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.

From a non-targeted metabolomics approach to a targeted biomarkers strategy to highlight testosterone abuse in equine. Illustration of a methodological transfer between platforms and laboratories.

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids abuses in animal competitions, a non-targeted liquid chromatography coupled to high resolution mass spectrometry based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on four potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput liquid chromatography-selected reaction monitoring (LC-SRM). The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.

Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling.

Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.

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.

Extending the Lipidome Coverage by Combining Different Mass Spectrometric Platforms: An Innovative Strategy to Answer Chemical Food Safety Issues.

From a general public health perspective, a strategy combining non-targeted and targeted lipidomics MS-based approaches is proposed to identify disrupted patterns in serum lipidome upon growth promoter treatment in pigs. Evaluating the relative contributions of the platforms involved, the study aims at investigating the potential of innovative analytical approaches to highlight potential chemical food safety threats. Serum samples collected during an animal experiment involving control and treated pigs, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer™ platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The strategy enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation to cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of non-targeted and targeted MS approaches, various compartments of the pig serum lipidome could be explored, including commonly characterised lipids (Lipidyzer™), triglyceride isomers (Triglyceride platform) and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced coverage, level of characterization and applicability.

Moderate High Caloric Maternal Diet Impacts Dam Breast Milk Metabotype and Offspring Lipidome in a Sex-Specific Manner.

Lactation is a critical period during which maternal sub- or over-nutrition affect milk composition and offspring development that can have lasting health effects. The consequences of moderate high-fat, high-simple carbohydrate diet (WD) consumption by rat dams, during gestation and lactation, on milk composition and offspring blood lipidome and its growth, at weaning, were investigated by using a comprehensive lipidomic study on mass-spectrometric platform combined to targeted fatty- and free amino-acids analysis. This holistic approach allowed clear-cut differences in mature milk-lipidomic signature according to maternal diet with a similar content of protein, lactose and leptin. The lower WD-milk content in total fat and triglycerides (TGs), particularly in TGs-with saturated medium-chain, and higher levels in both sphingolipid (SL) and TG species with unsaturated long-chain were associated to a specific offspring blood-lipidome with decreased levels in TGs-containing saturated fatty acid (FA). The sexual-dimorphism in the FA-distribution in TG (higher TGs-rich in oleic and linoleic acids, specifically in males) and SL species (increased levels in very long-chain ceramides, specifically in females) could be associated with some differences that we observed between males and females like a higher total body weight gain in females and an increased preference for fatty taste in males upon weaning.

Correction: Alexandre-Gouabau et al. "Comprehensive Preterm Breast Milk Metabotype Associated with Optimal Infant Early Growth Pattern", Nutrients, 2019, 11, 528.

The authors wish to make a correction to Section 2 [...].

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.

Ammonium Fluoride as Suitable Additive for HILIC-Based LC-HRMS Metabolomics.

Hydrophilic Interaction Liquid Chromatography (HILIC) chromatography is widely applied in metabolomics as a complementary strategy to reverse phase chromatography. Nevertheless, it still faces several issues in terms of peak shape and compounds ionization, limiting the automatic de-convolution and data semi-quantification performed through dedicated software. A way to improve the chromatographic and ionization performance of a HILIC method is to modify the electrostatic interactions of the analytes with both mobile and stationary phases. In this study, using a ZIC-HILIC chromatographic phase, we evaluated the performance of ammonium fluoride (AF) as additive salt, comparing its performance to ammonium acetate (AA). Three comparative criteria were selected: (1) identification and peak quality of 34 standards following a metabolomics-specific evaluation approach, (2) an intraday repeatability test with real samples and (3) performing two real metabolomics fingerprints with the AF method to evaluate its inter-day repeatability. The AF method showed not only higher ionization efficiency and signal-to-noise ratio but also better repeatability and robustness than the AA approach. A tips and tricks section is then added, aiming at improving method replicability for further users. In conclusion, ammonium fluoride as additive salt presents several advantages and might be considered as a step forward in the application of robust HILIC methods in metabolomics.

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

Toward the characterisation of non-intentionally added substances migrating from polyester-polyurethane lacquers by comprehensive gas chromatography-mass spectrometry technologies.

