Fluxomics: mass spectrometry versus quantitative imaging

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using ¹³C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

A Fluorescence Resonance Energy Transfer-Based Analytical Tool for Nitrate Quantification in Living Cells

Nitrate (NO₃ ^-) is a critical source of nitrogen (N) available to microorganisms and plants. Nitrate sensing activates signaling pathways in the plant system that impinges upon, developmental, molecular, metabolic, and physiological responses locally, and globally. To sustain, the high crop productivity and high nutritional value along with the sustainable environment, the study of rate-controlling steps of a metabolic network of N assimilation through fluxomics becomes an attractive strategy. To monitor the flux of nitrate, we developed a non-invasive genetically encoded fluorescence resonance energy transfer (FRET)-based tool named "FLIP-NT" that monitors the real-time uptake of nitrate in the living cells. The developed nanosensor is suitable for real-time monitoring of nitrate flux in living cells at subcellular compartments with high spatio-temporal resolution. The developed FLIP-NT nanosensor was not affected by the pH change and have specificity for nitrate with an affinity constant (K _d) of ∼5 μM. A series of affinity mutants have also been generated to expand the physiological detection range of the sensor protein with varying K _d values. It has been found that this sensor successfully detects the dynamics of nitrate fluctuations in bacteria and yeast, without the disruption of cellular organization. This FLIP-NT nanosensor could be a very important tool that will help us to advance the understanding of nitrate signaling.

Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches

Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.

Live cell monitoring of glycine betaine by FRET-based genetically encoded nanosensor

Glycine betaine (GB) is one of the key compatible solutes that accumulate in the cell at exceedingly high level under the conditions of high salinity. It plays a crucial role in the maintenance of osmolarity of the cell without affecting the physiological processes. Analysis of stress-induced physiological conditions in living cells, therefore, requires real-time monitoring of cellular GB level. Glycine Betaine Optical Sensor (GBOS), a genetically-encoded FRET-based nanosensor developed in this study, allows the real-time monitoring of GB levels inside living cells. This nanosensor has been developed by sandwiching GB binding protein (ProX) between the Förster resonance energy transfer (FRET) pair, the cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP). Conformational change in ProX, which was used as sensory domain, reported the change in the level of this compatible solute in in vitro and in vivo conditions. Binding of the GB to the sensory domain fetches close to both the fluorescent moieties that result in the form of increased FRET ratio. So, any change in the concentration of GB is correlated with change in FRET ratio. This sensor also reported the GB cellular dynamics in real-time in Escherichia coli cells after the addition of its precursor, choline. The GBOS was also expressed in yeast and mammalian cells to monitor the intracellular GB. Therefore, the GBOS represents a unique FRET-based nanosensor which allows the non-invasive ratiometric analysis of the GB in living cells.

Reduced mitochondrial malate dehydrogenase activity has a strong effect on photorespiratory metabolism as revealed by 13C labelling

Mitochondrial malate dehydrogenase (mMDH) catalyses the interconversion of malate and oxaloacetate (OAA) in the tricarboxylic acid (TCA) cycle. Its activity is important for redox control of the mitochondrial matrix, through which it may participate in regulation of TCA cycle turnover. In Arabidopsis, there are two isoforms of mMDH. Here, we investigated to which extent the lack of the major isoform, mMDH1 accounting for about 60% of the activity, affected leaf metabolism. In air, rosettes of mmdh1 plants were only slightly smaller than wild type plants although the fresh weight was decreased by about 50%. In low CO2 the difference was much bigger, with mutant plants accumulating only 14% of fresh weight as compared to wild type. To investigate the metabolic background to the differences in growth, we developed a (13)CO2 labelling method, using a custom-built chamber that enabled simultaneous treatment of sets of plants under controlled conditions. The metabolic profiles were analysed by gas- and liquid- chromatography coupled to mass spectrometry to investigate the metabolic adjustments between wild type and mmdh1 The genotypes responded similarly to high CO2 treatment both with respect to metabolite pools and (13)C incorporation during a 2-h treatment. However, under low CO2 several metabolites differed between the two genotypes and, interestingly most of these were closely associated with photorespiration. We found that while the glycine/serine ratio increased, a concomitant altered glutamine/glutamate/α-ketoglutarate relation occurred. Taken together, our results indicate that adequate mMDH activity is essential to shuttle reductants out from the mitochondria to support the photorespiratory flux, and strengthen the idea that photorespiration is tightly intertwined with peripheral metabolic reactions.

