High-Throughput, Accurate Mass Metabolome Profiling of Cellular Extracts by Flow Injection–Time-of-Flight Mass Spectrometry

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals

Mitochondrial stress activates a mitonuclear response to safeguard and repair mitochondrial function and to adapt cellular metabolism to stress. Using a multiomics approach in mammalian cells treated with four types of mitochondrial stressors, we identify activating transcription factor 4 (ATF4) as the main regulator of the stress response. Surprisingly, canonical mitochondrial unfolded protein response genes mediated by ATF5 are not activated. Instead, ATF4 activates the expression of cytoprotective genes, which reprogram cellular metabolism through activation of the integrated stress response (ISR). Mitochondrial stress promotes a local proteostatic response by reducing mitochondrial ribosomal proteins, inhibiting mitochondrial translation, and coupling the activation of the ISR with the attenuation of mitochondrial function. Through a trans-expression quantitative trait locus analysis, we provide genetic evidence supporting a role for Fh1 in the control of Atf4 expression in mammals. Using gene expression data from mice and humans with mitochondrial diseases, we show that the ATF4 pathway is activated in vivo upon mitochondrial stress. Our data illustrate the value of a multiomics approach to characterize complex cellular networks and provide a versatile resource to identify new regulators of mitochondrial-related diseases.

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

Reciprocal crosstalk between metabolism and cellular signaling pathways plays a crucial role in cellular decision-making. In recent years, this premise has motivated several metabolomics studies that aimed to gain a mechanistic understanding of metabolic phenotypes. However, due to complex interactions within metabolic networks, it remains a challenge to infer mechanisms that underlie metabolome changes. We present the metabolic network segmentation approach, a novel method that aimed to identify the sites and sequential order of metabolic regulatory events, based merely on steady state or dynamic metabolomics data. This method employs probabilistic graphical models to partition the entire metabolic network into modules with correlated metabolites. It identifies fractures between modules (i.e., reactions that connect non-correlated metabolites) as sites of regulation. We performed validation and benchmark analyses in hundreds of E. coli knockout mutants deficient in enzymes and transcription factors. Moreover, we verified the capability of our method for identifying the sequential order of metabolic regulatory events by testing it on fibroblasts exposed to oxidative stress. Our metabolic network segmentation algorithm is widely applicable, and thus, it will enhance our mechanistic understanding of metabolic phenotypes and their connections to cellular signaling and regulatory processes.

PknG senses amino acid availability to control metabolism and virulence of Mycobacterium tuberculosis

Sensing and response to changes in nutrient availability are essential for the lifestyle of environmental and pathogenic bacteria. Serine/threonine protein kinase G (PknG) is required for virulence of the human pathogen Mycobacterium tuberculosis, and its putative substrate GarA regulates the tricarboxylic acid cycle in M. tuberculosis and other Actinobacteria by protein-protein binding. We sought to understand the stimuli that lead to phosphorylation of GarA, and the roles of this regulatory system in pathogenic and non-pathogenic bacteria. We discovered that M. tuberculosis lacking garA was severely attenuated in mice and macrophages and furthermore that GarA lacking phosphorylation sites failed to restore the growth of garA deficient M. tuberculosis in macrophages. Additionally we examined the impact of genetic disruption of pknG or garA upon protein phosphorylation, nutrient utilization and the intracellular metabolome. We found that phosphorylation of GarA requires PknG and depends on nutrient availability, with glutamate and aspartate being the main stimuli. Disruption of pknG or garA caused opposing effects on metabolism: a defect in glutamate catabolism or depletion of intracellular glutamate, respectively. Strikingly, disruption of the phosphorylation sites of GarA was sufficient to recapitulate defects caused by pknG deletion. The results suggest that GarA is a cellular target of PknG and the metabolomics data demonstrate that the function of this signaling system is in metabolic regulation. This function in amino acid homeostasis is conserved amongst the Actinobacteria and provides an example of the close relationship between metabolism and virulence.

A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways

RATIONALE

The implementation of mass spectrometry (MS)-based metabolomics is advancing many areas of biomedical research. The time associated with traditional chromatographic methods for resolving metabolites prior to mass analysis has limited the potential to perform large-scale, highly powered metabolomics studies and clinical applications.

