13C Metabolic Flux Analysis Research Articles

Metabolic engineering of isopropyl alcohol-producing Escherichia coli strains with 13 C-metabolic flux analysis.

Metabolic engineering of isopropyl alcohol (IPA)-producing Escherichia coli strains was conducted along with 13 C-metabolic flux analysis (MFA). A metabolically engineered E. coli strain expressing the adc gene derived from Clostridium acetobutylicum and the IPADH gene from C. beijerinckii did not produce IPA during its exponential growth phase in the aerobic batch culture. 13 C-MFA was carried out, and revealed a deficiency in NADPH regeneration for IPA production in growth phase. Based on these findings, we used nitrogen-starved culture conditions to reduce NADPH consumption for biomass synthesis. As a result, IPA yield was increased to 20% mol/mol glucose. 13 C-MFA revealed that the relative flux levels through the oxidative pentose phosphate (PP) pathway and the TCA cycle were elevated in nitrogen-starved condition relative to glucose uptake rate. To prevent CO2 release in the 6-phosphogluconate dehydrogenase (6PGDH) reaction, metabolism of this E. coli strain was further engineered to redirect glycolytic flux to the glucose 6-phosphate dehydrogenase (G6PDH) and Entner-Doudoroff (ED) pathway. IPA yield of 55% mol/mol glucose was achieved by combining the nitrogen-starved culture condition with the metabolic redirection. The 13 C-MFA data and intracellular NADPH levels obtained under these IPA production conditions revealed linear correlations between the specific IPA production rate and NADPH concentration, as well as between IPA yield and the pyruvate dehydrogenase (PDH) flux. Our results showed that 13 C-MFA is a helpful tool for metabolic engineering studies, and that further improvement in IPA production by E. coli may be achieved by fine-tuning the cofactor ratio and concentrations, as well as optimizing the metabolic pathways and culture conditions.

Abstract 439: Metabolic flux analysis reveals targets to sensitize chemoresistance in acute myeloid leukemia induced by mesenchymal stromal cell-derived exosomes

Abstract The tumor microenvironment has a pleiotropic role in supporting cancer cell growth, metastasis and drug resistance. Exosomes from mesenchymal stromal cells (MSC) were found to regulate metabolism of acute myeloid leukemia (AML) cells that led to a chemoresistant phenotype. Exosomes carry a host of proteins, nucleotides and metabolites that can induce metabolic reprogramming recipient cells via direct supply of metabolite cargo or through signaling. We utilized 13C tracer techniques and two metabolic flux analysis techniques to reveal the mechanism of metabolic reprogramming induced by MSC-derived exosomes. First, 13C metabolic flux analysis was used to quantify intracellular fluxes of central carbon, amino acids and fatty acid metabolism in AML cells cultured with and without exosomes in media with 13C substrates. Second, we employed a novel technique, exosome-mediated metabolic flux analysis (Exo-MFA), to estimate direct support provided by metabolite supply from exosome cargo by introducing 13C-labeled exosomes. The combined analysis dissected the metabolite supply from exosome cargo from the overall reprogramming of metabolic pathways due to exosomes. Our results revealed key metabolic pathways that could be targeted to inhibit exosome-induced reprogramming to reverse the chemoresistant phenotype. Citation Format: Abhinav Achreja, Hongyun Zhao, Ziwen Zhu, Jonathan Gerszberg, Marina Y. Konopleva, Michael Andreeff, Deepak Nagrath. Metabolic flux analysis reveals targets to sensitize chemoresistance in acute myeloid leukemia induced by mesenchymal stromal cell-derived exosomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 439. doi:10.1158/1538-7445.AM2017-439

Abstract 5431: Androgen receptor regulates the mitochondrial pyruvate carrier to fuel oncometabolism in prostate cancer

