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Background & Aims: Acute decompensation (AD) of cirrhosis is a heterogeneous clinical entity associated with moderate mortality. In some patients, this condition develops quickly into the more deadly acute-on-chronic liver failure (ACLF), in which other organs such as the kidneys or brain fail. The aim of this study was to characterize the blood lipidome in a large series of patients with cirrhosis and identify specific signatures associated with AD and ACLF development. Methods: Serum untargeted lipidomics was performed in 561 patients with AD (518 without and 43 with ACLF) (discovery cohort) and in 265 patients with AD (128 without and 137 with ACLF) in whom serum samples were available to perform repeated measurements during the 28-day follow-up (validation cohort). Analyses were also performed in 78 patients with AD included in a therapeutic albumin trial (43 patients with compensated cirrhosis and 29 healthy individuals). Results: The circulating lipid landscape associated with cirrhosis was characterized by a generalized suppression, which was more manifest during AD and in non-surviving patients. By computing discriminating accuracy and the variable importance projection score for each of the 223 annotated lipids, we identified a sphingomyelin fingerprint specific for AD of cirrhosis and a distinct cholesteryl ester and lysophosphatidylcholine finger-print for ACLF. Liver dysfunction and infections were the prin-cipal net contributors to these fingerprints, which were dynamic and interchangeable between patients with AD whose condition worsened to ACLF and those who improved. Notably, blood lysophosphatidylcholine levels increased in these patients after albumin therapy. Conclusions: Our findings provide insights into the lipid land-scape associated with decompensation of cirrhosis and ACLF progression and identify unique non-invasive diagnostic bio-markers of advanced cirrhosis. Lay summary: Analysis of lipids in blood from patients with advanced cirrhosis reveals a general suppression of their levels in the circulation of these patients. A specific group of lipids known as sphingomyelins are useful to distinguish between patients with compensated and decompensated cirrhosis. Another group of lipids designated cholesteryl esters further distinguishes patients with decompensated cirrhosis who are at risk of developing organ failures. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.

Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.

© 2021 by the authors., Insulin receptor substrate (IRS) 2 is a key mediator of insulin signaling and IRS-2 knockout (IRS2−/−) mice are a preclinical model to study the development of diabetes, as they develop peripheral insulin resistance and beta-cell failure. The differential inflammatory profile and insulin signaling in the hypothalamus of non-diabetic (ND) and diabetic (D) IRS2−/− mice might be implicated in the onset of diabetes. Because the lipid profile is related to changes in inflammation and insulin sensitivity, we analyzed whether ND IRS2−/− mice presented a different hypothalamic fatty acid metabolism and lipid pattern than D IRS2−/− mice and the relationship with inflammation and markers of insulin sensitivity. ND IRS2−/− mice showed elevated hypothalamic anti-inflammatory cytokines, while D IRS2−/− mice displayed a proinflammatory profile. The increased activity of enzymes related to the pentose-phosphate route and lipid anabolism and elevated polyunsaturated fatty acid levels were found in the hypothalamus of ND IRS2−/− mice. Conversely, D IRS2−/− mice have no changes in fatty acid composition, but hypothalamic energy balance and markers related to anti-inflammatory and insulin-sensitizing properties were reduced. The data suggest that the concurrence of an anti-inflammatory profile, increased insulin sensitivity and polyunsaturated fatty acids content in the hypothalamus may slow down or delay the onset of diabetes., This work was supported by the Spanish Ministry of Science and Innovation with the help of European FEDER funding (grant numbers FIS PI19/00166, BFU 2017-82565-C2-1-R, and RTI2018-094052-B-100), Comunidad de Madrid, Spain (S2017/BMD-3684) and the Network Center for Biomedical Research on Obesity and Nutrition (CIBEROBN) and Diabetes (CIBERDEM) Instituto Carlos III. S.C. was supported by CIBEROBN and A.G.M. by Fundación para la Investigación Biomédica Hospital Infantil Universitario Niño Jesús.

