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Comparative Transcriptome Analysis Depicts Candidate Genes Involved in Skin Color Differentiation in Red Tilapia

PJZ_55_2_535-543

Comparative Transcriptome Analysis Depicts Candidate Genes Involved in Skin Color Differentiation in Red Tilapia

Jinzhao He1, Pengfei Feng2, Junqi Qin2, Yun Teng1, Xu Luo2*, Zigui Chen1*, Huawei Ma2* and Dayan Zhou1

1Breeding Laboratory, Guangxi Aquatic Products Introduction and Breeding Center, 30 Baisha Avenue, Jiangnan District, Nanning 530031, China.

2Biotechnology Laboratory, Guangxi Academy of Fishery Sciences, No. 8 Qingshan Road, Qingxiu District, Nanning 530021, China.

Jinzhao He, Pengfei Feng and Junqi Qin have contributed equally as first co-authors.

ABSTRACT

This study aimed to investigate the genetic molecular mechanism of body color differentiation and variation of red tilapia, selecting the main genes related to the variation and cultivating the pure and stable red tilapia variety. The effects of different temperature treatments on body color and survival of Guam red tilapia, pearl white red tilapia and Florida red tilapia were compared. Besides, comparative transcriptome analysis was used to screen the candidate genes linked to the skin color differentiation of pearl white red tilapia. Among them, the body color of Guam red tilapia changed when the water temperature dropped to 16-14°C, and continued to drop to about 11°C, it was discolored in a large area reaching above 90%. According to the differential analysis: Tyrosine Kinase STYK1, HSP70, HSP30 and Transcription factor Sp6 expressions were significantly increased in the low temperature group, while MC1R, Transcription factor (MafB, jun-D, AP-1, E2F5, ETV6, Sp9, Sp7, E2F1, Sp4) expressions were notably decreased. Further, the color variation of red tilapia at low temperature may result from the change of tyrosine activity in the body. The in-depth study of its regulatory mechanism contributes to understanding the genetic mechanism of red tilapia and improving the body shape.


Article Information

Received 26 March 2020

Revised 14 December 2020

Accepted 20 January 2021

Available online 08 March 2022

(early access)

Published 15 December 2022

Authors’ Contribution

JH, PF and JQ presented the concept of the study and wrote the manuscript. YT and XL designed the study, interpreted the results, wrote and revised the manuscript. ZC performed the experiments while DZ helped him. HM helped in writing and revising the manuscript.

Key words

Comparative transcriptome analysis, Genetic molecular mechanism, Skin color differentiation, Red tilapia, Candidate genes for skin color differentiation, Tyrosine Kinase, HSP70, HSP30, Transcription factor Sp6, Oreochromis spp.

DOI: https://dx.doi.org/10.17582/journal.pjz/20200326090308

* Corresponding authors: 260229251@qq.com; 0czg@163.com; ma463543285@126.com

0030-9923/2023/0002-0535 $ 9.00/0

Copyright 2023 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).



INTRODUCTION

Red Tilapia (genus Oreochromis), an essential tropical freshwater species is one of the most important edible fish in the world (Melo et al., 2014). It is widely cultured because of its rapid growth and tender meat (Noraini et al., 2013).

Fish belongs to poikilothermic animals whose body temperature varies with the environmental temperature (Yang et al., 2004), water temperature affects the survival, growth, metabolism, reproduction of fish (Ross et al., 2013). Tilapia is a warm-water fish with a temperature tolerance of 6-42 °C, but the tolerance to low temperature is influenced by water quality, fish age and health status (Nitzan et al., 2016). The extreme freezing weather in China always caused great economic loss to the tilapia farming industry (Luan, 2010). A larger number of red tilapia and wintering seedings that were ready for market were frozen and die under 8-10 °C of water temperature (Wang et al., 2018a). Additionally, the time of seedling release of the early spring red tilapia with low cold tolerance would be delayed, leading to its market price volatile (He et al., 2016). The supply and demand relationship of red tilapia was unbalanced, which had a major impact on the red tilapia breeding industry (El-Ebiary et al., 2013). Therefore, the physiological study on the cold tolerance of red tilapia can provide data supporting for the breeding of cold-resistant red tilapia in the future.

