Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ.

Biological processes are regulated by intermolecular interactions and chemical modifications that do not affect protein levels, thus escaping detection in classical proteomic screens. We demonstrate here that a global protein structural readout based on limited proteolysis-mass spectrometry (LiP-MS) detects many such functional alterations, simultaneously and in situ, in bacteria undergoing nutrient adaptation and in yeast responding to acute stress. The structural readout, visualized as structural barcodes, captured enzyme activity changes, phosphorylation, protein aggregation, and complex formation, with the resolution of individual regulated functional sites such as binding and active sites. Comparison with prior knowledge, including other 'omics data, showed that LiP-MS detects many known functional alterations within well-studied pathways. It suggested distinct metabolite-protein interactions and enabled identification of a fructose-1,6-bisphosphate-based regulatory mechanism of glucose uptake in E. coli. The structural readout dramatically increases classical proteomics coverage, generates mechanistic hypotheses, and paves the way for in situ structural systems biology.

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication.

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.

Endoplasmic reticulum stress in the intestinal epithelium initiates purine metabolite synthesis and promotes Th17 cell differentiation in the gut.

Intestinal IL-17-producing T helper (Th17) cells are dependent on adherent microbes in the gut for their development. However, how microbial adherence to intestinal epithelial cells (IECs) promotes Th17 cell differentiation remains enigmatic. Here, we found that Th17 cell-inducing gut bacteria generated an unfolded protein response (UPR) in IECs. Furthermore, subtilase cytotoxin expression or genetic removal of X-box binding protein 1 (Xbp1) in IECs caused a UPR and increased Th17 cells, even in antibiotic-treated or germ-free conditions. Mechanistically, UPR activation in IECs enhanced their production of both reactive oxygen species (ROS) and purine metabolites. Treating mice with N-acetyl-cysteine or allopurinol to reduce ROS production and xanthine, respectively, decreased Th17 cells that were associated with an elevated UPR. Th17-related genes also correlated with ER stress and the UPR in humans with inflammatory bowel disease. Overall, we identify a mechanism of intestinal Th17 cell differentiation that emerges from an IEC-associated UPR.

L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity.

Metabolic activity is intimately linked to T cell fate and function. Using high-resolution mass spectrometry, we generated dynamic metabolome and proteome profiles of human primary naive T cells following activation. We discovered critical changes in the arginine metabolism that led to a drop in intracellular L-arginine concentration. Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity. Proteome-wide probing of structural alterations, validated by the analysis of knockout T cell clones, identified three transcriptional regulators (BAZ1B, PSIP1, and TSN) that sensed L-arginine levels and promoted T cell survival. Thus, intracellular L-arginine concentrations directly impact the metabolic fitness and survival capacity of T cells that are crucial for anti-tumor responses.

Antibodies Set Boundaries Limiting Microbial Metabolite Penetration and the Resultant Mammalian Host Response.

Although the mammalian microbiota is well contained within the intestine, it profoundly shapes development and metabolism of almost every host organ. We questioned the range and depth of microbial metabolite penetration into the host, and how this is modulated by intestinal immunity. Chemically identical microbial and host metabolites were distinguished by stable isotope tracing from 13C-labeled live non-replicating Escherichia coli, differentiating 12C host isotopes with high-resolution mass spectrometry. Hundreds of endogenous microbial compounds penetrated 23 host tissues and fluids after intestinal exposure: subsequent 12C host metabolome signatures included lipidemia, reduced glycolysis, and inflammation. Penetrant bacterial metabolites from the small intestine were rapidly cleared into the urine, whereas induced antibodies curtailed microbial metabolite exposure by accelerating intestinal bacterial transit into the colon where metabolite transport mechanisms are limiting. Pervasive penetration of microbial molecules can cause extensive host tissue responses: these are limited by immune and non-immune intestinal mucosal adaptations to the microbiota.

(p)ppGpp Regulates a Bacterial Nucleosidase by an Allosteric Two-Domain Switch.

