Aldehyde perception induces specific molecular responses in Laminaria digitata and affects algal consumption by a specialist grazer.

In the marine environment, distance signaling based on water-borne cues occurs during interactions between macroalgae and herbivores. In the brown alga Laminaria digitata from North-Atlantic Brittany, oligoalginates elicitation or grazing was shown to induce chemical and transcriptomic regulations, as well as emission of a wide range of volatile aldehydes, but their biological roles as potential defense or warning signals in response to herbivores remain unknown. In this context, bioassays using the limpet Patella pellucida and L. digitata were carried out for determining the effects of algal transient incubation with 4-hydroxyhexenal (4-HHE), 4-hydroxynonenal (4-HNE) and dodecadienal on algal consumption by grazers. Simultaneously, we have developed metabolomic and transcriptomic approaches to study algal molecular responses after treatments of L. digitata with these chemical compounds. The results indicated that, unlike the treatment of the plantlets with 4-HNE or dodecadienal, treatment with 4-HHE decreases algal consumption by herbivores at 100 ng.ml-1 . Moreover, we showed that algal metabolome was significantly modified according to the type of aldehydes, and more specifically the metabolite pathways linked to fatty acid degradation. RNAseq analysis further showed that 4-HHE at 100 ng.ml-1 can activate the regulation of genes related to oxylipin signaling pathways and specific responses, compared to oligoalginates elicitation. As kelp beds constitute complex ecosystems consisting of habitat and food source for marine herbivores, the algal perception of specific aldehydes leading to targeted molecular regulations could have an important biological role on kelps/grazers interactions.

FragHub: A Mass Spectral Library Data Integration Workflow.

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Electrospray ionization and heterogeneous matrix effects in liquid chromatography/mass spectrometry based meta-metabolomics: A biomarker or a suppressed ion?

RATIONALE: Correct biomarker determination in metabolomics is crucial for unbiased conclusions and reliable applications. However, this determination is subject to several drifts, e.g. matrix effects and ion suppression in Liquid Chromatography/Mass Spectrometry (LC/MS)-based approaches. This phenomenon provokes critical issues for biomarker determination, particularly during comparative studies dealing with samples exhibiting heterogeneous complexities. METHODS: Occurrence of the issue was coincidentally noticed when studying the environmental impact of a complex bioinsecticide: Bacillus thuringiensis israelensis. The studied samples comprised insecticide-spiked sediments and untreated control sediments. QuEChERS extractions followed by LC/ESI-Q/ToF analyses were performed on sediments after 15 days of incubation. Meta-metabolomes containing pesticide xenometabolites and sediment endometabolites were analyzed in depth using XCMS-based computational data preprocessing. Multivariate statistical analyses (PCA, OPLS-DA) and raw data crosschecks were performed to search for environmental biomarkers. RESULTS: Multivariate analyses and raw data crosschecks led to the selection of nine metabolites as biomarker candidates. However, when exploring the mass spectra, co-elutions were noticed between seven of these metabolites and multi-charged macromolecules originating from the pesticide. Provoked false positives were thus suspected due to a potential ion suppression exclusively occurring in the spiked samples. A dilution-based approach was then applied. It confirmed five metabolites as suppressed ions. CONCLUSIONS: Ion suppression should be considered as a critical issue for biomarker determination when comparing heterogeneous metabolic profiles. Raw chromatograms and mass spectra crosschecks are mandatory to reveal potential ion suppressions in such cases. Dilution is a suitable approach to filter reliable biomarker candidates before their identification and absolute quantification.

Simultaneous exploration of nutrients and pollutants in human milk and their impact on preterm infant growth: An integrative cross-platform approach.

