In Vitro and In Vivo Biotransformation Profiling of 6PPD-Quinone toward Their Detection in Human Urine.

The antioxidant N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its oxidized quinone product 6PPD-quinone (6PPD-Q) in rubber have attracted attention due to the ecological risk that they pose. Both 6PPD and 6PPD-Q have been detected in various environments that humans cohabit. However, to date, a clear understanding of the biotransformation of 6PPD-Q and a potential biomarker for exposure in humans are lacking. To address this issue, this study presents a comprehensive analysis of the extensive biotransformation of 6PPD-Q across species, encompassing both in vitro and in vivo models. We have tentatively identified 17 biotransformation metabolites in vitro, 15 in mice in vivo, and confirmed the presence of two metabolites in human urine samples. Interestingly, different biotransformation patterns were observed across species. Through semiquantitative analysis based on peak areas, we found that almost all 6PPD-Q underwent biotransformation within 24 h of exposure in mice, primarily via hydroxylation and subsequent glucuronidation. This suggests a rapid metabolic processing of 6PPD-Q in mammals, underscoring the importance of identifying effective biomarkers for exposure. Notably, monohydroxy 6PPD-Q and 6PPD-Q-O-glucuronide were consistently the most predominant metabolites across our studies, highlighting monohydroxy 6PPD-Q as a potential key biomarker for epidemiological research. These findings represent the first comprehensive data set on 6PPD-Q biotransformation in mammalian systems, offering insights into the metabolic pathways involved and possible exposure biomarkers.

Eosinophilic pulmonary disease with ulcerative colitis: A case report.

RATIONALE: Eosinophilic pulmonary disease (EPD) is a general term for a large group of diseases with complex etiology. Ulcerative colitis is an inflammatory bowel disease (IBD). Patients with IBD may have pulmonary involvement. We herein present a case of ulcerative colitis complicated with EPD. PATIENT CONCERNS: A 34-year-old woman with ulcerative colitis presented with dry cough. She had peripheral eosinophilia and apical ground glass opacities on CT (computed tomography) of her chest. Antibiotic treatment was ineffective. DIAGNOSES: Lung biopsy revealed eosinophil infiltration in the alveolar space and interstitial space, so EPD was considered. INTERVENTIONS: After oral administration of prednisone, the lung shadow on CT disappeared when the cough symptoms resolved. However, the symptoms recurred after drug withdrawal, and the lung shadow reappeared on imaging. The cough symptoms and lung shadow disappeared after oral prednisone was given again. Prednisone was slowly discontinued after 6 months of treatment. OUTCOMES: The patient stopped prednisone for half a year. No recurrence or abnormal CT findings were detected during the half-year follow-up. LESSONS: The clinical manifestations of EPD are atypical, laboratory and imaging findings are not specific, and it is difficult to make a definite diagnosis before lung biopsy. The diagnosis depends on pathological examination. Glucocorticoid treatment is effective, but some patients may relapse after drug withdrawal. Active follow-up after glucocorticoid treatment is very important for identifying disease recurrence. Patients with IBD are relatively prone to developing EPD. The etiology of EPD is complex. In clinical practice, we need to make a diagnosis and differential diagnosis to clarify its etiology.

Using Machine Learning to Construct the Blood-Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data.

Permeabilities of various trace elements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood-follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19-4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076-0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998-1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

Ascertaining sensitive exposure biomarkers of various metal(loid)s to embryo implantation.

Close relationships exist between metal(loid)s exposure and embryo implantation failure (EIF) from animal and epidemiological studies. However, there are still inconsistent results and lacking of sensitive metal(loid) exposure biomarkers associated with EIF risk. We aimed to ascertain sensitive metal(loid) biomarkers to EIF and provide potential biological explanations. Candidate metal(loid) biomarkers were measured in the female hair (FH), female serum (FS), and follicular fluid (FF) with various exposure time periods. An analytical framework was established by integrating epidemiological association results, comprehensive literature searching, and knowledge-based adverse outcome pathway (AOP) networks. The sensitive biomarkers of metal(loid)s along with potential biological pathways to EIF were identified in this framework. Among the concerned 272 candidates, 45 metal(loid)s biomarkers across six time periods and three biomatrix were initially identified by single-metal(loid) analyses. Two biomarkers with counterfactual results according to literature summary results were excluded, and a total of five biomarkers were further determined from 43 remained candidates in mixture models. Finally, four sensitive metal(loid) biomarkers were eventually assessed by overlapping AOP networks information, including Se and Co in FH, and Fe and Zn in FS. AOP networks also identified key GO pathways and proteins involved in regulation of oxygen species biosynthetic, cell proliferation, and inflammatory response. Partial dependence results revealed Fe in FS and Co in FH at their low levels might be potential sensitive exposure levels for EIF. Our study provided a typical framework to screen the crucial metal(loid) biomarkers and ascertain that Se and Co in FH, and Fe and Zn in FS played an important role in embryo implantation.