Abstract The metabolic underpinnings of androgen receptor (AR)-driven growth in prostate cancer (PCa) are underexplored, hindering the development of strategies to leverage the metabolic properties differentiating PCa from normal tissue. To this end, we discovered a subunit of the mitochondrial pyruvate carrier (MPC), MPC2, is a direct AR target gene that is increased in PCa specimens and associated with clinical outcomes. Our observation suggests a targetable link between AR signaling and carbon trafficking in PCa. Indeed, MPC inhibition delays proliferation and alters metabolic properties in hormone sensitive and castrate resistant AR-driven PCa models. Next, using 13C metabolic flux analysis coupled with a reverse phase protein array, we determined MPC inhibition results in activation of the eIF2α/ATF4 integrated stress response pathway to partially compensate for MPC inhibition by coordinating increased glutamine incorporation into the citric acid cycle. Therefore, to amplify the effect of MPC inhibition, we restricted glutamine availability during MPC inhibition and found this combination resulted in proliferative arrest. Last, we extended our efforts in-vivo and demonstrated combinatorial MPC/glutaminase inhibition resulted in a significant reduction in castrate-resistant prostate tumor xenograft growth in mice with no overt host toxicity. We anticipate our findings will accelerate the development of clinical strategies to therapeutically manipulate carbon flux to starve clinically lethal castrate-resistant PCa. Citation Format: David A. Bader, Nagireddy Putluri, Sean M. Hartig, Sean E. McGuire. Androgen receptor regulates the mitochondrial pyruvate carrier to fuel oncometabolism in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5431. doi:10.1158/1538-7445.AM2017-5431

Abstract 2852: Monitoring the impact on metabolic flux in vivo of a newly developed LDH inhibitor using hyperpolarized 13C magnetic resonance spectroscopic imaging

Abstract [aim] Increased lactate production is a feature of many neoplasms, and Lactate Dehydrogenase A (LDH-A) plays a key role in conversion of pyruvate to lactate. LDHA inhibition, therefore, is considered to be a promising approach toward developing a new therapeutic strategy for cancer treatment focused on targeting cancer metabolism. Non-invasive imaging approaches able to monitor metabolic fluxes in vivo will be useful for this purpose. Hyperpolarized 13C Magnetic Resonance Imaging (MRI) has been well known as a valuable technology to investigate metabolic processes in tumor xenografts, allowing us to perform dynamic 13C-metabolic flux analysis in vivo. Use of [1-13C]pyruvate with this technology provides the ability to monitor LDHA activity in real time through dynamic observation of conversion of [1-13C]pyruvate to [1-13C]lactate. This study aimed to monitor drug efficacy of a newly developed LDH inhibitor (LDHI, obtained from National Cancer Institute Experimental Therapeutics Program, NExT) in a xenograft tumor model using 13C MRI technology with hyperpolarized 13C-labeled pyruvate. [Results] Hyperpolarized [1-13C]pyruvate MR studies were performed before and after LDHI administration to assess the impact on metabolic flux in vivo. Using hyperpolarized [1-13C]pyruvate MR Spectroscopy (MRS), we found that lactate production was significantly suppressed by LDHI administration in MiaPaca (a glycolytic pancreatic cancer cell line) tumors, as was the [1-13C]lactate to [1-13C]pyruvate ratio ([1-13C]-Lac/Pyr), which was calculated from the areas under the curves (AUC) using time-intensity data. This ratio decreased from 1.08 to 0.128 (88.1% decrease) 30 minutes after intravenous administration of the LDHI. In addition, hyperpolarized [1-13C]pyruvate MRS revealed that LDHI significantly suppressed lactate production in a dose dependent manner. Furthermore, Chemical Shift Imaging with 13C MRI demonstrated that the [1-13C]lactate signal in each voxel clearly decreased, compared to that before LDHI administration. The sum of [1-13C]lactate signals in the tumor region decreased after LDHI administration, resulting in a significant decrease in the tumor-specific [1-13C] Lac/Pyr ratio (1.463±0.31 before LDHI to 0.134±0.036 30 minutes after LDHI administration, a 90.67±2.56% decrease, n=3, p<0.01). [Conclusions] These results indicate that hyperpolarized 13C-MRI is a useful method to evaluate on-target efficacy of novel LDH inhibitors in vivo, and this technique can be used to determine optimum dose and exposure time of the LDHI in the tumor region. The current method can be of great value in providing an in vivo pharmacodynamic biomarker for this novel anti-cancer therapeutic targeting deregulated tumor metabolism. Citation Format: Nobu Oshima, Shun Kishimoto, Kristin Beebe, Keita Saito, Kazutoshi Yamamoto, Jeffery Brender, Anastasia Sowers, Ganesha Rai, Bryan T. Mott, David J. Maloney, James B. Mitchell, Murali K. Cherukuri, Leonard M. Neckers. Monitoring the impact on metabolic flux in vivo of a newly developed LDH inhibitor using hyperpolarized 13C magnetic resonance spectroscopic imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2852. doi:10.1158/1538-7445.AM2017-2852

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism.