METHODS

Here we describe a three-minute method for the rapid profiling of central metabolic pathways through UHPLC/MS, tracing experiments in vitro and in vivo, and targeted quantification of compounds of interest using spiked in heavy labeled standards.

RESULTS

This method has shown to be linear, reproducible, selective, sensitive, and robust for the semi-targeted analysis of central carbon and nitrogen metabolism. Isotopically labeled internal standards are used for absolute quantitation of steady-state metabolite levels and de novo synthesized metabolites in tracing studies. We further propose exploratory applications to biofluids, cell and tissue extracts derived from relevant biomedical/clinical samples.

CONCLUSIONS

While limited to the analysis of central carbon and nitrogen metabolism, this method enables the analysis of hundreds of samples per day derived from diverse biological matrices. This approach makes it possible to analyze samples from large patient populations for translational research, personalized medicine, and clinical metabolomics applications. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolic constraints on the evolution of antibiotic resistance

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.

An integrative metabolomics and transcriptomics study to identify metabolic alterations in aged skin of humans in vivo

Background

Aging human skin undergoes significant morphological and functional changes such as wrinkle formation, reduced wound healing capacity, and altered epidermal barrier function. Besides known age-related alterations like DNA-methylation changes, metabolic adaptations have been recently linked to impaired skin function in elder humans. Understanding of these metabolic adaptations in aged skin is of special interest to devise topical treatments that potentially reverse or alleviate age-dependent skin deterioration and the occurrence of skin disorders.

Results

We investigated the global metabolic adaptions in human skin during aging with a combined transcriptomic and metabolomic approach applied to epidermal tissue samples of young and old human volunteers. Our analysis confirmed known age-dependent metabolic alterations, e.g. reduction of coenzyme Q10 levels, and also revealed novel age effects that are seemingly important for skin maintenance. Integration of donor-matched transcriptome and metabolome data highlighted transcriptionally-driven alterations of metabolism during aging such as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis. Together, we identified several age-dependent metabolic alterations that might affect cellular signaling, epidermal barrier function, and skin structure and morphology.

Conclusions

Our study provides a global resource on the metabolic adaptations and its transcriptional regulation during aging of human skin. Thus, it represents a first step towards an understanding of the impact of metabolism on impaired skin function in aged humans and therefore will potentially lead to improved treatments of age related skin disorders.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3547-3) contains supplementary material, which is available to authorized users.

Genomewide landscape of gene-metabolome associations in Escherichia coli

Metabolism is one of the best-understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non-enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single-celled organisms. Here, we systematically mapped the association between > 3,800 single-gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene-metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism-related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Frontiers of high-throughput metabolomics

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics

We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line-specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways.

Untargeted metabolomics unravels functionalities of phosphorylation sites in Saccharomyces cerevisiae

Background

Coordinated through a complex network of kinases and phosphatases, protein phosphorylation regulates essentially all cellular processes in eukaryotes. Recent advances in proteomics enable detection of thousands of phosphorylation sites (phosphosites) in single experiments. However, functionality of the vast majority of these sites remains unclear and we lack suitable approaches to evaluate functional relevance at a pace that matches their detection.

Results

Here, we assess functionality of 26 phosphosites by introducing phosphodeletion and phosphomimic mutations in 25 metabolic enzymes and regulators from the TOR and HOG signaling pathway in Saccharomyces cerevisiae by phenotypic analysis and untargeted metabolomics. We show that metabolomics largely outperforms growth analysis and recovers 10 out of the 13 previously characterized phosphosites and suggests functionality for several novel sites, including S79 on the TOR regulatory protein Tip41. We analyze metabolic profiles to identify consequences underlying regulatory phosphorylation events and detecting glycerol metabolism to have a so far unknown influence on arginine metabolism via phosphoregulation of the glycerol dehydrogenases. Further, we also find S508 in the MAPKK Pbs2 as a potential link for cross-talking between HOG signaling and the cell wall integrity pathway.

Conclusions

We demonstrate that metabolic profiles can be exploited for gaining insight into regulatory consequences and biological roles of phosphosites. Altogether, untargeted metabolomics is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of phosphosites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0350-8) contains supplementary material, which is available to authorized users.

