Metabolic Flux Measurements Research Articles

Biocalorimetry-aided monitoring of fungal pretreatment of lignocellulosic agricultural residues

The present study aimed to investigate whether and how non-invasive biocalorimetric measurements could serve for process monitoring of fungal pretreatment during solid-state fermentation (SSF) of lignocellulosic agricultural residues such as wheat straw. Seven filamentous fungi representing different lignocellulose decay types were employed. Water-soluble sugars being immediately available after fungal pretreatment and those becoming water-extractable after enzymatic digestion of pretreated wheat straw with hydrolysing (hemi)cellulases were considered to constitute the total bioaccessible sugar fraction. The latter was used to indicate the success of pretreatments and linked to corresponding species-specific metabolic heat yield coefficients (YQ/X) derived from metabolic heat flux measurements during fungal wheat straw colonisation. An YQ/X range of about 120 to 140 kJ/g was seemingly optimal for pretreatment upon consideration of all investigated fungi and application of a non-linear Gaussian fitting model. Upon exclusion from analysis of the brown-rot basidiomycete Gloeophyllum trabeum, which differs from all other here investigated fungi in employing extracellular Fenton chemistry for lignocellulose decomposition, a linear relationship where amounts of total bioaccessible sugars were suggested to increase with increasing YQ/X values was obtained. It remains to be elucidated whether an YQ/X range being optimal for fungal pretreatment could firmly be established, or if the sugar accessibility for post-treatment generally increases with increasing YQ/X values as long as “conventional” enzymatic, i.e. (hemi)cellulase-based, lignocellulose decomposition mechanisms are operative. In any case, metabolic heat measurement–derived parameters such as YQ/X values may become very valuable tools supporting the assessment of the suitability of different fungal species for pretreatment of lignocellulosic substrates.Key points• Biocalorimetry was used to monitor wheat straw pretreatment with seven filamentous fungi.• Metabolic heat yield coefficients (YQ/X) seem to indicate pretreatment success.• YQ/X values may support the selection of suitable fungal strains for pretreatment.

Aerobic Glycolysis in Photoreceptors Supports Energy Demand in the Absence of Mitochondrial Coupling.

Metabolism is adapted to meet energetic needs. Based on the amount of ATP required to maintain plasmamembrane potential, photoreceptor energy demands must be high. The available evidence suggests that photoreceptors primarily generate metabolic energy through aerobic glycolysis, though this evidence is based primarily on protein expression and not measurement of metabolic flux. Aerobic glycolysis can be validated by measuring flux of glucose to lactate. Aerobic glycolysis is also inefficient and thus an unexpected adaptation for photoreceptors to make. We measured metabolic rates to determine the energy-generating pathways that support photoreceptor metabolism. We found that photoreceptors indeed perform aerobic glycolysis and this is associated with mitochondrial uncoupling.

Oxidative Glucose Metabolism Promotes Senescence in Vascular Endothelial Cells.

Vascular aging is based on the development of endothelial dysfunction, which is thought to be promoted by senescent cells accumulating in aged tissues and is possibly affected by their environment via inflammatory mediators and oxidative stress. Senescence appears to be closely interlinked with changes in cell metabolism. Here, we describe an upregulation of both glycolytic and oxidative glucose metabolism in replicative senescent endothelial cells compared to young endothelial cells by employing metabolic profiling and glucose flux measurements and by analyzing the expression of key metabolic enzymes. Senescent cells exhibit higher glycolytic activity and lactate production together with an enhanced expression of lactate dehydrogenase A as well as increases in tricarboxylic acid cycle activity and mitochondrial respiration. The latter is likely due to the reduced expression of pyruvate dehydrogenase kinases (PDHKs) in senescent cells, which may lead to increased activity of the pyruvate dehydrogenase complex. Cellular and mitochondrial ATP production were elevated despite signs of mitochondrial dysfunction, such as an increased production of reactive oxygen species and extended mitochondrial mass. A shift from glycolytic to oxidative glucose metabolism induced by pharmacological inhibition of PDHKs in young endothelial cells resulted in premature senescence, suggesting that alterations in cellular glucose metabolism may act as a driving force for senescence in endothelial cells.

