Lactate Metabolism in Human Lung Tumors.

Cancer cells consume glucose and secrete lactate in culture. It is unknown whether lactate contributes to energy metabolism in living tumors. We previously reported that human non-small-cell lung cancers (NSCLCs) oxidize glucose in the tricarboxylic acid (TCA) cycle. Here, we show that lactate is also a TCA cycle carbon source for NSCLC. In human NSCLC, evidence of lactate utilization was most apparent in tumors with high 18fluorodeoxyglucose uptake and aggressive oncological behavior. Infusing human NSCLC patients with 13C-lactate revealed extensive labeling of TCA cycle metabolites. In mice, deleting monocarboxylate transporter-1 (MCT1) from tumor cells eliminated lactate-dependent metabolite labeling, confirming tumor-cell-autonomous lactate uptake. Strikingly, directly comparing lactate and glucose metabolism in vivo indicated that lactate's contribution to the TCA cycle predominates. The data indicate that tumors, including bona fide human NSCLC, can use lactate as a fuel in vivo.

Lactate is an epigenetic metabolite that drives survival in model systems of glioblastoma.

Lactate accumulates to a significant amount in glioblastomas (GBMs), the most common primary malignant brain tumor with an unfavorable prognosis. However, it remains unclear whether lactate is metabolized by GBMs. Here, we demonstrated that lactate rescued patient-derived xenograft (PDX) GBM cells from nutrient-deprivation-mediated cell death. Transcriptome analysis, ATAC-seq, and ChIP-seq showed that lactate entertained a signature of oxidative energy metabolism. LC/MS analysis demonstrated that U-13C-lactate elicited substantial labeling of TCA-cycle metabolites, acetyl-CoA, and histone protein acetyl-residues in GBM cells. Lactate enhanced chromatin accessibility and histone acetylation in a manner dependent on oxidative energy metabolism and the ATP-citrate lyase (ACLY). Utilizing orthotopic PDX models of GBM, a combined tracer experiment unraveled that lactate carbons were substantially labeling the TCA-cycle metabolites. Finally, pharmacological blockage of oxidative energy metabolism extended overall survival in two orthotopic PDX models in mice. These results establish lactate metabolism as a novel druggable pathway for GBM.

Combinatorial CRISPR-Cas9 Metabolic Screens Reveal Critical Redox Control Points Dependent on the KEAP1-NRF2 Regulatory Axis.

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway (PPP). This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts was validated using 13C and 2H isotope tracing, and these assays together revealed key nodes controlling redox homeostasis along the KEAP-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP genes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin-Benson cycle explains photosynthesis labeling anomalies.

SignificancePhotosynthesis metabolites are quickly labeled when 13CO2 is fed to leaves, but the time course of labeling reveals additional contributing processes involved in the metabolic dynamics of photosynthesis. The existence of three such processes is demonstrated, and a metabolic flux model is developed to explore and characterize them. The model is consistent with a slow return of carbon from cytosolic and vacuolar sugars into the Calvin-Benson cycle through the oxidative pentose phosphate pathway. Our results provide insight into how carbon assimilation is integrated into the metabolic network of photosynthetic cells with implications for global carbon fluxes.

Pharmacological SERCA activation limits diet-induced steatohepatitis and restores liver metabolic function in mice.

Metabolic dysfunction-associated steatotic liver disease is the most common form of liver disease and poses significant health risks to patients who progress to metabolic dysfunction-associated steatohepatitis. Fatty acid overload alters endoplasmic reticulum (ER) calcium stores and induces mitochondrial oxidative stress in hepatocytes, leading to hepatocellular inflammation and apoptosis. Obese mice have impaired liver sarco/ER Ca2+-ATPase (SERCA) function, which normally maintains intracellular calcium homeostasis by transporting Ca2+ ions from the cytoplasm to the ER. We hypothesized that restoration of SERCA activity would improve diet-induced steatohepatitis in mice by limiting ER stress and mitochondrial dysfunction. WT and melanocortin-4 receptor KO (Mc4r-/-) mice were placed on either chow or Western diet (WD) for 8 weeks. Half of the WD-fed mice were administered CDN1163 to activate SERCA, which reduced liver fibrosis and inflammation. SERCA activation also restored glucose tolerance and insulin sensitivity, improved histological markers of metabolic dysfunction-associated steatohepatitis, increased expression of antioxidant enzymes, and decreased expression of oxidative stress and ER stress genes. CDN1163 decreased hepatic citric acid cycle flux and liver pyruvate cycling, enhanced expression of mitochondrial respiratory genes, and shifted hepatocellular [NADH]/[NAD+] and [NADPH]/[NADP+] ratios to a less oxidized state, which was associated with elevated PUFA content of liver lipids. In sum, the data demonstrate that pharmacological SERCA activation limits metabolic dysfunction-associated steatotic liver disease progression and prevents metabolic dysfunction induced by WD feeding in mice.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802.

