Pleuronectiformes) to Boost Aquaculture Production

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Integrating genomic resources of flatfish (Pleuronectiformes) to boost aquaculture production

Citation for published version: Robledo Sanchez, D, Hermida, M, Rubiolo, JA, Fernández, C, Blanco, A, Bouza, C & Martínez, P 2016, 'Integrating genomic resources of flatfish (Pleuronectiformes) to boost aquaculture production', Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics. https://doi.org/10.1016/j.cbd.2016.12.001

Digital Object Identifier (DOI): 10.1016/j.cbd.2016.12.001

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Published In: Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics

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Integrating genomic resources of ﬂatﬁsh (Pleuronectiformes) to boost aquaculture production

Diego Robledo, Miguel Hermida, Juan A. Rubiolo, Carlos Fern«andez, Andr«es Blanco, Carmen Bouza, Paulino Mart«õnez

PII: S1744-117X(16)30091-0 DOI: doi:10.1016/j.cbd.2016.12.001 Reference: CBD 435

To appear in: Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics

Received date: 28 September 2016 Revised date: 9 December 2016 Accepted date: 13 December 2016

Please cite this article as: Robledo, Diego, Hermida, Miguel, Rubiolo, Juan A., Fernández, Carlos, Blanco, Andrés, Bouza, Carmen, Mart´ınez, Paulino, Integrat- ing genomic resources of flatfish (Pleuronectiformes) to boost aquaculture production, Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics (2016), doi:10.1016/j.cbd.2016.12.001

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Integrating genomic resources of flatfish (Pleuronectiformes) to boost aquaculture production

Diego Robledoa, Miguel Hermidab, Juan A. Rubiolob, Carlos Fernándezb, Andrés Blancob, Carmen Bouzab, Paulino Martínezb*

aDepartment of Zoology, Genetics and Physical Anthropology, Faculty of Biology (CIBUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain bDepartment of Zoology, Genetics and Physical Anthropology, Faculty of Veterinary, Universidade de Santiago de Compostela, 27002 Lugo, Spain

Running title: Flatfish integrative genomics in aquaculture

* Corresponding author Dr. Paulino Martínez Department of Zoology, Genetics and Physical Anthropology, Faculty of Veterinary, Universidade deACCEPTED Santiago de Compostela, MANUSCRIPT 27002 Lugo, Spain

Tel/Fax: + 34 982822428

E-mail: [email protected]

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Abstract

Flatfish have a high market acceptance thus representing a profitable aquaculture production. The main farmed species is the turbot (Scophthalmus maximus) followed by Japanese flounder (Paralichthys olivaceous) and tongue sole (Cynoglossus semilaevis), but other species like Atlantic halibut (Hippoglossus hippoglossus), Senegalese sole (Solea senegalensis) and common sole (Solea solea) also register an important production and are very promising for farming. Important genomic resources are available for most of these species including whole genome sequencing projects, genetic maps and transcriptomes. In this work, we integrate all available genomic information of these species within a common framework, taking as reference the whole assembled genomes of turbot and tongue sole (> 210x coverage). New insights related to the genetic basis of productive traits and new data useful to understand the evolutionary origin and diversification of this group were obtained. Despite a general 1:1 chromosome syntenic relationship between species, the comparison of turbot and tongue sole genomes showed huge intrachromosomic reorganizations. The integration of available mapping information supported specific chromosome fusions along flatfish evolution and facilitated the comparison between species of previously reported genetic associations for productive traits. When comparing transcriptomic resources of the six species, a common set of ~2,500 othologues and ~150 common miRNAs were identified, and specific sets of putative missing genes were detected in flatfish transcriptomes, likely reflecting their evolutionary diversification.

ACCEPTED MANUSCRIPT Key words: Pleuronectiformes, aquaculture, genetic map, transcriptome, genome, comparative mapping, evolution

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Introduction

Pleuronectiformes is an order of fish with special adaptations to demersal life. Their origin has been the focus of a long controversy since Darwin's time. Flatfish suffer a drastic metamorphosis from the bilateral symmetry of pelagic larvae to the flat morphology typical of the group, which represents its most oustanding adaptation to demersal life. Additionally, the sea bottom poses new environmental challenges as compared to pelagic life such as low light conditions, skin damage risk, and low temperature and oxygen concentration (Figueras et al., 2016). All available data suggest a quick adaptive radiation of this group ~40 MYA (Friedman, 2008), reflected by a high molecular evolutionary rate (Vernau et al., 1994; Castro et al., 2006) and major genomic reorganizations (Figueras et al., 2016). This probably explains the low inter- family/suborder statistical support reported in phylogenetic studies (Pardo et al., 2005; Azevedo et al., 2008), especially between the two suborders of the group, Psettoidei and Pleuronectoidei (Campbell et al., 2014). This low phylogenetic support has led some authors to not discard a polyphyletic origin of the group (Campbell et al., 2014). Genetic evidences on the diversification of flatfish adaptation to sea bottom have been reported from the whole genome sequencing projects of the tongue sole (Cynoglossus semilaevis; Chen et al., 2014) and the turbot (Scophthalmus maximus; Figueras et al., 2016), suggesting that different strategies may have been involved in the adaptation to a demersal lifestyle.

Flatfish are a group of great commercial value. Fisheries of several flatfish species are exploited all overACCEPTED the world reaching a total MANUSCRIPT production of ~150,000 tonnes (Gibson et al., 2014). Reduction of captures as a consequence of fisheries exhaustion has , promoted flatfish aquaculture mainly in Europe and Asia (Cerdá and Manchado, 2013). Although the usual inland production of flatfish increases production costs, the high appreciation by consumers allows higher market prices, making it profitable. Flatfish aquaculture production increased from 52,005 tonnes in 2003 to 131,254 tonnes in 2014, with PR China and Spain as the leading countries (FAO, 2014). Production records of the main six farmed species indicate that turbot has the largest production (77,000 tonnes; (https://aquatrace.eu/leaflets/turbot), followed by Japanese flounder (Paralichthys olivaceus; 44,733 tonnes), and tongue sole (7,120 tonnes) (FAO, 2014). Other farmed species with high potential are Atlantic halibut (Hippoglossus

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hippoglossus; 1,327 tonnes), Senegalese sole (Solea senegalensis; 1,066 tonnes) and common sole (S. solea; 88 tonnes).

Improving nutrition and growth, controlling reproduction and sex ratio, and obtaining disease resistant broodstock are transversal issues to improve flatfish production (Cerdá and Manchado, 2013). Specifically, larval viability is a major concern in Atlantic halibut (Shields et al., 1999); reproduction represents the main bottleneck for Senegalese and common soles (Imsland et al., 2003; Oliveira et al., 2011); obtaining more resistant or tolerant broodstock to some diseases is the main challenge for turbot (enteromyxosis and scuticociliatosis; Rodríguez-Ramilo et al., 2013; Robledo et al., 2014) and Japanese flounder (scuticociliatosis and lymphocystosis; Fuji et al., 2007; Kang and Kim, 2015); and sex control in turbot and tongue sole is highly relevant because of their size dimorphism in favour of females (Liao et al., 2014; Taboada et al., 2014a). An important but uneven effort has been done to understand the physiological, molecular and genetic basis of these traits in the different flatfish with the goal of improving production. Further, heritabilities have been estimated for growth-related traits to predict their response to selection in turbot (Gjerde et al., 1997; Guan et al., 2016), Japanese flounder (Liu et al., 2014), tongue sole (Liu et al., 2016) and common sole (Blonk et al., 2010), and genotype x environment interactions have been evaluated in turbot (Guan et al., 2016) and common sole (Mas-Muñoz et al., 2013). These and other studies have suggested the potential of genetic breeding programs in flatfish species. However, turbot is the only species with well established familiar breeding programs, currently in the fifth generation of selection (Chavanne et al., 2016; Janssen et al., 2016). SACCEPTEDome advances have also been MANUSCRIPT made using marker assisted selection in Japanese flounder for achieving more resistant broodstock to lymphocystis (Hwang et al., 2011; Ozaki et al., 2012), as well as for obtaining all-female populations in tongue sole (Chen et al., 2008a) and turbot (Taboada et al., 2014a).

