Nontarget High-resolution Mass Spectrometry Research Articles

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Plastic Food Packaging from Five Countries Contains Endocrine- and Metabolism-Disrupting Chemicals.

Plastics are complex chemical mixtures of polymers and various intentionally and nonintentionally added substances. Despite the well-established links between certain plastic chemicals (bisphenols and phthalates) and adverse health effects, the composition and toxicity of real-world mixtures of plastic chemicals are not well understood. To assess both, we analyzed the chemicals from 36 plastic food contact articles from five countries using nontarget high-resolution mass spectrometry and reporter-gene assays for four nuclear receptors that represent key components of the endocrine and metabolic system. We found that chemicals activating the pregnane X receptor (PXR), peroxisome proliferator receptor γ (PPARγ), estrogen receptor α (ERα), and inhibiting the androgen receptor (AR) are prevalent in plastic packaging. We detected up to 9936 chemical features in a single product and found that each product had a rather unique chemical fingerprint. To tackle this chemical complexity, we used stepwise partial least-squares regressions and prioritized and tentatively identified the chemical features associated with receptor activity. Our findings demonstrate that most plastic food packaging contains endocrine- and metabolism-disrupting chemicals. Since samples with fewer chemical features induce less toxicity, chemical simplification is key to producing safer plastic packaging.

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study.

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data.

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

Hydrothermal Alkaline Treatment (HALT) of Foam Fractionation Concentrate Derived from PFAS-Contaminated Groundwater.

While foam fractionation (FF) process has emerged as a promising technology for removal of per- and polyfluoroalkyl substances (PFASs) from contaminated groundwater, management of the resulting foam concentrates with elevated concentrations of PFASs (e.g., >1 g/L) remains a challenge. Here, we applied hydrothermal alkaline treatment (HALT) to two foam concentrates derived from FF field demonstration projects that treated aqueous film-forming foam (AFFF)-impacted groundwater. Results showed >90% degradation and defluorination within 90 min of treatment (350 °C, 1 M NaOH) of all 62 PFASs (including cations, anions, and zwitterions) identified in foam concentrates. Observed rate constants for degradation of individual perfluoroalkyl sulfonates (PFSAs, CnF2n+1-SO3-), the most recalcitrant class of PFASs, in both foam concentrates were similar to values measured previously in other aqueous matrices, indicating that elevated initial PFAS concentrations (e.g., PFHxSinit = 0.55 g/L), dissolved organic carbon (DOC; up to 4.5 g/L), and salt levels (e.g., up to 325 mg/L chloride) do not significantly affect PFAS reaction kinetics. DOC was partially mineralized by treatment, but a fraction (∼15%) was recalcitrant. Spectroscopic characterization revealed molecular features of the HALT-recalcitrant DOC fraction, and nontarget high-resolution mass spectrometry tentatively identified 129 nonfluorinated HALT-recalcitrant molecules. Analysis of process energy requirements shows that treating PFAS-contaminated foam concentrates with HALT would add minimally (<5%) to the overall energy requirements of an integrated FF-HALT treatment train.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

Efficient PFAS prioritization in non-target HRMS data: systematic evaluation of the novel MD/C-m/C approach

Non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) is necessary to comprehensively characterize per- and polyfluoroalkyl substances (PFAS) in environmental, biological, and technical samples due to the very limited availability of authentic PFAS reference standards. Since in trace analysis, MS/MS information is not always achievable and only selected PFAS are present in homologous series, further techniques to prioritize measured HRMS data (features) according to their likelihood of being PFAS are highly desired due to the importance of efficient data reduction during NTS. Kaufmann et al. (J AOAC Int, 2022) presented a very promising approach to separate selected PFAS from sample matrix features by plotting the mass defect (MD) normalized to the number of carbons (MD/C) vs. mass normalized to the number of C (m/C). We systematically evaluated the advantages and limitations of this approach by using ~ 490,000 chemical formulas of organic chemicals (~ 210,000 PFAS, ~ 160,000 organic contaminants, and 125,000 natural organic matter compounds) and calculating how efficiently, and especially which, PFAS can be prioritized. While PFAS with high fluorine content (approximately: F/C > 0.8, H/F < 0.8, mass percent of fluorine > 55%) can be separated well, partially fluorinated PFAS with a high hydrogen content are more difficult to prioritize, which we discuss for selected PFAS. In the MD/C-m/C approach, even compounds with highly positive MDs above 0.5 Da and hence incorrectly assigned to negative MDs can still be separated from true negative mass defect features by the normalized mass (m/C). Furthermore, based on the position in the MD/C-m/C plot, we propose the estimation of the fluorine fraction in molecules for selected PFAS classes. The promising MD/C-m/C approach can be widely used in PFAS research and routine analysis. The concept is also applicable to other compound classes like iodinated compounds.Graphical

Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research

A CNN was developed to classify extracted features from nontarget mass spectrometry workflows. The CNN accuracy ranged from 85% to 100%. These tools will be important in data-driven research enabling rapid processing of large volume and complex datasets.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS.

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log KOW.

Metabolism of 3-Chlorobiphenyl (PCB 2) in a Human-Relevant Cell Line: Evidence of Dechlorinated Metabolites.

Lower chlorinated polychlorinated biphenyls (LC-PCBs) and their metabolites make up a class of environmental pollutants implicated in a range of adverse outcomes in humans; however, the metabolism of LC-PCBs in human models has received little attention. Here we characterize the metabolism of PCB 2 (3-chlorobiphenyl), an environmentally relevant LC-PCB congener, in HepG2 cells with in silico prediction and nontarget high-resolution mass spectrometry. Twenty PCB 2 metabolites belonging to 13 metabolite classes, including five dechlorinated metabolite classes, were identified in the cell culture media from HepG2 cells exposed for 24 h to 10 μM or 3.6 nM PCB 2. The PCB 2 metabolite profiles differed from the monochlorinated metabolite profiles identified in samples from an earlier study with PCB 11 (3,3'-dichlorobiphenyl) under identical experimental conditions. A dechlorinated dihydroxylated metabolite was also detected in human liver microsomal incubations with monohydroxylated PCB 2 metabolites but not PCB 2. These findings demonstrate that the metabolism of LC-PCBs in human-relevant models involves the formation of dechlorination products. In addition, untargeted metabolomic analyses revealed an altered bile acid biosynthesis in HepG2 cells. Our results indicate the need to study the disposition and toxicity of complex PCB 2 metabolites, including novel dechlorinated metabolites, in human-relevant models.

Stored Reference Samples Enable Efficient Non-Target HRMS Screening for Novel Chemical Contamination in Drinking Water

Producers of drinking water (DW) occasionally require chemical identification of new or unexpected contamination, e.g., caused by an incident. The state-of-the-art technique for the identification of organic compounds is High-Resolution Mass Spectrometry (HRMS). The ability to discover unexpected compounds at low concentrations in any sample by HRMS is facilitated by comparison to non-contaminated reference samples. Samples of raw and drinking water were collected regularly over one whole year from six Swedish drinking water treatment plants (DWTPs). The samples were analyzed by LC-HRMS together with spiked samples mimicking an incident. This setup enabled evaluation of the significance of having access to a collection of matrix-matched reference samples. The main variation in the organic compound profile in the data set was explained by the uniqueness of the raw water and purification steps in the individual DWTPs. Seasonal variations were also significant but subordinate. This subject was further explored by the analysis of drinking water sampled once from 90 of the 1750 Swedish DWTPs, where a similarity between DW originating from rock aquifers was observed. No other significant correlation between samples was observed—e.g., other types of raw water, which types of purification steps were involved, or which additives or process chemicals were added—which could aid in the selection of relevant reference samples. The conclusion from the study is that it would be imperative for the DWTPs to have access to their individual reference samples for use in the investigation of an incident. A library of such reference samples, e.g., collected monthly and covering the last 12 months, could be stored and used together with a fresh “suspected sample” for non-target HRMS investigations as described.

