MetaboLabPy-An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.

Metabolic Characterization of Deceased Donor Kidneys Undergoing Hypothermic Machine Perfusion Before Transplantation Using 13C-enriched Glucose

Background

The provision of a metabolic substrate is one mechanism by which hypothermic machine perfusion (HMP) of kidneys provides clinical benefit. This study aimed to describe de novo metabolism in ex vivo human kidneys undergoing HMP before transplantation using 13C-labeled glucose as a metabolic tracer.

Methods

Cadaveric human kidneys were perfused with modified clinical-grade perfusion fluid (kidney perfusion solution [KPS-1], Organ Recovery Systems), in which glucose was uniformly enriched with the stable isotope 13C ([U-13C] glucose). The sampled perfusion fluid was analyzed using a blood gas analyzer, and metabolic profiling was performed using 1-dimensional and 2-dimensional nuclear magnetic resonance spectroscopy and mass spectrometry. Functional outcome measures included serum creatinine levels and the development of delayed graft function.

Results

Fourteen kidneys were perfused with the modified KPS-1 and successfully transplanted. The mean duration of HMP was 8.7 h. There was a sustained increase in the conversion of glucose into de novo glycolytic end products, such as lactate, in donor kidneys during HMP. There was no significant association between functional outcomes and metabolism during the HMP. De novo anaerobic metabolism was indicated by continuing lactate production, as indicated by increasing concentrations of universally 13C-labeled lactate ([U-13C] lactate) in perfusion fluid from all kidneys. This was more evident in donation after circulatory death donor kidneys.

Conclusions

Our study is the first to use [U-13C] glucose to describe the metabolism during HMP. The consequences of an initial warm ischemic insult on circulatory death in donor kidneys continue during the preservation period.

Mitochondrial Metabolism in the Spotlight: Maintaining Balanced RNAP III Activity Ensures Cellular Homeostasis

RNA polymerase III (RNAP III) holoenzyme activity and the processing of its products have been linked to several metabolic dysfunctions in lower and higher eukaryotes. Alterations in the activity of RNAP III-driven synthesis of non-coding RNA cause extensive changes in glucose metabolism. Increased RNAP III activity in the S. cerevisiae maf1Δ strain is lethal when grown on a non-fermentable carbon source. This lethal phenotype is suppressed by reducing tRNA synthesis. Neither the cause of the lack of growth nor the underlying molecular mechanism have been deciphered, and this area has been awaiting scientific explanation for a decade. Our previous proteomics data suggested mitochondrial dysfunction in the strain. Using model mutant strains maf1Δ (with increased tRNA abundance) and rpc128-1007 (with reduced tRNA abundance), we collected data showing major changes in the TCA cycle metabolism of the mutants that explain the phenotypic observations. Based on 13C flux data and analysis of TCA enzyme activities, the present study identifies the flux constraints in the mitochondrial metabolic network. The lack of growth is associated with a decrease in TCA cycle activity and downregulation of the flux towards glutamate, aspartate and phosphoenolpyruvate (PEP), the metabolic intermediate feeding the gluconeogenic pathway. rpc128-1007, the strain that is unable to increase tRNA synthesis due to a mutation in the C128 subunit, has increased TCA cycle activity under non-fermentable conditions. To summarize, cells with non-optimal activity of RNAP III undergo substantial adaptation to a new metabolic state, which makes them vulnerable under specific growth conditions. Our results strongly suggest that balanced, non-coding RNA synthesis that is coupled to glucose signaling is a fundamental requirement to sustain a cell's intracellular homeostasis and flexibility under changing growth conditions. The presented results provide insight into the possible role of RNAP III in the mitochondrial metabolism of other cell types.

Automated analysis for multiplet identification from ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra

Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- ¹H, ¹³C-HSQC NMR spectra arising from ¹³C - ¹³C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- ¹H, ¹³C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- ¹H, ¹³C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Automated analysis for multiplet identification from ultra-high resolution 2D-1H,13C-HSQC NMR spectra

Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D-1H,13C-HSQC NMR spectra arising from 13C -13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D-1H,13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D-1H,13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

NMR-based isotope editing, chemoselection and isotopomer distribution analysis in stable isotope resolved metabolomics

NMR is a very powerful tool for identifying and quantifying compounds within complex mixtures without the need for individual standards or chromatographic separation. Stable Isotope Resolved Metabolomics (or SIRM) is an approach to following the fate of individual atoms from precursors through metabolic transformation, producing an atom-resolved metabolic fate map. However, extracts of cells or tissue give rise to very complex NMR spectra. While multidimensional NMR experiments may partially overcome the spectral overlap problem, additional tools may be needed to determine site-specific isotopomer distributions. NMR is especially powerful by virtue of its isotope editing capabilities using NMR active nuclei such as 13C, 15N, 19F and 31P to select molecules containing just these atoms in a complex mixture, and provide direct information about which atoms are present in identified compounds and their relative abundances. The isotope-editing capability of NMR can also be employed to select for those compounds that have been selectively derivatized with an NMR-active stable isotope at particular functional groups, leading to considerable spectral simplification. Here we review isotope analysis by NMR, and methods of chemoselection both for spectral simplification, and for enhanced isotopomer analysis.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Optimised collection of non-uniformly sampled 2D-HSQC NMR spectra for use in metabolic flux analysis

