Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity

Evolution of molecular switches for regulation of transgene expression by clinically licensed gluconate

Synthetic biology holds great promise to improve the safety and efficacy of future gene and engineered cell therapies by providing new means of endogenous or exogenous control of the embedded therapeutic programs. Here, we focused on gluconate as a clinically licensed small-molecule inducer and engineered gluconate-sensitive molecular switches to regulate transgene expression in human cell cultures and in mice. Several switch designs were assembled based on the gluconate-responsive transcriptional repressor GntR from Escherichia coli. Initially we assembled OFF- and ON-type switches by rewiring the native gluconate-dependent binding of GntR to target DNA sequences in mammalian cells. Then, we utilized the ability of GntR to dimerize in the presence of gluconate to activate gene expression from a split transcriptional activator. By means of random mutagenesis of GntR combined with phenotypic screening, we identified variants that significantly enhanced the functionality of the genetic devices, enabling the construction of robust two-input logic gates. We also demonstrated the potential utility of the synthetic switch in two in vivo settings, one employing implantation of alginate-encapsulated engineered cells and the other involving modification of host cells by DNA delivery. Then, as proof-of-concept, the gluconate-actuated genetic switch was connected to insulin secretion, and the components encoding gluconate-induced insulin production were introduced into type-1 diabetic mice as naked DNA via hydrodynamic tail vein injection. Normoglycemia was restored, thereby showcasing the suitability of oral gluconate to regulate in situ production of a therapeutic protein.

VAMS-Based Blood Capillary Sampling for Mass Spectrometry-Based Human Metabolomics Studies

Volumetric absorptive microsampling (VAMS) is a recently developed sample collection method that enables single-drop blood collection in a minimally invasive manner. Blood biomolecules can then be extracted and processed for analysis using several analytical platforms. The integration of VAMS with conventional mass spectrometry (MS)-based metabolomics approaches is an attractive solution for human studies representing a less-invasive procedure compared to phlebotomy with the additional potential for remote sample collection. However, as we recently demonstrated, VAMS samples require long-term storage at -80 °C. This study investigated the stability of VAMS samples during short-term storage and compared the metabolome obtained from capillary blood collected from the fingertip to those of plasma and venous blood from 22 healthy volunteers. Our results suggest that the blood metabolome collected by VAMS samples is stable at room temperature only for up to 6 h requiring subsequent storage at -80 °C to avoid significant changes in the metabolome. We also demonstrated that capillary blood provides better coverage of the metabolome compared to plasma enabling the analysis of several intracellular metabolites presented in red blood cells. Finally, this work demonstrates that with the appropriate pre-analytical protocol capillary blood can be successfully used for untargeted metabolomics studies.

ROS and pentose phosphate pathway: mathematical modelling of the metabolic regulation in response to xenobiotic-induced oxidative stress and the proposed Impact of the gluconate shunt

Elevated intracellular levels of reactive oxygen species (ROS), e.g. resulting from exposure to xenobiotics, can cause severe damages. Antioxidant defence mechanisms, which involve regulation of enzyme activities, protect cells to a certain extent. Nevertheless, continuous or increased exposure can overwhelm this system resulting in an adverse cellular state. To simulate exposure scenarios and to investigate the transition to an adverse cellular state, a mathematical model for the dynamics of ROS in response to xenobiotic-induced oxidative stress has been developed. It is based on exposure experiments of human urothelial cells (RT4) to the nitrated polycyclic aromatic hydrocarbon 3-nitrobenzanthrone (3-NBA), a component of diesel engine exhaust, and takes into account the following metabolic pathways of the antioxidant defence system: glutathione redox cycle scavenging directly ROS, the pentose phosphate pathway and the gluconate shunt as NADPH supplier and the beginning of glycolysis. In addition, ROS generation due to the bioactivation of 3-NBA has been implemented. The regulation of enzyme activities plays an important role in the presented mathematical model. The in silico model consists of ordinary differential equations on the basis of enzyme kinetics and mass action for the metabolism of 3-NBA. Parameters are either estimated from performed in vitro experiments via least-squares fitting or obtained from the literature. The results underline the importance of the pentose phosphate pathway to cope with oxidative stress and suggest an important role of the gluconate shunt during low-dose exposure.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

The gluconate shunt is an alternative route for directing glucose into the pentose phosphate pathway in fission yeast

