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      Characterization of oral and cloacal microbial communities of wild and rehabilitated loggerhead sea turtles ( Caretta caretta)

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          Abstract

          Background

          Microbial communities of wild animals are being increasingly investigated to provide information about the hosts’ biology and promote conservation. Loggerhead sea turtles ( Caretta caretta) are a keystone species in marine ecosystems and are considered vulnerable in the IUCN Red List, which led to growing efforts in sea turtle conservation by rescue centers around the world. Understanding the microbial communities of sea turtles in the wild and how affected they are by captivity, is one of the stepping stones in improving the conservation efforts. Describing oral and cloacal microbiota of wild animals could shed light on the previously unknown aspects of sea turtle holobiont biology, ecology, and contribute to best practices for husbandry conditions.

          Results

          We describe the oral and cloacal microbiota of Mediterranean loggerhead sea turtles by 16S rRNA gene sequencing to compare the microbial communities of wild versus turtles in, or after, rehabilitation at the Adriatic Sea rescue centers and clinics. Our results show that the oral microbiota is more sensitive to environmental shifts than the cloacal microbiota, and that it does retain a portion of microbial taxa regardless of the shift from the wild and into rehabilitation. Additionally, Proteobacteria and Bacteroidetes dominated oral and cloacal microbiota, while Kiritimatiellaeota were abundant in cloacal samples. Unclassified reads were abundant in the aforementioned groups, which indicates high incidence of yet undiscovered bacteria of the marine reptile microbial communities.

          Conclusions

          We provide the first insights into the oral microbial communities of wild and rehabilitated loggerhead sea turtles, and establish a framework for quick and non-invasive sampling of oral and cloacal microbial communities, useful for the expansion of the sample collection in wild loggerhead sea turtles. Finally, our investigation of effects of captivity on the gut-associated microbial community provides a baseline for studying the impact of husbandry conditions on turtles’ health and survival upon their return to the wild.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s42523-021-00120-5.

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          Most cited references69

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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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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                klara.filek@biol.pmf.hr
                adriana.trotta@uniba.it
                romana.gracan@biol.pmf.hr
                antonio.dibello@uniba.it
                marialaura.corrente@uniba.it
                suncica.bosak@biol.pmf.hr
                Journal
                Anim Microbiome
                Anim Microbiome
                Animal Microbiome
                BioMed Central (London )
                2524-4671
                3 September 2021
                3 September 2021
                2021
                : 3
                : 59
                Affiliations
                [1 ]GRID grid.4808.4, ISNI 0000 0001 0657 4636, Department of Biology, Faculty of Science, , University of Zagreb, ; Rooseveltov trg 6, 10 000 Zagreb, Croatia
                [2 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Department of Veterinary Medicine, , University of Bari “Aldo Moro”, ; Str. Prov. Per Casamassima Km 3, 70010 Valenzano, BA Italy
                Author information
                http://orcid.org/0000-0002-4604-2324
                Article
                120
                10.1186/s42523-021-00120-5
                8417999
                34479653
                24b29595-b57a-46ae-9504-beee0f9462dc
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 April 2021
                : 24 August 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004488, Hrvatska Zaklada za Znanost;
                Award ID: UIP-2017-05-5635
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                microbiota,bacterial diversity,reptile,rehabilitation,adriatic sea,conservation

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