Polyester-polyurethane lacquer, used to cover the inner surface of metallic food contact materials, may transfer non-intentionally added substances (NIAS) to the food. The identification of such a diversity of compounds, considered as migrating substances, requires taking advantage of complementary analytical platforms. Therefore, four types of gas chromatography-mass spectrometry (GCMS) couplings were investigated and compared for their abilities to identify migrating substances after acetonitrile extraction of two commercialised lacquers. In parallel, various ionisation sources, i.e. electron ionisation (EI) (70 eV and soft energies) and atmospheric-pressure chemical ionisation (APCI) as well as various mass analysers, i.e. quadrupole, time-of-flight (low and high resolution) and Orbitrap, were tested. Comparison of mass spectra with a commercial library for EI ionisation source led to the identification of two NIAS compounds, isophorone diisocyanate and 4,4'-diphenylmethane diisocyanate. Additionally, many cyclic oligoesters (four monomer units) were unambiguously identified according to supplier's declaration on starting materials used, primarily based on the molecular ion observed by APCI mode and characteristic fragment ions. High resolution mass analysers also enhanced confidence level in such NIAS identification. One- and two-dimensional GC were also investigated for separation assessment. Although GC × GC did not reveal additional NIAS, its use provided a valuable mapping of oligomers according to monomers composition. These results were compared to our previously published LC-MS study, carried out on the same lacquer samples. This study shows that LC and GC, along with their related ionisation techniques and their own selectivity, are complementary approaches, revealing different classes of compounds covering a wide range of volatility and polarity.

Comprehensive Preterm Breast Milk Metabotype Associated with Optimal Infant Early Growth Pattern.

Early nutrition impacts preterm infant early growth rate and brain development but can have long lasting effects as well. Although human milk is the gold standard for feeding new born full-term and preterm infants, little is known about the effects of its bioactive compounds on breastfed preterm infants' growth outcomes. This study aims to determine whether breast milk metabolome, glycome, lipidome, and free-amino acids profiles analyzed by liquid chromatography-mass spectrometry had any impact on the early growth pattern of preterm infants. The study population consisted of the top tercile-Z score change in their weight between birth and hospital discharge ("faster grow", n = 11) and lowest tercile ("slower grow", n = 15) from a cohort of 138 premature infants (27⁻34 weeks gestation). This holistic approach combined with stringent clustering or classification statistical methods aims to discriminate groups of milks phenotype and identify specific metabolites associated with early growth of preterm infants. Their predictive reliability as biomarkers of infant growth was assessed using multiple linear regression and taking into account confounding clinical factors. Breast-milk associated with fast growth contained more branched-chain and insulino-trophic amino acid, lacto-N-fucopentaose, choline, and hydroxybutyrate, pointing to the critical role of energy utilization, protein synthesis, oxidative status, and gut epithelial cell maturity in prematurity.

When LC-HRMS metabolomics gets ISO17025 accredited and ready for official controls - application to the screening of forbidden compounds in livestock.

Within the particular context of controlling chemical residues in food, an alternative to targeted approaches has emerged; it consists in the characterisation of physiological perturbations induced upon exposure of animals to a given chemical substance/class of substances to highlight suitable biomarkers addressing safety and/or regulatory issues. Metabolomics in particular has been investigated in the hope of identifying such biomarkers, and a range of studies have demonstrated the efficiency of the strategy. Until very recently, steps remained to be taken towards official or commercial implementation of corresponding tools. In particular, the lack of guidelines and criteria to validate such methods that do not target specific chemical species per se, constituted a bottleneck. In the present work, a metabolomics model dedicated to the detection of ß-agonist administration in bovines has been developed and fully validated; criteria (selectivity, robustness, stability, suspicion threshold definition, false positive and false negative rates) have been proposed in agreement with EU expectations (Dec 2002/657), enabling demonstration that performances comply with screening requirements. Although some of the biomarkers involved in the prediction model remain un-elucidated, the corresponding LC-HRMS method has recently been ISO17025 accredited, allowing for the very first official implementation of a metabolomics based strategy within French National Monitoring Plans.

Consequences of blunting the mevalonate pathway in cancer identified by a pluri-omics approach.

