Fabienne Jeanneret

Evaluation of steroidomics by liquid chromatography hyphenated to mass spectrometry as a powerful analytical strategy for measuring human steroid perturbations.

This review presents the evolution of steroid analytical techniques, including gas chromatography coupled to mass spectrometry (GC-MS), immunoassay (IA) and targeted liquid chromatography coupled to mass spectrometry (LC-MS), and it evaluates the potential of extended steroid profiles by a metabolomics-based approach, namely steroidomics. Steroids regulate essential biological functions including growth and reproduction, and perturbations of the steroid homeostasis can generate serious physiological issues; therefore, specific and sensitive methods have been developed to measure steroid concentrations. GC-MS measuring several steroids simultaneously was considered the first historical standard method for analysis. Steroids were then quantified by immunoassay, allowing a higher throughput; however, major drawbacks included the measurement of a single compound instead of a panel and cross-reactivity reactions. Targeted LC-MS methods with selected reaction monitoring (SRM) were then introduced for quantifying a small steroid subset without the problems of cross-reactivity. The next step was the integration of metabolomic approaches in the context of steroid analyses. As metabolomics tends to identify and quantify all the metabolites (i.e., the metabolome) in a specific system, appropriate strategies were proposed for discovering new biomarkers. Steroidomics, defined as the untargeted analysis of the steroid content in a sample, was implemented in several fields, including doping analysis, clinical studies, in vivo or in vitro toxicology assays, and more. This review discusses the current analytical methods for assessing steroid changes and compares them to steroidomics. Steroids, their pathways, their implications in diseases and the biological matrices in which they are analysed will first be described. Then, the different analytical strategies will be presented with a focus on their ability to obtain relevant information on the steroid pattern. The future technical requirements for improving steroid analysis will also be presented.

Human urinary biomarkers of dioxin exposure: Analysis by metabolomics and biologically driven data dimensionality reduction

Untargeted metabolomic approaches offer new opportunities for a deeper understanding of the molecular events related to toxic exposure. This study proposes a metabolomic investigation of biochemical alterations occurring in urine as a result of dioxin toxicity. Urine samples were collected from Czech chemical workers submitted to severe dioxin occupational exposure in a herbicide production plant in the late 1960s. Experiments were carried out with ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution quadrupole time-of-flight (QTOF) mass spectrometry. A chemistry-driven feature selection was applied to focus on steroid-related metabolites. Supervised multivariate data analysis allowed biomarkers, mainly related to bile acids, to be highlighted. These results supported the hypothesis of liver damage and oxidative stress for long-term dioxin toxicity. As a second step of data analysis, the information gained from the urine analysis of Victor Yushchenko after his poisoning was examined. A subset of relevant urinary markers of acute dioxin toxicity from this extreme phenotype, including glucuro- and sulfo-conjugated endogenous steroid metabolites and bile acids, was assessed for its ability to detect long-term effects of exposure. The metabolomic strategy presented in this work allowed the determination of metabolic patterns related to dioxin effects in human and the discovery of highly predictive subsets of biologically meaningful and clinically relevant compounds. These results are expected to provide valuable information for a deeper understanding of the molecular events related to dioxin toxicity. Furthermore, it presents an original methodology of data dimensionality reduction by using extreme phenotype as a guide to select relevant features prior to data modeling (biologically driven data reduction).

