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1.  Metabolic responses induced by DNA damage and poly (ADP-ribose) polymerase (PARP) inhibition in MCF-7 cells 
Genomic instability is one of the hallmarks of cancer. Several chemotherapeutic drugs and radiotherapy induce DNA damage to prevent cancer cell replication. Cells in turn activate different DNA damage response (DDR) pathways to either repair the damage or induce cell death. These DDR pathways also elicit metabolic alterations which can play a significant role in the proper functioning of the cells. The understanding of these metabolic effects resulting from different types of DNA damage and repair mechanisms is currently lacking. In this study, we used NMR metabolomics to identify metabolic pathways which are altered in response to different DNA damaging agents. By comparing the metabolic responses in MCF-7 cells, we identified the activation of poly (ADP-ribose) polymerase (PARP) in methyl methanesulfonate (MMS)-induced DNA damage. PARP activation led to a significant depletion of NAD+. PARP inhibition using veliparib (ABT-888) was able to successfully restore the NAD+ levels in MMS-treated cells. In addition, double strand break induction by MMS and veliparib exhibited similar metabolic responses as zeocin, suggesting an application of metabolomics to classify the types of DNA damage responses. This prediction was validated by studying the metabolic responses elicited by radiation. Our findings indicate that cancer cell metabolic responses depend on the type of DNA damage responses and can also be used to classify the type of DNA damage.
PMCID: PMC4606886  PMID: 26478723
DNA damage; PARP; metabolomics; NMR; MCF-7
2.  Quantification of 1H NMR Spectra from Human Plasma 
Human plasma is a biofluid that is high in information content, making it an excellent candidate for metabolomic studies. 1H NMR has been a popular technique to detect several dozen metabolites in blood plasma. In order for 1H NMR to become an automated, high-throughput method, challenges related to (1) the large signal from lipoproteins and (2) spectral overlap between different metabolites have to be addressed. Here diffusion-weighted 1H NMR is used to separate lipoprotein and metabolite signals based on their large difference in translational diffusion. The metabolite 1H NMR spectrum is then quantified through spectral fitting utilizing full prior knowledge on the metabolite spectral signatures. Extension of the scan time by 3 minutes or 15% per sample allowed the acquisition of a 1H NMR spectrum with high diffusion weighting. The metabolite 1H NMR spectra could reliably be modeled with 28 metabolites. Excellent correlation was found between results obtained with diffusion NMR and ultrafiltration. The combination of minimal sample preparation together with minimal user interaction during processing and quantification provides a metabolomics technique for automated, quantitative 1H NMR of human plasma.
PMCID: PMC4624446  PMID: 26526515
Human plasma; 1H NMR; diffusion; spectral quantification; lipoproteins
Parasitoid wasps inject insect hosts with a cocktail of venoms to manipulate the physiology, development, and immunity of the hosts and to promote development of the parasitoid offspring. The jewel wasp Nasonia vitripennis is a model parasitoid with at least 79 venom proteins. We conducted a high-throughput analysis of Nasonia venom effects on temporal changes of 249 metabolites in pupae of the flesh fly host (Sarcophaga bullata), over a five-day time course. Our results show that venom does not simply arrest the metabolism of the fly host. Rather, it targets specific metabolic processes while keeping hosts alive for at least five days post venom injection by the wasp. We found that venom: (a) Activates the sorbitol biosynthetic pathway while maintaining stable glucose levels, (b) Causes a shift in intermediary metabolism by switching to anaerobic metabolism and blocking the tricarboxylic acid cycle, (c) Arrests chitin biosynthesis that likely reflects developmental arrest of adult fly structures, (d) Elevates the majority of free amino acids, and (e) May be increasing phospholipid degradation. Despite sharing some metabolic effects with cold treatment, diapause, and hypoxia, the venom response is distinct from these conditions. Because Nasonia venom dramatically increases sorbitol levels without changing glucose levels, it could be a useful model for studying the regulation of the sorbitol pathway, which is relevant to diabetes research. Our findings generally support the view that parasitoid venoms are a rich source of bioactive molecules with potential biomedical applications.
PMCID: PMC5113827  PMID: 27867325
Venom; Nasonia; Sorbitol; Anaerobic respiration; Chitin; Amino acids
4.  Circulating sterols as predictors of early allograft dysfunction and clinical outcome in patients undergoing liver transplantation 
Metabolomics  2016;12(12):182.