L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity

Metabolic activity is intimately linked to T cell fate and function. Using high-resolution mass spectrometry, we generated dynamic metabolome and proteome profiles of human primary naive T cells following activation. We discovered critical changes in the arginine metabolism that led to a drop in intracellular L-arginine concentration. Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity. Proteome-wide probing of structural alterations, validated by the analysis of knockout T cell clones, identified three transcriptional regulators (BAZ1B, PSIP1, and TSN) that sensed L-arginine levels and promoted T cell survival. Thus, intracellular L-arginine concentrations directly impact the metabolic fitness and survival capacity of T cells that are crucial for anti-tumor responses.

L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity

Metabolic activity is intimately linked to T cell fate and function. Using high-resolution mass spectrometry, we generated dynamic metabolome and proteome profiles of human primary naive T cells following activation. We discovered critical changes in the arginine metabolism that led to a drop in intracellular L-arginine concentration. Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity. Proteome-wide probing of structural alterations, validated by the analysis of knockout T cell clones, identified three transcriptional regulators (BAZ1B, PSIP1, and TSN) that sensed L-arginine levels and promoted T cell survival. Thus, intracellular L-arginine concentrations directly impact the metabolic fitness and survival capacity of T cells that are crucial for anti-tumor responses.

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Dataset on dynamic metabolome/proteome profiles of activated human naive T cells

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Intracellular L-arginine levels regulate several metabolic pathways in T cells

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T cells with increased L-arginine display enhanced survival and anti-tumor activity

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LiP-MS identified proteins that are structurally modified by high L-arginine levels

A rheostat mechanism governs the bifurcation of carbon flux in mycobacteria

Fatty acid metabolism is an important feature of the pathogenicity of Mycobacterium tuberculosis during infection. Consumption of fatty acids requires regulation of carbon flux bifurcation between the oxidative TCA cycle and the glyoxylate shunt. In Escherichia coli, flux bifurcation is regulated by phosphorylation-mediated inhibition of isocitrate dehydrogenase (ICD), a paradigmatic example of post-translational mechanisms governing metabolic fluxes. Here, we demonstrate that, in contrast to E. coli, carbon flux bifurcation in mycobacteria is regulated not by phosphorylation but through metabolic cross-activation of ICD by glyoxylate, which is produced by the glyoxylate shunt enzyme isocitrate lyase (ICL). This regulatory circuit maintains stable partitioning of fluxes, thus ensuring a balance between anaplerosis, energy production, and precursor biosynthesis. The rheostat-like mechanism of metabolite-mediated control of flux partitioning demonstrates the importance of allosteric regulation during metabolic steady-state. The sensitivity of this regulatory mechanism to perturbations presents a potentially attractive target for chemotherapy.

Microbes survive in dynamic environments by modulating their intracellular metabolism. Here, the authors reveal that mycobacteria employ a rheostat-like mechanism to regulate carbon flux between the oxidative TCA cycle and the glyoxylate shunt during glucose-acetate diauxic shift.

Current state and challenges for dynamic metabolic modeling

While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters.

Next-Generation "-omics" Approaches Reveal a Massive Alteration of Host RNA Metabolism during Bacteriophage Infection of Pseudomonas aeruginosa

As interest in the therapeutic and biotechnological potentials of bacteriophages has grown, so has value in understanding their basic biology. However, detailed knowledge of infection cycles has been limited to a small number of model bacteriophages, mostly infecting Escherichia coli. We present here the first analysis coupling data obtained from global next-generation approaches, RNA-Sequencing and metabolomics, to characterize interactions between the virulent bacteriophage PAK_P3 and its host Pseudomonas aeruginosa. We detected a dramatic global depletion of bacterial transcripts coupled with their replacement by viral RNAs over the course of infection, eventually leading to drastic changes in pyrimidine metabolism. This process relies on host machinery hijacking as suggested by the strong up-regulation of one bacterial operon involved in RNA processing. Moreover, we found that RNA-based regulation plays a central role in PAK_P3 lifecycle as antisense transcripts are produced mainly during the early stage of infection and viral small non coding RNAs are massively expressed at the end of infection. This work highlights the prominent role of RNA metabolism in the infection strategy of a bacteriophage belonging to a new characterized sub-family of viruses with promising therapeutic potential.

