Erratum: Comparative Transcriptome Analysis Identifies Candidate Genes Related to Skin Color Differentiation in Red Tilapia

This corrects the article DOI: 10.1038/srep31347.

and dozens of genes have been reported to be involved in the pigmentation process through collecting and identifying the pigmentation mutations [13][14][15] . In non-model fish, transcriptome analysis in different color varieties of common carp was done to understand the genetic basis 4,16 . But the molecular mechanisms of fish skin color variation remain less understood. The biosynthesis of other types of pigments may involve different pathways and regulatory networks, but are in general less studied 17 .
Tilapia is the common name for nearly a hundred species of cichlid fish which originated in Africa 18 . It is currently one of the most important food fish in the world. In recent years, red tilapia is becoming more popular for aquaculture production in many parts of the world, such as China, Malaysia and Thailand 19 . While the genetic background of red tilapia varieties is not well documented, their derivation is generally attributed to the crossbreeding between mutant reddish-orange Mozambique tilapia (Oreochromis mossambicus) with other tilapia species like Nile tilapia (O. niloticus) and blue tilapia (O. aureus). Red tilapia has been gaining popularity due to its very fast growth, the absence of black peritoneum, salinity tolerance and adaptability to any culture system 20 . However, the pigmentation differentiation in genetic breeding and skin color variation during the overwintering period are the main problems limiting the development of commercial red tilapia culture. The pigmentation differentiation is not reversible and skin color variation during overwintering period is reversible with the environmental temperature increasing. Coloration patterns including whole pink (WP), pink with scattered black spots (PB) and pink with scattered red spots (PR) have been found in our breeding population (Fig. 1). And the market prices of PB and PR red tilapia are lower for unacceptance by consumers.
In previous study, significant genetic differences were revealed by molecular markers among the different color varieties of red tilapia, the WP grew faster than PB and PR body color types of the Malaysia red tilapia 21 . Here, we utilized the powerful approach of comparative transcriptomics analysis using next-generation sequencing and examined transcript profiles from the skins of WP, PB and PR three color Malaysia red tilapia in genetic breeding. We obtained candidate genes that may be involved in the skin pigmentation process and found a set of candidate simple sequence repeats (SSR) markers that can be used for molecular selective breeding in the future. Understanding the molecular mechanisms of skin pigmentation in red tilapia will advance our knowledge of skin color genetics in fish and accelerate the molecular selection of a specific strain with consistent skin colors.

Results and Discussions
Sequencing and short reads generation. To better understand fish skin color genetics, we conducted a comparative transcriptomic analysis among three skin color red tilapia, WP, PB and PR, using next-generation sequencing. Four cDNA libraries were constructed using total RNA from nine tissue samples of one red tilapia and three skin sample sets from six WP, PB and PR red tilapia individuals, respectively. Each library was sequenced on the Illumina HiSeq2500 platform. In total, high-throughput sequencing created a mean of 56,741,505 paired-end (PE) reads for each library (Table 1). After data filtering, a mean of 56,223,940 PE clean reads were obtained and the sequencing quality was high with Q20 ratio larger than 93% for all samples. All the data are available at NCBI SRA database (SRP076062).
De novo assembly of the red tilapia transcriptome. All of the clean short reads were mapped to the Nile tilapia genome (Oreochromis niloticusOrenil 1.0) using Tophat software within known gene annotation. We found that about 82.2% of the reads could be mapped when the mismatch rate was set to 10% of a read. Based   Table 3). Cumulatively, 88,763 assembled contigs had at least one significant hit against at least one of the four databases, corresponding to the prediction of 74,918 unique genes. Based on the BLASTx results of the unique contigs compared with Nr database, a pie chart showed the distribution of all annotated contigs ranged from species ( Fig. 2A). 91.75% contigs were annotated to 10 fish species, of which 5 species from Cichlidae and represented 82.17% of all annotated contigs. This result was consisted with the evolutionary relationship in a certain degree as the red tilapia is one of the Cichlidae fish. Among the top 10 fish species which had the homology with red tilapia, the Oreochromis niloticus (53.70%) had the highest number of hits, this may due to Nile tilapia was the one parent of red tilapia. The followed 4 fish species which come from Cichlidae occupied 28.47% annotated contigs. Some contigs were annotated to zebrafish (Danio rerio) and medaka (Oryzias latipes) which are model fishes and their annotations are more complete. Additionally, 0.46% of the annotated contigs had hits with unknown species, suggesting that they may represent some lineage-specific genes in red tilapia transcriptome.
