Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard.

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Neuroactive metabolites and bile acids are altered in extremely premature infants with brain injury.

The gut microbiome is associated with pathological neurophysiological evolvement in extremely premature infants suffering from brain injury. The exact underlying mechanism and its associated metabolic signatures in infants are not fully understood. To decipher metabolite profiles linked to neonatal brain injury, we investigate the fecal and plasma metabolome of samples obtained from a cohort of 51 extremely premature infants at several time points, using liquid chromatography (LC)-high-resolution mass spectrometry (MS)-based untargeted metabolomics and LC-MS/MS-based targeted analysis for investigating bile acids and amidated bile acid conjugates. The data are integrated with 16S rRNA gene amplicon gut microbiome profiles as well as patient cytokine, growth factor, and T cell profiles. We find an early onset of differentiation in neuroactive metabolites between infants with and without brain injury. We detect several bacterially derived bile acid amino acid conjugates in plasma and feces. These results provide insights into the early-life metabolome of extremely premature infants.

Polyphenol exposure of mothers and infants assessed by LC-MS/MS based biomonitoring in breast milk.

Exposure to polyphenols is relevant throughout critical windows of infant development, including the breastfeeding phase. However, the quantitative assessment of polyphenols in human breast milk has received limited attention so far, though polyphenols may positively influence infant health. Therefore, a targeted LC-MS/MS assay was developed to investigate 86 analytes representing different polyphenol classes in human breast milk. The sample preparation consisted of liquid extraction, salting out, freeze-out, and a dilution step. Overall, nearly 70% of the chemically diverse polyphenols fulfilled all strict validation criteria for full quantitative assessment. The remaining analytes did not fulfill all criteria at every concentration level, but can still provide useful semi-quantitative insights into nutritional and biomedical research questions. The limits of detection for all analyzed polyphenols were in the range of 0.0041-87 ng*mL-1, with a median of 0.17 ng*mL-1. Moreover, the mean recovery was determined to be 82% and the mean signal suppression and enhancement effect was 117%. The developed assay was applied in a proof-of-principle study to investigate polyphenols in breast milk samples provided by twelve Nigerian mothers at three distinct time points post-delivery. In total, 50 polyphenol analytes were detected with almost half being phenolic acids. Phase II metabolites, including genistein-7-ß-D-glucuronide, genistein-7-sulfate, and daidzein-7-ß-D-glucuronide, were also detected in several samples. In conclusion, the developed method was demonstrated to be fit-for-purpose to simultaneously (semi-) quantify a wide variety of polyphenols in breast milk. It also demonstrated that various polyphenols including their biotransformation products were present in breast milk and therefore likely transferred to infants where they might impact microbiome development and infant health.

Biomonitoring of Dietary Mycotoxin Exposure and Associated Impact on the Gut Microbiome in Nigerian Infants.

Mycotoxins are toxic chemicals that adversely affect human health. Here, we assessed the influence of mycotoxin exposure on the longitudinal development of early life intestinal microbiota of Nigerian neonates and infants (NIs). Human biomonitoring assays based on liquid chromatography tandem mass spectrometry were applied to quantify mycotoxins in breast milk (n = 68) consumed by the NIs, their stool (n = 82), and urine samples (n = 15), which were collected longitudinally from month 1-18 postdelivery. Microbial community composition was characterized by 16S rRNA gene amplicon sequencing of stool samples and was correlated to mycotoxin exposure patterns. Fumonisin B1 (FB1), FB2, and alternariol monomethyl ether (AME) were frequently quantified in stool samples between months 6 and 18. Aflatoxin M1 (AFM1), AME, and citrinin were quantified in breast milk samples at low concentrations. AFM1, FB1, and ochratoxin A were quantified in urine samples at relatively high concentrations. Klebsiella and Escherichia/Shigella were dominant in very early life stool samples (month 1), whereas Bifidobacterium was dominant between months 3 and 6. The total mycotoxin levels in stool were significantly associated with NIs' gut microbiome composition (PERMANOVA, p < 0.05). However, no significant correlation was observed between specific microbiota and the detection of certain mycotoxins. Albeit a small cohort, this study demonstrates that mycotoxins may influence early life gut microbiome composition.

Enhancing microbiome research in sub-Saharan Africa.

