NTFD--a stand-alone application for the non-targeted detection of stable isotope-labeled compounds in GC/MS data

Exogenous pyruvate promotes gentamicin uptake to kill antibiotic-resistant Vibrio alginolyticus

OBJECTIVES

Elucidating antibiotic resistance mechanisms is necessary for developing novel therapeutic strategies. The increasing incidence of antibiotic-resistant Vibrio alginolyticus infection threatens both human health and aquaculture, but the mechanism has not been fully elucidated.

METHODS

Here, an isobaric tags for relative and absolute quantification (iTRAQ) functional proteomics analysis was performed on gentamicin-resistant V. alginolyticus (VA-RGEN) and a gentamicin-sensitive strain in order to characterize the global protein expression changes upon gentamicin resistance. Then, the bacterial killing assay and bacterial gentamicin pharmacokinetics were performed.

RESULTS

Proteomics analysis demonstrated a global metabolic downshift in VA-RGEN, where the pyruvate cycle (the P cycle) was severely compromised. Exogenous pyruvate restored the P cycle activity, disrupting the redox state and increasing the membrane potential. It thereby potentiated gentamicin-mediated killing by approximately 3000- and 150-fold in vitro and in vivo, respectively. More importantly, bacterial gentamicin pharmacokinetics indicated that pyruvate enhanced gentamicin influx to a degree that exceeded the gentamicin expelled by the bacteria, increasing the intracellular gentamicin.

CONCLUSION

Thus, our study suggests a metabolism-based approach to combating gentamicin-resistant V. algonolyticus, which paves the way for combating other types of antibiotic-resistant bacterial pathogens.

Stable Isotope-Assisted Untargeted Metabolomics Identifies ALDH1A1-Driven Erythronate Accumulation in Lung Cancer Cells

Using an untargeted stable isotope-assisted metabolomics approach, we identify erythronate as a metabolite that accumulates in several human cancer cell lines. Erythronate has been reported to be a detoxification product derived from off-target glycolytic metabolism. We use chemical inhibitors and genetic silencing to define the pentose phosphate pathway intermediate erythrose 4-phosphate (E4P) as the starting substrate for erythronate production. However, following enzyme assay-coupled protein fractionation and subsequent proteomics analysis, we identify aldehyde dehydrogenase 1A1 (ALDH1A1) as the predominant contributor to erythrose oxidation to erythronate in cell extracts. Through modulating ALDH1A1 expression in cancer cell lines, we provide additional support. We hence describe a possible alternative route to erythronate production involving the dephosphorylation of E4P to form erythrose, followed by its oxidation by ALDH1A1. Finally, we measure increased erythronate concentrations in tumors relative to adjacent normal tissues from lung cancer patients. These findings suggest the accumulation of erythronate to be an example of metabolic reprogramming in cancer cells, raising the possibility that elevated levels of erythronate may serve as a biomarker of certain types of cancer.

Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics

Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of ¹³C-labeled contaminants. Therefore, we developed a ²H-SIAM pipeline coupled with a highly flexible algorithm ²H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of ²H-SIAM. We demonstrate that the proposed ²H-SIAM pipeline has unique advantages over ¹³C-SIAM, for example, cost-effective ²H-labeled contaminants, easy synthesis of ²H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed ²H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.

Pesticides and Male Fertility: A Dangerous Crosstalk

In recent decades, an increasing incidence of male infertility has been reported. Interestingly, and considering that pesticides have been used for a long time, the high incidence of this pathological state is concomitant with the increasing use of these chemicals, suggesting they are contributors for the development of human infertility. Data from literature highlight the ability of certain pesticides and/or their metabolites to persist in the environment for long periods of time, as well as to bioaccumulate in the food chain, thus contributing for their chronic exposure. Furthermore, pesticides can act as endocrine disrupting chemicals (EDCs), interfering with the normal function of natural hormones (which are responsible for the regulation of the reproductive system), or even as obesogens, promoting obesity and associated comorbidities, like infertility. Several in vitro and in vivo studies have focused on the effects and possible mechanisms of action of these pesticides on the male reproductive system that cause sundry negative effects, even though through diverse mechanisms, but all may lead to infertility. In this review, we present an up-to-date overview and discussion of the effects, and the metabolic and molecular features of pesticides on somatic cells and germinal tissues that affect germ cell differentiation.