Cell-tak coating interferes with DNA-based normalization of metabolic flux data

Metabolic flux investigations of cells and tissue samples are a rapidly advancing tool in diverse research areas. Reliable methods of data normalization are crucial for an adequate interpretation of results and to avoid a misinterpretation of experiments and incorrect conclusions. The most common methods for metabolic flux data normalization are to cell number, DNA and protein. Data normalization may be affected by a variety of factors, such as density, healthy state, adherence efficiency, or proportional seeding of cells. The mussel-derived adhesive Cell-Tak is often used to immobilize poorly adherent cells. Here we demonstrate that this coating strongly affects the fluorescent detection of DNA leading to an incorrect and highly variable normalization of metabolic flux data. Protein assays are much less affected and cell counting can virtually completely remove the effect of the coating. Cell-Tak coating also affects cell shape in a cell line-specific manner and may change cellular metabolism. Based on these observations we recommend cell counting as a gold standard normalization method for Seahorse metabolic flux measurements with protein content as a reasonable alternative.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Advancements in Pulsed Stable Isotope-Resolved Metabolomics.

The understanding of biochemical processes of metabolism is gained through the measurement of the concentration of intermediates and the rate of metabolite conversion. However, the measurement of metabolite concentrations does not give a full representation of this dynamic system. To understand the kinetics of metabolism, the systemmust be described and quantified in terms of metabolite flow as a function of time. In order to measure the metabolite flow, or more precisely the metabolic flux through a biological system, substrates of the cell are labelled with stable isotopes. The usage of these substrates by the cell leads to the incorporation of the isotopes into downstream intermediates.The most important metabolic pathways are encompassed in the central carbon metabolism (CCM). According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the central carbon metabolism "is the most basic aspect of life". It includes all metabolites and enzymatic reactions within: glycolysis and gluconeogenesis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), amino acids and nucleotide metabolic pathways. Some molecules are at the crossroad of metabolic pathways, interconnecting diverse metabolic and therefore functional outcomes. Labelling these nodal metabolites and analysing their isotopic composition allows the precise determination of the metabolic flow within the biochemical networks that they are in.Application of stable isotope labelled substrates allows the measurement of metabolic flux through a biochemical pathway. The rapid turnover of metabolites in pathways requires pulse-feeding cells with a labelled substrate. This method allows for the determination of different cell states. For example, the action of a drug from immediate impact until the compensatory response of the metabolic system (cell, organs, organisms). Pulsed labelling is an elegant way to analyse the action of small molecules and drugs and enables the analysis of regulatory metabolic processes in short time scales.

Applicability and information value of biocalorimetry for the monitoring of fungal solid-state fermentation of lignocellulosic agricultural by-products

The applicability of biocalorimetry for monitoring fungal conversion of lignocellulosic agricultural by-products during solid-state fermentation (SSF) was substantiated through linking the non-invasive measurement of metabolic heat fluxes to conventional invasive determination of fungal activity (growth, substrate degradation, enzyme activity) parameters. For this, the fast-growing, cellulose-utilising ascomycete Stachybotrys chlorohalonata and the comparatively slow-growing litter-decay basidiomycete Stropharia rugosoannulata were investigated as model organisms during growth on solid wheat straw. Both biocalorimetric and non-calorimetric data may suggest R (ruderal)- and C (combative)-selected life history strategies in S. chlorohalonata and S. rugosoannulata, respectively. For both species, a strong linear correlation of the released metabolic heat with the corresponding fungal biomass was observed. Species-specific YQ/X values (metabolic heat released per fungal biomass unit) were obtained, which potentially enable use of biocalorimetric signals for the quantification of fungal biomass during single-species SSF processes. Moreover, YQ/X values may also indicate different fungal life history strategies and therefore be considered as useful parameters aiding fungal ecology research.

