Next Article in Journal
DUSP2 Deletion Inhibits Macrophage Migration by Inhibiting ERK Activation in Zebrafish
Next Article in Special Issue
RNA-Seq Reveals Differential Gene Expression Patterns Related to Reproduction in the Golden Mahseer
Previous Article in Journal
Immunoregulation and Resistance to Aquatic Pathogens with Dietary Nucleotides in Pacific White Shrimp, Litopenaeus vannamei
Previous Article in Special Issue
De Novo Transcriptome Analysis of the Early Hybrid Triploid Loach (Misgurnus anguillicaudatus) Provides Novel Insights into Fertility Mechanism
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fishes
Volume 8
Issue 6
10.3390/fishes8060309
fishes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptome Analysis Reveals Differences in Gene Expression in the Muscle of the Brown-Marbled Grouper (Epinephelus fuscoguttatus) with Different Growth Rates

by
Tong Wang
1,
Yang Yang
1,
Shirui Gong
1,
Xi Wu
1,
Leilei Zeng
1,
Yuhao Tao
1,
Chaoyue Zhong
1,
Leling Song
1 and
Xiaochun Liu
1,2,*
1
State Key Laboratory of Biocontrol, Institute of Aquatic Economic Animals and Guangdong Provincial Key Laboratory for Aquatic Economic Animals, Life Sciences School, Sun Yat-sen University, Guangzhou 510275, China
2
Southern Laboratory of Ocean Science and Engineering, Zhuhai 519000, China
*
Author to whom correspondence should be addressed.
Fishes 2023, 8(6), 309; https://doi.org/10.3390/fishes8060309
Submission received: 18 April 2023 / Revised: 2 June 2023 / Accepted: 6 June 2023 / Published: 8 June 2023
(This article belongs to the Special Issue Application of Transcriptomics in Fish)
Browse Figures
Review Reports Versions Notes

Abstract

:
Brown-marbled grouper is one of the most important mariculture species in China and is widely used as a crossbreeding parent in the grouper industry. Enhancing growth rates is a key target in fish breeding, and gaining insight into the underlying mechanisms responsible for growth differences between individuals can aid in the improvement of grouper growth rates. However, the mechanism behind growth differences in this fish remains unclear. We analyzed the transcriptome profiles of muscle tissues between fast- and slow-growing brown-marbled grouper using RNA-seq and identified 77 significantly up-regulated genes and 92 significantly down-regulated genes in the extreme growth groups. Our findings suggest that up-regulated genes such as ghr and tnni2, as well as down-regulated genes such as stc2 and pdp1, are associated with growth advantages in brown-marbled grouper. We used differentially expressed genes (DEGs) for GO and KEGG enrichment analyses. The results of the GO enrichments showed that the significantly up-regulated genes in the fast-growing group were involved in protein folding, the actin cytoskeleton, the myosin complex, and other processes. The results of the KEGG enrichments showed that the significantly up-regulated genes in the fast-growing group were involved in various pathways such as glycolysis/gluconeogenesis, adipocytokine signaling, MAPK signaling, carbon metabolism, and PI3K-Akt signaling. Additionally, gene set enrichment analysis (GSEA) showed that the PI3K/AKT/mTOR pathway was up-regulated in the fast-growing group, which may be responsible for the higher nutrient absorption efficiency and muscle growth in these fish. Our results contribute to a better understanding of the molecular mechanisms and regulatory pathways underlying fast growth in brown-marbled grouper. However, further studies are necessary to fully elucidate the mechanisms behind growth differences between individuals.
Keywords:
brown-marbled grouper; muscle; growth; RNA-seq
Key Contribution: The manuscript presents findings from transcriptome analysis of muscle tissue in fast- and slow-growing brown-marbled grouper, identifying genes and pathways associated with fast growth. The study contributes to a better understanding of the molecular mechanisms and regulatory pathways underlying fast growth in this important mariculture species.

Graphical Abstract

1. Introduction

Brown-marbled grouper (Epinephelus fuscoguttatus) belongs to the Epinephelus genus of the Epinephelidae family and is an important, economically valuable, edible fish found in tropical and subtropical seas of Asia [1]. Due to its excellent meat quality, high nutrient content, and rapid growth, the brown-marbled grouper has a high market value and is widely cultured in southern China [2]. Additionally, its long and stable spawning cycle and high spawning quality and quantity make it an ideal choice as a female parent in crossbreeding programs [3,4,5]. Currently, the Hulong grouper, the grouper with the largest production in China, is produced by crossing the brown-marbled grouper with the giant grouper (E. lanceolatus) [6].
Growth is a critical trait for economically significant aquaculture species, and it mainly depends on the increase in muscle mass. The muscle content can comprise up to 50–70% of the fish’s body weight, and it primarily drives the fish’s growth [7]. Ray-finned fish’s muscle growth differs from that of mammals, with hyperplasia continuing throughout the fish’s lifespan. The intricate regulatory mechanisms of muscle growth are mainly regulated by the GH/IGF system, which promotes hyperplasia and hypertrophy through the stimulation of cell proliferation, differentiation, myogenesis, protein synthesis, and protein degradation [8,9]. Environmental factors such as light, temperature, and food supply can also affect muscle growth by influencing the proliferation of myogenic progenitor cells (MPCs), driving hyperplasia [10,11].
It has been reported that in seven species of freshwater fish, muscle growth occurs until approximately 40% of the maximum body length is reached, beyond which hypertrophy becomes the primary driver of growth [12]. Growth signaling pathways such as the GH/IGF axis regulate the synthesis and degradation of protein and control the rate of fiber expansion. The GH/IGF system stimulates the synthesis and secretion of IGF-1 in the liver and other tissues, which then promotes muscle growth through the activation of MAPK/ERK, PI3K/AKT/TOR, and other signaling pathways [13,14]. However, our understanding of the detailed molecular regulatory mechanisms in teleost fish is limited, and the biological functions and roles of many genes involved in fish growth are poorly understood. The analysis of ortholog and paralog genes generated in fish through whole-genome duplication (WGD) further hinders their study [15]. In addition, the genetically based responses of growth to environmental inputs can be highly varied, leading to potentially conflicting results. A more systematic understanding of the genes responsible for muscle growth is needed, and improved knowledge of the molecular genetic basis will provide new opportunities for enhancing muscle growth.
RNA-seq is a recently developed approach that utilizes high-throughput sequencing technologies for transcriptome profiling [16]. Over the past few decades, RNA-seq has been widely employed in aquaculture to identify candidate genes associated with economically important traits. For instance, RNA-seq has been utilized to identify differentially expressed genes involved in disease and stress responses in catfish (Ictalurus punctatus) [17,18]. Additionally, RNA-seq has been used to explore differentially expressed genes related to growth in various fish species, such as rainbow trout (Oncorhynchus mykiss), largemouth bass (Micropterus salmoides), and schizothoracine fish (Schizothorax prenanti) [19,20,21]. Numerous genes and signaling pathways involved in the regulation of the growth process of ray-finned fish have been discovered. In a transcriptome study focusing on muscle growth in mandarin fish (Siniperca chuatsi), differentially expressed genes (DEGs) associated with GH-IGF pathways, protein synthesis, ribosome synthesis, and energy metabolism were found to be expressed at significantly higher levels in larger individuals compared to smaller ones [7]. Another study examined muscle and liver tissues of black carps (Mylopharyngodon piceus) from the same batch with different growth rates, using RNA-seq to evaluate their growth traits. The findings revealed significant up-regulation of growth-related pathways, including the FoxO signaling pathway, p53 signaling pathway, PI3K-Akt signaling pathway, and insulin signaling pathway, in black carps with fast growth rates [22]. Although the present study serves as a foundation for investigating the growth of the brown-marbled grouper, it is important to note that variations in species or environmental factors may yield different results.
In this study, we employed RNA-seq to analyze the muscle transcriptome of fast- and slow-growing brown-marbled grouper in a half-sibling family, aiming to identify important candidate genes associated with brown-marbled grouper growth. The results of this study contribute to a better understanding of the mechanisms underlying growth regulation in brown-marbled grouper.

