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Keywords:

  • markers development;
  • sea cucumber (Apostichopus japonicus);
  • transcriptome sequencing

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

Sea cucumber (Apostichopus japonicus) is an ecologically and economically important species in East and South-East Asia. This project aimed to identify large numbers of gene-associated markers and differentially expressed genes (DEGs) after lipopolysaccharides (LPS) challenge in A. japonicus using high-throughput transcriptome sequencing. A total of 162 million high-quality reads of 174 million raw reads were obtained by deep sequencing using Illumina HiSeq™ 2000 platform. Assembly of these reads generated 94 704 unigenes, with read length ranging from 200 to 16 153 bp (average length of 810 bp). A total of 36 005 were identified as coding sequences (CDSs), 32 479 of which were successfully annotated. Based on the assembly transcriptome, we identified 142 511 high-quality single nucleotide polymorphisms (SNPs). Among them, 33 775, 63 120 and 45 616 were located in sequences without predicted CDS (non-CDSs), CDSs and untranslated regions (UTRs), respectively. These putative SNPs included 82 664 transitions and 59 847 transversions. Totally, 89 375 (59.1%) were distributed in 15 473 known genes. A total of 6417 microsatellites were detected in 5970 unigenes, 3216 of which were annotated and 2481 were successfully subjected for primer design. The numbers of simple sequence repeats (SSRs) identified in non-CDSs, CDSs and UTRs were 2367, 2316 and 1734. These potential SNPs and SSRs are expected to provide abundant resources for genetic, evolutionary and ecological studies in sea cucumber. Transcriptome comparison revealed 1330, 1347 and 1291 DEGs in the coelomocytes of A. japonicus at 4 h, 24 h and 72 h after LPS challenge, respectively. Approximately 58.4% (1802) of total DEGs have been successfully annotated.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

Sea cucumber (Apostichopus japonicus), an ecologically and economically important species, is naturally distributed in the north-western Pacific Ocean along the coasts of China, Japan, Korea and Russia. In addition to nutritional and supposed medicinal values, this sea cucumber species is considered to be one of the most flavourful species in markets in East and Southeast Asian countries. In the past two decades, A. japonicus aquaculture has expanded rapidly and become a vigorous industry along the coasts of northern China. To analyse the genetic diversity and improve economic properties of this species for aquaculture including growth rate and disease resistance, genetic breeding programmes have been conducted, such as the development of molecular markers (Zhan et al. 2007; Sun et al. 2010; Peng et al. 2012; Yang et al. 2012), construction of genetic maps (Li et al. 2009a), characterization and expression patterns of immune-related genes (Yang et al. 2010; Zhou et al. 2011). While more attentions were focused on the commercial exploitation, the restoration and management of wild populations and genetic diversity assessment of A. japonicus raised concerns regarding the maintenance of genetic diversity among cultured stocks, especially because their seedlings are released into natural habitats (Kang et al. 2011). However, genomic resources of A. japonicus are still insufficient in the postgenomic era. So far, approximately 8000 expressed sequence tags (ESTs) generated by traditional Sanger sequencing method are available from GenBank. Among them, 5728 ESTs were deposited by Yang et al. to identify immune-related genes (Yang et al. 2009), and 730 ESTs were deposited by Zheng et al. to identify candidate genes involved in intestine regeneration (Zheng et al. 2006). Although these ESTs have been used in the single nucleotide polymorphisms (SNPs) mining, the available SNPs are limited due to sequencing coverage and depth (Sun et al. 2010; Yang et al. 2012). Besides the Sanger sequencing ESTs, recently, Sun et al. performed a large-scale transcriptome profiling for the intestine and body wall tissues of A. japonicus by 454 pyrosequencing to identify potential regeneration candidate genes (Sun et al. 2011). Du et al. performed a large-scale transcriptome sequencing of diverse A. japonicus cDNA libraries representing different developmental stages and adult tissues to identify candidate genes potentially involved in aestivation (Du et al. 2012).

Continued improvements in next generation sequencing technologies have transformed our ability to obtain significant sequencing depth and coverage in a rapid and low-cost manner (Shendure & Ji 2008). However, challenges still exist when working with nonmodel organisms; for example, the short sequence read length and huge data sets have to be analysed de novo, without reference genomes. Optimistically, following initial steps of assembly and annotation, putative genetic markers can be more easily obtained and validated from the large numbers of overlapped sequence reads (Roberts et al. 2012).