Each categorized COG term represents an ancient conserved domain, but the results implied that there is only a small proportion of contigs with annotation for COG categories. Among the functional classes, the largest cluster was the general function prediction only (4,570, 33.64%), followed by replication, recombination and repair (2,133,15.70%), transcription (1,670, 12.29%) (see Supplementary Table S1). Only a few contigs were assigned to nuclear structure (0.10%) and cell motility (0.10%), which represented the smallest groups (Fig. 3).
To identify the biological pathway in the red tilapia transcriptome, all contigs were mapped to the KEGG database and 321 pathways were associated (see Supplementary Table S1). The top 20 pathways with the largest group of contigs are shown in Fig. 2B.
Gene ontology (GO) annotation was then performed based on the Nr annotation, 26,515 contigs (13.96%) were assigned to GO terms, corresponding to 22,439 unique genes ( Table 3, Fig. 2C) (see Supplementary Table S1). As shown in Fig. 2C, a total of 62 terms were assigned, including 23 (37.10%) biological process terms, 19 (30.65%) cellular component terms and 20 (32.26%) molecular function terms. In the biological process category, cellular process (15,755) was the most abundant term, followed by single-organism process term (14,662) and metabolic process term (12,670). For the cellular component category, cell (10,906) and cell part terms (10,906) were the predominant terms and they was followed by organelle term (7,351). Within the molecular function category, binding (13,415) was the most predominant term, and it was followed by catalytic activity term (10,909) and transporter activity term (1,650). Assessment of transcriptome assembly. All contigs of red tilapia transcriptome were compared with Ensembl proteins of zebrafish, fugu, medaka, three-spined stickleback, tilapia and proteins of common carp download from CarpBase using BLASTx program with an e-value cutoff of 1e-5. The mapping rate of red tilapia's contigs had significant hits on proteins ranged from 39.98% to 49.27%. And the contigs of red tilapia transcriptome had hits to 53.4% -86.7% of the unique proteins of zebrafish, fugu, medaka, three-spined stickleback, common carp and tilapia (Table 4). Identification of SSRs. SSR markers are highly polymorphic and have proven to be a valuable tool for various applications in genetics and breeding. Therefore, to discover a novel set of SSRs in the contigs of red tilapia, a total of 160,762 sequences were used to determine potential microsatellite motifs by MISA. Total numbers of 77,423 SSRs were identified in 44,723 sequences, with 16,646 sequences containing more than one SSR (Table 5). On average, one SSR could be found every 4.0 kb in the red tilapia transcriptome. Taking into account that mononucleotide repeats may be a result of sequencing errors and assembly mistakes, 46,643 detected mononucleotide repeats were excluded. The most abundant type of repeat motifs was dinucleotide (24.9%), followed by trinucleotide (12.1%), tetranucleotide (2.2%), pentanucleotide (0.6%) and hexanucleotide (0.024%) repeats. Future research direction is to discover reliable markers that can be used to distinguish WP, PR and PB red tilapia. The more information of the SSRs was in the Supplementary Table S2. ORF identification and prediction. Based 4A). Among the annotated contigs, a total of 12,055 (13.64%) sequences that had 100% complete ORF region were identified (Fig. 4B), the number of the sequences that had 95% integrity ORF region was 2,187 (2.48%), and most of the sequences (63,221, 71.55%) had less than 70% integrity ORF region. To identify the potential ORF in the unannotated contigs, 72,427 contigs were analyzed by Transdecoder program. A total of 28,514 sequences were predicted containing ORF, with an average length of 220.01 bp ranging   from 147 bp to 3,318 bp (Fig. 4C). The remaining 43,913 contigs contained no ORF, indicating that they may come from non-coding genes or uncompleted assembled untranslated regions (UTR).

Comparative analysis of the skin transcriptome.
To reveal the differences in the skin color of red tilapia, we performed a comparative analysis of three skin transcriptomes. Based on the criteria that |logFC| ≥ 1 and FDR ≤ 0.5, we identified 4,366 differentially expressed genes in PB skin compared with WP skin, which include 2,291 up-regulated genes and 2,075 down-regulated genes in PB skin. We also identified 2,734 differential expressed genes (DEGs) in PR skin compared with WP skin including 1,594 up-regulated genes and 1,140 down-regulated genes in PR skin. In addition, 4,476 DEGs were detected in PR skin compared with PB skin, of    which there were 2,184 up-regulated genes and 2,292 down-regulated genes in PR skin (Fig. 5). Further analysis indicated that a total of 148 unigenes showed significantly different expression levels in all three groups (Fig. 5).
