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      Effects of different temperatures on Leiocassis longirostris gill structure and intestinal microbial composition

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          Abstract

          Fish are poikilothermic vertebrates and their physiological activities are affected by water temperature. In recent years, extreme weather has occurred frequently, and temperature changes have adversely affected the growth of farmed fish. To explore the changes in gill tissue structure caused by changing the water temperature and the relationship between the intestinal microbiota and the Leiocassis longirostris host adaptation mechanism, gill tissue sections and intestinal microbial 16S rRNA amplicon sequencing were conducted under different temperature stress (low temperature 4 °C, normal temperature 26 °C and high temperature 32 °C). The results showed that heat stress and cold stress caused injury and swelling, terminal congestion, cell vacuolation, and necrosis of the gill tissue of L. longirostris. For intestinal microbiota, the abundances of Pseudomonadota and Bacillota increased at the cold stress, while the abundances of Fusobacteriota and Bacteroidota increased at the heat stress. The number of opportunistic bacteria, mainly Aeromonas and Acinetobacter, was the highest under cold stress. In addition, the richness of the intestinal microbiota decreased significantly at heat and cold stresses, while evenness increased. Prediction of intestinal microbiota function showed that most common functions, such as metabolism of cofactors and vitamins, energy metabolism and replication and repair, were decreased significantly at heat stress and cold stress, and phylogenetic relationship analysis revealed significant differences among the groups. In conclusion, the change of temperature altered the gill tissue structure, and affected the structure and homeostasis of the intestinal microbiota, thus affecting the survival time of L. longirostris, and cold stress had a greater effect than heat stress.

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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            Metagenomic biomarker discovery and explanation

            This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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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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                Author and article information

                Contributors
                q_-l@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 March 2024
                26 March 2024
                2024
                : 14
                : 7150
                Affiliations
                [1 ]Fisheries Institute, Sichuan Academy of Agricultural Sciences, ( https://ror.org/05f0php28) Chengdu, Sichuan China
                [2 ]Sichuan Water Conservancy Vocational College, Chongzhou, Sichuan China
                [3 ]Present Address: 1611 Xiyuan Avenue, Chengdu, China
                Article
                57731
                10.1038/s41598-024-57731-6
                10966105
                38532085
                7f80db08-426f-4fd2-abd7-77f0d527bf70
                © The Author(s) 2024

                Open Access This 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
                : 12 January 2024
                : 21 March 2024
                Funding
                Funded by: The National Key Research and Development Program of China
                Award ID: 2022YFD2400903
                Funded by: The China Agriculture Research System of MOF and MARA
                Award ID: CARS-46
                Funded by: Technology Program of Sichuan Academy of Agricultural Sciences
                Award ID: 1 + 9KJGG004
                Funded by: The Sichuan Freshwater Fish Innovation Team of the National Modern Agricultural Industrial Technology System
                Categories
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                © Springer Nature Limited 2024

                Uncategorized
                microbiology,microbial communities
                Uncategorized
                microbiology, microbial communities

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