Journal of Ocean University of China  2022, Vol. 21 Issue (2): 430-444  DOI: 10.1007/s11802-022-4882-9

Citation  

LI Yuquan, CHEN Yigeng, CUI Yanting, et al. Transcriptome Analysis of Pacific White Shrimp (Litopenaeus vannamei) Under Prolonged High-Salinity Stress[J]. Journal of Ocean University of China, 2022, 21(2): 430-444.

Corresponding author

WANG Zhongkai, E-mail: zkwang@qau.edu.cn.

History

Received December 15, 2020
revised February 6, 2021
accepted June 8, 2021
Transcriptome Analysis of Pacific White Shrimp (Litopenaeus vannamei) Under Prolonged High-Salinity Stress
LI Yuquan1),2) #, CHEN Yigeng1) #, CUI Yanting1) , SHEN Min1) , WANG Renjie1) , and WANG Zhongkai1)     
1) School of Marine Science and Engineering, Qingdao Agricultural University, Qingdao 266109, China;
2) Shandong Key Laboratory of Disease Control in Mariculture, Marine Biology Institute of Shandong Province, Qingdao 266104, China
Abstract: The Pacific white shrimp (Litopenaeus vannamei) is a marine species commonly farmed worldwide. In northern China, it has been increasingly cultured in high-salinity waters (> 40), but exhibits poor growth performance. In this study, postlarval shrimps were acclimated to salinity 55, cultivated for 3 months at this salinity, and compared with a control group reared at general salinity 25. Subsequently, high-throughput RNA sequencing was applied to compare the transcriptomic responses in the gills and hepatopancreas of the shrimps in the control group and the treatment group, while the weights of the shrimps in these two groups were significantly different. The results revealed that 11834 and 2115 genes were significantly differentially expressed in the gills and hepatopancreas, respectively. Additionally, enrichment analysis of the differentially expressed genes indicated that osmoregulation-associated Gene Ontology terms and KEGG pathways were similar between the two subgroups of the shrimp maintained at high salinity, suggesting that the growth rate of shrimp at high salinity is independent of osmoregulation. Furthermore, examination of the shrimp with different growth rates (i.e., weights) at high salinity revealed molt-associated processes, namely, increased expression of ecdysone response genes and downstream effector genes in the gills and hepatopancreas of slow-growing shrimp, suggesting a role of the molt-associated processes in the regulation of shrimp growth at high salinity. Thus, we not only report adaptive transcriptomic responses of L. vannamei to prolonged high-salinity stress, but also provide new insights into the shrimp growth regulation at high salinity.
Key words: salinity    growth    osmoregulation    transcriptome    Litopenaeus vannamei    
1 Introduction

Salinity is an influential environmental factor that exerts crucial selective pressure on aquatic organisms. Salinity variations can directly affect the composition and osmolality of body fluids of aquatic animals (Charmantier and Charmantier-Daures, 2001). Crustaceans naturally inhabit aquatic environments with varying salinity levels, ranging from freshwater to highly saline seawater. This situation consequently requires these organisms to control their hemolymph osmotic pressure via osmoregulation of their hemolymph osmolytes in relation to the environment they inhabit (Charmantier et al., 2008; Romano and Zeng, 2012).

The Pacific white shrimp (Litopenaeus vannamei) is a typical euryhaline species possessing potent osmoregulation abilities and can grow in inland, coastal, or oceanic environments. As a result of its rapid growth, strong disease resistance, suitability for high-density cultivation, and high tolerance to salinity variation, L. vannamei is widely cultured worldwide (Pante, 1990; Saoud et al., 2003; Kumaran et al., 2017). There are many high-salinity water bodies in the coastal and northwestern regions of China and waters with salinity levels of 40–70 can be found in approximately 130000 ha along the coast of Shandong Province. This water is unsuitable for other aquatic animals owing to the extreme environmental conditions, while shrimp farming is an effective means of using high-salinity waters (Li et al., 2020). The development of shrimp farming in these high-salinity water bodies can not only expand the scope of aquaculture and raise farmers' incomes but also provide a novel method for effective utilization of high-salinity waters in China. However, the cultivation of L. vannamei in high-salinity waters still faces many obstacles, such as slow growth rates, low survival rates, and low disease resistance (Ramos-Carreño et al., 2014; Li et al., 2017a, 2017b; Shen et al., 2019; Zhao et al., 2019). Therefore, it is necessary to comprehensively elucidate the molecular pathways responsive to high salinity in shrimp to establish improved cultivation techniques for this species.

