Profiling the Metabolism of Human Cells by Deep 13C Labeling

Dietary approaches for exploiting metabolic vulnerabilities in cancer

Renewed interest in tumor metabolism sparked an enthusiasm for dietary interventions to prevent and treat cancer. Changes in diet impact circulating nutrient levels in the plasma and the tumor microenvironment, and preclinical studies suggest that dietary approaches, including caloric and nutrient restrictions, can modulate tumor initiation, progression, and metastasis. Cancers are heterogeneous in their metabolic dependencies and preferred energy sources and can be addicted to glucose, fructose, amino acids, or lipids for survival and growth. This dependence is influenced by tumor type, anatomical location, tissue of origin, aberrant signaling, and the microenvironment. This review summarizes nutrient dependencies and the related signaling pathway activations that provide targets for nutritional interventions. We examine popular dietary approaches used as adjuvants to anticancer therapies, encompassing caloric restrictions, including time-restricted feeding, intermittent fasting, fasting-mimicking diets (FMDs), and nutrient restrictions, notably the ketogenic diet. Despite promising results, much of the knowledge on dietary restrictions comes from in vitro and animal studies, which may not accurately reflect real-life situations. Further research is needed to determine the optimal duration, timing, safety, and efficacy of dietary restrictions for different cancers and treatments. In addition, well-designed human trials are necessary to establish the link between specific metabolic vulnerabilities and targeted dietary interventions. However, low patient compliance in clinical trials remains a significant challenge.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

Integrated experimental-computational analysis of a HepaRG liver-islet microphysiological system for human-centric diabetes research

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Microphysiological systems (MPS) are powerful tools to unravel biological knowledge underlying disease. MPS provide a physiologically relevant, human-based in vitro setting, which can potentially yield better translatability to humans than current animal models and traditional cell cultures. However, mechanistic interpretation and extrapolation of the experimental results to human outcome remain significant challenges. In this study, we confront these challenges using an integrated experimental-computational approach. We present a computational model describing glucose metabolism in a previously reported MPS integrating liver and pancreas. This MPS supports a homeostatic feedback loop between HepaRG/HHSteC spheroids and pancreatic islets, and allows for detailed investigations of mechanisms underlying type 2 diabetes in humans. We show that the computational model captures the complex dynamics of glucose-insulin regulation observed in the system, and can provide mechanistic insight into disease progression features, such as insulin resistance and β-cell dynamics. Furthermore, the computational model can explain key differences in temporal dynamics between MPS and human responses, and thus provides a tool for translating experimental insights into human outcome. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with ¹³C, to use mass spectrometry to find out where the ¹³C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the ¹³C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions.

Functional Characterization of the Solute Carrier LAT-1 (SLC7A5/SLC2A3) in Human Brain Capillary Endothelial Cells with Rapid UPLC-MS/MS Quantification of Intracellular Isotopically Labelled L-Leucine

The solute carrier L-type amino acid transporter 1 (LAT-1/SLC7A5) is a viable target for drug delivery to the central nervous system (CNS) and tumors due to its high abundance at the blood-brain barrier and in tumor tissue. LAT-1 is only localized on the cell surface as a heterodimer with CD98, which is not required for transporter function. To support future CNS drug-delivery development based on LAT-1 targeting, we established an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) assay for stable isotopically labeled leucine ([¹³C₆, ¹⁵N]-L-leucine), with a dynamic range of 0.1-1000 ng/mL that can be applied for the functional testing of LAT-1 activity when combined with specific inhibitors and, consequently, the LAT-1 inhibition capacity of new compounds. The assay was established in a 96-well format, facilitating high-throughput experiments, and, hence, can support the screening for novel inhibitors. Applicable recommendations of the US Food and Drug Administration and European Medicines Agency for bioanalytical method validation were followed to validate the assay. The assay was applied to investigate the IC₅₀ of two well-known LAT-1 inhibitors on hCMEC/D3 cells: the highly specific LAT-1 inhibitor JPH203, which was also used to demonstrate LAT-1 specific uptake, and the general system L inhibitor BCH. In addition, the [¹³C₆, ¹⁵N]-L-leucine uptake was determined on two human brain capillary endothelial cell lines (NKIM-6 and hCMEC/D3), which were characterized for their expressional differences of LAT-1 at the protein and mRNA level and the surface amount of CD98. The IC₅₀ values of the inhibitors were in concordance with previously reported values. Furthermore, the [¹³C₆, ¹⁵N]-L-leucine uptake was significantly higher in hCMEC/D3 cells compared to NKIM-6 cells, which correlated with higher expression of LAT-1 and a higher surface amount of CD98. Therefore, the UPLC-MS/MS quantification of ([¹³C₆, ¹⁵N]-L-leucine is a feasible strategy for the functional characterization of LAT-1 activity in cells or tissue.