2. Methods

2.1. Ethics Statement

All experiments conducted in the present study were approved by the Animal Care and Use Committee of the School of Life Sciences, Sun Yet-Sen University.

2.2. Experimental Fish and Sample Collection

The brown-marbled groupers were collected from Hainan Chenhai Aquaculture Co., Ltd., a half-sibling family constructed through single-sire and multiple-dam mating. To mitigate the potential impact of gender-related growth differences, we conducted sampling of the brown-marbled groupers after one year of aquaculture cultivation, before the occurrence of sexual reversal. A total of 300 fish were randomly selected and measured for their body length and weight. Subsequently, three fish with the fastest growth rate (average body length: 37.7 ± 0.62 cm; average body weight: 1071.7 ± 5.79 g) and three fish with the slowest growth rate (average body length: 29.7 ± 0.94 cm; average body weight: 393.3 ± 30.35 g) were chosen. The two extreme groups were anesthetized with tricaine eugenol for subsequent sampling, and white muscle was collected from the base of the dorsal fin of the brown-marbled groupers. Precautions were taken to prevent contamination of the muscle tissues with blood vessels and nerve tissues during the sampling process. The tissues were rapidly frozen in liquid nitrogen and stored at −80 °C.

2.3. RNA Extraction and Library Construction

Total RNA from each sample of 6 fish was extracted using the Trizol Reagent (Invitrogen, City of Carlsbad, CA, USA). RNA integrity was assessed using 1% agarose gel electrophoresis after removing the genomic DNA with DNase I (TaKaRa, Dalian, China). RNA quality and quantity were confirmed using the Agilent 2100 Bioanalyzer (Agilent Technologies, City of Santa Clara, CA, USA) and ND-2000 platform (NanoDrop Technologies, City of Wilmington, DE, USA). mRNA was isolated using oligo (dT) beads and subjected to reverse transcription and fragmentation. Double-stranded cDNA was synthesized using random hexamer primers. Agarose gel electrophoresis was used to select and purify 300–400 bp cDNA fragments, which were then subjected to end repair, “A” base addition, and adapter connection. Library sequencing was performed after amplification with 15 PCR cycles. The cDNA libraries were sequenced on the Illumina HiSeq 2000 platform with a strategy of paired-end 150 bp (PE 150).

2.4. DEG Analysis and GSEA Analysis

Quality filtering was performed using Fastp v0.20.1 [23], and the default settings were used to remove residual adapter sequences from the reads. Illumina-specific adapters were clipped from the reads, and bases with a Phred score less than 20 at the leading and trailing ends were eliminated. Only read pairs with both pairs longer than 36 bp were retained after filtering. The high-quality clean reads were mapped to the brown-marbled grouper genome [24] using HISAT2 v2.1.0 [25]. The uniquely mapped paired-end reads were counted and assigned to genes using FeatureCounts [26] in the Subread package v1.5.0, available on SourceForge. Differential expression analysis was conducted between the fast- and slow-growing brown-marbled grouper samples using the edgeR package (http://www.r-project.org/, accessed on 2 June 2023) [27]. Genes with an absolute log2 fold change (|log2FC|) greater than 1 and a false discovery rate (FDR) less than 0.05 were considered significant differential genes.
The DEGs were compared with the GO database (http://www.geneontology.org/, accessed on 2 June 2023) to obtain a list and count of transcripts associated with GO functions. Hypergeometric tests were conducted to identify significantly enriched GO entries, as compared to the entire set of transcripts. Similarly, the DEGs were compared with the KEGG database, and hypergeometric tests were used to identify significantly enriched KEGG pathways [28]. The analysis of GO terms and pathways can provide insight into the biological functions of transcripts. All expression data were statistically analyzed and visualized using R (version 4.1.3, https://www.r-project.org, accessed on 2 June 2023).
Gene set enrichment analysis (GSEA) was utilized to determine whether a pre-defined set of genes exhibited statistically significant, concordant differences between the two biological states. The expressed genes were analyzed using GSEA software version 4.1.0 [29] to identify pathways that contributed to differences in growth. The GSEA analysis was conducted according to |NES| > 1, NOM p-val < 0.05 and FDR q-val < 0.25 [29].

2.5. Real-Time PCR

To validate the transcriptome data, 25 DEGs were selected as verification genes to confirm the reliability of the RNA-seq results. The expression levels of the selected growth-related genes were verified using real-time PCR (RT-PCR) analysis with three replicates for each RNA sample. Specific primers were designed for each gene using Primer Premier 6.0 (Table S1) [30]. The beta-actin gene was used as an internal control. Each reaction system had a total volume of 10 μL, containing 100 ng of cDNA, 0.5 μL of each primer (10 μM), and 5 μL of Power SYBR Green PCR MasterMix (Vazyme Biotech, Nanjing, China), and the remaining volume was filled with double-distilled water to reach 10 μL (Vazyme Biotech, Nanjing, China). A quantitative fluorescence PCR assay was performed using the LightCyclerR® 480 II Real-Time PCR System (Roche, City of South San Francisco, CA, USA) and 384-well plates with Power SYBR Green PCR MasterMix (Vazyme Biotech, Nanjing, China). Each reaction was repeated three times, and the relative expression levels were calculated using the 2−ΔΔCT method.