Extensive research has been conducted to understand the effects of ambient salinity and osmoregulation on the molecular mechanism of salinity adaptation in L. vannamei. Various genes involved in osmoregulation have been cloned, including those coding the Na+/K+-ATPase (NKA) α-subunit, vacuolar-type H+-ATPase (V-ATPase) β-subunit (Pan et al., 2014), carbonic anhydrase (CA) (Liu et al., 2015), glutamate dehydratase (GDH) (Li et al., 2009, 2011), and crustacean hyperglycemic hormone (CHH) (Lago-Lestón et al., 2007; Tiu et al., 2007; Shinji et al., 2012). Nowadays, high-throughput sequencing technology is becoming increasingly popular as a tool for revealing the molecular mechanisms behind various physiological changes in organisms. Several transcriptomic and proteomic studies have been carried out to date to reveal the osmoregulation mechanism in L. vannamei exposed to long-term or acute low-salinity stress (Chen et al., 2015; Hu et al., 2015; Wang et al., 2015; Zhao et al., 2015; Xu et al., 2017). Nonetheless, few studies have addressed the osmotic regulation that operates under high-salinity stress in L. vannamei. Therefore, transcriptomic characterization of the molecular mechanisms that govern the high-salinity acclimation of L. vannamei is still necessary to obtain better insights that can improve the production as well as management of this commercially important species.

As a vital organ for nutrient storage in L. vannamei, the hepatopancreas performs various functions related to energy metabolism, nutrient absorption, and digestive-enzyme synthesis while also playing a key role in immune function and detoxification (Gibson and Barker, 1979; Navarrete del Toro and García-Carreño, 2019). Gill tissues, on the other hand, are directly exposed to ambient water; consequently, changes in salinity should directly affect physiological status of the gills. Furthermore, gills are the primary organ for breathing and ion exchangeand and have been shown to play a major part in osmoregulation in crustaceans (Morris, 2001; Leone et al., 2017). To gain insights into the mechanisms underlying high-salinity adaptation in L. vannamei, it is necessary to identify differentially expressed genes (DEGs) in the gills and hepatopancreas.

In the present study, we employed high-throughput RNA sequencing (RNA-seq) technology to examine transcriptomic responses of the gills and hepatopancreas from shrimps exposed to high-salinity and the control salinity. Our study highlighted the genes and pathways that react to prolonged high-salinity stress and regulate shrimp growth. These data should improve the understanding of geneticlevel responses to long-term high-salinity stress in L. vannamei. Consequently, this information can be utilized for improving the growth and development performance of L. vannamei under high-salinity conditions.

2 Materials and Methods 2.1 Animal Maintenance and Experimental Design

Postlarval L. vannamei (PL20) weighing 0.12 g ± 0.02 g were obtained from a commercial farm in Haiyang, Yantai, China. The shrimps were acclimated for 7 days in two tanks (500 L) containing aerated seawater at a salinity of 25. At the beginning of the experiment, the shrimps in one tank were maintained at salinity 25, while the shrimps in the other tank were acclimated to salinity 55 through daily 2 increments in salinity by the addition of high-salinity seawater. After that, the shrimps in each tank were randomly assigned to triplicate tanks (100 L) of each salinity treatment, at a density of 60 shrimps per tank. The 55 salinity tank was designated as the treatment group, while the 25 salinity tank was served as the control.

During the acclimation and experimental periods, the shrimps were fed a commercial diet thrice daily, at 08:00, 16:00, and 22:00. The culture water was exchanged at a daily rate of 50% tank volume, while the unconsumed feed was removed daily with a siphon tube. Sea water was pumped from the Aoshan Coast (Qingdao, China) and filtered through an activated carbon cartridge for at least 3 d before entering the culture system. The water temperature was maintained at 28℃ ± 0.5℃ throughout the experiment, whereas pH fluctuated spontaneously between 7.9 and 8.1. Dissolved oxygen levels exceeded 6.0 mg L−1, and the ammonia-nitrogen level was less than 0.05−1, while with a natural photoperiod was maintained.

2.2 Sample Collection

Prior to the experimentation, no significant difference in the weight of the shrimps was observed between the groups. Following a 3-month trial, the shrimps were fasted for 24 h preceding the sampling. The body weight (BW) and total length (TL) of the shrimps were assessed at the end of the experiment. The molt stage of the shrimps was discerned by observing partial retraction of the epidermis in the uropod. Then, shrimps at the intermolt stage were randomly chosen for sample collection from the 55_B subgroup (high BW & TL in the treatment group), 55_S subgroup (low BW & TL in the treatment group), and 25_Z group (control group) with one per replicate tank (three shrimp per group). The gills and hepatopancreas of the shrimps were rapidly excised, frozen in liquid nitrogen, and stored at −80℃ until RNA isolation.

