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Differential Impacts of Land-Based Sources of Pollution on the Microbiota of Southeast Florida Coral Reefs

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      ABSTRACT

      Coral reefs are dynamic ecosystems known for decades to be endangered due, in large part, to anthropogenic impacts from land-based sources of pollution (LBSP). In this study, we utilized an Illumina-based next-generation sequencing approach to characterize prokaryotic and fungal communities from samples collected off the southeast coast of Florida. Water samples from coastal inlet discharges, oceanic outfalls of municipal wastewater treatment plants, treated wastewater effluent before discharge, open ocean samples, and coral tissue samples (mucus and polyps) were characterized to determine the relationships between microbial communities in these matrices and those in reef water and coral tissues. Significant differences in microbial communities were noted among all sample types but varied between sampling areas. Contamination from outfalls was found to be the greatest potential source of LBSP influencing native microbial community structure among all reef samples, although pollution from inlets was also noted. Notably, reef water and coral tissue communities were found to be more greatly impacted by LBSP at southern reefs, which also experienced the most degradation during the course of the study. The results of this study provide new insights into how microbial communities from LBSP can impact coral reefs in southeast Florida and suggest that wastewater outfalls may have a greater influence on the microbial diversity and structure of these reef communities than do contaminants carried in runoff, although the influences of runoff and coastal inlet discharge on coral reefs are still substantial.

      IMPORTANCE Coral reefs are known to be endangered due to sewage discharge and to runoff of nutrients, pesticides, and other substances associated with anthropogenic activity. Here, we used next-generation sequencing to characterize the microbial communities of potential contaminant sources in order to determine how environmental discharges of microbiota and their genetic material may influence the microbiomes of coral reef communities and coastal receiving waters. Runoff delivered through inlet discharges impacted coral microbial communities, but impacts from oceanic outfalls carrying treated wastewater were greater. Geographic differences in the degree of impact suggest that coral microbiomes may be influenced by the microbiological quality of treated wastewater.

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      Most cited references 57

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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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          UCHIME improves sensitivity and speed of chimera detection

          Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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            Author and article information

            Affiliations
            [a ]BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, USA
            [b ]Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, Florida, USA
            [c ]Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, Florida, USA
            [d ]Department of Civil Engineering and National Resources Research Institute, University of Minnesota Duluth, Duluth, Minnesota, USA
            Georgia Institute of Technology
            Author notes
            Address correspondence to Chan Lan Chun, chun0157@ 123456d.umn.edu .

            M.J.S. and C.L.C. contributed equally to this article.

            Citation Staley C, Kaiser T, Gidley ML, Enochs IC, Jones PR, Goodwin KD, Sinigalliano CD, Sadowsky MJ, Chun CL. 2017. Differential impacts of land-based sources of pollution on the microbiota of southeast Florida coral reefs. Appl Environ Microbiol 83:e03378-16. https://doi.org/10.1128/AEM.03378-16.

            Contributors
            Role: Editor,
            Georgia Institute of Technology
            Journal
            Appl Environ Microbiol
            Appl. Environ. Microbiol
            aem
            aem
            AEM
            Applied and Environmental Microbiology
            American Society for Microbiology (1752 N St., N.W., Washington, DC )
            0099-2240
            1098-5336
            24 March 2017
            1 May 2017
            15 May 2017
            1 May 2017
            : 83
            : 10
            28341673 5411493 03378-16 10.1128/AEM.03378-16
            Copyright © 2017 Staley et al.

            This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

            Counts
            supplementary-material: 1, Figures: 6, Tables: 3, Equations: 0, References: 57, Pages: 16, Words: 8693
            Product
            Funding
            Funded by: DOC | National Oceanic and Atmospheric Administration (NOAA) https://doi.org/10.13039/100000192
            Award ID: CRCP Project 1114
            Award Recipient : Christopher Sinigalliano Award Recipient : Kelly Goodwin Award Recipient : Maribeth Gidley Award Recipient : Ian Enochs Award Recipient : Chan Lan Chun Award Recipient : Michael Sadowsky
            Categories
            Environmental Microbiology
            Custom metadata
            May 2017

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