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

Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition

Oxygen limitation is regarded as a useful strategy to improve enzyme production by mycelial fungus like Aspergillus niger. However, the intracellular metabolic response of A. niger to oxygen limitation is still obscure. To address this, the metabolism of A. niger was studied using multi-omics integrated analysis based on the latest GEMs (genome-scale metabolic model), including metabolomics, fluxomics and transcriptomics. Upon sharp reduction of the oxygen supply, A. niger metabolism shifted to higher redox level status, as well as lower energy supply, down-regulation of genes for fatty acid synthesis and a rapid decrease of the specific growth rate. The gene expression of the glyoxylate bypass was activated, which was consistent with flux analysis using the A. niger GEMs iHL1210. The increasing flux of the glyoxylate bypass was assumed to reduce the NADH formation from TCA cycle and benefit maintenance of the cellular redox balance under hypoxic conditions. In addition, the relative fluxes of the EMP pathway were increased, which possibly relieved the energy demand for cell metabolism. The above multi-omics integrative analysis provided new insights on metabolic regulatory mechanisms of A. niger associated with enzyme production under oxygen-limited condition, which will benefit systematic design and optimization of the A. niger microbial cell factory.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.

Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data

Motivation

Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime.

Results

We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in Escherichia coli and Lactococcus lactis, and provides evidence for acetate uptake by L. lactis after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites.

Availability and implementation

The Matlab code for the estimation method is available for download at https://team.inria.fr/ibis/rate-estimation-software/, together with the datasets.

Supplementary information

Supplementary data are available at Bioinformatics online.

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.

Microbes vs. chemistry in the origin of the anaerobic gut lumen

The succession from aerobic and facultative anaerobic bacteria to obligate anaerobes in the infant gut along with the differences between the compositions of the mucosally adherent vs. luminal microbiota suggests that the gut microbes consume oxygen, which diffuses into the lumen from the intestinal tissue, maintaining the lumen in a deeply anaerobic state. Remarkably, measurements of luminal oxygen levels show nearly identical pO₂ (partial pressure of oxygen) profiles in conventional and germ-free mice, pointing to the existence of oxygen consumption mechanisms other than microbial respiration. In vitro experiments confirmed that the luminal contents of germ-free mice are able to chemically consume oxygen (e.g., via lipid oxidation reactions), although at rates significantly lower than those observed in the case of conventionally housed mice. For conventional mice, we also show that the taxonomic composition of the gut microbiota adherent to the gut mucosa and in the lumen throughout the length of the gut correlates with oxygen levels. At the same time, an increase in the biomass of the gut microbiota provides an explanation for the reduction of luminal oxygen in the distal vs. proximal gut. These results demonstrate how oxygen from the mammalian host is used by the gut microbiota, while both the microbes and the oxidative chemical reactions regulate luminal oxygen levels, shaping the composition of the microbial community throughout different regions of the gut.

Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5'-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.

Understanding D-Ribose and Mitochondrial Function

Mitochondria are important organelles referred to as cellular powerhouses for their unique properties of cellular energy production. With many pathologic conditions and aging, mitochondrial function declines, and there is a reduction in the production of adenosine triphosphate. The energy carrying molecule generated by cellular respiration and by pentose phosphate pathway, an alternative pathway of glucose metabolism. D-ribose is a naturally occurring monosaccharide found in the cells and particularly in the mitochondria is essential in energy production. Without sufficient energy, cells cannot maintain integrity and function. Supplemental D-ribose has been shown to improve cellular processes when there is mitochondrial dysfunction. When individuals take supplemental D-ribose, it can bypass part of the pentose pathway to produce D-ribose-5-phosphate for the production of energy. In this article, we review how energy is produced by cellular respiration, the pentose pathway, and the use of supplemental D-ribose.

High-Temperature Induced Changes of Extracellular Metabolites in Pleurotus ostreatus and Their Positive Effects on the Growth of Trichoderma asperellum

