Introduction

Dissolved oxygen (DO) is an important factor for the survival of aquatic animals, and hypoxia directly affects the growth, development, and reproduction of aquatic animals (Miller Neilan and Rose 2014; Hou et al. 2020). In general, hypoxia is defined as a DO concentration less than 2 mg/L (Galic et al. 2019) and can be accelerated by many factors, such as high temperature, high-density farming, and poor water quality (Stephen Hopkins et al. 1994). During the shrimp aquaculture process, an extension of the farming time is associated with increased deterioration of the water quality of shrimp ponds (Jiang et al. 2009). Moreover, the bottom layer of pond waters might become hypoxic or even anoxic due to the respiration of the shrimp and the decomposition of organic matter, particularly at night, and these hypoxic conditions can certainly threaten the life of shrimp (Cheng et al. 2003). Typically, shrimp respond to aquatic hypoxia via a series of behavioral, physiological, and biochemical and molecular changes. The mysid shrimp Tenagomysis novae-zealandiae reduces its activity to tolerate moderate hypoxia but shows an escape response at a DO level of 0.5 mg/L (Larkin et al. 2008). To avoid oxidative damage caused by the reactive oxygen species (ROS) and free radicals induced under hypoxia, Litopenaeus vannamei can activate its antioxidant enzyme system to eliminate these molecules (Kniffin et al. 2014; Li et al. 2016). In Litopenaeus vannamei, Palaemonetes sinensis, and Pandalus borealis, hypoxia also induces a change in respiratory metabolism by regulating the expression of key enzymes of aerobic or anaerobic metabolism to ensure an adequate energy supply (Keni et al. 2015; Simón et al. 2018; Bao et al. 2018; Pillet et al. 2016). In addition, many studies have shown that different types of metabolism genes, antioxidants, autophagy, and the cell cycle play important roles in the responses of shrimp to hypoxia stress (Camacho-Jiménez et al. 2018; Trasviña-Arenas et al. 2013; Sun et al. 2019; Nuñez-Hernandez et al. 2019). Hypoxia-inducible factor (HIF), a master regulator of many physiological responses to hypoxia, can affect the physiological state of the body by regulating the transcription of genes involved in glucose metabolism, erythropoiesis, cell growth, and cell proliferation (Kodama et al. 2012). Cota-Ruiz et al. (2016) found that HIF-1 participates in the upregulation of phosphofructokinase transcripts under short-term hypoxia. Hypoxia can also activate apoptosis signaling pathways, and Nuñez-Hernandez et al. (2018) found that caspase-3 expression is significantly increased in the hepatopancreas of Litopenaeus vannamei after 48 h of hypoxia, which indicates that caspase-3 is involved in the molecular process of hypoxia-induced apoptosis.

The ridgetail white prawn (Exopalaemon carinicauda) is one of the most important economic shrimp for marine fisheries and pond culture in China (Wang et al. 2010; Ma et al. 2020). In its natural environment, E. carinicauda can live in seawater or brackish waters in the Indo-West Pacific, Korea, and China (Gao et al. 2017). Due to its various advantages, including rapid growth, strong environmental adaptation, and reproductive performance, the scale of the cultivation of ridgetail white prawns has expanded rapidly and currently contributes to at least 1/3 of the overall output of pond polyculture in eastern China (Li et al. 2019). With the expansion of farming scales, high-density breeds have become the primary mode of pond culture. However, as stocking densities increase, the deterioration of water quality and eutrophication observed in recent years has increased the likelihood of hypoxia, which inevitably leads to hypoxic stress (Sun et al. 2020a, 2020b). Therefore, understanding the mechanisms of the adaptation of ridgetail white shrimp to hypoxia can help its farming industry and the breeding of hypoxia tolerance.

Weighted gene coexpression network analysis (WGCNA) is a transcriptional data analysis method based on large samples that can be used for the rapid identification of core relevant pathways and genes involved in the regulation of transcription and makes it easy to infer the regulatory relationships of unknown genes based on known genes in the gene network (Zhou et al. 2019).

