Quantification of central metabolic fluxes in the facultative methylotroph methylobacterium extorquens AM1 using 13C-label tracing and mass spectrometry

Charting the Metabolic Landscape of the Facultative Methylotroph Bacillus methanolicus

Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.

Bacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support B. methanolicus growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol. Because fluxomics provides the most direct readout of the cellular phenotype, we hypothesized that comparing methylotrophic and nonmethylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. In this study, we designed and performed a ¹³C metabolic flux analysis (¹³C-MFA) of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol, and arabitol to compare the associated metabolic states. On methanol, results showed a greater flux in the ribulose monophosphate (RuMP) pathway than in the tricarboxylic acid (TCA) cycle, thus validating previous findings on the methylotrophy of B. methanolicus. New insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP variants were generated. Importantly, we demonstrated that the linear detoxification pathways and the malic enzyme shared with the pentose phosphate pathway have an important role in cofactor regeneration. Finally, we identified, for the first time, the metabolic pathway used to assimilate arabitol. Overall, those data provide a better understanding of this strain under various environmental conditions.

IMPORTANCE Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.

Designing and Engineering Methylorubrum extorquens AM1 for Itaconic Acid Production

Methylorubrum extorquens (formerly Methylobacterium extorquens) AM1 is a methylotrophic bacterium with a versatile lifestyle. Various carbon sources including acetate, succinate and methanol are utilized by M. extorquens AM1 with the latter being a promising inexpensive substrate for use in the biotechnology industry. Itaconic acid (ITA) is a high-value building block widely used in various industries. Given that no wildtype methylotrophic bacteria are able to utilize methanol to produce ITA, we tested the potential of M. extorquens AM1 as an engineered host for this purpose. In this study, we successfully engineered M. extorquens AM1 to express a heterologous codon-optimized gene encoding cis-aconitic acid decarboxylase. The engineered strain produced ITA using acetate, succinate and methanol as the carbon feedstock. The highest ITA titer in batch culture with methanol as the carbon source was 31.6 ± 5.5 mg/L, while the titer and productivity were 5.4 ± 0.2 mg/L and 0.056 ± 0.002 mg/L/h, respectively, in a scaled-up fed-batch bioreactor under 60% dissolved oxygen saturation. We attempted to enhance the carbon flux toward ITA production by impeding poly-β-hydroxybutyrate accumulation, which is used as carbon and energy storage, via mutation of the regulator gene phaR. Unexpectedly, ITA production by the phaR mutant strain was not higher even though poly-β-hydroxybutyrate concentration was lower. Genome-wide transcriptomic analysis revealed that phaR mutation in the ITA-producing strain led to complex rewiring of gene transcription, which might result in a reduced carbon flux toward ITA production. Besides poly-β-hydroxybutyrate metabolism, we found evidence that PhaR might regulate the transcription of many other genes including those encoding other regulatory proteins, methanol dehydrogenases, formate dehydrogenases, malate:quinone oxidoreductase, and those synthesizing pyrroloquinoline quinone and thiamine co-factors. Overall, M. extorquens AM1 was successfully engineered to produce ITA using acetate, succinate and methanol as feedstock, further supporting this bacterium as a feasible host for use in the biotechnology industry. This study showed that PhaR could have a broader regulatory role than previously anticipated, and increased our knowledge of this regulator and its influence on the physiology of M. extorquens AM1.

Metabolomics Revealed an Association of Metabolite Changes and Defective Growth in Methylobacterium extorquens AM1 Overexpressing ecm during Growth on Methanol

Methylobacterium extorquens AM1 is a facultative methylotroph capable of growth on both single-carbon and multi-carbon compounds. The ethylmalonyl-CoA (EMC) pathway is one of the central assimilatory pathways in M. extorquens during growth on C1 and C2 substrates. Previous studies had shown that ethylmalonyl-CoA mutase functioned as a control point during the transition from growth on succinate to growth on ethylamine. In this study we overexpressed ecm, phaA, mcmAB and found that upregulating ecm by expressing it from the strong constitutive mxaF promoter caused a 27% decrease in growth rate on methanol compared to the strain with an empty vector. Targeted metabolomics demonstrated that most of the central intermediates in the ecm over-expressing strain did not change significantly compared to the control strain; However, poly-β-hydroxybutyrate (PHB) was 4.5-fold lower and 3-hydroxybutyryl-CoA was 1.6-fold higher. Moreover, glyoxylate, a toxic and highly regulated essential intermediate, was determined to be 2.6-fold higher when ecm was overexpressed. These results demonstrated that overexpressing ecm can manipulate carbon flux through the EMC pathway and divert it from the carbon and energy storage product PHB, leading to an accumulation of glyoxylate. Furthermore, untargeted metabolomics discovered two unusual metabolites, alanine (Ala)-meso-diaminopimelic acid (mDAP) and Ala-mDAP-Ala, each over 45-fold higher in the ecm over-expressing strain. These two peptides were also found to be highly produced in a dose-dependent manner when glyoxylate was added to the control strain. Overall, this work has explained a direct association of ecm overexpression with glyoxylate accumulation up to a toxic level, which inhibits cell growth on methanol. This research provides useful insight for manipulating the EMC pathway for efficiently producing high-value chemicals in M. extorquens.

