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Fish Nutrition, Metabolism and Physiology

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 2214

Special Issue Editors


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Guest Editor
Institute of Aquaculture Torre de la Sal (IATS), Consejo Superior de Investigaciones Científicas (CSIC), 12595 Ribera de Cabanes, Spain
Interests: aquaculture; fish nutrition; fish physiology; metabolic regulation; intestinal microbiota; omics; bioinformatics

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Guest Editor
Department of Biology, Faculty of Marine and Environmental Sciences, Instituto Universitario de Investigación Marina (INMAR), Campus de Excelencia Internacional del Mar (CEI·MAR), University of Cádiz, 11519 Puerto Real, Cádiz, Spain
Interests: aquaculture; fish nutrition; functional additives; fish physiology; stress; animal welfare; metabolism; microbiota

Special Issue Information

Dear Colleagues,

The global commercial production of fish from capture fisheries and aquaculture is a vital future food source for human consumption, due to its high nutritional value and health benefits compared to those of other animals. However, to ensure production, these sectors have to tackle some future challenges, such as climate change and marine resource depletion. Fish nutrition is crucial to overcoming these challenges, as it can promote better growth and fish flesh quality, or can lead to stress in fish, affecting their physiology and metabolism. Research on fish nutrition focuses on nutrient requirements and metabolism, but recent decades have seen increased attention on the use of omics technologies (transcriptomics, metataxonomics, and metagenomics) to understand how fish respond to nutrient, dietary, and environmental changes. Thus, this Special Issue invites researchers to submit studies that delve into fish nutrition, metabolism, and physiology. Additionally, we welcome submissions focusing on the application of omics approaches in these areas, all with the ultimate aim of advancing knowledge about the global productive chain involving fish species.

Dr. Fernando Naya-Català
Dr. Paula Simó-Mirabet
Guest Editors

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Keywords

  • fish
  • nutrition
  • metabolism
  • physiology
  • growth performance
  • fish stress
  • transcriptomics
  • metataxonomics
  • metagenomics
  • aquaculture

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Published Papers (4 papers)

