Determination of bile acids in serum of pigs exposed to polychlorinated biphenyls by liquid chromatography-mass spectrometry.

Exposure to polychlorinated biphenyls (PCBs) has been linked to dyslipidemia. Under acute exposure to PCBs, it has been observed that the secretion of bile acids (BAs) can be impacted, limiting (indirectly) lipid absorption in the gut. In this context, two non-targeted metabolomics studies on pig serum have recently suggested that BA concentrations may fluctuate under exposure to current non-dioxin-like (NDL)-PCB levels in food, reflecting the acute effects of such chronic exposure. The objective of this research is to implement a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for BA analysis in order to validate the findings of previous metabolomics studies, in which BA levels in serum samples from pigs exposed to environmental doses of NDL-PCBs were highlighted to be affected. The proposed LC-MS method involves the use of a C18-pentafluorophenyl LC column, which is not usually selected for the separation of BAs, but shows better performance for the separation of isomers than typical C18 columns. This LC-MS method shows excellent analytical performance such as low limits of detection (LODs) (≤1 ng/mL for most BAs) and good linearity (R2 > 0.994), while no matrix effect was observed. A total of 13 BAs have been quantified, while further BA isomers could be detected and semi-quantified. The application of this targeted LC-MS method confirmed previous findings, suggesting that exposure to low doses of NDL-PCBs decreases the concentration of BAs (i.e., glycochenodeoxycholic acid, hyodeoxycholic acid and taurochenodeoxycholic acid) while the effect on the precursors (cholic acid and chenodeoxycholic acid) is less pronounced.

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.

A comparison of hydrophilic interaction liquid chromatography and capillary electrophoresis for the metabolomics analysis of human serum.

Cationic, anionic, zwitterionic and, partially polar metabolites are very important constituents of blood serum. Several of these metabolites underpin the core metabolism of cells (e.g., Krebs cycle, urea cycle, proteins synthesis, etc.), while others might be considered ancillary but still important to grasp the status of any organism through blood serum analysis. Due to its wide chemical diversity, modern metabolomics analysis of serum is still struggling to provide a complete and comprehensive picture of the polar metabolome, due to the limitations of each specific analytical method. In this study, two metabolomics-based analytical methods using the most successful techniques for polar compounds separation in human serum samples, namely hydrophilic interaction liquid chromatography (HILIC) and capillary electrophoresis (CE), are evaluated, both coupled to a high-resolution time-of-flight mass spectrometer via electrospray ionization (ESI-Q-TOF-MS). The performance of the two methods have been compared using five terms of comparison, three of which are specific to metabolomics, such as (1) compounds' detectability (2) Pezzatti score (Pezzatti et al. 2018), (3) intra-day precision (repeatability), (4) ease of automatic analysis of the data (through a common deconvolution alignment and extrapolation software, MS-DIAL, and (5) time & cost analysis. From this study, HILIC-MS proved to be a better tool for polar metabolome analysis, while CE-MS helped identify some interesting variables that gave it interest in completing metabolome coverage in metabolomics studies. Finally, in this framework, MS-DIAL demonstrates for the first time its ability to process CE data for metabolomics, although it is not designed for it.

Metabolomics and lipidomics to identify biomarkers of effect related to exposure to non-dioxin-like polychlorinated biphenyls in pigs.