Transcriptome analysis using next generation sequencing on multiple individuals has been approved as effective way for SNP identification and validation (Novaes et al. 2008). Recently, 454 pyrosequencing was employed for the identification of gene-derived SNPs in numerous species including many marine animals, such as turbot, marine annelid, sea bream, pacific herring and coral (Meyer et al. 2009; Roberts et al. 2012; Zakas et al. 2012; Calduch-Giner et al. 2013; Vera et al. 2013). While the 454 pyrosequencing technology has been widely used for transcriptome analysis, Illumina platform is being gradually accepted for its dramatically improved sequencing throughput and quality (Surget-Groba & Montoya-Burgos 2010; Kircher et al. 2011). Paired-end sequencing technology and deeper sequencing reads make it possible to assemble contigs of transcripts. Such assembly can be aided by the availability of reference genome and/or reference transcriptome database (Liu et al. 2011; Xu et al. 2012). For those species without any references, the assembly quality of transcriptome was also acceptable after a strategy of an optimum sequencing amount and a robust de novo assembly method (Chen et al. 2010).

Here, we performed a large-scale transcriptome sequencing of A. japonicus by Illumina HiSeq™ 2000 platform (BGI, Shenzhen, China), to improve A. japonicus transcriptome resources by combining the data from this study with all the ESTs from NCBI database including the 454 sequencing data (SRA046386) (Du et al. 2012). We aimed to identify large numbers of gene-associated markers [(SNPs) and simple sequence repeats (SSRs)] and analyse the differentially expressed genes (DEGs) in the coelomocytes of A. japonicus after lipopolysaccharides (LPS) challenge. Compared with 54 000 SNPs and approximately 700 SSRs detected by Du et al., we obtained a total of 142 511 high-quality SNPs, 89 375 of which were from the annotated contigs. In addition, 2481 SSRs with sufficient flanking regions were identified, and at least one candidate primer pair can be designed for each locus. These gene-associated markers presented here will be beneficial to numerous researches, such as genetic diversity assessment, genome mapping and reproductive ecology analysis in A. japonicus.

In addition to gene-associated markers identification, due to the rapidly declining price for large-scale sequencing, ability to capture rare transcripts and splicing variants and development of algorithms for assessing gene expression levels is largely increasing (Nagalakshmi et al. 2010; Malone & Oliver 2011). In this study, we generated a reference transcriptome of A. japonicus assembled by the Illumina sequencing reads and public database. By mapping back the transcripts of coelomocytes at different sampling times after LPS challenge to the reference transcriptome and comparing with the control group, the transcripts were quantified and differentially expressed genes were identified. In natural marine environments, bacteria not only occupy a crucial position in the food chain of sea cucumbers, but also affect their normal growth, especially Gram-negative bacteria which may cause skin ulceration (Eeckhaut et al. 2004). Studies have shown that LPS derived from the cell wall of Gram-negative bacteria could induce significantly immune responses in sea cucumbers (Yang et al. 2010; Zhou et al. 2011).

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

Animals and RNA extraction

To achieve large coverage of the transcripts and to develop SNPs efficiently, 150 adults of sea cucumber (Apostichopus japonicus) were collected from three areas, Wafangdian, Lvshun and Zhuanghe located in Liaoning Province, China in 2010. Animals were kept in aquaria at seawater temperature 18–19 °C, pH 8.0–8.2 and salinity of 31 for 1 week prior to sampling. About 5.0 g of every tissues, including body wall, intestine, respiratory trees, coelomocytes, male and female gonad, were dissected and mixed, respectively. To obtain larval materials, fertilization and larval culture were performed according to the documented method (Yang et al. 2010). Samples from every development stages, including unfertilized eggs, fertilized eggs, cellulous stages, blastula prior to hatching, gastrula, early auricularia, auricularia, late auricularia, doliolaria, pentactula and 1-mm long juvenile, were cultured in aquaria with the conditions described previously. No less than 20 000 of the eggs and larvae were collected, respectively, by sieving using a 60-μm filter, pelleted by centrifugation (Labnet Spectrafuge, USA) and then stored in 1.5-mL microcentrifuge tubes. About 1000 juveniles were collected using a sieve with mesh size 180 μm. All samples were frozen immediately with liquid nitrogen and ground to a fine powder with mortar and pestle in the presence of liquid nitrogen and then stored at −80 °C prior to RNA isolation.

To investigate the expression difference of immune-related genes in response to pathogen infection, 500 μL LPS (1 g/L; Sigma, USA) diluted with phosphate-buffered saline (PBS) was injected into the healthy sea cucumbers with average body weight 12.5 g. The sea cucumbers injected with 500 μL PBS were treated as the control group (Ramírez-Gómez et al. 2009). The cultured conditions were as mentioned previously. Coelomocytes were collected, respectively, at 4 h, 24 h and 72 h after injection according to the methods reported previously (Santiago-Cardona et al. 2003). For each sampling time, the coelomocytes taken from 15 different individuals were mixed in 1.5-mL microcentrifuge tubes. All samples were frozen immediately with liquid nitrogen and then stored at −80 °C prior to RNA isolation.