High-level production of ethylmalonyl-CoA pathway-derived dicarboxylic acids by Methylobacterium extorquens under cobalt-deficient conditions and by polyhydroxybutyrate negative strains

Bio-based production of dicarboxylic acids is an emerging research field with remarkable progress during the last decades. The recently established synthesis of the ethylmalonyl-CoA pathway (EMCP)-derived dicarboxylic acids, mesaconic acid and (2S)-methylsuccinic acid, from the alternative carbon source methanol (Sonntag et al., Appl Microbiol Biotechnol 98:4533-4544, 2014) gave a proof of concept for the sustainable production of hitherto biotechnologically inaccessible monomers. In this study, substantial optimizations of the process by different approaches are presented. Abolishment of mesaconic and (2S)-methylsuccinic acid reuptake from culture supernatant and a productivity increase were achieved by 30-fold decreased sodium ion availability in culture medium. Undesired flux from EMCP into polyhydroxybutyrate (PHB) cycle was hindered by the knockout of polyhydroxyalkanoate synthase phaC which was concomitant with 5-fold increased product concentrations. However, frequently occurring suppressors of strain ΔphaC lost their beneficial properties probably due to redirected channeling of acetyl-CoA. Pool sizes of the product precursors were increased by exploiting the presence of two cobalt-dependent mutases in the EMCP: Fine-tuned growth-limiting cobalt concentrations led to 16-fold accumulation of mesaconyl- and (2S)-methylsuccinyl-CoA which in turn resulted in 6-fold increased concentrations of mesaconic and (2S)-methylsuccinic acids, with a combined titer of 0.65 g/l, representing a yield of 0.17 g/g methanol. This work represents an important step toward an industrially relevant production of ethylmalonyl-CoA pathway-derived dicarboxylic acids and the generation of a stable PHB synthesis negative Methylobacterium extorquens strain.

Ethylmalonyl coenzyme A mutase operates as a metabolic control point in Methylobacterium extorquens AM1

The metabolism of one- and two-carbon compounds by the methylotrophic bacterium Methylobacterium extorquens AM1 involves high carbon flux through the ethylmalonyl coenzyme A (ethylmalonyl-CoA) pathway (EMC pathway). During growth on ethylamine, the EMC pathway operates as a linear pathway carrying the full assimilatory flux to produce glyoxylate, malate, and succinate. Assimilatory carbon enters the ethylmalonyl-CoA pathway directly as acetyl-CoA, bypassing pathways for formaldehyde oxidation/assimilation and the regulatory mechanisms controlling them, making ethylamine growth a useful condition to study the regulation of the EMC pathway. Wild-type M. extorquens cells were grown at steady state on a limiting concentration of succinate, and the growth substrate was then switched to ethylamine, a condition where the cell must make a sudden switch from utilizing the tricarboxylic acid (TCA) cycle to using the ethylmalonyl-CoA pathway for assimilation, which has been an effective strategy for identifying metabolic control points. A 9-h lag in growth was observed, during which butyryl-CoA, a degradation product of ethylmalonyl-CoA, accumulated, suggesting a metabolic imbalance. Ethylmalonyl-CoA mutase activity increased to a level sufficient for the observed growth rate at 9 h, which correlated with an upregulation of RNA transcripts for ecm and a decrease in the levels of ethylmalonyl-CoA. When the wild-type strain overexpressing ecm was tested with the same substrate switchover experiment, ethylmalonyl-CoA did not accumulate, growth resumed earlier, and, after a transient period of slow growth, the culture grew at a higher rate than that of the control. These findings demonstrate that ethylmalonyl-CoA mutase is a metabolic control point in the EMC pathway, expanding our understanding of its regulation.

Methylobacterium extorquens: methylotrophy and biotechnological applications

Methylotrophy is the ability to use reduced one-carbon compounds, such as methanol, as a single source of carbon and energy. Methanol is, due to its availability and potential for production from renewable resources, a valuable feedstock for biotechnology. Nature offers a variety of methylotrophic microorganisms that differ in their metabolism and represent resources for engineering of value-added products from methanol. The most extensively studied methylotroph is the Alphaproteobacterium Methylobacterium extorquens. Over the past five decades, the metabolism of M. extorquens has been investigated physiologically, biochemically, and more recently, using complementary omics technologies such as transcriptomics, proteomics, metabolomics, and fluxomics. These approaches, together with a genome-scale metabolic model, facilitate system-wide studies and the development of rational strategies for the successful generation of desired products from methanol. This review summarizes the knowledge of methylotrophy in M. extorquens, as well as the available tools and biotechnological applications.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.