2.6. Statistical Analyses

Data analysis was performed using R version 4.1.3, and the results were presented as means ± SEM. The body weights of fish in the fast-growing and slow-growing groups were compared using Student’s t-test, with the significance level set at p < 0.05.

3. Result

3.1. Next-Generation Sequencing and Mapping to the Reference Genome

In total, transcriptome sequencing of the six samples produced a total of 361,058,385 high-quality clean reads (Table S2). All the clean reads were mapped to the brown-marbled grouper genome using HISAT2 and then compressed and sorted using the SAMtools software. More than 97% of the data from the six samples were mapped to the reference genome.

3.2. Identification of Differentially Expressed Genes

A total of 77 significantly up-regulated genes and 92 significantly down-regulated genes were identified between the fast- and slow-growing groups. In the process of the identification of differential genes, many functional genes related to growth were recognized, such as myh1, myh4, tnni2, gtr12, fmo5, ttc9a, and ghr. Based on the results of the analysis, a volcano plot was drawn using R and important genes associated with growth were marked (Figure 1).

3.3. Enrichment Analysis of GO and KEGG Pathways on the Basis of DEGs

GO annotation was used to categorize the 110 DEGs into biological process, cell composition, and molecular function groups, which were further classified into 27 functional categories across 3 ontologies. The cellular component, molecular function, and biological process groups contained nine functional categories each (Figure S1). Notably, significant changes were observed in the myosin complex, cytoskeleton, actin cytoskeleton (cellular component), and motor activity (molecular function) based on the results of the GO analysis (Figure S2).
After excluding pathways associated with human disease, KEGG pathway analysis identified 125 DEGs enriched in various metabolic and organismal system pathways. The major pathways related to growth included steroid biosynthesis (ko00100), glycolysis/gluconeogenesis (ko00010), carbon metabolism (ko01200), the biosynthesis of amino acids (ko01230), MAPK signaling pathway (ko04010), the PI3K-Akt signaling pathway (ko04151), and the glucagon signaling pathway (ko04922) (Figure 2 and Table 1). These significant pathway enrichments provide insight into the molecular mechanisms underlying growth-related traits in the brown-marbled grouper.

3.4. GSEA Analysis

A total of 14 gene sets were found that were significantly enriched for differentially expressed genes in the fast-growing group, as compared with the slow-growing group. The full list of significantly enriched gene sets can be found in Table 2. Of these, seven gene sets were significantly up-regulated and seven gene sets were down-regulated in the fast-growing group. The important pathways which were related to growth and enriched in the fast- and slow-growing groups are shown in Figure 3.
Compared with the slow-growing group, the gene set involved in the development of skeletal muscle (myogenesis), activation of the PI3K/AKT/mTOR pathway (PI3K-Akt-mTOR signaling), and mitotic spindle assembly (mitotic spindle) was significantly up-regulated in the fast-growing group. Compared with the slow-growing group, the gene set encoding proteins involved in oxidative phosphorylation (oxidative phosphorylation), activation of the mTORC1 complex (mTORC1 signaling), and encoding of the cell-cycle-related targets of E2F transcription factors (E2F targets) was significantly up-regulated in the fast-growing group. The heat map of the top 50 features for each phenotype in the GSEA_data is shown in Figure S3.

3.5. qPCR

To verify the accuracy of the RNA-seq data, we screened 25 DEGs involved in muscle contraction (myh1, myh4), growth hormone receptor (ghr), Troponin I, fast skeletal muscle (tnni2), facilitated glucose transporter member 12 (gtr12), skeletal development (ttc9a), glycolysis (pdp1), the citric acid cycle (sdhb), beta-1,3-galactosyltransferase 2 (b3gt2), immunity (mxra5), and other processes (Figure S4). The gene expression changes obtained via qPCR were remarkably consistent with those obtained from the RNA-seq results, with R2 values of 0.9043 (Figure 4). Therefore, the qPCR results confirmed the reliability and accuracy of the RNA-seq data.

