Insulin resistance plays a central role in the development of the metabolic syndrome, but how it relates to cardiovascular disease remains controversial. Liver insulin receptor knockout (LIRKO) mice have pure hepatic insulin resistance. On a chow diet, LIRKO mice have a proatherogenic lipoprotein profile with reduced HDL cholesterol and VLDL particles that are markedly enriched in cholesterol. This is due to increased secretion and decreased clearance of apoB-containing lipoproteins, coupled with decreased triglyceride secretion secondary to increased expression of PGC-1β, which promotes VLDL secretion, but decreased expression of SREBP-1c, SREBP-2 and their targets, the lipogenic enzymes and the LDL receptor. Within twelve weeks on an atherogenic diet, LIRKO mice show marked hypercholesterolemia, and 100% of LIRKO mice, but 0% of controls, develop severe atherosclerosis. Thus, insulin resistance at the level of the liver is sufficient to produce the dyslipidemia and increased risk of atherosclerosis associated with the metabolic syndrome.
Metformin inhibits cancer cell proliferation and epidemiology studies suggest an association with increased survival in cancer patients taking metformin, however, the mechanism by which metformin improves cancer outcomes remains controversial. To explore how metformin might directly affect cancer cells, we analyzed how metformin altered the metabolism of prostate cancer cells and tumors. We found that metformin decreased glucose oxidation and increased dependency on reductive glutamine metabolism in both cancer cell lines and in a mouse model of prostate cancer. Inhibition of glutamine anaplerosis in the presence of metformin further attenuated proliferation while increasing glutamine metabolism rescued the proliferative defect induced by metformin. These data suggest that interfering with glutamine may synergize with metformin to improve outcomes in patients with prostate cancer.
Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from 13C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of 13C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the 13C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the 13C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in 13C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC–MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in 13C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.
Isotope Effects; Isotopomer Modeling; Metabolic Engineering; Metabolic Flux Analysis; Modeling errors
Proliferating mammalian cells use glutamine as a source of nitrogen and as a key anaplerotic source to provide metabolites to the tricarboxylic acid cycle (TCA) for biosynthesis. Recently, mTORC1 activation has been correlated with increased nutrient uptake and metabolism, but no molecular connection to glutaminolysis has been reported. Here, we show that mTORC1 promotes glutamine anaplerosis by activating glutamate dehydrogenase (GDH). This regulation requires transcriptional repression of SIRT4, the mitochondrial-localized sirtuin that inhibits GDH. Mechanistically, mTORC1 represses SIRT4 by promoting the proteasome-mediated destabilization of cAMP response element binding-2 (CREB2). Thus, a relationship between mTORC1, SIRT4 and cancer is suggested by our findings. Indeed, SIRT4 expression is reduced in human cancer, and its overexpression reduces cell proliferation, transformation and tumor development. Finally, our data indicate that targeting nutrient metabolism in energy-addicted cancers with high mTORC1 signaling may be an effective therapeutic approach.
Hypoxic and VHL-deficient cells use glutamine to generate citrate and lipids through reductive carboxylation (RC) of α-ketoglutarate. To gain insights into the role of HIF and the molecular mechanisms underlying RC, we took advantage of a panel of disease-associated VHL mutants and showed that HIF expression is necessary and sufficient for the induction of RC in human renal cell carcinoma (RCC) cells. HIF expression drastically reduced intracellular citrate levels. Feeding VHL-deficient RCC cells with acetate or citrate or knocking down PDK-1 and ACLY restored citrate levels and suppressed RC. These data suggest that HIF-induced low intracellular citrate levels promote the reductive flux by mass action to maintain lipogenesis. Using [1–13C] glutamine, we demonstrated in vivo RC activity in VHL-deficient tumors growing as xenografts in mice. Lastly, HIF rendered VHL-deficient cells sensitive to glutamine deprivation in vitro, and systemic administration of glutaminase inhibitors suppressed the growth of RCC cells as mice xenografts.