The ability of flux balance analysis to predict evolution of central metabolism scales with the initial distance to the optimum

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a ¹³C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.

The most common method of modeling genome-scale metabolism, flux balance analysis, involves using known stoichiometry to define feasible metabolic states and then choosing between these states by proposing that evolution has selected a metabolic flux that optimizes fitness. But does evolution optimize metabolism, and if so, what component of metabolism equates to fitness? We directly tested the underlying assumption of stoichiometric optimality by comparing predicted flux distributions with changes in fluxes that occurred following experimental evolution. Across three experiments ranging in length from a few hundred to fifty thousand generations, we found that substrate uptake – an input to the model – always increased, but supposed optimality criteria such as yield only increased sometimes. Despite this, there was a clear trend. Highly optimal ancestors evolved slightly lower yield in the course of increasing the overall rate, whereas more sub-optimal strains were able to increase both. These results suggest that flux balance analysis is capable of predicting either the initial metabolic behavior of strains or how they will evolve, but not both.

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

GENERIC ENZYMATIC RATE EQUATION UNDER LIVING CONDITIONS

Based on our experience in kinetic modeling of coupled multiple metabolic pathways, we propose a generic rate equation for the dynamical modeling of metabolic kinetics. It is symmetric for forward and backward reactions. Its Michaelis-Menten-King-Altman form makes the kinetic parameters (or functions) easy to relate to experimental values in the database and to use in computation. In addition, such a uniform form is ready to arbitrary number of substrates and products with different stiochiometry. We explicitly show how to obtain such rate equations rigorously for three well-known binding mechanisms. Hence, the proposed rate equation is formally exact under the quasi-steady state condition. Various features of this generic rate equation are discussed. In particular, for irreversible reactions, the product inhibition which directly arises from enzymatic reaction is eliminated in a natural way. We also discuss how to include the effects of modifiers and cooperativity.

Genome-scale reconstruction and system level investigation of the metabolic network of Methylobacterium extorquens AM1

Background

Methylotrophic microorganisms are playing a key role in biogeochemical processes - especially the global carbon cycle - and have gained interest for biotechnological purposes. Significant progress was made in the recent years in the biochemistry, genetics, genomics, and physiology of methylotrophic bacteria, showing that methylotrophy is much more widespread and versatile than initially assumed. Despite such progress, system-level description of the methylotrophic metabolism is currently lacking, and much remains to understand regarding the network-scale organization and properties of methylotrophy, and how the methylotrophic capacity emerges from this organization, especially in facultative organisms.

Results

In this work, we report on the integrated, system-level investigation of the metabolic network of the facultative methylotroph Methylobacterium extorquens AM1, a valuable model of methylotrophic bacteria. The genome-scale metabolic network of the bacterium was reconstructed and contains 1139 reactions and 977 metabolites. The sub-network operating upon methylotrophic growth was identified from both in silico and experimental investigations, and ¹³C-fluxomics was applied to measure the distribution of metabolic fluxes under such conditions. The core metabolism has a highly unusual topology, in which the unique enzymes that catalyse the key steps of C1 assimilation are tightly connected by several, large metabolic cycles (serine cycle, ethylmalonyl-CoA pathway, TCA cycle, anaplerotic processes). The entire set of reactions must operate as a unique process to achieve C1 assimilation, but was shown to be structurally fragile based on network analysis. This observation suggests that in nature a strong pressure of selection must exist to maintain the methylotrophic capability. Nevertheless, substantial substrate cycling could be measured within C2/C3/C4 inter-conversions, indicating that the metabolic network is highly versatile around a flexible backbone of central reactions that allows rapid switching to multi-carbon sources.

Conclusions

This work emphasizes that the metabolism of M. extorquens AM1 is adapted to its lifestyle not only in terms of enzymatic equipment, but also in terms of network-level structure and regulation. It suggests that the metabolism of the bacterium has evolved both structurally and functionally to an efficient but transitory utilization of methanol. Besides, this work provides a basis for metabolic engineering to convert methanol into value-added products.

Fast growth increases the selective advantage of a mutation arising recurrently during evolution under metal limitation

Understanding the evolution of biological systems requires untangling the molecular mechanisms that connect genetic and environmental variations to their physiological consequences. Metal limitation across many environments, ranging from pathogens in the human body to phytoplankton in the oceans, imposes strong selection for improved metal acquisition systems. In this study, we uncovered the genetic and physiological basis of adaptation to metal limitation using experimental populations of Methylobacterium extorquens AM1 evolved in metal-deficient growth media. We identified a transposition mutation arising recurrently in 30 of 32 independent populations that utilized methanol as a carbon source, but not in any of the 8 that utilized only succinate. These parallel insertion events increased expression of a novel transporter system that enhanced cobalt uptake. Such ability ensured the production of vitamin B₁₂, a cobalt-containing cofactor, to sustain two vitamin B₁₂–dependent enzymatic reactions essential to methanol, but not succinate, metabolism. Interestingly, this mutation provided higher selective advantages under genetic backgrounds or incubation temperatures that permit faster growth, indicating growth-rate–dependent epistatic and genotype-by-environment interactions. Our results link beneficial mutations emerging in a metal-limiting environment to their physiological basis in carbon metabolism, suggest that certain molecular features may promote the emergence of parallel mutations, and indicate that the selective advantages of some mutations depend generically upon changes in growth rate that can stem from either genetic or environmental influences.