2.3 RNA Isolation, Library Construction, and RNA-seq

Total RNA was isolated from gill and hepatopancreas tissues using the TRIzol® reagent (Invitrogen, USA). The genomic DNA was removed from the RNA by means of RNase-free DNase I (Takara, China), followed by analysis of the degradation and contamination of RNA by electrophoresis of the samples in 1.0% agarose gels. RNA purity was verified on the NanoPhotometer® spectrophotometer (Implen, Germany). RNA concentration was measured with the Qubit® RNA Assay Kit on a Qubit® 2.0 Fluorometer (Life Technologies, USA). Lastly, RNA integrity was assessed using the RNA Nano 6000 Assay Kit and the Agilent Bioanalyzer 2100 system (Agilent Technologies, USA) and was expressed as an RNA Integrity Number (RIN).

According to the results, RNA samples with high quality (OD260/OD280 = 2.0–2.2, OD260/OD230 ≥ 2.0, RIN ≥ 8.0, and 28S: 18S ≥ 1.0) were used for library construction. A total of 3 μg of RNA per sample was used as the input material for the RNA sample preparation. Sequencing libraries were generated via the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA), and were then sequenced on the Illumina HiSeq 2500 platform with paired-end reads.

2.4 Data Analysis 2.4.1 Quality control of DNA sequence readings

Raw reads in the fastq format were initially processed using Perl scripts. The clean reads were obtained by the removal of reads containing adapters, reads containing polyN, and low-quality reads from the raw data. At the same time, Q20, Q30, and GC contents of the clean data were calculated. All the downstream analyses were performed on these high-quality clean data.

2.4.2 Mapping of reads to the reference genome

Reference genome and gene model annotation files for L. vannamei were directly downloaded from the NCBI genome website (https://www.ncbi.nlm.nih.gov/genome/?term=penaeus+vannamei). The index of the reference genome was constructed in Hisat2 v2.0.5 software, which was also utilized for aligning the paired-end clean reads to the reference genome (Kim et al., 2015). We selected Hisat2 as the mapping tool because it can generate a database of splice junctions based on the gene model annotation file and thus may yield a better mapping result than other (nonsplice) mapping tools.

2.4.3 Quantification of gene expression

The featureCounts v1.5.0-p3 software was used to determine the read numbers mapped to each gene (Yang et al., 2014). Then, the fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM) of each gene was calculated based on the length of a gene and the read count mapped to this gene. FPKM takes into account simultaneously the effects of sequencing depth and gene length on the read count, and is currently the most popular metric for estimating gene expression levels.

2.4.4 Differential expression analysis and functional enrichment

Differential expression analysis of genes was performed between the two groups of shrimps with the help of the DESeq2 R package (1.16.1) (Love et al., 2014). DESeq2 offers statistical routines for determining differential expression in digital gene expression data using a model based on a negative binomial distribution. The resulting P-values were adjusted by the Benjamini-Hochberg procedure for controlling the false discovery rate. Genes with an adjusted P-value < 0.05 and |log2(Fold change)| > 1.0 were regarded as differentially expressed.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs were implemented by means of the clusterProfiler R package, in which gene length bias was corrected (Yu et al., 2012). GO terms or KEGG pathways with corrected P-values less than 0.05 were considered significantly enriched in a set of DEGs.

2.5 Experimental Validation of RNA-seq Profiles by qPCR

To validate our Illumina sequencing data, nine DEGs were chosen for quantitative PCR (qPCR) analysis in the same RNA samples that were used for the transcriptome profiling. Primers were designed in Primer 5 software (Table 1). First-strand cDNA was synthesized from 1 μg of total RNA using the PrimeScript RT Reagent Kit with gDNA Eraser (Takara, China). Amplicons were first examined by gel electrophoresis to confirm that a single product of expected size was amplified, and the efficiency of the primers was examined by means of real-time PCR Miner (Zhao and Fernald, 2005). Furthermore, these specific PCR products were verified via Sanger sequencing.

Table 1 Primers used in quantitative PCR

The amplifications were performed in a 96-well plate, and the reaction mixture (10 μL volume) consisted of 5.0 μL of 2 × ChamQ Universal SYBR qPCR Master Mix (Vazyme, China), 1.0 μL of cDNA (10 ng μL−1), 0.2 μL each of 10 μmol L−1 forward and reverse primers, and 3.6 μL of RNase-free water. qPCR was carried out on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, USA) as follows: 95℃ (30 s) for pre-incubation, followed by 40 cycles at 95℃ (10 s) and 60℃ (30 s). Finally, the melting curve was analyzed to verify amplification specificity. The accumulation of fluorescence signals from the SYBR Green dye was recorded in the 60℃ (30 s) phase during each cycle. A negative control without template cDNA was included throughout. Each sample was analyzed in triplicate, along with the reference genes ubiquitin (Ub) and ribosomal protein S12 (S12) (Álvarez-Ruiz et al., 2015; Ventura-López et al., 2016; Trejo-Flores et al., 2018; Galindo-Torres et al., 2019), and the expression level was normalized to the geometric mean of the two host genes (Vandesompele et al., 2002). The relative gene expression levels were calculated by the comparative Ct method using the formula 2−∆∆Ct (Livak and Schmittgen, 2001). qPCR data were statistically evaluated by one-way analysis of variance (ANOVA), followed by Tukey's post hoc test in SPSS 21.0 (SPSS, IL, USA), wherein P < 0.05 denoted a statistically significant difference. The qPCR results were then compared with transcriptome data (FPKM values) to assess a possible correlation between the two expression results for nine selected DEGs.