In this study, we performed RNA-seq of hepatopancreas tissue from ridgetail white shrimp subjected to hypoxia and reoxygenation for different time points. Transcriptome and WGCNA analyses were performed to determine the mechanisms and key regulatory genes involved in the response of E. carinicauda to of hypoxia and reoxygenation.

Materials and methods

Experimental shrimp collection and rearing

Healthy E. carinicauda (weight: 2.57 ± 0.21 g, body length: 5.3 ± 0.1 cm) were collected from the Rudong base of Jiangsu Institute of Marine Fisheries, Nantong, China. Before the experiment, all the shrimp were placed in two 800-L constant-aeration cylindrical tanks (each tank contained approximately 500 shrimp and 400 L of marine water) and allowed to acclimate for 1 week. The acclimation conditions were 20~21 °C and 25 ppt, and the tank was continuously aerated to maintain a DO level above 7.5 mg/L. During the acclimation period, the shrimp were regularly fed a commercial diet obtained from Nantong Haid Bio-tech Co., LTD., which has a protein content of 42%, once a day at 3–5% of their wet body weight and were fasted for 24 h before the experiment. Any uneaten food, feces, and dead shrimp were removed in a timely manner through a siphon tube, and one-third of the marine water in each tank was replaced once daily with new marine water (20~21 °C, 25 ppt, DO above 7.5 mg/L).

Experimental design and sample collection

A schematic diagram of the experimental design is shown in Fig. 1. Before the experiments, the terminal time (6 h) of hypoxia stress was determined in a preliminary experiment. After 1 week of acclimatization, the shrimps were randomly transferred into 21 conical flasks (10 shrimps in each bottle) with 5 L of constant-aeration seawater (temperature: 20~21°C, salinity: 25 ppt) and divided into groups (each group was placed in three conical flasks): hypoxia for 0 h (0 h) (as the control group), 3 h (3 h), 4.5 h (4.5 h), and 6 h (6 h) and recovery under constant aeration for 1 h (7 h), 4 h (10 h), and 8 h (14 h). The shrimps were allowed to acclimate in the conical flasks for 30 min, and the conical flasks of the experimental groups were then immediately plugged with rubber plugs; after 6 h of hypoxia, the reoxygenation groups were immediately aerated. At the corresponding time points, water samples were collected in a water bottle and fixed with a solution of manganese chloride and alkaline potassium iodide to determinate the DO level. Hepatopancreas tissue of six/five living shrimp from one treatment bottle was then pooled together to obtain a single replicate, and three biological replicates of each group were obtained; the samples were named as follows: jg-0-1 to 3, jg-3-1 to 3, jg-4.5-1 to 3, jg-6-1 to 3, jg-7-1 to 3, jg-10-1 to 3, and jg-14-1 to 3. All hepatopancreas tissue samples were immediately frozen in liquid nitrogen and stored at −80 °C for further analysis.

Fig. 1
figure 1

Schematic diagram of experimental design. a. inflatable tube. b. conical flasks with 5L seawater. c. air stone. d. clip. e. air inlet. f. rubber plug. g. exhalent siphon

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Determination of the DO level in the water samples

As described in “Marine Monitoring Specification——Part 4: Seawater Analysis (GB17378.4–2007),” the water samples were fixed for 1 h (complete precipitation), and the DO level was then measured using the iodometric method.

RNA extraction, library construction, and sequencing

According to the results obtained in “Determination of the DO level in the water samples,” total RNA was extracted from the hepatopancreas tissue obtained after 0 h (control), 3 h (hypoxic starting point), and 6 h (hypoxic lethal half point) of gradual changes in hypoxia and after 1 and 8 h of reoxygenation using a TRIzol reagent kit (Invitrogen) according to the manufacturer’s protocol. The integrity value, purity, and integrity of the extracted RNA were assessed using an Agilent 2100 Bioanalyzer (Agilent), NanoDrop microspectrophotometer, and RNase-free agarose gel electrophoresis, respectively. After quality control, the mRNA was enriched with oligo (dT) beads, and the enriched mRNA was cleaved into short fragments using fragmentation buffer and reverse transcribed into cDNA with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, dNTPs, and buffer. The cDNA fragments were then purified using a QIAquick PCR extraction kit (Qiagen), subjected to end repair and poly(A) addition, and ligated to Illumina sequencing adapters. The ligation products were size selected by agarose gel electrophoresis, amplified by PCR, and sequenced using an Illumina HiSeqTM 4000 instrument by Gene Denovo Biotechnology Co. (Guangzhou, China).