Recent epidemiological studies show that current levels of exposure to polychlorinated biphenyls (PCBs) remain of great concern, as there is still a link between such exposures and the development of chronic environmental diseases. In this sense, most studies have focused on the health effects caused by exposure to dioxin-like PCBs (DL-PCBs), although chemical exposure to non-dioxin-like PCB (NDL-PCB) congeners is more significant. In addition, adverse effects of PCBs have been documented in humans after accidental and massive exposure, but little is known about the effect of chronic exposure to low-dose PCB mixtures. In this work, exposure to Aroclor 1260 (i.e. a commercially available mixture of PCBs consisting primarily of NDL-PCB congeners) in pigs is investigated as new evidence in the risk assessment of NDL-PCBs. This animal model has been selected due to the similarities with human metabolism and to support previous toxicological studies carried out with more frequently used animal models. Dietary exposure doses in the order of few ng/kg body weight (b.w.) per day were applied. As expected, exposure to Aroclor 1260 led to the bioaccumulation of NDL-PCBs in perirenal fat of pigs. Metabolomics and lipidomics have been applied to reveal biomarkers of effect related to Aroclor 1260 exposure, and by extension to NDL-PCB exposure, for 21 days. In the metabolomics analysis, 33 metabolites have been identified (level 1 and 2) as significantly altered by the Aroclor 1260 administration, while in the lipidomics analysis, 39 metabolites were putatively annotated (level 3) and associated with NDL-PCB exposure. These biomarkers are mainly related to the alteration of fatty acid metabolism, glycerophospholipid metabolism and tryptophan-kynurenine pathway.

(Poly)phenolic compounds and gut microbiome: new opportunities for personalized nutrition.

For decades, (poly)phenols have been linked to cardiometabolic health, but population heterogeneity limits their apparent efficacy and the development of tailored, practical protocols in dietary interventions. This heterogeneity is likely determined by the existence of different metabotypes, sub-populations of individuals metabolizing some classes of (poly)phenols differently. The gut microbiota plays a major role in this process. The impact of microbiota-related phenolic metabotypes on cardiometabolic health is becoming evident, although the picture is still incomplete, and data are absent for some classes of (poly)phenols. The lack of a complete understanding of the main microbial actors involved in the process complicates the picture. Elucidation of the mechanisms behind phenolic metabotypes requires novel experimental designs that can dissect the inter-individual variability. This paper, in addition to providing an overview on the current state-of-the-art, proposes wider metabotyping approaches as a means of paving the way towards effective personalized nutrition with dietary (poly)phenols.

Coupling Complete Blood Count and Steroidomics to Track Low Doses Administration of Recombinant Growth Hormone: An Anti-Doping Perspective.

Growth Hormone (GH) under its human recombinant homologue (rhGH), may be abused by athletes to take advantage of its well-known anabolic and lipolytic properties; hence it is prohibited in sports by the World Anti-Doping Agency. Due to the rapid turnover of rhGH, anti-doping screening tests have turned to monitor two endocrine biomarkers (IGF-I and P-III-NP), but unfortunately, they show population-wise variability, limiting the identification rate of rhGH users. Previous studies have evidenced the numerous effects of GH on human physiology, especially in hematopoiesis and steroidogenesis. In this work, aiming to discover novel physiological rhGH biomarkers, we analyzed the complete blood count and the steroidomics profile of healthy, physically active, young males treated either with EPO + rhGH or EPO + placebo. The time-trends of these two physiological routes have been analyzed through geometric trajectory analysis (GTA) and OPLS-DA. Individuals supplemented with micro-doses of rhGH exhibited different leukopoietic and steroidal profiles compared to the control population, suggesting a role of the rhGH in both pathways. In the article, hypotheses on the observed differences are discussed according to the most recent literature and compared to results in animal models. The use of leukopoietic and steroidal biomarkers together with endocrine biomarkers (IGF-1 and P-III-NP) allows to correctly classify over 98% of samples with no false positives, miss-classifying only one single sample (false negative) over a total of 56; a promising result, if compared to the current rhGH detection strategies.

Non-targeted screening methodology to characterise human internal chemical exposure: Application to halogenated compounds in human milk.