OM-FBA: Integrate Transcriptomics Data with Flux Balance Analysis to Decipher the Cell Metabolism

Constraint-based metabolic modeling such as flux balance analysis (FBA) has been widely used to simulate cell metabolism. Thanks to its simplicity and flexibility, numerous algorithms have been developed based on FBA and successfully predicted the phenotypes of various biological systems. However, their phenotype predictions may not always be accurate in FBA because of using the objective function that is assumed for cell metabolism. To overcome this challenge, we have developed a novel computational framework, namely omFBA, to integrate multi-omics data (e.g. transcriptomics) into FBA to obtain omics-guided objective functions with high accuracy. In general, we first collected transcriptomics data and phenotype data from published database (e.g. GEO database) for different microorganisms such as Saccharomyces cerevisiae. We then developed a “Phenotype Match” algorithm to derive an objective function for FBA that could lead to the most accurate estimation of the known phenotype (e.g. ethanol yield). The derived objective function was next correlated with the transcriptomics data via regression analysis to generate the omics-guided objective function, which was next used to accurately simulate cell metabolism at unknown conditions. We have applied omFBA in studying sugar metabolism of S. cerevisiae and found that the ethanol yield could be accurately predicted in most of the cases tested (>80%) by using transcriptomics data alone, and revealed valuable metabolic insights such as the dynamics of flux ratios. Overall, omFBA presents a novel platform to potentially integrate multi-omics data simultaneously and could be incorporated with other FBA-derived tools by replacing the arbitrary objective function with the omics-guided objective functions.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

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.

Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks.

In-depth metabolic phenotyping of genetically engineered mouse models in obesity and diabetes

The world-wide prevalence of obesity and diabetes has increased sharply during the last two decades. Accordingly, the metabolic phenotyping of genetically engineered mouse models is critical for evaluating the functional roles of target genes in obesity and diabetes, and for developing new therapeutic targets. In this review, we discuss the practical meaning of metabolic phenotyping, the strategy of choosing appropriate tests, and considerations when designing and performing metabolic phenotyping in mice.

Elements required for an efficient NADP-malic enzyme type C4 photosynthesis

C4 photosynthesis has higher light, nitrogen, and water use efficiencies than C3 photosynthesis. Although the basic anatomical, cellular, and biochemical features of C4 photosynthesis are well understood, the quantitative significance of each element of C4 photosynthesis to the high photosynthetic efficiency are not well defined. Here, we addressed this question by developing and using a systems model of C4 photosynthesis, which includes not only the Calvin-Benson cycle, starch synthesis, sucrose synthesis, C4 shuttle, and CO₂ leakage, but also photorespiration and metabolite transport between the bundle sheath cells and mesophyll cells. The model effectively simulated the CO₂ uptake rates, and the changes of metabolite concentrations under varied CO₂ and light levels. Analyses show that triose phosphate transport and CO₂ leakage can help maintain a high photosynthetic rate by balancing ATP and NADPH amounts in bundle sheath cells and mesophyll cells. Finally, we used the model to define the optimal enzyme properties and a blueprint for C4 engineering. As such, this model provides a theoretical framework for guiding C4 engineering and studying C4 photosynthesis in general.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Recent applications of metabolomics toward cyanobacteria

