Protein pathway analysis to study development-dependent effects of acute and repeated trimethyltin (TMT) treatments in 3D rat brain cell cultures.

Trimethyltin is an organometallic compound, described to be neurotoxic and to trigger neuroinflammation and oxidative stress. Previous studies associated TMT with the perturbation of mitochondrial function, or neurotransmission. However, the mechanisms of toxicity may differ depending on the duration of exposure and on the stage of maturation of brain cells. This study aim at elucidating whether the toxicity pathways triggered by a known neurotoxicant (TMT) differs depending on cell maturation stage or duration of exposure. To this end omics profiling of immature and differentiated 3D rat brain cell cultures exposed for 24 h or 10 days (10-d) to 0.5 and 1 µM of TMT was performed to better understand the underlying mechanisms of TMT associated toxicity. Proteomics identified 55 and 17 proteins affected by acute TMT treatment in immature and differentiated cultures respectively, while 10-day treatment altered 96 proteins in immature cultures versus 353 in differentiated. The results suggest different sensitivity to TMT depending on treatment duration and cell maturation. In accordance with known TMT mechanisms oxidative stress and neuroinflammation was observed after 10-d treatment at both maturation stages, whereas the neuroinflammatory process was more prominent in differentiated cultures than in the immature, no development-dependent difference could be detected for oxidative stress or synaptic neurodegeneration. Pathway analysis revealed that both vesicular trafficking and the synaptic machinery were strongly affected by 10-d TMT treatment in both maturation stages, as was GABAergic and glutamatergic neurotransmission. This study shows that omics approaches combined with pathway analysis constitutes an improved tool-set in elucidating toxicity mechanisms.

An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments.

Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.

Removal of batch effects using stratified subsampling of metabolomic data for in vitro endocrine disruptors screening.

The human adrenal cell line H295R constitutes a well-established model to evaluate potential alterations of steroidogenic pathways as a result of chemical exposure. However, to date most assays are based on the targeted investigation of a limited number of steroid hormones, thus preventing in-depth mechanistic interpretation with respect to steroidogenesis. In that context, analytical strategies coupling liquid chromatography and high-resolution mass spectrometry (LC-HRMS) have been reported as promising methods for an extended monitoring of steroid metabolites. However, unwanted sources of variability occurring during the acquisition process, including batch effects, may prevent relevant biochemical information to be properly highlighted. Dedicated data mining strategies are therefore needed to overcome these limitations, and extract relevant extended steroidomic profiles. The present study combines an untargeted LC-HRMS acquisition strategy with automated steroid metabolite annotation based on accurate mass and isotopic patterns, and a chemometric tool allowing the different sources of variability to be decomposed based on experimental design. This workflow was applied to the extended monitoring of steroidogenic dysregulations due to endocrine disrupting chemicals (EDCs) exposure in H295R cell cultures. A series of six chemicals, including acetyl tributylcitrate, octyl methoxycinnamate, torcetrapib, forskolin, linuron, and octocrylene, and dimethylsulfoxide as solvent control, were investigated through the simultaneous monitoring of 130 potential steroid metabolites, repeating the whole experiment independently three times. A stratified subsampling strategy was carried out to remove efficiently systematic batch variations and highlight subgroups of chemicals with similar steroid patterns. The proposed approach was reported as a potent screening strategy, as it allowed specific alterations of the steroid hormone biosynthesis and metabolism related to distinct mechanisms of action to be distinguished.

Steroid profiles in both blood serum and seminal plasma are not correlated and do not reflect sperm quality: Study on the male reproductive health of fifty young Swiss men.