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Research

27 pages, 19927 KiB  
Article
Metabonomics and Transcriptomics Analyses Reveal the Underlying HPA-Axis-Related Mechanisms of Lethality in Larimichthys polyactis Exposed to Underwater Noise Pollution
by Qinghua Jiang, Yu Zhang, Ting Ye, Xiao Liang and Bao Lou
Int. J. Mol. Sci. 2024, 25(23), 12610; https://doi.org/10.3390/ijms252312610 - 24 Nov 2024
Viewed by 211
Abstract
The problem of marine noise pollution has a long history. Strong noise (>120 dB re 1 µPa) will affects the growth, development, physiological responses, and behaviors of fish, and also can induce the stress response, posing a mortal threat. Although many studies have [...] Read more.
The problem of marine noise pollution has a long history. Strong noise (>120 dB re 1 µPa) will affects the growth, development, physiological responses, and behaviors of fish, and also can induce the stress response, posing a mortal threat. Although many studies have reported that underwater noise may affect the survival of fish by disturbing their nervous system and endocrine system, the underlying causes of death due to noise stimulation remain unknown. Therefore, in this study, we used the underwater noise stress models to conduct underwater strong noise (50–125 dB re 1 µPa, 10–22,000 Hz) stress experiments on small yellow croaker for 10 min (short-term noise stress) and 6 days (long-term noise stress). A total of 150 fishes (body weight: 40–60 g; body length: 12–14 cm) were used in this study. Omics (metabolomics and transcriptomics) studies and quantitative analyses of important genes (HPA (hypothalamic–pituitary–adrenal)-axis functional genes) were performed to reveal genetic and metabolic changes in the important tissues associated with the HPA axis (brain, heart, and adrenal gland). Finally, we found that the strong noise pollution can significantly interfere with the expression of HPA-axis functional genes (including corticotropin releasing hormone (CRH), corticotropin releasing hormone receptor 2 (CRHR2), and arginine vasotocin (AVT)), and long-term stimulation can further induce metabolic disorders of the functional tissues (brain, heart, and adrenal gland), posing a lethal threat. Meanwhile, we also found that there were two kinds of death processes, direct death and chronic death, and both were closely related to the duration of stimulation and the regulation of the HPA axis. Full article
(This article belongs to the Special Issue Fish Nutrition, Metabolism and Physiology)
19 pages, 2697 KiB  
Article
Synergistic Effects of Dietary Tryptophan and Dip Vaccination in the Immune Response of European Seabass Juveniles
by Diogo Peixoto, Inês Carvalho, André Cunha, Paulo Santos, Lourenço Ramos-Pinto, Marina Machado, Rita Azeredo and Benjamín Costas
Int. J. Mol. Sci. 2024, 25(22), 12200; https://doi.org/10.3390/ijms252212200 - 13 Nov 2024
Viewed by 368
Abstract
Vaccination is an effective, cost-efficient method to preventing disease outbreaks. However, vaccine procedures can induce adverse reactions due to stress, increasing plasma cortisol in the short term. In this context, tryptophan may prove to be fundamental as it has been demonstrated to have [...] Read more.
Vaccination is an effective, cost-efficient method to preventing disease outbreaks. However, vaccine procedures can induce adverse reactions due to stress, increasing plasma cortisol in the short term. In this context, tryptophan may prove to be fundamental as it has been demonstrated to have various desirable neuroendocrine attributes in different fish species. Therefore, this study aimed to evaluate both short-term (3 days) and long-term (21 days) effects of dietary tryptophan supplementation on European seabass juveniles’ (26.23 ± 7.22 g) response to vaccination and disease resistance to Tenacibaculum maritimum. The short-term tryptophan-fed fish exhibited increased hepatic superoxide dismutase and plasma cortisol levels, along with the downregulation of immune-related genes. Despite these changes, disease resistance was unaffected. When fish were later dip vaccinated, tryptophan prevented the stress-induced plasma cortisol increase and upregulated the gene expression of igm, suggesting tryptophan’s role in enhancing vaccination efficiency by counteracting stress-associated effects. In the long term, the lowest supplementation dose counteracted vaccine-mediated reduced gene expression, and fish fed this diet showed a more modest molecular response. Overall, the findings suggest a complex interplay between tryptophan supplementation, immune responses, and vaccine efficiency in fish. Further research is necessary to clarify how tryptophan could consistently improve vaccine efficiency in aquaculture. Full article
(This article belongs to the Special Issue Fish Nutrition, Metabolism and Physiology)
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Figure 1

