SummaryThe citric acid cycle (CAC) metabolite fumarate has been proposed to be cardioprotective; however, its mechanisms of action remain to be determined. To augment cardiac fumarate levels and to assess fumarate's cardioprotective properties, we generated fumarate hydratase (Fh1) cardiac knockout (KO) mice. These fumarate-replete hearts were robustly protected from ischemia-reperfusion injury (I/R). To compensate for the loss of Fh1 activity, KO hearts maintain ATP levels in part by channeling amino acids into the CAC. In addition, by stabilizing the transcriptional regulator Nrf2, Fh1 KO hearts upregulate protective antioxidant response element genes. Supporting the importance of the latter mechanism, clinically relevant doses of dimethylfumarate upregulated Nrf2 and its target genes, hence protecting control hearts, but failed to similarly protect Nrf2-KO hearts in an in vivo model of myocardial infarction. We propose that clinically established fumarate derivatives activate the Nrf2 pathway and are readily testable cytoprotective agents.

This study employs spectroscopy-based metabolic profiling of fecal extracts from healthy subjects and patients with active or inactive ulcerative colitis (UC) and Crohn's disease (CD) to substantiate the potential use of spectroscopy as a non-invasive diagnostic tool and to characterize the fecal metabolome in inflammatory bowel disease (IBD). Stool samples from 113 individuals (UC 48, CD 44, controls 21) were analyzed by 1 H nuclear magnetic resonance (NMR) spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with principal component analysis and orthogonal-projection to latent structure-discriminant analysis using SIMCA-P ? 12 and MATLAB. Significant differences were found in the metabolic profiles making it possible to differentiate between active IBD and controls and between UC and CD. The metabolites holding differential power primarily belonged to a range of amino acids, microbiota-related short chain fatty acids, and lactate suggestive of an inflammation-driven malabsorption and dysbiosis of the normal bacterial ecology. However, removal of patients with intestinal surgery and anti-TNF-a antibody treatment eliminated the discriminative power regarding UC versus CD. This study consequently demonstrates that 1 H NMR spectroscopy of fecal extracts is a potential non-invasive diagnostic tool and able to characterize the inflammationdriven changes in the metabolic profiles related to malabsorption and dysbiosis. Intestinal surgery and medication are to be accounted for in future studies, as it seems to be factors of importance in the discriminative process.

The tricarboxylic acid (TCA) cycle is a central metabolic pathway responsible for supplying reducing potential for oxidative phosphorylation and anabolic substrates for cell growth, repair and proliferation. As such it thought to be essential for cell proliferation and tissue homeostasis. However, since the initial report of an inactivating mutation in the TCA cycle enzyme complex, succinate dehydrogenase (SDH) in paraganglioma (PGL), it has become clear that some cells and tissues are not only able to survive with a truncated TCA cycle, but that they are also able of supporting proliferative phenotype observed in tumours. Here, we show that loss of SDH activity leads to changes in the metabolism of non-essential amino acids. In particular, we demonstrate that pyruvate carboxylase is essential to re-supply the depleted pool of aspartate in SDH-deficient cells. Our results demonstrate that the loss of SDH reduces the metabolic plasticity of cells, suggesting vulnerabilities that can be targeted therapeutically.

Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1 H, projections of 2D 1 H, 1 H J-resolved (pJRES), and intact 2D J-resolved (JRES).

One-dimensional (1D) (1)H NMR spectroscopy remains a leading analytical technology in metabolomics. Advantages of this approach include relatively rapid spectral acquisition and NMR resonances that provide a direct measure of metabolite concentration based upon a single internal standard. Severe spectral congestion can, however, significantly hinder both metabolite identification and quantification. Two-dimensional (1)H J-resolved (JRES) NMR spectroscopy retains many of the benefits of 1D NMR, but additionally disperses the overlapping resonances into a second dimension, reducing congestion and increasing metabolite specificity. The usefulness of this approach to metabolomics was first realised six years ago, and since then it has been used in biological, medical and environmental studies of plants and animals. Here we provide a basic introduction to the 2D JRES NMR experiment and then discuss strategies for spectral acquisition and processing in the context of metabolomics applications, concluding with some key recommendations: acquisition using a double spin-echo sequence with excitation sculpting; processing using the SEM window function, tilting and symmetricising, optionally followed by a skyline projection. Strategies for implementing JRES spectroscopy into the metabolomics toolbox are then considered, including its roles in metabolic fingerprinting, metabolite identification and metabolite quantification. Public resources and data standards for JRES metabolomics are reviewed. We conclude by evaluating the advantages (e.g. increased spectral dispersion and confidence in metabolite identification; fully automated processing; reduced batch-to-batch variation) and disadvantages (e.g. longer acquisition times; higher technical variability; phase-twisted lineshapes resulting in quantification errors) of 2D JRES NMR vs the established 1D approach for metabolomics.