Systems proteomics of liver mitochondria function

Recent improvements in quantitative proteomics approaches, including Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), permit reproducible large-scale protein measurements across diverse cohorts. Together with genomics, transcriptomics, and other technologies, transomic data sets can be generated that permit detailed analyses across broad molecular interaction networks. Here, we examine mitochondrial links to liver metabolism through the genome, transcriptome, proteome, and metabolome of 386 individuals in the BXD mouse reference population. Several links were validated between genetic variants toward transcripts, proteins, metabolites, and phenotypes. Among these, sequence variants in Cox7a2l alter its protein's activity, which in turn leads to downstream differences in mitochondrial supercomplex formation. This data set demonstrates that the proteome can now be quantified comprehensively, serving as a key complement to transcriptomics, genomics, and metabolomics--a combination moving us forward in complex trait analysis.

Metabolomic Profiling of Bradyrhizobium diazoefficiens-Induced Root Nodules Reveals Both Host Plant-Specific and Developmental Signatures

Bradyrhizobium diazoefficiens is a nitrogen-fixing endosymbiont, which can grow inside root-nodule cells of the agriculturally important soybean and other host plants. Our previous studies described B. diazoefficiens host-specific global expression changes occurring during legume infection at the transcript and protein level. In order to further characterize nodule metabolism, we here determine by flow injection-time-of-flight mass spectrometry analysis the metabolome of (i) nodules and roots from four different B. diazoefficiens host plants; (ii) soybean nodules harvested at different time points during nodule development; and (iii) soybean nodules infected by two strains mutated in key genes for nitrogen fixation, respectively. Ribose (soybean), tartaric acid (mungbean), hydroxybutanoyloxybutanoate (siratro) and catechol (cowpea) were among the metabolites found to be specifically elevated in one of the respective host plants. While the level of C4-dicarboxylic acids decreased during soybean nodule development, we observed an accumulation of trehalose-phosphate at 21 days post infection (dpi). Moreover, nodules from non-nitrogen-fixing bacteroids (nifA and nifH mutants) showed specific metabolic alterations; these were also supported by independent transcriptomics data. The alterations included signs of nitrogen limitation in both mutants, and an increased level of a phytoalexin in nodules induced by the nifA mutant, suggesting that the tissue of these nodules exhibits defense and stress reactions.

Safe and Efficient Gene Therapy for Pyruvate Kinase Deficiency

Pyruvate kinase deficiency (PKD) is a monogenic metabolic disease caused by mutations in the PKLR gene that leads to hemolytic anemia of variable symptomatology and that can be fatal during the neonatal period. PKD recessive inheritance trait and its curative treatment by allogeneic bone marrow transplantation provide an ideal scenario for developing gene therapy approaches. Here, we provide a preclinical gene therapy for PKD based on a lentiviral vector harboring the hPGK eukaryotic promoter that drives the expression of the PKLR cDNA. This therapeutic vector was used to transduce mouse PKD hematopoietic stem cells (HSCs) that were subsequently transplanted into myeloablated PKD mice. Ectopic RPK expression normalized the erythroid compartment correcting the hematological phenotype and reverting organ pathology. Metabolomic studies demonstrated functional correction of the glycolytic pathway in RBCs derived from genetically corrected PKD HSCs, with no metabolic disturbances in leukocytes. The analysis of the lentiviral insertion sites in the genome of transplanted hematopoietic cells demonstrated no evidence of genotoxicity in any of the transplanted animals. Overall, our results underscore the therapeutic potential of the hPGK-coRPK lentiviral vector and provide high expectations toward the gene therapy of PKD and other erythroid metabolic genetic disorders.

