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      Fine grained compositional analysis of Port Everglades Inlet microbiome using high throughput DNA sequencing

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          Abstract

          Background

          Similar to natural rivers, manmade inlets connect inland runoff to the ocean. Port Everglades Inlet (PEI) is a busy cargo and cruise ship port in South Florida, which can act as a source of pollution to surrounding beaches and offshore coral reefs. Understanding the composition and fluctuations of bacterioplankton communities (“microbiomes”) in major port inlets is important due to potential impacts on surrounding environments. We hypothesize seasonal microbial fluctuations, which were profiled by high throughput 16S rRNA amplicon sequencing and analysis.

          Methods & Results

          Surface water samples were collected every week for one year. A total of four samples per month, two from each sampling location, were used for statistical analysis creating a high sampling frequency and finer sampling scale than previous inlet microbiome studies. We observed significant differences in community alpha diversity between months and seasons. Analysis of composition of microbiomes (ANCOM) tests were run in QIIME 2 at genus level taxonomic classification to determine which genera were differentially abundant between seasons and months. Beta diversity results yielded significant differences in PEI community composition in regard to month, season, water temperature, and salinity. Analysis of potentially pathogenic genera showed presence of Staphylococcus and Streptococcus. However, statistical analysis indicated that these organisms were not present in significantly high abundances throughout the year or between seasons.

          Discussion

          Significant differences in alpha diversity were observed when comparing microbial communities with respect to time. This observation stems from the high community evenness and low community richness in August. This indicates that only a few organisms dominated the community during this month. August had lower than average rainfall levels for a wet season, which may have contributed to less runoff, and fewer bacterial groups introduced into the port surface waters. Bacterioplankton beta diversity differed significantly by month, season, water temperature, and salinity. The 2013–2014 dry season (October–April), was warmer and wetter than historical averages. This may have driven significant differences in beta diversity. Increased nitrogen and phosphorous concentrations were observed in these dry season months, possibly creating favorable bacterial growth conditions. Potentially pathogenic genera were present in the PEI. However their relatively low, non-significant abundance levels highlight their relatively low risk for public health concerns. This study represents the first to sample a large port at this sampling scale and sequencing depth. These data can help establish the inlet microbial community baseline and supplement the vital monitoring of local marine and recreational environments, all the more poignant in context of local reef disease outbreaks and worldwide coral reef collapse in wake of a harsh 2014–16 El Niño event.

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          Most cited references36

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          Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus.

          The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on >35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 10(27) and 7.0 ± 0.3 × 10(26) cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles.
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            Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data

            Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets. In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches. Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance. Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.
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              Cultivation of the ubiquitous SAR11 marine bacterioplankton clade.

              The alpha-proteobacterial lineage that contains SAR11 and related ribosomal RNA gene clones was among the first groups of organisms to be identified when cultivation-independent approaches based on rRNA gene cloning and sequencing were applied to survey microbial diversity in natural ecosystems. This group accounts for 26% of all ribosomal RNA genes that have been identified in sea water and has been found in nearly every pelagic marine bacterioplankton community studied by these methods. The SAR11 clade represents a pervasive problem in microbiology: despite its ubiquity, it has defied cultivation efforts. Genetic evidence suggests that diverse uncultivated microbial taxa dominate most natural ecosystems, which has prompted widespread efforts to elucidate the geochemical activities of these organisms without the benefit of cultures for study. Here we report the isolation of representatives of the SAR11 clade. Eighteen cultures were initially obtained by means of high-throughput procedures for isolating cell cultures through the dilution of natural microbial communities into very low nutrient media. Eleven of these cultures have been successfully passaged and cryopreserved for future study. The volume of these cells, about 0.01 micro m(3), places them among the smallest free-living cells in culture.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                8 May 2018
                2018
                : 6
                : e4671
                Affiliations
                [1 ]Department of Biological Sciences, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University , Dania Beach, FL, United States of America
                [2 ]Division of Natural Sciences, Duke Kunshan University , Kunshan, Jiangsu, China
                [3 ]Department of Marine and Environmental Sciences, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University , Dania Beach, FL, United States of America
                [4 ]College of Osteopathic Medicine, Nova Southeastern University , Davie, FL, United States of America
                Article
                4671
                10.7717/peerj.4671
                5947159
                f30db1a9-6ddf-4bff-9b0a-3c9bae620ecd
                ©2018 O’Connell et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 24 October 2017
                : 5 April 2018
                Funding
                Funded by: Nova Southeastern University
                The work was supported by a President’s Grant through Nova Southeastern University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Environmental Sciences
                Genomics
                Marine Biology
                Microbiology
                Environmental Impacts

                microbiome,16s,port everglades inlet,bacterioplankton,miseq
                microbiome, 16s, port everglades inlet, bacterioplankton, miseq

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