five

Data_Sheet_1_Improving Cadmium Resistance in Escherichia coli Through Continuous Genome Evolution.PDF

收藏
NIAID Data Ecosystem2026-03-10 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Improving_Cadmium_Resistance_in_Escherichia_coli_Through_Continuous_Genome_Evolution_PDF/7745795
下载链接
链接失效反馈
官方服务:
资源简介:
Cadmium (Cd) is a heavy metal that is extremely toxic to many organisms; however, microbes are highly adaptable to extreme conditions, including heavy metal contamination. Bacteria can evolve in the natural environment, generating resistant strains that can be studied to understand heavy-metal resistance mechanisms, but obtaining such adaptive strains usually takes a long time. In this study, the genome replication engineering assisted continuous evolution (GREACE) method was used to accelerate the evolutionary rate of the Escherichia coli genome to screen for E. coli mutants with high resistance to cadmium. As a result, a mutant (8mM-CRAA) with a minimum inhibitory concentration (MIC) of 8 mM cadmium was generated; this MIC value was approximately eightfold higher than that of the E. coli BL21(DE3) wild-type strain. Sequencing revealed 329 single nucleotide polymorphisms (SNPs) in the genome of the E. coli mutant 8mM-CRAA. These SNPs as well as RNA-Seq data on gene expression induced by cadmium were used to analyze the genes related to cadmium resistance. Overexpression, knockout and mutation of the htpX (which encodes an integral membrane heat shock protein) and gor (which encodes glutathione reductase) genes revealed that these two genes contribute positively to cadmium resistance in E. coli. Therefore, in addition to the previously identified cadmium resistance genes zntA and capB, many other genes are also involved in bacterial cadmium resistance. This study assists us in understanding the mechanism of microbial cadmium resistance and facilitating the application of heavy-metal remediation.
创建时间:
2019-02-20
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作