Differentiation during genetic selection and variation in body color has been a growing limitation to the commercial value of red tilapia (Wang et al., 2018b). The differentiation of body color in red tilapia is irreversible, while the change of skin color is reversible with the change of ambient temperature (Wang et al., 2018a). Fish pigment cells are mainly distributed in skin, and fins and scales are derivatives of the skin, and pigment cells are widely distributed. Previous study has shown that the two types of melanin produced by melanocytes extremely influence on body color phenotype, one of which is eumelanin being responsible for producing black and brown phenotypes, the other is brown melanin contributing to producing the yellow and red phenotypes (Kottler et al., 2015). The formation of melanin is a complex process requiring the maturation of melanocytes, the synthesis and transportation of melanin, in which regulatory factors and signaling molecules are involved in each stage (Poletini et al., 2016). The adenylate cyclase pathway, the protein kinase c pathway and the tyrosine pathway are three molecules and transduction pathways in melanocytes that synthesize melanin (Higanakamine et al., 2015). The common genetic signaling pathways of two pigments have been discovered (Mandal et al., 2010). The eumelanin is synthesized from tyrosine in vivo. Under the action of tyrosinase, tyrosine forms a eumelanin through a series of oxidation and catalytic polymerization (Chodurek et al., 2013). It turns to the synthetic pathway of brown melanin after the reduction of tyrosinase activity (Chodurek et al., 2013). Compared with other fish, little is known to the body color differentiation in red tilapia. Zhu et al. (2016) used comparative transcriptome sequencing to find the following genes related to skin color in red tilapia of three different colors: tyr, tyrp1, silv, sox10, slc24a5, CBS and slc7a11 (Zhu et al., 2016). The results showed that the miRNAs associated with red tilapia color mainly included slc7a11, mc1r and asip, predicating that miR-138-5p and miR-722 played an important role in the regulation of pigmentation. However, the molecular mechanism of body color differentiation remains unclear.

Thus, in this study, transcriptome sequencing was performed on the tissue samples of pearly white tilapia fin strips at low temperature and normal temperature to screen out the genes related to body color variation of red tilapia and provide a theoretical basis for the breeding of fine red tilapia strains.

MATERIALS AND METHODS

Sample collection

Thirtysamples, each of Guam red tilapia (GR), pearl white red tilapia (WR) and Florida red tilapia (FR), which were bred in the same year and weighed 10g, 80g and 120g, respectively were selected for the experiment. The animal experiment complying with the ARRIVE guidelines was carried out in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and associated guidelines, EU Directive 2010/63/EU for animal experiments. Different red tilapia strains were placed in diverse aquarium for 5 days at 20 °C to adapt to the living environment of the aquarium for subsequent temperature culture test. Fish were fasted for 24h before the trial. The circulating water filtration system was adopted in this experiment, and feeding was conducted at 9:00 am and 4:00 pm, respectively every day. The laboratory temperature was artificially cooled, from 20 °C to 6 °C with gradient of 0.2 °C/2 h. During this period, water temperature was recorded every 2 h during the day and every 4 h at night. Meanwhile, data such as the deaths, the temperature at which the body color began to change, the location and degree of discoloration of red tilapia in each group were recorded. In the normal water temperature (20 °C) and cooling process (after 24 h of body color change), 3 fin tissues of WR were extracted for transcriptome sequencing respectively, stored in a -80 °C freezer before sequencing.

RNA isolation and cDNA library construction

Total RNA was extracted from tissue samples using MagZol Reagent. Then concentration and mass of each RNA sample was detected by nucleic acid protein detector (OD260: OD280) (Eppendorf, Germany) and agarose gel electrophoresis, respectively. The qualified RNA samples were enriched with magnetic beads with Oligo (dT). cDNA strand was synthesized with six base primers using enriched mRNA as template, and cDNA library was obtained by a series of modifications and PCR enrichment. The cDNA library was then quantitatively analyzed by Qbuit3.0, and the library insert size was detected by Agilent 2100 Bioanalyzer to ensure that the library effective concentration and quality meet the requirements of the RNA sequencing.

Transcriptome sequencing and de novo assembly of transcriptome

Each qualified cDNA library was sequenced using Illumina HiSeq2500 for 2 × 125 bp pair-end (PE) sequencing. Quality control of all raw reads was conducted by Fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) software. Raw data obtained from sequencing were filtered, and connector sequences and low-quality reads were removed to obtain high quality clean data. Gene assembly analysis was performed on high quality clean data after quality control. Trinity (v2.2.0) analysis software based on DE Bruijn graphs was used for subsequent analysis to assemble the transcriptome sequencing data from scratch. The sequences output from Trinity software can be used for further analysis.