Data analysis

De novo assembly and gene function annotation

The raw reads were further filtered by fastp (version 0.18.0) (Chen et al. 2018) to remove the adaptor sequences, low-quality reads containing more than 50% low-quality (Q-value≤20) bases, and reads with >10% poly-N to obtain high-quality clean reads. The Trinity package was used for the de novo assembly of the remaining high-quality reads (Grabherr et al. 2011). All unigenes were subjected to a BLASTX similarity search using an E-value threshold of 1e−5 to the NCBI nonredundant protein (NR) database (http://www.ncbi.nlm.nih.gov), the Swiss-Prot protein database (http://www.expasy.ch/sprot), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg), and the COG/KOG database (http://www.ncbi.nlm.nih.gov/COG).

Differential expression analysis

The expression of the unigenes was calculated and normalized to RPKM (reads per kb per million reads) (Mortazavi et al. 2008). RNA differential expression analysis between two groups was performed using DESeq2 (Love et al. 2014). Genes with a false discovery rate (FDR) less than 0.05 and an absolute fold change ≥ 2 were considered differentially expressed genes (DEGs).

Coexpression network construction and module and hub gene selection

Coexpression networks were constructed using Omicsmart (www.omicsmart.com/) from Gene Denovo Biotechnology Co. Genes with RPKM values < 1 were removed from the samples, and the remaining genes were used for WGCNA. As shown in Fig. 2, a soft threshold power of 5 (R2 = 0.9050) was used in this study. Subsequently, a hierarchical clustering dendrogram was constructed based on similar gene expression profiles. We then set the minimum number of genes in each module to 50 and used a threshold of 0.7 to merge similar modules. The correlation coefficients for the samples were calculated using the module eigengenes to identify modules with biological or clinical significance. The intracellular connectivity was calculated for each gene, and genes with high connectivity were often found to be hub genes, which might have important functions. The network of each module was visualized directly in Omicsmart.

Fig. 2
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Analysis of network topology for different soft threshold power. The left panel displays the effect of soft threshold power on scale free topology model fit index. The right panel shows the effect of soft threshold power on the mean connectivity

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Functional analysis of module genes

The biological functions of modules highly associated with hypoxia and reoxygenation were analyzed by gene ontology (GO) (Ashburner et al. 2000) and KEGG (Kanehisa and Goto 2000) pathway enrichment analysis. GO terms and KEGG pathways with P ≤ 0.05 were considered to be significantly enriched.

Experimental validation of DEGs

To further validate the reliability of the transcriptome sequencing, 10 DEGs were randomly selected and analyzed by qRT-PCR. All primers were designed as shown in Table 1. Total RNA was extracted from the samples that were also used for sequencing, and cDNA was synthesized using the PrimeScript™ RT Reagent Kit with gDNA Eraser (Perfect Real Time) (TaKaRa, Dalian, China). qRT-PCR was performed using TB Green® Premix Ex Taq™ (Tli RNaseH Plus) (TaKaRa, Dalian, China) in 20-μl reactions, which contained 10 μl of TB Green Premix Ex Taq (Tli RNaseH Plus) (2X), 0.8 μl of the reverse primer (10 μM), 0.4 μl of ROX Reference Dye (50X), 2 μl of cDNA, and 6.8 μl of ddH2O. The PCR amplification procedure was performed in a StepOnePlus Real Time PCR System (Applied Biosystems, USA) with the following temperature program: 95 °C for 30 s; 40 cycles of 95 °C for 5 s and 60 °C for 30 s; melt curve detection at 95 °C for 15 s; and 60 °C for 60 s to 95 °C for 15 s at increments of 0.3 °C. All reactions were performed in triplicate. The expression of each gene was normalized using β-actin as the reference gene and calculated using the comparative threshold cycle (2−ΔΔCt) method.