4. Discussion

Mammalian muscle growth is influenced by various factors, including metabolic, behavioral, and reproductive activities [31]. Similarly, ray-finned fish muscle growth is a complex process, which is controlled by genes, environment, diet, and other factors [32]. We conducted this study after finding that fast- and slow-growing brown-marbled grouper developed differently during cultivation. This study is the first to report on the difference in the brown-marbled grouper muscle transcriptome between fast- and slow-growing groups. Using the resulting muscle transcriptome sequences, we obtained the DEGs of the fast- and slow-growing brown-marbled grouper which potentially influence muscle growth. GO, KEGG, and GSEA analyses were used to explore closely related signaling pathways that may contribute to growth differences.
In our experiments, we found that the genes ghr and tnni2 were significantly up-regulated in the fast-growing group compared with the slow-growing group. ghr (the growth hormone receptor) is a cell membrane receptor that specifically binds to growth hormone (GH). Upon binding, ghr initiates a phosphorylation cascade that regulates signaling and gene expression, ultimately regulating various physiological functions such as growth, metabolism, appetite, reproduction, and social behavior [33,34]. Based on a study of Rohu (Labeo rohita), it was found that several molecules have agonistic effects on the growth hormone receptor protein, and these molecules can be utilized to enhance ray-finned fish growth [35]. tnni2 is a member of the troponin I gene family that encodes a fast-twitch skeletal muscle protein. It is a constituent of the troponin complex, which comprises troponin T, troponin C, and troponin I subunits [36]. This complex, along with tropomyosin, plays a crucial role in regulating striated muscle contraction in a calcium-dependent manner. Studies on mice showed that tnni2 knock-out affects bone development in mice, leading to exhibit growth retardation more frequently than wild-type mice [37]. stc2 (mammalian stanniocalcin-2) is a secreted polypeptide which prevents the PAPP-A cleavage of insulin-like growth factor-binding protein (IGFBP)-4 and, hence, its release within tissues of bioactive IGF, required for normal growth [38]. stc2 showed down-regulated expression in the fast-growing group, which indicated that low levels of expression were more beneficial for grouper growth. Furthermore, among the differentially expressed genes, we also identified several genes that may be associated with the growth of brown-marbled grouper, such as pdp1, g6pi, and s27a2. pdp1, a transcription factor found in the muscle activator region, has been shown to play a role in regulating the expression of the tropomyosin I gene in somatic wall and pharyngeal muscles. This is achieved through binding to specific DNA sequences within the muscle activator that are essential for activation function [39]. g6pi is involved in glycolysis [40], and s27a2 is involved in fatty acid synthesis [41]. Further research is needed to validate the associations between these genes and muscle growth in brown-marbled grouper.
Both the GO and KEGG enrichment analyses revealed that the DEGs were enriched in muscle growth synthesis and in protein and carbohydrate metabolism pathways. In general, muscle growth is associated with increased protein anabolism and glycogen anabolism [42]. Although the GH/IGF-axis-related pathway was not observed in the differential gene enrichment results, its downstream-related phosphonate and phosphinate metabolism, glucose metabolism, carbon metabolism, and amino acid biosynthesis pathways were observed in the KEGG pathway enrichment analysis. In the Hulong grouper (E. fuscogutatus ♀ × E. lanceolatus ♂), the growth advantage appears to be associated with the up-regulation of DEGs related to the GH/IGF axis and its downstream signaling pathways, which enhance protein and glycogen synthesis [4].
In this study, the KEGG pathway enrichment analysis revealed that certain DEGs were enriched in the PI3K-Akt signaling pathway. Furthermore, the GSEA analysis indicated a significant up-regulation of the PI3K/AKT/mTOR pathway in the fast-growing group compared to the slow-growing group. Prior studies have established that the core components of the PI3K/AKT/mTOR signaling pathway include phosphatidylinositol 3-kinase (PI3Ks) and its downstream targets, Protein Kinase B (AKT) and the mammalian target of rapamycin (mTOR) [31]. mTOR is a downstream effector protein of the PI3K protein kinase family signaling pathway. In mammals, it is present in two separate complexes, mTORC1 and mTORC2, which are differentiated by their respective accessory proteins, raptor and rictor [43].
mTORC1 is a key regulator of nutrient storage that promotes the synthesis of enzymes involved in glucose biosynthesis, as well as the production of proteins, lipids, nucleotides, and ATP, thereby facilitating muscle growth [44,45]. Additionally, the PI3K/AKT/mTOR cascade serves as a crucial effector of insulin signaling and plays a major role in inducing glycolysis and lipogenesis while simultaneously repressing glycogenolysis [46]. Activation of the PI3K/AKT/mTOR pathway through feeding can stimulate mRNA translation and protein synthesis while also regulating a variety of processes, such as nutrient absorption, cell growth, proliferation, and anabolism [44,47]. Insulin binds to cell surface receptors to activate the PI3K-AKT pathway via IRS1. AKT promotes glucose absorption and activates mTORC1 activity through the AKT-TSC1/2-RheB-mTORC1 pathway [48]. In a recent study involving crucian carp, the heterozygous deletion of pik3r1 was utilized to enhance PI3K/AKT/mTOR signaling pathway activity, leading to improvements in somatic cell growth and food utilization efficiency [49]. Some studies have also shown that the swimming training of zebrafish (Danio rerio) can increase the activation and expression of the mTOR pathway in the skeletal muscle and then increase the growth of zebrafish skeletal muscle [50]. Other studies have shown that amino acid supplementation in mice can stimulate the mTOR pathway to optimize muscle synthesis after feeding but does not increase the speed of protein synthesis [51]. Juvenile rainbow trout can regulate the activation of the mTOR pathway through feeding, subsequently enhancing protein synthesis [52,53]. Therefore, compared with the slow-growing group, the PI3K/AKT/mTOR pathway of the fast-growing group was significantly up-regulated, possibly indicating that the expression of this signaling pathway led to a higher nutrient absorption efficiency in the fast-growing group. In the growth process of grouper, nutrition, digestion, and absorption play important roles. Higher nutrition utilization is beneficial for the growth of muscle and the increase in the body length and weight of ray-finned fish.

5. Conclusions

In the present experiments, RNA samples from brown-marbled grouper muscle were subjected to RNA-seq analysis, and many DEGs were obtained. Genes such as myh1, myh4, ghr, gtr12, and tnni2 were up-regulated in the fast-growing fish, while sct2, pdp1, mxra5, and others were down-regulated, which might promote muscle growth. GO and KEGG enrichment analyses revealed significant differential gene involvement in steroid biosynthesis, carbohydrate metabolism, glycolysis, gluconeogenesis, skeletal system development, motor activity, and cellular macromolecule biosynthetic processes. The GSEA analysis results showed that the PI3K/AKT/mTOR pathway was significantly up-regulated in the fast-growing group, indicating that fast-growing groupers have higher nutritional utilization. The transcriptome sequences generated in this study have great potential to facilitate gene function analysis and molecular marker development based on gene sequence data, accelerating research progress. Furthermore, the availability of sample data can enable more in-depth investigation of the growth and other traits of brown-marbled grouper.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes8060309/s1, Table S1: The specific primers for RT-PCR; Table S2: Summary of transcriptome data generated from the brown-marbled grouper samples; Figure S1: GO assignment of the differentially expressed genes; Figure S2: Bar plots of down-regulated genes in fast-growing grouper of GO functional enrichment analyses; Figure S3: Bar plots of up-regulated genes in fast-growing grouper of GO functional enrichment analyses; Figure S4: A: Bar plot of up-regulated genes in the fast-growing group based on KEGG enrichment analyses. B: Bar plot of down-regulated genes in the fast-growing group based on KEGG enrichment analyses; Figure S5: Heat map of the top 50 features for each phenotype in the GSEA data; Figure S6: L represent the slow-growing group; H represent the fast-growing group.

Author Contributions

T.W. and Y.Y. conceived and designed the study. L.Z. and Y.T. were responsible for the fish rearing and sampling. C.Z. and L.S. performed the RNA-seq and qPCR analyses, respectively, inferring the biological significance of the DE genes. T.W., S.G. and X.W. analyzed the data and wrote the manuscript. X.L. made modifications to the article and provided guidance for the experiment. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (U22A20531, 32273132), China Agriculture Research System of MOF and MARA (CARS-47), National Key Research and Development Program (2018YFD0900203), and Project of Hainan Academician Team Innovation Center (YSPTZX202122).

Institutional Review Board Statement

The Animal Care and Use Committee in School of Life Sciences, Sun Yet-Sen University SYSU-IACUC-2022-B0131.

