Zi-Yi Zheng, Jing-Xuan Zhou, Zhao-Xing Peng, Hong-Gang Ni
{"title":"Computational simulation of bioaccumulation and trophic transfer of antibiotics mechanisms in aquatic food chain","authors":"Zi-Yi Zheng, Jing-Xuan Zhou, Zhao-Xing Peng, Hong-Gang Ni","doi":"10.1016/j.watres.2024.122951","DOIUrl":null,"url":null,"abstract":"Numerous antibiotics have been detected in aquatic ecosystems and induced severe toxic effects on aquatic organisms. However, mechanisms of bioaccumulation and trophic transfer of antibiotics are not adequately discussed, to the best of our knowledge. In this context, the bidirectional selective effect values (BSEV) and trophic transfer efficiency ratio (TTER) of 24 antibiotics in a simulated food chain (<em>Chlorella sorokiniana</em>-<em>Daphnia magna</em>-<em>Danio rerio</em>) were first calculated to mirror the bioaccumulation and biomagnification. Based on estimates above, the multi-output machine learning (ML) models, including K nearest neighbor (KNN), Support vector machine (SVM), Extremely randomized trees (ERT) and Extreme gradient boosting (XGBoost), were constructed, followed by molecular dynamics (MD) simulation and density functional theory (DFT) calculation to explore the bioaccumulation and biomagnification mechanism. According to our results, sulfonamide antibiotics had greater capacity biomagnification, while <em>β</em>-lactam and tetracycline antibiotics showed opposite results. Meanwhile Cytochromes P450 (CYP450) in <em>Danio rerio</em> played a key role in the food chain. The ERT model exhibited reliable prediction with indicators of R<sup>2</sup> = 0.816, MAE = 0.039, MSE = 0.003, RMSE = 0.053 and MAPE = 8.923. The <em>AATS5s</em> was identified as the most contributing descriptor. The differences in the atomic composition, structure and binding ability to enzymes of antibiotics lead to the differences in their bioaccumulation. Van der Waals interactions (ΔE<sub>vdw</sub>) and non-polar interactions (ΔG<sub>nonpolar</sub>) were the main driving energy for the biometabolism capability of antibiotics. Tetracyclines are the most readily biometabolized, whereas sulfonamides are more difficult to biometabolize due to their low binding capacity and low reactivity.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"37 1","pages":""},"PeriodicalIF":11.4000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2024.122951","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Numerous antibiotics have been detected in aquatic ecosystems and induced severe toxic effects on aquatic organisms. However, mechanisms of bioaccumulation and trophic transfer of antibiotics are not adequately discussed, to the best of our knowledge. In this context, the bidirectional selective effect values (BSEV) and trophic transfer efficiency ratio (TTER) of 24 antibiotics in a simulated food chain (Chlorella sorokiniana-Daphnia magna-Danio rerio) were first calculated to mirror the bioaccumulation and biomagnification. Based on estimates above, the multi-output machine learning (ML) models, including K nearest neighbor (KNN), Support vector machine (SVM), Extremely randomized trees (ERT) and Extreme gradient boosting (XGBoost), were constructed, followed by molecular dynamics (MD) simulation and density functional theory (DFT) calculation to explore the bioaccumulation and biomagnification mechanism. According to our results, sulfonamide antibiotics had greater capacity biomagnification, while β-lactam and tetracycline antibiotics showed opposite results. Meanwhile Cytochromes P450 (CYP450) in Danio rerio played a key role in the food chain. The ERT model exhibited reliable prediction with indicators of R2 = 0.816, MAE = 0.039, MSE = 0.003, RMSE = 0.053 and MAPE = 8.923. The AATS5s was identified as the most contributing descriptor. The differences in the atomic composition, structure and binding ability to enzymes of antibiotics lead to the differences in their bioaccumulation. Van der Waals interactions (ΔEvdw) and non-polar interactions (ΔGnonpolar) were the main driving energy for the biometabolism capability of antibiotics. Tetracyclines are the most readily biometabolized, whereas sulfonamides are more difficult to biometabolize due to their low binding capacity and low reactivity.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.