Glycolysis and the pentose phosphate pathway both play a central role in the degradation of glucose in all domains of life. Another metabolic route that can facilitate glucose breakdown is the gluconate shunt. In this shunt glucose dehydrogenase and gluconate kinase catalyze the two-step conversion of glucose into the pentose phosphate pathway intermediate 6-phosphogluconate. Despite the presence of these enzymes in many organisms, their only established role is in the production of 6-phosphogluconate for the Entner-Doudoroff pathway. In this report we performed metabolic profiling on a strain of Schizosaccharomyces pombe lacking the zinc-responsive transcriptional repressor Loz1 with the goal of identifying metabolic pathways that were altered by cellular zinc status. This profiling revealed that loz1Δ cells accumulate higher levels of gluconate. We show that the altered gluconate levels in loz1Δ cells result from increased expression of gcd1 By analyzing the activity of recombinant Gcd1 in vitro and by measuring gluconate levels in strains lacking enzymes of the gluconate shunt we demonstrate that Gcd1 encodes a novel NADP⁺-dependent glucose dehydrogenase that acts in a pathway with the Idn1 gluconate kinase. We also find that cells lacking gcd1 and zwf1, which encode the first enzyme in the pentose phosphate pathway, have a more severe growth phenotype than cells lacking zwf1 We propose that in S. pombe Gcd1 and Idn1 act together to shunt glucose into the pentose phosphate pathway, creating an alternative route for directing glucose into the pentose phosphate pathway that bypasses hexokinase and the rate-limiting enzyme glucose-6-phosphate dehydrogenase.

Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires

BACKGROUND

The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context.

RESULTS

We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides.

CONCLUSIONS

We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

Kinetic analysis of gluconate phosphorylation by human gluconokinase using isothermal titration calorimetry

Gluconate is a commonly encountered nutrient, which is degraded by the enzyme gluconokinase to generate 6-phosphogluconate. Here we used isothermal titration calorimetry to study the properties of this reaction. ΔH, KM and kcat are reported along with substrate binding data. We propose that the reaction follows a ternary complex mechanism, with ATP binding first. The reaction is inhibited by gluconate, as it binds to an Enzyme-ADP complex forming a dead-end complex. The study exemplifies that ITC can be used to determine mechanisms of enzyme catalyzed reactions, for which it is currently not commonly applied.

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.

Systematic genomic analysis reveals the complementary aerobic and anaerobic respiration capacities of the human gut microbiota

Because of the specific anatomical and physiological properties of the human intestine, a specific oxygen gradient builds up within this organ that influences the intestinal microbiota. The intestinal microbiome has been intensively studied in recent years, and certain respiratory substrates used by gut inhabiting microbes have been shown to play a crucial role in human health. Unfortunately, a systematic analysis has not been previously performed to determine the respiratory capabilities of human gut microbes (HGM). Here, we analyzed the distribution of aerobic and anaerobic respiratory reductases in 254 HGM genomes. In addition to the annotation of known enzymes, we also predicted a novel microaerobic reductase and novel thiosulfate reductase. Based on this comprehensive assessment of respiratory reductases in the HGM, we proposed a number of exchange pathways among different bacteria involved in the reduction of various nitrogen oxides. The results significantly expanded our knowledge of HGM metabolism and interactions in bacterial communities.

Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic acid as determined by constraint based metabolic network analysis

The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role of gluconate in humans. Here we report the recombinant expression, purification and biochemical characterization of isoform I of human gluconokinase alongside substrate specificity and kinetic assays of the enzyme catalyzed reaction. The enzyme, shown to be a dimer, had ATP dependent phosphorylation activity and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase, the results highlight that little is known of the mechanism of gluconate metabolism in humans despite its widespread use in medicine and consumer products.

Promise and reality in the expanding field of network interaction analysis: metabolic networks

In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Comparative evaluation of open source software for mapping between metabolite identifiers in metabolic network reconstructions: application to Recon 2

Background

An important step in the reconstruction of a metabolic network is annotation of metabolites. Metabolites are generally annotated with various database or structure based identifiers. Metabolite annotations in metabolic reconstructions may be incorrect or incomplete and thus need to be updated prior to their use. Genome-scale metabolic reconstructions generally include hundreds of metabolites. Manually updating annotations is therefore highly laborious. This prompted us to look for open-source software applications that could facilitate automatic updating of annotations by mapping between available metabolite identifiers. We identified three applications developed for the metabolomics and chemical informatics communities as potential solutions. The applications were MetMask, the Chemical Translation System, and UniChem. The first implements a “metabolite masking” strategy for mapping between identifiers whereas the latter two implement different versions of an InChI based strategy. Here we evaluated the suitability of these applications for the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We applied the best suited application to updating identifiers in Recon 2, the latest reconstruction of human metabolism.

Results

All three applications enabled partially automatic updating of metabolite identifiers, but significant manual effort was still required to fully update identifiers. We were able to reduce this manual effort by searching for new identifiers using multiple types of information about metabolites. When multiple types of information were combined, the Chemical Translation System enabled us to update over 3,500 metabolite identifiers in Recon 2. All but approximately 200 identifiers were updated automatically.

Conclusions

We found that an InChI based application such as the Chemical Translation System was better suited to the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We identified several features, however, that could be added to such an application in order to tailor it to this task.

Constraint-based models predict metabolic and associated cellular functions

The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

A community-driven global reconstruction of human metabolism

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.