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      Fish Skin Microbiomes Are Highly Variable Among Individuals and Populations but Not Within Individuals

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          Abstract

          Fish skin-associated microbial communities are highly variable among populations and species and can impact host fitness. Still, the sources of variation in microbiome composition, and particularly how they vary among and within host individuals, have rarely been investigated. To tackle this issue, we explored patterns of variation in fish skin microbiomes across different spatial scales. We conducted replicate sampling of dorsal and ventral body sites of perch ( Perca fluviatilis) from two populations and characterized the variation of fish skin-associated microbial communities with 16S rRNA gene metabarcoding. Results showed a high similarity of microbiome samples taken from the left and right side of the same fish individuals, suggesting that fish skin microbiomes can be reliably assessed and characterized even using a single sample from a specific body site. The microbiome composition of fish skin differed markedly from the bacterioplankton communities in the surrounding water and was highly variable among individuals. No ASV was present in all samples, and the most prevalent phyla, Actinobacteria, Bacteroidetes, and Proteobacteria, varied in relative abundance among fish hosts. Microbiome composition was both individual- and population specific, with most of the variation explained by individual host. At the individual level, we found no diversification in microbiome composition between dorsal and ventral body sites, but the degree of intra-individual heterogeneity varied among individuals. To identify how genetic and phenotypic characteristics of fish hosts impact the rate and nature of intra-individual temporal dynamics of the skin microbiome, and thereby contribute to the host-specific patterns documented here, remains an important task for future research.

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          Most cited references103

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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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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                21 January 2022
                2021
                : 12
                : 767770
                Affiliations
                [1] 1Ecology and Evolution in Microbial Model Systems (EEMiS), Department of Biology and Environmental Science, Linnaeus University , Kalmar, Sweden
                [2] 2Department of Ecology, Environment and Plant Sciences, Stockholm University , Stockholm, Sweden
                [3] 3Baltic Sea Centre, Stockholm University , Stockholm, Sweden
                Author notes

                Edited by: Lifeng Zhu, Nanjing Normal University, China

                Reviewed by: Ram Negi, University of Delhi, India; Ke-yi Tang, Sichuan Normal University, China

                *Correspondence: Hanna Berggren, hanna.berggren@ 123456lnu.se

                This article was submitted to Microbial Symbioses, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2021.767770
                8813977
                35126324
                af8c9b04-4975-43c5-bef2-cfd7d1f4edb0
                Copyright © 2022 Berggren, Tibblin, Yıldırım, Broman, Larsson, Lundin and Forsman.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 September 2021
                : 13 December 2021
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 104, Pages: 13, Words: 9881
                Funding
                Funded by: Svenska Forskningsrådet Formas, doi 10.13039/501100001862;
                Funded by: Linnéuniversitetet, doi 10.13039/501100005967;
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                community ecology,diversity,fish,heterogeneity,repeatability,richness,skin microbiota,spatial variation

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