Using in Vitro and Machine Learning Approaches to Determine Species-Specific Dioxin-like Potency and Congener-Specific Relative Sensitivity among Birds for Brominated Dioxin Analogues

Zhang，Rui1; Wu，Qiuxuan1; Qi，Xiaoyi2,3; Wang，Xiaoxiang4,5; Zhang，Xuesheng6; Song，Chao7,8; Peng，Ying9; Crump，Doug10; Zhang，Xiaowei11

2021

10.1021/acs.est.1c05951

ENVIRONMENTAL SCIENCE & TECHNOLOGY   影响因子和分区

0013-936X

1520-5851

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.

dioxin-like compounds ecotoxicological risk assessment mechanism-driven prediction model non- ortho polybrominated biphenyls polybrominated dibenzo- p-dioxins polybrominated dibenzofurans random forest

EI

英语

NI期刊

通讯

20214711203802

Approximation algorithms ; Birds ; Decision trees ; Forecasting ; Hydrogen bonds ; Learning algorithms ; Machine learning ; Molecular dynamics ; Nanoparticles ; Risk assessment ; Toxicity

Health Care:461.7 ; Machine Learning:723.4.2 ; Nanotechnology:761 ; Physical Chemistry:801.4 ; Organic Compounds:804.1 ; Accidents and Accident Prevention:914.1 ; Mathematics:921 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Solid State Physics:933 ; Systems Science:961

ENVIRONMENT/ECOLOGY

2-s2.0-85119413941

Scopus

1.School of Water Conservancy and Environment,University of Jinan,Jinan,250022,China
2.Department of Gynecology,Shandong Provincial Hospital,Cheeloo College of Medicine,Shandong University,Jinan,250021,China
3.Department of Gynecology,Shandong Provincial Hospital Affiliated to Shandong First Medical University,Jinan,250021,China
4.State Environ. Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
5.Yuanshang Technology Co.,Ltd.,Shenzhen,518126,China
6.School of Resources and Environmental Engineering,Anhui University,Hefei,230601,China
7.Freshwater Fisheries Research Center,Chinese Academy of Fishery Sciences,Wuxi,214081,China
8.Lab. of Quality and Safety Risk Assessment for Aquatic Products on Environmental Factors (Wuxi),Ministry of Agriculture,Wuxi,214081,China
9.Research and Development Center for Watershed Environmental Eco-Engineering,Beijing Normal University,Zhuhai,519087,China
10.Ecotoxicology and Wildlife Health Division,Environment and Climate Change Canada,National Wildlife Research Centre,Carleton University,Ottawa,K1A 0H3,Canada
11.State Key Laboratory of Pollution Control and Resources Reuse,School of the Environment,Nanjing University,Nanjing,210023,China

Zhang，Rui,Wu，Qiuxuan,Qi，Xiaoyi,et al. Using in Vitro and Machine Learning Approaches to Determine Species-Specific Dioxin-like Potency and Congener-Specific Relative Sensitivity among Birds for Brominated Dioxin Analogues[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2021,55:16056-16066.

Zhang，Rui.,Wu，Qiuxuan.,Qi，Xiaoyi.,Wang，Xiaoxiang.,Zhang，Xuesheng.,...&Zhang，Xiaowei.(2021).Using in Vitro and Machine Learning Approaches to Determine Species-Specific Dioxin-like Potency and Congener-Specific Relative Sensitivity among Birds for Brominated Dioxin Analogues.ENVIRONMENTAL SCIENCE & TECHNOLOGY,55,16056-16066.

Zhang，Rui,et al."Using in Vitro and Machine Learning Approaches to Determine Species-Specific Dioxin-like Potency and Congener-Specific Relative Sensitivity among Birds for Brominated Dioxin Analogues".ENVIRONMENTAL SCIENCE & TECHNOLOGY 55(2021):16056-16066.