https://orcid.org/0000-0001-9841-1147
Figures
Abstract
The hybrid grouper (Cromileptes altivelas, ♀ × Epinephelus lanceolatus, ♂) is an economically important aquaculture species that exhibits certain growth advantages compared to its female parent, Cromileptes altivelas. However, the current understanding of the molecular mechanisms underlying the growth of hybrid groupers is lacking. Herein, we performed full-length transcriptome sequencing and next-generation sequencing on the hybrid grouper and its parents to identify growth-related genes and comprehensively analyze the regulatory mechanism of growth heterosis in the hybrid grouper. Approximately 44.70, 40.44, and 45.32 Gb of single-molecule real-time sequencing data were generated in C. altivelas (Cal), E. lanceolatus (Ela), and the hybrid (Hyb), which were combined into 204,322 non-redundant isoforms using the PacBio sequencing platform. Differentially expressed genes (DEGs) were identified between Hyb and Cal (3,494, 2,125, and 1,487 in brain, liver, and muscle tissues, respectively) and Hyb and Ela (3,415, 2,351, and 1,675 in brain, liver, and muscle tissues, respectively). Then, 27 DEGs (13 in the brain and 14 in the muscle) related to growth traits were identified using cluster and correlation network analysis. Quantitative RT-PCR validated 15 DEGs consistent with transcriptome sequencing (RNA-seq) trends. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that these 15 genes were mainly involved in regulating the actin cytoskeleton, suggesting that this pathway plays an essential role in fish growth. In addition, we found that the phosphatase and tensin homologue (PTEN) is a key regulator of growth heterosis in Hyb. These results shed light on the regulatory mechanism of growth in the Hyb, which is important for marker-assisted selection programs to improve the growth quality of groupers.
Citation: Cao L, Ma J, Lu Y, Chen P, Hou X, Yang N, et al. (2024) Combining full-length transcriptome sequencing and next generation sequencing to provide insight into the growth superiority of the hybrid grouper (Cromileptes altivelas (♀) × Epinephelus lanceolatus (♂)). PLoS ONE 19(10): e0308802. https://doi.org/10.1371/journal.pone.0308802
Editor: Ishtiyaq Ahmad, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, INDIA
Received: July 3, 2024; Accepted: July 31, 2024; Published: October 9, 2024
Copyright: © 2024 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Raw data have submitted to GenBank database (BioProject ID: PRJNA1112737, https://dataview.ncbi.nlm.nih.gov/object/PRJNA1112737?reviewer=qa7p8lskotat189o94h7oeuue1).
Funding: The research was supported by National Natural Science Foundation of China (32160861), The Major Science and Technology plan of Hainan Province (ZDKJ2021017), Major Science and Technology Program of Yazhou Bay Innovation Institute of Hainan Tropical Ocean University (2022CXYZD001), State Key Laboratory of Developmental Biology of Freshwater Fish (2020KF001) and the Youth Project of Yazhou Bay Innovation Institute of Hainan Tropical Ocean University(2022CXYQNXM06). The funders had a role in study design, data collection and analysis.
Competing interests: The author(s) declare that they have no conflict of interest.
Introduction
Growth, a pivotal economic aspect of aquaculture, profoundly influences profitability and productivity. Its intricate regulation has been extensively investigated in various aquatic species, including Pangasianodon hypophthalmus [1], Trachinotus ovatus [2], Lateolabrax maculatus [3] and Epinephelus coioides [4]. Growth dynamics are governed by intricate signaling pathways originating from multiple tissues. Among these, the growth hormone/insulin-like growth factor (GH/IGF) system stands out as a principal promoter of growth in vertebrates [5]. Growth hormone (GH) orchestrates somatic growth by binding to GH receptors [6,7]. Structurally akin to insulin, insulin-like growth factors (IGFs), including IGF1 and IGF2, stimulate myogenic cell proliferation, differentiation, and protein synthesis [8,9]. GH/IGF system mediates its biological functions via signaling pathways such as mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK), phosphatidylinositol 3-kinase/protein kinase B/Forkhead box O (PI3K/Akt/FOXO), and JAK2-STAT5 [10–12]. Expression of growth-related genes within the GH/IGF axis predominantly occurs in brain, liver, and muscle tissues [13,14].
Body weight serves as a pivotal metric for assessing the growth rate of aquatic species, with muscle mass often constituting 50%-70% of total body weight [15]. Hence, enhancing muscle growth is crucial for augmenting the yield of cultured fish. Research underscores the indispensable role of high-quality muscle contractions in muscle growth, a process intricately intertwined with actin cytoskeleton dynamics [16,17]. Mounting evidence suggests that smooth muscle contraction necessitates actin filament polymerization and various cytoskeletal processes [18].