Total RNA was isolated using the UNIQ-10 Column Total RNA Isolation Kit (Sangon, Shanghai, China) according to the manufacturer's instructions. The quality and quantity of extracted total RNA were measured using the NanoPhotometer (Implen GmbH, Munich, Germany) and agarose gel electrophoresis.

cDNA library construction and sequencing

Poly (A) mRNA was isolated using Oligo (dT) beads from total RNA and sheared into short fragments using fragmentation buffer. Taking these short fragments as templates, random hexamer primer was used to synthesize the first-strand cDNA, followed by synthesis of the second-strand cDNA using RNaseH and DNA polymerase I. The cDNA fragments underwent end repair process, addition of ‘A’ base and ligation of sequencing adapters. After agarose gel electrophoresis, the suitable fragments were selected for the PCR amplification to generate final cDNA libraries. Totally, five cDNA libraries were constructed. One was constructed by pooled equal quantities of high-quality RNA from each material, and other four were constructed by control and stimulated coelomocytes RNA after LPS challenge at 4 h, 24 h and 72 h, respectively. High-throughput sequencing was conducted using the Illumina HiSeq™ 2000 platform to generate 100-bp paired-end reads.

Transcriptome analysis

The raw reads were first preprocessed by discarding reads with adaptors, reads with unknown nucleotides larger than 5%, low quality reads (quality scores < 20) or reads less than 20 bp. Transcriptome de novo assembly was carried out with a short read assembling programme—Trinity (Grabherr et al. 2011).

The 454 reads and ESTs obtained from GenBank were first preprocessed by trimming adaptors and Poly A. The preprocessed sequences were assembled using the program Newbler 2.6 (Roche; cDNA assembly mode). The assembled sequences from the Illumina HiSeq™ 2000 sequencing and 454 reads were taken into further process of sequence splicing and redundancy removing with sequence clustering software Tgicl to acquire nonredundant unigenes as long as possible (Pertea et al. 2003).

To annotate the sea cucumber (A. japonicus) transcriptome, we performed BLAST searches against the nonredundant (nr) database in NCBI, Swiss-Prot, KEGG and COG with an e-value cut-off of 1e-5. Gene Ontology terms were assigned by Blast2GO (Aparicio et al. 2006) through a search of the nr database. KEGG analysis was performed to gain an overview of gene pathways networks. For coding sequence (CDS) prediction, we performed blastx alignment (e-value < 1e-5) between unigenes and protein databases of relative species whole protein set (RSWP, which contained Strongylocentrotus purpuratus, Ciona intestinalis, Branchiostoma floridae and Crassostrea gigas), nr, Swiss-Prot, KEGG and COG, and the best aligning results were used to decide sequence direction of unigenes. If results obtained from different databases conflicted with each other, a priority order of nr, Swiss-Prot, KEGG and COG was used when deciding sequence direction of unigenes. When a unigene happened to be unaligned to none of the above databases, the software ESTScan (Iseli et al. 1999) was introduced to decide its sequence direction. The peptide sequences were translated using standard codons for CDS with length larger than 100 bp.

To identify gene families, we selected the following reference species to represent sequenced marine animals and model species: C. intestinalis, A. japonicus, B. floridae, S. purpuratus, Petromyzon marinus, C. gigas and Homo sapiens. For comparative analysis, we clustered individual genes into gene families using Treefam (Li et al. 2006). First, we collected protein sequences longer than 30 amino acids from these seven species. The longest protein isoform was retained for each gene. Second, blastp was used against a database containing a protein data set of all species with the e-value of 1e-7. Then, it was conjoined fragmental alignments for each gene pair by Solar (Yu et al. 2006). Third, gene families were extracted by Hcluster with default parameters. The single-copy ortholog genes were used for constructing the phylogenetic tree using PhyML with default parameters (Guindon & Gascuel 2003; Guindon et al. 2010).

SNPs and SSRs identification and validation

Assembled contigs were scanned for SNPs with SNP detection software SOAPsnp (Li et al. 2009ab). SNP detection was performed using the following quality and significance filters: (i) the minimum average quality of surrounding bases and minimum quality of the central base were set as 15 and 20 quality score units, respectively; (ii) minimum coverage was set at 5 reads; (iii) minimum variant frequency or count was set at 10%; and (iv) SNPs located in read ends (last three bases) were not considered in the analysis due to possible sequencing errors.

We employed MIcroSAtellite (MISA; http://pgrc.ipk-gatersleben.de/misa/) for microsatellite mining. The parameters were designed for identification of perfect di-, tri-, tetra-, penta- and hexanucleotide motifs with minimum of repeat numbers of 6, 5, 5, 4 and 4, respectively. Based on MISA results, Primer3 v2.23 (http://primer3.sourceforge.net) was used to design primers. Primer design parameters were set as follows: length range = 18–28 nucleotides with 23 as optimum; PCR product size range  from  80 to 300 bp; average annealing temperature = 60 °C; GC content 40–60%, with 50% as optimum.