Reductively metabolized glutamine is a major cellular carbon source for fatty acid synthesis during hypoxia or when mitochondrial respiration is impaired. Yet, a mechanistic understanding of what determines reductive metabolism is missing. Here we identify several cellular conditions where the α-ketoglutarate/citrate ratio is changed due to altered acetyl-CoA to citrate conversion, and demonstrate that reductive glutamine metabolism is initiated in response to perturbations that results in an increase in the α-ketoglutarate/citrate ratio. Thus, targeting reductive glutamine conversion for a therapeutic benefit might require distinct modulations of metabolite concentrations rather than targeting the upstream signaling, which only indirectly affects the process.
Synergistic multi-ligand treatments that can induce neuronal differentiation offer valuable strategies to regulate and modulate neurite outgrowth. Whereas the signaling pathways mediating single ligand-induced neurite outgrowth, such as Akt, extracellular signal-regulated kinase (Erk), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (P38), have been extensively studied, the mechanisms underlying multi-ligand synergistic neurite outgrowth are poorly understood. In an attempt to gain insight into synergistic neurite outgrowth, PC12 cells were treated with one of three combinations: pituitary adenylate cyclase-activating peptide (PACAP) with epidermal growth factor (EP), basic fibroblast growth factor (FP), or nerve growth factor (NP) and then challenged with the appropriate kinase inhibitors to assess the signaling pathways involved in the process.
Response surface analyses indicated that synergistic neurite outgrowth was regulated by distinct pathways in these systems. Synergistic increases in the phosphorylation of Erk and JNK, but not Akt or P38, were observed with the three growth factor-PACAP combinations. Unexpectedly, we identified a synergistic increase in JNK phosphorylation, which was involved in neurite outgrowth in the NP and FP, but not EP, systems. Inhibition of JNK using the SP600125 inhibitor reduced phosphorylation of 90 kDa ribosomal S6 kinase (P90RSK) in the NP and FP, but not EP, systems. This suggested the involvement of P90RSK in mediating the differential effects of JNK in synergistic neurite outgrowth.
Taken together, these findings reveal the involvement of distinct signaling pathways in regulating neurite outgrowth in response to different synergistic growth factor-PACAP treatments. Our findings demonstrate a hitherto unrecognized mechanism of JNK-P90RSK in mediating synergistic neurite outgrowth induced by the co-treatment of growth factors and PACAP.
PC12; Synergistic; NGF; FGFb; EGF; PACAP; Neurite outgrowth; JNK; P90RSK
In vitro synthesis of chemicals and pharmaceuticals using enzymes is of considerable interest as these biocatalysts facilitate a wide variety of reactions under mild conditions with excellent regio-, chemo- and stereoselectivities. A significant challenge in a multi-enzymatic reaction is the need to optimize the various steps involved simultaneously so as to obtain high-yield of a product. In this study, statistical experimental design was used to guide the optimization of a total synthesis of amorpha-4,11-diene (AD) using multienzymes in the mevalonate pathway. A combinatorial approach guided by Taguchi orthogonal array design identified the local optimum enzymatic activity ratio for Erg12:Erg8:Erg19:Idi:IspA to be 100∶100∶1∶25∶5, with a constant concentration of amorpha-4,11-diene synthase (Ads, 100 mg/L). The model also identified an unexpected inhibitory effect of farnesyl pyrophosphate synthase (IspA), where the activity was negatively correlated with AD yield. This was due to the precipitation of farnesyl pyrophosphate (FPP), the product of IspA. Response surface methodology was then used to optimize IspA and Ads activities simultaneously so as to minimize the accumulation of FPP and the result showed that Ads to be a critical factor. By increasing the concentration of Ads, a complete conversion (∼100%) of mevalonic acid (MVA) to AD was achieved. Monovalent ions and pH were effective means of enhancing the specific Ads activity and specific AD yield significantly. The results from this study represent the first in vitro reconstitution of the mevalonate pathway for the production of an isoprenoid and the approaches developed herein may be used to produce other isopentenyl pyrophosphate (IPP)/dimethylallyl pyrophosphate (DMAPP) based products.
