Introduction

The swimming crab, Portunus trituberculatus (Crustacea: Decapoda: Brachyura), is a commercially important fishery species widely distributed from Korea, Japan, China, through Southeast Asia, to the Indian Ocean (Dai et al. 1986). It is also an important aquaculture species in China (Sun 1984a). P. trituberculatus is a euryhaline crab species, surviving in wide-range salinity conditions, but different water salinity condition might influence its distribution and migration route (Shen and Liu 1965; Dai 1977; Dai et al. 1986; Xue et al. 1997). The water salinity condition is also an important factor for artificial propagation of the swimming crab, especially in larval development and molt stage (Ji 2005). Therefore, the research on the genetic mechanisms to environmental salinity changes is of great interest for marine biologists and would possibly help the crab artificial propagation in the future.

The recent sequencing of whole genomes of model species has accelerated the development of various transcriptomic profiling techniques, including microarray-based gene expression profiling, which allows scientists to monitor the activities of thousands of genes simultaneously and characterize genetic pathways involved in response to environmental stressors and toxicants (Currie et al. 2005; Ju et al. 2007). One alternative method to work around the limited genomic information is sequencing cDNA libraries and designing cDNA microarrays by spotting genes identified by various techniques (Li and Brouwer 2009).

Expressed sequence tags (ESTs) analysis is a powerful approach for investigating gene expression related to specific biological functions, especially in organisms where genomic data are not available (Gueguen et al. 2003). ESTs contain enough sequence information to design and construct DNA microarrays for determining gene expression patterns. Expressed sequence tag (EST) libraries have been successfully utilized in partial sequencing of various aquatic crustacean species, such as fiddler crab (Uca pugilator) (Durica et al. 2003), blue crab (Callinectes sapidus) (Coblentz et al. 2006), black tiger shrimp (Penaeus monodon) (Supungul et al. 2002), porcelain crab (Petrolisthes cinctipes) (Stillman et al. 2006), green shore crab (Carcinus maenas) (Towle and Smith 2006), America lobster (Homarus americanus) (Towle and Smith 2004, 2006), and white shrimp (Litopenaeus vannamei) (Alejandra et al. 2007). However, EST-microarray approach is limited to only a few crustacean species, including American lobster (Homarus americanus) (Stepanyan et al. 2006), porcelain crab (Petrolisthes cinctipes) (Teranishi and Stillman 2007), grass shrimp (Palaemonetes pugio) (Li and Brouwer 2009), and Callinectes maenas (Towle et al. 2010).

The swimming crab is an ecologically important crustacean. Although an extensive literature that describes the development, physiology, behavior, and propagation techniques of this species exists (Sun 1984a, b; Song et al. 1989; Ji 2005), there is very limited genomic and annotation information available for swimming crab (Yamauchia et al. 2003; Kong et al. 2008; Zhang et al. 2009), and little is known about the genomic response of swimming crab exposed to environmental stressors. In our previous study, gill cDNA library of ESTs was constructed from the swimming crab exposed to two different salinity challenges (10 and 35‰) (Xu et al. 2010). A total of 2426 transcripts were selected for microarray construction based on criteria described in Xu et al. (2010).

Here, we report cDNA microarray analysis of salinity-challenged swimming crab. The 2426 cDNA spotted microarray was constructed from 4433 ESTs (GenBank accession no. GW397727–GW402159). The purpose of the present study was to demonstrate the utility of this custom-designed DNA microarray as a useful tool to monitor gene expression changes in swimming crab during salinity acclimation, identify potential salinity adaptation pathways, networks, or important genes, and validate them using real-time quantitative RT-PCR.

Materials and methods

Laboratory exposures

Twenty-seven male adult crabs were collected from Zhoushan Archipelago of the East China Sea. All the samples were acclimated in the laboratory (25 ppt, 18°C) for 3 days before the experiment treatment. The crabs were divided into 3 groups (9 crabs for one group) and acclimated to three different salinity challenges (10, 25, or 40 ppt) at 18°C. The sixth pair of gills from each crab collected from each treatment was sampled at day 3, 4, and 6, respectively. All the 27 gill samples were used for RNA extraction, individually.