Table 1 Corresponding PCR primers used for quantitative real-time PCR.
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Results

DO concentration at corresponding time points

The results of the DO analysis are shown in Fig. 3. The DO level at 3 h, 4.5 h, and 6 h was significantly (P < 0.05) lower than that in the control group. At 3 h, the DO level was 2.12 mg/L, which was approximately equal to the hypoxia level; thus, 3 h was selected as the start time of hypoxia stress. At 6 h, the survival rate of the shrimp was 47%; thus, 6 h was selected as the longest hypoxia exposure duration, and the DO level at this time point was 1.04 mg/L. In contrast, the DO level in the reoxygenation groups returned to that in the control group level rapidly after the start of reoxygenation, and the levels at 7 h, 10 h, and 14 h were not significantly different from that in the control group (P > 0.05).

Fig. 3
figure 3

Results of DO concentration. Different lowercases indicate significance difference (P<0.05) in different time points

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Sequencing, assembly, and gene functional annotation

From 15 samples, a total of 864,905,274 raw reads were generated. After filtering, the clean reads from each sample ranged from 44,659,922 to 90,874,932. The average rate of clean reads was 99.91%. The detailed statistics of each sample, including the numbers of raw and clean bases, the Q20 and Q30 values, and the GC percentage, are listed in Table 2. Trinity software was used to assemble and cluster the clean reads into unigenes. A total of 93,227 unigenes, with an average length of 834 bp and an N50 length of 1352 bp, were obtained (Table 3). The assembled unigenes were annotated using four public databases, namely, COG/KOG, NR, KEGG, and Swiss-Prot (Fig. 4). A total of 40,719 unigenes (43.68% of the unigenes) were annotated. The numbers of unigenes annotated with the NR, KEGG, COG/KOG, and Swiss-Prot databases were 37346 (40.01%), 33955 (36.42%), 24238 (26.00%), and 29042 (31.15%), respectively (Table 4). The NR species annotation demonstrated that the majority of unigenes were found in Hyalella azteca, Branchiostoma belcheri, Saccoglossus kowalevskii, Tetrahymena thermophila SB210, Daphnia magna, and Lingula anatine (Fig. 5). The unigenes of E. carinicauda were classified into 25 KOG clusters according to the COG/KOG functional classification, and the cluster with the highest number of unigenes (4949) was the general function prediction cluster, followed by the signal transduction mechanism cluster and the posttranslational modification, protein turnover, and chaperone cluster (4332 and 3020 unigenes, respectively; Fig. 6). In the KEGG classification, 33,955 (36.42%) unigenes were mapped to the five categories of KEGG metabolic pathways, including metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems, and most of the genes belonged to global and overview maps (1870 unigenes), followed by translation (1283 unigenes), transport and catabolism (1019 unigenes), signal transduction (843 unigenes), and folding, sorting, and degradation (652 unigenes) (Fig. 7). In addition, we also classified the unigenes into three functional categories based on GO classification. In the biological process category, the unigenes were clustered into 24 classifications, and the highest number of genes belonged to metabolic process, followed by cellular process and single-organism process. The unigenes were also classified into 20 classifications in the cellular component category, and cell and cell parts were the most representative. In addition, among the 12 molecular function classifications, catalytic activity and binding were the most representative (Fig. 8).

Table 2 Sequencing dataoutput statistics
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Table 3 Statistics ofassembly results of RNA-seq
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Fig. 4
figure 4

The Venn diagram of unigenes annotated in COG/KOG, KEGG, NR and Swissprot databases. The number in each color block indicated the number of unigenes that was annotated by single or multiple databases

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Table 4 Annotationstatistics of unigenes
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Fig. 5
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The top-hits species distribution of unigenes by the NR database

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Fig 6
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Functional classification of unigenes in COG/KOG

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Fig. 7
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KEGG annotation analysis of all unigenes

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Fig. 8
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GO annotation analysis of all unigenes

