Next Article in Journal
Network Candidate Genes in Breeding for Drought Tolerant Crops
Next Article in Special Issue
Molecular and Functional Characterization of Thioredoxin 1from Korean Rose Bitterling (Rhodeus uyekii)
Previous Article in Journal
Interferon-Beta Therapy of Multiple Sclerosis Patients Improves the Responsiveness of T Cells for Immune Suppression by Regulatory T Cells
Previous Article in Special Issue
Transcriptome Profile Analysis of Ovarian Tissues from Diploid and Tetraploid Loaches Misgurnus anguillicaudatus
Article

Transcriptome Analysis and Discovery of Genes Involved in Immune Pathways from Coelomocytes of Sea Cucumber (Apostichopus japonicus) after Vibrio splendidus Challenge

1
Fisheries College, Ocean University of China, Qingdao 266100, China
2
Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Li Lin
Int. J. Mol. Sci. 2015, 16(7), 16347-16377; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms160716347
Received: 4 March 2015 / Revised: 26 June 2015 / Accepted: 29 June 2015 / Published: 17 July 2015
(This article belongs to the Special Issue Fish Molecular Biology)

Abstract

Vibrio splendidus is identified as one of the major pathogenic factors for the skin ulceration syndrome in sea cucumber (Apostichopus japonicus), which has vastly limited the development of the sea cucumber culture industry. In order to screen the immune genes involving Vibrio splendidus challenge in sea cucumber and explore the molecular mechanism of this process, the related transcriptome and gene expression profiling of resistant and susceptible biotypes of sea cucumber with Vibrio splendidus challenge were collected for analysis. A total of 319,455,942 trimmed reads were obtained, which were assembled into 186,658 contigs. After that, 89,891 representative contigs (without isoform) were clustered. The analysis of the gene expression profiling identified 358 differentially expression genes (DEGs) in the bacterial-resistant group, and 102 DEGs in the bacterial-susceptible group, compared with that in control group. According to the reported references and annotation information from BLAST, GO and KEGG, 30 putative bacterial-resistant genes and 19 putative bacterial-susceptible genes were identified from DEGs. The qRT-PCR results were consistent with the RNA-Seq results. Furthermore, many DGEs were involved in immune signaling related pathways, such as Endocytosis, Lysosome, MAPK, Chemokine and the ERBB signaling pathway.
Keywords: sea cucumber (Apostichopus japonicus); Vibrio splendidus; transcriptome sequencing; differentially expressed genes; bacteria-resistant gene; bacteria-susceptible gene sea cucumber (Apostichopus japonicus); Vibrio splendidus; transcriptome sequencing; differentially expressed genes; bacteria-resistant gene; bacteria-susceptible gene

1. Introduction

Currently, the sea cucumber (Apostichopus japonicus) has become one of the most important aquaculture species in China, achieving valuable profits [1]. However, many diseases occurred along with the rapid expansion and intensification of farming, causing serious economic losses and disrupting the sustainable development of this industry [2]. Skin ulceration syndrome with clinical signs of anorexia, shaking head, mouth timidity, viscera ejection and skin ulceration, is the most serious disease and is highly infectious and lethal to the species. Etiological studies [3,4,5] indicated that a bacterial species (Vibrio/Pseudoalteromonas/Aeromonas spp.), a parasite (Parasitic nematode) and virus (a spherical virus) are the major pathogens for this serious disease, including Vibrio splendidus [6].
Like other invertebrates, sea cucumber lacks adaptive immunity and relies solely on innate immunity, which is composed of cellular responses such as phagocytosis and encapsulation, as well as humoral immunity that produces immune-related factors [7,8]. Coelomocytes function as the major sites for the elimination of pathogens [9,10]. Moreover, there are plentiful hydrolases in coelomic fluid such as lysozyme (LSZ), phenoloxidase activity (PO), total nitric oxide synthase (T-NOS), superoxide dismutase (SOD), and alkaline phosphatase (AKP), which could hydrolyze foreign pathogens [11,12]. In recent years, various studies have been conducted to investigate the immunity of sea cucumber, trying to identify the immune factors, clone immune-related genes, and obtain immune-related expressed sequence tags (EST) [13,14,15,16,17,18]. In addition, the studies of different microRNAs and proteins between healthy and natural skin ulceration syndrome Apostichopus japonicus have also been performed [19,20,21]. Nevertheless, the responsive mechanism of sea cucumber to pathogenic bacteria remains unclear.
In the present study, a mid-sensitive full-sib family was chosen for artificial-challenge experiment using Vibrio splendidus. The sea cucumbers were divided into a disease-resistant group and susceptibility group according to a series of symptoms of skin ulcer syndrome [4]. The transcriptome and expression profile of bacterial-resistant and bacterial-susceptible sea cucumber juveniles’ coelomocytes were analyzed using the Illumina sequencing method and bioinformatics analysis. Putative disease-resistant genes and susceptibility genes of sea cucumber were also screened. Additionally, gene-associated markers were screened for potential genetic research.

2. Results and Discussion

2.1. Illumina Sequencing and Assembly

To obtain an overview of the sea cucumber coelomocytes transcriptome, a cDNA library was generated from an equal mixture of RNA from nine individuals of the three groups (control group, bacterial-resistant group, and bacterial-susceptible group) using Illumina Hiseq2500 platform. After cleaning and quality checks, approximately 319 million (319,455,942) trimmed reads from 320 million (320,106,172) raw reads were generated using deep sequencing. Assembling analysis obtained 186,658 contigs with a median of contigs (N50) length of 1245, and 89,891 representative contigs (without isoform) with a N50 length of 791 using the 25-mer parameter in Trinity [22,23] (Table 1). Length statistics of assembled contigs and representative contigs are displayed in Figure 1 and Figure 2. The transcriptome database was next used as a source for the large set of functional genes (disease-resistant genes and susceptibility genes). The abundant data could also be a reference for further study including molecular markers and the genome of sea cucumber.
Table 1. Summary statistics of assembled transcriptome length for Apostichopus japonicus coelomocytes.
Table 1. Summary statistics of assembled transcriptome length for Apostichopus japonicus coelomocytes.
Assembled TranscriptomeAllMinMedianMeanN50MaxTotal
contigs186,658201543832124515,051155,375,852
representative contigs (without isoform)89,89120137659379115,05153,310,798
N50, contig length—weighted median.
Figure 1. The length distribution of assembled contigs in the sequenced cDNA library.
Figure 1. The length distribution of assembled contigs in the sequenced cDNA library.
Ijms 16 16347 g001
Figure 2. The length distribution of representative contigs in the sequenced cDNA library.
Figure 2. The length distribution of representative contigs in the sequenced cDNA library.
Ijms 16 16347 g002

2.2. Gene Annotation

Representative contigs were first annotated by BLAST to protein databases nr, Swiss-prot, Pfam, KEGG and COG separately and then annotated to nucleotide databases Nt with an E-value cut-off of 10−5. The percent values of the representative contigs to these databases are listed in Table 2. Altogether, 20,060 (22.32%) had at least one significant match to these databases (Table 2).
Table 2. Result of functional annotation of the assembled representative contigs to the databases.
Table 2. Result of functional annotation of the assembled representative contigs to the databases.
No. of Representative ContigsSwiss-ProtNrPfamKEGGCOG
89,89113,95519,77714,39818,88713,126
Percentage15.52%22.00%16.02%21.01%14.60%

2.2.1. Nr Annotation

Among the annotated representative contigs to the Nr database, 6724 (46.7%) representative contigs were matched to Strongylocentrotus purpuratus, 1497 (10.4%) to Saccoglossus kowalevskii, 849 (5.9%) to Branchiostoma floridae, 230 (1.6%) to Nematostella vectensis, 230 (1.6%) to Crassostrea gigas, 202 (1.4%) to Capitella teleta, 173 (1.2%) to Aplysiacali fornica, 158 (1.1%) to Homo sapiens, 158 (1.1%) to Xenopus silurana, 130 (0.9%) to Mus musculus and 4047 (28.2%) to other species (Figure 3).

