Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.     

Early Steps in Isoprenoid Biosynthesis: Multilevel Regulation of the Supply of Common Precursors in Plant Cells

Isoprenoids are produced in all organisms but are especially abundant and diverse in plants. Two separate pathways operate in plant cells to synthesize prenyl diphosphate precursors common to all isoprenoids. Cytosolic and mitochondrial precursors are produced by the mevalonic acid (MVA) pathway whereas the recently discovered methylerythritol phosphate (MEP) pathway is located in plastids. However, both pathways may participate in the synthesis of at least some isoprenoids under certain circumstances. Although genes encoding all the enzymes from both pathways have already been cloned, little is known about the regulatory mechanisms that control the supply of isoprenoid precursors. Genetic approaches are providing valuable information on the regulation of both pathways. Thus, recent data from overexpression experiments in transgenic plants show that several enzymes share control over the metabolic flux through the MEP pathway, whereas a single regulatory step has been proposed for the MVA pathway. Identification of Arabidopsis thaliana mutants that are resistant to the inhibition of the MVA and the MEP pathways is a promising approach to uncover mechanisms involved in the crosstalk between pathways. The characterization of some of these mutants impaired in light perception and signaling has recently provided genetic evidence for a role of light as a key factor to modulate the availability of isoprenoid precursors in Arabidopsis seedlings. The picture emerging from recent data supports that a complex regulatory network appears to be at work in plant cells to ensure the supply of isoprenoid precursors when needed.     

Genetically constrained metabolic flux analysis

Significant progress has been made in using existing metabolic databases to estimate metabolic fluxes. Traditional metabolic flux analysis generally starts with a predetermined metabolic network. This approach has been employed successfully to analyze the behaviors of recombinant strains by manually adding or removing the corresponding pathway(s) in the metabolic map. The current work focuses on the development of a new framework that utilizes genomic and metabolic databases, including available genetic/regulatory network structures and gene chip expression data, to constrain metabolic flux analysis. The genetic network consisting of the sensing/regulatory circuits will activate or deactivate a specific set of genes in response to external stimulus. The activation and/or repression of this set of genes will result in different gene expression levels that will in turn change the structure of the metabolic map. Hence, the metabolic map will automatically "adapt" to the external stimulus as captured by the genetic network. This adaptation selects a subnetwork from the pool of feasible reactions and so performs what we term "environmentally driven dimensional reduction." The Escherichia coli oxygen and redox sensing/regulatory system, which controls the metabolic patterns connected to glycolysis and the TCA cycle, was used as a model system to illustrate the proposed approach.     

Metabolic capacity estimation of Escherichia coli as a platform for redox biocatalysis: constraint-based modeling and experimental verification

Whole-cell redox biocatalysis relies on redox cofactor regeneration by the microbial host. Here, we applied flux balance analysis based on the Escherichia coli metabolic network to estimate maximal NADH regeneration rates. With this optimization criterion, simulations showed exclusive use of the pentose phosphate pathway at high rates of glucose catabolism, a flux distribution usually not found in wild-type cells. In silico, genetic perturbations indicated a strong dependency of NADH yield and formation rate on the underlying metabolic network structure. The linear dependency of measured epoxidation activities of recombinant central carbon metabolism mutants on glucose uptake rates and the linear correlation between measured activities and simulated NADH regeneration rates imply intracellular NADH shortage. Quantitative comparison of computationally predicted NADH regeneration and experimental epoxidation rates indicated that the achievable biocatalytic activity is determined by metabolic and enzymatic limitations including non-optimal flux distributions, high maintenance energy demands, energy spilling, byproduct formation, and uncoupling. The results are discussed in the context of cellular optimization of biotransformation processes and may guide a priori design of microbial cells as redox biocatalysts.     

Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS.