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

Tubular dysfunction impairs renal excretion of pseudouridine in diabetic kidney disease.

Plasma nucleosides-pseudouridine (PU) and N2N2-dimethyl guanosine (DMG) predict the progression of type 2 diabetic kidney disease (DKD) to end-stage renal disease, but the mechanisms underlying this relationship are not well understood. We used a well-characterized model of type 2 diabetes (db/db mice) and control nondiabetic mice (db/m mice) to characterize the production and excretion of PU and DMG levels using liquid chromatography-mass spectrometry. The fractional excretion of PU and DMG was decreased in db/db mice compared with control mice at 24 wk before any changes to renal function. We then examined the dynamic changes in nucleoside metabolism using in vivo metabolic flux analysis with the injection of labeled nucleoside precursors. Metabolic flux analysis revealed significant decreases in the ratio of urine-to-plasma labeling of PU and DMG in db/db mice compared with db/m mice, indicating significant tubular dysfunction in diabetic kidney disease. We observed that the gene and protein expression of the renal tubular transporters involved with nucleoside transport in diabetic kidneys in mice and humans was reduced. In conclusion, this study strongly suggests that tubular handling of nucleosides is altered in early DKD, in part explaining the association of PU and DMG with human DKD progression observed in previous studies.NEW & NOTEWORTHY Tubular dysfunction explains the association between the nucleosides pseudouridine and N2N2-dimethyl guanosine and diabetic kidney disease.

Exercise training adaptations in liver glycogen and glycerolipids require hepatic AMP-activated protein kinase in mice.

Regular exercise elicits adaptations in glucose and lipid metabolism that allow the body to meet energy demands of subsequent exercise bouts more effectively and mitigate metabolic diseases including fatty liver. Energy discharged during the acute exercise bouts that comprise exercise training may be a catalyst for liver adaptations. During acute exercise, liver glycogenolysis and gluconeogenesis are accelerated to supply glucose to working muscle. Lower liver energy state imposed by gluconeogenesis and related pathways activates AMP-activated protein kinase (AMPK), which conserves ATP partly by promoting lipid oxidation. This study tested the hypothesis that AMPK is necessary for liver glucose and lipid adaptations to training. Liver-specific AMPKα1α2 knockout (AMPKα1α2fl/fl+AlbCre) mice and littermate controls (AMPKα1α2fl/fl) completed sedentary and exercise training protocols. Liver nutrient fluxes were quantified at rest or during acute exercise following training. Liver metabolites and molecular regulators of metabolism were assessed. Training increased liver glycogen in AMPKα1α2fl/fl mice, but not in AMPKα1α2fl/fl+AlbCre mice. The inability to increase glycogen led to lower glycogenolysis, glucose production, and circulating glucose during acute exercise in trained AMPKα1α2fl/fl+AlbCre mice. Deletion of AMPKα1α2 attenuated training-induced declines in liver diacylglycerides. In particular, training lowered the concentration of unsaturated and elongated fatty acids comprising diacylglycerides in AMPKα1α2fl/fl mice, but not in AMPKα1α2fl/fl+AlbCre mice. Training increased liver triacylglycerides and the desaturation and elongation of fatty acids in triacylglycerides of AMPKα1α2fl/fl+AlbCre mice. These lipid responses were independent of differences in tricarboxylic acid cycle fluxes. In conclusion, AMPK is required for liver training adaptations that are critical to glucose and lipid metabolism.NEW & NOTEWORTHY This study shows that the energy sensor and transducer, AMP-activated protein kinase (AMPK), is necessary for an exercise training-induced: 1) increase in liver glycogen that is necessary for accelerated glycogenolysis during exercise, 2) decrease in liver glycerolipids independent of tricarboxylic acid (TCA) cycle flux, and 3) decline in the desaturation and elongation of fatty acids comprising liver diacylglycerides. The mechanisms defined in these studies have implications for use of regular exercise or AMPK-activators in patients with fatty liver.