Figure 1
<p>Plasma cortisol levels (<b>A</b>), hepatic superoxide dismutase activity (<b>B</b>), and head-kidney relative expression of genes (<b>C</b>–<b>G</b>) related to the immune response of vaccinated European seabass fed experimental diets (CTRL, TRP1 and TRP2) for 3 days before the dip vaccine. SOD—superoxide dismutase activity; <span class="html-italic">il1β</span>—interleukin 1 beta; <span class="html-italic">ido2</span>—indoleamine-dioxygenase 2; <span class="html-italic">il10</span>—interleukin 10; <span class="html-italic">tph1α</span>—tryptophan 5-hydroxylase-like alpha and <span class="html-italic">igm</span>—immunoglobulin M. Values are presented as means ± SD (n = 9). Multivariate ANOVA followed by Tukey <span class="html-italic">post-hoc</span> test (<span class="html-italic">p</span> ≤ 0.05). If the interaction was significant, Tukey <span class="html-italic">post-hoc</span> test was used to identify differences among treatments. Capital letters stand for significant differences between sampling times. Different low-case letters stand for statistically significant differences between dietary treatments.</p>
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<p>Heatmap of relative mRNA gene expression on the head-kidney of vaccinated and non-vaccinated European seabass fed experimental diets (CTRL, TRP1, and TRP2) for 3 days before (<b>A</b>) and 3 days after the dip vaccine, and then vaccinated or not fish were fed CTRL until day 21 (<b>B</b>). <span class="html-italic">tph1α</span>—tryptophan 5-hydroxylase-like alpha; <span class="html-italic">ido2</span>—indoleamine-dioxygenase 2; <span class="html-italic">c3</span>—complement factor 3; <span class="html-italic">il1β</span>—interleukin 1 beta; <span class="html-italic">il10</span>—interleukin 10; and <span class="html-italic">igm</span>—immunoglobulin M. Lines represent the different dietary treatments and columns represent genes assessed. Pearson’s method was used to calculate the distance metric and relative mRNA levels were hierarchically clustered with the centroid linkage algorithm. Colours represent the intensity of the analysed gene; green more intense; red less intense.</p>
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<p>Survival rate of European seabass juveniles fed dietary treatments for 3 days and subsequently bath challenged with <span class="html-italic">T. maritimum</span> (ACC13.1).</p>
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<p>Haematologic profile (<b>A</b>–<b>C</b>), plasma alternative complement pathway and cortisol levels (<b>D</b>,<b>E</b>), as well as head-kidney relative expression of genes (<b>F</b>–<b>H</b>) related to the immune response of vaccinated and non-vaccinated European seabass fed experimental diets (CTRL, TRP1 and TRP2) for 3 days before and 3 days after the dip vaccine, and then vaccinated or not fish were fed CTRL until day 21. WBC—white blood cell count; ACH50—alternative complement pathway; <span class="html-italic">il10</span>—interleukin 10; <span class="html-italic">ido2</span>—indoleamine-dioxygenase 2; and <span class="html-italic">il1β</span>—interleukin 1 beta. Values are presented as means ± SD (n = 9). Multivariate ANOVA followed by Tukey <span class="html-italic">post-hoc</span> test (<span class="html-italic">p</span> ≤ 0.05). If the interaction was significant, Tukey <span class="html-italic">post-hoc</span> test was used to identify differences among treatments. Capital letters stand for significant differences between vaccinated or not fish. Different low-case letters stand for statistically significant differences between dietary treatments.</p>
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<p>Canonical discriminant analysis of European seabass fed experimental diets (CTRL, TRP1, and TRP2) for 3 days and sampled before (0 h) and after 1 and 6 h after dip vaccination. (<b>A</b>)—Canonical discriminant scores of each group. Small circle marks represent group centroids. (<b>B</b>)—Variable/factor correlation (factor loads) for two main discriminant functions (F1 and F2). ◆ Plasma: ACH50—alternative complement pathway activity. ■ Liver: GSH: GSSG—reduced: oxidised glutathione ratio; tGSH—total glutathione content; rGSH—reduced glutathione content. • Head kidney: <span class="html-italic">il1β</span>—interleukin 1 beta; <span class="html-italic">il10</span>—interleukin 10; <span class="html-italic">igm</span>—immunoglobulin M.</p>
