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      Cross-Talk Between Intestinal Microbiota and Host Gene Expression in Gilthead Sea Bream ( Sparus aurata) Juveniles: Insights in Fish Feeds for Increased Circularity and Resource Utilization

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          Abstract

          New types of fish feed based on processed animal proteins (PAPs), insect meal, yeast, and microbial biomasses have been used with success in gilthead sea bream. However, some drawback effects on feed conversion and inflammatory systemic markers were reported in different degrees with PAP- and non-PAP-based feed formulations. Here, we focused on the effects of control and two experimental diets on gut mucosal-adherent microbiota, and how it correlated with host transcriptomics at the local (intestine) and systemic (liver and head kidney) levels. The use of tissue-specific PCR-arrays of 93 genes in total rendered 13, 12, and 9 differentially expressed (DE) genes in the intestine, liver, and head kidney, respectively. Illumina sequencing of gut microbiota yielded a mean of 125,350 reads per sample, assigned to 1,281 operational taxonomic unit (OTUs). Bacterial richness and alpha diversity were lower in fish fed with the PAP diet, and discriminant analysis displayed 135 OTUs driving the separation between groups with 43 taxa correlating with 27 DE genes. The highest expression of intestinal pcna and alpi was achieved in PAP fish with intermediate values in non-PAP, being the pro-inflammatory action of alpi associated with the presence of Psychrobacter piscatorii. The intestinal muc13 gene was down-regulated in non-PAP fish, with this gene being negatively correlated with anaerobic (Chloroflexi and Anoxybacillus) and metal-reducing ( Pelosinus and Psychrosinus) bacteria. Other inflammatory markers ( igm, il8, tnfα) were up-regulated in PAP fish, positively correlating the intestinal igm gene with the inflammasome activator Escherichia/Shigella, whereas the systemic expression of il8 and tnfα was negatively correlated with the Bacilli class in PAP fish and positively correlated with Paracoccus yeei in non-PAP fish. Overall changes in the expression pattern of il10, galectins ( lgals1, lgals8), and toll-like receptors ( tlr2, tlr5, tlr9) reinforced the anti-inflammatory profile of fish fed with the non-PAP diet, with these gene markers being associated with a wide range of OTUs. A gut microbiota-liver axis was also established, linking the microbial generation of short chain fatty acids with the fueling of scd1- and elovl6-mediated lipogenesis. In summary, by correlating the microbiome with host gene expression, we offer new insights in the evaluation of fish diets promoting gut and metabolism homeostasis, and ultimately, the health of farmed fish.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                05 October 2021
                2021
                : 12
                : 748265
                Affiliations
                [1] 1Nutrigenomics and Fish Growth Endocrinology Group, Institute of Aquaculture Torre de la Sal (IATS-CSIC) , Castellón, Spain
                [2] 2SPAROS Lda, Area Empresarial de Marim , Olhăo, Portugal
                [3] 3Fish Pathology Group, Institute of Aquaculture Torre de la Sal (IATS-CSIC) , Castellón, Spain
                [4] 4Faculty of Biosciences and Aquaculture, Nord University , Bodø, Norway
                Author notes

                Edited by: Enric Gisbert, Institute of Agrifood Research and Technology (IRTA), Spain

                Reviewed by: Fotini Kokou, Wageningen University and Research, Netherlands; Miguel Angel Moriñigo, University of Malaga, Spain

                *Correspondence: Jaume Pérez-Sánchez jaime.perez.sanchez@ 123456csic.es

                This article was submitted to Aquatic Physiology, a section of the journal Frontiers in Physiology

                Article
                10.3389/fphys.2021.748265
                8523787
                34675821
                20d4b7f4-eada-4830-b9b9-bd7e9b7e3b8f
                Copyright © 2021 Naya-Català, do Vale Pereira, Piazzon, Fernandes, Calduch-Giner, Sitjà-Bobadilla, Conceição and Pérez-Sánchez.

                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
                : 27 July 2021
                : 02 September 2021
                Page count
                Figures: 8, Tables: 2, Equations: 0, References: 145, Pages: 20, Words: 15246
                Funding
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Award ID: 652831
                Award ID: 773330
                Funded by: Ministerio de Ciencia e Innovación, doi 10.13039/501100004837;
                Award ID: RTI2018-094128-B-I00
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: RYC2018-024049-I/AEI/10.13039/501100011033
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                fish meal,processed animal proteins,insect proteins,algae meal,gut microbiota,host transcriptomics,inflammatory markers,lipid metabolism

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