We describe here a novel methodology for rapid diagnosis of metabolic changes, which is based on probabilistic equations that relate GC-MS-derived mass distributions in proteinogenic amino acids to in vivo enzyme activities. This metabolic flux ratio analysis by GC-MS provides a comprehensive perspective on central metabolism by quantifying 14 ratios of fluxes through converging pathways and reactions from [1-13C] and [U-13C]glucose experiments. Reliability and accuracy of this method were experimentally verified by successfully capturing expected flux responses of Escherichia coli to environmental modifications and seven knockout mutations in all major pathways of central metabolism. Furthermore, several mutants exhibited additional, unexpected flux responses that provide new insights into the behavior of the metabolic network in its entirety. Most prominently, the low in vivo activity of the Entner-Doudoroff pathway in wild-type E. coli increased up to a contribution of 30% to glucose catabolism in mutants of glycolysis and TCA cycle. Moreover, glucose 6-phosphate dehydrogenase mutants catabolized glucose not exclusively via glycolysis, suggesting a yet unidentified bypass of this reaction. Although strongly affected by environmental conditions, a stable balance between anaplerotic and TCA cycle flux was maintained by all mutants in the upper part of metabolism. Overall, our results provide quantitative insight into flux changes that bring about the resilience of metabolic networks to disruption.     

Comparison of different strategies to reduce acetate formation in Escherichia coli

E. coli cells produce acetate as an extracellular coproduct of aerobic cultures. Acetate is undesirable because it retards growth and inhibits protein formation. Most process designs or genetic modifications to minimize acetate formation aim at balancing growth rate and oxygen consumption. In this research, three genetic approaches to reduce acetate formation were investigated: (1) direct reduction of the carbon flow to acetate (ackA-pta, poxB knock-out); (2) anticipation on the underlying metabolic and regulatory mechanisms that lead to acetate (constitutive ppc expression mutant); and (3) both (1) and (2). Initially, these mutants were compared to the wild-type E. coli via batch cultures under aerobic conditions. Subsequently, these mutants were further characterized using metabolic flux analysis on continuous cultures. It is concluded that a combination of directly reducing the carbon flow to acetate and anticipating on the underlying metabolic and regulatory mechanism that lead to acetate, is the most promising approach to overcome acetate formation and improve recombinant protein production. These genetic modifications have no significant influence on the metabolism when growing the micro-organisms under steady state at relatively low dilution rates (less than 0.4 h(-1)).     

Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control

Cybernetic modeling strives to uncover the inbuilt regulatory programs of biological systems and leverage them toward computational prediction of metabolic dynamics. Because of its focus on incorporating the global aims of metabolism, cybernetic modeling provides a systems-oriented approach for describing regulatory inputs and inferring the impact of regulation within biochemical networks. Combining cybernetic control laws with concepts from metabolic pathway analysis has culminated in a systematic strategy for constructing cybernetic models, which was previously lacking. The newly devised framework relies upon the simultaneous application of local controls that maximize the net flux through each elementary flux mode and global controls that modulate the activities of these modes to optimize the overall nutritional state of the cell. The modeling concepts are illustrated using a simple linear pathway and a larger network representing anaerobic E. coli central metabolism. The E. coli model successfully describes the metabolic shift that occurs upon deleting the pta-ackA operon that is responsible for fermentative acetate production. The model also furnishes predictions that are consistent with experimental results obtained from additional knockout strains as well as strains expressing heterologous genes. Because of the stabilizing influence of the included control variables, the resulting cybernetic models are more robust and reliable than their predecessors in simulating the network response to imposed genetic and environmental perturbations.     

Directed evolution of metabolically engineered Escherichia coli for carotenoid production

We have previously introduced a reconstructed isoprenoid pathway into Escherichia coli that exhibits amplified biosynthetic flux to geranylgeranyl diphosphate (GGPP), a common isoprenoid precursor. It was shown that GGPP synthase is an important rate-controlling enzyme in this reconstructed isoprenoid pathway. In this investigation, we applied directed evolution to GGPP synthase from Archaeoglobus fulgidus to enable the enhanced production of carotenoids in metabolically engineered E. coli. Eight mutants were isolated, and the best one increased lycopene production by 100%. Among the mutants that were isolated, mutation points were clustered in four "hot regions". The "hottest" region is located in the sequence upstream of the coding region, which presumably improves the expression level of the enzyme. The other three are within the coding sequence and are believed to improve the enzyme-specific activity in E coli. These results demonstrate that modulating both enzymatic expression and specific activity are important for optimizing the metabolic flux distribution.     

Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

Increasing the flux through central carbon metabolism is difficult because of rigidity in regulatory structures, at both the genetic and the enzymatic levels. Here we describe metabolic engineering of a regulatory network to obtain a balanced increase in the activity of all the enzymes in the pathway, and ultimately, increasing metabolic flux through the pathway of interest. By manipulating the GAL gene regulatory network of Saccharomyces cerevisiae, which is a tightly regulated system, we produced prototroph mutant strains, which increased the flux through the galactose utilization pathway by eliminating three known negative regulators of the GAL system: Gal6, Gal80, and Mig1. This led to a 41% increase in flux through the galactose utilization pathway compared with the wild-type strain. This is of significant interest within the field of biotechnology since galactose is present in many industrial media. The improved galactose consumption of the gal mutants did not favor biomass formation, but rather caused excessive respiro-fermentative metabolism, with the ethanol production rate increasing linearly with glycolytic flux.     

Metabolic network analysis of lysine producing Corynebacterium glutamicum at a miniaturized scale

We present a straightforward approach comprising (13)C tracer experiments at 200-microL volume in 96-well microtiter plates with on-line measurement of dissolved oxygen for quantitative high-throughput metabolic network analysis at a miniaturized scale. This method was successfully applied for cultivation and (13)C metabolic flux analysis of two mutants of lysine producing Corynebacterium glutamicum (ATCC 13287 and ATCC 21543). Microtiter-plate cultivations showed excellent accordance in kinetics and stoichiometry of growth and product formation as well as in intracellular flux distributions as compared with parallel shake-flask experiments. These cultivations further allowed clear identification of strain-specific flux differences such as increased flux toward lysine, increased flux through the pentose phosphate pathway (PPP), decreased flux through the tricarboxylic (TCA) cycle, and increased dihydroxyacetone formation in C. glutamicum ATCC 21543 compared with ATCC 13287. The present approach has strong potential for broad quantitative screening of metabolic network activities, especially those involving high-cost tracer substrates.     

The landscape of metabolic pathway dependencies in cancer cell lines

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.     

Metabolic pathway analysis of enzyme-deficient human red blood cells

Five enzymopathies (G6PDH, TPI, PGI, DPGM and PGK deficiencies) in the human red blood cells are investigated using a stoichiometric modeling approach, i.e., metabolic pathway analysis. Elementary flux modes (EFMs) corresponding to each enzyme deficiency case are analyzed in terms of functional capabilities. When available, experimental findings reported in literature related to metabolic behavior of the human red blood cells are compared with the results of EFM analysis. Control-effective flux (CEF) calculation, a novel approach which allows quantification and interpretation of determined EFMs, is performed for further analysis of enzymopathies. Glutathione reductase reaction is found to be the most effective reaction in terms of its CEF value in all enzymopathies in parallel with its known essential role for red blood cells. Efficiency profiles of the enzymatic reactions upon the degree of enzyme deficiency are obtained by the help of the CEF approach, as a basis for future experimental studies. CEF analysis, which is found to be promising in the analysis of erythrocyte enzymopathies, has the potential to be used in modeling efforts of human metabolism.     

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.     

CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.     

Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy

Viruses rely on the metabolic network of their cellular hosts to provide energy and building blocks for viral replication. We developed a flux measurement approach based on liquid chromatography-tandem mass spectrometry to quantify changes in metabolic activity induced by human cytomegalovirus (HCMV). This approach reliably elucidated fluxes in cultured mammalian cells by monitoring metabolome labeling kinetics after feeding cells (13)C-labeled forms of glucose and glutamine. Infection with HCMV markedly upregulated flux through much of the central carbon metabolism, including glycolysis. Particularly notable increases occurred in flux through the tricarboxylic acid cycle and its efflux to the fatty acid biosynthesis pathway. Pharmacological inhibition of fatty acid biosynthesis suppressed the replication of both HCMV and influenza A, another enveloped virus. These results show that fatty acid synthesis is essential for the replication of two divergent enveloped viruses and that systems-level metabolic flux profiling can identify metabolic targets for antiviral therapy.     

On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis

Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.