In Journal of hazardous materials
Microplastics are a growing marine environmental concern globally due to their high abundance and persistent degradation. We created a global map for predicting marine microplastic pollution using a machine-learning model based on 9445 samples and found that microplastics converged in zones of accumulation in subtropical gyres and near polar seas. The predicted global potential for the biodegradation of microplastics in 1112 metagenome-assembled genomes from 485 marine metagenomes indicated high potential in areas of high microplastic pollution, such as the northern Atlantic Ocean and the Mediterranean Sea. However, the limited number of samples hindered our prediction, a priority issue that needs to be addressed in the future. We further identified hosts with microplastic degradation genes (MDGs) and found that Proteobacteria accounted for a high proportion of MDG hosts, mainly Alphaproteobacteria and Gammaproteobacteria, with host-specific patterns. Our study is essential for raising awareness, identifying areas with microplastic pollution, providing a prediction method of machine learning to prioritize surveillance, and identifying the global potential of marine microbiomes to degrade microplastics, providing a reference for selecting bacteria that have the potential to degrade microplastics for further applied research.
Chen Bingfeng, Zhang Zhenyan, Wang Tingzhang, Hu Hang, Qin Guoyan, Lu Tao, Hong Wenjie, Hu Jun, Penuelas Josep, Qian Haifeng
2023-Mar-11
Biodegradation potential, Global microplastic distribution, Machine learning, Metagenome, Microplastic
