Increasing mTORC1 Pathway Activity or Methionine Supplementation during Pregnancy Reverses the Negative Effect of Maternal Malnutrition on the Developing Kidney.

BACKGROUND: Low nephron number at birth is associated with a high risk of CKD in adulthood because nephrogenesis is completed in utero. Poor intrauterine environment impairs nephron endowment via an undefined molecular mechanism. A calorie-restricted diet (CRD) mouse model examined the effect of malnutrition during pregnancy on nephron progenitor cells (NPCs). METHODS: Daily caloric intake was reduced by 30% during pregnancy. mRNA expression, the cell cycle, and metabolic activity were evaluated in sorted Six2 NPCs. The results were validated using transgenic mice, oral nutrient supplementation, and organ cultures. RESULTS: Maternal CRD is associated with low nephron number in offspring, compromising kidney function at an older age. RNA-seq identified cell cycle regulators and the mTORC1 pathway, among other pathways, that maternal malnutrition in NPCs modifies. Metabolomics analysis of NPCs singled out the methionine pathway as crucial for NPC proliferation and maintenance. Methionine deprivation reduced NPC proliferation and lowered NPC number per tip in embryonic kidney cultures, with rescue from methionine metabolite supplementation. Importantly, in vivo, the negative effect of caloric restriction on nephrogenesis was prevented by adding methionine to the otherwise restricted diet during pregnancy or by removing one Tsc1 allele in NPCs. CONCLUSIONS: These findings show that mTORC1 signaling and methionine metabolism are central to the cellular and metabolic effects of malnutrition during pregnancy on NPCs, contributing to nephrogenesis and later, to kidney health in adulthood.

Sensitivity of pituitary gonadotropes to hyperglycemia leads to epigenetic aberrations and reduced follicle-stimulating hormone levels.

The connection between metabolism and reproductive function is well recognized, and we hypothesized that the pituitary gonadotropes, which produce luteinizing hormone and follicle-stimulating hormone (FSH), mediate some of the effects directly via insulin-independent glucose transporters, which allow continued glucose metabolism during hyperglycemia. We found that glucose transporter 1 is the predominant glucose transporter in primary gonadotropes and a gonadotrope precursor-derived cell line, and both are responsive to culture in high glucose; moreover, metabolite levels were altered in the cell line. Several of the affected metabolites are cofactors for chromatin-modifying enzymes, and in the gonadotrope precursor-derived cell line, we recorded global changes in histone acetylation and methylation, decreased DNA methylation, and increased hydroxymethylation, some of which did not revert to basal levels after cells were returned to normal glucose. Despite this weakening of epigenetic-mediated repression seen in the model cell line, FSH ß-subunit ( Fshb) mRNA levels in primary gonadotropes were significantly reduced, apparently due in part to increased autocrine/paracrine effects of inhibin. However, unlike thioredoxin interacting protein and inhibin subunit α, Fshb mRNA levels did not recover after the return of cells to normal glucose. The effect on Fshb expression was also seen in 2 hyperglycemic mouse models, and levels of circulating FSH, required for follicle growth and development, were reduced. Thus, hyperglycemia seems to target the pituitary gonadotropes directly, and the likely extensive epigenetic changes are sensed acutely by Fshb. This scenario would explain clinical findings in which, even after restoration of optimal blood glucose levels, fertility often remains adversely affected. However, the relative accessibility of the pituitary provides a possible target for treatment, particularly crucial in the young in which hyperglycemia is increasingly common and fertility most relevant.-Feldman, A., Saleh, A., Pnueli, L., Qiao, S., Shlomi, T., Boehm, U., Melamed, P. Sensitivity of pituitary gonadotropes to hyperglycemia leads to epigenetic aberrations and reduced follicle-stimulating hormone levels.

Quantitative flux analysis reveals folate-dependent NADPH production.