Effects of mutations can change under different genetic backgrounds or environmental factors, also known as epistasis and genotype-by-environment interactions (G×E), respectively. Though epistasis and G×E are traditionally treated as distinct phenomena, our study of a beneficial mutation highlights their commonality. This mutation resulted from insertion of the same transposable element upstream of a novel cobalt transport system in 30 of 32 independent populations during evolution in metal-limited media. The resulting increased cobalt uptake provided a selective benefit that depended upon two environmental factors: cobalt limitation and growth substrates whose metabolism requires a particular vitamin B₁₂ (which contains cobalt) -dependent biochemical pathway. Furthermore, this mutation exhibited epistatic and G×E interactions with other cellular processes in a generic way, such that its selective advantage increased as cells were able to grow faster. This growth-rate dependence accords with a simple model: the slowest of multiple physiological processes needed for growth exerts the greatest control over an organism's growth rate. It suggests that as growth results from the performance of the entire physiological system, genes or environmental factors that affect distinct physiological processes may thus interact through their convergent effects on growth phenotypes.

Effect of CO2 on succinate production in dual-phase Escherichia coli fermentations

Succinate production under different concentrations of carbon dioxide (CO(2)) was studied in Escherichia coli AFP111, which contains mutations in pyruvate formate lyase (pfl), lactate dehydrogenase (ldhA) and the phosphotransferase system glucosephosphotransferase enzyme II (ptsG). A series of two-phase fermentations were conducted in which an aerobic cell growth phase was followed by an anaerobic succinate production phase using several constant concentrations of CO(2). As the concentration of CO(2) in the gas phase increased from 0% to 50%, the succinate specific productivity increased from 1.9 mg/gh to 225 mg/gh, and the succinate yield increased from 0.04 g/g to 0.75 g/g. Above 50% CO(2), succinate production did not increase further. Intracellular fluxes were determined at three different CO(2) concentrations (3%, 10%, and 50%) using (13)C-label tracing coupled with LC-MS analysis. The fraction of carbon flux into the pentose phosphate pathway increased from 0.04 at 3% CO(2) to 0.17 at 50% CO(2). Also, the fractional flux through anaplerotic carboxylation at the phosphoenolpyruvate (PEP) node increased slightly from 0.53 at 3% CO(2) to 0.63 at 50% CO(2). The increased flux into the pentose phosphate pathway is attributed to an increased demand for reduced cofactors with elevated CO(2). A four-process explicit model to describe the CO(2) transfer and utilization was proposed. The model predicted that at CO(2) concentrations below about 30-40% the system becomes limited by gas phase CO(2), while at higher CO(2) concentrations the system is limited by PEP carboxylase enzyme kinetics.

Increasing the accuracy of mass isotopomer analysis through calibration curves constructed using biologically synthesized compounds

Mass isotopomer analysis is an important technique to measure the production and flow of metabolites in living cells, tissues, and organisms. This technique depends on accurate quantifications of different mass isotopomers using mass spectrometry. Constructing calibration curves using standard samples is the most universal approach to convert raw mass spectrometry measurements into quantitative distributions of mass isotopomers. Calibration curve approach has been, however, of very limited use in comprehensive analyses of biological systems, mainly suffering from the lack of extensive range of standard samples with accurately known isotopic enrichment. Here, we present a biological method capable of synthesizing specifically labeled amino acids. These amino acids have well-determined and estimable mass isotopomer distributions and thus can serve as standard samples. In this method, the bacterium strain Methylobacterium salsuginis sp. nov. was cultivated with partially 13C-labeled methanol as the only carbon source to produce 13C-enriched compounds. We show that the mass isotopomer distributions of the various biosynthesized amino acids are well determined and can be reasonably estimated based on proposed binomial approximation if the labeling state of the biomass reached an isotopic steady state. The interference of intramolecular inhomogeneity of 13C isotope abundances caused by biological isotope fractionation was eliminated by estimating average 13C isotope abundance. Further, the predictions are tested experimentally by mass spectrometry (MS) spectra of the labeled glycine, alanine, and aspartic acid. Most of the error in mass spectrometry measurements was less than 0.74 mol% in the test case, significantly reduced as compared with uncalibrated results, and this error is expected to be less than 0.4 mol% in real experiment as revealed by theoretical analysis.