All the DEGs were listed in the Supplementary Table S3. To validate the differentially expressed genes identified by comparative transcriptomic analysis, we selected 20 genes which were pigment biosynthesis related genes for qRT-PCR confirmation of differential expression from three comparative groups. As shown in Fig. 6, the qRT-PCR expression patterns of 16 out of 20 randomly selected DEGs were in agreement with the results of RNA-Seq analysis. In addition, we chose 9 out of the 20 genes and gene β -actin for validation of RNA-Seq assembly accuracy, the Sanger sequencing results showed that PCR products were consistent with the assembly sequences.
All these genes are involved in the production of melanin, which is the substance that gives skin, hair, and eyes their color. The most two common types of melanin are eumelanin and pheomelanin, which correspond to brown/black color and red/yellow color respectively. In the comparative transcriptome analysis of PB skin and WP skin, it was obvious that the amount and density of eumelanin were much higher in PB skin than in WP skin, and the results of DEGs analysis corresponded to observed result. The expression level of tyrosinase-related protein 1 (tyrp1), sex determining region Y-box 10 (sox10), premelanosome protein (pmel/silv), solute carrier family 24 (sodium/potassium/calcium exchanger) member 5 (slc24a5), solute carrier family 45 member 2 (slc45a2) and tyrosinase (tyr) were up-regulated in PB skin, implying that these genes play a key role in the contribution to black coloration in red tilapia in the eumelanin synthesis pathway. tyrp1, pmel and tyr are involved in the production of melanin, their characters and functions were reported a lot in other vertebrate species and teleost fishes 11,22,23 . A gene we should take a note of is sox10, which belongs to a family of genes that play a critical role in the formation of tissues and organs during embryonic development. sox10 can activate the mitf and raise mitf expression 24 Increased expression of mitf and its activation stimulate the transcription of tyr, tyrp1 and dct 7,12 , which are responsible for the synthesis of melanin. Another significantly differentially expressed gene was slc24a5. This protein has a major influence on natural skin color variation in humans, the removal of which disrupts melanogenesis in human and mice melanocytes, causing a significant reduction in melanin pigment production 25 . Consistent with these studies, our results showed that the expression level of slc24a5 was 8.88-fold up-regulated  in PB skin when compared with WP skin. While investigating the DEGs list for the comparison between PR skin and WP skin, we also found that the expression level of tyrp1, pmel and tyr genes were up-regulated in PR skin, the results again verified that these genes are important in the production of melanin again.
Lastly, we compared the PR skin and PB skin transcriptome and found that tyrp1 and sox10 genes were up-regulated in the PB skin. Another gene that should be taken note of is cbs (cystathionine beta synthase), also known as cysteine synthase. The protein encoded by this gene performs a crucial role in the biosynthetic pathway of cysteine. The cbs gene was up-regulated in the PR skin, suggesting that more cysteine was synthesized, which was in agreement with our results that high-level expression of cbs contributed to the production of pheomelanin 26 . It is well established, cysteine is involved in the biosynthetic pathway for pheomelanin. In the presence of cysteine, a sulphur-rich amino-acid, DOPA-quinone is transformed into cysteinyl-DOPA, an intermediary in the synthesis of pheomelanin 26 . Some studies suggested that slc7a11/xCT, a cysteine/glutamate exchanger, can directly affects the pheomelanin synthesis 4,27,28 . In our results, we found that the gene slc7a11 was assembled into several assemblies, the differential expression analysis showed that there was no significant expression difference. But when we use the annotation genes of tilapia to identify the DEGs directly, the results showed that the slc7a11 was significantly up-regulated in PR skin. It is therefore again confirmed that the slc7a11 plays a key role in the production of pheomelanin.
Melanogenesis is under complex regulatory control by multiple agents interacting via pathways. It is activated by receptor-dependent mechanisms in hormonal, auto-, para-, or intracrine fashion 29 . The most important positive regulator of melanogenesis is the MC1 receptor with its ligands melanocortic peptides determining intensity of melanogenesis and the type of synthesized melanin 29 . mc1r activates the cyclic AMP (cAMP) response-element binding protein (creb), and its cascade involves the up-regulation of the expression of microphthalmia associated transcription factor (mitf), which could bind and activate melanogenic gene promoters and increase their expression, resulting in increased melanin synthesis 30 . The putative genes and pathways involved in the red tilapia skin pigmentation process are showed in Fig. 7. Briefly, mc1r positive regulates cAMP expression levels resulting in melanin synthesis process. The synthesis of eumelanin is then mediated by tyr, sox10, pmel, slc24a5 and slc45a2. The pheomelano genesis during melanin biosynthesis may depend on the presence of cbs, which synthesizes the cysteine and consequently promote the synthesis of pheomelanin.