The tobacco-specific nitrosamine 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a carcinogenic and ubiquitous environmental pollutant which carcinogenic and cytotoxic activity has been thoroughly investigated in murine models and human tissues. However, its potential deleterious effects on vertebrate early development are yet poorly understood. In this work, we characterized the impact of NNK exposure during early developmental stages of zebrafish embryos, a known alternative model for mammalian toxicity studies. Embryos exposed to different NNK concentrations were monitored for lethality and for the appearance of malformations during the first five days after fertilization. LC/MS-based untargeted metabolomics was subsequently performed for a wide-scope assay of NNK-related metabolic alterations. Our results revealed the presence of not only the parental compound, but also of two NKK known metabolites, 4-Hydroxy-4-(3-pyridyl)-butyric acid (HPBA) and 4-(Methylnitrosamino)-1-(3-pyridyl-N-oxide)-1-butanol (NNAL-N-oxide) in exposed embryos likely resulting from active CYP450-mediated α-hydroxylation and NNK detoxification pathways, respectively. This was paralleled by a disruption in purine and pyrimidine metabolisms and the activation of the base excision repair pathway. Our results confirm NNK as a harmful embryonic agent and demonstrate zebrafish embryos to be a suitable early development model to monitor NNK toxicity.

Muscle is an essential organ for glucose metabolism and can be influenced by metabolic disorders and physical activity. Elevated muscle carnosine levels have been associated with insulin resistance and cardiometabolic risk factors. Little is known about muscle carnosine in type 1 diabetes (T1D) and how it is influenced by physical activity. The aim of this study was to characterize muscle carnosine in vivo by proton magnetic resonance spectroscopy (16 men with T1D (10 athletes/6 sedentary) and 14 controls without diabetes (9/5) were included. Body composition by DXA, cardiorespiratory capacity (VOSubjects with T1D presented higher carnosine CR levels compared to controls. T1D patients with a lower VOElevated muscle carnosine levels in persons with T1D and their effect on atherogenic lipoproteins can be modulated by physical activity.

Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization. HERMES is a molecular-formula-oriented and peak-detection-free method that uses LC/MS1 information to optimize MS2 acquisition for LC/MS-based metabolomic analysis.

Comprehensive metabolome analyses are hampered by low identification rates of metabolites due to suboptimal strategies in MS and MS2 acquisition, and data analysis. Here we present a molecular formula-oriented and peak detection-free method, HERMES, that improves sensitivity and selectivity for metabolite profiling in MS and structural annotation in MS2. An analysis of environmental water, E. coli, and human plasma extracts by HERMES showed increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared to iterative data-dependent acquisition (DDA). HERMES is available as an R package with a user-friendly graphical interface to allow data analysis and interactive tracking of compound annotations.

Isotopic labelling experiments have been utterly valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC’s superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode or selected ion clusters using optimized narrow-windows (~10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway and urea cycle from LRMS and HRMS data.

The structural similarity among lipid species and the low sensitivity and spectral resolution of nuclear magnetic resonance (NMR) have traditionally hampered the routine use of 1H NMR lipid profiling of complex biological samples in metabolomics, which remains mostly manual and lacks freely available bioinformatics tools. However, 1H NMR lipid profiling provides fast quantitative screening of major lipid classes (fatty acids, glycerolipids, phospholipids, and sterols) and some individual species and has been used in several clinical and nutritional studies, leading to improved risk prediction models. In this Article, we present LipSpin, a free and open-source bioinformatics tool for quantitative 1H NMR lipid profiling. LipSpin implements a constrained line shape fitting algorithm based on voigt profiles and spectral templates from spectra of lipid standards, which automates the analysis of severely overlapped spectral regions and lipid signals with complex coupling patterns. LipSpin provides the most detailed quantification of fatty acid families and choline phospholipids in serum lipid samples by 1H NMR to date. Moreover, analytical and clinical results using LipSpin quantifications conform with other techniques commonly used for lipid analysis.

A novel metabolomics approach for NMR-based stable isotope tracer studies called PEPA is presented, and its performance validated using human cancer cells. PEPA detects the position of carbon label in isotopically enriched metabolites and quantifies fractional enrichment by indirect determination of 13 C-satellite peaks using 1D-1 H-NMR spectra. In comparison with 13 C-NMR, TOCSY and HSQC, PEPA improves sensitivity, accelerates the elucidation of 13 C positions in labeled metabolites and the quantification of the percentage of stable isotope enrichment. Altogether, PEPA provides a novel framework for extending the high-throughput of 1 H-NMR metabolic profiling to stable isotope tracing in metabolomics, facilitating and complementing the information derived from 2D-NMR experiments and expanding the range of isotopically enriched metabolites detected in cellular extracts.

The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.

Metabolomics is the scientific study of the chemical processes in a living system, environment and nutrition. It is a relatively new omics science, but the potential applications are wide, including medicine, personalized medicine and intervention studies, food and nutrition, plants, agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field, from masters to postgraduate students, PhDs, postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies, but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather, the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples, computing and exercises.

