Transposon insertion in pmel17 rewired skin and muscle transcriptomes in Mozambique tilapia

doi:10.21203/rs.3.rs-3204520/v1

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Transposon insertion in pmel17 rewired skin and muscle transcriptomes in Mozambique tilapia

https://doi.org/10.21203/rs.3.rs-3204520/v1

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The pmel17 gene plays a crucial role in melanin pigmentation. Our previous studies showed that in Mozambique tilapia, a transposon inserted into the 3’ untranslated region (3′ UTR) of pmel17 resulted in the silencing of pmel17and led to the loss of melanin pigments (golden mutant phenotype). Additionally, the transposon insertion caused reduced growth performance and increased locomotion. In this study, to investigate the mechanisms underlying these phenotypic changes, we sequenced transcriptomes of the skin and muscle samples collected from wildtype and mutant tilapias. A total of 51 and 141 differentially expressed genes (DEGs) were identified in the skin and muscle transcriptomes, respectively. DEGs in the skin were primarily down-regulated in golden genotypes and associated with neural crest development and melanin pigmentation pathways. Besides these DEGs involved in the classic melanin pigmentation pathway of vertebrates, 14 DEGs were also observed to be related to melanogenesis. In muscle transcriptomes, there was an enrichment of GO terms associated with growth factors and cellular lipid catabolic processes. Specifically, DEGs related to growth factor binding exhibited a down-regulation, while those related to lipid metabolism showed an up-regulation in mutant genotypes. These findings agree with observed phenotypic changes. Furthermore, several DEGs associated with muscle function and mobility were up-regulated. Our study sheds light on how a single mutation in a gene can modulate multiple phenotypes by rewiring gene regulation networks. The research also provides valuable insights into the complex genetic mechanisms underlying the regulation of diverse phenotypic traits by a single gene.

tilapia

pmel17

transcriptome

regulation

melanin

growth

A single gene typically operates in a complicated gene regulation network and plays important roles in different stages of the development, growth, and functioning of living organisms (Davidson, 2010). Therefore, a single gene can take part in various biological processes and cause numerous phenotypic variations by influencing the associated regulatory network, a phenomenon known as pleiotropy (Alberts, 2017 ). Studies pertaining to gene regulatory networks are rapidly evolving, aiming to unravel the complexity of these processes (Mercatelli et al., 2020). In a gene regulation network, cascading interactions in a pathway are common, wherein a single gene can activate or inhibit the expression of multiple associated genes through diverse mechanisms, such as interactions with regulatory elements or the production of signalling molecules (Karlebach and Shamir, 2008, McGinnis and Madden, 2004). Therefore, altering the expression pattern or function of a single gene in a regulatory network or cascading pathway can lead to the rewiring of the whole regulatory network and result in multiple phenotypic variations (Muntoni et al., 2003).

In our previous study, we found a natural recessive golden color mutant (loss of melanin) in Mozambique tilapia, Oreochromis mossambicus (Liu et al., 2022a). This mutation was due to the silencing of pmel17, resulting from a transposon insertion into the 3’ UTR of this gene (Liu et al., 2022b). Other than the loss of melanin in the skin and eyes, mutant genotypes also showed reduced growth performance and increased locomotion. This is consistent with the data from another study where the mutant genotypes had significantly lower body weight, denser overall swimming trajectory and longer average swimming distance at four months old compared to wild types in the same family (Wang et al., 2023). pmel17, also known as the silver gene, encodes a premelanosome protein and is involved in the melanin polymerization in the pigment organelle, which is the melanosome of melanocytes (Theos et al., 2005). Loss-of-function of this gene causes the disappearance of melanin granules in vertebrates (Watt et al., 2013, McGinnis and Madden, 2004, Liu et al., 2022b). However, the mechanisms through which a gene which is associated with melanin pigmentation is implicated in the regulation of growth and locomotion remains unknown. We hypothesized that pmel17 could modulate both the growth and locomotion regulatory pathways through its involvement in a coregulatory network or cascading pathway.

Transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a robust tool enabling the identification of potential candidate genes, pathways, and regulatory networks that are responsible for intricate phenotypic changes in organisms. This approach holds the potential to offer valuable molecular insights into the underlying mechanisms driving these changes (Wang et al., 2009). In this study, through transcriptome sequencing, we identified DEGs between the wildtype and golden mutant genotypes at the pmel17 locus of Mozambique tilapia. Our first purpose is to identify candidate genes, pathways, and networks that are involved in the regulation of melanin pigmentation, growth performance, and locomotion. In addition, we aim to understand the underlying mechanisms of how pmel17 rewires pathways or networks, which finally lead to decrease growth and increased locomotion besides reduced melanin pigmentation. This study enhances our understanding of the coregulatory network governing melanin pigmentation, growth performance, and locomotion.