To validate the putative SSRs and SNPs and evaluate the polymorphism of these markers, 32 individuals of A. japonicus collected from Dalian City (Liaoning Province, China) were used at 20 SSRs and 23 SNPs loci. In total, 20 SSR primer pairs were used, and for SNPs validation, we designed 15 primers to amplify 23 putative SNPs. Genomic DNA was extracted from tube feet using cetyl trimethyl ammonium bromide (CTAB) method (Nelson et al. 2002). PCR amplifications were performed in 20 μL volume containing 50 ng of genomic DNA, 1 × universal PCR buffer, 0.4 μm of each primer, 200 μm of each dNTPs and 1 U of Taq polymerase (Takara, China). The PCR programme was as follows: 5 min at 94 °C for an initial denaturation, followed by 30 cycles of 30 s at 94 °C, 45 s at primer-specific annealing temperature, 1 min at 72 °C and a final extension at 72 °C for 10 min.

The amplified PCR products by SSRs primer pairs were separated on 10% nondenaturing polyacrylamide gel at 200 V for 2 h and stained with GeneFinder nucleic acid dye (Biovision, China) for 15 min and visualized under ultraviolet light. Each PCR product amplified by SNPs prime pairs was checked on a 1.5% agarose gel, purified by gel extraction and then sequenced using traditional Sanger sequencing method.

Differentially expressed genes

The unigene expression was calculated using RPKM method (reads per kb per million reads) (Mortazavi et al. 2008). The RPKM method is able to eliminate the influence of different gene length and sequencing level on the calculation of gene expression. Therefore, the calculated gene expression can be directly used for comparing the difference of gene expression between samples.

To identify the DEGs in the coelomocytes of A. japonicus at different time points after LPS challenge, a rigorous algorithm was developed for statistical analysis according to ‘The significance of digital gene expression profiles’ (Audic & Claverie 1997). False Discovery Rate (FDR) is a statistical method used in multiple hypothesis testing to correct for P-value (Benjamini et al. 2001). When we got FDR, we used the ratio of RPKMs of the two samples at the same time. The larger the ratio indicates the larger difference of the expression level between the two samples. In our analysis, we chose those with FDR ≤ 0.001 and ratio larger than 2 (|log2 ratio| ≥ 1). Subsequently, all DEGs were subjected to Gene Ontology (GO) enrichment analysis compared with the transcriptome background using hypergeometric test. The calculated P-value goes through Bonferroni correction, taking corrected P-value ≤ 0.05 as a threshold. GO terms fulfilling this condition were defined as significantly enriched terms in DEGs. The similar method was used in KEGG pathways analysis.

Results and discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

Sequence analysis and assembly

Approximately 162 million (161 923 766) high-quality trimmed reads from 174 million (173 910 356) raw reads were generated from the deep sequencing of sea cucumber (Apostichopus japonicus) transcriptome using Illumina HiSeq™ 2000 platform. The raw reads produced by sequencing five cDNA libraries have been deposited in the NCBI SRA database (accession number: SRA050267 including SRX122152, SRX122619 and SRX122622). The high-quality reads containing 1 235 031 preprocessed 454 sequencing reads and 6731 ESTs from the public database were also included in the A. japonicus transcriptome analysis (Table 1).

Table 1. Summary statistics of transcriptome assembly for Apostichopus japonicus
Type of dataTotal clean readsTotal clean nucleotidesTotal consensus sequencesTotal lengthMean lengthN50
454 reads1 235 031624 161 07934 937 (454Isotigs)43 000 00212301550
Sanger reads67314 044 5476731 (ESTs)4 044 547600689
Illumina reads161 923 76614 573 138 94094 528 (Contigs)57 761 480611837
Assembly Results  94 704 (Unigenes)76 736 1268101335

Assembly of these reads generated 94 704 tentative consensus sequences (nonredundant sequences or unigenes), ranging from 200 to 16 153 bp, with an average size of 810 bp (Table 1). The total assembled unigenes included 38 587 transcripts which were attributed to the different sequence splicing of 22 000 genes. Each of these unigenes contained at least two sequences with pairwise sequence similarity larger than 70%. The number of different splicing isoforms in A. japonicus is not as high as in vertebrate (Wang et al. 2008). Combined with the 454 sequencing data (Du et al. 2012), 84.9% of the high-quality reads were incorporated into the unigenes, and the quality of assembled transcriptome was improved greatly than only de novo assembly using Illumina or 454 sequencing reads. The summary statistics of transcriptome assembly indicated that 454 pyrosequencing and Sanger reads facilitated effective assembly of the Illumina short reads and the assembled transcripts by Illumina reads covered nearly all the 454 and Sanger sequences (Table 1). The size distribution of unigenes is shown in Fig. 1. More than 50 000 of the total unigenes were not matched with the transcriptome assembly result from the 454 sequences (Du et al. 2012). With deeper sequencing and coverage in this study, this result suggests more representative collections of A. japonicus genes obtained. Another reason is that huge amount of short reads generated makes the transcriptome assembly difficult, which is not only impeded by repeats but also by alternatively spliced transcripts. The allelic variation is also a significant impact factor for the assembly of transcriptome.