Isolation and quantification of total RNA

The harvested 27 tissue samples were treated with TRIZOL reagent (Invitrogen) according to the manufacturer’s protocol to extract total RNA, individually. After precipitation, RNA was DNase treated and stored in DEPC water. The concentration of RNA was determined spectrophotometrically. All 260/280 ratios were between 1.8 and 2.1. Quality of the RNA was checked by observing intact rRNA on denaturing RNA gels. In order to obtain enough RNA for microarray analysis, 10ug total RNA from each sample was extracted, and total RNA of 3 crabs challenged by the same salinity (10, 25, or 40 ppt) cultured for day 3, day 4, and day 6 was pooled. Three total RNA samples named G-10, G-25, and G-40 accordingly were therefore acquired. Then, two treated samples (G-10 and G-40) and control samples (G-25) were precipitated in 100% ethanol and sent to CapitalBio Corporation (http://www.capitalbio.com) for microarray analysis (See detailed in 2.4 and 2.5) or stored at −80°C for further quantitative real-time RT-PCR (qRT-PCR) analysis.

cDNA library and cDNA clones

Gill cDNA library was performed as described in Xu et al. (2010) from swimming crab. A total of 2426 unique cDNA fragments were PCR amplified using T3 and T7 primers in a 100 μl reaction. The PCR conditions were 1 cycle of 94°C for 3 min; 30 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 1 min; and 1 cycle of 72°C for 5 min and then hold at 4°C. Aliquots of products were run on a 1.5% agarose gel containing ethidium bromide to confirm the correct insert size of amplicon as well as to ascertain minimal primer dimer content. The PCR bands were purified by isopropanol precipitation and then dissolved in about 50 μl ddH2O. Concentrations were measured using a Spectrophotometer (UV1800, Shimadzu, Japan). Using an Eppendorf Vacufuge (Thermo Jouan), 2,500 ng of DNA was speed vacuum dried and resuspended in 10 μl ddH2O with a final concentration of 250 ng/μl for each clone. Samples were stored in 96-well polypropylene plates and sent to CapitalBio Corporation for microarray construction.

Microarray design and construction

The swimming crab cDNA GeneChip was constructed by CapitalBio Corporation and briefly described as follow. The cDNA clones were dissolved in EasyArray™ spotting solution and finally spotted on aminosilane-coated slides using a SmartArray™ microarrayer (CapitalBio Corporation). On one slide, there were 48 blocks, and every block had 15 columns and 14 rows. The probes for four housekeeping genes and eight yeast intergenic sequences were spotted on every block as internal and external controls, respectively. Each set of samples was spotted, in triplicate, on a glass slide.

RNA amplification, labeling, and hybridization

Before amplification, total RNA was purified using NucleoSpin® RNA clean-up kit (MACHEREY–NAGEL, Germany). Then, formaldehyde denaturing gel electrophoresis was used to detect the RNA quality. Total RNA (50 μg) was used to prepare the fluorescent dye-labeled cDNA using the linear mRNA amplification procedure was performed by CapitalBio Corporation. Then, the RNA samples of treatment and control were labeled with either fluorochromes cyanine-3 (Cy3)-dCTP or cyanine-5 (Cy5)-dCTP. Analyses were performed twice per sample, using a dye-swapping experiment (technical replicate) in which cDNA from the control (G-25) was labeled with Cy3 and cDNA from low salinity (G-10) or high salinity (G-40) challenge was labeled with Cy5. In the second analysis, control cDNA was labeled with Cy5, and cDNA from salinity challenge was labeled with Cy3. This dye reversal helps to minimize error due to fluor-associated bias. The resulting labeled cDNAs were dissolved in 80 μl of hybridization solution containing 3 × SSC, 0.2% SDS, 5 × Denhardt’s solutions, and 25% formamide and then denatured at 95°C for 3 min before hybridization. Labeled cDNA was hybridized to the swimming crab cDNA GeneChip (CapitalBio) at 42°C in a hybridization chamber for 16 h. After hybridization, slides were washed with washing solution 1 (0.2% SDS, 2 × SSC) and solution 2 (2 × SSC) at 42°C for 5 min, respectively. After wash, the arrays were dried by spinning at 1,500 rpm for 5 min.

Microarray imaging and data analysis

Chips were scanned using a LuxScan 10KA dual pathways laser scanner (CapitalBio Corp.), and images were analyzed by LuxScan3.0 image analysis software (CapitalBio Corp.). For the individual channel data extracts, we removed any faint spots, with signal intensities below 400 units after subtracting the background, from both channels (Cy3 and Cy5). For each hybridized spot, the background intensity was subtracted and normalized by nonlinear LOWESS normalization (Yang et al. 2002). The signal intensity was calculated as the mean intensities of the three replicates minus the background signal. Average ratios and standard deviations were calculated.

Transcript regulation is expressed as the ratio of intensities between stress and control. A postnormalization cutoff of twofold up- or down-regulation (ratio ≥2.0 or ≤0.5) was defined as the threshold for significant up- or down-regulation, respectively. Hierarchical cluster analysis is used to identify genes with certain patterns of expression under two different salinity challenge using Cluster 3.0 (de Hoon et al. 2004). The settings for the calculations were as follows: similarity is measured by standard correlation, and clustering method is average linkage. The genes are clustered according to similarities in expression profiles across two salinity conditions.

Gene identification

All the 2426 ESTs used for our microarray analysis were performed by a BLASTx search against the NCBI nonredundant protein database (http://www.ncbi.nlm.nih.gov/BLAST). E value less than 10−4 was used. Genes were identified according to the best hits against known sequences in Swiss-Prot database (http://www.ncbi.nlm.nih.gov/).