Early nutritional management including fortified human breastmilk is currently recommended to fulfil the energy demands and counterbalance risks associated to preterm birth. However, little is known about the potential adverse effects of exposure to persistent organic pollutants (POPs) carried in human milk on preterm infant growth. We conducted a pilot study proving the application of an integrative analytical approach based on mass spectrometry (MS) coupled to advanced statistical models, favouring the comprehensive molecular profiling to support the identification of multiple biomarkers. We applied this workflow in the frame of a preterm infants' cohort to explore environmental determinants of growth. The combination of high resolution gas and liquid chromatography MS platforms generated a large molecular profile, including 102 pollutants and nutrients (targeted analysis) and 784 metabolites (non-targeted analysis). Data analysis consisted in a preliminary examination of associations between the signatures of POPs and the normalised growth of preterm infants, using multivariate linear regression adjusting for known confounding variables. A second analysis aimed to identify multidimensional biomarkers using a multiblock algorithm allowing the integration of multiple datasets in the growth model of preterm infants. The preliminary results did not suggest an impairment of preterm growth associated to the milk concentrations of POPs. The multiblock approach however revealed complex interrelated molecular networks of POPs, lipids, metabolites and amino acids in breastmilk associated to preterm infant growth, supporting the high potential of biomarkers exploration of this proposed workflow. Whereas the present study intended to identify simultaneously pollutant and nutrient exposure profiles associated to early preterm infant growth, this workflow may be easily adapted and applied to other matrices (e.g. serum) and research settings, favouring the functional exploration of environmental determinants of complex and multifactorial diseases.

HaloSeeker 1.0: A User-Friendly Software to Highlight Halogenated Chemicals in Nontargeted High-Resolution Mass Spectrometry Data Sets.

In the present work, we address the issue of nontargeted screening of organohalogenated chemicals in complex matrixes. A global strategy aiming to seek halogenated signatures in full-scan high-resolution mass spectrometry (HRMS) fingerprints was developed. The resulting all-in-one user-friendly application, HaloSeeker 1.0, was developed to promote the accessibility of associated in-house bioinformatics tools to a large audience. The ergonomic web user interface avoids any interactions with the coding component while allowing interactions with the data, including peak detection (features), deconvolution, and comprehensive accompanying manual review for chemical formula assignment. HaloSeeker 1.0 was successfully applied to a marine sediment HRMS data set acquired on a liquid chromatography-heated electrospray ionization [LC-HESI(-)] Orbitrap instrument ( R = 140 000 at m/z 200). Among the 4532 detected features, 827 were paired and filtered in 165 polyhalogenated clusters. HaloSeeker was also compared to three similar tools and showed the best performances. HaloSeeker's ability to filter and investigate halogenated signals was demonstrated and illustrated by a potential homologue series with C12H xBr yCl zO2 as a putative general formula.

Resolution of quantitative resistance to clubroot into QTL-specific metabolic modules.

Plant disease resistance is often under quantitative genetic control. Thus, in a given interaction, plant cellular responses to infection are influenced by resistance or susceptibility alleles at different loci. In this study, a genetic linkage analysis was used to address the complexity of the metabolic responses of Brassica napus roots to infection by Plasmodiophora brassicae. Metabolome profiling and pathogen quantification in a segregating progeny allowed a comparative mapping of quantitative trait loci (QTLs) involved in resistance and in metabolic adjustments. Distinct metabolic modules were associated with each resistance QTL, suggesting the involvement of different underlying cellular mechanisms. This approach highlighted the possible role of gluconasturtiin and two unknown metabolites in the resistance conferred by two QTLs on chromosomes C03 and C09, respectively. Only two susceptibility biomarkers (glycine and glutathione) were simultaneously linked to the three main resistance QTLs, suggesting the central role of these compounds in the interaction. By contrast, several genotype-specific metabolic responses to infection were genetically unconnected to resistance or susceptibility. Likewise, variations of root sugar profiles, which might have influenced pathogen nutrition, were not found to be related to resistance QTLs. This work illustrates how genetic metabolomics can help to understand plant stress responses and their possible links with disease.

A multidimensional 1H NMR lipidomics workflow to address chemical food safety issues.

INTRODUCTION: Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability. OBJECTIVES: The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization. METHOD: The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes-including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes-are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from ß-agonists diet fed pigs. RESULTS: Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework. CONCLUSION: This work demonstrates the potential of fast multidimensional 1H NMR-suited with an appropriate sample preparation-for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.