The ability to assemble multiple fragments of DNA into a plasmid in a single step is invaluable to studies in metabolic engineering and synthetic biology. Using phosphorothioate chemistry for high efficiency and site specific cleavage of sequences, a novel ligase independent cloning method (cross-lapping in vitro assembly, CLIVA) was systematically and rationally optimized in E. coli. A series of 16 constructs combinatorially expressing genes encoding enzymes in the 1-deoxy-D-xylulose 5-phosphate (DXP) pathway were assembled using multiple DNA modules. A plasmid (21.6 kb) containing 16 pathway genes, was successfully assembled from 7 modules with high efficiency (2.0 x 103 cfu/ µg input DNA) within 2 days. Overexpressions of these constructs revealed the unanticipated inhibitory effects of certain combinations of genes on the production of amorphadiene. Interestingly, the inhibitory effects were correlated to the increase in the accumulation of intracellular methylerythritol cyclodiphosphate (MEC), an intermediate metabolite in the DXP pathway. The overexpression of the iron sulfur cluster operon was found to modestly increase the production of amorphadiene. This study demonstrated the utility of CLIVA in the assembly of multiple fragments of DNA into a plasmid which enabled the rapid exploration of biological pathways.
The metabolic engineer's toolbox, comprising stable isotope tracers, flux estimation and analysis, pathway identification, and pathway kinetics and regulation, among other techniques, has long been used to elucidate and quantify pathways primarily in the context of engineering microbes for producing small molecules of interest. Recently, these tools are increasingly finding use in cancer biology due to their unparalleled capacity for quantifying intracellular metabolism of mammalian cells. Here we review basic concepts that are used to derive useful insights about the metabolism of tumor cells, along with a number of illustrative examples highlighting the fundamental contributions of these methods to elucidating cancer cell metabolism. This area presents unique opportunities for metabolic engineering to expand its portfolio of applications into the realm of cancer biology and help develop new cancer therapies based on a new class of metabolically derived targets.
Isoprenoids are a large and diverse class of compounds that includes many high value natural products and are thus in great demand. To meet the increasing demand for isoprenoid compounds, metabolic engineering of microbes has been used to produce isoprenoids in an economical and sustainable manner. To achieve high isoprenoid yields using this technology, the availability of metabolic precursors feeding the deoxyxylulose phosphate (DXP) pathway, responsible for isoprenoid biosynthesis, has to be optimized. In this study, phosphoenolpyruvate, a vital DXP pathway precursor, was enriched by deleting the genes encoding the carbohydrate phosphotransferase system (PTS) in E. coli. Production of lycopene (a C40 isoprenoid) was maximized by optimizing growth medium and culture conditions. In optimized conditions, the lycopene yield from PTS mutant was seven fold higher than that obtained from the wild type strain. This resulted in the highest reported specific yield of lycopene produced from the DXP pathway in E. coli to date (20,000 µg/g dry cell weight). Both the copy number of the plasmid encoding the lycopene biosynthetic genes and the expression were found to be increased in the optimized media. Deletion of PTS together with a similar optimization strategy was also successful in enhancing the production of amorpha-1,4-diene, a distinct C15 isoprenoid, suggesting that the approaches developed herein can be generally applied to optimize production of other isoprenoids.
Efforts to improve the production of a compound of interest in Saccharomyces cerevisiae have mainly involved engineering or overexpression of cytoplasmic enzymes. We show that targeted expression of metabolic pathways to mitochondria can increase production levels compared with expression of the same pathways in the cytoplasm. Compartmentalisation of the Ehrlich pathway into mitochondria increased isobutanol production by 260%, whereas overexpression of the same pathway in the cytoplasm only improved yields by 10%, compared with a strain overexpressing only the first three steps of the biosynthetic pathway. Subcellular fractionation of engineered strains reveals that targeting the enzymes of the Ehrlich pathway to the mitochondria achieves higher local enzyme concentrations. Other benefits of compartmentalization may include increased availability of intermediates, removing the need to transport intermediates out of the mitochondrion, and reducing the loss of intermediates to competing pathways.
