High information throughput analysis of nucleotides and their isotopically enriched isotopologues by direct-infusion FTICR-MS

L-Glyceraldehyde Inhibits Neuroblastoma Cell Growth via a Multi-Modal Mechanism on Metabolism and Signaling

Glyceraldehyde (GA) is a three-carbon monosaccharide that can be present in cells as a by-product of fructose metabolism. Bruno Mendel and Otto Warburg showed that the application of GA to cancer cells inhibits glycolysis and their growth. However, the molecular mechanism by which this occurred was not clarified. We describe a novel multi-modal mechanism by which the L-isomer of GA (L-GA) inhibits neuroblastoma cell growth. L-GA induces significant changes in the metabolic profile, promotes oxidative stress and hinders nucleotide biosynthesis. GC-MS and 13C-labeling was employed to measure the flow of carbon through glycolytic intermediates under L-GA treatment. It was found that L-GA is a potent inhibitor of glycolysis due to its proposed targeting of NAD(H)-dependent reactions. This results in growth inhibition, apoptosis and a redox crisis in neuroblastoma cells. It was confirmed that the redox mechanisms were modulated via L-GA by proteomic analysis. Analysis of nucleotide pools in L-GA-treated cells depicted a previously unreported observation, in which nucleotide biosynthesis is significantly inhibited. The inhibitory action of L-GA was partially relieved with the co-application of the antioxidant N-acetyl-cysteine. We present novel evidence for a simple sugar that inhibits cancer cell proliferation via dysregulating its fragile homeostatic environment.

Differential Inhibition of Anaplerotic Pyruvate Carboxylation and Glutaminolysis-Fueled Anabolism Underlies Distinct Toxicity of Selenium Agents in Human Lung Cancer

Past chemopreventive human trials on dietary selenium supplements produced controversial outcomes. They largely employed selenomethionine (SeM)-based diets. SeM was less toxic than selenite or methylseleninic acid (MSeA) to lung cancer cells. We thus investigated the toxic action of these Se agents in two non-small cell lung cancer (NSCLC) cell lines and ex vivo organotypic cultures (OTC) of NSCLC patient lung tissues. Stable isotope-resolved metabolomics (SIRM) using 13C6-glucose and 13C5,15N2-glutamine tracers with gene knockdowns were employed to examine metabolic dysregulations associated with cell type- and treatment-dependent phenotypic changes. Inhibition of key anaplerotic processes, pyruvate carboxylation (PyC) and glutaminolysis were elicited by exposure to MSeA and selenite but not by SeM. They were accompanied by distinct anabolic dysregulation and reflected cell type-dependent changes in proliferation/death/cell cycle arrest. NSCLC OTC showed similar responses of PyC and/or glutaminolysis to the three agents, which correlated with tissue damages. Altogether, we found differential perturbations in anaplerosis-fueled anabolic pathways to underlie the distinct anti-cancer actions of the three Se agents, which could also explain the failure of SeM-based chemoprevention trials.

Bayesian kinetic modeling for tracer-based metabolomic data

BACKGROUND

Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.

RESULTS

We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .

CONCLUSIONS

Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Glutamine uptake and catabolism is required for myofibroblast formation and persistence

BACKGROUND

Fibrosis and extracellular matrix remodeling are mediated by resident cardiac fibroblasts (CFs). In response to injury, fibroblasts activate, differentiating into specialized synthetic and contractile myofibroblasts producing copious extracellular matrix proteins (e.g., collagens). Myofibroblast persistence in chronic diseases, such as HF, leads to progressive cardiac dysfunction and maladaptive remodeling. We recently reported that an increase in αKG (alpha-ketoglutarate) bioavailability, which contributes to enhanced αKG-dependent lysine demethylase activity and chromatin remodeling, is required for myofibroblast formation. Therefore, we aimed to determine the substrates and metabolic pathways contributing to αKG biosynthesis and their requirement for myofibroblast formation.

METHODS

Stable isotope metabolomics identified glutaminolysis as a key metabolic pathway required for αKG biosynthesis and myofibroblast formation, therefore we tested the effects of pharmacologic inhibition (CB-839) or genetic deletion of glutaminase (Gls1-/-) on myofibroblast formation in both murine and human cardiac fibroblasts. We employed immunofluorescence staining, functional gel contraction, western blotting, and bioenergetic assays to determine the myofibroblast phenotype.

RESULTS

Carbon tracing indicated enhanced glutaminolysis mediating increased αKG abundance. Pharmacological and genetic inhibition of glutaminolysis prevented myofibroblast formation indicated by a reduction in αSMA+ cells, collagen gel contraction, collagen abundance, and the bioenergetic response. Inhibition of glutaminolysis also prevented TGFβ-mediated histone demethylation and supplementation with cell-permeable αKG rescued the myofibroblast phenotype. Importantly, inhibition of glutaminolysis was sufficient to prevent myofibroblast formation in CFs isolated from the human failing heart.

CONCLUSIONS

These results define glutaminolysis as necessary for myofibroblast formation and persistence, providing substantial rationale to evaluate several new therapeutic targets to treat cardiac fibrosis.

Polarization and β-Glucan Reprogram Immunomodulatory Metabolism in Human Macrophages and Ex Vivo in Human Lung Cancer Tissues

Immunomodulatory (IM) metabolic reprogramming in macrophages (Mϕs) is fundamental to immune function. However, limited information is available for human Mϕs, particularly in response plasticity, which is critical to understanding the variable efficacy of immunotherapies in cancer patients. We carried out an in-depth analysis by combining multiplex stable isotope-resolved metabolomics with reversed phase protein array to map the dynamic changes of the IM metabolic network and key protein regulators in four human donors' Mϕs in response to differential polarization and M1 repolarizer β-glucan (whole glucan particles [WGPs]). These responses were compared with those of WGP-treated ex vivo organotypic tissue cultures (OTCs) of human non-small cell lung cancer. We found consistently enhanced tryptophan catabolism with blocked NAD+ and UTP synthesis in M1-type Mϕs (M1-Mϕs), which was associated with immune activation evidenced by increased release of IL-1β/CXCL10/IFN-γ/TNF-α and reduced phagocytosis. In M2a-Mϕs, WGP treatment of M2a-Mϕs robustly increased glucose utilization via the glycolysis/oxidative branch of the pentose phosphate pathway while enhancing UDP-N-acetyl-glucosamine turnover and glutamine-fueled gluconeogenesis, which was accompanied by the release of proinflammatory IL-1β/TNF-α to above M1-Mϕ's levels, anti-inflammatory IL-10 to above M2a-Mϕ's levels, and attenuated phagocytosis. These IM metabolic responses could underlie the opposing effects of WGP, i.e., reverting M2- to M1-type immune functions but also boosting anti-inflammation. Variable reprogrammed Krebs cycle and glutamine-fueled synthesis of UTP in WGP-treated OTCs of human non-small cell lung cancer were observed, reflecting variable M1 repolarization of tumor-associated Mϕs. This was supported by correlation with IL-1β/TNF-α release and compromised tumor status, making patient-derived OTCs unique models for studying variable immunotherapeutic efficacy in cancer patients.