Real-time RT-PCR confirmation of microarray data

Fourteen genes with down- or up-regulated expression in microarray data were selected for transcription quantification using quantitative real-time RT-PCR to check for consistency in gene expression patterns observed with the microarrays. Quantitative real-time PCR was performed on cDNA generated from 2 μg of RNA obtained from the same pooled RNA samples (G-10, G-25, and G-40) used in the microarray study. cDNA was generated by reverse transcription using Superscript II RNase H-Reverse Transcriptase (Invitrogen) and random hexamers (50 ng/μl). Primers (Table 1) were designed to amplify a short fragment (100–200 bp) using DNAStar Inc. 1996 and synthesized by Sangong (Sangong company, Shanghai, China). The real-time RT-PCR confirmation results were performed using the SYBR Premix Ex Taq kit (TaKaRa, China), and each reaction was prepared in 25 μl containing 70 ng cDNA (2 μl), SYBR PremixEx Taq 12.5 μl, 10 mM each of sense and antisense primers 0.5 μl. Real-time PCR was performed using the iCycler iQ Real-Time PCR Detection System (Bio-Rad, CA, USA).

Table 1 Primer sequences and product size of target and reference genes used for quantitative RT-PCR
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The standard curve testing was performed using a series of diluted samples (tenfold serial dilutions: 1×, 10×, 100×, 1,000×, 10,000×), respectively, for each gene. The slopes of the standard curves were calculated. According to the formula E = 10^ (−1/slope), the values of PCR efficiency for all the genes were calculated. In all cases, PCR efficiencies were >90%. Efficiency curve analysis of amplification (tenfold serial dilutions from 100 to 0.01 ng of cDNA) was carried out with blank controls to confirm that the fluorescent real-time RT-PCR data are precise and trustworthy.

The following PCR program was used for gene amplification: 95°C for 2 min; 95°C for 15 s, optimal anneal temperature for each pair of primers for 15 s (40 cycles); and 72°C for 20 s, followed by a melt curve. Melt curve analysis was performed to ensure single products by using a temperature step gradient from 55°C to 95°C in 0.5°C increments with fluorescence measured after a 20-s incubation at each temperature. At the end of the reaction, the products of qRT-PCR were run on 1.5% agarose gel electrophoresis and displayed an equal-sized band as predicted. The real-time RT-PCR for each gene was performed in duplicate for at least three replicates.

Four candidate reference genes, including β-actin, 18S rRNA (18S ribosomal RNA), RpL8, and RpL18, which showed no significant changes from microarray analysis, were selected for reference gene selection analysis. The primers for amplifying the four candidate reference genes were also shown in Table 1.

The cycle threshold values (Ct-values) were first determined for the reference genes. The average Ct value of each triplicate reaction was then used for subsequent analysis with the geNorm (Vandesompele et al. 2002) programs in order to determine the best reference gene. This approach relies on the principle that the expression of a perfect reference gene should be identical in all samples, independent of experimental conditions. The program calculates the gene expression normalization factor based on geometric mean of the number of candidate genes and determines the most stable internal controls, which can be used as housekeeping genes for specific experimental condition in control and salinity-challenged samples.

The relative mRNA expression levels for all of these genes in swimming crab subjected to two salinity stresses were calculated using the equation 2−ΔΔCT normalized with the selected reference gene (ΔCT = CT of target gene minus CT of the selected reference gene, ΔΔCT = ΔCT of treated sample (G-10 or G-40) minus ΔCT of control sample (G-25)). Data are expressed as mean and standard error of the mean (SEM). Statistical significance was determined using a two-tailed unpaired Student’s t test using p < 0.05.

Results

Gene expression profiling

In our experiment, labeled cDNAs with Cy3 or Cy5 for control crabs (G-25, 25‰) were derived from pools of the sixth gills sampled from 9 crabs (every 3 crabs sampled from days 3, 4, and 6, individually) were compared with the equivalent labeled, time- and tissue-matched cDNAs from RNA pooled from 9 low salinity (G-10, 10‰) or high salinity (G-40, 40‰)-challenged crabs. A simple and effective design for the direct comparison of two samples in dye swap initial experiments was chosen. Therefore, both the low-salinity-inducible and high-salinity-inducible ESTs could be acquired from our microarray hybridization.

The constructed microarrays were properly prepared since hybridizations with Cy3- and Cy5-labeled targets prepared from the same RNA under unstressed conditions (G-25) resulted in spots with signal intensities within the 2–0.5 ratio area as shown in a scatter plot (Fig. 1). To assess our microarray, we employed the salt-stressed condition as a model. Figure 1a shows the scatter plot for hybridizations with low salt-inducible targets (G-10, Cy5-labeled) and unstressed targets (G-25, Cy3-labeled). About 6.5% of the spots were up-regulated (≥twofold), and 1.4% were down-regulated (≤0.5-fold). On the other hand, Fig. 1b shows the scatter plot for hybridizations with high salt-inducible targets (G-40, Cy5-labeled) and unstressed targets (G-25, Cy3-labeled). And about 5.1% of the spots were up-regulated expression (≥twofold), and 9.2% were down-regulated expression (≤0.5-fold).