Functional Analysis of Immune Signature Genes in Th1* Memory Cells Links ISOC1 and Pyrimidine Metabolism to IFN-γ and IL-17 Production

CCR6+CXCR3+CCR4-CD4+ memory T cells, termed Th1*, are important for long-term immunity to Mycobacterium tuberculosis and the pathogenesis of autoimmune diseases. Th1* cells express a unique set of lineage-specific transcription factors characteristic of both Th1 and Th17 cells and display distinct gene expression profiles compared with other CD4+ T cell subsets. To examine molecules and signaling pathways important for the effector function of Th1* cells, we performed loss-of-function screening of genes selectively enriched in the Th1* subset. The genetic screen yielded candidates whose depletion significantly impaired TCR-induced IFN-γ production. These included genes previously linked to IFN-γ or M. tuberculosis susceptibility and novel candidates, such as ISOC1, encoding a metabolic enzyme of unknown function in mammalian cells. ISOC1-depleted T cells, which produced less IFN-γ and IL-17, displayed defects in oxidative phosphorylation and glycolysis and impairment of pyrimidine metabolic pathway. Supplementation with extracellular pyrimidines rescued both bioenergetics and IFN-γ production in ISOC1-deficient T cells, indicating that pyrimidine metabolism is a key driver of effector functions in CD4+ T cells and Th1* cells. Results provide new insights into the immune-stimulatory function of ISOC1 as well as the particular metabolic requirements of human memory T cells, providing a novel resource for understanding long-term T cell-driven responses.

Transporters at the Interface between Cytosolic and Mitochondrial Amino Acid Metabolism

Mitochondria are central organelles that coordinate a vast array of metabolic and biologic functions important for cellular health. Amino acids are intricately linked to the bioenergetic, biosynthetic, and homeostatic function of the mitochondrion and require specific transporters to facilitate their import, export, and exchange across the inner mitochondrial membrane. Here we review key cellular metabolic outputs of eukaryotic mitochondrial amino acid metabolism and discuss both known and unknown transporters involved. Furthermore, we discuss how utilization of compartmentalized amino acid metabolism functions in disease and physiological contexts. We examine how improved methods to study mitochondrial metabolism, define organelle metabolite composition, and visualize cellular gradients allow for a more comprehensive understanding of how transporters facilitate compartmentalized metabolism.

The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters

Induction of the one-carbon cycle is an early hallmark of mitochondrial dysfunction and cancer metabolism. Vital intermediary steps are localized to mitochondria, but it remains unclear how one-carbon availability connects to mitochondrial function. Here, we show that the one-carbon metabolite and methyl group donor S-adenosylmethionine (SAM) is pivotal for energy metabolism. A gradual decline in mitochondrial SAM (mitoSAM) causes hierarchical defects in fly and mouse, comprising loss of mitoSAM-dependent metabolites and impaired assembly of the oxidative phosphorylation system. Complex I stability and iron-sulfur cluster biosynthesis are directly controlled by mitoSAM levels, while other protein targets are predominantly methylated outside of the organelle before import. The mitoSAM pool follows its cytosolic production, establishing mitochondria as responsive receivers of one-carbon units. Thus, we demonstrate that cellular methylation potential is required for energy metabolism, with direct relevance for pathophysiology, aging, and cancer.

Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction

Endothelial cells play a critical role in the adaptation of tissues to injury. Tissue ischemia induced by infarction leads to profound changes in endothelial cell functions and can induce transition to a mesenchymal state. Here we explore the kinetics and individual cellular responses of endothelial cells after myocardial infarction by using single cell RNA sequencing. This study demonstrates a time dependent switch in endothelial cell proliferation and inflammation associated with transient changes in metabolic gene signatures. Trajectory analysis reveals that the majority of endothelial cells 3 to 7 days after myocardial infarction acquire a transient state, characterized by mesenchymal gene expression, which returns to baseline 14 days after injury. Lineage tracing, using the Cdh5-CreERT2;mT/mG mice followed by single cell RNA sequencing, confirms the transient mesenchymal transition and reveals additional hypoxic and inflammatory signatures of endothelial cells during early and late states after injury. These data suggest that endothelial cells undergo a transient mes-enchymal activation concomitant with a metabolic adaptation within the first days after myocardial infarction but do not acquire a long-term mesenchymal fate. This mesenchymal activation may facilitate endothelial cell migration and clonal expansion to regenerate the vascular network.

Endothelial cells play a critical role in the adaptation of tissues to injury and show a remarkable plasticity. Here the authors show, using single cell sequencing, that endothelial cells acquire a transient mesenchymal state associated with metabolic adaptation after myocardial infarction.

Mapping metabolic oscillations during cell cycle progression

Proliferating cells must synthesize a wide variety of macromolecules while progressing through the cell cycle, but the coordination between cell cycle progression and cellular metabolism is still poorly understood. To identify metabolic processes that oscillate over the cell cycle, we performed comprehensive, non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics of HeLa cells isolated in the G₁ and SG₂M cell cycle phases, capturing thousands of diverse metabolite ions. When accounting for increased total metabolite abundance due to cell growth throughout the cell cycle, 18% of the observed LC-HRMS peaks were at least twofold different between the stages, consistent with broad metabolic remodeling throughout the cell cycle. While most amino acids, phospholipids, and total ribonucleotides were constant across cell cycle phases, consistent with the view that total macromolecule synthesis does not vary across the cell cycle, certain metabolites were oscillating. For example, ribonucleotides were highly phosphorylated in SG₂M, indicating an increase in energy charge, and several phosphatidylinositols were more abundant in G₁, possibly indicating altered membrane lipid signaling. Within carbohydrate metabolism, pentose phosphates and methylglyoxal metabolites were associated with the cycle. Interestingly, hundreds of yet uncharacterized metabolites similarly oscillated between cell cycle phases, suggesting previously unknown metabolic activities that may be synchronized with cell cycle progression, providing an important resource for future studies.

Yeast homologs of human MCUR1 regulate mitochondrial proline metabolism

Mitochondria house evolutionarily conserved pathways of carbon and nitrogen metabolism that drive cellular energy production. Mitochondrial bioenergetics is regulated by calcium uptake through the mitochondrial calcium uniporter (MCU), a multi-protein complex whose assembly in the inner mitochondrial membrane is facilitated by the scaffold factor MCUR1. Intriguingly, many fungi that lack MCU contain MCUR1 homologs, suggesting alternate functions. Herein, we characterize Saccharomyces cerevisiae homologs Put6 and Put7 of MCUR1 as regulators of mitochondrial proline metabolism. Put6 and Put7 are tethered to the inner mitochondrial membrane in a large hetero-oligomeric complex, whose abundance is regulated by proline. Loss of this complex perturbs mitochondrial proline homeostasis and cellular redox balance. Yeast cells lacking either Put6 or Put7 exhibit a pronounced defect in proline utilization, which can be corrected by the heterologous expression of human MCUR1. Our work uncovers an unexpected role of MCUR1 homologs in mitochondrial proline metabolism.