Rapid analysis of S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) isotopologues in stable isotope-resolved metabolomics (SIRM) using direct infusion nanoelectrospray ultra-high-resolution Fourier transform mass spectrometry (DI-nESI-UHR-FTMS)

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are important metabolites in the one-carbon cycle that modulates cellular methylation required for proliferation and epigenetic regulation. Their concentrations, synthesis, and turnover are difficult to determine conveniently and reliably. We have developed such a method by coupling a simple and rapid purification scheme that efficiently captures both compounds, with high sensitivity, sample throughput direct infusion nanoelectrospray ultra-high-resolution Fourier transform mass spectrometry (DI-nESI-UHR-FTMS). This method is compatible with Stable Isotope-Resolved Metabolomic (SIRM) analysis of numerous other metabolites. The limits of detection for both SAM and SAH were <1 nM, and the linearity range was up to 1000 nM. The method was first illustrated for SAM/SAH analysis of mouse livers, and lung adenocarcinoma A549 cells. We then applied the method to track ¹³C₁-CH₃-Met incorporation into SAM and ¹³C₆-glucose transformation into SAM and SAH via de novo synthesis. We further used the method to show the distinct effects on A549 and H1299 cells with treatment of anti-cancer methylseleninic acid (MSA), selenite, and selenomethionine, notably SAM depletion and increased SAM to SAH ratio by MSA, which implicates altered epigenetic regulation.

An Overview of Pretreatment and Analysis of Nucleotides in Different Samples (Update since 2010)

Nucleotides, which are important low-molecular-weight compounds present in organisms, are precursors of nucleic acids and participate in various regulatory and metabolic functions. Sensitive and valid methods for monitoring and determining nucleotides and nucleosides in different samples are urgently required. Due to the presence of numerous endogenous interferences in complex matrices and the high polarity of the molecules of the phosphate moiety, the determination of nucleotide content is challenging. This review summarizes the pretreatment and analysis methods of nucleotides in different samples. Advanced pretreatment methods, including different microextraction methods, solid-phase extraction based on novel materials, QuEChERS, are clearly displayed, and continuous progress which has been made in LC, LC-MS/MS and capillary electrophoresis methods are discussed. Moreover, the strengths and weaknesses of different methods are discussed and compared. Highlight:Advanced pretreatment and detection methods of nucleotides were critically reviewed.Microextraction technology was one of the trends of nucleotides pretreatment in the future.Applications of novel materials and supercritical fluid were highlighted.The evolution and advance of HRMS analyzers were in detailed.

Blockade of 6-phosphogluconate dehydrogenase generates CD8+ effector T cells with enhanced anti-tumor function

Although T cell expansion depends on glycolysis, T effector cell differentiation requires signaling via the production of reactive oxygen species (ROS). Because the pentose phosphate pathway (PPP) regulates ROS by generating nicotinamide adenine dinucleotide phosphate (NADPH), we examined how PPP blockade affects T cell differentiation and function. Here, we show that genetic ablation or pharmacologic inhibition of the PPP enzyme 6-phosphogluconate dehydrogenase (6PGD) in the oxidative PPP results in the generation of superior CD8+ T effector cells. These cells have gene signatures and immunogenic markers of effector phenotype and show potent anti-tumor functions both in vitro and in vivo. In these cells, metabolic reprogramming occurs along with increased mitochondrial ROS and activated antioxidation machinery to balance ROS production against oxidative damage. Our findings reveal a role of 6PGD as a checkpoint for T cell effector differentiation/survival and evidence for 6PGD as an attractive metabolic target to improve tumor immunotherapy.

Inhibiting phosphoglycerate dehydrogenase counteracts chemotherapeutic efficacy against MYCN-amplified neuroblastoma

Here we sought metabolic alterations specifically associated with MYCN amplification as nodes to indirectly target the MYCN oncogene. Liquid chromatography-mass spectrometry-based proteomics identified seven proteins consistently correlated with MYCN in proteomes from 49 neuroblastoma biopsies and 13 cell lines. Among these was phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in de novo serine synthesis. MYCN associated with two regions in the PHGDH promoter, supporting transcriptional PHGDH regulation by MYCN. Pulsed stable isotope-resolved metabolomics utilizing ¹³ C-glucose labeling demonstrated higher de novo serine synthesis in MYCN-amplified cells compared to cells with diploid MYCN. An independence of MYCN-amplified cells from exogenous serine and glycine was demonstrated by serine and glycine starvation, which attenuated nucleotide pools and proliferation only in cells with diploid MYCN but did not diminish these endpoints in MYCN-amplified cells. Proliferation was attenuated in MYCN-amplified cells by CRISPR/Cas9-mediated PHGDH knockout or treatment with PHGDH small molecule inhibitors without affecting cell viability. PHGDH inhibitors administered as single-agent therapy to NOG mice harboring patient-derived MYCN-amplified neuroblastoma xenografts slowed tumor growth. However, combining a PHGDH inhibitor with the standard-of-care chemotherapy drug, cisplatin, revealed antagonism of chemotherapy efficacy in vivo. Emergence of chemotherapy resistance was confirmed in the genetic PHGDH knockout model in vitro. Altogether, PHGDH knockout or inhibition by small molecules consistently slows proliferation, but stops short of killing the cells, which then establish resistance to classical chemotherapy. Although PHGDH inhibition with small molecules has produced encouraging results in other preclinical cancer models, this approach has limited attractiveness for patients with neuroblastoma.

Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research

Metabolomics-the endpoint of the omics cascade-is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks

Metabolism is a complex network of compartmentalized and coupled chemical reactions, which often involve transfers of substructures of biomolecules, thus requiring metabolite substructures to be tracked. Stable isotope resolved metabolomics (SIRM) enables pathways reconstruction, even among chemically identical metabolites, by tracking the provenance of stable isotope-labeled substructures using NMR and ultrahigh resolution (UHR) MS. The latter can resolve and count isotopic labels in metabolites and can identify isotopic enrichment in substructures when operated in tandem MS mode. However, MS2 is difficult to implement with chromatography-based UHR-MS due to lengthy MS1 acquisition time that is required to obtain the molecular isotopologue count, which is further exacerbated by the numerous isotopologue source ions to fragment. We review here recent developments in tandem MS applications of SIRM to obtain more detailed information about isotopologue distributions in metabolites and their substructures.