3 Results 3.1 Differential Growth of the Shrimp Under Prolonged High-Salinity Stress

The growth of L. vannamei substantially varied under prolonged high-salinity stress. Therefore, to elucidate the molecular mechanisms underlying the differences in the growth of the shrimps under high-salinity stress, the shrimps in the experimental group were distributed into two subgroups. The 83 shrimps with final weight less than 1.0 g were set as 55_S (small shrimps), while the other 15 shrimps with final weight higher than 2.0 g were named 55_B (big shrimps). The 135 shrimps in the control group were designated as the 25_Z group. As shown in Fig.1, final BW and TL of the shrimps were greater in the 55_B subgroup than in the 55_S subgroup (F = 588.208, df = 97, P = 0; F = 544.507, df = 97, P = 0), while there were no significant differences in BW and TL between 55_B and 25_Z shrimps (F = 2.510, df = 149, P = 0.115; F = 2.566, df = 149, P = 0.111).

Fig. 1 The final body weight (A) and total length (B) of the shrimp in the control group and two subgroups at high-salinity. All data are presented as mean ± SEM (standard error of mean). Different superscripts indicate significant differences (P < 0.05).
3.2 RNA-seq and Mapping Statistics

Total RNA was separately extracted from the gills and hepatopancreas of nine shrimps (three in each subgroup). After examination of the quality and integrity of the RNA, two RNA samples with poor quality were discarded. The remaining 16 RNA samples of high quality were used for the creation of cDNA libraries, followed by sequencing. The details were listed in Table 2. A total of 835084646 paired-end reads with 150-bp read length were generated from the 16 samples. The number of sequences in each sample ranged from 41.98 to 67.50 million. After removing the reads containing adapters and/or poly-N, and low-quality reads, 829261486 clean reads were selected for further analyses. The cleaned sequences in each sample ranged from 41.52 to 67.08 million reads with average Q20% at 96.52%, Q30% at 93.29%, and GC% at 47.21%, thus confirming the stability and consistency of sampling, library preparation, and sequencing methodologies. Next, 710425508 clean reads were aligned to the L. vannamei genome using Hisat2; the average mapping rate was 85.60% among the samples. Raw reads were archived in the National Center for Biotechnology Information (NCBI) Sequence Read Ar-Bchive database (accession No. PRJNA649598).

Table 2 Summary of sequence data generated from transcriptome sequencing, quality filtering, and genome mapping
3.3 Identification of DEGs

The DEGs in the hepatopancreas and gills between different groups of shrimps were chosen by means of the following criteria: an adjusted P-value < 0.05 and |log2(Fold change)| > 1.0. In total, 11834 and 2115 DEGs were obtained from the gill and hepatopancreas tissues, respectively. It is obvious that the quantity of DEGs was much less in the hepatopancreas than in the gills in both subgroups (Fig.2). For example, in the comparison between 55_B and 25_Z shrimp, 873 DEGs were found in the hepatopancreas (55_ B_H vs. 25_Z_H), while there were 9315 DEGs in the gills (55_B_G vs. 25_Z_G). In addition, downregulated genes outnumbered upregulated ones in the gills, whereas a great number of upregulated genes was found in the hepatopancreas. Furthermore, the number of DEGs identified in the comparison between the subgroups at high salinity (i.e., 55_B_G vs. 55_S_G and 55_B_H vs. 55_S_H) was less than that identified in the comparisons of shrimps at different salinity levels (i.e., 55_B_G vs. 25_Z_G and 55_B_H vs. 25_Z_H) (Fig.2).

