Pyrethroids, an effective and widely used class of pesticides, have attracted considerable concerns considering their frequent detection in environmental matrices. However, their potential health risks to amphibians remain unclear. In our study, female Xenopus laevis were exposed to 0, 0.06, and 0.3 μg/L typical pyrethroid, cis-bifenthrin (cis-BF), for 3 months. Elevated activities of both aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were observed, indicating an ongoing liver injury. Furthermore, exposure to cis-BF led to hyperlipidemia and lipid accumulation in the liver of Xenopus. The targeted lipidomic analysis further revealed that treatment with cis-BF perturbed liver steroid homeostasis, as evidenced by the enriched lipids in the steroid biosynthesis pathway. Consistent with the targeted lipidomic result, treatment with cis-BF changed the liver transcriptome profile with induction of 808 and 1230 differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes analysis underlined the adverse effects of cis-BF exposure on steroid biosynthesis, primary bile acid biosynthesis, and the PPAR signaling pathway in the Xenopus liver. Taken together, our study revealed that exposure to cis-BF at environmentally relevant concentrations resulted in lipid metabolic disorder associated with nonalcoholic fatty liver disease of X. laevis, and our results provided new insight into the potential long-term hazards of pyrethroids.
Accurately identifying the source and controlling the total amount of pollutants are the basis for achieving regulation of pollution sources, which is critical for the prevention and control of surface water pollution. For this purpose, this study used the Xinjian River in Jinyun County, Lishui City, Zhejiang Province, China, as a case study to explore whether and how the tributary inflow impacts the downstream water quality. The main pollution sources in the upstream, midstream, and downstream of the Xinjian River were apportioned using the Positive Matrix Factorization (PMF) model based on the water quality data from four sample stations from January 2018 to September 2022. According to the unmatched factor in different sections, it is plausible to infer that the TN and TP are mainly caused by the tributaries. To enhance the reliability of pollution source apportionment based on the receptor model, a series of remote sensing images with high resolution were used to derive the water quality concentrations to present the spatial distribution and reveal the long-term trend of the local water environment. It is anticipated that the apportionment results could be of great assistance to local authorities for the control and management of pollution, as well as the protection of riverine water quality.
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