Integrating liver and brain transcriptomic analyses provides new insights into the growth regulation mechanism of Rice flower carp

doi:10.21203/rs.3.rs-2002126/v1

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Integrating liver and brain transcriptomic analyses provides new insights into the growth regulation mechanism of Rice flower carp

https://doi.org/10.21203/rs.3.rs-2002126/v1

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As the main economic fish species in integrated rice-fish farming, the growth performance of rice flower carp (Cyprinus carpio var. Quanzhounensis) directly affects farmers’ economic benefits. In this study, whole-brain and liver tissues of rice flower carp of different sizes were used for transcriptome analysis to identify differentially expressed genes (DEGs) in the two organs and to determine genes associated with the growth of rice flower carp. In the liver tissues, 28,186 genes were generated, including 880 DEGs. In the brain tissues, 50,373 genes were generated, including 2223 DEGs. Twelve and eight genes from the liver and brain, respectively, were selected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The high correlation between the expression of these genes in RNA-seq and qRT-PCR provided reliable transcriptome information. The transcriptome results suggested that the slow-growth individuals were affected by the feeding regulators, neuropeptide y (NPY) and cholecystokinin (CCK), less food was uptake and needed to maintain normal life activities through lipid metabolism and gluconeogenesis in the liver, thus, reducing the amount of energy available for growth and resulting in slow growth. This study provides new insights into the growth regulation mechanisms of rice flower carp and increases our understanding of growth regulation mechanisms in different fish species.

Rice flower carp

Growth

Liver and whole-brain

RNA-seq

Feeding regulators

Rice flower carp (Cyprinus carpio var. Quanzhounensis) is an essential economic animal in the practice of integrated rice-fish culture in China. It is native to Quanzhou County, Guangxi Zhuang Autonomous Region (Fisheries Research Institute of Guangxi Zhuang Autonomous Region, 2006) and has been cultured in rice fields for over 2000 years (Peng et al., 2019). Rice flower carp feed on fallen rice flowers during their long-term cultivation in rice fields, thus, explaining the origin which of the name “rice flower carp” (He et al., 2019; Peng et al., 2019). They have several features, such as short and thick body shape, light purple abdomen, and soft edible intermuscular bones, which are unique among cyprinid fishes. In addition, their meat has no earthy odor (He et al., 2019; Peng et al., 2019). These unique properties make rice flower carp more attractive to consumers, consequently, increasing its economic value and making it the main species in integrated rice-fish culture. A 2015 report showed that culturing rice flower carp in rice fields increased farmers’ income by 240 yuan in Quanzhou County, Guangxi Zhuang Autonomous Region (Yuan, 2015). However, issues with growth performance, such as significant differences in individual body mass, emerged during the development of the rice flower carp aquaculture industry. Our preliminary data indicated that the body mass of the largest individuals was more than 10 times those of the smallest individuals in the same generation, with the same breeding environment. Thus, rice flower carp could not be harvested in the same batch, consequently, causing economic loss. Therefore, exploring the mechanisms of growth regulation in rice flower carp is crucial.

The growth status of fish depends on the balance between energy intake and expenditure (Dai et al., 2015). Growth is a complex, quantitative trait that is regulated by many factors, including the environment, energy metabolism, and reproductive behavior (De-Santis and Jerry, 2007). Similar to mammals, fish growth is regulated by the brain-pituitary-liver axis (Xiao and Lin, 1999). The major genes involved in this axis are those encoding for the growth hormone (GH) and growth hormone-releasing hormone (GHRH), which are secreted by the hypothalamus; pituitary adenylate cyclase-activating polypeptide (PACAP) and somatostatin (SST), which are secreted by the pituitary gland; and insulin-like growth factors (IGFs), which are released by target organs, such as the liver (Dai et al., 2015; Fu et al., 2016, 2019). Many studies have explored the functions of these genes in the axis. However, the regulatory mechanisms of growth differ among species, especially in the largest group of vertebrates and fishes. Large-scale gene expression studies comparing groups with different growth rates are still lacking. Moreover, transcriptome analysis of rice flower carp of different sizes is essential for understanding the regulatory mechanisms of fish growth and are is therefore, worth studying.