Steroids play an important role in sperm production and quality. These hormones have been extensively studied in blood, but poorly investigated in semen. The purpose of our study was to evaluate the relationship between sperm quality and steroid profiles in blood and semen in a small cohort of young Swiss men. Another objective was to determine whether the presence of xenobiotics or drugs could influence these profiles. Semen analysis was performed according to WHO guidelines, and steroid profiles in blood serum and seminal plasma were determined by two complementary approaches: a targeted investigation involving the quantification of a limited number of relevant steroids for testing putative correlations with sperm parameters and a global "steroidomic" analysis highlighting their complex metabolic relationship. Results showed that steroid profiles are distinct within blood and seminal fluid. No significant correlation was found between individual steroids measured in blood and in semen, demonstrating the relevance of assessing hormone levels in both fluids. Moreover, testosterone and androstenedione levels were significantly correlated in semen but not in blood. None of the evaluated spermiogram parameters was linked to steroid levels measured in any medium. The steroidomic analyses confirmed that the steroids present in both fluids are different and that there is no correlation with spermiogram parameters. Finally, upon toxicological screening, we observed that all the three samples positive for tetrahydrocannabinol, which is known to act as an endocrine disruptor, displayed low seminal testosterone concentrations. In conclusion, we did not find any evidence suggesting using steroid profiles, neither in blood nor in semen, as surrogates for sperm analyses. However, steroid profiles could be useful biomarkers of individual exposure to endocrine disruptors.

Steroid profiling in H295R cells to identify chemicals potentially disrupting the production of adrenal steroids.

The validated OECD test guideline 456 based on human adrenal H295R cells promotes measurement of testosterone and estradiol production as read-out to identify potential endocrine disrupting chemicals. This study aimed to establish optimal conditions for using H295R cells to detect chemicals interfering with the production of key adrenal steroids. H295R cells' supernatants were characterized by liquid chromatography-mass spectrometry (LC-MS)-based steroid profiling, and the influence of experimental conditions including time and serum content was assessed. Steroid profiles were determined before and after incubation with reference compounds and chemicals to be tested for potential disruption of adrenal steroidogenesis. The H295R cells cultivated according to the OECD test guideline produced progestins, glucocorticoids, mineralocorticoids and adrenal androgens but only very low amounts of testosterone. However, testosterone contained in Nu-serum was metabolized during the 48h incubation. Thus, inclusion of positive and negative controls and a steroid profile of the complete medium prior to the experiment (t=0h) was necessary to characterize H295R cells' steroid production and indicate alterations caused by exposure to chemicals. Among the tested chemicals, octyl methoxycinnamate and acetyl tributylcitrate resembled the corticosteroid induction pattern of the positive control torcetrapib. Gene expression analysis revealed that octyl methoxycinnamate and acetyl tributylcitrate enhanced CYP11B2 expression, although less pronounced than torcetrapib. Further experiments need to assess the toxicological relevance of octyl methoxycinnamate- and acetyl tributylcitrate-induced corticosteroid production. In conclusion, the extended profiling and appropriate controls allow detecting chemicals that act on steroidogenesis and provide initial mechanistic evidence for prioritizing chemicals for further investigations.

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification.

The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q(2)) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification.

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 3ß-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 11ß-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.

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.

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.

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).

Extraction of amino acids by reverse iontophoresis in vivo.

Reverse iontophoresis across the skin is a potentially useful alternative for non-invasive clinical and therapeutic drug monitoring. In this work, the reverse iontophoretic extraction of 17 amino acids was studied in vivo in healthy volunteers. Charged amino acids were primarily extracted towards the electrode of opposite polarity, while zwitterionic species were extracted, more or less equally, to both anode and cathode, suggesting that the net charge on the skin, under the conditions of the experiment, was close to zero. The significant presence of a 'skin reservoir' of several amino acids, presumably originating from the barrier's so-called 'natural moisturizing factor', was deduced from the results. While this phenomenon had been observed in an earlier in vitro investigation, the levels of certain amino acids (including serine and glycine) in the skin were found to be much higher in vivo. Hence, while the results of this study confirm the feasibility of extracting some amino acids at physiologically relevant levels in vivo, the objective of achieving a correlation between iontophoretically extracted fluxes and blood plasma levels may not be a practically realisable goal in all cases.

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.