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Figure 1. The length distribution of unigenes. Unigenes were generated from de novo assembly of Illumina sequencing reads and public database, and were compared with the 454 sequences assembly.

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Sequence annotation

Unigene sequences were first annotated by blastx to protein databases nr, Swiss-Prot, KEGG and COG (e-value < 1e-5), and then annotated by blastn to nucleotide databases nt (e-value < 1e-5). Of all the 94 704 assembled sequences, 29 357 showed significant matches to nr, 24 573 to Swiss-Prot, 21 139 to KEGG, 10 124 to COG and 11 003 to nt databases. Altogether, 32 479 had significant matches, at least one hit to these databases.

Among the 94 704 unigenes, 36 005 (38.2%) were predicted as CDSs (>100 bp), ranging from 102 bp to 14 625 bp with an average length of 721 bp (Fig. 2). The transcripts without predicted CDSs consist of assembled transcripts with low coverage values and short length. For instance, 49 817 (52.6% of all unigenes) of these transcripts are shorter than 500 bp, and only 4.0% of all reads were mapped to these transcripts. These short and low coverage transcripts may represent chimeras resulting from assembly errors, fragmented transcripts of low expressed genes, as well as noncoding RNA.

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Figure 2. The length distribution of coding sequences (CDSs).

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Approximately 34.3% of unique consensus sequences were successfully annotated in this study. This ratio is comparable with those reported in other de novo transcriptome sequencing studies for nonmodel organisms (Clark et al. 2010; Franchini et al. 2011; Hou et al. 2011; Du et al. 2012; Qin et al. 2012). Among the annotated unigenes, about 13 706 (42.2%) were matched to Strongylocentrotus purpuratus, 4255 (13.1%) to Saccoglossus kowalevskii, 2696 (8.3%) to Branchiostoma floridae, 877 (2.7%) to Nematostella vectensis, 812 (2.5%) to Danio rerio, 10 133 (31.2%) to Oreochromis niloticus and a small amount to other species.

Gene Ontology (GO) annotation was further performed for the annotated unigenes in terms of biological process, molecular function and cellular component. In total, 14 597 unigenes were assigned with at least one GO term for a total of 116 226 GO assignments. Distribution of the unigenes in three different GO categories is shown in Fig. S1 (Supporting Information). For biological process, cellular process was the most abundant GO terms (17.2%), followed by metabolic process (12.9%) and biological regulation (8.7%). For molecular function, genes encoding binding (47.6%) and catalytic activity (35.7%) proteins were highly represented in GO terms. For cellular component, major categories were cell (22.2%), cell part (22.1%) and organelle (16.2%).

Gene Ontology term assignment for molecular function revealed that a total of 2009 unigenes were annotated with the category of protein binding (GO: 0005515), which contained the most abundant genes, including some important immune-related and stress-related genes, such as thioredoxin, interleukin enhancer binding factor 3, scavenger receptor cysteine-rich protein type 12 precursor, Hsc70-interacting protein, heat shock protein 26 and melanotransferrin. These results might be due to several cDNA libraries used for the Illumina sequencing derived from the samples after LPS challenge.

KEGG pathway analysis showed that 21 139 unigenes were mapped to 255 pathways, of which metabolic pathways were the most abundant (7402). Several pathways, such as complement and coagulation cascades, chemokine signalling, cytokine–cytokine receptor interaction and Toll-like receptor signalling pathways, are clearly linked with immune responses. These results will provide a basis for future studies, such as gene-associated markers identification, gene cloning and expression analysis.

Evolutionary analysis

Being the basal deuterostomes, echinoderms occupy the top taxonomic position of invertebrates, where is the evolutionary bridge between invertebrates and vertebrates. In addition to sea urchin S. purpuratus whose genome has been sequenced and published, the availability of transcriptomes and genomes from other classes in echinoderms is expected to promote phylogenetic and comparative evolutionary genomics, and to enable the characterization of the function gene repertoire for different echinoderm species.