In this study, we sequenced the whole-brain transcriptome and liver transcriptome of rice flower carp of different sizes. We aimed to identify differentially expressed genes in the two organs and to determine some of the essential candidate genes related to the growth of rice flower carp.

Experimental animals and tissue sampling

All the experimental fish were 20-month-old and from one population, and they were cultured in the Guangxi Agricultural Fine Variety Hainan South Propagation and Breeding Base, China. Eighteen individuals according to the body mass, nine individuals with the maximum body mass were considered as the max group (the average weight was 1641.06 ± 306.35g), and another nine individuals with the minimum body mass were considered as the min group (the average weight was 233.5 ± 19.94g). All of the above experimental fish were anesthetized with tricaine methanesulfonate (MS-222, 100 mg/L) for subsequent sampling, liver and whole-brain tissues of each individual were taken, cut into pieces, and placed in tissue RNA protective solution (RNAsafetyTM), then the tissues were stored at −80℃ until use. All experiments were performed in compliance with the Animal Management Regulations of the Animal Welfare and Ethical Committee of Guangxi Academy of Fisheries Science (Nanning, RP China) (GACUC number 201703021), Southwest University (Chongqing, RP China) (Approval ID: 20160922). RNA extraction, library construction and sequencing

For whole-brain RNA extraction, each sample was homogenized, followed by total RNA extraction, and the three RNA samples were mixed in equal amounts for transcriptome sequencing. Total RNA was extracted using Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. The liver tissue was treated with the same methods as before. RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked using RNase-free agarose gel electrophoresis. The cDNA libraries were constructed as previously described (Lu et al., 2020). The cDNA libraries were sequenced on an Illumina Novaseq 6000 by Gene Denovo Biotechnology Co. (Guangzhou, China).

Data Processing, Assembly, and Functional Annotation

To get high-quality clean reads, reads obtained from the sequencing machines were further filtered by fastp (version 0.18.0). The parameters were referred to as the ones described by Ye et al.(Ye et al., 2018). Transcriptome de novo assembly was carried out with short reads assembling program–Trinity (v 2.8.4) (Grabherr et al., 2011). Basic annotation of genes includes protein functional annotation, pathway annotation, COG/KOG functional annotation, and Gene Ontology (GO) annotation. To annotate the genes, we used the BLASTx program (http://www.ncbi.nlm.nih.gov/BLAST/) with an E-value threshold of 1e-5 to the NCBI non-redundant protein (Nr) database (http://www.ncbi.nlm.nih.gov), the Swiss-Prot protein database (http://www.expasy.ch/sprot), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg), and the COG/KOG database (http://www.ncbi.nlm.nih.gov/COG). Protein functional annotations could then be obtained according to the best alignment results. All raw sequencing data was uploaded to NCBI SRA (PRJNA862684).

Analysis of DEGs

For each transcription region, expression abundance was determined using RSEM (http://deweylab.biostat.wisc.edu/rsem/) (v1.2.19). RNAs differential expression analysis was performed by DESeq2 software between two different groups(Love et al., 2014; Robinson et al., 2010). The transcripts with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change≥2 were considered DEGs. DEGs were further annotated by GO and KEGG pathway analyses.

Real time-polymerase chain reaction (PCR) confirmation of Illumina sequencing data

To verify the reliability of the transcriptome data, 12 genes in the liver were selected based on their expression levels in different groups and gene functions. These included interferon-stimulated gene 15 (ISG15), perilipin, ring finger protein 144ab (RNF144AB), and polyubiquitin 4 (UBI4), which are related to the ubiquitin proteasome pathway; solute carrier family 27 member 1 (SLC27A1), acyl-CoA synthetase bubblegum family member 2 (ACSBG2), perilipin-2 (PLIN2),fatty acid binding protein 1b(FABP1B.1), and unigene0058103 [apolipoprotein-AⅡ (Apo-AⅡ)], which are related to lipid metabolism; and DNA damage inducible transcript 4 (DDIT4), prostaglandin d2 synthase (PTGDS), and cytochrome p450 family 24 subfamily a member 1 (CYP24A1). Eight genes in the brain were selected using the same methods and included neuropeptide y (NPY), transforming growth factor beta induced(TGFBI), and fibroblast growth factor 12 (FGF12), which are related to the brain-pituitary-liver axis; MCH 2-like (MCH2); oxytocin (OXT); THAP domain-containing protein 6(THAP6); major histocompatibility complex class I-related(MR1); and unigene0018980. Their mRNA levels were measured by quantitative reverse transcription- polymerase chain reaction (qRT-PCR), using liver and whole-brain tissue samples from 18 rice flower carp. The primers for each gene were designed using Primer 5 (Supplementary Table S1). Total RNA was extracted using TRIzol reagent kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. qRT-PCR was performed using an ABI StepOnePlus instrument (ABI, USA). Ribosomal protein L13 (RPL13) was used as a normalization control to calculate relative expression levels (Li, 2020). Melting was analyzed to confirm target specificity. All samples were analyzed in triplicate, and relative gene expression levels were calculated using the 2−^△△CT method (Livak and Schmittgen, 2001).

