Transcriptome analysis reveal alterations in hepatic glycan biosynthesis and metabolism of grass carp (Ctenopharyngodon idellus) fed with broad beans

Transcriptome analysis reveal alterations in hepatic glycan biosynthesis and metabolism of grass carp (Ctenopharyngodon idellus) fed with broad beans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptome analysis reveal alterations in hepatic glycan biosynthesis and metabolism of grass carp (Ctenopharyngodon idellus) fed with broad beans Meilin Hao, Junhong Zhu, Yuxiao Xie, Wenjie Cheng, Lanlan Yi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3320206/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The meat of grass carp ( Ctenopharyngodon idellus ) fed broad beans is crispy, called crisp grass carp. In order to better understand the changes mechanistic in liver tissue of crisp grass carp, gene expression profiles and pathways of liver tissues were performed by using RNA-seq. As a result of the transcriptome analysis, the total number of reads produced for each liver sample ranged from 35,914,404 to 42,460,834. A total of 2519 differentially expressed genes (DEGs) were identified. Among them, 1156 genes were up-regulated and 1363 genes were down-regulated. Gene Ontology (GO) annotations indicated that DEGs were mainly enriched in biological processes of ribosome and structural constituent of ribosome. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that DEGs were mainly enriched in metabolism of energy, amino acid, carbohydrate, and lipid acid, and the genes in these pathways were up-regulated. The protein-protein interaction (PPI) network with 260 nodes and 249 edges was constructed and 3 modules were extracted from the entire network. ITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3 genes were the top 10 hub genes with the closest connections to other nodes. In summary, this study identified several candidate genes and focused on glycan biosynthesis and metabolism pathways, providing a reference for further investigation into the mechanism of liver metabolism in grass carp fed with broad beans. grass carp broad bean liver transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Grass carp is one of the freshwater fish with the largest amount of cultivation in China, Which also is the most consumed fish due to its delicious taste and rich protein content(Yu et al., 2017 ). With the continuous improvement of the living standards of Chinese residents, the cultivation of higher quality grass carp products has become a key issue. Currently in fish feed, fishmeal and soybean meal are the main protein sources for grass carp(Peng et al., 2020 ). With the rapid development of the aquaculture industry, fishmeal and soybean meal became scarce and their prices increased(Sarker et al., 2018 ). Therefore, it is essential to find an alternative plant protein feed for grass carp. The broad bean is a herbaceous plant belonging to the subfamily Papilionaceae of the family Fabaceae of the order Rosacea, and is widely planted all over the world. Broad beans are rich in protein, carbohydrates and trace elements(Mejri et al., 2018 ). It is commonly used as a nutritional feed for animals such as pigs, poultry, ruminants and fish(Shi et al., 2022 ). In the early 1970s, the technicians of the May 7th Cadre School in Guangdong Province accidentally discovered that in the adult grass carp breeding stage, after feeding a single feed of broad beans for 90–120 days, the muscle hardness increased, the meat was firm, and the taste was crisp(Yu et al., 2014 ;Fu et al., 2022). The modified grass carp is called crisp grass carp. Crisp grass carp shows higher muscle hardness and crispness(Fu et al., 2022;Yu et al., 2017 ), which is extremely popular among consumers. Compared to common grass carp, crisped grass carp showed significant increase in muscle hardness, elasticity, chewing power and adhesion, collagen content, myofibril length and density, and reduction in myofibril diameter(Zhang et al., 2021 ;Fu et al., 2020 ). In addition, broad beans affect the fatty acid content of fish and increase fat deposition in viscera(Tian et al., 2019 ). It has been shown that continuous consumption of broad beans by grass carp leads to permanent inflammation-induced intestinal mucosal damage and hepatic steatosis(Li et al., 2018 ;Lin et al., 2012 ), which seriously affects the health and quality of grass carp. The liver plays an important role in maintaining metabolic homeostasis as the main site of synthesis, metabolism and storage of carbohydrates, proteins and lipids(Trefts et al., 2017 ). Therefore, in order to explore the effect of broad bean on the growth and metabolism of grass carp, this study took grass carp as the research object, and set up two groups of experiments, namely the group fed with broad bean and the group without fed with broad bean. Transcriptome sequencing analysis was carried out on the grass carp livers of the two groups of experiments, and the effect of broad beans on the liver tissue metabolism of grass carp was clarified from the perspective of molecular biology, so as to provide a reference for the healthy breeding and quality improvement of grass carp. 2 Materials and Methods 2.1 Animal and Sample Collection Healthy grass carp were purchased from an aquaculture farm in Zunyi, Guizhou Province, China. The fish were first temporarily cultured in a cement pond (5 m × 5 m ×1.5 m) for 1 week and the feed amount for each day was 2–3% of fish weight. A total of 180 fish with initial weight of 768 ± 75 g were randomly divided into crisp grass carp (Group A) and ordinary grass carp groups (Group B), with three replicates each group. They were cultured in six cement ponds (2 × 2 × 1.5 m), with 30 fish in each pond. Crisp grass carp were fed solely with whole faba beans, the feed was soaked in about 0.15% salt water for 24 h, and then soaked in water for 12 h until the broad beans were opened after the germ. The single feeding amount of broad bean accounted for 2%-3% of the body weight of fish. The ordinary grass carp were fed with commercial diet (crude protein: 329.9 g kg − 1 ; crude lipid: 43.8 g kg − 1 ; Tongwei Company, China). The fish were fed twice per day (at 8:00 and 17:00). The water temperature was kept at 25–30℃, pH was 6.5–7.5, and dissolved oxygen was above 5.0 mg/L. The final weights of crisp grass carp and ordinary grass carp were 1,992 ± 125 g and 2,457 ± 132 g after 120 days, respectively. One fish was randomly selected from each pond to collect its muscle tissue, which was placed at − 80°C until RNA extraction. The procedure in this experiment was approved by the Ethics Committee of Experimental Animal of Zunyi Normal College. 2.2 RNA Preparation Total RNA was isolated from liver tissue of grass carps and crisp grass carps using the TRIzol reagent (Takara, Dalian, China) according to the manufacturer’s instruction. The concentration of the isolated RNA was determined by measuring absorbance at 260 nm. The integrity of the RNA was determined by agarose gel electrophoresis and Agilent BioAnalyzer 2100 (Agilent Technologies, San Jose, CA, USA). The RNA was used for transcriptomics analysis. 2.3 Library Construction and Sequencing Six RNA samples with high quality (RIN > 8.7) and concentration (average concentration: 477.4 ng/µL) were used to construct the sequencing libraries and high-throughput sequencing was performed on the Illumina novaseq 6000 platform (San Diego, CA, USA) following the manufacturer’s recommendations, generating 150 bp paired-end reads (Table S1 ). The high-quality clean reads were obtained by fifiltering the raw reads and removing: (1) the sequences containing adapters; (2) the sequences with more than 10% of N bases; (3) the sequences with more than 50% base quality values less than 10. 2.4 Differential Expression Analysis Illumina HiSeq 4000 sequencer reads were paired-end and quality controlled by Q30. Amplifification of the 30 adaptor and the removal of low-quality reads were performed by cutadapt software (v1.9.3), followed by alignment with the reference genome (C_idella_female_scaffolds. fasta V1) using hisat2 software (v2.0.4). Guided by the Ensembl gtf gene annotation file, cuffdiff software was then used to get the gene level fragments per kilobase per million (FPKM) as the expression profiles of mRNA and fold change. The number of clean reads for each gene was calculated and FPKM was used to estimate the expression abundance of transcripts from different samples(Roberts et al.,2011). Differential expression analyses of the A and B groups were performed using the DESeq R package(Wang et al., 2009 ;Love et al., 2014 ) and genes with an p-value ≤ 0.05 and an expression | log2 Fold | ≥ 1were identified as DEGs. 2.5 GO and KEGG Analysis To annotate the function of these DEGs, Gene Ontology (GO) analysis was conducted by using the GOseq software for each of the three main categories: biological process, cellular component and molecular function. Biological pathways enriched for the identified DEGs through Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were carried out using the KOBAS software. 