Fig. 1
figure 1

Scatter plot of the microarray hybridization obtained from salinity-challenged Portunus trituberculatus. Total RNAs from nontreated tissues (25‰) and low (10‰, a) or high (40‰, b) salinity acclimation tissues were labeled with Cy3 and Cy5, respectively. Each dot represents the hybridization intensity for each gene. Red (or green) dot represents the ≥2.0 (or ≤0.5) ratio between the hybridization intensity in 10‰ (a) or 40‰ (b) and the hybridization intensity in 25‰, indicating the significantly up-regulated (down-regulated) genes. Black dot represents the 0.5–2.0 ratio, indicating the nonsignificantly different expressed genes (color figure online)

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Venn diagram analysis of gene expression of swimming crab

Accordingly, from the cDNA microarray analysis, 417 genes, about 17.19% of the total analyzed genes (417/2,426), showed significantly different expression (≥twofold up-regulation or down-regulation) after two different salt challenges. The degree of overlap in differentially expressed genes in high-salinity (40‰) and low-salinity environment (10‰) is shown in Venn diagram in Fig. 2 with the criteria of no less than twofold change.

Fig. 2
figure 2

Venn diagrams of salinity challenges up-regulated and down-regulated genes identified on the basis of microarray data. Classifications of the salinity-challenged up-/down-regulated genes were identified by their expression patterns. Salinity-acclimated significantly deferent regulated genes (≥2.0 fold) identified were put into the following four groups: ≥2.0-fold up-regulated genes in 10‰, ≥2.0-fold down-regulated genes in 10‰, ≥2.0-fold up-regulated genes in 40‰, and ≥2.0-fold down-regulated genes in 40‰

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These diagrams provide an important overview showing the distributions of gene expression changes responding to different salinity challenges. Fifty-four genes were down-regulated and 158 were up-regulated significantly when facing low salinity challenge, while 222 genes were down-regulated and 123 were up-regulated significantly responding to high salinity challenge (See Fig. 2).

The overlap genes between up-regulation in 10‰, down-regulation in 10‰, up-regulation in 40‰, and down-regulation in 40‰ salinity challenges were also shown in Fig. 2. These results indicated the greatest overlapping gene regulation (120 genes) was existed between up-regulation in 10‰ water and down-regulation in 40‰ salinity environment, and the lowest overlapping (5 genes) was existed between up-regulation in 10 and 40‰ salinity challenges (Fig. 2).

Cluster analysis

The expression profiles in swimming crab were further categorized into several patterns by using hierarchical clustering analysis (Eisen et al. 1998) for the two different salinity challenge including 417 genes that showed more than 1.5-fold differential expression at least one condition (Fig. 3, hierarchical clustering analysis). At least 8 different patterns of transcript regulation could be distinguished (see Fig. 3): genes highly up-regulated in low and high salinity conditions (Cluster VII, 18 genes), only in low salinity challenge (Cluster I, 23 genes) and only in high salinity conditions (Cluster VIII, 87 genes). These categories are in contrast with three clusters that showed different kinetics of down-regulation in the two salinity conditions (Cluster IV, 24 genes), only in low salinity challenge (Cluster V, 24 genes), and only in high-salinity environment (Cluster III, 73 genes). Moreover, Cluster II (138 genes) showed the genes highly up-regulated in low salinity and down-regulated in high salinity challenge, while Cluster VI (30 genes) exhibited the genes highly up-regulated in high salinity and down-regulated in low salinity challenge.

Fig. 3
figure 3

Hierarchical cluster analysis of significantly up- or down-regulated transcripts that exhibited 1.5-fold or more expression regulation for at least one of the stress treatments in the 10 or 40‰. Expression ratios in columns are shown for cy5 and cy3 labeled in the high (40‰) and low (10‰) salinity challenges. Ratios equal to 1:1 are black, and ratios greater/smaller than 1:1 are indicated red/green. Color scales assigned to each fold change are shown at the bottom of each cluster. The gene number for each cluster was shown on the right. For a detailed view, see Table S1–S8 (color figure online)

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In addition, clustering analysis for the same-directionally regulated genes in the both low and high salinity conditions was shown in Fig. 4. Five genes were simultaneously highly induced in the two salinity conditions, while 10 genes were repressed significantly in the two conditions (Fig. 4).