Fish samples

The golden mutant phenotype (Fig. 1) in Mozambique tilapia was a recessive trait resulting from the insertion of a transposon into the 3’ UTR of pmel17 and subsequent silencing of its expression (Liu et al., 2022a, Liu et al., 2022b). The golden mutant (bb) showed a loss of melanin pigments in the skin and eye, reduced growth, and increased locomotion in comparison to the wildtype (BB). We identified fish with BB or bb genotypes from a F₂ population by genotyping the pmel17 locus using an insertion/deletion (InDel) marker developed in our previous study which was strictly linked with this gene (Liu et al., 2022a, Liu et al., 2022b). Skin and muscle samples were collected from four-month-old fish of both BB and bb genotypes for RNA extraction. The fish had an average body weight of 14.16 ± 3.97 g (BB) vs 12.26 ± 3.31g (bb) at the same age (four months post hatch) and under the same culture conditions at the marine fish facility at the Temasek Life Sciences Laboratory, Singapore.

RNA extraction and sequencing

Total RNA was extracted from the skin and muscle tissues of three golden mutants (bb) and three wildtype individuals (BB), using TRIzol reagent (ThermoFisher Scientific, MA, USA) according to the manufacturer's protocol. RNA quality was examined using NanoDrop spectrophotometer (ThermoFisher, MA, USA) and electrophoresis using a 1% agarose gel. RNAseq libraries were constructed using the Illumina TruSeq RNA Library Prep Kit v2 (Illumina, CA, USA) according to the manufacturer's protocol. In brief, this process involved mRNA enrichment using poly-T oligo-attached magnetic beads, fragmentation of the enriched mRNA, cDNA synthesis, end repair, A-tailing, adapter ligation, and PCR enrichment. The libraries were quantified using the KAPA Illumina Library Quantification Kit (Roche, Switzerland). Finally, the libraries were sequenced with 2×75 bp pair-end reads on the Illumina NextSeq500 platform (Illumina, CA, USA).

Identifying differentially expressed genes (DEGs)

Raw sequencing reads were processed and filtered using the program process_shortreads from the Stacks package (Catchen et al., 2011) to remove adaptors and low quality reads, following our previously established method (Wang et al., 2019). Clean reads were then aligned and mapped to the reference genome of Mozambique tilapia (unpublished) using the STAR aligner (Dobin and Gingeras, 2015) with default parameters. Only the uniquely mapped reads were extracted for further analyses. Uniquely mapped reads were counted against annotated gene features using HTSeq-count (Anders et al., 2015). To account for variations in sequencing depth among samples, transcript counts were normalized for detection of DEGs using DESeq2 (Love et al., 2014). Transcripts with a low expression level, defined as having less than 1 count per million mapped reads (CPM), were excluded from further analysis. DEGs were determined based on a fold change (FC) cut-off of 2 and a significance cut-off of 0.05 after adjustment for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) correction (Thissen et al., 2002).

Analysing gene ontology and networks

Corresponding protein sequences of DEGs were extracted from the annotation files of the reference genome. These sequences were then mapped to the zebrafish protein database (Ensembl release 99) using BLASTP (McGinnis and Madden, 2004) with an e-value threshold of 1-e10 to retrieve gene ontology (GO) accessions. DEGs from multiple samples were clustered using principal component analysis (PCA) and heatmap approaches based on normalized expression values. These analyses were performed using the ClustVis program (Metsalu and Vilo, 2015). Functional classification of the GO terms associated with the DEGs was carried out using REVIGO (Supek et al., 2011) with zebrafish as a reference. To understand the interactions among different functional groups of the DEGs, network analysis based on the GO annotations was conducted using the program Metascape (Zhou et al., 2019) with reference to the zebrafish gene functioning network. Enrichment analyses were performed using default parameters.

Validating DEGs using quantitative PCR (qPCR)

The relative expression of randomly selected DEGs was examined using quantitative real-time PCR (q-RT-PCR) and compared with the mRNA sequencing data. Transcript sequences of selected DEGs were extracted from the annotated reference genome of Mozambique tilapia, and primers (Table S4) were designed using Primer3 Plus (Untergasser et al., 2012). The housekeeping gene beta-actin was used as a reference for normalization, and qPCR reactions were conducted according to our previous study (Wang et al., 2014). Briefly, two micrograms of DNase-treated RNA were used to synthesize cDNA with the reverse transcriptase M-MLV (Promega, WI, USA). Real-time PCR was carried out in triplicates using the KAPA™ SYBR® FAST qPCR Kits (Kapa Biosystems, MA, USA) according to the manufacturer's protocol and in a BioRad CFX96 Real Time PCR Detection System (Bio-Rad, CA, USA). The 2^−ΔΔCT method was employed to quantify the relative expression of individual genes. Pearson’s correlation test was applied to examine the consistency between different data sets (Mukaka, 2012).

