Intracellular Flux Prediction of Recombinant Escherichia coli Producing Gamma-Aminobutyric Acid.

Genome-scale metabolic model (GEM) can be used to simulate cellular metabolic phenotypes under various environmental or genetic conditions. This study utilized the GEM to observe the internal metabolic fluxes of recombinant Escherichia coli producing gamma-aminobutyric acid (GABA). Recombinant E. coli was cultivated in a fermenter under three conditions: pH 7, pH 5, and additional succinic acids. External fluxes were calculated from cultivation results, and internal fluxes were calculated through flux optimization. Based on the internal flux analysis, glycolysis and pentose phosphate pathways were repressed under cultivation at pH 5, even though glutamate dehydrogenase increased GABA production. Notably, this repression was halted by adding succinic acid. Furthermore, proper sucA repression is a promising target for developing strains more capable of producing GABA.

Adjusting for false discoveries in constraint-based differential metabolic flux analysis.

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.

A continuous flow bioreactor system for high-throughput hyperpolarized metabolic flux analysis.

Hyperpolarized carbon-13 labeled compounds are increasingly being used in medical MR imaging (MRI) and MR imaging (MRI) and spectroscopy (MRS) research, due to its ability to monitor tissue and cell metabolism in real-time. Although radiological biomarkers are increasingly being considered as clinical indicators, biopsies are still considered the gold standard for a large variety of indications. Bioreactor systems can play an important role in biopsy examinations because of their ability to provide a physiochemical environment that is conducive for therapeutic response monitoring ex vivo. We demonstrate here a proof-of-concept bioreactor and microcoil receive array setup that allows for ex vivo preservation and metabolic NMR spectroscopy on up to three biopsy samples simultaneously, creating an easy-to-use and robust way to simultaneously run multisample carbon-13 hyperpolarization experiments. Experiments using hyperpolarized [1-13C]pyruvate on ML-1 leukemic cells in the bioreactor setup were performed and the kinetic pyruvate-to-lactate rate constants ( k PL ) extracted. The coefficient of variation of the experimentally found k PL s for five repeated experiments was C V = 35 % . With this statistical power, treatment effects of 30%-40% change in lactate production could be easily differentiable with only a few hyperpolarization dissolutions on this setup. Furthermore, longitudinal experiments showed preservation of ML-1 cells in the bioreactor setup for at least 6 h. Rat brain tissue slices were also seen to be preserved within the bioreactor for at least 1 h. This validation serves as the basis for further optimization and upscaling of the setup, which undoubtedly has huge potential in high-throughput studies with various biomarkers and tissue types.

Flux sampling in genome-scale metabolic modeling of microbial communities.

BACKGROUND: Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. RESULTS: In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. CONCLUSIONS: Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

Ex Vivo 13C-Metabolic Flux Analysis of Porcine Circulating Immune Cells Reveals Cell Type-Specific Metabolic Patterns and Sex Differences in the Pentose Phosphate Pathway.

In general, females present with stronger immune responses than males, but scarce data are available on sex-specific differences in immunometabolism. In this study, we characterized porcine peripheral blood mononuclear cell (PBMC) and granulocyte energy metabolism using a Bayesian 13C-metabolic flux analysis, which allowed precise determination of the glycolytic, pentose phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) fluxes, together with an assessment of the superoxide anion radical (O2•-) production and mitochondrial O2 consumption. A principal component analysis allowed for identifying the cell type-specific patterns of metabolic plasticity. PBMCs displayed higher TCA cycle activity, especially glutamine-derived aspartate biosynthesis, which was directly related to mitochondrial respiratory activity and inversely related to O2•- production. In contrast, the granulocytes mainly utilized glucose via glycolysis, which was coupled to oxidative PPP utilization and O2•- production rates. The granulocytes of the males had higher oxidative PPP fluxes compared to the females, while the PBMCs of the females displayed higher non-oxidative PPP fluxes compared to the males associated with the T helper cell (CD3+CD4+) subpopulation of PBMCs. The observed sex-specific differences were not directly attributable to sex steroid plasma levels, but we detected an inverse correlation between testosterone and aldosterone plasma levels and showed that aldosterone levels were related with non-oxidative PPP fluxes of both cell types.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Model validation and selection in metabolic flux analysis and flux balance analysis.

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Valoración del impacto de la tasa libre de litiasis en los resultados de la evaluación metabólica / Is stone free status really necessary for metabolic evaluation following stone removal procedures?