Fig. 2 Statistics of DEGs between different groups. Black columns represent the number of up-regulated genes while grey columns represent the number of down-regulated genes. The quantities of DEGs are shown above the columns.
3.4 GO Enrichment Analysis of the DEGs

In total, 74, 62, and 108 GO terms were significantly enriched in the gills of the three subgroups (55_B_G vs. 55_ S_G, 55_B_G vs. 25_Z_G, and 55_S_G vs. 25_Z_G), respectively (P < 0.05). In addition, 52, 106, and 126 GO terms were separately significantly enriched in the hepatopancreas of the three compared subgroups (55_B_H vs. 55_ S_H, 55_B_H vs. 25_Z_H, and 55_S_H vs. 25_Z_H; P < 0.05). The top five most significantly enriched GO terms in the DEG sets of gills and hepatopancreas are presented in Tables 3 and 4, respectively, including the q-value of the GO terms and the number of upregulated and downregulated genes for each GO term. An overall comparison of the shrimp at high salinity (55_B_G vs. 55_S_G and 55_ B_H vs. 55_S_H) revealed that the significantly enriched GO terms were all downregulated in the shrimp with higher weight (the 55_B subgroup). In addition, several GO terms, including chitin metabolic process (GO: 0006030), hormone-mediated signaling pathway (GO: 0009755), extracellular region (GO: 0005576), and chitin binding (GO: 0008061), were proved to be enriched both in the gill DEG set and in the hepatopancreas DEG set. Regarding the comparison between the shrimp at different salinity levels, a majority of the most enriched GO terms turned out to be significantly upregulated under the high-salinity stress relative to the control (salinity of 25). Moreover, several GO terms such as translation (GO: 0006412), cytoplasm (GO: 00057 37), ribosome (GO: 0005840), structural constituent of ribosome (GO: 0003735), and transferase activity, transferring phosphorus-containing groups (GO: 0016772) were found to be the most enriched GO terms in the gill DEG set, in the comparisons between 55_B_G and 25_Z_G and between 55_S_G and 25_Z_G. By contrast, only two GO terms, purine ribonucleoside monophosphate biosynthetic process (GO: 0009168) and oxidoreductase activity (GO: 0016491) were identified in the hepatopancreas DEG set in the comparisons between 55_B_H and 25_Z_H and between 55_ S_H and 25_Z_H.

Table 3 The top five most significantly enriched GO terms in gills
Table 4 The top five most significantly enriched GO terms in hepatopancreas
3.5 KEGG Enrichment Analysis of the DEGs

This analysis was performed to reveal the pathways that were significantly affected after the prolonged salinity challenge. All the KEGG pathways that are significantly enriched in the gill DEG set and significantly up-regulated in the hepatopancreas DEG set are presented in Tables 5 and 6, respectively, including the P value of the pathways. In the gills, six pathways were shown to be upregulated, while three pathways were downregulated in the comparison between subgroups 55_B_G and 55_S_G. These included oxidative-stress-related pathways, such as 'oxidative phosphorylation' (dme00190), and metabolism-associated pathways, such as 'citrate cycle' (dme00020) (Table 5). Similar significantly downregulated pathways were identified in the comparison between 55_B_G and 25_Z_G and between 55_S_G and 25_Z_G. These pathways comprised signaling-related cascades, such as the 'Hippo signaling pathway-fly' (dme04391), 'TGF-beta signaling pathway' (dme 04350), 'phosphatidylinositol signaling system' (dm04070), and 'MAPK signaling pathway-fly' (dme04013). The 'ribosome' (dme03010) was also prominent among the upregulated pathways in both comparisons (Table 5).

Table 5 The significantly enriched KEGG pathways in gills
Table 6 The significantly upregulated KEGG pathways in hepatopancreas

No significantly altered pathway was identified in the hepatopancreas in the comparison between 55_B_H and 55_S_H, whereas 13 upregulated pathways were identified when both high-salinity subgroups were separately compared with the control group (Table 6). The 'oxidative phosphorylation' (dme00190) pathway was proved to be the most significantly enriched one in the two comparisons. Other significantly altered pathways that were also common to both comparisons were associated with energy metabolism, lipid metabolism, and protein metabolism, including 'fatty acid elongation' (dme00062), 'fatty acid degradation' (dme 00071), 'valine, leucine, and isoleucine degradation' (dme 00280), and 'carbon metabolism' (dme01200) (Table 6). Furthermore, 'sphingolipid metabolism' (dme00600) was the single pathway that was significantly downregulated in the comparison of subgroups 55_B_H vs. 25_Z_H.

3.6 Validation of Selected DEGs' Expressions by qPCR

Nine DEGs related to osmoregulation, digestive enzymes, molting, and immunity were selected to validate the differential expression of the genes by qPCR analysis. The melting-curve analysis confirmed a single specific amplicon for all the tested genes. Fold changes from the qPCR data were compared with the results of the differential expression analysis (Fig.3). Overall, the differential expression of these genes was confirmed by the qPCR analysis, indicating the reliability and accuracy of our differential expression analysis.