2.6 Statistical Analysis Data are expressed as mean ± standard mean of error (SEM). The statistical significance of the difference between the two groups were conducted using one-way ANOVA with Dunnet’s t-test at p < 0.05 probability levels in SPSS 25.0. “*” was considered significant difference ( p < 0.05); “**” was considered an extremely significant difference ( p < 0.01). 3 Results 3.1 High-Throughput Sequencing and Read Mapping In this study, a total of six libraries in liver tissue were established by high-throughput RNA sequencing. The clean reads of each sample ranged from 35 million to 43 million, the mapped reads and unique mapped reads were more than 92.13% and 94.16%, respectively. In addition, more than 84.12% mapped to gene were detected in each sample (Table 1 ). According to the correlation analysis of 6 samples, the differences between biological replicates were small and the repeatability was high, which indicated that the selection of experimental samples was consistent and reliable (Fig. 1). Table 1 Summary of reads and matches. Samples Clean Reads Mapped Reads Unique Mapped Reads Mapped to Gene Group_A1 36354078 92.70% 94.27% 84.78% Group_A2 39272704 92.13% 94.34% 84.12% Group_A3 35914404 92.85% 94.16% 85.50% Group_B1 42460834 93.83% 94.73% 86.63% Group_B2 40967930 93.82% 94.80% 86.47% Group_B3 39474196 93.81% 95.01% 86.52% 3.2 Analysis of Differentially Expressed Genes A comparison of the liver transcriptomes of A and B groups revealed 2519 DEGs, with criteria of |log2FoldChange| > 1, a p -value of less than 0.05, including 1156 up-regulated and 1363 down-regulated DEGs (Fig. 2). However, with an adjusted p -adjust less than 0.05, 1399 genes were detected in liver tissues. A number of differentially expressed genes were highly expressed in the liver tissues of both groups. 3.3 Gene ontology enrichment for functional analysis of differentially expressed genes To dissect the functional categories of DEGs, GO enrichment analysis were performed. GO enrichment revealed that most of the DEGs were classified into three major functional categories, including cellular component, biological process, and molecular function (Fig. 3A). In the cellular component category, most genes were enriched in nuclear outer membrane-endoplasmic reticulum membrane network (GO:0042175, 53 genes), endoplasmic reticulum membrane (GO:0005789, 52 genes), endomembrane system (GO:0012505, 118 genes), endoplasmic reticulum (GO:0005783, 40 genes) and organelle membrane (GO:0031090, 96 genes). In the molecular function category, DEGs were enriched in iron ion binding (GO:0005506, 28 genes), oxidoreductase activity (GO:0016491, 72 genes), heme binding (GO:0020037, 24 genes), tetrapyrrole binding (GO:0046906, 24 genes) and catalytic activity (GO:0003824, 339 genes). Meanwhile, the DEGs involved in fatty acid metabolic process (GO:0006631, 21 genes), small molecule metabolic process (GO:0044281, 78 genes), lipid metabolic process (GO:0006629, 54 genes), and oxidation-reduction process (GO:0055114, 69 genes) were enriched in the biological process category (Table S2). GO enrichment showed that up-regulated genes were significantly enriched for cellular components and biological process (Fig. 3B). In the molecular function category, up-regulated genes were enriched in iron ion binding (GO:0005506, 19 genes), oxidoreductase activity (GO:0016491, 42 genes), monooxygenase activity (GO:0004497, 16 genes), heme binding (GO:0020037, 16 genes), tetrapyrrole binding (GO:0046906, 16 genes), and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (GO:0016705, 17 genes). Meanwhile, in the biological process category, up-regulated genes were involved in oxidation-reduction process (GO:0055114, 38 genes) mainly (Table S3). GO enrichment showed that down-regulated genes were significantly enriched for cellular components (Fig. 3C). In the molecular function category, down-regulated genes were enriched in endomembrane system (GO:0012505, 95 genes), nuclear outer membrane-endoplasmic reticulum membrane network (GO:0042175, 45 genes), endoplasmic reticulum membrane (GO:0005789, 44 genes), endoplasmic reticulum (GO:0005783, 37 genes), and organelle membrane (GO:0031090, 74 genes) (Table S4). 3.4 Kyoto encyclopedia of genes and genomes enrichment for functional analysis of differentially expressed genes Kyoto encyclopedia of genes and genomes pathway classification and functional enrichment for DEGs were performed to determine the main biochemical metabolic pathways and signal transduction pathways. KEGG pathway enrichment analysis showed that the DEGs were statistically enriched in 20 pathways (Fig. 4A), and 10 of the pathways were related to metabolism of energy, amino acid, carbohydrate, and lipid acid metabolism, including steroid biosynthesis, glutathione metabolism, metabolism of xenobiotics by cytochrome P450, fatty acid degradation, drug metabolism - cytochrome P450, terpenoid backbone biosynthesis, N-Glycan biosynthesis, and steroid hormone biosynthesis. The genes in these pathways were up-regulated (Table S5). KEGG pathway enrichment analysis showed that the up-regulated genes were statistically enriched in 20 pathways (Fig. 4B), and 17 of the pathways were related to metabolism and organismal systems. Metabolism pathways included metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, fatty acid degradation, arachidonic acid metabolism, drug metabolism - other enzymes, linoleic acid metabolism, tryptophan metabolism. Organismal systems pathways included protein digestion and absorption, longevity regulating pathway - worm, cholesterol metabolism, ovarian steroidogenesis, vitamin digestion and absorption, and carbohydrate digestion and absorption (Table S6). KEGG pathway enrichment analysis showed that the down-regulated genes were statistically enriched in 20 pathways (Fig. 4C), and 13 of the pathways were related to metabolism and organismal systems. Metabolism pathways included amino sugar and nucleotide sugar metabolism, steroid biosynthesis, terpenoid backbone biosynthesis, N-Glycan biosynthesis, various types of N-glycan biosynthesis, fatty acid elongation, and fatty acid biosynthesis. Organismal systems pathways included IL-17 signaling pathway, fat on and absorption, adipocytokine signaling pathway, osteoclast differentiation, and C-type lectin receptor signaling pathway (Table S7). This result, in consistence with GO analysis, further indicating metabolic and immune enhancement in grass carp fed broad bean. 3.5 PPI network construction and hub gene identification After STRING analysis of the DEGs, the PPI network was constructed and visualized by Cytoscape with 260 nodes and 249 interactions (Fig. 5), and the whole PPI network was analyzed by cytoHubba. After the connectivity degree of each node was calculated, ITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3 were the top 10 hub genes with the closest connections to other nodes. The whole PPI network was analyzed by MCODE (Fig. 6). A total of 8 modules were mined from the PPI network. Among these, three modules (Modules 1–3) with both MCODE score > 3 and nodes > 3 were further selected for functional analysis. The pathway enrichment analysis revealed that DEGs in module 1 were mostly enriched in glycan biosynthesis and metabolism. The hub genes ITM1, STT3B, SEL1L, UGGT1 and MLEC participated in the pathway. Module 2 was mainly associated with immunity, and included the hub genes IL1B, NFKB2, BCL3 , and IRAK3 . Module 3, including RAB7, STX8, RAB20 , and TBC1D2 , exhibited a close relationship with intrinsic transport and secretion processes of the cell. 3.6 Hub gene identification of metabolism The metabolic pathway of differential gene enrichment was further analyzed. Matebolism pathway enrichment analysis of the up-regulated genes including Amino acid metabolism, Lipid metabolism, Metabolism of cofactors and vitamins and Xenobiotics biodegradation and metabolism (Table 2 ). After STRING analysis of the up-regulated genes, the PPI network was constructed and analyzed by cytoHubba (Fig. 7A). After the connectivity