Groupers are esteemed for their economic significance in marine aquaculture with production volume of over 200,000 tons in 2022 [19]. However, the protogynous hermaphroditism characteristic of groupers poses challenges for attaining the F2 generation via self-crossing [20]. Hybridization emerges as a valuable strategy in grouper mariculture, yielding numerous farmed species, including Epinephelus fuscoguttatus (♀) × Epinephelus polyphekadion (♂) [21], E. awoara (♀) × E. tukula (♂) [22], E. fuscoguttatus ♀ × E. lanceolatus ♂ [23] and Cromileptes altivelas (Cal, ♀) × E. lanceolatus (Ela, ♂) [24]. C. altivelis × E. lanceolatus (Hyb) are important mariculture species in the southern coastal region of China. Previous research has demonstrated the superior growth performance of Hyb over Cal [25]. However, the mechanisms underlying the growth superiority of Hyb have not yet been reported.
Transcriptome sequencing (RNA-seq) is a high-throughput, low-cost technology that utilizes next-generation sequencing to reveal the presence and quantity of RNA in a sample at a given moment. This technique is most widely applied in analyzing the regulatory mechanisms of various important economic traits in aquatic animals [26]. Regulatory factors and mechanisms associated with growth and skin pigmentation in E. fuscoguttatus (♀) × E. lanceolatus (♂), as well as body pigmentation formation in Plectropomus leopardus, have been discovered by next-generation sequencing technology [12,27,28]. However, next-generation sequencing technology has apparent disadvantages, such as the short length of sequencing reads, unreliable assembly results, and limited accuracy of transcriptome abundance calculations [29,30]. Overcoming these drawbacks, third-generation PacBio sequencing technology yields full-length reads with uniform coverage, offering significant advantages in constructing comprehensive transcriptomes. Here, we integrate data from full-length transcriptome sequencing and RNA-seq of Hyb and its parental species for the first time, shedding light on molecular-level transcriptomic changes underlying growth heterosis in Hyb.
Materials and methods
Sample preparation and ethics statement
Three-year-old Cal, Ela and Hyb were used as experimental samples. They were cultivated under the same breeding conditions at Hainan Chenhai Aquatic Co., Ltd. in Hainan Province, China. Five individuals of each species were selected randomly. The mean body weight was 0.46±0.03 kg, 2.68±0.07 kg and 1.84±0.4 kg in Cal, Ela and Hyb, respectively. The brain, liver, and muscle tissues were dissected from each fish after euthanasia by immersion in MS-222 buffered solution (3 g/L) on ice. All experimental procedures strictly adhered to the guidelines stipulated by Administration of Affairs Concerning Animal Experimentation of China and were approved by the Institutional Review Board on Bioethics and Biosafety of BGI (FT14015).
Full-length transcriptome sequencing
Total RNA was extracted from the three tissues (brain, liver, and muscle) using a TRIzol kit (Invitrogen) according to the manufacturer’s instructions. Total RNA quality was determined using an Agilent 2100 Bioanalyzer, and RNA integrity was analyzed for subsequent experiments. The concentration of RNA in each sample was greater than 300 ng/ul, and the total amount of the RNA used for the cDNA library was greater than 5 ug. NEBNext®Single Cell/Low Input cDNA Synthesis & Amplification Module was used to synthesize full-length cDNA. The harvested cDNA was processed for end-repair/A tailing. Finally, single-molecule real-time (SMRT) hairpin adapters were ligated to the ends of double-stranded cDNA molecules. The purity, concentration, and insert size of the library were assessed to ensure library quality; qualified libraries were processed for full-length transcriptome sequencing on the PacBio sequencing platform. Full-length transcripts were obtained in three main steps: full-length sequence identification, consensus sequence clustering, and consensus sequence polishing [31]. Corrected transcript sequences were clustered using CD-HIT 4.6.7 software based on 95% similarity between the sequences [32]. The benchmarking universal single-copy ortholog (BUSCO) 3.0.2 software was employed to evaluate the completeness of the full-length transcriptome sequence [33].