Genomic resources have largely increased in the last decade as a consequence of the lowering cost of Next Generation Sequencing (NGS) technologies. As a consequence, significant transcriptomic resources are now available in flatfish, which are essential for refined functional studies (Cerdá et al., 2008; Ribas et al., 2013; Gomes et al., 2014; Robledo et al., 2014). NGS technologies also facilitated the whole genome assembly of the turbot and tongue sole, suitable references for both structural and functional

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genomics studies (Vilas et al., 2015; Zhang et al., 2015a; Robledo et al., 2016a; Ronza et al., 2016). Further, high-throughput SNP identification and genotyping costs are now very low (Davey et al., 2011; Peterson et al., 2012; Guo et al., 2014). These markers are the basis to construct high-density genetic maps, which in turn are very useful to look for associations and for comparative mapping with model species (Bouza et al., 2012; Diopere et al., 2014; Shao et al., 2015).

Flatfish genomes are among the most compact ones within fish ranging between 500- 700 Mb (Table 1). Important genomic resources and tools have been reported in the main farmed flatfish species belonging to different families of the main suborder Pleuronectoidei: the tongue sole (Cynoglossidae), the turbot (Scophthalmidae), the Atlantic halibut (Pleuronectidae), the Japanese flounder (Paralichthydae), and the Senegalese and common soles (Soleidae). The resources available for these flatfish include genetic maps of medium-high density that have been applied for QTL screening, comprehensive transcriptomic and microRNA databases for gene expression studies, and complete or ongoing whole genome sequencing projects (Table 1).

In the present work, we review and compare the genomic resources currently available in the main farmed flatfish species (turbot, Japanese flounder, Atlantic halibut, tongue sole, common sole and Senegalese sole). Further, we integrate the existing resources for identifying transpecific genomic regions or genes associated with productive traits, useful for future marker assisted selection programs. Additionally, the information gathered involving five of the principal flatfish families provides clues onto the genomic diversificationACCEPTED of flatfish to adapt MANUSCRIPT to a benthic lifestyle. Material and methods

Genome organization: turbot vs tongue sole

We evaluated genomic reorganizations between the two species with available genomes, turbot and tongue sole. We also compared the genomes of turbot and stickleback (Gasterosteus aculeatus; the closest related to Pleuronectiformes among model fish; Chen et al., 2014; Figueras et al., 2016) to have a reference for evaluating the extent of genomic reorganizations between the two flatfish species. Syntenic dotplots were generated using tongue sole (NCBI accession GCF_000523025.1), turbot

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(European Nucleotide Archive, study accession PRJEB11743) and stickleback (ENSEMBL BROAD S1) publicly available genome assemblies. Pair-wise asssembly comparisons were done using BLASTZ v1.02 (Schwartz et al., 2003) filtering low quality BLAST hits with a c-score < 0.5. DAGChainer software v.02-06-2008 (Haas et al., 2004) was used to find chains of BLASTZ matches in order to identify syntenic regions. A minimum of five aligned pairs and a maximum distance of 120 kb between two consecutive matches were required to establish a synteny between two genomic regions. Syntenic relationships between genomes were represented using the SynMap.pl script of CoGe (Accessed April 2016; https://genomevolution.org/CoGe/SynMap.pl; Lyons and Freeling, 2008; Lyons et al., 2008).

Genetic maps

Marker sequences from the genetic maps of the six target flatfish species were compiled for mapping integration. For tongue sole, the map of Song et al. (2012a) was used as reference since it integrates previous mapping information in this species; marker sequences were downloaded from GenBank Data Library (Accession numbers JN902087 - JN903037). For Atlantic halibut, microsatellite sequences reported by Reid et al. (2007) were downloaded, and SNP associated sequences by Palaiokostas et al. (2013), including RAD sequences and map positions, were gently provided by authors. For Japanese flounder, microsatellite and Expressed Sequence Tag (EST) sequences provided by Castaño-Sánchez et al. (2010) were downloaded from GenBank (Accession numbers DQ865460-865479, DQ868392, EF112607-112700, AB459284-459473), while SNP-associatedACCEPTED RAD sequences by MANUSCRIPT Shao et al. (2015) were available as Supplemental Material. SNPs and flanking sequences of microsatellites included in the genetic maps of common sole (Diopere et al., 2014) and Senegalese sole (Molina-Luzón et al., 2015b) were downloaded from GenBank following the information provided in the Supplementary Material of their respective manuscripts (S1 Table, Diopere et al. 2014; ESM 1, Molina-Luzón et al. 2015b). Finally, the map by Hermida et al. (2013), which integrates all previous mapping information of turbot, was used.

All marker sequences were positioned in the turbot genome (Figueras et al., 2016) using BLAST v. 2.2.28+ (Altschul et al. 1990, e-value < 1e-05) . To evaluate the correspondence between genetic maps, only those markers matching to scaffolds anchored to the turbot genetic map were considered. Correspondence between linkage

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groups of the different flatfish species was represented using the igraph package v1.0.1 in R v.3.2.3 (Csardi and Nepusz, 2006) considering only syntenic relationships (two or more homologous markers between linkage groups).

Integrating quantitative trait loci (QTL)

Information on genomic regions associated with productive traits for all target species was retrieved from the literature and integrated taking the turbot genome and genetic map as reference. We identified associations for sex determination (SD) and growth in tongue sole (Chen et al., 2007, 2008a; Song et al., 2012a); for growth and resistance to the bacteria Vibrio anguillarum (vibriosis) and Streptococcus iniae in Japanese flounder (Ozaki et al., 2010; Song et al. 2012b; Wang et al., 2014a; Shao et al., 2015); for SD in Atlantic halibut (Palaiokostas et al., 2013); and for growth in Scophthalmus rhombus (Hermida et al., 2014). All of them were integrated with the previous information available for SD, growth and resistance to the parasite Philasterides dicentrarchi (scuticociliatosis), the bacteria Aeromonas salmonicida (furunculosis) and the hemorraghic septicaemia virus (VHSV) in turbot (Martínez et al., 2009; Ruan et al., 2010; Sánchez-Molano et al., 2011; Rodríguez-Ramilo et al., 2011, 2013, 2014; Hermida et al., 2013). The sequences of the highest associated marker/s, when available, were positioned in the turbot genome using BLAST v. 2.2.28+ (Altschul et al. 1990) with a cut-off E value < 1e-10, and then located in the genetic map using the established relationship between both (Figueras et al., 2016). Alternatively, those sequences without significant matches to the turbot genome were blasted against the genomes of the five model fish species (G. aculeatus, Tetraodon nigroviridis, Oryzias latipes, TakifuguACCEPTED rubripes and Danio rerio MANUSCRIPT) using the BLAT tool (Kent, 2002) of Ensembl v.83 (Yates et al., 2016). Then, their position was predicted in the turbot genetic map using the established syntenies between turbot and model fish genomes (Bouza et al., 2012; Figueras et al., 2016). The gene content of the genomic regions next to selected flatfish QTL was explored in order to identify candidate genes and enriched pathways or functions using BLAST2GO v.3.1 (Conesa et al., 2005) taking the turbot transcriptome (22,751 genes) as background (FDR < 5%).