2.2.2. GO Annotation

Gene Ontology (GO) [24] analysis was carried out, and for the three major functional categories: biological process, cellular component and molecular function, there were 9574, 11,078 and 10,994 representative contigs, respectively (Figure 4). For biological process, genes involved in transcription and DNA-dependent (752) was highest represented, followed by regulation of transcription DNA-dependent (630), and translation (528). Regarding cellular component, the top three categories were integral to membrane (2095), nucleus (2075) and cytoplasm (2037). For molecular function, ATP binding was the most represented GO term, followed by zinc ion binding.
Figure 3. Species distribution of the BLAST matches of the transcriptome representative contigs.
Figure 3. Species distribution of the BLAST matches of the transcriptome representative contigs.
Ijms 16 16347 g003
Figure 4. Classification of the gene ontology (GO) for the sea cucumber coelomocytes transcriptome representative contigs.
Figure 4. Classification of the gene ontology (GO) for the sea cucumber coelomocytes transcriptome representative contigs.
Ijms 16 16347 g004

2.2.3. COG Annotation

The COG database was used to classify orthologous gene products. 13,126 representative contigs were allocated to 25 COG classifications (Figure 5). Among them, “general function prediction only” (1946, 14.83%) and “signal transduction mechanisms” (1844, 14.05%) was the largest group, which indicated multiple genes were involved in signaling pathways after Vibrio splendidus infection.

2.2.4. KEGG Annotation

KEGG was used as a powerful tool to analyze biological metabolism and study metabolism networks. In all, 18,887 representative contigs were consequently classified into specific pathways (Figure 6), among which maximum members fell into “metabolism” (2346) and “human diseases” (2151), followed by “organism system” (1779), “cellular processes” (1625) and “genetic information processing” (1308), while the least amount of members were assigned to “environmental information processing” (1009). The highest number of genes were involved in signal transduction and immune system in KEGG, indicating many genes could respond to Vibrio splendidus challenge.
Figure 5. Clusters of Orthologous Groups (COG) classification of the sea cucumber coelomocytes transcriptome representative contigs.
Figure 5. Clusters of Orthologous Groups (COG) classification of the sea cucumber coelomocytes transcriptome representative contigs.
Ijms 16 16347 g005
Figure 6. KEGG classification of the sea cucumber coelomocytes transcriptome representative contigs.
Figure 6. KEGG classification of the sea cucumber coelomocytes transcriptome representative contigs.
Ijms 16 16347 g006

2.3. Single Nucleotide Polymorphism (SNP) and Simple Sequence Repeat (SSR) Detecting

The transcriptome is also an important EST resource for rapid and effective mining of genetic markers, such as SNP and SSR [25]. The molecular markers have also been widely used in identifying functional genes, genetic breeding, genome mapping, and cloning genes.
In total, 149,745 high-quality SNPs were detected using Bowtie and the SAMTOOLS software. The dominant type of variation was transition (86,345, 57.67%), followed by transversion (63,391, 42.33%). The most common transition type was A→G and C→T (Table 3).
Table 3. Summary of SNP identified from the sea cucumber coelomocytes transcriptome.
Table 3. Summary of SNP identified from the sea cucumber coelomocytes transcriptome.
SNP Type NO. of SNP
Transition86,354
A-G41,651
C-T44,703
Transversion63,391
A-C16,243
A-T21,687
C-G10,327
G-T15,134
Total 149,745
In addition, 8009 SSRs (simple sequence repeats) were identified from the assembled sequences. The most abundant repeat motifs were mono-nucleotides (3869), which accounted for 48.31% of all SSRs, followed by dinucleotides (2350, 29.34%), trinucleotides (1591, 19.87%), tetranucleotides (111, 1.39%), pentanucleotides (70, 0.87%), and hexanucleotides (18, 0.22%) (Figure 7).
Figure 7. Summary of simple sequence repeat (SSR) identified from the sea cucumber coelomocytes transcriptome.
Figure 7. Summary of simple sequence repeat (SSR) identified from the sea cucumber coelomocytes transcriptome.
Ijms 16 16347 g007

2.4. Construction of Digital Expression Profiling for Differentially Expressed Genes

The digital gene expression profiling (DGE) is a rapid and efficient approach for gene expression analysis [26,27]. Many significantly differentially expressed genes (DEGs) were acquired by comparing the gene expressions in disease-resistant or susceptibility group (A or S, respectively) with the control group (K), under the criteria of p-value ≤ 0.01 and |log2 fold-change (FC)| ≥ 1 (FDR ≤ 0.05). As a result, we obtained 358 DEGs in the disease-resistant group (13 up-regulated and 345 down-regulated) (Figure 8) and 102 DEGs in the susceptibility group (86 up-regulated and 16 down-regulated) (Figure 9).
Figure 8. Differentially expressed genes from disease-resistant group (A), comparing with control group (K). Red points represent 13 up-regulated genes, and green points represent 345 down-regulated genes.
Figure 8. Differentially expressed genes from disease-resistant group (A), comparing with control group (K). Red points represent 13 up-regulated genes, and green points represent 345 down-regulated genes.
Ijms 16 16347 g008
Figure 9. Differentially expressed genes from susceptibility group (S) comparing with control group (K). Red points represent 86 up-regulated genes, green point represents 16 down-regulated genes.
Figure 9. Differentially expressed genes from susceptibility group (S) comparing with control group (K). Red points represent 86 up-regulated genes, green point represents 16 down-regulated genes.
Ijms 16 16347 g009