Quantification and Characterization of Fine Plastic Particles as Considerable Components in Atmospheric Fine Particles.

The negative effects of air pollution, especially fine particulate matter (PM2.5, particles with an aerodynamic diameter of ≤2.5 µm), on human health, climate, and ecosystems are causing significant concern. Nevertheless, little is known about the contributions of emerging pollutants such as plastic particles to PM2.5 due to the lack of continuous measurements and characterization methods for atmospheric plastic particles. Here, we investigated the levels of fine plastic particles (FPPs) in PM2.5 collected in urban Shanghai at a 2 h resolution by using a novel versatile aerosol concentration enrichment system that concentrates ambient aerosols up to 10-fold. The FPPs were analyzed offline using the combination of spectroscopic and microscopic techniques that distinguished FPPs from other carbon-containing particles. The average FPP concentrations of 5.6 µg/m3 were observed, and the ratio of FPPs to PM2.5 was 13.2% in this study. The FPP sources were closely related to anthropogenic activities, which pose a potential threat to ecosystems and human health. Given the dramatic increase in plastic production over the past 70 years, this study calls for better quantification and control of FPP pollution in the atmosphere.

Environmentally realistic dose of tire-derived metabolite 6PPD-Q exposure causes intestinal jejunum and ileum damage in mice via cannabinoid receptor-activated inflammation.

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) is a quinone derivative of a common tire additive 6PPD, whose occurrence has been widely reported both in the environment and human bodies including in adults, pregnant women and children. Yet, knowledge on the potential intestinal toxicity of 6PPD-Q in mammals at environmentally relevant dose remain unknown. In this study, the effects of 6PPD-Q on the intestines of adult ICR mice were evaluated by orally administering environmentally relevant dose or lower levels of 6PPD-Q (0.1, 1, 10, and 100 µg/kg) for 21 days. We found that 6PPD-Q disrupted the integrity of the intestinal barrier, mostly in the jejunum and ileum, but not in the duodenum or colon, in a dose-dependent manner. Moreover, intestinal inflammation manifested with elevated levels of TNF-α, IL-1, and IL-6 mostly observed in doses at 10 and 100 µg/kg. Using reverse target screening technology combining molecular dynamic simulation modeling we identified key cannabinoid receptors including CNR2 activation to be potentially mediating the intestinal inflammation induced by 6PPD-Q. In summary, this study provides novel insights into the toxic effects of emerging contaminant 6PPD-Q on mammalian intestines and that the chemical may be a cannabinoid receptor agonist to modulate inflammation.

Kidney organoid models reveal cilium-autophagy metabolic axis as a therapeutic target for PKD both in vitro and in vivo.

Human pluripotent stem cell-derived kidney organoids offer unprecedented opportunities for studying polycystic kidney disease (PKD), which still has no effective cure. Here, we developed both in vitro and in vivo organoid models of PKD that manifested tubular injury and aberrant upregulation of renin-angiotensin aldosterone system. Single-cell analysis revealed that a myriad of metabolic changes occurred during cystogenesis, including defective autophagy. Experimental activation of autophagy via ATG5 overexpression or primary cilia ablation significantly inhibited cystogenesis in PKD kidney organoids. Employing the organoid xenograft model of PKD, which spontaneously developed tubular cysts, we demonstrate that minoxidil, a potent autophagy activator and an FDA-approved drug, effectively attenuated cyst formation in vivo. This in vivo organoid model of PKD will enhance our capability to discover novel disease mechanisms and validate candidate drugs for clinical translation.