ATP is the dominant energy source in animals for mechanical and electrical work (for example, muscle contraction or neuronal firing). For chemical work, there is an equally important role for NADPH, which powers redox defence and reductive biosynthesis. The most direct route to produce NADPH from glucose is the oxidative pentose phosphate pathway, with malic enzyme sometimes also important. Although the relative contribution of glycolysis and oxidative phosphorylation to ATP production has been extensively analysed, similar analysis of NADPH metabolism has been lacking. Here we demonstrate the ability to directly track, by liquid chromatography-mass spectrometry, the passage of deuterium from labelled substrates into NADPH, and combine this approach with carbon labelling and mathematical modelling to measure NADPH fluxes. In proliferating cells, the largest contributor to cytosolic NADPH is the oxidative pentose phosphate pathway. Surprisingly, a nearly comparable contribution comes from serine-driven one-carbon metabolism, in which oxidation of methylene tetrahydrofolate to 10-formyl-tetrahydrofolate is coupled to reduction of NADP(+) to NADPH. Moreover, tracing of mitochondrial one-carbon metabolism revealed complete oxidation of 10-formyl-tetrahydrofolate to make NADPH. As folate metabolism has not previously been considered an NADPH producer, confirmation of its functional significance was undertaken through knockdown of methylenetetrahydrofolate dehydrogenase (MTHFD) genes. Depletion of either the cytosolic or mitochondrial MTHFD isozyme resulted in decreased cellular NADPH/NADP(+) and reduced/oxidized glutathione ratios (GSH/GSSG) and increased cell sensitivity to oxidative stress. Thus, although the importance of folate metabolism for proliferating cells has been long recognized and attributed to its function of producing one-carbon units for nucleic acid synthesis, another crucial function of this pathway is generating reducing power.

Systemic Succinate Homeostasis and Local Succinate Signaling Affect Blood Pressure and Modify Risks for Calcium Oxalate Lithogenesis.

BACKGROUND: In the kidney, low urinary citrate increases the risk for developing kidney stones, and elevation of luminal succinate in the juxtaglomerular apparatus increases renin secretion, causing hypertension. Although the association between stone formation and hypertension is well established, the molecular mechanism linking these pathophysiologies has been elusive. METHODS: To investigate the relationship between succinate and citrate/oxalate levels, we assessed blood and urine levels of metabolites, renal protein expression, and BP (using 24-hour telemetric monitoring) in male mice lacking slc26a6 (a transporter that inhibits the succinate transporter NaDC-1 to control citrate absorption from the urinary lumen). We also explored the mechanism underlying this metabolic association, using coimmunoprecipitation, electrophysiologic measurements, and flux assays to study protein interaction and transport activity. RESULTS: Compared with control mice, slc26a6-/- mice (previously shown to have low urinary citrate and to develop calcium oxalate stones) had a 40% decrease in urinary excretion of succinate, a 35% increase in serum succinate, and elevated plasma renin. Slc26a6-/- mice also showed activity-dependent hypertension that was unaffected by dietary salt intake. Structural modeling, confirmed by mutational analysis, identified slc26a6 and NaDC-1 residues that interact and mediate slc26a6's inhibition of NaDC-1. This interaction is regulated by the scaffolding protein IRBIT, which is released by stimulation of the succinate receptor SUCNR1 and interacts with the NaDC-1/slc26a6 complex to inhibit succinate transport by NaDC-1. CONCLUSIONS: These findings reveal a succinate/citrate homeostatic pathway regulated by IRBIT that affects BP and biochemical risk of calcium oxalate stone formation, thus providing a potential molecular link between hypertension and lithogenesis.

Inferring cancer dependencies on metabolic genes from large-scale genetic screens.

BACKGROUND: Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. RESULTS: We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~ 2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. CONCLUSIONS: Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

A key role for mitochondrial gatekeeper pyruvate dehydrogenase in oncogene-induced senescence.

In response to tenacious stress signals, such as the unscheduled activation of oncogenes, cells can mobilize tumour suppressor networks to avert the hazard of malignant transformation. A large body of evidence indicates that oncogene-induced senescence (OIS) acts as such a break, withdrawing cells from the proliferative pool almost irreversibly, thus crafting a vital pathophysiological mechanism that protects against cancer. Despite the widespread contribution of OIS to the cessation of tumorigenic expansion in animal models and humans, we have only just begun to define the underlying mechanism and identify key players. Although deregulation of metabolism is intimately linked to the proliferative capacity of cells, and senescent cells are thought to remain metabolically active, little has been investigated in detail about the role of cellular metabolism in OIS. Here we show, by metabolic profiling and functional perturbations, that the mitochondrial gatekeeper pyruvate dehydrogenase (PDH) is a crucial mediator of senescence induced by BRAF(V600E), an oncogene commonly mutated in melanoma and other cancers. BRAF(V600E)-induced senescence was accompanied by simultaneous suppression of the PDH-inhibitory enzyme pyruvate dehydrogenase kinase 1 (PDK1) and induction of the PDH-activating enzyme pyruvate dehydrogenase phosphatase 2 (PDP2). The resulting combined activation of PDH enhanced the use of pyruvate in the tricarboxylic acid cycle, causing increased respiration and redox stress. Abrogation of OIS, a rate-limiting step towards oncogenic transformation, coincided with reversion of these processes. Further supporting a crucial role of PDH in OIS, enforced normalization of either PDK1 or PDP2 expression levels inhibited PDH and abrogated OIS, thereby licensing BRAF(V600E)-driven melanoma development. Finally, depletion of PDK1 eradicated melanoma subpopulations resistant to targeted BRAF inhibition, and caused regression of established melanomas. These results reveal a mechanistic relationship between OIS and a key metabolic signalling axis, which may be exploited therapeutically.