MIAMI--a tool for non-targeted detection of metabolic flux changes for mode of action identification

Summary

Mass isotopolome analysis for mode of action identification (MIAMI) combines the strengths of targeted and non-targeted approaches to detect metabolic flux changes in gas chromatography/mass spectrometry datasets. Based on stable isotope labeling experiments, MIAMI determines a mass isotopomer distribution-based (MID) similarity network and incorporates the data into metabolic reference networks. By identifying MID variations of all labeled compounds between different conditions, targets of metabolic changes can be detected.

Availability and implementation

We implemented the data processing in C++17 with Qt5 back-end using MetaboliteDetector and NTFD libraries. The data visualization is implemented as web application. Executable binaries and visualization are freely available for Linux operating systems, the source code is licensed under General Public License version 3.

Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life

Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.

Non-Targeted Mass Isotopolome Analysis Using Stable Isotope Patterns to Identify Metabolic Changes

Gas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.

Glycine, serine and threonine metabolism confounds efficacy of complement-mediated killing

Serum resistance is a poorly understood but common trait of some difficult-to-treat pathogenic strains of bacteria. Here, we report that glycine, serine and threonine catabolic pathway is down-regulated in serum-resistant Escherichia coli, whereas exogenous glycine reverts the serum resistance and effectively potentiates serum to eliminate clinically-relevant bacterial pathogens in vitro and in vivo. We find that exogenous glycine increases the formation of membrane attack complex on bacterial membrane through two previously unrecognized regulations: 1) glycine negatively and positively regulates metabolic flux to purine biosynthesis and Krebs cycle, respectively. 2) α-Ketoglutarate inhibits adenosine triphosphate synthase, which in together promote the formation of cAMP/CRP regulon to increase the expression of complement-binding proteins HtrE, NfrA, and YhcD. The results could lead to effective strategies for managing the infection with serum-resistant bacteria, an especially valuable approach for treating individuals with weak acquired immunity but a normal complement system.

Serum-resistant bacteria can escape complement killing in the bloodstream. Here, using metabolomics and metabolite perturbations, the authors describe an altered metabolic state in serum-resistant Escherichia coli and show that exogenous glycine potentiates elimination of pathogenic bacteria in vivo.

Comprehensive Profiling by Non-targeted Stable Isotope Tracing Capillary Electrophoresis-Mass Spectrometry: A New Tool Complementing Metabolomic Analyses of Polar Metabolites

Mass spectrometry (MS) driven metabolomics is a frequently used tool in various areas of life sciences; however, the analysis of polar metabolites is less commonly included. In general, metabolomic analyses lead to the detection of the total amount of all covered metabolites. This is currently a major limitation with respect to metabolites showing high turnover rates, but no changes in their concentration. Such metabolites and pathways could be crucial metabolic nodes (e.g., potential drug targets in cancer metabolism). A stable-isotope tracing capillary electrophoresis-mass spectrometry (CE-MS) metabolomic approach was developed to cover both polar metabolites and isotopologues in a non-targeted way. An in-house developed software enables high throughput processing of complex multidimensional data. The practicability is demonstrated analyzing [U-¹³ C]-glucose exposed prostate cancer and non-cancer cells. This CE-MS-driven analytical strategy complements polar metabolite profiles through isotopologue labeling patterns, thereby improving not only the metabolomic coverage, but also the understanding of metabolism.

Peak Annotation and Verification Engine for Untargeted LC-MS Metabolomics

Untargeted metabolomics can detect more than 10 000 peaks in a single LC-MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/ z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/ z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE enables systematic annotation of LC-MS metabolomics data with only ∼4% of peaks annotated as apparent metabolites.

