Rapid Screening of Consumer Products by GCxGC-HRT and Machine Learning Assisted Data Processing.

This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia.

BACKGROUND: Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology. OBJECTIVES: We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures. METHODS: A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia. RESULTS: A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes. CONCLUSIONS: These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Uncertainty estimation strategies for quantitative non-targeted analysis.

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

Review of the environmental prenatal exposome and its relationship to maternal and fetal health.

Environmental chemicals comprise a major portion of the human exposome, with some shown to impact the health of susceptible populations, including pregnant women and developing fetuses. The placenta and cord blood serve as important biological windows into the maternal and fetal environments. In this article we review how environmental chemicals (defined here to include man-made chemicals [e.g., flame retardants, pesticides/herbicides, per- and polyfluoroalkyl substances], toxins, metals, and other xenobiotic compounds) contribute to the prenatal exposome and highlight future directions to advance this research field. Our findings from a survey of recent literature indicate the need to better understand the breadth of environmental chemicals that reach the placenta and cord blood, as well as the linkages between prenatal exposures, mechanisms of toxicity, and subsequent health outcomes. Research efforts tailored towards addressing these needs will provide a more comprehensive understanding of how environmental chemicals impact maternal and fetal health.

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples.

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.

Using prepared mixtures of ToxCast chemicals to evaluate non-targeted analysis (NTA) method performance.

Non-targeted analysis (NTA) methods are increasingly used to discover contaminants of emerging concern (CECs), but the extent to which these methods can support exposure and health studies remains to be determined. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) was launched in 2016 to address this need. As part of ENTACT, 1269 unique substances from EPA's ToxCast library were combined to make ten synthetic mixtures, with each mixture containing between 95 and 365 substances. As a participant in the trial, we first performed blinded NTA on each mixture using liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS). We then performed an unblinded evaluation to identify limitations of our NTA method. Overall, at least 60% of spiked substances could be observed using selected methods. Discounting spiked isomers, true positive rates from the blinded and unblinded analyses reached a maximum of 46% and 65%, respectively. An overall reproducibility rate of 75% was observed for substances spiked into more than one mixture and observed at least once. Considerable discordance in substance identification was observed when comparing a subset of our results derived from two separate reversed-phase chromatography methods. We conclude that a single NTA method, even when optimized, can likely characterize only a subset of ToxCast substances (and, by extension, other CECs). Rigorous quality control and self-evaluation practices should be required of labs generating NTA data to support exposure and health studies. Accurate and transparent communication of performance results will best enable meaningful interpretations and defensible use of NTA data. Graphical abstract ᅟ.

Effects of Chromophoric Dissolved Organic Matter on Anthracene Photolysis Kinetics in Aqueous Solution and Ice.

We measured photolysis kinetics of the PAH anthracene in aqueous solution, in bulk ice, and at ice surfaces in the presence and absence of chromophoric dissolved organic matter (CDOM). Self-association, which occurs readily at ice surfaces, may be responsible for the faster anthracene photolysis observed there. Photolysis rate constants in liquid water increased under conditions where anthracene self-association was observed. Concomitantly, kinetics changed from first-order to second-order, indicating that the photolysis mechanism at ice surfaces might be different than that in aqueous solution. Other factors that could lead to faster photolysis at ice surfaces were also investigated. Increased photon fluxes due to scattering in the ice samples can account for at most 20% of the observed rate increase, and other factors including singlet oxygen (1O2*) production and changes in pH and polarity were determined not to be responsible for the faster photolysis. CDOM (in the form of fulvic acid (FA)) did not affect anthracene photolysis kinetics in aqueous solution but suppressed photolysis in ice cubes and ice granules (by 30% and 56%, respectively). This was primarily due to competitive photon absorption (the inner filter effect). Freeze-concentration (or "salting out") appears to slightly increase the suppressing effects of FA on anthracene photolysis. This may be due to increased competitive photon absorption or to physical interactions between anthracene and FA.