The main objective of a metabolomics experiment is to determine the composition of the set of small molecules in one or more biological samples, both with respect to quantity and chemical characteristics. Two detection platforms are most commonly used in metabolomics: mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Since biological samples often consist of very complex matrices, containing several hundred to thousands of metabolites, these techniques are often coupled to different chromatographic separation techniques. In this chapter, we will treat the basic principles of the most commonly used chromatographic techniques and the very foundations of MS and NMR as major detection techniques in metabolomics. Numerous chromatographic techniques exist, but here we focus on three that are commonly used in metabolomics: gas chromatography, liquid chromatography and capillary electrophoresis. The focus is on concepts that are of particular importance for subsequent analysis and interpretation of the data. Less common detection methods based on vibrational and UV-Vis spectroscopy are treated briefly at the end of the chapter.

Background Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects. Results We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature. Conclusions The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license. Electronic supplementary material The online version of this article (10.1186/s12859-018-2487-5) contains supplementary material, which is available to authorized users.

At present, mass spectrometry (MS)-based metabolomics has been widely used to obtain new insights into human, plant, and microbial biochemistry; drug and biomarker discovery; nutrition research; and food control. Despite the high research interest, identifying and characterizing the structure of metabolites has become a major drawback for converting raw MS data into biological knowledge. Comprehensive and well-annotated MS-based spectral databases play a key role in serving this purpose via the formation of metabolite annotations. The main characteristics of the mass spectral databases currently used in MS-based metabolomics are reviewed in this study, underlining their advantages and limitations. In addition, the overlap of compounds with MSn (n ≥ 2) spectra from authentic chemical standards in most public and commercial databases has been calculated for the first time. Finally, future prospects of mass spectral databases are discussed in terms of the needs posed by novel applications and instrumental advancements.

Metabolomics is a relatively new “omics” approach used to characterize metabolites in a biological system at baseline and following a diversity of stimuli. However, the metabolomic response to exercise in hypoxia currently remains unknown. To examine this, 24 male participants completed 1 h of exercise at a workload corresponding to 75% of pre-determined O2max in hypoxia (Fio2 = 0.16%), and repeated in normoxia (Fio2 = 0.21%), while pre- and post-exercise and 3 h post-exercise metabolites were analyzed using a LC ESI-qTOF-MS untargeted metabolomics approach in serum samples. Exercise in hypoxia and in normoxia independently increased metabolism as shown by a change in a combination of twenty-two metabolites associated with lipid metabolism (p < 0.05, pre vs. post-exercise), though hypoxia per se did not induce a greater metabolic change when compared with normoxia (p > 0.05). Recovery from exercise in hypoxia independently decreased seventeen metabolites associated with lipid metabolism (p < 0.05, post vs. 3 h post-exercise), compared with twenty-two metabolites in normoxia (p < 0.05, post vs. 3 h post-exercise). Twenty-six metabolites were identified as responders to exercise and recovery (pooled hypoxia and normoxia pre vs. recovery, p < 0.05), including metabolites associated with purine metabolism (adenine, adenosine and hypoxanthine), the amino acid phenylalanine, and several acylcarnitine molecules. Our novel data provides preliminary evidence of subtle metabolic differences to exercise and recovery in hypoxia and normoxia. Specifically, exercise in hypoxia activates metabolic pathways aligned to purine and lipid metabolism, but this effect is not selectively different from exercise in normoxia. We also show that exercise per se can activate pathways associated with lipid, protein and purine nucleotide metabolism.

The structural similarity among lipid species and the low sensitivity and spectral resolution of nuclear magnetic resonance (NMR) have traditionally hampered the routine use of