Partner switching and metabolic flux in a model cnidarian-dinoflagellate symbiosis

Metabolite exchange is fundamental to the viability of the cnidarian-Symbiodiniaceae symbiosis and survival of coral reefs. Coral holobiont tolerance to environmental change might be achieved through changes in Symbiodiniaceae species composition, but differences in the metabolites supplied by different Symbiodiniaceae species could influence holobiont fitness. Using 13C stable-isotope labelling coupled to gas chromatography-mass spectrometry, we characterized newly fixed carbon fate in the model cnidarian Exaiptasia pallida (Aiptasia) when experimentally colonized with either native Breviolum minutum or non-native Durusdinium trenchii Relative to anemones containing B. minutum, D. trenchii-colonized hosts exhibited a 4.5-fold reduction in 13C-labelled glucose and reduced abundance and diversity of 13C-labelled carbohydrates and lipogenesis precursors, indicating symbiont species-specific modifications to carbohydrate availability and lipid storage. Mapping carbon fate also revealed significant alterations to host molecular signalling pathways. In particular, D. trenchii-colonized hosts exhibited a 40-fold reduction in 13C-labelled scyllo-inositol, a potential interpartner signalling molecule in symbiosis specificity. 13C-labelling also highlighted differential antioxidant- and ammonium-producing pathway activities, suggesting physiological responses to different symbiont species. Such differences in symbiont metabolite contribution and host utilization may limit the proliferation of stress-driven symbioses; this contributes valuable information towards future scenarios that select in favour of less-competent symbionts in response to environmental change.

Flux analysis of inborn errors of metabolism

Patients with an inborn error of metabolism (IEM) are deficient of an enzyme involved in metabolism, and as a consequence metabolism reprograms itself to reach a new steady state. This new steady state underlies the clinical phenotype associated with the deficiency. Hence, we need to know the flux of metabolites through the different metabolic pathways in this new steady state of the reprogrammed metabolism. Stable isotope technology is best suited to study this. In this review the progress made in characterizing the altered metabolism will be presented. Studies done in patients to estimate the residual flux through the metabolic pathway affected by enzyme deficiencies will be discussed. After this, studies done in model systems will be reviewed. The focus will be on glycogen storage disease type I, medium-chain acyl-CoA dehydrogenase deficiency, propionic and methylmalonic aciduria, urea cycle defects, phenylketonuria, and combined D,L-2-hydroxyglutaric aciduria. Finally, new developments are discussed, which allow the tracing of metabolic reprogramming in IEM on a genome-wide scale. In conclusion, the outlook for flux analysis of metabolic derangement in IEMs looks promising.

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.

13C metabolomics reveals widespread change in carbon fate during coral bleaching

INTRODUCTION

Rising seawater temperatures are threatening the persistence of coral reefs; where above critical thresholds, thermal stress results in a breakdown of the coral-dinoflagellate symbiosis and the loss of algal symbionts (coral bleaching). As symbiont-derived organic products typically form a major portion of host energy budgets, this has major implications for the fitness and persistence of symbiotic corals.

OBJECTIVES

We aimed to determine change in autotrophic carbon fate within individual compounds and downstream metabolic pathways in a coral symbiosis exposed to varying degrees of thermal stress and bleaching.

METHODS

We applied gas chromatography-mass spectrometry coupled to a stable isotope tracer (¹³C), to track change in autotrophic carbon fate, in symbiont and host individually, following exposure to elevated water temperature.

RESULTS

Thermal stress resulted in partner-specific changes in carbon fate, which progressed with heat stress duration. We detected modifications to carbohydrate and fatty acid metabolism, lipogenesis, and homeostatic responses to thermal, oxidative and osmotic stress. Despite pronounced photodamage, remaining in hospite symbionts continued to produce organic products de novo and translocate to the coral host. However as bleaching progressed, we observed minimal ¹³C enrichment of symbiont long-chain fatty acids, also reflected in ¹³C enrichment of host fatty acid pools.