RNA sequencing data

A total of 12 mRNA libraries from skin and muscle samples were constructed and sequenced, with each consisting of three wildtype (BB) individuals and three mutants (bb). The average number of clean reads throughout these mRNA libraries was 36.0 million (M), where the bb genotypes had a higher sequence coverage compared to the wildtype (BB) (with an average of ~ 40.4 M vs. 37.6 M in skin and ~ 36.3 M vs 29.8 M in muscle). In 11 samples, more than 82.2% of the clean reads were found to be uniquely mapped to the reference genome, while in one sample, 67.0% of the clean reads were uniquely mapped to the reference genome (Supplementary Table S1).

DEGs in the skin and muscle

A total of 51 DEGs were identified in the skin between the wildtype and golden mutant. Among these DEGs, 13 were upregulated and 38 were downregulated in the golden mutant (Fig. 2A & 2B and supplementary Table S2). These DEGs showed distinct expression patterns in contrast to the expression of background genes (Fig. 2C). In muscle samples, a total of 141 DEGs were identified, within which 60 were up-regulated and 81 were down-regulated in the golden mutant (Fig. 3A & 3B and supplementary Table S3). Consistently, the expression patterns of these DEGs were also observed to be clearly different from those of the background genes (Fig. 3C).

We further verified the relative expression patterns of these DEGs using q-RT-PCR by randomly selecting four up-regulated and six down-regulated DEGs from either skin or muscle samples. The same RNA samples used for RNA-Seq were used for validation by qPCR. The relative expression patterns were consistent between RNA-Seq and q-RT-PCR data sets for these selected 10 DEGs (Figure S1). The correlation between the transcriptome data (transcripts per million) and qPCR data sets (relative mRNA expression) assessed by Pearson’s correlation coefficient was 0.874 (P < 0.001) (Figure S1), indicating the reliability of the DEGs identified by RNA-Seq.

GO enrichment based on all identified DEGs

A total of 18 GO terms and/or pathways were significantly enriched, with the most significant two terms being related to melanosomes (GO:0042470) and kinesins (zebrafish pathway: R-DRE-983189) (Fig. 5A). There were also some enriched GO terms related to growth and fatty acid metabolism, e.g., growth factor binding (GO:0019838). Only one protein-protein interaction network which was significantly enriched and primarily related to kinesins was identified. Within this network, almost all the DEGs showed down-regulation in the golden mutant except for zic3 (Fig. 5B).

Genes involved in melanin pigmentation patterns

Gene ontology and protein-protein interaction enrichment analyses of DEGs in skin transcriptomes identified seven significantly enriched GO terms (Fig. 4A). The most significant enriched GO term was related to Neural crest development (Fig. 4A). Melanocytes which differentiate into pigment-producing cells, originate from the neural crest. Consistently, GO enrichment in the category biological processes showed that most of the enrichments were related to melanin pigmentation (Fig. 4B). We then mapped the identified DEGs to the melanin pigmentation pathways of vertebrates (Stuckert et al., 2019). We found that most of the DEGs were down-regulated, involved in the melanogenesis, and played roles downstream of the causal gene pmel17 (Fig. 5). Interestingly, we found that several genes, e.g., tyr, tyrp1, and ednrb, were upstream of pmel17. These genes play important roles in the regulation of pmel17 expression and melanin synthesis (Serre et al., 2018). In addition to the known genes implicated in melanin synthesis, we conducted a comprehensive literature mining to explore the potential functions of the remaining DEGs. Our search found that certain DEGs were also potentially related to melanin pigmentation patterns, although they were not included in the above signalling pathway. These genes included the down-regulated zgc:153031, hsdl2, ahnak, titin, kif5ba, marcksl1b, viml, lepr, ppardb, cldn11a, and up-regulated ecel1, calphotin, cx47.1, and tbx5a (Fig. 5).