Embryonic Lethality of Mitochondrial Pyruvate Carrier 1 Deficient Mouse Can Be Rescued by a Ketogenic Diet

Mitochondrial import of pyruvate by the mitochondrial pyruvate carrier (MPC) is a central step which links cytosolic and mitochondrial intermediary metabolism. To investigate the role of the MPC in mammalian physiology and development, we generated a mouse strain with complete loss of MPC1 expression. This resulted in embryonic lethality at around E13.5. Mouse embryonic fibroblasts (MEFs) derived from mutant mice displayed defective pyruvate-driven respiration as well as perturbed metabolic profiles, and both defects could be restored by reexpression of MPC1. Labeling experiments using 13C-labeled glucose and glutamine demonstrated that MPC deficiency causes increased glutaminolysis and reduced contribution of glucose-derived pyruvate to the TCA cycle. Morphological defects were observed in mutant embryonic brains, together with major alterations of their metabolome including lactic acidosis, diminished TCA cycle intermediates, energy deficit and a perturbed balance of neurotransmitters. Strikingly, these changes were reversed when the pregnant dams were fed a ketogenic diet, which provides acetyl-CoA directly to the TCA cycle and bypasses the need for a functional MPC. This allowed the normal gestation and development of MPC deficient pups, even though they all died within a few minutes post-delivery. This study establishes the MPC as a key player in regulating the metabolic state necessary for embryonic development, neurotransmitter balance and post-natal survival.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

High coverage metabolomics analysis reveals phage-specific alterations to Pseudomonas aeruginosa physiology during infection

Phage-mediated metabolic changes in bacteria are hypothesized to markedly alter global nutrient and biogeochemical cycles. Despite their theoretic importance, experimental data on the net metabolic impact of phage infection on the bacterial metabolism remains scarce. In this study, we tracked the dynamics of intracellular metabolites using untargeted high coverage metabolomics in Pseudomonas aeruginosa cells infected with lytic bacteriophages from six distinct phage genera. Analysis of the metabolomics data indicates an active interference in the host metabolism. In general, phages elicit an increase in pyrimidine and nucleotide sugar metabolism. Furthermore, clear phage-specific and infection stage-specific responses are observed, ranging from extreme metabolite depletion (for example, phage YuA) to complete reorganization of the metabolism (for example, phage phiKZ). As expected, pathways targeted by the phage-encoded auxiliary metabolic genes (AMGs) were enriched among the metabolites changing during infection. The effect on pyrimidine metabolism of phages encoding AMGs capable of host genome degradation (for example, YuA and LUZ19) was distinct from those lacking nuclease-encoding genes (for example, phiKZ), which demonstrates the link between the encoded set of AMGs of a phage and its impact on host physiology. However, a large fraction of the profound effect on host metabolism could not be attributed to the phage-encoded AMGs. We suggest a potentially crucial role for small, 'non-enzymatic' peptides in metabolism take-over and hypothesize on potential biotechnical applications for such peptides. The highly phage-specific nature of the metabolic impact emphasizes the potential importance of the 'phage diversity' parameter when studying metabolic interactions in complex communities.

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.

Model-based media selection to minimize the cost of metabolic cooperation in microbial ecosystems

MOTIVATION

Simple forms of mutualism between microorganisms are widespread in nature. Nevertheless, the role played by the environmental nutrient composition in mediating cross-feeding in microbial ecosystems is still poorly understood.

RESULTS

Here, we use mixed-integer bilevel linear programming to investigate the cost of sharing metabolic resources in microbial communities. The algorithm infers an optimal combination of nutrients that can selectively sustain synergistic growth for a pair of species and guarantees minimum cost of cross-fed metabolites. To test model-based predictions, we selected a pair of Escherichia coli single gene knockouts auxotrophic, respectively, for arginine and leucine: ΔargB and ΔleuB and we experimentally verified that model-predicted medium composition significantly favors mutualism. Moreover, mass spectrometry profiling of exchanged metabolites confirmed the predicted cross-fed metabolites, supporting our constraint based modeling approach as a promising tool for engineering microbial consortia.

AVAILABILITY AND IMPLEMENTATION

The software is freely available as a matlab script in the Supplementary materials.

