Molecular pathway and gene responses of the Pacific white shrimp Litopenaeus vannamei to acute low salinity stress.
KEY WORDS: shrimp. RNA-Seq, oxidative stress, signal transduction, energy metabolism, Litopenaeus vannamei
Aquatic crustaceans inhabit a variety of environments with different salinities from freshwater to seawater (Pequeux 1995). As one of the most important environmental factor, salinity can directly cause physiological stress on aquatic animals by changing osmoregulation. Therefore, osmoregulation is an essential physiological process for the majority of aquatic crustaceans because it enables them to cope with the discrepancy of ion concentrations inside the body and the environment (Pequeux 1995). Nevertheless, due to the diverse habitats of crustacean species, the osmoregulation of crustacean is a quite complex process (Pequeux 1995, Augusto et al. 2007, Sokolova et al. 2012), and the underlying mechanism of osmoregulation of crustacean is limited especially at the molecular level.
The Pacific white shrimp Litopenaeus vannamei is a typical euryhaline species and can adapt to both coastal and oceanic environments, as a strong osmoregulator (Cheng et al. 2006). Salinity tolerance research has shown that L. vannamei can tolerate a wide range of salinity from 0.5 to 50 (Pante 1990). Due to the special ecological niche of L. vannamei and its importance in aquaculture, research on the effects of ambient salinity and osmoregulation of L. vannamei has been extensively conducted, but the understanding on this animal is limited to its isoosmotic point (Diaz et al. 2001), growth and survival (Li et al. 2007), immunity (Wang & Chen 2005, Lin et al. 2012,), stress resistance (Li et al. 2007, Li et al. 2008b, Wang et al. 2013a), and genes controlling osmoregulation of L. vannamei (Lago-Leston et al. 2007, Li et al. 2009, Wang et al. 2013b). Nevertheless, the integrative molecular pathways of L. vannamei in response to salinity stress have not been studied.
To understand the complex molecular biological process of stress physiology at the whole transcriptome level, RNA-seq (whole-transcriptome shotgun sequencing) is an emerging technology to identify the relatively complete genes and pathways involved in the physiological response (Morin et al. 2008, Chu & Corey 2012). The RNA-seq has been used in the analysis of stress-response pathways in various aquatic species (Scott & Johnston 2012, Liu et al. 2013, Smith et al. 2013, Xia et al. 2013, Xu et al. 2013, Li et al. 2014), and is an proven tool to capture the genes and metabolic pathways at a specified physiological condition for aquatic animals (Li & Li 2014). Suppression subtractive hybridization has been used to capture the genes and pathways in juvenile Litopenaeus vannamei under a long-term low salinity stress. The most common genes in these libraries are immunity-related proteins and enzymes (Gao et al. 2012), but other genes or metabolic pathways contributing to salinity adaptation may be also included. Similarly, RNS-seq analysis in hemocytes of the Pacific white shrimp reveals that many immune pathways are involved into the process coping with acute salinity stress (31-16 for 24 h), and some energy metabolisms are found in this study but are not fully discussed (Zhao et al. 2015). Despite the low salinity environment (<5) for L. vannamei farming, the knowledge on the metabolic pathways and genes regulation of this euryhaline crustacean at low salinity is still limited.
Previous literature has shown that various organs are involved in the osmoregulation of aquatic animals (Pequeux 1995). Relatively to other organs in crustacean species, such as hepatopancreas and eyes stalk, gill, and muscle (with skin) are directly exposed to ambient water, and water salinity change, especially, would directly affect the physiological status of these two organs. Furthermore, gill is the main site for breathing and ion exchanging, and has been proved significant in osmoregulation in both fish and shrimp (Welcomme & Devos 1991, Fiol et al. 2006, Kiilerich et al. 2007). Because osmoregulation is a high energy cost process (Tseng & Hwang 2008), muscle has also been found involved in this process as the largest energy and osmolyte pool (Li et al. 2009).
Therefore, the aim of this study is to identify the change of pathways and genes responding to acute salinity stress in gill and muscle of juvenile Litopenaeus vannamei. On the basis of the data analysis, putative hypotheses are formulated on how L. vannamei responds to acute salinity stress in an attempt to improve the understanding on the underlying molecular mechanism of salinity adaptation in crustacean.
MATERIALS AND METHODS
Experimental A nimals
Juvenile Litopenaeus vannamei (2.6 [+ or -] 0.4 g) were obtained from a shrimp farm close to the Jinshan District, Shanghai, China. Shrimps were acclimated in tanks for 2 wk with fully aerated water at a salinity of 20 and at 24.3-27.4[degrees]. Then, the shrimps were deprived of food for 24 h and then divided into two treatment groups with three tanks for each group, and there were 30 shrimps in each tank. The group remains at salinity of 20 was the control, and the other group of shrimp was directed exposed into water with a low salinity of 3. No shrimp died within the 24 h. Shrimp at the intermolt stage C were anesthetized with cold ice, and then muscle and gill were sampled for RNA extraction at 24 h after low salinity challenge. Fifteen shrimps from each treatment were pooled for RNA-seq analysis (five shrimp in each tank). During the experimental period, shrimp were all derived of feed to reduce waste production and maintain water quality. Gill and muscle were preserved in liquid nitrogen and then transferred to -80[degrees] for storage before the RNA extraction.
RNA Extraction, Library Construction, and Sequencing
Total RNA from gill and muscle were extracted using Unizol reagent kit (Biostar, Shanghai, China) according to the protocol, and then the extracted RNA was treated with DNAase I. The quality of total RNA was determined using bioanalyzer 2100 and Quibt meter. The RNA-seq library preparation and sequencing were conducted at the HudsonAlpha Genomic Services Laboratory (Huntsville, AL) as previously described (Li et al. 2014). Raw read are available at NCB1 SRA under accession number SRP013882.
De Novo Assembly
All reads with quality scores less than 20 and length less than 15 bp were trimmed by removing ambiguous nucleotides with CLC Genomics Workbench (version 4.9; CLC bio, Aarhus, Denmark) before assembly, and then were used for further subsequent assembly using de Brujin graph assemblers (Li et al. 2012) with ABySS (version 1.2.5) and TransABySS (version 1.2.2) (Simpson et al. 2009). The k-mer size was from 25 to 49 in ABySS, and assemblies from all k-mer lengths were merged into one assembly by TransABySS to generate the transcriptome assembly. CD-Hit (4.5.4) CAP3 was used to remove redundancy and contigs greater than 200 bp (Kal et al. 1999. Li & Godzik 2006). The identify threshold was set at equal to 1 in CD-Hit, and the minimal overlap length and identity was set at equal to 100 bp and 99% in CAP3, respectively.
