Predicting bioconcentration factor and estrogen receptor bioactivity of bisphenol a and its analogues in adult zebrafish by directed message passing neural networks

Predicting Bioconcentration Factors of Per- and Polyfluoroalkyl Substances Using a Directed Message Passing Neural Network with Multimodal Feature Fusion

Amid growing concerns regarding the ecological risks posed by emerging contaminants, per- and polyfluoroalkyl substances (PFASs) present significant challenges for risk assessment due to their structural diversity and the paucity of experimental data on their bioaccumulation. This study investigated the bioconcentration factors (BCFs) of 18 emerging and legacy PFASs using zebrafish in a flow-through exposure system and constructed a robust BCF prediction model to address the data gaps associated with numerous novel PFASs. Experimental results indicated that perfluoro(3,5,7,9,11-pentaoxadodecanoic) acid (PFO5DoDA) and perfluoro-2,5-dimethyl-3,6-dioxanonanoic acid (C9 HFPO-TA) exhibited higher bioaccumulation potential than perfluorooctanoic acid (PFOA). A multimodal feature-fused directed message passing neural network (FF-DMPNN) model was constructed, integrating molecular graph representations, physicochemical descriptors, and bioassay data reflecting absorption, distribution, metabolism, and excretion characteristics. The FF-DMPNN model demonstrated superior predictive performance compared to conventional machine learning approaches by providing a more complete representation of molecular structures and physicochemical properties, achieving higher accuracy (R2 = 0.742) and robustness in predicting BCF values for PFASs. Application of the model to a comprehensive PFAS database identified 2.45% of chemicals as bioaccumulative, highlighting the need for regulatory attention. Overall, this study provides critical insights into the bioconcentration risks associated with PFASs and offers a reliable framework for prioritizing regulatory actions for these emerging contaminants, addressing a pressing need for their effective environmental management.

Integrated analysis of microplastics origins and impact on prominent aquaculture ecosystems in Bangladesh

Microplastics (MPs) have become a pressing environmental challenge in aquaculture-farmed ponds, particularly in Bangladesh, where research on their prevalence and impact is sparse. This research systematically investigates the distribution, abundance, and features of MPs in water and fish from aquaculture ponds in the western region of Bangladesh. The study reveals that MPs were widespread in water samples, with quantities ranging from 0.095 to 0.36 items/L, predominantly fibers accounted for 60.86 % of the total, followed by 26.08 % fragments, 11.30 % lines, and 1.76 % pellets. Fish samples demonstrated an average MP concentration of 1.19 items/g (23.37 items/individual), in fish gill ranging from 1.05 to 5.04 items/g and in GIT 0.40 to 2.26 items/g across eleven species, predominantly fibers with a 100 % detection rate, showing variability in MP concentration based on tissue type, species, and feeding habits. Fourier transform infrared spectroscopy (FT-IR) was employed to analyze the polymer composition, revealing significant proportions of SSP (W-43.17 %, F-35.22 %), PE (W-5.06 %, F-23.14 %), PP (W-5.57 %, F-8.19 %), nylon (W-15.76 %, F-14.84 %), PVC (W-7.16 %, F-3.58 %), and acrylic (W-5.57 %, F-4.93 %). Strong correlations were found between fish size and MP abundance, indicating that pond environmental contamination is a significant factor in MP ingestion. Pollution risk assessment revealed high contamination MP levels in both water and fish. Among the probable sources, MPs contributions are from agricultural runoff, tires of vehicles (each 14.11 %), fishing nets, fish feed, household wastage, plastic-made feeding equipment, laundry wastage (each 11.76 %), and so on. The research underscores the need for further research on MP exposure to human health and sustainable aquaculture production practices.

Toxicity and bioconcentration of bisphenol A alternatives in the freshwater pulmonate snail Planorbella pilsbryi