Mitochondria are subcellular organelles that are critical for meeting the bioenergetic and biosynthetic needs of the cell. Mitochondrial function relies on genes and RNA species encoded both in the nucleus and mitochondria, and on their coordinated translation, import and respiratory complex assembly. Here, we characterize EXD2 (exonuclease 3′–5′ domain-containing 2), a nuclear-encoded gene, and show that it is targeted to the mitochondria and prevents the aberrant association of messenger RNAs with the mitochondrial ribosome. Loss of EXD2 results in defective mitochondrial translation, impaired respiration, reduced ATP production, increased reactive oxygen species and widespread metabolic abnormalities. Depletion of the Drosophila melanogaster EXD2 orthologue (CG6744) causes developmental delays and premature female germline stem cell attrition, reduced fecundity and a dramatic extension of lifespan that is reversed with an antioxidant diet. Our results define a conserved role for EXD2 in mitochondrial translation that influences development and ageing., We thank the following bodies for funding: Ministerio de Economía y Competitividad (MINECO) (T.H.S.: BFU2012-39521, BFU2015-68354, Ayudas para incentivar la incorporación estable de doctores (IED) 2015; L.R.d.P.: BIO2015-64572; T.H.S. and L.R.d.P: institutional funding through the Centres of Excellence Severo Ochoa award and from the CERCA Programme of the Catalan Government; and O.Y.: SAF2011-30578 and BFU2014-57466); the Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), an initiative of Instituto de Investigacion Carlos III (ISCIII) to O.Y.; and the Biotechnology and Biological Sciences Research Council (BBSRC: BB/H019723/1 and BB/M008800/1) to A.J.D. and L.B. S.A. was supported by a Finnish Cultural Society Fellowship, J.S. by a fellowship from Fundação para a Ciência e a Tecnologia (SFRH/BD/87025/2012), P.A.K. by an Advanced Postdoc Mobility fellowship from the Swiss National Science Foundation (SNF), I.G.-C. by an Asociación Española Contra el Cáncer (AECC) fellowship, A.A.-F. by a Severo Ochoa FPI fellowship (MINECO; SVP2014068398), and A.A.J. by an EMBO long-term fellowship (ALTF 554-2015).

The successful implementation of synthetic biology for chemicals biosynthesis relies on the availability of large libraries of well-characterized enzymatic building blocks. Here we present a scalable pipeline that applies the methodology of synthetic biology itself to bootstrap the creation of such a library. By designing and building a cytochrome P450 enzyme collection and testing it in a custom-made untargeted GC/MS-metabolomics-based approach, we were able to rapidly create and characterize a comprehensive enzyme library for the controlled oxyfunctionalisation of terpene scaffolds with a wide range of activities and selectivities towards several monoterpenes. This novel resource can now be used to access the extensive chemical diversity of terpenoids by pathway engineering and the assembly of biocatalytic cascades to subsequently produce libraries of oxygenated terpenoids and their derivatives for diverse applications, including drug discovery.

Determination of lipoprotein particle size and number using advanced lipoprotein tests (ALTs) is of particular importance to improve cardiovascular risk prediction. Here we present the Liposcale test, a novel ALT based on 2D diffusion-ordered 1H NMR spectroscopy. Our method uses diffusion coefficients to provide a direct measure of the mean particle sizes and numbers. Using 177 plasma samples from healthy individuals and the concentration of ApoB and ApoA from isolated lipoprotein fractions, our test showed a stronger correlation between the NMR-derived lipoprotein particle numbers and apolipoprotein concentrations than the LipoProfile ® test commercialized by Liposcience. We also converted LDL particle numbers to ApoB equivalents (milligrams per deciliter) and our test yielded similar values of LDL-ApoB to the LipoProfile ® test (absolute mean bias of 8.5 and 7.4 mg/dl, respectively). In addition, our HDL particle number values were more concordant with the calibrated values determined recently using ion mobility. Finally, principal component analysis distinguished type 2 diabetic patients with and without atherogenic dyslipidemia (AD) on a second cohort of 307 subjects characterized using the Liposcale test (area under the curve = 0.88) and showed concordant relationships between variables explaining AD. Altogether, our method provides reproducible and reliable characterization of lipoprotein particles and it is applicable to pathological states such as AD. Copyright © 2015 by the American Society for Biochemistry and Molecular Biology, Inc.

