The intracellular equilibrium thermodynamic and steady-state concentrations of metabolites

A new model for the organization and flow of metabolites through a metabolic pathway is presented. The model is based on four major findings. (1) The intracellular concentrations of enzyme sites exceed the concentrations of intermediary metabolites that bind specifically to these sites. (2) The concentration of the excessive enzyme sites in the cell is sufficiently high so that nearly all the cellular intermediary metabolites are enzyme-bound. (3) Enzyme conformations are perturbed by the interactions with substrates and products; the conformations of enzyme-substrate and enzyme-product complexes are different. (4) Two enzymes, catalyzing reactions that are sequential in a metabolic pathway, transfer the common metabolite back and forth via an enzyme-enzyme complex without the intervention of the solvent environment. The model proposes that the enzyme-enzyme recognition is ligand-induced. Conversion of E₂S and E₂P results in the loss of recognition of E₂ by E₁ and the concomitant recognition of E₂ by E₃. This model substantially alters existent views of the bioenergetics and the kinetics of intracellular metabolism. The rates of direct transfer of metabolite from enzyme to enzyme are comparable to the rates of interconversion between substrate and product within an individual enzyme. Consequently, intermediary metabolites are nearly equipartitioned among their high-affinity enzyme sites within a metabolic pathway. Metabolic flux involves the direct transfer of metabolite from enzyme to enzyme via a set of low and nearly equal energy barriers.     

Expression of a bacterial feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase of the shikimate pathway in Arabidopsis elucidates potential metabolic bottlenecks between primary and secondary metabolism

The shikimate pathway of plants mediates the conversion of primary carbon metabolites via chorismate into the three aromatic amino acids and to numerous secondary metabolites derived from them. However, the regulation of the shikimate pathway is still far from being understood. We hypothesized that 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (DAHPS) is a key enzyme regulating flux through the shikimate pathway. To test this hypothesis, we expressed a mutant bacterial AroG gene encoding a feedback-insensitive DAHPS in transgenic Arabidopsis plants. The plants were subjected to detailed analysis of primary metabolism, using GC-MS, as well as secondary metabolism, using LC-MS. Our results exposed a major effect of bacterial AroG expression on the levels of shikimate intermediate metabolites, phenylalanine, tryptophan and broad classes of secondary metabolite, such as phenylpropanoids, glucosinolates, auxin and other hormone conjugates. We propose that DAHPS is a key regulatory enzyme of the shikimate pathway. Moreover, our results shed light on additional potential metabolic bottlenecks bridging plant primary and secondary metabolism.     

NMR adventures in the metabolic labyrinth within plants

The era of metabolic engineering has begun, but there is only limited knowledge about metabolic fluxes and how they are regulated in plants. Particular challenges are the non-linearities between enzyme abundances, metabolite concentrations and metabolic fluxes, and the existence of metabolic networks that provide multiple routes between many important metabolites. NMR offers the means to distinguish and quantitate the fluxes along different routes to key metabolites. NMR can therefore help us understand and resolve the apparent paradox of, on the one hand, great metabolic flexibility evident in the natural responses of plants and, on the other hand, the unpredictable changes in metabolism reported in genetically engineered plants.     

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric,thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.     

Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology

Metabolomics embraces several strategies that aim to quantify cell metabolites in order to increase our understanding of how metabolite levels and interactions influence phenotypes. Metabolic footprinting represents a niche within metabolomics, because it focuses on the analysis of extracellular metabolites. Although metabolic footprinting represents only a fraction of the entire metabolome, it provides important information for functional genomics and strain characterization, and it can also provide scientists with a key understanding of cell communication mechanisms, metabolic engineering and industrial biotechnological processes. Due to the tight and convoluted relationship between intracellular metabolism and metabolic footprinting, metabolic footprinting can provide precious information about the intracellular metabolic status. Hereby, we state that integrative information from metabolic footprinting can assist in further interpretation of metabolic networks.     