CONTACT

zampieri@imsb.biol.ethz.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Life span extension by targeting a link between metabolism and histone acetylation in Drosophila

Old age is associated with a progressive decline of mitochondrial function and changes in nuclear chromatin. However, little is known about how metabolic activity and epigenetic modifications change as organisms reach their midlife. Here, we assessed how cellular metabolism and protein acetylation change during early aging in Drosophila melanogaster. Contrary to common assumptions, we find that flies increase oxygen consumption and become less sensitive to histone deacetylase inhibitors as they reach midlife. Further, midlife flies show changes in the metabolome, elevated acetyl-CoA levels, alterations in protein-notably histone-acetylation, as well as associated transcriptome changes. Based on these observations, we decreased the activity of the acetyl-CoA-synthesizing enzyme ATP citrate lyase (ATPCL) or the levels of the histone H4 K12-specific acetyltransferase Chameau. We find that these targeted interventions both alleviate the observed aging-associated changes and promote longevity. Our findings reveal a pathway that couples changes of intermediate metabolism during aging with the chromatin-mediated regulation of transcription and changes in the activity of associated enzymes that modulate organismal life span.

Branched-chain amino acid catabolism is a conserved regulator of physiological ageing

Ageing has been defined as a global decline in physiological function depending on both environmental and genetic factors. Here we identify gene transcripts that are similarly regulated during physiological ageing in nematodes, zebrafish and mice. We observe the strongest extension of lifespan when impairing expression of the branched-chain amino acid transferase-1 (bcat-1) gene in C. elegans, which leads to excessive levels of branched-chain amino acids (BCAAs). We further show that BCAAs reduce a LET-363/mTOR-dependent neuro-endocrine signal, which we identify as DAF-7/TGFβ, and that impacts lifespan depending on its related receptors, DAF-1 and DAF-4, as well as ultimately on DAF-16/FoxO and HSF-1 in a cell-non-autonomous manner. The transcription factor HLH-15 controls and epistatically synergizes with BCAT-1 to modulate physiological ageing. Lastly and consistent with previous findings in rodents, nutritional supplementation of BCAAs extends nematodal lifespan. Taken together, BCAAs act as periphery-derived metabokines that induce a central neuro-endocrine response, culminating in extended healthspan.

High-throughput platforms for metabolomics

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.

Nrf2 Activation Promotes Keratinocyte Survival during Early Skin Carcinogenesis via Metabolic Alterations

Pharmacologic activation of the transcription factor NRF2 has been suggested to offer a strategy for cancer prevention. In this study, we present evidence from murine tumorigenesis experiments suggesting there may be limitations to this possibility, based on tumorigenic effects of Nrf2 in murine keratinocytes that have not been described previously. In this setting, Nrf2 expression conferred metabolic alterations in keratinocytes that were protumorigenic in nature, affecting enzymes involved in glutathione biosynthesis or in the oxidative pentose phosphate pathway and other NADPH-producing enzymes. Under stress conditions, coordinate increases in NADPH, purine, and glutathione levels promoted the survival of keratinocytes harboring oncogenic mutations, thereby promoting tumor development. The protumorigenic activity of Nrf2 in keratinocytes was particularly significant in a mouse model of skin tumorigenesis that did not rely upon chemical carcinogenesis. In exploring the clinical relevance of our findings, we confirm that NRF2 and protumorigenic NRF2 target genes were activated in some actinic keratoses, the major precancerous lesion in human skin. Overall, our results reveal an unexpected tumor-promoting activity of activated NRF2 during early phases of skin tumorigenesis.

Dynamic exometabolome analysis reveals active metabolic pathways in non-replicating mycobacteria

An organism's metabolic activity leaves an extracellular footprint and dynamic changes in this exometabolome inform about nutrient uptake, waste disposal and signalling activities. Using non-targeted mass spectrometry, we report exometabolome dynamics of hypoxia-induced, non-replicating mycobacteria that are thought to play a role in latent tuberculosis. Despite evidence of active metabolism, little is known about the mechanisms enabling obligate aerobic mycobacteria to cope with hypoxia, resulting in long-term survival and increased chemotherapeutic tolerance. The dynamics of 379 extracellular compounds of Mycobacterium smegmatis were deconvoluted with a genome-scale metabolic reaction-pair network to generate hypotheses about intracellular pathway usage. Time-resolved (13) C-tracing and mutant experiments then demonstrated a crucial, energy-generating role of asparagine utilization and non-generic usage of the glyoxylate shunt for hypoxic fitness. Experiments with M. bovis and M. tuberculosis revealed the general relevance of asparagine fermentation and a variable contribution of the glyoxylate shunt to non-replicative, hypoxic survival between the three species.