Our knowledge on cyanobacterial molecular biology increased tremendously by the application of the "omics" techniques. Only recently, metabolomics was applied systematically to model cyanobacteria. Metabolomics, the quantitative estimation of ideally the complete set of cellular metabolites, is particularly well suited to mirror cellular metabolism and its flexibility under diverse conditions. Traditionally, small sets of metabolites are quantified in targeted metabolome approaches. The development of separation technologies coupled to mass-spectroscopy- or nuclear-magnetic-resonance-based identification of low molecular mass molecules presently allows the profiling of hundreds of metabolites of diverse chemical nature. Metabolome analysis was applied to characterize changes in the cyanobacterial primary metabolism under diverse environmental conditions or in defined mutants. The resulting lists of metabolites and their steady state concentrations in combination with transcriptomics can be used in system biology approaches. The application of stable isotopes in fluxomics, i.e. the quantitative estimation of carbon and nitrogen fluxes through the biochemical network, has only rarely been applied to cyanobacteria, but particularly this technique will allow the making of kinetic models of cyanobacterial systems. The further application of metabolomics in the concert of other "omics" technologies will not only broaden our knowledge, but will also certainly strengthen the base for the biotechnological application of cyanobacteria.

Nuclear magnetic resonance methods for metabolic fluxomics

Fluxomics, through its core methodology of metabolic flux analysis (MFA), enables quantification of carbon traffic through cellular biochemical pathways. Isotope labeling experiments aid MFA by providing information on intracellular fluxes, especially through parallel and cyclic pathways. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are two complementary methods to measure abundances of isotopomers generated in these experiments. 2-D [(13)C, (1)H] heteronuclear correlation NMR spectra can detect (13)C isotopes coupled to protons and thus noninvasively separate molecules and atoms with a specific isotopic content from a mixture of molecular species. Furthermore, the fine structures of the peaks in these spectra can reveal scalar couplings between chemically bonded carbon atoms in the sample, from which isotopomer abundances can be quantified. This chapter introduces methods for NMR sample preparation and spectral acquisition of 2-D [(13)C, (1)H] correlation maps, followed by a detailed presentation of methods to process the spectra and quantify isotopomer abundances. We explain the use of the software NMRViewJ for spectral visualization and processing, as well as MATLAB scripts developed by us for peak extraction, deconvolution of overlapping peaklets, and isotopomer abundance quantification. Finally, we discuss the applications of NMR-derived isotopomer data toward quantitatively understanding metabolic pathways.

Linking post-translational modifications and variation of phenotypic traits

Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits.

Comparative metabolic profiling between desiccation-sensitive and desiccation-tolerant species of Selaginella reveals insights into the resurrection trait

Spike mosses (Selaginellaceae) represent an ancient lineage of vascular plants in which some species have evolved desiccation tolerance (DT). A sister-group contrast to reveal the metabolic basis of DT was conducted between a desiccation-tolerant species, Selaginella lepidophylla, and a desiccation-sensitive species, Selaginella moellendorffii, at 100% relative water content (RWC) and 50% RWC using non-biased, global metabolomics profiling technology, based on GC/MS and UHLC/MS/MS(2) platforms. A total of 301 metabolites, including 170 named (56.5%) and 131 (43.5%) unnamed compounds, were characterized across both species. S.  lepidophylla retained significantly higher abundances of sucrose, mono- and polysaccharides, and sugar alcohols than did S. moellendorffii. Aromatic amino acids, the well-known osmoprotectant betaine and flavonoids were also more abundant in S. lepidophylla. Notably, levels of γ-glutamyl amino acid, linked with glutathione metabolism in the detoxification of reactive oxygen species, and with possible nitrogen remobilization following rehydration, were markedly higher in S. lepidophylla. Markers for lipoxygenase activity were also greater in S. lepidophylla, especially at 50% RWC. S. moellendorffii contained more than twice the number of unnamed compounds, with only a slightly greater abundance than in S. lepidophylla. In contrast, S. lepidophylla contained 14 unnamed compounds of fivefold or greater abundance than in S. moellendorffii, suggesting that these compounds might play critical roles in DT. Overall, S. lepidophylla appears poised to tolerate desiccation in a constitutive manner using a wide range of metabolites with some inducible components, whereas S. moellendorffii mounts only limited metabolic responses to dehydration stress.