Data Availability Statement

The raw sequence reads were deposited in NCBI’s Short Read Archive (SRR19851482-SRR19851487) under the BioProject accession number PRJNA850911.

Conflicts of Interest

The authors declare that they have no competing interest.

References

  1. Mukai, Y.; Seng Lim, L. Morphogenesis of free neuromasts in the larvae of brown-marbled grouper Epinephelus fuscoguttatus. Mar. Freshw. Behav. Physiol. 2016, 49, 159–171. [Google Scholar] [CrossRef]
  2. Liu, G.; Gong, Z.; Li, Q. The complete mitochondrial genome of the breed Ningbo brown-marbled grouper (Epinephelus fuscoguttatus). Mitochondrial DNA B Resour. 2019, 4, 2109–2110. [Google Scholar] [CrossRef] [Green Version]
  3. Amenyogbe, E.; Chen, G.; Wang, Z. Identification, characterization, and expressions profile analysis of growth hormone receptors (GHR1 and GHR2) in Hybrid grouper (Epinephelus fuscoguttatus ♀ × Epinephelus polyphekadion ♂). Genomics 2020, 112, 1–9. [Google Scholar] [CrossRef]
  4. Sun, Y.; Guo, C.Y.; Wang, D.D.; Li, X.F.; Xiao, L.; Zhang, X.; You, X.; Shi, Q.; Hu, G.J.; Fang, C.; et al. Transcriptome analysis reveals the molecular mechanisms underlying growth superiority in a novel grouper hybrid (Epinephelus fuscogutatus ♀ × E. lanceolatus ♂). BMC Genet. 2016, 17, 24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Zhu, K.; Zhang, D.; Wei, J.; Huang, G.; Guo, Y.; Jiang, S.; Yu, D. The complete mitochondrial genome of the hybrid grouper Epinephelus fuscoguttatus (♀) × Epinephelus lanceolatus (♂). Mitochondrial DNA A DNA Mapp. Seq. Anal. 2016, 27, 1968–1969. [Google Scholar] [CrossRef]
  6. Yang, Y.; Wu, L.; Wu, X.; Li, B.; Huang, W.; Weng, Z.; Lin, Z.; Song, L.; Guo, Y.; Meng, Z.; et al. Identification of Candidate Growth-Related SNPs and Genes Using GWAS in Brown-Marbled Grouper (Epinephelus fuscoguttatus). Mar. Biotechnol. 2020, 22, 153–166. [Google Scholar] [CrossRef]
  7. Liu, X.; Zeng, S.; Liu, S.; Wang, G.; Lai, H.; Zhao, X.; Bi, S.; Guo, D.; Chen, X.; Yi, H.; et al. Identifying the Related Genes of Muscle Growth and Exploring the Functions by Compensatory Growth in Mandarin Fish (Siniperca chuatsi). Front. Physiol. 2020, 11, 553563. [Google Scholar] [CrossRef] [PubMed]
  8. Fuentes, E.N.; Bjornsson, B.T.; Valdes, J.A.; Einarsdottir, I.E.; Lorca, B.; Alvarez, M.; Molina, A. IGF-I/PI3K/Akt and IGF-I/MAPK/ERK pathways in vivo in skeletal muscle are regulated by nutrition and contribute to somatic growth in the fine flounder. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2011, 300, R1532–R1542. [Google Scholar] [CrossRef]
  9. Johnston, I.A.; Bower, N.I.; Macqueen, D.J. Growth and the regulation of myotomal muscle mass in teleost fish. J. Exp. Biol. 2011, 214, 1617–1628. [Google Scholar] [CrossRef] [Green Version]
  10. Johnston, I.A. Environment and plasticity of myogenesis in teleost fish. J. Exp. Biol. 2006, 209, 2249–2264. [Google Scholar] [CrossRef] [Green Version]
  11. Rehfeldt, C.; Te Pas, M.F.; Wimmers, K.; Brameld, J.M.; Nissen, P.M.; Berri, C.; Valente, L.M.; Power, D.M.; Picard, B.; Stickland, N.C.; et al. Advances in research on the prenatal development of skeletal muscle in animals in relation to the quality of muscle-based food. II--Genetic factors related to animal performance and advances in methodology. Animal 2011, 5, 718–730. [Google Scholar] [CrossRef] [Green Version]
  12. Weatherley, A.H.; Gill, H.S.; Lobo, A.F. Recruitment and maximal diameter of axial muscle fibres in teleosts and their relationship to somatic growth and ultimate size. J. Fish Biol. 1988, 33, 851–859. [Google Scholar] [CrossRef]
  13. Wood, A.W.; Duan, C.; Bern, H.A. Insulin-like growth factor signaling in fish. Int. Rev. Cytol. 2005, 243, 215–285. [Google Scholar] [CrossRef]
  14. Laviola, L.; Natalicchio, A.; Giorgino, F. The IGF-I signaling pathway. Curr. Pharm. Des. 2007, 13, 663–669. [Google Scholar] [CrossRef] [PubMed]
  15. Fuentes, E.N.; Valdes, J.A.; Molina, A.; Bjornsson, B.T. Regulation of skeletal muscle growth in fish by the growth hormone--insulin-like growth factor system. Gen. Comp. Endocrinol. 2013, 192, 136–148. [Google Scholar] [CrossRef] [PubMed]
  16. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef]
  17. Liu, S.; Wang, X.; Sun, F.; Zhang, J.; Feng, J.; Liu, H.; Rajendran, K.V.; Sun, L.; Zhang, Y.; Jiang, Y.; et al. RNA-Seq reveals expression signatures of genes involved in oxygen transport, protein synthesis, folding, and degradation in response to heat stress in catfish. Physiol. Genom. 2013, 45, 462–476. [Google Scholar] [CrossRef] [Green Version]
  18. Sun, F.; Peatman, E.; Li, C.; Liu, S.; Jiang, Y.; Zhou, Z.; Liu, Z. Transcriptomic signatures of attachment, NF-kappaB suppression and IFN stimulation in the catfish gill following columnaris bacterial infection. Dev. Comp. Immunol. 2012, 38, 169–180. [Google Scholar] [CrossRef]
  19. Garcia de la Serrana, D.; Devlin, R.H.; Johnston, I.A. RNAseq analysis of fast skeletal muscle in restriction-fed transgenic coho salmon (Oncorhynchus kisutch): An experimental model uncoupling the growth hormone and nutritional signals regulating growth. BMC Genom. 2015, 16, 564. [Google Scholar] [CrossRef] [Green Version]
  20. Li, R.; Zhang, R.; Yi, J.; Guo, W.; Cheng, Q.; Zhi, L.; Lin, Y. Characterization and expression profiles of muscle transcriptome in Schizothoracine fish, Schizothorax prenanti. Gene 2019, 685, 156–163. [Google Scholar] [CrossRef]
  21. Li, S.; Liu, H.; Bai, J.; Zhu, X. Transcriptome assembly and identification of genes and SNPs associated with growth traits in largemouth bass (Micropterus salmoides). Genetica 2017, 145, 175–187. [Google Scholar] [CrossRef] [PubMed]
  22. Zhang, J.; Shen, Y.; Xu, X.; Dai, Y.; Li, J. Transcriptome Analysis of the Liver and Muscle Tissues of Black Carp (Mylopharyngodon piceus) of Different Growth Rates. Mar. Biotechnol. 2020, 22, 706–716. [Google Scholar] [CrossRef]
  23. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  24. Yang, Y.; Wang, T.; Chen, J.; Wu, L.; Wu, X.; Zhang, W.; Luo, J.; Xia, J.; Meng, Z.; Liu, X. Whole-genome sequencing of brown-marbled grouper (Epinephelus fuscoguttatus) provides insights into adaptive evolution and growth differences. Mol. Ecol. Resour. 2021, 22, 711–723. [Google Scholar] [CrossRef]
  25. Kim, D.; Paggi, J.M.; Park, C.; Bennett, C.; Salzberg, S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019, 37, 907–915. [Google Scholar] [CrossRef]
  26. Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef] [Green Version]
  27. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Kanehisa, M.; Araki, M.; Goto, S.; Hattori, M.; Hirakawa, M.; Itoh, M.; Katayama, T.; Kawashima, S.; Okuda, S.; Tokimatsu, T.; et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, 36, D480–D484. [Google Scholar] [CrossRef]
  29. Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550. [Google Scholar] [CrossRef] [Green Version]
  30. Ye, J.; Coulouris, G.; Zaretskaya, I.; Cutcutache, I.; Rozen, S.; Madden, T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 2012, 13, 134. [Google Scholar] [CrossRef] [Green Version]