Comparative transcriptomics

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Transcriptomes or proteomes of five of the six target flatfish species are publicly available and were retrieved for their comparison: tongue sole (Chen et al., 2014); turbot (Figueras et al., 2016); Atlantic halibut (Gomes et al., 2014); Senegalese sole and common sole (Benzekri et al. 2014). The Japanese flounder transcriptome assembly was not publicly available, so we performed a de novo assembly using the raw reads deposited by authors (Wang et al., 2014c; Huang et al., 2015). Reads, downloaded from two paired-end projects on NCBI Sequence Read Archive (SRX500343, Wang et al., 2014c; SRR1515192, Huang et al., 2015) were filtered using Trimmomatic v.0.32 (Bolger et al., 2014). Briefly, residual Illumina specific adaptors were clipped from the reads, leading and trailing bases with Phred score < 15 were removed, and finally, if the average Phred score over four bases using a sliding window was < 20, these bases were also removed. Only reads where both pairs had a length > 32 bp post-filtering were retained. The retained reads were assembled with AbySS v.1.3.7 (Simpson et al., 2009) (k-mer=64), and the resulting de novo transcriptome re-assembled with CAP3 v.3 (Huang and Madan, 1999) to remove redundancy and retain the longest transcripts.

Turbot and tongue sole proteomes, and Atlantic halibut, Japanese flounder, Senegalese sole and common sole transcriptomes were compared using BLAST v. 2.2.28+ (Altschul et al. 1990; e-value < 1e-5) to identify orthology relationships, which were established following a reciprocal best-match strategy (Tatusov et al., 1997). We first looked for orthology between turbot and tongue sole proteomes, since these are the species with more genomic resources, including genome sequencing projects, thus, representing the most complete and robust set of genes. Afterwards, the transcriptome of each of the otherACCEPTED species was compared MANUSCRIPT with the turbot-tongue sole pairs of orthologues to establish sets of orthologues between species trios.

In order to identify genes missing in the turbot transcriptome, the full transcriptomes / proteomes of tongue sole, Atlantic halibut, common sole, Senegalese sole and Japanese flounder were compared to the turbot transcriptome using local BLAST v. 2.2.28+ (Altschul et al., 1990; BLASTp, BLASTx, tBLASTn depending on the information available, e-value < 1e-5). The reverse procedure was followed to identify turbot transcripts missing in the rest of the species included in the analysis. BLAST results were filtered using GeneValidator v.1.6.2 (Drăgan et al., 2016) in order to discard transcripts not represented in public databases or generated by assembly errors. The

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following filtering parameters were considered: transcript length (comparing the length of the query sequence to the length of the most significant BLAST hits using hierarchical clustering); coverage (determining whether the hit regions matched the query sequence more than once using a Wilcoxon test); conserved regions (aligning the query to a position using a specific scoring matrix profile derived from a multiple alignment of the ten most significant BLAST hits); single gene origin (deviation from unimodality of HSP -high score pairs- start and stop coordinates would indicate that HSPs map to multiple regions of the query); and ORFs similarities (considering BLAST which aligned within a single ORF). Only those sequences with a validation confidence higher than 90% were considered real gene transcripts.

MicroRNAs

MicroRNA (miRNA) sequences for the target species were compiled both from miRbase v.21 (Kozamara and Griffiths-Jones, 2014) and from the following publications: tongue sole (Sha et al., 2014), Japanese flounder (Fu et al., 2011; Gu et al., 2014; Zhang et al., 2014b), Atlantic halibut (Bizuayehu et al., 2012a, 2012b) and turbot (Robledo et al., 2016a). All available sequences were compared using BLAST v. 2.2.28+ (Altschul et al., 1990) and those sequences with 100% homology were adscribed to the same miRNA, even if they had different lengths. Furthermore, those sequences showing a single mismatch were also endorsed to the same miRNA, unless both variants were present in at least two different flatfish species, in which case they were assigned to two different miRNAs.

A phylogeneticACCEPTED analysis of the let7 miRNA MANUSCRIPT family was carried out to assess how a specially conserved and diverse miRNA family (Roush and Slack, 2008) evolved in an order showing a high evolutionary rate such as Pleuronectiformes (Figueras et al., 2016). A phylogenetic tree was constructed using MEGAv7.0 (Kumar et al., 2016). The evolutionary history of the miRNAs was inferred by using the Maximum Likelihood method based on the Tamura-Nei model (Tamura and Nei, 1993). Initial trees for the heuristic search were obtained automatically by applying the Neighbor-Joining and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MLC) approach, and then, selecting the topology with superior log likelihood value.

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Results and Discussion

Comparative genomics represents a useful strategy to transfer genomic resources between related model and non-model species. Its application to teleosts has allowed to address comparative studies related to their genome organization and evolution within the framework of the whole genome duplications (WGD) occurring in vertebrates (Jaillon et al., 2004; Kasahara et al., 2007; Zhang et al., 2013; Vij et al., 2016) and to detect candidate gene associations for complex traits of productive or evolutionary interest (Norman et al., 2012; Chen et al., 2014; Figueras et al., 2016; Vij et al., 2016). Pleuronectiformes is an order of evolutionarily iconic fish because of their asymmetrical morphology and adaptations to demersal life, which includes several economically important species for aquaculture and wild fisheries worldwide. The recent completion of the draft genome sequences of turbot and tongue sole (Chen et al., 2014; Figueras et al., 2016), along with the availability of important resources on genetic mapping and transcriptomics in farmed flatfish species, have enabled us to deep into their genome organization, gene annotation and genetic architecture of complex traits related to domestication and breeding programs. In Figure 1 is shown the workflow followed in this study indicating the resources available, the references used for integrating genomic data and its interest for evolutionary and productive applications.

Genome organization: turbot vs tongue sole

The whole genome sequence is publicly available for two flatfish species, the tongue sole (Chen et al., 2014) and the turbot (Figueras et al., 2016). Both species have compact genomesACCEPTED of similar size (~500-560 MANUSCRIPT Mb; Table 1), which have been sequenced to high coverage (> 210x). Despite their similarities, the turbot karyotype displays 22 chromosome pairs while that of tongue sole 21, suggesting a single major reorganization between both karyotypes (see below). However, a refined homology analysis between the ten largest turbot scaffolds and their tongue sole orthologues showed high intra-chromosomic reorganization between both species (Figueras et al., 2016). Here, we extended this comparison to the complete sequence of all tongue sole and turbot chromosomes / linkage groups inferred from the scaffolds anchored to the turbot genetic map (Figure 2), and performed a similar comparison between turbot and stickleback, as the closest model fish genome. A first overview shows a high macrosyntenic correspondence between chromosomes in both comparisons: turbot vs

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tongue sole and turbot vs stickleback. However, while a higher amount of minor translocations (dispersed spots) is observed with regard to stickleback, a lower collinearity is detected between tongue sole and turbot orthologous chromosomes. Only Chr20 of tongue sole showed high collinearity with that of turbot (LG01), while important collinear stretches were observed for several stickleback chromosomes (i.e. GrpII, VIII, XIII, XVI, XXI). Data suggest a high intrachromosomic reorganization rate in flatfish, although more information (incoming genomes) will be required to confirm this hypothesis. Higher molecular evolutionary rates have been reported in flatfish than in other related fish (Vernau et al., 1994), which has been corroborated by the low cross-species amplification observed for microsatellites in this group (Castro et al., 2006). The low statistical support of the flatfish phylogeny, especially at the most internal nodes, and the long branches observed for some families like Cynoglossidae (Pardo et al., 2005) are consistent with a rapid adaptive radiation. Intrachromosomal reorganizations during evolution of specific lineages would be favored during the diploidization process after the fish-specific WDG, since they may reduce homeologous chromosome pairing, decreasing the production of aneuploid gametes and contributing to genomic stability (Comai, 2005; Amores et al., 2011; Lien et al., 2016).

