Metabolic reprogramming of cancer cells provides energy and multiple intermediates critical for cell growth. Hypoxia in tumors represents a hostile environment that can encourage these transformations. We report that glycogen metabolism is upregulated in tumors in vivo and in cancer cells in vitro in response to hypoxia. In vitro, hypoxia induced an early accumulation of glycogen, followed by a gradual decline. Concordantly, glycogen synthase (GYS1) showed a rapid induction, followed by a later increase of glycogen phosphorylase (PYGL). PYGL depletion and the consequent glycogen accumulation led to increased reactive oxygen species (ROS) levels that contributed to a p53-dependent induction of senescence and markedly impaired tumorigenesis in vivo. Metabolic analyses indicated that glycogen degradation by PYGL is important for the optimal function of the pentose phosphate pathway. Thus, glycogen metabolism is a key pathway induced by hypoxia, necessary for optimal glucose utilization, which represents a targetable mechanism of metabolic adaptation.

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.

Oral cancer is the eighth most common cancer worldwide and represents a significant disease burden. If detected at an early stage, survival from oral cancer is better than 90% at 5 years, whereas late stage disease survival is only 30%. Therefore, there is an obvious clinical utility for novel metabolic markers that help to diagnose oral cancer at an early stage and to monitor treatment response. In the current study, blood samples of oral cancer patients were analyzed using nuclear magnetic resonance spectroscopy to derive a metabolic signature for oral cancer. Using multivariate chemometric analysis, we obtained an excellent discrimination between serum samples from cancer patients and from a control group and could also discriminate between different stages of disease. The metabolic profile obtained for oral cancer is significant, even for early stage disease and relatively small tumors. This suggests a systemic metabolic response to cancer, which bears great potential for early diagnosis.

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).

BackgroundDespite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR data sets for statistical analysis.ResultsHere we present MetaboLab, a MATLAB based software package that facilitates NMR data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (1H,13C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments.ConclusionsThe MetaboLab software is an integrated software package for NMR data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.

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Public databases of NMR spectra of low molecular weight metabolites must be constructed to remove the major bottleneck of metabolite identification and quantification in the analysis of metabolomics data. Two-dimensional (2-D) 1 H J-resolved spectroscopy represents a popular alternative to 1-D NMR methods, resolving the highly overlapped signals characteristic of complex metabolite mixtures across two frequency dimensions. Here we report the design, measurement and curation of, primarily, a database of 2-D J-resolved NMR spectra.Metabolites were selected based upon their importance within metabolic pathways and their detection potential by NMR, and prepared for analysis at pH 6.6, 7.0 and 7.4. Sixteen NMR spectra were recorded for each metabolite using a 500 MHz spectrometer, including 1-D and 2-D J-resolved spectra, different water suppression methods and different acquisition parameters. Some metabolites were removed due to limited solubility, poor NMR signal quality or contamination, and the final dataset comprised of 3328 NMR spectra arising from 208 metabolite standards. These data are housed in a purpose-built MySQL database (Birmingham Metabolite Library; BML-NMR) containing over 100 separate tables and allowing the efficient storage of raw free-induction-decays (FIDs), 1-D and 2-D NMR spectra and associated metadata. The database is compliant with the Metabolomics Standards Initiative (MSI) endorsed reporting requirements, with some necessary amendments. Library data can be accessed freely and searched through a custom written web interface (www.bml-nmr.org). FIDs, NMR spectra and associated metadata can be downloaded according to a newly implemented MSI-compatible XML schema.