Air pollution-derived particulate matter dysregulates hepatic Krebs cycle, glucose and lipid metabolism in mice

Exposure to ambient air particulate matter (PM_2.5) is well established as a risk factor for cardiovascular and pulmonary disease. Both epidemiologic and controlled exposure studies in humans and animals have demonstrated an association between air pollution exposure and metabolic disorders such as diabetes. Given the central role of the liver in peripheral glucose homeostasis, we exposed mice to filtered air or PM_2.5 for 16 weeks and examined its effect on hepatic metabolic pathways using stable isotope resolved metabolomics (SIRM) following a bolus of ¹³C₆-glucose. Livers were analyzed for the incorporation of ¹³C into different metabolic pools by IC-FTMS or GC-MS. The relative abundance of ¹³C-glycolytic intermediates was reduced, suggesting attenuated glycolysis, a feature found in diabetes. Decreased ¹³C-Krebs cycle intermediates suggested that PM_2.5 exposure led to a reduction in the Krebs cycle capacity. In contrast to decreased glycolysis, we observed an increase in the oxidative branch of the pentose phosphate pathway and ¹³C incorporations suggestive of enhanced capacity for the de novo synthesis of fatty acids. To our knowledge, this is one of the first studies to examine ¹³C₆-glucose utilization in the liver following PM_2.5 exposure, prior to the onset of insulin resistance (IR).

Integration of flux measurements and pharmacological controls to optimize stable isotope-resolved metabolomics workflows and interpretation

Stable isotope-resolved metabolomics (SIRM) provides information regarding the relative activity of numerous metabolic pathways and the contribution of nutrients to specific metabolite pools; however, SIRM experiments can be difficult to execute, and data interpretation is challenging. Furthermore, standardization of analytical procedures and workflows remain significant obstacles for widespread reproducibility. Here, we demonstrate the workflow of a typical SIRM experiment and suggest experimental controls and measures of cross-validation that improve data interpretation. Inhibitors of glycolysis and oxidative phosphorylation as well as mitochondrial uncouplers serve as pharmacological controls, which help define metabolic flux configurations that occur under well-controlled metabolic states. We demonstrate how such controls and time course labeling experiments improve confidence in metabolite assignments as well as delineate metabolic pathway relationships. Moreover, we demonstrate how radiolabeled tracers and extracellular flux analyses integrate with SIRM to improve data interpretation. Collectively, these results show how integration of flux methodologies and use of pharmacological controls increase confidence in SIRM data and provide new biological insights.

De novo synthesis of serine and glycine fuels purine nucleotide biosynthesis in human lung cancer tissues

Nucleotide synthesis is essential to proliferating cells, but the preferred precursors for de novo biosynthesis are not defined in human cancer tissues. We have employed multiplexed stable isotope-resolved metabolomics to track the metabolism of [¹³C₆]glucose, D₂-glycine, [¹³C₂]glycine, and D₃-serine into purine nucleotides in freshly resected cancerous and matched noncancerous lung tissues from nonsmall cell lung cancer (NSCLC) patients, and we compared the metabolism with established NSCLC PC9 and A549 cell lines in vitro Surprisingly, [¹³C₆]glucose was the best carbon source for purine synthesis in human NSCLC tissues, in contrast to the noncancerous lung tissues from the same patient, which showed lower mitotic indices and MYC expression. We also observed that D₃-Ser was preferentially incorporated into purine rings over D₂-glycine in both tissues and cell lines. MYC suppression attenuated [¹³C₆]glucose, D₃-serine, and [¹³C₂]glycine incorporation into purines and reduced proliferation in PC9 but not in A549 cells. Using detailed kinetic modeling, we showed that the preferred use of glucose as a carbon source for purine ring synthesis in NSCLC tissues involves cytoplasmic activation/compartmentation of the glucose-to-serine pathway and enhanced reversed one-carbon fluxes that attenuate exogenous serine incorporation into purines. Our findings also indicate that the substrate for de novo nucleotide synthesis differs profoundly between cancer cell lines and fresh human lung cancer tissues; the latter preferred glucose to exogenous serine or glycine but not the former. This distinction in substrate utilization in purine synthesis in human cancer tissues should be considered when targeting one-carbon metabolism for cancer therapy.

Improved segmented-scan spectral stitching for stable isotope resolved metabolomics (SIRM) by ultra-high-resolution Fourier transform mass spectrometry

We have implemented a linear ion trap (LIT)-based SIM-stitching method for ultra-high-resolution Fourier transform mass spectrometry (FTMS) that increases the S/N over a wide m/z range compared to non-segmented wide full-scan (WFS) spectra. Here we described an improved segmented spectral scan stitching method that was based on quadrupole mass filter (QMF)-SIM, which overcame previous limitations of ion signal loss in LIT. This allowed for accurate representation of isotopologue distributions, both at natural abundance and in stable isotope-resolved metabolomics (SIRM)-based experiments. We also introduced a new spectral binning method that provided more precise and resolution-independent bins for irreversibly noise-suppressed FTMS spectra. We demonstrated a substantial improvement in S/N and sensitivity (typically > 10-fold) for ¹³C labeled lipid extracts of human macrophages grown as three-dimensional (3D) cell culture, with detection of an increased number of ¹³C isotopologue ions. The method also enabled analysis of extracts from very limited biological samples.

Metabolic Labeling of Cultured Mammalian Cells for Stable Isotope-Resolved Metabolomics: Practical Aspects of Tissue Culture and Sample Extraction

Stable isotope-resolved metabolomics (SIRM) methods are used increasingly by cancer researchers to probe metabolic pathways and identify vulnerabilities in cancer cells. Analytical and computational advances are being made constantly, but tissue culture and sample extraction procedures are often variable and not elaborated in the literature. This chapter discusses basic aspects of tissue culture practices as they relate to the use of stable isotope tracers and provides a detailed metabolic labeling and metabolite extraction procedure designed to maximize the amount of information that can be obtained from a single tracer experiment.