Genetic maps

Linkage maps are valuable tools for aquaculture research, establishing a suitable framework for marker-association studies related to productive characters, for the identification of genes related to specific traits through fine mapping and positional cloning, or forACCEPTED comparative mapping strategies MANUSCRIPT using model species (MacKay, 2001; Sarropoulou et al., 2008; Martínez et al., 2009; Moen et al., 2009; Wang et al., 2011). Genetic maps have been developed in several farmed fish species to assist selection for complex productive traits (Danzmann and Gharbi, 2007; Canario et al., 2008).

At least one genetic map has been published for each of the six target flatfish species in our study and, in some cases, more than one (Table 1). These maps range from low density, as those available in Senegalese sole (Molina-Luzón et al., 2015b) and common sole (Diopere et al., 2014), to high density, as the most comprehensive ones reported in Atlantic halibut (Palaiokostas et al., 2013) and Japanese flounder (Shao et al., 2015). Tongue sole and turbot have also important mapping resources and, additionally, the whole genome sequence is available, which have facilitated the integration of physical

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and genetic maps. Two low density (Liao et al., 2009; Jiang et al., 2013) and one high density (Song et al., 2012a) maps have been described in tongue sole. The last genetic map comprises 1,007 microsatellite markers and includes markers from the previous one enabling anchorage. In turbot, the first linkage map included 236 anonymous microsatellites (Bouza et al., 2007), and this map was successively enriched mostly with Expression Sequence Tag (EST)-linked markers for a total of 496 markers (Bouza et al., 2008, 2012). In a parallel study Ruan et al. (2010) developed male and female turbot maps using mostly anonymous microsatellites. All previous turbot genetic maps were finally integrated by Hermida et al. (2013) generating a genetic map of ~600 markers. Centromeres were also located and integrated with previous turbot mapping data using half tetrad analysis on diploid gynogenetics (Martínez et al., 2008). Finally, a highly dense RADseq based map has been recently reported in turbot (6,647 SNP; Wang et al., 2015), but unfortunately it cannot be anchored to previous mapping resources due to insufficient marker sequence information. In Japanese flounder and Atlantic halibut low-medium density linkage maps comprising microsatellite and AFLP markers are available (Japanese flounder: Coimbra et al., 2003; Kang et al., 2008; Castaño-Sánchez, et al., 2010; Song et al., 2012b; Atlantic halibut: Reid et al., 2007), but new generation high density maps based on RADseq have largely increased mapping information for both species (Shao et al., 2015 and Palaiokostas et al., 2013, respectively). These maps comprehend 12,712 and 5,703 SNPs for Japanese flounder and Atlantic halibut, respectively. Unfortunately, previous markers are not included in these high-density SNP maps, although a correspondence between different maps has been provided for the Japanese flounder through comparative genomics (Shao et al., 2015). Finally, genetic maps forACCEPTED common sole and Senegalese MANUSCRIPT sole have recently been published comprehending 423 SNPs (Diopere et al., 2014) and 129 microsatellites (Molina-Luzón, et al., 2015b), respectively, the last one only including female segregation from haploid gynogenetics reference families.

The list of 1,317 microsatellite markers of tongue sole yielded 202 positive matches on the turbot genome (Table 2; Supplementary file 1). After removing tongue sole unlinked markers (45) and markers matching to unanchored turbot scaffolds only 133 markers were retained (Table 2; Supplementary file 1). For halibut, 5,959 sequences (258 microsatellites and 5,701 SNPs from RADseq) were compared using BLAST against the turbot genome and 614 positive matches were identified, 535 of them to anchored

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scaffolds (86 from microsatellites and 449 SNPs) (Table 2; Supplementary file 1). For Japanese flounder, 1,267 microsatellites, 99 EST-associated markers and 12,712 SNPs from RADseq were compared. A total of 606 positive matches were found, 527 markers matching to anchored scaffolds in the turbot map. Only 116 of these 527 positive matches proceeded from RAD sequences. Finally, from the 431 and 114 marker sequences for common sole and Senegale sole respectively, 35 and 41 positive matches with anchored scaffolds were retained (Table 2; Supplementary file 1).

Some studies have addressed the relationship among flatfish genetic maps as a suitable frame for applied and evolutionary studies. Reid et al. (2007) analyzed the correspondence between the genetic maps of Atlantic halibut and Japanese flounder, and Diopere et al. (2014) between those of turbot and common sole, but the broadest comparison was reported by Molina-Luzón et al. (2015b). Here, using as reference the highest coverage flatfish genome (turbot, 219x; Figueras et al., 2016), we established the relationships between all flatfish genetic maps. Comparative mapping using the turbot genetic map as reference (n = 22; Table 1) strongly supports the existence of several fusions (or fisions) in the evolution of the order Pleuronectiformes, especially for those species with the highest marker density, tongue sole, Atlantic halibut and Japanese flounder (Table 2). Thus, two turbot chromosomes, represented by linkage groups (LG) 16 and LG21+24, would correspond to the C8 of tongue sole (n = 21), while linkage groups P10 and P19 of Japanese flounder (n = 24) as well as H17 and H22 of halibut (n = 24) would correspond to turbot LG02, the largest metacentric pair of the turbot karyotype (Taboada et al., 2014b). Additionally, linkage groups P18 and P15 of JapaneseACCEPTED flounder and H16 and H23 MANUSCRIPT of halibut would correspond to LG16, the second largest metancentric pair of the turbot karyotype (Taboada et al., 2014b) (Table 2; Figure 3; Supplementary file 2). Our comparative mapping analysis is in accordance with the occurrence of chromosomal rearrangements in Pleuronectiforms derived from their primitive karyotypes (2n = 48 acrocentrics; LeGrande, 1975; Fujiwara et al., 2007) and aids to explain the diversity in chromosome and arm number in this group (2n = 28–48; fundamental number (NF) = 37–70). For Senegalese sole and common sole, the low number of markers makes non-viable such comparison, since some linkage groups did not show any marker matching to the turbot map (Figure 3; Supplementary file 2). Some turbot linkage groups like LG04, LG15, and LG20, or even LG03, LG06, LG11 and LG17, appear to be highly stable across flatfish evolution mostly with a

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single correspondent linkage group in the other species. Conversely, others like LG01, LG02, LG04, LG10 and LG08+18 show syntenic relationships with more than one LG in the other species denoting higher unstability. Among the species with highest mapping resources, the Japanese flounder displayed the lowest amount of discordant markers (out of the 1:1 macrosyntenic relationship) with turbot (9.7%), while the Atlantic halibut (16.4%) and tongue sole (16.5%) nearly doubled it (Table 2; Figure 3; Supplementary file 2). These data may suggest different rate of interchromosomal reorganizations among flatfish lineages, but may also be explained by ambiguous homologies related to the retention of duplicated sequences along vertebrate and fish evolution (Jaillon et al., 2004; Kasahara et al., 2007; Bouza et al., 2012).