degree of each node was calculated, BBOX1 were the hub gene with the closest connections to other nodes. Table 2 Matebolism pathway enrichment analysis of the up-regulated genes Level2 PathwayID Pathway gene Pvalue FDR Amino acid metabolism ko00380 Tryptophan metabolism 8 0.0012222 0.0189039 ko00310 Lysine degradation 9 0.013493 0.1159456 ko00280 Valine, leucine and isoleucine degradation 6 0.0323558 0.2144733 ko00480 Glutathione metabolism 14 0.0000130 0.0005026 Carbohydrate metabolism ko00052 Galactose metabolism 6 0.0197993 0.1435455 Lipid metabolism ko00071 Fatty acid degradation 12 0.0000028 0.0002197 ko00590 Arachidonic acid metabolism 16 0.0000045 0.0002353 ko00591 Linoleic acid metabolism 9 0.0004435 0.0081857 ko00592 alpha-Linolenic acid metabolism 7 0.0042224 0.0434531 ko00140 Steroid hormone biosynthesis 7 0.0151536 0.1212289 ko00565 Ether lipid metabolism 8 0.0193122 0.1435455 Metabolism of cofactors and vitamins ko00790 Folate biosynthesis 5 0.0187898 0.1435455 Xenobiotics biodegradation and metabolism ko00980 Metabolism of xenobiotics by cytochrome P450 16 0.0000000 0.0000103 ko00982 Drug metabolism - cytochrome P450 14 0.0000015 0.0001704 ko00983 Drug metabolism - other enzymes 15 0.0000255 0.000846 Matebolism pathway enrichment analysis of the down-regulated genes including Amino acid metabolism, Carbohydrate metabolism, and Lipid metabolism (Table 3 ). After STRING analysis of the down-regulated genes, the PPI network was constructed and analyzed by cytoHubba (Fig. 7B). After the connectivity degree of each node was calculated, NANSA, ALG5, NSDH1 were the hub genes with the closest connections to other nodes. Table 3 Matebolism pathway enrichment analysis of the down-regulated genes Level2 PathwayID Pathway gene Pvalue FDR Amino acid metabolism ko00250 Alanine, aspartate and glutamate metabolism 11 0.0006308 0.0150148 ko00330 Arginine and proline metabolism 12 0.010288 0.1084174 ko00220 Arginine biosynthesis 6 0.0171949 0.1375596 ko00480 Glutathione metabolism 12 0.0086045 0.0983378 ko00440 Phosphonate and phosphinate metabolism 3 0.0225633 0.1692247 Carbohydrate metabolism ko00520 Amino sugar and nucleotide sugar metabolism 21 0.0000000 0.0000003 ko00630 Glyoxylate and dicarboxylate metabolism 7 0.0127493 0.1223941 ko00051 Fructose and mannose metabolism 8 0.0170353 0.1375596 ko00010 Glycolysis / Gluconeogenesis 10 0.0366885 0.2515786 ko00510 N-Glycan biosynthesis 16 0.0000118 0.0004729 ko00513 Various types of N-glycan biosynthesis 11 0.002317 0.0397289 Lipid metabolism ko00100 Steroid biosynthesis 12 0.0000000 0.0000005 ko00062 Fatty acid elongation 8 0.0040163 0.0566961 ko00061 Fatty acid biosynthesis 6 0.0042522 0.0566961 ko01040 Biosynthesis of unsaturated fatty acids 7 0.0165191 0.1375596 ko00564 Glycerophospholipid metabolism 18 0.0201315 0.1558571 ko00140 Steroid hormone biosynthesis 8 0.0438928 0.2772179 4 Discussion Aquaculture provides people with high-quality protein(Fiorella et al.,2021). Grass carp is one of the economically important and widely farmed freshwater fish species in China(Lu et al.,2020). The crisp grass carp is very popular among consumers because of its improved taste, and some products are exported to Southeast Asia and North America(Lin et al.,2009;Fu et al.,2020). Here, transcriptome sequencing was performed on liver tissues of grass carp using Illumina platform, and the metabolic changes of grass carp fed broad beans were comprehensively analyzed. In this study, transcriptome databases were constructed using liver tissues of grass carp. One reason was that the liver plays an important role in maintaining the metabolic stability of fish(Trefts et al.,2017). Another reason was that dietary nutrient levels or dietary changes have significant effects on the liver of fish. Studies have shown that broad bean affects the lipid and fatty acid content of grass carp liver, and further affects the meat quality of grass carp(Yu et al.,2017). The clean reads of each sample ranged from 35 million to 43 million, the mapped reads and unique mapped reads were more than 92.13% and 94.16%, respectively. In addition, more than 84.12% mapped to gene were detected in each sample. Consistent with most studies(Wang et al.,2009;Soneson et al.,2015), our results also showed that the sequencing depth is sufficient and the sequencing quality is high enough to meet the requirements of later analysis. The identified 2519 DEGs help to illustrate the underlying differences between A and B groups, which exhibit significant different metabolism, and will be valuable for future studies on the mechanism of liver metabolism in grass carp fed with broad bean. In this study, GO and KEGG analysis results of DEGs showed that metabolism capacity of the liver of crisp grass carp were enhanced. Among them, lipid acid metabolism, amino acid metabolism, glycan biosynthesis and metabolism were significantly enriched. This may also further explain the characteristics of crisp grass carp. The liver mainly performs metabolic functions in the body(Madrigal et al.,2014). Previous studies have shown that crisp grass carp has hard meat and crisp taste, which may be caused by the enhancement of liver lipid acid metabolism and amino acid metabolism(Xu et al.,2020;Coda et al.,2015). Studies have shown that feeding broad beans enhances muscle stiffness and liver metabolism in fish, which is consistent with the results of this study(Smith et al.,2013). In this work, the construction of the PPI network and the identification of the hub genes were carried out for the differential genes. Ten hub genes were screened, including ITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3 genes. The hub genes ITM1, STT3B, SEL1L, UGGT1 and MLEC genes are mainly involved in the upregulation of glycan biosynthesis and metabolism. These glycosyl biosynthetic and metabolic pathways play critical roles in the normal function of cells and organisms(Mikolajczyk et al.,2020). For example, the synthesis and modification of glycoproteins and glycolipids are critical for cellular signaling, cell adhesion, and cell-cell interactions. UGGT1 (UDP-glucose: glycoprotein glucosyltransferase 1) is an enzyme in the endoplasmic reticulum (ER), which is involved in the glycosylation modification process of proteins(Adeva et al.,2016). ALG5 (Asparagine-linked glycosylation 5 homolog) is a glycosyltransferase involved in the synthesis of N-glycan chains of proteins. ALG5 and UGGT1 genes play an important role in the protein quality control of the endoplasmic reticulum and the maintenance of cellular homeostasis. Adams found that UGGT1 gene is central hubs in the chaperone network of the endoplasmic reticulum (ER), acting as gatekeepers to the early secretory pathway. IL1B (Interleukin-1 beta) is a cytokine (cytokine), which plays an important regulatory role in the immune system and inflammatory response. In this study, IL1B and IRAK3 genes down-regulated in liver tissue of the cripe grass carp. A series of inflammatory reactions in the body will lead to excessive levels of IL1B (Lopez et al.,2011). Previous studies have shown Interleukin-1 receptor-associated kinase 3 ( IRAK3 ) is a pseudokinase mediator in the human inflammatory pathway, and ablation of its function is associated with enhanced antitumor immunity(Rowley et al.,2022). The decline in IL1B transcripts of cripe grass carp in this study implied that broad bean diet might inhibit the inflammatory response(Zhong et al.,2022). KRTCAP2 (Keratinocyte associated protein 2) is an intracellular structural protein that is related to keratin and may play a role in cellular structures such as the cytoskeleton and intercellular connections. Sun et al. findings suggest that KRTCAP2 is a prognostic marker for hepatic carcinoma patients with potential clinical implications for predicting immunotherapeutic responsiveness(Sun et al.,2022;Ito et al.,2015). NFKB (nuclear factor kappa B) is the important gene in the nuclear factor kappa B pathway. The nuclear factor kappa B pathway is an important cell signaling pathway that plays a key role in cellular immune and inflammatory responses(De et al.,2020;Shen et al.,2022;Adams et al.,2020). These hub genes were significantly up-regulated in the liver of crisp grass carp, confirming the results of the GO and KEGG pathways. Feeding broad beans enhanced the metabolic capacity of grass carp liver, especially glycan biosynthesis and metabolism. However, some of these candidate genes are related to immunity, and more changes in their functions and immune mechanisms still need to be further investigated. 