Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Substrate cycles in Penicillium chrysogenum quantified by isotopic non-stationary flux analysis

Background

Penicillium chrysogenum, the main production strain for penicillin-G, has a high content of intracellular carbohydrates, especially reduced sugars such as mannitol, arabitol, erythritol, as well as trehalose and glycogen. In previous steady state ¹³C wash-in experiments a delay of labeling enrichments in glycolytic intermediates was observed, which suggests turnover of storage carbohydrates. The turnover of storage pools consumes ATP which is expected to reduce the product yield for energy demanding production pathways like penicillin-G.

Results

In this study, a ¹³C labeling wash-in experiment of 1 hour was performed to systematically quantify the intracellular flux distribution including eight substrate cycles. The experiments were performed using a mixed carbon source of 85% Cmol_Glc/Cmol_Glc+EtOH labeled glucose (mixture of 90% [1-¹³C₁] and 10% [U-¹³C₆]) and 15% ethanol [U-¹³C₂]. It was found, that (1) also several extracellular pools are enriched with ¹³C labeling rapidly (trehalose, mannitol, and others), (2) the intra- to extracellular metabolite concentration ratios were comparable for a large set of metabolites while for some carbohydrates (mannitol, trehalose, and glucose) the measured ratios were much higher.

Conclusions

The fast enrichment of several extracellular carbohydrates and a concentration ratio higher than the ratio expected from cell lysis (2%) indicate active (e.g. ATP consuming) transport cycles over the cellular membrane. The flux estimation indicates, that substrate cycles account for about 52% of the gap in the ATP balance based on metabolic flux analysis.

Expanding the concepts and tools of metabolic engineering to elucidate cancer metabolism

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.

Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation

The combination of high-resolution LC–MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly ¹³C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

Recent advances in mapping environmental microbial metabolisms through 13C isotopic fingerprints

After feeding microbes with a defined (13)C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of (13)C approaches to analyse novel cell metabolisms for the past 3 years. The isotopic fingerprints (defined as unique isotopomers useful for pathway identifications) have revealed the operations of the Entner-Doudoroff pathway, the reverse tricarboxylic acid cycle, new enzymes for biosynthesis of central metabolites, diverse respiration routes in phototrophic metabolism, co-metabolism of carbon nutrients and novel CO(2) fixation pathways. This review also discusses new isotopic methods to map carbon fluxes in global metabolisms, as well as potential factors influencing the metabolic flux quantification (e.g. metabolite channelling, the isotopic purity of (13)C substrates and the isotopic effect). Although (13)C labelling is not applicable to all biological systems (e.g. microbial communities), recent studies have shown that this method has a significant value in functional characterization of poorly understood micro-organisms, including species relevant for biotechnology and human health.

13C-flux analysis reveals NADPH-balancing transhydrogenation cycles in stationary phase of nitrogen-starving Bacillus subtilis

In their natural habitat, microorganisms are typically confronted with nutritional limitations that restrict growth and force them to persevere in a stationary phase. Despite the importance of this phase, little is known about the metabolic state(s) that sustains it. Here, we investigate metabolically active but non-growing Bacillus subtilis during nitrogen starvation. In the absence of biomass formation as the major NADPH sink, the intracellular flux distribution in these resting B. subtilis reveals a large apparent catabolic NADPH overproduction of 5.0 ± 0.6 mmol g(-1)h(-1) that was partly caused by high pentose phosphate pathway fluxes. Combining transcriptome analysis, stationary (13)C-flux analysis in metabolic deletion mutants, (2)H-labeling experiments, and kinetic flux profiling, we demonstrate that about half of the catabolic excess NADPH is oxidized by two transhydrogenation cycles, i.e. isoenzyme pairs of dehydrogenases with different cofactor specificities that operate in reverse directions. These transhydrogenation cycles were constituted by the combined activities of the glyceraldehyde 3-phosphate dehydrogenases GapA/GapB and the malic enzymes MalS/YtsJ. At least an additional 6% of the overproduced NADPH is reoxidized by continuous cycling between ana- and catabolism of glutamate. Furthermore, in vitro enzyme data show that a not yet identified transhydrogenase could potentially reoxidize ∼20% of the overproduced NADPH. Overall, we demonstrate the interplay between several metabolic mechanisms that concertedly enable network-wide NADPH homeostasis under conditions of high catabolic NADPH production in the absence of cell growth in B. subtilis.