Simultaneous isotope dilution quantification and metabolic tracing of deoxyribonucleotides by liquid chromatography high resolution mass spectrometry

Quantification of cellular deoxyribonucleoside mono- (dNMP), di- (dNDP), triphosphates (dNTPs) and related nucleoside metabolites are difficult due to their physiochemical properties and widely varying abundance. Involvement of dNTP metabolism in cellular processes including senescence and pathophysiological processes including cancer and viral infection make dNTP metabolism an important bioanalytical target. We modified a previously developed ion pairing reversed phase chromatography-mass spectrometry method for the simultaneous quantification and ¹³C isotope tracing of dNTP metabolites. dNMPs, dNDPs, and dNTPs were chromatographically resolved to avoid mis-annotation of in-source fragmentation. We used commercially available ¹³C¹⁵N-stable isotope labeled analogs as internal standards and show that this isotope dilution approach improves analytical figures of merit. At sufficiently high mass resolution achievable on an Orbitrap mass analyzer, stable isotope resolved metabolomics allows simultaneous isotope dilution quantification and ¹³C isotope tracing from major substrates including ¹³C-glucose. As a proof of principle, we quantified dNMP, dNDP and dNTP pools from multiple cell lines. We also identified isotopologue enrichment from glucose corresponding to ribose from the pentose-phosphate pathway in dNTP metabolites.

NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism

There is considerable interest in defining metabolic reprogramming in human diseases, which is recognized as a hallmark of human cancer. Although radiotracers have a long history in specific metabolic studies, stable isotope-enriched precursors coupled with modern high resolution mass spectrometry and NMR spectroscopy have enabled systematic mapping of metabolic networks and fluxes in cells, tissues and living organisms including humans. These analytical platforms are high in information content, are complementary and cross-validating in terms of compound identification, quantification, and isotope labeling pattern analysis of a large number of metabolites simultaneously. Furthermore, new developments in chemoselective derivatization and in vivo spectroscopy enable tracking of labile/low abundance metabolites and metabolic kinetics in real-time. Here we review developments in Stable Isotope Resolved Metabolomics (SIRM) and recent applications in cancer metabolism using a wide variety of stable isotope tracers that probe both broad and specific aspects of cancer metabolism required for proliferation and survival.

Stable Isotope-Resolved Metabolomic Differences between Hormone-Responsive and Triple-Negative Breast Cancer Cell Lines

Purpose

To conduct an exploratory study to identify mechanisms that differentiate Luminal A (BT474 and MCF-7) and triple-negative (MDA-MB-231 and MDA-MB-468) breast cancer (BCa) cell lines to potentially provide novel therapeutic targets based on differences in energy utilization.

Methods

Cells were cultured in media containing either [U-¹³C]-glucose or [U-¹³C]-glutamine for 48 hours. Conditioned media and cellular extracts were analyzed by ¹H and ¹³C NMR spectroscopy.

Results

MCF-7 cells consumed the most glucose, producing the most lactate, demonstrating the greatest Warburg effect-associated energy utilization. BT474 cells had the highest tricarboxylic acid cycle (TCA) activity. The majority of energy utilization patterns in MCF-7 cells were more similar to MDA-MB-468 cells, while the patterns for BT474 cells were more similar to MDA-MB-231 cells. Compared to the Luminal A cell lines, TNBC cell lines consumed more glutamine and less glucose. BT474 and MDA-MB-468 cells produced high amounts of ¹³C-glycine from media [U-¹³C]-glucose which was integrated into glutathione, indicating de novo synthesis.

Conclusions

Stable isotopic resolved metabolomics using ¹³C substrates provided mechanistic information about energy utilization that was difficult to interpret using ¹H data alone. Overall, cell lines that have different hormone receptor status have different energy utilization requirements, even if they are classified by the same clinical BCa subtype; and these differences offer clues about optimizing treatment strategies.

New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis

INTRODUCTION

Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems.

OBJECTIVES

Our goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies.

RESULTS

We observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness.

CONCLUSION

Our methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis.

Quantitative profiling of carbonyl metabolites directly in crude biological extracts using chemoselective tagging and nanoESI-FTMS

The extensive range of chemical structures, wide range of abundances, and chemical instability of metabolites present in the metabolome pose major analytical challenges that are difficult to address with existing technologies. To address these issues, one approach is to target a subset of metabolites that share a functional group, such as ketones and aldehydes, using chemoselective tagging. Here we report a greatly improved chemoselective method for the quantitative analysis of hydrophilic and hydrophobic carbonyl-containing metabolites directly in biological samples. This method is based on direct tissue or cells extraction with simultaneous derivatization of stable and labile carbonylated metabolites using N-[2-(aminooxy)ethyl]-N,N-dimethyl-1-dodecylammonium (QDA) and ¹³CD₃ labeled QDA. We combined innovations of direct quenching of biological sample with frozen derivatization conditions under the catalyst N,N-dimethyl-p-phenylenediamine, which facilitated the formation of oxime stable-isotope ion pairs differing by m/z 4.02188 while minimizing metabolite degradation. The resulting oximes were extracted by HyperSep C8 tips to remove interfering compounds, and the products were detected using nano-electrospray ionization interfaced with a Thermo Fusion mass spectrometer. The quaternary ammonium tagging greatly increased electrospray MS detection sensitivity and the signature ions pairs enabled simple identification of carbonyl compounds. The improved method showed the lower limits of quantification for carbonyl standards to be in the range of 0.20-2 nM, with linearity of R² > 0.99 over 4 orders of magnitude. We have applied the method to assign 66 carbonyls in mouse tumor tissues, many of which could not be assigned solely by accurate mass and tandem MS. Fourteen of the metabolites were quantified using authentic standards. We also demonstrated the suitability of this method for determining ¹³C labeled isotopologues of carbonyl metabolites in ¹³C₆-glucose-based stable isotope-resolved metabolomic (SIRM) studies.