Steroidomic Footprinting Based on Ultra-High Performance Liquid Chromatography Coupled with Qualitative and Quantitative High-Resolution Mass Spectrometry for the Evaluation of Endocrine Disrupting Chemicals in H295R Cells

The screening of endocrine disrupting chemicals (EDCs) that may alter steroidogenesis represents a highly important field mainly due to the numerous pathologies, such as cancer, diabetes, obesity, osteoporosis, and infertility that have been related to impaired steroid-mediated regulation. The adrenal H295R cell model has been validated to study steroidogenesis by the Organization for Economic Co-operation and Development (OECD) guideline. However, this guideline focuses solely on testosterone and estradiol monitoring, hormones not typically produced by the adrenals, hence limiting possible in-depth mechanistic investigations. The present work proposes an untargeted steroidomic footprinting workflow based on ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution MS for the screening and mechanistic investigations of EDCs in H295R cell supernatants. A suspected EDC, triclocarban (TCC), used in detergents, cosmetics, and personal care products, was selected to demonstrate the efficiency of the reported methodology, allowing the simultaneous assessment of a steroidomic footprint and quantification of a selected subset of steroids in a single analysis. The effects of exposure to increasing TCC concentrations were assessed, and the selection of features with database matching followed by multivariate analysis has led to the selection of the most salient affected steroids. Using correlation analysis, 11 steroids were associated with a high, 18 with a medium, and 8 with a relatively low sensitivity behavior to TCC. Among the candidates, 13 identified steroids were simultaneously quantified, leading to the evaluation and localization of the disruption of steroidogenesis caused by TCC upstream of the formation of pregnenolone. The remaining candidates could be associated with a specific steroid class (progestogens and corticosteroids, or androgens) and represent a specific footprint of steroidogenesis disruption by TCC. This strategy was devised to be compatible with medium/high-throughput screening and could be useful for the mechanistic elucidation of EDCs.

Evaluation and identification of dioxin exposure biomarkers in human urine by high-resolution metabolomics, multivariate analysis and in vitro synthesis.

A previous high-resolution metabolomic study pointed out a dysregulation of urinary steroids and bile acids in human cases of acute dioxin exposure. A subset of 24 compounds was highlighted as putative biomarkers. The aim of the current study was (i) to evaluate the 24 biomarkers in an independent human cohort exposed to dioxins released from the incineration fumes of a municipal waste incinerator and; (ii) to identify them by comparison with authentic chemical standards and biosynthesised products obtained with in vitro metabolic reactions. An orthogonal projection to latent structures discriminant analysis built on biomarker profiles measured in the intoxicated cohort and the controls separated both groups with reported values of 93.8%; 100% and 87.5% for global accuracy; sensitivity and specificity; respectively. These results corroborated the 24 compounds as exposure biomarkers; but a definite identification was necessary for a better understanding of dioxin toxicity. Dehydroepiandrosterone 3β-sulfate, androsterone 3α-glucuronide, androsterone 3α-sulfate, pregnanediol 3α-glucuronide and 11-ketoetiocholanolone 3α-glucuronide were identified by authentic standards. Metabolic reactions characterised four biomarkers: glucuronide conjugates of 11β-hydroxyandrosterone; glycochenodeoxycholic acid and glycocholic acid produced in human liver microsomes and glycoursodeoxycholic acid sulfate generated in cytosol fraction. The combination of metabolomics by high-resolution mass spectrometry with in vitro metabolic syntheses confirmed a perturbed profile of steroids and bile acids in human cases of dioxin exposure.

Extraction of amino acids by reverse iontophoresis: simulation of therapeutic monitoring in vitro.

Reverse iontophoresis across the skin has been investigated as alternative, non-invasive method for clinical and therapeutic drug monitoring. This research investigated the reverse iontophoretic extraction of 19 amino acids present at clinically relevant levels in the subdermal compartment of an in vitro diffusion cell. Over a simulated, systemic concentration range of 0-500 microM, the extraction of amino acids was linear. Charged amino acids were extracted towards the electrode of opposite polarity, while zwitterionic species were extracted to both anode and cathode with the latter predominating. The reverse iontophoretic extraction flux was a linear function of amino acid isoelectric point, reflecting the different contributions of electromigration and electroosmosis to electrotransport. Overall, the results confirm the feasibility of monitoring amino acids at clinically relevant levels and provide an incentive for in vivo research to further explore the clinical potential of reverse iontophoresis for the non-invasive monitoring of amino acids.