Analysis of carbon and nitrogen co-metabolism in yeast by ultrahigh-resolution mass spectrometry applying 13C- and 15N-labeled substrates simultaneously

Alternative metabolic pathways inside a cell can be deduced using stable isotopically labeled substrates. One prerequisite is accurate measurement of the labeling pattern of targeted metabolites. Experiments are generally limited to the use of single-element isotopes, mainly (13)C. Here, we demonstrate the application of direct infusion nanospray, ultrahigh-resolution Fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) for metabolic studies using differently labeled elemental isotopes simultaneously--i.e., (13)C and (15)N--in amino acids of a total protein hydrolysate. The optimized strategy for the analysis of metabolism by a hybrid linear ion trap-FTICR-MS comprises the collection of multiple adjacent selected ion monitoring scans. By limiting both the width of the mass range and the number of ions entering the ICR cell with automated gain control, sensitive measurements of isotopologue distribution were possible without compromising mass accuracy and isotope intensity mapping. The required mass-resolving power of more than 60,000 is only achievable on a routine basis by FTICR and Orbitrap mass spectrometers. Evaluation of the method was carried out by comparison of the experimental data to the natural isotope abundances of selected amino acids and by comparison to GC/MS results obtained from a labeling experiment with (13)C-labeled glucose. The developed method was used to shed light on the complexity of the yeast Saccharomyces cerevisiae carbon-nitrogen co-metabolism by administering both (13)C-labeled glucose and (15)N-labeled alanine. The results indicate that not only glutamate but also alanine acts as an amino donor during alanine and valine synthesis. Metabolic studies using FTICR-MS can exploit new possibilities by the use of multiple-labeled elemental isotopes.

Development of quantitative metabolomics for Pichia pastoris

Accurate, reliable and reproducible measurement of intracellular metabolite levels has become important for metabolic studies of microbial cell factories. A first critical step for metabolomic studies is the establishment of an adequate quenching and washing protocol, which ensures effective arrest of all metabolic activity and removal of extracellular metabolites, without causing leakage of metabolites from the cells. Five different procedures based on cold methanol quenching and cell separation by filtration were tested for metabolomics of Pichia pastoris regarding methanol content and temperature of the quenching solution as key parameters. Quantitative evaluation of these protocols was carried out through mass balance analysis, based on metabolite measurements in all sample fractions, those are whole broth, quenched and washed cells, culture filtrate and quenching and washing solution. Finally, the optimal method was used to study the time profiles of free amino acid and central carbon metabolism intermediates in glucose-limited chemostat cultures. Acceptable recoveries (>90%) were obtained for all quenching procedures tested. However, quenching at -27°C in 60% v/v methanol performed slightly better in terms of leakage minimization. We could demonstrate that five residence times under glucose limitation are enough to reach stable intracellular metabolite pools. Moreover, when comparing P. pastoris and S. cerevisiae metabolomes, under the same cultivation conditions, similar metabolite fingerprints were found in both yeasts, except for the lower glycolysis, where the levels of these metabolites in P. pastoris suggested an enzymatic capacity limitation in that part of the metabolism. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0308-1) contains supplementary material, which is available to authorized users.

Mass spectrometry-based metabolomics to elucidate functions in marine organisms and ecosystems

Marine systems are very diverse and recognized as being sources of a wide range of biomolecules. This review provides an overview of metabolite profiling based on mass spectrometry (MS) approaches in marine organisms and their environments, focusing on recent advances in the field. We also point out some of the technical challenges that need to be overcome in order to increase applications of metabolomics in marine systems, including extraction of chemical compounds from different matrices and data management. Metabolites being important links between genotype and phenotype, we describe added value provided by integration of data from metabolite profiling with other layers of omics, as well as their importance for the development of systems biology approaches in marine systems to study several biological processes, and to analyze interactions between organisms within communities. The growing importance of MS-based metabolomics in chemical ecology studies in marine ecosystems is also illustrated.

Stable isotope-resolved metabolomics and applications for drug development

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality.

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or "awakening" of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.

