The Austrian children's biomonitoring survey 2020 Part B: Mycotoxins, phytotoxins, phytoestrogens and food processing contaminants.

This study assessed the levels of environment and food-related exposures in urine of Austrian school children aged six to ten (n = 85) focusing on mycotoxins, phytoestrogens, and food processing by-products using two multi-analyte LC-MS/MS methods. Out of the 55 biomarkers of exposure reported in this study, 22 were quantified in the first void urine samples. Mycotoxins frequently quantified included zearalenone (detection rate 100%; median 0.11 ng/mL), deoxynivalenol (99%; 15 ng/mL), alternariol monomethyl ether (75%; 0.04 ng/mL), and ochratoxin A (19%; 0.03 ng/mL). Several phytoestrogens, including genistein, daidzein, and its metabolite equol, were detected in all samples at median concentrations of 22 ng/mL, 43 ng/mL, and 14 ng/mL, respectively. The food processing by-product 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), was detected in 4% of the samples (median 0.016 ng/mL). None of the investigated samples contained the tested phytotoxins that were rarely considered for human biomonitoring previously (pyrrolizidine alkaloids, tropane alkaloids, aristolochic acids). When relating estimated exposure to current health-based guidance values, 22% of the children exceeded the tolerable daily intake for deoxynivalenol, and the estimated MOE for OTA indicates possible health risks for some children. The results clearly demonstrate frequent low-level (co-)exposure and warrant further exposome-scale exposure assessments, especially in susceptible sub-populations and longitudinal settings.

Comparing the sensitivity of a low- and a high-resolution mass spectrometry approach for xenobiotic trace analysis: An exposome-type case study.

The chemical exposome consists of environmental exposures experienced throughout a lifetime but to date analytical approaches to investigate the plethora of low-abundance chemicals remain very limited. Liquid chromatography high-resolution mass spectrometry (HRMS) is commonly applied in untargeted exposome-wide analyses of xenobiotics in biological samples; however, human biomonitoring approaches usually utilize targeted low-resolution triple quadrupole (QQQ) mass spectrometry tailored to a small number of chemicals. HRMS can cover a broader chemical space but the detection of molecules from low-level exposure amidst a background of highly-abundant endogenous molecules has proven to be difficult. In this study, a triple quadrupole (QQQ) and a high-resolution mass spectrometer (HRMS) with identical chromatography were utilized to determine the limits of quantitation (LOQ) of >100 xenobiotics and estrogenic hormones in pure solvent and human urine. Both instrumental platforms are currently applied in exposure assessment studies and were operated in their most frequently used acquisition mode (full scan for HRMS and multiple reaction monitoring for QQQ) to mimic typical applications. For HRMS analyses, the median LOQ was 0.9 and 1.2 ng/mL in solvent and urine, respectively, while for low-resolution QQQ measurements, the median LOQ was 0.1 and 0.2 ng/mL in solvent and urine, respectively. To evaluate the calculated LOQs in complex biological samples, spot urine samples from 24 Nigerian female volunteers were investigated. The higher LOQ values for HRMS resulted in less quantified low-abundance analytes and decreased the number of compounds detected below the LOQ. Even at chronic low-dose exposure, such compounds might be relevant for human health because of high individual toxicity or potential mixture effects. Nevertheless, HRMS enabled the additional screening for exposure to unexpected/unknown analytes, including emerging compounds and biotransformation products. Therefore, a synergy between high- and low-resolution mass spectrometry may currently be the best option to elucidate and quantify xenobiotics in comprehensive exposome-wide association studies (ExWAS).

Results of the Austrian Children's Biomonitoring Survey 2020 part A: Per- and polyfluorinated alkylated substances, bisphenols, parabens and other xenobiotics.