5 Conclusions In summary, this study did a transcriptomic analysis in the liver tissue of grass carps and crisp grass carps. A substantial number of DEGs have been identified, which are associated with crucial metabolic processes including lipid acid metabolism, amino acid metabolism, and glycan biosynthesis and metabolism. These hub genes are significantly up-regulated in the liver of crisp grass carp, which further indicates that broad bean diet enhances liver metabolism, especially glycan biosynthesis and metabolism. These findings could serve as a reference for further investigation into the liver metabolic changes in animals fed with a broad bean diet. Declarations Author contribution: Meilin Hao participated in the design of the study. Wenjie Cheng, Lanlan Yi and Yuxiao Xie did the experiments and did the data analysis. Meilin Hao, Sumei Zhao and Junhong Zhu drafted the manuscript and all authors contributed to finalizing the writing. Funding: This work was supported by Guizhou Province colleges and universities youth science and technology talent development project (Grant numbers [Qian Jiao He KY[2020]106]) and Zunyi Normal University PhD start-up fund (Grant numbers [BS[2019]26] ). Author Meilin Hao has received research support from Zunyi Normal University. Data availability: The data that support the fndings of this study are available from the corresponding author upon reasonable request. Declarations Ethics approval: The experiment was carried out in accordance with the research plan of the Institutional Animal Care and Use Committee of Zunyi Normal College. Consent to participate: All authors agree to participate in this study. Consent for publication: All authors agree to participate in the publication of this article. Competing interests: The authors declare no competing interests. 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Transcriptome analysis of grass carp (Ctenopharyngodon idella) between fast- and slow-growing fish. Comp Biochem Physiol Part D Genomics Proteomics. 35,100688. https://doi.org/10.1016/j.cbd.2020.100688. Madrigal SE, Madrigal BE, Álvarez GI, Sumaya MMT, Gutiérrez SJ, Bautista M, Morales GÁ, García GM, Aguilar FJL, Morales GJA., 2014. Review of natural products with hepatoprotective effects. World J Gastroenterol. 20(40),14787-14804. https://doi.org/10.3748/wjg.v20.i40.14787. Mejri F, Selmi S, Martins A, Benkhoud H, Baati T, Chaabane H, Njim L, Serralheiro MLM, Rauter AP, Hosni K., 2018. Broad bean (Vicia faba L.) pods: a rich source of bioactive ingredients with antimicrobial, antioxidant, enzyme inhibitory, anti-diabetic and health-promoting properties. Food Funct. 9(4),2051-2069. https://doi.org/10.1039/C8FO00055G. Mikolajczyk K, Kaczmarek R, Czerwinski M., 2020. How glycosylation affects glycosylation: the role of N-glycans in glycosyltransferase activity. Glycobiology. 30(12),941-969. https://doi.org/10.1093/glycob/cwaa041. Peng KS, Wu N, Cui ZW, Zhang XY, Lu XB, Wang ZX, Chen DD, Zhang YA., 2020. Effect of the complete replacement of dietary fish meal by soybean meal on histopathology and immune response of the hindgut in grass carp (Ctenopharyngodon idellus). Vet Immunol Immunopathol. 221,110009. https://doi.org/10.1016/j.vetimm.2020.110009. Roberts A, Trapnell,C, Donaghey J, Rinn JL, Pachter L., 2011. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 12,R22. https://doi.org/10.1186/gb-2011-12-3-r22. Rowley A, Brown BS, Stofega M, Hoh H, Mathew R, Marin V, Ding RX, McClure RA, Bittencourt FM, Chen J, Gururaja T, Kinoshita T, Wang X, Rivkin A, Woller KR., 2022. Targeting IRAK3 for Degradation to Enhance IL-12 Pro-inflammatory Cytokine Production. ACS Chem Biol. 17(6),1315-1320. https://doi.org/10.1021/ACSCHEMBIO.2C00037. Sarker PK, Kapuscinski AR, Bae AY, Donaldson E, Sitek AJ, Fitzgerald DS, Edelson OF., 2018. Towards sustainable aquafeeds: Evaluating substitution of fishmeal with lipid-extracted microalgal co-product (Nannochloropsis oculata) in diets of juvenile Nile tilapia (Oreochromis niloticus). PLoS One. 13(7),e0201315. https://doi.org/10.1371/journal.pone.0201315. Shen M, Jiang Z, Zhang K, Chen L, Fang L, Yi H, Shan Z, Rong Z., 2022. Transcriptome analysis of grass carp (Ctenopharyngodon idella) and Holland's spinibarbel (Spinibarbus hollandi) infected with Ichthyophthirius multifiliis. Fish Shellfish Immunol. 121,305-315. https://doi.org/10.1016/J.FSI.2022.01.008. Shi SH, Lee SS, Zhu YM, Jin ZQ, Wu FB, Qiu CW., 2022. Comparative Metabolomic Profiling Reveals Key Secondary Metabolites Associated with High Quality and Nutritional Value in Broad Bean ( Vicia faba L.). Molecules. 27(24),8995. https://doi.org/10.3390/MOLECULES27248995. Smith LA, Houdijk JG, Homer D, Kyriazakis I., 2013. Effects of dietary inclusion of pea and faba bean as a replacement for soybean meal on grower and finisher pig performance and carcass quality. J Anim Sci. 91(8),3733-3741. https://doi.org/10.2527/jas.2012-6157. Soneson C, Love MI, Robinson MD., 2015. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4,1521. https://doi.org/10.12688/f1000research.7563.1. Sun P, Zhang H, Shi J, Xu M, Cheng T, Lu B, Yang L, Zhang X, Huang J., 2023. KRTCAP2 as an immunological and prognostic biomarker of hepatocellular carcinoma. Colloids Surf B Biointerfaces. 222,113124. https://doi.org/10.1016/J.COLSURFB.2023.113124. Tian JJ, Ji H, Wang YF, Xie J, Wang GJ, Li ZF, Yu EM, Yu DG, Zhang K, Gong WB., 2019. Lipid accumulation in grass carp (Ctenopharyngodon idellus) fed faba beans (Vicia faba L.). Fish Physiol. Biochem. 45(2),631-642. https://doi.org/10.1007/s10695-018-0589-7. Trefts E, Gannon M, Wasserman DH., 2017. The liver. Curr Biol. 27(21),R1147-R1151. https://doi.org/10.1016/j.cub.2017.09.019. Wang L, Feng Z, Wang X, Wang X, Zhang X., 2009. DEGseq: An R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26,136-138. https://doi.org/10.1093/bioinformatics/btp612. Wang Z, Gerstein M, Snyder M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10(1),57-63. https://doi.org/10.1038/nrg2484. Xu WH, Guo HH, Chen SJ, Wang YZ, Lin ZH, Huang XD, Tang HJ, He YH, Sun JJ, Gan L., 2020. Transcriptome analysis revealed changes of multiple genes involved in muscle hardness in grass carp (Ctenopharyngodon idellus) fed with faba bean meal. Food Chem. 314,126205. https://doi.org/10.1016/j.foodchem.2020.126205. Yu E, Xie J, Wang G, Yu D, Gong W, Li Z, Wang H, Xia Y, Wei N., 2014. Gene Expression Profiling of Grass Carp (Ctenopharyngodon idellus) and Crisp Grass Carp. Int J Genomics. 2014,639687. https://doi.org/10.1155/2014/639687. Yu EM, Zhang HF, Li ZF, Wang GJ, Wu HK, Xie J, Yu DG, Xia Y, Zhang K, Gong WB., 2017. Proteomic signature of muscle fibre hyperplasia in response to faba bean intake in grass carp. Sci Rep. 7,45950. https://doi.org/10.1038/srep45950. Zhang J, Kaneko G, Sun J, Wang G, Xie J, Tian J, Li Z, Gong W, Zhang K, Xia Y, Yu E., 2021. Key Factors Affecting the Flesh Flavor Quality and the Nutritional Value of Grass Carp in Four Culture Modes. Foods. 10(9),2075. https://doi.org/10.3390/foods10092075. Zhong ZM, Zhang J, Tang BG, Yu FF, Lu YS, Hou G, Chen JY, Du ZX., 2022. Transcriptome and metabolome analyses of the immune response to light stress in the hybrid grouper (Epinephelus lanceolatus ♂ × Epinephelus fuscoguttatus ♀). Animal. 16(2),100448. https://doi.org/10.1016/j.animal.2021.100448. Additional Declarations No competing interests reported. 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version\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3320206/v1/6e1f0f819aad4cf7004bed42.png"},{"id":42850938,"identity":"4ba26ea6-98c3-446d-b599-55ef92e0a5fa","added_by":"auto","created_at":"2023-09-08 18:42:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":116025,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3320206/v1/9ee2290dd9a498f8f9d68edc.png"},{"id":42852273,"identity":"ead6b2c0-abf7-409d-a5eb-c6a9b61d7006","added_by":"auto","created_at":"2023-09-08 18:50:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31064,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3320206/v1/e0ff2b569025f8d8920278a0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome analysis reveal alterations in hepatic glycan biosynthesis and metabolism of grass carp (Ctenopharyngodon idellus) fed with broad beans","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGrass carp is one of the freshwater fish with the largest amount of cultivation in China, Which also is the most consumed fish due to its delicious taste and rich protein content(Yu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). With the continuous improvement of the living standards of Chinese residents, the cultivation of higher quality grass carp products has become a key issue. Currently in fish feed, fishmeal and soybean meal are the main protein sources for grass carp(Peng et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the rapid development of the aquaculture industry, fishmeal and soybean meal became scarce and their prices increased(Sarker et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, it is essential to find an alternative plant protein feed for grass carp.