Resumen del trabajo presentado al 19th European Congress of Endocrinology (ECE), celebrado en Lisboa (Portugal) del 20 al 23 de mayo de 2017., [Background]: IRS2-deficient (IRS2-/-) mice are considered a good model to analyze the development of diabetes as some of them present an increase in glycemia comparable to that observed in diabetes onset in humans, whereas a high proportion of these mice do not develop diabetes. Energy homeostasis regulation by the hypothalamus can be disturbed by an inflammatory environment, which predisposes an individual to the onset of diabetes. Saturated fatty acids induce hypothalamic dysfunction, whereas unsaturated fatty acids mediate severa! anti-inflammatory actions. [Objectives]: Our aim was to determine the pattern of fatty acids in the hypothalamus of non-diabetic IRS2-/- (ND) and diabetic IRS2-/- (D) mice and its possible assodation with hypothalamic inflammation. [Methods]: We studied 18 male mice including controls, ND and D mice. We analyzed enzymes involved in the generation of NADPH, fatty acid synthesis and regulation of energy homeostasis by western blotting and pro- and anti-inflammatory chemokines/cytokines by multiplexed bead immunoassay. Metabolomic studies were performed by proton nuclear magnetic resonance (H-NMR) after extraction of hypothalamic metabolites in organic solvents. [Results]: Malic enzyme was increased in ND mice and fatty acid synthase in D and ND, with a greater increase in ND. Acetyl·CoA carboxylase was inhibited and AMPK activated in D mice, with no changes in ND mice. Among the studied chemokines/cytokines, monocyte chemoattractant protein-1, fractalkine and interleukin (IL)-2 were increased in D and IL-4 en ND mice. Metabolomic studies revealed an increase in the levels of ω3-fatty acids, phosphatydil-ethanolamine, linolenic acid and MUFA plus PUFA in the hypothalamus of ND m ice with respect to controls. A negative correlation of analyzed metabolites with fractalkine and a positive correlation with IL-4 was observed. [Conclusion]: The favorable hypothalamic lipid profile suggests a beneficia! role against hypothalamic inflammation in non-diabetic IRS2-/- mice.

One-dimensional (1)H NMR represents one of the most commonly used analytical techniques in metabolomic studies. The increase in the number of samples analyzed as well as the technical improvements involving instrumentation and spectral acquisition demand increasingly accurate and efficient high-throughput data processing workflows. We present FOCUS, an integrated and innovative methodology that provides a complete data analysis workflow for one-dimensional NMR-based metabolomics. This tool will allow users to easily obtain a NMR peak feature matrix ready for chemometric analysis as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. The algorithm development has been focused on solving the critical difficulties that appear at each data processing step and that can dramatically affect the quality of the results. As well as method integration, simplicity has been one of the main objectives in FOCUS development, requiring very little user input to perform accurate peak alignment, peak picking, and metabolite identification. The new spectral alignment algorithm, RUNAS, allows peak alignment with no need of a reference spectrum, and therefore, it reduces the bias introduced by other alignment approaches. Spectral alignment has been tested against previous methodologies obtaining substantial improvements in the case of moderate or highly unaligned spectra. Metabolite identification has also been significantly improved, using the positional and correlation peak patterns in contrast to a reference metabolite panel. Furthermore, the complete workflow has been tested using NMR data sets from 60 human urine samples and 120 aqueous liver extracts, reaching a successful identification of 42 metabolites from the two data sets. The open-source software implementation of this methodology is available at http://www.urr.cat/FOCUS.

Objective This study compares the patterns of visceral (VIS) and subcutaneous (SC) adipose tissue (AT)-derived metabolites from non-obese (BMI 24-26 kg/m2) and obese subjects (BMI > 40 kg/m2) with no major metabolic risk factors other than BMI. Methods SC- and VIS- AT obtained from obese (Ob) and non-obese (NOb) subjects during surgery were incubated to obtain their metabolites. Differences related to obesity or anatomical provenances of AT were assessed using an untargeted metabolomics approach based on gas chromatography-mass spectrometry. Results The overall effect of obesity on the metabolite profile resulted more remarkable than the effect of regional AT. Only the depletion of 2-ketoisocaproic (2-KIC) acid reached statistical significance for the SC-AT alone, although it was observed in both depots. Obesity induced more significant changes in several amino acids levels of the VIS-AT metabolites. On the one hand, higher released levels of glutamine and alanine were detected in the VIS- obese AT, whereas on the other, the VIS- obese AT presented a diminished uptake of essential amino acids (methionine, threonine, lysine), BCAAs, leucine, and serine. Conclusion This study shows that obesity markedly affects the amino acid metabolic signature of the AT before the clinical onset of other significant metabolic alterations aside from BMI.