The stringent response alarmones pppGpp and ppGpp are essential for rapid adaption of bacterial physiology to changes in the environment. In Escherichia coli, the nucleosidase PpnN (YgdH) regulates purine homeostasis by cleaving nucleoside monophosphates and specifically binds (p)ppGpp. Here, we show that (p)ppGpp stimulates the catalytic activity of PpnN both in vitro and in vivo causing accumulation of several types of nucleobases during stress. The structure of PpnN reveals a tetramer with allosteric (p)ppGpp binding sites located between subunits. pppGpp binding triggers a large conformational change that shifts the two terminal domains to expose the active site, providing a structural rationale for the stimulatory effect. We find that PpnN increases fitness and adjusts cellular tolerance to antibiotics and propose a model in which nucleotide levels can rapidly be adjusted during stress by simultaneous inhibition of biosynthesis and stimulation of degradation, thus achieving a balanced physiological response to constantly changing environments.

Genome-wide RNAi screen identifies novel players in human 60S subunit biogenesis including key enzymes of polyamine metabolism.

Ribosome assembly is an essential process that is linked to human congenital diseases and tumorigenesis. While great progress has been made in deciphering mechanisms governing ribosome biogenesis in eukaryotes, an inventory of factors that support ribosome synthesis in human cells is still missing, in particular regarding the maturation of the large 60S subunit. Here, we performed a genome-wide RNAi screen using an imaging-based, single cell assay to unravel the cellular machinery promoting 60S subunit assembly in human cells. Our screen identified a group of 310 high confidence factors. These highlight the conservation of the process across eukaryotes and reveal the intricate connectivity of 60S subunit maturation with other key cellular processes, including splicing, translation, protein degradation, chromatin organization and transcription. Intriguingly, we also identified a cluster of hits comprising metabolic enzymes of the polyamine synthesis pathway. We demonstrate that polyamines, which have long been used as buffer additives to support ribosome assembly in vitro, are required for 60S maturation in living cells. Perturbation of polyamine metabolism results in early defects in 60S but not 40S subunit maturation. Collectively, our data reveal a novel function for polyamines in living cells and provide a rich source for future studies on ribosome synthesis.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests. RESULTS: Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models. CONCLUSIONS: We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

Glycolysis/gluconeogenesis specialization in microbes is driven by biochemical constraints of flux sensing.

Central carbon metabolism is highly conserved across microbial species, but can catalyze very different pathways depending on the organism and their ecological niche. Here, we study the dynamic reorganization of central metabolism after switches between the two major opposing pathway configurations of central carbon metabolism, glycolysis, and gluconeogenesis in Escherichia coli, Pseudomonas aeruginosa, and Pseudomonas putida. We combined growth dynamics and dynamic changes in intracellular metabolite levels with a coarse-grained model that integrates fluxes, regulation, protein synthesis, and growth and uncovered fundamental limitations of the regulatory network: After nutrient shifts, metabolite concentrations collapse to their equilibrium, rendering the cell unable to sense which direction the flux is supposed to flow through the metabolic network. The cell can partially alleviate this by picking a preferred direction of regulation at the expense of increasing lag times in the opposite direction. Moreover, decreasing both lag times simultaneously comes at the cost of reduced growth rate or higher futile cycling between metabolic enzymes. These three trade-offs can explain why microorganisms specialize for either glycolytic or gluconeogenic substrates and can help elucidate the complex growth patterns exhibited by different microbial species.

High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study.

INTRODUCTION: Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. METHODS: Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites' ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. RESULTS: Six eGFR slope models selected 9-30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. CONCLUSIONS: Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.

2H/1H variation in microbial lipids is controlled by NADPH metabolism.

The hydrogen-isotopic compositions (2H/1H ratios) of lipids in microbial heterotrophs are known to vary enormously, by at least 40% (400‰) relative. This is particularly surprising, given that most C-bound H in their lipids appear to derive from the growth medium water, rather than from organic substrates, implying that the isotopic fractionation between lipids and water is itself highly variable. Changes in the lipid/water fractionation are also strongly correlated with the type of energy metabolism operating in the host. Because lipids are well preserved in the geologic record, there is thus significant potential for using lipid 2H/1H ratios to decipher the metabolism of uncultured microorganisms in both modern and ancient ecosystems. But despite over a decade of research, the precise mechanisms underlying this isotopic variability remain unclear. Differences in the kinetic isotope effects (KIEs) accompanying NADP+ reduction by dehydrogenases and transhydrogenases have been hypothesized as a plausible mechanism. However, this relationship has been difficult to prove because multiple oxidoreductases affect the NADPH pool simultaneously. Here, we cultured five diverse aerobic heterotrophs, plus five Escherichia coli mutants, and used metabolic flux analysis to show that 2H/1H fractionations are highly correlated with fluxes through NADP+-reducing and NADPH-balancing reactions. Mass-balance calculations indicate that the full range of 2H/1H variability in the investigated organisms can be quantitatively explained by varying fluxes, i.e., with constant KIEs for each involved oxidoreductase across all species. This proves that lipid 2H/1H ratios of heterotrophic microbes are quantitatively related to central metabolism and provides a foundation for interpreting 2H/1H ratios of environmental lipids and sedimentary hydrocarbons.