Gene Annotation and Ontology
The databases of nonreluctant (NR) protein and UniProtKb/SwissProt were used to BLAST the assembly contigs. The cutoff E-value was le-5, and only the top gene ID and the name were assigned to each contig. The UniProtKb/SwissProt BLAST results from Blast2GO (version 2.5.0) were used for the gene ontology (GO) annotation analysis (Gotz et al. 2008). After the gene ID mapping, GO term assignment, annotation augmentation, and generic GO-slim process; the annotation result was categorized into biological process, molecular function, and cellular component.
Identification of Differentially Expressed Contigs
The high-quality reads from each sample were mapped onto the TransABySS reference assembly with CLC Genomics Workbench software. The total number of mapped reads for each transcript was first determined, and then normalized to determine the reads per kilobase of exon model per million mapped reads. To identify the differently expressed genes between the control and the treated samples, the proportions-based test was used with P value setting at less than 0.05 for significant difference (Kal et al. 1999). Transcripts with absolute fold change values greater than 2, total read number greater than 5, and corrected P value < 0.05 were used in the analysis as differently expressed contigs.
Gene Ontology and Pathway Analysis by IPA Program
The analysis of significantly expressed GO terms was performed on Ontologizer 2.0 using the parent-child-intersection method with a Benjammini-Hochberg multiple testing correction to identify the overrepresented GO annotations by comparing to the broader reference assembly (Grossmann et al. 2007, Bauer et al. 2008). Gene ontology terms for each gene were obtained using the UniProtKb/SwissProt annotation.
The GO term assignment in the differentially expressed genes sets were compared with the reference assembly. The threshold was set at the FDR value less than 0.1. The differentiation of significantly expressed genes was analyzed by the Ingenuity Pathway Analysis (IPA) program (https:// analysis.ingenuity.com). All the pathways with one or more genes overlapping the candidate genes were extracted. In IPA, each of these pathways was assigned to a P value via Fisher's exact test, but only pathways with P < 0.01 were selected.
RNA-Seq Sequencing and De Novo Assembly
Totally, 281.4 million reads were obtained, including 68.5 and 67.0 million reads from the control muscle and gill, respectively, and 74.2 and 71.7 million reads from the gill and muscle of Litopenaeus vannamei under low salinity stress, respectively. After filtering, 256.9 million reads accounting for 91.29% of the total reads were used for transcriptome assembly.
De novo assembly with TransABySS generated 557,740 contigs in total with an average length of 333.4 bp and N50 size of 1,198 bp, including 156,686 contigs greater than 200 bp and 49,162 contigs greater than 1,000 bp. After redundancy filtering, 105,153 contigs were generated with an average length of 591.2 bp (Table 1).
Gene Identification and Annotation
Totally, 24,676 out of 105,153 (23.47%) TransAbySS contigs showed significant BLAST hit against the UniProt database with 11,551 unigene matches (Table 2). In contrast, 29,158 out of the TransAbySS contigs (27.73%) had significant BLAST hit against the NR database with 16,765 unigene matches. A total of 7,546 and 10,764 genes from the UniProt and NR databases were identified, which matched protein sequences in the public databases with the stringent criteria of a BLAST score greater than or equal to 100 and E-value less than or equal to le-20.
Identification and Analysis of Differentially Expressed Genes
After annotation, 991 (468 up and 523 down) and 3,709 (316 up and 3,393 down) unigenes differentially expressed after the low salinity challenge for 24 h relative to the control in the muscle and gill, respectively. More than half of the differentially expressed genes with a fold change below two and five, only less than 10% of the differentially expressed genes have been regulated more than 20-fold. In detail, there are 340 out of 991 unigenes were significantly changed more than 5-fold, and only 47 out of 991 unigenes were significantly changed more than 20-fold in muscle; and there are 1,623 out of 3,709 unigenes were differentially expressed more than 5-fold, and only 345 out of 3,709 unigenes were differentially expressed more than 5-fold in gill. Read coverage (average contig size) within the differentially expressed contigs ranged from 407.7 reads/contig in gill to 509.1 reads/contig in muscle. In muscle, the putative aquaporin gene was the top upregulated gene with a fold change of 113 relative to the control, and much higher than other genes. Similarly, in shrimp muscle, the antilipolysaccharide factor isoform and molt-inhibiting hormone 1(MIH1, sinus gland peptide A precursor) were found the most upregulated genes with fold changes of 66.33 and 60.05, respectively.
Gene Ontology and Pathway Analysis
Totally, 2,587 GO terms for muscle including 1,043 biological process terms, 705 molecular function terms and 839 cellular component terms were assigned to 1,300 unique gene matches using Blast2GO. In shrimp gill, GO terms including 3708 biological process terms, 2,179 molecular function terms, and 2,742 cellular component terms were assigned to 3,709 unique gene matches by Blast2GO. The metabolic process (GO: 0,008,152), binding (GO: 0,005,488), and cell (GO: 0,005,623) were the most common annotation terms in the three GO categories for both muscle and gill of the shrimp.
In both muscle and gill, three pathway categories, including oxidative stress-related pathways, signal transduction-related pathways, and metabolism-related pathways (especially energy metabolism pathways), significantly changed after the low salinity stress relative to the salinity control at salinity of 20 revealed by the IPA (Tables 3 and 4). In both muscle and gill, the protein ubiquitination pathway, oxidative phosphorylation and mitochondrial dysfunction significantly changed after the low salinity challenge among the oxidative stress pathways. Relatively fewer pathways related to the signal transduction and metabolism were found in muscle than in gill. For signal transduction-related pathways, hypoxia signaling in the cardiovascular system, integrin-linked kinase signaling and PI3K/ AKT signaling pathways toped in muscle, whereas in gill, aryl hydrocarbon receptor signaling, xenobiotic metabolism signaling, and rac signaling were the top three pathways. Other three pathways, including glucocorticoid receptor signaling, integrin signaling, and protein kinase A signaling were common in both gill and muscle. Among the metabolism pathways, those related to energy metabolism are dominant. In muscle, citrate cycle, purine metabolism and aminosugar metabolism were the top three pathways. Whereas in gill, valine, leucine, and isoleucine degradation; lysine degradation; and propanoate metabolism were the top three pathways.
