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      Microbial communities of wild-captured Kemp’s ridley (Lepidochelys kempii) and green sea turtles (Chelonia mydas)

      1 , 2 , 3 , 3 , 4
      Endangered Species Research
      Inter-Research Science Center

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          Abstract

          Conservation efforts for endangered sea turtle species, such as Kemp’s ridley turtles Lepidochelys kempii and green turtles Chelonia mydas, may benefit from information on the microbial communities that contribute to host health. Previous studies examining host-associated microbiomes of these species have been limited in geographic region, life stage, and/or health. Here, we characterized the microbiome of the oral cavity and cloaca from wild-captured Kemp’s ridley and green turtles off the west coast of Florida, USA, by using Illumina sequencing to analyze the 16S rRNA gene. Microbial communities were distinct between body sites as well as between turtle species, suggesting that the turtle species is more important than the local environment in determining the microbiome of sea turtles. We identified the core microbiome for each species at each body site and determined that there were very few bacteria shared among the oral samples of both species, and no taxa co-occurred in the cloaca samples among both species. The core microbiome of the green turtle cloaca was primarily from the order Clostridiales, which plays an important role in digestion for other herbivorous species. Due to high prevalence of fibropapillomatosis in the green turtles (90%), we also investigated the correlation between the microbiome and the severity of fibropapillomatosis, and we identified changes in beta diversity associated with the total number of tumors. This study provides the first glimpse of the microbiome in 2 sympatric species of sea turtle and sheds an important species-specific light on the microbiome of these endangered species.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Journal
                Endangered Species Research
                Endang. Species. Res.
                Inter-Research Science Center
                1863-5407
                1613-4796
                May 06 2021
                May 06 2021
                : 45
                : 21-36
                Affiliations
                [1 ]Animal Health Department, New England Aquarium, Boston, Massachusetts 02110, USA
                [2 ]University of Massachusetts, Boston, Massachusetts 20125, USA
                [3 ]Inwater Research Group, Inc., Jensen Beach, Florida 34957, USA
                [4 ]Department of Marine and Environmental Sciences, Marine Science Center, Northeastern University, Nahant, Massachusetts 01908, USA
                Article
                10.3354/esr01116
                21add15f-d005-4e39-8e9c-aa250ced266a
                © 2021

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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