Screening of potential oestrogen receptor α agonists in pesticides via in silico, in vitro and in vivo methods

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.

Black phosphorus quantum dots induce myocardial inflammatory responses and metabolic disorders in mice

As an ultrasmall derivative of black phosphorus (BP) sheets, BP quantum dots (BP-QDs) have been effectively used in many fields. Currently, information on the cardiotoxicity induced by BP-QDs remains limited. We aimed to evaluate BP-QD-induced cardiac toxicity in mice. Histopathological examination of heart tissue sections was performed. Transcriptome sequencing, real-time quantitative PCR (RT‒qPCR), western blotting, and enzyme-linked immunosorbent assay (ELISA) assays were used to detect the mRNA and/or protein expression of proinflammatory cytokines, nuclear factor kappa B (NF-κB), phosphatidylinositol 3 kinase-protein kinase B (PI3K-AKT), peroxisome proliferator-activated receptor gamma (PPARγ), and glucose/lipid metabolism pathway-related genes. We found that heart weight and heart/body weight index (HBI) were significantly reduced in mice after intragastric administration of 0.1 or 1 mg/kg BP-QDs for 28 days. In addition, obvious inflammatory cell infiltration and increased cardiomyocyte diameter were observed in the BP-QD-treated groups. Altered expression of proinflammatory cytokines and genes related to the NF-κB signaling pathway further confirmed that BP-QD exposure induced inflammatory responses. In addition, BP-QD treatment also affected the PI3K-AKT, PPARγ, thermogenesis, oxidative phosphorylation, and cardiac muscle contraction signaling pathways. The expression of genes related to glucose/lipid metabolism signaling pathways was dramatically affected by BP-QD exposure, and the effect was primarily mediated by the PPAR signaling pathway. Our study provides new insights into the toxicity of BP-QDs to human health.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Toxicity and ecological risk assessment for two AhR agonistic pesticides mepanipyrim and cyprodinil and their metabolites

Mepanipyrim and cyprodinil are widely used to control and/or prevent fungal diseases in fruit culture. They are frequently detected in the aquatic environment and some food commodities. Different from TCDD, mepanipyrim and cyprodinil are more easily metabolised in the environments. However, the risk of their metabolites to the ecological environment is unclear and needs to be further confirmed. In this study, we investigated the temporal pattern of mepanipyrim- and cyprodinil-induced CYP1A and AhR2 expression and EROD enzyme activity at different time frames during zebrafish embryonic and larval development. Then, we assessed the ecological risk of mepanipyrim, cyprodinil, and their metabolites to aquatic organisms. Our results showed that mepanipyrim and cyprodinil exposure could increase the expression level of cyp1a and ahr2 genes and EROD activity by a dynamic pattern in different developmental stages of zebrafish. Besides, their several metabolites showed strong AhR agonistic activity. Importantly, these metabolites could cause potential ecological risks to aquatic organisms and should be paid more attention to. Our results would provide an important reference value for environmental pollution control and the use management of mepanipyrim and cyprodinil.

Computer-aided toxicity prediction and potential risk assessment of two novel neonicotinoids, paichongding and cycloxaprid, to hydrobionts

Paichongding (IPP) and cycloxaprid (CYC) have been effectively used as the alternative products of imidacloprid (IMI) against IMI-resistant insects and exhibit a great market potential. However, risk assessment of IPP and CYC for non-target organisms, especially ecological risk assessment for non-target aquatic organisms, is still lacking. Here, we predicted the toxicity and potential risks of IPP, CYC, and their transformation products (TPs) to hydrobionts. The results indicated that IPP and CYC could generate 428 and 113 TPs, respectively, via aerobic microbial transformation. Nearly half of the IPP TPs and nearly 41 % of the CYC TPs exhibited high or moderate toxicity to Daphnia or fish. Moreover, we found that IPP, CYC, and 80 TPs of them posed potential risks to aquatic ecosystems. Almost all harmful TPs contained a 6-chloropyridine ring structure, suggesting that this structure may be associated with the strong toxicity of these TPs to aquatic organisms, and these TPs (IPP-TP2 or CYC-TP2, IPP-TP197 or CYC-TP71, IPP-TP198 or CYC-TP72, and IPP-TP212 or CYC-TP80) may appear in aquatic environments as final products. The risks posed by these TPs to aquatic ecosystems require more attention. This study provides insights into the toxicity and ecological risks of IPP and CYC.