Pleurotus ostreatus is a widely cultivated edible fungus in China. Green mold disease of P. ostreatus which can seriously affect yield is a common disease during cultivation. It occurs mostly after P. ostreatus mycelia have been subjected to high temperatures. However, little information is available on the relationship between high temperature and green mold disease. The aim of this study is to prove that extracellular metabolites of P. ostreatus affected by high temperature can promote the growth of Trichoderma asperellum. After P. ostreatus mycelia was subjected to high temperature, the extracellular fluid of P. ostreatus showed a higher promoting effect on mycelial growth and conidial germination of T. asperellum. The thiobarbituric acid reactive substance (TBARS) content reached the maximum after 48 h at 36°C. A comprehensive metabolite profiling strategy involving gas chromatography-mass spectrometry (GC/MS) combined with liquid chromatography-mass spectrometry (LC/MS) was used to analyze the changes of extracellular metabolites in response to high temperature. A total of 141 differential metabolites were identified, including 84.4% up-regulated and 15.6% down-regulated. Exogenous metabolites whose concentrations were increased after high temperature were randomly selected, and nearly all of them were able to promote the mycelial growth and conidial germination of T. asperellum. The combination of all selected exogenous metabolites also has the promotion effects on the mycelial growth and conidial germination of T. asperellum in a given concentration range in vitro. Overall, these results provide a first view that high temperature affects the extracellular metabolites of P. ostreatus, and the extensive change in metabolites promotes T. asperellum growth.

Quantification of biomolecules responsible for biomarkers in the surface-enhanced Raman spectra of bacteria using liquid chromatography-mass spectrometry

Biomarkers in SERS spectra of bacteria originate from bacterial purine metabolites, whose quantification indicates their continuous release in a stressful water environment.

Metabolic Dysfunction in Heart Failure: Diagnostic, Prognostic, and Pathophysiologic Insights From Metabolomic Profiling

Metabolic impairment is an intrinsic component of heart failure (HF) pathophysiology. Although initially conceived as a myocardial defect, metabolic dysfunction is now recognized as a systemic process with complex interplay between the myocardium and peripheral tissues and organs. Specifically, HF-associated metabolic dysfunction includes alterations in substrate utilization, insulin resistance, defects in energy production, and imbalanced anabolic-catabolic signaling leading to cachexia. Each of these metabolic abnormalities is associated with significant morbidity and mortality in patients with HF; however, their detection and therapeutic management remains challenging. Given the difficulty in obtaining human cardiac tissue for research purposes, peripheral blood metabolomic profiling, a well-established approach for characterizing small-molecule metabolite intermediates from canonical biochemical pathways, may be a useful technology for dissecting biomarkers and mechanisms of metabolic impairment in HF. In this review, metabolic abnormalities in HF will be discussed with particular emphasis on the application of metabolomic profiling to detecting, risk stratifying, and identifying novel targets for metabolic therapy in this heterogeneous population.

Methanol assimilation in Escherichia coli is improved by co-utilization of threonine and deletion of leucine-responsive regulatory protein

Methane, the main component of natural gas, can be used to produce methanol which can be further converted to other valuable products. There is increasing interest in using biological systems for the production of fuels and chemicals from methanol, termed methylotrophy. In this work, we have examined methanol assimilation metabolism in a synthetic methylotrophic E. coli strain. Specifically, we applied ¹³C-tracers and evaluated 25 different co-substrates for methanol assimilation, including amino acids, sugars and organic acids. In particular, co-utilization of threonine significantly enhanced methylotrophy. Through our investigations, we proposed specific metabolic pathways that, when activated, correlated with increased methanol assimilation. These pathways are normally repressed by the leucine-responsive regulatory protein (lrp), a global regulator of metabolism associated with the feast-or-famine response in E. coli. By deleting lrp, we were able to further enhance the methylotrophic ability of our synthetic strain, as demonstrated through increased incorporation of ¹³C carbon from ¹³C-methanol into biomass.

Microdosing, isotopic labeling, radiotracers and metabolomics: relevance in drug discovery, development and safety

This review discusses the use of stable (¹³C, ²D) or radioactive isotopes (¹⁴C, ¹¹C, ¹⁸F, ¹³¹I, ⁶⁴Cu, ⁶⁸Ga) incorporated into the molecular structure of new drug entities for the purpose of pharmacokinetic or -dynamic studies. Metabolite in safety testing requires the administration of pharmacologically active doses. In such studies, radiotracers find application mainly in preclinical animal investigations, whereby LC-MS/MS is used to identify metabolite structure and drug-related effects. In contrast, first-in-human metabolite studies have to be carried out at nonpharmacological doses not exceeding 100 μg (microdose), which is generally too low for metabolite detection by LC-MS/MS. This short-coming can be overcome by specific radio- or isotopic labeling of the drug of interest and measurements using accelerator mass spectroscopy, single-photon emission computed tomography and positron emission tomography. Such combined radioisotope-based approaches permit Phase 0, first-in-human metabolite study.