While there are lighthouse examples of microbiome research in sub-Saharan Africa (SSA), a significant proportion of local researchers face several challenges. Here, we highlight prevailing issues limiting microbiome research in SSA and suggest potential technological, societal, and research-based solutions. We emphasize the need for considerable investment in infrastructures, training, and appropriate funding to democratize modern technologies with a view to providing useful data to improve human health.

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).

Ecophysiology and interactions of a taurine-respiring bacterium in the mouse gut.

Taurine-respiring gut bacteria produce H2S with ambivalent impact on host health. We report the isolation and ecophysiological characterization of a taurine-respiring mouse gut bacterium. Taurinivorans muris strain LT0009 represents a new widespread species that differs from the human gut sulfidogen Bilophila wadsworthia in its sulfur metabolism pathways and host distribution. T. muris specializes in taurine respiration in vivo, seemingly unaffected by mouse diet and genotype, but is dependent on other bacteria for release of taurine from bile acids. Colonization of T. muris in gnotobiotic mice increased deconjugation of taurine-conjugated bile acids and transcriptional activity of a sulfur metabolism gene-encoding prophage in other commensals, and slightly decreased the abundance of Salmonella enterica, which showed reduced expression of galactonate catabolism genes. Re-analysis of metagenome data from a previous study further suggested that T. muris can contribute to protection against pathogens by the commensal mouse gut microbiota. Together, we show the realized physiological niche of a key murine gut sulfidogen and its interactions with selected gut microbiota members.

Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening.

Polyphenols, prevalent in plants and fungi, are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties. Due to their complexity, analysis with untargeted approaches is favorable, which typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrometry (LRMS). Here, the advantages of HRMS were evaluated by thoroughly testing untargeted techniques and available online resources. By applying data-dependent acquisition on real-life urine samples, 27 features were annotated with spectral libraries, 88 with in silico fragmentation, and 113 by MS1 matching with PhytoHub, an online database containing >2000 polyphenols. Moreover, other exogenous and endogenous molecules were screened to measure chemical exposure and potential metabolic effects using the Exposome-Explorer database, further annotating 144 features. Additional polyphenol-related features were explored using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, the gap between the two instrumental approaches was quantified in three spiked human matrices (urine, serum, plasma) as well as real-life urine samples. Both instruments showed feasible sensitivity, with median limits of detection in the spiked samples being 10-18 ng/mL for HRMS and 4.8-5.8 ng/mL for LRMS. The results demonstrate that, despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human polyphenol exposure. In the future, this work is expected to allow for linking human health effects with exposure patterns and toxicological mixture effects with other xenobiotics.

Metabolomics as an emerging approach for deciphering the biological impact and toxicity of food contaminants: the case of mycotoxins.

Exposure to mycotoxins through the dietary route occurs on a daily basis while their deleterious effects are exhibited in the form of ailments, such as inflammation, cancer, and hormonal imbalance. The negative impact of mycotoxins can be attributed to their interaction with various biomolecules and their interference in metabolic pathways. The activity of biomolecules, such as enzymes/receptors, which engage the intricate mechanism of endogenous metabolism, is more susceptible to disruption by metabolites of high toxicity, which gives rise to adverse health effects. Metabolomics is a useful analytical approach that can assist in unraveling such information. It can simultaneously and comprehensively analyze a large number of endogenous and exogenous molecules present in biofluids and can, thus, reveal biologically relevant perturbations following mycotoxin exposure. Information provided by genome, transcriptome and proteome analyses, which have been utilized for the elucidation of biological mechanisms so far, are further complemented by the addition of metabolomics in the available bioanalytics toolbox. Metabolomics can offer insight into complex biological processes and their respective response to several (co-)exposures. This review focuses on the most extensively studied mycotoxins reported in literature and their respective impact on the metabolome upon exposure.

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.

Understanding the Chemical Exposome During Fetal Development and Early Childhood: A Review.

Early human life is considered a critical window of susceptibility to external exposures. Infants are exposed to a multitude of environmental factors, collectively referred to as the exposome. The chemical exposome can be summarized as the sum of all xenobiotics that humans are exposed to throughout a lifetime. We review different exposure classes and routes that impact fetal and infant metabolism and the potential toxicological role of mixture effects. We also discuss the progress in human biomonitoring and present possiblemodels for studying maternal-fetal transfer. Data gaps on prenatal and infant exposure to xenobiotic mixtures are identified and include natural biotoxins, in addition to commonly reported synthetic toxicants, to obtain a more holistic assessment of the chemical exposome. We highlight the lack of large-scale studies covering a broad range of xenobiotics. Several recommendations to advance our understanding of the early-life chemical exposome and the subsequent impact on health outcomes are proposed.