Sequencing and Annotation

The six cDNA libraries of liver tissues were sequenced separately using the Illumina novaseq 6000 sequencing platform. In total, we obtained 311,985,890 clean reads (99.55%) and 46.49 Gb clean data (98.90%), and in all six pools, Q30>95%. A total of 28,186 genes were generated after all the clean reads were assembled with the average length and N50 length was 2,161 bp, and 2,963 bp, respectively. These results reflect the high quality of the de novo transcriptome assembly (Table 1). After screening, 21,255 (75.41%) genes were annotated into four databases, including Nr (21,145, 75.02%), Swissprot (17603, 62.45%), KOG (14,376, 51.00%), and KEGG (14,565, 51.67%) (Fig. 1). Through Nr databases annotation, rice flower carp has high homology with Cyprinus carp (7,749, 36.70%), the following is Carassius auratus (3,304, 15.64%), and Sinocyclocheilus rhinocerous (2,358, 11.15%) (Table 2).

And a total of 328,548,250 raw reads were obtained from six cDNA libraries of whole-brain tissues. After filtering, 156,148,664 clean reads (99.54%) and 171,066,936 clean reads (99.64%) were obtained in the max group and min group, respectively. All sequencing results were submitted to NCBI. Using the Trinity assembly program, we generated a total of 50,373 genes, with an average length of 1,708 bp, N50 length of 2,523 bp, 43,726 genes greater than 500 bp, and 28,857 genes greater than 1,000 bp (Table 3). The annotation of these genes using the Swissprot, Nr, KEGG, and KOG databases resulted in 23,409, 31,196, 20,152, and 18,689 genes annotations, respectively (Fig. 2). Through Nr databases annotation, rice flower carp has high homology with Cyprinus carp (7,749, 21.99%), the following is Carassius auratus (3,304, 9.40%) (Table 2).

Analysis of DEGs

A total of 880 DEGs (different expression genes) were identified in liver tissues between the two groups. Of these, 495 DEGs were upregulated and 385 DEGs were downregulated (Fig. 3a). And 2,223 DEGs were identified in brain tissues between the two groups, 1,432 DEGs were upregulated and 791 DEGs were downregulated (Fig. 3b). One hundred and twenty-three genes have been found that differentially expressed in the two organs (Fig. 3c), including myosin regulatory light chain 2 (MYL2), myosin regulatory light chain 4 (MYL4), cAMP-response element binding protein (CREB), signal transducer and activator of transcription 1 (STAT1), Myosin Heavy Chain 4 (MHY4). Some genes that are related to the brain-pituitary-liver axis were identified, such as growth hormone receptor (GHR), somatostatin-2 (SST2), somatostatin-1a (SST1A), insulin like growth factor binding protein 2 (IGFBP2A), neuropeptide y (NPY), and insulin like growth factor 2 (IGF2). These DEGs potentially influenced the growth of rice flower carp.

GO/KEGG enrichment analysis of DEGs

GO enrichment and KEGG pathway enrichment analyses were performed using the corresponding annotations of the DEGs to understand the functional relevance of the DEGs. A total of 55 level 2 GO terms and 59 level 2 GO terms were obtained in liver tissues and brain tissues, respectively (Fig. 4). In the liver tissues, most DEGs were mainly classified into the cellular process (441 DEGs), single-organism process (427 DEGs), cell part (402 DEGs), cell (402 DEGs), and binding (390 DEGs) (Fig. 4a). In the brain tissues, most DEGs were mainly classified into the cellular process (869 DEGs), single-organism process (856 DEGs), binding (809 DEGs), cell part (804 DEGs), cell (803 DEGs), and organelle (733 DEGs) (Fig. 4b).