In 85 Austrian school children aged 6-10 years, two multi-analyte LC-MS/MS methods were used to study the concentrations of 33 chemical substances in urine, including per- and polyfluorinated alkylated substances (PFAS), bisphenols, parabens, benzophenones, triclosan, polycyclic aromatic hydrocarbon metabolites, and cotinine. Each of the children was exposed to 14-21 substances simultaneously. Correlations were found between compounds of the same and of divergent substance groups supporting the strong need to consider multiple exposures and mixture effects. Eight compounds, including perfluorohexanoic acid (PFHxA), perfluorononanoic acid (PFOA), methyl paraben (n-MeP), ethyl paraben (n-EtP), propyl paraben (n-PrP), benzophenone-1 (BP-1), 2-naphthol, and 3-hydroxyphenanthrene were detected in all urine samples. In the PFAS group the medians of detectable substances ranged between <0.0005 µg/l for perfluorononanoic acid (PFNA) and 0.004 µg/l for PFHxA. For other environmental contaminants investigated, a maximum urinary level of 893 µg/l was identified for n-MeP. The highest median value was 2.5 µg/l for 2-naphthol. Daily intakes were calculated for bisphenol A (BPA), triclosan (TCS), and four parabens. These values did not exceed the tolerable or acceptable daily intakes currently in force. Based on a recently proposed TDI for BPA, daily intakes of all children exceeded this value. A cumulative risk assessment was conducted for four parabens not showing exceedances of acceptable exposures. The results demonstrate simultaneous exposure to several different chemicals, with the majority showing impact on the endocrine system being of particular concern with respect to mixture effects. Further assessments with a stronger focus on mixtures are warranted. The results also highlight the need of policy actions as foreseen in the EU Chemicals Strategy for Sustainability.

Integrated Exposomics/Metabolomics for Rapid Exposure and Effect Analyses.

The totality of environmental exposures and lifestyle factors, commonly referred to as the exposome, is poorly understood. Measuring the myriad of chemicals that humans are exposed to is immensely challenging, and identifying disrupted metabolic pathways is even more complex. Here, we present a novel technological approach for the comprehensive, rapid, and integrated analysis of the endogenous human metabolome and the chemical exposome. By combining reverse-phase and hydrophilic interaction liquid chromatography (HILIC) and fast polarity-switching, molecules with highly diverse chemical structures can be analyzed in 15 min with a single analytical run as both column's effluents are combined before analysis. Standard reference materials and authentic standards were evaluated to critically benchmark performance. Highly sensitive median limits of detection (LODs) with 0.04 µM for >140 quantitatively assessed endogenous metabolites and 0.08 ng/mL for the >100 model xenobiotics and human estrogens in solvent were obtained. In matrix, the median LOD values were higher with 0.7 ng/mL (urine) and 0.5 ng/mL (plasma) for exogenous chemicals. To prove the dual-column approach's applicability, real-life urine samples from sub-Saharan Africa (high-exposure scenario) and Europe (low-exposure scenario) were assessed in a targeted and nontargeted manner. Our liquid chromatography high-resolution mass spectrometry (LC-HRMS) approach demonstrates the feasibility of quantitatively and simultaneously assessing the endogenous metabolome and the chemical exposome for the high-throughput measurement of environmental drivers of diseases.

Homologue series detection and management in LC-MS data with homologueDiscoverer.

SUMMARY: Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AVAILABILITY AND IMPLEMENTATION: homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Next-generation biomonitoring of the early-life chemical exposome in neonatal and infant development.

Exposure to synthetic and natural chemicals is a major environmental risk factor in the etiology of many chronic diseases. Investigating complex co-exposures is necessary for a holistic assessment in exposome-wide association studies. In this work, a sensitive liquid chromatography-tandem mass spectrometry approach was developed and validated. The assay enables the analysis of more than 80 highly-diverse xenobiotics in urine, serum/plasma, and breast milk; with detection limits generally in the pg-ng mL-1 range. In plasma of extremely-premature infants, 27 xenobiotics are identified; including contamination with plasticizers, perfluorinated alkylated substances and parabens. In breast milk samples collected longitudinally over the first 211 days post-partum, 29 analytes are detected, including pyrrolizidine- and tropane alkaloids which have not been identified in this matrix before. A preliminary estimation of daily toxicant intake via breast milk is conducted. In conclusion, we observe significant early-life co-exposure to multiple toxicants, and demonstrate the method's applicability for large-scale exposomics-type cohort studies.

PeakBot: machine-learning-based chromatographic peak picking.

MOTIVATION: Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. RESULTS: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AVAILABILITY AND IMPLEMENTATION: The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Elucidation of xenoestrogen metabolism by non-targeted, stable isotope-assisted mass spectrometry in breast cancer cells.

