Summary: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation.Availability and implementation: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure.W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB).Contact: contact@workflow4metabolomics.org

The liver is a key organ of metabolic homeostasis with functions that oscillate in response to food intake. Although liver and gut microbiome crosstalk has been reported, microbiome-mediated effects on peripheral circadian clocks and their output genes are less well known. Here, we report that germ-free (GF) mice display altered daily oscillation of clock gene expression with a concomitant change in the expression of clock output regulators. Mice exposed to microbes typically exhibit characterized activities of nuclear receptors, some of which (PPARα, LXRβ) regulate specific liver gene expression networks, but these activities are profoundly changed in GF mice. These alterations in microbiome-sensitive gene expression patterns are associated with daily alterations in lipid, glucose, and xenobiotic metabolism, protein turnover, and redox balance, as revealed by hepatic metabolome analyses. Moreover, at the systemic level, daily changes in the abundance of biomarkers such as HDL cholesterol, free fatty acids, FGF21, bilirubin, and lactate depend on the microbiome. Altogether, our results indicate that the microbiome is required for integration of liver clock oscillations that tune output activators and their effectors, thereby regulating metabolic gene expression for optimal liver function.

Background: Bisphenol A (BPA) is a well-known endocrine disruptor used to manufacture polycarbonate plastics and epoxy resins. Exposure of pregnant rodents to low doses of BPA results in pleiotropic effects in their offspring.Objective: We used metabolomics—a method for determining metabolic changes in response to nutritional, pharmacological, or toxic stimuli—to examine metabolic shifts induced in vivo by perinatal exposure to low doses of BPA in CD-1 mice.Methods: Male offspring born to pregnant CD-1 mice that were exposed to vehicle or to 0.025, 0.25, or 25 µg BPA/kg body weight/day, from gestation day 8 through day 16 of lactation, were examined on postnatal day (PND) 2 or PND21. Aqueous extracts of newborns (PND2, whole animal) and of livers, brains, and serum samples from PND21 pups were submitted to 1H nuclear magnetic resonance spectroscopy. Data were analyzed using partial least squares discriminant analysis.Results: Examination of endogenous metabolic fingerprints revealed remarkable discrimination in whole extracts of the four PND2 newborn treatment groups, strongly suggesting changes in the global metabolism. Furthermore, statistical analyses of liver, serum, and brain samples collected on PND21 successfully discriminated among treatment groups. Variations in glucose, pyruvate, some amino acids, and neurotransmitters (γ-aminobutyric acid and glutamate) were identified.Conclusions: Low doses of BPA disrupt global metabolism, including energy metabolism and brain function, in perinatally exposed CD-1 mouse pups. Metabolomics can be used to highlight the effects of low doses of endocrine disruptors by linking perinatal exposure to changes in global metabolism.

Metabolomics is a key approach in modern functional genomics and systems biology.Due to the complexity of metabolomics data, the variety of experimental designs, and the variety of existing bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues.Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI).Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows.

: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, Springer Verlag, 2017, 10.1007/s11306-017-1244 Abstract:Introduction Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues. ResultsThe performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion ASICS is a completely automated procedure to identify and quantify metabolites in 1 H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

Human exposure to xenobiotics is usually estimated by indirect methods. Biological monitoring has emerged during the last decade to improve assessment of exposure. However, biomonitoring is still an analytical challenge, because the amounts of sample available are often very small yet analysis must be as thorough and sensitive as possible. The purpose of this work was to develop an untargeted "exposomics" approach by using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS), which was applied to the characterization of pesticide metabolites in urine from pregnant women from a French epidemiological cohort. An upgradable list of pesticides commonly used on different crops, with their metabolites (more than 400 substances) was produced. Raw MS data were then processed to extract signals from these substances. Metabolites were identified by tandem mass spectrometry; putative identifications were validated by comparison with standards and metabolites generated by experiments on animals. Finally, signals of identified compounds were statistically analyzed by use of multivariate methods. This enabled discrimination of exposure groups, defined by indirect methods, on the basis of four metabolites from two fungicides (azoxystrobin, fenpropimorph) used in cereal production. This original approach applied to pesticide exposure can be extended to a variety of contaminant families for upstream evaluation of exposure from food and the environment.