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium.

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii.

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.

Metabolic impact of nutrient starvation in mevalonate-producing Escherichia coli

The aim of this work was to enhance mevalonate yield from glucose in Escherichia coli by essential nutrient starvations and to reveal these effects on the central carbon metabolism. Stationary phase culture without essential nutrients such as nitrogen, sulfur, and magnesium was evaluated using an engineered E. coli introducing mvaE and mvaS genes from Enterococcus faecalis. Sulfur starvation resulted in the highest mevalonate yield of 0.61C-molC-mol−1 from glucose. The metabolic impacts of nutrient starvation were investigated by 13C-metabolic flux analysis. Under nitrogen starvation, the flux of the TCA cycle was large, causing high CO2 production. This was caused by degradation of mevalonate synthesis pathway enzymes. Under magnesium starvation, NADPH production was decreased, which limited mevalonate synthesis and promoted an overflow of acetate. Sulfur starvation not only suppressed the TCA cycle flux, but also supplied NADPH for mevalonate synthesis.

Isotopically nonstationary 13C flux analysis of cyanobacterial isobutyraldehyde production

We applied isotopically nonstationary 13C metabolic flux analysis (INST-MFA) to compare the pathway fluxes of wild-type (WT) Synechococcus elongatus PCC 7942 to an engineered strain (SA590) that produces isobutyraldehyde (IBA). The flux maps revealed a potential bottleneck at the pyruvate kinase (PK) reaction step that was associated with diversion of flux into a three-step PK bypass pathway involving the enzymes PEP carboxylase (PEPC), malate dehydrogenase (MDH), and malic enzyme (ME). Overexpression of pk in SA590 led to a significant improvement in IBA specific productivity. Single-gene overexpression of the three enzymes in the proposed PK bypass pathway also led to improvements in IBA production, although to a lesser extent than pk overexpression. Combinatorial overexpression of two of the three genes in the proposed PK bypass pathway (mdh and me) led to improvements in specific productivity that were similar to those achieved by single-gene pk overexpression. Our work demonstrates how 13C flux analysis can be used to identify potential metabolic bottlenecks and novel metabolic routes, and how these findings can guide rational metabolic engineering of cyanobacteria for increased production of desired molecules.

Updates to a 13C metabolic flux analysis model for evaluating energy metabolism in cultured cerebellar granule neurons from neonatal rats

A hexose phosphate recycling model previously developed to infer fluxes through the major glucose consuming pathways in cultured cerebellar granule neurons (CGNs) from neonatal rats metabolizing [1,2–13C2]glucose was revised by considering reverse flux through the non-oxidative pentose phosphate pathway (PPP) and symmetrical succinate oxidation within the tricarboxylic acid (TCA) cycle. The model adjusts three flux ratios to effect 13C distribution in the hexose, pentose, and triose phosphate pools, and in TCA cycle malate to minimize the error between predicted and measured 13C labeling in exported lactate (i.e., unlabeled, single-, double-, and triple-labeled; M, M1, M2, and M3, respectively). Inclusion of reverse non-oxidative PPP flux substantially increased the number of calculations but ultimately had relatively minor effects on the labeling of glycolytic metabolites. From the error-minimized solution in which the predicted M-M3 lactate differed by 0.49% from that measured by liquid chromatography-triple quadrupole mass spectrometry, the neurons exhibited negligible forward non-oxidative PPP flux. Thus, no glucose was used by the pentose cycle despite explicit consideration of hexose phosphate recycling. Mitochondria consumed only 16% of glucose while 45% was exported as lactate by aerobic glycolysis. The remaining 39% of glucose was shunted to pentose phosphates presumably for de novo nucleotide synthesis, but the proportion metabolized through the oxidative PPP vs. the reverse non-oxidative PPP could not be determined. The lactate exported as M1 (2.5%) and M3 (1.2%) was attributed to malic enzyme, which was responsible for 7.8% of pyruvate production (vs. 92.2% by glycolysis). The updated model is more broadly applicable to different cell types by considering bi-directional flux through the non-oxidative PPP. Its application to cultured neurons utilizing glucose as the sole exogenous substrate has demonstrated substantial oxygen-independent glucose utilization by aerobic glycolysis as well as the oxidative PPP and/or reverse non-oxidative PPP, but negligible glucose consumption by the pentose cycle.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