  31. Saxton, R.A.; Sabatini, D.M. mTOR Signaling in Growth, Metabolism, and Disease. Cell 2017, 168, 960–976. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Dutta, H. Growth in fishes. Gerontology 1994, 40, 97–112. [Google Scholar] [CrossRef] [PubMed]
  33. Zhu, T.; Goh, E.L.K.; Graichen, R.; Ling, L.; Lobie, P.E. Signal transduction via the growth hormone receptor. Cell. Signal. 2001, 13, 599–616. [Google Scholar] [CrossRef] [PubMed]
  34. Behncken, S.N.; Waters, M.J. Molecular recognition events involved in the activation of the growth hormone receptor by growth hormone. J. Mol. Recognit. 1999, 12, 355–362. [Google Scholar] [CrossRef]
  35. Dhandare, B.C.; Rather, M.A.; Bhosale, B.P.; Pawar, R.; Guttula, P.K.; Pagarkar, A.U. Molecular modeling, docking and dynamic simulations of growth hormone receptor (GHR) of Labeo rohita. J. Biomol. Struct. Dyn. 2022, 40, 3024–3037. [Google Scholar] [CrossRef]
  36. Mullen, A.J.; Barton, P.J.R. Structural characterization of the human fast skeletal muscle troponin I gene (TNNI2). Gene 2000, 242, 313–320. [Google Scholar] [CrossRef]
  37. Zhu, X.; Wang, F.; Zhao, Y.; Yang, P.; Chen, J.; Sun, H.; Liu, L.; Li, W.; Pan, L.; Guo, Y.; et al. A gain-of-function mutation in Tnni2 impeded bone development through increasing Hif3a expression in DA2B mice. PLoS Genet. 2014, 10, e1004589. [Google Scholar] [CrossRef] [Green Version]
  38. Jepsen, M.R.; Kloverpris, S.; Mikkelsen, J.H.; Pedersen, J.H.; Fuchtbauer, E.M.; Laursen, L.S.; Oxvig, C. Stanniocalcin-2 inhibits mammalian growth by proteolytic inhibition of the insulin-like growth factor axis. J. Biol. Chem. 2015, 290, 3430–3439. [Google Scholar] [CrossRef] [Green Version]
  39. Lin, S.C.; Lin, M.H.; Horvath, P.; Reddy, K.L.; Storti, R.V. PDP1, a novel Drosophila PAR domain bZIP transcription factor expressed in developing mesoderm, endoderm and ectoderm, is a transcriptional regulator of somatic muscle genes. Development 1997, 124, 4685–4696. [Google Scholar] [CrossRef]
  40. Yang, W.J.; Wu, Y.B.; Chen, L.; Xu, K.K.; Xie, Y.F.; Wang, J.J. Two Chitin Biosynthesis Pathway Genes in Bactrocera dorsalis (Diptera: Tephritidae): Molecular Characteristics, Expression Patterns, and Roles in Larval-Pupal Transition. J. Econ. Entomol. 2015, 108, 2433–2442. [Google Scholar] [CrossRef]
  41. Ignatz, E.H.; Hori, T.S.; Kumar, S.; Benfey, T.J.; Braden, L.M.; Runighan, C.D.; Westcott, J.D.; Rise, M.L. RNA-Seq Analysis of the Growth Hormone Transgenic Female Triploid Atlantic Salmon (Salmo salar) Hepatic Transcriptome Reveals Broad Temperature-Mediated Effects on Metabolism and Other Biological Processes. Front. Genet. 2022, 13, 852165. [Google Scholar] [CrossRef] [PubMed]
  42. Valente, L.M.P.; Moutou, K.A.; Conceição, L.E.C.; Engrola, S.; Fernandes, J.M.O.; Johnston, I.A. What determines growth potential and juvenile quality of farmed fish species? Rev. Aquac. 2013, 5, S168–S193. [Google Scholar] [CrossRef] [Green Version]
  43. Laplante, M.; Sabatini, D.M. mTOR signaling in growth control and disease. Cell 2012, 149, 274–293. [Google Scholar] [CrossRef] [Green Version]
  44. Liu, G.Y.; Sabatini, D.M. mTOR at the nexus of nutrition, growth, ageing and disease. Nat. Rev. Mol. Cell. Biol. 2020, 21, 183–203. [Google Scholar] [CrossRef] [PubMed]
  45. Schiaffino, S.; Dyar, K.A.; Ciciliot, S.; Blaauw, B.; Sandri, M. Mechanisms regulating skeletal muscle growth and atrophy. FEBS J. 2013, 280, 4294–4314. [Google Scholar] [CrossRef]
  46. Ribback, S.; Cigliano, A.; Kroeger, N.; Pilo, M.G.; Terracciano, L.; Burchardt, M.; Bannasch, P.; Calvisi, D.F.; Dombrowski, F. PI3K/AKT/mTOR pathway plays a major pathogenetic role in glycogen accumulation and tumor development in renal distal tubules of rats and men. Oncotarget 2015, 6, 13036–13048. [Google Scholar] [CrossRef] [Green Version]
  47. Hietakangas, V.; Cohen, S.M. Regulation of tissue growth through nutrient sensing. Annu. Rev. Genet. 2009, 43, 389–410. [Google Scholar] [CrossRef]
  48. Menon, S.; Dibble, C.C.; Talbott, G.; Hoxhaj, G.; Valvezan, A.J.; Takahashi, H.; Cantley, L.C.; Manning, B.D. Spatial control of the TSC complex integrates insulin and nutrient regulation of mTORC1 at the lysosome. Cell 2014, 156, 771–785. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Huang, J.; Shi, C.; Gao, Y.; Su, J.; Shu, Y.; Zeng, N.; Zhai, G.; Jin, X.; Han, D.; He, J.; et al. Heterozygous depletion of pik3r1 improves growth and feed conversion efficiency in Gibel carp (Carassius gibelio). Aquaculture 2021, 545, 737207. [Google Scholar] [CrossRef]
  50. Rovira, M.; Arrey, G.; Planas, J.V. Exercise-Induced Hypertrophic and Oxidative Signaling Pathways and Myokine Expression in Fast Muscle of Adult Zebrafish. Front. Physiol. 2017, 8, 1063. [Google Scholar] [CrossRef] [Green Version]
  51. Norton, L.E.; Layman, D.K.; Bunpo, P.; Anthony, T.G.; Brana, D.V.; Garlick, P.J. The leucine content of a complete meal directs peak activation but not duration of skeletal muscle protein synthesis and mammalian target of rapamycin signaling in rats. J. Nutr. 2009, 139, 1103–1109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Lansard, M.; Panserat, S.; Seiliez, I.; Polakof, S.; Plagnes-Juan, E.; Geurden, I.; Medale, F.; Kaushik, S.; Corraze, G.; Skiba-Cassy, S. Hepatic protein kinase B (Akt)-target of rapamycin (TOR)-signalling pathways and intermediary metabolism in rainbow trout (Oncorhynchus mykiss) are not significantly affected by feeding plant-based diets. Br. J. Nutr. 2009, 102, 1564–1573. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Seiliez, I.; Gabillard, J.C.; Skiba-Cassy, S.; Garcia-Serrana, D.; Gutierrez, J.; Kaushik, S.; Panserat, S.; Tesseraud, S. An in vivo and in vitro assessment of TOR signaling cascade in rainbow trout (Oncorhynchus mykiss). Am. J. Physiol. Regul. Integr. Comp. Physiol. 2008, 295, R329–R335. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Fishes 08 00309 g001 550
Figure 1. Volcano plot showing the relationship between the magnitude of gene expression change (log2 of fold change) on the x-axis and the statistical significance of this change (log10 of false discovery rate (FDR)) on the y-axis in a comparison between the fast-growing and slow-growing groups. Colored points represent differentially expressed genes (cutoff FDR 0.05) that were either up-regulated (red) or down-regulated (blue) in the fast-growing group compared with the slow-growing group. In the volcano plot, important genes associated with growth are marked, and the size of the points represents the value of gene expression change (log2 of fold change).
Figure 1. Volcano plot showing the relationship between the magnitude of gene expression change (log2 of fold change) on the x-axis and the statistical significance of this change (log10 of false discovery rate (FDR)) on the y-axis in a comparison between the fast-growing and slow-growing groups. Colored points represent differentially expressed genes (cutoff FDR 0.05) that were either up-regulated (red) or down-regulated (blue) in the fast-growing group compared with the slow-growing group. In the volcano plot, important genes associated with growth are marked, and the size of the points represents the value of gene expression change (log2 of fold change).