Asymmetric, bimodal trade-offs during adaptation of Methylobacterium to distinct growth substrates

Trade-offs between selected and nonselected environments are often assumed to exist during adaptation. This phenomenon is prevalent in microbial metabolism, where many organisms have come to specialize on a narrow breadth of substrates. One well-studied example is methylotrophic bacteria that can use single-carbon (C(1)) compounds as their sole source of carbon and energy, but generally use few, if any, multi-C compounds. Here, we use adaptation of experimental populations of the model methylotroph, Methylobacterium extorquens AM1, to C(1) (methanol) or multi-C (succinate) compounds to investigate specialization and trade-offs between these two metabolic lifestyles. We found a general trend toward trade-offs during adaptation to succinate, but this was neither universal nor showed a quantitative relationship with the extent of adaptation. After 1500 generations, succinate-evolved strains had a remarkably bimodal distribution of fitness values on methanol: either an improvement comparable to the strains adapted on methanol or the complete loss of the ability to grow on C(1) compounds. In contrast, adaptation to methanol resulted in no such trade-offs. Based on the substantial, asymmetric loss of C(1) growth during growth on succinate, we suggest that the long-term maintenance of C(1) metabolism across the genus Methylobacterium requires relatively frequent use of C(1) compounds to prevent rapid loss.

pH and base counterion affect succinate production in dual-phase Escherichia coli fermentations

Succinate production was studied in Escherichia coli AFP111, which contains mutations in pyruvate formate lyase (pfl), lactate dehydrogenase (ldhA) and the phosphotransferase system glucosephosphotransferase enzyme II (ptsG). Two-phase fermentations using a defined medium at several controlled levels of pH were conducted in which an aerobic cell growth phase was followed by an anaerobic succinate production phase using 100% (v/v) CO(2). A pH of 6.4 yielded the highest specific succinate productivity. A metabolic flux analysis at a pH of 6.4 using (13)C-labeled glucose showed that 61% of the PEP partitioned to oxaloacetate and 39% partitioned to pyruvate, while 93% of the succinate was formed via the reductive arm of the TCA cycle. The flux distribution at a pH of 6.8 was also analyzed and was not significantly different compared to that at a pH of 6.4. Ca(OH)(2) was superior to NaOH or KOH as the base for controlling the pH. By maintaining the pH at 6.4 using 25% (w/v) Ca(OH)(2), the process achieved an average succinate productivity of 1.42 g/l h with a yield of 0.61 g/g.

Methanol-based industrial biotechnology: current status and future perspectives of methylotrophic bacteria

Methanol is one of the building blocks in the chemical industry and can be synthesized either from petrochemical or renewable resources, such as biogas. Bioprocess technology with methylotrophic bacteria is well established, as illustrated by large-scale single-cell protein production in the past. During recent years, the first genomes of methylotrophs have been sequenced and significant progress in elucidating their metabolism has been made. In addition, the tool set for genetic engineering of methylotrophic bacteria has expanded greatly and strategies to produce fine and bulk chemicals with methylotrophs have been described. This review highlights the potential of these bacteria for the development of economically competitive bioprocesses based on methanol as an alternative carbon source, bringing together biological, technical and economic considerations.

Formate as the main branch point for methylotrophic metabolism in Methylobacterium extorquens AM1

In serine cycle methylotrophs, methylene tetrahydrofolate (H4F) is the entry point of reduced one-carbon compounds into the serine cycle for carbon assimilation during methylotrophic metabolism. In these bacteria, two routes are possible for generating methylene H4F from formaldehyde during methylotrophic growth: one involving the reaction of formaldehyde with H4F to generate methylene H4F and the other involving conversion of formaldehyde to formate via methylene tetrahydromethanopterin-dependent enzymes and conversion of formate to methylene H4F via H4F-dependent enzymes. Evidence has suggested that the direct condensation reaction is the main source of methylene H4F during methylotrophic metabolism. However, mutants lacking enzymes that interconvert methylene H4F and formate are unable to grow on methanol, suggesting that this route for methylene H4F synthesis should have a significant role in biomass production during methylotrophic metabolism. This problem was investigated in Methylobacterium extorquens AM1. Evidence was obtained suggesting that the existing deuterium assay might overestimate the flux through the direct condensation reaction. To test this possibility, it was shown that only minor assimilation into biomass occurred in mutants lacking the methylene H4F synthesis pathway through formate. These results suggested that the methylene H4F synthesis pathway through formate dominates assimilatory flux. A revised kinetic model was used to validate this possibility, showing that physiologically plausible parameters in this model can account for the metabolic fluxes observed in vivo. These results all support the suggestion that formate, not formaldehyde, is the main branch point for methylotrophic metabolism in M. extorquens AM1.