Differentially expressed genes were mapped to 290 pathways and 303 pathways in the KEGG database for the liver tissues and brain tissues, respectively. Twenty-seven KEGG pathways were significant from 290 KEGG pathways in total for liver tissues (P<0.05), Fig. 5a are showing the top 20 KEGG enrichment of differentially expressed genes. And 72 KEGG pathways were significant from 303 KEGG pathways in total for brain tissues (P<0.05), the top 20 of KEGG enrichment of differentially expressed genes are shown in Fig. 5b. Through significant pathway enrichment analysis, some pathways that may affect growth were enriched in liver tissues, such as PPAR signaling pathway (18 DEGs) and Glycolysis / Gluconeogenesis (18 DEGs).

Validation of RNA-Seq profiles by qRT-PCR

To validate the gene expression from RNA-seq data, twelve and eight genes were selected from liver and brain tissues, respectively, for qRT-PCR. Melting curve analysis revealed a single product for all tested genes. The high correlation between the expression of these genes in RNA-seq and qRT-PCR indicated that the transcriptome information was reliable (Fig 6).

Endocrine regulation of fish growth is mainly mediated by the growth hormone/insulin-like growth factor (GH/IGF) axis (Blanco, 2020). Our original assumption was that genes related to the GH/IGF axis caused a difference in the body mass of rice flower carp. GH is a key hormone that regulates longitudinal growth in mammals and promotes fish growth (Davidson, 1987). It exerts its physiological function by binding to the growth hormone receptor (GHR) (Brooks et al., 2014; Waters, 2016). GHR was identified in the liver tissues, and the mRNA expression of GHR was upregulated in the min group. This differed from previous studies, in which high expression of GHR promoted fish growth. We speculated that the mRNA expression of GHR did not fit the growth status, and was influenced by other factors. Studies have shown that the binding of GH to GHR activates janus kinase 2 (JAK2), which subsequently phosphorylates GHR and signals the transducer and activator of transcription 5 (STAT5) proteins on tyrosine residues, leading to the transduction of GH signals (Herrington and Carter-Su, 2001; J et al., 2003; S et al., 2008). However, the expression of JAK2 and STAT5 was not detected by RNA-seq. This also confirmed that the high mRNA expression of GHR is influenced by other factors, such as temperature and starvation (Wang, 2018). The mRNA expression of GHR in Trachinotus blochii (Lacepède, 1801) increased after 3 d of starvation (Wang, 2018). We also identified some genes [hepatocyte nuclear factor 4 alpha (HNF4A), SST1A, and SST2] that were related to the mRNA expression of GHR by RNA-seq. A study on human subjects showed that HNF4A represses GHR expression (Cg et al., 2008), while the mRNA expression of HNF4A was upregulated in the max group. SST was reported to have potent inhibitory effects on pituitary GH secretion (Shimon et al., 1997), and the mRNA expression of SST1A and SST2 was upregulated in the min group, which also indicated that the high mRNA expression of GHR in the min group was influenced by other factors.

After a preliminary analysis of the genes involved in the growth axis, we established a new hypothesis in which the differences in the growth of rice flower carp might be influenced by energy intake. The brain is the most complex organ in vertebrates (Lytton et al., 2017). Two genes related to feeding, NPY and cholecystokinin (CCK), were identified in the brain. Feeding is the only way to acquire energy and nutrients in all heterotrophic organisms, including fish (Sarabian et al., 2018). As one of the most abundant and ubiquitously distributed neuropeptides, NPY plays an important role in food intake and energy homeostasis regulation (Huang et al., 2014). In Carassius auratus (Lin et al., 2000), Danio rerio (Yokobori et al., 2012), Hypophthalmichthys molitrix (Yang, 2015), Silurus meridionalis (Yang, 2013), and Oncorhynchus kisutch (Silverstein et al., 1998), NPY mRNA expression increased during starvation, and normal values were re-established after re-feeding. Research has also shown that the feeding intake of Carassius auratus significantly increased after NPY injection into the brain cavity (Narnaware and Peter, 2001). These results indicated that NPY is involved in feeding regulation and that high mRNA expression of NPY is a potent orexigenic factor in fish. In the present study, the mRNA expression of NPY was upregulated in the max group, which indicated that high mRNA expression of NPY promoted the growth of rice flower carp by increasing feeding intake. In mammals, CCK is the first brain-gut peptide found to be involved in the regulation of feeding, and it is also involved in the regulation of fish feeding (Gibbs et al., 1973; Xv, 2021). In the present study, the mRNA expression of CCK was downregulated in the min group. Similarly, in fasting experiments with Salmo salar and Ctenopharyngodon idellus, the mRNA expression of CCK was downregulated (Feng et al., 2012; Murashita et al., 2009), which suggested that the mRNA expression level of CCK in the min group may be influenced by energy intake. In the present study, experimental individuals were cultured in the same environment, and the different mRNA expression of NPY and CCK between the max and min groups may be the result of genetic factors. The low mRNA expression of NPY in the min group may have inhibited feeding, reduced energy acquisition, and slowed growth. Thus, the expression levels of NPY and CCK supported our hypothesis.