Bisphenol A (BPA) is an industrial chemical identified as a vertebrate endocrine disruptor. Numerous alternatives have been developed, for which toxicity data are lacking. The present study assessed the toxicity of BPA and its replacement products bisphenol F (BPF), bisphenol S (BPS), and bisphenol AF (BPAF) in freshwater snail (Planorbella pilsbryi) embryos and adults. The chronic toxicity of BPA and BPAF was further characterized in 28-day tests with adult snails, followed by 21-day assessments of hatching and survival of embryos produced at the end of the test (F1 generation). In acute tests, BPAF was the most toxic of the substances tested (maximum acceptable toxicant concentration [MATC], 136 µg/L), followed by BPA (MATC, 1404 µg/L), BPF (MATC, 1525 µg/L), and BPS (MATC > 8590 µg/L). In the chronic test with BPA, although we observed no significant effects on adult snails up to 479 µg/L, the hatching and survival of juveniles from the F1 generation decreased (MATC, 13 µg/L), and was delayed by 7.5 days, on average. In contrast, we did not observe any decrease in hatching or survival of juveniles from the F1 generation during exposure to BPAF. Effects were observed at concentrations above most reported environmental exposure concentrations, although there was an overlap between exposure and effect concentrations. Given that concentrations of alternative substances are expected to increase, and in the absence of data on potential effects of mixtures, further research is needed.

Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms

The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R2 = 0.727 and Q2(LOO) = 0.723) and external validation metrics (Q2F1 = 0.739, Q2F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.

BCDPi: An interpretable multitask deep neural network model for predicting chemical bioconcentration in fish

Predicting the bioconcentration of chemical compounds plays a crucial role in assessing environmental risks and toxicological impacts. This study presents a robust multitask deep learning model for predicting the bioconcentration potential. The model can predict the bioconcentration of compounds in multiple categories, including non-bioconcentrative (non-BC), weakly bioconcentrative (weak-BC), and strongly bioconcentrative (strong-BC). We also employed the SHapley Additive exPlanations (SHAP) technology for the model interpretation. The binary classification models (non-BC vs BC and weak-BC vs strong-BC) showed good predictive performance, which achieved accuracy values over 90% and area under the curve (AUC) values with 0.95. The final ternary classification model provided an overall accuracy with 91.11%. Comparative analysis of molecular physicochemical properties showed that the importance of molecular weight, polar surface area, solubility, and hydrogen bonding are important for chemical bioconcentration. Besides, we identified eight structural alerts responsible for chemical bioconcentration. We made the model available as an online tool named BCdpi-predictor, which is accessible at http://bcdpi.sapredictor.cn/. Users can predict the bioconcentration potential of chemical compounds freely. The model has significant implications for environmental policy and regulatory frameworks, such as REACH, by providing a more accurate and interpretable method for assessing chemical risks. We hope that the results of this study can provide helpful tools and meaningful information for chemical bioconcentration prediction in environmental risk assessment.

Potential use of gammarus (Pontogammarus maeoticus) and shrimp (Palaemon elegans) as biomonitors of microplastics pollution in coastal environments

Microplastics (MPs) pose a significant threat to marine ecosystems, necessitating robust biomonitoring to assess aquatic risks and inform effective policymaking. In this study we investigated MPs pollution in gammarus (Pontogammarus maeoticus), shrimp (Palaemon elegans), sediment and water samples of southern coast of the Caspian Sea to assess the potential use of these two crustaceans as biomonitors of MPs pollution, bioconcentration of MPs in organisms' tissue and the pollution risks of MPs in environmental matrices. Samples were collected from 6 stations during June to August 2023. MPs were found in all compartments with an average of 100 ± 45.34 items/kg dry weight, 0.45 ± 0.06 items/L, 0.38 ± 0.21 items/individual or 0.58 ± 0.34 items/g wet weight (ww) and 0.26 ± 0.15 items/individual or 8.69 ± 7.88 items/g ww, for sediments, seawaters, P. elegans and P. maeoticus, respectively. MPs were prevailed by class 300-1000 μm in size, polyamide in polymer, fiber in shape and black in color. P. maeoticus and P. elegans did not meet the selection criteria as MPs biomonitors. However, bioconcentration factor (BCF) illustrated that both crustaceans can absorb and accumulate MPs from their surrounding water (BCF >1). Based on contamination factors (CF) values, sampling stations were polluted with MPs (1 ≤ CF < 6). The overall pollution load index (PLI) for sediment and seawater stations were 2.47 and 1.88, respectively, indicating minor contamination with MPs in the risk level I. Current research provides useful information on MPs pollution in crustaceans species and the risk level of MPs in environmental matrices that can be suitable for bioaccumulation hazard assessment and future monitoring programs.

