Disinfection by-products, including those arising from secondary reactions between chlorinated reagents and natural organic matter such as trihalomethanes (THMs), have been associated with several adverse health outcomes, such as bladder and colorectal cancer.
Aims
We aim to identify metabolic signatures of THM exposure in a high exposure setting (swimming pool), validate these markers in an external dataset with low exposure contrast (residential tap water), and characterise the multi-omic responses to THM exposures.
Materials And Methods
In PISCINA-II study (discovery data), n = 58 volunteers swam in a chlorinated pool for 40 min. Metabolomics and proteomics in serum and THM concentrations in exhaled breath were measured before and after swimming. Metabolome-wide association study (MWAS) using multivariate normal models was used to identify signatures of exposure to individual THM components. Multivariate analysis coupling stability-calibrated consensus clustering and sparse partial least square models selected sets of features jointly affected by exposures. Stability-calibrated conditional independence networks integrated selected metabolic features and immune proteomic markers.Selected features were finally annotated and further validated in the MCC-Spain study with n = 293 cancer-free individuals.
Results
MWAS identified 188 features significantly associated with at least one THM component. Multivariate analysis selected 21 metabolic clusters, containing 72 individual features, of which 12 were tentatively identified. Of these, L-kynurenine, Lyso-PE (20:4), piperine and cortisone were also identified in the MCC-Spain study despite much lower exposure contrasts. We visualised the complex correlations across molecular features jointly associated with THM exposures.
Conclusions
This study provides detailed characterisation of molecular responses to THM exposure from different routes and at different levels. Future studies are needed to understand the health implications of the involved metabolites.
In chemical forensics, chemical source inference is crucial for the determination of the origin of toxic agents through various analytical and chemometric methodologies. Recent advancements in the study of organophosphorus compounds as nerve agents have mainly focused on the identification of specific production pathways and source materials. Using Chlorpyrifos as a model organophosphorus compound and previously generated data, the present work aims to assess the robustness of the approach for the discrimination of chemical production routes by introducing environmental matrix complexity. Various crude Chlorpyrifos synthesis samples were spiked onto three complex environmental matrices (sand, soil, and surface river water) to simulate real-case samples. Experiments were carried out at two concentrations (20 ng/μL and 1 ng/μL) in crude samples. Sampling and extraction protocols were developed, and recovery rates and matrix effects were assessed to track down impurity attribution signatures linked to the synthetic pathways from each matrix. Ultra-high-performance liquid chromatography (UHPLC) coupled to a hybrid trapped ion mobility-quadrupole time-of-flight system (TIMS-Q-ToF) was used in a metabolomics-based analytical strategy. Synthetic routes for contaminated environmental samples were predicted using a training data set from various raw Chlorpyrifos synthetic pathways. Precision and the number of discriminant features in samples from Non-Targeted Analysis (NTA) versus Suspect Screening Analysis (SSA) approaches were evaluated with confusion matrices and univariate analysis to assess the relevance of the features employed. Finally, highly concentrated explosive residues were added to evaluate analytical perturbance. This application in complex environmental conditions represents a key step toward validating the proof of concept's transferability and exploring its analytical limitations.
C. Orlandi, G. Delaporte, Christine Albaret et al.· Journal of the American Soci...· 0 citations
BACKGROUND
The association between organochlorine pesticides (OCPs)/synthetic pyrethroids (SPs) and thyroid cancer (TC) remains poorly understood, with metabolic mechanisms unexplored.
METHODS
We conducted a 1:1 age- and sex-matched case-control study (n = 668). Serum levels of 27 target analytes (19 OCPs and 8 SPs) were quantified; subsequent analyses were restricted to 13 compounds (10 OCPs and 3 SPs) with detection frequencies ≥85%. Eight machine learning (ML) algorithms with Shapley Additive Explanations (SHAP) were used to identify key pollutants in the 334 case-control pairs. Untargeted metabolomics was performed in a subset of 50 age- and sex-matched case-control pairs. Mixture effects were assessed by Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression. Furthermore, the Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) model was employed to integrate exposure and metabolic data, enabling the identification of TC patient subgroups and the exploration of underlying metabolic mechanisms.