Assembled sequence annotation: Functional annotation

Functional annotation of the assembled sequences was performed by homology searches against the Uni Prot-Swiss Prot (The Universal Protein Resource) database, the GO (Gene Ontology) database, the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and the TrEMBL protein database, and searches were conducted by the BLAST program.

RT-PCR analysis

Total RNA was isolated from samples by TRIzol method (Invitrogen, CA, USA), and cDNA was obtained by reverse transcription after DNA elimination. The results were calculated by ABI Prism 7500 RT-PCR system (Applied Biosystem, Foster City, CA, USA). Amplification was performed in a mixture (10 μl) containing 5 μl of 2X SYBR Green Master Mix, 1 μl of cDNA and 0.2 μl of each primer (10 μM). The thermal cycling curve consisted of an initial denaturation at 95°C for 5 min, followed by 40 cycles of 95 °C for 15 s, annealing/extension at 60 °C for 45s. Melting curve was generated by heating to 95 °C and cooling to 65 °C. All reactions were performed in triplicate and included a negative control without template. The value is based on identifying two biological replicates, each repeated 3 times. The gene expression level was normalized to β-actin expression in the same sample. A two-tailed t-test was used to compare expression levels.

RESULTS

Effect of temperature on apparent body color of red tilapia

When the temperature dropped to 15 °C, body color of the PR and FR with the various body weight began to change; body color of the PR and FR changed over large areas when the temperature was decreased to 11 °C, and the discoloration ratio is all over 90%. When the temperature dropped to 10.2 °C, GR (10 g) began to change color, as temperature continued to reduce, the total body color change ratio was about 6%. However, for GR (80 g and 120 g), the decrease of temperature has no effect on the change of body color (Table I).

Table I. Changes of body color of different species of red tilapia at different temperatures.

Weight

Species

T1 (°C)

T2 (°C)

Discoloration ratio (%)

10 g

A

10.2

0

6

B

15~13

11~9

91

C

14~13

11.5~9.6

95

80 g

A

0

0

0

B

15~14

11.6~9.8

94

C

16~15

12.1~9.8

97

120 g

A

0

0

0

B

16~14

11.8~10.3

96

C

16~15

11.8~10.0

97

T1, The water temperature starting to change color; T2, Water temperature when large areas of color changing. (A) Guam red tilapia; (B) Pearl white red tilapia; (C) Florida red tilapia.

 

Transcriptome sequencing and assembly

In the gradient cooling culture process, the apparent body color of WR changed obviously. Thus, 3 fin tissues of WR in the normal water temperature (20 °C) and cooling process (after 24 h of body color change) were selected for transcriptome sequencing. In total, a mean of 8, 194, 153 paired-end (PE) clean reads for each library were obtained after data filtering and the sequencing quality was high with Q30 ratio larger than 96% for all samples (Table II).

We pooled all the clean reads from six libraries and de novo assembled them using Trinity. A total of 117,609 contigs, corresponding to unique genes of which length ranged from 201 bp to 9,868 bp. There were 27,273 (23.19%) contigs longer than 1,000 bp. The detailed length distribution of transcripts from the red tilapia is shown in Figure 1.

 

Table II. Transcriptome sequencing data of red tilapia.

Sample

Number of clean reads

Number of bases

Length (bp)

GC (%)

Q20

Q30

HLF3

8213990

1026748750

125

48.57

98.93

98.02

HLF7

8174316

1021789500

125

48.58

97.89

96.26

Read_num, Number of sequences sequenced; Base_num (bp), Number of bases sequenced; Length (bp), Sequencing read length; GC%, Ratio of G base and C base to total base number; Q20, Ratio of the number of bases having a mass value greater than 20 (error rate less than 1%) to the total number of bases; Q30, Ratio of the number of bases whose mass value is greater than 30 (error rate less than 0.1%) to the total number of bases.