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Identification and analysis of DEGs

Pairwise comparisons of the different sample groups analyzed (jg-0-vs-jg-3; jg-0-vs-jg-6; jg-0-vs-jg-7; jg-0-vs-jg-14; jg-3-vs-jg-6; jg-3-vs-jg-7; jg-3-vs-jg-14; jg-6-vs-jg-7; jg-6-vs-jg-14; jg-7-vs-jg-14) detected a total of 4315 DEGs (3374 upregulated and 1569 downregulated genes) in the hepatopancreas of E. carinicauda (Fig. 9). Compared with the control group, a total of 274 DEGs (166 upregulated and 108 downregulated genes) were identified in the hypoxia experimental groups (3 h and 6 h), and 1704 DEGs (1470 upregulated and 239 downregulated genes) were identified in the reoxygenation experimental groups (7 h and 14 h). Remarkably, the number of DEGs was higher after hypoxia and reoxygenation. A GO functional enrichment analysis showed that the biological processes of the DEGs were mostly related to metabolic processes, cellular processes, and single-organism processes. Cell and cell part were most enriched GO terms in the cellular component category, and catalytic activity and binding were the most highly enriched molecular function terms (Fig. 10). We also mapped all DEGs to KEGG pathways, and the results showed that the DEGs were significantly enriched (q < 0.05) in ribosome biogenesis in eukaryotes, apoptosis-multiple species, apoptosis-fly, longevity regulating pathway-multiple species, MAPK signaling pathway, MAPK signaling pathway-fly, and protein processing in the endoplasmic reticulum (ER) (Fig. 11).

Fig. 9
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The number of differential expression genes in fifteen pairwise comparisons. UP: Up-regulated genes, DOWN: Down-regulated genes

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Fig. 10
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Gene ontology annotated analysis of all DEGs

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Fig. 11
figure 11

Top 20 of KEGG enrichment pathway of all DEGs

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Coexpression network

A weighted gene coexpression network was constructed for the hepatopancreas of E. carinicauda after the hypoxia and reoxygenation treatments. The network contained 20,203 genes and was divided into 20 modules (Fig. 12). In the figure, the blue module had the largest number of genes (11968), whereas the dark magenta module had the smallest number of genes (115) (Fig. 13). Three of the identified modules were found to be specifically related to hypoxia and reoxygenation: the dark gray module with 234 genes, the green module with 1126 genes, and the blue module with 11968 genes were significantly positively correlated with jg-3-1, 2, 3, jg-7-1, 2, 3, and jg-14-1, 2, 3, respectively. Notably, no modules exhibited significant negative correlations with the hypoxia and reoxygenation treatments (Fig. 14).

Fig. 12
figure 12

Clustering dendrograms of 20203 genes. Dissimilarity was based on topological overlap, together with assigned module colors. The 20 co-expression modules are shown in different colors

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Fig. 13
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The number of genes of each module

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Fig. 14
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Correlation heatmap of module-sample association. The colors, ranging from green through white to red, indicate low to high correlations

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GO and KEGG enrichment analyses of modules

To further investigate the potential functions of the dark gray, green, and blue modules, GO and KEGG enrichment analyses were performed. The GO enrichment analysis was divided into three categories, biological process, molecular function, and cellular component, and revealed that most of the genes belonging to the three abovementioned modules were enriched in metabolic process, cellular process, single-organism process, cell, cell part, catalytic activity, and binding at level 2 (Fig. 15). A KEGG pathway analysis was performed with the same modules, and the results revealed that 12 pathways, including glycosphingolipid biosynthesis-ganglio series, glycosphingolipid biosynthesis-globo and isoglobo series, and amino sugar and nucleotide sugar metabolism, were significantly (P < 0.05) enriched with the genes in the dark gray module. In addition, 19 pathways, such as the MAPK signaling pathway-fly, MAPK signaling pathway, apoptosis-fly, and Wnt signaling pathway, were significantly (P < 0.05) enriched with the genes in the green module, and the blue module was significantly (P < 0.05) enriched in 21 pathways, such as spliceosome, RNA transport, and N-glycan biosynthesis (Table 5).