CONCLUSION

These data have major implications for our understanding of coral symbiosis function during bleaching. Our findings suggest that during early stage bleaching, remaining symbionts continue to effectively translocate a variety of organic products to the host, however under prolonged thermal stress there is likely a reduction in the quality of these products.

The fate of lysine: Non-targeted stable isotope analysis reveals parallel ways for lysine catabolization in Phaeobacter inhibens

For a detailed investigation of the degradation of lysine in Phaeobacter inhibens DSM 17395, stable isotope experiments with uniformly ¹³C labeled L-lysine were carried out with lysine adapted cells and the metabolites were analyzed using GC/MS and HPLC/MS. A non-targeted stable isotope analysis was used which compares labeled and not labeled samples to determine the Mass Isotopomer Distribution not only for known metabolites but for all labeled compounds in our GC/MS analysis. We show that P. inhibens uses at least two parallel pathways for the first degradation steps of lysine. Further investigations identified L-pipecolate as an L-lysine degradation intermediate in P. inhibens. The analysis of HPLC/MS data as well as the labeling data of tricarboxylic acid (TCA) cycle intermediates show that L-lysine is not only catabolized directly to acetyl-CoA but also via the ethylmalonyl-CoA-pathway, leading to entry points into the TCA cycle via acetyl-CoA, succinyl-CoA, and malate. Altogether the presented data give a detailed insight into the catabolization of L-lysine following the fate of ¹³C labeled carbon via several ways into the TCA cycle.

Comparative Metabolomics of Mycoplasma bovis and Mycoplasma gallisepticum Reveals Fundamental Differences in Active Metabolic Pathways and Suggests Novel Gene Annotations

Mycoplasmas are pathogenic bacteria that cause serious chronic infections in production animals, resulting in considerable losses worldwide, as well as causing disease in humans. These bacteria have extremely reduced genomes and are thought to have limited metabolic flexibility, even though they are highly successful persistent parasites in a diverse number of species. The extent to which different Mycoplasma species are capable of catabolizing host carbon sources and nutrients, or synthesizing essential metabolites, remains poorly defined. We have used advanced metabolomic techniques to identify metabolic pathways that are active in two species of Mycoplasma that infect distinct hosts (poultry and cattle). We show that these species exhibit marked differences in metabolite steady-state levels and carbon source utilization. This information has been used to functionally characterize previously unknown genes in the genomes of these pathogens. These species-specific differences are likely to reflect important differences in host nutrient levels and pathogenic mechanisms.

Mycoplasmas are simple, but successful parasites that have the smallest genome of any free-living cell and are thought to have a highly streamlined cellular metabolism. Here, we have undertaken a detailed metabolomic analysis of two species, Mycoplasma bovis and Mycoplasma gallisepticum, which cause economically important diseases in cattle and poultry, respectively. Untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analyses of mycoplasma metabolite extracts revealed significant differences in the steady-state levels of many metabolites in central carbon metabolism, while ¹³C stable isotope labeling studies revealed marked differences in carbon source utilization. These data were mapped onto in silico metabolic networks predicted from genome wide annotations. The analyses elucidated distinct differences, including a clear difference in glucose utilization, with a marked decrease in glucose uptake and glycolysis in M. bovis compared to M. gallisepticum, which may reflect differing host nutrient availabilities. The ¹³C-labeling patterns also revealed several functional metabolic pathways that were previously unannotated in these species, allowing us to assign putative enzyme functions to the products of a number of genes of unknown function, especially in M. bovis. This study demonstrates the considerable potential of metabolomic analyses to assist in characterizing significant differences in the metabolism of different bacterial species and in improving genome annotation.

