different disease cell types while the cells are still proliferating in lactate concentrations that do not significantly decrease exponential growth rates.

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. ¹³C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying ¹³C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of ¹³C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for ¹³C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of ¹³C MFA.

EMUlator: An Elementary Metabolite Unit (EMU) Based Isotope Simulator Enabled by Adjacency Matrix

Stable isotope based metabolic flux analysis is currently the unique methodology that allows the experimental study of the integrated responses of metabolic networks. This method primarily relies on isotope labeling and modeling, which could be a challenge in both experimental and computational biology. In particular, the algorithm implementation for isotope simulation is a critical step, limiting extensive usage of this powerful approach. Here, we introduce EMUlator a Python-based isotope simulator which is developed on Elementary Metabolite Unit (EMU) algorithm, an efficient and powerful algorithm for isotope modeling. We propose a novel adjacency matrix method to implement EMU modeling and exemplify it stepwise. This method is intuitively straightforward and can be conveniently mastered for various customized purposes. We apply this arithmetic pipeline to understand the phosphoketolase flux in the metabolic network of an industrial microbe Clostridium acetobutylicum. The resulting design enables a high-throughput and non-invasive approach for estimating phosphoketolase flux in vivo. Our computational insights allow the systematic design and prediction of isotope-based metabolic models and yield a comprehensive understanding of their limitations and potentials.

Elevated temperatures do not trigger a conserved metabolic network response among thermotolerant yeasts

BACKGROUND

Thermotolerance is a highly desirable trait of microbial cell factories and has been the focus of extensive research. Yeast usually tolerate only a narrow temperature range and just two species, Kluyveromyces marxianus and Ogataea polymorpha have been described to grow at reasonable rates above 40 °C. However, the complex mechanisms of thermotolerance in yeast impede its full comprehension and the rare physiological data at elevated temperatures has so far not been matched with corresponding metabolic analyses.

RESULTS

To elaborate on the metabolic network response to increased fermentation temperatures of up to 49 °C, comprehensive physiological datasets of several Kluyveromyces and Ogataea strains were generated and used for 13C-metabolic flux analyses. While the maximum growth temperature was very similar in all investigated strains, the metabolic network response to elevated temperatures was not conserved among the different species. In fact, metabolic flux distributions were remarkably irresponsive to increasing temperatures in O. polymorpha, while the K. marxianus strains exhibited extensive flux rerouting at elevated temperatures.

CONCLUSIONS

While a clear mechanism of thermotolerance is not deducible from the fluxome level alone, the generated data can be valued as a knowledge repository for using temperature to modulate the metabolic activity towards engineering goals.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC-MS/MS

Targeted tandem mass spectrometry (LC-MS/MS) has been extremely useful for profiling small molecules extracted from biological sources, such as cells, bodily fluids and tissues. Here, we present a protocol for analysing incorporation of the non-radioactive stable isotopes carbon-13 (¹³C) and nitrogen-15 (¹⁵N) into polar metabolites in central carbon metabolism and related pathways. Our platform utilizes selected reaction monitoring (SRM) with polarity switching and amide hydrophilic interaction liquid chromatography (HILIC) to capture transitions for carbon and nitrogen incorporation into selected metabolites using a hybrid triple quadrupole (QQQ) mass spectrometer. This protocol represents an extension of a previously published protocol for targeted metabolomics of unlabeled species and has been used extensively in tracing the metabolism of nutrients such as ¹³C-labeled glucose, ¹³C-glutamine and ¹⁵N-glutamine in a variety of biological settings (e.g., cell culture experiments and in vivo mouse labelling via i.p. injection). SRM signals are integrated to produce an array of peak areas for each labelling form that serve as the output for further analysis. The processed data are then used to obtain the degree and distribution of labelling of the targeted molecules (termed fluxomics). Each method can be customized on the basis of known unlabeled Q1/Q3 SRM transitions and adjusted to account for the corresponding ¹³C or ¹⁵N incorporation. The entire procedure takes ~6-7 h for a single sample from experimental labelling and metabolite extraction to peak integration.

