莱茵衣藻
生物转化
氯霉素
化学
水解酶
衣原体
生物化学
酶
抗生素
基因
突变体
作者
Xin Qi,Jingyu Qin,Shaoguo Ru,Jiu-Qiang Xiong
出处
期刊:Water Research
[Elsevier]
日期:2024-08-22
卷期号:265: 122285-122285
被引量:1
标识
DOI:10.1016/j.watres.2024.122285
摘要
Microalgae-based biotechnology is one of the most promising alternatives to conventional methods for the removal of antibiotic contaminants from diverse water matrices. However, current knowledge regarding the biochemical mechanisms and catabolic enzymes involved in microalgal biodegradation of antibiotics is scant, which limits the development of enhancement strategies to increase their engineering feasibility. In this study, we investigated the removal dynamics of amphenicols (chloramphenicol, thiamphenicol, and florfenicol), which are widely used in aquaculture, by Chlamydomonas reinhardtii under different growth modes (autotrophy, heterotrophy, and mixotrophy). We found C. reinhardtii removed >92 % chloramphenicol (CLP) in mixotrophic conditions. Intriguingly, gamma-glutamyl hydrolase (GGH) in C. reinhardtii was most significantly upregulated according to the comparative proteomics, and we demonstrated that GGH can directly bind to CLP at the Pro77 site to induce acetylation of the hydroxyl group at C3 position, which generated CLP 3-acetate. This identified role of microalgal GGH is mechanistically distinct from that of animal counterparts. Our results provide a valuable enzyme toolbox for biocatalysis and reveal a new enzymatic function of microalgal GGH. As proof of concept, we also analyzed the occurrence of these three amphenicols and their degradation intermediate worldwide, which showed a frequent distribution of the investigated chemicals at a global scale. This study describes a novel catalytic enzyme to improve the engineering feasibility of microalgae-based biotechnologies. It also raises issues regarding the different microalgal enzymatic transformations of emerging contaminants because these enzymes might function differently from their counterparts in animals.
科研通智能强力驱动
Strongly Powered by AbleSci AI