Osmoregulation in crustacean is a complex process where many organs and systems are involved and contribute to the body hemostasis (Pequeux 1995). Litopenaeus vannamei has been recognized as one of the most euryhaline penaeid species, with adults and juveniles exhibiting a hyper- and hypoosmoregulatory pattern and being able to tolerate a wide salinity range, shown by the stable hemolymph osmolality, ionic concentration, or gill Na/K ATPase at various salinities (Gong et al. 2004, Sowers et al. 2006, Huong et al. 2010, Jennyfers et al. 2014). In this study, both gill and muscle responded significantly to acute low salinity challenge with a similar pattern in juvenile L. vannamei. Much more unigenes were differentially expressed in gill than muscle, but most of the genes were regulated between 2-fold and 5-fold in both tissues. The modest fold changes may indicate the ability of L. vannamei to quickly adapt the acute low salinity challenge. After annotation, more pathways changed in the gill than in the muscle after salinity challenge, which is similar to the finding in Eriocheir sinensis after the challenge with an acute salinity for 24 h (Li et al. 2014). This may be because gills are directly exposed to the environment, and are the primary site for the balance of ions between excretion and absorption (Evans et al. 2005, Evans & Somero 2008, Li et al. 2014). When facing an acute salinity challenge, gills are more responsible than the muscle to keep the hemostatics of shrimp. Though the number of pathway changes is different between these two organs, the patterns of change in the signal transduction pathways, oxidative pathways, and metabolism pathways in the gill and muscle were similar, and these patterns are also found in E. sinensis exposed to an acute salinity challenge for 24 h (Li et al. 2014).
Response of Signal Transduction Pathways to Acute Salinity Challenge
Aquatic animals need to quickly activate appropriate signal transduction pathways and send a message to specific target molecules to affect related function when suffered in osmotic stress (Evans 2002, Evans et al. 2005, Fiol et al. 2006, Fiol & Kultz 2007, Evans & Somero 2008). In this study, seven pathways in muscle and 13 pathways in gills were significantly changed and these pathways included glucocorticoid receptor signaling, integrin signaling, and protein kinase A signaling pathways in both gill and muscle. The osmosensory signal transduction pathway is hypothesized to regulate glucocorticoid expression in rainbow trout, and salinity exposure can modulate glucocorticoid expression and glucocorticoid signaling (Singer et al. 2007). Besides, in zebrafish, the glucocorticoid receptor can mediate Cortisol regulation of epidermal ionocyte development and ion transport (Cruz et al. 2013). These finding indicated that the glucocorticoid receptor signaling pathway was included in fish under salinity stress. Similarly, the glucocorticoid signaling in crustacean may have similar function of mediating the Cortisol regulation and ion transportation in respond to acute salinity challenge as in fish.
The protein kinase-A signaling pathway also significantly changed in both gill and muscle of Eriocheir sinensis after acute salinity challenge (Li et al. 2014). Because the protein kinase-A signaling can activate the adenylyl cyclase enzymes for catalyzing the formation of cAMP from ATP, and can regulate the metabolism of glycogen, sugar, and lipid metabolism (Li et al. 2014), the significant change of this pathway may indicate the increase of energy metabolism. It is known that shrimp need more energy to keep homeostasis by ion regulation under acute salinity stress (Tseng & Hwang 2008).
Integrins are a transmembrane receptor for extracellular matrix components and can modulate the calcium channel of animals (Chao et al. 2011) and participate in chondrocyte transduction under osmotic stress (Jablonski et al. 2014). Therefore, the significant response of the integrin signaling pathway in this study indicates that under acute low salinity challenge, the integrins in Litopenaeus vannamei would play an important role in ion modulation during osmoregulation. But further research should be done to explore how integrins work in osmoregulation during salinity stress.
In conclusion, the glucocorticoid receptor signaling, integrin signaling, and protein kinase A signaling pathways played important roles in Litopenaeus vannamei under salinity stress. They may active the subsequent process of ion transportation or energy metabolism during osmoregulation.
Energy Metabolism during Acute Salinity Challenge
Once shrimp senses the ambient salinity change and the signal transduction network is activated, signal are sent to osmo-effectors, which are responsible for acclimation to changes in environmental salinity (Fiol & Kultz 2007). Among these effector mechanism, energy metabolism plays a critical role for animal survival and maintains normal physiological status of organisms under stressful conditions (Sokolova et al. 2012). During salinity acclimation, aquatic animals need additional energy to modulate and stimulate ion transport (Tseng & Hwang 2008).
In this study, in both gill and muscle of shrimp, amino acid metabolism pathways were significantly changed by low salinity challenge, such as glutamate metabolism and arginine and proline metabolism, which is similar to the findings in Eriocheir sinensis (Li et al. 2014). A total of 10 amino acid metabolism pathways in gill and six pathways in muscle participating in the process of osmoregulation in E. sinensis. It may be because that muscle is the main site of gluconeogenesis (Vinagre & Da Silva 2002) in crustaceans, and this process also appears to occur in the gill (Oliveira et al. 2004). In addition, muscle is also the main amino acid pool in crustaceans, which plays a role in storing and supplying amino acids to the other tissues during stress (Wang et al. 2012). The amount of free amino acids in the muscle of E. sinensis will decrease after 12 h under salinity stress (Wang et al. 2012). Moreover, free amino acid and glycerol will be used by Neoheiice granulata to maintain the adequate glucose in other tissues through gluconeogenesis under hypoosmotic stress (Nery & Santos, 1993, Lauer et al. 2012). The finding in this study further corroborates the notion that free amino acids are important intracellular osmotic effectors in crustacean (Edwards 1982, McNamara et al. 2004, Augusto et al. 2007, Augusto et al. 2009, Wang et al. 2012).
Carbohydrate metabolism pathways for starch and sucrose metabolism, galactose metabolism, glycolysis/glucongeogenesis, and glycosaminoglycan degradation in gill, and aminosugars metabolism in muscle were also involved into the physiological process related to the osmo/ion regulation of shrimp after acute low salinity change in this study. Previous studies have showed that carbohydrate can directly meet the high energy demand of aquatic animals in a stress condition, especially in salinity stress (Welcomme & Devos 1991, Tseng & Hwang 2008). In previous study, it was found that suitable dietary carbohydrate of Litopenaeus vannamei can improve growth performance at low salinity by providing direct extra energy for growth and osmoregulation (Wang et al. 2014a). Moreover, under the basic demand for protein, the addition of appropriate carbohydrate can improve the survival of L. vannamei at low salinity (Wang et al. 2014b). The changes of these carbohydrate metabolism pathways can also reveal that carbohydrate or glucose can alleviate the osmotic stress by providing extra energy for shrimp during low salinity challenge.