Sensitive and specific assessment of the hepatic graft metabolism after liver transplantation (LTX) is essential for early detection of postoperative dysfunction implying the need for consecutive therapeutic interventions.
Here, we assessed circulating liver metabolites of the cholesterol pathway, amino acids and acylcarnitines and evaluated their predictive value on early allograft dysfunction (EAD) and clinical outcome in the context of LTX.
The metabolites were quantified in the plasma of 40 liver graft recipients one day pre- and 10 days post-LTX by liquid chromatography/tandem mass spectrometry (LC–MS/MS). Plant sterols as well as cholesterol and its precursors were determined in the free and esterified form; lanosterol in the free form only. Metabolites and esterification ratios were compared to the model for early allograft function scoring (MEAF) which is calculated at day 3 post-LTX from routine parameters defining EAD.
The hepatic esterification ratio of all sterols, but not amino acids and acylcarnitine concentrations, showed substantial metabolic disturbances post-LTX and correlated to the MEAF. In ROC analysis, the low esterification ratio of β-sitosterol and stigmasterol from day 1 and of the other sterols from day 3 were predictive for a high MEAF, i.e. EAD. Additionally, the ratio of esterified β-sitosterol and free lanosterol were predictive for all days and the esterification ratio of the other sterols at day 3 or 4 post-LTX for 3-month mortality.
Low ratios of circulating esterified sterols are associated with a high risk of EAD and impaired clinical outcome in the early postoperative phase following LTX.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1129-z) contains supplementary material, which is available to authorized users.
PMCID: PMC5078158  PMID: 27840599
Liver transplantation; Sterol metabolism; MEAF
5.  Non-targeted metabolomics of Brg1/Brm double-mutant cardiomyocytes reveals a novel role for SWI/SNF complexes in metabolic homeostasis 
Mammalian SWI/SNF chromatin-remodeling complexes utilize either BRG1 or Brm as alternative catalytic subunits to alter the position of nucleosomes and regulate gene expression. Genetic studies have demonstrated that SWI/SNF complexes are required during cardiac development and also protect against cardiovascular disease and cancer. However, Brm constitutive null mutants do not exhibit a cardiomyocyte phenotype and inducible Brg1 conditional mutations in cardiomyocyte do not demonstrate differences until stressed with transverse aortic constriction, where they exhibit a reduction in cardiac hypertrophy. We recently demonstrated the overlapping functions of Brm and Brg1 in vascular endothelial cells and sought here to test if this overlapping function occurred in cardiomyocytes. Brg1/Brm double mutants died within 21 days of severe cardiac dysfunction associated with glycogen accumulation and mitochondrial defects based on histological and ultrastructural analyses. To determine the underlying defects, we performed nontargeted metabolomics analysis of cardiac tissue by GC/MS from a line of Brg1/Brm double-mutant mice, which lack both Brg1 and Brm in cardiomyocytes in an inducible manner, and two groups of controls. Metabolites contributing most significantly to the differences between Brg1/Brm double-mutant and control-group hearts were then determined using the variable importance in projection analysis. Increased cardiac linoleic acid and oleic acid suggest alterations in fatty acid utilization or intake are perturbed in Brg1/Brm double mutants. Conversely, decreased glucose-6-phosphate, fructose-6-phosphate, and myoinositol suggest that glycolysis and glycogen formation are impaired. These novel metabolomics findings provide insight into SWI/SNF-regulated metabolic pathways and will guide mechanistic studies evaluating the role of SWI/SNF complexes in homeostasis and cardiovascular disease prevention.