Coupling of activity, metabolism and behaviour across the Drosophila brain.

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species1-5. These global patterns alter energy metabolism over seconds to hours, which underpins the widespread use of oxygen consumption and glucose uptake as proxies of neural activity6,7. However, whether changes in neural activity are causally related to metabolic flux in intact circuits on the timescales associated with behaviour is unclear. Here we combine two-photon microscopy of the fly brain with sensors that enable the simultaneous measurement of neural activity and metabolic flux, across both resting and active behavioural states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across brain networks. Using local optogenetic perturbation, we demonstrate that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, which suggests that neuronal metabolism predictively allocates resources to anticipate the energy demands of future activity. Finally, our studies reveal that the initiation of even minimal behavioural movements causes large-scale changes in the pattern of neural activity and energy metabolism, which reveals a widespread engagement of the brain. As the relationship between neural activity and energy metabolism is probably evolutionarily ancient and highly conserved, our studies provide a critical foundation for using metabolic proxies to capture changes in neural activity.

Dynamic metabolic reprogramming in dendritic cells: An early response to influenza infection that is essential for effector function.

Infection with the influenza virus triggers an innate immune response that initiates the adaptive response to halt viral replication and spread. However, the metabolic response fueling the molecular mechanisms underlying changes in innate immune cell homeostasis remain undefined. Although influenza increases parasitized cell metabolism, it does not productively replicate in dendritic cells. To dissect these mechanisms, we compared the metabolism of dendritic cells to that of those infected with active and inactive influenza A virus and those treated with toll-like receptor agonists. Using quantitative mass spectrometry, pulse chase substrate utilization assays and metabolic flux measurements, we found global metabolic changes in dendritic cells 17 hours post infection, including significant changes in carbon commitment via glycolysis and glutaminolysis, as well as mitochondrial respiration. Influenza infection of dendritic cells led to a metabolic phenotype distinct from that induced by TLR agonists, with significant resilience in terms of metabolic plasticity. We identified c-Myc as one transcription factor modulating this response. Restriction of c-Myc activity or mitochondrial substrates significantly changed the immune functions of dendritic cells, such as reducing motility and T cell activation. Transcriptome analysis of inflammatory dendritic cells isolated following influenza infection showed similar metabolic reprogramming occurs in vivo. Thus, early in the infection process, dendritic cells respond with global metabolic restructuring, that is present in inflammatory lung dendritic cells after infection, and this is important for effector function. These findings suggest metabolic switching in dendritic cells plays a vital role in initiating the immune response to influenza infection.

Magnetic resonance spectroscopy in the rodent brain: Experts' consensus recommendations.

In vivo MRS is a non-invasive measurement technique used not only in humans, but also in animal models using high-field magnets. MRS enables the measurement of metabolite concentrations as well as metabolic rates and their modifications in healthy animals and disease models. Such data open the way to a deeper understanding of the underlying biochemistry, related disturbances and mechanisms taking place during or prior to symptoms and tissue changes. In this work, we focus on the main preclinical 1H, 31P and 13C MRS approaches to study brain metabolism in rodent models, with the aim of providing general experts' consensus recommendations (animal models, anesthesia, data acquisition protocols). An overview of the main practical differences in preclinical compared with clinical MRS studies is presented, as well as the additional biochemical information that can be obtained in animal models in terms of metabolite concentrations and metabolic flux measurements. The properties of high-field preclinical MRS and the technical limitations are also described.

Spectroscopic measurements of metabolic fluxes

The conversion of a deuterated substrate into cellular metabolites in vivo can be quantified via decreases in the proton nuclear magnetic resonance signals of these metabolites.