Fig. 4
figure 4

Hierarchical cluster analysis of the concomitant up-/down-regulated genes in the high (40‰) and low (10‰) salinity challenges. Expression ratios in columns are shown for cy5 and cy3 labeled in the high (40‰) and low (10‰) salinity challenges. Color scales assigned to fold changes are shown at the bottom of each cluster. The gene IDs are shown on the right, and the asterisks indicated known genes. For a detailed view, see Table S4 and Table S7 (color figure online)

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The list of genes in the 8 clusters that showed distinguished patterns in hierarchical clustering was indicated in Table S1–S8 (See supplementary data). And the annotations for each unigene were also shown in Table S1–S8. It is noticed that 64.03% (267/417) of the salinity acclimation candidate genes do not match with any known sequences when compared to the Swiss-Prot protein database using BLASTx (E value < 10−4), which indicated that these unmatched ESTs probably represent novel genes.

Absolute transcriptional profiling by qRT-PCR

To verify that candidate genes identified in the heterologous microarrays were indeed differentially expressed, we quantified the transcript copy numbers of 14 selected genes. These genes were chosen because their reported functions made them interesting candidates, and they represent a variety of functional classes and appeared either up- or down-regulated in the microarray experiments (see Table S1–S8; selected genes for qRT-PCR quantification are highlighted in gray). The primers used for qRT-PCR and their length of its corresponding PCR products were also indicated in Table 1.

In order to get reliable qRT-PCR results, four candidate reference genes (β-actin, 18S rRNA, RpL8, and RpL18) were selected for reference gene selection analysis. The primers for amplifying the four candidate reference genes were also shown in Table 1. The best reference genes were decided by using geNorm programs (Vandesompele et al. 2002). For the comparison of control and salinity-challenged individuals, RpL8 gene was considered as the most stable genes in the swimming crab, and it therefore was selected as an internal control for qPCR analysis.

Validation by qRT-PCR was performed first with the same pooled samples employed in the microarray experiments. The results are presented as conventional fold variations in Table 2 in order to compare both methodologies. Of the 28 genes examined, 26 genes showed the same changing direction (up or down) and similar changing folds as detected by the microarray assay (Table 2), suggesting that the majority of the salinity responsive genes found from the microarray analysis are authentic.

Table 2 Gene expression levels of 14 selected genes for the comparison of control (25‰) and salinity-challenged individuals (10 or 40‰) according to microarray and qPCR analysis
Full size table

Discussion

Expression patterns of swimming crab

Microarray analysis is a powerful and rapid technique used to investigate gene expression. Additionally, cDNA microarrays have contributed to the understanding of the mechanisms of stress tolerance in variety of species (Teranishi and Stillman 2007; Li and Brouwer 2009; Wang et al. 2007; Towle et al. 2010). The aim of the cluster analysis was to evaluate the microarray technique by examining the expression profiles of a number of genes known to be involved in osmoregulation and to determine whether other genes exhibited similar expression profiles, thus implicating them to be involved in the same phenomena.

In our study, 2426 unique genes were selected for cDNA microarray analysis. In total, 417 significantly up-regulated or down-regulated expressed genes (≥twofold) were identified. Then, the differentially expressed genes were grouped into eight clusters by hierarchical clustering. About 71.5% (298/417) of the differentially expressed genes belonged to three expression patterns (clusters II, III, and VIII) (Fig. 3). Genes displaying similar expression patterns may be coordinately regulated or involved in similar pathways or networks (Nadler and Attie 2001). Therefore, these three expression patterns may represent three important salinity adaptation pathways or networks in P. trituberculatus.

In addition, our cDNA microarray data suggested that there were differences in gene expression patterns of P. trituberculatus for low salinity and high salinity acclimation, suggesting that two salinity challenges resulted in a wide variation of gene expression in P. trituberculatus, and further investigation of these patterns may help identify salinity adaptation mechanisms.

Genes restrictively involved in low salinity acclimation

In total, there were 67 genes restrictively involved in low salinity acclimation, among which 33 genes showed significantly up-regulated (≥twofold) and 34 genes exhibited significantly depressed expression (≤0.5-fold) (See Fig. 2).

For the 33 up-regulated genes, 23 came from cluster I (See Table S1), 5 genes from cluster II (See Table S2, EST underlined), and 5 genes come from cluster VII (See Table S7, EST underlined). Cluster I showed genes significantly up-regulated (≥2.0-fold) in low salinity conditions and comparatively inconspicuous variations (<1.5-fold) in high salinity challenge (Cluster I, see Table S1). The genes in cluster I include 23 genes, among which 39.1% (9/23) matched to the annotated genes. Catalase and calcyclin-binding protein were the two most interesting ones. Catalase can scavenge superoxide anions and hydrogen peroxide, and the overexpression of catalase can rescue cells from ROS (reactive oxygen scavengers)-related deaths (Yu et al. 2006). Calcyclin-binding protein has been reported as being involved in calcium-dependent ubiquitination and subsequent proteosomal degradation of target proteins. In this study, a catalase gene (PT0026G08) and a calcyclin-binding protein gene (PT0030E11) were significantly up-regulated during low salinity challenge (Table S1, cluster I), indicating a role for catalase and calcyclin-binding protein in low salinity acclimation.