Next-generation sequencing
The amount of 1 μg RNA per sample was used for RNA-seq library construction. First-strand cDNA was synthesized using fragmented mRNA as a template and random hexamers as primers, followed by second-strand synthesis with the addition of RNase H and DNA polymerase I. cDNA was purified using AMPure XP beads. Double-stranded cDNA was then subjected to end repair. Adenosine was added to the end and ligated to the adapters. AMPure XP beads were used to select fragments within a specific size range. Transcriptome sequencing was performed using the Illumina NovaSeq6000 sequencing platform. The full-length non-chimeric transcript per species was used as a reference sequence to obtain the isoform after removing redundancy with CD-HIT, and STAR software was used to compare the next-generation sequencing data with the above reference sequence. Gene expression levels were quantified using Kallisto, based on transcripts generated from circular consensus data [34]. Fragments Per Kilobase of transcript per million mapped reads (FPKM) were used as the standard method to estimate gene expression levels. Raw data have been submitted to GenBank database (BioProject ID: PRJNA1112737, https://dataview.ncbi.nlm.nih.gov/object/PRJNA1112737?reviewer=qa7p8lskotat189o94h7oeuue1).
Differential expression and cluster analysis
Differential expression analysis was performed using edgeR (version 4.0.16) based on read counts. The screening criteria of differentially expressed genes (DEGs): FDR < 0.01 and | log2(Fold Change)| ≥ 2.5 were set as the threshold. Analysis of similarities (ANOSIM) and principal coordinate analysis (PCoA) were implemented for DEGs using the vegan (2.6–4) and ape packages (5.7–1) [35]. Clustering analyses were conducted to search for DEGs with similar expression behavior using the upset R package 1.4.0 and the pheatmap v1.0.12 package. The ggplot2 R package was used to draw pictures [36].
Function and pathway analysis
All DEGs in different tissues were submitted to DAVID (https://david.ncifcrf.gov/summary.jsp) for enrichment analysis of the differential expression gene by Gene Ontology (GO) number, GO biological processes and molecular function terminology. The relationships among the DEGs were predicted using correlation network analysis. Corr. was used to calculate the correlation coefficient using the psych package (version 1.9.6). When the correlation coefficient (r) was > 0.2, P < 0.001 was considered significant and listed. P-value was inferred by adjusting the p-value using the Benjamini and Hochberg’s method [37]. The igraph v2.0.2 was used for network analysis, and Gephi v0.10.1 realized the network visualization.
Based on publicly available pathway databases, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapper (https://www.kegg.jp/kegg/mapper/) was used to identify the metabolic pathways of genes related to growth traits.
Data analysis
To verify the results of the transcriptional sequencing, the real-time quantitative PCR (qRT-PCR) was conducted on the brain, liver, and muscle tissues of Hyb and its parents (primers were listed in S1 Table). The 2−ΔΔCt method [38] was applied to determine the gene expression abundance. All other data were analyzed using SPSS17.0 software.
Results
Data analysis of full-length transcriptome sequencing and next-generation sequencing
Full-length transcriptome data for Cal, Ela, and Hyb were obtained using the PacBio Sequel platform, with 44.70Gb, 40.44Gb, and 45.32Gb of SMRT sequencing data, which generated 481,134, 440,996, and 494,591 circular consensus (CCS) reads, respectively. After filtering out incomplete circular consensus reads, 399,162, 337,744, and 392,975 full-length non-chimeric (FLNC) sequences with complete 5’-3’ ends were obtained, which correspond to Cal, Ela, and Hyb. After removing redundant sequences, 74,516, 77,419, and 98,523 consensus sequences were obtained in Cal, Ela, and Hyb by CD-HIT, respectively, and then combined into 204,322 non-redundant isoforms (Fig 1A). Based on BUSCO analysis, 70.29% (Cal), 71.95% (Ela), and 75.58% (Hyb) of the complete orthologs were identified (S2 Table). A total of 78,672 complete coding sequences (CDS) were predicted (Table 1).
(a) Length distribution of the consensus isoform and full-length non-chimeric read (FLNC). (b) Gene Ontology (GO) classification of the assembled full-length transcripts. (c) Cluster of Orthologous Groups of proteins (COG) classification of the assembled full-length transcripts.
In this study, 125,305 reads were annotated as combined non-redundant FLNC reads based on seven databases. The number of annotated reads in the seven databases ranged from 93,622 (45.82%; KEGG pathways) to 120,463 (58.96%; Non-Redundant Protein (NR) database) (S3 Table). In addition, 96,729 reads were annotated in the GO database (Fig 1B), and 16,541 reads were annotated in the Clusters of Orthologous Groups of proteins (COG) database (Fig 1C).