Fig. 3 qPCR validation of RNA-seq data. Nine genes were selected for validation. X axis represents the groups. Columns and bars represent the means and standard error of relative expression levels from qPCR results (Y axis at left). Lines represent the FPKM value from transcriptome results (Y axis at right). Values with different superscripts indicated statistical significance (P < 0.05), which were calculated via one-way ANOVA.
4 Discussion

In this work, shrimps maintained at 25 salinity (control) showed better growth performance than those maintained at 55 salinity. Li et al. (2017a) reported the optimal salinity for growth and survival of L. vannamei was 20–25. In addition, 25 salinity was also similar to the salinity of the farming ponds. Therefore, we chose 25 as the control salinity. A previous experiment in our laboratory also revealed that the relative weight gain rate and specific growth rate of L. vannamei decrease with an increase in salinity from 30 to 60 and the survival rate of the shrimp is lower than 40% at the salinity of 60 (Li et al., 2017b). Therefore, we selected 55 as the treatment salinity to obtain better comparative effects in our study. This finding in agreement with some research on optimal salinity conditions for L. vannamei growth. Huang (1983) reported that L. vannamei grows best at about 20 salinity and poorliest at both 5 and 45. Similarly, according to Bray et al. (1994), shrimps cultivated at 5 and 15 salinities exhibit a significantly higher growth rate than those at any other concentrations tested, and the shrimps in hypersaline water (49) show significantly slower growth than those in the low-salinity treatment groups. Nevertheless, in the present work, some shrimps grew well at 55. The final weight of these shrimps (the 55_B subgroup) was not significantly lower than that in the control group (the 25_Z group), while it was clearly higher relative to the other shrimps maintained at 55 salinity (the 55_S subgroup) (Fig.1).

4.1 Growth Performance of Shrimp at High Salinity Is Unrelated to Osmoregulation

Both gill and hepatopancreas tissues of L. vannamei responded significantly to prolonged high-salinity exposure, though via different patterns. The number of DEGs in the gill tissues greatly exceeded that in the hepatopancreas tissues (Fig.2). This result may be attributed to the direct environmental exposure of the gill, an organ that plays a considerable role in the control of osmotic pressure and ion exchange (Freire et al., 2008; Mcnamara and Faria, 2012; Li et al., 2014). During the high-salinity challenge in this study, the gill was demonstrated to be more responsible for ensuring homeostasis in the shrimps in comparison with the hepatopancreas. Furthermore, more DEGs were identified in the gill tissues than hepatopancreas tissues in the comparison of shrimps at different salinity levels (Fig.2). Functional analysis of these DEGs suggests that several GO terms and KEGG pathways such as signaling-associated pathways in the gills are enriched in the DEG set of shrimps maintained at 55 salinity when compared with the control (25 salinity) (Table 5). Apparently, once the gills detected an increase in salinity, this stimulus was transformed into stress signals and transmitted to other parts of the body, thereby initiating the regulatory mechanism used by the gills. Some transcriptomic studies have revealed that these signal transduction pathways also perform important functions in response to low-salinity stress in the gills of L. vannamei (Hu et al., 2015; Wang et al., 2015) and Scylla paramamosain (Wang et al., 2018). Therefore, the pathways enriched in the DEG set of shrimps at 55 salinity may be related to adaptation to high-salinity stress.

Similar to the above results obtained in the gills, GO terms and KEGG pathways associated with oxidation resistance, energy metabolism, fatty acid metabolism, and amino acid metabolism are upregulated in the hepatopancreas of the shrimp maintained at 55 salinity (Table 4, Table 6). In aquatic animals, alterations in salinity can elicit various physiological responses, such as elevation of plasma hormone levels, accelerated metabolism, and electrolyte disequilibrium due to the overproduction of reactive oxygen species (ROS) caused by salinity stress (Liu et al., 2007). Under oxidative stress, the elevated concentrations of ROS, which are a noxious product of aerobic metabolism, can damage cellular constituents (Lushchak, 2011). Therefore, upregulation of a gene associated with 'oxidoreductase activity' may safeguard the shrimp from the high level of ROS or related hazardous substances produced by oxidative stress induced by the high-salinity challenge in L. vannamei. Moreover, osmoregulation is an energy consuming process, which involves the hepatopancreas for provision of the required energy. Consequently, the upregulated energy metabolism-associated pathways may supply extra energy for osmoregulation in the shrimp, as reported in other transcriptomic research articles on L. vannamei (Hu et al., 2015; Wang et al., 2015) and Eriocheir sinensis (Li et al., 2014) subjected to a salinity challenge. Additionally, the upregulated 'fatty acid elongation' (dme00062) and 'fatty acid degradation' (dme00071) pathways may take part in a negative feedback loop by adjusting the permeability of the gill membrane. This approach can be a highly effective way to reduce ion diffusion and water afflux as opposed to exclusive use of ion transport mechanisms that are more energy-consuming. Similar results have been reported in transcriptomic and proteomic researches on L. vannamei under low-salinity stress (Chen et al., 2015; Wang et al., 2015; Xu et al., 2017). The previous findings suggest that these amino acid metabolism pathways may participate in the regulation of free amino acids, which contribute to osmoregulation capacity in crustaceans when ambient salinity changes (Lv et al., 2013; Li et al., 2014; Wang et al., 2015; Xu et al., 2017; Wang et al., 2018). Moreover, an increase in the amount of total free amino acids has been detected in L. vannamei and other crustaceans after exposure to high salinity (Huong et al., 2001; Silvia et al., 2004; Koyama et al., 2018; Liu et al., 2018). Therefore, the upregulated GO terms and KEGG pathways in the shrimps at 55 salinity can be implicated in the osmoregulation driven by the hepatopancreas.