The study of nutritional interventions in humans is difficult to assess because the induced metabolic changes are lower than the natural biological variability between subjects. Due to its holistic approach, 1H NMR is one of the preferred technologies for this type of studies, even though it has a very low sensitivity. This work shows how the use of several chemometric algorithms on the measured data compensates for these drawbacks and allows the study of the effects of the nutritional intervention isolating them from the natural variability inherent to human studies. Mild to moderate hypercholesterolemic patients received either placebo or soluble fiber in a low saturated fat diet. Plasma samples were collected at week 0 and week 8. Spectra obtained with NMR equipment were processed with ANOVA simultaneous component analysis (ASCA). The application of clustering techniques revealed different responses based on the patient's basal state, which allowed the identification of responders from non-responders. Results showed a triglyceride level reduction of up to 15% (p = 0.0032), with a higher reduction for those patients with a higher initial lipid profile. Moreover, line-shape fitting techniques applied to the NMR spectra allowed the conclusion that LDL (and VLDL) lipoprotein particles, and more noticeably triglycerides, moved to a profile configuration associated with lower cardiovascular risk. Results shed light on some of the metabolic modifications that husk fiber induces in humans which could not be seen with more conventional data analysis approaches. Our conclusion is that by using the right chemometric techniques it is possible to assess nutritional intervention effects in human NMR human studies despite the low sensitivity and selectivity that the technique offers today.

A novel metabolomics approach for NMR‐based stable isotope tracer studies called PEPA is presented, and its performance validated using human cancer cells. PEPA detects the position of carbon label in isotopically enriched metabolites and quantifies fractional enrichment by indirect determination of 13C‐satellite peaks using 1D‐1H‐NMR spectra. In comparison with 13C‐NMR, TOCSY and HSQC, PEPA improves sensitivity, accelerates the elucidation of 13C positions in labeled metabolites and the quantification of the percentage of stable isotope enrichment. Altogether, PEPA provides a novel framework for extending the high‐throughput of 1H‐NMR metabolic profiling to stable isotope tracing in metabolomics, facilitating and complementing the information derived from 2D‐NMR experiments and expanding the range of isotopically enriched metabolites detected in cellular extracts.

The mechanisms that allow breast cancer (BCa) cells to metabolically sustain rapid growth are poorly understood. Here we report that BCa cells are dependent on a mechanism to supply precursors for intracellular lipid production derived from extracellular sources and that the endothelial lipase (LIPG) fulfils this function. LIPG expression allows the import of lipid precursors, thereby contributing to BCa proliferation. LIPG stands out as an essential component of the lipid metabolic adaptations that BCa cells, and not normal tissue, must undergo to support high proliferation rates. LIPG is ubiquitously and highly expressed under the control of FoxA1 or FoxA2 in all BCa subtypes. The downregulation of either LIPG or FoxA in transformed cells results in decreased proliferation and impaired synthesis of intracellular lipids.

Proanthocyanidins (PACs) have been reported to modulate multiple targets by simultaneously controlling many pivotal metabolic pathways in the liver. However, the precise mechanism of PAC action on the regulation of the genes that control hepatic metabolism remains to be clarified. Accordingly, we used a metabolomic approach combining both nuclear magnetic resonance and mass spectrometry analysis to evaluate the changes induced by different doses of grape-seed PACs in the liver of healthy rats. Here, we report that PACs significantly increased the hepatic nicotinamide adenine dinucleotide (NAD+) content in a dose-dependent manner by specifically modulating the hepatic concentrations of the major NAD+ precursors as well as the mRNA levels of the genes that encode the enzymes involved in the cellular metabolism of NAD+. Notably, Sirtuin 1 (Sirt1) gene expression was also significantly up-regulated in a dose-response pattern. The increase in both the NAD+ availability and Sirt1 mRNA levels, in turn, resulted in the hepatic activation of SIRT1, which was significantly associated with improved protection against hepatic triglyceride accumulation. Our data clearly indicates that PAC consumption could be a valid tool to enhance hepatic SIRT1 activity through the modulation of NAD+ levels.

DOI: 10.1038/srep24977 Proanthocyanidins (PACs) have been reported to modulate multiple targets by simultaneously controlling many pivotal metabolic pathways in the liver. However, the precise mechanism of PAC action on the regulation of the genes that control hepatic metabolism remains to be clarified. Accordingly, we used a metabolomic approach combining both nuclear magnetic resonance and mass spectrometry analysis to evaluate the changes induced by different doses of grape-seed PACs in the liver of healthy rats. Here, we report that PACs significantly increased the hepatic nicotinamide adenine dinucleotide (NAD+) content in a dose-dependent manner by specifically modulating the hepatic concentrations of the major NAD+ precursors as well as the mRNA levels of the genes that encode the enzymes involved in the cellular metabolism of NAD+. Notably, Sirtuin 1 (Sirt1) gene expression was also significantly up-regulated in a dose-response pattern. The increase in both the NAD+ availability and Sirt1 mRNA levels, in turn, resulted in the hepatic activation of SIRT1, which was significantly associated with improved protection against hepatic triglyceride accumulation. Our data clearly indicates that PAC consumption could be a valid tool to enhance hepatic SIRT1 activity through the modulation of NAD+ levels.