In this study, using TreeFam and the pipeline described in method, we obtained 23 145 gene families from A. japonicus and the six reference genomes. The common and unique gene families among A. japonicus, Ciona intestinalis, B. floridae and S. purpuratus were summarized in Fig. 3. To reconstruct the evolutionary relationship between A. japonicus and other animals, a set of 127 single-copy gene families was obtained by Treefam method. And it was concatenated to a super peptide (84 261 peptide sites) for constructing phylogenetic tree using PhyML (Fig. 4). The results showed that except Crassostrea gigas, other six species formed three clades by every two species, and A. japonicus was closely related with S. purpuratus. Both S. purpuratus and A. japonicus belong to echinoderms, and our phylogenetic analysis reaffirmed their taxonomic closeness. Additionally, the phylogenetic analysis also revealed the evolutionary relationships of the represent marine species and the vertebrate. Molecular evidence is considered to be more effective for taxonomic classification compared with other existing methods. The transcriptome data and mitochondrial genome for phylogeny and orthology analysis have become increasingly popular (Castoe et al. 2009; Meusemann et al. 2010; Kocot et al. 2011). Especially in certain taxon, such as arthropods, echinoderms, tetrapods and snakes which possess radical transformations both morphologically and physiologically (Carroll 1997), molecular data can make robust contributions in phylogenetic analysis.

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Figure 3. Venn diagram of unique and common genes among the Apostichopus japonicus, Branchiostoma floridae, Strongylocentrotus purpuratus and Ciona intestinalis.

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Figure 4. Species tree from the orthologous data set across seven species.

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Identification of gene-associated markers

Owing to their potentials for high genotyping efficiency, automation, data quality, genome-wide coverage and analytical simplicity (Morin et al. 2004), SNPs have rapidly become the marker of choice for many applications in genetics and genomics studies (Liu et al. 2011). In this study, we identified 142 511 high-quality SNPs from 94 704 unigenes (Table 2). The putative SNPs included 82 664 transitions and 59 847 transversions. The minor allele frequencies of SNPs including transitions and transversions were estimated from the sequence data (Fig. 5). The overall frequency of all types of SNPs in the transcriptome was one per 538 bp. The distribution of filtered SNPs in per contigs is shown in Fig. 6. Of all the putative SNPs, 137 616 (91.0%) were identified from contigs composed by more than ten reads, suggesting that the majority of SNPs identified in this study were covered at sufficient sequencing depth and more likely represent ‘true’ SNPs. Among these SNPs, 89 375 (59.1%) were identified from contigs with annotation information, and they were distributed in 15 473 known genes (Table 2).

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Figure 5. Distribution of minor allele frequencies of single nucleotide polymorphisms (SNPs) identified for Apostichopus japonicus. The X-axis represents the SNP sequence derived minor allele frequency in percentage, while the Y-axis represents the number of SNPs with given minor allele frequency.

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Figure 6. Distribution of filtered single nucleotide polymorphisms (SNPs) per contig. Histograms depict frequency of contigs with a given number of SNPs identified.

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Table 2. Summary of single nucleotide polymorphisms (SNPs) identified from the Apostichopus japonicus transcriptome
SNP informationCounts
Transition82 664
A/G41 302
C/T41 362
Transversion59 847
A/C14 613
A/T20 030
C/G10 301
G/T14 903
Total SNPs142 511
Number of contigs with SNPs26 186
Number of known genes containing SNPs15 473

Deep analysis of the functional SNPs showed that 33 775, 63 120 and 45 616 SNPs were distributed in sequences without predicted CDS (non-CDSs), coding sequences (CDSs) and untranslated regions (UTRs), respectively. SNPs occur in protein-coding regions are beneficial for assessing the polymorphisms that directly affect the phenotype related to important economic traits. On the other hand, high proportion of coding SNPs developed from disease or stress-associated functional genes implying the result of natural selection. Thus, the transcript region containing sequences variations can be used for explaining the influences of natural selection at the gene and protein levels (Ellegren 2008). In some cases, mutations in coding regions may cause the loss of protein functions leading to species extinction. Compared with this destructive mutation, beneficial mutations can be explained by the retained traits during evolution (Lynch et al. 2006; Zhu et al. 2012).

Recently, comparative genomic analyses have been conducted on noncoding region where the level of conserved sequences is similar to protein-coding genes (Bejerano et al. 2004; Dermitzakis et al. 2004). Nevertheless, the mutations occurred in which regions ultimately determine the molecular function and organism's fitness need profound discussion (Kryukov et al. 2005).

For the DEGs, GO term assignment and KEGG pathway analyses showed that important genes and signalling pathways associated with growth, metabolism, disease, immunity and stress responses have been identified in the transcriptome data. Insights into SNPs in these genes including Cu/Zn superoxide dismutase (14 SNPs), heat shock protein 70 (6 SNPs) and cytochrome P450 (15 SNPs) help to understand individual's resistance to hypoxia or oxidative stress from marine environment. It is noteworthy that mannan receptor possesses 89 SNPs showing highest polymorphism among all the unigenes. Previous studies demonstrated that mannan receptors involved in many biological process especially in clearance of extracellular pathogens and peroxidases to keep homoeostasis (Vigerust et al. 2012).