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

BackgroundModeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.ResultsThe jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S-13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.ConclusionsjQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Tracking the flow of carbon toward oil synthesis in an emerging crop lesquerella (Physaria fendleri)

Lesquerella (Physaria fendleri) is a Brassicaceae that produces hydroxy fatty acids (HFAs) in its embryos; a type of oil that is very valuable and widely used in the industry of cosmetics, coatings, greases, plastics, paints, and biofuel, among others. The current source of HFAs is ricinoleic acid which comes from castor (Ricinus communis). Unfortunately, its large‐scale production has been banned from the United States because castor contains the highly toxic compound ricin. On the other hand, free of toxins and rich in HFAs, lesquerella is an attractive alternative to imported castor oil, and is hence in the verge of commercialization. Moreover, lesquerella has tremendous potentials for oil production, has a short maturity time and is not used for food compared to other oilseed crops. However, to become economically viable, lesquerella first must be engineered to synthesize more HFAs. A lack of knowledge of the metabolic pathways underlying oil synthesis in lesquerella embryos presents a major constraint.In plants, fatty acid synthesis occurs in plastids and requires carbon in the form of acetyl‐CoA units as well as reducing power and energy. In order to assess the contribution of the pathways involved in fatty acid biosynthesis in terms of carbon, reductant and energy, a 13C‐Metabolic Flux Analysis, aka fluxomics, is being conducted. 13C‐labeling of developing embryos is required for this purpose. Our most recent work done towards meeting this goal is: (i) obtaining culture conditions for growing lesquerella embryos in vivo so that their biomass composition matches that of embryos grown in planta, (ii) determining the efficiency at which the embryos convert carbon into biomass (iii) tracking the flow of carbon towards oil synthesis using labeled substrates (13C‐glucose or 13C‐glutamine). The combination of the data from the above studies will permit to construct a flux map that will unravel possible “road blocks” in fatty acid production. Future work will consist in targeting these limiting steps through metabolic engineering to increase total fatty acids in lesquerella embryos.Support or Funding InformationAgriculture and Food Research Initiative competitive grant # 2016‐67013‐24605 of the USDA National Institute of Food and Agriculture.

Cyanobacterial carbon metabolism: Fluxome plasticity and oxygen dependence

Synechocystis sp. strain PCC 6803 has been widely used as a photo-biorefinery chassis. Based on its genome annotation, this species contains a complete TCA cycle, an Embden-Meyerhof-Parnas pathway (EMPP), an oxidative pentose phosphate pathway (OPPP), and an Entner-Doudoroff pathway (EDP). To evaluate how Synechocystis 6803 catabolizes glucose under heterotrophic conditions, we performed 13 C metabolic flux analysis, metabolite pool size analysis, gene knockouts, and heterologous expressions. The results revealed a cyclic mode of flux through the OPPP. Small, but non-zero, fluxes were observed through the TCA cycle and the malic shunt. Independent knockouts of 6-phosphogluconate dehydrogenase (gnd) and malic enzyme (me) corroborated these results, as neither mutant could grow under dark heterotrophic conditions. Our data also indicate that Synechocystis 6803 metabolism relies upon oxidative phosphorylation to generate ATP from NADPH under dark or insufficient light conditions. The pool sizes of intermediates in the TCA cycle, particularly acetyl-CoA, were found to be several fold lower in Synechocystis 6803 (compared to E. coli metabolite pool sizes), while its sugar phosphate intermediates were several-fold higher. Moreover, negligible flux was detected through the native, or heterologous, EDP in the wild type or Δgnd strains under heterotrophic conditions. Comparing photoautotrophic, photomixotrophic, and heterotrophic conditions, the Calvin cycle, OPPP, and EMPP in Synechocystis 6803 possess the ability to regulate their fluxes under various growth conditions (plastic), whereas its TCA cycle always maintains at low levels (rigid). This work also demonstrates how genetic profiles do not always reflect actual metabolic flux through native or heterologous pathways. Biotechnol. Bioeng. 2017;114: 1593-1602. © 2017 Wiley Periodicals, Inc.