Lipids play significant roles in osmoregulation as reported in previous studies (Lemos et al. 2001, Luvizotto-Santos et al. 2003, Sang & Fotedar 2004). In this study, acute low salinity change led to a significant change in pathways of fatty acid elongation in mitochondria, fatty acid metabolism, sphingolipid metabolism, and propanoate metabolism in gill. No pathway related to lipid metabolism was found in shrimp muscle, which may be because this acute salinity change stopped after 24 h and there was not enough time to respond by muscle compared with shrimp gill. Previous literature shows that modification of fatty acids composition in the gill with a high level of (n-3) PUFA can result in a large gill area to enhance osmoregulatory capacity of shrimp at low salinity, and increase survival (Palacios et al. 2004). Because the (n-3) HUFAs, especially docosahexaenoic acid, are mainly incorporated in cell membranes, and can increase membranes permeability and hence their fluidity (Martins et al. 2006, Sui et al. 2007). Furthermore, sphingolipid is a key factor in gill cell membrane and plays important roles for salt transport in aquatic animals (el Babili et al. 1996).
In summary, energy metabolism including protein, carbohydrate, and lipid metabolism, plays a critical role in the osmoregulation process in Litopenaeus vannamei after acute salinity stress. Free amino acids are important intracellular osmotic effectors in L. vannamei. Carbohydrate may alleviate the osmotic stress by providing extra energy for shrimp during low salinity challenge. Meanwhile, lipids may play some role in by increasing the ion transport under salinity stress.
Reactive Oxygen Species Overproduction and Scavenging
Similar to the findings in Eriocheir sinensis (Li et al. 2014), this study identified significant changes of the protein ubiquitination. mitochondria dysfunction, and oxidative phosphorylation pathways in both gill and muscle of Litopenaeus vannamei. The activity of oxidative phosphorylation can be modulated by tissue metabolic stress to maintain energy metabolism homeostasis. Under acute salinity stress, the energy metabolism in both gill and muscle reached the maximum as measured by the process of oxidative phosphorylation (Phillips et al. 2012).
Under oxidative stress, the elevation of reactive oxygen species (ROS), which is an unenviable product in aerobic metabolism, can damage cellular constituents (Lushchak 2011). In aquatic animals, salinity change can lead to various physiological responses such as elevation of plasma hormone, high metabolism and electrolyte disequilibrium due to ROS generation by salinity stress (Liu et al. 2007). Uniquinone functions as an antioxidant and serves as a cofactor of mitochondrial uncoupling proteins, and modification of the ubiquinone pool in mice results in increased longevity and higher resistance to oxidative stress (Liu et al. 2005). Oxidative stress could increase the levels of ubiquitin conjugates in various cell types (Shang et al. 1997, Liu et al. 2005, Shang & Taylor 2011) to remove the overproduced ROS. Protein ubiquitination and ubiquinone biosynthesis pathways could be activated by the oxidative stress of acute salinity challenge and increased energy metabolism to help shrimp eliminate the high level of ROS or related metabolite waste products. Similar findings were found in Eriocheir sinensis under acute salinity stress (Li et al. 2014) and Leuciscus waleckii in an extremely alkaline saline environment (Xu et al. 2013).
In summary, Litopenaeus vannamei may get more energy through by the process of oxidative phosphorylation. The increased protein ubiquitination and ubiquinone biosynthesis pathways may prevent the shrimp from the high level of ROS or related hazardous substances produced by oxidative stress of acute salinity challenge.
Potential Genes Coping with Acute Salinity Challenge
In this study, 468 genes in muscle and 316 genes in gill were upregulated, but the putative aquaporin gene was the top upregulated gene with a fold change of 113 times more in muscle at 3 than in the control (20). Aquaporin is a family of water-specific channel proteins that allow the transport of water and other solutes such as glycerol or urea in the presence of osmotic gradients (Borgnia et al. 1999). Therefore, under osmotic stress, the aquaporin can possibly regulate water and ions movement across membranes (Giffard-Mena et al. 2007). Aquaporin 3 from Sparus aurata express in various tissues except for blood, and higher expression is found in S. aurata under a hyposaline than a hypersaline condition (Deane & Woo 2006). Aquaporin 1 shows a major osmoregulatory role in water transport in the kidney and gut in seawater acclimated seabass Dicentrarchus labrax, whereas aquaporin 3 has a role in water transport in the gill of D. labrax acclimated to freshwater (Giffard-Mena et al. 2007). Similarly, aquaporin 3 from Cyprinus carpio also shows direct relation with environmental salinity C. carpio (Salati et al. 2014). Though no information is reported in aquaporin in crustaceans, all the findings together with the upregulated expression of the aquaporin gene in this study indicate its importance in regulating water transport under osmotic stress, especially at a hypoosmotic condition in both fish and shrimp.
In the gill of shrimp after 24 h-low salinity challenge, the top upregulated genes are antilipolysaccharide factor isoform and MIH1 (sinus gland peptide A precursor) with fold changes more than 60 times. Molt-inhibiting hormone is usually responsible for maintaining animals in at an intermolt stage due to the effect of MIH on the synthesis of a steroid molecule secreted by Yorgan (ecdysterioid) (Lago-Leston et al. 2007), but also has more possible biological functions such as hyperglycemic activity (Cheng et al. 2006, Lago-Leston et al. 2007). Two alternative slicing variants of MIH present in the eyestalks of Litopenaeus vannamei, and MIH 1 is the dominated hormone in L. vannamei regardless of ambient salinities. The highest expression values for MIH 1 were observed at the lowest salinity (10) tested in the study (Lago-Leston et al. 2007), which is in certain degree similar to the result of this study. Therefore, as in the Pacific rock crab Cancer antennarius, the high MIH expression induced by stress can inhibit ecdysteroid synthesis in the Y-organ during the intermolt stage of the crustacean and therefore inhibit the process of molting (Spaziani et al. 1989).