 

GO annotation and analysis of DEGs

Gene ontology (GO) annotation was then performed based on the Nr annotation and 34,542 contigs (29.37%) were assigned to GO term (Fig. 2). As shown in Figure 2, a total of 49 terms were assigned, including 23 (46.94%) biological process category, 13 (26.53%) cellular component terms and 13 (26.53%) molecular function terms. In the biological process category, cellular process was the most abundant term, followed by metabolic process biological regulation and pigmentation. For the cellular component category, cell and cell part were the predominant terms which were followed by organelle. For the molecular function category, binding was the main term, and it was followed by catalytic molecular transducer and transcription regulator (Fig. 2).

Functional annotation

All contigs were compared with four data bases including EMBL database, the UniProt-SwissProt database, KEGG database and GO database for functional annotation. There were 27997, 35370, 34542 assembled contigs that had significant hits against EMBL database, the UniProt-SwissProt database and GO database,

 

 

respectively (Table III, Fig. 2). To identify the biological pathway in the red tilapia transcriptome, all contigs were mapped to the KEGG database and were associated with 216 pathways (Supplementary Table I).

 

Table III. Statistics of function annotation.

Annotated database

Annotated number

Swiss-PROT

35370

EMBL

27997

GO

34542

All contigs

117609

 

Identifying candidate genes involved in skin color and pigmentation

To show the differences in the skin color of red tilapia, we made a comparative analysis of two skin transcriptomes. Based on the standard that |logFC| ≥ 1 and FDR ≤ 0.5, 3, 228 differentially expressed transcripts were identified between HLF7 and HLF3, which include 1,327 up-regulated transcripts and 1,901 down-regulated transcripts in HLF7. Candidate gene enrichment analysis identified 49 candidate genes involved in the processes of skin color and pigmentation from the two transcriptome data sets. Interestingly, six genes were revealed to be specifically expressed in HLF7, while 16 genes were only found expressed in HLF3 (Table IV).

DISCUSSION

We employed the transcriptome analysis to investigate the different gene expression of fin tissues of WR, indicating that the color change of red tilapia at low temperature arose from the co-regulation of multiple genes (tyrosine protein kinase STYK1, HSP 70, HSP 30, HSP 90, MC1R and transcription factor).

Heat shock protein (HSP70) is one of the most conservative and important proteins in HSPP family which is synthesized by the reaction of organism to the physical, chemical and biological stress agents in the environment (Shen et al., 2016). HSP70 gene can produce abundant heat stress proteins, remove the denatured proteins, protect the cells from the damage of denatured proteins, and reduce the impact of external stress environment on the body of fish when the cells are under stress (González-Aravena et al., 2018). Studies have found that the HSP70 mRNA expression in the liver was significantly increased after 12 h of low-temperature stress on the fish (Ming et al., 2012). It has been demonstrated that both low temperature and high temperature caused the rapid expression of HSP70 gene in the body cells of platy bream and the synthesis of heat stress protein (Ming et al., 2012). In our study, the expression of HSP70 in HLF7 cultured at low temperature was significantly higher than that in normal temperature group, suggesting that the low temperature environment outside led to the changes in the physiological function of red tilapia. Besides, it is worth noting that the fry that has not yet fully developed are more susceptible to temperature changes and change their body color in our experiments, such as GR. Consistently, studies have shown that huge genetic variation in body color can provide a good indication of its potential improvement, and the body length and width of red tilapia are negatively correlated with body color (-0.47 to -0.25) (Hamzah et al., 2017). However, the specific mechanism of this change still needs further study.

The biological basis of body color change in fish is the number, morphology and distribution of different pigment cells on skin and scales (Steffen et al., 2015). Compared with only a kind of pigment cell in the mammalian (melanocytes), bony fishes were found to have 6 kinds of pigment cells, including the melanocytes, red pigment cells, yellow pigment cells, rainbow, white and blue pigment cells, making the formation and changes of the fish body color more complicated and different red tilapia showing different body colors (Erickson, 2010). It has also been reported that MC1R gene, ASIP gene and TYRP1 gene are the major candidate genes of fish body color formation (Jian-Xiong et al., 2014). MC1R gene, as a key gene to produce melanin, expressed slightly in HLF7 of the low temperature group, and it was not the main reason for the formation of black spots (San-Jose et al., 2015). It is speculated that the formation of melanin by MC1R gene was affected by the albino gene. Meanwhile, the MC1R gene, which was related to the synthesis of melanin, represents a critical role in the formation of fish body color. However, MC1R expression was low in HLF7 in black-spotted red tilapia of the low-temperature group in our study, probably because it was an acute regulator and made pigments significantly deposited in the short term. And hence the surface of the skin was colored with streaks, and then black markings or red markings, but the expression level was stable.

