Impact of kinetic isotope effects in isotopic studies of metabolic systems

6-Phosphogluconolactonase is critical for the efficient functioning of the pentose phosphate pathway

The metabolic networks of microorganisms are remarkably robust to genetic and environmental perturbations. This robustness stems from redundancies such as gene duplications, isoenzymes, alternative metabolic pathways, and also from non-enzymatic reactions. In the oxidative branch of the pentose phosphate pathway (oxPPP), 6-phosphogluconolactone hydrolysis into 6-phosphogluconate is catalysed by 6-phosphogluconolactonase (Pgl) but in the absence of the latter, the oxPPP flux is thought to be maintained by spontaneous hydrolysis. However, in Δpgl Escherichia coli, an extracellular pathway can also contribute to pentose phosphate synthesis. This raises question as to whether the intracellular non-enzymatic reaction can compensate for the absence of 6-phosphogluconolactonase and, ultimately, on the role of 6-phosphogluconolactonase in central metabolism. Our results validate that the bypass pathway is active in the absence of Pgl, specifically involving the extracellular spontaneous hydrolysis of gluconolactones to gluconate. Under these conditions, metabolic flux analysis reveals that this bypass pathway accounts for the entire flux into the oxPPP. This alternative metabolic route-partially extracellular-sustains the flux through the oxPPP necessary for cell growth, albeit at a reduced rate in the absence of Pgl. Importantly, these findings imply that intracellular non-enzymatic hydrolysis of 6-phosphogluconolactone does not compensate for the absence of Pgl. This underscores the crucial role of Pgl in ensuring the efficient functioning of the oxPPP.

Tissue-specific reprogramming of glutamine metabolism maintains tolerance to sepsis

Reprogramming metabolism is of great therapeutic interest for reducing morbidity and mortality during sepsis-induced critical illness. Disappointing results from randomized controlled trials targeting glutamine and antioxidant metabolism in patients with sepsis have begged a deeper understanding of the tissue-specific metabolic response to sepsis. The current study sought to fill this gap. We analyzed skeletal muscle transcriptomics of critically ill patients, versus elective surgical controls, which revealed reduced expression of genes involved in mitochondrial metabolism and electron transport, with increases in glutathione cycling, glutamine, branched chain, and aromatic amino acid transport. We then performed untargeted metabolomics and 13C isotope tracing to analyze systemic and tissue specific metabolic phenotyping in a murine polymicrobial sepsis model. We found an increased number of correlations between the metabolomes of liver, kidney, and spleen, with loss of correlations between the heart and quadriceps and all other organs, pointing to a shared metabolic signature within vital abdominal organs, and unique metabolic signatures for muscles during sepsis. A lowered GSH:GSSG and elevated AMP:ATP ratio in the liver underlie the significant upregulation of isotopically labeled glutamine's contribution to TCA cycle anaplerosis and glutamine-derived glutathione biosynthesis; meanwhile, the skeletal muscle and spleen were the only organs where glutamine's contribution to the TCA cycle was significantly suppressed. These results highlight tissue-specific mitochondrial reprogramming to support liver energetic demands and antioxidant synthesis, rather than global mitochondrial dysfunction, as a metabolic consequence of sepsis.

Tracer or toxicant: Does stable isotope labeling affect central processes in aquatic food webs?

Stable isotope analysis (SIA) is an elementary technique in food web ecology, but its insights become increasingly ambiguous in complex systems. One approach to elevate the utility of SIA in such systems is the use of heavy isotope tracers (i.e., labeling). However, the fundamental assumption that the addition of such tracers does not affect in situ conditions has been challenged. This study tests if labeling is suitable for autotrophy-based and detritus-based aquatic food webs. For the former, the survival and reproduction of Daphnia magna fed with phytoplankton cultured at different levels of ¹⁵N addition were assessed. For the latter, the microbial decomposition of leaf litter was assessed at the same tracer levels. While no significant differences were observed, effect patterns were comparable to a previous study, supporting the isotopic redundancy hypothesis that postulates discrete quantum mechanical states at which the reaction speeds of metabolic processes are altered. Although physiology (reproduction) and activity (microbial decomposition) might not be altered to an ecologically significant level, labeling with heavy stable isotopes could potentially affect isotopic fractionation in biochemical processes and bias conclusions drawn from resulting SI ratios.

Control and regulation of acetate overflow in Escherichia coli

Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.

Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here, we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with ¹⁵N hydrolysate from Ralstonia eutropha, we identified 12 326 nonredundant unlabeled peptides, of which 8256 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. MetaProfiler and the bioinformatic pipeline are available at https://github.com/northomics/MetaProfiler.git.

