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.

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.

Metabolic Flux Analysis Reveals the Roles of Stearate and Oleate on CPT1C-mediated Tumor Cell Senescence.

Cellular senescence is a state of proliferative arrest, and the development of carcinoma can be suppressed by conferring tumor cell senescence. Recently, we found that carnitine palmitoyltransferase 1C (CPT1C) controls tumor cell proliferation and senescence via regulating lipid metabolism and mitochondrial function. Here, 13C-metabolic flux analysis (13C-MFA) was performed and the results revealed that CPT1C knockdown in MDA-MB-231 cells significantly induced cellular senescence accompanied by altered fatty acid metabolism. Strikingly, stearate synthesis was decreased while oleate was increased. Furthermore, stearate significantly inhibited proliferation while oleate reversed the senescent phenotype induced by silencing CPT1C in MDA-MB-231 cells as well as PANC-1 cells. A939572, an inhibitor of stearoyl-Coenzyme A desaturase 1, had the same effect as stearate to inhibit cellular proliferation. These results demonstrated that stearate and oleate are involved in CPT1C-mediated tumor cellular senescence, and the regulation of stearate/oleate rate via inhibition of SCD-1 could be an additional strategy with depletion of CPT1C for cancer therapy.

Effect of light fluctuations on photosynthesis and metabolic flux in Synechocystis sp. PCC 6803.

In nature, photosynthetic organisms are exposed to fluctuating light, and their physiological systems must adapt to this fluctuation. To maintain homeostasis, these organisms have a light fluctuation photoprotective mechanism, which functions in both photosystems and metabolism. Although the photoprotective mechanisms functioning in the photosystem have been studied, it is unclear how metabolism responds to light fluctuations within a few seconds. In the present study, we investigated the metabolic response of Synechocystis sp. PCC 6803 to light fluctuations using 13 C-metabolic flux analysis. The light intensity and duty ratio were adjusted such that the total number of photons or the light intensity during the low-light phase was equal. Light fluctuations affected cell growth and photosynthetic activity under the experimental conditions. However, metabolic flux distributions and cofactor production rates were not affected by the light fluctuations. Furthermore, the estimated ATP and NADPH production rates in the photosystems suggest that NADPH-consuming electron dissipation occurs under fluctuating light conditions. Although we focused on the water-water cycle as the electron dissipation path, no growth effect was observed in an flv3-disrupted strain under fluctuating light, suggesting that another path contributes to electron dissipation under these conditions.

13C metabolic flux analysis clarifies distinct metabolic phenotypes of cancer cell spheroid mimicking tumor hypoxia.

Cancer cells adapt their intracellular energy metabolism to the oxygen-deprived tumor microenvironment (TME) to ensure tumor progression. This adaptive mechanism has focused attention on the metabolic phenotypes of tumor cells under hypoxic TME for developing novel cancer therapies. Although widely used monolayer (2D) culture does not fully reflect in vivo hypoxic TME, spheroid (3D) culture can produce a milieu similar to the TME in vivo. However, how different metabolic phenotypes are expressed in 3D cultures mimicking tumor hypoxia compared with 2D cultures under hypoxia remains unclear. To address this issue, we investigated the metabolic phenotypes of 2D- and 3D-cultured cancer cells by 13C-metabolic flux analysis (13C-MFA). Principal component analysis of 13C mass isotopomer distributions clearly demonstrated distinct metabolic phenotypes of 3D-cultured cells. 13C-MFA clarified that 3D culture significantly upregulated pyruvate carboxylase flux in line with the pyruvate carboxylase protein expression level. On the other hand, 3D culture downregulated glutaminolytic flux. Consistent with our findings, 3D-cultured cells are more resistant to a glutaminase inhibitor than 2D-cultured cells. This study suggests the importance of considering the metabolic characteristics of the particular in vitro model used for research on cancer metabolism.

Isotope tracing in health and disease.

Biochemical characterization of metabolism provides molecular insights for understanding biology in health and disease. Over the past decades, metabolic perturbations have been implicated in cancer, neurodegeneration, and diabetes, among others. Isotope tracing is a technique that allows tracking of labeled atoms within metabolites through biochemical reactions. This technique has become an integral component of the contemporary metabolic research. Isotope tracing measures substrate contribution to downstream metabolites and indicates its utilization in cellular metabolic networks. In addition, isotopic labeling data are necessary for quantitative metabolic flux analysis. Here, we review recent work utilizing metabolic tracing to study health and disease, and highlight its application to interrogate subcellular, intercellular, and in vivo metabolism. We further discuss the current challenges and opportunities to expand the utility of isotope tracing to new research areas.

