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Article

Transcriptome Analysis Revealed the Immune and Metabolic Responses of Grass Carp (Ctenopharyngodon idellus) Under Acute Salinity Stress

by
Leshan Ruan
1,†,
Baocan Wei
1,†,
Yanlin Liu
1,
Rongfei Mu
1,
Huang Li
1 and
Shina Wei
1,2,*
1
College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China
2
Nansha-South China Agricultural University Fishery Research Institute, Guangzhou 511457, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Fishes 2025, 10(8), 380; https://doi.org/10.3390/fishes10080380
Submission received: 3 July 2025 / Revised: 25 July 2025 / Accepted: 28 July 2025 / Published: 5 August 2025
(This article belongs to the Special Issue Adaptation and Response of Fish to Environmental Changes)
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Versions Notes

Abstract

Freshwater salinization, an escalating global environmental stressor, poses a significant threat to freshwater biodiversity, including fish communities. This study investigates the grass carp (Ctenopharyngodon idellus), a species with the highest aquaculture output in China, to elucidate the molecular underpinnings of its physiological adaptations to fluctuating salinity gradients. We used high-throughput mRNA sequencing and differential gene expression profiling to analyze transcriptional dynamics in intestinal and kidney tissues of grass carp exposed to heterogeneous salinity stressors. Concurrent serum biochemical analyses showed salinity stress significantly increased Na+, Cl−, and osmolarity, while decreasing lactate and glucose. Salinity stress exerted a profound impact on the global transcriptomic landscape of grass carp. A substantial number of co-regulated differentially expressed genes (DEGs) in kidney and intestinal tissues were enriched in immune and metabolic pathways. Specifically, genes associated with antigen processing and presentation (e.g., cd4-1, calr3b) and apoptosis (e.g., caspase17, pik3ca) exhibited upregulated expression, whereas genes involved in gluconeogenesis/glycolysis (e.g., hk2, pck2) were downregulated. KEGG pathway enrichment analyses revealed that metabolic and cellular structural pathways were predominantly enriched in intestinal tissues, while kidney tissues showed preferential enrichment of immune and apoptotic pathways. Rigorous validation of RNA-seq data via qPCR confirmed the robustness and cross-platform consistency of the findings. This study investigated the core transcriptional and physiological mechanisms regulating grass carp’s response to salinity stress, providing a theoretical foundation for research into grass carp’s resistance to salinity stress and the development of salt-tolerant varieties.
Keywords:
grass carp; transcriptome; salinity stress; immune; metabolism
Key Contribution: This study explores molecular responses of grass carp, China’s dominant aquaculture species, to salinity stress via transcriptomic profiling of intestinal/kidney tissues and serum analysis.