Comparative metabolic profiling between desiccation-sensitive and desiccation-tolerant species of Selaginella reveals insights into the resurrection trait

Spike mosses (Selaginellaceae) represent an ancient lineage of vascular plants in which some species have evolved desiccation tolerance (DT). A sister-group contrast to reveal the metabolic basis of DT was conducted between a desiccation-tolerant species, Selaginella lepidophylla, and a desiccation-sensitive species, Selaginella moellendorffii, at 100% relative water content (RWC) and 50% RWC using non-biased, global metabolomics profiling technology, based on GC/MS and UHLC/MS/MS(2) platforms. A total of 301 metabolites, including 170 named (56.5%) and 131 (43.5%) unnamed compounds, were characterized across both species. S.  lepidophylla retained significantly higher abundances of sucrose, mono- and polysaccharides, and sugar alcohols than did S. moellendorffii. Aromatic amino acids, the well-known osmoprotectant betaine and flavonoids were also more abundant in S. lepidophylla. Notably, levels of γ-glutamyl amino acid, linked with glutathione metabolism in the detoxification of reactive oxygen species, and with possible nitrogen remobilization following rehydration, were markedly higher in S. lepidophylla. Markers for lipoxygenase activity were also greater in S. lepidophylla, especially at 50% RWC. S. moellendorffii contained more than twice the number of unnamed compounds, with only a slightly greater abundance than in S. lepidophylla. In contrast, S. lepidophylla contained 14 unnamed compounds of fivefold or greater abundance than in S. moellendorffii, suggesting that these compounds might play critical roles in DT. Overall, S. lepidophylla appears poised to tolerate desiccation in a constitutive manner using a wide range of metabolites with some inducible components, whereas S. moellendorffii mounts only limited metabolic responses to dehydration stress.

Ecogenomics of microbial communities in bioremediation of chlorinated contaminated sites

Organohalide compounds such as chloroethenes, chloroethanes, and polychlorinated benzenes are among the most significant pollutants in the world. These compounds are often found in contamination plumes with other pollutants such as solvents, pesticides, and petroleum derivatives. Microbial bioremediation of contaminated sites, has become commonplace whereby key processes involved in bioremediation include anaerobic degradation and transformation of these organohalides by organohalide respiring bacteria and also via hydrolytic, oxygenic, and reductive mechanisms by aerobic bacteria. Microbial ecogenomics has enabled us to not only study the microbiology involved in these complex processes but also develop tools to better monitor and assess these sites during bioremediation. Microbial ecogenomics have capitalized on recent advances in high-throughput and -output genomics technologies in combination with microbial physiology studies to address these complex bioremediation problems at a system level. Advances in environmental metagenomics, transcriptomics, and proteomics have provided insights into key genes and their regulation in the environment. They have also given us clues into microbial community structures, dynamics, and functions at contaminated sites. These techniques have not only aided us in understanding the lifestyles of common organohalide respirers, for example Dehalococcoides, Dehalobacter, and Desulfitobacterium, but also provided insights into novel and yet uncultured microorganisms found in organohalide respiring consortia. In this paper, we look at how ecogenomic studies have aided us to understand the microbial structures and functions in response to environmental stimuli such as the presence of chlorinated pollutants.

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.

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.

Optical sensors for measuring dynamic changes of cytosolic metabolite levels in yeast

Optical sensors allow dynamic quantification of metabolite levels with subcellular resolution. Here we describe protocols for analyzing cytosolic glucose levels in yeast using genetically encoded Förster resonance energy transfer (FRET) sensors. FRET glucose sensors with different glucose affinities (K(d)) covering the low nano- to mid- millimolar range can be targeted genetically to the cytosol or to subcellular compartments. The sensors detect the glucose-induced conformational change in the bacterial periplasmic glucose/galactose binding protein MglB using FRET between two fluorescent protein variants. Measurements can be performed with a single sensor or multiple sensors in parallel. In one approach, cytosolic glucose accumulation is measured in yeast cultures in a 96-well plate using a fluorimeter. Upon excitation of the cyan fluorescent protein (CFP), emission intensities of CFP and YFP (yellow fluorescent protein) are captured before and after glucose addition. FRET sensors provide temporally resolved quantitative data of glucose for the compartment of interest. In a second approach, reversible changes of cytosolic free glucose are measured in individual yeast cells trapped in a microfluidic platform, allowing perfusion of different solutions while FRET changes are monitored in a microscope setup. By using the microplate fluorimeter protocol, 96 cultures can be measured in less than 1 h; analysis of single cells of a single genotype can be completed in <2 h. FRET-based analysis has been performed with glucose, maltose, ATP and zinc sensors, and it can easily be adapted for high-throughput screening using a wide spectrum of sensors.