Cancer cells engage in a metabolic program to enhance biosynthesis and support cell proliferation. The regulatory properties of pyruvate kinase M2 (PKM2) influence altered glucose metabolism in cancer. PKM2 interaction with phosphotyrosine-containing proteins inhibits enzyme activity and increases availability of glycolytic metabolites to support cell proliferation. This suggests that high pyruvate kinase activity may suppress tumor growth. We show that expression of PKM1, the pyruvate kinase isoform with high constitutive activity, or exposure to published small molecule PKM2 activators inhibit growth of xenograft tumors. Structural studies reveal that small molecule activators bind PKM2 at the subunit interaction interface, a site distinct from that of the endogenous activator fructose-1,6-bisphosphate (FBP). However, unlike FBP, binding of activators to PKM2 promotes a constitutively active enzyme state that is resistant to inhibition by tyrosine-phosphorylated proteins. These data support the notion that small molecule activation of PKM2 can interfere with anabolic metabolism.
Acetyl coenzyme A (AcCoA) is the central biosynthetic precursor for fatty acid synthesis and protein acetylation. In the conventional view of mammalian cell metabolism, AcCoA is primarily generated from glucose-derived pyruvate through the citrate shuttle and adenosine triphosphate citrate lyase (ACL) in the cytosol1-3. However, proliferating cells that exhibit aerobic glycolysis and those exposed to hypoxia convert glucose to lactate at near stoichiometric levels, directing glucose carbon away from the tricarboxylic acid cycle (TCA) and fatty acid synthesis4. Although glutamine is consumed at levels exceeding that required for nitrogen biosynthesis5, the regulation and utilization of glutamine metabolism in hypoxic cells is not well understood. Here we show that human cells employ reductive metabolism of alpha-ketoglutarate (αKG) to synthesize AcCoA for lipid synthesis. This isocitrate dehydrogenase 1 (IDH1) dependent pathway is active in most cell lines under normal culture conditions, but cells grown under hypoxia rely almost exclusively on the reductive carboxylation of glutamine-derived αKG for de novo lipogenesis. Furthermore, renal cell lines deficient in the von Hippel-Lindau (VHL) tumor suppressor protein preferentially utilize reductive glutamine metabolism for lipid biosynthesis even at normal oxygen levels. These results identify a critical role for oxygen in regulating carbon utilization in order to produce AcCoA and support lipid synthesis in mammalian cells.
Most tumors display increased glucose metabolism compared to that of normal tissues. The preferential conversion of glucose to lactate in cancer cells (the Warburg Effect) has been emphasized1; however, the extent to which metabolic fluxes originating from glucose are utilized for alternative processes is poorly understood2,3. Here we used a combination of mass spectrometry and NMR with stable isotope labeling to investigate the alternate pathways derived from glucose metabolism in cancer cells. We found that in some cancer cells, a relatively large amount of glycolytic carbon is diverted into serine and glycine biosynthesis through phosphoglycerate dehydrogenase (PHGDH). A bioinformatics analysis of 3131 human cancers revealed that the gene PHGDH at 1p12 is recurrently amplified in a genomic region of focal copy number gain most commonly found in melanoma in which amplification was associated with increased protein expression. Decreased PHGDH expression by RNA interference impaired growth and flux into serine metabolism in PHGDH-amplified cell lines. Increased expression was also associated with breast cancer subtypes and ectopic expression of PHGDH in mammary epithelial cells (MCF-10a) disrupted acinar morphogenesis, induced loss of polarity, and preserved the viability of the extracellular matrix-deprived cells, each being phenotypic alterations that may predispose cells to transformation. Our findings demonstrate that altered metabolic flux from glucose into a specific alternate pathway can be selected during tumor development and may contribute to the pathogenesis of human cancer.