Antilipopolysaccharide factor is an important shrimp immune gene (Tharntada et al. 2008), and was significantly downregulated in gill and gut tissues when compared with muscle tissues of Penaeus monodon acclimated from salinity of 28 to 3 for 2 wk (Shekhar et al. 2006). High expressions of lipopolysaccharide were found in Litopenaeus vannamei reared at 2.5 and 5 than that at 15,25, and 35 for a period of 24 wk (Lin et al. 2012). This finding reveals either acute or chronic salinity stress will stimulate the immune response of L. vannamei, which will decrease its resistance against pathogens due to the reduction of immune parameters when exposed to low salinity conditions (Wang & Chen 2005, Li et al. 2010). A recently RNA-seq study with hemocytes of L. vannamei also collaborate this finding, though shrimp was only transferred from 31 to 16 for 24 h (Zhao et al. 2015).
After acute low salinity challenge, shrimp could respond to ambient salinity change using various signal transduction pathways as summarized in Figure 1. Under low salinity stress, the glucocorticoid receptor signaling, integrin signaling, and protein kinase A signaling pathways played important roles in Litopenaeus vannamei under salinity stress. They may active the subsequent process of ion transportation or energy metabolism during osmoregulation. The energy metabolism including protein, carbohydrate, and lipid metabolism, plays a critical role in the osmoregulation process in L. vannamei after acute salinity stress. Free amino acids are important intracellular osmotic effectors in L. vannamei. Carbohydrate may alleviate the osmotic stress by providing extra energy for shrimp during low salinity challenge. Meanwhile, lipids may play some role in by increasing the ion transport under salinity stress. Under acute salinity stress, L. vannamei may get more energy through by the process of oxidative phosphorylation. The increased protein ubiquitination and ubiquinone biosynthesis pathways may prevent the shrimp from the high level of ROS or related hazardous substances produced by oxidative stress of acute salinity challenge. Despite the identification of various pathways under salinity stress, many functional responses to the transcriptome analysis remains unclear. Due to the complex of osmoregulation in crustacean, the functional roles of many genes involved on osmoregulation at low salinity are not fully discussed in this study. Therefore, the detained and specific response of vitally important pathways and functional genes should be further studied in the future.
This research was supported by grants from the National Natural Science Foundation of China (No. 31472291, 31172422), the Special Fund for Agro-scientific Research in the Public Interest (No. 201203065), National "Twelfth Five-Year" Plan for Science & Technology Support (2012BAD25B03), the National Basic Research Program (973Program, No. 2014CB138803), and partly by the E-Institute of Shanghai Municipal Education Commission (No. E03009) and ECNU innovation fund.
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XIAODAN WANG, (1) SHAOLIN WANG, (2) CHAO LI, (3) KE CHEN, (1) JIAN G. QIN, (4) LIQIAO CHEN (1) AND ERCHAO LI (1) *
(1) Laboratory of Aquaculture and Environmental Health, School of Life Sciences, East China Normal University, Shanghai, 200241, China; (2) Department of Psychiatry & Neurobiology Science, University of Virginia, Charlottesville, VA 22911; (3) Marine Science and Engineering College, Qingdao Agricultural University, Qingdao, 266109, China; School of Biological Sciences, Flinders University, Adelaide, SA 5001, Australia
* Corresponding author. E-mail: email@example.com
TABLE 1. Summary of de novo assembly results of Illumina sequence data of Litopenaeus vananmei. Trans-ABySS Contigs ([greater than or equal to] 100 bp) 557,740 Large contigs ([greater than or equal to] 1,000 bp) 49,162 Maximum length (bp) 13,707 Average length (bp) 333.4 N50 (bp) 1,198 Contigs after length filtering ([greater than or equal 156,686 to] 200 bp) Percentage contigs kept after length filtering 28.09% Average contig length after length filtering (bp) 984.2 Contigs (After CD-HIT-EST + CAP3) 105,153 Average length (bp) (After CD-HIT-EST + CAP3) 591.2 Reads mapped to final reference (%) 70.62 TABLE 2. Summary of gene identification and annotation of assembled contigs based on