Multi-omics analysis unravels a segregated metabolic flux network that tunes co-utilization of sugar and aromatic carbons in Pseudomonas putida

Pseudomonas species thrive in different nutritional environments and can catabolize divergent carbon substrates. These capabilities have important implications for the role of these species in natural and engineered carbon processing. However, the metabolic phenotypes enabling Pseudomonas to utilize mixed substrates remain poorly understood. Here, we employed a multi-omics approach involving stable isotope tracers, metabolomics, fluxomics, and proteomics in Pseudomonas putida KT2440 to investigate the constitutive metabolic network that achieves co-utilization of glucose and benzoate, respectively a monomer of carbohydrate polymers and a derivative of lignin monomers. Despite nearly equal consumption of both substrates, metabolite isotopologues revealed nonuniform assimilation throughout the metabolic network. Gluconeogenic flux of benzoate-derived carbons from the tricarboxylic acid cycle did not reach the upper Embden-Meyerhof-Parnas pathway nor the pentose-phosphate pathway. These latter two pathways were populated exclusively by glucose-derived carbons through a cyclic connection with the Entner-Doudoroff pathway. We integrated the ¹³C-metabolomics data with physiological parameters for quantitative flux analysis, demonstrating that the metabolic segregation of the substrate carbons optimally sustained biosynthetic flux demands and redox balance. Changes in protein abundance partially predicted the metabolic flux changes in cells grown on the glucose:benzoate mixture versus on glucose alone. Notably, flux magnitude and directionality were also maintained by metabolite levels and regulation of phosphorylation of key metabolic enzymes. These findings provide new insights into the metabolic architecture that affords adaptability of P. putida to divergent carbon substrates and highlight regulatory points at different metabolic nodes that may underlie the high nutritional flexibility of Pseudomonas species.

Tandem Mass Spectrometry for 13C Metabolic Flux Analysis: Methods and Algorithms Based on EMU Framework

In the past two decades, ¹³C metabolic flux analysis (¹³C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing ¹³C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances.

A Cyclic Metabolic Network in Pseudomonas protegens Pf-5 Prioritizes the Entner-Doudoroff Pathway and Exhibits Substrate Hierarchy during Carbohydrate Co-Utilization

The genetic characterization of Pseudomonas protegens Pf-5 was recently completed. However, the inferred metabolic network structure has not yet been evaluated experimentally. Here, we employed 13C-tracers and quantitative flux analysis to investigate the intracellular network for carbohydrate metabolism. In lieu of the direct phosphorylation of glucose by glucose kinase, glucose catabolism was characterized primarily by the oxidation of glucose to gluconate and 2-ketogluconate before the phosphorylation of these metabolites to feed the Entner-Doudoroff (ED) pathway. In the absence of phosphofructokinase activity, a cyclic flux from the ED pathway to the upper Embden-Meyerhof-Parnas (EMP) pathway was responsible for routing glucose-derived carbons to the non-oxidative pentose phosphate (PP) pathway. Consistent with the lack of annotated genes in P. protegens Pf-5 for the transport or initial catabolism of pentoses and galactose, only glucose was assimilated into intracellular metabolites in the presence of xylose, arabinose, or galactose. However, when glucose was fed simultaneously with fructose or mannose, co-uptake of these hexoses was evident, but glucose was preferred over fructose (3 to 1) and over mannose (4 to 1). Despite gene annotation of mannose catabolism to fructose-6-phosphate, metabolite labeling patterns revealed that mannose was assimilated into fructose-1,6-bisphosphate, similarly to fructose catabolism. Remarkably, carbons from mannose and fructose were also found to cycle backward through the upper EMP pathway toward the ED pathway. Therefore, the operational metabolic network for processing carbohydrates in P. protegens Pf-5 prioritizes flux through the ED pathway to channel carbons to EMP, PP, and downstream pathways.IMPORTANCE Species of the Pseudomonas genus thrive in various nutritional environments and have strong biocatalytic potential due to their diverse metabolic capabilities. Carbohydrate substrates are ubiquitous both in environmental matrices and in feedstocks for engineered bioconversion. Here, we investigated the metabolic network for carbohydrate metabolism in Pseudomonas protegens Pf-5. Metabolic flux quantitation revealed the relative involvement of different catabolic routes in channeling carbohydrate carbons through a cyclic metabolic network. We also uncovered that mannose catabolism was similar to fructose catabolism, despite the annotation of a different pathway in the genome. Elucidation of the constitutive metabolic network in P. protegens is important for understanding its innate carbohydrate processing, thus laying the foundation for targeting metabolic engineering of this untapped Pseudomonas species.