Elucidation of non-intentionally added substances migrating from polyester-polyurethane lacquers using automated LC-HRMS data processing.

An untargeted strategy aiming at identifying non-intentionally added substances (NIAS) migrating from coatings was developed. This innovative approach was applied to two polyester-polyurethane lacquers, for which suppliers previously provided the identity of the monomers involved. Lacquers were extracted with acetonitrile and analyzed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Data, acquired in the full scan mode, were processed using an open-source R-environment (xcms and CAMERA packages) to list the detected features and deconvolute them in groups related to individual compounds. The most intense groups, accounting for more than 85% of cumulated feature intensities, were then investigated. A homemade database, populated with predicted polyester oligomer combinations from a relevant selection of diols and diacids, enabled highlighting the presence of 14 and 17 cyclic predicted polyester oligomers in the two lacquers, including three mutual combinations explained by common known monomers. Combination hypotheses were strengthened by chromatographic considerations and by the investigation of fragmentation patterns. Regarding unpredicted migrating substances, four monomers were hypothesised to explain several polyester or caprolactam oligomer series. Finally, considering both predicted and tentatively elucidated unpredicted oligomers, it was possible to assign hypotheses to features representing up to 82% and 90% of the cumulated intensities in the two lacquers, plus 9% and 3% (respectively) originating from the procedural blank. Graphical abstract Elucidation of non-intentionally added substances.

Automated Detection of Natural Halogenated Compounds from LC-MS Profiles-Application to the Isolation of Bioactive Chlorinated Compounds from Marine-Derived Fungi.

A collection of culture extracts obtained from several marine-derived fungal strains collected on the French Atlantic coast was investigated by high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) in order to prospect for halogenated compounds and to identify potentially new ones. To achieve a fast, automated, and efficient data analysis, a bioinformatics tool named MeHaloCoA (Marine Halogenated Compound Analysis) was developed and included into R. After extraction of all the peaks from the metabolic fingerprints and their associated mass spectra, a mathematical filter based on mass isotopic profiles allowed the selective detection of halogenated (Cl and Br) molecules. Integrating MeHaloCoA into a dereplication approach allowed the identification of known and new halogenated compounds in a competitive amount of time. Subsequent targeted purification led to the isolation of several chlorinated metabolites, including two new natural products with bioactive potential, griseophenone I and chlorogriseofulvin, from a marine-derived Penicillium canescens strain.

Time Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced Penicillium.

This work aimed at studying metabolome variations of marine fungal strains along their growth to highlight the importance of the parameter "time" for new natural products discovery. An untargeted time-scale metabolomic study has been performed on two different marine-derived Penicillium strains. They were cultivated for 18 days and their crude extracts were analyzed by HPLC-DAD-HRMS (High Performance Liquid Chromatography-Diode Array Detector-High Resolution Mass Spectrometry) each day. With the example of griseofulvin biosynthesis, a pathway shared by both strains, this work provides a new approach to study biosynthetic pathway regulations, which could be applied to other metabolites and more particularly new ones. Moreover, the results of this study emphasize the interest of such an approach for the discovery of new chemical entities. In particular, at every harvesting time, previously undetected features were observed in the LC-MS (Liquid Chromatography-Mass Spectrometry) data. Therefore, harvesting times for metabolite extraction should be performed at different time points to access the hidden metabolome.

Functional characterization of terpene synthases and chemotypic variation in three lavender species of section Stoechas.