\u003c/p\u003e \u003cp\u003eThe broad bean is a herbaceous plant belonging to the subfamily Papilionaceae of the family Fabaceae of the order Rosacea, and is widely planted all over the world. Broad beans are rich in protein, carbohydrates and trace elements(Mejri et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is commonly used as a nutritional feed for animals such as pigs, poultry, ruminants and fish(Shi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the early 1970s, the technicians of the May 7th Cadre School in Guangdong Province accidentally discovered that in the adult grass carp breeding stage, after feeding a single feed of broad beans for 90\u0026ndash;120 days, the muscle hardness increased, the meat was firm, and the taste was crisp(Yu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e;Fu et al., 2022). The modified grass carp is called crisp grass carp. Crisp grass carp shows higher muscle hardness and crispness(Fu et al., 2022;Yu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which is extremely popular among consumers.\u003c/p\u003e \u003cp\u003eCompared to common grass carp, crisped grass carp showed significant increase in muscle hardness, elasticity, chewing power and adhesion, collagen content, myofibril length and density, and reduction in myofibril diameter(Zhang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;Fu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, broad beans affect the fatty acid content of fish and increase fat deposition in viscera(Tian et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has been shown that continuous consumption of broad beans by grass carp leads to permanent inflammation-induced intestinal mucosal damage and hepatic steatosis(Li et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Lin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which seriously affects the health and quality of grass carp. The liver plays an important role in maintaining metabolic homeostasis as the main site of synthesis, metabolism and storage of carbohydrates, proteins and lipids(Trefts et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, in order to explore the effect of broad bean on the growth and metabolism of grass carp, this study took grass carp as the research object, and set up two groups of experiments, namely the group fed with broad bean and the group without fed with broad bean. Transcriptome sequencing analysis was carried out on the grass carp livers of the two groups of experiments, and the effect of broad beans on the liver tissue metabolism of grass carp was clarified from the perspective of molecular biology, so as to provide a reference for the healthy breeding and quality improvement of grass carp.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Animal and Sample Collection\u003c/h2\u003e \u003cp\u003eHealthy grass carp were purchased from an aquaculture farm in Zunyi, Guizhou Province, China. The fish were first temporarily cultured in a cement pond (5 m \u0026times; 5 m \u0026times;1.5 m) for 1 week and the feed amount for each day was 2\u0026ndash;3% of fish weight. A total of 180 fish with initial weight of 768\u0026thinsp;\u0026plusmn;\u0026thinsp;75 g were randomly divided into crisp grass carp (Group A) and ordinary grass carp groups (Group B), with three replicates each group. They were cultured in six cement ponds (2 \u0026times; 2 \u0026times; 1.5 m), with 30 fish in each pond. Crisp grass carp were fed solely with whole faba beans, the feed was soaked in about 0.15% salt water for 24 h, and then soaked in water for 12 h until the broad beans were opened after the germ. The single feeding amount of broad bean accounted for 2%-3% of the body weight of fish. The ordinary grass carp were fed with commercial diet (crude protein: 329.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; crude lipid: 43.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Tongwei Company, China). The fish were fed twice per day (at 8:00 and 17:00). The water temperature was kept at 25\u0026ndash;30℃, pH was 6.5\u0026ndash;7.5, and dissolved oxygen was above 5.0 mg/L. The final weights of crisp grass carp and ordinary grass carp were 1,992\u0026thinsp;\u0026plusmn;\u0026thinsp;125 g and 2,457\u0026thinsp;\u0026plusmn;\u0026thinsp;132 g after 120 days, respectively. One fish was randomly selected from each pond to collect its muscle tissue, which was placed at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction. The procedure in this experiment was approved by the Ethics Committee of Experimental Animal of Zunyi Normal College.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 RNA Preparation\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from liver tissue of grass carps and crisp grass carps using the TRIzol reagent (Takara, Dalian, China) according to the manufacturer\u0026rsquo;s instruction. The concentration of the isolated RNA was determined by measuring absorbance at 260 nm. The integrity of the RNA was determined by agarose gel electrophoresis and Agilent BioAnalyzer 2100 (Agilent Technologies, San Jose, CA, USA). The RNA was used for transcriptomics analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003eSix RNA samples with high quality (RIN\u0026thinsp;\u0026gt;\u0026thinsp;8.7) and concentration (average concentration: 477.4 ng/\u0026micro;L) were used to construct the sequencing libraries and high-throughput sequencing was performed on the Illumina novaseq 6000 platform (San Diego, CA, USA) following the manufacturer\u0026rsquo;s recommendations, generating 150 bp paired-end reads (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The high-quality clean reads were obtained by fifiltering the raw reads and removing: (1) the sequences containing adapters; (2) the sequences with more than 10% of N bases; (3) the sequences with more than 50% base quality values less than 10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eIllumina HiSeq 4000 sequencer reads were paired-end and quality controlled by Q30. Amplifification of the 30 adaptor and the removal of low-quality reads were performed by cutadapt software (v1.9.3), followed by alignment with the reference genome (C_idella_female_scaffolds. fasta V1) using hisat2 software (v2.0.4). Guided by the Ensembl gtf gene annotation file, cuffdiff software was then used to get the gene level fragments per kilobase per million (FPKM) as the expression profiles of mRNA and fold change. The number of clean reads for each gene was calculated and FPKM was used to estimate the expression abundance of transcripts from different samples(Roberts et al.,2011). Differential expression analyses of the A and B groups were performed using the DESeq R package(Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e;Love et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and genes with an p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and an expression | log2 Fold | \u0026ge; 1were identified as DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 GO and KEGG Analysis\u003c/h2\u003e \u003cp\u003eTo annotate the function of these DEGs, Gene Ontology (GO) analysis was conducted by using the GOseq software for each of the three main categories: biological process, cellular component and molecular function. Biological pathways enriched for the identified DEGs through Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were carried out using the KOBAS software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard mean of error (SEM). The statistical significance of the difference between the two groups were conducted using one-way ANOVA with Dunnet\u0026rsquo;s t-test at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 probability levels in SPSS 25.0. \u0026ldquo;*\u0026rdquo; was considered significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); \u0026ldquo;**\u0026rdquo; was considered an extremely significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 High-Throughput Sequencing and Read Mapping\u003c/h2\u003e \u003cp\u003eIn this study, a total of six libraries in liver tissue were established by high-throughput RNA sequencing. The clean reads of each sample ranged from 35\u0026nbsp;million to 43\u0026nbsp;million, the mapped reads and unique mapped reads were more than 92.13% and 94.16%, respectively. In addition, more than 84.12% mapped to gene were detected in each sample (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the correlation analysis of 6 samples, the differences between biological replicates were small and the repeatability was high, which indicated that the selection of experimental samples was consistent and reliable (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of reads and matches.