Genes involved in growth and locomotion

Enrichment analyses identified 14 significantly enriched GO terms and protein-protein interaction pathways being identified at significance level of 0.05. Notably, the diversity of the enriched terms and pathways in the muscle transcriptome was found to be greater compared to that observed in the skin transcriptome (Figure S3, Figure S2 and S4). Within these enriched GO terms, we found the terms: growth factor binding (GO:0019838) and cellular lipid catabolic process (GO:0044242) were more likely related to growth performance (Table 1). Among the DEGs identified, three were found to be associated with growth factor binding. Specifically, in the muscle tissue of golden genotypes (bb) compared to the wildtype (BB), two of these genes, namely glypican 1b (gpc1b) and growth factor binding protein 5b (igfbp5b), exhibited downregulation. Notably, the gene insulin-like growth factor binding protein 1a (igfbp1a) which has previously been reported to impact growth, metabolism, and development through the inhibition of IGF activity (Lee et al., 1997), showed up-regulation within the muscle tissue of golden genotypes. Above all, the expression patterns of all three genes in golden genotypes were in line with growth retardation. In addition to the previously mentioned DEGs, several genes were found to be associated with the cellular lipid catabolic process in the muscle tissue of the golden genotypes. These genes included beta-carotene oxygenase 2a (bco2a), glutaryl-CoA dehydrogenase, mitochondrial (gcdh) and carnitine palmitoyltransferase 1b (cpt1b). Interestingly, these DEGs exhibited up-regulation in the muscle of golden genotypes. These genes play crucial roles in fatty acid metabolism, particularly in reducing body adiposity (Amengual et al., 2011) and facilitating the transport of long-chain fatty acids into the mitochondria, thereby promoting energy production (Kölker et al., 2004, McGarry and Brown, 1997). The potential functions of the remaining DEGs were also investigated through literature mining. We found nine up-regulated genes in the muscle of golden mutant in contrast to the wildtype, which were revealed to be related to muscle functions (Table 2). These DEGs included motor protein genes involved in muscle contraction and movement, e.g., myosin heavy chain b (myhb) and myosin light chain, phosphorylatable, and fast skeletal muscle b (mylpfb). The other up-regulated DEGs related to the regulation of growth and development, energy homeostasis, and mobility included angiopoietin-like 4 (angptl4) and transcription factor SMAD family member 6b (smad6b).

Table 1

Differentially expressed genes that are related to growth and lipid metabolisms identified in muscle between golden and wildtype of Mozambique tilapia.
Gene ID	log2FC	P (FDR)	Regulation	Gene name
Growth factor binding (GO:0019838)
OmoY00013606	-1.24	3.25E-03	Down	glypican 1b (gpc1b)
OmoY00008803	-1.05	1.88E-02	Down	insulin-like growth factor binding protein 5b (igfbp5b)
OmoY00030094	1.33	8.24E-03	Up	insulin-like growth factor binding protein 1a (igfbp1a)
Cellular lipid catabolic process (GO:0044242)
OmoY00027565	1.66	2.22E-05	Up	beta-carotene oxygenase 2a (bco2a)
OmoY00007787	1.40	2.59E-04	Up	glutaryl-CoA dehydrogenase, mitochondrial (gcdh)
OmoY00004660	1.16	2.15E-02	Up	carnitine palmitoyltransferase 1b (cpt1b)

Table 2

Differentially expressed genes that are related to muscle function identified in muscle between golden and wildtype of Mozambique tilapia.
Gene ID	log2FC	P (FDR)	Regulation	Gene name
OmoY00016321	1.83	1.64E-04	Up	myosin, heavy chain b (myhb)
OmoY00005602	1.49	3.19E-05	Up	transmembrane protein 120B (tmem120b)
OmoY00024115	1.41	9.25E-03	Up	myosin light chain, phosphorylatable, fast skeletal muscle b (mylpfb)
OmoY00009624	1.31	2.43E-02	Up	obscurin, cytoskeletal calmodulin and titin-interacting RhoGEF b (obscnb)
OmoY00013768	1.24	8.10E-03	Up	angiopoietin-like 4 (angptl4)
OmoY00025111	1.09	2.02E-02	Up	SMAD family member 6b (smad6b)
OmoY00025987	1.05	1.98E-02	Up	delta-like 2 homolog (dlk2)
OmoY00026460	1.03	2.11E-03	Up	GTP binding protein 2b (gtpbp2b)
OmoY00019529	1.02	2.39E-03	Up	SET and MYND domain containing 2b (smyd2b)

Transcriptome sequencing is an effective approach for the identification of differentially expressed genes (DEGs), reconstruction of gene regulation pathways and networks, thereby offering valuable insights into the mechanisms of phenotypic innovation (Ozsolak and Milos, 2011). In this work, we studied the transcriptomes of the skin and muscle samples of golden mutant and wildtype Mozambique tilapias. We identified DEGs between the two genotypes. The consensus observed between the RNA-seq, and qPCR data sets indicates a high level of confidence in the DEGs identified through RNA-seq. Gene ontology and protein-protein interaction network enrichment analyses of these DEGs identified genes, regulation pathways, and networks that are related to the phenotypic changes of the golden mutant. These data provide valuable information for understanding genetic mechanisms by which silencing of pmel17 leads to loss of melanin pigments, reduced growth, and increased locomotion.