Hidden resources in the Escherichia coli genome restore PLP synthesis and robust growth after deletion of the essential gene pdxB.

PdxB (erythronate 4-phosphate dehydrogenase) is expected to be required for synthesis of the essential cofactor pyridoxal 5'-phosphate (PLP) in Escherichia coli Surprisingly, incubation of the ∆pdxB strain in medium containing glucose as a sole carbon source for 10 d resulted in visible turbidity, suggesting that PLP is being produced by some alternative pathway. Continued evolution of parallel lineages for 110 to 150 generations produced several strains that grow robustly in glucose. We identified a 4-step bypass pathway patched together from promiscuous enzymes that restores PLP synthesis in strain JK1. None of the mutations in JK1 occurs in a gene encoding an enzyme in the new pathway. Two mutations indirectly enhance the ability of SerA (3-phosphoglycerate dehydrogenase) to perform a new function in the bypass pathway. Another disrupts a gene encoding a PLP phosphatase, thus preserving PLP levels. These results demonstrate that a functional pathway can be patched together from promiscuous enzymes in the proteome, even without mutations in the genes encoding those enzymes.

Co-catabolism of arginine and succinate drives symbiotic nitrogen fixation.

Biological nitrogen fixation emerging from the symbiosis between bacteria and crop plants holds promise to increase the sustainability of agriculture. One of the biggest hurdles for the engineering of nitrogen-fixing organisms is an incomplete knowledge of metabolic interactions between microbe and plant. In contrast to the previously assumed supply of only succinate, we describe here the CATCH-N cycle as a novel metabolic pathway that co-catabolizes plant-provided arginine and succinate to drive the energy-demanding process of symbiotic nitrogen fixation in endosymbiotic rhizobia. Using systems biology, isotope labeling studies and transposon sequencing in conjunction with biochemical characterization, we uncovered highly redundant network components of the CATCH-N cycle including transaminases that interlink the co-catabolism of arginine and succinate. The CATCH-N cycle uses N2 as an additional sink for reductant and therefore delivers up to 25% higher yields of nitrogen than classical arginine catabolism-two alanines and three ammonium ions are secreted for each input of arginine and succinate. We argue that the CATCH-N cycle has evolved as part of a synergistic interaction to sustain bacterial metabolism in the microoxic and highly acid environment of symbiosomes. Thus, the CATCH-N cycle entangles the metabolism of both partners to promote symbiosis. Our results provide a theoretical framework and metabolic blueprint for the rational design of plants and plant-associated organisms with new properties to improve nitrogen fixation.

Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli.

Our understanding of metabolism is limited by a lack of knowledge about the functions of many enzymes. Here, we develop a high-throughput mass spectrometry approach to comprehensively profile proteins for in vitro enzymatic activity. Overexpressed or purified proteins are incubated in a supplemented metabolome extract containing hundreds of biologically relevant candidate substrates, and accumulating and depleting metabolites are determined by nontargeted mass spectrometry. By combining chemometrics and database approaches, we established an automated pipeline for unbiased annotation of the functions of novel enzymes. In screening all 1,275 functionally uncharacterized Escherichia coli proteins, we discovered 241 potential novel enzymes, 12 of which we experimentally validated. Our high-throughput in vitro metabolomics method is generally applicable to any purified protein or crude cell lysate of its overexpression host and enables performing up to 1,200 nontargeted enzyme assays per working day.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years. PREDICTORS: 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. OUTCOMES: Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). ANALYTICAL APPROACH: Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT. RESULTS: During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]). LIMITATIONS: Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. CONCLUSIONS: Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

Purity by design: Reducing impurities in bioproduction by stimulus-controlled global translational downregulation of non-product proteins.