It has been revealed that the body color of Mozambique tilapia became black in the dark after 25 h, but there was no significant change in MC1R expression in the epidermis and the α-MSH level in the serum. Therefore, the MC1R gene could also act as an acute regulator in the regulation of fish pigmentation, but the pigment is significantly deposited in a short period sine this activity expression cannot be maintained for a long time (Moorman et al., 2015). So even if the red tilapia epidermis exhibited a certain color, there was no significant change in MC1R mRNA expression in fish tissues.

 

Table IV. Expression and annotation of candidate genes involved in skin color and pigmentation between HLF7 and HLF3.

Gene_ID

FPKMHLF3

FPKM HLF7

Fold change (log2)

Annotation

TRINITY_DN24220_c0_g4

0

62.65

10.41823029

Heat shock protein 30

TRINITY_DN24220_c0_g1

0

62.17

10.0705945

Heat shock protein 30

TRINITY_DN45765_c0_g1

0

19.36

9.377526023

Heat shock protein HSP 90-alpha

TRINITY_DN24220_c0_g2

0

61.72

8.461914888

Heat shock protein 30

TRINITY_DN24220_c0_g3

0

55.3

8.292501236

Heat shock protein 30

TRINITY_DN22422_c0_g1

0.49

50.84

8.0814764

Heat shock 70 kDa protein 1

TRINITY_DN2485_c0_g1

0

1.36

7.204597264

Transcription factor Sp6

TRINITY_DN40960_c0_g1

2.04

61.57

5.358205112

Heat shock protein HSP 90-beta

TRINITY_DN55897_c0_g1

1.4

21.72

4.455123638

Heat shock protein HSP 90-alpha

TRINITY_DN290_c0_g1

0.33

3.55

3.922387454

Heat shock protein beta-8

TRINITY_DN7424_c0_g1

0.45

4.57

3.839197418

Myosin-binding protein C, fast-type

TRINITY_DN30976_c0_g1

4.41

38.63

3.665320161

Heat shock protein HSP 90-alpha

TRINITY_DN45441_c0_g3

0.23

2.1

3.523276762

Mitogen-activated protein kinase 8

TRINITY_DN21271_c0_g1

2.58

12.4

2.880544153

Tyrosine-protein kinase STYK1

TRINITY_DN44078_c0_g1

2.11

10.63

2.861156424

Heat shock protein HSP 90-alpha

TRINITY_DN20921_c0_g1

13.79

67.1

2.829661276

Carbonic anhydrase 4

TRINITY_DN12210_c0_g1

60.47

219.48

2.40838373

Dual specificity protein phosphatase 5

TRINITY_DN10113_c0_g1

1.69

7.21

2.37892893

Cyclin-dependent kinase 16

TRINITY_DN23272_c0_g2

5.5

18.25

2.278654253

Heat shock 70 kDa protein 4

TRINITY_DN11134_c0_g1

219.16

30.3

-2.305170585

Transcription factor jun-D

TRINITY_DN19725_c1_g2

14.92

1.89

-2.426863715

Fibroblast growth factor receptor 1-A

TRINITY_DN19383_c0_g1

0.48

0

-2.558910746

Sphingosine 1-phosphate receptor 1

TRINITY_DN25429_c2_g1

7.02

0.74

-2.644540997

Calcium/calmodulin-dependent protein kinase type II subunit gamma

TRINITY_DN5260_c0_g1

21.2

2.24

-2.686316481

Protein kinase C beta type

TRINITY_DN22985_c0_g1

32.56

4.14

-2.716853168

Dual specificity protein phosphatase 2

TRINITY_DN24675_c0_g2

11.08

1.16

-2.731113656

Myosin light chain kinase, smooth muscle

TRINITY_DN25683_c1_g3

5

0.37

-2.789473925

Heat shock 70 kDa protein 12A

TRINITY_DN15797_c0_g1

1.23

0

-2.805596395

Serine/threonine-protein kinase N1

TRINITY_DN20404_c0_g1

4.67

0.36

-3.138131195

Mast/stem cell growth factor receptor Kit

TRINITY_DN5929_c0_g1

295.71

22.79

-3.148401717

Transcription factor jun-D

TRINITY_DN1493_c0_g1

4.86

0.36

-3.168662628

Epithelial discoidin domain-containing receptor 1

TRINITY_DN2926_c0_g1

5.24

0.36

-3.272375487

Traf2 and NCK-interacting protein kinase

TRINITY_DN11441_c0_g1

196.91

9.25

-3.862108956

Transcription factor AP-1

TRINITY_DN20977_c0_g1

5.43

0

-4.447806609

Transcription factor E2F5

TRINITY_DN63906_c0_g1

4.11

0.11

-4.585107644

Transcription factor Sp7

TRINITY_DN9373_c0_g1

6.33

0

-6.800394984