Screening of pesticides and emerging contaminants in eighteen Greek lakes by using target and non-target HRMS approaches: Occurrence and ecological risk assessment

Lakes, albeit ecosystems of vital importance, are insufficiently investigated with respect to the degradation of water quality due to the organic micropollutants load. As regards Greece, screening of lake waters is scarce and concerns a limited number of contaminants. However, understanding the occurrence of contaminants of emerging concern (CECs) and other micropollutants in lakes is essential to appraise their potential ecotoxicological effects. The aim of this study was to deploy a multiresidue screening approach based on liquid chromatography−high-resolution mass spectrometry (HRMS) to get a first snapshot for >470 target CECs, including pesticides, pharmaceuticals, personal care products (PPCPs), per- and polyfluoroalkyl substances (PFASs), as well as organophosphate flame retardants (OPFRs) in eighteen Greek lakes in Central, Northern and West Northern Greece. The omnipresent compounds were DEET (N,N-diethyl-meta-toluamide), caffeine and TCPP (tris (1-chloro-2-propyl) phosphate). Maximum concentrations varied among the different classes. DEET was detected at a maximum average concentration of >1000 ng/L in Lake Orestiada, while its mean concentration was estimated at 233 ng/L. The maximum total concentrations for pesticides, PPCPs, PFASs, and OPFRs were 5807, 2669, 33.1, and 1214 ng/L, respectively, indicating that Greek lakes are still threatened by the intense agricultural activity. Besides, HRMS enabled a non-target screening by exploiting the rich content of the full-scan raw data, allowing the ‘discovery’ of tentative candidates, such as surfactants, pharmaceuticals, and preservatives among others, without reference standards. The potential ecotoxicity was assessed by both the risk quotient method and ECOSAR (Ecological Structure Activity Relationships) revealing low risk for most of the compounds.

Studying the Parkinson’s disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease, with an increasing incidence in recent years due to the aging population. Genetic mutations alone only explain <10% of PD cases, while environmental factors, including small molecules, may play a significant role in PD. In the present work, 22 plasma (11 PD, 11 control) and 19 feces samples (10 PD, 9 control) were analyzed by non-target high-resolution mass spectrometry (NT-HRMS) coupled to two liquid chromatography (LC) methods (reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC)). A cheminformatics workflow was optimized using open software (MS-DIAL and patRoon) and open databases (all public MSP-formatted spectral libraries for MS-DIAL, PubChemLite for Exposomics, and the LITMINEDNEURO list for patRoon). Furthermore, five disease-specific databases and three suspect lists (on PD and related disorders) were developed, using PubChem functionality to identifying relevant unknown chemicals. The results showed that non-target screening with the larger databases generally provided better results compared with smaller suspect lists. However, two suspect screening approaches with patRoon were also good options to study specific chemicals in PD. The combination of chromatographic methods (RP and HILIC) as well as two ionization modes (positive and negative) enhanced the coverage of chemicals in the biological samples. While most metabolomics studies in PD have focused on blood and cerebrospinal fluid, we found a higher number of relevant features in feces, such as alanine betaine or nicotinamide, which can be directly metabolized by gut microbiota. This highlights the potential role of gut dysbiosis in PD development.

Communicating Confidence of Per- and Polyfluoroalkyl Substance Identification via High-Resolution Mass Spectrometry.

Per- and polyfluoroalkyl substances (PFASs) are important environmental contaminants, yet relatively few analytical reference standards exist for this class. Nontarget analyses performed by means of high-resolution mass spectrometry (HRMS) are increasingly common for the discovery and identification of PFASs in environmental and biological samples. The certainty of PFAS identifications made via HRMS must be communicated through a reliable and harmonized approach. Here, we present a confidence scale along with identification criteria specific to suspect or nontarget analysis of PFASs by means of nontarget HRMS. Confidence levels range from level 1a—“Confirmed by Reference Standard,” and level 1b—“Indistinguishable from Reference Standard,” to level 5—“Exact Masses of Interest,” which are identified by suspect screening or data filtering, two common forms of feature prioritization. This confidence scale is consistent with general criteria for communicating confidence in the identification of small organic molecules by HRMS (e.g., through a match to analytical reference standards, library MS/MS, and/or retention times) but incorporates the specific conventions and tools used in PFAS classification and analysis (e.g., detection of homologous series and specific ranges of mass defects). Our scale clarifies the level of certainty in PFAS identification and, in doing so, facilitates more efficient identification.

Nontarget mass spectrometry and in silico molecular characterization of air pollution from the Indian subcontinent

Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget high-resolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0.5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter. Compared to clean air from the southern Indian Ocean, molecular structures from highly-polluted regions were dominated by organic nitrogen compounds, many with computed physicochemical properties of high toxicological and climate relevance. We conclude that combining nontarget analysis with computational mass spectrometry can advance molecular-level understanding of the sources and impacts of polluted air.