Capitalizing on the ability of mammalian cells to conduct complex post-translational modifications, most protein therapeutics are currently produced in cell culture systems. Addition of a signal peptide to the product protein enables its accumulation in the cell culture supernatant, but separation of the product from endogenously secreted proteins remains costly and labor-intensive. We considered that global downregulation of translation of non-product proteins would be an efficient strategy to minimize downstream processing requirements. Therefore, taking advantage of the ability of mammalian protein kinase R (PKR) to switch off most cellular translation processes in response to infection by viruses, we fused a caffeine-inducible dimerization domain to the catalytic domain of PKR. Addition of caffeine to this construct results in homodimerization and activation of PKR, effectively rewiring rapid global translational downregulation to the addition of the stimulus in a dose-dependent manner. Then, to protect translation of the target therapeutic, we screened viral and cellular internal ribosomal entry sites (IRESes) known or suspected to be resistant to PKR-induced translational stress. After choosing the best-in-class Seneca valley virus (SVV) IRES, we additionally screened for IRES transactivation factors (ITAFs) as well as for supplementary small molecules to further boost the production titer of the product protein under conditions of global translational downregulation. Importantly, the residual global translation activity of roughly 10% under maximal downregulation is sufficient to maintain cellular viability during a production timeframe of at least five days. Standard industrially used adherent as well as suspension-adapted cell lines transfected with this synthetic biology-inspired Protein Kinase R-Enhanced Protein Production (PREPP) system could produce several medicinally relevant protein therapeutics, such as the blockbuster drug rituximab, in substantial quantities and with significantly higher purity than previous culture technologies. We believe incorporation of such purity-by-design technology in the production process will alleviate downstream processing bottlenecks in future biopharmaceutical manufacturing.

Synthesis and degradation of FtsZ quantitatively predict the first cell division in starved bacteria.

In natural environments, microbes are typically non-dividing and gauge when nutrients permit division. Current models are phenomenological and specific to nutrient-rich, exponentially growing cells, thus cannot predict the first division under limiting nutrient availability. To assess this regime, we supplied starving Escherichia coli with glucose pulses at increasing frequencies. Real-time metabolomics and microfluidic single-cell microscopy revealed unexpected, rapid protein, and nucleic acid synthesis already from minuscule glucose pulses in non-dividing cells. Additionally, the lag time to first division shortened as pulsing frequency increased. We pinpointed division timing and dependence on nutrient frequency to the changing abundance of the division protein FtsZ. A dynamic, mechanistic model quantitatively relates lag time to FtsZ synthesis from nutrient pulses and FtsZ protease-dependent degradation. Lag time changed in model-congruent manners, when we experimentally modulated the synthesis or degradation of FtsZ. Thus, limiting abundance of FtsZ can quantitatively predict timing of the first cell division.

Real-time metabolome profiling of the metabolic switch between starvation and growth.

Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15-30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation-induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

Genomewide landscape of gene-metabolome associations in Escherichia coli.

Metabolism is one of the best-understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non-enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single-celled organisms. Here, we systematically mapped the association between > 3,800 single-gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene-metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism-related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.

Glycolysis without pyruvate kinase in Clostridium thermocellum.

The metabolism of Clostridium thermocellum is notable in that it assimilates sugar via the EMP pathway but does not possess a pyruvate kinase enzyme. In the wild type organism, there are three proposed pathways for conversion of phosphoenolpyruvate (PEP) to pyruvate, which differ in their cofactor usage. One path uses pyruvate phosphate dikinase (PPDK), another pathway uses the combined activities of PEP carboxykinase (PEPCK) and oxaloacetate decarboxylase (ODC). Yet another pathway, the malate shunt, uses the combined activities of PEPCK, malate dehydrogenase and malic enzyme. First we showed that there is no flux through the ODC pathway by enzyme assay. Flux through the remaining two pathways (PPDK and malate shunt) was determined by dynamic 13C labeling. In the wild-type strain, the malate shunt accounts for about 33±2% of the flux to pyruvate, with the remainder via the PPDK pathway. Deletion of the ppdk gene resulted in a redirection of all pyruvate flux through the malate shunt. This provides the first direct evidence of the in-vivo function of the malate shunt.