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.

Markers for DNA damage are induced in the rat colon by the Alternaria toxin altertoxin-II, but not a complex extract of cultured Alternaria alternata.

Mycotoxins produced by Alternaria spp. act genotoxic in cell-based studies, but data on their toxicity in vivo is scarce and urgently required for risk assessment. Thus, male Sprague-Dawley rats received single doses of a complex Alternaria toxin extract (CE; 50 mg/kg bw), altertoxin II (ATX-II; 0.21 mg/kg bw) or vehicle by gavage, one of the most genotoxic metabolites in vitro and were sacrificed after 3 or 24 h, respectively. Using SDS-PAGE/Western Blot, a significant increase of histone 2a.X phosphorylation and depletion of the native protein was observed for rats that were exposed to ATX-II for 24 h. Applying RT-PCR array technology we identified genes of interest for qRT-PCR testing, which in turn confirmed an induction of Rnf8 transcription in the colon of rats treated with ATX-II for 3 h and CE for 24 h. A decrease of Cdkn1a transcription was observed in rats exposed to ATX-II for 24 h, possibly indicating tissue repair after chemical injury. In contrast to the observed response in the colon, no markers for genotoxicity were induced in the liver of treated animals. We hereby provide the first report of ATX-II as a genotoxicant in vivo. Deviating results for similar concentrations of ATX-II in a natural Alternaria toxin mixture argue for substantial mixture effects.

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.

Quantifying up to 90 polyphenols simultaneously in human bio-fluids by LC-MS/MS.

Establishing a method for human biomonitoring (HBM) of polyphenols enables the assessment of internal concentrations of these food bio-actives and the correlation with potential health effects such as antioxidant or anti-inflammatory properties. Thus, a targeted LC-MS/MS method for quantifying up to 90 analytes, representing the main polyphenol classes including flavanones, isoflavones, stilbenes, and phenolic acids, was developed for human urine, serum, and plasma. The method was established for low sample volumes and with a cost and time efficient sample preparation protocol for high-throughput, which is critical for its application in large cohort and exposome-wide association studies. On average, the sample preparation yielded extraction efficiencies of 98% for urine, 98% for serum, and 87% for plasma. Limits of detection were between 0.11 ng mL-1 and 300 ng mL-1 for urine, 0.12 ng mL-1 and 190 ng mL-1 for serum, and 0.12 ng mL-1 and 340 ng mL-1 for plasma, excluding one analyte. In-house validation revealed that 66, 49, and 64 analytes for urine, serum, and plasma, respectively, fulfilled all stringent requirements, that are usually utilized for tailored single analyte methods, at all evaluated concentration levels. After validation, this method was applied in a proof-of-principle study that detected 39 polyphenols in urine. Changes in the concentrations of the analytes after the ingestion of a high polyphenol smoothie was examined over 24 h. The study further confirmed that the majority of polyphenols detected were phenolic acids, and phase II conjugated metabolites were more abundant than their respective non-conjugated forms.

Early-life chemical exposome and gut microbiome development: African research perspectives within a global environmental health context.

The gut microbiome of neonates, infants, and toddlers (NITs) is very dynamic, and only begins to stabilize towards the third year of life. Within this period, exposure to xenobiotics may perturb the gut environment, thereby driving or contributing to microbial dysbiosis, which may negatively impact health into adulthood. Despite exposure of NITs globally, but especially in Africa, to copious amounts and types of xenobiotics - such as mycotoxins, pesticide residues, and heavy metals - little is known about their influence on the early-life microbiome or their effects on acute or long-term health. Within the African context, the influence of fermented foods, herbal mixtures, and the delivery environment on the early-life microbiome are often neglected, despite being potentially important factors that influence the microbiome. Consequently, data on in-depth understanding of the microbiome-exposome interactions is lacking in African cohorts. Collecting and evaluating such data is important because exposome-induced gut dysbiosis could potentially favor disease progression.

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.