Table 5 Results of KEGG pathway significantly enriched in darkgrey, blue, and green module
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Fig. 15
figure 15

Gene ontology annotated analysis of dark grey, blue, and green module. a Dark grey module, b blue module, c green module

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Identification of hub genes and network construction

To identify hub genes from the significant modules based on weight values, the top 100 pairs between two genes in each module were selected to construct a gene correlation network. The gene with the highest connectivity was considered a hub gene. In the dark gray module with 35 unigenes, the green module with 38 unigenes, and the blue module with 89 unigenes, unigene0018514 (unknown gene), unigene0018395 (Ras-responsive element-binding protein 1-like, RREB1), and unigene0073542 (ubiquitin-activating enzyme E1, UBE1) were in core positions. RREB1 is a Krüppel-type zinc finger protein that can bind to Ras-responsive elements in promoters and might exert its control via the Ras/Raf pathway (Jiang et al. 2010). UBE1 belongs to the ubiquitin-activating E1 family of enzymes and plays a crucial role in ubiquitylation to activate ubiquitin (Lambert-Smith et al. 2020) (Fig. 16).

Fig. 16
figure 16

Network relationship among the modules. a 35 unigenes with highest weight are in dark grey module, b 38 unigenes with highest weight are in green module, c 89 unigenes with highest weight are in blue module. Note: the direction of the arrow indicates a positive relationship between two genes; the color of lines, ranging from blue through cyan to red, indicate low to high weight values; the size of the circle represents the level of genetic connectivity

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Validation of gene expression patterns by qRT-PCR

To verify the reliability of the RNA-seq results, ten candidate DEGs were randomly selected for detection by qRT-PCR. As expected, the qPCR results were generally consistent with the RNA-seq results, confirming the reliability of the RNA-seq results (Fig. 17).

Fig. 17
figure 17

qRT-PCR validation of ten DEGs. The y-axis on the left shows the normalized expression level (RPKM) of RNA-seq (blue bar), and the y-axis on the right shows relative expression level of qRT-PCR (green line). The relative expressions of ten genes were calculated using the 2−△△Ct method, and β-actin was used as a reference gene. The results were expressed as mean ± SD

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Discussion

Differential expression of genes during hypoxia and reoxygenation

Hypoxia is an important stressor for shrimp in aquatic environments that can cause respiratory abnormalities and metabolic disturbances, which result in reduced efficiency of bait conversion and retarded growth and development (Bao et al., 2018), and these effects need to be regulated by the underlying genes. The hepatopancreas is an important organ for digestion, absorption, storage, and metabolism in decapods and plays a key role in the initiation of defense responses in shrimp (Jiang et al. 2009). In this study, a total of 4315 DEGs were identified in the hepatopancreas of E. carinicauda after hypoxia and reoxygenation. KEGG analyses showed that the pathways involving these genes were significantly enriched in ribosome biogenesis in eukaryotes, apoptosis, the longevity regulating pathway, the MAPK signaling pathway, and protein processing in the ER.

