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Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on

There is a paucity of experimental data regarding dioxin-like toxicity of polybrominated dibenzo-p-dioxins/dibenzofurans (PBDD/Fs) and non-ortho polybrominated biphenyls (PBBs). In this study, avian aryl hydrocarbon receptor 1 (AHR1)-luciferase reporter gene assays were used to determine their species-specific dioxin-like potencies (DLPs) and congener-specific interspecies relative sensitivities in birds. The results suggested that DLPs of the brominated congeners for chicken-like (Ile324_Ser380) species did not always follow World Health Organization toxicity equivalency factors of their chlorinated analogues. For ring-necked pheasant-like (Ile324_Ala380) and Japanese quail-like (Val324_Ala380) species, the difference in DLP for several congeners was 1 or even 2 orders of magnitude. Moreover, molecular docking and molecular dynamics simulation were performed to explore the interactions between the brominated congeners and AHR1-ligand-binding domain (LBD). The molecular mechanics energy (EMM) between each congener and each individual amino acid (AA) residue in AHR1-LBD was calculated. These EMM values could finely characterize the final conformation of species-specific AHR1-LBD for each brominated congener. Based on this, mechanism-driven generalized linear models were successfully built using machine learning algorithms and the spline approximation method, and these models could qualitatively predict the complex relationships between AHR1 conformations and DLPs or avian interspecies relative sensitivity to brominated dioxin-like compounds (DLCs). In addition, several AAs conserved among birds were found to potentially interact with species-specific AAs, thereby inducing species-specific interactions between AHR1 and brominated DLCs. The present study provides a novel strategy to facilitate the development of mechanism-driven computational prediction models for supporting safety assessment of DLCs, as well as a basis for the ecotoxicological risk assessment of brominated congeners in birds.