Metabolic pathway confirmation and discovery through (13)C-labeling of proteinogenic amino acids

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine cell central metabolism and discover new enzymes is (13)C-assisted metabolism analysis 1. This technique is based on isotopic labeling, whereby microbes are fed with a (13)C labeled substrates. By tracing the atom transition paths between metabolites in the biochemical network, we can determine functional pathways and discover new enzymes. As a complementary method to transcriptomics and proteomics, approaches for isotopomer-assisted analysis of metabolic pathways contain three major steps (2). First, we grow cells with (13)C labeled substrates. In this step, the composition of the medium and the selection of labeled substrates are two key factors. To avoid measurement noises from non-labeled carbon in nutrient supplements, a minimal medium with a sole carbon source is required. Further, the choice of a labeled substrate is based on how effectively it will elucidate the pathway being analyzed. Because novel enzymes often involve different reaction stereochemistry or intermediate products, in general, singly labeled carbon substrates are more informative for detection of novel pathways than uniformly labeled ones for detection of novel pathways(3, 4). Second, we analyze amino acid labeling patterns using GC-MS. Amino acids are abundant in protein and thus can be obtained from biomass hydrolysis. Amino acids can be derivatized by N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS) before GC separation. TBDMS derivatized amino acids can be fragmented by MS and result in different arrays of fragments. Based on the mass to charge (m/z) ratio of fragmented and unfragmented amino acids, we can deduce the possible labeled patterns of the central metabolites that are precursors of the amino acids. Third, we trace 13C carbon transitions in the proposed pathways and, based on the isotopomer data, confirm whether these pathways are active (2). Measurement of amino acids provides isotopic labeling information about eight crucial precursor metabolites in the central metabolism. These metabolic key nodes can reflect the functions of associated central pathways. (13)C-assisted metabolism analysis via proteinogenic amino acids can be widely used for functional characterization of poorly-characterized microbial metabolism(1). In this protocol, we will use Cyanothece 51142 as the model strain to demonstrate the use of labeled carbon substrates for discovering new enzymatic functions.

Fast sampling of the cellular metabolome

Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells, and may interfere with the endometabolome measurements. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, leakage can be minimized by adaptation of the quenching method. For prokaryotic cells this had not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction for measurement of metabolites in total broth samples, washed cell samples and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

Metabolic pathway determination and flux analysis in nonmodel microorganisms through 13C-isotope labeling

C-isotope labeling is a commonly used technique for determining and quantifying pathways in microorganisms under various growth conditions. The experimental protocol consists of feeding the cell with a composition-defined substrate and measuring isotopic labeling patterns in the synthesized metabolites (often the amino acids). Not only can the labeling information be cross-referenced with genomic information to identify the novel pathways, but it can also be used to decipher absolute carbon fluxes through the metabolic network of interest. This technique can be widely used for functional characterization of nonmodel microbial species, and thus we provide a (13)C-pathway and flux analysis protocol. The five key procedures are: (1) growing cells using labeled substrates, (2) measuring extracellular metabolite and biomass component, (3) analyzing isotopic labeling patterns in amino acids and central metabolites using gas chromatography-mass spectrometry, (4) tracing (13)C carbon transitions in metabolites and discovering new pathways, and (5) estimating flux distributions based on isotopomer constraints. This protocol provides complementary information to the recently published protocol for (13)C-based metabolic flux analysis of the model species Escherichia coli (Nat Protoc 4:878-892, 2009).

Development of constraint-based system-level models of microbial metabolism

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

influx_s: increasing numerical stability and precision for metabolic flux analysis in isotope labelling experiments

MOTIVATION

The problem of stationary metabolic flux analysis based on isotope labelling experiments first appeared in the early 1950s and was basically solved in early 2000s. Several algorithms and software packages are available for this problem. However, the generic stochastic algorithms (simulated annealing or evolution algorithms) currently used in these software require a lot of time to achieve acceptable precision. For deterministic algorithms, a common drawback is the lack of convergence stability for ill-conditioned systems or when started from a random point.

RESULTS

In this article, we present a new deterministic algorithm with significantly increased numerical stability and accuracy of flux estimation compared with commonly used algorithms. It requires relatively short CPU time (from several seconds to several minutes with a standard PC architecture) to estimate fluxes in the central carbon metabolism network of Escherichia coli.