Hypoxia can induce the production of ROS, and the sudden increase in O2 (reoxygenation) after hypoxia can produce higher levels of ROS (Parrilla-Taylor and Zenteno-Savín 2011). Increased production of ROS mediates oxidative damage, which might affect ER homeostasis, interfere with improper protein folding, and induce apoptosis (Yang et al. 2016). In addition, oxidative stress leads to RNA damage and the incorporation of oxidized nucleotides during RNA synthesis, which impairs protein synthesis and other RNA functions (Magagnin et al. 2007). The ribosome is essential for protein synthesis, and the ER is the site at which newly synthesized, secreted, and transmembrane proteins are folded and plays a key role in the molecular life of each cell (Dominik et al. 2019; Thomson et al. 2013; Magagnin et al. 2007). The biogenesis of ribosomes is a tightly regulated activity that is inevitably linked to other fundamental cellular processes, including growth and cell division (Thomson et al. 2013). However, eukaryotic ribosome biogenesis is a complex process that requires numerous ribosomal proteins to facilitate the modification, folding, and cleavage of rRNA and the binding of ribosomal proteins to pre-rRNA (Horn et al. 2011). The protein synthesis requirements in cells are coordinated with ribosomal biosynthetic activity, and upregulation of the levels of certain key ribosomal proteins can affect cell growth and metabolism by regulating the expression of other proteins (Antonio et al. 2015). We also found that the nucleolar proteins NOP56 and NOP1, which are important components of the Box C/D snoRNAs involved in the early processing of pre-rRNA, were differentially expressed (Andersen et al. 2018; David et al. 1991). A previous study showed that cells exposed to severe hypoxia exhibit an increased synthesis rate of specific proteins (oxygen-regulated proteins, ORPs) (Wilson and Sutherland 1989). However, protein synthesis is an energy-expensive process that consumes a large proportion of the total ATP turnover of all cells and organs. The pelagic red crab, Pleuroncodes planipes, achieves metabolic suppression in zones with minimal oxygen by reducing protein synthesis (Seibel et al. 2018). After 4 h of hypoxia treatment, the Sillago sihama liver inhibits translation and protein synthesis by regulating ribosome activity and tRNA synthesis (Tian et al. 2020). These findings might indicate that ridgetail white prawns also enhance or inhibit protein synthesis under hypoxia and reoxygenation. As a result of hypoxia, the ATP levels decrease, and cell function cannot be maintained; if the stress is not relieved, the cell will eventually die (Sun et al. 2020a, 2020b). Moreover, when energy is depleted, ribosome synthesis is promptly repressed, and nucleolar proteins are degraded by nucleophagy (autophagic degradation of the nucleolus) (Morshed et al. 2019). In addition, oxidative stress can not only alter the function of many cells, which leads to cell death, but also cause ER stress-induced apoptosis (Iurlaro & Munoz-Pinedo., 2016; Sun et al. 2020a, 2020b). The DEG ATF4 is the main transcriptional regulator of the cellular hypoxic response to the unfolded protein response (UPR) and activates genes that promote restoration of normal ER function and survival under hypoxia (Rzymski et al. 2009), and the caspase-7 gene plays primary roles as the executioner of apoptosis (Thomsen et al. 2013). These results are similar to previous findings that hypoxia can initiate apoptosis (Nuñez-Hernandez et al. 2018). Furthermore, stress resistance genes that belong to the longevity regulating pathway (such as superoxide dismutase, SOD, catalase, and CAT) play an important role in resistance to oxidative damage. For example, CAT expression in the gills of white shrimp (Litopenaeus vannamei) is 3.2- and 3-fold higher under hypoxia and reoxygenation conditions (6 and 24 h of hypoxia followed by 1 h of reoxygenation) than under normoxia conditions (Trasviña-Arenas et al. 2013). We also found similar results in our study: this gene was differentially expressed in hepatopancreas tissue under hypoxia and reoxygenation. The MAPK signaling pathway plays a critical role in responding to cellular stress and promoting cell growth and survival (Conrad et al. 2000). ROS can stimulate MAPKs to promote a variety of cellular processes, including apoptosis and the inflammatory response (Lin et al. 2018). In humans, the MAPK signaling pathways are activated during the hypoxia and reoxygenation of villous explants in vitro, and the downstream consequences of this activation is the enhancement of apoptosis (Cindrova-Davies et al. 2007). In response to hypoxia, many genes involved in the MAPK signaling pathway are expressed at higher levels in channel catfish, Ictalurus punctatus (Yang et al. 2018). In this study, abundant MAPK signaling pathway genes, including MEK1, MNK1/2, and MKK3, which directly or indirectly mediate cell apoptosis, were identified as DEGs, which suggests that the hepatopancreas of E. carinicauda might be an important tissue in the response to hypoxia and reoxygenation.