Stable Isotope-Resolved Metabolomics Shows Metabolic Resistance to Anti-Cancer Selenite in 3D Spheroids versus 2D Cell Cultures

Conventional two-dimensional (2D) cell cultures are grown on rigid plastic substrates with unrealistic concentration gradients of O₂, nutrients, and treatment agents. More importantly, 2D cultures lack cell–cell and cell–extracellular matrix (ECM) interactions, which are critical for regulating cell behavior and functions. There are several three-dimensional (3D) cell culture systems such as Matrigel, hydrogels, micropatterned plates, and hanging drop that overcome these drawbacks but they suffer from technical challenges including long spheroid formation times, difficult handling for high throughput assays, and/or matrix contamination for metabolic studies. Magnetic 3D bioprinting (M3DB) can circumvent these issues by utilizing nanoparticles that enable spheroid formation and growth via magnetizing cells. M3DB spheroids have been shown to emulate tissue and tumor microenvironments while exhibiting higher resistance to toxic agents than their 2D counterparts. It is, however, unclear if and how such 3D systems impact cellular metabolic networks, which may determine altered toxic responses in cells. We employed a Stable Isotope-Resolved Metabolomics (SIRM) approach with ¹³C₆-glucose as tracer to map central metabolic networks both in 2D cells and M3DB spheroids formed from lung (A549) and pancreatic (PANC1) adenocarcinoma cells without or with an anti-cancer agent (sodium selenite). We found that the extent of ¹³C-label incorporation into metabolites of glycolysis, the Krebs cycle, the pentose phosphate pathway, and purine/pyrimidine nucleotide synthesis was largely comparable between 2D and M3DB culture systems for both cell lines. The exceptions were the reduced capacity for de novo synthesis of pyrimidine and sugar nucleotides in M3DB than 2D cultures of A549 and PANC1 cells as well as the presence of gluconeogenic activity in M3DB spheroids of PANC1 cells but not in the 2D counterpart. More strikingly, selenite induced much less perturbation of these pathways in the spheroids relative to the 2D counterparts in both cell lines, which is consistent with the corresponding lesser effects on morphology and growth. Thus, the increased resistance of cancer cell spheroids to selenite may be linked to the reduced capacity of selenite to perturb these metabolic pathways necessary for growth and survival.

Integration of flux measurements to resolve changes in anabolic and catabolic metabolism in cardiac myocytes

Although ancillary pathways of glucose metabolism are critical for synthesizing cellular building blocks and modulating stress responses, how they are regulated remains unclear. In the present study, we used radiometric glycolysis assays, [¹³C₆]-glucose isotope tracing, and extracellular flux analysis to understand how phosphofructokinase (PFK)-mediated changes in glycolysis regulate glucose carbon partitioning into catabolic and anabolic pathways. Expression of kinase-deficient or phosphatase-deficient 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase in rat neonatal cardiomyocytes co-ordinately regulated glycolytic rate and lactate production. Nevertheless, in all groups, >40% of glucose consumed by the cells was unaccounted for via catabolism to pyruvate, which suggests entry of glucose carbons into ancillary pathways branching from metabolites formed in the preparatory phase of glycolysis. Analysis of ¹³C fractional enrichment patterns suggests that PFK activity regulates glucose carbon incorporation directly into the ribose and the glycerol moieties of purines and phospholipids, respectively. Pyrimidines, UDP-N-acetylhexosamine, and the fatty acyl chains of phosphatidylinositol and triglycerides showed lower ¹³C incorporation under conditions of high PFK activity; the isotopologue ¹³C enrichment pattern of each metabolite indicated limitations in mitochondria-engendered aspartate, acetyl CoA and fatty acids. Consistent with this notion, high glycolytic rate diminished mitochondrial activity and the coupling of glycolysis to glucose oxidation. These findings suggest that a major portion of intracellular glucose in cardiac myocytes is apportioned for ancillary biosynthetic reactions and that PFK co-ordinates the activities of the pentose phosphate, hexosamine biosynthetic, and glycerolipid synthesis pathways by directly modulating glycolytic intermediate entry into auxiliary glucose metabolism pathways and by indirectly regulating mitochondrial cataplerosis.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Exploring cancer metabolism using stable isotope-resolved metabolomics (SIRM)

Metabolic reprogramming is a hallmark of cancer. The changes in metabolism are adaptive to permit proliferation, survival, and eventually metastasis in a harsh environment. Stable isotope-resolved metabolomics (SIRM) is an approach that uses advanced approaches of NMR and mass spectrometry to analyze the fate of individual atoms from stable isotope-enriched precursors to products to deduce metabolic pathways and networks. The approach can be applied to a wide range of biological systems, including human subjects. This review focuses on the applications of SIRM to cancer metabolism and its use in understanding drug actions.

The MYC mRNA 3'-UTR couples RNA polymerase II function to glutamine and ribonucleotide levels

Deregulated expression of MYC enhances glutamine utilization and renders cell survival dependent on glutamine, inducing "glutamine addiction". Surprisingly, colon cancer cells that express high levels of MYC due to WNT pathway mutations are not glutamine-addicted but undergo a reversible cell cycle arrest upon glutamine deprivation. We show here that glutamine deprivation suppresses translation of endogenous MYC via the 3'-UTR of the MYC mRNA, enabling escape from apoptosis. This regulation is mediated by glutamine-dependent changes in adenosine-nucleotide levels. Glutamine deprivation causes a global reduction in promoter association of RNA polymerase II (RNAPII) and slows transcriptional elongation. While activation of MYC restores binding of MYC and RNAPII function on most promoters, restoration of elongation is imperfect and activation of MYC in the absence of glutamine causes stalling of RNAPII on multiple genes, correlating with R-loop formation. Stalling of RNAPII and R-loop formation can cause DNA damage, arguing that the MYC 3'-UTR is critical for maintaining genome stability when ribonucleotide levels are low.

Analysis of Stable Isotope Assisted Metabolomics Data Acquired by High Resolution Mass Spectrometry

Stable isotope assisted metabolomics (SIAM) uses stable isotope tracers to support studies of biochemical mechanisms. We report a suite of data analysis algorithms for automatic analysis of SIAM data acquired on a high resolution mass spectrometer. To increase the accuracy of isotopologue assignment, metabolites detected in the unlabeled samples were used as reference metabolites to generate possible isotopologue candidates for analysis of peaks detected in the labeled samples. An iterative linear regression model was developed to deconvolute the overlapping isotopic peaks of isotopologues present in a full MS spectrum, where the threshold for the weight factor was determined by a simulation study assuming different levels of Gaussian white noise contamination. A normalization method enabling isotope ratio-based normalization was implemented to study the difference of isotopologue abundance distribution between sample groups. The developed method can analyze SIAM data acquired by direct infusion MS and LC-MS, and can handle metabolite tracers containing different tracer elements. Analysis of SIAM data acquired from mixtures of known compounds showed that the developed algorithms accurately identify metabolites and quantify stable isotope enrichment. Application of SIAM data acquired from a biological study further demonstrated the effectiveness and accuracy of the developed method for analysis of complex samples.