Integrating QTL and associations of productive traits

The existence of interspecific conserved genomic regions (syntenies) enables searching and comparing specific genomic sequences from one species into another (Sarropoulou et al., 2008). Similarly, QTL associated with traits of economic interest in one species may be predictively located in other species (Li et al., 2011; Loukovitis et al., 2011). Localization of QTL for the same trait in the same genomic region of different species would provide additional support for the associations detected and also suggest their transpecific conservation. Additionally, this information might provide clues on putative QTL not yet identified in other species.

We retrieved from the literature information on QTL and genomic associations for productive traits in flatfish species (Supplementary file 3) and compared their genomic location with thatACCEPTED previously reported for MANUSCRIPTtubot QTL (Martínez et al., 2009; Ruan et al., 2010; Sánchez-Molano et al., 2011; Rodríguez-Ramilo et al., 2011, 2013, 2014; Hermida et la., 2013). Several QTL associated markers showed a significant homology to the turbot genome and were predictively located in the turbot genetic map (Figure 4). Interestingly, QTL markers for resistence to Streptococcus iniae in Japanese flounder (Ozaki et al., 2010) were located within the confidence intervals of resistence QTL for A. salmonicida, P. dicentrarchi and VHSV previously reported in turbot at LG06 and LG09. Two ubiquitine related genes (TOPORS, USP53) at LG06 and TRIM16, pertaining to the tripartite motif family, at LG09, were identified within ±1 Mb around the highest associated markers. These genes have been suggested to play a general role in resistance to pathologies (Figueras et al., 2016) and might be related to the observed

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transpecific QTL conservation. Also, growth QTL reported in turbot by Ruan et al. (2010) and in S. rhombus by Hermida et al. (2014) colocalize with that described in this species by Sánchez-Molano et al. (2011) at LG16, suggesting the presence of growth- related genetic variation in this chromosome across turbot populations and even among flatfish. Indeed, major candidate genes involved in regulating the somatotrophic axis and myogenesis in vertebrates were identified in this region of the turbot genome, such as IGF1 and MYF5 (Figueras et al., 2016). Furthermore, these genes have been linked to QTL for growth related traits in salmonids (Moghadam et al., 2007; Wringe et al., 2010), supporting the interest of this region for marker-assisted selection in turbot and other flatfish.

Other associated markers were located close to previous turbot QTL, but out of their confidence intervals, suggesting different underlying genomic regions (Supplementary files 3 and 4). This is the case of two Japanese flounder QTL related to resistence to Vibrio anguillarum (Wang et al., 2014a) at turbot LG01 and to S. iniae at turbot LG06 and LG09 (Ozaki et al., 2010). A tight cluster of immune related genes was identified at LG01 in the syntenic region of the reported QTL, including several genes involved in defence response, response to wounding and immune response (CD48, CD59, FCER1G, LTB4R, NFATC4, VAV3; Supplementary file 4). Similarly, enriched immune related GO terms were identified at turbot LG06 in the syntenic region of the previously mentioned QTL including innate immune response, inflammatory response or response to wounding (CBFB, E2F4, IL34, PLLP, TGM2, TMEM189; Supplementary file 4). Other associations were detected at linkage groups where no QTL had been reported in turbot, such asACCEPTED two growth-related QTL (qWiMANUSCRIPT-fl4-1 and qWi-m14) at LG14 of Japanese flounder (Song et al., 2012b; Supplementary file 3). qWi-fl4-1 was physically anchored to turbot LG08 enabling gene mining which showed functional enrichment of growth regulation and primary metabolic process (ADORA1, CD81, CREB3, ETV5, GRM4, KCNH8, NGF, RERG, SATB2, SEC14L2, TADA2B, TEAD3, UBE2L3), while qWi-m14 was achored to turbot LG02 and included enriched GO terms related to growth factor binding and primary metabolic process (ADORA2A, BMPR2, CSRNP3, DMD, GBX2, KCNH7, NUFIP1). Finally, sex related QTL (and/or associated markers) reported in flatfish were anchored to the turbot genome, but their syntenic locations did not overlap with the intervals described for the major sex determination in turbot at LG05 (Martínez et al., 2009; Figueras et al., 2016). Tongue sole sex determination QTL were located at

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turbot LG12 and LG17 (Song et al., 2012a) and Atlantic halibut at turbot LG14 (Palaikostas et al., 2013). The sex determination QTL of Japanese flounder, although syntenic to turbot LG05 (Sakamoto et al., 2008), was located far away from the major SD region of turbot. The lack of concordance for sex-associated regions in species of different families and even genera is a common observation in fish, where sex determining regions show a high evolutionary turnover (Martínez et al., 2014).

Comparative transcriptomics

De novo assembly of the Japanese flounder transcriptome in our work rendered 61,637 sequences, which contrasts with the original 96,627 reported by Wang et al. (2014c) (Supplementary file 5). Differences are explained by the stronger read filtering criteria and the additional reassembly step with Cap3 performed in our study. This is the strategy we followed in previous studies to avoid redundancy obtaining consistent results for transcriptome assembly and downstream applications such as marker identification or microarray design (Hasanuzzaman et al., 2016).

Comparative transcriptomics among flatfish was first tackled by identifying a list of orthologous genes between all pairs of species analyzed in order to provide common sets of genes useful for functional genomics studies, but also to be used as anchors for comparative mapping (Chen et al., 2014; Figueras et al., 2016; Supplementary file 6). Tongue sole, Japanese flounder, Senegalese sole and common sole transcriptomes are the result of broad RNA sequencing projects, but still based on an incomplete number of tissues and experimental conditions. On the contrary, turbot and tongue sole display transcriptomesACCEPTED derived from both empirical MANUSCRIPT and predictive data using their whole genomes. Thus, it is expected that these two last transcriptomes are the most complete and robust. Accordingly, we first assessed the orthology relationships between these two species and out of the 28,189 and 34,032 turbot and tongue sole transcripts, respectively, a total of 16,199 orthologs were identified. Then, this set was used to identify triplets of orthologues common to turbot and tongue sole and to each of the other four target species. The largest set of triplets was that with Senegalese sole (11,827) and common sole (12,827). Both Japanese flounder and Atlantic halibut rendered a much lower amount ( 6,031 and 7,473 respectively). Finally, all these sets of triplets were compared to identify common orthologues to all the six flatfish studied species, obtaining a total of 2,810 common flatfish orthologs (Figure 5). The Japanese

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flounder and Atlantic halibut transcriptomes greatly hampered the identification of a higher number of common orthologs, and when only the turbot, tongue sole, Senegalese sole and common sole transcriptomes were considered, a common set of 10,581 orthologs was obtained.