PMCID: PMC4574504  PMID: 26392817
SWI/SNF complex; BRG1; BRM; Cardiomyocyte; Metabolomics; Fatty acid; Glucose
6.  Production of hyperpolarized 13CO2 from [1-13C]pyruvate in perfused liver does reflect total anaplerosis but is not a reliable biomarker of glucose production 
In liver, 13CO2 can be generated from [1-13C] pyruvate via pyruvate dehydrogenase or anaplerotic entry of pyruvate into the TCA cycle followed by decarboxylation at phosphoenolpyruvate carboxykinase (PEPCK), the malic enzyme, isocitrate dehydrogenase, or α-ketoglutarate dehydrogenase. The purpose of this study was to determine the relative importance of these pathways in production of hyperpolarized (HP) 13CO2 after administration of hyper-polarized pyruvate in livers supplied with a fatty acid plus substrates for gluconeogenesis. Isolated mouse livers were perfused with a mixture of thermally-polarized 13C-enriched pyruvate, lactate and octanoate in various combinations prior to exposure to HP pyruvate. Under all perfusion conditions, HP malate, aspartate and fumarate were detected within ~ 3 s showing that HP [1-13C]pyruvate is rapidly converted to [1-13C]oxaloacetate which can subsequently produce HP 13CO2 via decarboxylation at PEPCK. Measurements using HP [2-13C]pyruvate allowed the exclusion of reactions related to TCA cycle turnover as sources of HP 13CO2. Direct measures of O2 consumption, ketone production, and glucose production by the intact liver combined with 13C isotopomer analyses of tissue extracts yielded a comprehensive profile of metabolic flux in perfused liver. Together, these data show that, even though the majority of HP 13CO2 derived from HP [1-13C]pyruvate in livers exposed to fatty acids reflects decarboxylation of [4-13C]oxaloacetate (PEPCK) or [4-13C]malate (malic enzyme), the intensity of the HP 13CO2 signal is not proportional to glucose production because the amount of pyruvate returned to the TCA cycle via PEPCK and pyruvate kinase is variable, depending upon available substrates.
PMCID: PMC4629494  PMID: 26543443
Hyperpolarization; Liver metabolism; Hyperpolarized 13CO2 production; Gluconeogenesis; Pyruvate cycling
7.  Exposure to ionizing radiation reveals global dose- and time-dependent changes in the urinary metabolome of rat 
The potential for exposures to ionizing radiation has increased in recent years. Although advances have been made, understanding the global metabolic response as a function of both dose and exposure time is challenging considering the complexity of the responses. Herein we report our findings on the dose- and time-dependency of the urinary response to ionizing radiation in the male rat using radiation metabolomics. Urine samples were collected from adult male rats, exposed to 0.5 to 10 Gy γ–radiation, both before from 6 to 72 h following exposures. Samples were analyzed by liquid chromatography coupled with time-of-flight mass spectrometry, and deconvoluted mass chromatographic data were initially analyzed by principal component analysis. However, the breadth and complexity of the data necessitated the development of a novel approach to summarizing biofluid constituents after exposure, called Visual Analysis of Metabolomics Package (VAMP). VAMP revealed clear urine metabolite profile differences to as little as 0.5 Gy after 6 h exposure. Via VAMP, it was discovered that the response to radiation exposure found in rat urine is characterized by an overall net down-regulation of ion excretion with only a modest number of ions excreted in excess over pre-exposure levels. Our results show both similarities and differences with the published mouse urine response and a dose- and time-dependent net decrease in urine ion excretion associated with radiation exposure. These findings mark an important step in the development of minimally invasive radiation biodosimetry. VAMP should have general applicability in metabolomics to visualize overall differences and trends in many sample sets.
PMCID: PMC4635442  PMID: 26557048
Radiation; biodosimetry; bioinformatics
8.  Loss of ncm5 and mcm5 wobble uridine side chains results in an altered metabolic profile 
Metabolomics  2016;12(12):177.
The Elongator complex, comprising six subunits (Elp1p-Elp6p), is required for formation of 5-carbamoylmethyl (ncm5) and 5-methoxycarbonylmethyl (mcm5) side chains on wobble uridines in 11 out of 42 tRNA species in Saccharomyces cerevisiae. Loss of these side chains reduces the efficiency of tRNA decoding during translation, resulting in pleiotropic phenotypes. Overexpression of hypomodified \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text {tRNA}_{{\rm s^{2} {\rm UUU}}}^{{\rm Lys}} , {\rm tRNA}_{{\rm s^{2} {\rm UUG}}}^{{\rm Gln }} \;{\rm and}\;{\rm tRNA}_{{\rm s^{2} {\rm UUC}}}^{{\rm Glu}}} $$\end{document}tRNAs2UUULys,tRNAs2UUGGlnandtRNAs2UUCGlu, which in wild-type strains are modified with mcm5s2U, partially suppress phenotypes of an elp3Δ strain.