Semisynthesis of fluorescent metabolite sensors on cell surfaces

Progress in understanding signal transduction and metabolic pathways is hampered by a shortage of suitable sensors for tracking metabolites, second messengers, and neurotransmitters in living cells. Here we introduce a class of rationally designed semisynthetic fluorescent sensor proteins, called Snifits, for measuring metabolite concentrations on the cell surface of mammalian cells. Functional Snifits are assembled on living cells through two selective chemical labeling reactions of a genetically encoded protein scaffold. Our best Snifit displayed fluorescence intensity ratio changes on living cells significantly higher than any previously reported cell-surface-targeted fluorescent sensor protein. This work establishes a generally applicable and rational strategy for the generation of cell-surface-targeted fluorescent sensor proteins for metabolites of interest.

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Dynamic imaging of glucose flux impedance using FRET sensors in wild-type Arabidopsis plants

Quantitative and dynamic analysis of metabolites and signalling molecules is limited by technical challenges in obtaining temporally resolved information at the cellular and compartmental level. Real-time information on signalling and metabolite levels with subcellular granularity can be obtained with the help of genetically encoded FRET (Förster resonance energy transfer) nanosensors. FRET nanosensors represent powerful tools for gene discovery, and analysis of regulatory networks, for example by screening mutants. However, RNA silencing has impaired our ability to express FRET nanosensors functionally in Arabidopsis plants. This drawback was overcome here by expressing the nanosensors in RNA silencing mutants. However, the use of silencing mutants requires the generation of homozygous lines deficient in RNA silencing as well as the mutation of interest and co-expression of the nanosensor. Here it is shown that dynamic changes in cytosolic glucose levels can readily be quantified in wild-type Arabidopsis plants at early stages of development (7-15 d) before silencing had a major effect on fluorescence intensity. A detailed protocol for screening 10-20 mutant seedlings per day is provided. The detailed imaging protocol provided here is suitable for analysing sugar flux in young wild-type plants as well as mutants affected in sugar signalling, metabolism, or transport using a wide spectrum of FRET nanosensors.

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.

Reconstructing bulk isotope ratios from compound-specific isotope ratios

Carbon isotope analysis by bulk elemental analysis coupled with isotope ratio mass spectrometry has been the mainstay of delta(13)C analyses both at natural abundance and in tracer studies. More recently, compound-specific isotope analysis (CSIA) has become established, whereby organic constituents are separated online by gas or liquid chromatography before oxidation and analysis of CO(2) for constituent delta(13)C. Theoretically, there should be concordance between bulk delta(13)C measurements and carbon-weighted delta(13)C measurements of carbon-containing constituents. To test the concordance between the bulk and CSIA, fish oil was chosen because the majority of carbon in fish oil is in the triacylglycerol form and approximately 95% of this carbon is amenable to CSIA in the form of fatty acids. Bulk isotope analysis was carried out on aliquots of oil extracted from 55 fish samples and delta(13)C values were obtained. Free fatty acids (FFAs) were produced from the oil samples by saponification and derivatised to fatty acid methyl esters (FAMEs) for CSIA by gas chromatography/combustion/isotope ratio mass spectrometry. A known amount of an internal standard (C15:0 FAME) was added to allow analyte quantitation. This internal standard was also isotopically calibrated in both its FFA (delta(13)C = -34.30 per thousand) and FAME (delta(13)C = -34.94 per thousand) form. This allowed reporting of FFA delta(13)C from measured FAME delta(13)C values. The bulk delta(13)C was reconstructed from CSIA data based on each FFA delta(13)C and the relative amount of CO(2) produced by each analyte. The measured bulk mean delta(13)C (SD) was -23.75 per thousand (1.57 per thousand) compared with the reconstructed bulk mean delta(13)C of -23.76 (1.44 per thousand) from CSIA and was not significantly different. Further analysis of the data by the Bland-Altman method did not show particular bias in the data relative to the magnitude of the measurement. Good agreement between the methods was observed with the mean difference between methods (range) of 0.01 per thousand (-1.50 to 1.30).