Enrichment and pathway analysis of DEGs.
In the further analysis of the GO term enrichment and KEGG pathway of DEGs, all the DEGs were classified into different gene ontologies and pathway (see Supplementary Table S4). The results showed that many DEGs were assigned to pigmentation-related term, such as melanin biosynthetic process (GO:0042438), melanin metabolic process (GO:0006582) and melanosome organization (GO:0032438). The genes enriched in these pigmentation-related terms were informative and worth further study.
The KEGG pathway analysis results showed that some DEGs were associated with pigmentation-related pathways. We focused on the melanogenesis, Wnt signaling pathway and MAPK signaling pathway. It's reported that, in vertebrates both Wnt and MAPK signaling pathways were involved in pigment cell development 31 . In our study, some identified DEGs were enriched in the Wnt or MAPK signaling pathway, and they are likely to be involved in melanin synthesis in red tilapia (see Supplementary Table S4). For example, sfrp5 (secreted frizzled-related protein 5), acts as a soluble modulator of Wnt signaling pathway (Fig. 7). It's reported that sfrp5 is highly expressed in the retinal pigment epithelium 32 , corresponding to the findings of this study that the expression of sfrp5 is up-regulated in PR and PB skin. Another gene hsp70, member of the heat shock proteins, is involved in the MAPK signaling pathway (Fig. 7). Recent studies suggest that the roles of the hsp70 molecular chaperone and proteasomal and lysosomal proteolytic pathways are evaluated in human retinal pigment epithelium 33 . In our results, hsp70 was expressed higher in PR skin compared to WP skin, suggesting that hsp70 may be involved in the skin pigmentation process. The details of interactions of the melanogenetic pathway with other regulatory pathways such as nervous system, immune system or circulatory system in red tilapia skin remain to be further investigated. Nine tissue samples including brain, muscle, liver, intestine, heart, kidney, ovary, skin, and spleen were collected from one red tilapia. Another three sets of skin tissues were collected from six WP red tilapia, six PB (pink with 2/3 or above 2/3 scattered black spots of all skin) red tilapia and six PR (pink with 2/3 or above 2/3 scattered red spots of all skin) red tilapia individuals, respectively. And the skins were sampled from the back skin with black spot and red spot regions. All fresh tissue samples were frozen immediately in liquid nitrogen and then stored at − 80 °C before RNA isolation.
RNA isolation, cDNA library construction and sequencing. Total RNA was obtained from red tilapia samples using TRIzol (Invitrogen, UK) according to the manufacturer's protocol. Then genomic DNA was removed from RNA sample using DNase (New England Biolabs). RNA purity was assessed using the Nanodrop-2000 (Thermo Scientific, USA). Each RNA sample had an A260:A280 ratio above 1.9 and A260:A230 ratio above 1.8. Total RNA integrity was then subsequently checked using an Agilent Technologies 2100 Bioanalyzer with an RNA Integrity Number (RIN) value greater than or equal to 8. An equal amount of total RNA from nine tissue samples was pooled, and an equal amount of total RNA from six individuals of each color group (WP, PB and PR red tilapia) was pooled separately. Next, four sequencing libraries were constructed by TruSeq ™ RNA Sample Preparation Kit according to the product instruction (Illumina). Each library was sequenced using Illumina HiSeq2500 for 2 × 125 bp pair-end (PE) sequencing.