Melanism related genes beyond melanin pigmentation pathway

The identification of DEGs related to melanism in this study provides valuable insights into the underlying molecular pathways. The significant enrichment of the GO term "Neural crest development" highlights the association between melanin pigmentation and the developmental processes of neural crest cells, which have been well-established in previous studies (Mayor and Theveneau, 2013, Adameyko et al., 2009, Kelsh, 2004). This finding suggests that melanin production is intricately linked to the proper development and differentiation of neural crest cells. Furthermore, our analysis reveals that the majority of the DEGs are downstream of the causal gene pmel17 and involved in the Eumelanin production pathway. pmel17 encodes a scaffolding fibrils protein that optimize sequestration and condensation of the pigment melanin, and is essential for the normal maturation of melanosomes and normal deposition of the melanin pigment therein (Watt et al., 2013). Therefore, our finding aligns with the established melanin pigmentation pathway in vertebrates. The genes tyr, tyrp1, and ednrb, identified as upstream regulators of pmel17, are well-known for their crucial roles in the regulation of pmel17 gene expression and melanin synthesis. Their involvement in pigmentation and coloration patterns has been extensively studied in various organisms and highlighted the importance of these genes in determining the intensity and distribution of pigmentation in different tissues and species (Bennett and Lamoreux, 2003, Braasch et al., 2009, Boonanuntanasarn et al., 2004, Mao et al., 2023). The regulation of upstream genes by pmel17 could occur through various mechanisms, including feedback loops or interactions with other genes in the regulatory network. The exact mechanisms involved in the regulation of upstream genes by pmel17 are still unknown or require further investigation. Gene regulatory networks can be complex, with interconnected pathways and feedback loops, and understanding the precise interactions between genes and their regulators is an ongoing area of research. In addition to these genes, we conducted a literature mining search to explore the potential functions of the remaining DEGs. Interestingly, we discovered that some of these DEGs are associated with pigmentation patterns, although they do not belong to the traditional melanin pigmentation pathway observed in vertebrates. These genes include down-regulated genes of zgc:153031, hsdl2, ahnak, titin, kif5ba, marcksl1b, viml, lepr, ppardb, cldn11a and up-regulated genes of ecel1, calphotin, cx47.1, tbx5a. These genes are likely involved in pigmentation patterns. For example, zgc:153031 (Dihydrofolate reductase, dhfr) catalyses the production of 5,6,7,8-tetrahydrobiopterin (BH4) in the xanthophores (Danish-Daniel et al., 2023); hsdl2 likely plays a role in retinal pigment epithelium (Kobayashi et al., 1997); and lepr is also suggested to be involved in the leptin-melanocortin pathway in skin and hair pigmentation (Kanti et al., 2021). By comparing our findings with other studies on pigmentation patterns (Hoekstra and Nachman, 2003, Ducrest et al., 2008, Mao et al., 2023), we underscore the importance of our work in expanding the current understanding of melanin synthesis and regulation. The enrichment of GO terms related to melanin pigmentation, along with the identification of genes both in the known pigmentation pathway and beyond, contributes to a more comprehensive picture of the molecular mechanisms underlying pigmentation processes.

Genes related to growth and locomotion

In muscle transcriptomes, we found that significant enrichment of GO terms, protein-protein interaction pathways, and networks were related to growth performance and locomotion. The down-regulation of genes involved in growth factor binding, such as glypican 1b and insulin-like growth factor binding protein 5b (igfbp5), suggests a potential decrease in growth performance in the golden mutant. On the other hand, the up-regulation of genes involved in cellular lipid catabolic processes, such as beta-carotene oxygenase 2a (bco2a) and carnitine palmitoyltransferase 1b (cpt1b), indicates an accelerated metabolism of fatty acids, potentially leading to increased mobility and decreased growth performance. Furthermore, we identified several other up-regulated genes that were associated with muscle function, regulation of growth and development, energy homeostasis, and mobility. These genes include myosin heavy chain b (myhb), myosin light chain, phosphorylatable, fast skeletal muscle b (mylpfb), angiopoietin-like 4 (angptl4), and transcription factor SMAD family member 6b (smad6b).In this group, myhb is a key component of muscle contraction and is responsible for generating force and movement in skeletal muscles (Bottinelli, 2001). The mylpfb gene plays a role in the regulation of muscle contraction and contributes to the fast-twitch muscle fiber phenotype (Wang et al., 2007). The angptl4 gene is involved in angiogenesis, which is the formation of new blood vessels, and also has roles in lipid metabolism and energy homeostasis (Carbone et al., 2018). Lastly, smad6b is a regulator of gene expression and is involved in various signalling pathways, including those related to muscle development and differentiation (Imamura et al., 1997). The up-regulation of these genes suggests their potential involvement in the observed phenotypic variations related to muscle performance and locomotion in the golden mutant. Their roles in muscle contraction, mobility regulation, angiogenesis, and signalling pathways indicate their importance in muscle function and mobility. This provides additional evidence for the altered molecular processes underlying the phenotype of the golden mutant. Through a comprehensive exploration of the molecular mechanisms governing growth, mobility, and muscle functions, our study substantially contributes to the advancement of knowledge in this field. However, it is essential to acknowledge that further research is required to fully ascertain the specific roles of these genes in the context of muscle function and mobility.