Information processing without brains--the power of intercellular regulators in plants

Plants exhibit different developmental strategies than animals; these are characterized by a tight linkage between environmental conditions and development. As plants have neither specialized sensory organs nor a nervous system, intercellular regulators are essential for their development. Recently, major advances have been made in understanding how intercellular regulation is achieved in plants on a molecular level. Plants use a variety of molecules for intercellular regulation: hormones are used as systemic signals that are interpreted at the individual-cell level; receptor peptide-ligand systems regulate local homeostasis; moving transcriptional regulators act in a switch-like manner over small and large distances. Together, these mechanisms coherently coordinate developmental decisions with resource allocation and growth.

Advances in molecular and "-omics" technologies to gauge microbial communities and bioremediation at xenobiotic/anthropogen contaminated sites

Microbial bioremediation has been well-demonstrated as an ecofriendly and cost-competitive strategy for elimination of xenobiotic and or anthropogenic compounds from the polluted environments. However, successful execution of these versatile bioremediation strategies requires a thorough understanding of factors governing the growth, metabolism, dynamics and functions of indigenous microbial communities at contaminated sites. Recent innovative breakthroughs in genotypic profiling, ultrafast genome pyrosequencing, metagenomics, metatranscriptomics, metaproteomics and metabolomics along with bioinformatics tools have provided crucial in-sights of microbial communities and their mechanisms in bioremediation of environmental pollutants. Moreover, advances in these technologies have significantly improved the process of efficacy determination and implementation of microbial bioremediation strategies. The current review is focused on application of these molecular and "-omics" technologies in gauging the innate microbial community structures, dynamics and functions at contaminated sites or pollution containment facilities.

Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed-batch culture

How do intracellular fluxes respond to dynamically increasing glucose limitation when the physiology changes from strong overflow metabolism near to exclusively maintenance metabolism? Here we investigate this question in a typical industrial, glucose-limited fed-batch cultivation with a riboflavin overproducing Bacillus subtilis strain. To resolve dynamic flux changes, a novel approach to (13)C flux analysis was developed that is based on recording (13)C labeling patterns in free intracellular amino acids. Fluxes are then estimated with stationary flux ratio and iterative isotopomer balancing methods, for which a decomposition of the process into quasi-steady states and estimation of isotopic steady state (13)C labeling patterns was necessary. By this approach, we achieve a temporal resolution of 30-60 min that allows us to resolve the slow metabolic transients that typically occur in such cultivations. In the late process phase we found, most prominently, almost exclusive respiratory metabolism, significantly increased pentose phosphate pathway contribution and a strongly decreased futile cycle through the PEP carboxykinase. As a consequence, higher catabolic NADPH formation occurred than was necessary to satisfy the anabolic demands, suggesting a transhydrogenase-like mechanism to close the balance of reducing equivalents.

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.

Imaging approach for monitoring cellular metabolites and ions using genetically encoded biosensors

The spatiotemporal patterns of ion and metabolite levels in living cells are important in understanding signal transduction and metabolite flux. Imaging approaches using genetically encoded sensors are ideal for detecting such molecule dynamics, which are hard to capture otherwise. Recent years have seen iterative improvements and evaluations of sensors, which in turn are starting to make applications in more challenging experimental settings possible. In this review, we will introduce recent progress made in the variety and properties of biosensors, and how biosensors are used for the measurement of metabolite and ion in live cells. The emerging field of applications, such as parallel imaging of two separate molecules, high-resolution transport studies and high-throughput screening using biosensors, will be discussed.