Graphical Abstract

1. Introduction

Soil salinization represents a significant global challenge. Estimates from UNESCO and FAO indicate that the global area of saline-alkali land is approximately 954.38 million hectares, with China’s saline-alkali land accounting for around 99.13 million hectares [1]. This area is expanding at an alarming rate. Concurrently, sea level rise induced by global warming exacerbates the situation, as high-salinity seawater infiltrates underground aquifers and causes river backflow, resulting in the salinization of freshwater resources [2]. This dual stress poses a substantial threat to aquaculture industries that depend on freshwater environments. According to the China Fishery Yearbook 2024, the area of freshwater aquaculture in China is 5409.73 thousand hectares. Consequently, enhancing the adaptability of fish to salinity fluctuations within aquaculture systems is now a critical strategy for optimizing aquaculture productivity.
In recent years, to better promote saline aquaculture, numerous studies have focused on the mechanisms by which aquatic organisms adapt to salinity stress. Among these, the intestine and kidney being the primary metabolic and immune organs have garnered widespread attention [3]. The fish intestine responds to changes in salinity by dynamically regulating water and salinity absorption as well as ion exchange, thereby playing a crucial role in osmoregulation, which is a key adaptive mechanism for aquatic organisms in varying salinity environments [4,5]. For instance, research has shown that when killifish are transferred from brackish water to freshwater, both water content and ion absorption in their intestines increase; however, the activity of Na+/K+-ATPase decreases by the seventh day, indicating the plasticity of intestinal function [6]. Additionally, the kidney serves as one of the primary osmoregulatory organs in salinity-tolerant fish [7]. For example, the kidneys of large yellow croaker (Larimichthys crocea) enhance osmoregulation and energy metabolism through methylation and demethylation events, thereby adapting to low-salinity environments [8]. Research on tilapia (Oreochromis niloticus) also indicates that genes from the solute carrier family in the kidneys are closely related to ion transport and glucose transport, both of which are crucial for osmoregulation [9]. In summary, changes in salinity levels affect physiological and developmental characteristics and influence the expression patterns of various genes in aquatic organisms, with these expression patterns varying among different tissues.
Grass carp is an economically important species prized for rapid growth and high nutritional value. According to China Fisheries Yearbook 2024, the total output of grass carp in China was about 5.94 million tons in 2023, which was the highest output among all aquaculture species in China. However, the increase in grass carp production in 2023 was only 0.62% compared with that in 2022. Research on grass carp aquaculture in saline-alkali land will help boost grass carp production growth, and the development of new varieties of grass carp with high salt tolerance is a feasible and effective strategy. However, the response mechanism of grass carp to salinity stress still needs further study, in which the key is to gain a deeper understanding of the molecular and genetic mechanisms of salt tolerance traits in grass carp.
This study employed a transcriptome sequencing strategy to investigate the responses of the grass carp’s intestine and kidney under different salinity conditions, and also analyzed serum biochemical indicators under stress conditions. The research aims to enhance our understanding of the defensive roles of the grass carp’s intestines and kidneys when subjected to high osmotic stress, providing a theoretical foundation for the development of new salt-tolerant varieties with enhanced salinity tolerance and productivity. The findings may contribute to elucidating the tolerance mechanisms of grass carp under salinity stress conditions, thereby supporting the sustainable development of the aquaculture industry.

2. Materials and Methods

2.1. Animals and Salinity Challenges

Grass carp were from Heshan City, Guangdong Province, China, and the culture experiment was carried out in the aquaculture experimental base of Yanshan District Marine College, South China Agricultural University. All grass carp were acclimatized in freshwater for 14 days before the start of the experiment, and were fed twice a day. The water temperature was maintained at 23 °C with full automation of water circulation and mild aeration.
We calculated the median lethal concentration (LC50) of grass carp under salinity stress at different time points via non-linear fitting analysis using GraphPad Prism 9.5.0 (GraphPad Software, San Diego, CA, USA; www.graphpad.com) (Table A1), with the 96 h LC50 being 13.72 ppt. At the end of the acclimatization period, 160 healthy, active and uniformly sized (113.2 ± 9.5 g) grass carp were randomly divided into four groups, namely, control group (0 ppt), 3 ppt group, 6 ppt group, and 12 ppt group. Fish were housed in uniform tanks (1.8 × 1 × 1.5 m) and fed twice daily (7:00 and 16:00). The circulating filtration system was cleaned daily (mild aeration maintained), and the acute salinity test lasted 7 days. Daily mortality per tank was recorded. Saline water was made by mixing fresh water and sea salt (Jiangxi Yantong Technology Co., Ltd., Ji’an, China).
The use of grass carp in this study were approved by the Animal Research and Ethics South China Agricultural University (approval code: 2020g009 and date: 8 September 2020). The techniques employed in this study strictly adhered to the Guidelines for the Use of Laboratory Animals of South China Agricultural University.

2.2. Sample Collection

After the acute salinity tolerance experiment, the fish were anesthetized with 100 mg/L MS-222 (MedChem Express, Monmouth Junction, NJ, USA) before sampling and measurement. Six fish were randomly selected from treatment groups (3, 6, 12 ppt) and control group (0 ppt) for sampling. Blood was stored at 4 °C for 12 h after collection and then centrifuged at 4000 rpm for 10 min. The serum supernatant obtained was stored in a −80 °C freezer. The internal organs of the fish were dissected with sterile forceps, the intestinal contents were extruded, and the hindgut and kidney samples were collected and quickly stored in a −80 °C freezer.

2.3. Hematological Parameters Analysis

Nitric oxide (NO), blood glucose (Glu), lactic acid (LAC), Na+, K+ and Cl− detection kits were purchased from Beijing Huaying Biotechnology Institute (Beijing, China) and measured via colorimetry using a microplate reader (Wuxi Huawei Delang Instrument Co., Ltd., Wuxi, China). The osmolality of the serum samples was measured using a BS-200 freezing point osmometer (Shanghai Ida Medical Devices Co., Ltd., Shanghai, China).