2.5. Selecting Disease-Resistant and Susceptibility Genes

The information of genes involved in immune response of other species were collected, which were used in combination of the GO, KEGG, NCBI annotation for the identification of potential bacterial-resistant genes, and 30 genes were identified (listed in Table 4 and Table 5). Some of these 30 genes had been reported in sea cucumber, such as heat shock protein70 (HSP70-like), which is a member of the heat shock protein family, stimulates the innate immune response [28,29] and plays crucial roles in environmental stress tolerance and adaptation in sea cucumber [30,31]. HSP70-like expression was significantly up-regulated after Vibrio splendidus infection, which is consistent with the result of LPS challenge [32].
In addition, many disease-resistant genes that had not been previously linked to the immune response, including heat-responsive protein 12 (Hrp12-like), serine/threonine-protein kinase RIO3 (RIOK3-like) and Interferon-induced very large GTPase 1 (Gvin1-like) were involved in the immune response. However, these genes had been investigated in other species. For instance, Hrp12 has significant similarity to Hsp70 [33] and Hrp12-like may also play an important role in protein transport, protein folding and cell signaling. RIOK3 is a novel regulator of the antiviral type I interferon pathway and plays a crucial role in the antiviral type I interferon pathway. However, type I interferon is involved in the innate immune response which functions as the first line of defense and limits infectious pathogens directly [34], therefore we predicted that RIOK3-like is related closely to innate immunity. Mitogen-activated protein kinase kinase 6 (MP2K6), a member of the MAPK family which are signal transduction mediators that have been implicated in cell survival and death [35], is also activated during engagement of the Type I IFN receptor and plays important roles in Type I IFN signaling and generation of IFN responses [36]. Gvin1 that contributes to the cellular response to both type I and type II IFNs could lead to cell-autonomous resistance against various pathogens [37].
Nineteen potential susceptibility genes (Table 4 and Table 6) were identified from the DEGs, including Rho GTPase-activating protein 39 (ARHGAP39-like), scavenger receptor cysteine-rich protein type 12 precursor (DMBT1-like), and nuclear factor NF-κB p105 subunit (NFkB-like). The Rho GTPase-activating proteins (RhoGAPs) are one of the major classes of regulators of Rho GTPase which are important in cell cytoskeletal organization, membrane trafficking, transcriptional regulation, cell growth and differentiation, neuronal morphogenesis, and endocytosis [38,39]. Thus, ARHGAP39-like may be related with the immune response. Members of the scavenger receptor cysteine-rich (SRCR) superfamily have diverse functions, including pathogen recognition and immune-regulation [40], and we inferred that DMBT1-like might be also involved in the immune response.
Table 4. A subset of candidate Vibrio splendidus-resistant and susceptibility genes that are involved in the immune signaling pathway.
Table 4. A subset of candidate Vibrio splendidus-resistant and susceptibility genes that are involved in the immune signaling pathway.
Coding NumberContig IDGene NamePredict FunctionRegulationLog2 FCAccession Number in Nr DatabaseIdentities (%)
Chemokine signaling pathway
A1comp76725_c0_seq6FOXO1-likeFork head box protein (Strongylocentrotus purpuratus)Down−2.23XP_790591.378
A2comp78415_c0_seq14ADCY2-likeAdenylatecyclase type 2 (Strongylocentrotus purpuratus) Down−5.91XP_780688.372
A3comp79708_c0_seq1STAT5B-likeSignal transducer and activator of transcription 5B (Strongylocentrotus purpuratus)Down−3.81XP_003723422.170
S1comp79328_c1_seq13NFKB-likeNuclear factor NF-κB p105 subunit (Apostichopus japonicus)Up1.8AEP33644.168
S2comp74502_c1_seq4ADCY2-likeAdenylatecyclase type 2-like (Strongylocentrotus purpuratus)Up4.18XP_780688.375
Lysosome
A4comp74062_c0_seq5NEU1-likeSialidase-1 (Strongylocentrotus purpuratus)Down−1.88DAA35227.185
A5com78701_c0_seq2AP-1-likeAP-1 complex subunit mu-1-like (Strongylocentrotus purpuratus)Down−2.67XP_789616.377
S3comp78293_c0_seq2ABCA2-likeATP-binding cassette sub-family A member 2-like (Cricetulus griseus)Up2.49XP_003514719.170
S4comp78293_c0_seq4ABCA2-likeATP-binding cassette sub-family A member 2-like (Cricetulus griseus)Up2.49XP_003514719.170
S5comp79570_c0_seq6SGSH-likeN-sulphoglucosamine sulphohydrolase-like (Strongylocentrotus purpuratus)Up1.01XP_794467.170
S6comp77223_c0_seq3ABCA2-likeATP-binding cassette sub-family A member 2, partial (Strongylocentrotus purpuratus)Up1.77XP_798273.368
S7comp80153_c0_seq15AP-3-likeAdaptor-related protein complex 3, δ 1 subunit-like (Strongylocentrotus purpuratus)Up1.79XP_002733668.169
S8comp78750_c3_seq11DNase-II likePlancitoxin-1 (Capitella teleta)Up1.89ELU06802.175
Endocytosis
A6comp76401_c0_seq2VPS37-likeESCRT-I complex subunit VPS37 (Nematostella vectensis)Down−3.6XP_001624048.182
S9comp77471_c1_seq34rabaptin5-likeRabGTPase -binding effector protein 1-like (Strongylocentrotus purpuratus)Up1.8XP_789966.377
S10comp80156_c1_seq5AP-2-likeAP-2 complex subunit alpha-2 (Rattus norvegicus)Up1.16NP_112270.274
S11comp77877_c0_seq1CHMP5-likeCharged multivesicular body protein 5-like (Strongylocentrotus purpuratus)Up1.76XP_786663.172
S12comp75233_c0_seq13PAR6-likepartitioning defective 6 (Hemicentrotus pulcherrimus)Down−2.11BAF99001.177
S13comp79698_c0_seq6EGFR/ RTK-likeEpidermal growth factor receptor (Apostichopus japonicas)Up1.32AEY55412.197
ERBB signaling pathway
A7comp76122_c1_seq21NCK2-likeCytoplasmic protein NCK2 (Strongylocentrotus purpuratus)Up3.62XP_784072.172
A8comp76122_c1_seq7NCK2-likeCytoplasmic protein NCK2 (Strongylocentrotus purpuratus)Up2.72XP_784072.272
A3comp79708_c0_seq1STAT5B-likeSignal transducer and activator of transcription 5B (Strongylocentrotus purpuratus)Down−3.81XP_003723422.170
S13comp79698_c0_seq6EGFR/RTK-likeEpidermal growth factor receptor (Apostichopus japonicas)Up1.32AEY55412.197
MAPK signaling pathway
A9comp77146_c0_seq3MAP3K4-likeMitogen-activated protein kinase kinase kinase 4 (Strongylocentrotus purpuratus)Down−2.23XP_784029.372
A10comp78357_c1_seq8MAPK10-likeMitogen-activated protein kinase 10 (Strongylocentrotus purpuratus)Down−4.43XP_786040.375
S1comp79328_c1_seq13NFKB-likeNuclear factor NF-κB p105 subunit (Apostichopus japonicas)Up1.8AEP33644.168
S13comp79698_c0_seq6EGFR/RTK-likeEpidermal growth factor receptor (Apostichopus japonicas)Up1.32AEY55412.197
S14comp80408_c0_seq17FLNA-likeFilamin-A (Strongylocentrotus purpuratus)Up1.75XP_792145.374
A, represents disease-resistant gene; S, represents susceptibility gene. Log2 FC (fold change) indicates differential expression level of disease-resistant group (A) relative to the control group (K). “−”, indicates fold change of down-regulation.
Table 5. Other putative disease-resistant genes.
Table 5. Other putative disease-resistant genes.
Coding NumberContig IDGene NamePredict Function RegulationLog2 FCAccession Number in Nr DatabaseIdentities (%)
A11comp71589_c0_seq4COX19-likecytochrome c oxidase assembly protein COX19 (Danio rerio)Down−2.64NP_001104010.172
A12comp72396_c0_seq2DDX47-likeprobable ATP-dependent RNA helicase DDX47-like (Strongylocentrotus purpuratus)Down−4.9XP_786173.376
A13comp72841_c2_seq2Trmt1-liketRNA methyltransferase 1-like (Saccoglossus kowalevskii)Down−3.12XP_002736321.167
A14comp73256_c0_seq3Hrsp12-likeheat-responsive protein 12 (Mus musculus)Down−3.8EDL08846.177
A15comp74533_c0_seq6CNOT10-likeCCR4-NOT transcription complex subunit 10-like (Ornithorhynchus anatinus)Down−2.52XP_001509062.176
A16comp74754_c1_seq1phyhd1-likephytanoyl-CoA dioxygenase domain-containing protein 1-like (Strongylocentrotus purpuratus)Down−3.55XP_789562.269
A17comp74908_c0_seq5ehhadh-likePeroxisomal bifunctional enzyme (Branchiostoma floridae)Down−3.86XP_002593843.173
A18comp75055_c2_seq2DHX35-likeprobable ATP-dependent RNA helicase DHX35-like (Strongylocentrotus purpuratus)Down−3.6XP_783015.166
A19comp75531_c0_seq3RIOK3-likeSerine/threonine-protein kinase RIO3 (Saccoglossus kowalevskii)Down−2.19XP_002736242.169
A20comp76071_c1_seq12Map2k6-likeDual specificity mitogen-activated protein kinase kinase 6 (Capitella teleta)Down−2.71ELT91393.172
A21comp76305_c0_seq6Gvin1-likeinterferon-induced very large GTPase 1-like isoform X2 (Danio rerio)Down−2.67XP_684086.483
A22comp76655_c1_seq14Ndufb3-likeNADH dehydrogenase (ubiquinone) 1 β subcomplex subunit 3-like (Strongylocentrotus purpuratus) Down−1.02XP_783578.181
A23comp76725_c0_seq4PRPFF19-likepre-mRNA-processing factor 19 (Strongylocentrotus purpuratus)Down−2.97XP_787949.374
A24comp77143_c0_seq19Mapkap1-liketarget of rapamycin complex 2 subunit MAPKAP1-like (Strongylocentrotus purpuratus)Down−7.52XP_787234.265
A25comp77913_c0_seq1V1g163483-likeInosine triphosphate pyrophosphatase (Rana catesbeiana)Down−3.36ACO51724.175
A26comp78256_c0_seq1SMU1-likeWD40 repeat-containing protein SMU1 (Gallus gallus)Down−2.46NP_001007980.176
A27comp78900_c0_seq70ND5-likeNADH dehydrogenase subunit 5 (Apostichopus japonicas)Up1.15YP_002836162.1100
A28comp79236_c0_seq23YPEL5-likeprotein yippee-like 5-like isoform 2 (Strongylocentrotus purpuratus)Down−3.51XP_786314.175
A29comp80082_c0_seq9Usp39-liketri-snRNP-associated protein 2 (Strongylocentrotus purpuratus)Down−4.33XP_001185686.271
A30comp80196_c0_seq6Hsp70Ab-likeheat shock protein 70 (Apostichopus japonicas)Up4.6ACJ54702.175
Log2 FC (fold change) indicates differential expression level of susceptibility group (S) relative to the control group (K). “−”, indicates fold change of down-regulation.
Table 6. Other putative susceptibility genes.
Table 6. Other putative susceptibility genes.
Coding NumberContig IDGene NamePredict Function RegulationLog2 FCAccession Number in Nr DatabaseIdentities (%)
S15comp73644_c0_seq2ARHGAP39-likeRho GTPase-activating protein 39 (Capitella teleta)Up3.14ELT94447.166
S16comp74218_c0_seq25ftsjd2-likecap-specific mRNA (nucleoside-2'-O-)-methyltransferase 1-like (Danio rerio)Up2.04XP_003729301.170
S17comp75066_c0_seq3DMBT1-likescavenger receptor cysteine-rich protein type 12 precursor (Strongylocentrotus purpuratus)Up1.38NP_999762.170
S18comp73655_c0_seq9Calr-likeCalreticulin (Strongylocentrotus purpuratus)Up1.33XM_006792233.177
S19comp72192_c0_seq1ATG5-likeautophagy-related protein 5 (Strongylocentrotus purpuratus)Up1.32XM_011665174.170
Log2 FC (fold change) indicates differential expression level of susceptibility group (S) relative to the control group (K). “−”, indicates fold change of down-regulation.