IMPORTANCE Mycoplasmas are pathogenic bacteria that cause serious chronic infections in production animals, resulting in considerable losses worldwide, as well as causing disease in humans. These bacteria have extremely reduced genomes and are thought to have limited metabolic flexibility, even though they are highly successful persistent parasites in a diverse number of species. The extent to which different Mycoplasma species are capable of catabolizing host carbon sources and nutrients, or synthesizing essential metabolites, remains poorly defined. We have used advanced metabolomic techniques to identify metabolic pathways that are active in two species of Mycoplasma that infect distinct hosts (poultry and cattle). We show that these species exhibit marked differences in metabolite steady-state levels and carbon source utilization. This information has been used to functionally characterize previously unknown genes in the genomes of these pathogens. These species-specific differences are likely to reflect important differences in host nutrient levels and pathogenic mechanisms.

MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics

Stable isotope labeling (SIL) techniques have the potential to enhance different aspects of liquid chromatography–high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics methods including metabolite detection, annotation of unknown metabolites, and comparative quantification. In this work, we present MetExtract II, a software toolbox for detection of biologically derived compounds. It exploits SIL-specific isotope patterns and elution profiles in LC-HRMS(/MS) data. The toolbox consists of three complementary modules: M1 (AllExtract) uses mixtures of uniformly highly isotope-enriched and native biological samples for selective detection of the entire accessible metabolome. M2 (TracExtract) is particularly suited to probe the metabolism of endogenous or exogenous secondary metabolites and facilitates the untargeted screening of tracer derivatives from concurrently metabolized native and uniformly labeled tracer substances. With M3 (FragExtract), tandem mass spectrometry (MS/MS) fragments of corresponding native and uniformly labeled ions are evaluated and automatically assigned with putative sum formulas. Generated results can be graphically illustrated and exported as a comprehensive data matrix that contains all detected pairs of native and labeled metabolite ions that can be used for database queries, metabolome-wide internal standardization, and statistical analysis. The software, associated documentation, and sample data sets are freely available for noncommercial use at http://metabolomics-ifa.boku.ac.at/metextractII.

Mapping carbon fate during bleaching in a model cnidarian symbiosis: the application of 13 C metabolomics

Coral bleaching is a major threat to the persistence of coral reefs. Yet we lack detailed knowledge of the metabolic interactions that determine symbiosis function and bleaching-induced change. We mapped autotrophic carbon fate within the free metabolite pools of both partners of a model cnidarian-dinoflagellate symbiosis (Aiptasia-Symbiodinium) during exposure to thermal stress via the stable isotope tracer (¹³ C bicarbonate), coupled to GC-MS. Symbiont photodamage and pronounced bleaching coincided with substantial increases in the turnover of non¹³ C-labelled pools in the dinoflagellate (lipid and starch store catabolism). However, ¹³ C enrichment of multiple compounds associated with ongoing carbon fixation and de novo biosynthesis pathways was maintained (glucose, fatty acid and lipogenesis intermediates). Minimal change was also observed in host pools of ¹³ C-enriched glucose (a major symbiont-derived mobile product). However, host pathways downstream showed altered carbon fate and/or pool composition, with accumulation of compatible solutes and nonenzymic antioxidant precursors. In hospite symbionts continue to provide mobile products to the host, but at a significant cost to themselves, necessitating the mobilization of energy stores. These data highlight the need to further elucidate the role of metabolic interactions between symbiotic partners, during the process of thermal acclimation and coral bleaching.

MIA: non-targeted mass isotopolome analysis

MIA detects and visualizes isotopic enrichment in gas chromatography electron ionization mass spectrometry (GC-EI-MS) datasets in a non-targeted manner. It provides an easy-to-use graphical user interface that allows for visual mass isotopomer distribution analysis across multiple datasets. MIA helps to reveal changes in metabolic fluxes, visualizes metabolic proximity of isotopically enriched compounds and shows the fate of the applied stable isotope labeled tracer.

AVAILABILITY AND IMPLEMENTATION

Linux and Windows binaries, documentation, and sample data are freely available for download at http://massisotopolomeanalyzer.lu MIA is a stand-alone application implemented in C ++  and based on Qt5, NTFD and the MetaboliteDetector framework.

CONTACT

karsten.hiller@uni.lu.