Nuclear magnetic resonance (NMR) spectroscopy is integral to metabolic studies; yet, it can suffer from the long acquisition times required to collect data of sufficient signal strength and resolution. The use of non-uniform sampling (NUS) allows faster collection of NMR spectra without loss of spectral integrity. When planning experimental methodologies to perform metabolic flux analysis (MFA) of cell metabolism, a variety of options are available for the acquisition of NUS NMR data. Before beginning data collection, decisions have to be made regarding selection of pulse sequence, number of transients and NUS specific parameters such as the sampling level and sampling schedule. Poor choices will impact data quality, which may have a negative effect on the subsequent analysis and biological interpretation. Herein, we describe factors that should be considered when setting up non-uniformly sampled 2D-¹ H,¹³ C HSQC NMR experiments for MFA and provide a standard protocol for users to follow.

In vivo [U-13C]glucose labeling to assess heart metabolism in murine models of pressure and volume overload

Alterations in the metabolism of substrates such as glucose are integrally linked to the structural and functional changes that occur in the remodeling heart. Assessment of such metabolic changes under in vivo conditions would provide important insights into this interrelationship. We aimed to investigate glucose carbon metabolism in pressure-overload and volume-overload cardiac hypertrophy by using an in vivo [U-¹³C]glucose labeling strategy to enable analyses of the metabolic fates of glucose carbons in the mouse heart. Therefore, [U-¹³C]glucose was administered in anesthetized mice by tail vein infusion, and the optimal duration of infusion was established. Hearts were then excised for ¹³C metabolite isotopomer analysis by NMR spectroscopy. [U-¹³C]glucose infusions were performed in mice 2 wk following transverse aortic constriction (TAC) or aortocaval fistula (Shunt) surgery. At this time point, there were similar increases in left ventricular (LV) mass in both groups, but TAC resulted in concentric hypertrophy with impaired LV function, whereas Shunt caused eccentric hypertrophy with preserved LV function. TAC was accompanied by significant changes in glycolysis, mitochondrial oxidative metabolism, glucose metabolism to anaplerotic substrates, and de novo glutamine synthesis. In contrast to TAC, hardly any metabolic changes could be observed in the Shunt group. Taken together, in vivo [U-¹³C]glucose labeling is a valuable method to investigate the fate of nutrients such as glucose in the remodeling heart. We find that concentric and eccentric cardiac remodeling are accompanied by distinct differences in glucose carbon metabolism.

NEW & NOTEWORTHY This study implemented a method for assessing the fate of glucose carbons in the heart in vivo and used this to demonstrate that pressure and volume overload are associated with distinct changes. In contrast to volume overload, pressure overload-induced changes affect the tricarboxylic acid cycle, glycolytic pathways, and glutamine synthesis. A better understanding of cardiac glucose metabolism under pathological conditions in vivo may provide new therapeutic strategies specific for different types of hemodynamic overload.

Listen to this article’s corresponding podcast at: https://ajpheart.podbean.com/e/u-13c-glucose-and-in-vivo-heart-metabolism/.

Introduction of a new method for two-dimensional NMR quantitative analysis in metabolomics studies

NMR is one of the most important platforms for metabolomic studies. Though 2D NMR has been applied in metabolomics, most applications have mainly focused on metabolite identification whilst limitations causing a bottle-neck for applying high-throughput 2D NMR data for quantity related statistical analysis lies on the data interpretation methods. In this study, instead of using the traditional methods of calculating the 2D NMR data to search for the important features, a new procedure, which applies the high-resolution 1D NMR metabolites chemical shift range to filter the 2D NMR data, was developed. This new method was demonstrated using both a mixture of standard metabolites and a case study on plant extracts using 2D non-uniform sampling (NUS) total correlation spectroscopy (TOCSY) data. As a result, our method successfully filtered out the important features with a high success rate, and the extracted peaks showed high linearity between the calculated intensities and the concentrations of metabolites from a range of 0.05 mM-2 mM. The method was successfully applied to a metabolomics case study which included 18 Begonia samples that showed excellent peak extractions. In summary, our study has provided a practical new 2D NMR data extraction method for use in future metabolomics studies.