Industrial biotechnology promises to revolutionize conventional chemical manufacturing in the years ahead, largely owing to the excellent progress in our ability to re-engineer cellular metabolism. However, most successes of metabolic engineering have been confined to over-producing natively synthesized metabolites in E. coli and S. cerevisiae. A major reason for this development has been the descent of metabolic engineering, particularly secondary metabolic engineering, to a collection of demonstrations rather than a systematic practice with generalizable tools. Synthetic biology, a more recent development, faces similar criticisms. Herein, we attempt to lay down a framework around which bioreaction engineering can systematize itself just like chemical reaction engineering. Central to this undertaking is a new approach to engineering secondary metabolism known as ‘multivariate modular metabolic engineering’ (MMME), whose novelty lies in its assessment and elimination of regulatory and pathway bottlenecks by re-defining the metabolic network as a collection of distinct modules. After introducing the core principles of MMME, we shall then present a number of recent developments in secondary metabolic engineering that could potentially serve as its facilitators. It is hoped that the ever-declining costs of de novo gene synthesis; the improved use of bioinformatic tools to mine, sort and analyze biological data; and the increasing sensitivity and sophistication of investigational tools will make the maturation of microbial metabolic engineering an autocatalytic process. Encouraged by these advances, research groups across the world would take up the challenge of secondary metabolite production in simple hosts with renewed vigor, thereby adding to the range of products synthesized using metabolic engineering.
Metabolic Engineering; Synthetic Biology; Multivariate Analysis; Modularization; Natural Products; Secondary Metabolic Pathways; Terpenoids
Summary: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a software for the non-targeted analysis of all detectable compounds derived from a stable isotope-labeled tracer present in a GC/MS dataset. In contrast to traditional metabolic flux analysis approaches, NTFD does not depend on any a priori knowledge or library information. To obtain dynamic information on metabolic pathway activity, NTFD determines mass isotopomer distributions for all detected and labeled compounds. These data provide information on relative fluxes in a metabolic network. The graphical user interface allows users to import GC/MS data in netCDF format and export all information into a tab-separated format.
Availability: NTFD is C++- and Qt4-based, and it is freely available under an open-source license. Pre-compiled packages for the installation on Debian- and Redhat-based Linux distributions, as well as Windows operating systems, along with example data, are provided for download at http://ntfd.mit.edu/.
Mammalian cells consume and metabolize various substrates from their surroundings for energy generation and biomass synthesis. Glucose and glutamine, in particular, are the primary carbon sources for proliferating cancer cells. While this combination of substrates generates static labeling patterns for use in 13C metabolic flux analysis (MFA), the inability of single tracers to effectively label all pathways poses an obstacle for comprehensive flux determination within a given experiment. To address this issue we applied a genetic algorithm to optimize mixtures of 13C-labeled glucose and glutamine for use in MFA. We identified tracer combinations that minimized confidence intervals in an experimentally determined flux network describing central carbon metabolism in tumor cells. Additional simulations were used to determine the robustness of the [1,2-13C2]glucose/[U-13C5]glutamine tracer combination with respect to perturbations in the network. Finally, we experimentally validated the improved performance of this tracer set relative to glucose tracers alone in a cancer cell line. This versatile method allows researchers to determine the optimal tracer combination to use for a specific metabolic network, and our findings applied to cancer cells significantly enhance the ability of MFA experiments to precisely quantify fluxes in higher organisms.