Lavandula pedunculata (Mill.) Cav. subsp. lusitanica, Lavandula stoechas L. subsp. stoechas and Lavandula viridis l'Hér. are three lavender taxa that belong to the botanical section Stoechas and are widely used as aromatherapy, culinary herb or folk medicine in many Mediterranean regions. The analysis of their bioactive volatile constituents revealed the presence of 124 substances, the most abundant being the bicyclic monoterpenes fenchone, camphor and 1,8-cineole that give these three species their respective chemotypes. Most noteworthy was fenchone which, with its reduced form fenchol, made 48% of the total volatile constituents of L. pedunculata while present at 2.9% in L. stoechas and undetectable in L. viridis. In order to provide a molecular explanation to the differences in volatile compounds of these three species, two monoterpene synthases (monoTPS) and one sesquiterpene synthase (sesquiTPS) were cloned in L. pedunculata and functionally characterized as fenchol synthase (LpFENS), α-pinene synthase (LpPINS) and germacrene A synthase (LpGEAS). The two other lavender species contained a single orthologous gene for each of these three classes of TPS with similar enzyme product specificities. Expression profiles of FENS and PINS genes matched the accumulation profile of the enzyme products unlike GEAS. This study provides one of the rare documented cases of chemotype modification during plant speciation via changes in the level of plant TPS gene expression, and not functionality.

Isolation and functional characterization of a τ-cadinol synthase, a new sesquiterpene synthase from Lavandula angustifolia.

In this paper we characterize three sTPSs: a germacrene D (LaGERDS), a (E)-ß-caryophyllene (LaCARS) and a τ-cadinol synthase (LaCADS). τ-cadinol synthase is reported here for the first time and its activity was studied in several biological models including transiently or stably transformed tobacco species. Three dimensional structure models of LaCADS and Ocimum basilicum γ-cadinene synthase were built by homology modeling using the template structure of Gossypium arboreum δ-cadinene synthase. The depiction of their active site organization provides evidence of the global influence of the enzymes on the formation of τ-cadinol: instead of a unique amino-acid, the electrostatic properties and solvent accessibility of the whole active site in LaCADS may explain the stabilization of the cadinyl cation intermediate. Quantitative PCR performed from leaves and inflorescences showed two patterns of expression. LaGERDS and LaCARS were mainly expressed during early stages of flower development and, at these stages, transcript levels paralleled the accumulation of the corresponding terpene products (germacrene D and (E)-ß-caryophyllene). By contrast, the expression level of LaCADS was constant in leaves and flowers. Phylogenetic analysis provided informative results on potential duplication process leading to sTPS diversification in lavender.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome.

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

A comprehensive multiplatform metabolomic analysis reveals alterations of 2-hydroxybutyric acid among women with deep endometriosis related to the pesticide trans-nonachlor.

BACKGROUND: Exposure to persistent organic pollutants (POPs) has been related to the risk of endometriosis however the mechanisms remain unclear. The objective of the present study was to characterize the metabolic profiles underpinning the associations between POPs and endometriosis risk. METHODOLOGY: A hospital-based case-control study was conducted in France to recruit women with and without surgically confirmed deep endometriosis. Women's serum was analyzed using gas and liquid chromatography coupled to high-resolution mass spectrometry (HRMS) to measure the levels of polychlorinated biphenyls (PCBs), organochlorinated pesticides (OCPs) and per-/polyfluoroalkyl substances (PFAS). A comprehensive metabolomic profiling was conducted using targeted HRMS and 1H nuclear magnetic resonance (1H NMR) to cover polar and non-polar fractions. A "meet-in-the-middle" statistical framework was applied to identify the metabolites related to endometriosis and POP levels, using multivariate linear and logistic regressions adjusting for confounding variables. RESULTS: Fourteen PCBs, six OCPs and six PFAS were widely found in almost all serum samples. The pesticide trans-nonachlor was the POP most strongly and positively associated with deep endometriosis risk, with odds ratio (95 % confidence interval) of 2.42 (1.49; 4.12), followed by PCB180 and 167. Women with endometriosis exhibited a distinctive metabolic profile, with elevated serum levels of lactate, ketone bodies and multiple amino acids and lower levels of bile acids, phosphatidylcholines (PCs), cortisol and hippuric acid. The metabolite 2-hydroxybutyrate was simultaneously associated to endometriosis risk and exposure to trans-nonachlor. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive metabolome-wide association study of endometriosis, integrating ultra-trace profiling of POPs. The results confirmed a metabolic alteration among women with deep endometriosis that could be also associated to the exposure to POPs. Further observational and experimental studies will be required to delineate the causal ordering of those associations and gain insight on the underlying mechanisms.