Elucidation of an alternate isoleucine biosynthesis pathway in Geobacter sulfurreducens

The central metabolic model for Geobacter sulfurreducens included a single pathway for the biosynthesis of isoleucine that was analogous to that of Escherichia coli, in which the isoleucine precursor 2-oxobutanoate is generated from threonine. 13C labeling studies performed in G. sulfurreducens indicated that this pathway accounted for a minor fraction of isoleucine biosynthesis and that the majority of isoleucine was instead derived from acetyl-coenzyme A and pyruvate, possibly via the citramalate pathway. Genes encoding citramalate synthase (GSU1798), which catalyzes the first dedicated step in the citramalate pathway, and threonine ammonia-lyase (GSU0486), which catalyzes the conversion of threonine to 2-oxobutanoate, were identified and knocked out. Mutants lacking both of these enzymes were auxotrophs for isoleucine, whereas single mutants were capable of growth in the absence of isoleucine. Biochemical characterization of the single mutants revealed deficiencies in citramalate synthase and threonine ammonia-lyase activity. Thus, in G. sulfurreducens, 2-oxobutanoate can be synthesized either from citramalate or threonine, with the former being the main pathway for isoleucine biosynthesis. The citramalate synthase of G. sulfurreducens constitutes the first characterized member of a phylogenetically distinct clade of citramalate synthases, which contains representatives from a wide variety of microorganisms.

Metabolite profiling analysis ofMethylobacterium extorquens AM1 by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry

Methylobacterium extorquens AM1 is a facultative methylotroph, which is a potential candidate to be used in commercial processes to convert simple one-carbon compounds to a variety of multicarbon chemicals and products. To better understand C(1) metabolism in M. extorquens AM1 at the systems level, metabolite profiling tools were developed and applied in this bacterium. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC x GC-TOFMS) was used to obtain metabolite profiles of M. extorquens AM1 (primarily organic acids) and to identify the metabolite differences between cells grown on methanol (C(1) substrate) and succinate (multicarbon substrate). In this study, a list of compounds that included amino acids and major intermediates of central C(1) and multicarbon metabolism were studied as target metabolites. For these, calibration curves were obtained for absolute quantification by spiking different amounts of standard mixtures to cell cultures. Parallel factor analysis (PARAFAC) was used for accurate peak quantification. Unknown chemical differences between cells grown on methanol and succinate were identified by applying Fisher ratio analysis at a selective mass channel (m/z 147). Thirty-six compounds were discovered to be statistically differentially expressed between C(1) and multicarbon metabolism. Among these, 13 were identified by matching to library mass spectra, and the rest were novel compounds that were not included in libraries. These differentially expressed compounds have provided clues to new pathways that are specifically linked to C(1) metabolism.

Identification of a fourth formate dehydrogenase in Methylobacterium extorquens AM1 and confirmation of the essential role of formate oxidation in methylotrophy

A mutant of Methylobacterium extorquens AM1 with lesions in genes for three formate dehydrogenase (FDH) enzymes was previously described by us (L. Chistoserdova, M. Laukel, J.-C. Portais, J. A. Vorholt, and M. E. Lidstrom, J. Bacteriol. 186:22-28, 2004). This mutant had lost its ability to grow on formate but still maintained the ability to grow on methanol. In this work, we further investigated the phenotype of this mutant. Nuclear magnetic resonance experiments with [13C]formate, as well as 14C-labeling experiments, demonstrated production of labeled CO2 in the mutant, pointing to the presence of an additional enzyme or a pathway for formate oxidation. The tungsten-sensitive phenotype of the mutant suggested the involvement of a molybdenum-dependent enzyme. Whole-genome array experiments were conducted to test for genes overexpressed in the triple-FDH mutant compared to the wild type, and a gene (fdh4A) was identified whose translated product carried similarity to an uncharacterized putative molybdopterin-binding oxidoreductase-like protein sharing relatively low similarity with known formate dehydrogenase alpha subunits. Mutation of this gene in the triple-FDH mutant background resulted in a methanol-negative phenotype. When the gene was deleted in the wild-type background, the mutant revealed diminished growth on methanol with accumulation of high levels of formate in the medium, pointing to an important role of FDH4 in methanol metabolism. The identity of FDH4 as a novel FDH was also confirmed by labeling experiments that revealed strongly reduced CO2 formation in growing cultures. Mutation of a small open reading frame (fdh4B) downstream of fdh4A resulted in mutant phenotypes similar to the phenotypes of fdh4A mutants, suggesting that fdh4B is also involved in formate oxidation.

A transient isotopic labeling methodology for 13C metabolic flux analysis of photoautotrophic microorganisms

Metabolic flux analysis is increasingly recognized as an integral component of systems biology. However, techniques for experimental measurement of system-wide metabolic fluxes in purely photoautotrophic systems (growing on CO(2) as the sole carbon source) have not yet been developed due to the unique problems posed by such systems. In this paper, we demonstrate that an approach that balances positional isotopic distributions transiently is the only route to obtaining system-wide metabolic flux maps for purely autotrophic metabolism. The outlined transient (13)C-MFA methodology enables measurement of fluxes at a metabolic steady-state, while following changes in (13)C-labeling patterns of metabolic intermediates as a function of time, in response to a step-change in (13)C-label input. We use mathematical modeling of the transient isotopic labeling patterns of central intermediates to assess various experimental requirements for photoautotrophic MFA. This includes the need for intracellular metabolite concentration measurements and isotopic labeling measurements as a function of time. We also discuss photobioreactor design and operation in order to measure fluxes under precise environmental conditions. The transient MFA technique can be used to measure and compare fluxes under different conditions of light intensity, nitrogen sources or compare strains with various mutations or gene deletions and additions.