BLAST homology searches against UniProt and NR. Annotated Annotated Contigs with contigs [greater contigs [greater putative gene than or equa than or equal matches to] 500 bp to] 1,000 bp UniProt 24,676 12,306 8,229 NR 29,158 13,846 8,960 Hypothetical Quality Unigene gene unigene matches matches matches UniProt 11,551 0 7,546 NR 16.765 5,339 10,764 TABLE 3. Significantly changed pathways in muscle of Litopenaeus vannamei at salinity of three relative to the control group at 20. Pathways -log (P value) Oxidative Protein 1.62E01 stress-related ubiquitination pathways pathway Oxidative 1.17E01 phosphorylation Mitochondrial 7.68E00 dysfunction Ubiquinone 3.94E00 biosynthesis Signaling-related Hypoxia signaling 2.87E00 pathways in the cardiovascular system Integrin-linked 2.8E00 kinase signaling PI3K/AKT signaling 2.17E00 Actin cytoskeleton 2.17E00 signaling Glucocorticoid 2.11E00 receptor Signaling Integrin signaling 2.11E00 Protein kinase A 2E00 signaling Metabolism-related Citrate cycle 5.43E00 pathways Purine metabolism 4.66E00 Aminosugars 3.78E00 metabolism Pentose phosphate 3.36E00 pathway Glutamate 2.74E00 metabolism Arginine and 2.5E00 proline metabolism Urea cycle and 2.39E00 metabolism of amino groups D-glutamine and 2.18E00 D-glutamate metabolism Pathways Molecules Oxidative Protein CRYAB, USP14, PSMA3, UBE2A, stress-related ubiquitination PSMD7, UBE2D2, USP2, pathways pathway HSPA5, TCEB1, PSMB6, HSP90B1, PSMC6, PSMD10, USP47, UCHL5, PSMA2, UCHL3, PSMA6, PSMB5, DNAJC19, HSPA9, PSMD6, PSMA1, THOP1, HSPD1, PSMD5, UBE3A, SKP1/ SKP1P2, PSMD8, DNAJC21, PSMB7, PSMC1, PSMB2, UBE2G1, PSMD12, PSMA5, PSMB1, PSMA4, HSP90AA1, PSMD4, PSMC3 Oxidative NDUFA10, UQCR11, NDUFB8, phosphorylation ATP6AP1, NDUFS1, NDUFA5, UQCRFS1, ATP5F1, NDUFS4, ATP6V0E2, ATP5A1, ATP5C1, UQCR10, Atp5h, NDUFV2, ATP6V1H, NDUFA11, NDUFA6, UQCRC2, NDUFB7, ATP6V0D1, COX5A, SDHD, CYCl, UQCRQ, NDUFA7 Mitochondrial NDUFA10, ATP5A1, NDUFB8, dysfunction ATP5C1, NDUFS1, PRDX3, NDUFA5, PARK7, NDUFV2, NDUFA11, NDUFA6, NDUFB7, UQCRC2, COX5A, UQCRFS1, SDHD, CYCl, NDUFA7, PINK1, NDUFS4 Ubiquinone NDUFA5, NDUFS1, NDUFA10, biosynthesis NDUFV2, NDUFA11, NDUFA6, NDUFB7, NDUFB8, NDUFA7, NDUFS4 Signaling-related Hypoxia signaling P4HB, HSP90B1, NFKB1A, pathways in the UBE2A, UBE2G1, UBE2D2, cardiovascular HSP90AA1, LDHA system Integrin-linked ITGB1, nuILMYH10, PXN, kinase MYL6, RHOC, PIK3R1, MYH7, signaling PARVB, MTOR, FLNA, PPP2R3A, PPM1L, MYH9, RPS6KA5 PI3K/AKT signaling ITGB1, MTOR, HSP90B1, NFKB1A, YWHAE, PPP2R3A, PIK3R1, PPM1L, HSP90AA1, CDKN1B Actin cytoskeleton 1TGB1, MYH10, ARHGEF4, PXN, signaling MYL6, PIK3R1, ARPC5, CRK, MYH7, TTN, MYL12A, ACTR3, FLNA, MYH9, ARPC1A Glucocorticoid PRKAB1, PIK3Rl, HSPA9, receptor SLP1, PCK1, HSPA5, null, Signaling HSP90B1, NFKB1A, NFAT5, HMGB1, PCK2, POLR2H, HSP90AA1, A2M, PPP3CA, POLR2K Integrin signaling RAP1B, ITGB1, CAPN5, PXN, RHOC, PIK3R1, ARPC5, CRK, TTN, PARVB, MYL12A, ARF1, ACTR3, ARPC1A Protein kinase A RAP1B, MYH10, PXN, MYL6, signaling YWHAE, PPP1R3C, RYR2, TTN, null, PHKA2, MYL12A, PYGM, NFKB1A, NFAT5, FLNA, ADD1, PDE5A, PPP1CA, PPP3CA Metabolism-related Citrate cycle SUCLA2, PCK2, SUCLG2, AC02, pathways SDHSD, PCK1, IDH3A, ACLY Purine metabolism Pkm, ATP5A1, PAICS, MYH7, TYMP, HSPD1, HSPA5, AMPD2, null, POLR1C, ATP5C1, null, PSMC1, Atp5h, PSMC6, ADK, AK1, PNP, POLR2H, HSP90AA1, MYH9, PDE5A, ATP5F1, POLR2K Aminosugars HK1, NAGK, CHIT1, HEXB, metabolism PDE5A, PPM1F, UAP1, CHIA, null, GFPT2 Pentose phosphate DERA, PGD, PGLS, PGM1, RGN, pathway PFKP Glutamate NAGK, SUCLG2, GLS, GLUD1, metabolism GOT2, GFPT2 Arginine and P4HB, PRODH, OAT, GLUD1, proline GOT2, ODC1, ALDH7A1, metabolism PYCR1 Urea cycle and OAT, GLUD1, ALDH18A1, ODC1, metabolism of PYCR1 amino groups D-glutamine and GLS, GLUD1 D-glutamate metabolism TABLE 4. Significantly changed pathways in gill of Litopenaeus vannamei at salinity of three relative to the control group at 20. Pathways log (P value) Oxidative Mitochondrial 2.93E00 stress-related dysfunction pathways Oxidative 1.89E00 phosphorylation * Protein 1.83E00 ubiquitination pathway * Signaling-related Aryl hydrocarbon 3.5E00 pathways receptor signaling Xenobiotic 3.07E00 metabolism signaling Rac signaling 2.85E00 Sertoli 2.78E00 cell-sertoli cell junction signaling Estrogen receptor 2.48E00 signaling Glucocorticoid 2.38EOO receptor signaling Huntington's 2.38E00 disease