抵抗性
基因组
机制(生物学)
抗生素
化学
电化学
还原(数学)
计算生物学
微生物学
生化工程
抗生素耐药性
生物
生物化学
工程类
基因
物理
整合子
电极
几何学
物理化学
量子力学
数学
作者
Rui Wang,Jinping Chen,Haiyang Chen
标识
DOI:10.1016/j.jhazmat.2025.137350
摘要
Electronic mediators are an effective means of enhancing the efficiency of microbial electrochemical electron transfer; however, there are still gaps in understanding the strengthening mechanisms and the efficiency of removing antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). This study systematically elucidates the effects of various electron mediators on bioelectrochemical processes, electron transfer efficiency, and the underlying mechanisms that inhibit ARG propagation within sediment microbial fuel cell systems (SMFCs). The results indicate that the addition of electron mediators significantly increased the output voltage (33.3 %-61.1 %) and maximum power density (14 %-106 %) of SMFCs, while also reducing ARB abundance and transmission risk. The enhancement effect follows the order of biochar, nanoscale zero-valent iron, graphene, and carbon nanotubes, with biochar emerging as the most economical and efficient choice for generating electricity and removing human pathogenic bacteria carrying ARGs. Procrustes analysis revealed that electron mediators facilitated the removal of ARGs by altering the structure of the microbiome, particularly the electricity-generating microorganisms (EGMs). Voltage and mobile genetic elements were the primary drivers of ARGs in the SMFCs. The network analysis results show that multiple carbohydrate-active enzymes, cluster of orthologous groups, and EGMs were negatively correlated with ARGs, indicating that the electron mediator-enhanced SMFCs mainly inhibit the spread of ARGs by promoting cell division, carbohydrate metabolism, and electricity generation. This study provides novel insights into how electron mediators affect ARG removal in microbial electrochemistry, which can inform economically viable strategies for sustainable environmental remediation.
科研通智能强力驱动
Strongly Powered by AbleSci AI