Identification of bacterial lipopeptides as key players in IBS.

OBJECTIVES: Clinical studies revealed that early-life adverse events contribute to the development of IBS in adulthood. The aim of our study was to investigate the relationship between prenatal stress (PS), gut microbiota and visceral hypersensitivity with a focus on bacterial lipopeptides containing γ-aminobutyric acid (GABA). DESIGN: We developed a model of PS in mice and evaluated, in adult offspring, visceral hypersensitivity to colorectal distension (CRD), colon inflammation, barrier function and gut microbiota taxonomy. We quantified the production of lipopeptides containing GABA by mass spectrometry in a specific strain of bacteria decreased in PS, in PS mouse colons, and in faeces of patients with IBS and healthy volunteers (HVs). Finally, we assessed their effect on PS-induced visceral hypersensitivity. RESULTS: Prenatally stressed mice of both sexes presented visceral hypersensitivity, no overt colon inflammation or barrier dysfunction but a gut microbiota dysbiosis. The dysbiosis was distinguished by a decreased abundance of Ligilactobacillus murinus, in both sexes, inversely correlated with visceral hypersensitivity to CRD in mice. An isolate from this bacterial species produced several lipopeptides containing GABA including C14AsnGABA. Interestingly, intracolonic treatment with C14AsnGABA decreased the visceral sensitivity of PS mice to CRD. The concentration of C16LeuGABA, a lipopeptide which inhibited sensory neurons activation, was decreased in faeces of patients with IBS compared with HVs. CONCLUSION: PS impacts the gut microbiota composition and metabolic function in adulthood. The reduced capacity of the gut microbiota to produce GABA lipopeptides could be one of the mechanisms linking PS and visceral hypersensitivity in adulthood.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.

INTRODUCTION: Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. OBJECTIVES: To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. METHODS: We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. RESULTS: Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination. CONCLUSIONS: This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples. TRIAL REGISTRATION NUMBER: NCT03335644.

Safety assessment of cosmetics by read across applied to metabolomics data of in vitro skin and liver models.

As a result of the cosmetics testing ban, safety evaluations of cosmetics ingredients must now be conducted using animal-free methods. A common approach is read across, which is mainly based on structural similarities but can also be conducted using biological endpoints. Here, metabolomics was used to assess biological effects to enable a read across between a candidate cosmetic ingredient, DIV665, only studied using in vitro assays, and a structurally similar reference compound, PA102, previously investigated using traditional in vivo toxicity methods. The (1) cutaneous distribution after topical application, (2) skin metabolism, (3) liver metabolism and (4) effect on the intracellular metabolomic profiles of in vitro skin and hepatic models, SkinEthic®RHE model and HepaRG® cells were investigated. The compounds exhibited similar skin penetration and skin and liver metabolism, with small differences attributed to their physicochemical properties. The effects of both compounds on the metabolome of RHE and HepaRG® cells were similarly small, both in terms of the metabolites modulated and the magnitude of changes. The patterns of metabolome changes did not fit with any known signature relating to a mode of action known to be linked to liver toxicity e.g. modification of the Krebs cycle, urea synthesis and lipid metabolism, were more reflective of transient adaptive responses. Overall, these studies indicate that PA102 is biologically similar to DIV665, allowing read across of safety endpoints, such as in vivo sub-chronic (but not reproduction toxicity) studies, for the former to be applied to DIV665. Based on this, in the absence of animal data (which is prohibited for new chemicals), it could be concluded that DIV665 applied according to the consumer topical use scenario, is similar to PA102, and is predicted to exhibit low local skin and systemic toxicity.

Does the Phytochemical Diversity of Wild Plants Like the Erythrophleum genus Correlate with Geographical Origin?