To identify metabolic alterations in an elp3Δ strain and elucidate whether these metabolic alterations are suppressed by overexpression of hypomodified \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text {tRNA}_{{\rm s^{2} {\rm UUU}}}^{{\rm Lys}} , {\rm tRNA}_{{\rm s^{2} {\rm UUG}}}^{{\rm Gln }} \;{\rm and}\;{\rm tRNA}_{{\rm s^{2} {\rm UUC}}}^{{\rm Glu}}} $$\end{document}tRNAs2UUULys,tRNAs2UUGGlnandtRNAs2UUCGlu.
Metabolic profiles were obtained using untargeted GC-TOF-MS of a temperature-sensitive elp3Δ strain carrying either an empty low-copy vector, an empty high-copy vector, a low-copy vector harboring the wild-type ELP3 gene, or a high-copy vector overexpressing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text {tRNA}_{{\rm s^{2} {\rm UUU}}}^{{\rm Lys}} , {\rm tRNA}_{{\rm s^{2} {\rm UUG}}}^{{\rm Gln }} \;{\rm and}\;{\rm tRNA}_{{\rm s^{2} {\rm UUC}}}^{{\rm Glu}}} $$\end{document}tRNAs2UUULys,tRNAs2UUGGlnandtRNAs2UUCGlu. The temperature sensitive elp3Δ strain derivatives were cultivated at permissive (30 °C) or semi-permissive (34 °C) growth conditions.
Culturing an elp3Δ strain at 30 or 34 °C resulted in altered metabolism of 36 and 46 %, respectively, of all metabolites detected when compared to an elp3Δ strain carrying the wild-type ELP3 gene. Overexpression of hypomodified \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text {tRNA}_{{\rm s^{2} {\rm UUU}}}^{{\rm Lys}} , {\rm tRNA}_{{\rm s^{2} {\rm UUG}}}^{{\rm Gln }} \;{\rm and}\;{\rm tRNA}_{{\rm s^{2} {\rm UUC}}}^{{\rm Glu}}} $$\end{document}tRNAs2UUULys,tRNAs2UUGGlnandtRNAs2UUCGlu suppressed a subset of the metabolic alterations observed in the elp3Δ strain.
Our results suggest that the presence of ncm5- and mcm5-side chains on wobble uridines in tRNA are important for metabolic homeostasis.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1120-8) contains supplementary material, which is available to authorized users.
PMCID: PMC5037161  PMID: 27738410
Elongator complex; tRNA wobble uridine modifications; Translation; ELP3; Metabolomics; Metabolic profiling
9.  Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction 
Metabolomics  2016;12(11):173.
Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.
Our aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.
Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.
In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package (‘batchCorr’).
The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1124-4) contains supplementary material, which is available to authorized users.
PMCID: PMC5031781  PMID: 27746707
Metabolomics; LC-MS; Data correction; Batch alignment; Drift correction
10.  Modelling the acid/base 1H NMR chemical shift limits of metabolites in human urine 
Metabolomics  2016;12(10):152.
Despite the use of buffering agents the 1H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.
To investigate the acid, base and metal ion dependent 1H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.
Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl2, MgCl2, NaCl or KCl, and their 1H NMR spectra were acquired.
Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na+, K+, Ca2+ and Mg2+, were also measured.
These data will be a valuable resource for 1H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for 1H NMR spectra.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1101-y) contains supplementary material, which is available to authorized users.
PMCID: PMC5025509  PMID: 27729829
NMR; pH; Peak shift; Acid/base limits
11.  Removing the bottlenecks of cell culture metabolomics: fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method 
Metabolomics  2016;12(10):151.
Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.
We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.
We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.
We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.
We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1104-8) contains supplementary material, which is available to authorized users.
PMCID: PMC5025493  PMID: 27729828
Cell culture metabolomics; Normalization method; Harvesting; Metabolite–cell number correlation
12.  Fortune telling: metabolic markers of plant performance 
Metabolomics  2016;12(10):158.
In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC–MS, LC–MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.
Aim of review
(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.
Key message
Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.
PMCID: PMC5025497  PMID: 27729832
Breeding; Metabolic marker; Metabolomics; Plant performance; Prediction
13.  Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome 
Metabolomics  2016;12(10):159.
Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.
Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.
Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.
Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.
Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1109-3) contains supplementary material, which is available to authorized users.