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<p>Canonical discriminant analysis of vaccinated (V) and non-vaccinated (NV) European seabass fed experimental diets (CTRL, TRP1 and TRP2) for 3 days before and 3 days after dip vaccination and sampled 21 days after vaccination. (<b>A</b>)—Canonical discriminant scores of each group. Small circle marks represent group centroids. (<b>B</b>)—Variables/factors correlation (factor loads) for two main discriminant functions (F1 and F2). ◆ Plasma: ACH50—alternative complement pathway activity; IgM—plasma immunoglobulin M levels. • Head-kidney: <span class="html-italic">il1β</span>—interleukin 1 beta; <span class="html-italic">il10</span>—interleukin 10; <span class="html-italic">igm</span>—immunoglobulin M; <span class="html-italic">ido2</span>—indoleamine-dioxygenase 2; <span class="html-italic">c3</span>—complement factor 3; <span class="html-italic">tph1α</span>—tryptophan 5-hydroxylase-like alpha.</p>
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<p>Experimental setup of a short-term dietary tryptophan supplementation prior to vaccination or to a bacterial challenge.</p>
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16 pages, 8629 KiB  
Article
The Identification of Proteomic Signatures Associated with Alkaline Tolerance in the Skin Mucus of Crucian Carp (Carassius auratus)
by Zhipeng Sun, Jing Huang, Xiaofeng Zhang, Yumei Chang and Guo Hu
Int. J. Mol. Sci. 2024, 25(21), 11618; https://doi.org/10.3390/ijms252111618 - 29 Oct 2024
Viewed by 479
Abstract
The skin is covered by a protective mucus layer, which is essential to the innate defense mechanism of fish. Investigating the response of skin mucus to various toxic stresses is crucial for enhancing its ability to tackle environmental challenges and developing strategies to [...] Read more.
The skin is covered by a protective mucus layer, which is essential to the innate defense mechanism of fish. Investigating the response of skin mucus to various toxic stresses is crucial for enhancing its ability to tackle environmental challenges and developing strategies to mitigate toxic effects. Alkalinity stress assays (50 mmol/L NaHCO3) were conducted on crucian carp (Carassius auratus) from Lake Dali Nur (pH = 9.6) and Ping Xiang red crucian carp from freshwater (pH = 7) over 7 days. The expression of skin mucous proteins was analyzed using the liquid chromatography (LC)-spectrometry (MS)/MS Analysis-Data-independent acquisition (DIA) mode. A total of 12,537 proteins were identified across 20 samples from four groups, with 12,025 quantified. In the alkaline water population, high alkali stress resulted in the up-regulation of 139 proteins and the down-regulation of 500 proteins. In contrast, the freshwater population showed an increase in 112 proteins and a decrease in 120; both populations had a total of 23 genes up-regulated and 21 down-regulated. The protein regulatory network for the alkaline water group included 3146 pairwise interactions among 464 nodes, with only 20 being differentially expressed proteins. Conversely, the freshwater group’s network comprised just 1027 specific interactions across 337 nodes, with 6 corresponding to differentially expressed proteins. A common protein regulatory network responding to high alkali stress was extracted and visualized for both populations. Based on their regulatory relationships and expression levels, these proteins are hypothesized to play similar roles under high alkali stress. Notably, the alpha-globin fragment and keratin type I cytoskeletal 13-like proteins showed markedly up-regulated expression, with the alpha-globin fragment increasing nearly a thousandfold from an extremely low level. This suggests it could serve as a potential biomarker for alkali tolerance, warranting further investigation. Full article
(This article belongs to the Special Issue Fish Nutrition, Metabolism and Physiology)
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Figure 1