Engineering Escherichia coli for malate production by integrating modular pathway characterization with CRISPRi-guided multiplexed metabolic tuning

The application of rational design in reallocating metabolic flux to overproduce desired chemicals is always restricted by the native regulatory network. Here, we demonstrated that in vitro modular pathway optimization combined with in vivo multiplexed combinatorial engineering enables effective characterization of the bottleneck of a complex biosynthetic cascade and improves the output of the engineered pathway. As a proof of concept, we systematically identified the rate-limiting step of a five-gene malate biosynthetic pathway by combinatorially tuning the enzyme loads of a reconstituted biocatalytic reaction in a cell-free system. Using multiplexed CRISPR interference, we subsequently eliminated the metabolic constraints by rationally assigning an optimal gene expression pattern for each pathway module. The present engineered strain Escherichia coli B0013-47 exhibited a 2.3-fold increase in malate titer compared with that of the parental strain, with a yield of 0.85 mol/mol glucose in shake-flask culture and titer of 269 mM (36 g/L) in fed-batch cultivation. The strategy reported herein represents a powerful method for improving the efficiency of multi-gene pathways and advancing the success of metabolic engineering.

Mathematical modelling of microbes: metabolism, gene expression and growth

The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

Comprehensive Real-Time Analysis of the Yeast Volatilome

While yeast is one of the most studied organisms, its intricate biology remains to be fully mapped and understood. This is especially the case when it comes to capture rapid, in vivo fluctuations of metabolite levels. Secondary electrospray ionization-high resolution mass spectrometry SESI-HRMS is introduced here as a sensitive and noninvasive analytical technique for online monitoring of microbial metabolic activity. The power of this technique is exemplarily shown for baker’s yeast fermentation, for which the time-resolved abundance of about 300 metabolites is demonstrated. The results suggest that a large number of metabolites produced by yeast from glucose neither are reported in the literature nor are their biochemical origins deciphered. With the technique demonstrated here, researchers interested in distant disciplines such as yeast physiology and food quality will gain new insights into the biochemical capability of this simple eukaryote.

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms and they remain a topic of active investigation. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.

Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols

Sample preparation is one of the most important steps in metabolome analysis. The challenges of determining microbial metabolome have been well discussed within the research community and many improvements have already been achieved in last decade. The analysis of intracellular metabolites is particularly challenging. Environmental perturbations may considerably affect microbial metabolism, which results in intracellular metabolites being rapidly degraded or metabolized by enzymatic reactions. Therefore, quenching or the complete stop of cell metabolism is a pre-requisite for accurate intracellular metabolite analysis. After quenching, metabolites need to be extracted from the intracellular compartment. The choice of the most suitable metabolite extraction method/s is another crucial step. The literature indicates that specific classes of metabolites are better extracted by different extraction protocols. In this review, we discuss the technical aspects and advancements of quenching and extraction of intracellular metabolite analysis from microbial cells.

Engineering Microbial Metabolite Dynamics and Heterogeneity

As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.

Metabolism in time and space – exploring the frontier of developmental biology

Despite the fact that metabolic studies played a prominent role in the early history of developmental biology research, the field of developmental metabolism was largely ignored following the advent of modern molecular biology. Metabolism, however, has recently re-emerged as a focal point of biomedical studies and, as a result, developmental biologists are once again exploring the chemical and energetic forces that shape growth, development and maturation. In May 2017, a diverse group of scientists assembled at the EMBO/EMBL Symposium ‘Metabolism in Time and Space’ to discuss how metabolism influences cellular and developmental processes. The speakers not only described how metabolic flux adapts to the energetic needs of a developing organism, but also emphasized that metabolism can directly regulate developmental progression. Overall, and as we review here, this interdisciplinary meeting provided a valuable forum to explore the interface between developmental biology and metabolism.

Characterization of rapid extraction protocols for high-throughput metabolomics

RATIONALE

In the last five years, high-throughput metabolomics has significantly advanced scientific research and holds the potential to promote strides in the fields of clinical metabolomics and personalized medicine. While innovations in the field of flow-injection mass spectrometry and three-minute metabolomics methods now allow investigators to process hundreds to thousands of samples per day, time-sensitive clinical applications, particularly in the emergency department, are limited by a lack of rapid extraction methods.

METHODS

Here we characterized the efficacy of fast liquid-liquid extractions for characterization of hydrophilic compounds through ultra-high-pressure liquid chromatography/mass spectrometry. Internal stable-isotope-labeled standards were used to quantitatively characterize markers of energy and oxidative metabolism in human whole blood, plasma and red blood cells - three common matrices of clinical relevance.