2.4. Sample Preparation and RNA-Seq Analysis

Total RNA was isolated using TRIzol reagent (MedChem Express, Monmouth Junction, NJ, USA) followed by purification with the RNeasy Mini Kit (QIAGEN, Duesseldorf, Germany) according to the manufacturer’s instructions. The integrity and concentration of total RNA were assessed using an Agilent 2100 RNA Nano 6000 Assay Kit (Agilent Technologies, Santa Clara, CA, USA). Subsequently, all samples were sent to BGI (Beijing Genomics Institute, Wuhan, China) for further RNA-seq detection and analysis using the DNBSEQ-G99 sequencer (Beijing Genomics Institute, Wuhan, China).

2.5. Identification of DEGs

Differentially expressed genes (DEGs) between the freshwater control group and salinity-stressed groups were identified from the RNA-seq dataset using R scripts. To ensure reliability, only genes meeting the following criteria were retained as DEGs for subsequent analysis: an absolute log2 fold change (|log2FC|) of ≥1 (indicating at least a 2-fold difference in expression between groups) and a Q-value (false discovery rate-adjusted p-value) of ≤0.05 (indicating statistical significance after correcting for multiple testing).

2.6. KEGG Pathway Analysis

To explore the biological functions and signaling pathways associated with the differentially expressed genes (DEGs), two complementary enrichment analyses were performed using the RNA-seq transcriptomic data via BGI’s DR.TOM bioinformatics analysis system. First, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was conducted to identify which metabolic or signaling pathways were significantly enriched with DEGs. Second, gene ontology (GO) enrichment analysis was separately performed to categorize DEGs into functional terms across three main domains: biological processes (e.g., cellular activities), molecular functions (e.g., protein binding), and cellular components (e.g., organelle structures). For both analyses, statistical significance of pathway/term enrichment was determined using hypergeometric testing, with a strict threshold of p < 0.05 applied to filter meaningful results.

2.7. Real-Time Quantitative PCR

Total intestinal and kidney RNA were extracted using the RNA Isolation Kit (Foregene Co., Ltd., Chengdu, China). The ReverTra Ace qPCR RT Kit (TOYOBO, Osaka, Japan) was used for the reverse transcription of RNA. The QuantStudio 5 Real-Time Detection System (Thermo Fisher Scientific, Waltham, MA, USA) from Applied Biosystems was used for real-time fluorescent quantitative PCR (qPCR) analysis. The specific reaction system is shown in Table 1. The cycling conditions are as follows: 1 min at 95 °C for activation followed by 40 cycles for 15 s at 95 °C, 15 s at 60 °C, and 45 s at 72 °C. To enhance the reliability and reproducibility of the results, our qPCR experiments included three independent biological replicates and three technical replicates.
All qPCR primers are detailed in Appendix A Table A2. Each primer pair specifically amplified a single PCR product of the expected size with comparable melting temperatures (Tm), as validated by agarose gel electrophoresis and melting curve analysis, and were further subjected to sequencing to confirm sequence accuracy. The relative expression level of the target gene, normalized to the reference gene β-actin, was calculated using the 2−ΔΔCt method [10].

2.8. Statistical Analysis

The obtained data were subjected to one-way analysis of variance (ANOVA) using SPSS software (version 25.0, IBM, Armonk, NY, USA), followed by Duncan’s multiple comparison test when a significant difference was indicated (p < 0.05). All data were expressed as mean ± SD.

3. Results

3.1. Survival Percentage

Figure 1 shows the survival percentage of grass carp under different salinity stresses at different times. The 12 ppt group started to die at day 2, while 3 ppt and 6 ppt groups started to die at day 3 and day 4. The 12 ppt group had a lower survival rate than the other groups.
Survival   percentage   ( % ) = s u r v i v i n g   f i s h ÷ t o t a l   f i s h

3.2. Serum Biochemistry

As shown in Figure 2, the serum Cl− and Na+ contents in the 6 ppt and 12 ppt groups were significantly higher than the control and 3 ppt groups (p < 0.05). The serum LAC and Glu contents in the 3, 6 and 12 ppt groups were significantly lower than those in the control group (p < 0.05). However, serum K+ content in the 3 ppt and 6 ppt groups were significantly higher than that in the control group and 12 ppt group (p < 0.05). In addition, serum osmolarity in the 12 ppt group was significantly higher than in the other groups (p < 0.05).