It should also be noticed that, among the 33 low salinity acclimation candidate genes, only 10 genes found to match with at least one known sequence with E-value < 10−4 (See Table S1, S2, and S7). Since most of the 33 up-regulated genes (23/33) are unknown function or novel genes, the pathways they involved are yet not known.

As for the 34 down-regulated low salinity acclimation genes, 24 genes came from cluster V (See Table S5), 3 genes from cluster IV (See Table S4, EST underlined), and 7 genes from cluster VI (See Table S6, EST underlined). It was clear that the 34 down-regulated salinity acclimation genes were involved in variety of functional groups, such as fatty acid binding protein (myelin P2 protein, PT0035E08, Table S5) (Narayanan et al. 1988), metabolism (lactoylglutathione lyase, PT0026C11, Table S5), transcription genes (elongation factor 1-alpha, PT0021G03, Table S5), and ROS scavenging genes (glutathione S-transferase 1, PT0019D10, Table S6). Glutathione S-transferase (GST) can detoxify ROS and is involved in stress responses (Nutricati et al. 2006). In this study, the GST transcripts were significantly down-regulated in the low salinity challenge, which meant that transcriptional regulation of ROS scavenging genes may allow P. trituberculatus to adapt to low salinity stress.

It also should be noticed that lots of protein synthesis and destination genes were involved in low salinity acclimation, such as transgelin-2 (PT0037B08, Table S5) (Assinder et al. 2009), vitamin K-dependent protein C light chain (PT0034E08, Table S6), ubiquitin (PT0014B01, Table S6), and ribosomal genes (PT0010A08 in Table S4, PT0007C12 and PT0032F06 in Table S5), which indicated that lots of protein synthesis process were highly depressed during the low salinity stress in swimming crab.

Genes restrictively involved in high salinity acclimation

As shown in Fig. 2, there were 200 genes restrictively involved in high salinity acclimation, among which 108 genes showed significantly up-regulated (≥twofold) and 92 genes exhibited significantly depressed expression (≤0.5-fold).

For the 108 up-regulated genes, 87 came from cluster VIII (See Table S8), 13 genes from cluster VI (See Table S6, EST double underlined), and 8 from cluster VII (See Table S7, EST double underlined).

In our microarray hybridizations, it was clearly showed that plenty of protein synthesis and destination genes were involved in high salinity acclimation. For example, 2 NSFL1 cofactor p47 genes (PT0019C06 in Table S6; PT0029B10 in Table S8), 9 tubulin genes (See Table S8, EST underlined), 2 arginine kinase (PT0024G06 and PT0040G08, Table S7), and 4 serine proteinase genes (See Table S8, double underlined) showed highly up-regulated expressions during high salinity challenges, which suggested that the gill had an active metabolism involved in high salinity acclimation.

In addition, signaling components or transcription regulators such as Rho guanine nucleotide exchange factor 10 (PT0023H05, Table S8); 26S protease regulatory subunit 6B (PT0018G06, Table S8); 26S protease regulatory subunit S10B (PT0009B08, Table S8); and eukaryotic translation initiation factor 3 subunit E (PT0030B07, Table S8) were found significantly up-regulated, which indicated an effective signaling cascade in P. trituberculatus under high salinity stress and those genes may represent the potential sensors of salinity acclimation signals.

Heat shock proteins (HSPs) are commonly induced by thermal stress and play a role in stabilizing and facilitating there folding of proteins that have been denatured during exposure to various stresses (Feder and Hofmann 1999). It has been suggested that the HSPs might serve as biomarkers of stress and environmental insult in aquatic organisms (Iwama et al. 1999). Some research found that HSPs were inhibited by abiotic stresses (Zang and Komatsu 2007), whereas significant increase in HSPs expression was detected in crustaceans when facing osmotic and environmental stress (Cimino et al. 2002; Tanguay et al. 2004; Chang 2005). In this study, we identified three HSP genes, HSP 90-alpha (PT0034C05, Table S6), HSP 70 (PT0028G08, Table S8), and HSP 60 (PT0005F02, Table S8), which were significantly up-regulated by high salinity stress. Similar to our results, numerous studies suggested that HSP70 and HSP90 may be important in response to hyperosmotic stress. For example, Tilapia (Sarotherodon melanotheron) living in hypersaline areas exhibited higher levels of HSP70 compared with fish from less saline locations (Tine et al. 2010). Higher levels of HSP70 and HSP90 were detected in horseshoe crab (Limulus polyphemus) embryos responding to hyperosmotic stress (Greene et al. 2011). Up-regulation of the HSP gene might increase protein stability. More detailed research can be done according to the connections between the salinity changes and HSPs in the future.