MSeasy: unsupervised and untargeted GC-MS data processing.

UNLABELLED: MSeasy performs unsupervised data mining on gas chromatography-mass spectrometry data. It detects putative compounds within complex metabolic mixtures through the clustering of mass spectra. Retention times or retention indices are used after clustering, together with other validation criteria, for quality control of putative compounds. The package generates a fingerprinting or profiling matrix compatible with NIST mass spectral search program and ARISTO webtool (Automatic Reduction of Ion Spectra To Ontology) for molecule identification. Most commonly used file formats, NetCDF, mzXML and ASCII, are acceptable. A graphical and user-friendly interface, MSeasyTkGUI, is available for R novices. AVAILABILITY: MSeasy and MSeasytkGUI are implemented as R packages available at http://cran.r-project.org/web/packages/MSeasy/index.html and http://cran.r-project.org/web/packages/MSeasyTkGUI/index.html.

Cytotoxicity, fractionation and dereplication of extracts of the dinoflagellate Vulcanodinium rugosum, a producer of pinnatoxin G.

Pinnatoxin G (PnTX-G) is a marine toxin belonging to the class of cyclic imines and produced by the dinoflagellate Vulcanodinium rugosum. In spite of its strong toxicity to mice, leading to the classification of pinnatoxins into the class of "fast-acting toxins", its hazard for human health has never been demonstrated. In this study, crude extracts of V. rugosum exhibited significant cytotoxicity against Neuro2A and KB cells. IC50 values of 0.38 µg mL⁻¹ and 0.19 µg mL⁻¹ were estimated on Neuro2A cells after only 24 h of incubation and on KB cells after 72 h of incubation, respectively. In the case of Caco-2 cells 48 h after exposure, the crude extract of V. rugosum induced cell cycle arrest accompanied by a dramatic increase in double strand DNA breaks, although only 40% cytotoxicity was observed at the highest concentration tested (5 µg mL⁻¹). However, PnTX-G was not a potent cytotoxic compound as no reduction of the cell viability was observed on the different cell lines. Moreover, no effects on the cell cycle or DNA damage were observed following treatment of undifferentiated Caco-2 cells with PnTX-G. The crude extract of V. rugosum was thus partially purified using liquid-liquid partitioning and SPE clean-up. In vitro assays revealed strong activity of some fractions containing no PnTX-G. The crude extract and the most potent fraction were evaluated using full scan and tandem high resolution mass spectrometry. The dereplication revealed the presence of a major compound that could be putatively annotated as nakijiquinone A, N-carboxy-methyl-smenospongine or stachybotrin A, using the MarinLit™ database. Further investigations will be necessary to confirm the identity of the compounds responsible for the cytotoxicity and genotoxicity of the extracts of V. rugosum.

Deeper insights into the effects of low dietary levels of polychlorinated biphenyls on pig metabolism using gas chromatography-high resolution mass spectrometry metabolomics.

Polychlorinated biphenyls (PCBs) are a class of contaminants of great concern, linked to the development of many chronic diseases. Adverse effects of PCBs have been documented in humans after accidental and massive exposure. However, little is known about the effect of chronic exposure to low-dose PCB mixtures, and studies regarding scattered lifetime exposures to non-dioxin-like (NDL)-PCBs are especially missing. In this work, serum samples from pigs chronically exposed through their diet during 22 days to Aroclor 1260 (i.e. a commercially available mixture of NDL-PCBs) underwent a metabolomics analysis using gas chromatography-high resolution mass spectrometry (GC-HRMS), with the objective to investigate the effect of exposure to low doses of NDL-PCBs (few ng/kg body weight (b.w.) per day). The study showed that the serum profiles of 84 metabolites are significantly altered by the administration of Aroclor 1260, of which 40 could be identified at level 1. The aggregate interpretation of the results of this study, together with the outcome of a previous one involving LC-HRMS profiling, provided a substantial and concise overview of the effect of low dose exposure to NDL-PCBs, reflecting the hepatotoxic and neurotoxic effects already reported in literature at higher and longer exposures. These results are intended to contribute to the debate on the current toxicological reference values for these substances.