WGCNA

WGCNA is a computational method for constructing a weighted gene coexpression network based on gene expression, and this network can be further divided into coexpression modules. Genes in the same coexpression module are similarly expressed and might have closely related functions (Jia et al. 2020). WGCNA has been widely used to identify genes and modules associated with stress in fish (Huang et al. 2020; Ning and Sun 2020; Kim et al. 2020). However, few studies have investigated the complex coexpression networks in shrimp under hypoxia reoxygenation stress. In this study, 20 modules were identified by WGCNA. Interestingly, three module genes (dark grey, green, and blue) were significantly positively correlated with hypoxia (jg-3-1, 2, 3) and reoxygenation (jg-7-1, 2, and 3 and jg-14-1, 2, and 3). The results of the GO analysis revealed that the genes in the dark gray, green, and blue modules were enriched in metabolic processes. Notably, metabolic processes reportedly play a pivotal role in the hypoxia and reoxygenation responses of shrimp (Li & Brouwer., 2013). The KEGG enrichment analysis showed that the genes in the dark gray module were most significantly enriched in glycosphingolipid biosynthesis. It is worth noting that all the genes in this pathway were annotated as hexosaminidase. Hexosaminidases are exoglycosidases that can cleave N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine (GalNAc) residues from the nonreducing terminals of glycoconjugates (Venugopal et al. 2020). In insects and marine invertebrates, hexosaminidases degrade chitin along with chitinases (Venugopal et al. 2020). The role of hexosaminidase in the regulation of the GlcNAc levels is directly related to the cytoprotection of mammalian cardiac tissues against hypoxia and ischemic injury (Jayasundara et al. 2015). We hypothesized that the high expression of hexosaminidase in shrimp might play a role in degradation during exposure to hypoxia for 3 h. The KEGG analysis revealed that genes in the green module were most significantly enriched in the MAPK signaling pathway. The functions of the MAPK signaling pathway have been described previously, and the results suggest that a sudden increase in ROS under reoxygenation for 1 h might stimulate this pathway. The KEGG enrichment analysis showed that the genes in the blue module were mostly enriched in spliceosome and RNA transport. Each pre-mRNA splicing event is performed by an exceptionally dynamic ribonucleoprotein machinery known as the spliceosome (Chen and Moore 2015), and mature spliced mRNAs are then transported to their sites of function throughout the cell (Nakielny et al. 1997). We found a significantly positive correlation between the blue module and reoxygenation for 8 h, which appeared to indicate that the transcription process of shrimp was active.

The identification of hub genes is key to exploring the mechanism of hypoxia and reoxygenation tolerance. In this study, the hub genes of the dark gray, green, and blue modules were unigene0018514 (unknown gene), unigene0018395 (RREB1), and unigene0073542 (UBE1), respectively. Although the hub gene of the dark gray module is unknown, we found that both unigene0000372 (trypsin, TRY) and unigene0002163 (suppressor of tumorigenicity 14 protein, ST14), which showed high correlations, have proteolytic enzyme activity (Baird 2013; Silva et al. 2017), which suggests that these hub genes might also have proteolytic functions and provide energy under hypoxia. RREB1, which is also named HNT, FINB, and LZ321, is a zinc finger transcription factor involved in a variety of biological processes, including DNA damage repair, cell growth and proliferation, cell differentiation, fat development, fasting glucose balance, zinc transport, and transcriptional regulation (Deng et al. 2020). RREB1 has been confirmed to be a downstream effector of the MAPK signaling pathway, which can activate or repress the expression of target genes to respond to Ras signaling (Deng et al. 2020; Kent et al. 2013). This finding is consistent with our results from the KEGG enrichment analysis of the green module. Ubiquitination is often used as a signal of protein degradation, defines the fate and function of proteins, and plays multiple roles in cell survival, differentiation, and development (Carlos et al., 2012). UBE1 catalyzes the first step of the ubiquitin-dependent proteolytic pathway and plays a role in the ubiquitination process (Tokumoto et al. 2000). In addition, the degradation of hypoxia inducible factor-1 alpha (HIF-1α) protein is dependent on ubiquitination (Wang et al. 2016). We thus speculated that UBE1 expression plays a role in the degradation of HIF-1α after reoxygenation for 8 h. In summary, the three hub genes identified from the three related modules might play a key role in the response of E. carinicauda to hypoxia and reoxygenation.