13C-Fingerprinting and Metabolic Flux Analysis of Bacterial Metabolisms

¹³C-assisted metabolism analysis provides rigorous calculations of the intracellular reaction rates (i.e., fluxes) within the central metabolism of microbial hosts. This mapping of the intracellular network within microbes has proven to be essential for understanding the cell physiology. The approach is also a key to identifying central metabolic nodes, probing the rigidity of a metabolic network, revealing cofactor balances, and delineating hidden pathways. Here we present the methodology of using stable isotopic carbon substrates for both qualitative (¹³C-fingerprinting of functional pathways) and quantitative (Metabolic Flux Analysis) metabolism studies on bacterial species. In this methodology, we include step-by-step instructions to use the open source WUflux software for the steady-state flux calculations based on labeling information of amino acids or free metabolites.

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

BACKGROUND

The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins-which favors correct folding and facilitates downstream processing-as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and [Formula: see text]-based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)-previously shown to be successfully overexpressed in S. lividans-was taken as an example protein.

RESULTS

RNA-seq and [Formula: see text]-based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion.

CONCLUSIONS

Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.

Flux Connections Between Gluconate Pathway, Glycolysis, and Pentose-Phosphate Pathway During Carbohydrate Metabolism in Bacillus megaterium QM B1551

Bacillus megaterium is a bacterium of great importance as a plant-beneficial bacterium in agricultural applications and in industrial bioproduction of proteins. Understanding intracellular processing of carbohydrates in this species is crucial to predicting natural carbon utilization as well as informing strategies in metabolic engineering. Here, we applied stable isotope-assisted metabolomics profiling and metabolic flux analysis to elucidate, at high resolution, the connections of the different catabolic routes for carbohydrate metabolism immediately following substrate uptake in B. megaterium QM B1551. We performed multiple 13C tracer experiments to obtain both kinetic and long-term 13C profiling of intracellular metabolites. In addition to the direct phosphorylation of glucose to glucose-6-phosphate (G6P) prior to oxidation to 6-phosphogluconate (6P-gluconate), the labeling data also captured glucose catabolism through the gluconate pathway involving glucose oxidation to gluconate followed by phosphorylation to 6P-gluconate. Our data further confirmed the absence of the Entner-Doudoroff pathway in B. megaterium and showed that subsequent catabolism of 6P-gluconate was instead through the oxidative pentose-phosphate (PP) pathway. Quantitative flux analysis of glucose-grown cells showed equal partition of consumed glucose from G6P to the Embden-Meyerhof-Parnas (EMP) pathway and from G6P to the PP pathway through 6P-gluconate. Growth on fructose alone or xylose alone was consistent with the ability of B. megaterium to use each substrate as a sole source of carbon. However, a detailed 13C mapping during simultaneous feeding of B. megaterium on glucose, fructose, and xylose indicated non-uniform intracellular investment of the different carbohydrate substrates. Flux of glucose-derived carbons dominated the gluconate pathway and the PP pathway, whereas carbon flux from both glucose and fructose populated the EMP pathway; there was no assimilatory flux of xylose-derived carbons. Collectively, our findings provide new quantitative insights on the contribution of the different catabolic routes involved in initiating carbohydrate catabolism in B. megaterium and related Bacillus species.

The challenge and potential of photosynthesis: unique considerations for metabolic flux measurements in photosynthetic microorganisms

Photosynthetic microorganisms have the potential for sustainable production of chemical feedstocks and products but have had limited success due to a lack of tools and deeper understanding of metabolic pathway regulation. The application of instationary metabolic flux analysis (INST-MFA) to photosynthetic microorganisms has allowed researchers to quantify fluxes and identify bottlenecks and metabolic inefficiencies to improve strain performance or gain insight into cellular physiology. Additionally, flux measurements can also highlight deviations between measured and predicted fluxes, revealing weaknesses in metabolic models and highlighting areas where a lack of understanding still exists. In this review, we outline the experimental steps necessary to successfully perform photosynthetic flux experiments and analysis. We also discuss the challenges unique to photosynthetic microorganisms and how to account for them, including: light supply, quenching, concentration, extraction, analysis, and flux calculation. We hope that this will enable a larger number of researchers to successfully apply isotope assisted metabolic flux analysis (¹³C-MFA) to their favorite photosynthetic organism.