A total of 953,794,305 clean reads were generated using the Illumina platform with an average of 21,195,429 reads per library. All clean reads were mapped to full-length transcriptome databases of Cal, Ela, and Hyb using Spliced Transcripts Alignment to a Reference (STAR). The final alignment comprised 838,451,691 reads, and the average mapping rate of all RNA-seq libraries was 87.91%, indicating that the PacBio library had a high degree of integrity (S4 Table).
Identification DEGs and enrichment analyses
ANOSIM showed that the difference between species was greater than that within species (R = 0.639, p = 0.001 in brain tissue; R = 0.664, p = 0.001 in liver tissue; and R = 0.87, p = 0.001 in muscle tissue), indicating that statistical significance existed in the comparison between different species (Fig 2A). PCoA showed that different individuals of each species could be clearly clustered or grouped, indicating good repeatability within the species. The differences in transcription levels in the muscle tissue were the greatest among the three species (Fig 2B).
Analysis of similarities (ANOSIM) and principal coordinates analysis (PCoA) of Cal, Ela and Hyb in brain tissue (a), liver tissue (b) and muscle tissue (c), respectively.
To fully compare the differences between the hybrid groupers and parents, we set up two comparisons: Hyb vs. Cal and Hyb vs. Ela. In the brain, there were 3,494 differences between Hyb and Cal (18 upregulated, 3,476 downregulated) and 3,415 differences between Hyb and Ela (45 upregulated, 3,370 downregulated) (Figs 3 and 4A). The number of common DEGs that only exist between the two comparisons was 19. In the liver tissue, 2,125 (66 upregulated, 2,059 downregulated) and 2,351 (106 upregulated, 2,245 downregulated) DEGs were found in the Hyb vs. Cal and Hyb vs. Ela comparisons, respectively (Figs 3 and 4B). Only 92 common DEGs were found between these two comparisons in liver tissue. In muscle tissue, 1,487 (38 upregulated, 1,449 downregulated) and 1,675 (93 upregulated, 1,582 downregulated) DEGs were identified in Hyb vs. Cal and Hyb vs. Ela comparisons, respectively (Figs 3 and 4C). Only 48 common DEGs were detected between the two comparisons in the muscle tissue.
Volcano plot showing differentially regulated genes between Hyb vs Cal, Hyb vs Ela in brain tissue (a), liver tissue (b) and muscle tissue (c), respectively.
The GO analysis of the total DEGs produced three major functional categories: cellular component (CC), molecular function (MF), and biological processes (BP). In CC, the cytoskeleton was significantly enriched in brain tissue. For BP, most of the enriched subcategories were related to protein transport in the brain, liver, and muscle tissues (Fig 5).
GO enrichment of differentially expressed genes (DEGs) in brain tissue (a), liver tissue (b) and muscle tissue (c), respectively.
Clustering and correlation network analysis of DEGs
According to the relative expression trends, the DEGs were mainly categorized into three groups: those with high expression in Cal, high expression in Ela, and high expression in Hyb (Fig 6). In the liver tissue, the number of DEGs was the lowest (Fig 6B). In all tissues, more DEGs were highly expressed in Cal or Ela, while the expression levels of DEGs in the Hyb were intermediate.
Heatmap of DEGs in brain tissue (a), liver tissue (b) and muscle tissue (c), respectively.
The correlation analysis to screen genes related to growth traits and explore their regulatory relationships found that 13 growth-related DEGs were detected in the brain tissues and 14 growth-related DEGs were detected in the muscle tissues. These DEGs formed a network in the brain and muscle tissues, respectively (Fig 7). There were two growth-related DEGs (MYZAP and TNNT2) found in liver tissue, but they did not form a network. The qRT-PCR expression patterns of the 15 DEGs were consistent with the data obtained from RNA-seq (AN32A, G137B, and PTEN from brain tissue; ACTC, CAZA1, FGFR3, HSPB1, MLRS, MYSS, MYPC2, RHOAB, STYL2, TBA, TBB1, and TPM1 from muscle tissue) (Fig 8 and S5 Table). The relative expression levels of the 15 DEGs in Hyb were lower than those in Cal and higher than those in Ela. Unfortunately, no DEGs with similar expression patterns between qRT-PCR and RNA-seq were found in the liver tissue.
Construction of the growth-related genes relationship network through correlation network analysis in brain tissue (a) and muscle tissue (b). Red arrow indicates positive regulation, green arrow indicates negative regulation. Red node shows the expression level trend of this gene is consistent with transcriptome sequencing (RNA-seq). Blue node shows the expression level trend of this gene is not consistent with RNA-seq.