PMCID: PMC5025507  PMID: 27729833
Centrifugation; Plasma; Metabolome; Relative centrifugal force; Spinning time; Preanalytics
14.  Metabolomics reveals dose effects of low-dose chronic exposure to uranium in rats: identification of candidate biomarkers in urine samples 
Metabolomics  2016;12(10):154.
Data are sparse about the potential health risks of chronic low-dose contamination of humans by uranium (natural or anthropogenic) in drinking water. Previous studies report some molecular imbalances but no clinical signs due to uranium intake.
In a proof-of-principle study, we reported that metabolomics is an appropriate method for addressing this chronic low-dose exposure in a rat model (uranium dose: 40 mg L−1; duration: 9 months, n = 10). In the present study, our aim was to investigate the dose–effect pattern and identify additional potential biomarkers in urine samples.
Compared to our previous protocol, we doubled the number of rats per group (n = 20), added additional sampling time points (3 and 6 months) and included several lower doses of natural uranium (doses used: 40, 1.5, 0.15 and 0.015 mg L−1). LC–MS metabolomics was performed on urine samples and statistical analyses were made with SIMCA-P+ and R packages.
The data confirmed our previous results and showed that discrimination was both dose and time related. Uranium exposure was revealed in rats contaminated for 9 months at a dose as low as 0.15 mg L−1. Eleven features, including the confidently identified N1-methylnicotinamide, N1-methyl-2-pyridone-5-carboxamide and 4-hydroxyphenylacetylglycine, discriminated control from contaminated rats with a specificity and a sensitivity ranging from 83 to 96 %, when combined into a composite score.
These findings show promise for the elucidation of underlying radiotoxicologic mechanisms and the design of a diagnostic test to assess exposure in urine, in a dose range experimentally estimated to be above a threshold between 0.015 and 0.15 mg L−1.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1092-8) contains supplementary material, which is available to authorized users.
PMCID: PMC5025510  PMID: 27729830
Metabolomics; Chronic; Low dose; Contamination; Uranium; N1-methylnicotinamide
15.  NMR-based metabolic characterization of chicken tissues and biofluids: a model for avian research 
Metabolomics  2016;12(10):157.
Poultry is one of the most consumed meat in the world and its related industry is always looking for ways to improve animal welfare and productivity. It is therefore essential to understand the metabolic response of the chicken to new feed formulas, various supplements, infections and treatments.
As a basis for future research investigating the impact of diet and infections on chicken’s metabolism, we established a high-resolution proton nuclear magnetic resonance (NMR)-based metabolic atlas of the healthy chicken (Gallus gallus).
Metabolic extractions were performed prior to 1H-NMR and 2D NMR spectra acquisition on twelve biological matrices: liver, kidney, spleen, plasma, egg yolk and white, colon, caecum, faecal water, ileum, pectoral muscle and brain of 6 chickens. Metabolic profiles were then exhaustively characterized.
Nearly 80 metabolites were identified. A cross-comparison of these matrices was performed to determine metabolic variations between and within each section and highlighted that only eight core metabolites were systematically found in every matrice.
This work constitutes a database for future NMR-based metabolomic investigations in relation to avian production and health.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1105-7) contains supplementary material, which is available to authorized users.
PMCID: PMC5025519  PMID: 27729831
Chicken; Metabolome; Nuclear magnetic resonance spectroscopy (NMR); Metabolite
16.  Metabolomics enables precision medicine: “A White Paper, Community Perspective” 
Metabolomics  2016;12(9):149.
Introduction: Background to metabolomics
Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.
Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine
We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.
Conclusions: Key scientific concepts and recommendations for precision medicine
Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
PMCID: PMC5009152  PMID: 27642271
Metabolomics; Metabonomics; Pharmacometabolomics; Pharmacometabonomics; Precision medicine; Personalized medicine
17.  The metabolome 18 years on: a concept comes of age 
Metabolomics  2016;12(9):148.
The term ‘metabolome’ was introduced to the scientific literature in September 1998.
Aim and key scientific concepts of the review
To mark its 18-year-old ‘coming of age’, two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.
PMCID: PMC5009154  PMID: 27695392
Metabolome; Functional genomics; Systems biology; Precision medicine
18.  Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics 
Metabolomics  2016;12(9):146.
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.
A literature search of the current relevant primary research was performed.
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.
PMCID: PMC4987388  PMID: 27616976
Mass spectrometry; Nuclear magnetic resonance; Leukemia; Stratified medicine; Nutraceutical; Drug redeployment
19.  Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis 
Metabolomics  2016;12(9):144.
Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.
The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.
The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).
Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.
In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1090-x) contains supplementary material, which is available to authorized users.
PMCID: PMC5047924  PMID: 27547172
Metabolomics; Polyphenols; Secondary metabolism; Rubus idaeus; Data analysis
20.  An Interactive Cluster Heat Map to Visualize and Explore Multidimensional Metabolomic Data 
Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online ( is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.
PMCID: PMC4505375  PMID: 26195918
XCMS Online; Metabolomics; Bioinformatics software; Interactive cluster heat map; Anatomical brain regions; Brain Metabolomics
21.  The Warburg effect: a balance of flux analysis 
Cancer metabolism is characterized by increased macromolecular syntheses through coordinated increases in energy and substrate metabolism. The observation that cancer cells produce lactate in an environment of oxygen sufficiency (aerobic glycolysis) is a central theme of cancer metabolism known as the Warburg effect. Aerobic glycolysis in cancer metabolism is accompanied by increased pentose cycle and anaplerotic activities producing energy and substrates for macromolecular synthesis. How these processes are coordinated is poorly understood. Recent advances have focused on molecular regulation of cancer metabolism by oncogenes and tumor suppressor genes which regulate numerous enzymatic steps of central glucose metabolism. In the past decade, new insights in cancer metabolism have emerged through the application of stable isotopes particularly from 13C carbon tracing. Such studies have provided new evidence for system-wide changes in cancer metabolism in response to chemotherapy. Interestingly, experiments using metabolic inhibitors on individual biochemical pathways all demonstrate similar system-wide effects on cancer metabolism as in targeted therapies.
Since biochemical reactions in the Warburg effect place competing demands on available precursors, high energy phosphates and reducing equivalents, the cancer metabolic system must fulfill the condition of balance of flux (homeostasis). In this review, the functions of the pentose cycle and of the tricarboxylic acid (TCA) cycle in cancer metabolism are analyzed from the balance of flux point of view. Anticancer treatments that target molecular signaling pathways or inhibit metabolism alter the invasive or proliferative behavior of the cancer cells by their effects on the balance of flux (homeostasis) of the cancer metabolic phenotype.
PMCID: PMC4507278  PMID: 26207106
22.  Untargeted metabolomics studies employing NMR and LC-MS reveal metabolic coupling between Nanoarcheum equitans and its archaeal host Ignicoccus hospitalis 
Interspecies interactions are the basis of microbial community formation and infectious diseases. Systems biology enables the construction of complex models describing such interactions, leading to a better understanding of disease states and communities. However, before interactions between complex organisms can be understood, metabolic and energetic implications of simpler real-world host-microbe systems must be worked out. To this effect, untargeted metabolomics experiments were conducted and integrated with proteomics data to characterize key molecular-level interactions between two hyperthermophilic microbial species, both of which have reduced genomes. Metabolic changes and transfer of metabolites between the archaea Ignicoccus hospitalis and Nanoarcheum equitans were investigated using integrated LC-MS and NMR metabolomics. The study of such a system is challenging, as no genetic tools are available, growth in the laboratory is challenging, and mechanisms by which they interact are unknown. Together with information about relative enzyme levels obtained from shotgun proteomics, the metabolomics data provided useful insights into metabolic pathways and cellular networks of I. hospitalis that are impacted by the presence of N. equitans, including arginine, isoleucine, and CTP biosynthesis. On the organismal level, the data indicate that N. equitans exploits metabolites generated by I. hospitalis to satisfy its own metabolic needs. This finding is based on N. equitans’s consumption of a significant fraction of the metabolite pool in I. hospitalis that cannot solely be attributed to increased biomass production for N. equitans. Combining LC-MS and NMR metabolomics datasets improved coverage of the metabolome and enhanced the identification and quantitation of cellular metabolites.