Engineering Escherichia coli for efficient glutathione production.

Glutathione is a tripeptide of excellent value in the pharmaceutical, food, and cosmetic industries that is currently produced during yeast fermentation. In this case, glutathione accumulates intracellularly, which hinders high production. Here, we engineered Escherichia coli for the efficient production of glutathione. A total of 4.3 g/L glutathione was produced by overexpressing gshA and gshB, which encode cysteine glutamate ligase and glutathione synthetase, respectively, and most of the glutathione was excreted into the culture medium. Further improvements were achieved by inhibiting degradation (Δggt and ΔpepT); deleting gor (Δgor), which encodes glutathione oxide reductase; attenuating glutathione uptake (ΔyliABCD); and enhancing cysteine production (PompF-cysE). The engineered strain KG06 produced 19.6 g/L glutathione after 48 h of fed-batch fermentation with continuous addition of ammonium sulfate as the sulfur source. We also found that continuous feeding of glycine had a crucial role for effective glutathione production. The results of metabolic flux and metabolomic analyses suggested that the conversion of O-acetylserine to cysteine is the rate-limiting step in glutathione production by KG06. The use of sodium thiosulfate largely overcame this limitation, increasing the glutathione titer to 22.0 g/L, which is, to our knowledge, the highest titer reported to date in the literature. This study is the first report of glutathione fermentation without adding cysteine in E. coli. Our findings provide a great potential of E. coli fermentation process for the industrial production of glutathione.

Elucidation of triacylglycerol catabolism in Yarrowia lipolytica: How cells balance acetyl-CoA and excess reducing equivalents.

Yarrowia lipolytica is an industrial yeast that can convert waste oil to value-added products. However, it is unclear how this yeast metabolizes lipid feedstocks, specifically triacylglycerol (TAG) substrates. This study used 13C-metabolic flux analysis (13C-MFA), genome-scale modeling, and transcriptomics analyses to investigate Y. lipolytica W29 growth with oleic acid, glycerol, and glucose. Transcriptomics data were used to guide 13C-MFA model construction and to validate the 13C-MFA results. The 13C-MFA data were then used to constrain a genome-scale model (GSM), which predicted Y. lipolytica fluxes, cofactor balance, and theoretical yields of terpene products. The three data sources provided new insights into cellular regulation during catabolism of glycerol and fatty acid components of TAG substrates, and how their consumption routes differ from glucose catabolism. We found that (1) over 80% of acetyl-CoA from oleic acid is processed through the glyoxylate shunt, a pathway that generates less CO2 compared to the TCA cycle, (2) the carnitine shuttle is a key regulator of the cytosolic acetyl-CoA pool in oleic acid and glycerol cultures, (3) the oxidative pentose phosphate pathway and mannitol cycle are key routes for NADPH generation, (4) the mannitol cycle and alternative oxidase activity help balance excess NADH generated from ß-oxidation of oleic acid, and (5) asymmetrical gene expressions and GSM simulations of enzyme usage suggest an increased metabolic burden for oleic acid catabolism.

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference.

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

One-shot 13 C15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux.

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed 13 C15 N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based 13 C15 N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics.

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13 C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13 C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13 C-based proteomics for uncovering microbial interactions.

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

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

Elevated nutrient availability enhances chondrocyte metabolism and biosynthesis in tissue-engineered cartilage.

OBJECTIVE: Chondrocytes, which typically rely on anaerobic metabolism, exhibit upregulated biosynthetic activity when subjected to conditions that elicit mixed aerobic-anaerobic metabolism. Previously, we observed that increasing media volume resulted in the transition from anaerobic to mixed aerobic-anaerobic metabolism. Maximal extracellular matrix (ECM) accumulation occurred at this transition as a result of changes in hypoxia-inducible factor 1α signaling and associated hypoxic gene expression. This study aimed to explore the effect of further increases in media availability on ECM synthesis and chondrocyte metabolism. METHODS: Primary bovine chondrocytes were grown in 3D high-density tissue culture under varying levels of media availability (4-16 mL/106 cells). Changes in ECM accumulation and metabolism were determined through biochemical assays and 13C-metabolic flux analysis (13C-MFA). RESULTS: Increasing media volumes resulted in higher accumulation of cartilaginous ECM (collagen and proteoglycans) and cellularity. Extracellular metabolite measurements revealed that elevated media availability led to increased glucose and glutamine metabolism, along with increased anaerobic activity. 13C-MFA utilizing [U-13C] glucose demonstrated that increased media availability significantly impacted central carbon metabolism, upregulating all glucose-related metabolic pathways (glycolysis, lactate fermentation, the tricarboxylic acid (TCA) cycle, hexosamine biosynthetic pathway, and the malate-aspartate shuttle). Furthermore, 13C-MFA indicated that glutamine was donating carbons to the TCA cycle, and additional studies involving [U-13C] glutamine tracing supported this notion. CONCLUSIONS: Elevated media availability upregulates ECM synthesis and leads to significant changes in metabolic phenotype. Glutamine plays an important role in chondrocyte metabolism and increases in glutamine metabolism correlate with increases in ECM accumulation.