On the whole, our functional analyses revealed strengthened osmoregulation in the shrimps at high ambient salinity. Of note, the enriched GO terms and KEGG pathways are similar between the two shrimp subgroups maintained at high salinity. These similarities suggest that their growth rates are independent of osmoregulation.

We also identified differential expression of ion transport enzymes and proteins, such as NKA, V-ATPase subunit α, CA, and aquaporins (AQPs) (Table 7). NKA is important for the regulation of hematopoietic osmotic pressure at different salinity levels (Castilho et al., 2001), whereas V-ATPase is responsible for maintaining acid-base balance and nitrogen excretion. Several reports indicate that gene expression levels of NKA α-subunit and a V-ATPase subunit are highly up-regulated during salinity stress (Luquet et al., 2005; Hu et al., 2015). On the other hand, CA participates in ion transport, acid-base balance, and pH regulation by supplying H+ and HCO3 through catalyzed hydration of respiratory CO2, which is capable of diffusing through the gills (Henry, 1987). CA upregulation has also been identified in crustaceans subjected to a salinity challenge (Pongsomboon et al., 2009; Pan et al., 2016; Ge et al., 2019; Huang et al., 2019). AQPs help to maintain the balance of cellular osmolality by facilitating the transport of water and some small-molecule solutes across the plasma membrane. In crabs such as Portunus trituberculatus and Callinectes sapidus and in the shrimp L. vannamei, the osmoregulatory role of AQPs is evidenced by their different expressions under salinity stress (Chung et al., 2012; Lv et al., 2013; Wang et al., 2015). The above genes are significantly upregulated or downregulated in our both subgroups of the shrimp at high salinity, as compared to their expressions in the control group. By contrast, they are not differentially expressed between the two shrimp subgroups with different growth rates at high salinity. These results further support our previous conclusion. Nevertheless, further study is still required to clarify the correlation between osmoregulation and growth at high salinity.

Table 7 Characterization of DEGs related to osmoregulation in gills
4.2 Functional Analysis of DEGs Related to Growth Performance of Shrimp at High Salinity

In the comparison of gene expression profiles between the 55_B subgroup and 55_S subgroup, several enriched GO terms, including chitin metabolic process, hormonemediated signaling pathway, extracellular region, and chitin binding, were found to be upregulated in the hepatopancreas and gills of shrimp in the 55_S subgroup and may affect their growth performance at high salinity (Table 3, Table 4). One study uncovered regulatory roles of chitin metabolic process and extracellular region (GO terms) during molting processes in L. vannamei (Gao et al., 2017). Accordingly, we hypothesized that the molt-associated processes may exert important physiological effects on the growth of shrimp at high salinity. Further evidence supporting this hypothesis was obtained in the analysis of differential expression of molt-associated genes in the shrimp at high salinity. Notably, ecdysone receptor and ecdysone response genes are upregulated both in the hepatopancreas and gills in the 55_S subgroup (Table 8). In L. vannamei, the ecdysone function in promoting molting is implemented by products of those downstream genes belonging to the ecdysone signaling pathway (Zhang et al., 2019). In the present study, this overexpression of ecdysone response genes is consistent with their function in the regulation of the expression of downstream effectors such as chitinase (Table 8) and cuticle proteins (Fig.4, Table 8) in the shrimps of the 55_S subgroup. During the molting cycle in crustaceans, chitinase dissolves chitin in the old exoskeleton into more soluble substances, which can then be partially reabsorbed into the body and utilized to synthesize the new exoskeleton (Huang et al., 2010). Cuticle proteins are some of the main structural proteins involved in the construction of the cuticle during molting (Roer et al., 2015). In addition to the GO terms common to both organs, myosin complex and actin cytoskeleton are the most significantly enriched GO terms in the cellular component category in the gills in our work. Consistent with this result, a number of genes encoding skelemin-associated proteins, such as actin, myosin, and troponin, are markedly upregulated in the gills of 55_S shrimps as well (Fig.5).