RESULTS
Participants (mean age 45.2 years, 82.3% female) had serum OCPs at 0.007-0.333 ng/mL and SPs at 0.046-0.095 ng/mL. ML algorithms identified fenpropathrin, β-BHC, cyhalothrin, α-BHC, and p,p'-DDD as the top five contributors to TC. Elevated OCPs/SPs exposure was significantly associated with increased TC risk (WQS: adjusted OR = 1.45, 95%CI = 1.34-2.24, P = 0.019; LUCID: OR = 9.33). Fenpropathrin was the primary contributor (BKMR posterior inclusion probability = 1.00; WQS weight = 67.6%). A total of 45 significant differential metabolites (DMs) were identified (VIP ≥1, P < 0.05, and qualitative level 1). LUCID revealed a distinct TC cluster characterized by upregulated S-sulfo-L-cysteine/adenosine and downregulated 2-hydroxycaprylic acid.
CONCLUSION
OCPs/SPs mixtures, driven by fenpropathrin, disrupt amino acid/nucleotide metabolism while suppressing organic acid metabolism, representing a potential TC-associated metabolic signature.
Fei Wang, Chunxiang Li, Linfang Zou et al.· Environment International· 0 citations
Organochlorine pesticides (OCPs), despite being banned, persist in the environment and continue to pose potential risks to human health. This exploratory study investigated the concentrations of multiple OCPs in serum samples from 10 local residents and 52 surrounding environmental samples, including soil (n = 10), air (n = 12), indoor dust (n = 10), and food (n = 20), collected in Taizhou, Zhejiang Province, in 2022. Multiple exposure pathways were quantified, and associated health risks were assessed. The results showed evidence of selective accumulation in humans, with serum ΣOCP concentrations ranging from 107.01 to 552.41 ng/g lipid weight (lw). p,p′-DDE was the predominant compound, accounting for 67.81% of total serum OCPs, while β-HCH showed relatively higher enrichment among HCH congeners. Lower proportions of p,p′-DDT and α-/γ-HCH were observed. Source contributions varied across media: food was the primary contributor to p,p′-DDE exposure, indoor dust and soil contributed more to β-HCH and certain DDT isomers, and the atmosphere showed a higher contribution of methoxychlor. Differences between integrated exposure profiles and serum composition highlighted the potential roles of metabolic transformation and lipid-associated accumulation in shaping internal OCP burdens. Risk assessment indicated that DDT metabolites and β-HCH were the major contributors to potential exposure risks. Overall, this study provides preliminary insights into the environmental–human distribution patterns of OCPs observed in Taizhou under post-ban conditions and highlights that environmental concentrations alone may not fully represent chronic internal exposure. These findings improve the understanding of potential OCP exposure pathways and emphasize the need for larger-scale studies incorporating population characteristics and refined exposure parameters for more comprehensive risk assessment
Yuhao Fan, Lei Hou, Ke-Cheng Guo et al.· Environmental Contamination:...· 0 citations
Disinfection by-products (DBPs) in drinking water pose significant health risks due to their cytotoxicity, bioaccumulation potential, and interactions with biological targets. Here, we develop a toxicity-guided and interpretable framework integrating molecular modeling, machine learning, network toxicology, and experimental characterization to systematically predict and prioritize potentially high-risk DBPs. A curated dataset of 271 DBPs was assembled, including cytotoxicity (pLC50) and bioconcentration factors (BCF), along with binding affinities to 15 bladder-related proteins obtained through molecular modeling. Machine learning models achieved strong predictive performance (R2 > 0.83), and SHAP analysis identified lipophilicity, electronic structure, and molecular connectivity as key determinants of toxicity. Prioritization of over 271 DBPs revealed that halobenzoquinones (HBQs), particularly halogenated naphthoquinone derivatives, rank among the highest-risk classes. Our results suggest that redox-active structural features, including electrophilicity and polarizability, may contribute to biological interactions and oxidative stress-related responses. Experimental validation across structurally diverse DBPs supported the predicted toxicity prioritization, with concentration-dependent cytotoxicity and Caspase-3-associated apoptosis observed for selected compounds, while network toxicology identified potential associations with oxidative stress, apoptosis, and inflammation-related pathways. Overall, this study establishes a cross-scale, toxicity-guided assessment framework for relating molecular properties to potential biological responses and prioritizing emerging DBPs, supporting evidence-based water quality management.