signaling Integrin signaling 2.36E00 Tight junction 2.07E00 signaling Synaptic long-term 2.06E00 potentiation BMP signaling 2.04E00 pathway Protein kinase A 2.03E00 signaling ERK/MAPK signaling 2E00 Metabolism-related Valine, leucine, 1.02E01 pathways and isoleucine degradation Lysine degradation 7.83EOO Propanoate 7.81E00 metabolism Butanoate 7.22E00 metabolism [beta]-alanine 5.74E00 metabolism Citrate cycle 5.61E00 Ascorbate and 5.44E00 aldarate metabolism Glycine, serine, 4.38E00 and threonine metabolism Tryptophan 4.29E00 metabolism Glutamate 4.09E00 metabolism Pyruvate 4.O8EOO metabolism Arginine and 3.33EOO proline metabolism Purine metabolism 3.29E00 Calcium signaling 3.14E00 Nucleotide sugars 2.51E00 metabolism Fatty acid 2.48E00 elongation in mitochondria Fatty acid 2.39E00 metabolism Pantothenate and 2.35E00 CoA biosynthesis Starch and sucrose 2.3IE00 metabolism Galactose 2.25E00 metabolism Sphingolipid 2.23E00 metabolism Glyoxylate and 2.12E00 dicarboxylate metabolism Sulfur metabolism 2.12E00 Glycolysis/ 2.09 E00 gluconeogenesis Glycosaminoglycan 2.05E00 degradation Pyrimidine 2.03EOO metabolism Assembly of RNA 2.99E00 polymerase II complex Pathways Molecules Oxidative Mitochondrial COX1, NDUFV1, COX7A2, stress-related dysfunction CPT1A, NDUFA10, CASP3, pathways PRDX5, XDH, PSENEN, NDUFB8, GPX7, App, ATP5C1, NDUFA5, ATP5B, CYB5R3, NDUFA12, SDHD, CYC1, OGDH, NDUFA7, PINK1, AIFM1 Oxidative COX1, NDUFV1, COX7A2, phosphorylation * ATP6V1D, NDUFA10, UQCR11, NDUFB8, ATP6V1A, ATP6AP1, ATP6V0A1, ATP5C1, NDUFA5, Atp5h, ATP5B, ATP6V0D1, NDUFA12, SDHD, CYC1, ATP5F1, NDUFA7, ATP6V1B2 Protein COX1, NDUFV1, COX7A2, ubiquitination ATP6V1D, NDUFA10, pathway * UQCR11, NDUFB8, ATP6V1A, ATP6AP1, ATP6V0A1, ATP5C1, NDUFA5, Atp5h, ATP5B, ATP6V0D1, NDUFA12, SDHD, CYC1, ATP5F1, NDUFA7, ATP6V1B2 Signaling-related Aryl hydrocarbon ALDH4A1, GSTM5, NFKB1, pathways receptor SMARCA4, EP300, ARNT, signaling GSTT1, NCOA7, ALDH1A1, NCOA2, ALDH5A1, CHEK2, GSTK1, ALDH7A1, ALDH1B1, SRC, MGST1, ALDH8A1, SLC35A2, ALDH9A1, GSTO1, A1P, CDKN1B, ALDHI6A1, MCM7 Xenobiotic ALDH4A1, CHST4, SULT1C4, metabolism CAMK1, GSTM5, ABCC2, signaling PIK3R1, NFKB1, P1K3R4, ARNT, EP300, CUL3, GSTT1, CAMK2D, ALDH1A1, UGT2B17, MAP3K7, PPM1L, CHST11, SMOX, null, ALDH5A1, GSTK1, ALDH7A1, ALDH1B1, MGST1, ALDH8A1, GRIP1, UGT8, Sult1d1, ALDH9A1, GSTO1, AIP, PPP2R1A, PRK, CI, MAPK14, SULT1A1, ALDH16A1, ABCC3, EIF2AK3, DNAJC7 Rac signaling ITGB1, ABI2, PAK2, PIK3R1, ARPC5, PIK3R4, NFKB1, ANK1, MCF2L, PIP5K1A, CYFIP2, ACTR3, PRKCI, CYFIP1, ARPC1A, PARD3, PI4KA, NCKAP1, ITGA4 Sertoli PRKACB, SPTBN1, AXIN1, cell-sertoli MLLT4, MPP6, CTNNA2, cell junction SORBS1, MAP3K7, AKT3, signaling MTMR2, CTNNB1, ACTN1, ITGA4, 1TGB1, PLS1, DLG1, SRC, EPN1, TJP2, TUBG1, TUBA1B, ATF2, null, MAPK14, TUBA1A, PRKAG2, MAGI2, PRKAR1A Estrogen receptor MED12L, SRC, TAF11, signaling POLR2D, MED23, null, MED21, TAF10, ERCC2, SMARCA4, EP300, null, GTF2B, TAF1, POLR2A, TAF5, NCOA2, MED13L, NCOR1, TAF3, POLR21 Glucocorticoid PRKACB, TAF11, POLR2D, receptor PRKAB1, PIK3R1, SMAD3, signaling SLP1, PBX1, TAF10, HSPA5, NFKB1, PIK3R4, SMARCA4, EP300, null, GTF2B, POLR2A, NFAT5, NCOA2, MAP3K7, AKT3, NCOR1, TAF3, TAB1, PPP3CA, null, HSPA9, CHP1, TAT, ERCC2, TRAF6, MAPK14, TAFl, TAF5, PRKAG2, POLR21 Huntington's POLR2D, VTI1A, PIK3R1, disease GNB2L1, HSPA5, PIK3R4, signaling VTI1B, EP300, null, POLR2A, HDAC7, AKT3, PLCB1, TCERG1, NCOR1, GOSR1, CASP3, GLS, HSPA9, CLTC, ITPR1, STX1A, NAPG, ATF2, DNM1, DYNC112, PLCB4, PRKC1, ATP5B, CAPN9, POLR21 Integrin signaling RAP1B, RAPGEF1, ARHGAP26, PIK3R1, ARPC5, CRK, TLN1, NCK1, PIK3R4, MYLK, BRAF, PARVB, ACTR3, ITGAV, AKT3, ARPC1A, ACTN1, ITGA4, ITGB1, SRC, PAK2, RHOC, TTN, RAC3, ARF1, PPP1R12B, CAPN9, ITGA7, CTTN Tight junction PRKACB, CPSF2, TJP2, signaling MARK2, MLLT4, MYH7, null, NFKB1, SMURF1, MYLK, PPP2R1A, PRKCI, LLGL1, MPP5, PPM1L, PRKAG2, AKT3, MYH9, INADL, MAGI2, CTNNB1, CSTF3, PRKAR1A Synaptic long-term RAP1B, PRKACB, PPP1R3C, potentiation CHP1, ITPR1, ATF2, GR1NA, EP300, PLCB4, CAMK2D, PRKC1, PPP1R7, ADCY1, PRKAG2, PLCB1, PPP3CA, PRKAR1A BMP signaling PRKACB, SMAD6, NFKB1, pathway SMURF1, CHRD, ATF2, BMPR1B, MAPK14, MAP3K7, PRKAG2, SMAD1, TAB1, PRKAR1A Protein kinase A RAP1B, PRKACB, FLNB, signaling