3.3. Transcriptome Sequencing and Analysis of DEGs

After trimming adaptors, low-quality reads, and ambiguous bases, 158.62 Gb of clean data was generated from 12 intestinal samples. The proportion of clean reads aligned to the reference genome was 91.59%, and the Q20 and Q30 values exceeded 97.53% and 92.88%, respectively, indicating high quality of the transcriptome sequencing (Table 2). Clean reads were assembled into 32,523 unigenes (hereafter referred to as genes). The lengths ranged from 181 bp to 16,770 bp, with an N50 length of 3482 bp and an average length of 1322 bp. To demonstrate the transcriptional differences among grass carp under various salinity stresses, principal component analysis (PCA) and Venn analyses were utilized to illustrate the differences in gene transcription between the control group and the salinity-stressed groups. The PCA score plot revealed a distinct separation between the control group and the intestinal or kidney groups under salinity stress, indicating that genes associated with salinity resistance underwent significant alterations following salinity exposure (Figure 3A,B). The Venn diagram illustrated that 305 and 760 genes were commonly upregulated or downregulated in the intestine and kidney, respectively, following salinity stimulation (Figure 3C,D). When compared to the 0 ppt group, 1123 upregulated and 1223 downregulated DEGs were observed in the 3 ppt group; the 6 ppt group had 1089 DEGs upregulated and 836 DEGs downregulated; and the 12 ppt group had 1882 DEGs upregulated and 922 DEGs downregulated (Figure 4).

3.4. KEGG Pathways Enrichment Analysis of DEGs

To analyze the significantly enriched KEGG pathways associated with salinity stress, we classified the co-regulated differentially expressed genes (DEGs) in the intestine and kidney using the KEGG database (Figure 5A). As illustrated in the figure, both the intestine and kidney demonstrated notable enrichment in immune-related KEGG pathways, including antigen processing and presentation, Th1/Th2 cell differentiation, and Th17 cell differentiation. Additionally, significant enrichment was observed in metabolic and bioenergetic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism, and carbon metabolism, as well as in pathways related to cell apoptosis and cell adhesion molecules (Figure 5A). For intestinal KEGG enrichment, the predominant pathways identified are those related to metabolism, including fat digestion and absorption, vitamin digestion and absorption, the PPAR signaling pathway, cholesterol metabolism, and mineral absorption (Figure 5B). Conversely, KEGG enrichment linked to the kidneys is primarily associated with immune-related pathways, such as antigen processing and presentation, cell adhesion molecules, Th17 cell differentiation, the NOD-like receptor signaling pathway, and B cell receptor signaling pathway (Figure 5C).

3.5. Salinity Stress Involves Immune and Metabolic Pathways

First, we conducted a comprehensive analysis of the differentially expressed genes that are co-regulated in the intestine and kidney in response to salinity stress. This analysis revealed an enrichment of these genes in signaling pathways associated with cellular metabolism and immune response. As illustrated in Figure 6, the heatmap displays the expression profiles of differentially expressed genes (DEGs) that are enriched in the antigen processing and presentation, apoptosis, and gluconeogenesis/glycolysis signaling pathways. Notably, caspase17 and pik3ca, which are associated with apoptosis, as well as hk2 and pck2, involved in the gluconeogenesis/glycolysis signaling pathway, were significantly upregulated in the kidney and intestine following salinity stress. In contrast, cd4-1 and calr3b, related to antigen processing and presentation, exhibited a downregulation.
In addition to the co-regulated differentially expressed genes (DEGs), the DEGs specifically regulated by salinity stress in the intestine and kidney were also analyzed. As illustrated in Figure 7, the expression profiles of DEGs in the intestine were significantly enriched in KEGG pathways associated with cell adhesion, intestinal mineral absorption, and PPAR signaling pathways, as depicted in the heatmap. For instance, several genes, including mhc-i and mhc-ii related to cell adhesion, atp1b4a and hmox1a associated with intestinal minerals, and gapdh and ldha linked to the PPAR signaling pathway, were found to be downregulated following salinity stress. Conversely, alcamb and siglec1 in cell adhesion, as well as slc16a1 and clcn2 in the intestinal mineral pathway, exhibited upregulation. In the kidney, the specifically regulated DEGs were enriched in KEGG pathways related to immune signaling pathways, as represented in the heatmap in Figure 8. Notably, key genes such as mhc-i and cd22 in cell adhesion, golga6 and gptase1b in the B cell receptor signaling pathway, mp3k5 and gptase1.2 in the TNF signaling pathway, and ctid1 in antigen generation and presentation were all downregulated after exposure to salinity stress, while il-8 and dhx33 in the NOD-like receptor signaling pathway were upregulated.