A dual control mechanism synchronizes riboflavin and sulphur metabolism in Bacillus subtilis

Flavin mononucleotide (FMN) riboswitches are genetic elements, which in many bacteria control genes responsible for biosynthesis and/or transport of riboflavin (rib genes). Cytoplasmic riboflavin is rapidly and almost completely converted to FMN by flavokinases. When cytoplasmic levels of FMN are sufficient ("high levels"), FMN binding to FMN riboswitches leads to a reduction of rib gene expression. We report here that the protein RibR counteracts the FMN-induced "turn-off" activities of both FMN riboswitches in Bacillus subtilis, allowing rib gene expression even in the presence of high levels of FMN. The reason for this secondary metabolic control by RibR is to couple sulfur metabolism with riboflavin metabolism.

Quantitative prediction of genome-wide resource allocation in bacteria

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

The outer mucus layer hosts a distinct intestinal microbial niche

The overall composition of the mammalian intestinal microbiota varies between individuals: within each individual there are differences along the length of the intestinal tract related to host nutrition, intestinal motility and secretions. Mucus is a highly regenerative protective lubricant glycoprotein sheet secreted by host intestinal goblet cells; the inner mucus layer is nearly sterile. Here we show that the outer mucus of the large intestine forms a unique microbial niche with distinct communities, including bacteria without specialized mucolytic capability. Bacterial species present in the mucus show differential proliferation and resource utilization compared with the same species in the intestinal lumen, with high recovery of bioavailable iron and consumption of epithelial-derived carbon sources according to their genome-encoded metabolic repertoire. Functional competition for existence in this intimate layer is likely to be a major determinant of microbiota composition and microbial molecular exchange with the host.

The Convergence of Systems and Reductionist Approaches in Complex Trait Analysis

Research into the genetic and environmental factors behind complex trait variation has traditionally been segregated into distinct scientific camps. The reductionist approach aims to decrypt phenotypic variability bit by bit, founded on the underlying hypothesis that genome-to-phenome relations are largely constructed from the additive effects of their molecular players. In contrast, the systems approach aims to examine large-scale interactions of many components simultaneously, on the premise that interactions in gene networks can be both linear and non-linear. Both approaches are complementary, and they are becoming increasingly intertwined due to developments in gene editing tools, omics technologies, and population resources. Together, these strategies are beginning to drive the next era in complex trait research, paving the way to improve agriculture and toward more personalized medicine.

Real-time metabolome profiling of the metabolic switch between starvation and growth

Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15-30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation-induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

Analytics for Metabolic Engineering

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.

Acute Activation of Oxidative Pentose Phosphate Pathway as First-Line Response to Oxidative Stress in Human Skin Cells

Integrity of human skin is endangered by exposure to UV irradiation and chemical stressors, which can provoke a toxic production of reactive oxygen species (ROS) and oxidative damage. Since oxidation of proteins and metabolites occurs virtually instantaneously, immediate cellular countermeasures are pivotal to mitigate the negative implications of acute oxidative stress. We investigated the short-term metabolic response in human skin fibroblasts and keratinocytes to H2O2 and UV exposure. In time-resolved metabolomics experiments, we observed that within seconds after stress induction, glucose catabolism is routed to the oxidative pentose phosphate pathway (PPP) and nucleotide synthesis independent of previously postulated blocks in glycolysis (i.e., of GAPDH or PKM2). Through ultra-short (13)C labeling experiments, we provide evidence for multiple cycling of carbon backbones in the oxidative PPP, potentially maximizing NADPH reduction. The identified metabolic rerouting in oxidative and non-oxidative PPP has important physiological roles in stabilization of the redox balance and ROS clearance.

Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis

Mycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%-7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.