AVAILABILITY

The software package influx_s implementing this algorithm is distributed under an OpenSource licence at http://metasys.insa-toulouse.fr/software/influx/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics-assisted synthetic biology

As the world progresses from a fossil-fuel based economy to a more sustainable one, synthetic biology will become increasingly important for the production of high-value fine chemicals as well as low-value commodities in bulk. The integration of metabolomics and fluxomics within synthetic biology projects will be vital at all levels, including the initial design of the pathways to be generated, through to the optimisation of those pathways so that more efficient conversion of low-cost starting materials into highly desirable products can be achieved. This review highlights these areas and details the most important and exciting advances being made in this area.

Collisional fragmentation of central carbon metabolites in LC-MS/MS increases precision of ¹³C metabolic flux analysis

Experimental determination of fluxes by (13)C-tracers relies on detection of (13)C-patterns in metabolites or by-products. In the field of (13)C metabolic flux analysis, the most recent developments point toward recording labeling patterns by liquid chromatography (LC)-mass spectrometry (MS)/MS directly in intermediates in central carbon metabolism (CCM) to increase temporal resolution. Surprisingly, the flux studies published so far with LC-MS measurements were based on intact metabolic intermediates-thus neglected the potential benefits of using positional information to improve flux estimation. For the first time, we exploit collisional fragmentation to obtain more fine-grained (13)C-data on intermediates of CCM and investigate their impact in (13)C metabolic flux analysis. For the case study of Bacillus subtilis grown in mineral medium with (13)C-labeled glucose, we compare the flux estimates obtained by iterative isotopologue balancing of (13)C-data obtained either by LC-MS/MS for solely intact intermediates or LC-MS/MS for intact and fragmented intermediates of CCM. We show that with LC-MS/MS data, fragment information leads to more precise estimates of fluxes in pentose phosphate pathway, glycolysis, and to the tricarboxylic acid cycle. Additionally, we present an efficient analytical strategy to rapidly acquire large sets of (13)C-patterns by tandem MS, and an in-depth analysis of the collisional fragmentation of primary intermediates. In the future, this catalogue will enable comprehensive in silico calculability analyses to identify the most sensitive measurements and direct experimental design.

From cancer metabolism to new biomarkers and drug targets

Great interest is presently given to the analysis of metabolic changes that take place specifically in cancer cells. In this review we summarize the alterations in glycolysis, glutamine utilization, fatty acid synthesis and mitochondrial function that have been reported to occur in cancer cells and in human tumors. We then propose considering cancer as a system-level disease and argue how two hallmarks of cancer, enhanced cell proliferation and evasion from apoptosis, may be evaluated as system-level properties, and how this perspective is going to modify drug discovery. Given the relevance of the analysis of metabolism both for studies on the molecular basis of cancer cell phenotype and for clinical applications, the more relevant technologies for this purpose, from metabolome and metabolic flux analysis in cells by Nuclear Magnetic Resonance and Mass Spectrometry technologies to positron emission tomography on patients, are analyzed. The perspectives offered by specific changes in metabolism for a new drug discovery strategy for cancer are discussed and a survey of the industrial activity already going on in the field is reported.

Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris

Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models. The validity of the trained neural network model was evaluated using biological information (i.e. operons). To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons (N(c)) and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected. In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets.

Systems biology: the next frontier for bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from 'omics platform technologies, in particular "downstream" technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.

Subpopulation-specific metabolic pathway usage in mixed cultures as revealed by reporter protein-based 13C analysis

Most large-scale biological processes, like global element cycling or decomposition of organic matter, are mediated by microbial consortia. Commonly, the different species in such consortia exhibit mutual metabolic dependencies that include the exchange of nutrients. Despite the global importance, surprisingly little is known about the metabolic interplay between species in particular subpopulations. To gain insight into the intracellular fluxes of subpopulations and their interplay within such mixed cultures, we developed here a (13)C flux analysis approach based on affinity purification of the recombinant fusion glutathione S-transferase (GST) and green fluorescent protein (GFP) as a reporter protein. Instead of detecting the (13)C labeling patterns in the typically used amino acids from the total cellular protein, our method detects these (13)C patterns in amino acids from the reporter protein that has been expressed in only one species of the consortium. As a proof of principle, we validated our approach by mixed-culture experiments of an Escherichia coli wild type with two metabolic mutants. The reporter method quantitatively resolved the expected mutant-specific metabolic phenotypes down to subpopulation fractions of about 1%.