Figure 1
<p>Principal component analysis (PCA) of 20 sequenced individuals in four groups. The dots imply diverse specimens, and the colors denote distinct experimental groups. The circles of multiple hues evince the clustering among disparate groups, and the magnitude of the circles typically reflects the degree of dispersion of the samples within the group. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; “F” stands for the freshwater population; “0” indicates that no NaHCO<sub>3</sub> is added to the aquaculture water; “50” indicates the addition of 50 mmol/L NaHCO<sub>3</sub> in the aquaculture water; and 1 to 5 are the number of sequenced individuals randomly assigned within the group.</p>
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<p>Overview of correlation analysis of expression patterns within and between groups for the 20 sequenced individuals. Correlation coefficients from 0 to 1 are shown in blank to dark purple, showing 5 sequenced individuals in 4 groups. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; “F” stands for the freshwater population; “0” indicates that no NaHCO<sub>3</sub> is added to the aquaculture water; “50” indicates the addition of 50 mmol/L NaHCO<sub>3</sub> in the aquaculture water; and 1 to 5 are the number of sequenced individuals randomly assigned within the group.</p>
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<p>The number of differentially expressed proteins in and among all 4 groups in the high alkali stress experiment. Intersection is the number of differentially expressed proteins shared by groups. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; and “F” stands for the freshwater population; “Up” represents proteins that are up-regulated; and “Down” represents down-regulated proteins.</p>
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<p>The expressional profiles of the differentially expressed proteins in and among all 4 groups in the high alkali stress experiment. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; “F” stands for the freshwater population; “0” indicates that no NaHCO<sub>3</sub> is added to the aquaculture water; and “50” indicates the addition of 50 mmol/L NaHCO<sub>3</sub> in the aquaculture water.</p>
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<p>Gene Ontology enrichment analysis of differentially expressed proteins for each population in high alkali stress experiment. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; and “F” stands for the freshwater population. Red dots correspond to GO terms associated with biological processes (BP), blue dots correspond to GO terms associated with molecular functions (MF), and green dots correspond to GO terms associated with cellular components (CC). The size of each dot represents the number of genes involved in the respective GO term. The X-axis represents the <span class="html-italic">p</span>-value of the topGO enrichment analysis, which was transformed using −log10 (<span class="html-italic">p</span>). The Y-axis represents the GO terms themselves.</p>
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<p>The KEGG pathway enrichment analysis of differentially expressed proteins for each population in a high alkali stress experiment. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; and “F” stands for the freshwater population. The dot size represents the number of genes involved in the KEGG term; the X-axis is the <span class="html-italic">p</span>-value of the KEGG enrichment analysis, with −log10 transformation, −log10 (<span class="html-italic">p</span>), while the Y-axis is KEGG terms.</p>
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<p>Gene regulatory network analysis of differentially expressed proteins for each population in the high alkali stress experiment. (<b>a</b>) shows the regulatory network of proteins identified in the skin mucus of the alkaline water population; (<b>b</b>) shows the regulatory network of proteins identified in the skin mucus of the freshwater population. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; and “F” stands for the freshwater population. The size of the nodes is indicative of the number of connections among them; red nodes represent up-regulated expression, whereas blue nodes indicate down-regulated expression. The color intensity serves as a quantitative measure of the magnitude of differential expression. The node names labeled on the figure are differentially expressed proteins.</p>
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<p>Gene regulatory network analysis of differentially expressed proteins shared in both populations in the high alkali stress experiment. (<b>a</b>) shows the regulatory network of proteins with expression level identified in the skin mucus of the alkaline water population shared with freshwater population; (<b>b</b>) shows the regulatory network of proteins with expression level identified in the skin mucus of the freshwater population shared with alkaline water population. The interpretation of the group name abbreviations: “Ca” represents the Latin abbreviation for <span class="html-italic">Carassius auratus</span>; “A” stands for the alkaline water population; and “F” stands for the freshwater population. The size of the nodes is indicative of the number of connections among them; red nodes represent up-regulated expression, whereas blue nodes indicate down-regulated expression. The color intensity serves as a quantitative measure of the magnitude of differential expression. The node names labeled on the figure are differentially expressed proteins.</p>
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23 pages, 3098 KiB  
Article
Exploring Multifunctional Markers of Biological Age in Farmed Gilthead Sea Bream (Sparus aurata): A Transcriptomic and Epigenetic Interplay for an Improved Fish Welfare Assessment Approach
by Álvaro Belenguer, Fernando Naya-Català, Josep Àlvar Calduch-Giner and Jaume Pérez-Sánchez
Int. J. Mol. Sci. 2024, 25(18), 9836; https://doi.org/10.3390/ijms25189836 - 11 Sep 2024
Viewed by 822
Abstract
DNA methylation clocks provide information not only about chronological but also biological age, offering a high-resolution and precise understanding of age-related pathology and physiology. Attempts based on transcriptomic and epigenetic approaches arise as integrative biomarkers linking the quantification of stress responses with specific [...] Read more.