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments

Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of ¹³C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

Designing experiments is obligatory in the biosciences to valorize their scientific outcome. When the experiments are expensive, unfortunately, in practice often the costs emerge to be showstoppers. In this situation the question arises: How to get the most out of the experiment for your invest in terms of time and money? We approach this question by formulating the design task as a multiple-criteria optimization problem. Its solution produces a set of Pareto-optimal design proposals that feature the trade-off between information gain, as measured by different metrics, and the costs. Then, exploration of the design proposals allows us to make the best decision on information-economic experiments under given circumstances. Implemented in the field of isotope-based metabolic flux analysis, practical application of the Pareto approach provides detailed insight into the tight interplay of plenty of information carriers and cost factors. Supported by an innovative tailored visual representation scheme, the investigator is enabled to explore the options before conducting the experiment. With a practical showcase at hand, our computational study highlights the benefits of incorporating multiple information criteria apart from the costs, balancing the shortcomings of conventional single-objective experimental design strategies.

Engineering an in vivo EP-bifido pathway in Escherichia coli for high-yield acetyl-CoA generation with low CO2 emission

The low carbon yield from native metabolic machinery produces unfavorable process economics during the biological conversion of substrates to desirable bioproducts. To obtain higher carbon yields, we constructed a carbon conservation pathway named EP-bifido pathway in Escherichia coli by combining Embden-Meyerhof-Parnas Pathway, Pentose Phosphate Pathway and "bifid shunt", to generate high yield acetyl-CoA from glucose. ¹³C-Metabolic flux analysis confirmed the successful and appropriate employment of the EP-bifido pathway. The CO₂ release during fermentation significantly reduced compared with the control strains. Then we demonstrated the in vivo effectiveness of the EP-bifido pathway using poly-β-hydroxybutyrate (PHB), mevalonate and fatty acids as example products. The engineered EP-bifido strains showed greatly improved PHB yield (from 26.0 mol% to 63.7 mol%), fatty acid yield (from 9.17% to 14.36%), and the highest mevalonate yield yet reported (64.3 mol% without considering the substrates used for cell mass formation). The synthetic pathway can be employed in the production of chemicals that use acetyl-CoA as a precursor and can be extended to other microorganisms.

On structural identifiability analysis of the cascaded linear dynamic systems in isotopically non-stationary 13C labelling experiments

The isotopically non-stationary 13C labelling experiments, as an emerging experimental technique, can estimate the intracellular fluxes of the cell culture under an isotopic transient period. However, to the best of our knowledge, the issue of the structural identifiability analysis of non-stationary isotope experiments is not well addressed in the literature. In this work, the local structural identifiability analysis for non-stationary cumomer balance equations is conducted based on the Taylor series approach. The numerical rank of the Jacobian matrices of the finite extended time derivatives of the measured fractions with respect to the free parameters is taken as the criterion. It turns out that only one single time point is necessary to achieve the structural identifiability analysis of the cascaded linear dynamic system of non-stationary isotope experiments. The equivalence between the local structural identifiability of the cascaded linear dynamic systems and the local optimum condition of the nonlinear least squares problem is elucidated in the work. Optimal measurements sets can then be determined for the metabolic network. Two simulated metabolic networks are adopted to demonstrate the utility of the proposed method.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease

Metabolic dysfunction has reemerged as an essential hallmark of tumorigenesis, and metabolic phenotypes are increasingly being integrated into pre-clinical models of disease. The complexity of these metabolic networks requires systems-level interrogation, and metabolic flux analysis (MFA) with stable isotope tracing present a suitable conceptual framework for such systems. Here we review efforts to elucidate mechanisms through which metabolism influences tumor growth and survival, with an emphasis on applications using stable isotope tracing and MFA. Through these approaches researchers can now quantify pathway fluxes in various in vitro and in vivo contexts to provide mechanistic insights at molecular and physiological scales respectively. Knowledge and discoveries in cancer models are paving the way toward applications in other biological contexts and disease models. In turn, MFA approaches will increasingly help to uncover new therapeutic opportunities that enhance human health.