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus ‘Kuenenia stuttgartiensis’ using time-series ¹³C and ²H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO₂ fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis, and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood–Ljungdahl pathway instead of oxidizing it completely to CO₂ followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor’s side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Simultaneous Measurement of Metabolite Concentration and Isotope Incorporation by Mass Spectrometry

Studies of the topology, functioning, and regulation of metabolic systems are based on two main types of information that can be measured by mass spectrometry: the (absolute or relative) concentration of metabolites and their isotope incorporation in ¹³C-labeling experiments. These data are currently obtained from two independent experiments because the ¹³C-labeled internal standard (IS) used to determine the concentration of a given metabolite overlaps the ¹³C-mass fractions from which its ¹³C-isotopologue distribution (CID) is quantified. Here, we developed a generic method with a dedicated processing workflow to obtain these two sets of information simultaneously in a unique sample collected from a single cultivation, thereby reducing by a factor of 2 both the number of cultivations to perform and the number of samples to collect, prepare, and analyze. The proposed approach is based on an IS labeled with other isotope(s) that can be resolved from the ¹³C-mass fractions of interest. As proof-of-principle, we analyzed amino acids using a doubly labeled ¹⁵N¹³C-cell extract as IS. Extensive evaluation of the proposed approach shows a similar accuracy and precision compared to state-of-the-art approaches. We demonstrate the value of this approach by investigating the dynamic response of amino acids metabolism in mammalian cells upon activation of the protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), a key component of the unfolded protein response. Integration of metabolite concentrations and isotopic profiles reveals a reduced de novo biosynthesis of amino acids upon PERK activation. The proposed approach is generic and can be applied to other (micro)organisms, analytical platforms, isotopic tracers, or classes of metabolites.

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

INTRODUCTION

Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.

OBJECTIVE

To introduce a software tool for the identification of isotopologues from mass spectrometry data.

METHODS

DeltaMS relies on XCMS peak detection and X¹³CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X¹³CMS.

RESULTS

To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.

CONCLUSION

DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

Methodology for the Validation of Isotopic Analyses by Mass Spectrometry in Stable-Isotope Labeling Experiments

Stable-isotope labeling experiments (ILEs) are widely used to investigate the topology and operation of metabolic networks. The quality of isotopic data collected in ILEs is of utmost importance to ensure reliable biological interpretations, but current evaluation approaches are limited due to a lack of suitable reference material and relevant evaluation criteria. In this work, we present a complete methodology to evaluate mass spectrometry (MS) methods used for quantitative isotopic studies of metabolic systems. This methodology, based on a biological sample containing metabolites with controlled labeling patterns, exploits different quality metrics specific to isotopic analyses (accuracy and precision of isotopologue masses, abundances, and mass shifts and isotopic working range). We applied this methodology to evaluate a novel LC-MS method for the analysis of amino acids, which was tested on high resolution (Orbitrap operating in full scan mode) and low resolution (triple quadrupole operating in multiple reaction monitoring mode) mass spectrometers. Results show excellent accuracy and precision over a large working range and revealed matrix-specific as well as mode-specific characteristics. The proposed methodology can identify reliable (and unreliable) isotopic data in an easy and straightforward way and efficiently supports the identification of sources of systematic biases as well as of the main factors that influence the overall accuracy and precision of measurements. This approach is generic and can be used to validate isotopic analyses on different matrices, analytical platforms, labeled elements, or classes of metabolites. It is expected to strengthen the reliability of isotopic measurements and thereby the biological value of ILEs.

The rate of lactate production from glucose in hearts is not altered by per-deuteration of glucose