2.6. Immune Signaling Pathway

2.6.1. MAPK Signaling Pathway

Five immune genes were enriched in the mitogen-activated protein kinase (MAPK) pathway, including JNK-like, MEKK4-like, NFkB-like, FLNA-like and EGFR-like (Figure 10). The MAPK cascade is a highly conserved module that is involved in various cellular functions, such as cell proliferation, differentiation and migration.
Figure 10. MAPK signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Figure 10. MAPK signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Ijms 16 16347 g010
The MAPK signaling pathway exists widely in all eukaryotes from yeast to human. The genes involved in the MAPK pathway in other species have been identified including Eriocheir sinensis [7], Pseudosciaena crocea [41], but not in sea cucumber. The genes found in sea cucumber: EGFR, JNK, MEKK4, NF-κB and FLNA, had been identified in Eriocheir sinensis, but not in Pseudosciaena crocea. EGFR exists on the cell surface and is activated by binding with its specific ligands, including epidermal growth factor and transforming growth factor α (TGFα), and it activates several signaling cascades to convert extracellular cues into appropriate cellular responses, principally the MAPK, protein kinase B (Akt) and Jun N-terminal kinase (JNK) pathways, leading to DNA synthesis and cell proliferation [42]. FLNA gene serves as a scaffold for a wide range of cytoplasmic signaling proteins. Moreover, the lack of filamin A damages cyclinB relevant proteins, and consequently delays the initiation and progression of mitosis [43]. MEKK4 is a member of MAPK family and could activate downstream MAPK kinase [44]. The c-Jun N-terminal kinases (JNKs) belonging to a large group of serine/threonine (Ser/Thr) protein kinase from the MAPK family, could phosphorylate and activate the transcription factor c-Jun [45]. NF-kappa-B gene is a transcription factor, regulating expression of a number of genes that participate in the inflammatory response, immune response, cell growth and apoptosis [46]. NF-κB p105 plays a role in the activation of NF-κB as a MAPK kinase signaling regulatory protein [47].

2.6.2. ERBB Signaling Pathway

Four genes of sea cucumber were enriched in the ERBB pathway on the basis of our analysis (Figure 11), including ERBB-1-like (EFGR), STAT5-like, JNK-like, and Nck-like. The ERBB family of receptor tyrosine kinases (RTKs) couples binding of extracellular growth factor ligands to intracellular signaling pathways and regulates diverse biologic responses, including proliferation, differentiation, cell motility, and survival.
The ERBB pathway has been investigated widely in human, however, few studies have been done in other animal species, including sea cucumber. STAT5 is phosphorylated by EGFR stimulation and binds with specific DNA, further activating or inactivating transcription [48,49]. Nck regulates cell cycle arrest after DNA damage in some pathways including a translocation of Nck to the nucleus [50].
Figure 11. ERBB signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Figure 11. ERBB signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Ijms 16 16347 g011

2.6.3. Lysosomes

The hydrolysis of lysosomes is an important process of immune response for sea cucumber, which degrades pathogens through hydrolytic enzymes. The acidic hydrolytic enzymes of lysosomes include protease, glycosidase, sulfatase and lipase. Lysosomes include some membrane proteins such as LAMP, LIMP, and ABCA2. SGSH-like, DNaseII-like, ABCA2-like, AP-3-like, NEU1-like and AP-1-like (Figure 12) involved in lysosome were identified, among which NEU1-like belongs to glycosidase, SGSH-like belongs to sulfatase, DNaseII-like belongs to nuclease, ABCA2-like belongs to membrane proteins, and AP-1-like and AP-3-like are members of the clathrin family. The lysosome produces various acidic hydrolysis after Vibrio splendidus infection, therefore SGSH-like, DNaseII-like and NEU1-like could represent up-regulated expression. ABCA2-like may be relevant to transmembrane transport, hence, foreign pathogens are transported into the cell via membrane proteins and further hydrolysis [51]. AP-1 is mainly localized to the trans-Golgi network (TGN) and mediates protein trafficking between the TGN and endosomes [52,53]. AP-3 localized to endosomes and/or the TGN and in mammals, is thought to mediate protein sorting to lysosomes and specialized endosomal-lysosomal organelles [54,55]. Therefore, we inferred that AP-1-like and AP-3-like also change in response to the infection of Vibrio splendidus.
Figure 12. Lysosome signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Figure 12. Lysosome signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Ijms 16 16347 g012

2.6.4. Endocytosis

Endocytosis is a process which brings extracellular large molecules or other cells (like bacteria) into the cell interior by cytomorphosis. There are three types of endocytosis: phagocytosis, pinocytosis and receptor mediated endocytosis. RTK/EGFR-like, AP-2-like, PAR6-like, rabaptin5-like, CHMP5-like, and VPS37-like (Figure 13) were the DEGs enriched in endocytosis, among which VPS37-like and PAR6-like were down-regulated. AP-2 is localized to the plasma membrane and mediates receptor endocytosis [56,57,58]. CHMP5 participated in biosynthesis of multivesicular bodies in endocytosis [59], therefore its expression will be up-regulated after Vibrio splendidus infection. Depletion of HCRP1/hVps37A in mammals hinders degradation of EGFR [60]. Rabaptin 5 is a major component of clathrin-coated vesicles in the Golgi network, taking part in endosomal recycling compartment with Rab4 [61]. PAR6 is also a key adaptor that links Cdc42 and atypical PKCs to Par3 in endocytosis [62].
Figure 13. Endocytosis signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Figure 13. Endocytosis signaling pathway. Red boxes represent up-regulated genes, and green boxes represent down-regulated genes.
Ijms 16 16347 g013

2.6.5. Chemokine Signaling Pathway

The chemokine receptor is one kind of small protein polypeptide of the cytokines superfamily and contains specific Cysteine motifs in their amino acid sequence [63]. Chemokines and their receptors are also important factors in B and T cell development [64,65], infections, angiogenesis, and tumor growth as well as metastasis. AC-like, NFkB-like, FOXO-like and STAT5B-like (Figure 14) participated in chemokine signaling pathways, of which FOXO-like and STAT-like were down-regulated. We predicted that the pathways including FOXO and STAT were inhibited, and immune response was depended mainly on the pathways involving in NFkB. Although above-mentioned genes are involved in immune signaling pathways, it is very necessary to validate their functions further.
Figure 14. Chemokine signaling pathway. Red boxes represent up-regulated genes, green boxes represent down-regulated genes, and blue box includes two genes of which one is an up-regulated gene and the other is a down-regulated gene.
Figure 14. Chemokine signaling pathway. Red boxes represent up-regulated genes, green boxes represent down-regulated genes, and blue box includes two genes of which one is an up-regulated gene and the other is a down-regulated gene.
Ijms 16 16347 g014