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClean Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMapped Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnique Mapped Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMapped to Gene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36354078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39272704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35914404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42460834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_B2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40967930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup_B3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39474196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eA comparison of the liver transcriptomes of A and B groups revealed 2519 DEGs, with criteria of |log2FoldChange| \u0026gt; 1, a \u003cem\u003ep\u003c/em\u003e-value of less than 0.05, including 1156 up-regulated and 1363 down-regulated DEGs (Fig.\u0026nbsp;2). However, with an adjusted \u003cem\u003ep\u003c/em\u003e-adjust less than 0.05, 1399 genes were detected in liver tissues. A number of differentially expressed genes were highly expressed in the liver tissues of both groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Gene ontology enrichment for functional analysis of differentially expressed genes\u003c/h2\u003e \u003cp\u003eTo dissect the functional categories of DEGs, GO enrichment analysis were performed. GO enrichment revealed that most of the DEGs were classified into three major functional categories, including cellular component, biological process, and molecular function (Fig.\u0026nbsp;3A). In the cellular component category, most genes were enriched in nuclear outer membrane-endoplasmic reticulum membrane network (GO:0042175, 53 genes), endoplasmic reticulum membrane (GO:0005789, 52 genes), endomembrane system (GO:0012505, 118 genes), endoplasmic reticulum (GO:0005783, 40 genes) and organelle membrane (GO:0031090, 96 genes). In the molecular function category, DEGs were enriched in iron ion binding (GO:0005506, 28 genes), oxidoreductase activity (GO:0016491, 72 genes), heme binding (GO:0020037, 24 genes), tetrapyrrole binding (GO:0046906, 24 genes) and catalytic activity (GO:0003824, 339 genes). Meanwhile, the DEGs involved in fatty acid metabolic process (GO:0006631, 21 genes), small molecule metabolic process (GO:0044281, 78 genes), lipid metabolic process (GO:0006629, 54 genes), and oxidation-reduction process (GO:0055114, 69 genes) were enriched in the biological process category (Table S2).\u003c/p\u003e \u003cp\u003eGO enrichment showed that up-regulated genes were significantly enriched for cellular components and biological process (Fig.\u0026nbsp;3B). In the molecular function category, up-regulated genes were enriched in iron ion binding (GO:0005506, 19 genes), oxidoreductase activity (GO:0016491, 42 genes), monooxygenase activity (GO:0004497, 16 genes), heme binding (GO:0020037, 16 genes), tetrapyrrole binding (GO:0046906, 16 genes), and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (GO:0016705, 17 genes). Meanwhile, in the biological process category, up-regulated genes were involved in oxidation-reduction process (GO:0055114, 38 genes) mainly (Table S3). GO enrichment showed that down-regulated genes were significantly enriched for cellular components (Fig.\u0026nbsp;3C). In the molecular function category, down-regulated genes were enriched in endomembrane system (GO:0012505, 95 genes), nuclear outer membrane-endoplasmic reticulum membrane network (GO:0042175, 45 genes), endoplasmic reticulum membrane (GO:0005789, 44 genes), endoplasmic reticulum (GO:0005783, 37 genes), and organelle membrane (GO:0031090, 74 genes) (Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Kyoto encyclopedia of genes and genomes enrichment for functional analysis of differentially expressed genes\u003c/h2\u003e \u003cp\u003eKyoto encyclopedia of genes and genomes pathway classification and functional enrichment for DEGs were performed to determine the main biochemical metabolic pathways and signal transduction pathways. KEGG pathway enrichment analysis showed that the DEGs were statistically enriched in 20 pathways (Fig.\u0026nbsp;4A), and 10 of the pathways were related to metabolism of energy, amino acid, carbohydrate, and lipid acid metabolism, including steroid biosynthesis, glutathione metabolism, metabolism of xenobiotics by cytochrome P450, fatty acid degradation, drug metabolism - cytochrome P450, terpenoid backbone biosynthesis, N-Glycan biosynthesis, and steroid hormone biosynthesis. The genes in these pathways were up-regulated (Table S5).\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis showed that the up-regulated genes were statistically enriched in 20 pathways (Fig.\u0026nbsp;4B), and 17 of the pathways were related to metabolism and organismal systems. Metabolism pathways included metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, fatty acid degradation, arachidonic acid metabolism, drug metabolism - other enzymes, linoleic acid metabolism, tryptophan metabolism. Organismal systems pathways included protein digestion and absorption, longevity regulating pathway - worm, cholesterol metabolism, ovarian steroidogenesis, vitamin digestion and absorption, and carbohydrate digestion and absorption (Table S6).\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis showed that the down-regulated genes were statistically enriched in 20 pathways (Fig.\u0026nbsp;4C), and 13 of the pathways were related to metabolism and organismal systems. Metabolism pathways included amino sugar and nucleotide sugar metabolism, steroid biosynthesis, terpenoid backbone biosynthesis, N-Glycan biosynthesis, various types of N-glycan biosynthesis, fatty acid elongation, and fatty acid biosynthesis. Organismal systems pathways included IL-17 signaling pathway, fat on and absorption, adipocytokine signaling pathway, osteoclast differentiation, and C-type lectin receptor signaling pathway (Table S7). This result, in consistence with GO analysis, further indicating metabolic and immune enhancement in grass carp fed broad bean.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 PPI network construction and hub gene identification\u003c/h2\u003e \u003cp\u003eAfter STRING analysis of the DEGs, the PPI network was constructed and visualized by Cytoscape with 260 nodes and 249 interactions (Fig.\u0026nbsp;5), and the whole PPI network was analyzed by cytoHubba. After the connectivity degree of each node was calculated, \u003cem\u003eITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3\u003c/em\u003e were the top 10 hub genes with the closest connections to other nodes.\u003c/p\u003e \u003cp\u003eThe whole PPI network was analyzed by MCODE (Fig.\u0026nbsp;6). A total of 8 modules were mined from the PPI network. Among these, three modules (Modules 1\u0026ndash;3) with both MCODE score\u0026thinsp;\u0026gt;\u0026thinsp;3 and nodes\u0026thinsp;\u0026gt;\u0026thinsp;3 were further selected for functional analysis. The pathway enrichment analysis revealed that DEGs in module 1 were mostly enriched in glycan biosynthesis and metabolism. The hub genes \u003cem\u003eITM1, STT3B, SEL1L, UGGT1\u003c/em\u003e and \u003cem\u003eMLEC\u003c/em\u003e participated in the pathway. Module 2 was mainly associated with immunity, and included the hub genes \u003cem\u003eIL1B, NFKB2, BCL3\u003c/em\u003e, and \u003cem\u003eIRAK3\u003c/em\u003e. Module 3, including \u003cem\u003eRAB7, STX8, RAB20\u003c/em\u003e, and \u003cem\u003eTBC1D2\u003c/em\u003e, exhibited a close relationship with intrinsic transport and secretion processes of the cell.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Hub gene identification of metabolism\u003c/h2\u003e \u003cp\u003eThe metabolic pathway of differential gene enrichment was further analyzed. Matebolism pathway enrichment analysis of the up-regulated genes including Amino acid metabolism, Lipid metabolism, Metabolism of cofactors and vitamins and Xenobiotics biodegradation and metabolism (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After STRING analysis of the up-regulated genes, the PPI network was constructed and analyzed by cytoHubba (Fig.