Gas Chromatography Coupled to Mass Spectrometry (GC-MS) to Study Metabolism in Cultured Cells

Cancer cells exhibit characteristic patterns of metabolic behaviour that can be exploited for therapeutic purposes. Conditions found within the tumour microenvironment, such as hypoxia and selective nutrient availability, are known to influence the metabolism of cancer and stromal cells. Understanding cancer metabolism requires the use of analytical methods that allow detection and quantification of many metabolites simultaneously. Gas chromatography-mass spectrometry (GC-MS) is a versatile method to quantify metabolite abundance and, in combination with stable isotope labelled compounds, can yield important insights into the activity of metabolic pathways in cancer cells. This chapter provides an overview of the use of GC-MS for metabolic analysis of adherent cancer cells with an emphasis on the technical background that should be taken into consideration when designing and executing GC-MS-based metabolomics experiments.

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis

BACKGROUND

Metabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand.

RESULTS

We present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells.

CONCLUSIONS

Differential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.

Non-targeted Tracer Fate Detection

Stable isotopes have been used to trace atoms through metabolism and quantify metabolic fluxes for several decades. Only recently non-targeted stable isotope labeling approaches have emerged as a powerful tool to gain insights into metabolism. However, the manual detection of isotopic enrichment for a non-targeted analysis is tedious and time consuming. To overcome this limitation, the non-targeted tracer fate detection (NTFD) algorithm for the automated metabolome-wide detection of isotopic enrichment has been developed. NTFD detects and quantifies isotopic enrichment in the form of mass isotopomer distributions (MIDs) in an automated manner, providing the means to trace functional groups, determine MIDs for metabolic flux analysis, or detect tracer-derived molecules in general. Here, we describe the algorithmic background of NTFD, discuss practical considerations for the freely available NTFD software package, and present potential applications of non-targeted stable isotope labeling analysis.

Exogenous alanine and/or glucose plus kanamycin kills antibiotic-resistant bacteria

Multidrug-resistant bacteria are an increasingly serious threat to human and animal health. However, novel drugs that can manage infections by multidrug-resistant bacteria have proved elusive. Here we show that glucose and alanine abundances are greatly suppressed in kanamycin-resistant Edwardsiella tarda by GC-MS-based metabolomics. Exogenous alanine or glucose restores susceptibility of multidrug-resistant E. tarda to killing by kanamycin, demonstrating an approach to killing multidrug-resistant bacteria. The mechanism underlying this approach is that exogenous glucose or alanine promotes the TCA cycle by substrate activation, which in turn increases production of NADH and proton motive force and stimulates uptake of antibiotic. Similar results are obtained with other Gram-negative bacteria (Vibrio parahaemolyticus, Klebsiella pneumoniae, Pseudomonas aeruginosa) and Gram-positive bacterium (Staphylococcus aureus), and the results are also reproduced in a mouse model for urinary tract infection. This study establishes a functional metabolomics-based strategy to manage infection by antibiotic-resistant bacteria.

Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS

An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC-HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.

(13)C metabolic flux analysis of recombinant expression hosts

Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

Fragment formula calculator (FFC): determination of chemical formulas for fragment ions in mass spectrometric data