Chloroformate derivatization for tracing the fate of Amino acids in cells and tissues by multiple stable isotope resolved metabolomics (mSIRM)

Amino acids have crucial roles in central metabolism, both anabolic and catabolic. To elucidate these roles, steady-state concentrations of amino acids alone are insufficient, as each amino acid participates in multiple pathways and functions in a complex network, which can also be compartmentalized. Stable Isotope-Resolved Metabolomics (SIRM) is an approach that uses atom-resolved tracking of metabolites through biochemical transformations in cells, tissues, or whole organisms. Using different elemental stable isotopes to label multiple metabolite precursors makes it possible to resolve simultaneously the utilization of these precursors in a single experiment. Conversely, a single precursor labeled with two (or more) different elemental isotopes can trace the allocation of e.g. C and N atoms through the network. Such dual-label experiments however challenge the resolution of conventional mass spectrometers, which must distinguish the neutron mass differences among different elemental isotopes. This requires ultrahigh resolution Fourier transform mass spectrometry (UHR-FTMS). When combined with direct infusion nano-electrospray ion source (nano-ESI), UHR-FTMS can provide rapid, global, and quantitative analysis of all possible mass isotopologues of metabolites. Unfortunately, very low mass polar metabolites such as amino acids can be difficult to analyze by current models of UHR-FTMS, plus the high salt content present in typical cell or tissue polar extracts may cause unacceptable ion suppression for sources such as nano-ESI. Here we describe a modified method of ethyl chloroformate (ECF) derivatization of amino acids to enable rapid quantitative analysis of stable isotope labeled amino acids using nano-ESI UHR-FTMS. This method showed excellent linearity with quantifiable limits in the low nanomolar range represented in microgram quantities of biological specimens, which results in extracts with total analyte abundances in the low to sub-femtomole range. We have applied this method to profile amino acids and their labeling patterns in ¹³C and ²H doubly labeled PC9 cell extracts, cancerous and non-cancerous tissue extracts from a lung cancer patient and their protein hydrolysates as well as plasma extracts from mice fed with a liquid diet containing ¹³C₆-glucose (Glc). The multi-element isotopologue distributions provided key insights into amino acid metabolism and intracellular pools in human lung cancer tissues in high detail. The ¹³C labeling of Asp and Glu revealed de novo synthesis of these amino acids from ¹³C₆-Glc via the Krebs cycle, specifically the elevated level of ¹³C₃-labeled Asp and Glu in cancerous versus non-cancerous lung tissues was consistent with enhanced pyruvate carboxylation. In addition, tracking the fate of double tracers, (¹³C₆-Glc + ²H₂-Gly or ¹³C₆-Glc + ²H₃-Ser) in PC9 cells clearly resolved pools of Ser and Gly synthesized de novo from ¹³C₆-Glc (¹³C₃-Ser and ¹³C₂-Gly) versus Ser and Gly derived from external sources (²H₃-Ser, ²H₂-Gly). Moreover the complex ²H labeling patterns of the latter were results of Ser and Gly exchange through active Ser-Gly one-carbon metabolic pathway in PC9 cells.

NMR-based Stable Isotope Resolved Metabolomics in systems biochemistry

Metabolism is the basic activity of live cells, and monitoring the metabolic state provides a dynamic picture of the cells or tissues, and how they respond to external changes, for in disease or treatment with drugs. NMR is an extremely versatile analytical tool that can be applied to a wide range of biochemical problems. Despite its modest sensitivity its versatility make it an ideal tool for analyzing biochemical dynamics both in vitro and in vivo, especially when coupled with its isotope editing capabilities, from which isotope distributions can be readily determined. These are critical for any analyses of flux in live organisms. This review focuses on the utility of NMR spectroscopy in metabolomics, with an emphasis on NMR applications in stable isotope-enriched tracer research for elucidating biochemical pathways and networks with examples from nucleotide biochemistry. The knowledge gained from this area of research provides a ready link to genomic, epigenomic, transcriptomic, and proteomic information to achieve systems biochemical understanding of living cells and organisms.

Metabolomics Analysis of Hormone-Responsive and Triple-Negative Breast Cancer Cell Responses to Paclitaxel Identify Key Metabolic Differences

To date, no targeted therapies are available to treat triple negative breast cancer (TNBC), while other breast cancer subtypes are responsive to current therapeutic treatment. Metabolomics was conducted to reveal differences in two hormone receptor-negative TNBC cell lines and two hormone receptor-positive Luminal A cell lines. Studies were conducted in the presence and absence of paclitaxel (Taxol). TNBC cell lines had higher levels of amino acids, branched-chain amino acids, nucleotides, and nucleotide sugars and lower levels of proliferation-related metabolites like choline compared with Luminal A cell lines. In the presence of paclitaxel, each cell line showed unique metabolic responses, with some similarities by type. For example, in the Luminal A cell lines, levels of lactate and creatine decreased while certain choline metabolites and myo-inositol increased with paclitaxel. In the TNBC cell lines levels of glutamine, glutamate, and glutathione increased, whereas lysine, proline, and valine decreased in the presence of drug. Profiling secreted inflammatory cytokines in the conditioned media demonstrated a greater response to paclitaxel in the hormone-positive Luminal cells compared with a secretion profile that suggested greater drug resistance in the TNBC cells. The most significant differences distinguishing the cell types based on pathway enrichment analyses were related to amino acid, lipid and carbohydrate metabolism pathways, whereas several biological pathways were differentiated between the cell lines following treatment.

Type 2 Diabetes Dysregulates Glucose Metabolism in Cardiac Progenitor Cells

Type 2 diabetes is associated with increased mortality and progression to heart failure. Recent studies suggest that diabetes also impairs reparative responses after cell therapy. In this study, we examined potential mechanisms by which diabetes affects cardiac progenitor cells (CPCs). CPCs isolated from the diabetic heart showed diminished proliferation, a propensity for cell death, and a pro-adipogenic phenotype. The diabetic CPCs were insulin-resistant, and they showed higher energetic reliance on glycolysis, which was associated with up-regulation of the pro-glycolytic enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3). In WT CPCs, expression of a mutant form of PFKFB, which mimics PFKFB3 activity and increases glycolytic rate, was sufficient to phenocopy the mitochondrial and proliferative deficiencies found in diabetic cells. Consistent with activation of phosphofructokinase in diabetic cells, stable isotope carbon tracing in diabetic CPCs showed dysregulation of the pentose phosphate and glycero(phospho)lipid synthesis pathways. We describe diabetes-induced dysregulation of carbon partitioning using stable isotope metabolomics-based coupling quotients, which relate relative flux values between metabolic pathways. These findings suggest that diabetes causes an imbalance in glucose carbon allocation by uncoupling biosynthetic pathway activity, which could diminish the efficacy of CPCs for myocardial repair.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