\u0026nbsp;7A). After the connectivity degree of each node was calculated, \u003cem\u003eBBOX1\u003c/em\u003e were the hub gene with the closest connections to other nodes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMatebolism pathway enrichment analysis of the up-regulated genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathwayID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAmino acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTryptophan metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0012222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0189039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysine degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1159456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValine, leucine and isoleucine degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0323558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2144733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlutathione metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGalactose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0197993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1435455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatty acid degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArachidonic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinoleic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0004435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0081857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ealpha-Linolenic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0042224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0434531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0151536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1212289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEther lipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0193122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1435455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolism of cofactors and vitamins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFolate biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0187898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1435455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eXenobiotics biodegradation and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug metabolism - cytochrome P450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug metabolism - other enzymes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMatebolism pathway enrichment analysis of the down-regulated genes including Amino acid metabolism, Carbohydrate metabolism, and Lipid metabolism (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After STRING analysis of the down-regulated genes, the PPI network was constructed and analyzed by cytoHubba (Fig.\u0026nbsp;7B). After the connectivity degree of each node was calculated, \u003cem\u003eNANSA, ALG5, NSDH1\u003c/em\u003e were the hub genes with the closest connections to other nodes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMatebolism pathway enrichment analysis of the down-regulated genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathwayID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAmino acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlanine, aspartate and glutamate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0006308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0150148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArginine and proline metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1084174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArginine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0171949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1375596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlutathione metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0086045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0983378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhosphonate and phosphinate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0225633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1692247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCarbohydrate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmino sugar and nucleotide sugar metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlyoxylate and dicarboxylate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0127493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1223941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFructose and mannose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0170353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1375596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycolysis / Gluconeogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0366885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2515786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN-Glycan biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVarious types of N-glycan biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0397289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatty acid elongation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0040163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0566961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatty acid biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0042522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0566961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko01040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiosynthesis of unsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0165191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1375596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0201315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1558571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0438928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2772179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAquaculture provides people with high-quality protein(Fiorella et al.,2021). Grass carp is one of the economically important and widely farmed freshwater fish species in China(Lu et al.,2020). The crisp grass carp is very popular among consumers because of its improved taste, and some products are exported to Southeast Asia and North America(Lin et al.,2009;Fu et al.,2020). Here, transcriptome sequencing was performed on liver tissues of grass carp using Illumina platform, and the metabolic changes of grass carp fed broad beans were comprehensively analyzed.\u003c/p\u003e \u003cp\u003eIn this study, transcriptome databases were constructed using liver tissues of grass carp. One reason was that the liver plays an important role in maintaining the metabolic stability of fish(Trefts et al.,2017). Another reason was that dietary nutrient levels or dietary changes have significant effects on the liver of fish. Studies have shown that broad bean affects the lipid and fatty acid content of grass carp liver, and further affects the meat quality of grass carp(Yu et al.,2017).\u003c/p\u003e \u003cp\u003eThe clean reads of each sample ranged from 35\u0026nbsp;million to 43\u0026nbsp;million, the mapped reads and unique mapped reads were more than 92.13% and 94.16%, respectively. In addition, more than 84.12% mapped to gene were detected in each sample. Consistent with most studies(Wang et al.,2009;Soneson et al.,2015), our results also showed that the sequencing depth is sufficient and the sequencing quality is high enough to meet the requirements of later analysis. The identified 2519 DEGs help to illustrate the underlying differences between A and B groups, which exhibit significant different metabolism, and will be valuable for future studies on the mechanism of liver metabolism in grass carp fed with broad bean.\u003c/p\u003e \u003cp\u003eIn this study, GO and KEGG analysis results of DEGs showed that metabolism capacity of the liver of crisp grass carp were enhanced. Among them, lipid acid metabolism, amino acid metabolism, glycan biosynthesis and metabolism were significantly enriched. This may also further explain the characteristics of crisp grass carp. The liver mainly performs metabolic functions in the body(Madrigal et al.,2014). Previous studies have shown that crisp grass carp has hard meat and crisp taste, which may be caused by the enhancement of liver lipid acid metabolism and amino acid metabolism(Xu et al.,2020;Coda et al.,2015). Studies have shown that feeding broad beans enhances muscle stiffness and liver metabolism in fish, which is consistent with the results of this study(Smith et al.,2013).