Bridging the gap between fluxomics and industrial biotechnology

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. ¹³C-based metabolic flux analysis (¹³C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both ¹³C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and ¹³C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes.

Isotopomer enrichment assay for very short chain fatty acids and its metabolic applications

The present work illustrated an accurate GC/MS measurement for the low isotopomer enrichment assay of formic acid, acetic acid, propionic aicd, butyric acid, and pentanoic acid. The pentafluorobenzyl bromide derivatives of these very short chain fatty acids have high sensitivity of isotopoic enrichment due to their low natural isotopomer distribution in negative chemical ionization mass spectrometric mode. Pentafluorobenzyl bromide derivatization reaction was optimized in terms of pH, temperature, reaction time, and the amount of pentafluorobenzyl bromide versus sample. The precision, stability, and accuracy of this method for the isotopomer analysis were validated. This method was applied to measure the enrichments of formic acid, acetic acid, and propionic acid in the perfusate from rat liver exposed to Krebs-Ringer bicarbonate buffer only, 0-1mM [3,4-(13)C(2)]-4-hydroxynonanoate, and 0-2mM [5,6,7-(13)C(3)]heptanoate. The enrichments of acetic acid and propionic acid in the perfusate are comparable to the labeling pattern of acetyl-CoA and propionyl-CoA in the rat liver tissues. The enrichment of the acetic acid assay is much more sensitive and precise than the enrichment of acetyl-CoA by LC-MS/MS. The reversibility of propionyl-CoA from succinyl-CoA was confirmed by the low labeling of M1 and M2 of propionic acid from [5,6,7-(13)C(3)]heptanoate perfusates.

Metabolic flux analysis of the mixotrophic metabolisms in the green sulfur bacterium Chlorobaculum tepidum

The photosynthetic green sulfur bacterium Chlorobaculum tepidum assimilates CO(2) and organic carbon sources (acetate or pyruvate) during mixotrophic growth conditions through a unique carbon and energy metabolism. Using a (13)C-labeling approach, this study examined biosynthetic pathways and flux distributions in the central metabolism of C. tepidum. The isotopomer patterns of proteinogenic amino acids revealed an alternate pathway for isoleucine synthesis (via citramalate synthase, CimA, CT0612). A (13)C-assisted flux analysis indicated that carbons in biomass were mostly derived from CO(2) fixation via three key routes: the reductive tricarboxylic acid (RTCA) cycle, the pyruvate synthesis pathway via pyruvate:ferredoxin oxidoreductase, and the CO(2)-anaplerotic pathway via phosphoenolpyruvate carboxylase. During mixotrophic growth with acetate or pyruvate as carbon sources, acetyl-CoA was mainly produced from acetate (via acetyl-CoA synthetase) or citrate (via ATP citrate lyase). Pyruvate:ferredoxin oxidoreductase converted acetyl-CoA and CO(2) to pyruvate, and this growth-rate control reaction is driven by reduced ferredoxin generated during phototrophic growth. Most reactions in the RTCA cycle were reversible. The relative fluxes through the RTCA cycle were 80∼100 units for mixotrophic cultures grown on acetate and 200∼230 units for cultures grown on pyruvate. Under the same light conditions, the flux results suggested a trade-off between energy-demanding CO(2) fixation and biomass growth rate; C. tepidum fixed more CO(2) and had a higher biomass yield (Y(X/S), mole carbon in biomass/mole substrate) in pyruvate culture (Y(X/S) = 9.2) than in acetate culture (Y(X/S) = 6.4), but the biomass growth rate was slower in pyruvate culture than in acetate culture.