Resolving the role of plant glutamate dehydrogenase. I. In vivo real time nuclear magnetic resonance spectroscopy experiments

In higher plants the glutamate dehydrogenase (GDH) enzyme catalyzes the reversible amination of 2-oxoglutarate to form glutamate, using ammonium as a substrate. For a better understanding of the physiological function of GDH either in ammonium assimilation or in the supply of 2-oxoglutarate, we used transgenic tobacco (Nicotiana tabacum L.) plants overexpressing the two genes encoding the enzyme. An in vivo real time (15)N-nuclear magnetic resonance (NMR) spectroscopy approach allowed the demonstration that, when the two GDH genes were overexpressed individually or simultaneously, the transgenic plant leaves did not synthesize glutamate in the presence of ammonium when glutamine synthetase (GS) was inhibited. In contrast we confirmed that the primary function of GDH is to deaminate Glu. When the two GDH unlabeled substrates ammonium and Glu were provided simultaneously with either [(15)N]Glu or (15)NH(4)(+) respectively, we found that the ammonium released from the deamination of Glu was reassimilated by the enzyme GS, suggesting the occurrence of a futile cycle recycling both ammonium and Glu. Taken together, these results strongly suggest that the GDH enzyme, in conjunction with NADH-GOGAT, contributes to the control of leaf Glu homeostasis, an amino acid that plays a central signaling and metabolic role at the interface of the carbon and nitrogen assimilatory pathways. Thus, in vivo NMR spectroscopy appears to be an attractive technique to follow the flux of metabolites in both normal and genetically modified plants.

Novel biological insights through metabolomics and 13C-flux analysis

Metabolomics and (13)C-flux analysis have become instrumental for analyzing cellular metabolism and its regulation. Driven primarily by technical advances in mass spectrometry-based analytics, they provide unmatched readouts on metabolic state and activity. Functional genomics leverages metabolomics for the discovery of novel enzymes and unexpected secondary activities of annotated enzymes. (13)C-flux analyses are frequently used for empirical elucidation of pathways in poorly characterized species and for network-wide analysis of mechanisms that realize energy and redox balancing. Integration of metabolomics, (13)C-flux analysis and other data enable the condition-dependent characterization of regulatory circuits that ultimately govern the metabolic phenotype.

Semisynthetic fluorescent sensor proteins based on self-labeling protein tags

Genetically encoded fluorescent sensor proteins offer the possibility to probe the concentration of key metabolites in living cells. The approaches currently used to generate such fluorescent sensor proteins lack generality, as they require a protein that undergoes a conformational change upon metabolite binding. Here we present an approach that overcomes this limitation. Our biosensors consist of SNAP-tag, a fluorescent protein and a metabolite-binding protein. SNAP-tag is specifically labeled with a synthetic molecule containing a ligand of the metabolite-binding protein and a fluorophore. In the labeled sensor, the metabolite of interest displaces the intramolecular ligand from the binding protein, thereby shifting the sensor protein from a closed to an open conformation. The readout is a concomitant ratiometric change in the fluorescence intensities of the fluorescent protein and the tethered fluorophore. The observed ratiometric changes compare favorably with those achieved in genetically encoded fluorescent sensor proteins. Furthermore, the modular design of our sensors permits the facile generation of ratiometric fluorescent sensors at wavelengths not covered by autofluorescent proteins. These features should allow semisynthetic fluorescent sensor proteins based on SNAP-tag to become important tools for probing previously inaccessible metabolites.