Comparative secretome metabolic dysregulation by six engineered dietary nanoparticles (EDNs) on the simulated gut microbiota.

The potential use of engineered dietary nanoparticles (EDNs) in diet has been increasing and poses a risk of exposure. The effect of EDNs on gut bacterial metabolism remains largely unknown. In this study, liquid chromatography-mass spectrometry (LC-MS) based metabolomics was used to reveal significantly altered metabolites and metabolic pathways in the secretome of simulated gut microbiome exposed to six different types of EDNs (Chitosan, cellulose nanocrystals (CNC), cellulose nanofibrils (CNF) and polylactic-co-glycolic acid (PLGA); two inorganic EDNs including TiO2 and SiO2) at two dietary doses. We demonstrated that all six EDNs can alter the composition in the secretome with distinct patterns. Chitosan, followed by PLGA and SiO2, has shown the highest potency in inducing the secretome change with major pathways in tryptophan and indole metabolism, bile acid metabolism, tyrosine and phenol metabolism. Metabolomic alterations with clear dose response were observed in most EDNs. Overall, phenylalanine has been shown as the most sensitive metabolites, followed by bile acids such as chenodeoxycholic acid and cholic acid. Those metabolites might be served as the representative metabolites for the EDNs-gut bacteria interaction. Collectively, our studies have demonstrated the sensitivity and feasibility of using metabolomic signatures to understand and predict EDNs-gut microbiome interaction.

Emerging organic contaminants of liquid crystal monomers: Environmental occurrence, recycling and removal technologies, toxicities and health risks.

Liquid crystal monomers (LCMs) are a family of synthetic organic chemicals applied in the liquid crystal displays (LCDs) of various electric and electronic products (e-products). Due to their unique properties (i.e., persistence, bioaccumulative potential, and toxicity) and widespread environmental distributions, LCMs have attracted increasing attention across the world. Recent studies have focused on the source, distribution, fate, and toxicity of LCMs; however, a comprehensive review is scarce. Herein, we highlighted the persistence and bioaccumulation potential of LCMs by reviewing their physical-chemical properties. The naming rules were suggested to standardize the abbreviations regarding LCMs. The sources and occurrences of LCMs in different environmental compartments, including dust, sediment, soil, leachate, air and particulate, human serum, and biota samples, were reviewed. It is concluded that the LCMs in the environment mainly originate from the usage and disassembly of e-products with LCDs. Moreover, the review of the potential recycling and removal technologies regarding LCMs from waste LCD panels suggests that a combination of natural attenuation and physic-chemical remediation should be developed for LCMs remediations in the future. By reviewing the health risks and toxicity of LCMs, it is found that a large gap exists in their toxicity and risk to organisms. The fate and toxicity investigation of LCMs, and further investigations on the effects on the human exposure risks of LCMs to residents, especially to occupational workers, should be considered in the future.

Unveiling the Microfiber Release Footprint: Guiding Control Strategies in the Textile Production Industry.

Microplastic fibers from textiles have been known to significantly contribute to marine microplastic pollution. However, little is known about the microfiber formation and discharge during textile production. In this study, we have quantified microfiber emissions from one large and representative textile factory during different stages, spanning seven different materials, including cotton, polyester, and blended fabrics, to further guide control strategies. Wet-processing steps released up to 25 times more microfibers than home laundering, with dyeing contributing to 95.0% of the total emissions. Microfiber release could be reduced by using white coloring, a lower dyeing temperature, and a shorter dyeing duration. Thinner, denser yarns increased microfiber pollution, whereas using tightly twisted fibers mitigated release. Globally, wet textile processing potentially produced 6.4 kt of microfibers in 2020, with China, India, and the US as significant contributors. The study underlined the environmental impact of textile production and the need for mitigation strategies, particularly in dyeing processes and fiber choice. In addition, no significant difference was observed between the virgin polyesters and the used ones. Replacing virgin fibers with recycled fibers in polyester fabrics, due to their increasing consumption, might offer another potential solution. The findings highlighted the substantial impact of textile production on microfiber released into the environment, and optimization of material selection, knitting technologies, production processing, and recycled materials could be effective mitigation strategies.