Fishes 08 00309 g001
Fishes 08 00309 g002 550
Figure 2. The top 20 pathways enriched among the 106 differentially expressed genes identified via KEGG after eliminating pathways associated with human disease. Circle 1: The top 20 pathways in the enrichment analysis, where the outside circle is the coordinate scale for the number of DEGs. Different colors represent different classes. Circle 2: The number of genes in the background category and their corresponding p-values are shown. Longer bars represent a higher number of genes, while a smaller p-value is indicated by a more intense red color. Circle 3: The number of DEGs in the pathway. Circle 4: The Rich_Factor value of each pathway.
Figure 2. The top 20 pathways enriched among the 106 differentially expressed genes identified via KEGG after eliminating pathways associated with human disease. Circle 1: The top 20 pathways in the enrichment analysis, where the outside circle is the coordinate scale for the number of DEGs. Different colors represent different classes. Circle 2: The number of genes in the background category and their corresponding p-values are shown. Longer bars represent a higher number of genes, while a smaller p-value is indicated by a more intense red color. Circle 3: The number of DEGs in the pathway. Circle 4: The Rich_Factor value of each pathway.
Fishes 08 00309 g002
Fishes 08 00309 g003 550
Figure 3. Gene set enrichment analysis (GSEA) enrichment plots of representative gene sets from Table 2: myogenesis (A), PI3K-Akt-mTOR signaling (B), mitotic spindle (C), oxidative phosphorylation (D), mTORC1 signaling (E), E2F targets (F). NES, normalized enrichment score; NOM p-val, the statistical significance of the enrichment score; FDR q-val, false discovery rate. Positive and negative NES indicate higher and lower expression in the fast-growing group, respectively.
Figure 3. Gene set enrichment analysis (GSEA) enrichment plots of representative gene sets from Table 2: myogenesis (A), PI3K-Akt-mTOR signaling (B), mitotic spindle (C), oxidative phosphorylation (D), mTORC1 signaling (E), E2F targets (F). NES, normalized enrichment score; NOM p-val, the statistical significance of the enrichment score; FDR q-val, false discovery rate. Positive and negative NES indicate higher and lower expression in the fast-growing group, respectively.
Fishes 08 00309 g003
Fishes 08 00309 g004 550
Figure 4. Comparison of gene expression changes measured via RNA-seq and qPCR. The log2 (fold change) values from the qPCR results are plotted on the x-axis, while those from the RNA-seq results are plotted on the y-axis. The correlation between the two methods is shown.
Figure 4. Comparison of gene expression changes measured via RNA-seq and qPCR. The log2 (fold change) values from the qPCR results are plotted on the x-axis, while those from the RNA-seq results are plotted on the y-axis. The correlation between the two methods is shown.
Fishes 08 00309 g004
Table
Table 1. The top 20 pathways enriched among the 106 differentially expressed genes identified via KEGG after eliminating pathways associated with human disease t.
Table 1. The top 20 pathways enriched among the 106 differentially expressed genes identified via KEGG after eliminating pathways associated with human disease t.
Pathway IDKEGG_B_ClassPathway
ko00440Metabolism of other amino acidsPhosphonate and phosphinate metabolism
ko00100Lipid metabolismSteroid biosynthesis
ko00010Carbohydrate metabolismGlycolysis/gluconeogenesis
ko01200Global and overview mapsCarbon metabolism
ko01230Global and overview mapsBiosynthesis of amino acids
ko01120Global and overview mapsMicrobial metabolism in diverse environments
ko03050Folding, sorting, and degradationProteasome
ko03020TranscriptionRNA polymerase
ko04066Signal transductionHIF-1 signaling pathway
ko04010Signal transductionMAPK signaling pathway
ko04151Signal transductionPI3K-Akt signaling pathway
ko04214Cell growth and deathApoptosis—fly
ko04530Cellular community—eukaryotesTight junction
ko04626Environmental adaptationPlant–pathogen interaction
ko04920Endocrine systemAdipocytokine signaling pathway
ko04657Immune systemIL-17 signaling pathway
ko04922Endocrine systemGlucagon signaling pathway
ko04612Immune systemAntigen processing and presentation
ko04923Endocrine systemRegulation of lipolysis in adipocyte
ko04915Endocrine systemEstrogen signaling pathway
Note: These pathways are ranked in ascending order, with the top 20 pathways having the lowest p-values.
Table
Table 2. Top down-regulated gene sets from the GSEA analysis.
Table 2. Top down-regulated gene sets from the GSEA analysis.
GSBiological FeaturesNumber of Genes in SetSizeES (Fast vs. Slow)NES (Fast vs. Slow)Nominal p-ValueFDR q-Value
UPSET
MYOGENESISGenes involved in the development of skeletal muscle (myogenesis).2001140.5142.189<0.001<0.001
EPITHELIAL_MESENCHYMAL_TRANSITIONGenes defining epithelial–mesenchymal transition, as in wound healing, fibrosis, and metastasis.200890.4301.782<0.0010.013
ESTROGEN_RESPONSE_EARLYGenes defining early response to estrogen.200780.4291.7310.0030.019
APICAL_JUNCTIONGenes encoding components of the apical junction complex.200910.4181.7070.0010.021
PI3K_AKT_MTOR_SIGNALINGGenes up-regulated by activation of the PI3K/AKT/mTOR pathway.105430.4351.5580.0180.068
MITOTIC_SPINDLEGenes important for mitotic spindle assembly.199990.3691.5340.0070.069
HYPOXIAGenes up-regulated in response to low oxygen levels (hypoxia).200950.3481.4360.0200.134
DOWNSET
MYC_TARGETS_V1A subgroup of genes regulated by MYC-version 1 (v1).20083−0.507−2.3270.0000.000
MYC_TARGETS_V2A subgroup of genes regulated by MYC-version 2 (v2).5828−0.627−2.2100.0000.000
UNFOLDED_PROTEIN_RESPONSEGenes up-regulated during unfolded protein response, a cellular stress response related to the endoplasmic reticulum.11359−0.416−1.7720.0000.010
OXIDATIVE_PHOSPHORYLATIONGenes encoding proteins involved in oxidative phosphorylation.200109−0.343−1.6340.0040.027
MTORC1_SIGNALINGGenes up-regulated through activation of the mTORC1 complex.20098−0.323−1.5040.0040.066
DNA_REPAIRGenes involved in DNA repair.15064−0.341−1.4820.0180.066
E2F_TARGETSGenes encoding cell-cycle-related targets of E2F transcription factors.20069−0.320−1.4140.0260.095
GS: gene set name; SIZE: number of genes in the gene set after filtering out those genes not included in the expression dataset; ES: enrichment score for the gene set; NES: normalized enrichment score; NOM p-value (<0.05): the statistical significance of the enrichment score; FDR q-value (<0.25): false discovery rate.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, T.; Yang, Y.; Gong, S.; Wu, X.; Zeng, L.; Tao, Y.; Zhong, C.; Song, L.; Liu, X. Transcriptome Analysis Reveals Differences in Gene Expression in the Muscle of the Brown-Marbled Grouper (Epinephelus fuscoguttatus) with Different Growth Rates. Fishes 2023, 8, 309. https://doi.org/10.3390/fishes8060309