Protein design provides lead(II) ion biosensors for imaging molecular fluxes around red blood cells

Metalloprotein design and semiconductor nanoparticles have been combined to generate a reagent for selective fluorescence imaging of Pb(2+) ions in the presence red blood cells. A biosensor system based on semiconductor nanoparticles provides the photonic properties for small molecule measurement in and around red blood cells. Metalloprotein design was used to generate a Pb(2+) ion selective receptor from a protein that is structurally homologous to a protein used previously in this biosensing system. Parameters for the Pb(2+) ion binding site were derived from crystallographic structures of low molecular weight Pb(2+) ion complexes that contain a stereoactive lone pair. When the designed protein was produced and attached to ZnS-coated CdSe nanoparticles, two Pb(NO(3))(2)-associated binding events were observed (2-fold emission decrease; K(A1) = 1 x 10(9) M(-1); K(A2) = 3.5 x 10(6) M(-1)). The fluorescence response had a 100 pM Pb(NO(3))(2) detection limit, while no response was observed with Ca(2+) ions (10 mM), Zn(2+) ions (100 muM), or Cd(2+) ions (100 muM). Metal ion selectivity presumably comes from the coordination geometry selected to favor lone pair formation on Pb(2+) ions and electrostatically disfavor tetrahedral coordination. Replacement of ZnS-coated CdSe with ZnS-coated InGaP nanoparticles provided similar biosensors (100 pM limit of detection; K(A1) = 1 x 10(9) M(-1); K(A2) = 1 x 10(7) M(-1)) but with excitation/emission wavelengths longer than the major absorbance of red blood cell hemoglobin (>620 nm). The InGaP nanoparticle-based biosensors provided a 5 nM Pb(NO(3))(2) detection limit in the presence of red blood cells. The modularity of the biosensor system provides exchangeable Pb(2+) ion detection around red blood cells.

Comparison of quantitative metabolite imaging tools and carbon-13 techniques for fluxomics

The recent development of analytic technologies allows fast analysis of metabolism in real time. Fluxomics aims to define the genes involved in regulation of flux through a metabolic or signaling pathway. Flux through a metabolic or signaling pathway is determined by the activity of its individual components; regulation can occur at many levels, including transcriptional, posttranslational, and allosteric levels. Currently two technologies are used to monitor fluxes. The first is pulse labeling of the organism with a tracer such as C13, followed by mass spectrometric analysis of the partitioning of label into different compounds. The second approach is based on the use of flux sensors, proteins that respond with a conformational change to ligand binding. Fluorescence resonance energy transfer (FRET) detects the conformational change and serves as a proxy for ligand concentration. Both methods provide high time resolution. In contrast to mass spectrometry assays, FRET nanosensors monitor only a single compound, but the advantage of FRET nanosensors is that they yield data with cellular and subcellular resolution.

Fluorescence resonance energy transfer sensors for quantitative monitoring of pentose and disaccharide accumulation in bacteria

BACKGROUND

Engineering microorganisms to improve metabolite flux requires detailed knowledge of the concentrations and flux rates of metabolites and metabolic intermediates in vivo. Fluorescence resonance energy transfer sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. These sensors have been applied successfully in mammalian and plant cells but potentially could also be used to monitor steady-state levels of metabolites in microorganisms using fluorimetric assays. Sensors for hexose and pentose carbohydrates could help in the development of fermentative microorganisms, for example, for biofuels applications. Arabinose is one of the carbohydrates to be monitored during biofuels production from lignocellulose, while maltose is an important degradation product of starch that is relevant for starch-derived biofuels production.

RESULTS

An Escherichia coli expression vector compatible with phage lambda recombination technology was constructed to facilitate sensor construction and was used to generate a novel fluorescence resonance energy transfer sensor for arabinose. In parallel, a strategy for improving the sensor signal was applied to construct an improved maltose sensor. Both sensors were expressed in the cytosol of E. coli and sugar accumulation was monitored using a simple fluorimetric assay of E. coli cultures in microtiter plates. In the case of both nanosensors, the addition of the respective ligand led to concentration-dependent fluorescence resonance energy transfer responses allowing quantitative analysis of the intracellular sugar levels at given extracellular supply levels as well as accumulation rates.

CONCLUSION

The nanosensor destination vector combined with the optimization strategy for sensor responses should help to accelerate the development of metabolite sensors. The new carbohydrate fluorescence resonance energy transfer sensors can be used for in vivo monitoring of sugar levels in prokaryotes, demonstrating the potential of such sensors as reporter tools in the development of metabolically engineered microbial strains or for real-time monitoring of intracellular metabolite during fermentation.