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

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

13C-metabolic flux analysis of Clostridium ljungdahlii illuminates its core metabolism under mixotrophic culture conditions.

Carbon dioxide-fixing acetogenic bacteria (acetogens) utilizing the Wood-Ljungdahl Pathway (WLP) play an important role in CO2 fixation in the biosphere and in the development of biological processes - alone or in cocultures, under both autotrophic and mixotrophic conditions - for production of chemicals and fuels. To date, limited work has been reported in experimentally validating and quantifying reaction fluxes of their core metabolic pathways. Here, the core metabolic model of the acetogen Clostridium ljungdahlii was interrogated using 13C-metabolic flux analysis (13C-MFA), which required the development of a new defined culture medium. Autotrophic, heterotrophic, and mixotrophic growth in defined medium was possible by adding 1 mM methionine to replace yeast extract. Our 13C-MFA found an incomplete TCA cycle and inactive core pathways/reactions, notably those of the oxidative pentose phosphate pathway, Entner-Doudoroff pathway, and malate dehydrogenase. 13C-MFA during mixotrophic growth using the parallel tracers [1-13C]fructose, [1,2-13C]fructose, [1,2,3-13C]fructose, and [U-13C]asparagine found that externally supplied CO2 contributed the majority of carbon consumed. All internally-produced CO2 from the catabolism of asparagine and fructose was consumed by the WLP. While glycolysis of fructose was active, it was not a major contributor to overall production of ATP, NADH, and acetyl-CoA. Gluconeogenic reactions were active despite the availability of organic carbon. Asparagine was catabolized equally via conversion to threonine and subsequent cleavage to produce acetaldehyde and glycine, and via deamination to fumarate and then the anaplerotic conversion of malate to pyruvate. Both pathways for asparagine catabolism produced acetyl-CoA, either directly via pyruvate or indirectly via the WLP. Cofactor stoichiometry based on our data predicted an essentially zero flux through the ferredoxin-dependent transhydrogenase (Nfn) reaction. Instead, nearly all of NADPH generated from the hydrogenase reaction was consumed by the WLP. Reduced ferredoxin produced by the hydrogenase reaction and glycolysis was mostly used for ATP generation via the RNF/ATPase system, with the remainder consumed by the WLP. NADH produced by RNF/ATPase was entirely consumed via the WLP.

Comparative 13 C-metabolic flux analysis indicates elevation of ATP regeneration, carbon dioxide, and heat production in industrial Saccharomyces cerevisiae strains.

BACKGROUND: Various industrial Saccharomyces cerevisiae strains are used for specific processes, such as sake, wine brewing and bread making. Understanding mechanisms underlying the fermentation performance of these strains would be useful for further engineering of the S. cerevisiae metabolism. However, the relationship between the fermentation performance, intra-cellular metabolic states, and other phenotypic characteristics of industrial yeasts is still unclear. In this study, 13 C-metabolic flux analysis of four diploid yeast strains-laboratory, sake, bread, and wine yeasts-was conducted. RESULTS: While the Crabtree effect was observed for all strains, the metabolic flux level of glycolysis was elevated in bread and sake yeast. Furthermore, increased flux levels of the TCA cycle were commonly observed in the three industrial strains. The specific rates of CO2 production, net ATP regeneration, and metabolic heat generation estimated from the metabolic flux distribution were two to three times greater than those of the laboratory strain. The elevation in metabolic heat generation was correlated with the tolerance to low-temperature stress. CONCLUSION: These results indicate that the metabolic flux distribution of sake and bread yeast strains contributes to faster production of ethanol and CO2 . It is also suggested that the generation of metabolic heat is preferable under the actual industrial fermentation conditions.

Metabolic flux analysis of the non-transitory starch tradeoff for lipid production in mature tobacco leaves.

The metabolic plasticity of tobacco leaves has been demonstrated via the generation of transgenic plants that can accumulate over 30% dry weight as triacylglycerols. In investigating the changes in carbon partitioning in these high lipid-producing (HLP) leaves, foliar lipids accumulated stepwise over development. Interestingly, non-transient starch was observed to accumulate with plant age in WT but not HLP leaves, with a drop in foliar starch concurrent with an increase in lipid content. The metabolic carbon tradeoff between starch and lipid was studied using 13CO2-labeling experiments and isotopically nonstationary metabolic flux analysis, not previously applied to the mature leaves of a crop. Fatty acid synthesis was investigated through assessment of acyl-acyl carrier proteins using a recently derived quantification method that was extended to accommodate isotopic labeling. Analysis of labeling patterns and flux modeling indicated the continued production of unlabeled starch, sucrose cycling, and a significant contribution of NADP-malic enzyme to plastidic pyruvate production for the production of lipids in HLP leaves, with the latter verified by enzyme activity assays. The results suggest an inherent capacity for a developmentally regulated carbon sink in tobacco leaves and may in part explain the uniquely successful leaf lipid engineering efforts in this crop.