Docosahexaenoic acid and etanercept could reduce functional and metabolic alterations during collagen-induced arthritis in rats without any synergistic effect.

AIMS: Rheumatoid arthritis is an autoimmune disease which induces chronic inflammation and increases the risk for sarcopenia and metabolic abnormalities. Nutritional strategies using omega 3 polyunsaturated fatty acids could be proposed to alleviate inflammation and improve the maintenance of lean mass. Independently, pharmacological agents targeting key molecular regulators of the pathology such as TNF alpha could be proposed, but multiple therapies are frequently necessary increasing the risk for toxicity and adverse effects. The aim of the present study was to explore if the combination of an anti-TNF therapy (Etanercept) with dietary supplementation with omega 3 PUFA could prevent pain and metabolic effects of RA. MATERIALS AND METHODS: RA was induced using collagen-induced arthritis (CIA) in rats to explore of supplementation with docosahexaenoic acid, treatment with etanercept or their association could alleviate symptoms of RA (pain, dysmobility), sarcopenia and metabolic alterations. KEY FINDINGS: We observed that Etanercept had major benefits on pain and RA scoring index. However, DHA could reduce the impact on body composition and metabolic alterations. SIGNIFICANCE: This study revealed for the first time that nutritional supplementation with omega 3 fatty acid could reduce some symptoms of rheumatoid arthritis and be an effective preventive treatment in patients who do not need pharmacological therapy, but no sign of synergy with an anti-TNF agent was observed.

Urinary metabolomic profiling from spontaneous tolerant kidney transplanted recipients shows enrichment in tryptophan-derived metabolites.

BACKGROUND: Operational tolerance is the holy grail in solid organ transplantation. Previous reports showed that the urinary compartment of operationally tolerant recipients harbor a specific and unique profile. We hypothesized that spontaneous tolerant kidney transplanted recipients (KTR) would have a specific urinary metabolomic profile associated to operational tolerance. METHODS: We performed metabolomic profiling on urine samples from healthy volunteers, stable KTR under standard and minimal immunosuppression and spontaneous tolerant KTR using liquid chromatography in tandem with mass spectrometry. Supervised and unsupervised multivariate computational analyses were used to highlight urinary metabolomic profile and metabolite identification thanks to workflow4metabolomic platform. FINDINGS: The urinary metabolome was composed of approximately 2700 metabolites. Raw unsupervised clustering allowed us to separate healthy volunteers and tolerant KTR from others. We confirmed by two methods a specific urinary metabolomic signature in tolerant KTR mainly driven by kynurenic acid independent of immunosuppressive drugs, serum creatinine and gender. INTERPRETATION: Kynurenic acid and tryptamine enrichment allowed the identification of putative pathways and metabolites associated with operational tolerance like IDO, GRP35 and AhR and indole alkaloids. FUNDING: This study was supported by the ANR, IRSRPL and CHU de Nantes.

Nandrolone and estradiol biomarkers identification in bovine urine applying a liquid chromatography high-resolution mass spectrometry metabolomics approach.

With the aim of specifically investigating patterns associated with three steroid treatments (17ß-nandrolone, 17ß-estradiol, and 17ß-nandrolone + 17ß-estradiol) in bovine, an reversed phase liquid chromatography (RPLC)-electrospray ionization (ESI)(+/-)-high-resolution mass spectrometry (HRMS) study was conducted to characterize the urinary profiles of involved animals. Although specific fingerprints with strong differences could be highlighted between urinary metabolite profiles within urine samples collected on control and treated animals, it appeared further that significant discriminations could also be observed between steroid treatments, evidencing thus specific patterns and candidate biomarkers associated to each treatment. An MS-2 structural elucidation step enabled level-1 identification of two biomarkers mainly involved in energy pathways, in relation to skeletal muscle functioning. These results make it possible to envisage a global strategy for the detection of anabolic practices involving steroids, while at the same time providing clues as to the compounds used, which would facilitate the confirmation stage to follow.