Plant sulphur metabolism is stimulated by photorespiration

Intense efforts have been devoted to describe the biochemical pathway of plant sulphur (S) assimilation from sulphate. However, essential information on metabolic regulation of S assimilation is still lacking, such as possible interactions between S assimilation, photosynthesis and photorespiration. In particular, does S assimilation scale with photosynthesis thus ensuring sufficient S provision for amino acids synthesis? This lack of knowledge is problematic because optimization of photosynthesis is a common target of crop breeding and furthermore, photosynthesis is stimulated by the inexorable increase in atmospheric CO2. Here, we used high-resolution 33S and 13C tracing technology with NMR and LC-MS to access direct measurement of metabolic fluxes in S assimilation, when photosynthesis and photorespiration are varied via the gaseous composition of the atmosphere (CO2, O2). We show that S assimilation is stimulated by photorespiratory metabolism and therefore, large photosynthetic fluxes appear to be detrimental to plant cell sulphur nutrition.

Different Dimensions in Microbial Community Adaptation and Function.

With the omics tool, the challenges in understanding the microbial community functions are becoming more intriguing. It is the environment created scenario, which demands alignment of the different members of the community for the desired output leading to common condition for their survival. The resultant community pathways provide a broad umbrella of metabolic options giving the desired plasticity, which playsdecision making role in the adaptation process. The initial step in community characterization must involve the discovery of key and core member of the community and monitoring the fluctuations in functional abundance over the space and time. The concept of entropy and metabolic fluxes must reflect the inner metabolic machinery of the taxon selection and route of functional operation in a community. The segregation of member based on their functional role and hierarchical level in the community must be an essential step to be followed by interaction mapping and measurement of metabolic fluxes to derive the flow of metabolites within the community. This conceptual framework and integrated omics tools with supported statistical modeling algorithm can help in bringing out finer details in the process of community functional adaptation in any given scenario.

Nuclear Magnetic Resonance Measurement of Metabolic Flux Using 13C and 1H Signals.

Metabolic flux modeling is a complementary technique to standard metabolomics/metabonomics as routinely practiced in biological systems. Here we outline carbon-13-based isotopomer analysis using nuclear magnetic resonance as the primary analytical technique. Isotopomer analysis produces relative rates of tricarboxylic acid cycle turnover. If these measures are normalized to O2 consumption, absolute rates can be inferred. The primary biological system targeted in this review is cell culture.

Characterization of Brain Metabolism by Nuclear Magnetic Resonance.

The noninvasive, quantitative ability of nuclear magnetic resonance (NMR) spectroscopy to characterize small molecule metabolites has long been recognized as a major strength of its application in biology. Numerous techniques exist for characterizing metabolism in living, excised, or extracted tissue, with a particular focus on 1 H-based methods due to the high sensitivity and natural abundance of protons. With the increasing use of high magnetic fields, the utility of in vivo 1 H magnetic resonance spectroscopy (MRS) has markedly improved for measuring specific metabolite concentrations in biological tissues. Higher fields, coupled with recent developments in hyperpolarization, also enable techniques for complimenting 1 H measurements with spectroscopy of other nuclei, such as 31 P and 13 C, and for combining measurements of metabolite pools with metabolic flux measurements. We compare ex vivo and in vivo methods for studying metabolism in the brain using NMR and highlight insights gained through using higher magnetic fields, the advent of dissolution dynamic nuclear polarization, and combining in vivo MRS and ex vivo NMR approaches.

Non-Canonical Control of Neuronal Energy Status by the Na+ Pump

Neurons have limited intracellular energy stores but experience acute and unpredictable increases in energy demand. To better understand how these cells repeatedly transit from a resting to active state without undergoing metabolic stress, we monitored their early metabolic response to neurotransmission using ion-sensitive probes and FRET sensors invitro and invivo. A short theta burst triggered immediate Na+ entry, followed by a delayed stimulation of the Na+/K+ ATPase pump. Unexpectedly, cytosolic ATP and ADP levels were unperturbed across a wide range of physiological workloads, revealing strict flux coupling between the Na+ pump and mitochondria. Metabolic flux measurements revealed a "priming" phase of mitochondrial energization by pyruvate, whereas glucose consumption coincided with delayed Na+ pump stimulation. Experiments revealed that the Na+ pump plays a permissive role for mitochondrial ATP production and glycolysis. We conclude that neuronal energy homeostasis is not mediated by adenine nucleotides or by Ca2+, but by a mechanism commanded by the Na+ pump.