Rewiring of gene pathways by a single gene bring about multiple phenotypic changes

The rewiring of gene regulation networks resulting from a single gene variation can have profound effects on phenotypic innovations. In our study, we observed multiple phenotypic changes along with extensive alterations in the skin and muscle transcriptomes of the Mozambique tilapia golden mutant. These alterations were due to an insertion of a transposon in the pmel17 locus. This transposon insertion silenced pmel17 and resulted in the alteration of the gene regulation pathways and regulatory networks related to the gene, leading to multiple phenotypic changes. “A gene for speed”, actn3, a notable example of a monogenic influence on multiple phenotypic characteristics, has been linked to the regulation of diverse phenotypic traits, such as muscle fiber composition, muscle strength, endurance performance, susceptibility to muscle injury, and even susceptibility to specific diseases. These associations arise from the multifunctionality of the gene within related gene regulation pathways and regulatory networks(Ma et al., 2013, Pickering and Kiely, 2017). All these data highlight that variation of a single gene can generate multiple phenotypes by affecting its related gene regulatory pathways and networks.

Why pmel17 can regulate pigment, growth, and locomotion

The involvement of pmel17 in multiple pathways related to pigment, growth, and mobility can be explained by its association with neural crest cell development. Neural crest cells have a remarkable capacity to differentiate into various cell types. They play a crucial role in the development of not only pigmentation-related structures but also numerous other systems, including the musculoskeletal, cardiovascular, and nervous systems (Bronner and LeDouarin, 2012, Le Douarin and Dupin, 2003, Le Douarin and Kalcheim, 1999). As pigmentation genes, including pmel17, are expressed in neural crest-derived cells, they can influence not only pigmentation, but also other developmental processes mediated by neural crest cells. For instance, genes downstream of pmel17 in the pigment synthesis pathway may also have roles in growth and locomotion due to their expression in tissues derived from neural crest cells. Additionally, the genes involved in neural crest development have been linked to growth and locomotion, suggesting that the neural crest-related pathways and networks have broader implications beyond pigmentation. Therefore, the connections between pigmentation genes, neural crest development, and various phenotypic traits such as growth and locomotion are not surprising. The shared involvement of these genes and pathways highlights the interplay between pigmentation and other aspects of development, further emphasizing the multi-faceted nature of genes, like pmel17, in shaping the phenotype through neural crest-related mechanisms.

These findings support the notion that even a single genetic variation can have far-reaching consequences through the rewiring of gene regulation networks. Understanding the mechanisms by which gene regulatory networks are rewired by a single gene is crucial for unravelling the underlying genetic basis of complex phenotypic innovations.

This work contributes to the reconstruction of the melanin pigmentation pathway, identification of novel genes, pathways, and regulatory networks governing melanism, growth, and mobility. Our discoveries offer valuable insights not only into the genes and pathways influencing pigmentation, growth, and locomotion, but also shed light on the mechanisms through which a single gene can govern multiple phenotypes by reconfiguring the associated gene regulatory network. Further exploration is warranted to gain a comprehensive understanding of the functional implications of the identified DEGs. Functional validation techniques, such as knockdown or overexpression experiments (Yang et al., 2022), will be instrumental in unravelling the precise roles of these genes in pigmentation, growth, locomotion, and muscle functions. Overall, our research emphasizes that altering the expression pattern of a single gene can lead to the rewiring of gene regulatory networks and, consequently, trigger diverse effects on phenotypic innovations.

Statement on ethics approval and consent to participate

All fish handling in the study followed the guidelines set up by the Institutional Animal Care and Use Committee (IACUC) of Temasek Life Sciences Laboratory (TLL), Singapore (approval number: TLL (F)-20–003). This paper does not involve the use of any human data or tissue.

Consent for publication

Not applicable

Data Availability

The transcriptome data have been deposited in the DDBJ database with the accession number Bioproject no. PRJDB16155.

Competing Interest

The authors declare no competing interests.

Funding

This study was supported by the internal fund (5020) of the Temasek Life Sciences Laboratory, Singapore.

Author contributions

Gen Hua Yue initiated the project. Le Wang and Gen Hua Yue designed the study. Fei Sun cultured fish, collected samples, constructed, and sequenced the libraries, and carried out qPCR. Le Wang analysed the data. Fei Sun and Le Wang drafted the manuscript. Gen Hua Yue supervised the project. All authors provided critical feedback and helped shape the research, analysis, and manuscript.

Acknowledgements

We thank our colleagues May Lee and Shadame Yeo for editing the language and grammar of this manuscript.