Studies have shown that, when faced with starvation stress, most fish first deplete their liver stores to maintain their vital activity requirements (Navarro and Gutiérrez, 1995; Li et al., 2018; Fan and Li, 2011). In this study, the expression of pyruvate kinase, a key enzyme in glycolysis, was upregulated in the liver of the max group. Additionally, the key enzyme of gluconeogenesis, fructose-1,6-bisphosphatase, was also upregulated in the livers of the max group. In contrast, phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G-6-Pase) were upregulated in the livers of the min group. Fasting experiments with Oncorhynchus mykiss (Soengas et al., 2006), Silurus meridionalis (Fan and Li, 2011), and Sparus aurata (Metón et al., 2004) showed that pyruvate kinase activity was inhibited, which is consistent with the results of this experiment. In starved conditions, glucagon activates protein kinase a and promotes dephosphorylation of the transducer of regulated cAMP response element-binding (CREB) activity 2 (TORC2). TORC2 then enters the nucleus and binds to CREB protein-specific sites. This increases the transcriptional levels of PEPCK, G-6-Pase, and peroxisome proliferator-activated receptor gamma coactivator 1a (PGC-1a), and promotes gluconeogenesis (Du and Liu, 2007). In this study, glucagon receptor-like isoform X2, CREB, G-6-Pase, and PEPCK were identified as differentially expressed genes and were upregulated in the min group. Gluconeogenesis was also enhanced in the min group. This demonstrated that the individuals in the min group had insufficient energy intake and maintained blood glucose stabilization and normal life activities by enhancing gluconeogenesis in the liver.

Lipids are the primary source of metabolic energy in fish for growth, development, and reproduction (Tocher, 2003). Some genes related to lipid transport, lipogenesis, cholesterol metabolism, fatty acid transport, and fatty acid oxidation have been identified, including peroxisome proliferator activated receptor alpha (PPARA), Apo-AⅡ, FABP1B, fatty acid transporter protein 1/4 (FATP1/4), SLC27A1, and carnitine palmitoyltransferase 1 (CPT1). All genes were upregulated in the min group. As a lipid sensor, PPARA plays an important role in regulating fatty acid metabolism. Long-term fasting experiments with Oreochromis niloticus (Ning, 2016) and Aplodinotus grunniens (Liu et al., 2022) showed that mRNA expression of PPARA was upregulated in the liver. High mRNA expression of SLC27A1 also enhances the uptake of extrahepatic fatty acids (Jiang, 2021). A fasting experiment with A. grunniens also showed that mRNA expression of CPT1A was upregulated in the liver (Liu et al., 2022). This suggested that the individuals in the min group had more vigorous fatty acid β-oxidation activity. Our previous research on the analysis of muscle composition of different sizes of rice flower carp also showed that the individuals in the max group had a high saturated fatty acid (SFA) content and monounsaturated fatty acid (MUFA) content (unpublished). The results showed that individuals in the min group maintained normal life activities by enhancing fatty acid β-oxidation in the liver. This was consistent with previous studies that showed SFA and MUFA readily underwent fatty acid β-oxidation in the mitochondria to provide energy (Mckenzie et al., 1998; Sidell et al., 1995).