\u003c/p\u003e \u003cp\u003eIn this work, the construction of the PPI network and the identification of the hub genes were carried out for the differential genes. Ten hub genes were screened, including \u003cem\u003eITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3\u003c/em\u003e genes. The hub genes \u003cem\u003eITM1, STT3B, SEL1L, UGGT1\u003c/em\u003e and \u003cem\u003eMLEC\u003c/em\u003e genes are mainly involved in the upregulation of glycan biosynthesis and metabolism. These glycosyl biosynthetic and metabolic pathways play critical roles in the normal function of cells and organisms(Mikolajczyk et al.,2020). For example, the synthesis and modification of glycoproteins and glycolipids are critical for cellular signaling, cell adhesion, and cell-cell interactions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eUGGT1\u003c/em\u003e (UDP-glucose: glycoprotein glucosyltransferase 1) is an enzyme in the endoplasmic reticulum (ER), which is involved in the glycosylation modification process of proteins(Adeva et al.,2016). \u003cem\u003eALG5\u003c/em\u003e (Asparagine-linked glycosylation 5 homolog) is a glycosyltransferase involved in the synthesis of N-glycan chains of proteins. \u003cem\u003eALG5\u003c/em\u003e and \u003cem\u003eUGGT1\u003c/em\u003e genes play an important role in the protein quality control of the endoplasmic reticulum and the maintenance of cellular homeostasis. Adams found that \u003cem\u003eUGGT1\u003c/em\u003e gene is central hubs in the chaperone network of the endoplasmic reticulum (ER), acting as gatekeepers to the early secretory pathway. \u003cem\u003eIL1B\u003c/em\u003e (Interleukin-1 beta) is a cytokine (cytokine), which plays an important regulatory role in the immune system and inflammatory response. In this study, \u003cem\u003eIL1B\u003c/em\u003e and \u003cem\u003eIRAK3\u003c/em\u003e genes down-regulated in liver tissue of the cripe grass carp. A series of inflammatory reactions in the body will lead to excessive levels of \u003cem\u003eIL1B\u003c/em\u003e(Lopez et al.,2011). Previous studies have shown Interleukin-1 receptor-associated kinase 3 (\u003cem\u003eIRAK3\u003c/em\u003e) is a pseudokinase mediator in the human inflammatory pathway, and ablation of its function is associated with enhanced antitumor immunity(Rowley et al.,2022). The decline in \u003cem\u003eIL1B\u003c/em\u003e transcripts of cripe grass carp in this study implied that broad bean diet might inhibit the inflammatory response(Zhong et al.,2022).\u003c/p\u003e \u003cp\u003e \u003cem\u003eKRTCAP2\u003c/em\u003e (Keratinocyte associated protein 2) is an intracellular structural protein that is related to keratin and may play a role in cellular structures such as the cytoskeleton and intercellular connections. Sun et al. findings suggest that \u003cem\u003eKRTCAP2\u003c/em\u003e is a prognostic marker for hepatic carcinoma patients with potential clinical implications for predicting immunotherapeutic responsiveness(Sun et al.,2022;Ito et al.,2015). \u003cem\u003eNFKB\u003c/em\u003e (nuclear factor kappa B) is the important gene in the nuclear factor kappa B pathway. The nuclear factor kappa B pathway is an important cell signaling pathway that plays a key role in cellular immune and inflammatory responses(De et al.,2020;Shen et al.,2022;Adams et al.,2020). These hub genes were significantly up-regulated in the liver of crisp grass carp, confirming the results of the GO and KEGG pathways. Feeding broad beans enhanced the metabolic capacity of grass carp liver, especially glycan biosynthesis and metabolism. However, some of these candidate genes are related to immunity, and more changes in their functions and immune mechanisms still need to be further investigated.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, this study did a transcriptomic analysis in the liver tissue of grass carps and crisp grass carps. A substantial number of DEGs have been identified, which are associated with crucial metabolic processes including lipid acid metabolism, amino acid metabolism, and glycan biosynthesis and metabolism. These hub genes are significantly up-regulated in the liver of crisp grass carp, which further indicates that broad bean diet enhances liver metabolism, especially glycan biosynthesis and metabolism. These findings could serve as a reference for further investigation into the liver metabolic changes in animals fed with a broad bean diet.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u0026nbsp;\u003c/strong\u003eMeilin Hao participated in the design of the study. Wenjie Cheng, Lanlan Yi and Yuxiao Xie did the experiments and did the data analysis. Meilin Hao, Sumei Zhao and Junhong Zhu drafted the manuscript and all authors contributed to finalizing the writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by Guizhou Province colleges and universities youth science and technology talent development project (Grant numbers [Qian Jiao He KY[2020]106]) and Zunyi Normal University PhD start-up fund (Grant numbers [BS[2019]26] ). Author Meilin Hao has received research support from Zunyi Normal University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data that support the fndings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe experiment was carried out in accordance with the research plan of the Institutional Animal Care and Use Committee of Zunyi Normal College.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e All authors agree to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All authors agree to participate in the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams BM, Canniff NP, Guay KP, Larsen ISB, Hebert DN, 2020. 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Animal. 16(2),100448. https://doi.org/10.1016/j.animal.2021.100448.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"grass carp, broad bean, liver, transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-3320206/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3320206/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe meat of grass carp (\u003cem\u003eCtenopharyngodon idellus\u003c/em\u003e) fed broad beans is crispy, called crisp grass carp. In order to better understand the changes mechanistic in liver tissue of crisp grass carp, gene expression profiles and pathways of liver tissues were performed by using RNA-seq.\u0026nbsp;As a result of the transcriptome analysis, the total number of reads produced for each liver sample ranged from 35,914,404 to 42,460,834. A total of 2519 differentially expressed genes (DEGs) were identified. Among them, 1156 genes were up-regulated and 1363 genes were down-regulated. Gene Ontology (GO) annotations indicated that DEGs were mainly enriched in biological processes of ribosome and structural constituent of ribosome. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that DEGs were mainly enriched in metabolism of energy, amino acid, carbohydrate, and lipid acid, and the genes in these pathways were up-regulated. The protein-protein interaction (PPI) network with 260 nodes and 249 edges was constructed and 3 modules were extracted from the entire network. \u003cem\u003eITML, STT3B, SEL1L, UGGT1, MLEC, IL1B, ALG5, KRTCAP2, NFKB2, IRAK3\u003c/em\u003e genes were the top 10 hub genes with the closest connections to other nodes. In summary, this study identified several candidate genes and focused on glycan biosynthesis and metabolism pathways, providing a reference for further investigation into the mechanism of liver metabolism in grass carp fed with broad beans.\u003c/p\u003e","manuscriptTitle":"Transcriptome analysis reveal alterations in hepatic glycan biosynthesis and metabolism of grass carp (Ctenopharyngodon idellus) fed with broad beans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-08 18:42:31","doi":"10.21203/rs.3.rs-3320206/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20fe5823-a0d2-418b-b38a-3d63e1669290","owner":[],"postedDate":"September 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-15T07:59:28+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-08 18:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3320206","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3320206","identity":"rs-3320206","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}