Adameyko I, Lallemend F, Aquino JB, Pereira JA, Topilko P, Müller T, Fritz N, Beljajeva A, Mochii M, Liste I (2009) Schwann cell precursors from nerve innervation are a cellular origin of melanocytes in skin. Cell 139:366-379
Alberts B (2017) Molecular Biology of the Cell. Garland Science, New York
Amengual J, Gouranton E, Van Helden YG, Hessel S, Ribot J, Kramer E, Kiec-Wilk B, Razny U, Lietz G, Wyss A, Dembinska-Kiec A, Palou A, Keijer J, Landrier JF, Bonet ML, Von Lintig J (2011) Beta-carotene reduces body adiposity of mice via BCMO1. PLoS ONE 6:e20644
Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166-169
Bennett DC, Lamoreux ML (2003) The color loci of mice–a genetic century. Pigm Cell Res 16:333-344
Boonanuntanasarn S, Yoshizaki G, Iwai K, Takeuchi T (2004) Molecular cloning, gene expression in albino mutants and gene knockdown studies of tyrosinase mRNA in rainbow trout. Pigm Cell Res 17:413-421
Bottinelli R (2001) Functional heterogeneity of mammalian single muscle fibres: do myosin isoforms tell the whole story? Pflügers Archiv 443:6-17
Braasch I, Liedtke D, Volff JN, Schartl M (2009) Pigmentary function and evolution of tyrp1 gene duplicates in fish. Pigment Cell Melanoma Res 22:839-850
Bronner ME, Ledouarin NM (2012) Development and evolution of the neural crest: an overview. Dev Biol 366:2-9
Carbone C, Piro G, Merz V, Simionato F, Santoro R, Zecchetto C, Tortora G, Melisi D (2018) Angiopoietin-like proteins in angiogenesis, inflammation and cancer. Int J Mol Sci 19:431
Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes Genom Genet 1:171-182
Danish-Daniel M, Ng TT, Lau CC, Tan MP, Wong LL, Sung YY, Muhammad TST, Sui L (2023) Transcriptomic analysis reveals the differential genes associated with skin growth and pigmentation between Red Melon and Red Cover Discus. in press, https://doiorg/1021203/rs3rs-2888205/v1
Davidson EH (2010) The regulatory genome: gene regulatory networks in development and evolution. Elsevier, Singapore
Dobin A, Gingeras TR (2015) Mapping RNA‐seq reads with STAR. Curr Protoc Bioinform 51:11.14. 1-11.14. 19
Ducrest A-L, Keller L, Roulin A (2008) Pleiotropy in the melanocortin system, coloration and behavioural syndromes. Trends Ecol Evol 23:502-510
Hoekstra HE, Nachman MW (2003) Different genes underlie adaptive melanism in different populations of rock pocket mice. Mol Ecol 12:1185-1194
Imamura T, Takase M, Nishihara A, Oeda E, Hanai J-I, Kawabata M, Miyazono K (1997) Smad6 inhibits signalling by the TGF-β superfamily. Nature 389:622-626
Kanti V, Puder L, Jahnke I, Krabusch PM, Kottner J, Vogt A, Richter C, Andruck A, Lechner L, Poitou C (2021) A melanocortin-4 receptor agonist induces skin and hair pigmentation in patients with monogenic mutations in the leptin-melanocortin pathway. Skin Pharmacol Physiol 34:307-316
Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9:770-780
Kelsh RN (2004) Genetics and evolution of pigment patterns in fish. Pigm Cell Res 17:326-336
Kobayashi K, Kobayashi H, Ueda M, Honda Y (1997) Expression of 17β-hydroxysteroid dehydrogenase type IV in chick retinal pigment epithelium. Exp Eye Res 64:719-726
Kölker S, Koeller DM, Okun JG, Hoffmann GF (2004) Pathomechanisms of neurodegeneration in glutaryl‐CoA dehydrogenase deficiency. Ann Neurol 55:7-12
Le Douarin N, Kalcheim C (1999) The Neural Crest. Cambridge university press, Cambridge
Le Douarin NM, Dupin E (2003) Multipotentiality of the neural crest. Curr Opin Genet Dev 13:529-536
Lee PD, Giudice LC, Conover CA, Powell DR (1997) Insulin-like growth factor binding protein-1: recent findings and new directions. Proc Soc Exp Biol Med 216:319-357
Liu F, Sun F, Kuang GQ, Wang L, Yue GH (2022a) Identification of Pmel17 for golden skin color using linkage mapping in Mozambique tilapia. Aquaculture 548:737703
Liu F, Sun F, Kuang GQ, Wang L, Yue GH (2022b) The insertion in the 3′ utr of pmel17 is the causal variant for golden skin color in tilapia. Mar Biotechnol 24:566-573
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
Ma F, Yang Y, Li X, Zhou F, Gao C, Li M, Gao L (2013) The association of sport performance with ACE and ACTN3 genetic polymorphisms: a systematic review and meta-analysis. PLoS ONE 8:e54685
Mao L, Zhu Y, Yan J, Zhang L, Zhu S, An L, Meng Q, Zhang Z, Wang X (2023) Full-length transcriptome sequencing analysis reveals differential skin color regulation in snakeheads fish Channa argus. Aquac Fish in press, https://doi.org/10.1016/j.aaf.2022.11.008
Mayor R, Theveneau E (2013) The neural crest. Development 140:2247-2251
Mcgarry JD, Brown NF (1997) The mitochondrial carnitine palmitoyltransferase system—from concept to molecular analysis. Eur J Biochem 244:1-14
Mcginnis S, Madden TL (2004) BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 32:W20-W25
Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM (2020) Gene regulatory network inference resources: A practical overview. Biochim Biophys Acta Gene Regul Mech BBA-Gene Regul Mech 1863:194430
Metsalu T, Vilo J (2015) ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res 43:W566-W570
Mukaka MM (2012) A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69-71
Muntoni F, Torelli S, Ferlini A (2003) Dystrophin and mutations: one gene, several proteins, multiple phenotypes. Lancet Neurol 2:731-740
Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87-98
Pickering C, Kiely J (2017) ACTN3: more than just a gene for speed. Front Physiol 8:1080
Serre C, Busuttil V, Botto JM (2018) Intrinsic and extrinsic regulation of human skin melanogenesis and pigmentation. Int J Cosmet Sci 40:328-347
Stuckert AM, Moore E, Coyle KP, Davison I, Macmanes MD, Roberts R, Summers K (2019) Variation in pigmentation gene expression is associated with distinct aposematic color morphs in the poison frog Dendrobates auratus. BMC Evol Biol 19:1-15
Supek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6:e21800
Theos AC, Truschel ST, Raposo G, Marks MS (2005) The Silver locus product Pmel17/gp100/Silv/ME20: controversial in name and in function. Pigm Cell Res 18:322-336
Thissen D, Steinberg L, Kuang D (2002) Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. J Edu Behav Sta 27:77-83
Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3—new capabilities and interfaces. Nucleic Acids Res 40:e115-e115
Wang L, Chua E, Sun F, Wan ZY, Ye B, Pang H, Wen Y, Yue GH (2019) Mapping and validating QTL for fatty acid compositions and growth traits in Asian seabass. Mar Biotechnol 21:643-654
Wang L, Sun F, Yang ZT, Ye Y.P., Wen YF, Yue GH (2023) Transposon insertion into pmel-17 reduced melanin pigmentation, and growth, but increased locomotion of Mozambique tilapia. Aquaculture submitted
Wang L, Xia JH, Liu XJ, Liu P, Wan ZY, Yue GH (2014) Molecular characterization and mapping of Fgf21 gene in a foodfish species Asian seabass. PLoS ONE 9:e90172
Wang Y, Szczesna-Cordary D, Craig R, Diaz-Perez Z, Guzman G, Miller T, Potter JD (2007) Fast skeletal muscle regulatory light chain is required for fast and slow skeletal muscle development. FASEB J 21:2205-2214
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57-63
Watt B, Van Niel G, Raposo G, Marks MS (2013) PMEL: a pigment cell‐specific model for functional amyloid formation. Pigment Cell Melanoma Res 26:300-315
Yang Z, Yu Y, Tay YX, Yue GH (2022) Genome editing and its applications in genetic improvement in aquaculture. Rev Aquac 14:178-191
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523