Stage-specific metabolic features of differentiating neurons: Implications for toxicant sensitivity

Developmental neurotoxicity (DNT) may be induced when chemicals disturb a key neurodevelopmental process, and many tests focus on this type of toxicity. Alternatively, DNT may occur when chemicals are cytotoxic only during a specific neurodevelopmental stage. The toxicant sensitivity is affected by the expression of toxicant targets and by resilience factors. Although cellular metabolism plays an important role, little is known how it changes during human neurogenesis, and how potential alterations affect toxicant sensitivity of mature vs. immature neurons. We used immature (d0) and mature (d6) LUHMES cells (dopaminergic human neurons) to provide initial answers to these questions. Transcriptome profiling and characterization of energy metabolism suggested a switch from predominantly glycolytic energy generation to a more pronounced contribution of the tricarboxylic acid cycle (TCA) during neuronal maturation. Therefore, we used pulsed stable isotope-resolved metabolomics (pSIRM) to determine intracellular metabolite pool sizes (concentrations), and isotopically non-stationary ¹³C-metabolic flux analysis (INST ¹³C-MFA) to calculate metabolic fluxes. We found that d0 cells mainly use glutamine to fuel the TCA. Furthermore, they rely on extracellular pyruvate to allow continuous growth. This metabolic situation does not allow for mitochondrial or glycolytic spare capacity, i.e. the ability to adapt energy generation to altered needs. Accordingly, neuronal precursor cells displayed a higher sensitivity to several mitochondrial toxicants than mature neurons differentiated from them. In summary, this study shows that precursor cells lose their glutamine dependency during differentiation while they gain flexibility of energy generation and thereby increase their resistance to low concentrations of mitochondrial toxicants.

13C-assisted metabolic flux analysis to investigate heterotrophic and mixotrophic metabolism in Cupriavidus necator H16

INTRODUCTION

Cupriavidus necator H16 is a gram-negative bacterium, capable of lithoautotrophic growth by utilizing hydrogen as an energy source and fixing carbon dioxide (CO2) through Calvin-Benson-Bassham (CBB) cycle. The potential to utilize synthesis gas (Syngas) and the prospects of rerouting carbon from polyhydroxybutyrate synthesis to value-added compounds makes C. necator an excellent chassis for industrial application.

OBJECTIVES

In the context of lack of sufficient quantitative information of the metabolic pathways and to advance in rational metabolic engineering for optimized product synthesis in C. necator H16, we carried out a metabolic flux analysis based on steady-state 13C-labelling.

METHODS

In this study, steady-state carbon labelling experiments, using either d-[1-13C]fructose or [1,2-13C]glycerol, were undertaken to investigate the carbon flux through the central carbon metabolism in C. necator H16 under heterotrophic and mixotrophic growth conditions, respectively.

RESULTS

We found that the CBB cycle is active even under heterotrophic condition, and growth is indeed mixotrophic. While Entner-Doudoroff (ED) pathway is shown to be the major route for sugar degradation, tricarboxylic acid (TCA) cycle is highly active in mixotrophic condition. Enhanced flux is observed in reductive pentose phosphate pathway (redPPP) under the mixotrophic condition to supplement the precursor requirement for CBB cycle. The flux distribution was compared to the mRNA abundance of genes encoding enzymes involved in key enzymatic reactions of the central carbon metabolism.

CONCLUSION

This study leads the way to establishing 13C-based quantitative fluxomics for rational pathway engineering in C. necator H16.

Understanding metabolism with flux analysis: From theory to application

Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.

To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis

¹³ C Metabolic Fluxes Analysis (¹³ C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of ¹³ C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to ¹³ C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in ¹³ C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer.

A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis

Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for ¹³C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.

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Compartmentation influences computed intracellular fluxes.

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Improper localization constraints potentially produce false flux solutions.