3.6. Validation of the Reliability of Transcriptome Data by qPCR

To assess the reliability of the transcriptome data, we examined the expression changes of several randomly selected genes using qPCR. As illustrated in Figure 9, the expression levels of atp1b1a, atp1b2a, abcg2b, hsp70.3, btk, ccl19, il-20ra, and tnf-b in the intestine under salinity stress were consistent with the transcriptome results. Similarly, Figure 10 shows that the expression levels of ptgs2b, safb, hsp70.3, ciita, mhc-i, cd22, il-8, and gpt in the kidney are consistent with the transcriptomic results. Therefore, these findings confirm the reliability of the transcriptome data.

4. Discussion

This study demonstrated the transcriptomic and physiological parameters and alterations of grass carp under salinity stress, revealing the comprehensive osmoregulatory capabilities of this species. Blood indicators in fish serve as important reflections of their health status. Previous studies have demonstrated that the biochemical indicators in fish blood exhibit significant changes in response to stress [11]. Although serum biochemistry can vary among fish due to factors such as age, sex, and stage of sexual maturity, they remain important indicators of an animal’s response to environmental changes [12]. This is primarily because these indicators can reflect physiological stress in fish earlier than the outward manifestations of signs and symptoms [13,14,15]. In terms of serum biochemistry, the lactate and glucose levels in salinity-stressed groups were significantly lower than those in the freshwater group, while nitric oxide (NO) levels showed no significant changes. Typically, glucose levels increase during feeding but decrease when insulin is released for glucose storage or protein synthesis [16]. However, under stress conditions, pancreatic glucagon facilitates the breakdown of glycogen into glucose to meet the heightened energy demands necessary for alleviating stress [17,18]. In our study, heatmaps show that hk2 and pck2, which participate in the gluconeogenesis/glycolysis signaling pathway, are significantly upregulated in the kidneys and intestines after salinity stress, and the decrease in serum glucose content indicates that grass carp enhance their glucose metabolism capacity after receiving salinity stress, consuming more glucose to resist salinity stress. It has been previously noted that when fish are subjected to stressful conditions such as increased salinity, they require a greater proportion of available energy to maintain homeostasis [19]. In response to osmotic stress, freshwater fish tend to consume more saline water to counteract water loss from their tissues in a hyperosmotic environment [20,21]. When faced with changes in salinity, fish maintain their osmotic pressure and ionic balance by modifying osmoregulatory activity and plasma ion concentrations [22]. Osmoregulation involves various ionic processes, requiring fish to adjust and balance the increases and losses of serum ions, particularly Na+, Cl−, and K+. The regulation of plasma ion levels is commonly employed to evaluate the osmoregulatory capacity of fish under varying salinity conditions [23]. For instance, studies have reported a significant increase in plasma chloride ion concentration in catfish (Alexander catfish) subjected to high salinity [24]. This complex physiological process also results in the accumulation of excess sodium (Na+) and chloride (Cl−) ions in the blood, necessitating additional energy for their excretion [25]. Similarly, in our study, after saline stress, the Na+ and Cl− concentrations in the salinity-stressed fish were higher than those in the control group, leading to an increased demand for energy for ion exchange, which ultimately resulted in a decrease in blood glucose levels.
The study shows that grass carp exposed to salinity stress exhibit differentially expressed genes (DEGs) in both the kidney and intestine, with shared pathways identified through KEGG analysis. Notably, these pathways include antigen processing and presentation, glycolysis/gluconeogenesis, apoptosis, and cell adhesion molecules, among others. The findings indicate that the downregulation of eif2ak3 and ripk1 expression following salinity stress enhances endoplasmic reticulum stress, which promotes apoptosis. This activation subsequently affects metabolic activities and osmoregulation [26,27]. Furthermore, anti-apoptotic genes mcl1a and bcl2a inhibited mitochondrial apoptosis by preventing bax/bak oligomerization across all salinity treatments. In contrast, the pro-apoptotic factor BCL2L11 (BIM) facilitates the release of cytochrome c by binding to Bcl-2 family proteins, such as Mcl1, thereby establishing a dynamic antagonistic regulation that balances cell survival and damage clearance [28,29]. Transcriptomic analysis of grass carp following salinity stress revealed significant upregulation of kras and pik3ca in the salt-stressed group. These genes concurrently modulated the expression of fas, a key regulator enriched in the fatty acid metabolism pathway. Furthermore, pathway enrichment analysis indicated their involvement in the PI3K-AKT/mTORC1 signaling axis, suggesting a potential role in maintaining cellular energy homeostasis, promoting cell proliferation, and facilitating membrane repair. These findings align with prior studies demonstrating the functional impact of these genes on such physiological processes [30,31]. Furthermore, the transcriptional changes in calr3b regulate the calcium ion signaling pathway, exerting a significant impact on cellular osmoregulation and the secretion of stress hormones such as cortisol. Simultaneously, calr3b collaborates with the transcription factor nfkb to activate ion transport genes—particularly Na+/K+-ATPase—thereby enhancing the osmoregulatory capacity of key osmoregulatory organs such as the gills and kidneys [32,33]. Compared to previous studies, Aldob, Gapdh, and Eno3 make up the core glycolytic enzyme module. Under salt stress, their concerted