Cellular metabolomics of Escherchia coli

Escherichia coli is among the simplest and best-understood free-living organisms. It has served as a valuable model for numerous biological processes, including cellular metabolism. Just as E. coli stood at the front of the genomic revolution, it is playing a leading role in the development of cellular metabolomics: the study of the complete metabolic contents of cells, including their dynamic concentration changes and fluxes. This review briefly describes the essentials of cellular metabolomics and its fundamental differentiation from biomarker metabolomics and lipidomics. Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry. In particular emphasis is given to the cell handling and sample preparation steps required for collecting data of high biological reliability, such as fast metabolome quenching. Future challenges, both in terms of data collection and application of the data to obtain a comprehensive understanding of metabolic dynamics, are discussed.

Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

BACKGROUND

In recent years, constrained optimization - usually referred to as flux balance analysis (FBA) - has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions.

RESULTS

To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of Escherichia coli (931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state.

CONCLUSION

Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered.

Metabolic flux elucidation for large-scale models using 13C labeled isotopes

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large scale. When cells are fed a growth substrate with certain carbon positions labeled with (13)C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the antimalarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-(13)C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

Engineering primary metabolic pathways of industrial micro-organisms

Metabolic engineering is a powerful tool for the optimisation and the introduction of new cellular processes. This is mostly done by genetic engineering. Since the introduction of this multidisciplinary approach, the success stories keep accumulating. The primary metabolism of industrial micro-organisms has been studied for long time and most biochemical pathways and reaction networks have been elucidated. This large pool of biochemical information, together with data from proteomics, metabolomics and genomics underpins the strategies for design of experiments and choice of targets for manipulation by metabolic engineers. These targets are often located in the primary metabolic pathways, such as glycolysis, pentose phosphate pathway, the TCA cycle and amino acid biosynthesis and mostly at major branch points within these pathways. This paper describes approaches taken for metabolic engineering of these pathways in bacteria, yeast and filamentous fungi.

Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition

Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied.

Isotopomer analysis of myocardial substrate metabolism: a systems biology approach

The increasing accessibility of mass isotopomer data via GC-MS and NMR technology has necessitated the use of a systematic and reliable method to take advantage of such data for flux analysis. Here we applied a nonlinear, optimization-based method to study substrate metabolism in cardiomyocytes using (13)C data from perfused mouse hearts. The myocardial metabolic network used in this study accounts for 257 reactions and 240 metabolites, which are further compartmentalized into extracellular space, cytosol, and mitochondrial matrix. Analysis of the perfused mouse heart showed that the steady-state ATP production rate was 16.6 +/- 2.3 micromol/min . gww, with 30% of the ATP coming from glycolysis. Of the four substrates available in the perfusate (glucose, pyruvate, lactate, and oleate), exogenous glucose forms the majority of cytosolic pyruvate. Pyruvate decaboxylation is significantly higher than carboxylation, suggesting that anaplerosis is low in the perfused heart. Exchange fluxes were predicted to be high for reversible enzymes in the citric acid cycle (CAC), but low in the glycolytic pathway. Pseudoketogenesis amounted to approximately 50% of the net ketone body uptake. Sensitivity analysis showed that the estimated flux distributions were relatively insensitive to experimental errors. The application of isotopomer data drastically improved the estimation of reaction fluxes compared to results computed with respect to reaction stoichiometry alone. Further study of 12 commonly used (13)C glucose mixtures showed that the mixtures of 20% [U-(13)C(6)] glucose, 80% [3 (13)C] glucose and 20% [U-(13)C(6)] glucose, 80% [4 (13)C] were best for resolving fluxes in the current network.

Measurement of respiration rates of Methylobacterium extorquens AM1 cultures by use of a phosphorescence-based sensor

Respiration rates of bacterial cultures can be a powerful tool in gauging the effects of genetic manipulation and environmental changes affecting overall metabolism. We present an optical method for measuring respiration rates using a robust phosphorescence lifetime-based sensor and off-the-shelf technology. This method was tested with the facultative methylotroph Methylobacterium extorquens AM1 to demonstrate subtle mutant phenotypes.