RESULTS

For all the tested matrices, vortexing time (4-60 min) did not significantly affect extraction yields for the tested hydrophilic metabolites. Coefficients of variations <<20% for all tested compounds, except for the redox-sensitive metabolite cystine (accumulating over time). Internal standards and second extractions confirmed recoveries >80% for all tested metabolites, except for basic amino acids and polyamines, which showed reproducible yields ranging from 50 to 75%. Global profiling and absolute quantitation of 24 metabolites revealed similarities between the plasma and red blood cell metabolomes.

CONCLUSIONS

Rapid extraction (~4 min) of hydrophilic compounds is a viable and potentially automatable strategy to perform quantitative analysis of whole blood, plasma and red blood cells for research or clinical applications.

Time-Resolved Pharmacological Studies using Automated, On-line Monitoring of Five Parallel Suspension Cultures

Early stage pharmacological studies rely on in vitro methodologies for screening and testing compounds. Conventional assays based on endpoint measurements provide limited information because the lack in temporal resolution may not determine the pharmacological effect at its maximum. We developed an on-line, automated system for near real-time monitoring of extracellular content from five parallel suspension cultures, combining cell density measurements with a high-resolution separations every 12 minutes for 4 days. Selector and switching valves provide the fluidic control required to sample from one culture during the analysis of the previous sample from another culture, a time-saving measure that is fundamental to the throughput of the presented system. The system was applied to study the metabolic effects of the drugs rotenone, β-lapachone and clioquinol using lactate as metabolic indicator. For each drug, 96 assays were executed on the extracellular matrix at three concentrations with two controls in parallel, consuming only 5.78 mL of media from each culture over four days, less than 60 μL per analysis. The automated system provides high sample throughput, good temporal resolution and low sample consumption combined with a rugged analytical method with adequate sensitivity, providing a promising new platform for pharmacological and biotechnological studies.

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.

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.

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.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Metabolomics: A Primer

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

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.

Uncertainty budgeting in fold change determination and implications for non-targeted metabolomics studies in model systems

Uncertainty budgeting provides error intervals for fold change values and complements significance testing in non-targeted metabolomics.

Non-invasive real time monitoring of yeast volatilome by PTR-ToF-MS

INTRODUCTION

Producing a wide range of volatile secondary metabolites Saccharomyces cerevisiae influences wine, beer, and bread sensory quality and hence selection of strains based on their volatilome becomes pivotal. A rapid on-line method for volatilome assessing of strains growing on standard solid media is still missing.

OBJECTIVES

Methodologically, the aim of this study was to demonstrate the automatic, real-time, direct, and non-invasive monitoring of yeast volatilome in order to rapidly produce a robust large data set encompassing measurements relative to many strains, replicates and time points. The fundamental scope was to differentiate volatilomes of genetically similar strains of oenological relevance during the whole growing process.

METHOD

Six different S. cerevisiae strains (four meiotic segregants of a natural strain and two laboratory strains) inoculated onto a solid medium have been monitored on-line by Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry for 11 days every 4 h (3540 time points). FastGC PTR-ToF-MS was performed during the stationary phase on the 5th day.

RESULTS

More than 300 peaks have been extracted from the average spectra associated to each time point, 70 have been tentatively identified. Univariate and multivariate analyses have been performed on the data matrix (3640 measurements × 70 peaks) highlighting the volatilome evolution and strain-specific features. Laboratory strains with opposite mating type, and meiotic segregants of the same natural strain showed significantly different profiles.

CONCLUSIONS

The described set-up allows the on-line high-throughput screening of yeast volatilome of S. cerevisiae strains and the identification of strain specific features and new metabolic pathways, discriminating also genetically similar strains, thus revealing a novel method for strain phenotyping, identification, and quality control.

Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth

Bacteria growing under different conditions experience a broad range of demand on the rate of protein synthesis, which profoundly affects cellular resource allocation. During fast growth, protein synthesis has long been known to be modulated by adjusting the ribosome content, with the vast majority of ribosomes engaged at a near-maximal rate of elongation. Here, we systematically characterize protein synthesis by Escherichia coli, focusing on slow-growth conditions. We establish that the translational elongation rate decreases as growth slows, exhibiting a Michaelis-Menten dependence on the abundance of the cellular translational apparatus. However, an appreciable elongation rate is maintained even towards zero growth, including the stationary phase. This maintenance, critical for timely protein synthesis in harsh environments, is accompanied by a drastic reduction in the fraction of active ribosomes. Interestingly, well-known antibiotics such as chloramphenicol also cause a substantial reduction in the pool of active ribosomes, instead of slowing down translational elongation as commonly thought.