Mixture effect of parental exposure to triazophos and fenvalerate on the early development of zebrafish offspring

Triazophos (TRI) and fenvalerate (FEN) have been extensively used in the world and frequently coexist in the water environments, might pose health risk to aquatic species. However, investigations of their mixture toxic effects on offspring after parental exposure have been neglected, especially for aquatic vertebrates such fish. To address this knowledge gap, parental zebrafish (F0 generation) were exposed to TRI, FEN and their mixture for 60 days, as well as the embryos (F1 generation) were hatched without or with continued corresponding exposures at the same concentrations until 7 days post fertilization. The results exhibited that exposure to TRI and FEN altered the expression levels of biomarkers associated with several biological processes, such as apoptosis and inflammatory response. Compared to individual exposure in the F1 generation, the co-exposure to TRI and FEN resulted in increased the expression of T4 and cc-chem mRNA and decreased the expression of ROS, trα, il-8, and gpx mRNA when the F0 generation was similarly exposed. These results revealed that the co-exposure to TRI and FEN has detrimental effects on fish progeny following parental exposure, even if the progeny are not directly exposed to the pesticides, and such negative effects may be intensified if the offspring continue to be exposed. This study enhances the understanding of the harmful impacts of parental exposure to the pesticide mixture on descendants and holds implications for the ecological risk assessment of pesticide mixtures in aquatic vertebrates. Further mechanistic studies are necessary to gain a deeper insight into the mixture effects of pesticides and other kinds of pollutants on subsequent offspring following parental exposure.

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.

Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals

Accurate prediction of parameters related to the environmental exposure of chemicals is crucial for the sound management of chemicals. However, the lack of large data sets for training models may result in poor prediction accuracy and robustness. Herein, integrated transfer learning (TL) and multitask learning (MTL) was proposed for constructing a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model) using n-octanol/water partition coefficients as a source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation parameters based on enlarged data sets that cover 2496 compounds with at least one bioaccumulation parameter. Results show that the TL-MTL-GNN model outperformed single-task GNN models with and without the TL, as well as conventional machine learning models trained with molecular descriptors or fingerprints. Applicability domains were characterized by a state-of-the-art structure-activity landscape-based (abbreviated as ADSAL) methodology. The TL-MTL-GNN model coupled with the optimal ADSAL was employed to predict bioaccumulation parameters for around 60,000 chemicals, with more than 13,000 compounds identified as bioaccumulative chemicals. The high predictive accuracy and robustness of the TL-MTL-GNN model demonstrate the feasibility of integrating the TL and MTL strategy in modeling small-sized data sets. The strategy holds significant potential for addressing small data challenges in modeling environmental chemicals.

Prediction of Potential Risk for Flupyradifurone and Its Transformation Products to Hydrobionts

Flupyradifurone (FPF) is considered the latest generation of neonicotinoid insecticides. Here, we investigated the toxicity and ecological risk of FPF and its aerobic transformation products (TPs) to aquatic species using the method of prediction. We found that FPF exhibited moderate or high toxicity to some aquatic species. The 5% hazardous concentration of FPF was 3.84 μg/L for aquatic organisms. We obtained 91 aerobic TPs for FPF, and almost half of FPF TPs exhibited toxicity to fish or Daphnia. Eleven of the TPs of FPF exhibited a high or moderate risk to aquatic ecosystems. All FPF TPs with high and moderate risks contained a 6-chloropyridine ring structure, indicating that the derivant of a pyridine ring exhibits potential risks to aquatic ecosystems. Our results provide insight into the potential risk of FPF to aquatic ecosystems and could be used to help set criteria to control pollution caused by FPF.

Construction of functionalized In-based metal organic framework/BiOCl1-xIx Z-scheme heterojunction for efficient photocatalytic degradation of tetracycline: Performance and mechanism

The environmental implications of antibiotics have drawn widespread attention. Numerous monomer-based bismuth oxide halide catalysts have been extensively studied to remove tetracycline (TC) from aquatic environments. Integrating bismuth oxide halide composites with In-based metal organic framework (NH2-MIL-68(In)) might potentially serve as a novel strategy. By meticulously adjusting Cl and I within the composite bismuth halide oxide (B-x), a suite of purpose built heterojunctions (NMB-x) were synthesized, which were engineered to facilitate the efficient photodegradation of TC in simulated and actual aquatic environments. The incorporation of Z-scheme heterojunctions yielded a significant enhancement in photocatalytic responsiveness and charge carrier separation. Notably, NMB-0.3 demonstrated remarkable TC removal efficiency of 88.52 ± 3.05%, which is 3.74 times of B-0.3 within 90 min. The apparent quantum yield was also increased from 8.97% (B-0.3) to 19.68% (NMB-0.3). The removal of TC from natural water bodies was also assessed. Moreover, the photocatalyst concentration, assessed using response surface method, was found to show influential factors on TC removal. In addition, density functional theory (DFT) simulations were employed to identify vulnerable sites within TC. Intermediates and pathways in the photodegradation of TC have also been inferred. Furthermore, a comprehensive environmental toxicity assessment of representative intermediates demonstrated that these intermediates exhibited significantly reduced environmental toxicity compared to TC. This study provides a new approach to the design strategy of efficient and environmentally friendly MOF-based photocatalysts.