No competing interests reported.

SupplementaryTables14.xlsx
Table S1 Summary statistics of cleaned paired-end reads and aligned efficiency against the reference genome of Mozambique tilapia for individual samples. Table S2 Differentially expressed genes identified in skin between golden and wildtype of Mozambique tilapia. Table S3 Differentially expressed genes identified in muscle between golden and wildtype of Mozambique tilapia. Table S4 Primers used for verification of the transcriptome data with real-time quantitative PCR.
FigureS1.jpg
Figure S1 Verification of the differentially expressed genes (DEGs) by quantitative real-time PCR (qPCR). The correlation between transcriptome (transcripts per million) and qPCR data sets (relative mRNA expression) were assessed by Pearson’s correlation coefficient.
FigureS2.jpg
Figure S2 Gene ontology analysis of the DEGs in skin transcriptome. The enriched GO terms in the category of Biological Processes at the significance level of 0.05 are shown.
FigureS3.jpg
Figure S3 Gene ontology analysis of the DEGs in muscle transcriptome. The enriched GO terms at the significance level of 0.05 are shown.
FigureS4.jpg
Figure S4 Gene ontology analysis of the DEGs in muscle transcriptome. The enriched GO terms both in the category of Cellular Components and Molecular Functions, at the significance level of 0.05 are shown.

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Transposon insertion in pmel17 rewired skin and muscle transcriptomes in Mozambique tilapia

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Abstract

Figures

Introduction

Materials and methods

Fish samples

RNA extraction and sequencing

Identifying differentially expressed genes (DEGs)

Analysing gene ontology and networks

Validating DEGs using quantitative PCR (qPCR)

Results

RNA sequencing data

DEGs in the skin and muscle

GO enrichment based on all identified DEGs

Genes involved in melanin pigmentation patterns

Genes involved in growth and locomotion

Discussion

Melanism related genes beyond melanin pigmentation pathway

Genes related to growth and locomotion

Rewiring of gene pathways by a single gene bring about multiple phenotypic changes

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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