Legionnaires’ Disease: State of the Art Knowledge of Pathogenesis Mechanisms of Legionella

Legionella species are environmental gram-negative bacteria able to cause a severe form of pneumonia in humans known as Legionnaires’ disease. Since the identification of Legionella pneumophila in 1977, four decades of research on Legionella biology and Legionnaires’ disease have brought important insights into the biology of the bacteria and the molecular mechanisms that these intracellular pathogens use to cause disease in humans. Nowadays, Legionella species constitute a remarkable model of bacterial adaptation, with a genus genome shaped by their close coevolution with amoebae and an ability to exploit many hosts and signaling pathways through the secretion of a myriad of effector proteins, many of which have a eukaryotic origin. This review aims to discuss current knowledge of Legionella infection mechanisms and future research directions to be taken that might answer the many remaining open questions. This research will without a doubt be a terrific scientific journey worth taking.

Large-Scale Profiling of Cellular Metabolic Activities Using Deep 13C Labeling Medium

The recently developed deep labeling method allows for large-scale profiling of metabolic activities in human cells or tissues using isotope tracing with a highly ¹³C enriched culture medium in combination with liquid chromatography-high resolution mass spectrometry. This method generates mass spectrometry data sets where endogenous cellular products can be identified, and active pathways can be determined from observed ¹³C mass isotopomers of the various metabolites measured. Here we describe in detail the experimental procedures for deep labeling experiments in cultured mammalian cells, including synthesis of the deep labeling medium, experimental considerations for cell culture, metabolite extractions and sample preparation, and liquid chromatography-mass spectrometry. We also outline a workflow for the downstream data analysis using publicly available software.

Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability

Cancer cell metabolism is heavily influenced by microenvironmental factors, including nutrient availability. Therefore, knowledge of microenvironmental nutrient levels is essential to understand tumor metabolism. To measure the extracellular nutrient levels available to tumors, we utilized quantitative metabolomics methods to measure the absolute concentrations of >118 metabolites in plasma and tumor interstitial fluid, the extracellular fluid that perfuses tumors. Comparison of nutrient levels in tumor interstitial fluid and plasma revealed that the nutrients available to tumors differ from those present in circulation. Further, by comparing interstitial fluid nutrient levels between autochthonous and transplant models of murine pancreatic and lung adenocarcinoma, we found that tumor type, anatomical location and animal diet affect local nutrient availability. These data provide a comprehensive characterization of the nutrients present in the tumor microenvironment of widely used models of lung and pancreatic cancer and identify factors that influence metabolite levels in tumors.

In the body, cancer cells can rely on different nutrients than normal cells, and they can use these nutrients in a different way. What cancer cells consume also depends on what is available in their immediate environment. In a tumor, cells grab nutrients from the ‘interstitial’ fluid that surrounds them, but what is present in this liquid may vary within tumors arising in different locations. Understanding what nutrients are ‘on the menu’ in specific tumors would help to target diseased cells while sparing healthy ones, but this knowledge has been difficult to obtain.

To investigate this, Sullivan et al. used a technique called mass spectrometry to measure the amounts of 120 nutrients present in the interstitial fluid of mouse pancreas and lung tumors. Different levels of nutrients were found in the two types of tumors, and analyses showed that what was present in the interstitial fluid depended on the type of cancer cells, where the tumor was located, and what the animals ate. This suggests that cancer cells may have different needs because they are limited in what they have access to.

It remains to be seen whether the nutrients levels found in mouse tumors are the same as those in humans. Armed with this knowledge, it may then be possible to feed cancer cells grown in the laboratory with the nutrient menu that they would have access to in the body. This could help identify new cancer treatments.

Current Status and Future Prospects of Clinically Exploiting Cancer-specific Metabolism-Why Is Tumor Metabolism Not More Extensively Translated into Clinical Targets and Biomarkers?