Porphobilinogen deaminase (PBGD) haploinsufficiency (acute intermittent porphyria, AIP) is characterized by neurovisceral attacks when hepatic heme synthesis is activated by endogenous or environmental factors including fasting. While the molecular mechanisms underlying the nutritional regulation of hepatic heme synthesis have been described, glucose homeostasis during fasting is poorly understood in porphyria. Our study aimed to analyse glucose homeostasis and hepatic carbohydrate metabolism during fasting in PBGD-deficient mice. To determine the contribution of hepatic PBGD deficiency to carbohydrate metabolism, AIP mice injected with a PBGD-liver gene delivery vector were included. After a 14 h fasting period, serum and liver metabolomics analyses showed that wild-type mice stimulated hepatic glycogen degradation to maintain glucose homeostasis while AIP livers activated gluconeogenesis and ketogenesis due to their inability to use stored glycogen. The serum of fasted AIP mice showed increased concentrations of insulin and reduced glucagon levels. Specific over-expression of the PBGD protein in the liver tended to normalize circulating insulin and glucagon levels, stimulated hepatic glycogen catabolism and blocked ketone body production. Reduced glucose uptake was observed in the primary somatosensorial brain cortex of fasted AIP mice, which could be reversed by PBGD-liver gene delivery. In conclusion, AIP mice showed a different response to fasting as measured by altered carbohydrate metabolism in the liver and modified glucose consumption in the brain cortex. Glucose homeostasis in fasted AIP mice was efficiently normalized after restoration of PBGD gene expression in the liver.

Background: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. Methods: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. Results: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P-FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an overrepresentation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. Conclusions: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

The sizes of certain types of lipoprotein particles have been associated with an increased risk of cardiovascular disease. However, there is currently no gold standard technique for the determination of this parameter. Here, we propose an analytical procedure to measure lipoprotein particles sizes using diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY). The method was tested on six lipoprotein fractions, VLDL, IDL, LDL1, LDL2, HDL2, and HDL3, which were obtained by sequential ultracentrifugation from four patients. We performed a pulsed-field gradient experiment on each fraction to obtain a mean diffusion coefficient, and then determined the apparent hydrodynamic radius using the Stokes–Einstein equation. To validate the hydrodynamic radii obtained, the particle size distribution of these lipoprotein fractions was also measured using transmission electron microscopy (TEM). The standard errors of duplicate measurements of diffusion coefficient ranged from 0.5% to 1.3%, confirming the repeatability of the technique. The coefficient of determination between the hydrodynamic radii and the TEM-derived mean particle size was r2 = 0.96, and the agreement between the two techniques was 85%. Thus, DOSY experiments have proved to be accurate and reliable for estimating lipoprotein particle sizes.

Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facilitate the unbiased mapping of labeled moieties through metabolic pathways. However, due to the complexity of the resulting experimental data sets few computational tools are available for data analysis. Here we introduce geoRge, a novel computational approach capable of analyzing untargeted LC/MS data from stable isotope-labeling experiments. geoRge is written in the open language R and runs on the output structure of the XCMS package, which is in widespread use. As opposed to the few existing tools, which use labeled samples to track stable isotopes by iterating over all MS signals using the theoretical mass difference between the light and heavy isotopes, geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. Isotopically enriched compounds change their isotopic distribution as compared to unlabeled compounds. This is directly reflected in a number of new m/z peaks and higher intensity peaks in the mass spectra of labeled samples relative to the unlabeled equivalents. The automated untargeted isotope annotation and relative quantification capabilities of geoRge are demonstrated by the analysis of LC/MS data from a human retinal pigment epithelium cell line (ARPE-19) grown on normal and high glucose concentrations mimicking diabetic retinopathy conditions in vitro. In addition, we compared the results of geoRge with the outcome of X(13)CMS, since both approaches rely entirely on XCMS parameters for feature selection, namely m/z and retention time values. To ensure data traceability and reproducibility, and enabling for comparison with other existing and future approaches, raw LC/MS files have been deposited in MetaboLights (MTBLS213) and geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.