HIF-driven SF3B1 induces KHK-C to enforce fructolysis and heart disease

Fructose is a major component of dietary sugar and its overconsumption exacerbates key pathological features of metabolic syndrome. The central fructose-metabolising enzyme is ketohexokinase (KHK), which exists in two isoforms: KHK-A and KHK-C, generated through mutually exclusive alternative splicing of KHK pre-mRNAs. KHK-C displays superior affinity for fructose compared with KHK-A and is produced primarily in the liver, thus restricting fructose metabolism almost exclusively to this organ. Here we show that myocardial hypoxia actuates fructose metabolism in human and mouse models of pathological cardiac hypertrophy through hypoxia-inducible factor 1α (HIF1α) activation of SF3B1 and SF3B1-mediated splice switching of KHK-A to KHK-C. Heart-specific depletion of SF3B1 or genetic ablation of Khk, but not Khk-A alone, in mice, suppresses pathological stress-induced fructose metabolism, growth and contractile dysfunction, thus defining signalling components and molecular underpinnings of a fructose metabolism regulatory system crucial for pathological growth.

Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization

Macrophage polarization involves a coordinated metabolic and transcriptional rewiring that is only partially understood. By using an integrated high-throughput transcriptional-metabolic profiling and analysis pipeline, we characterized systemic changes during murine macrophage M1 and M2 polarization. M2 polarization was found to activate glutamine catabolism and UDP-GlcNAc-associated modules. Correspondingly, glutamine deprivation or inhibition of N-glycosylation decreased M2 polarization and production of chemokine CCL22. In M1 macrophages, we identified a metabolic break at Idh, the enzyme that converts isocitrate to alpha-ketoglutarate, providing mechanistic explanation for TCA cycle fragmentation. (13)C-tracer studies suggested the presence of an active variant of the aspartate-arginosuccinate shunt that compensated for this break. Consistently, inhibition of aspartate-aminotransferase, a key enzyme of the shunt, inhibited nitric oxide and interleukin-6 production in M1 macrophages, while promoting mitochondrial respiration. This systems approach provides a highly integrated picture of the physiological modules supporting macrophage polarization, identifying potential pharmacologic control points for both macrophage phenotypes.

Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome

Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown. Recent advances for system-wide experimental data generation have facilitated the quantification of signaling systems, but the integration of multi-level dynamic data remains challenging. Here, we co-designed dynamic experiments and a probabilistic, model-based method to infer causal relationships between metabolism, signaling, and gene regulation. We analyzed the dynamic regulation of nitrogen metabolism by the target of rapamycin complex 1 (TORC1) pathway in budding yeast. Dynamic transcriptomic, proteomic, and metabolomic measurements along shifts in nitrogen quality yielded a consistent dataset that demonstrated extensive re-wiring of cellular networks during adaptation. Our inference method identified putative downstream targets of TORC1 and putative metabolic inputs of TORC1, including the hypothesized glutamine signal. The work provides a basis for further mechanistic studies of nitrogen metabolism and a general computational framework to study cellular processes.

The neurometabolic fingerprint of excessive alcohol drinking

'Omics' techniques are widely used to identify novel mechanisms underlying brain function and pathology. Here we applied a novel metabolomics approach to further ascertain the role of frontostriatal brain regions for the expression of addiction-like behaviors in rat models of alcoholism. Rats were made alcohol dependent via chronic intermittent alcohol vapor exposure. Following a 3-week abstinence period, rats had continuous access to alcohol in a two-bottle, free-choice paradigm for 7 weeks. Nontargeted flow injection time-of-flight mass spectrometry was used to assess global metabolic profiles of two cortical (prelimbic and infralimbic) and two striatal (accumbens core and shell) brain regions. Alcohol consumption produces pronounced global effects on neurometabolomic profiles leading to a clear separation of metabolic phenotypes between treatment groups, particularly. Further comparisons of regional tissue levels of various metabolites, most notably dopamine and Met-enkephalin, allow the extrapolation of alcohol consumption history. Finally, a high-drinking metabolic fingerprint was identified indicating a distinct alteration of central energy metabolism in the accumbens shell of excessively drinking rats that could indicate a so far unrecognized pathophysiological mechanism in alcohol addiction. In conclusion, global metabolic profiling from distinct brain regions by mass spectrometry identifies profiles reflective of an animal's drinking history and provides a versatile tool to further investigate pathophysiological mechanisms in alcohol dependence.