dysregulation weakens cellular energy supply and intensifies oxidative stress. In detail, high salinity triggers upregulated aldob expression, causing excessive glycolytic flux and boosting metabolic energy consumption. At the same time, salt stress enhances Gapdh activity. This increases ATP production and, through Nadph-dependent antioxidant mechanisms, reduces ROS accumulation [34,35]. In addition, the changes in the expression levels of pck2 and aldoaa in the intestine and kidney of grass carp under salt stress are crucial to the regulation of the gluconeogenesis–glycolysis axis. As a key enzyme in gluconeogenesis, pck2 catalyzes the conversion of oxaloacetic acid (OAA) to phosphoenolpyruvate (PEP), thereby promoting gluconeogenesis. On the other hand, Alodaa, the key enzyme in glycolysis, generates energy by regulating glucose decomposition. Through these two pathways, pck2 and aldoaa jointly ensure energy homeostasis [36,37]. As critical metabolic and immune organs, the kidneys and intestines show a significant overlap in differentially expressed genes (DEGs) under salinity stress, indicating a close regulatory relationship between them.
The outcomes of this investigation suggest that the intestinal tissue of grass carp exhibits multi-level and multi-system adaptive mechanisms in response to salinity stress, which involve the dynamic reorganization of the cytoskeleton and the coordinated regulation of solute carrier family (slc) genes. Consistent with previous research, the dynamic alterations in microtubules and actin within the cytoskeleton are closely associated with the physiological and metabolic activities of fish, facilitating their survival and reproduction in environments characterized by varying salinity levels [38,39]. This dynamic adjustment of the cytoskeleton not only preserves cellular integrity by repairing damaged macromolecules, but also interacts with the osmotic sensing signal transduction network through reversible protein phosphorylation mechanisms, forming a physiological basis for rapid responses to environmental changes. As the primary organ for metabolic absorption, the intestinal tissue undergoes structural modifications, while the slc gene family plays a crucial role in osmotic balance by precisely regulating ion transport: slc34a2b mediates kidney sodium-phosphate cotransport, slc26a3.2 collaborates with nhe3 to regulate chloride/bicarbonate exchange in the gills, and slc5a1 optimizes the metabolic balance between nutrient absorption and osmotic regulation through intestinal glucose-sodium cotransport [40,41,42,43]. Notably, sodium-potassium ATPase (Atp1a1/Atp1b4) enhances sodium excretion in the gills by upregulating β-subunit expression [44,45], while the clcn2 chloride channel dynamically regulates its expression levels to maintain transmembrane chloride gradients [46,47]. RNA-Seq analysis revealed that the differential expression of 36 slc genes and nramp metal transporters forms a molecular network underlying salinity adaptation, with their expression patterns significantly correlating with the ecotypes of fish, including euryhaline species [48].
The immune system of fish primarily comprises innate immunity and acquired immunity. Innate immunity serves as the first line of defense against pathogens, primarily through mechanisms such as physical barriers and cytokines that prevent pathogen invasion. Acquired immunity involves the production of specific immune cells and antibodies, with the kidneys of fish playing a crucial role in this process. The major histocompatibility complex (Mhc) family operates in conjunction with multidimensional molecular mechanisms to form a complex immune system that enhances the pathogen clearance efficiency of cytotoxic T cells [49,50]. Mhc class ii molecules play a central regulatory role in the presentation of exogenous antigens, while Mhc class i molecules regulate cellular immune responses through the endogenous antigen presentation pathway. Their expression levels were significantly influenced by fluctuations in salinity. High-salinity environments may inhibit their function by disrupting the endoplasmic reticulum stress signaling pathway, thereby reducing immune function. In such conditions, fish may experience dehydration stress, which can adversely affect immune system functionality. Studies have demonstrated that, under high-salinity conditions, the expression of Mhc class ii molecules in certain fish species may increase to enhance immune response capabilities and mitigate infection risks associated with environmental stress. This study found that some Mhc class ii molecules also exhibit enhanced expression under low salinity stimulation, yielding similar results [39,51]. Salinity changes significantly affect the efficiency of antigen delivery by modulating the physicochemical properties of the mucus layer and the osmotic pressure of epithelial cells. The enhanced uptake of antigens under high-salinity conditions may be attributed to altered cell membrane fluidity and the activation of phagocytosis [52]. The cell adhesion system plays a crucial role in maintaining tissue barrier function through integrin-cadherin complex-mediated mechanotransduction. Integrin a1b1 and B-cadherin collaboratively regulate the dynamic reorganization of the cytoskeleton, driving the polar distribution of Na+/K+-ATPase via the Rho GTPase signaling axis; this process may involve stress fiber remodeling and phosphorylation modifications of tight junction proteins [53,54]. At the epigenetic regulation level, Prmt1-mediated protein methylation may influence the expression of osmotic pressure-related genes by affecting transcription factor activity [55]. Cd22, as an inhibitory receptor on B cells, potentially plays a role in maintaining immune homeostasis under salinity stress through its signaling regulatory network. Under long-term salinity stress, such as in freshwater fish like Oreochromis aureus, the immune pressure on the spleen increases, which may elevate the risk of disease in these fish [56].