2.7. Differential Expression Verification of Putative Genes

The primers of 36 genes were designed and all primer sequences are listed in Table S1. The candidate immune genes were used for qRT-PCR validation. The results showed the differential expression level of these genes between the control group and the disease-resistant group or susceptibility group (Figure 15 and Figure 16). Among them, up-regulation or down-regulation of 35 genes are consistent with the results of RNA-Seq. In conclusion, validation results (qRT-PCR vs. RNA-Seq) are overall reliable with the clear exception of PAR6-like, but further studies are still needed to verify the functions of these genes.
Figure 15. Comparison of 22 putative disease-resistant gene expression levels between RNA-Seq (blue bar) and RT-PCR (red bar). “−”indicates down-regulation.
Figure 15. Comparison of 22 putative disease-resistant gene expression levels between RNA-Seq (blue bar) and RT-PCR (red bar). “−”indicates down-regulation.
Ijms 16 16347 g015
Figure 16. Comparison of 14 putative susceptibility gene expression levels between RNA-Seq (blue bar) and RT-PCR (red bar). “−”indicates down-regulation.
Figure 16. Comparison of 14 putative susceptibility gene expression levels between RNA-Seq (blue bar) and RT-PCR (red bar). “−”indicates down-regulation.
Ijms 16 16347 g016

3. Experimental Section

3.1. Sea Cucumber and Microbial Challenge

Two hundred healthy sea cucumber juveniles from a culture corporation in Qingdao with average weight of 10.3 ± 2.1 g were randomly selected from a full-sib family which is medium-sensitive to Vibrio splendidus and acclimatized in 4 plastic tanks (83 cm × 64 cm × 60 cm) with filtered ozone sterilized seawater for a week. The temperature was maintained at 15 ± 1.5 °C during the whole period of the experiment. The water was changed every three days and the sea cucumbers of treatment group were fed with formula every three days after changing the water.
Vibrio splendidus strain used in the experiment was initially isolated from a skin ulceration syndrome sea cucumber and identified as the pathogen of this syndrome in our laboratory [6]. Cryopreserved Vibrio splendidus strain were revived on tryptone soy broth medium (TSB) medium, and then inoculated into liquid TSB medium at 28 °C for 12 h with shaking at 200 rpm. The cultured bacteria were collected by centrifuging at 4000 rpm for 2 min, and then re-suspended in sterilized seawater.
For microbial challenge, 1 tank with 50 samples was served as control (without any treatment), and the other 3 tanks with 150 samples were immersed with high density of Vibrio splendidus at the final pathogen concentration of 108 cfu·mL−1. The challenge experiment lasted for 30 days and the water was changed every three days. The onset of skin ulceration syndrome was observed from 3 to 25 days in succession after the challenge. The skin ulceration syndrome appeared in 96 sea cucumbers (64% of the challenged sea cucumbers) and the residual 54 sea cucumbers (36%) were not infected by the disease. The first 25% infected sea cucumbers (24 sea cucumbers) were considered as bacterial-susceptible group (S) and the 36 uninfected sea cucumbers were considered as bacterial-resistant group (A). In all, three groups: control group (K), bacterial-resistant group (A) and bacterial-susceptible group (S) were used for the next steps.

3.2. Total RNA Extraction and cDNA Library Construction

Three sea cucumber individuals from each group were collected (S1, S2, and S3 from bacterial-susceptible group with obvious symptoms, A1, A2 and A3from bacterial-resistant group, K1 K2 and K3 from the control group). Coelomic fluid weas extracted and centrifuged at 3000 rpm for 2 min to harvest the coelomocytes. Total RNA was extracted using TaKaRa Mini BEST Universal RNA Extract Kit (Takara, Dalian, China). Total RNA quantity and purity were analyzed using Bioanalyzer 2100 and RNA 6000 Nano Lab Chip Kit (Agilent, Palo Alto, CA, USA) with RIN number >7.0 [66,67]. Nine RNA samples were collected from the 9 samples. RNA-Seq of every sample was performed respectively for gene expression profile analysis. Approximately 10 μg of total coelomocyte RNA was subjected to isolate Poly (A) mRNA with poly-T oligo attached magnetic beads (Invitrogen, Carlsbad, CA, USA). Following purification, the mRNA is fragmented into small pieces using divalent cations under elevated temperature. Then the cleaved RNA fragments were reverse-transcribed to create the final cDNA library in accordance with the protocol of the mRNA-Seq sample preparation kit (Illumina, San Diego, CA, USA), and the average insert size for the paired-end libraries was 300 bp (±50 bp). Subsequently, we performed the paired-end sequencing on an Illumina Hiseq2500 (LC Sciences, Hongzhou, China), following the recommended protocol from the vendor.

3.3. Sequencing and Assembly

Transcriptome sequence of the pooled nine samples was conducted using the Illumina paired-end RNA-Seq approach with Illumina 2500 sequence platform. Prior to assembly, the raw reads were first filtered by removing the adapter sequences, primer sequences and potential contaminations, which are the reads with unknown base greater than 5 and also with low-quality (<Q20) with existing tools: CutAdapt, NGS QC Toolkit and Trimmomatic. After that, paired-end trimmed reads were produced. The raw sequence data were then submitted to the NCBI Short Read Archive with accession number of SRP 057956. Also, trimmed reads were assembled using Trinity (Available online: http://trinityrnaseq.sourceforge.net/) and to remove the effect of different isoforms or alternative splicing, the longest contig of each isoform set was selected as the representative contig in the downstream analysis.

3.4. Annotation of Representative Contig

Representative contigs were first annotated to protein databases (download date: 7 March 2014) Nr, Swiss-prot, Pfam, KEGG and COG separately. Gene names were assigned to each assembled sequence based on the best BLAST hit (highest score). For homologous annotation, sample representative contigs were compared with NCBI non-redundant protein (Nr) (Available online: ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz), Swiss-Prot (Available online: ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz), Cluster of Orthologous Groups (COG) (Available online: http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/COG/grace/shokog.cgi), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Available online: http://www.kegg.jp/kegg/download/) and Pfam (Available online: ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam27.0/Pfam-A.fasta.gz) database using algorithm blastx with E-value cut-off of 10−5. Gene ontology (GO) categories [68] were used for gene annotation using the BLAST 2 GO software [69,70]. The top 20 hits extracted from the blastx results were used for gene annotation and GO analysis (level 2), illustrating general functional categories. KEGG pathways were assigned to the assembled sequences using the online KEGG Automatic Annotation Server (KAAS, available online: http://www.genome. jp/kegg/kaas/). The bi-directional best hit (BBH) method was used to obtain KEGG Orthology (KO) assignment [71].

3.5. SNPs and SSRs Detection

The SNPs in the transcriptome level were analyzed based on the massively parallel Illumina technology. The Bowtie (Available online: http://bowtie-bio.sourceforge.net/) and Samtools (Available online: http://samtools.sourceforge.net/) software with default parameters (cDNA mode) were used to identify the SNPs. The SNP identification was limited to the transcripts (≥200 bp) containing at least 100 reads for each allele. The sample data were mapped to the contig via the Bowtie software after pretreatment, based on the library of transcription. Further SNP analysis was done according to the mapping results, and then variable sites with higher possibility were further filtered using the software of Samtools.
The MISA (Microsatellite) Perl script (Available online: http://pgrc.ipkgatersleben.de/misa) was used for the identification of SSRs. The BatchPrimer3 V1.0 program was used to design primers pairs for amplification of the SSR motifs [72]. Monomers, Dimers, Trimers, Quadmers, Pentamers and Hexamers were all considered as the searching criteria for SSRs in MISA script.

3.6. Identification of Differentially Expressed Genes (DEGs)

To investigate the expression level of each representative contig in different groups, digital gene expression profiles of the three groups were constructed and transcripts expression levels were calculated using RPKM (Reads per kilobase of exon model per million mapped reads) [73]. p-Value was used to identify the DEGs between two groups using chi-square test (2 × 2), and the significance threshold of the p-value in multiple tests was set based on the FDR (FDR ≤ 0.05). The fold changes (log2 (RERPKM/PERPKM)) were also estimated according to the normalized gene expression levels. Considering the above researches, “p-value < 0.01 and |log2fold change| ≥ 1 (FDR ≤ 0.05)” were set as the threshold. Putative Vibrio splendidus-resistant genes were screened through comparing the expression level of genes between disease-resistant group (A) and control group (K), and susceptibility genes were screened through comparing the expression difference between bacterial-susceptible group (S) and control group (K).