Investigating the K+ sensitivity of cellular metabolism by extracellular flux analysis.

We have recently demonstrated that the activity of hexokinase 2 is dependent on the intracellular potassium ion (K+) concentration ([K+]). To analyze the K+ dependency of the cell metabolism in cell populations, we used an extracellular flux analyzer to assess oxygen consumption and acidification rates as well-established measures of oxidative- and glycolytic metabolic activities. This protocol describes in detail how a potential K+ sensitivity of the cell metabolism can be elucidated by extracellular flux analysis. For complete details on the use and execution of this protocol, please refer to Bischof et al. (2021).

Contribution of branched chain amino acids to energy production and mevalonate synthesis in cancer cells.

Leucine, isoleucine and valine, known as branched chain amino acids (BCAAs), have been reported to be degraded by different cancer cells, and their biodegradation pathways have been suggested as anticancer targets. However, the mechanisms by which the degradation of BCAAs could support the growth of cancer cells remains unclear. In this work, 13C experiments have been carried out in order to elucidate the metabolic role of BCAA degradation in two breast cancer cell lines (MCF-7 and BCC). The results revealed that up to 36% of the energy production via respiration by MCF-7 cells was supported by the degradation of BCAAs. Also, 67% of the mevalonate (the precursor of cholesterol) synthesized by the cells was coming from the degradation of leucine. The results were lower for BCC cells (14 and 30%, respectively). The non-tumorigenic epythelial cell line MCF-10A was used as a control, showing that 10% of the mitochondrial acetyl-CoA comes from the degradation of BCAAs and no mevalonate production. Metabolic flux analysis around the mevalonate node, also revealed that significant amounts of acetoacetate are being produced from BCAA derived carbon, which could be at the source of lipid synthesis. From these results we can conclude that the degradation of BCAAs is an important energy and carbon source for the proliferation of some cancer cells and its therapeutic targeting could be an interesting option.

Overexpression of MRP3 in HeLa-UGT1A9 Cells Enhances Glucuronidation Capability of the Cells.

BACKGROUND: The interplay between phase II enzymes and efflux transporters leads to extensive metabolism and low systemic bioavailability of flavonoids. OBJECTIVE: In this study, the dynamic interplay between multiple UGTs and multiple efflux transporters that occur inside the cells was fully investigated. METHODS: A new HeLa-UGT1A9-MRP3 cell was established to overexpress two dominant efflux transporters MRP3 and BCRP, and two UGT isoforms UGT1A9 and UGT1A3. The metabolism and glucuronides excretion for a model flavonoid genistein were determined in HeLa-UGT1A9-MRP3 cells and HeLa-UGT1A9-Con cells that overexpressed one UGT (1A9) and one efflux transporter (BCRP). RESULTS: The excretion rate grew nearly 6-fold, cellular clearance of glucuronides increased about 3-fold, and fraction of genistein metabolized (fmet) increased (14%, p<0.01) in the new cells. Small interfering (siRNA)-mediated MRP3 functional knockdown resulted in marked decreases in the excretion rates (26%-78%), intracellular amounts (56%-93%), and cellular clearance (54%-96%) in both cells, but the magnitude of the differences in HeLa- UGT1A9-Con cells was relatively small. Reductions in fmet values were similarly moderate (11%-14%). In contrast, UGT1A9 knockdown with siRNA caused large decreases in the excretion rates (46%-88%), intracellular amounts (80%-97%), cellular clearance (80%-98%) as well as fmet value (33%-43%, p<0.01) in both UGT1A9 cells. Comparisons of the kinetic parameters and profiles of genistein glucuronidation as well as UGT mRNA expression suggest that HeLa-UGT1A9-MRP3 has increased expression of both MRP3 and UGT1A3. CONCLUSION: The newly engineered HeLa-UGT1A9-MRP3 cells is an appropriate model to study the kinetic interplay between multiple UGTs and efflux transporters, and a promising biosynthetic tool to obtain flavonoid glucuronides of high purity.

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations.

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.

Maternal metabolic health drives mesenchymal stem cell metabolism and infant fat mass at birth.