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements.

Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.

Temporal fluxomics reveals oscillations in TCA cycle flux throughout the mammalian cell cycle.

Cellular metabolic demands change throughout the cell cycle. Nevertheless, a characterization of how metabolic fluxes adapt to the changing demands throughout the cell cycle is lacking. Here, we developed a temporal-fluxomics approach to derive a comprehensive and quantitative view of alterations in metabolic fluxes throughout the mammalian cell cycle. This is achieved by combining pulse-chase LC-MS-based isotope tracing in synchronized cell populations with computational deconvolution and metabolic flux modeling. We find that TCA cycle fluxes are rewired as cells progress through the cell cycle with complementary oscillations of glucose versus glutamine-derived fluxes: Oxidation of glucose-derived flux peaks in late G1 phase, while oxidative and reductive glutamine metabolism dominates S phase. These complementary flux oscillations maintain a constant production rate of reducing equivalents and oxidative phosphorylation flux throughout the cell cycle. The shift from glucose to glutamine oxidation in S phase plays an important role in cell cycle progression and cell proliferation.

Metabolite concentrations, fluxes and free energies imply efficient enzyme usage.

In metabolism, available free energy is limited and must be divided across pathway steps to maintain a negative ΔG throughout. For each reaction, ΔG is log proportional both to a concentration ratio (reaction quotient to equilibrium constant) and to a flux ratio (backward to forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site dissociation constant (Km or Ki). The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints.

Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase.

Fumarate hydratase (FH) is an enzyme of the tricarboxylic acid cycle (TCA cycle) that catalyses the hydration of fumarate into malate. Germline mutations of FH are responsible for hereditary leiomyomatosis and renal-cell cancer (HLRCC). It has previously been demonstrated that the absence of FH leads to the accumulation of fumarate, which activates hypoxia-inducible factors (HIFs) at normal oxygen tensions. However, so far no mechanism that explains the ability of cells to survive without a functional TCA cycle has been provided. Here we use newly characterized genetically modified kidney mouse cells in which Fh1 has been deleted, and apply a newly developed computer model of the metabolism of these cells to predict and experimentally validate a linear metabolic pathway beginning with glutamine uptake and ending with bilirubin excretion from Fh1-deficient cells. This pathway, which involves the biosynthesis and degradation of haem, enables Fh1-deficient cells to use the accumulated TCA cycle metabolites and permits partial mitochondrial NADH production. We predicted and confirmed that targeting this pathway would render Fh1-deficient cells non-viable, while sparing wild-type Fh1-containing cells. This work goes beyond identifying a metabolic pathway that is induced in Fh1-deficient cells to demonstrate that inhibition of haem oxygenation is synthetically lethal when combined with Fh1 deficiency, providing a new potential target for treating HLRCC patients.

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity.

Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.

Fatty acid labeling from glutamine in hypoxia can be explained by isotope exchange without net reductive isocitrate dehydrogenase (IDH) flux.

Acetyl-CoA is an important anabolic precursor for lipid biosynthesis. In the conventional view of mammalian metabolism, acetyl-CoA is primarily derived by the oxidation of glucose-derived pyruvate in mitochondria. Recent studies have employed isotope tracers to show that in cancer cells grown in hypoxia or with defective mitochondria, a major fraction of acetyl-CoA is produced via another route, reductive carboxylation of glutamine-derived α-ketoglutarate (catalyzed by reverse flux through isocitrate dehydrogenase, IDH). Here, we employ a quantitative flux model to show that in hypoxia and in cells with defective mitochondria, oxidative IDH flux persists and may exceed the reductive flux. Therefore, IDH flux may not be a net contributor to acetyl-CoA production, although we cannot rule out net reductive IDH flux in some compartments. Instead of producing large amounts of net acetyl-CoA reductively, the cells adapt by reducing their demand for acetyl-CoA by importing rather than synthesizing fatty acids. Thus, fatty acid labeling from glutamine in hypoxia can be explained by spreading of label without net reductive IDH flux.

High throughput synthetic lethality screen reveals a tumorigenic role of adenylate cyclase in fumarate hydratase-deficient cancer cells.