Global Metabolic Responses to Salt Stress in Fifteen Species

Cells constantly adapt to unpredictably changing extracellular solute concentrations. A cornerstone of the cellular osmotic stress response is the metabolic supply of energy and building blocks to mount appropriate defenses. Yet, the extent to which osmotic stress impinges on the metabolic network remains largely unknown. Moreover, it is mostly unclear which, if any, of the metabolic responses to osmotic stress are conserved among diverse organisms or confined to particular groups of species. Here we investigate the global metabolic responses of twelve bacteria, two yeasts and two human cell lines exposed to sustained hyperosmotic salt stress by measuring semiquantitative levels of hundreds of cellular metabolites using nontargeted metabolomics. Beyond the accumulation of osmoprotectants, we observed significant changes of numerous metabolites in all species. Global metabolic responses were predominantly species-specific, yet individual metabolites were characteristically affected depending on species’ taxonomy, natural habitat, envelope structure or salt tolerance. Exploiting the breadth of our dataset, the correlation of individual metabolite response magnitudes across all species implicated lower glycolysis, tricarboxylic acid cycle, branched-chain amino acid metabolism and heme biosynthesis to be generally important for salt tolerance. Thus, our findings place the global metabolic salt stress response into a phylogenetic context and provide insights into the cellular phenotype associated with salt tolerance.     

In vitro microbiotic fermentation causes an extensive metabolite turnover of rye bran phytochemicals

The human gut hosts a microbial community which actively contributes to the host metabolism and has thus remarkable effect on our health. Intestinal microbiota is known to interact remarkably with the dietary constituents entering the colon, causing major metabolic conversions prior to absorption. To investigate the effect of microbial metabolism on the phytochemical pool of rye bran, we applied an in vitro simulated colonic fermentation where samples were collected with intervals and analyzed by LC-MS based non-targeted metabolite profiling. The analyses revealed extensive metabolic turnover on the phytochemical composition of the bran samples, and showed effects on all the metabolite classes detected. Furthermore, the majority of the metabolites, both the precursors and the conversion products, remained unidentified indicating that there are numerous yet unknown phytochemicals, which can potentially affect on our health. This underlines the importance of comprehensive profiling assays and subsequent detailed molecular investigations in order to clarify the effect of microbiota on phytochemicals present in our everyday diet.     

A network perspective on metabolism and aging

Aging affects a myriad of genetic, biochemical, and metabolic processes, and efforts to understand the underlying molecular basis of aging are often thwarted by the complexity of the aging process. By taking a systems biology approach, network analysis is well-suited to study the decline in function with age. Network analysis has already been utilized in describing other complex processes such as development, evolution, and robustness. Networks of gene expression and protein-protein interaction have provided valuable insight into the loss of connectivity and network structure throughout lifespan. Here, we advocate the use of metabolic networks to expand the work from genomics and proteomics. As metabolism is the final fingerprint of functionality and has been implicated in multiple theories of aging, metabolomic methods combined with metabolite network analyses should pave the way to investigate how relationships of metabolites change with age and how these interactions affect phenotype and function of the aging individual. The metabolomic network approaches highlighted in this review are fundamental for an understanding of systematic declines and of failure to function with age.     

Thermodynamically based profiling of drug metabolism and drug-drug metabolic interactions: a case study of acetaminophen and ethanol toxic interaction

Drug-drug metabolic interactions can result in unwanted side effects, including reduced drug efficacy and formation of toxic metabolic intermediates. In this work, thermodynamic constraints on non-equilibrium metabolite concentrations are used to reveal the biochemical interactions between the metabolic pathways of ethanol and acetaminophen (N-acetyl-p-aminophenol), two drugs known to interact unfavorably. It is known that many reactions of these pathways are coupled to the central energy metabolic reactions through a number of metabolites and the cellular redox potential. Based on these observations, a metabolic network model has been constructed and a database of thermodynamic properties for all participating metabolites and reactions has been compiled. Constraint-based computational analysis of the feasible metabolite concentrations reveals that the non-toxic pathways for APAP metabolism and the pathway for detoxifying N-acetyl-p-benzoquinoneimine (NAPQI) are inhibited by network interactions with ethanol metabolism. These results point to the potential utility of thermodynamically based profiling of metabolic network interactions in screening of drug candidates and analysis of potential toxicity.     