Secondary metabolites are essential for plant survival and reproduction. Wild undomesticated and tropical plants are expected to harbor highly diverse metabolomes. We investigated the metabolomic diversity of two morphologically similar trees of tropical Africa, Erythrophleum suaveolens and E. ivorense, known for particular secondary metabolites named the cassaine-type diterpenoids. To assess how the metabolome varies between and within species, we sampled leaves from individuals of different geographic origins but grown from seeds in a common garden in Cameroon. Metabolites were analyzed using reversed phase LC-HRMS(/MS). Data were interpreted by untargeted metabolomics and molecular networks based on MS/MS data. Multivariate analyses enabled us to cluster samples based on species but also on geographic origins. We identified the structures of 28 cassaine-type diterpenoids among which 19 were new, 10 were largely specific to E. ivorense and five to E. suaveolens. Our results showed that the metabolome allows an unequivocal distinction of morphologically-close species, suggesting the potential of metabolite fingerprinting for these species. Plant geographic origin had a significant influence on relative concentrations of metabolites with variations up to eight (suaveolens) and 30 times (ivorense) between origins of the same species. This shows that the metabolome is strongly influenced by the geographical origin of plants (i.e., genetic factors).

Longitudinal analysis of the salivary metabolome of breast-fed and formula-fed infants over the first year of life.

INTRODUCTION: The salivary metabolome has been increasingly studied over the past ten years due to the potential of saliva as a non-invasive source of biomarkers. However, although saliva has been studied in relation to various diseases, its dynamic evolution during life is not known. This is particularly true for the first months of life. Infancy is indeed a critical period during which numerous behavioural and physiological events occur, such as dietary transitions and tooth eruption, which can lead to important biological modifications in the oral cavity. OBJECTIVES: The aim of this work was therefore to study the evolution of the salivary metabolome during the first months of life by 1H NMR. METHODS: Saliva of 32 infants with different milk feeding histories (breast vs formula) was collected at 6 stages, including 3 months old, 15 days before the onset of complementary feeding (CF), approximately 15 days after the onset of CF, approximately 21 days after the onset of CF and at approximately 11 and 15 months, and analysed. RESULTS: The longitudinal analysis showed a significant modification of the profiles of 18 metabolites over time; 14 presented an increase in abundance whereas 4 presented a decrease. These modifications seemed to be linked, for the most part, to an increase in oral microbial metabolism. Milk feeding history during the first months of life had no effect on metabolites. CONCLUSION: This work shows that the salivary metabolome should be considered when studying the changes occurring during infancy.

The food contaminant, deoxynivalenol, modulates the Thelper/Treg balance and increases inflammatory bowel diseases.

The incidence of inflammatory bowel diseases (IBD) is increasing in both Western and developing countries. IBD are multifactorial disorders involving complex interactions between genetic, immune, and environmental factors such as exposure to food contaminants. Deoxynivalenol (DON) is the most prevalent mycotoxin that contaminates staple food and induces intestinal breakdown and inflammatory response. To delineate the role of DON oral exposure in IBD, we used a Dextran sulfate sodium (DSS) colitis model in rats fed with a DON-contaminated diet or a control diet for 4 weeks. Colitis was induced in the 4th week by increasing concentrations of DSS in the drinking water (0, 2, 3 or 5%). DON exacerbated body weight loss and accelerated the appearance of symptoms in animals treated with DSS. DON increased morphological damage, pro-inflammatory markers (myeloperoxidase, CXCL-1 and IL-1ß) and immune cell responses. In lamina propria of the rat with colitis, DON increased adaptive and innate immune responses after anti-CD3/28 or LPS stimulation, respectively. In the spleen, DON increased IFNγ secretion and reduced Treg populations. Interestingly, De-epoxy-DON (DOM-1) a detoxified form of DON did not have any consequences on colitis. These results suggest that DON is a risk factor in the onset of IBD.

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

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.

Potential input from metabolomics for exploring and understanding the links between environment and health.

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Untargeted profiling of pesticide metabolites by LC-HRMS: an exposomics tool for human exposure evaluation.

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.