Revisiting the 13C-label distribution of the non-oxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence

The currently applied reaction structure in stoichiometric flux balance models for the nonoxidative branch of the pentose phosphate pathway is not in accordance with the established ping-pong kinetic mechanism of the enzymes transketolase (EC 2.2.1.1) and transaldolase (EC 2.2.1.2). Based upon the ping-pong mechanism, the traditional reactions of the nonoxidative branch of the pentose phosphate pathway are replaced by metabolite specific, reversible, glycolaldehyde moiety (C(2)) and dihydroxyacetone moiety (C(3)) fragments producing and consuming half-reactions. It is shown that a stoichiometric model based upon these half-reactions is fundamentally different from the currently applied stoichiometric models with respect to the number of independent C(2) and C(3) fragment pools in the pentose phosphate pathway and can lead to different label distributions for (13)C-tracer experiments. To investigate the actual impact of the new reaction structure on the estimated flux patterns within a cell, mass isotopomer measurements from a previously published (13)C-based metabolic flux analysis of Saccharomyces cerevisiae were used. Different flux patterns were found. From a genetic point of view, it is well known that several micro-organisms, including Escherichia coli and S. cerevisiae, contain multiple genes encoding isoenzymes of transketolase and transaldolase. However, the extent to which these gene products are also actively expressed remains unknown. It is shown that the newly proposed stoichiometric model allows study of the effect of isoenzymes on the (13)C-label distribution in the nonoxidative branch of the pentose phosphate pathway by extending the half-reaction based stoichiometric model with two distinct transketolase enzymes instead of one. Results show that the inclusion of isoenzymes affects the ensuing flux estimates.

OptStrain: a computational framework for redesign of microbial production systems

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

Flux analysis uncovers key role of functional redundancy in formaldehyde metabolism

Genome-scale analysis of predicted metabolic pathways has revealed the common occurrence of apparent redundancy for specific functional units, or metabolic modules. In many cases, mutation analysis does not resolve function, and instead, direct experimental analysis of metabolic flux under changing conditions is necessary. In order to use genome sequences to build models of cellular function, it is important to define function for such apparently redundant systems. Here we describe direct flux measurements to determine the role of redundancy in three modules involved in formaldehyde assimilation and dissimilation in a bacterium growing on methanol. A combination of deuterium and (14)C labeling was used to measure the flux through each of the branches of metabolism for growth on methanol during transitions into and out of methylotrophy. The cells were found to differentially partition formaldehyde among the three modules depending on the flux of methanol into the cell. A dynamic mathematical model demonstrated that the kinetic constants of the enzymes involved are sufficient to account for this phenomenon. We demonstrate the role of redundancy in formaldehyde metabolism and have uncovered a new paradigm for coping with toxic, high-flux metabolic intermediates: a dynamic, interconnected metabolic loop.

High-throughput phenomics: experimental methods for mapping fluxomes

Many technologies have been developed to help explain the phenotypic consequences of genetic and/or environmental modifications in areas like functional genomics, pharmaceutical research and metabolic engineering. The missing link in contemporary functional analyses that focus on the analysis of cellular components is the capacity to directly observe functional units. By linking genes and proteins to higher level biological functions, the molecular fluxes through metabolic networks (the fluxome) determine the cellular phenotype. Quantitative monitoring of such whole network operations by methods of metabolic flux analysis, thus bridges the gap by providing a global perspective of the integrated regulation at the transcriptional, translational and metabolic level. This review highlights recent developments towards high-throughput flux analysis.

The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks

Cellular functions are ultimately linked to metabolic fluxes brought about by thousands of chemical reactions and transport processes. The synthesis of the underlying enzymes and membrane transporters causes the cell a certain 'effort' of energy and external resources. Considering that those cells should have had a selection advantage during natural evolution that enabled them to fulfil vital functions (such as growth, defence against toxic compounds, repair of DNA alterations, etc.) with minimal effort, one may postulate the principle of flux minimization, as follows: given the available external substrates and given a set of functionally important 'target' fluxes required to accomplish a specific pattern of cellular functions, the stationary metabolic fluxes have to become a minimum. To convert this principle into a mathematical method enabling the prediction of stationary metabolic fluxes, the total flux in the network is measured by a weighted linear combination of all individual fluxes whereby the thermodynamic equilibrium constants are used as weighting factors, i.e. the more the thermodynamic equilibrium lies on the right-hand side of the reaction, the larger the weighting factor for the backward reaction. A linear programming technique is applied to minimize the total flux at fixed values of the target fluxes and under the constraint of flux balance (= steady-state conditions) with respect to all metabolites. The theoretical concept is applied to two metabolic schemes: the energy and redox metabolism of erythrocytes, and the central metabolism of Methylobacterium extorquens AM1. The flux rates predicted by the flux-minimization method exhibit significant correlations with flux rates obtained by either kinetic modelling or direct experimental determination. Larger deviations occur for segments of the network composed of redundant branches where the flux-minimization method always attributes the total flux to the thermodynamically most favourable branch. Nevertheless, compared with existing methods of structural modelling, the principle of flux minimization appears to be a promising theoretical approach to assess stationary flux rates in metabolic systems in cases where a detailed kinetic model is not yet available.