Secondly, we compared the turbot transcriptome with those of the other flatfish, trying to identify genes that were consistently missing in the genomes of turbot or of the other species. This information might be useful to ascertain the diversification of flatfish to adapt to their particular benthic lifestyles as previously reported (Chen et al., 2014; Figueras et al., 2016). Yet this is not a straightforward task, since it depends on the coverage and confident assembly of the transcriptomes, we estimated that the availability of several transcriptomes could render consistent trends if appropriate and conservative filtering pipelines were applied. As expected, when the turbot transcriptome was compared by BLAST with those of the other flatfish, most transcripts showed a significant match (Table 3). Those with no match varied in number from 2,903 to 7,262 depending on the species, but only a small number was validated with our filtering pipeline. From these results, it is evident that the Japanese flounder shows the most incomplete transcriptome. Among validated transcripts missing in the flatfish transcriptomes, the most representative functions (GO terms) were related to immune response and its modulation, skin secretion and transport and signaling, particularly G protein-coupled receptors (Supplementary file 7). Overall, five transcripts were exclusively found in turbot when compared to the remaining species, including interferon beta, involved in defense against viral infections (Perry et al., 2005); parvalbumin-6ACCEPTED, belonging to the calmodulin MANUSCRIPT family and involved in muscle relaxation, with nine isoforms in zebrafish (Friedberg, 2005); and the transcription factor ZFPM1, involved in haemostasis and the C-MYB transcription factor network (Lee et al., 2009) (Supplementary file 7).

When the reverse approach was applied (BLAST of flatfish transcriptomes against that of turbot), several genes were missing in the turbot transcriptome (Table 3; Supplementary file 8). A nearly full match was observed between tongue sole and turbot, species with the most consistent transcriptomes, with 609 genes not detected in the turbot database, 321 of them validated with our filtering pipeline. Among the other species, a much higher number of transcripts did not match against the turbot

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transcriptome. The Atlantic halibut showed the lowest number (6,798), while Senegalese sole, common sole and Japanese flounder presented very high numbers of unmatched transcripts (> 20,000). Although a minor fraction of this high discordance could be related to isoforms produced by alternative splicing, most of them are likely the result of inconsistent assembly since very few sequences (< 100) were finally validated. When the complete genomes of these species become available, this matter will be easily clarified.

Even though tongue sole showed the lowest number of unmatching transcripts with turbot, it showed the highest number of validated ones (321). Likely, those missing sequences in the turbot transcriptome are reliable, although we cannot exclude that a small fraction could be related to some assembling incompleteness of the turbot genome. These sequences are mostly related to immune response and its modulation, the nervous system and extracellular matrix formation (Supplementary file 8). Among the last, several collagen-related coding transcripts were identified including collagen α1 and SPARC1, a type I collagen binding protein involved in bone calcification and extracellular matrix synthesis (Mendoza-Londono et al., 2015). Different components of the protein polyubiquitinization machinery and the SUMO pathway were also missing including ubiquitin ligases, NEDD8, ubiquitin B and sentrin-specific proteases (SENP). Also a gene involved in sex determination in mouse (CBX2, chromobox homolog 2) (Katoh-Fukui et al., 1998) and an energy homeostasis regulator in mammals (UCN3, urocortin 3) (Li et al., 2007) were missing. A teleost specific urotensin (urotensin 1), with a suggested role in osmoregulation, and a corticotropin-releasing factor (Lederis et al., 1983) wereACCEPTED also found in tongue sole butMANUSCRIPT not in turbot. Comparison with Senegalese sole and common sole identified missing transcripts involved in the immune response among the most represented ones; however, no missing gene products were detected against the Atlantic halibut transcriptome. In Japanese flounder, similar to that observed in tongue sole, collagen α1, ubiquitin, polyubiquitin, and ubiquitin ligase were identified. Although most flatfish species have small-size scales compared to other fish (especially the pelagic ones), the turbot is the only flatfish without scales among those here analyzed. Reducing scales and even their complete loss appears to be an adaptation to the benthic life, likely to avoid skin scrapping due to chafing. This fact could explain the dissapearance or pseudogenization of some collagen-related genes involved in scale

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formation, but at the same time an alternative protection related to mucus production and immune defence enhancement would be expected. microRNAs

MicroRNAs (miRNAs) have been studied in five of the six target flatfish species: tongue sole (Sha et al., 2014), Japanese flounder (miRbase; Fu et al., 2011; Gu et al., 2014; Zhang et al., 2014b), Senegalese sole (Campos et al., 2014), Atlantic halibut (miRbase; Bizuayehu et al., 2012a, 2012b) and turbot (Robledo et al., 2016a) (Table 4; Supplementary file 9). A total of 168 mature miRNAs were found in at least four of the flatfish species (Figure 6). Japanese flounder shows the largest number of unique miRNAs (171), although many of them have been reported for the first time in this species (130) and will require further validation. The different tissues and conditions used for each species sequencing run may account for the variation observed and, especifically, for the miRNAs present only in one or two species (Figure 6). Future deep-sequencing projects will enable the detection of low expressed miRNAs (tissue and/or condition-specific) aiding to complete the flatfish miRNA dataset. Nonetheless, the current set of 168 common miRNAs is an important starting point to study the functional conservation of flatfish miRNAs and identify their targets, which so far have been mainly computationally predicted. The notable exception are three studies in Japanese flounder using luciferase reporter assay, which identified the gene serum response factor (SRF) as target of miR-133a (Su et al., 2015), histone deacetylase 4 (HDAC4) as target of miR-1 and miR-133a (Zhang et al., 2015b) and empty spiracles homeobox 2 (emx2)ACCEPTED as target of miR-26a MANUSCRIPTand miR-26b (Yin et al., 2015). These studies may be potentially useful for other flatfish species given the high conservation of the mature miRNA sequences, although the high evolutionary rate reported for the order Pleuronectiformes could hinder cross-species information transference. Thus, we analyzed the evolutionary relationships of the let7, a large and highly diverse miRNA family, to evaluate how this high evolutionary rate could affect miRNAs. Let7 phylogeny revealed a high conservation of the mature miRNAs of this family (Figure 7), which were identical in most cases. The mature miRNA conservation contrasts with the diversity of the 3p carrier strands (although these have not been described for tongue sole and Atlantic halibut), which do not present functional constraints. The let7 family is specially conserved across different taxa both in sequence and function (Roush and

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Slack, 2008). The central role of miRNAs in many regulatory networks will encourage for an increasing number of studies in the coming years contributing to understand the genetic basis of productive traits and their application for improving production in flatfish species.

Conclussion and perspectives

Comparative sequence analysis of multiple flatfish genomic resources was revised and integrated using the recently available reference draft genomes of turbot and tongue sole. These data allowed us to comparatively investigate genomic organization and gene content for identifying conserved syntenic blocks and QTL regions across flatfish. The generated integrative resources also provided fine information to deep into the karyotype evolution of flatfish and to explore specific transcriptome sequences associated to the evolutionary diversification of this group that should be further validated by functional analyses. Furthermore, integrating available QTL mapping for productive traits demostrated to be an effective way of obtaining key information on conserved transpecific QTL, which may accelerate the transference of knowledge to the industry. This study shows that flatfish include one of the largest collection of mapping and genomic resources for farmed fish, and will serve as a reference for further genomic analysis in vertebrates. Integrated turbot resources, including the genome assembly, the transcriptome and QTL mapping, have demonstrated to be a useful reference for comparative genomics in flatfish, and could further facilitate whole genome sequence assembly and annotation in other related species with evolutionary or productive purposes. ACCEPTED MANUSCRIPT Acknowledgements

We ackowledge the investigation funding provided by Xunta de Galicia local government (GRC2014/010) and the bioinformatic support of the Centro de Supercomputación de Galicia (CESGA). Halibut RAD-tag sequences were kindly supplied by H. Migaud & A. Davie, data generated from a Scottish Aquaculture Research Forum funded project, with the collaboration of C. Palaiokostas.