Microsatellite marker (SSR marker) is one of the most successful molecular markers in the construction of sea cucumber genetic map and in diversity analysis. In this study, a total of 6417 microsatellites were detected in 5970 unigenes, 3216 of which were annotated. These microsatellites included 2969 (46.3%) dinucleotide motifs, 2924 (45.6%) trinucleotide motifs, 291 (4.5%) tetranucleotide motifs, 170 (2.6%) pentanucleotide motifs and 63 (1.0%) hexanucleotide motifs (Table 2). Among these motifs, the most abundant was (AT/AT), followed by (AC/GT), (AG/CT), (AAT/ATT), (AAG/CTT), (AGG/CCT), (ATC/GAT) and (AAC/GTT; Fig. 7). Among these 6417 SSRs, 2481 were successfully designed at least one primer pair using Primer3 v2.23 (Table 3). There were 2367, 2316 and 1734 SSRs identified in non-CDSs, CDSs and UTRs, respectively.

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Figure 7. Frequency of classified repeat types (considering sequence complementary). Histograms depict the frequency of different SSR repeats.

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Table 3. Summary of simple sequence repeats (SSRs) identified from the Apostichopus japonicus transcriptome
SSR informationNumber
Total number of sequences examined94 704
Total number of identified SSRs6417
Di-nucleotide repeats2969
Trinucleotide repeats2924
Tetranucleotide repeats291
Pentanucleotide repeats170
Hexanucleotide repeats63
Number of unigenes containing SSRs5970
Number of unigenes containing SSRs with sufficient flanking sequence2481
Number of known genes containing SSRs3216

Previous comparisons between the effects of SSRs and SNPs markers on genetic diversity (Russell et al. 2004) have been discussed in detail. Within or between wild populations, these two markers showed uncorrelated patterns of diversity and divergence which might be limited by the difference of each marker's intrinsic properties (Defaveri et al. 2013). Statistics analysis of our result showed that 3216 (50.1%) SSRs and 89 375 (59.1%) SNPs were detected from annotated contigs which would be priority candidates for marker development and useful for further molecular ecology, evolution, genetic or genomic studies in this species.

Gene-associated markers validation

A total of 20 putative SSRs and 23 SNPs were tested by PCR amplification from 32 individuals. Of the 20 SSRs primer pairs, 16 amplified the expected products and 13 showed polymorphism (Table S1, Supporting Information). The remaining 4 primers failed to amplify any PCR product, which was also observed in the development of EST-SSRs in the same species (Peng et al. 2009). The nonamplification was probably due to primer sequences spanning across introns, and/or containing mutations and/or indels.

Of the 15 SNPs primer pairs for 23 SNPs, three failed in PCR amplification, and two were monomorphic, suggesting that they might not be true SNPs, or their minor alleles were too rare to be detected, or the primers did not work. Collectively, 18 SNP loci amplified by 10 primer pairs were polymorphic in the tested population (Table S2, Supporting Information).

It is difficult to compare the final putative SNPs and the results obtained by Du et al. because sequences alignments showed that many mismatches and gaps existed between the final assembled transcripts obtained here and the 454 sequences assembled results. This is probably caused by the inherent nature of different samples, the difference between two sequencing platforms, as well as deeper sequencing conducted by this study. SNPs validation results showed that 18 of 23 (78.3%) selected putative SNPs got the expected results by PCR amplification and sequencing, suggesting that the majority of the putative SNPs are expected to be true. A large number of SNPs obtained in the present study compared with 454 pyrosequencing results by Du et al. are probably due to the large quantities of sequencing data and more extensive coverage, which contributed to a large number of low-abundance SNPs being found. We integrated the 454 and Sanger sequencing data into our Illumina data assembly. The final putative SNPs contained the majority of SNPs from different data.

Currently, applications of SNPs have been developed rapidly for their broader genome coverage and widespread genetic variants. The SNPs we obtained are expected to address ecology and evolution questions such as genetic differentiation, natural selection and speciation (Geraldes et al. 2013). Due to markers' great potentials in ecological and evolutionary analyses (Garvin et al. 2010), persistent efforts are required to focus on identifying species-specific or new type diagnostic SNPs of sea cucumbers using the data in this study.

Differentially expressed genes after LPS challenge

During the past decade, skin ulceration diseases caused by Gram-negative bacteria pose the most serious threat to cultivated A. japonicus (Eeckhaut et al. 2004). Identification and characterization of immune-related genes will help us to understand the mechanism of immune responses to bacteria in sea cucumbers. Additionally, as a species of echinoderms which comprise the sister group of chordates and occupy a critical and largely unexplored phylogenetic position, studies of A. japonicus immune responses are crucial in understanding the evolution of the immune system in metazoans.