De novo assembly of reference sequences. Quality control of all raw reads was conducted by Fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) software. An initial filtering step was performed to exclude poor quality reads, including adaptor reads, ambiguous nucleotides and low-quality reads (reads having more than 50% bases with quality value). The clean reads were first mapped onto the Nile tilapia reference genome 34 (Oreochromis_niloticus.Orenil1.0) by Tophat 35 (v2.0.10). The '− G' option of Tophat together with the Gene Transfer Format (GTF) file of the Ensemble gene annotation was used for read mapping. Considering that the red tilapia is not exactly the same as the Nile tilapia, the mismatch rate of a read was set to 10% (-segment-mismatches 3 -N 12). The mapped reads were then assembled using Cufflinks 36 (V2.2.1) with default parameters. To get high-confidence isoforms, only those assembled transcripts with FPKM > 0 were retained for further analyses. Then we extracted the assembled transcripts sequence from the tilapia genome sequence according the GTF file generated by Cufflinks. Considering that the transcriptome of red tilapia was not exactly same with Nile tilapia, we corrected the difference of those assembled transcripts with high quality Illumina short reads. Proovread 37 a software which was designed to correct erroneous long reads sequenced by SMRT (Single Molecule Real-Time) with high quality short reads data as generated by Illumina sequencers, was used to correct the differences between the contigs of red tilapia and the transcriptome of tilapia. The most accurate correction results were used to perform the downstream analysis. Thirdly, we extracted the unmapped reads from the unmapped BAM file, and de novo assembly of the unmapped reads was performed using Trinity 38 (version trinityrnaseq-2.0.2) with default parameters.
The corrected assembled sequences generated by Cufflinks and sequences output from Trinity software were then combined. Cd-hit 39 was used to reduce redundancy of the combined assembled sequences. And the resulting contigs were considered as the red tilapia reference sequences.
Assembled sequence annotation and classification. Functional annotation of the assembled reference sequences was performed by homology searches against the NCBI Nr(Non-redundant protein) 40 database, the UniProt-SwissProt (The Universal Protein Resource) 41 database, the COG (Cluster of Orthologous Groups of protein) 42 database and the KEGG(Kyoto Encyclopedia of Genes and Genomes) 43 database. Searches were conducted by the BLASTx 44 program with an e-value cutoff of 1e-10. The gene name and description of the best blast hit was assigned to each contig with significant hits.
Assembly assessment. To compare the similarity to other teleost species, all assembled sequences were compared to Ensembl proteins of zebrafish (Danio rerio), fugu (Takifugu rubripes), medaka (Oryzias latipes), three-spined stickleback (Gasterosteus aculeatus) and Nile tilapia (Oreochromis niloticus) and proteins of the gynogenetic Songpu common carp (Cyprinus carpio) download from CarpBase 27 , by using BLASTx program with an e-value cutoff of 1e-5.
SSRs identification. All contigs were used to identify SSR markers by a microsatellite identification program, MIcroSAtellite (MISA) (http://pgrc.ipk-gatersleben.de/misa/misa.html). The parameters used to identify simple sequence repeats were at least for 6 repeats for di-nucleotide and 5 repeats for tri-, tetra-, penta-and hexa-nucleotide. The compound repeats which composed of two or more microsatellite sequences separated by 100 bases were identified.
ORF identification and prediction. The ORF regions of the contigs which were annotated with Nr database were identified by in-house Perl scripts. Putative ORFs of the unannotated contigs were predicted by Transdecoder (http://transdecoder.sourceforge.net/) program. We defined a contig which has full-length ORF region while it can cover the entire length of the subject protein. Otherwise we calculated the subject protein coverage as the completeness percentage.
Differential gene expression analysis. Clean reads of the skin tissues of each color were aligned to the assembled reference by Bowtie 45 , and then RSEM 46 program was used to estimate and quantify gene and isoform abundances. Gene expression was measured in fragments per kilobase of exon per million reads mapped (FPKM). Finally, edger 22 was used to normalize the expression level of each gene in 3 skin samples to identify the differentially expressed genes by pairwise comparisons. The threshold values of |logFC|≥ 1 and FDR (False Discovery Rate) ≤ 0.05 were used to judge the significance of DEGs. GO term enrichment and KEGG pathway analysis of DEGs was performed using KOBAS program.
qRT-PCR analysis. qRT-PCR was performed on the ABI PRISM 7500 Real-time PCR System. The amplifications were performed in a total volume of 10 μ l and included 5 μ l of 2X SYBR Green MasterMix reagent, 1 μ l of cDNA and 0.2 μ l of each primer (10 μ M). The thermal cycling profile consisted of an initial denaturation at 95 °C for 5 min followed by 40 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 45 s. An additional temperature-ramping step from 95 °C to 65 °C was used to produce the melting curve. All reactions were conducted in triplicate and included negative controls with no template. Values were determined based on two biology replicates, each with three technical replicates. The expression levels of genes were normalized to the levels of β -actinin the same sample. Two-side t test was used to compare expression levels.