High-throughput quantification of the levels and labeling abundance of free amino acids by liquid chromatography tandem mass spectrometry

Accurate assessment of mass isotopomer distributions (MIDs) of intracellular metabolites, such as free amino acids (AAs), is crucial for quantifying in vivo fluxes. To date, the majority of studies that measured AA MIDs have relied on the analysis of proteinogenic rather than free AAs by: i) GC–MS, which involved cumbersome process of derivatization, or ii) NMR, which requires large quantities of biological sample. In this work, the development and validation of a high-throughput LC–MS/MS method allowing the quantification of the levels and labeling of free AAs is described. Sensitivity in the order of the femtomol was achieved using multiple reaction monitoring mode (MRM). The MIDs of all free AAs were assessed without the need of derivatization, and were validated (except for Trp) on a mixture of unlabeled AA standards. Finally, this method was applied to the determination of the 13C-labeling abundance in free AAs extracted from maize embryos cultured with 13C-glutamine or 13C-glucose. Although Cys was below the limit of detection in these biological samples, the MIDs of a total of 18 free AAs were successfully determined. Due to the increased application of tandem mass spectrometry for 13C-Metabolic Flux Analysis, this novel method will enable the assessment of more complete and accurate labeling information of intracellular AAs, and therefore a better definition of the fluxes.

Acetate fluxes in Escherichia coli are determined by the thermodynamic control of the Pta-AckA pathway

Escherichia coli excretes acetate upon growth on fermentable sugars, but the regulation of this production remains elusive. Acetate excretion on excess glucose is thought to be an irreversible process. However, dynamic 13C-metabolic flux analysis revealed a strong bidirectional exchange of acetate between E. coli and its environment. The Pta-AckA pathway was found to be central for both flux directions, while alternative routes (Acs or PoxB) play virtually no role in glucose consumption. Kinetic modelling of the Pta-AckA pathway predicted that its flux is thermodynamically controlled by the extracellular acetate concentration in vivo. Experimental validations confirmed that acetate production can be reduced and even reversed depending solely on its extracellular concentration. Consistently, the Pta-AckA pathway can rapidly switch from acetate production to consumption. Contrary to current knowledge, E. coli is thus able to co-consume glucose and acetate under glucose excess. These metabolic capabilities were confirmed on other glycolytic substrates which support the growth of E. coli in the gut. These findings highlight the dual role of the Pta-AckA pathway in acetate production and consumption during growth on glycolytic substrates, uncover a novel regulatory mechanism that controls its flux in vivo, and significantly expand the metabolic capabilities of E. coli.

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

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

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

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

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

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

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants.

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary 13C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO2 over a diurnal period implying that the metabolic steady state is limited to less than 12h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient 13CO2 labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

LKB1 promotes metabolic flexibility in response to energy stress

The Liver Kinase B1 (LKB1) tumor suppressor acts as a metabolic energy sensor to regulate AMP-activated protein kinase (AMPK) signaling and is commonly mutated in various cancers, including non-small cell lung cancer (NSCLC). Tumor cells deficient in LKB1 may be uniquely sensitized to metabolic stresses, which may offer a therapeutic window in oncology. To address this question we have explored how functional LKB1 impacts the metabolism of NSCLC cells using 13C metabolic flux analysis. Isogenic NSCLC cells expressing functional LKB1 exhibited higher flux through oxidative mitochondrial pathways compared to those deficient in LKB1. Re-expression of LKB1 also increased the capacity of cells to oxidize major mitochondrial substrates, including pyruvate, fatty acids, and glutamine. Furthermore, LKB1 expression promoted an adaptive response to energy stress induced by anchorage-independent growth. Finally, this diminished adaptability sensitized LKB1-deficient cells to combinatorial inhibition of mitochondrial complex I and glutaminase. Together, our data implicate LKB1 as a major regulator of adaptive metabolic reprogramming and suggest synergistic pharmacological strategies for mitigating LKB1-deficient NSCLC tumor growth.