Suspect and non-targeted screening approaches are a matter of increasing interest notably with regard to the Exposome contextual framework, but their application to human samples still remains limited at this date. The aim of the present study was to develop a non-targeted workflow from sample preparation to data processing and method assessment to characterise the human internal chemical exposure at early life stage. The method was focused on human milk to investigate mother and newborn exposure to known organic contaminants and to extend the characterisation to unknown compounds. We specifically focused on halogenated biomarkers of exposure due to persistence and potential toxicological impact reasons. The newly developed approach was based on a simple and fast sample preparation followed by a comprehensive analysis by both liquid and gas phase chromatography coupled to high resolution mass spectrometry. Critical steps of the non-targeted workflow as the method assessment have been addressed with a reference mix of 30 chlorinated and brominated contaminants encompassing various substances groups and a statistical approach. Data processing until the identification of biomarkers of exposure was possible with homemade bioinformatics tools. On the other hand, the method was validated by the identification of historical chemicals as hexachlorobenzene and p,p'-DDE and emerging chemical as 4-hydroxychlorothalonil. This approach opens the door to further extensions and consolidations to offer new capabilities for exposomics and environmental health research.

Microbial community dynamics in phyto-thermotherapy baths viewed through next generation sequencing and metabolomics approach.

Phyto-thermotherapy is a treatment consisting in immersing oneself in baths of self-heating alpine grass, to benefit of the heat and rich aromatic components released by the process. The aim of this study was to characterize the bacterial and fungal diversity of three phyto-thermal baths (PTB) performed in three different months, and to compare the data with the profile of the volatile organic compounds (VOCs) of the process. All the data collected showed that PTBs were structured in two stages: the first three days were characterised by an exponential rise of the temperature, a fast bacterial development, higher microbial diversity and higher concentrations of plant aliphatic hydrocarbons. The second stage was characterised by a stable high temperature, shrinkage of the microbial diversity with a predominance of few bacterial and fungi species and higher concentrations of volatiles of microbial origin. Erwinia was the dominant microbial species during the first stage and probably responsible of the self-heating process. In conclusion, PTBs has shown both similarities with common self-heating processes and important peculiarities such as the absence of pathogenic bacteria and the dominance of plant terpenoids with health characteristics among the VOCs confirming the evidence of beneficial effects in particular in the first three days.

Applying metabolomics to detect growth hormone administration in athletes: Proof of concept.

Growth hormone (GH), an endogenous peptide regulating anabolism and lipolysis in humans, is known to be abused by athletes to improve their performance. Despite the development of two distinct screening methods, few positive cases have been reported by the antidoping authorities, probably due to the quick turnover of GH and the masking effects of age, ethnicity, and sex. Apart from growth regulation, GH is known to affect several metabolic pathways in humans including ketosis, amino-acid uptake, and protein breakdown. It is reasonable to imagine observing its markers of effects through the leading tool on metabolism study, metabolomics. In this proof-of-concept study, a cohort of well-trained volunteers was split in two equal groups and administered with micro-doses of EPO or EPO + GH every second day for 2 weeks. Urine and plasma samples were collected before, during, and after treatment and analyzed using metabolomics and lipidomics approaches. The results show that, by applying a direct discriminant analysis on the treated groups, it is possible to distinguish the treatments, and to use this difference to classify them correctly. High intragroup variability is observed, due to the subject-specific effect of the hormones. Through time 0 centering the data, a longitudinally tracking of the group was performed and a higher difference was observed between the groups, including a perfect classification of the samples before and after the treatments.

A role for metabolomics in the antidoping toolbox?

Evidence of the continuous rise of novel doping agents and novel doping strategies calls for the development of more accurate multi-target screening methods. Direct multi-target screening approaches are restricted to the targeted substances and their turnover. The development of effective "indirect" screening methods requires a priori a deep understanding of the metabolism of the substance. The biological passport has been demonstrated to be very effective, but it is limited to about 20 indirect parameters. The standard antidoping analytical methods are hence targeted and do not aim directly to identify unknown substances. Also, the detection of doping agents is limited by the excretion of the substance. This study considers metabolomics for the screening of performance enhancing hormone abuse by athletes, based on the following pieces of evidence: (1) hormones have a strong influence on human metabolism, changing several parameters in many tissues, organs, and bio-fluids; (2) metabolomics has been demonstrated to be a very accurate tool to depict the metabolic status of several organisms, tissues, and for several human diseases, including hormone deficiencies; (3) metabolomics has been demonstrated to be able to distinguish hormone-treated animals from controls in many species, without the need for a priori knowledge of the metabolism for the specific substance. The literature shows that metabolomics could be an appropriate tool to detect hormone abuse, keeping in mind the strength and the limitation of such an approach.