Table 8 The DEGs related to ecdysis in the shrimps at high salinity
Fig. 4 Expression profiles of genes of cuticle protein in the gills of shrimps at high salinity. The heatmap was drawn by TBtools (Chen et al., 2020). Colors represent relative mRNA expression as indicated in the color key. The red color shows high expression, and the blue color represents lower levels of expression. The color from red to blue represents the log2 (FPKM+1) from large to small.
Fig. 5 Expression profiles of skelemin-related genes in the gills of shrimps at high salinity. The heatmap was drawn by TBtools (Chen et al., 2020). Colors represent relative mRNA expression: the red color shows high expression; the blue color represents lower levels of expression; the color from red to blue represents the log2 (FPKM+1) from large to small.

Skelemin is important for body reconstruction after molting (Hooper and Thuma, 2005). At the postmolt stage, rapid enlargement and growth of shrimp entail not only accelerated water absorption but also skeleton expansion to provide a scaffold and muscle to fill the new body (Relaix and Zammit, 2012). One study uncovered increased expression of genes related to actin, myosin, and troponin in L. vannamei after molting (Gao et al., 2015).

Given that molting is a complex process, investigation was focused, for example, on the quantitation of circulating ecdysteroid concentration, molting frequency, and hormone levels must be conducted to further clarify the correlation between growth performance and molt-associated processes in the shrimps at high salinity.

5 Conclusions

Growth performance of some shrimps at the high salinity of 55 was comparable to that of shrimps cultivated at the control salinity of 25. Furthermore, we examined transcriptomic changes in the gills and hepatopancreas of the shrimps having significantly different final weights in the treatment group, as compared to the control group. In the comparisons of gene expression, numerous genes were found to be regulated by high-salinity stress. These data may help to understand the molecular basis of osmoregulation in L. vannamei. By contrast, salinity adaptation-associated GO terms and KEGG pathways were enriched similarly in the two subgroups of shrimps at high salinity, compared to that in the shrimps at control salinity, suggesting that the growth rate of shrimps at high salinity is independent of osmoregulation. Moreover, a substantial number of ecdysone response genes and downstream genes are upregulated in the gills and hepatopancreas of the slowgrowing shrimps, thereby providing insights into the molecular mechanism by which the molt-associated processes may play a role in the regulation of shrimp growth at high salinity.

Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 31802269), the Open Fund of Shandong Key Laboratory of Disease Control in Mariculture (No. KF20 1901), the Shrimp & Crab Innovation Team of Shandong Agriculture Research System (No. SD AIT-15-011), the High-Level Talent Research Fund of Qingdao Agricultural University (Nos. 663/1119054 and 663/1120027), and the First Class Fishery Discipline Program in Shandong Province.

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Table 1 Primers used in quantitative PCR
Fig. 1 The final body weight (A) and total length (B) of the shrimp in the control group and two subgroups at high-salinity. All data are presented as mean ± SEM (standard error of mean). Different superscripts indicate significant differences (P < 0.05).
Table 2 Summary of sequence data generated from transcriptome sequencing, quality filtering, and genome mapping
Fig. 2 Statistics of DEGs between different groups. Black columns represent the number of up-regulated genes while grey columns represent the number of down-regulated genes. The quantities of DEGs are shown above the columns.
Table 3 The top five most significantly enriched GO terms in gills
Table 4 The top five most significantly enriched GO terms in hepatopancreas
Table 5 The significantly enriched KEGG pathways in gills
Table 6 The significantly upregulated KEGG pathways in hepatopancreas
Fig. 3 qPCR validation of RNA-seq data. Nine genes were selected for validation. X axis represents the groups. Columns and bars represent the means and standard error of relative expression levels from qPCR results (Y axis at left). Lines represent the FPKM value from transcriptome results (Y axis at right). Values with different superscripts indicated statistical significance (P < 0.05), which were calculated via one-way ANOVA.
Table 7 Characterization of DEGs related to osmoregulation in gills
Table 8 The DEGs related to ecdysis in the shrimps at high salinity
Fig. 4 Expression profiles of genes of cuticle protein in the gills of shrimps at high salinity. The heatmap was drawn by TBtools (Chen et al., 2020). Colors represent relative mRNA expression as indicated in the color key. The red color shows high expression, and the blue color represents lower levels of expression. The color from red to blue represents the log2 (FPKM+1) from large to small.
Fig. 5 Expression profiles of skelemin-related genes in the gills of shrimps at high salinity. The heatmap was drawn by TBtools (Chen et al., 2020). Colors represent relative mRNA expression: the red color shows high expression; the blue color represents lower levels of expression; the color from red to blue represents the log2 (FPKM+1) from large to small.
Transcriptome Analysis of Pacific White Shrimp (Litopenaeus vannamei) Under Prolonged High-Salinity Stress
LI Yuquan , CHEN Yigeng , CUI Yanting , SHEN Min , WANG Renjie , and WANG Zhongkai