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Figures

Figure 1.- Scheme of the workflow followed to integrate flatfish genomics resources and their applications. The turbot and, to a minor extent, the tongue sole information were the main reference for all the integrative work. The species with data available at each section (maps, QTL, transcriptomes, miRNA and genomes) are shown in circles aroound the used reference/s (in the middle), when applied. ACCEPTED MANUSCRIPT

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Figure 2.- Syntenic dot plot of sequence collinearity between orthologous chromosomes of turbot vs tongue sole (A) and vs G. aculeatus (B). Syntenic regions are represented as dots in the Figure; green and blue dots represent positive and negative orientation, respectively.

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Figure 3.- Comparative mapping graph plot of available genetic maps in the main six flatfish aquaculture species. T: turbot; C: tongue sole; H: Atlantic halibut; P: Japanese flounder; L: Senegalese sole; S: common sole

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Figure 4.- Comparative QTL mapping in the main six flatfish aquaculture species. Flatfish QTL have been represented on the 24 turbot linkage groups (LG1-LG24). Green, blue and red colours represent associations related to resistance to pathologies, growth and sex determination, respectively. PO: Japanese flounder (P. olivaceus); CS: tongue sole (C. semilaevis); HH: Atlantic halibur (H. hippoglossus); SR: S. rhombus and SM: turbot (S. maximus) from Ruan et al. (2010).

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Figure 5.- Venn diagram showing orthology in the main flatfish aquaculture species. Orthologous sequences of the two species with the most extended genomic resources, the turbot and tongue sole, were used as reference.

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Figure 6.- Venn diagram showing shared and specific miRNAs between the main six flatfish aquaculture species.

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Figure 7.- Phylogenetic tree of the miRNA let7 family in five flatfish aquaculture species.

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Legends to Supplementary Files

Supplementary file 1.- Full comparative mapping information of available genetic maps in the main six flatfish aquaculture species

Supplementary file 2.- Macrosyntenic analysis of turbot regarding farmed flatfish (tongue sole, Japanese flounder, Atlantic halibut, Senegalese sole and common sole) and sticleback (G. aculeatus), the closest model teleost.

Supplementary file 3.- Full information on QTL studies from the main six aquaculture flatfish species

Supplementary file 4.- Gene mining in the most relevant QTL regions identified by comparative mapping in the main six flatfish aquaculture species

Supplementary file 5.- Full transcriptomic information in the main six flatfish aquaculture species

Supplementary file 6.- List of orthologous genes in the main six flatfish aquaculture species

Supplementary file 7.- List of genes present in the turbot transcriptome but missing in the flatfish target species

Supplementary file 8.- List of genes present in the flatfish target species transcriptomes but missing in ACCEPTEDthe turbot transcriptome MANUSCRIPT Supplementary file 9.- Full information of comparative miRNA resources in the main six flatfish aquaculture species

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Table 1. Genomic resources of the main flatfish aquaculture species

Tongue sole (C. Turbot (S. Japanese flounder Atlantic halibut Senegalese sole (S. Common sole (S. semilaevis) maximus) (P. olivaceous) (H. hippoglossus) senegalensis) solea)

M: 495Mb; F: 545 3 4 5 Genome size 568 Mb2 675 Mb 675Mb no 675Mb Mb1

Chromosome number 421 442 483 486 427 428 (2n)

Chromosome triploids10 triploids and gynogenetics9 gynogenetics12 no no manipulation gynogenetics11 gynogenetics13

Whole genome yes (212x)1 yes (219x)2 ongoing project no ongoing project no sequence (coverage)

Complete mtDNA 16,37114 no 17,09015 17,54616 16,65917 no sequence (bp)

BAC library (clones) 55,29618 46,08019 49,10020 no 29,18421 no

Genetic maps three22,23,24 five25,26,27,28,29 five30,31,32,33,34 two35,36 one37 one38

6026,7027, 664729, No. mapped SNPs no ACCEPTED13362 MANUSCRIPT30,10533 570336 no 42338 4389 No. mapped 3322,32523,100724, 24225,37826,41727, 11131,18032,126833, 25835 12937,41,42 838 microsatellites 31139 28428 162434, 15940

Comparative mapping yes55 yes25,26,27,2 yes30,33 yes35,36 yes37 yes38

22,24,43,44 22 36 QTL studies SD G SD27,29,45 G28,29,46 G34; PR30,40,50,51,52,53 SD no no

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PR47,48,49 SD54

Physical map yes18,55 yes19,2 yes20 no yes21 no

Bioinformatics yes (not public yes (not public yes (not public yes (not public yes56 yes56 databases available) available) available) available)

cDNA library/transcriptome 31,63257 22,7512,58 61,63759 22,27260 59,51456 54,00556 (No. unique sequences)

miRNAs 45261 31162 48663,64,65 22666,67 32068 no

Gene expression 30974; 55475 87176 1.9k69 2.7k70 4.3k71 (microarray; no. of no 1.2k77 1.9k78 9.9k79 9.383 10.3k84 5.1k85 43.4k56 12.8k86 43.4k72 43.8k73 probes) 13k80 13k81 13k82

Gene expression immune88 and brain, muscle and immune organs87 no larvae92 no (RNAseq) growth89 tissues liver90 gonads91

1Chen et al., 2014; 2Figueras et al., 2016; 3Fujiwara et al., 2007; 4Hardie and Hebert, 2004; 5Libertini et al., 2002; 6Ocalewitz et al., 2008; 7Cross et al., 2006; 8Pardo et al., 2001; 9Chen et al., 2009; 10Piferrer et al., 2000; 11Piferrer et al., 2004; 12Tabata et al., 1986; 13Molina-Luzón et al., 2015a; 14Kong et al., 2009; 15Saitoh et al., 2000; 16Mjelle et al., 2008; 17Manchado et al., 2007; 18Shao et al., 2010; 19Taboada et al., 2014b; 20Katagiri et al., 2000; 21García-Cegarra et al., 2013; 22Liao et al., 2009; 23Jiang et al., 2013; 24Song et al., 2012a; 25Bouza et al., 2007;ACCEPTED 26Bouza et al., 2012; MANUSCRIPT 27Hermida et al., 2013; 28Ruan et al., 2010; 29Wang et al., 2015; 30Shao et al., 2015; 31Coimbra et al., 2003; 32Kang et al., 2008; 33Castaño-Sánchez et al., 2010; 34Song et al., 2012b; 35Reid et al., 2007; 36Palaiokostas et al., 2013; 37Molina-Luzón et al., 2015b; 38Diopere et al., 2014; 39Sha et al., 2010; 40Ozaki et al., 2010; 41Funes et al., 2004; 42Chen et al., 2008b; 43Chen et al., 2007; 44Chen et al., 2008a; 45Martínez et al., 2009; 46Sánchez-Molano et al., 2011; 47Rodríguez-Ramilo et al., 2011; 48Rodríguez-Ramilo et al., 2013; 49Rodríguez-Ramilo et al., 2014; 50Fuji et al., 2006; 51Fuji et al., 2007; 52Wang et al., 2014a; 53Xu et al., 2008; 54He et al., 2008; 55Zhang et al., 2014a; 56Benzekri et al., 2014; 57Wang et al., 2014b; 58Ribas et al., 2013; 59Wang et al., 2014c; 60Gomes et al., 2014; 61Sha et al., 2014; 62Robledo et al., 2016a; 63Fu et al., 2011; 64Gu et al., 2014; 65Zhang et al., 2014b; 66Bizuayehu et al., 2012a; 67Bizuayehu et al., 2012b; 68Campos et al., 2014; 69Park et al., 2009; 70Millán et al., 2010; 71Hagenaars et al., 2011; 72Pereiro et al., 2014; 73Ribas et al., 2016; 74Nakayama et al., 2008; 75Matsuyama et al., 2011; 76Kurobe et al., 2005; 77Byon et al., 2006; 78Dumrongphol et al., 2009; 79Kato et al., 2012; 80Moon et al., 2014; 81Kondo et al., 2014; 82Iwakiri et al., 2014; 83Douglas et al., 2008; 84Mommens et al., 2014; 85Cerdá et al., 2008; 86Ferraresso et al., 2013; 87Zhang et al., 2015a; 88Robledo et al., 2014; 89Robledo et al., 2016b; 90Hu et al., 2014; 91Fan et al., 2014; 92Hachero-Cruzado et al., 2014.