Adipogenic Activity of Chemicals Used in Plastic Consumer Products.

Bisphenols and phthalates, chemicals frequently used in plastic products, promote obesity in cell and animal models. However, these well-known metabolism-disrupting chemicals (MDCs) represent only a minute fraction of all compounds found in plastics. To gain a comprehensive understanding of plastics as a source of exposure to MDCs, we characterized the chemicals present in 34 everyday products using nontarget high-resolution mass spectrometry and analyzed their joint adipogenic activities by high-content imaging. We detected 55,300 chemical features and tentatively identified 629 unique compounds, including 11 known MDCs. Importantly, the chemicals extracted from one-third of the products caused murine 3T3-L1 preadipocytes to proliferate, and differentiate into adipocytes, which were larger and contained more triglycerides than those treated with the reference compound rosiglitazone. Because the majority of plastic extracts did not activate the peroxisome proliferator-activated receptor γ and the glucocorticoid receptor, the adipogenic effects are mediated via other mechanisms and, thus, likely to be caused by unknown MDCs. Our study demonstrates that daily-use plastics contain potent mixtures of MDCs and can, therefore, be a relevant yet underestimated environmental factor contributing to obesity.

Plastic Products Leach Chemicals That Induce In Vitro Toxicity under Realistic Use Conditions.

Plastic products contain complex mixtures of extractable chemicals that can be toxic. However, humans and wildlife will only be exposed to plastic chemicals that are released under realistic conditions. Thus, we investigated the toxicological and chemical profiles leaching into water from 24 everyday plastic products covering eight polymer types. We performed migration experiments over 10 days at 40 °C and analyzed the migrates using four in vitro bioassays and nontarget high-resolution mass spectrometry (UPLC-QTOF-MSE). All migrates induced baseline toxicity, 22 an oxidative stress response, 13 antiandrogenicity, and one estrogenicity. Overall, between 17 and 8681 relevant chemical features were present in the migrates. In other words, between 1 and 88% of the plastic chemicals associated with one product were migrating. Further, we tentatively identified ∼8% of all detected features implying that most plastic chemicals remain unknown. While low-density polyethylene, polyvinyl chloride, and polyurethane induced most toxicological endpoints, a generalization for other materials is not possible. Our results demonstrate that plastic products readily leach many more chemicals than previously known, some of which are toxic in vitro. This highlights that humans are exposed to many more plastic chemicals than currently considered in public health science and policies.

The use of non-target high-resolution mass spectrometry screening to detect the presence of antibiotic residues in urban streams of Greensboro North Carolina.

The online version contains supplementary material available at 10.1007/s40201-021-00688-9.

Characterization of the Metabolic Pathways of 4-Chlorobiphenyl (PCB3) in HepG2 Cells Using the Metabolite Profiles of Its Hydroxylated Metabolites.

The characterization of the metabolism of lower chlorinated PCB, such as 4-chlorobiphenyl (PCB3), is challenging because of the complex metabolite mixtures formed in vitro and in vivo. We performed parallel metabolism studies with PCB3 and its hydroxylated metabolites to characterize the metabolism of PCB3 in HepG2 cells using nontarget high-resolution mass spectrometry (Nt-HRMS). Briefly, HepG2 cells were exposed for 24 h to 10 μM PCB3 or its seven hydroxylated metabolites in DMSO or DMSO alone. Six classes of metabolites were identified with Nt-HRMS in the culture medium exposed to PCB3, including monosubstituted metabolites at the 3′-, 4′-, 3-, and 4- (1,2-shift product) positions and disubstituted metabolites at the 3′,4′-position. 3′,4′-Di-OH-3 (4′-chloro-3,4-dihydroxybiphenyl), which can be oxidized to a reactive and toxic PCB3 quinone, was a central metabolite that was rapidly methylated. The resulting hydroxylated-methoxylated metabolites underwent further sulfation and, to a lesser extent, glucuronidation. Metabolomic analyses revealed an altered tryptophan metabolism in HepG2 cells following PCB3 exposure. Some PCB3 metabolites were associated with alterations of endogenous metabolic pathways, including amino acid metabolism, vitamin A (retinol) metabolism, and bile acid biosynthesis. In-depth studies are needed to investigate the toxicities of PCB3 metabolites, especially the 3′,4′-di-OH-3 derivatives identified in this study.