Bioaccumulation of microplastics in the edible tissues of fish collected from urban lakes of Bangladesh: a potential exposure to public health

Microplastic (MP) pollution is an emerging environmental problem, due to its universal dispersion. In the present study, we determined the MP pollution in water, sediment, and fish samples of three different urban lakes of Bangladesh to assess the bioaccumulation of MPs from the lake environment to fish's edible (flesh) and inedible tissue (gut), ecological risk and consequent human exposure to MPs by fish consumption. A total of forty-three fishes were collected from Jahangirnagar Co-Operative Housing Society (JCHS), Dhanmondi Lake (DL), and Saturia Thana Lake (MST). The average MP concentration in sediment and water of the lakes is 7588 ± 4353 MPs/kg dry weights; 142 ± 22 MPs/L, respectively. MPs were identified in the edible (2.8-20.2 MPs/g) and inedible (2.27-20.93 MPs/g) tissue of all fish species. The highest number of MPs was observed in the flesh of Labeo bata of the JCHS Lake and in the gut of Catla catla of DL. The most dominant shape of MPs was fiber and fragment, 0.1-0.4 mm was the dominant size range, and blue, purple, and transparent were the dominant colors. The presence of six polymer types was revealed by FT-IR analysis, which were polystyrene, polypropylene, nitrile, ethylene vinyl acetate, high-density polyethylene, and nylon. The concentration of MPs in fish is found to increase with the increment in body weight. The bioconcentration factor (BCF) analysis reveals that among all the fish species, Labeo bata and Oreochromis mossambicus accumulate the highest number of MPs from the lake environment. The pollution load index of MPs indicates that the sampling sites were within hazard levels III-IV. Estimated annual intake reveals that humans will be exposed to the highest number of MPs if they consume the flesh of Labeo bata of DL and JCHS Lake.

Combined toxicity of aflatoxin B1 and tebuconazole to the embryo development of zebrafish (Danio rerio)

Mycotoxins and pesticides are pervasive elements within the natural ecosystem. Furthermore, many environmental samples frequently exhibit simultaneous contamination by multiple mycotoxins and pesticides. Nevertheless, a significant portion of previous investigations has solely reported the occurrence and toxicological effects of individual chemicals. Global regulations have yet to consider the collective impacts of mycotoxins and pesticides. In our present study, we undertook a comprehensive analysis of multi-level endpoints to elucidate the combined toxicity of aflatoxin B1 (AFB1) and tebuconazole (TCZ) on zebrafish (Danio rerio). Our findings indicated that AFB1 (with a 10-day LC50 value of 0.018 mg L-1) exhibits higher toxicity compared to TCZ (with a 10-day LC50 value of 2.1 mg L-1) toward D. rerio. The co-exposure of AFB1 and TCZ elicited synergistic acute responses in zebrafish. The levels of GST, CYP450, SOD, and Casp-9 exhibited significant variations upon exposure to AFB1, TCZ, and their combined mixture, in contrast to the control group. Additionally, eight genes, namely cat, cxcl-cic, il-1β, bax, apaf-1, trβ, ugtlab, and vtg1, displayed marked alterations when exposed to the chemical mixture as opposed to individual substances. Therefore, further exploration of the underlying mechanisms governing joint toxicity is imperative to establish a scientific basis for evaluating the risk associated with the combined effects of AFB1 and TCZ. Moreover, it is essential to thoroughly elucidate the organ system toxicity triggered by the co-occurrence of mycotoxins and pesticides.