EISA-EXPOSOME: One Highly Sensitive and Autonomous Exposomic Platform with Enhanced in-Source Fragmentation/Annotation.

Lacking a highly sensitive exposome screening technique is one of the biggest challenges in moving exposomic research forward. Enhanced in-source fragmentation/annotation (EISA) has been developed to facilitate molecular identification in untargeted metabolomics and proteomics. In this work, with a mixture of 50 pesticides at three concentration levels (20, 4, and 0.8 ppb), we investigated the analytical performance of the EISA technique over the well-accepted targeted MS/MS mode (TMM) in the detection and identification of chemicals at low levels using a quadrupole time-of-flight (qTOF) instrument. Compared with the TMM method, the EISA technique can recognize additional 1, 20, and 23 chemicals, respectively, at the three concentration levels (20, 4, and 0.8 ppb, respectively) investigated. At the 0.8 ppb level, intensities of precursor ions and fragments observed using the EISA technique are 30-1,154 and 3-80 times higher, respectively, than those observed at the TMM mode. A higher matched fragment ratio (MFR) between the EISA technique and the TMM method was recognized for most chemicals. We further developed a chemical annotation informatics algorithm, EISA-EXPOSOME, which can automatically search each precursor ion (m/z) in the MS/MS library against the EISA MS1 spectra. This algorithm then calculated a weighted score to rank the candidate features by comparing the experimental fragment spectra to those in the library. The peak intensity, zigzag index, and retention time prediction model as well as the peak correlation coefficient were further adopted in the algorithm to filter false positives. The performance of EISA-EXPOSOME was demonstrated using a pooled dust extract with a pesticide mixture (n = 200) spiked at 5 ppb. One urine sample spiked with a contaminant mixture (n = 50) at the 5 ppb level was also used for the validation of the pipeline. Proof-of-principal application of EISA-EXPOSOME in the real sample was further evaluated on the pooled dust sample with a modified T3DB database (n = 1650). Our results show that the EISA-EXPOSOME algorithm can remarkably improve the detection and annotation coverage at trace levels beyond the traditional approach as well as facilitate the high throughput screening of suspected chemicals.

A missing jigsaw within the hygiene hypothesis: Low-dose bisphenol A exposure attenuates lipopolysaccharide-induced asthma protection.

The rising occurrence of allergic asthma in early life across industrialized countries suggests that environmental factors play a crucial role in determining asthma susceptibility and severity. While prior exposure to microbial lipopolysaccharides (LPSs) has been found to offer protection against allergic asthma, infants residing in urban environments are increasingly exposed to environmental pollutants. Utilizing limulus lysate test screens and virtual screening models, we identified pollutants that can modulate LPS bioactivity. This investigation revealed that bisphenol A (BPA), a chemical commonly used in numerous household items and previously implicated in obesity and cancer, effectively neutralizes LPS. In-depth mechanistic analyses showed that BPA specifically binds to the lipid A component of LPS, leading to inactivation. This interaction eliminates the immunostimulatory activity of LPS, making mice more susceptible to house dust mite (HDM)-induced allergic asthma. BPA reactivates lung epithelial cells, consequently amplifying type 2 responses to HDMs in dendritic cells. This chemical interplay provides new insights into the pathophysiology of asthma in relation to human exposure. Understanding the intricate relationships between environmental chemicals and microbial antigens, as well as their impacts on innate immunity, is critical for the development of intervention strategies to address immune disorders resulting from urbanization.

Essential role of lattice oxygen in methanol electrochemical refinery toward formate.

Developing technologies based on the concept of methanol electrochemical refinery (e-refinery) is promising for carbon-neutral chemical manufacturing. However, a lack of mechanism understanding and material properties that control the methanol e-refinery catalytic performances hinders the discovery of efficient catalysts. Here, using 18O isotope-labeled catalysts, we find that the oxygen atoms in formate generated during the methanol e-refinery reaction can originate from the catalysts' lattice oxygen and the O-2p-band center levels can serve as an effective descriptor to predict the catalytic performance of the catalysts, namely, the formate production rates and Faradaic efficiencies. Moreover, the identified descriptor is consolidated by additional catalysts and theoretical mechanisms from density functional theory. This work provides direct experimental evidence of lattice oxygen participation and offers an efficient design principle for the methanol e-refinery reaction to formate, which may open up new research directions in understanding and designing electrified conversions of small molecules.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening.