AMA Style

Wang T, Yang Y, Gong S, Wu X, Zeng L, Tao Y, Zhong C, Song L, Liu X. Transcriptome Analysis Reveals Differences in Gene Expression in the Muscle of the Brown-Marbled Grouper (Epinephelus fuscoguttatus) with Different Growth Rates. Fishes. 2023; 8(6):309. https://doi.org/10.3390/fishes8060309

Chicago/Turabian Style

Wang, Tong, Yang Yang, Shirui Gong, Xi Wu, Leilei Zeng, Yuhao Tao, Chaoyue Zhong, Leling Song, and Xiaochun Liu. 2023. "Transcriptome Analysis Reveals Differences in Gene Expression in the Muscle of the Brown-Marbled Grouper (Epinephelus fuscoguttatus) with Different Growth Rates" Fishes 8, no. 6: 309. https://doi.org/10.3390/fishes8060309

APA Style

Wang, T., Yang, Y., Gong, S., Wu, X., Zeng, L., Tao, Y., Zhong, C., Song, L., & Liu, X. (2023). Transcriptome Analysis Reveals Differences in Gene Expression in the Muscle of the Brown-Marbled Grouper (Epinephelus fuscoguttatus) with Different Growth Rates. Fishes, 8(6), 309. https://doi.org/10.3390/fishes8060309

Article Metrics

Back to TopTop