BACKGROUND: Synthetic lethality is an appealing technique for selectively targeting cancer cells which have acquired molecular changes that distinguish them from normal cells. High-throughput RNAi-based screens have been successfully used to identify synthetic lethal pathways with well-characterized tumor suppressors and oncogenes. The recent identification of metabolic tumor suppressors suggests that the concept of synthetic lethality can be applied to selectively target cancer metabolism as well. RESULTS: Here, we perform a high-throughput RNAi screen to identify synthetic lethal genes with fumarate hydratase (FH), a metabolic tumor suppressor whose loss-of-function has been associated with hereditary leiomyomatosis and renal cell carcinoma (HLRCC). Our unbiased screen identified synthetic lethality between FH and several genes in heme metabolism, in accordance with recent findings. Furthermore, we identified an enrichment of synthetic lethality with adenylate cyclases. The effects were validated in an embryonic kidney cell line (HEK293T) and in HLRCC-patient derived cells (UOK262) via both genetic and pharmacological inhibition. The reliance on adenylate cyclases in FH-deficient cells is consistent with increased cyclic-AMP levels, which may act to regulate cellular energy metabolism. CONCLUSIONS: The identified synthetic lethality of FH with adenylate cyclases suggests a new potential target for treating HLRCC patients.

Quantitation of cellular metabolic fluxes of methionine.

Methionine is an essential proteogenic amino acid. In addition, it is a methyl donor for DNA and protein methylation and a propylamine donor for polyamine biosynthesis. Both the methyl and propylamine donation pathways involve metabolic cycles, and methods are needed to quantitate these cycles. Here, we describe an analytical approach for quantifying methionine metabolic fluxes that accounts for the mixing of intracellular and extracellular methionine pools. We observe that such mixing prevents isotope tracing experiments from reaching the steady state due to the large size of the media pools and hence precludes the use of standard stationary metabolic flux analysis. Our approach is based on feeding cells with (13)C methionine and measuring the isotope-labeling kinetics of both intracellular and extracellular methionine by liquid chromatography-mass spectrometry (LC-MS). We apply this method to quantify methionine metabolism in a human fibrosarcoma cell line and study how methionine salvage pathway enzyme methylthioadenosine phosphorylase (MTAP), frequently deleted in cancer, affects methionine metabolism. We find that both transmethylation and propylamine transfer fluxes amount to roughly 15% of the net methionine uptake, with no major changes due to MTAP deletion. Our method further enables the quantification of flux through the pro-tumorigenic enzyme ornithine decarboxylase, and this flux increases 2-fold following MTAP deletion. The analytical approach used to quantify methionine metabolic fluxes is applicable for other metabolic systems affected by mixing of intracellular and extracellular metabolite pools.

An integrated computational approach for metabolic flux analysis coupled with inference of tandem-MS collisional fragments.

MOTIVATION: Metabolic flux analysis (MFA) is a commonly used approach for quantifying metabolic fluxes based on tracking isotope labeling of metabolite within cells. Tandem mass-spectrometry (MS/MS) has been recently shown to be especially useful for MFA by providing rich information on metabolite positional labeling, measuring isotopic labeling patterns of collisional fragments. However, a major limitation in this approach is the requirement that the positional origin of atoms in a collisional fragment would be known a priori, which in many cases is difficult to determine. RESULTS: Here we show that MS/MS data could also be used to improve flux inference even when the positional origin of fragments is unknown. We develop a novel method, metabolic flux analysis/unknown fragments, that extends on standard MFA and jointly searches for the most likely metabolic fluxes together with the most plausible position of collisional fragments that would optimally match measured MS/MS data. MFA/UF is shown to markedly improve flux prediction accuracy in a simulation model of gluconeogenesis and using experimental MS/MS data in Bacillus subtilis.

Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia.

Mammalian cells can generate ATP via glycolysis or mitochondrial respiration. Oncogene activation and hypoxia promote glycolysis and lactate secretion. The significance of these metabolic changes to ATP production remains however ill defined. Here, we integrate LC-MS-based isotope tracer studies with oxygen uptake measurements in a quantitative redox-balanced metabolic flux model of mammalian cellular metabolism. We then apply this approach to assess the impact of Ras and Akt activation and hypoxia on energy metabolism. Both oncogene activation and hypoxia induce roughly a twofold increase in glycolytic flux. Ras activation and hypoxia also strongly decrease glucose oxidation. Oxidative phosphorylation, powered substantially by glutamine-driven TCA turning, however, persists and accounts for the majority of ATP production. Consistent with this, in all cases, pharmacological inhibition of oxidative phosphorylation markedly reduces energy charge, and glutamine but not glucose removal markedly lowers oxygen uptake. Thus, glutamine-driven oxidative phosphorylation is a major means of ATP production even in hypoxic cancer cells.

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution.

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

Predicting selective drug targets in cancer through metabolic networks.

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.