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Minimal compartmentation constraints result in high-quality flux computations.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Background

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results

The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and ¹³C Metabolic Flux Analysis. Moreover, it introduces the capability to use ¹³C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale ¹³C Metabolic Flux Analysis (2S-¹³C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions

jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

Fluxomics links cellular functional analyses to whole-plant phenotyping

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

The importance of accurately correcting for the natural abundance of stable isotopes

The use of isotopically labeled tracer substrates is an experimental approach for measuring in vivo and in vitro intracellular metabolic dynamics. Stable isotopes that alter the mass but not the chemical behavior of a molecule are commonly used in isotope tracer studies. Because stable isotopes of some atoms naturally occur at non-negligible abundances, it is important to account for the natural abundance of these isotopes when analyzing data from isotope labeling experiments. Specifically, a distinction must be made between isotopes introduced experimentally via an isotopically labeled tracer and the isotopes naturally present at the start of an experiment. In this tutorial review, we explain the underlying theory of natural abundance correction of stable isotopes, a concept not always understood by metabolic researchers. We also provide a comparison of distinct methods for performing this correction and discuss natural abundance correction in the context of steady state 13C metabolic flux, a method increasingly used to infer intracellular metabolic flux from isotope experiments.

Metabolic Flux Analysis in Isotope Labeling Experiments Using the Adjoint Approach

Comprehension of metabolic pathways is considerably enhanced by metabolic flux analysis (MFA-ILE) in isotope labeling experiments. The balance equations are given by hundreds of algebraic (stationary MFA) or ordinary differential equations (nonstationary MFA), and reducing the number of operations is therefore a crucial part of reducing the computation cost. The main bottleneck for deterministic algorithms is the computation of derivatives, particularly for nonstationary MFA. In this article, we explain how the overall identification process may be speeded up by using the adjoint approach to compute the gradient of the residual sum of squares. The proposed approach shows significant improvements in terms of complexity and computation time when it is compared with the usual (direct) approach. Numerical results are obtained for the central metabolic pathways of Escherichia coli and are validated against reference software in the stationary case. The methods and algorithms described in this paper are included in the sysmetab software package distributed under an Open Source license at http://forge.scilab.org/index.php/p/sysmetab/.

Comprehensive and accurate tracking of carbon origin of LC-tandem mass spectrometry collisional fragments for 13C-MFA

In recent years the benefit of measuring positionally resolved 13C-labeling enrichment from tandem mass spectrometry (MS/MS) collisional fragments for improved precision of 13C-Metabolic Flux Analysis (13C-MFA) has become evident. However, the usage of positional labeling information for 13C-MFA faces two challenges: (1) The mass spectrometric acquisition of a large number of potentially interfering mass transitions may hamper accuracy and sensitivity. (2) The positional identity of carbon atoms of product ions needs to be known. The present contribution addresses the latter challenge by deducing the maximal positional labeling information contained in LC-ESI-MS/MS spectra of product anions of central metabolism as well as product cations of amino acids. For this purpose, we draw on accurate mass spectrometry, selectively labeled standards, and published fragmentation pathways to structurally annotate all dominant mass peaks of a large collection of metabolites, some of which with a complete fragmentation pathway. Compiling all available information, we arrive at the most detailed map of carbon atom fate of LC-ESI-MS/MS collisional fragments yet, comprising 170 intense and structurally annotated product ions with unique carbon origin from 76 precursor ions of 72 metabolites. Our 13C-data proof that heuristic fragmentation rules often fail to yield correct fragment structures and we expose common pitfalls in the structural annotation of product ions. We show that the positionally resolved 13C-label information contained in the product ions that we structurally annotated allows to infer the entire isotopomer distribution of several central metabolism intermediates, which is experimentally demonstrated for malate using quadrupole-time-of-flight MS technology. Finally, the inclusion of the label information from a subset of these fragments improves flux precision in a Corynebacterium glutamicum model of the central carbon metabolism.

