4.5 Article

Exploring links between pH and bacterial community composition in soils from the Craibstone Experimental Farm

期刊

FEMS MICROBIOLOGY ECOLOGY
卷 87, 期 2, 页码 403-415

出版社

OXFORD UNIV PRESS
DOI: 10.1111/1574-6941.12231

关键词

soil bacteria; pH; microbial diversity; agriculture; 16S rRNA genes; rare biosphere

资金

  1. Government of Ontario
  2. Natural Sciences and Engineering Research Council of Canada (NSERC)

向作者/读者索取更多资源

Soil pH is an important determinant of microbial community composition and diversity, yet few studies have characterized the specific effects of pH on individual bacterial taxa within bacterial communities, both abundant and rare. We collected composite soil samples over 2years from an experimentally maintained pH gradient ranging from 4.5 to 7.5 from the Craibstone Experimental Farm (Craibstone, Scotland). Extracted nucleic acids were characterized by bacterial and group-specific denaturing gradient gel electrophoresis and next-generation sequencing of bacterial 16S rRNA genes. Both methods demonstrated comparable and reproducible shifts within higher taxonomic bacterial groups (e.g. Acidobacteria, Alphaproteobacteria, Verrucomicrobia, and Gammaproteobacteria) across the pH gradient. In addition, we used non-negative matrix factorization (NMF) for the first time on 16S rRNA gene data to identify positively interacting (i.e. co-occurring) operational taxonomic unit (OTU) clusters (i.e. components'), with abundances that correlated strongly with pH, and sample year to a lesser extent. All OTUs identified by NMF were visualized within principle coordinate analyses of unifrac distances and subjected to taxonomic network analysis (SSUnique), which plotted OTU abundance and similarity against established taxonomies. Most pH-dependent OTUs identified here would not have been identified by previous methodologies for microbial community profiling and were unrelated to known lineages.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据