Analysis of metabolic network based on conservation of molecular structure

Recent work has revealed much about chemical reactions inside hundreds of organisms as well as universal characteristics of metabolic networks, which shed light on the evolution of the networks. However, characteristics of individual metabolites have been neglected. For example, some carbohydrates have structures that are decomposed into small molecules by metabolic reactions, but coenzymes such as ATP are mostly preserved. Such differences in metabolite characteristics are important for understanding the universal characteristics of metabolic networks. To quantify the structure conservation of metabolites, we defined the "structure conservation index" (SCI) for each metabolite as the fraction of metabolite atoms restored to their original positions through metabolic reactions. As expected, coenzymes and coenzyme-like metabolites that have reaction loops in the network show a higher SCI. Using the index, we found that the sum of metabolic fluxes is negatively correlated with the structure preservation of metabolite. Also, we found that each reaction path around high SCI metabolites changes independently, while changes in reaction paths involving low SCI metabolites coincide through evolution processes. These correlations may provide a clue to universal properties of metabolic networks.     

The complex genetic architecture of the metabolome

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype-metabolite associations distributed non-randomly within the genome. These clusters of genotype-metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype-metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.     

Identification of the pivotal role of glutamate in enhancing insect cell growth using factor analysis

Although the insect cell/baculovirus system is an important expression platform for recombinant protein production, our understanding of insect cell metabolism with respect to enhancing cell growth capability and productivity is still limited. Moreover, different host insect cell lines may have different growth characteristics associated with diverse product yields, which further hampers the elucidation of insect cell metabolism. To address this issue, the growth behaviors and utilization profiles of common metabolites among five cultured insect cell lines (derived from two insect hosts, Spodoptera frugiperda and Spodoptera exigua) were investigated in an attempt to establish a metabolic framework that can interpret the different cell growth behaviors. To analyze the complicated metabolic dataset, factor analysis was introduced to differentiate the crucial metabolic variations among these cells. Factor analysis was used to decompose the metabolic data to obtain the underlying factors with biological meaning that identify glutamate (a metabolic intermediate involved in glutaminolysis) as a key metabolite for insect cell growth. Notably, glutamate was consumed in significant amounts by fast-growing insect cell lines, but it was produced by slow-growing lines. A comparative experiment using cells grown in culture media with and without glutamine (the starting metabolite in glutaminolysis) was conducted to further confirm the pivotal role of glutamate. The factor analysis strategy allowed us to elucidate the underlying structure and inter-correlation between insect cell growth and metabolite utilization to provide some insights into insect cell growth and metabolism, and this strategy can be further extended to large-scale metabolomic analysis.     

Metabolomic profiling reveals bacterial metabolic adaptation strategies and new metabolites

How has metabolomics helped our understanding of infectious diseases? With the threat of antimicrobial resistance to human health around the world, metabolomics has emerged as a powerful tool to comprehensively characterize metabolic pathways to identify new drug targets. However, its output is constrained to known metabolites and their metabolic pathways. Recent advances in instrumentation, methodologies, and computational mass spectrometry have accelerated the use of metabolomics to understand pathogen–host metabolic interactions. This short review discusses a selection of recent publications using metabolomics in infectious/bacterial diseases. These studies unravel the links between metabolic adaptations to environments and host metabolic responses. Moreover, they highlight the importance of enzyme function and metabolite characterization in identifying new drug targets and biomarkers, as well as precision medicine in monitoring therapeutics and diagnosing diseases.     

Fungal metabolite analysis in genomics and phenomics

Metabolomics consists of strategies to quantitatively identify cellular metabolites and to understand how trafficking of these biochemical messengers through the metabolic network influences phenotype. The application of metabolomics to fungi has been strongly pursued because these organisms are widely used for the production of chemicals, are well known for their diverse metabolic landscape and serve as excellent eukaryotic model organisms for studying metabolism and systems biology. Within the context of fungal systems, recent progress has been made in the development of analytical tools and mathematical strategies used in metabolite analysis that have enhanced our ability to crack the code underpinning the cellular inventory, regulatory schemes and communication mechanisms that dictate cellular function. Metabolomics has played a key role in functional genomics and strain classification.