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.

The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification.

Compound identification is the main hurdle in LC-HRMS-based metabolomics, given the high number of 'unknown' metabolites. In recent years, numerous in silico fragmentation simulators have been developed to simplify and improve mass spectral interpretation and compound annotation. Nevertheless, expert mass spectrometry users and chemists are still needed to select the correct entry from the numerous candidates proposed by automatic tools, especially in the plant kingdom due to the huge structural diversity of natural compounds occurring in plants. In this work, we propose the use of a supervised machine learning approach to predict molecular substructures from isotopic patterns, training the model on a large database of grape metabolites. This approach, called 'Compounds Characteristics Comparison' (CCC) emulates the experience of a plant chemist who 'gains experience' from a (proof-of-principle) dataset of grape compounds. The results show that the CCC approach is able to predict with good accuracy most of the sub-structures proposed. In addition, after querying MS/MS spectra in Metfrag 2.2 and applying CCC predictions as scoring terms with real data, the CCC approach helped to give a better ranking to the correct candidates, improving users' confidence in candidate selection. Our results demonstrated that the proposed approach can complement current identification strategies based on fragmentation simulators and formula calculators, assisting compound identification. The CCC algorithm is freely available as R package (https://github.com/lucanard/CCC) which includes a seamless integration with Metfrag. The CCC package also permits uploading additional training data, which can be used to extend the proposed approach to other systems biological matrices. List of abbreviations: Acidic: acidic moiety; aliph: aliphatic chain; AUC: area under the ROC curve; bs: best glycosidic structure; CCC: Compounds' Characteristics Comparison; Cees: Carbons estimation errors; CO: Carbon to Oxygen ratio; Het: Heterocyclic moiety; IMD: Isotopic Mass Defect (and Pattern); LC-HRMS: Liquid Chromatography - High Resolution Mass Spectrometry; md: mass defect; MM: Monoisotopic Mass; MS: Mass Spectrometry; MSE: Mean Squared Error; nC: number of Carbons; NN: Nitrogen; pC: percentage of Carbon mass on the total mass; Pho: Phosphate; PLSr: Partial Least Square regression; ppm: parts per million; QSRR: Quantitative structure-retention relationship; RMD: Relative Mass Defect; ROC: Receiver Operating Characteristics; rRMD: residual Relative Mass Defect; RT: retention time; Sul: Sulphur; UPLC-ESI-Q-TOF-MS: Ultra Performance Liquid Chromatography - ElectroSpray Ionization -Quadropole - Time of Flight - Mass Spectrometry; VAT: Vitis arizonica Texas.

Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis: Following the volatiles fate in grass baths through passive diffusion sampling.

