Machine learning predicts ecological risks of nanoparticles to soil microbial communities
Machine learning predicts ecological risks of nanoparticles to soil microbial communities
复制标题
机器学习预测纳米颗粒对土壤微生物群落的生态风险
DOI：
10.1016/j.envpol.2022.119528
复制
发表时间：
2022
影响因子：
9.8
通讯作者：
Qian Haifeng
中科院分区：
文献类型：
--
作者：
Xu Nuohan;Kang Jian;Ye Yangqing;Zhang Qi;Ke Mingjing;Wang Yufei;Zhang Zhenyan;Lu Tao;Peijnenburg W.J.G.M.;Penuelas None Josep;Bao Guanjun;Qian Haifeng
随着纳米技术在农业中的迅速发展，纳米技术对土壤环境影响的评价日益迫切。本研究通过元数据分析和机器学习方法，对365份土壤样品的原始高通量测序（HTS）数据集进行合并，揭示NPs对土壤微生物群落的潜在生态效应。元数据分析表明，纳米颗粒处理对微生物群落的α多样性没有显著影响，但显著改变了β多样性。不幸的是，几种促进植物生长和提高致病性的有益细菌，如Dyella， Methylophilus, Streptomyces，在合成纳米颗粒的加入下，丰度降低了。此外，元数据还表明，纳米颗粒处理降低了辅因子、载体和维生素的生物合成能力，增强了芳香族化合物、氨基酸等的降解能力。这不利于土壤功能的发挥。除了土壤的异质性外，机器学习还发现a)纳米颗粒暴露时间是重塑土壤微生物群落的最重要因素，b)长期暴露降低了微生物群落的多样性和有益菌的丰度。本研究首次利用机器学习模型和元数据分析，从宏观角度探讨了纳米颗粒性质与土壤微生物群落危害之间的关系。这指导了纳米颗粒的合理使用，使其对土壤微生物群的影响降到最低。
With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.