4.7 Article

Metagenomic and quantitative insights into microbial communities and functional genes of nitrogen and iron cycling in twelve wastewater treatment systems

期刊

CHEMICAL ENGINEERING JOURNAL
卷 290, 期 -, 页码 21-30

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2016.01.024

关键词

Co-occurrence patterns; Functional gene group; MiSeq sequencing; Microbial community; N and Fe cycling; Network analysis

资金

  1. Science and Technology Innovative Program of Shaanxi Province [2011KTZB03-03-01]

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To gain a better understanding of bacterial community structures, ecological inter-correlations and functional generalists of nitrogen- and iron-cycling-related bacteria in various wastewater treatment systems (WWTSs), 16 samples collected from 3 industrial, 4 municipal and 5 anaerobic ammonium oxidation (anammox) WWTSs were used to perform metagenomic analysis. A total of 9394 to 17,130 effective reads for 16 samples were obtained from the bacterial 16S rRNA V3-V4 regions using MiSeq sequencing. Taxonomic analysis revealed that Bacteroidetes, Chloroflexi, Proteobacteria, and Planctomycetes were the dominant phyla in these samples. Furthermore, quantitative polymerase chain reaction (qPCR) was conducted and the results revealed that anammox, nrfA, FeOB (iron oxidizing bacteria) and FeRB (iron reducting bacteria) genes had higher abundance when Candidatus Brocadia was the dominant genera in anammox bioreactor. Finally, Spearman rank order coefficient correlation (SRCC) and redundancy discriminant analysis (RDA) analysis implicated that the groups of nitrification-anammox, denitrification-FeOB, and dissimilatory nitrate reduction to ammonium (DNRA)-FeRB showed positively correlations. Based on the co-occurrence pattern demonstrated by network analysis, the coupling of anammox, DNRA and FeRB was the noteworthy pathway for simultaneous removal of nitrogen and organic carbon. Overall, this work provides novel insights into the co-occurrence patterns and generalists of nitrogen and iron-cycling-related bacteria in wastewater treatment systems. (C) 2016 Elsevier B.V. All rights reserved.

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