Asterisk represents there is a significant difference between the two samples, as assessed by a t-test (p < 0.05).
Pathway analysis
Two genes (PTEN and HSPB1) were enriched in the MAPK signaling pathway. Five genes (PTEN, FGFR3, RHOAB, MLRS, and ACTC) were involved in the regulation of the actin cytoskeleton, forming the primary enrichment pathway (Fig 9). In addition, only PTEN was differentially expressed in the brain tissue, whereas other genes were differentially expressed in the muscle tissue. These findings suggest that PTEN may be a key upstream regulatory gene in Hyb growth.
Genes marked red are verified by qRT-PCR.
Discussion
Growth is a complex trait controlled by multiple genes within multiple organs. The complex genetic mechanisms associated with growth in groupers have attracted the attention of many researchers. RNA-seq technology can help acquire further insight into the molecular regulation mechanisms of biological processes by analyzing a variety of total transcript categories, structures, and expression levels [39,40]. RNA-seq is widely used to identify regulatory factors related to growth traits in groupers [41–43]. In this study, we adopted a combined strategy of RNA-Seq and SMRT sequencing to analyze the mechanism of Hyb growth superiority to correct the shortcomings of using RNA-seq alone.
Comparative transcript analysis between Hyb and its parents indicated that DEGs were related to growth traits, primarily in muscle and brain tissues. No DEGs were found in the liver tissue based on qRT-PCR results. These observations indicate that the brain and muscle are the main tissues controlling the growth of Hyb. Interestingly, the expression pattern of DEGs validated by qRT-PCR in Hyb was consistent, with the relative expression levels being higher than those in Ela and lower than those in Cal. In yeast, it was found that most genes whose expression differed between the hybrid and its parents displayed intermediate expression levels between the two parents [44]. In Arabidopsis, gene expression in the hybrids also exhibited intermediate levels between the two parents or was close to the level of one of the two parents. It has been proposed that gene expression levels are subjected to natural selection because living organisms must balance the costs and benefits of protein production and activity [45,46]. Intermediate expression levels in hybrids may further result in better resource efficiency, resulting in heterosis [44,47]. The findings from 16 maize hybrids, according to genome-wide expression analysis, imply a positive association between the proportion of genes showing mid-parent expression and yield heterosis [48].
KEGG pathway enrichment analysis revealed that these growth-related DEGs were enriched in the MAPK pathway and actin cytoskeleton regulation. MAKPs are kinase families that regulate myogenic cell proliferation, differentiation, and protein synthesis in fish [5]. The MAPK/ERK signaling pathway can promote muscle cell proliferation [49] and terminal differentiation [50], which can be activated by IGFs. Actin polymerization and cytoskeletal remodeling are closely associated with smooth muscle contraction. Dynamic changes in the actin cytoskeleton play a fundamental role in the regulation of tension development during smooth muscle contraction [18]. The p70S6k, as a potent stimulator of protein synthesis, is activated by increasing muscle contraction [16]. Activated p70S6k can promote muscle growth via the IGF-I/Akt/mTOR pathway [51]. Therefore, it can be inferred that Hyb growth is regulated by the GH/IGF system. The contribution of the GH/IGF system and its downstream signaling pathways in regulating growth superiority has been reported in other fish [5,42]. We also found that PTEN was enriched in two pathways, suggesting that it plays a vital role in the regulation of Hyb growth. PTEN is a crucial gene in regulating cell growth and division and executes its biological function depending on the Akt/PKB and PI3K/Akt signaling pathways [52,53]. PTEN has been generally studied in humans but rarely in fish. Our results indicate that PTEN plays an important role in the regulation of growth through the MAPK pathway and actin cytoskeleton. However, the specific mechanism of PTEN action requires further investigation.
Supporting information
S2 Table. The results of the transcriptome integrity assessment based on BUSCO using aneukaryota (ODB9) core gene dataset.
https://doi.org/10.1371/journal.pone.0308802.s002
(DOC)
S3 Table. BLAST analysis of the full-length transcripts against public databases.
https://doi.org/10.1371/journal.pone.0308802.s003
(DOC)
S4 Table. Summary of the alignment between next-generation sequencing reads and PacBio transcripts.
https://doi.org/10.1371/journal.pone.0308802.s004
(DOC)
S5 Table. Gene annotation of DEGs in the growth-related correlation network.
https://doi.org/10.1371/journal.pone.0308802.s005
(DOC)
Acknowledgments
We would like to sincerely thank many researchers who help to complete this manuscript.
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