Computational prediction of changes in brain metabolic fluxes during Parkinson's disease from mRNA expression.

BackgroundParkinson’s disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain.ResultsWe use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer’s disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.ConclusionOur method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson’s disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

A 13C isotope labeling method for the measurement of lignin metabolic flux in Arabidopsis stems

Background Metabolic fluxes represent the functional phenotypes of biochemical pathways and are essential to reveal the distribution of precursors among metabolic networks. Although analysis of metabolic fluxes, facilitated by stable isotope labeling and mass spectrometry detection, has been applied in the studies of plant metabolism, we lack experimental measurements for carbon flux towards lignin, one of the most abundant polymers in nature.ResultsWe developed a feeding strategy of excised Arabidopsis stems with 13C labeled phenylalanine (Phe) for the analysis of lignin biosynthetic flux. We optimized the feeding methods and found the stems continued to grow and lignify. Consistent with lignification profiles along the stems, higher levels of phenylpropanoids and activities of lignin biosynthetic enzymes were detected in the base of the stem. In the feeding experiments, 13C labeled Phe was quickly accumulated and used for the synthesis of phenylpropanoid intermediates and lignin. The intermediates displayed two different patterns of labeling kinetics during the feeding period. Analysis of lignin showed rapid incorporation of label into all three subunits in the polymers.ConclusionsOur feeding results demonstrate the effectiveness of the stem feeding system and suggest a potential application for the investigations of other aspects in plant metabolism. The supply of exogenous Phe leading to a higher lignin deposition rate indicates the availability of Phe is a determining factor for lignification rates.

Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering.

Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13C Metabolic Flux Analysis, and 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) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.

Increased Glutaminolytic Flux and Activation of Mitochondrial Metabolism by BCL2 Hyperactivity in Lymphoma

B-cell lymphoma 2 (BCL2) is an important apoptosis regulator during developmental and pathological states, and its overexpression is a key feature of several malignancies. Genomic data from The Cancer Genome Atlas (TCGA) reveals significant somatic copy number amplification, overexpression, and/or elevated protein activity of BCL2 in 50% of diffuse large B-cell lymphoma (DLBC) patients. While its canonical role in mitochondria-directed apoptosis is well established, the effect of BCL2 on transcriptional and metabolic networks remains elusive. Using an established lymphocytic pro-B-cell line overexpressing BCL2, we identified dysregulated transcriptional and metabolic networks by transcriptomic profiling arrays. Elevated BCL2 levels affect transcription factor complexes and mitogenic programs of NF-κB/REL, HIF1A/ARNT, AP1, E2F, and STAT factors. Using stable isotope-assisted metabolic flux measurements we quantify that elevated BCL2 expression increases carbon utilization boosting cellular proliferation. Tumorigenic overexpression of BCL2 significantly increases glycolytic flux, glutaminolysis, and anaplerotic flux into the TCA cycle. At the same time, the mitochondrial acetyl-CoA pool is separated from the glycolytic one by inactivating the pyruvate dehydrogenase complex via transcriptional regulation of pyruvate dehydrogenase kinase (PDK3). As compensatory fuel, mitochondrial TCA cycle metabolism is supported by asparagine synthase (ASNS) and oxidative glutaminolysis creating targets for small molecule inhibition of glutaminase. Lymphoma cells overexpressing BCL2 contained more mitochondrial mass and were more sensitive to L-glutamine deprivation and glutaminase inhibition. Cells overexpressing a mutant BCL2 G145E, which is incapable of binding BH domain members, failed to increase proliferation, glycolysis, or glutaminolysis. Taken together, the oncogene BCL2 has the ability to ramp up a metabolic phenotype supporting proliferation independent of its anti-apoptotic role. The cellular model of BCL2 activation supports NF-κB-positive subtypes of DLBC and identifies metabolic bottlenecks with dependency on anaplerotic flux as an actionable BCL2 effector network in cancer.