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Table 2. Comparative mapping of the main flatfish aquaculture species Tongue sole Atlantic halibut Japanese flounder Senegalese sole Common sole Turbot (S. maximus) (C. semilaevis) (H. hippoglossus) (P. olivaceous) (S. senegalensis) (S. solea) LG N. scaffolds LG N. markers LG N. markers LG N. markers LG N. markers LG N. markers LG01 14 C21 5 H18 20 P23 18 L03 1 S1 3 (C06,07,14,19) H05 2 P13 4 L05 1 (S10) H09 2 (P04,20) (H03,06,22) LG02 22 C14 6 H22 11 P10 25 L07 1 S5 2 C15 3 H17 10 P19 6 L20 1 (S14) (C04,19) H15 4 J12 3 (H03,07,11,13,21) (P03,13,15) LG03 7 C02 8 H20 20 P02 25 L15 1 (H02,09,24) (P05,07,08,10) LG04 9 C06 4 H24 18 P17 17 L03 2 S7 2 (C01,15) (H02,08,16,20,23) (P10,14,15) (L26) (S4) LG05 9 C03 8 H09 23 P09 27 L08 2 S1 1 H08 2 (P11) (H15,16) LG06 11 C07 8 H04ACCEPTED 17 MANUSCRIPTP11 17 L06 1 H16 2 (H05,24) LG07 8 C16 6 H19 15 P21 33 L11 1 (C09) (H04,21) (P04,05,20) L13 1 LG08&18 13 C02 2 H06 16 P14 13 L11 1 S22 1 C09 2 H13 2 P15 3 (C20) (H02,07,14,23) (P13,17,21,24)

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LG09 10 C10 5 H10 23 P07 34 L01 4 S4 5 (C02,05) H05 2 (P23) (L13,26) H07 2 (H09,13,16,18) LG10 12 C11 3 H11 14 P01 27 L23 1 S6 2 C21 2 H22 3 (P02,10,17) (S20) (C01) (H08,15,21) LG11 12 C13 2 H14 17 P16 30 L02 4 (C15) (H01,02,04,11,15,20) (P19) LG12 9 C01 9 H07 17 P04 30 L04 2 S11 2 H14 2 (P21,23,24) (L13) H18 2 H19 2 (H16) LG13 8 C18 7 H21 16 P24 22 L07 1 S2 3 (C15) H10 2 L14 1 (H08,19) LG14 6 C15 1 H13 12 P06 14 L09 3 H22 3 P10 5 (H02,07,12) (P03,22) LG15 10 C05 2 H12ACCEPTED 7 MANUSCRIPTP05 12 L22 1 S8 1 (H07,09,11,15,18,20) (P16,20) LG16 13 C09 6 H23 10 P18 11 S13 1 (C08) H16 5 P15 3 S23 1 (P09,11,12) LG17 6 C04 4 H08 12 P08 15 (H02,11,16,19) (P01,04,21) LG19 8 C12 9 H15 15 P12 18 L12 1

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(H08,18) LG20 5 C17 5 H01 9 P03 12 L17 2 (C03) (H04,18,21) (P08,12,13,23) (L25) LG21&24 5 C09 4 H02 14 P20 22 L19 1 S19 3 H18 2 (P07,10) (H01,08,21) LG22 4 C19 1 H03 12 P13 20 L01 1 S9 4 (H05) (P06,07) LG23 5 C08 5 H05 13 P22 14 L11 1 (H02,04,06,09,16) (P10)

The turbot genome (Figueras et al., 2016) was used as reference. Linkage groups: tongue sole (C), Atlantic halibut (H), Japanese flounder (P), Senegalese sole (L) and common sole (S). For Atlantic halibut and Japanese flounder, only the correspondence with the maps by Palaiokostas et al (2013) and Castaño-Sánchez et al (2010), respectively, are shown. In parentheses those markers in linkage groups with a single match.

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Table 3. Comparative transcriptomics of the main flatfish aquaculture species using the turbot transcriptome as reference

Validated Validated No. predicted Matched Transcripts with sequences with sequences with proteins/transcripts transcriptsc no matchc no matchd f GO termse f Turbot 22751a (S. maximus) Tongue sole 19,457 3,294 138 38 31632a (C. semilaevis) 31,023 609 321 174 Atlantic halibut 17,321 5,430 483 243 22272b (H. hippoglossus) 15,474 6,798 5 1 Common sole 19,848 2,903 49 21 54005b (S. solea) 33,341 20,644 34 30 Senegalese sole 19,694 3,057 69 27 59514b (S. senegalensis) 35,716 23,798 32 35 Japanese flounder 15,489 7,262 2,244 1,990 61637b (P. olivaceous) 23,073 38,564 17 13 a Proteome ACCEPTED MANUSCRIPT b Transcriptome c Turbot vs selected species (upper row), selected species vs turbot (lower row in grey), evalue=1e-5 d Turbot vs selected species (upper row), selected species vs turbot (lower row in grey), validation ranking > 90 % e Turbot vs selected species (upper row), selected species vs turbot (lower row in grey), determined with BLAST2GO with default options f See Supplementary files 6 and 7 for results

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Table 4. Comparative miRNA resources in the main flatfish aquaculture species or with potencial for aquaculture production

Tongue sole (C. Japanese Senegalese sole Atlantic halibut Turbot semilaevis) flounder (P. (Solea (Hippoglossus (Scophthalmus olivaceus) senegalensis) hippoglossus) maximus)

Tissues Liver, spleen, 1) Larvae; 2) Embryo and 1) Larvae; 2) 1) Larvae, 2)

head kidney and ovary and testis; larvae Brain and gonads brain, gonad, intestine 3) Spleen kidney, spleen, liver and muscle

Experimental Vibrio 1) Metamorphosis 75% epiboly, 20 1) Metamorphosis 1) 1, 5 and 15 conditions anguillarum (17, 21, 29, 36, somites, hatching, (2, 4 and 11 days days post infection and 42 days post and 8-9 and 14-15 post fertilization, fertilization, 2) hatching); 2) male days post 15 days post 120-130 days post and female adult hatching, at two fertilization or fertilization and 3 gonads; 3) rearingtemperatur hatching, 46, 61, years old megalocytivirus es, 21 and 15ºC 92 and 112 days RBIV-C1 post hatching); 2) infection Males and females 2-months, 3-years and 5- ACCEPTED MANUSCRIPT years old Total miRNAs 452 486 320 226 311

In five species 109 109 109 109 109 In four species 30 55 57 43 51

In three species 28 40 31 23 34 In two species 37 32 29 16 51 Unique 112 171 80 19 54

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