Redox biocatalysis and metabolism: molecular mechanisms and metabolic network analysis

Whole-cell biocatalysis utilizes native or recombinant enzymes produced by cellular metabolism to perform synthetically interesting reactions. Besides hydrolases, oxidoreductases represent the most applied enzyme class in industry. Oxidoreductases are attributed a high future potential, especially for applications in the chemical and pharmaceutical industries, as they enable highly interesting chemistry (e.g., the selective oxyfunctionalization of unactivated C-H bonds). Redox reactions are characterized by electron transfer steps that often depend on redox cofactors as additional substrates. Their regeneration typically is accomplished via the metabolism of whole-cell catalysts. Traditionally, studies towards productive redox biocatalysis focused on the biocatalytic enzyme, its activity, selectivity, and specificity, and several successful examples of such processes are running commercially. However, redox cofactor regeneration by host metabolism was hardly considered for the optimization of biocatalytic rate, yield, and/or titer. This article reviews molecular mechanisms of oxidoreductases with synthetic potential and the host redox metabolism that fuels biocatalytic reactions with redox equivalents. The tools discussed in this review for investigating redox metabolism provide the basis for studies aiming at a deeper understanding of the interplay between synthetically active enzymes and metabolic networks. The ultimate goal of rational whole-cell biocatalyst engineering and use for fine chemical production is discussed.

Mixotrophic and photoheterotrophic metabolism in Cyanothece sp. ATCC 51142 under continuous light

The unicellular diazotrophic cyanobacterium Cyanothece sp. ATCC 51142 (Cyanothece 51142) is able to grow aerobically under nitrogen-fixing conditions with alternating light-dark cycles or continuous illumination. This study investigated the effects of carbon and nitrogen sources on Cyanothece 51142 metabolism via (13)C-assisted metabolite analysis and biochemical measurements. Under continuous light (50 mumol photons m(-2) s(-1)) and nitrogen-fixing conditions, we found that glycerol addition promoted aerobic biomass growth (by twofold) and nitrogenase-dependent hydrogen production [up to 25 mumol H(2) (mg chlorophyll)( -1) h(-1)], but strongly reduced phototrophic CO(2) utilization. Under nitrogen-sufficient conditions, Cyanothece 51142 was able to metabolize glycerol photoheterotrophically, and the activity of light-dependent reactions (e.g. oxygen evolution) was not significantly reduced. In contrast, Synechocystis sp. PCC 6803 showed apparent mixotrophic metabolism under similar growth conditions. Isotopomer analysis also detected that Cyanothece 51142 was able to fix CO(2) via anaplerotic pathways, and to take up glucose and pyruvate for mixotrophic biomass synthesis.

Systems biology of microbial metabolism

One current challenge in metabolic systems biology is to map out the regulation networks that control metabolism. From progress in this area, we conclude that non-transcriptional mechanisms (e.g. metabolite-protein interactions and protein phosphorylation) are highly relevant in actually controlling metabolic function. Furthermore, recent results highlight more functions of enzymes and metabolites than currently appreciated in genome-scale metabolic reconstructions, thereby adding another level of complexity. Combining experimental analyses and modeling efforts we are also beginning to understand how metabolic behavior emerges. Particularly, we recognize that metabolism is not simply a dull workhorse process but rather takes very active control of itself and other cellular processes, rendering true system-level understanding of metabolism possibly more difficult than for other cellular systems.

Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research.

Alternative isoleucine synthesis pathway in cyanobacterial species

Cyanothece sp. ATCC 51142 is an aerobic N2-fixing and hydrogen-producing cyanobacterium. Isotopomer analysis of its amino acids revealed an identical labelling profile for leucine and isoleucine when Cyanothece 51142 was grown mixotrophically using 2-13C-labelled glycerol as the main carbon source. This indicated that Cyanothece 51142 employs the atypical alternative citramalate pathway for isoleucine synthesis, with pyruvate and acetyl-CoA as precursors. Utilization of the citramalate pathway was confirmed by an enzyme assay and LC-MS/MS analysis. Furthermore, the genome sequence of Cyanothece 51142 shows that the gene encoding the key enzyme (threonine ammonia-lyase) in the normal isoleucine pathway is missing. Instead, the cce_0248 gene in Cyanothece 51142 exhibits 53 % identity to the gene encoding citramalate synthase (CimA, GSU1798) from Geobacter sulfurreducens. Reverse-transcription PCR indicated that the cce_0248 gene is expressed and its transcriptional level is lower in medium with isoleucine than in isoleucine-free medium. Additionally, a blast search for citramalate synthase and threonine ammonia-lyase implies that this alternative isoleucine synthesis pathway may be present in other cyanobacteria, such as Cyanothece and Synechococcus. This suggests that the pathway is more widespread than originally thought, as previous identifications of the citramalate pathway are limited to mostly anaerobic bacteria or archaea. Furthermore, this discovery opens the possibility that such autrotrophic micro-organisms may be engineered for robust butanol and propanol production from 2-ketobutyrate, which is an intermediate in the isoleucine biosynthesis pathway.