Regulation of mammalian nucleotide metabolism and biosynthesis

Nucleotides are required for a wide variety of biological processes and are constantly synthesized de novo in all cells. When cells proliferate, increased nucleotide synthesis is necessary for DNA replication and for RNA production to support protein synthesis at different stages of the cell cycle, during which these events are regulated at multiple levels. Therefore the synthesis of the precursor nucleotides is also strongly regulated at multiple levels. Nucleotide synthesis is an energy intensive process that uses multiple metabolic pathways across different cell compartments and several sources of carbon and nitrogen. The processes are regulated at the transcription level by a set of master transcription factors but also at the enzyme level by allosteric regulation and feedback inhibition. Here we review the cellular demands of nucleotide biosynthesis, their metabolic pathways and mechanisms of regulation during the cell cycle. The use of stable isotope tracers for delineating the biosynthetic routes of the multiple intersecting pathways and how these are quantitatively controlled under different conditions is also highlighted. Moreover, the importance of nucleotide synthesis for cell viability is discussed and how this may lead to potential new approaches to drug development in diseases such as cancer.

Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation

Anabolic biosynthesis requires precursors supplied by the Krebs cycle, which in turn requires anaplerosis to replenish precursor intermediates. The major anaplerotic sources are pyruvate and glutamine, which require the activity of pyruvate carboxylase (PC) and glutaminase 1 (GLS1), respectively. Due to their rapid proliferation, cancer cells have increased anabolic and energy demands; however, different cancer cell types exhibit differential requirements for PC- and GLS-mediated pathways for anaplerosis and cell proliferation. Here, we infused patients with early-stage non-small-cell lung cancer (NSCLC) with uniformly 13C-labeled glucose before tissue resection and determined that the cancerous tissues in these patients had enhanced PC activity. Freshly resected paired lung tissue slices cultured in 13C6-glucose or 13C5,15N2-glutamine tracers confirmed selective activation of PC over GLS in NSCLC. Compared with noncancerous tissues, PC expression was greatly enhanced in cancerous tissues, whereas GLS1 expression showed no trend. Moreover, immunohistochemical analysis of paired lung tissues showed PC overexpression in cancer cells rather than in stromal cells of tumor tissues. PC knockdown induced multinucleation, decreased cell proliferation and colony formation in human NSCLC cells, and reduced tumor growth in a mouse xenograft model. Growth inhibition was accompanied by perturbed Krebs cycle activity, inhibition of lipid and nucleotide biosynthesis, and altered glutathione homeostasis. These findings indicate that PC-mediated anaplerosis in early-stage NSCLC is required for tumor survival and proliferation.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

Development and in silico evaluation of large-scale metabolite identification methods using functional group detection for metabolomics

Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultra-high resolution mass spectrometry (MS) enable comprehensive and rapid analysis of metabolites. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm, Chemically Aware Substructure Search (CASS) that efficiently detects functional groups within existing metabolite databases, allowing for combined molecular formula and functional group (from CS tagging) queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both Human Metabolome Database (HMDB) and KEGG Ligand demonstrated a high percentage of isomeric molecular formulae (43 and 28%, respectively), indicating the necessity for techniques such as CS-tagging. Furthermore, these two databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism within the biosphere. In silico analysis of various CS-tagging strategies under different conditions for adduct formation demonstrate that combined FT-MS derived molecular formulae and CS-tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database vs. 41 and 17%, respectively without adduct formation. This difference between database isomer disambiguation highlights the strength of CS-tagging for non-lipid metabolite identification. However, unique identification of complex lipids still needs additional information.

Knockdown of malic enzyme 2 suppresses lung tumor growth, induces differentiation and impacts PI3K/AKT signaling

Mitochondrial malic enzyme 2 (ME2) catalyzes the oxidative decarboxylation of malate to yield CO₂ and pyruvate, with concomitant reduction of dinucleotide cofactor NAD⁺ or NADP⁺. We find that ME2 is highly expressed in many solid tumors. In the A549 non-small cell lung cancer (NSCLC) cell line, ME2 depletion inhibits cell proliferation and induces cell death and differentiation, accompanied by increased reactive oxygen species (ROS) and NADP⁺/NADPH ratio, a drop in ATP, and increased sensitivity to cisplatin. ME2 knockdown impacts phosphoinositide-dependent protein kinase 1 (PDK1) and phosphatase and tensin homolog (PTEN) expression, leading to AKT inhibition. Depletion of ME2 leads to malate accumulation and pyruvate decrease, and exogenous cell permeable dimethyl-malate (DMM) mimics the ME2 knockdown phenotype. Both ME2 knockdown and DMM treatment reduce A549 cell growth in vivo. Collectively, our data suggest that ME2 is a potential target for cancer therapy.

Role in tumor growth of a glycogen debranching enzyme lost in glycogen storage disease

BACKGROUND

Bladder cancer is the most common malignancy of the urinary system, yet our molecular understanding of this disease is incomplete, hampering therapeutic advances.

METHODS

Here we used a genome-wide functional short-hairpin RNA (shRNA) screen to identify suppressors of in vivo bladder tumor xenograft growth (n = 50) using bladder cancer UMUC3 cells. Next-generation sequencing was used to identify the most frequently occurring shRNAs in tumors. Genes so identified were studied in 561 patients with bladder cancer for their association with stratification of clinical outcome by Kaplan-Meier analysis. The best prognostic marker was studied to determine its mechanism in tumor suppression using anchorage-dependent and -independent growth, xenograft (n = 20), and metabolomic assays. Statistical significance was determined using two-sided Student t test and repeated-measures statistical analysis.

RESULTS

We identified the glycogen debranching enzyme AGL as a prognostic indicator of patient survival (P = .04) and as a novel regulator of bladder cancer anchorage-dependent (P < .001), anchorage-independent (mean ± standard deviation, 180 ± 23.1 colonies vs 20±9.5 in control, P < .001), and xenograft growth (P < .001). Rescue experiments using catalytically dead AGL variants revealed that this effect is independent of AGL enzymatic functions. We demonstrated that reduced AGL enhances tumor growth by increasing glycine synthesis through increased expression of serine hydroxymethyltransferase 2.

CONCLUSIONS

Using an in vivo RNA interference screen, we discovered that AGL, a glycogen debranching enzyme, has a biologically and statistically significant role in suppressing human cancer growth.