Probing the bioconcentration and metabolism disruption of bisphenol A and its analogues in adult female zebrafish from integrated AutoQSAR and metabolomics studies

Plenty of emerging bisphenol A (BPA) substitutes rise to wait for assessment of bioconcentration and metabolism disruption. Computational methods are useful to fill the data gap in chemical risk assessment, such as automated quantitative structure-activity relationship (AutoQSAR). It is not clear how AutoQSAR performs in predicting the bioconcentration factor (BCF) in adult zebrafish. Herein, AutoQSAR was used to predict the logBCFs of BPA, bisphenol AF (BPAF), bisphenol B, bisphenol F and bisphenol S (BPS). For the test set, a linear relationship was shown between the observed and predicted logBCFs with a slope of 0.97. The predicted logBCFs of these five bisphenols were quite close to their experimental data with a slope of 0.94, suggesting better performance than directed message passing neural networks and EPI Suite with a slope of 0.69 and 0.61, respectively. Thus, AutoQSAR is powerful in modeling logBCFs in fish with minimal time and expertise. To link bioconcentration with metabolic effects, female zebrafish were exposed to BPA, BPAF and BPS for metabolomics analysis. BPA caused a significant disturbance in amino acid metabolism, while BPAF and BPS significantly altered another three metabolic pathways, showing chemical-specific responses. BPAF with the highest logBCF elicited the strongest metabolomic responses reflected by the metabolic effect level index, followed by BPA and BPS. Thus, BPAF and BPS elicited higher or similar metabolism disruption compared with BPA in female zebrafish, respectively, reflecting consequences of bioconcentration.

Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles

Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure-activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R²_test = 0.911, adjusted R²_test = 0.733, RMSE_test = 0.091, and MAE_test = 0.067) and for the combination of internal and external datasets (R²_test = 0.908, adjusted R²_test = 0.871, RMSE_test = 0.255, and MAE_test = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.

Interacted toxic mechanisms of ochratoxin A and tricyclazole on the zebrafish (Danio rerio)

Despite the current efforts to identify the mixtures of chemical pollutants, they are often "binned" into their corresponding pollutant groups. Limited studies have investigated complex mixtures of chemical pollutants co-occurring across different groups. The combined toxic impacts of several substances become a critical consideration in toxicology because chemical combinations can exert a greater deleterious effect than the single components in the mixture. In the current work, we assessed the joint impacts of ochratoxin A and tricyclazole on the zebrafish (Danio rerio) embryos and explored their underlying signaling pathways. Ochratoxin A displayed higher toxicity than tricyclazole, with a 10-day LC₅₀ of 0.16 mg L^-1, whereas that for tricyclazole was 1.94 mg L^-1. The combination of ochratoxin A and tricyclazole exhibited a synergistic impact on D. rerio. The activities of detoxification enzymes GST and CYP450, as well as apoptosis-associated enzyme caspase 3, were distinctly changed in most individual and mixture exposures comparing to the untreated group. Upon both individual and mixture exposures, more dramatic variations were detected in the expressions of nine genes, such as the apoptosis genes cas3 and bax, antioxidant gene mn-sod, immunosuppression gene il-1β, and the endocrine system genes trα, dio1, trβ, ugtlab, and crh, compared with the untreated group. These findings suggested that the simultaneous exposure to low doses of mycotoxins and pesticides in food commodities was more toxic than predicted from the individual chemicals. Considering the frequent co-occurrence of mycotoxins and pesticides in the diet, this synergy should be considered in future assessments.

Exposure to environmentally relevant concentrations of TnBP results in tissue-specific bio-accumulation and inhibits growth of silver carp (Hypophthalmichthys molitrix)

Tri-n-butyl phosphate (TnBP) is commonly used as flame retardant and rubber plasticizer, and has been widely detected in aquatic organisms and natural waters. However, the potential toxicity of TnBP in fish remains unclear. In the present study, silver carp (Hypophthalmichthys molitrix) larvae were treated with environmentally relevant concentrations (100 or 1000 ng/L) of TnBP for 60 d and then they were depurated in clean water for 15 d, and the accumulation and depuration of the chemical in six tissues of silver carp were measured. Furthermore, effects on growth were evaluated and potential molecular mechanisms were explored. Results indicated that TnBP could be rapidly accumulated and depurated in silver carp tissues. In addition, the bio-accumulation of TnBP displayed tissue-specificity, where intestine contained the greatest and vertebra had the smallest level of TnBP. Furthermore, exposure to environmentally relevant concentrations of TnBP led to time- and concentration-dependent growth inhibition of silver carp, even though TnBP was completely depurated in tissues. Mechanistic studies suggested that exposure to TnBP up- and down-regulated the expression of ghr and igf1 in liver, respectively, and increased GH contents in plasma of silver carp. TnBP exposure also up-regulated the expression of ugt1ab and dio2 in liver, as well as decreased T4 contents in plasma of silver carp. Our findings provide direct evidence of health hazards of TnBP to fish in natural waters, calling for more attention of environmental risks of TnBP in aquatic environment.