Exposure to maternal obesity may promote metabolic dysfunction in offspring. We used infant mesenchymal stem cells (MSCs) to experimentally examine cellular mechanisms of intergenerational health transmission. Our earlier reports show MSCs collected from infants of mothers with obesity had a dichotomous distribution in metabolic efficiency; they were either efficient (Ef-Ob) or inefficient (In-Ob) with respect to fatty acid oxidation (FAO). Here, we sought to determine if this was due to a primary defect in FAO. Accordingly, we measured FAO in myogenic differentiating MSCs under 3 conditions: (a) myogenesis alone, (b) excess fatty acid exposure, and (c) excess fatty acid exposure plus a chemical uncoupler to increase metabolic rate. Compared with normal weight and Ef-Ob MSCs, In-Ob displayed lower FAO in myogenesis alone and after fatty acid plus uncoupler, indicating In-Ob were less metabolically flexible after increasing lipid availability and metabolic rate, demonstrating a primary deficit in FAO. MSC FAO was negatively associated with fasting maternal glucose and insulin and positively associated with fasting HDL-cholesterol. MSC FAO was negatively associated with infant fat mass. These data indicate a less favorable maternal metabolic milieu, independent of maternal BMI, reduces intrinsic MSC FAO and is linked to higher infant adiposity as early as birth.

Metabolic analysis of mouse bone-marrow-derived dendritic cells using an extracellular flux analyzer.

Dendritic cell (DC) maturation induced by Toll-like receptor (TLR) agonists requires the activation of downstream metabolic changes. Here, we provide a detailed protocol to measure glycolysis, mitochondrial respiration, and fatty acid oxidation in mouse bone-marrow-derived DCs with the Seahorse XF24 extracellular flux (XF) analyzer. XF analysis with the Seahorse bioanalyzer has become a standard method to measure bioenergetic functions in cells, and this protocol can be adapted to other immune cells. For complete information on using this protocol, please refer to Gotoh et al. (2018).

Quantifying H+ exchange from muscle cytosolic energy catabolism using metabolite flux and H+ coefficients from multiple competitive cation binding: New evidence for consideration in established theories.

The purpose of this investigation was to present calculations of fractional H+ exchange (~H+e ) from the chemical reactions of non-mitochondrial energy catabolism. Data of muscle pH and metabolite accumulation were based on published research for intense exercise to contractile failure within ~3 min, from which capacities and time profiles were modeled. Data were obtained from prior research for multiple competitive cation dissociation constants of metabolites and the chemical reactions of non-mitochondrial energy catabolism, and pH dependent calculations of ~H+e from specific chemical reactions. Data revealed that the 3 min of intense exercise incurred a total ATP turnover of 142.5 mmol L-1 , with a total intramuscular ~H+ exchange (-'ve = release) of -187.9 mmol L-1 . Total ~H+ metabolic consumption was 130.6 mmol L-1 , revealing a net total ~H+e (~H+te ) of -57.3 mmol L-1 . Lactate production had a ~H+te of 44.2 mmol L-1 (for a peak accumulation = 45 mmol L-1 ). The net ~H+te for the sum of the CK, AK, and AMPD reactions was 36.33 mmol L-1 . The ~H+te from ATP turnover equaled -47.5 mmol L-1 . The total ~H+ release to lactate ratio was 4.3 (187.9/44). Muscle ~H+ release during intense exercise is up to ~4-fold larger than previously assumed based on the lactic acid construct.

Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection.

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.

Measurement of Metabolites from Migrating Cells.

Metastasis is a multistep process that involves responses to extrinsic and intrinsic signals at every step. It is thus only truly appreciated in the context of a whole organism. Nevertheless, in vitro studies can be used to facilitate understanding of the possible factors contributing to any phenotype that is associated with metastatic competence. The use of migration assays-where monolayers of cells migrate to cover gaps or "wounds"-has been described for decades to identify signaling pathways that regulate motile competence and to screen for ways of interfering with this ability. Here we depict the combination of such an assay with assessment of indicators of carbon metabolism using commercially available assays. This enables identification of changes in cellular metabolism associated with actively migrating cells.

Fatty Acid-Driven Polarization of Suppressive Bone Marrow-Derived Macrophages Including Metabolic and Functional Analysis.

Macrophages represent not only the first line of defense against pathogens and are the main drivers of inflammation but are also involved in the initiation, immune evasion as well as metastasis of tumors. Therefore, it has been suggested that diminishing the immune regulatory function of macrophages would support the natural immune surveillance or antitumor therapies, respectively. However, the plasticity of macrophages represents an obstacle in understanding and manipulating the role of macrophages in tumor tissue or the tumor microenvironment. Here, we describe a protocol to differentiate macrophages, based on changing their metabolic environment, from bone marrow precursors to tumor-associated macrophage-like cells of an immune suppressive phenotype. Based on these protocols, the inhibitory functional phenotype of macrophages can be manipulated and therefore further analyzed as described, by interrupting metabolic pathways.