This study was designed to determine whether perdeuterated glucose experiences a kinetic isotope effect (KIE) as glucose passes through glycolysis and is further oxidized in the tricarboxylic acid (TCA) cycle. Metabolism of deuterated glucose was investigated in two groups of perfused rat hearts. The control group was supplied with a 1:1 mixture of [U-¹³C₆]glucose and [1,6-¹³C₂]glucose, while the experimental group received [U-¹³C₆,U-²H₇]glucose and [1,6-¹³C₂]glucose. Tissue extracts were analyzed by ¹H, ²H and proton-decoupled ¹³C NMR spectroscopy. Extensive ²H-¹³C scalar coupling plus chemical shift isotope effects were observed in the proton-decoupled ¹³C NMR spectra of lactate, alanine and glutamate. A small but measureable (∼8%) difference in the rate of conversion of [U-¹³C₆]glucose vs. [1,6-¹³C₂]glucose to lactate, likely reflecting rates of CC bond breakage in the aldolase reaction, but conversion of [U-¹³C₆]glucose versus [U-¹³C₆,U-²H₇]glucose to lactate did not differ. This shows that the presence of deuterium in glucose does not alter glycolytic flux. However, there were two distinct effects of deuteration on metabolism of glucose to alanine and oxidation of glucose in the TCA. First, alanine undergoes extensive exchange of methyl deuterons with solvent protons in the alanine amino transferase reaction. Second, there is a substantial kinetic isotope effect in metabolism of [U-¹³C₆,U-²H₇]glucose to alanine and glutamate. In the presence of [U-¹³C₆,U-²H₇]glucose, alanine and lactate are not in rapid exchange with the same pool of pyruvate. These studies indicate that the appearance of hyperpolarized ¹³C-lactate from hyperpolarized [U-¹³C₆,U-²H₇]glucose is not substantially influenced by a deuterium kinetic isotope effect.

Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient

BACKGROUND

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

RESULTS

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

CONCLUSIONS

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.

Kinetic 15N-isotope effects on algal growth

Stable isotope labeling is a standard technique for tracing material transfer in molecular, ecological and biogeochemical studies. The main assumption in this approach is that the enrichment with a heavy isotope has no effect on the organism metabolism and growth, which is not consistent with current theoretical and empirical knowledge on kinetic isotope effects. Here, we demonstrate profound changes in growth dynamics of the green alga Raphidocelis subcapitata grown in 15N-enriched media. With increasing 15N concentration (0.37 to 50 at%), the lag phase increased, whereas maximal growth rate and total yield decreased; moreover, there was a negative relationship between the growth and the lag phase across the treatments. The latter suggests that a trade-off between growth rate and the ability to adapt to the high 15N environment may exist. Remarkably, the lag-phase response at 3.5 at% 15N was the shortest and deviated from the overall trend, thus providing partial support to the recently proposed Isotopic Resonance hypothesis, which predicts that certain isotopic composition is particularly favorable for living organisms. These findings confirm the occurrence of KIE in isotopically enriched algae and underline the importance of considering these effects when using stable isotope labeling in field and experimental studies.

Shifts in rotifer life history in response to stable isotope enrichment: testing theories of isotope effects on organismal growth

In ecology, stable isotope labelling is commonly used for tracing material transfer in trophic interactions, nutrient budgets and biogeochemical processes. The main assumption in this approach is that the enrichment with a heavy isotope has no effect on the organism growth and metabolism. This assumption is, however, challenged by theoretical considerations and experimental studies on kinetic isotope effects in vivo. Here, I demonstrate profound changes in life histories of the rotifer Brachionus plicatilis fed ¹⁵N-enriched algae (0.4-5.0 at%); i.e. at the enrichment levels commonly used in ecological studies. These findings support theoretically predicted effects of heavy isotope enrichment on growth, metabolism and ageing in biological systems and underline the importance of accounting for such effects when using stable isotope labelling in experimental studies.

Acetate fluxes in Escherichia coli are determined by the thermodynamic control of the Pta-AckA pathway

Escherichia coli excretes acetate upon growth on fermentable sugars, but the regulation of this production remains elusive. Acetate excretion on excess glucose is thought to be an irreversible process. However, dynamic ¹³C-metabolic flux analysis revealed a strong bidirectional exchange of acetate between E. coli and its environment. The Pta-AckA pathway was found to be central for both flux directions, while alternative routes (Acs or PoxB) play virtually no role in glucose consumption. Kinetic modelling of the Pta-AckA pathway predicted that its flux is thermodynamically controlled by the extracellular acetate concentration in vivo. Experimental validations confirmed that acetate production can be reduced and even reversed depending solely on its extracellular concentration. Consistently, the Pta-AckA pathway can rapidly switch from acetate production to consumption. Contrary to current knowledge, E. coli is thus able to co-consume glucose and acetate under glucose excess. These metabolic capabilities were confirmed on other glycolytic substrates which support the growth of E. coli in the gut. These findings highlight the dual role of the Pta-AckA pathway in acetate production and consumption during growth on glycolytic substrates, uncover a novel regulatory mechanism that controls its flux in vivo, and significantly expand the metabolic capabilities of E. coli.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

Evolution of E. coli on [U-13C]Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology

¹³C-Metabolic flux analysis (¹³C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled ¹³C-glucose, a commonly used isotope for ¹³C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (¹³C) or unlabeled (¹²C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between ¹²C and ¹³C-glucose as a carbon source for E. coli growth.