Predicting and assessing the toxicity and ecological risk of seven widely used neonicotinoid insecticides and their aerobic transformation products to aquatic organisms

Neonicotinoid insecticides (NIs) are widely used worldwide, accounting for 25 % of the global insecticide market, and are easily transported into surrounding aquatic ecological environments after application. At present, >80 % of surface water is contaminated by NIs globally. Some transformation products (TPs) of NIs can exhibit greater toxicity to aquatic organism than their parent products. However, few studies have evaluated the toxicity and ecological risk of the TPs of NIs. In this study, we aimed to assess the toxicity and ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms using a prediction method. We found that partial aerobic TPs of NIs have greater toxicity to aquatic organisms than their parent products, and some of them could severely damage aquatic ecosystems. Meanwhile, acetamiprid, thiacloprid, and several other TPs of NIs with a chlorinated ring structure showed strong bioconcentration abilities, which could potentially harm aquatic organisms through the food chain. Moreover, the widespread use of NIs has certain aquatic ecological risks, which should be controlled and limited. This study comprehensively evaluated the ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms for the first time. Our results could provide an important reference for assessment of the aquatic environmental risk posed by NIs and pollution control.

Diethyl phosphate disrupts hypothalamus-pituitary-adrenal axis endocrine hormones via nuclear receptors GR and Nur77: Integration of evidences from in vivo, in vitro and in silico approaches

Plenty of population epidemiology and cohort studies have found dialkyl phosphates (DAPs) in the urine were related to endocrine hormone disorders. However, we did not know whether these effects were caused by parent organophosphorus pesticides (OPs) or metabolite DAPs, especially the non-specific metabolite diethyl phosphate (DEP), which was the metabolic end product of most widely used diethyl OPs. In this study, animal experiments (in vivo), cell experiments (in vitro), small molecule-protein binding interaction experiments and computer molecular simulations (in silico) were used to explore the disturbing effects and molecular mechanisms of DEP on the hypothalamic-pituitary-adrenal (HPA) axis endocrine hormones. The animal experiments showed that chronic DEP exposure significantly disturbed the serum contents of HPA axis hormones in adult male rats. The target genes of glucocorticoid receptor (GR) in rat liver, including 11β-hsd1 and Pepck1 and PEPCK protein expressions, were down-regulated. Moreover, the gluconeogenic abilities of rats were impaired. However, it did not affect the expression of GR in the rat hypothalamus. These results indicated that the physiological functions of glucocorticoids and GR were damaged. Furthermore, spectroscopy experiments, cell experiments, molecular docking and molecular dynamics simulations also suggested that DEP can bind to nuclear receptors GR and Nur77, affecting their transcription factor functions, and the transcriptional expression levels of their downstream target genes were reduced. The biosynthesis and secretion of adrenocorticotropic hormone and glucocorticoids were blocked. Therefore, DEP can inhibit the production and physiological functions of HPA axis endocrine hormones by disrupting these related proteins and antagonizing nuclear receptors. These results were considered to provide a theoretical basis for strictly controlling the residue limits of OPs and their metabolites in foods, agricultural products and the environment. They also revealed new targets for evaluating the toxicities and risks of pesticide metabolites.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Gold nanoparticle-based lateral flow immunoassay for the rapid detection of flumetralin in orange

A sensitive monoclonal antibody (mAb) against flumetralin was produced and a gold nanoparticle-based lateral flow immunoassay (LFIA) strip was developed for screening flumetralin in orange.