DNA methylation clocks provide information not only about chronological but also biological age, offering a high-resolution and precise understanding of age-related pathology and physiology. Attempts based on transcriptomic and epigenetic approaches arise as integrative biomarkers linking the quantification of stress responses with specific fitness traits and may help identify biological age markers, which are also considered welfare indicators. In gilthead sea bream, targeted gene expression and DNA methylation analyses in white skeletal muscle proved sirt1 as a reliable marker of age-mediated changes in energy metabolism. To complete the list of welfare auditing biomarkers, wide analyses of gene expression and DNA methylation in one- and three-year-old fish were combined. After discriminant analysis, 668 differentially expressed transcripts were matched with those containing differentially methylated (DM) regions (14,366), and 172 were overlapping. Through enrichment analyses and selection, two sets of genes were retained: 33 showing an opposite trend for DNA methylation and expression, and 57 down-regulated and hypo-methylated. The first set displayed an apparently more reproducible and reliable pattern and 10 multifunctional genes with DM CpG in regulatory regions (sirt1, smad1, ramp1, psmd2—up-regulated; col5a1, calcrl, bmp1, thrb, spred2, atp1a2—down-regulated) were deemed candidate biological age markers for improved welfare auditing in gilthead sea bream. Full article
(This article belongs to the Special Issue Fish Nutrition, Metabolism and Physiology)
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Figure 1
<p>(<b>A</b>) Differentially expressed (DE) transcripts between one- (S + 1) and three-year-old fish (S + 3). Numbers indicate DE transcripts (based on the Wald test <span class="html-italic">p</span> &lt; 0.05 or FDR-adjusted <span class="html-italic">p</span> &lt; 0.05, in brackets). (<b>B</b>) Scores plot of partial least-squares discriminant analysis (PLS-DA) of muscular transcripts from S + 1 and S + 3 animals. In the analysis, RNA-seq data were normalized values of differentially expressed transcripts (Wald test, <span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Heatmap showing the abundance distribution (z-score) of the DE genes identified to be responsible for the separation between age groups.</p>
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<p>(<b>A</b>) Differentially methylated (DM) regions between one- (S + 1) and three-year-old fish (S + 3). Numbers indicate DM regions (based on the <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.05 or FDR-adjusted <span class="html-italic">p</span> &lt; 0.05, in brackets). (<b>B</b>) Scores plot of partial least-squares discriminant analysis (PLS-DA) of muscular methylated regions from S + 1 and S + 3 animals. MBD-seq data were the normalized values of differentially methylated 25 bp genomic regions (<span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Heatmap showing the abundance distribution (z-score) of the DM regions identified to be responsible for the separation between age groups.</p>
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<p>(<b>A</b>) Numbers of discriminant DE transcripts, discriminant DM transcripts (containing DM regions), and the overlapping discriminant DE transcripts with DM regions. Bar plots describing the results of an over-representation test conducted over the GO-BP terms of the discriminant DE transcripts (<b>B</b>) and of the overlapping discriminant DE transcripts with DM regions (<b>C</b>). * indicates that the supra-category is also detected in C. Met., metabolic; Cel., cellular; Reg., regulation; Multicel., multicellular.</p>
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<p>Correlation analysis between differentially methylation and differential expression (both as log2 fold change in rpkm values between S + 3 and S + 1 fish) of the overlapping transcripts (dots in figure) showing an opposite effect of DNA methylation and expression (<b>A</b>) and of the down-regulated overlapping transcripts associated to hypo-methylated CpGs (<b>B</b>). (<b>C</b>) Bar plot representing the numbers of selected genes showing an opposite trend for DNA methylation and expression (hyper-methylated + down-regulated and hypo-methylated + up-regulated).</p>
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<p>Proposed framework for transduction pathways of the 10 selected biological age markers in the white skeletal muscle of the gilthead sea bream and its mechanisms of methylation. The epigenetically mediated down-regulation of the gene encoding for the Na<sup>+</sup>/K<sup>−</sup>/ATPase subunit <span class="html-italic">atp1a2</span> induces a decrease in the NKA activity in old fish and the same pattern for collagen fibers-related <span class="html-italic">col5a1</span> provokes a loss of the mechanical support of cells. The hyper methylation of nuclear receptor for thyroid hormone (<span class="html-italic">thrb</span>), sprouty-related, EVH1 domain-containing protein 2(<span class="html-italic">spred2</span>) and bone morphogenetic protein (<span class="html-italic">bmp1</span>) stops the flow of MAPK signaling activation, avoiding advanced-age individuals to propel optimal myogenesis, muscle contraction and vasodilatation. Something similar occurs with calcitonin membrane receptor, in a compensatory mechanism with their components being hyper-methylated (<span class="html-italic">calcrl</span>) and hypo-methylated (<span class="html-italic">ramp1</span>). Hypo methylation leading to up-regulation is also present in the <span class="html-italic">smad1</span> gene, phosphorylated by <span class="html-italic">bmp1</span> and probably showing this trend to maximize the deficit on this protein. By last, the <span class="html-italic">sirt1</span> and <span class="html-italic">psmd2</span> genes are up-regulated by hypo methylation, being probably involved in the modulation of DNA damage repair. Altogether, this induces modulation of the white skeletal muscle activity in advanced-age organisms.</p>
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<p>Chart that represents the filters and criteria that were applied for matching wide transcriptomics (RNA-seq) and wide genomic DNA methylation (MBD-seq) approaches in order to record candidate markers of biological age in gilthead sea bream.</p>
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<p>Flow chart representing the multiomic layer integration and the different bioinformatic analysis and filters applied after wide transcriptomics (RNA-seq) and wide genomic DNA methylation (MBD-seq) approaches in order to obtain a list of candidate markers of biological age in gilthead sea bream.</p>
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