Jie Wang, Zhujun Liu, Ruipeng Lai et al.· Water Research· 0 citations
Per- and polyfluoroalkyl substances (PFAS) comprise thousands of persistent and structurally diverse chemicals that contribute to chronic human exposure. Yet, routine biomonitoring targets only a limited set of regulated legacy compounds, underestimating overall exposure. We present a retrospective, wide-scope suspect screening analysis framework to extract PFAS information from archived LC–HRMS exposomics datasets. A curated list of 1,666 PFAS, filtered for reversed-phase LC compatibility and enriched with structural descriptors, predicted retention indices, and MS2 fragments, was used to reprocess two human biomonitoring studies involving firefighter serum pools and population-based plasma samples. Candidate features were prioritized using precursor accuracy, isotopic patterns, and MS2 similarity, followed by spectral quality filtering and retention time-index regression models based on isotopically labeled standards, reinforcing identification confidence. The reanalysis recovered up to 80% of previously reported PFAS while revealing overlooked high-confidence carboxylic, sulfonic, and sulfonamidoacetic acid homologues, and >30 tentative PFAS series, encompassing isomeric clusters (alcohols and ethers) and structurally distinct subclasses (alkylpyrimidine PFAS). The reanalysis framework also revealed shared homologue patterns across exposure contexts. By framing PFAS chemical space and providing a reusable repository of confirmed and tentative structures, this framework reinterprets HRMS biobanked data, improving PFAS pattern assessment.
Lapo Renai, Federica Calabró, V. Turkina et al.· Environmental Science and Te...· 0 citations
Air pollution, particularly particulate matter (PM), is a major driver of global morbidity and mortality, with increasing evidence linking it to neurological disorders. This study investigates the chemical composition and neurotoxic potential of PM collected simultaneously in three sites in Catalonia (Spain): Bellver de Cerdanya (rural background), Manlleu (suburban), and Mollet del Vallès (suburban-industrial). Fifty-four filter samples collected in 2022 were analyzed by GC-MS for 30 organic molecular tracers, including polycyclic aromatic hydrocarbons (PAHs) and levoglucosan. Extracts were tested in SH-SY5Y human neuroblastoma cells across six toxicity endpoints: cell viability, reactive oxygen species (ROS), acetylcholinesterase (AChE) activity, antioxidant response, xenobiotic response, and p53 activation (DNA damage response). Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on the combined chemical-biological dataset resolved four components: a winter biomass burning component enriched in levoglucosan, dehydroabietic acid, and PAHs, inducing strong cytotoxicity, oxidative stress, and xenobiotic responses; a traffic component present throughout the year; a spring-summer secondary organic aerosol (SOA) component associated with selective AChE inhibition without cytotoxicity; and a summer primary organic aerosol (POA) component. Partial Least Squares (PLS) regression linked PM10 composition with toxicity responses. Five of six models were statistically significant (R2CV = 0.53-0.75), with the highest performance for ROS, p53 activation, and cell death (R2CV ≥ 0.62). Biomass burning markers and PAHs were the main predictors of oxidative stress and cytotoxicity, whereas biogenic SOA tracers showed low importance. These findings link specific PM10 sources to distinct neurotoxic effects and highlight the importance of controlling winter emissions.
Esmeralda Ayala, Jordi Puigdemasa, S. Platikanov et al.· Environmental Pollution· 0 citations