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ionization mass spectrometry (GC/EI-MS) measurements. The analysis of fragment ions formed during EI, which contain only specific parts of the original molecule can provide valuable information on the positional distribution of the label. The chemical formula of a fragment ion is usually applied to derive the correction matrix for accurate MID calculation. Hence, the correct assignment of chemical formulas to fragment ions is of crucial importance for correct MIDs. Moreover, the positional distribution of stable isotopes within a fragment ion is of high interest for stable isotope-assisted metabolomics techniques. For example, (13)C-metabolic flux analyses ((13)C-MFA) are dependent on the exact knowledge of the number and position of retained carbon atoms of the unfragmented molecule. Fragment ions containing different carbon atoms are of special interest, since they can carry different flux information. However, the process of mass spectral fragmentation is complex, and identifying the substructures and chemical formulas for these fragment ions is nontrivial. For that reason, we developed an algorithm, based on a systematic bond cleavage, to determine chemical formulas and retained atoms for EI derived fragment ions. Here, we present the fragment formula calculator (FFC) algorithm that can calculate chemical formulas for fragment ions where the chemical bonding (e.g., Lewis structures) of the intact molecule is known. The proposed algorithm is able to cope with general molecular rearrangement reactions occurring during EI in GC/MS measurements. The FFC algorithm is able to integrate stable isotope labeling experiments into the analysis and can automatically exclude candidate formulas that do not fit the observed labeling patterns.1 We applied the FFC algorithm to create a fragment ion repository that contains the chemical formulas and retained carbon atoms of a wide range of trimethylsilyl and tert-butyldimethylsilyl derivatized compounds. In total, we report the chemical formulas and backbone carbon compositions for 160 fragment ions of 43 alkylsilyl-derivatives of primary metabolites. Finally, we implemented the FFC algorithm in an easy-to-use graphical user interface and made it publicly available at http://www.ffc.lu .

Contextual inhibition of fatty acid synthesis by metformin involves glucose-derived acetyl-CoA and cholesterol in pancreatic tumor cells

Metformin, a generic glucose lowering drug, inhibits cancer growth expressly in models that employ high fat/cholesterol intake and/or low glucose availability. Here we use a targeted tracer fate association study (TTFAS) to investigate how cholesterol and metformin administration regulates glucose-derived intermediary metabolism and macromolecule synthesis in pancreatic cancer cells. Wild type K-ras BxPC-3 and HOM: GGT(Gly) → TGT(Cys) K12 transformed MIA PaCa-2 adenocarcinoma cells were cultured in the presence of [1,2-¹³C₂]-d-glucose as the single tracer for 24 h and treated with either 100 μM metformin (MET), 1 mM cholesteryl hemisuccinate (CHS), or the dose matching combination of MET and CHS (CHS-MET). Wild type K-ras cells used 11.43 % (SD = ±0.32) of new acetyl-CoA for palmitate synthesis that was derived from glucose, while K-ras mutated MIA PaCa-2 cells shuttled less than half as much, 5.47 % [SD = ±0.28 (P < 0.01)] of this precursor towards FAS. Cholesterol treatment almost doubled glucose-derived acetyl-CoA enrichment to 9.54 % (SD = ±0.24) and elevated the fraction of new palmitate synthesis by over 2.5-fold in MIA PaCa-2 cells; whereby 100 μM MET treatment resulted in a 28 % inhibitory effect on FAS. Therefore, acetyl-CoA shuttling towards its carboxylase, from thiolase, produces contextual synthetic inhibition by metformin of new palmitate production. Thereby, metformin, mutated K-ras and high cholesterol each contributes to limit new fatty acid and potentially cell membrane synthesis, demonstrating a previously unknown mechanism for inhibiting cancer growth during the metabolic syndrome.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research

Many untargeted LC-ESI-HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly ¹³C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-¹³C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC-HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87-135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-¹³C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.

Pulsed stable isotope-resolved metabolomic studies of cancer cells

Metabolic reprogramming is a key step in oncogenic transformation, and it involves alterations in both bioenergetic and anabolic metabolism. Sustained by these metabolic alterations, malignant cells acquire the ability to re-enter the cell cycle and proliferate. The so-called central carbon metabolism (CCM) is the ultimate source for energy and building blocks enabling cellular growth and proliferation. The time-resolved monitoring of the conversion of stable isotope-labeled metabolites provides profound insights into the metabolic dynamics of malignant cells and enables the tracking of individual carbon routes within the CCM. Specifically, the analysis of isotope incorporation rates within short time frames by means of pulsed stable isotope-resolved metabolomics (pSIRM) can be used to determine the dynamics of glycolysis and glutaminolysis-two metabolic circuitries that are often deregulated in malignant cells. Here, we detail a pSIRM-based method that can be applied to the study of metabolic alteration in cultured cancer cells.