Tumor cells exhibit a specialized metabolism supporting their superior ability for rapid proliferation, migration, and apoptotic evasion. It is reasonable to assume that the specific metabolic needs of the tumor cells can offer an array of therapeutic windows as pharmacological disturbance may derail the biochemical mechanisms necessary for maintaining the tumor characteristics, while being less important for normally proliferating cells. In addition, the specialized metabolism may leave a unique metabolic signature which could be used clinically for diagnostic or prognostic purposes. Quantitative global metabolic profiling (metabolomics) has evolved over the last two decades. However, despite the technology’s present ability to measure 1000s of endogenous metabolites in various clinical or biological specimens, there are essentially no examples of metabolomics investigations being translated into actual utility in the cancer clinic. This review investigates the current efforts of using metabolomics as a tool for translation of tumor metabolism into the clinic and further seeks to outline paths for increasing the momentum of using tumor metabolism as a biomarker and drug target opportunity.

Mapping Metabolic Events in the Cancer Cell Cycle Reveals Arginine Catabolism in the Committed SG2M Phase

Alterations in cell-cycle regulation and cellular metabolism are associated with cancer transformation, and enzymes active in the committed cell-cycle phase may represent vulnerabilities of cancer cells. Here, we map metabolic events in the G1 and SG2M phases by combining cell sorting with mass spectrometry-based isotope tracing, revealing hundreds of cell-cycle-associated metabolites. In particular, arginine uptake and ornithine synthesis are active during SG2M in transformed but not in normal cells, with the mitochondrial arginase 2 (ARG2) enzyme as a potential mechanism. While cancer cells exclusively use ARG2, normal epithelial cells synthesize ornithine via ornithine aminotransferase (OAT). Knockdown of ARG2 markedly reduces cancer cell growth and causes G2M arrest, while not inducing compensation via OAT. In human tumors, ARG2 is highly expressed in specific tumor types, including basal-like breast tumors. This study sheds light on the interplay between metabolism and cell cycle and identifies ARG2 as a potential metabolic target.

Application of Stable Isotope Labels for Metabolomics in Studies in Fatty Liver Disease

The progression of nonalcoholic fatty liver disease (NAFLD) increases the risks of cirrhosis and cardiovascular disease. Marked alteration of both cytosolic and mitochondrial metabolism, and in combination with insulin resistance, increases hepatic glucose production. Utilization of stable isotope tracers to study liver metabolism offers deep insight into rearrangements of metabolic pathways and substrate-product relationships under the conditions leading to fatty liver and induced by diseases, drugs, toxins, or genetic manipulations. Isotope tracing untargeted metabolomics (ITUM) recently emerged as a powerful platform in which the label can be tracked in an untargeted fashion, revealing the penetration of substrates into metabolic pathways, even at low abundance. Here, we describe a protocol that can be utilized to study the changes in utilization of any labeled substrate toward a wide range of metabolites either in isolated liver cells or whole liver tissue under conditions mimicking various stages of fatty liver disease. Furthermore, a routine protocol for extraction, separation, and mass spectrometric detection of isotopically labeled metabolites in an untargeted or targeted fashion. An informatic approach to analyze stable isotope untargeted metabolomic datasets is also described.

Gabapentin Can Suppress Cell Proliferation Independent of the Cytosolic Branched-Chain Amino Acid Transferase 1 (BCAT1)

The metabolism of branched-chain amino acids (BCAA) has recently been implicated in the growth of several cancer cell types. Gabapentin, a synthetic amino acid, is commonly used in high concentrations in this context to inhibit the cytosolic branched-chain amino acid transferase (BCAT1) enzyme. Here, we report that 10 mM gabapentin reduces the growth of HCT116 cells, which have an active branched-chain amino acid transferase but express very low levels of BCAT1, and presumably rely on the mitochondrial BCAT2 enzyme. Gabapentin did not affect transamination of BCAA to branched-chain keto acids (BCKA) in HCT116 cells, nor the reverse formation of BCAA from BCKA, indicating that the branched-chain amino acid transaminase is not inhibited. Moreover, the growth-inhibitory effect of gabapentin could not be rescued by supplementation with BCKA, and this was not due to the lack of uptake of BCKA, indicating that other effects of gabapentin are important. An untargeted LC-MS analysis of gabapentin-treated cells revealed a marked depletion of branched-chain carnitines. These results demonstrate that gabapentin at high concentrations can inhibit cell proliferation without affecting BCAT1 and may affect mitochondrial BCKA catabolism.