Metabolic flux analysis; Confidence intervals; Isotopic tracers; Tumor cells; Genetic algorithm
We developed a simple and accurate method for determining deuterium enrichment of glucose hydrogen atoms by electron impact gas chromatography mass spectrometry (GC-MS). First, we prepared 18 derivatives of glucose and screened over 200 glucose fragments to evaluate the accuracy and precision of mass isotopomer data for each fragment. We identified three glucose derivatives that gave six analytically useful ions: (1) glucose aldonitrile pentapropionate (m/z 173 derived from C4–C5 bond cleavage; m/z 259 from C3–C4 cleavage; m/z 284 from C4–C5 cleavage; and m/z 370 from C5–C6 cleavage); (2) glucose 1,2,5,6-di-isopropylidene propionate (m/z 301, no cleavage of glucose carbon atoms); and (3) glucose methyloxime pentapropionate (m/z 145 from C2–C3 cleavage). Deuterium enrichment at each carbon position of glucose was determined by least squares regression of mass isotopomer distributions. The validity of the approach was tested using labeled glucose standards and carefully prepared mixtures of standards. Our method determines deuterium enrichment of glucose hydrogen atoms with an accuracy of 0.3 mol%, or better, without the use of any calibration curves or correction factors. The analysis requires only 20 μL of plasma, which makes the method applicable for studying gluconeogenesis using deuterated water in cell culture and animal experiments.
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.
ensemble modeling; incremental identification; dynamic flux estimation; independent parameter set; generalized mass action model.
An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE). Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified).
In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates) exceeds that of metabolites (chemical species). Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA) models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented.
The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.
Incremental parameter estimation; Kinetic modeling; Metabolic network; GMA model
Recombinant proteins are routinely overexpressed in metabolic engineering. It is well known that some over-expressed heterologous recombinant enzymes are insoluble with little or no enzymatic activity. This study examined the solubility of over-expressed homologous enzymes of the deoxyxylulose phosphate pathway (DXP) and the impact of inclusion body formation on metabolic engineering of microbes.
Four enzymes of this pathway (DXS, ISPG, ISPH and ISPA), but not all, were highly insoluble, regardless of the expression systems used. Insoluble dxs (the committed enzyme of DXP pathway) was found to be inactive. Expressions of fusion tags did not significantly improve the solubility of dxs. However, hypertonic media containing sorbitol, an osmolyte, successfully doubled the solubility of dxs, with the concomitant improvement in microbial production of the metabolite, DXP. Similarly, sorbitol significantly improved the production of soluble and functional ERG12, the committed enzyme in the mevalonate pathway.
This study demonstrated the unanticipated findings that some over-expressed homologous enzymes of the DXP pathway were highly insoluble, forming inclusion bodies, which affected metabolite formation. Sorbitol was found to increase both the solubility and function of some of these over-expressed enzymes, a strategy to increase the production of secondary metabolites.
Isoprenoids; Protein solubility; Deoxyxylulose phosphate pathway; Activity analysis; Metabolic engineering
Isoprenoids are natural products that are all derived from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). These precursors are synthesized either by the mevalonate (MVA) pathway or the 1-Deoxy-D-Xylulose 5-Phosphate (DXP) pathway. Metabolic engineering of microbes has enabled overproduction of various isoprenoid products from the DXP pathway including lycopene, artemisinic acid, taxadiene and levopimaradiene. To date, there is no method to accurately measure all the DXP metabolic intermediates simultaneously so as to enable the identification of potential flux limiting steps. In this study, a solid phase extraction coupled with ultra performance liquid chromatography mass spectrometry (SPE UPLC-MS) method was developed. This method was used to measure the DXP intermediates in genetically engineered E. coli. Unexpectedly, methylerythritol cyclodiphosphate (MEC) was found to efflux when certain enzymes of the pathway were over-expressed, demonstrating the existence of a novel competing pathway branch in the DXP metabolism. Guided by these findings, ispG was overexpressed and was found to effectively reduce the efflux of MEC inside the cells, resulting in a significant increase in downstream isoprenoid production. This study demonstrated the necessity to quantify metabolites enabling the identification of a hitherto unrecognized pathway and provided useful insights into rational design in metabolic engineering.