Integrating multiple 'omics' analysis for microbial biology: application and methodologies

Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.

Central metabolism in Mycobacterium smegmatis during the transition from O2-rich to O2-poor conditions as studied by isotopomer-assisted metabolite analysis

Isotopomer-assisted metabolite analysis was used to investigate the central metabolism of Mycobacterium smegmatis and its transition from normal growth to a non-replicating state under a hypoxic environment. Tween 80 significantly promoted aerobic growth by improving O(2) transfer, while only small amount was degraded and metabolized via the TCA cycle for biomass synthesis. As the bacillus encountered hypoxic stress, isotopomer analysis suggested: (1) isocitrate lyase activity increased, which further induced glyoxylate pathway and glycine dehydrogenase for replenishing NAD(+); (2) the relative amount of acetyl-CoA entering the TCA cycle was doubled, whereas little entered the glycolytic and pentose phosphate pathways.

Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells

(13)C metabolic flux analysis (MFA) is the most comprehensive means of characterizing cellular metabolic states. Uniquely labeled isotopic tracers enable more focused analyses to probe specific reactions within the network. As a result, the choice of tracer largely determines the precision with which one can estimate metabolic fluxes, especially in complex mammalian systems that require multiple substrates. Here we have experimentally determined metabolic fluxes in a tumor cell line, successfully recapitulating the hallmarks of cancer cell metabolism. Using these data, we computationally evaluated specifically labeled (13)C glucose and glutamine tracers for their ability to precisely and accurately estimate fluxes in central carbon metabolism. These methods enabled us to identify the optimal tracer for analyzing individual fluxes, specific pathways, and central carbon metabolism as a whole. [1,2-(13)C(2)]glucose provided the most precise estimates for glycolysis, the pentose phosphate pathway, and the overall network. Tracers such as [2-(13)C]glucose and [3-(13)C]glucose also outperformed the more commonly used [1-(13)C]glucose. [U-(13)C(5)]glutamine emerged as the preferred isotopic tracer for the analysis of the tricarboxylic acid (TCA) cycle. These results provide valuable, quantitative information on the performance of (13)C-labeled substrates and can aid in the design of more informative MFA experiments in mammalian cell culture.

Characterization of the central metabolic pathways in Thermoanaerobacter sp. strain X514 via isotopomer-assisted metabolite analysis

Thermoanaerobacter sp. strain X514 has great potential in biotechnology due to its capacity to ferment a range of C(5) and C(6) sugars to ethanol and other metabolites under thermophilic conditions. This study investigated the central metabolism of strain X514 via (13)C-labeled tracer experiments using either glucose or pyruvate as both carbon and energy sources. X514 grew on minimal medium and thus contains complete biosynthesis pathways for all macromolecule building blocks. Based on genome annotation and isotopic analysis of amino acids, three observations can be obtained about the central metabolic pathways in X514. First, the oxidative pentose phosphate pathway in X514 is not functional, and the tricarboxylic acid cycle is incomplete under fermentative growth conditions. Second, X514 contains (Re)-type citrate synthase activity, although no gene homologous to the recently characterized (Re)-type citrate synthase of Clostridium kluyveri was found. Third, the isoleucine in X514 is derived from acetyl coenzyme A and pyruvate via the citramalate pathway rather than being synthesized from threonine via threonine ammonia-lyase. The functionality of the citramalate synthase gene (cimA [Teth514_1204]) has been confirmed by enzymatic activity assays, while the presence of intracellular citramalate has been detected by mass spectrometry. This study demonstrates the merits of combining (13)C-assisted metabolite analysis, enzyme assays, and metabolite detection not only to examine genome sequence annotations but also to discover novel enzyme activities.