Peroxisome proliferator activated receptor α (PPARα) acts as a fatty acid sensor to orchestrate the transcription of genes coding for rate-limiting enzymes required for lipid oxidation in hepatocytes. Mice only lacking Pparα in hepatocytes spontaneously develop steatosis without obesity in aging. Steatosis can develop into non alcoholic steatohepatitis (NASH), which may progress to irreversible damage, such as fibrosis and hepatocarcinoma. While NASH appears as a major public health concern worldwide, it remains an unmet medical need. In the current study, we investigated the role of hepatocyte PPARα in a preclinical model of steatosis. For this, we used High Fat Diet (HFD) feeding as a model of obesity in C57BL/6 J male Wild-Type mice (WT), in whole-body Pparαdeficient mice (Pparα −/−) and in mice lacking Pparα only in hepatocytes (Pparα hep−/−). We provide evidence that Pparα deletion in hepatocytes promotes NAFLD and liver inflammation in mice fed a HFD. This enhanced NAFLD susceptibility occurs without development of glucose intolerance. Moreover, our data reveal that non-hepatocytic PPARα activity predominantly contributes to the metabolic response to HFD. Taken together, our data support hepatocyte PPARα as being essential to the prevention of NAFLD and that extra-hepatocyte PPARα activity contributes to whole-body lipid homeostasis. Non alcoholic fatty liver disease (NAFLD) has become a major public health concern worldwide 1. NAFLD ranges from benign steatosis to non alcoholic steatohepatitis (NASH), which may progress to irreversible damage, such as fibrosis or hepatocarcinoma. The hallmark of NAFLD is an elevated level of neutral lipids, which accumulate as lipid droplets in hepatocytes 2. Although the aetiology of the disease is not fully understood, it is strongly associated with obesity and type 2 diabetes (T2D). In human NAFLD, the fatty acids that accumulate in hepatocytes originate from dietary fat 3,4 , adipose tissue lipolysis and hepatic de novo lipogenesis 3. In T2D, adipose tissue insulin resistance promotes lipolysis, whereas hyperglycaemia combined with hyperinsulinemia sustains hepatic de novo lipogenesis 5. Given the burden of the NAFLD epidemic, identifying molecular players that can be targeted is a rather important issue 6,7. Moreover, finding drugs that may be used to treat NASH and its progression to irreversible liver disease is a so far unmet medical need to be solved 8,9. Among drugs currently being tested in clinical trials

BackgroundThe use of pesticides and the related environmental contaminations can lead to human exposure to various molecules. In early-life, such exposures could be responsible for adverse developmental effects. However, human health risks associated with exposure to complex mixtures are currently under-explored.ObjectiveThis project aims at answering the following questions: What is the influence of exposures to multiple pesticides on the metabolome? What mechanistic pathways could be involved in the metabolic changes observed?MethodsBased on the PELAGIE cohort (Brittany, France), 83 pregnant women who provided a urine sample in early pregnancy, were classified in 3 groups according to the surface of land dedicated to agricultural cereal activities in their town of residence. Nuclear magnetic resonance-based metabolomics analyses were performed on urine samples. Partial Least Squares Regression-Discriminant Analysis (PLS-DA) and polytomous regressions were used to separate the urinary metabolic profiles from the 3 exposure groups after adjusting for potential confounders.ResultsThe 3 groups of exposure were correctly separated with a PLS-DA model after implementing an orthogonal signal correction with pareto standardizations (R2 = 90.7% and Q2 = 0.53). After adjusting for maternal age, parity, body mass index and smoking habits, the most statistically significant changes were observed for glycine, threonine, lactate and glycerophosphocholine (upward trend), and for citrate (downward trend).ConclusionThis work suggests that an exposure to complex pesticide mixtures induces modifications of metabolic fingerprints. It can be hypothesized from identified discriminating metabolites that the pesticide mixtures could increase oxidative stress and disturb energy metabolism.