Our study is the first to report the brain and liver transcriptomes of rice flower carp of different sizes. The transcriptome results suggested that the slow-growth individuals were affected by the feeding regulators NPY and CCK, less food was uptake and needed to maintain normal life activities through lipid metabolism and gluconeogenesis in the liver. This reduced the amount of energy available for growth, thus, slowing down growth. This study provides new insights into the growth regulation mechanisms of rice flower carp, as well as increases our understanding of growth regulation mechanisms in different fish species.

Authors’ Contributions

Z. L., X. D., and Y. H. analyzed the data, wrote the manuscript and have primary responsibility for the final content; Z. L., X. D., Y. H, Y. L., T. J., L. W., J. Q., and Z. C. conducted research; Y. H, L. W., J. Q., and Z. C. provided resources necessary to complete experiments; X. P., K. Z., H. L., Y. L., and H. Y. were responsible for reviewing manuscripts; H. L., Y. L., and H. Y. designed the study. All authors read and approved the final manuscript.

Funding

Research was supported by the National Natural Science Foundation of China (NSFC) through grant no. 31960730, Natural Science Foundation of Chongqing through grant cstc2021jcyj-msxmX0837, key projects of aquatic science and technology innovation in Chongqing, Specialty Freshwater Fish Industry Innovation Team of Guangxi, China through grant nycytxgxcxtd-20-01, and Guangxi Innovation-driven Development Special Foundation of Guangxi, China through grants GuikeAA17204095-3 and GuikeAA17204080-5.

Compliance with Ethical Standards

Disclaimer

The funders had no role in the study design, data collection, data analysis and interpretation, or preparation of this manuscript.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Ethics Approval

All experiments were performed in compliance with the Animal Management Regulations of the Animal Welfare and Ethical Committee of Guangxi Academy of Fisheries Science (Nanning, RP China) (GACUC number 201703021), Southwest University (Chongqing, RP China) (Approval ID: 20160922).

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Tab 1 Summary of transcriptome data generated from rice flower carp liver tissue

Sample	Raw reads	Raw bases (Gb)	Clean reads	Clean bases (Gb)		Q30	GC
Lmax-1	50090832	7.51	49830328	7.42	95.66%		47.00%
Lmax-2	53163036	7.97	52863560	7.88	95.67%		47.08%
Lmax-3	52300500	7.85	52034676	7.76	95.36%		47.47%
Lmin-1	56526624	8.48	56329114	8.39	95.60%		47.16%
Lmin-2	49188240	7.38	48996452	7.31	95.31%		47.81%
Lmin-3	52127262	7.82	51931760	7.74	95.49%		47.57%
Assembly statistics (all the clean reads from the 6 libraries were assembled together)
Term			Unigene
N50			2950
Total Length (bp)			60926926
Max Length (bp)			68029
Min Length (bp)			203
Average Length (bp)			2161

Tab 2 Comparison table of major species in Nr of rice flower carp liver and brain tissue transcriptome data

Species	Liver	Brain
Species	Matching ratio	Matching ratio
Cyprinus carpio	36.65%	35.51%
Carassius auratus	15.63%	15.18%
Sinocyclocheilus rhinocerous	11.15%	10.69%
Sinocyclocheilus anshuiensis	10.38%	9.71%
Sinocyclocheilus grahami	7.38%	7.51%

Tab 3 Summary of transcriptome data generated from rice flower carp brain tissue

Sample	Raw reads	Raw bases (Gb)	Clean reads	Clean bases (Gb)	Q30	GC
Bmax-1	53015374	7.95	52772532	7.86	94.16%	41.70%
Bmax-2	45960386	6.89	45756238	6.81	94.65%	42.54%
Bmax-3	57886564	8.68	57619894	8.58	94.67%	42.46%
Bmin-1	56090166	8.41	55880490	8.35	94.78%	43.58%
Bmin-2	49109402	7.37	48927238	7.30	94.76%	43.71%
Bmin-3	66486358	9.97	66259208	9.85	94.89%	43.04%
Assembly statistics (all the clean reads from the 6 libraries were assembled together)
Term			Unigene
N50			2523
Total Length (bp)			86040456
Max Length (bp)			68029
Min Length (bp)			201
Average Length (bp)			1708

No competing interests reported.

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Integrating liver and brain transcriptomic analyses provides new insights into the growth regulation mechanism of Rice flower carp

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