3.7. Identifying Potential Immune Genes and Pathway Analysis

The gene ontology (GO) was conducted for Functional classification of the putative disease resistant and susceptibility genes, and the pathway analysis was carried out by using KEGG.

3.8. Validation of Illumina Sequencing Results by qRT-PCR

Quantitative RT-PCR (qRT-PCR) was used to verify the expression level of putative immune genes that were identified in RNA-Seq analysis. Primers were designed using the Primer5 software and β-actin gene was used as the reference gene [74]. The qRT-PCR reactions were performed in a 20 mL volume composed of 2 μL of cDNA, 4 μM of each primer, and 10 mL Master mix, 2× conc (Roche, Penzberg, Germany) in the Eppendorf Real time PCR System. The thermal cycling program was 95 °C for 10 min, followed by 40 cycles of 95 °C for 10 s, 57 °C for 20 s and 72 °C for 30 s. Melting curve analysis was performed by the end of each PCR to confirm the PCR specificity. Three replications were used for each qRT-PCR validation. The relative expression of target genes was calculated using the 2−∆∆Ct method [75]. Differential expression level between control group and experimental group was determined using Log2 (A/K) or Log2 (S/K).

4. Conclusions

We conducted transcriptome sequencing and gene expression profile analysis of coelomocytes RNA in sea cucumber; 30 potential disease-resistant genes and 19 potential susceptibility genes were obtained, respectively, according to GO, KEGG, NCBI annotation and relevant published references. Furthermore, the genes were involved in immune signaling pathways, such as Endocytosis, Lysosome, MAPK, ERBB, and Chemokine, playing key roles in the interactive network of genes. Our study might provide useful information for future investigation of defense mechanism for Vibrio splendidus challenge.

Supplementary Materials

Acknowledgments

This study was supported by the National High Technology Research and Development Program, China (2012AA10A412), National Natural Science Foundation of China (No. 31202016), Agriculture Seed Improvement Project of Shandong Province, Special Funds for Technology R&D Program in Research Institutes (2011EG134219), and the Science and Technology Program of Qingdao (13-4-1-65-hy).