PMCID: PMC4529127  PMID: 26273237
LC-MS; NMR; metabolomics; Ignicoccus hospitalis; Nanoarcheum equitans; hyperthermophilic Archaea; interspecies interactions; metabolic pathway analysis; systems biology
23.  Metabolomics connects aberrant bioenergetic, transmethylation, and gut microbiota in sarcoidosis 
Sarcoidosis is a systemic granulomatous disease of unknown etiology. Granulomatous inflammation in sarcoidosis may affect multiple organs, including the lungs, skin, CNS, and the eyes, leading to severe morbidity and mortality. The underlying mechanisms for sustained inflammation in sarcoidosis are unknown. We hypothesized that metabolic changes play a critical role in perpetuation of inflammation in sarcoidosis. 1H nuclear magnetic resonance (NMR)-based untargeted metabolomic analysis was used to identify circulating molecules in serum to discriminate sarcoidosis patients from healthy controls. Principal component analyses (PCA) were performed to identify different metabolic markers and explore the changes of associated biochemical pathways. Using Chenomx 7.6 NMR Suite software, we identified and quantified metabolites responsible for such separation in the PCA models. Quantitative analysis showed that the levels of metabolites, such as 3-hydroxybutyrate, acetoacetate, carnitine, cystine, homocysteine, pyruvate, and trimethylamine N-oxide were significantly increased in sarcoidosis patients. Interestingly, succinate, a major intermediate metabolite involved in the tricyclic acid cycle was significantly decreased in sarcoidosis patients. Application of integrative pathway analyses identified deregulation of butanoate, ketone bodies, citric cycle metabolisms, and transmethylation. This may be used for development of new drugs or nutritional modification.
PMCID: PMC4960975  PMID: 27489531
Sarcoidosis; Metabolomics; 1H NMR; TCA cycle; β-Oxidation
24.  Preclinical models for interrogating drug action in human cancers using Stable Isotope Resolved Metabolomics (SIRM) 
In this review we compare the advantages and disadvantages of different model biological systems for determining the metabolic functions of cells in complex environments, how they may change in different disease states, and respond to therapeutic interventions.
All preclinical drug-testing models have advantages and drawbacks. We compare and contrast established cell, organoid and animal models with ex vivo organ or tissue culture and in vivo human experiments in the context of metabolic readout of drug efficacy. As metabolism reports directly on the biochemical state of cells and tissues, it can be very sensitive to drugs and/or other environmental changes. This is especially so when metabolic activities are probed by stable isotope tracing methods, which can also provide detailed mechanistic information on drug action. We have developed and been applying Stable Isotope-Resolved Metabolomics (SIRM) to examine metabolic reprogramming of human lung cancer cells in monoculture, in mouse xenograft/explant models, and in lung cancer patients in situ (Lane et al. 2011; T. W. Fan et al. 2011; T. W-M. Fan et al. 2012; T. W. Fan et al. 2012; Xie et al. 2014b; Ren et al. 2014a; Sellers et al. 2015b). We are able to determine the influence of the tumor microenvironment using these models. We have now extended the range of models to fresh human tissue slices, similar to those originally described by O. Warburg (Warburg 1923), which retain the native tissue architecture and heterogeneity with a paired benign versus cancer design under defined cell culture conditions. This platform offers an unprecedented human tissue model for preclinical studies on metabolic reprogramming of human cancer cells in their tissue context, and response to drug treatment (Xie et al. 2014a). As the microenvironment of the target human tissue is retained and individual patient's response to drugs is obtained, this platform promises to transcend current limitations of drug selection for clinical trials or treatments.
Conclusions and Future Work
Development of ex vivo human tissue and animal models with humanized organs including bone marrow and liver show considerable promise for analyzing drug responses that are more relevant to humans. Similarly using stable isotope tracer methods with these improved models in advanced stages of the drug development pipeline, in conjunction with tissue biopsy is expected significantly to reduce the high failure rate of experimental drugs in Phase II and III clinical trials.
PMCID: PMC4968890  PMID: 27489532
SIRM; cell culture; PDX models; tissue slices; metabolism
25.  Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington’s disease peripheral blood samples 
Metabolomics  2016;12:137.
Metabolic changes have been frequently associated with Huntington’s disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington’s disease patients especially when brain or other tissue samples are difficult to collect.
We investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations.
This analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington’s disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls.
By comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine—PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes (ALDH1B1, MBOAT1, MTRR and PLB1) that showed significant association with the changes in metabolite concentrations.
Our results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-016-1084-8) contains supplementary material, which is available to authorized users.
PMCID: PMC4963448  PMID: 27524956
Metabolomics; Gene expression; Biomarkers; Disease progression; Neurodegenerative; Integrated analysis

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