Phytothermotherapy ("grass baths") is a traditional phytotherapy for rheumatism consisting of taking baths in hot fermenting grass. Scientific studies have demonstrated its efficiency in treating several rheumatic diseases. However the efficiency and repeatability of the therapy is dependent on the wild fermentations, determining sometimes the appearance of unpleasant conditions leading to the early abandonment of the therapy. The metabolism undergoing in the grass baths is unknown and there is not an established method to evaluate and predict grass baths quality. The aim of this study is to establish a simple VOCs profiling method able to evaluate the grass baths, predicting their evolution, through the identification of marker volatiles related to the best conditions and/or the spoilage. After replicating in real scale the traditional grass baths, the volatile profiles were measured using passive diffusion samplers injected in a thermal desorption-comprehensive GC × GC-TOF-MS. The high dimensionality of the data coupled with the limited number of time points, required a rigorous method development for the analysis of the data, achieved through the development of a novel R package for variable selection in GC × GC data matrices. The further application of a fuzzy clustering approach demonstrated to be a useful tool dealing with short time series, allowing to discard un-trending volatiles and giving a clear snapshot of the main trends in the data. A broad coverage of the volatolome was provided, thus suitable to describe the main metabolic changes ongoing in the grass baths. Coupling this data with the temperature and pH, and comparing it to the data from similar processes, like silage and compost, we demonstrated that the established method can be helpful to evaluate short time series, allowing us to obtain a list of volatiles as candidate markers for the quality of the grass baths. The established method gave a list of markers applicable to real scale grass baths to predict spoilage; furthermore it provides a list of volatiles where to search for candidate markers with reported health-related effects and can be used to generate hypothesis on the mechanisms of action of the treatment.

Studying the effect of storage conditions on the metabolite content of red wine using HILIC LC-MS based metabolomics.

The main aim of this work was to develop an untargeted normal phase LC-MS method, starting from a targeted method already validated for the analysis of 135 polar metabolites. Since the LC instrument and column were the same, most of the chromatographic conditions remained identical, while the adaptations focused on maintaining the ionic strength of the eluents constant. The sample preparation was simplified and the effectiveness of LC-MS for long batches was evaluated, in order to record the maximum number of metabolites with good chromatographic resolution and the best MS stability and accuracy. The method was applied to study the influence of storage conditions on wine composition. Slightly sub-optimum storage conditions had a major impact on the polar metabolite fingerprint of the red wines analysed and the markers revealed included phenolics, vitamins and metabolites indentified in wine for the first time (4-amino-heptanedioic acid and its ethyl ester).

Comparing Wild American Grapes with Vitis vinifera: A Metabolomics Study of Grape Composition.

We analyzed via untargeted UHPLC-ESI-Q-TOF-MS the metabolome of the berry tissues (skin, pulp, seeds) of some American Vitis species (Vitis cinerea, Vitis californica, Vitis arizonica), together with four interspecific hybrids, and seven Vitis vinifera cultivars, aiming to find differences in the metabolomes of the American Vitis sp. versus Vitis vinifera. Apart from the known differences, that is, more complex content of anthocyanins and stilbenoids in the American grapes, we observed higher procyanidin accumulation (tens to hundreds of times) in the vinifera skin and seeds in comparison to American berries, and we confirmed this result via phloroglucinolysis. In the American grapes considered, we did not detect the accumulation of pleasing aroma precursors (terpenoids, glycosides), whereas they are common in vinifera grapes. We also found accumulation of hydrolyzable tannins and their precursors in the skin of the wild American grapes, which has never been reported earlier in any of the species under investigation. Such information is needed to improve the design of new breeding programs, lowering the risk of retaining undesirable characteristics in the chemical phenotype of the offspring.

Chemical composition of volatile aroma metabolites and their glycosylated precursors that can uniquely differentiate individual grape cultivars.

Every grape cultivar has its own unique genetic characteristics, leading to the production of a different secondary metabolite profile. Aroma is one of the most important aspects in terms of the quality of grapes and previous studies have assigned specific aromas to particular grape cultivars. In this study we present the molecular profiling of volatile aroma metabolites and their precursors in ten selected genotypes, including six Vitis vinifera cultivars, two American species (Arizonica Texas, Vitis cinerea) and two interspecific crosses. Chemical profiling was achieved through combined use of two orthogonal techniques, GC-MS and LC-HRMS, before and after enzymatic hydrolysis. The results show that both free and glycosidically bound aroma precursors behave differently in each different grape cultivar and species. As many as 66 free aroma volatile molecules (originally existing and released after hydrolysis) were profiled through GC-MS analysis, while 15 glycosylated precursors of volatiles were identified through LC-HRMS and correlation with GC-MS data.