Objetivo Evaluar si la tasa libre de litiasis afecta a los resultados del estudio metabólico en pacientes con alto riesgo de litiasis recidivante tras tratamiento completo mediante ureteroscopia. Pacientes y métodos Un total de 78 pacientes sometidos a cirugía retrógrada intrarrenal (CRIR) para el tratamiento de litiasis fueron incluidos en este estudio. Cuatro semanas después del tratamiento, los casos se dividieron en dos grupos en base a los resultados de la tomografía computarizada sin contraste (TCSC). Los casos del grupo 1 (n=54) presentaban una tasa libre de litiasis del 100% y los del grupo 2 (n=24) presentaban litiasis residuales en el riñón. Cuatro semanas después de la ureteroscopia flexible (URF) se realizó un análisis completo de orina de 24h a todos los pacientes de ambos grupos, para detectar los factores de riesgo implicados en la litogénesis. Los resultados del estudio metabólico (orina de 24h y suero) se compararon entre los dos grupos. Resultados La evaluación preoperatoria en orina y suero de los factores de riesgo asociados a la formación de cálculos no reveló diferencias estadísticas entre los dos grupos. El análisis comparativo de los factores de riesgo implicados en la formación de la litiasis mediante pruebas de orina de 24h tampoco reveló diferencias estadísticamente significativas entre los resultados preoperatorios y postoperatorios en los casos del grupo 2 con cálculos residuales. Tampoco se observaron diferencias significativas entre las medias de las variables séricas preoperatorias y postoperatorias de ambos grupos. Conclusiones Según nuestros resultados, y dada la similitud de los hallazgos obtenidos en los estudios metabólicos de los casos con y sin litiasis residual, la tasa libre de litiasis puede no constituir un factor imprescindible para la realización del estudio metabólico detallado (suero y orina de 24h) tras las intervenciones endourológicas para la extracción de los cálculos renales. (AU)

Objective To evaluate the impact of stone free status on the outcomes of metabolic evaluation in recurrent stone formers after ureteroscopic stone removal. Patients and methods A total of 78 patients undergoing retrograde intrarenal surgery (RIRS) for renal stones were included and cases were divided into two groups after 4 weeks based on the NCCT findings. While cases in the Group 1 (n=54) was completely stone free, cases in Group 2 (n=24) had residual fragments in the kidney. A full 24-hour urine analysis for relevant stone forming risk factors has been performed after 4 weeks following the fURS procedures in all patients of both groups. Outcomes of metabolic evaluation (24-hour urine and serum) have been comparatively evaluated in both groups. Results Evaluation of the preoperative serum and urine stone forming risk factors revelaed no statistical difference in both groups. Comparative evaluation of the 24-hour urinary stone forming risk factors also revealed no statistically significant difference between preoperative and postoperative findings in cases of Group 2 with residual stones. Last but not least, no significant difference was observed between the mean preoperative and postoperative serum variables between two groups. Conclusions Our results show that in the light of the similar metabolic evaluation outcomes obtained in cases with and without residual fragments, ‘stone free status’ may not be an essential factor to perform a detailed metabolic evaluation (24-hour urine analysis and serum parameters) after endourological stone removal procedures. (AU)

ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.

BACKGROUND: Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights. RESULTS: To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering. CONCLUSION: ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments.

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA. ONE SENTENCE SUMMARY: An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks.

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

Metabolic flux analysis of coenzyme Q10 synthesized by Rhodobacter sphaeroides under the influence of different pH regulators.

Coenzyme Q10 (CoQ10) is crucial for human beings, especially in the fields of biology and medicine. The aim of this experiment was to investigate the conditions for increasing CoQ10 production. At present, microbial fermentation is the main production method of CoQ10, and the production process of microbial CoQ10 metabolism control fermentation is very critical. Metabolic flux is one of the most important determinants of cell physiology in metabolic engineering. Metabolic flux analysis (MFA) is used to estimate the intracellular flux in metabolic networks. In this experiment, Rhodobacter sphaeroides was used as the research object to analyze the effects of aqueous ammonia (NH3·H2O) and calcium carbonate (CaCO3) on the metabolic flux of CoQ10. When CaCO3 was used to adjust the pH, the yield of CoQ10 was 274.43 mg·L-1 (8.71 mg·g-1 DCW), which was higher than that of NH3·H2O adjustment. The results indicated that when CaCO3 was used to adjust pH, more glucose-6-phosphate (G6P) entered the pentose phosphate (HMP) pathway and produced more NADPH, which enhanced the synthesis of CoQ10. At the chorismic acid node, more metabolic fluxes were involved in the synthesis of p-hydroxybenzoic acid (pHBA; the synthetic precursor of CoQ10), enhancing the anabolic flow of CoQ10. In addition, Ca2+ produced by the reaction of CaCO3 with organic acids promotes the synthesis of CoQ10. In summary, the use of CaCO3 adjustment is more favorable for the synthesis of CoQ10 by R. sphaeroides than NH3·H2O adjustment. The migration of metabolic flux caused by the perturbation of culture conditions was analyzed to compare the changes in the distribution of intracellular metabolic fluxes for the synthesis of CoQ10. Thus, the main nodes of the metabolic network were identified as G6P and chorismic acid. This provides a theoretical basis for the modification of genes related to the CoQ10 synthesis pathway.

Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis.

MOTIVATION: Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty. RESULTS: In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes. AVAILABILITY AND IMPLEMENTATION: The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis.

13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.

Integrative teaching of metabolic modeling and flux analysis with interactive python modules.

The modeling of rates of biochemical reactions-fluxes-in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C-metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners' self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

On paradoxes between optimal growth, metabolic control analysis, and flux balance analysis.

In Microbiology it is often assumed that growth rate is maximal. This may be taken to suggest that the dependence of the growth rate on every enzyme activity is at the top of an inverse-parabolic function, i.e. that all flux control coefficients should equal zero. This might seem to imply that the sum of these flux control coefficients equals zero. According to the summation law of Metabolic Control Analysis (MCA) the sum of flux control coefficients should equal 1 however. And in Flux Balance Analysis (FBA) catabolism is often limited by a hard bound, causing catabolism to fully control the fluxes, again in apparent contrast with a flux control coefficient of zero. Here we resolve these paradoxes (apparent contradictions) in an analysis that uses the 'Edinburgh pathway', the 'Amsterdam pathway', as well as a generic metabolic network providing the building blocks or Gibbs energy for microbial growth. We review and show that (i) optimization depends on so-called enzyme control coefficients rather than the 'catalytic control coefficients' of MCA's summation law, (ii) when optimization occurs at fixed total protein, the former differ from the latter to the extent that they may all become equal to zero in the optimum state, (iii) in more realistic scenarios of optimization where catalytically inert biomass is compensating or maintenance metabolism is taken into consideration, the optimum enzyme concentrations should not be expected to equal those that maximize the specific growth rate, (iv) optimization may be in terms of yield rather than specific growth rate, which resolves the paradox because the sum of catalytic control coefficients on yield equals 0, (v) FBA effectively maximizes growth yield, and for yield the summation law states 0 rather than 1, thereby removing the paradox, (vi) furthermore, FBA then comes more often to a 'hard optimum' defined by a maximum catabolic flux and a catabolic-enzyme control coefficient of 1. The trade-off between maintenance metabolism and growth is highlighted as worthy of further analysis.

Applying metabolic flux analysis to hydrogen fermentation using a metabolic network constructed for anaerobic mixed cultures.

In this study, a mixed-cultural metabolic network for anaerobic digestion that included the concept of a "universal bacterium" was constructed, and metabolic flux analysis (MFA) applying this network was conducted to evaluate the flow of electrons and materials during H2 fermentation under various conditions. The MFA results from two H2 fermenters feeding glucose with (GP) or without (GA) the addition of peptone suggest that hydraulic retention time (HRT) presents a significant impact on hydrogen production, and the reversed trends could be observed at HRTs below and above 4 h. From the MFA results of lactate/acetate-fed H2 fermenter, the highest flux of H2 production is associated with more significant acetate consumption and the following pathways toward the anaplerotic reactions cycle that produces NADH. The occurrence of acetogenesis in the H2 fermenters using various types of bioethanol-fermented residues (BEFRs) was also identified according to the MFA results. By analyzing the MFA results of all 49 sets of data from H2 fermenters via Pearson's correlation, it was revealed that the flux of H2 production positively correlates to the reduction of ferredoxin with pyruvate oxidation, acetate formation, and acetate emission when lactate was produced in the system. On the contrary, negative relationships were found between the flux of H2 production and these three fluxes. The extended application of MFA provides additional information, including the fluxes between intracellular metabolites, and the information has the potential to be used in decision-making systems during the future operation of anaerobic processes by connecting operational parameters.

High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux.

BACKGROUND: Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris. RESULTS: We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5. CONCLUSIONS: This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.