In our microarray hybridization, metabolism and energy genes such as enolase (PT0033H07, Table S7), uracil phosphoribosyl transferase (PT0018H01, Table S6), rRNA 2’-O-methyl transferase fibrillarin (PT0006F07, Table S6), phosphoglycerate mutase 2 (PT0040G02, Table S8), protein disulfide-isomerase (PT0039C06, Table S8), aspartate aminotransferase (PT0021B05, Table S8), and inorganic pyrophosphatase (PT0027C07, Table S8) exhibited highly increased expression, which indicated that high energy-demanding processes were up-regulated during high salinity challenge. Moreover, similarly to low salinity condition, ROS scavenging genes, peroxiredoxin-1 and superoxide dismutase (PT0033D08 and PT0004F09, Table S8), exhibited significantly induced expression, indicating that P. trituberculatus possessed an effective pathway to detoxification under salinity environment.

As for the 92 down-regulated high salinity acclimation genes, 73 genes came from cluster III (See Table S3), 8 genes from cluster II (See Table S2, EST double underlined), and 11 from cluster IV (See Table S4, EST double underlined). It should be noticed that 30.4% (28/92) of the 92 down-regulated genes matched annotated genes, among which 10 genes were designated as an antilipopolysaccharide factor (3 ESTs underlined in Table S3 and PT0026D10 in Table S4). Antilipopolysaccharide factors (ALFs) are important components of the innate immune systems of most living organisms (Tanaka et al. 1982). Significantly down-regulated expression levels of ALFs suggested that antimicrobial activity might be restricted when facing high salinity stress in the swimming crab.

Common genes involved in high and low salinity conditions

In total, 15 (5 + 10) genes showed simultaneously up/down-regulated (≥twofold variations) during low and high salinity challenges, whereas 130 (120 + 10) genes showed opposite directional regulation responding to the two different salinity challenges (Fig. 2). It should be noticed that 120 genes showed highly up-regulation in low salinity conditions while significantly down-regulation in high salinity challenge (120 genes neither marked by single nor double lines, See Table S2). However, 87.5% of them (105/120) are unknown function or novel genes (Table S2), and the pathways they involved are yet not known.

It is interesting that one important transporter protein, Na+/K+ATPase β-subunit, was found in this group. Na+/K+ATPase, mainly present in branchial and intestinal epithelia, plays an important role in maintaining osmotic homeostasis in low salinity acclimation as well as high salinity acclimation in variety of crab species (Schleich et al. 2001; Towle et al. 2001; Genovese et al. 2004; Tresguerres et al. 2003; Henry et al. 2002; Lucu and Towle 2003). Our microarray results showed that Na+/K+ATPase β-subunit (PT0027E09) showed significant up-regulation (2.2437) during low salinity challenge and significant down-regulation (0.3347) during high salinity challenges (Table S7 and Table S9), which suggested that Na+/K+ATPase β-subunit might play a very important role during salinity stress. Although lots of literature showed that gene expression levels of Na+/K+ATPase α subunit were highly up-regulated during salinity stress (Henry et al. 2002; Luquet et al. 2005; Jayasundara et al. 2007), significant up-/down-regulation of Na+/K+ATPase α subunit was not detected in our microarray test and quantitative real-time PCR. About 1.5-fold of up-regulation of Na+/K+ATPase α subunit gene was detected in low salinity challenge for both the microarray (1.6881) and the qPCR test (1.5639) (See Table 2). The expression level of Na+/K+ATPase α subunit was reported showing highly up-regulation between 6 h and 24 h in low salinity acclimation in the Carcinus maenas (Towle et al. 2010) and in the Neohelice (Chasmagnathus) granulata (Luquet et al. 2005). In the Pachygrapsus marmoratus, Na+/K+ATPase α-subunit mRNA levels in posterior gills 7 and 8 showed rapid responses to salinity dilution, with significant changes even by 2 h (Jayasundara et al. 2007). In our study, about 1.5-fold of down-regulation of Na+/K+ATPase α subunit gene was detected in high salinity challenge for both the microarray (0.8011) and the qPCR test (0.6638) (See Table 2), which was similar as Carcinus maenas when responding to high salinity adaptation (Jillette et al. 2011). None of the significant gene expression changes of Na+/K+ATPase α-subunit during salinity challenges do not negate its role for osmoregulation in swimming crab, and more future researches should be done toward Na+/K+ATPase α-subunit.

Results of the clustering analysis of the 15 genes that are up/down-regulated in the same direction under the low and high salinity challenges were shown in Fig. 4. Among the five genes showed significantly highly up-regulated (≥2.0 fold) responding to high and low salinity challenges (Table S7, Figs. 2, 4), three (PT0037G05, PT0020C07, and PT0035C08) are known genes (Table S7), including the transcription factor, CCAAT/enhancer-binding protein (PT0037G05). It showed nearly fourfold of expression level (3.988) during low salinity challenges and more than fivefold of expression (5.3379) during high salinity challenges, which were confirmed by quantitative real-time RT-PCR (See Table 2). The data shown here strongly indicated that CCAAT/enhancer-binding protein gene played a very important role in salinity acclimation and might be an effective transcriptional indicator of salinity adaptation in P. trituberculatus during salinity stress.