Metabolic tracing reveals novel adaptations to skeletal muscle cell energy production pathways in response to NAD + depletion

Background: Skeletal muscle is central to whole body metabolic homeostasis, with age and disease impairing its ability to function appropriately to maintain health. Inadequate NAD + availability is proposed to contribute to pathophysiology by impairing metabolic energy pathway use. Despite the importance of NAD + as a vital redox cofactor in energy production pathways being well-established, the wider impact of disrupted NAD + homeostasis on these pathways is unknown. Methods: We utilised skeletal muscle myotube models to induce NAD + depletion, repletion and excess and conducted metabolic tracing to provide comprehensive and detailed analysis of the consequences of altered NAD + metabolism on central carbon metabolic pathways. We used stable isotope tracers, [1,2-13C] D-glucose and [U- 13C] glutamine, and conducted combined 2D-1H,13C-heteronuclear single quantum coherence (HSQC) NMR spectroscopy and GC-MS analysis. Results: NAD + excess driven by nicotinamide riboside (NR) supplementation within skeletal muscle cells resulted in enhanced nicotinamide clearance, but had no effect on energy homeostasis or central carbon metabolism. Nicotinamide phosphoribosyltransferase (NAMPT) inhibition induced NAD + depletion and resulted in equilibration of metabolites upstream of glyceraldehyde phosphate dehydrogenase (GAPDH). Aspartate production through glycolysis and TCA cycle activity was increased in response to low NAD +, which was rapidly reversed with repletion of the NAD + pool using NR. NAD + depletion reversibly inhibits cytosolic GAPDH activity, but retains mitochondrial oxidative metabolism, suggesting differential effects of this treatment on sub-cellular pyridine pools. When supplemented, NR efficiently reversed these metabolic consequences. However, the functional relevance of increased aspartate levels after NAD + depletion remains unclear, and requires further investigation. Conclusions: These data highlight the need to consider carbon metabolism and clearance pathways when investigating NAD + precursor usage in models of skeletal muscle physiology.

The Effects of Oxygenation on Ex Vivo Kidneys Undergoing Hypothermic Machine Perfusion

BACKGROUND

Supplemental oxygenation of the standard hypothermic machine perfusion (HMP) circuit has the potential to invoke favorable changes in metabolism, optimizing cadaveric organs before transplantation.

METHODS

Eight pairs of porcine kidneys underwent 18 hours of either oxygenated (HMP/O2) or aerated (HMP/Air) HMP in a paired donation after circulatory death model of transplantation. Circulating perfusion fluid was supplemented with the metabolic tracer universally labeled glucose.Perfusate, end-point renal cortex, and medulla samples underwent metabolomic analysis using 1-dimension and 2-dimension nuclear magnetic resonance experiments in addition to gas chromatography-mass spectrometry. Analysis of C-labeled metabolic products was combined with adenosine nucleotide levels and differences in tissue architecture.

RESULTS

Metabolomic analysis revealed significantly higher concentrations of universally labeled lactate in the cortex of HMP/Air versus HMP/O2 kidneys (0.056 mM vs 0.026 mM, P < 0.05). Conversely, newly synthesized [4,5-C] glutamate concentrations were higher in the cortex of HMP/O2 kidneys inferring relative increases in tricarboxylic acid cycle activity versus HMP/Air kidneys (0.013 mmol/L vs 0.003 mmol/L, P < 0.05). This was associated with greater amounts of adenoside triphosphate in the cortex HMP/O2 versus HMP/Air kidneys (19.8 mmol/mg protein vs 2.8 mmol/mg protein, P < 0.05). Improved flow dynamics and favorable ultrastructural features were also observed in HMP/O2 kidneys. There were no differences in thiobarbituric acid reactive substances and reduced glutathione levels, tissue markers of oxidative stress, between groups.

CONCLUSIONS

The supplementation of perfusion fluid with high-concentration oxygen (95%) results in a greater degree of aerobic metabolism versus aeration (21%) in the nonphysiological environment of HMP, with reciprocal changes in adenoside triphosphate levels.

Quantitative Isotopomer Rates in Real-Time Metabolism of Cells Determined by NMR Methods

Tracer-based metabolism is becoming increasingly important for studying metabolic mechanisms in cells. NMR spectroscopy offers several approaches to measure label incorporation in metabolites, including ¹³ C- and ¹ H-detected spectra. The latter are generally more sensitive, but quantification depends on the proton-carbon ¹ J_CH coupling constant, which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites, and quantification of ¹ H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Herein, we compare ¹³ C-filtered and ¹³ C-edited methods for quantification and show the applicability of the methods for real-time NMR spectroscopy of cancer-cell metabolism, in which label incorporations are subject to constant flux. We find an approach using a double filter to be most suitable and sufficiently robust to reliably obtain ¹³ C incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24 h. The proposed method is equally well suited for calculating the level of label incorporation in labelled cell extracts in the context of metabolic flux analysis.