Environmental exposure to xenoestrogens, i.e., chemicals that imitate the hormone 17ß-estradiol, has the potential to influence hormone homeostasis and action. Detailed knowledge of xenobiotic biotransformation processes in cell models is key when transferring knowledge learned from in vitro models to in vivo relevance. This study elucidated the metabolism of two naturally-occurring phyto- and mycoestrogens; namely genistein and zearalenone, in an estrogen receptor positive breast cancer cell line (MCF-7) with the aid of stable isotope-assisted metabolomics and the bioinformatic tool MetExtract II. Metabolism was studied in a time course experiment after 2 h, 6 h and 24 h incubation. Twelve and six biotransformation products of zearalenone and genistein were detected, respectively, clearly demonstrating the abundant xenobiotic biotransformation capability of the cells. Zearalenone underwent extensive phase-I metabolism resulting in α-zearalenol (α-ZEL), a molecule known to possess a significantly higher estrogenicity, and several phase-II metabolites (sulfo- and glycoconjugates) of the native compound and the major phase I metabolite α-ZEL. Moreover, potential adducts of zearalenone with a vitamin and several hydroxylated metabolites were annotated. Genistein metabolism resulted in sulfation, combined sulfation and hydroxylation, acetylation, glucuronidation and unexpectedly adduct formation with pentose- and hexose sugars. Kinetics of metabolite formation and subsequent excretion into the extracellular medium revealed a time-dependent increase in most biotransformation products. The untargeted elucidation of biotransformation products formed during cell culture experiments enables an improved and more meaningful interpretation of toxicological assays and has the potential to identify unexpected or unknown metabolites.

Stable Isotope-Assisted Metabolomics for Deciphering Xenobiotic Metabolism in Mammalian Cell Culture.

Xenobiotics are ubiquitous in the environment and modified in the human body by phase I and II metabolism. Liquid chromatography coupled to high resolution mass spectrometry is a powerful tool to investigate these biotransformation products. We present a workflow based on stable isotope-assisted metabolomics and the bioinformatics tool MetExtract II for deciphering xenobiotic metabolites produced by human cells. Its potential was demonstrated by the investigation of the metabolism of deoxynivalenol (DON), an abundant food contaminant, in a liver carcinoma cell line (HepG2) and a model for colon carcinoma (HT29). Detected known metabolites included DON-3-sulfate, DON-10-sulfonate 2, and DON-10-glutathione as well as DON-cysteine. Conjugation with amino acids and an antibiotic was confirmed for the first time. The approach allows the untargeted elucidation of human xenobiotic products in tissue culture. It may be applied to other fields of research including drug metabolism, personalized medicine, exposome research, and systems biology to better understand the relevance of in vitro experiments.

Size-Resolved Identification, Characterization, and Quantification of Primary Biological Organic Aerosol at a European Rural Site.

Primary biological organic aerosols (PBOA) represent a major component of the coarse organic matter (OMCOARSE, aerodynamic diameter > 2.5 µm). Although this fraction affects human health and the climate, its quantification and chemical characterization currently remain elusive. We present the first quantification of the entire PBOACOARSE mass and its main sources by analyzing size-segregated filter samples collected during the summer and winter at the rural site of Payerne (Switzerland), representing a continental Europe background environment. The size-segregated water-soluble OM was analyzed by a newly developed offline aerosol mass spectrometric technique (AMS). Collected spectra were analyzed by three-dimensional positive matrix factorization (3D-PMF), showing that PBOA represented the main OMCOARSE source during summer and its contribution to PM10 was comparable to that of secondary organic aerosol. We found substantial cellulose contributions to OMCOARSE, which in combination with gas chromatography mass spectrometry molecular markers quantification, underlined the predominance of plant debris. Quantitative polymerase chain reaction (qPCR) analysis instead revealed that the sum of bacterial and fungal spores mass represented only a minor OMCOARSE fraction (<0.1%). X-ray photoelectron spectroscopic (XPS) analysis of C and N binding energies throughout the size fractions revealed an organic N increase in the PM10 compared to PM1 consistent with AMS observations.