Nonalcoholic fatty liver disease is considered to be the hepatic manifestation of metabolic syndrome and is usually related to high-fat, high-cholesterol diets. With the rationale that the identification and quantification of metabolites in different metabolic pathways may facilitate the discovery of clinically accessible biomarkers, we report the use of (1)H NMR metabolomics for quantitative profiling of liver extracts from LDLr(-/-) mice, a well-documented mouse model of fatty liver disease. A total of 55 metabolites were identified, and multivariate analyses in a diet- and time-comparative strategy were performed. Dietary cholesterol increased the hepatic concentrations of cholesterol, triglycerides, and oleic acid but also decreased the [PUFA/MUFA] ratio as well as the relative amount of long-chain polyunsaturated fatty acids in the liver. This was also accompanied by variations of the hepatic concentration of taurine, glutathione, methionine, and carnitine. Heat-map correlation analyses demonstrated that hepatic inflammation and development of steatosis correlated with cholesterol and triglyceride NMR derived signals, respectively. We conclude that dietary cholesterol is a causal factor in the development of both liver steatosis and hepatic inflammation.

In order to improve the mass spectra (MS)-based electronic nose (E-nose) performance, we have included the retention time data given by a new E-nose configuration based on a gas chromatograph–mass spectrometer (GC–MS) as a third dimension. The primary aim of this work is to show that the addition of the third dimension is useful, and brings extra information, helping in the classification of samples. By using this extra information our second goal is to optimize the chromatographic method in order to shorten the time of the chromatographic analysis to a minimum, while still having acceptable results. An experiment was designed in the form of 20 solutions with a high degree of similarity in mass spectra and chromatographic retention times. In order to optimize the system performance and reduce the time of the measurements to a minimum two different chromatographic methods were evaluated. By analyzing these solutions with two-way and three-way PCA, PARAFAC, PLS-DA and n-PLS-DA, and concatenated with supervised Fuzzy Artmap paradigms, we show that the addition of the extra information in the form of the chromatographic separation, even when using a short chromatographic separation, improves the results obtained, compared to the two-way analysis of the mass spectra or total ion chromatogram (TIC) alone. A third goal was to see which signal processing approach was the best suited. We found that when the retention time is used as a third dimension when chromatographic peaks are well resolved, two-way methods work better than their three-way counterparts, whereas in the case of a more challenging situation (a more coeluted chromatogram, with a much shorter measurement time) three-way methods perform better than classic two-way approaches.

Monocyte chemoattractant protein-1 (MCP-1) plays a relevant role in macrophage migration but recentfindings suggest an additional role in lipid and glucose metabolism. We report the use of 1 H NMRspectroscopy as a useful complementary method to assess the metabolic function of this gene ina comparative strategy. This metabonomic analysis was rapid, simple, quantitative and reproducible, andrevealed a suggestive relationship between the expression of the MCP-1 gene and hepatic glucose andtaurine concentrations. This approach should be considered in genetically modified mice when a meta-bolic alteration is suspected, or in routine assessment of metabolic phenotype. 2009 Elsevier Masson SAS. All rights reserved. 1. IntroductionThe evaluation of the activity of candidate genes or proteinsthrough transcriptomics and proteomics is usually expensive andcomplex. With the rationale that small differences in metabolismmay result in pronounced changes in metabolite concentrations,we designed a metabonomic approach to assess the function ofselected genes in genetically modified mice using a comparativestrategy between knockout and derived double-knockout mice. Totest this approach we chose MCP-1. Although the main rolereported for MCP-1 is related to monocyte trafficking and otherimmunological functions [1,2], there is evidence to suggest that itparticipates in metabolism [3]. We have demonstrated that MCP-1is involved in the development of obesity and in impaired glucosemetabolism in LDLr-deficient mice (LDLr

We report on the building of a simple and reproducible electronic nose based on commercially available metal oxide gas sensors aimed at monitoring the freshness of sardines stored at 4 °C. Sample delivery is based on the dynamic headspace method and four features are extracted from the transient response of each sensor. By using an unsupervised method, namely principal component analysis (PCA), we found that sardine samples could be grouped into three categories (fresh, medium and aged), which corresponded to an increasing number of days that sardines had spent under cold storage. Then, supervised linear or non-linear pattern recognition methods (PARC) such as discriminant factor analysis (DFA) or fuzzy ARTMAP neural networks (FANN) were successfully applied to build classification models to sort sardine samples according to these three states of freshness. The success rate in classification was 96.88% for the neural network classifier. Additionally, 10 volatile species that indicated the evolution of sardines with the number of days of cold storage were identified by SPME/MS/GC.