An Improved Method for Measuring Absolute Metabolite Concentrations in Small Biofluid or Tissue Samples

The measurement of absolute metabolite concentrations in small samples remains a significant analytical challenge. This is particularly the case when the sample volume is only a few microliters or less and cannot be determined accurately via direct measurement. We previously developed volume determination with two standards (VDTS) as a method to address this challenge for biofluids. As a proof-of-principle, we applied VDTS to NMR spectra of polar metabolites in the hemolymph (blood) of the tiny yet powerful genetic model Drosophila melanogaster. This showed that VDTS calculation of absolute metabolite concentrations in fed versus starved Drosophila larvae is more accurate than methods utilizing normalization to total spectral signal. Here, we introduce paired VDTS (pVDTS), an improved VDTS method for biofluids and solid tissues that implements the statistical power of paired control and experimental replicates. pVDTS utilizes new equations that also include a correction for dilution errors introduced by the variable surface wetness of solid samples. We then show that metabolite concentrations in Drosophila larvae are more precisely determined and logically consistent using pVDTS than using the original VDTS method. The refined pVDTS workflow described in this study is applicable to a wide range of different tissues and biofluids.

A framework for tracer-based metabolism in mammalian cells by NMR

Metabolism changes extensively during the normal proliferation and differentiation of mammalian cells, and in cancer and inflammatory diseases. Since changes in the metabolic network reflect interactions between genetic, epigenetic and environmental changes, it is helpful to study the flow of label from isotopically labelled precursors into other metabolites rather than static metabolite levels. For this Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive technique as it can quantify site-specific label incorporation. However, for applications using human cells and cell lines, the challenge is to optimize the process to maximize sensitivity and reproducibility. Here we present a new framework to analyze metabolism in mammalian cell lines and primary cells, covering the workflow from the preparation of cells to the acquisition and analysis of NMR spectra. We have applied this new approach in hematological and liver cancer cell lines and confirm the feasibility of tracer-based metabolism in primary liver cells.

Metabolic tracing reveals novel adaptations to skeletal muscle cell energy production pathways in response to NAD + depletion

Background: Skeletal muscle is central to whole body metabolic homeostasis, with age and disease impairing its ability to function appropriately to maintain health. Inadequate NAD + availability is proposed to contribute to pathophysiology by impairing metabolic energy pathway use. Despite the importance of NAD + as a vital redox cofactor in energy production pathways being well-established, the wider impact of disrupted NAD + homeostasis on these pathways is unknown. Methods: We utilised skeletal muscle myotube models to induce NAD + depletion, repletion and excess and conducted metabolic tracing to provide comprehensive and detailed analysis of the consequences of altered NAD + metabolism on central carbon metabolic pathways. We used stable isotope tracers, [1,2-13C] D-glucose and [U- 13C] glutamine, and conducted combined 2D-1H,13C-heteronuclear single quantum coherence (HSQC) NMR spectroscopy and GC-MS analysis. Results: NAD + excess driven by nicotinamide riboside (NR) supplementation within skeletal muscle cells results in enhanced nicotinamide clearance, but had no effect on energy homeostasis or central carbon metabolism. Nicotinamide phosphoribosyltransferase (NAMPT) inhibition induced NAD + depletion and resulted in equilibration of metabolites upstream of glyceraldehyde phosphate dehydrogenase (GAPDH). Aspartate production through glycolysis and TCA cycle activity is increased in response to low NAD +, which is rapidly reversed with repletion of the NAD + pool using NR. NAD + depletion reversibly inhibits cytosolic GAPDH activity, but retains mitochondrial oxidative metabolism, suggesting differential effects of this treatment on sub-cellular pyridine pools. When supplemented, NR efficiently reverses these metabolic consequences. However, the functional relevance of increased aspartate levels after NAD + depletion remains unclear, and requires further investigation. Conclusions: These data highlight the need to consider carbon metabolism and clearance pathways when investigating NAD + precursor usage in models of skeletal muscle physiology.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) offers high resolution data, yet is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting due to J-coupling in 1H,13C-HSQC NMR spectra, which allow the rapid collection of NMR data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways in vivo. We show how these novel techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ, and also show how the use of multiple nutrients, differentially labelled with 13C and 15N, can be used to provide additional information with which to profile metabolic pathways.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) spectroscopy offers high resolution data in terms of resolving metabolic pathway utilisation. Despite this, NMR spectroscopy is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting of  ¹³C signals due to homonuclear  ¹³C, ¹³C or heteronuclear  ¹³C, ¹⁵N J-coupling in  ¹H, ¹³C-HSQC NMR spectra. Together, these allow the rapid collection of NMR spectroscopy data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways  in vivo. We show how these techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ. In addition, we show how the use of multiple nutrients, differentially labelled with  ¹³C and  ¹⁵N, can be used to provide additional information with which to profile metabolic pathways.