College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China.
Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, PR China.
Sci Total Environ. 2022 Jul 1;828:154412. doi: 10.1016/j.scitotenv.2022.154412. Epub 2022 Mar 9.
In the past decades, hundreds of antibiotics have been isolated from microbial metabolites or have been artificially synthesized for protecting humans, animals and crops from microbial infections. Their everlasting usage results in impacts on the microbial community composition and causes well-known collateral damage to the functioning of microbial communities. Nevertheless, the impact of different antibiotic properties on aquatic microbial communities have so far only poorly been disentangled. Here we characterized the environmental risk of 50 main kinds of antibiotics from 9 classes at a concentration of 10 μg/L for aquatic bacterial communities via metadata analysis combined with machine learning. Metadata analysis showed that the alpha diversity of the bacterial community increased only after treatment with aminoglycoside and β-lactam antibiotics, while its structure was changed by almost all tested antibiotics. The antibiotic treatment also disturbed the functions of the bacterial community, especially with regard to metabolic pathways, including amino acids, cofactors, vitamins, xenobiotics and carbohydrate metabolism. The critical characteristics (atom stereocenter count, number of hydrogen atoms in the antibiotic, and the adipose water coefficient) of antibiotics affecting the composition of the bacterial community in aquatic habitats were screened by machine learning. The key characteristics of antibiotics affecting the function bacterial communities were the number of hydrogen atoms, molecular weight and complexity. In summary, by developing machine learning models and by performing metadata analysis, this study provides the relationship between the properties of antibiotics and their adverse impacts on aquatic microbial communities from a macro perspective. The study also provides guidance for the rational design of antibiotics.
在过去几十年中，已经从微生物代谢产物中分离出数百种抗生素，或者人工合成抗生素，以保护人类、动物和农作物免受微生物感染。它们的持续使用对微生物群落的组成产生了影响，并对微生物群落的功能造成了众所周知的附带损害。然而，不同抗生素特性对水生微生物群落的影响迄今尚未得到很好的区分。在这里，我们通过元数据分析结合机器学习，以 10μg/L 的浓度对 9 类 50 种主要抗生素对水生细菌群落的环境风险进行了特征描述。元数据分析表明，只有在使用氨基糖苷类和β-内酰胺类抗生素后，细菌群落的 alpha 多样性才会增加，而几乎所有测试的抗生素都会改变其结构。抗生素处理还扰乱了细菌群落的功能，特别是与代谢途径有关，包括氨基酸、辅因子、维生素、外源性化合物和碳水化合物代谢。通过机器学习筛选出影响水生生境中细菌群落组成的抗生素关键特征（抗生素中原子手性中心的数量、抗生素中氢原子的数量和脂肪水系数）。影响细菌群落功能的抗生素的关键特征是氢原子的数量、分子量和复杂性。总之，通过开发机器学习模型并进行元数据分析，本研究从宏观角度提供了抗生素性质与其对水生微生物群落的不良影响之间的关系。该研究还为合理设计抗生素提供了指导。