Application of stable isotope-assisted metabolomics for cell metabolism studies

The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other "omics" analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research.

Dynamic metabolic and transcriptional profiling of Rhodococcus sp. strain YYL during the degradation of tetrahydrofuran

Although tetrahydrofuran-degrading Rhodococcus sp. strain YYL possesses tetrahydrofuran (THF) degradation genes similar to those of other tetrahydrofuran-degrading bacteria, a much higher degradation efficiency has been observed in strain YYL. In this study, nuclear magnetic resonance (NMR)-based metabolomics analyses were performed to explore the metabolic profiling response of strain YYL to exposure to THF. Exposure to THF slightly influenced the metabolome of strain YYL when yeast extract was present in the medium. The metabolic profile of strain YYL over time was also investigated using THF as the sole carbon source to identify the metabolites associated with high-efficiency THF degradation. Lactate, alanine, glutarate, glutamate, glutamine, succinate, lysine, trehalose, trimethylamine-N-oxide (TMAO), NAD(+), and CTP were significantly altered over time in strain YYL grown in 20 mM THF. Real-time quantitative PCR (RT-qPCR) revealed changes in the transcriptional expression levels of 15 genes involved in THF degradation, suggesting that strain YYL could accumulate several disturbances in osmoregulation (trehalose, glutamate, glutamine, etc.), with reduced glycolysis levels, an accelerated tricarboxylic acid cycle, and enhanced protein synthesis. The findings obtained through (1)H NMR metabolomics analyses and the transcriptional expression of the corresponding genes are complementary for exploring the dynamic metabolic profile in organisms.

Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), over the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly widespread metabolic diseases such as cancer, diabetes, and obesity. Emerging NMR and MS atom-tracking technologies and informatics are poised to revolutionize metabolomics-based research because they deliver the high information throughput (HIT) that is needed for deciphering systems biochemistry. In particular, stable isotope-resolved metabolomics (SIRM) enables unambiguous tracking of individual atoms through compartmentalized metabolic networks in a wide range of experimental systems, including human subjects. MS offers a wide range of instrumental capabilities involving different levels of initial capital outlay and operating costs, ranging from gas-chromatography (GC) MS that is affordable by many individual laboratories to the HIT-supporting Fourier-transform (FT) class of MS that rivals NMR in cost and infrastructure support. This chapter focuses on sample preparation, instrument, and data processing procedures for these two extremes of MS instrumentation used in SIRM.

Metabolic reprogramming for producing energy and reducing power in fumarate hydratase null cells from hereditary leiomyomatosis renal cell carcinoma

Fumarate hydratase (FH)-deficient kidney cancer undergoes metabolic remodeling, with changes in mitochondrial respiration, glucose, and glutamine metabolism. These changes represent multiple biochemical adaptations in glucose and fatty acid metabolism that supports malignant proliferation. However, the metabolic linkages between altered mitochondrial function, nucleotide biosynthesis and NADPH production required for proliferation and survival have not been elucidated. To characterize the alterations in glycolysis, the Krebs cycle and the pentose phosphate pathways (PPP) that either generate NADPH (oxidative) or do not (non-oxidative), we utilized [U-¹³C]-glucose, [U-¹³C,¹⁵N]-glutamine, and [1,2- ¹³C₂]-glucose tracers with mass spectrometry and NMR detection to track these pathways, and measured the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of growing cell lines. This metabolic reprogramming in the FH null cells was compared to cells in which FH has been restored. The FH null cells showed a substantial metabolic reorganization of their intracellular metabolic fluxes to fulfill their high ATP demand, as observed by a high rate of glucose uptake, increased glucose turnover via glycolysis, high production of glucose-derived lactate, and low entry of glucose carbon into the Krebs cycle. Despite the truncation of the Krebs cycle associated with inactivation of fumarate hydratase, there was a small but persistent level of mitochondrial respiration, which was coupled to ATP production from oxidation of glutamine-derived α–ketoglutarate through to fumarate. [1,2- ¹³C₂]-glucose tracer experiments demonstrated that the oxidative branch of PPP initiated by glucose-6-phosphate dehydrogenase activity is preferentially utilized for ribose production (56-66%) that produces increased amounts of ribose necessary for growth and NADPH. Increased NADPH is required to drive reductive carboxylation of α-ketoglutarate and fatty acid synthesis for rapid proliferation and is essential for defense against increased oxidative stress. This increased NADPH producing PPP activity was shown to be a strong consistent feature in both fumarate hydratase deficient tumors and cell line models.

A Phos-tag-based magnetic-bead method for rapid and selective separation of phosphorylated biomolecules

A simple and efficient method based on magnetic-bead technology has been developed for the separation of phosphorylated and nonphosphorylated low-molecular-weight biomolecules, such as nucleotides, phosphorylated amino acids, or phosphopeptides. The phosphate-binding site on the bead is an alkoxide-bridged dinuclear zinc(II) complex with 1,3-bis(pyridin-2-ylmethylamino)propan-2-olate (Phos-tag), which is linked to a hydrophilic cross-linked agarose coating on a magnetic core particle. All steps for the phosphate-affinity separation are conducted in buffers of neutral pH with 50 μL of the magnetic beads in a 1.5-mL microtube. The entire separation protocol for phosphomonoester-type compounds, from addition to elution, requires less than 12 min per sample if the buffers and the zinc(II)-bound Phos-tag magnetic beads have been prepared in advance. The phosphate-affinity magnetic beads are reusable at least 15 times without a decrease in their phosphate-binding ability and they are stable for three months in propan-2-ol.

A Carbonyl Capture Approach for Profiling Oxidized Metabolites in Cell Extracts

Fourier-transform ion-cyclotron resonance mass spectrometry (FT-ICR-MS) detection of oxidized cellular metabolites is described using isotopologic, carbonyl-selective derivatizing agents that integrate aminooxy functionality for carbonyl capture, quaternary nitrogen for electrospray enhancement, and a hydrophobic domain for sample cleanup. These modular structural features enable rapid, sensitive analysis of complex mixtures of metabolite-derivatives by FT-ICR-MS via continuous nanoelectrospray infusion. Specifically, this approach can be used to globally assess levels of low abundance and labile aldehyde and ketone metabolites quantitatively and in high throughput manner. These metabolites are often key and unique indicators of various biochemical pathways and their perturbations. Analysis of lung adenocarcinoma A549 cells established a profile of carbonyl metabolites spanning multiple structural classes. We also demonstrate a procedure for metabolite quantification using pyruvate as a model analyte.