On the other hand, 10 genes exhibited substantial down-regulation (≤0.5-fold) in responding to the high and low salinity challenges, but only one gene matched an annotated gene (Table S4, Figs. 2, 4), antilipopolysaccharide factor. Since most of the genes are unknown in their functions or representing novel genes, the pathways involved remain to be determined. The current study provided important genetic information to deepen our understanding of the molecular mechanisms in salinity adaptation in the swimming crab.

Comparison with salinity adaptation studies on other crab species

So far, two other crab species (Carcinus maenas and Callinectes sapidus) have been the subjects of EST analysis and/or microarray studies to explore physiological mechanisms of salinity adaptation. Carcinus maenas is regarded as a good model species for ion and water balance studies due to its remarkable tolerance to environmental stresses including changes in salinity (Towle and Smith 2006; Stillman et al. 2008); 15,637 expressed sequence tags identified in a normalized multiple-tissue cDNA library from C. maenas (Towle and Smith 2006) and consequent microarray analysis on gill transcripts responding to salinity reduction had been reported (Towle et al. 2010). The Callinectes sapidus was also an important crab model species for physiological studies, and a variety of transporters and transport-related proteins from this species were reported (Coblentz et al. 2006), and 10563 ESTs were available (Stillman et al. 2008).

Compared with the EST libraries and/or microarray analysis in C. maenas and C. sapidus, the number of EST (4433) involved in this study is comparably low (Xu et al. 2010). However, by using microarray analysis (2426 cDNA spotted microarray), 417 differentially expressed genes of P. trituberculatus responding to high (40‰) and low (10‰) salinity stresses were indentified in our present study, which could be assigned as salinity stress responding genes.

By using microarray analysis, Towle et al. (2010) studied the gill transcripts of C. maenas responding to salinity reduction (from 32 to 10 or 15 ppt salinity). Some transport proteins such as Na+/K+ATPase α-subunit, cytoplasmic carbonic anhydrase showed transcriptional upregulation, whereas some (Na+/H+ exchanger, Na+/K+/2Cl− cotransporter, and V-type H+-ATPase B subunit) showed little transcriptional response. To our surprise, only two transporters or transport-related proteins (Na/K ATPase β-subunit (Table S2, PT0027E09) and Protein transport protein Sec61 subunit gamma (Table S3, PT0038D01)) showed significant changes responding to low/or high salinity stress. This result possibly resulted from the fact that fewer number of transport proteins were included in our microarray chip (Xu et al. 2010) than Towle et al. (2010)’s study.

On the other hand, some transporters or transport-related proteins such as V-type proton ATPase D subunit (PT0038G03), E subunit (PT0004G01) and F subunit (PT0014B10) (similar to Towle’s results), sodium bicarbonate cotransporter (PT0035B12), phosphate carrier protein (PT0001G04), and plasma membrane calcium transporting ATPase 3 (PT0027F02) in our study showed minor changes against salinity changes (detailed data not shown). The absence of transcriptional response to salinity changes does not negate a role for these proteins in gill osmoregulation; it may simply suggest that transcriptional regulation is not a part of the adaptive process for these transporters (Towle et al. 2010).

It should be noted that, deferent from Towle et al. (2010)’s results, lots of stress-related genes such as peroxiredoxin (PT0033D08, Table S8), superoxide dismutase (PT0004F09, Table S8), catalase (PT0026G08, Table S1), and three heat shock proteins, HSP90α (PT0034C05, Table S6), HSP70 (PT0028G08, Table S8), and HSP60 (PT0005F02, Table S8), 26S protease regulatory subunit S10B (PT0009B08, Table S8), 26S protease regulatory subunit 6B (PT0018G06, Table S8) were found, showing significantly up-regulated expression responding to low/or high salinity stress, which suggested that salinity dilution or elevation appeared to invoke a classic “stress response” at the transcriptional level in P. trituberculatus. This difference might reflect that P. trituberculatus possesses lower salinity adaptability than C. maenas, and it also suggested that the two crab species might have different signal pathways against the salinity “stress”.

Conclusions

In general, our work represents the first report of the utilization of microarray techniques for the study of salinity acclimation in the swimming crab. The results demonstrated the complexity of salinity adaptation pathways in P. trituberculatus and a substantial number of salinity responsive genes. This study revealed a few important salinity acclimation pathways, which may be helpful in understanding the molecular basis of osmoregulation and salinity adaptation in the crab. Further analysis using transfected cells or transgenic plants of those salinity responding genes will identify adaptive genes and their functions in salinity adaptation.