Metabolomics: A Primer

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

Exo-MFA - A 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism

Dissecting the pleiotropic roles of tumor micro-environment (TME) on cancer progression has been brought to the foreground of research on cancer pathology. Extracellular vesicles such as exosomes, transport proteins, lipids, and nucleic acids, to mediate intercellular communication between TME components and have emerged as candidates for anti-cancer therapy. We previously reported that cancer-associated fibroblast (CAF) derived exosomes (CDEs) contain metabolites in their cargo that are utilized by cancer cells for central carbon metabolism and promote cancer growth. However, the metabolic fluxes involved in donor cells towards packaging of metabolites in extracellular vesicles and exosome-mediated metabolite flux upregulation in recipient cells are still not known. Here, we have developed a novel empirical and computational technique, exosome-mediated metabolic flux analysis (Exo-MFA) to quantify flow of cargo from source cells to recipient cells via vesicular transport. Our algorithm, which is based on ¹³C metabolic flux analysis, successfully predicts packaging fluxes to metabolite cargo in CAFs, dynamic changes in rate of exosome internalization by cancer cells, and flux of cargo release over time. We find that cancer cells internalize exosomes rapidly leading to depletion of extracellular exosomes within 24h. However, metabolite cargo significantly alters intracellular metabolism over the course of 24h by regulating glycolysis pathway fluxes via lactate supply. Furthermore, it can supply up to 35% of the TCA cycle fluxes by providing TCA intermediates and glutamine. Our algorithm will help gain insight into (i) metabolic interactions in multicellular systems (ii) biogenesis of extracellular vesicles and their differential packaging of cargo under changing environments, and (iii) regulation of cancer cell metabolism by its microenvironment.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

FluxFix: automatic isotopologue normalization for metabolic tracer analysis

Background

Isotopic tracer analysis by mass spectrometry is a core technique for the study of metabolism. Isotopically labeled atoms from substrates, such as [¹³C]-labeled glucose, can be traced by their incorporation over time into specific metabolic products. Mass spectrometry is often used for the detection and differentiation of the isotopologues of each metabolite of interest. For meaningful interpretation, mass spectrometry data from metabolic tracer experiments must be corrected to account for the naturally occurring isotopologue distribution. The calculations required for this correction are time consuming and error prone and existing programs are often platform specific, non-intuitive, commercially licensed and/or limited in accuracy by using theoretical isotopologue distributions, which are prone to artifacts from noise or unresolved interfering signals.

Results

Here we present FluxFix (http://fluxfix.science), an application freely available on the internet that quickly and reliably transforms signal intensity values into percent mole enrichment for each isotopologue measured. ‘Unlabeled’ data, representing the measured natural isotopologue distribution for a chosen analyte, is entered by the user. This data is used to generate a correction matrix according to a well-established algorithm. The correction matrix is applied to labeled data, also entered by the user, thus generating the corrected output data. FluxFix is compatible with direct copy and paste from spreadsheet applications including Excel (Microsoft) and Google sheets and automatically adjusts to account for input data dimensions. The program is simple, easy to use, agnostic to the mass spectrometry platform, generalizable to known or unknown metabolites, and can take input data from either a theoretical natural isotopologue distribution or an experimentally measured one.

Conclusions

Our freely available web-based calculator, FluxFix (http://fluxfix.science), quickly and reliably corrects metabolic tracer data for natural isotopologue abundance enabling faster, more robust and easily accessible data analysis.

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.

WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism

Background

Flux analyses, including flux balance analysis (FBA) and ¹³C-metabolic flux analysis (¹³C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the ¹³C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the ¹³C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB^®.

Results

We constructed an open-source platform for steady-state ¹³C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify ¹³C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results.

Conclusion

Open-source software for ¹³C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1314-0) contains supplementary material, which is available to authorized users.

Carbon 13-Metabolic Flux Analysis derived constraint-based metabolic modelling of Clostridium acetobutylicum in stressed chemostat conditions

The metabolism of butanol producing bacteria Clostridium acetobutylicum was studied in chemostat with glucose limited conditions, butanol stimulus, and as a reference cultivation. COnstraint-Based Reconstruction and Analysis (COBRA) was applied using additional constraints from (13)C Metabolic Flux Analysis ((13)C-MFA) and experimental measurement results. A model consisting of 451 metabolites and 604 reactions was utilized in flux balance analysis (FBA). The stringency of the flux spaces considering different optimization objectives, i.e. growth rate maximization, ATP maintenance, and NADH/NADPH formation, for flux variance analysis (FVA) was studied in the different modelled conditions. Also a previously uncharacterized exopolysaccharide (EPS) produced by C. acetobutylicum was characterized on monosaccharide level. The major monosaccharide components of the EPS were 40n-% rhamnose, 34n-% glucose, 13n-% mannose, 10n-% galactose, and 2n-% arabinose. The EPS was studied to have butanol adsorbing property, 70(butanol)mg(EPS)g(-1) at 37°C.