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model.

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. There is growing interest in the use of GO and extensive in vitro and in vivo studies have been conducted to assess its nanotoxicity. Although it is known that GO can alter the composition of the gut microbiota in mice and zebrafish, studies on the potential impacts of GO on the human gut microbiome are largely lacking. This study addresses an important knowledge gap by investigating the impact of GO exposure- at low (25 mg/L) and high (250 mg/L) doses under both fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial communitys' structure and function, using an in vitro model. This model includes simulated oral, gastric, small intestinal phase digestion of GO followed by incubation in a colon bioreactor. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 mg/L GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 mg/L GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 mg/L GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 mg/L GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism at both low and high GO concentrations.

Analogy or fallacy, unsafe chemical alternatives: Mechanistic insights into energy metabolism dysfunction induced by Bisphenol analogs in HepG2 cells.

Bisphenol analogs (BPs) are widely used as industrial alternatives for Bisphenol A (BPA). Their toxicity assessment in humans has mainly focused on estrogenic activity, while other toxicity effects and mechanisms resulting from BPs exposure remain unclear. In this study, we investigated the effects of three BPs (Bisphenol AF (BPAF), Bisphenol G (BPG) and Bisphenol PH (BPPH)) on metabolic pathways of HepG2 cells. Results from comprehensive cellular bioenergetics analysis and nontarget metabolomics indicated that the most important process affected by BPs exposure was energy metabolism, as evidenced by reduced mitochondrial function and enhanced glycolysis. Compared to the control group, BPG and BPPH exhibited a consistent pattern of metabolic dysregulation, while BPAF differed from both, such as an increased ATP: ADP ratio (1.29-fold, p < 0.05) observed in BPAF and significantly decreased ATP: ADP ratio for BPG (0.28-fold, p < 0.001) and BPPH (0.45-fold, p < 0.001). Bioassay endpoint analysis revealed BPG/BPPH induced alterations in mitochondrial membrane potential and overproductions of reactive oxygen species. Taken together these data suggested that BPG/BPPH induced oxidative stress and mitochondrial damage in cells results in energy metabolism dysregulation. By contrast, BPAF had no effect on mitochondrial health, but induced a proliferation promoting effect on cells, which might contribute to the energy metabolism dysfunction. Interestingly, BPPH induced the greatest mitochondrial damage among the three BPs but did not exhibit Estrogen receptor alpha (ERα) activating effects. This study characterized the distinct metabolic mechanisms underlying energy metabolism dysregulation induced by different BPs in target human cells, providing new insight into the evaluation of the emerging BPA substitutes.

Oligomer nanoparticle release from polylactic acid plastics catalysed by gut enzymes triggers acute inflammation.

The health risks of exposure to 'eco-friendly' biodegradable plastics of anthropogenic origin and their effects on the gastrointestinal tract are largely unknown. Here we demonstrate that the enzymatic hydrolysis of polylactic acid microplastics generated nanoplastic particles by competing for triglyceride-degrading lipase during gastrointestinal processes. Nanoparticle oligomers were formed by hydrophobically driven self-aggregation. In a mouse model, polylactic acid oligomers and their nanoparticles bioaccumulated in the liver, intestine and brain. Hydrolysed oligomers caused intestinal damage and acute inflammation. A large-scale pharmacophore model revealed that oligomers interacted with matrix metallopeptidase 12. Mechanistically, high binding affinity (Kd = 13.3 µmol l-1) of oligomers to the catalytic zinc-ion finger domain led to matrix metallopeptidase 12 inactivation, which might mediate the adverse bowel inflammatory effects after exposure to polylactic acid oligomers. Biodegradable plastics are considered to be a solution to address environmental plastic pollution. Thus, understanding the gastrointestinal fates and toxicities of bioplastics will provide insights into potential health risks.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization.

BACKGROUND: Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. OBJECTIVES: Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern. METHODS: We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances. RESULTS: We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07µM, the mean absolute error (MAE) values of 1.28 and 1.56µM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 µM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants. DISCUSSION: We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis.

Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.