Author Contributions

Qiong Gao and Meijie Liao conducted the major part of this study including sample collection, bioinformatic analysis and manuscript preparation; Yingeng Wang conceived and designed the experiment, supervised the entire study and revised the manuscript; Bin Li, Zheng Zhang and Guiping Chen were involved in the sample preparation and microbial challenge experiment; Xiaojun Rong and Lan Wang were involved in data analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sloan, N.A. Echinoderm fisheries of the world: A review. In Proceedings of the 5th International Echinoderm Conference on Echinodermata, Galway, Ireland, 24–29 September 1984; Keegan, B.F., O’Connor, B.D.S., Eds.; A.A. Balkema: Rotterdam, The Netherlands, 1984; pp. 109–124. [Google Scholar]
  2. Huang, H.W.; Wang, Y.G. Current situation, questions and prospect in the sea cucumber industry. China Fish. 2007, 10, 50–53. [Google Scholar]
  3. Liu, H.Z.; Zheng, F.R.; Sun, X.Q.; Hong, X.G.; Dong, S.L.; Wang, B.; Tang, X.X.; Wang, Y.Q. Identification of the pathogens associated with skin ulceration and peristome tumescence in cultured sea cucumbers Apostichopus japonicus (Selenka). J. Invertebr. Pathol. 2010, 105, 236–242. [Google Scholar] [CrossRef] [PubMed]
  4. Wang, Y.G.; Zhang, C.Y.; Rong, X.J.; Chen, J.J.; Shi, C.Y. Diseases of cultured sea cucumber Apostichopus japonicus in China. FAO Fish. Tech. Paper 2005, 463, 297–310. [Google Scholar]
  5. Deng, H.; Zhou, Z.C.; Wang, N.B.; Liu, C. The syndrome of sea cucumber (Apostichopus) japonicas infected by virus and bacteria. Virol. Sin. 2008, 23, 63–67. [Google Scholar] [CrossRef]
  6. Zhang, C.Y.; Wang, Y.G.; Rong, X.J. Isolation and identification of causative pathogen for skin ulcerative syndrome in Apostichopus japonicus. J. Fish. China 2006, 30, 118–123. [Google Scholar]
  7. Li, X.H.; Cui, Z.X.; Liu, Y.; Song, C.W.; Shi, G.H. Transcriptome analysis and discovery of genes involved in immune pathways from Hepatopancreas of microbial challenged mitten crab Eriocheir sinensis. PLoS ONE 2013, 8, e68233. [Google Scholar] [CrossRef] [PubMed]
  8. Glin’ski, Z.; Jarosz, J. Immune phenomena in echinoderms. Arch. Immunol. Ther. Exp. 2000, 48, 189–193. [Google Scholar]
  9. Eliseikina, M.G.; Magarlamov, T.Y. Coelomocyte morphology in the Holothurians Apostichopus japonicus (Aspidochirota: Stichopodidae) and Cucumaria japonica (Dendrochirota: Cucumariidae). Russ. J. Mar. Biol. 2002, 28, 197–202. [Google Scholar] [CrossRef]
  10. Dolmatova, L.S.; Eliseikina, M.G.; Romashina, V.V. Antioxidant enzymatic activity of coelomocytes of the Far East sea cucumber Eupentacta fraudatrix. J. Evol. Biochem. Physiol. 2004, 40, 126–135. [Google Scholar] [CrossRef]
  11. Liu, Z.M.; Ma, Y.X.; Yang, Z.P.; Li, M.; Liu, J.; Bao, P.Y. Immune responses and disease resistance of the juvenile sea cucumber Apostichopus japonicus induced by Metschnikowia sp. C14. Aquaculture 2012, 368, 10–18. [Google Scholar] [CrossRef]
  12. Ma, Y.X.; Liu, Z.M.; Yang, Z.P.; Li, M.; Liu, J.; Song, J. Effects of dietary live yeast Hanseniaspora opuntiae C21 on the immune and disease resistance against Vibrio splendidus infection in juvenile sea cucumber Apostichopus japonicus. Fish Shellfish Immunol. 2012, 34, 66–73. [Google Scholar] [CrossRef] [PubMed]
  13. Gowda, N.M.; Goswami, U.; Khan, M.I. T-antigen binding lectin with antibacterial activity from marine invertebrate, sea cucumber (Holothuria scabra): Possible involvement in differential recognition of bacteria. J. Invertebr. Pathol. 2008, 99, 141–145. [Google Scholar] [CrossRef] [PubMed]
  14. Zhou, Z.C.; Sun, D.P.; Yang, A.F.; Dong, Y.; Chen, Z.; Wang, X.Y.; Guan, X.Y.; Jiang, B.; Wang, B. Molecular characterization and expression analysis of a complement component 3 in the sea cucumber (Apostichopus japonicus). Fish Shellfish Immunol. 2011, 31, 540–547. [Google Scholar] [CrossRef] [PubMed]
  15. Dong, Y.; Sun, H.J.; Zhou, Z.C.; Yang, A.F.; Chen, Z.; Guan, X.Y.; Gao, S.; Wang, B.; Jiang, B.; Jiang, J.W. Expression analysis of immune related genes identified from the coelomocytes of sea cucumber (Apostichopus japonicus) in response to LPS challenge. Int. J. Mol. Sci. 2014, 15, 19472–19486. [Google Scholar] [CrossRef] [PubMed]
  16. Yang, A.F.; Zhou, Z.C.; Dong, Y.; Jiang, B.; Wang, X.Y.; Chen, Z.; Guan, X.Y.; Wang, B.; Sun, D.P. Expression of immune-related genes in embryos and larvae of sea cucumber Apostichopus japonicus. Fish Shellfish Immunol. 2010, 29, 839–845. [Google Scholar] [CrossRef] [PubMed]
  17. Cong, L.; Yang, X.; Wang, X.; Tada, M.; Lu, M.; Liu, H.; Zhu, B. Characterization of an i-type lysozyme gene from the sea cucumber Stichopus japonicus, and enzymatic and nonenzymatic antimicrobial activities of its recombinant protein. J. Biosci. Bioeng. 2009, 107, 583–588. [Google Scholar] [CrossRef] [PubMed]
  18. Yang, A.F.; Zhou, Z.C.; He, C.B.; Hu, J.J.; Chen, Z.; Gao, X.G.; Dong, Y.; Jiang, H.; Liu, W.D.; Guan, X.Y.; et al. Analysis of expressed sequence tags from body wall, intestine and respiratory tree of sea cucumber (Apostichopus japonicus). Aquaculture 2009, 296, 193–199. [Google Scholar] [CrossRef]
  19. Li, C.H.; Feng, W.D.; Qiu, L.H.; Xia, C.G.; Su, X.R.; Jin, C.H.; Zhou, T.T.; Zeng, Y.; Li, T.W. Characterization of skin ulceration syndrome associated microRNAs in sea cucumber Apostichopus japonicus by deep sequencing. Fish Shellfish Immunol. 2012, 33, 436–441. [Google Scholar] [CrossRef] [PubMed]
  20. Zhang, P.; Li, C.H.; Li, Y.; Zhang, P.J.; Shao, Y.N.; Jin, C.H.; Li, T.W. Proteomic identification of differentially expressed proteins in sea cucumber Apostichopus japonicus coelomocytes after Vibrio splendidus infection. Dev. Comp. Immunol. 2014, 44, 370–377. [Google Scholar] [CrossRef] [PubMed]
  21. Zhang, P.J.; Li, C.H.; Zhang, P.; Jin, C.H.; Pan, D.D.; Bao, Y.B. iTRAQ-based proteomics reveals novel members involved in pathogen challenge in sea cucumber Apostichopus japonicus. PLoS ONE 2014, 9, e100492. [Google Scholar] [CrossRef] [PubMed]
  22. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [PubMed]
  23. Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; et al. De novo transcript sequence reconstruction from RNA-Seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 2013, 8, 1494–1512. [Google Scholar] [CrossRef] [PubMed]
  24. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25–29. [Google Scholar] [CrossRef] [PubMed]
  25. Du, H.X.; Bao, Z.M.; Hou, R.; Wang, S.; Su, H.L.; Yan, J.J.; Ti, M.L.; Li, Y.; Wei, W.; Lu, W.; et al. Transcriptome sequencing and characterization for the sea cucumber Apostichopus japonicus (Selenka, 1867). PLoS ONE 2012, 7, e33311. [Google Scholar] [CrossRef] [PubMed]
  26. Velculescu, V.E.; Kinzler, K.W. Gene expression analysis goes digital. Nat. Biotechnol. 2007, 25, 878–880. [Google Scholar] [CrossRef] [PubMed]
  27. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef] [PubMed]
  28. Wallin, R.P.; Lundqvist, A.; Moré, S.H.; von Bonin, A.; Kiessling, R.; Ljunggren, H.G. Heat-shock proteins as activators of the innate immune system. Trends Immunol. 2002, 23, 130–135. [Google Scholar] [CrossRef]
  29. Zügel, U.; Kaufmann, S.H.E. Role of heat shock proteins in protection from and pathogenesis of infectious diseases. Clin. Microbiol. Rev. 1999, 12, 19–39. [Google Scholar] [PubMed]
  30. Dong, Y.W.; Dong, S.L.; Meng, X.L. Effects of thermal and osmotic stress on growth, osmoregulation and Hsp70 in sea cucumber (Apostichopus japonicus Selenka). Aquaculture 2008, 276, 179–186. [Google Scholar] [CrossRef]
  31. Meng, X.L.; Ji, T.T.; Dong, Y.W.; Wang, Q.L. Thermal resistance in sea cucumbers (Apostichopus japonicus) with differing thermal history: The role of Hsp70. Aquaculture 2009, 294, 314–318. [Google Scholar] [CrossRef]
  32. Wang, X.Y.; Zhou, Z.C.; Yang, A.F.; Dong, Y.; Chen, Z.; Guan, X.Y.; Jiang, B.; Wang, B. Molecular characterization and expression analysis of heat shock cognate 70 after heat stress and lipopolysaccharide challenge in sea cucumber (Apostichopus japonicus). Biochem. Genet. 2013, 51, 443–457. [Google Scholar] [CrossRef] [PubMed]
  33. Samuel, S.J.; Tzung, S.P.; Cohen, S.A. Hrp12, a novel heat-responsive, tissue-specific, phosphorylated protein isolated from mouse liver. Hepatology 1997, 25, 1213–1222. [Google Scholar] [CrossRef] [PubMed]
  34. Feng, J.; de Jesus, P.D.; Su, V.; Han, S.; Gong, D.; Wu, N.C.; Tian, Y.; Li, X.; Wu, T.T.; Chanda, S.K.; et al. RIOK3 is an adaptor protein required for IRF3-mediated antiviral type I interfereon production. J. Virol. 2014, 88, 7987–7997. [Google Scholar] [CrossRef] [PubMed]
  35. Li, Y.Z.; Batra, S.; Sassano, A.; Majchrzak, B.; Levy, D.E.; Gaestel, M.; Fish, E.N.; Davis, R.J.; Platanias, L.C. Activation of mitogen-activated protein kinase kinase (MKK) 3 and MKK6 by Type I interferons. J. Biol. Chem. 2005, 280, 10001–10010. [Google Scholar]