Some immune-related genes in sea cucumbers have been characterized, and their expression patterns after LPS challenge have been analysed (Santiago et al. 2000; Santiago-Cardona et al. 2003; Ramírez-Gómez et al. 2008; Yang et al. 2009, 2010; Zhou et al. 2011). So far, large-scale identification of immune-related genes at the genome or transcriptome levels in sea cucumber has not been performed. In echinoderms, the main immune effector cells are the coelomocytes. Consequently, the transcripts of A. japonicus coelomocytes were quantified, and DEGs were analysed after LPS challenge in the present study.

Transcriptome comparison revealed 1330, 1347 and 1291 DEGs in the coelomocytes of A. japonicus at 4 h, 24 h and 72 h after LPS challenge, respectively (Table 4). Of these DEGs, 642, 890 and 837 were upregulated, while 688, 457 and 454 were downregulated at 4 h, 24 h and 72 h, respectively. At all three examined time points after LPS challenge, the total upregulated genes were more than downregulated genes. The imbalance was significant at 24 h and 72 h; the number of upregulated genes was nearly twice more than the downregulated. Some genes were upregulated or downregulated consistently at different time points after challenge (Fig. 8).

image

Figure 8. The differentially expressed genes at different time points after lipopolysaccharides (LPS) challenge.

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Table 4. Differentially expressed genes in the coelomocytes of Apostichopus japonicus after lipopolysaccharides (LPS) challenge
 4 h24 h72 h
Upregulated642890837
Downregulated688457454
Total133013471291

Approximately 58.4% (1802) of the DEGs in coelomocytes of A. japonicus were annotated. Gene Ontology annotation showed that 952 DEGs could be assigned with at least one GO term. The number of DEGs which have at least one GO term at three tested time points after challenge is shown in Fig. S2 (Supporting Information). KEGG pathway analysis showed that 1058 DEGs were mapped to 238 pathways, and metabolic pathways were the most abundant (190), followed by the focal adhesion (98), ECM–receptor interaction (71) and phagosome pathways (61). The DEGs contained some important immune-related genes, such as C-type lectin, lysozyme, interleukin 17C1 precursor, complement factors C3, Bf, H, Toll-like receptors, thioredoxin, tumour necrosis factor receptor-associated factor and lysosomal-associated transmembrane protein. The expression patterns of some DEGs corresponded to the previous data we obtained through qPCR detections (Yang et al. 2010; Zhou et al. 2011), and more should be further validated. These data provide important information of the coelomocytes in immune responses.

In conclusion, we performed a large-scale transcriptome sequencing of sea cucumber A. japonicus using an Illumina sequencing platform. The assembly transcriptome quality of A. japonicus was improved greatly by integrating the Illumina data and the public data, permitting gene discovery and characterization across a broad range of functional categories. A large number of potential genetic markers were identified from the A. japonicus transcriptome. The SNPs and SSRs identified here will provide sufficient resource for genetics and molecular ecology studies in sea cucumber. The DEGs in response to LPS were also identified. Such data will facilitate immune-related genes discovery and functional genomic studies of the sea cucumber.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

This work was supported by grants from National Nature Science Foundation of China (31272687), State 863 High-Technology R & D Project of China (2012AA10A412) and Science & Technology Project of Liaoning Province (2011203005).

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  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information
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Z.C.Z. conceived and designed the experiments, conducted the bioinformatic analysis and involved in writing the manuscript. Y.D., H.J.S., A.F.Y., Z.C., S.G., J.W.J., X.Y.G., B.J. and B.W. were involved in one or more processes of samples collection, data analysis and manuscript preparation.

Data accessibility

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information

Raw Illumina reads: NCBI SRA: SRA050267. Final DNA sequence assembly and SNP or SSR information: DRYAD entry doi: 10.5061/dryad.kj4n8.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Acknowledgements
  7. References
  8. Data accessibility
  9. Supporting Information
FilenameFormatSizeDescription
men12147-sup-0001-FigS1.pdfapplication/PDF2402KFig. S1 Gene Ontology (GO) of sea cucumber (Apostichopus japonicus).
men12147-sup-0002-FigS2.pdfapplication/PDF792KFig. S2 Differentially expressed genes with Gene Ontology (GO) term.
men12147-sup-0003-TableS1.docxWord document17KTable S1 Characterization of 13 novel microsatellite loci for sea cucumber (Apostichopus japonicus).
men12147-sup-0004-TableS2.docxWord document16KTable S2 Characterization of 18 novel single nucleotide polymorphism (SNP) loci for sea cucumber (Apostichopus japonicus).

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