We have previously shown that the combination of statins and taxanes was a powerful trigger of HGT-1 human gastric cancer cells' apoptosis1. Importantly, several genes involved in the "Central carbon metabolism pathway in cancer", as reported in the Kyoto Encyclopedia of Genes and Genomes, were either up- (ACLY, ERBB2, GCK, MYC, PGM, PKFB2, SLC1A5, SLC7A5, SLC16A3,) or down- (IDH, MDH1, OGDH, P53, PDK) regulated in response to the drug association. In the present study, we conducted non-targeted metabolomics and lipidomics analyses by complementary methods and cross-platform initiatives, namely mass spectrometry (GC-MS, LC-MS) and nuclear magnetic resonance (NMR), to analyze the changes resulting from these treatments. We identified several altered biochemical pathways involved in the anabolism and disposition of amino acids, sugars, and lipids. Using the Cytoscape environment with, as an input, the identified biochemical marker changes, we distinguished the functional links between pathways. Finally, looking at the overlap between metabolomics/lipidomics and transcriptome changes, we identified correlations between gene expression modifications and changes in metabolites/lipids. Among the metabolites commonly detected by all types of platforms, glutamine was the most induced (6-7-fold), pointing to an important metabolic adaptation of cancer cells. Taken together, our results demonstrated that combining robust biochemical and molecular approaches was efficient to identify both altered metabolic pathways and overlapping gene expression alterations in human gastric cancer cells engaging into apoptosis following blunting the cholesterol synthesis pathway.

Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples.

Metabolomics aims at detecting and semi-quantifying small molecular weight metabolites in biological samples in order to characterise the metabolic changes resulting from one or more given factors and/or to develop models based on diagnostic biomarker candidates. Nevertheless, whatever the objective of a metabolomic study, one critical step consists in the structural identification of mass spectrometric features revealed by statistical analysis and this remains a real challenge. Indeed, this requires both an understanding of the studied biological system, the correct use of various analytical information (retention time, molecular weight experimentally measured, isotopic golden rules, MS/MS fragment pattern interpretation…), or querying online databases. In gas chromatography-electro-ionisation (EI)-mass spectrometry, EI leads to a very reproducible fragmentation allowing establishment of universal EI mass spectra databases (for example, the NIST database -National Institute of Standards and Technology) and thus facilitates the identification step. Unfortunately, the situation is different when working with liquid chromatography-mass spectrometry (LC-MS) since atmospheric pressure ionisation exhibits high inter-instrument variability regarding fragmentation. Therefore, the constitution of LC-MS "in-house" spectral databases appears relevant in this context. The present study describes the procedure developed and applied to increment 133 and 130 metabolites in databanks dedicated to analyses performed with LC-HRMS in positive and negative electrospray ionisation, and the use of these databanks for annotating quickly untargeted metabolomics fingerprints. This study also describes the optimization of the parameters controlling the automatic processing in order to obtain a fast and reliable annotation of a maximum of organic compounds. This strategy was applied to bovine kidney samples collected from control animals or animals treated with steroid hormones. Thirty-eight compounds were identified successfully in the generated chemical phenotypes, among which five were found to be candidate markers of the administration of these anabolic agents, demonstrating the efficiency of the developed strategy to reveal and confirm metabolite structures according to the high-throughput objective expected from these integrative biological approaches.

Metabolomics in food analysis: application to the control of forbidden substances.

Metabolomics is a science of interest in food analysis to describe and predict properties of food products and processes. It includes the development of analytical methods with the ultimate goal being the identification of so-called 'quality markers', (i.e. sets of metabolites that correlate with, for example, quality, safety, taste, or fragrance of foodstuffs). In turn, these metabolites are influenced by factors as genetic differences of the raw food ingredients (such as animal breed or crop species differences), growth conditions (such as climate, irrigation strategy, or feeding) or production conditions (such as temperature, acidity, or pressure). In cases where the routine-based measurement of a food property faces some limitations such as the lack of knowledge regarding the target compounds to monitor, monitoring based on a limited set of crucial biomarkers is a good alternative, which is of great interest for food safety purposes regarding growth promoting practices. Such an approach may be more efficient than using a classic approach based on a limited set of known metabolites of anabolic compounds. In this context, screening strategies allowing detection of the physiological response resulting from anabolic compound administration are promising approaches to detect their misuse. The global metabolomics workflow implemented for such studies is presented and illustrated through various examples of biological matrices profiling (tissue, blood, urine) and for different classes of anabolic compounds (steroids, ß-agonists and somatotropin).