Glycolysis without pyruvate kinase in Clostridium thermocellum

The metabolism of Clostridium thermocellum is notable in that it assimilates sugar via the EMP pathway but does not possess a pyruvate kinase enzyme. In the wild type organism, there are three proposed pathways for conversion of phosphoenolpyruvate (PEP) to pyruvate, which differ in their cofactor usage. One path uses pyruvate phosphate dikinase (PPDK), another pathway uses the combined activities of PEP carboxykinase (PEPCK) and oxaloacetate decarboxylase (ODC). Yet another pathway, the malate shunt, uses the combined activities of PEPCK, malate dehydrogenase and malic enzyme. First we showed that there is no flux through the ODC pathway by enzyme assay. Flux through the remaining two pathways (PPDK and malate shunt) was determined by dynamic ¹³C labeling. In the wild-type strain, the malate shunt accounts for about 33±2% of the flux to pyruvate, with the remainder via the PPDK pathway. Deletion of the ppdk gene resulted in a redirection of all pyruvate flux through the malate shunt. This provides the first direct evidence of the in-vivo function of the malate shunt.

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.

Metabolic pathways in T cell activation and lineage differentiation

Recent advances in the field of immunometabolism support the concept that fundamental processes in T cell biology, such as TCR-mediated activation and T helper lineage differentiation, are closely linked to changes in the cellular metabolic programs. Although the major task of the intermediate metabolism is to provide the cell with a constant supply of energy and molecular precursors for the production of biomolecules, the dynamic regulation of metabolic pathways also plays an active role in shaping T cell responses. Key metabolic processes such as glycolysis, fatty acid and mitochondrial metabolism are now recognized as crucial players in T cell activation and differentiation, and their modulation can differentially affect the development of T helper cell lineages. In this review, we describe the diverse metabolic processes that T cells engage during their life cycle from naïve towards effector and memory T cells. We consider in particular how the cellular metabolism may actively support the function of T cells in their different states. Moreover, we discuss how molecular regulators such as mTOR or AMPK link environmental changes to adaptations in the cellular metabolism and elucidate the consequences on T cell differentiation and function.

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

Review of Microfluidic Photobioreactor Technology for Metabolic Engineering and Synthetic Biology of Cyanobacteria and Microalgae

One goal of metabolic engineering and synthetic biology for cyanobacteria and microalgae is to engineer strains that can optimally produce biofuels and commodity chemicals. However, the current workflow is slow and labor intensive with respect to assembly of genetic parts and characterization of production yields because of the slow growth rates of these organisms. Here, we review recent progress in the microfluidic photobioreactors and identify opportunities and unmet needs in metabolic engineering and synthetic biology. Because of the unprecedented experimental resolution down to the single cell level, long-term real-time monitoring capability, and high throughput with low cost, microfluidic photobioreactor technology will be an indispensible tool to speed up the development process, advance fundamental knowledge, and realize the full potential of metabolic engineering and synthetic biology for cyanobacteria and microalgae.

The metabolome 18 years on: a concept comes of age

BACKGROUND

The term 'metabolome' was introduced to the scientific literature in September 1998.

AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW

To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

Inflammation Thread Runs across Medical Laboratory Specialities

We work on the assumption that four major specialities or sectors of medical laboratory assays, comprising clinical chemistry, haematology, immunology, and microbiology, embraced by genome sequencing techniques, are routinely in use. Medical laboratory markers for inflammation serve as model: they are allotted to most fields of medical lab assays including genomics. Incessant coding of assays aligns each of them in the long lists of big data. As exemplified with the complement gene family, containing C2, C3, C8A, C8B, CFH, CFI, and ITGB2, heritability patterns/risk factors associated with diseases with genetic glitch of complement components are unfolding. The C4 component serum levels depend on sufficient vitamin D whilst low vitamin D is inversely related to IgG1, IgA, and C3 linking vitamin sufficiency to innate immunity. Whole genome sequencing of microbial organisms may distinguish virulent from nonvirulent and antibiotic resistant from nonresistant varieties of the same species and thus can be listed in personal big data banks including microbiological pathology; the big data warehouse continues to grow.