摘要
以湖北某铜矿尾矿库为主要研究对象,采集库内尾矿样品11个、对照样某小型废弃尾矿库尾矿样品2个和附近耕地土壤样品1个,采用变性梯度凝胶电泳方法对上述样品中细菌的16S r RNA V3~V6可变区扩增片段进行分析,利用分析得到的图谱数据与所测得样品的理化性质及重金属Cu、Zn含量进行相关性及冗余度(RDA)分析.结果表明,尾矿库内Cu、Zn污染严重并波及周边,与尾矿样品的理化性质存在不同的相关性,其中Zn的污染程度与有机质存在极显著正相关[R=0.668(P<0.01)].DGGE图谱分析结果发现,样品细菌多样性较低,相似性较高(最低相似度53.1%),优势菌群相对稳定,PCA分析表明,Cu和Zn对细菌多样性具有抑制作用.RDA分析结果说明Cu和Zn的含量对细菌种群分布影响很大,Cu对大部分种群具有抑制作用,而Zn一方面能促进某些种属数量,另一方面又能抑制其他种群的结构变化,这种影响并不是实验室研究的简单线性关系.
To examine the spatial changes of bacterial community in a copper mine tailing of Hubei, 14soil samples were collected, including 12 tailing samples and 3 compared samples (one from cultivated land ,two from another small copper mine tailing). DGGE (denaturing gradient gel electrophoresis) was employed to analyze V3~V6variable regions of bacterial 16S rRNA. Pearson correlation analysis and redundancy analysis (RDA) between bacterial community data and physicochemical parameters were carried out respectively to reveal relationships of the bacterial community structure, physical and chemical factors of the total samples, and contents of Cu, Zn. The results showed serious copper and zinc pollution, and there were significant differences for contents of Cu, Zn correlated with different physico^chemical characteristics of tailing samples, notably the significant position correlation [R=0.668(P〈0.01)] between the content of Zn and organic matter. According to analysis of gene separated by DGGE the bacterial structures were different between samples. Overall, there was certain rule in the samples that low bacterial diversity, high similarity (the lowest similarity 53.1%) and relatively stable dominant bacteria. RDA demonstrated that the content of Cu, Zn influenced on bacteria community, Cu can inhibit microbial population, so can Zn, but in a certain concentration range, Zn may contribute to the development of microbial diversity, that is not laboratory studied simply linear relationship with their concentrations.
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2014年第12期3182-3188,共7页
China Environmental Science
基金
国家自然科学基金资助项目(51204011)
北京市优秀博士学位论文指导教师科技项目(20121000803)