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      Contemporary environmental variation determines microbial diversity patterns in acid mine drainage

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          Abstract

          A wide array of microorganisms survive and thrive in extreme environments. However, we know little about the patterns of, and controls over, their large-scale ecological distribution. To this end, we have applied a bar-coded 16S rRNA pyrosequencing technology to explore the phylogenetic differentiation among 59 microbial communities from physically and geochemically diverse acid mine drainage (AMD) sites across Southeast China, revealing for the first time environmental variation as the major factor explaining community differences in these harsh environments. Our data showed that overall microbial diversity estimates, including phylogenetic diversity, phylotype richness and pairwise UniFrac distance, were largely correlated with pH conditions. Furthermore, multivariate regression tree analysis also identified solution pH as a strong predictor of relative lineage abundance. Betaproteobacteria, mostly affiliated with the ‘ Ferrovum' genus, were explicitly predominant in assemblages under moderate pH conditions, whereas Alphaproteobacteria, Euryarchaeota, Gammaproteobacteria and Nitrospira exhibited a strong adaptation to more acidic environments. Strikingly, such pH-dependent patterns could also be observed in a subsequent comprehensive analysis of the environmental distribution of acidophilic microorganisms based on 16S rRNA gene sequences previously retrieved from globally distributed AMD and associated environments, regardless of the long-distance isolation and the distinct substrate types. Collectively, our results suggest that microbial diversity patterns are better predicted by contemporary environmental variation rather than geographical distance in extreme AMD systems.

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          Most cited references 71

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          QIIME allows analysis of high-throughput community sequencing data.

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            Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

            mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Affiliations
                [1 ]State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University , Guangzhou, People's Republic of China
                Author notes
                [* ]School of Life Sciences, Sun Yat-sen University , No. 135, Xin-Gang-Xi Road, Guangzhou 510275, People's Republic of China. E-mail: shuws@ 123456mail.sysu.edu.cn
                [2]

                The first two authors contributed equally to this work.

                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group
                1751-7362
                1751-7370
                May 2013
                22 November 2012
                1 May 2013
                : 7
                : 5
                : 1038-1050
                23178673
                3635239
                ismej2012139
                10.1038/ismej.2012.139
                Copyright © 2013 International Society for Microbial Ecology

                This work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

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