  36. Hicks, S.D.; Parmele, K.T.; DeFranco, D.B.; Klann, E.; Callaway, C.W. Hypothermia differentially increases extracellular signal-regulated kinase and stress-activated protein kinase/c-Jun terminal kinase activation in the hippocampus during reperfusion after asphyxial cardiac arrest. Neuroscience 2000, 98, 677–685. [Google Scholar] [CrossRef]
  37. Klamp, T.; Boehm, U.; Schenk, D.; Pfeffer, K.; Howard, J.C. A giant GTPase, very large inducible GTPase-1, is inducible by IFNs. J. Immunol. 2003, 171, 1255–1265. [Google Scholar] [CrossRef] [PubMed]
  38. Moon, S.Y.; Zheng, Y. Rho GTPase-activating proteins in cell regulation. Trends Cell Biol. 2003, 13, 13–22. [Google Scholar] [CrossRef]
  39. Van Aelst, L.; D’Souza-Schorey, C. Rho GTPases and signaling networks. Genes Dev. 1997, 11, 2295–2322. [Google Scholar] [CrossRef] [PubMed]
  40. Qiu, R.; Sun, B.G.; Li, J.; Liu, X.; Sun, L. Identification and characterization of a cell surface scavenger receptor cysteine-rich protein of Sciaenops ocellatus: Bacterial interaction and its dependence on the conserved structural features of the SRCR domain. Fish Shellfish Immunol. 2013, 34, 810–818. [Google Scholar] [CrossRef] [PubMed]
  41. Mu, Y.N.; Ding, F.; Cui, P.; Ao, J.Q.; Hu, S.N.; Chen, X.H. Transcriptome and expression profiling analysis revealed changes of multiple signaling pathways involved in immunity in the large yellow croaker during Aeromonas hydrophila infection. BMC Genomics 2010, 11, 506. [Google Scholar] [CrossRef] [PubMed]
  42. Oda, K.; Matsuoka, Y.; Funahashi, A.; Kitano, H. A comprehensive pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 2005, 1. [Google Scholar] [CrossRef] [PubMed]
  43. Lian, G.; Lu, J.; Hu, J.; Zhang, J.; Cross, S.H.; Ferland, R.J.; Sheen, V.L. Filamin a regulates neural progenitor proliferation and cortical size through wee1-dependent Cdk1 phosphorylation. J. Neurosci. 2012, 32, 7672–7684. [Google Scholar] [CrossRef] [PubMed]
  44. Craig, E.A.; Stevens, M.V.; Vaillancourt, R.R.; Camenisch, T.D. MAP3Ks as central regulators of cell fate during development. Dev. Dyn. 2008, 237, 3102–3114. [Google Scholar] [CrossRef] [PubMed]
  45. Bogoyevitch, M.A.; Kobe, B. Uses for JNK: The many and varied substrates of the c-Jun N-terminal kinases. Microbiol. Mol. Biol. Rev. 2006, 70, 1061–1095. [Google Scholar] [CrossRef] [PubMed][Green Version]
  46. Beg, A.A.; Baldwin, A.S., Jr. Activation of multiple NF-κB/Rel DNA-binding complexes by tumor necrosis factor. Oncogene 1994, 9, 1487–1492. [Google Scholar] [PubMed]
  47. Beinke, S.; Ley, S.C. Functions of NF-κB1 and NF-κB2 in immune cell biology. Biochem. J. 2004, 382, 393–409. [Google Scholar] [PubMed]
  48. Lin, J.X.; Leonard, W.J. The role of Stat5a and Stat5b in signaling by IL-2 family cytokines. Oncogene 2000, 19, 2566–2576. [Google Scholar] [CrossRef] [PubMed]
  49. Farrar, M.A.; Harris, L.M. Turning transcription on or off with STAT5: When more is less. Nat. Immunol. 2011, 12, 1139–1140. [Google Scholar] [CrossRef] [PubMed]
  50. Kremer, B.E.; Adang, L.A.; Macara, I.G. Septins regulate actin organization and cell-cycle arrest through nuclear accumulation of NCK mediated by SOCS7. Cell 2007, 130, 837–850. [Google Scholar] [CrossRef] [PubMed]
  51. Mack, J.T.; Beljanski, V.; Tew, K.D.; Townsend, D.M. The ATP-binding cassette transporter ABCA2 as a mediator of intracellular trafficking. Biomed. Pharmacother. 2006, 60, 587–592. [Google Scholar] [CrossRef] [PubMed]
  52. Bonifacino, J.S.; Lippincott-Schwartz, J. Coat proteins: Shaping membrane transport. Nat. Rev. Mol. Cell Biol. 2003, 4, 409–414. [Google Scholar] [CrossRef] [PubMed]
  53. Mullins, C.; Hartnell, L.M.; Wassarman, D.A.; Bonifacino, J.S. Defective expression of the mu3 subunit of the AP-3 adaptor complex in the Drosophila pig mentation mutant carmine. Mol. Gen. Genet. 1999, 262, 401–412. [Google Scholar] [CrossRef] [PubMed]
  54. Le Borgne, R.; Alconada, A.; Bauer, U.; Hoflack, B. The mammalian AP-3 adaptor-like complex mediates the intracellular transport of lysosomal membrane glycoproteins. J. Biol. Chem. 1998, 273, 29451–29461. [Google Scholar] [CrossRef] [PubMed]
  55. Peden, A.A.; Oorschot, V.; Hesse, B.A.; Austin, C.D.; Scheller, R.H.; Klumperman, J. Localization of the AP-3 adaptor complex defines a novel endosomal exit site for lysosomal membrane proteins. J. Cell Biol. 2004, 164, 1065–1076. [Google Scholar] [CrossRef] [PubMed]
  56. Kirchhausen, T.; Bonifacino, J.S.; Riezman, H. Linking cargo to Vesicle formation: Receptor tail interactions with coat proteins. Curr. Opin. Cell Biol. 1997, 9, 488–495. [Google Scholar] [CrossRef]
  57. Lewin, D.A.; Mellman, I. Sorting out adaptors. BBA-Mol. Cell. Res. 1998, 1401, 129–145. [Google Scholar] [CrossRef]
  58. Hirst, J.; Robinson, M.S. Clathrin and adaptors. Biochim. Biophys. Acta 1998, 1404, 173–193. [Google Scholar] [CrossRef]
  59. Bache, K.G.; Slagsvold, T.; Cabezas, A.; Rosendal, K.R.; Raiborg, C.; Stenmark, H. The growth-regulatory protein HCRP1/hVps37A is a subunit of mammalian ESCRT-I and mediates receptor down-regulation. Mol. Biol. Cell 2004, 15, 4337–4346. [Google Scholar] [CrossRef] [PubMed]
  60. Tsang, H.T.; Connell, J.W.; Brown, S.E.; Thompson, A.; Reid, E.; Sanderson, C.M. A systematic analysis of human CHMP protein interactions: Additional MIT domain-containing proteins bind to multiple components of the human ESCRT III complex. Genomics 2006, 88, 333–346. [Google Scholar] [CrossRef] [PubMed]
  61. Vitale, G.; Rybin, V.; Christoforidis, S.; Thornqvist, P.; McCaffre, M.; Stenmark, H.; Zerial, M. Distinct Rab-binding domains mediate the interaction of Rabaptin5 with GTP-bound Rab4 and Rab5. EMBO J. 1998, 17, 1941–1951. [Google Scholar] [CrossRef] [PubMed]
  62. Joberty, G.; Petersen, C.; Gao, L.; Macara, I.G. The cell-polarity protein Par6 links Par3 and atypical protein kinase C to Cdc42. Nat. Cell Biol. 2000, 2, 531–539. [Google Scholar] [CrossRef] [PubMed]
  63. Rossi, D.; Zlotnik, A. The biology of chemokines and their receptors. Annu. Rev. Immunol. 2000, 18, 217–242. [Google Scholar] [CrossRef] [PubMed]
  64. Forster, R.; Emrich, T.; Kremmer, E.; Lipp, M. Expression of the G-protein-oupled receptor BLR1 defines mature, recirculating B cells and a subset of Thelper memory cells. Blood 1994, 84, 830–840. [Google Scholar] [PubMed]
  65. Vicari, A.P.; Figueroa, D.J.; Hedrick, J.A.; Foster, J.S.; Singh, K.P.; Menon, S.; Copeland, N.G.; Gilbert, D.J.; Jenkins, N.A.; Bacon, K.B.; et al. TECK: A novel CC chemokine specifically expressed by thymic dendritic cells and potentially involved in T cell development. Immunity 1997, 7, 291–301. [Google Scholar] [CrossRef]
  66. Schroeder, A.; Mueller, O.; Stocker, S.; Salowsky, R.; Leiber, M.; Gassmann, M.; Lightfoot, S.; Menzel, W.; Granzow, M.; Ragg, T. The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 2006, 7. [Google Scholar] [CrossRef] [PubMed]
  67. Mueller, O.; Lightfoot, S.; Schroeder, A. RNA integrity number (RIN)–standardization of RNA quality control. Agilent Appl. Note Publ. 2004, 1–8. [Google Scholar]
  68. Balakrishnan, R.; Harris, M.A.; Huntley, R.; van Auken, K.; Cherry, J.M. A guide to best practices for Gene Ontology (GO) manual annotation. Database (Oxf.) 2013. [Google Scholar] [CrossRef] [PubMed]
  69. Conesa, A.; Götz, S.; García-Gómez, J.M.; Terol, J.; Talón, M.; Robles, M. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005, 21, 3674–3676. [Google Scholar] [CrossRef] [PubMed]
  70. Götz, S.; García-Gómez, J.M.; Terol, J.; Williams, T.D.; Nagaraj, S.H.; Nueda, M.J.; Robles, M.; Talón, M.; Dopazo, J.; Conesa, A. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008, 36, 3420–3435. [Google Scholar] [CrossRef] [PubMed]
  71. Moriya, Y.; Itoh, M.; Okuda, S.; Yoshizawa, A.C.; Kanehisa, M. KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007, 35, W182–W185. [Google Scholar] [CrossRef] [PubMed]
  72. You, F.M.; Huo, N.; Gu, Y.Q.; Luo, M.C.; Ma, Y.; Hane, D.; Lazo, G.R.; Dvorak, J.; Anderson, O.D. BatchPrimer3: A high throughput web application for PCR and sequencing primer design. BMC Bioinform. 2008, 9. [Google Scholar] [CrossRef] [PubMed]
  73. Wang, X.; Wang, X.W.; Wang, L.K.; Feng, Z.X.; Zhang, X.G. A review on the processing and analysis of next-generation RNA-Seq data. Prog. Biochem. Biophys. 2010, 37, 834–846. [Google Scholar] [CrossRef]
  74. Yang, A.F.; Zhou, Z.C.; Dong, Y.; Jiang, B.; Wang, X.Y.; Chen, Z.; Guan, X.Y.; Wang, B.; Sun, D.P. Stability comparison of cytb and β-actin genes expression in sea cucumber (Apostichopus japonicus). J. Agric. Sci. Tech-Iran. 2010, 12, 79–84. [Google Scholar]
  75. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using realtime quantitative PCR and the 2−∆∆Ct method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
Back to TopTop