Hormonally up-regulated neu tumor-associated kinase homolog A

TRINITY_DN25415_c0_g1

1.25

0

-7.019203017

Collagen alpha-1(XII) chain

Table continued on next page .........................

Gene_ID

FPKMHLF3

FPKM HLF7

Fold change (log2)

Annotation

TRINITY_DN71751_c0_g1

2.12

0

-7.09264739

Transcription factor E2F1

TRINITY_DN9015_c0_g1

6.62

0

-7.09264739

Transcription factor Sp4

TRINITY_DN3355_c0_g1

1.84

0

-7.09264739

STE20-related kinase adapter protein beta

TRINITY_DN53770_c0_g1

3.26

0

-7.09264739

Ephrin type-A receptor 7

TRINITY_DN8410_c1_g1

3.07

0

-7.353919053

Heat shock 70 kDa protein 12A

TRINITY_DN4370_c0_g1

10.21

0

-7.468717718

Heat shock 70 kDa protein 12B

TRINITY_DN25415_c0_g2

1.93

0

-7.625416211

Collagen alpha-1(XII) chain

TRINITY_DN25683_c0_g1

12.05

0

-8.122847214

Heat shock 70 kDa protein 12A

TRINITY_DN5021_c0_g1

4.83

0

-8.670545959

Transcription factor Sp9

TRINITY_DN43769_c0_g1

100.52

0.14

-8.913081177

Neoverrucotoxin subunit alpha

TRINITY_DN24960_c0_g3

26.53

0

-9.961230712

Transcription factor ETV6

TRINITY_DN1001_c0_g1

22.66

0

-11.23238536

Transcription factor MafB

 

Tyrosinase is the rate-limiting enzyme of melanin synthesis vital for the rate and quantity of eumelanin and pheomelanin (Hirobe et al., 2011). During the formation of melanin, hormones, inorganic ions which are in vivo and external light, UV light physicochemical factors may affect the activity of TYR. Besides, the of the eumelanin to pheomelanin by ASIP gene was linked to the expression of the tyrosine family gene (TYR, TYRP1, DCT) and reduced its expression (Meng et al., 2014). Currently, the expression of gene related to the tyrosinase synthesis becomes an important indirect indicator of the energy of melanin formation in fish due to the absence of an effective method for determining the melanin content and body color index of fish (Chen et al., 2012). In addition, the coding transcription factors of pigment cells are also regulated by gene signal channels. It has been found that gene knockout can regulate the transcription factors of pigment cells in c-kit signal transduction, resulting in the loss of their function and pigmentation (Otsuki et al., 2020). Low temperature stress can also lead to changes in the expression of transcription factors. In our study, the expression of transcription factors decreased significantly at low temperature, but the related mechanism is unknown.

CONCLUSION

In this study, the body color of the GR with a small body weight changed significantly with temperature, and the melanin synthesis ability was enhanced in the low temperature group HLF7. The result was consistent with the tendency of red tilapia to become darker as the temperature gradient decreased. The pathway of melanin synthesis is a complex process involving the participation and regulation of polygene. Nevertheless, the overall melanin content, the ratio of eumelanin to brown melanin and the molecular metabolic mechanism of red tilapia body color variation was still needed to be further studied.

ACKNOWLEDGEMENTS

This study was financially supported by National Modern Agricultural Industry Technology System Guangxi Tilapia Innovation Team (No. 2016LFY05), Guangxi Science and Technology Base and Specialized Talents (AD17195016, AD18294012).

Supplementary material

There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20200326090308

Statement of conflict of interest

The authors have declared no conflict of interest.

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Pakistan Journal of Zoology

February

Vol. 55, Iss. 1, Pages 1-500

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