SMAD3, PPP1R3C, PHKG2, GNB2L1, NFKB1, PHKA2, MYLK, R0CK2, BRAF, CAMK2D, NFAT5, PHKB, PLCE1, PPP1R7, FLNA, PLCB1, CTNNB1, APEX1, PPP3CA, YWHAE, CHP1, ITPR1, ANAPC7, TTN, ATF2, PDE8A, AKAP13, PLCB4, PYGM, PRKC1, ADCY1, PRKAG2, ADD1, AKAP9, TCF7L2, CDC27, PRKAR1A ERK/MAPK signaling RAP1B, PRKACB, RAPGEF1, PIK3R1, PPP1R3C, CRK, TLN1, RAPGEF4, PIK3R4, KSR1, BRAF, PPP1R7, PPM1L, ITGA4, ITGB1, SRC, MYCN, PAK2, MKNK2, RAC3, ATF2, PLA2G6, PPP2R1A, PRKC1, PRKAG2, PRKAR1A Metabolism-related Valine, leucine, ALDH4A1, BCKDHB, HSD17B8, pathways and isoleucine ALDH1A1, BCAT2, BCKDHA, degradation OXCT1, MCCC1, ACADM, HSD17B4, HADHA, ALDH7A1, ALDH1B1, ECHS1, MUT, ALDH9A1, ELOVL6, ACADL, ACADVL, PCCA, HJBADH, AUH, AGXT2, HADH, MCCC2 Lysine degradation ALDH4A1, ALDH1B1, WHSC1, ECHS1, AASS, PLOD1, GCDH, ALDH9A1, ELOVL6, EP300, HSD17B8, TMLHE, ALDH1A1, FAM213B, AUH, OGDH, HSD17B4, HADHA, SHMT2, HADH, ALDH7A1 Propanoate ALDH4A1, ALDH1B1, ECHS1, metabolism SUCLG2, MUT, ALDH9A1, ACADL, SUCLA2, ALDH1A1, PCCA, ACADVL, SRD5A3, AUH, ACSS2, AGXT2, ACADM, LDHA, ACSL1, HADHA, ALDH7A1 Butanoate ALDH4A1, ALDH1B1, ECHS1, metabolism SUCLG2, PRDX6, ALDH9A1, ELOVL6, HSD17B8, BDHl, ALDH1A1, FAM213B, AUH, OXCT1, SDHD, HSD17B4, ALDH5A1, HADHA, HADH, ILVBL, ALDH7A1 [beta]-alanine ALDH4A1, ALDH1B1, SRM, metabolism DPYS, DPYD, ECHS1, ALDH9A1, ACADL, ALDH1A1, ACADVL, AUH, AGXT2, ACADM, HADHA, ALDH7A1 Citrate cycle SUCLA2, PC, SUCLG2, AC02, SDHD, 1DH2, IDH3A, MDH1, OGDH, ACLY, AC01, IDH3B Ascorbate and ALDH4A1, ALDH1B1, ALDH1A1, aldarate FAM213B, BCKDHA, RGN, metabolism ALDH9A1, GST01, ALDH7A1, BCKDHB Glycine, serine, GNMT, GLYCTK, TARS, and threonine ELOVL6, SARDH, PLCB4, metabolism PLCE1, FAM213B, PHGDH, PLCB1, SMOX, GOT1, ALAS2, GLDC, SARS, AGXT2, CHKB, SHMT2 Tryptophan ALDH4A1, BC02, GCDH, metabolism BCKDHB, ACR, HSD17B8, ACMSD, ALDH1A1, BCKDHA, SMOX, OGDH, HSD17B4, HADHA, ALDH7A1, ALDH1B1, ECHS1, HAAO, Nedd4, ALDH9A1, TMLHE, WARS, SRD5A3, AUH, Cyp2j9, KYNU, HADH Glutamate ALDH4A1, NAGK, GMPS, metabolism SUCLG2, NADSYN1, GLS, CCDC92, GOT1, CAD, ALDH5A1, GSTO1, GFPT2 Pyruvate ALDH4A1, ALDH1B1, PC, metabolism ACOT9, MDH1, ALDH9A1, GLO1, BCKDHB, ALDH1A1, BCKDHA, ACSS2, PDHX, LDHA, ACSL1, HAGH, HADHA, ALDH7A1 Arginine and ALDH4A1, ALDH1B1, SRM, proline OAT, AMD1, ALDH9A1, ACR, metabolism BCKDHB, P4HA1, ALDH1A1, PRODH, BCKDHA, GOT1, SMOX, ASL, ALDH7A1 Purine metabolism POLR2D, IMPAD1, XDH, POL1, HSPA5, POLRMT, SMARCA4, BCKDHB, MPP6, null, POLR2A, POLR3A, PRPS1, BCKDHA, ENTPD5, ADA, ATP5F1, DLG1, PEX6, TJP2, AK3, PAICS, MYH7, REV3L, NT5C2, PDE8A, ATP5C1, ENPP3, NT5C3, Atp5h, ATP5B, GMPS, MPP5, IMPDH1, ATF7IP, ADCY1, PNP, MYH9, GMPR2, POLR21, CANT1 Calcium signaling RAPlB, PRKACB, CAMK1, CHRFAM7A, ATP2A1, null, EP300, GR1NA, CAMK2D, NFAT5, TNNT3, HDAC7, ASPH, PPP3CA, CHRNA4, LETM1, TP63, ATP2C1, CHP1, CHRNA9, MEF2A, MYH7, ITPR1, ATF2, MICU1, CAMKK1, PRKAG2, MYH9, PRKAR1A Nucleotide sugars UGDH, FAM213B, UGP2, GALE metabolism Fatty acid ECHS1, AUH, HSD17B4, elongation in HADHA, HADH, HSDI7B8 mitochondria Fatty acid ALDH4A1, ALDH1B1, CPT1A, metabolism ECHS1, GCDH, ALDH9A1, HSD17B8, ACADL, ALDH1A1, ACADVL, AUH, ACSL4, Cyp2j9, HSD17B4, ADHFE1, ACADM, HADHA, ACSL1, HADH, ALDH7A1 Pantothenate and ENPP3, DPYS, DPYD, BCAT2, CoA biosynthesis PPCDC, ILVBL Starch and sucrose PGM2, PGM1, HPSE, GUSB, metabolism GANAB, ENPP3, DDX6, UGDH, PYGM, UGT2B17, UGP2, GAA, GBE1 Galactose B4GALT2, PGM2, FAM213B, metabolism GALK2, UGP2, GLB1, GALE, GAA, PGM1, GANAB Sphingolipid CERS6, ARSH, SPTLCl, PlGO, metabolism ARSD, PIGF, UGT8, SGMSl, ASAH1, PPAP2A, GLB1, SPTLC2, PPM1F, ARSB, KDSR Glyoxylate and APTX, GLYCTK, AC02, MDHl, dicarboxylate MTHFD1, ACO1 metabolism Sulfur metabolism SULT1A1, ETHE1, CHST1l, null, Sult1d1, SUOX Glycolysis/ PGK1, ALDH4A1, ALDH1B1, gluconeogenesis PGM2, PGM1, Tpi1 (includes others), ALDH9A1, GALM, ALDH1A1, PDHX, ACSS2, ADHFE1, LDHA, ACSL1, ALDH7A1 Glycosaminoglycan MGEA5, GLB1, HPSE, GALNS, degradation ARSB, GNS, GUSB Pyrimidine DPYS, DPYD, POLR2D, metabolism IMPAD1, AK3, POL1, REV3L, DCTD, POLRMT, CTPS1, NT5C2, ENPP3, NT5C3, null, POLR2A, POLR3A, ENTPD5, PNP, CAD, POLR21, CANT1 Assembly of RNA TAF11, GTF2B, null, polymerase II POLR2A, TAF1, POLR2D, complex TAF5, null, TAF3, TAF10, ERCC2, POLR21
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|Author:||Wang, Xiaodan; Wang, Shaolin; Li, Chao; Chen, Ke; Qin, Jian G.; Chen, Liqiao; Li, Erchao|
|Publication:||Journal of Shellfish Research|
|Date:||Dec 1, 2015|
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