Disproportionate Carbon Dioxide Efflux in Bacterial Metabolic Pathways for Different Organic Substrates Leads to Variable Contribution to Carbon-Use Efficiency.

Microbial organic matter turnover is an important contributor to the terrestrial carbon dioxide (CO2) budget. Partitioning of organic carbons into biomass relative to CO2 efflux, termed carbon-use efficiency (CUE), is widely used to characterize organic carbon cycling by soil microorganisms. Recent studies challenge proposals of CUE dependence on the oxidation state of the substrate carbon and implicate instead metabolic strategies. Still unknown are the metabolic mechanisms underlying variability in CUE. We performed a multiomics investigation of these mechanisms in Pseudomonas putida, a versatile soil bacterium of the Gammaproteobacteria, processing a mixture of plant matter derivatives. Our 13C-metabolomics data captured substrate carbons into different metabolic pathways: cellulose-derived sugar carbons in glycolytic and pentose-phosphate pathways; lignin-related aromatic carbons in the tricarboxylic acid cycle. Subsequent 13C-metabolic flux analysis revealed a 3-fold lower investment of sugar carbons in CO2 efflux compared to aromatic carbons, in agreement with reported substrate-dependent CUE. Proteomics analysis revealed enzyme-level regulation only for substrate uptake and initial catabolism, which dictated downstream fluxes through CO2-producing versus biomass-synthesizing reactions. Metabolic partitioning as shown here explained the substrate-dependent CUE calculated from reported metabolic flux analyses of other bacteria, further supporting a metabolism-guided perspective for predicting the microbial conversion of accessible organic matter to CO2 efflux.

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 .

Glucose-6-phosphatase catalytic subunit 2 negatively regulates glucose oxidation and insulin secretion in pancreatic ß-cells.

Elevated fasting blood glucose (FBG) is associated with increased risks of developing type 2 diabetes (T2D) and cardiovascular-associated mortality. G6PC2 is predominantly expressed in islets, encodes a glucose-6-phosphatase catalytic subunit that converts glucose-6-phosphate (G6P) to glucose, and has been linked with variations in FBG in genome-wide association studies. Deletion of G6pc2 in mice has been shown to lower FBG without affecting fasting plasma insulin levels in vivo. At 5 mM glucose, pancreatic islets from G6pc2 knockout (KO) mice exhibit no glucose cycling, increased glycolytic flux, and enhanced glucose-stimulated insulin secretion (GSIS). However, the broader effects of G6pc2 KO on ß-cell metabolism and redox regulation are unknown. Here we used CRISPR/Cas9 gene editing and metabolic flux analysis in ßTC3 cells, a murine pancreatic ß-cell line, to examine the role of G6pc2 in regulating glycolytic and mitochondrial fluxes. We found that deletion of G6pc2 led to ∼60% increases in glycolytic and citric acid cycle (CAC) fluxes at both 5 and 11 mM glucose concentrations. Furthermore, intracellular insulin content and GSIS were enhanced by approximately two-fold, along with increased cytosolic redox potential and reductive carboxylation flux. Normalization of fluxes relative to net glucose uptake revealed upregulation in two NADPH-producing pathways in the CAC. These results demonstrate that G6pc2 regulates GSIS by modulating not only glycolysis but also, independently, citric acid cycle activity in ß-cells. Overall, our findings implicate G6PC2 as a potential therapeutic target for enhancing insulin secretion and lowering FBG, which could benefit individuals with prediabetes, T2D, and obesity.

An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana.

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.

Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis.

BACKGROUND: Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. RESULTS: To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. CONCLUSIONS: Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.