5. Conclusions

This study analyzed differential gene expression in the intestine and kidney of grass carp under salinity stress through transcriptome analysis, revealing that metabolic processes, apoptosis, and immune functions are crucial for salinity adaptation. The intestine primarily regulates ion balance through metabolic pathways to maintain homeostasis, while the kidney exhibits more pronounced changes in immune functions, such as rapid adjustments in antigen presentation and cell adhesion. These findings suggest that fish achieve physiological homeostasis through modular collaboration of genes, enhancing their stress resistance. This provides key molecular targets for elucidating environmental adaptation mechanisms and breeding stress-resistant varieties, laying a theoretical foundation for the development of salt-tolerant grass carp. Further research is needed to elucidate the signaling pathways involved in salinity stress resistance.

Author Contributions

Conceptualization, L.R. and B.W.; formal analysis, L.R. and B.W.; investigation, L.R., B.W. and R.M.; methodology, Y.L. and H.L.; software, Y.L. and H.L.; data curation, L.R., Y.L. and H.L.; validation, Y.L. and R.M.; visualization, H.L. and R.M.; writing—original draft preparation, L.R. and B.W.; writing—review and editing, L.R., B.W. and S.W.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W.; resources, S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Biological Breeding-National Science and Technology Major Project (2023ZD04065).

Institutional Review Board Statement

The Grass Carp used in this study were approved by the Animal Research and Ethics South China Agricultural University (approval code: 2020g009 and approval date: 8 September 2022). The techniques employed in this study strictly adhered to the Guidelines for the Use of Laboratory Animals of South China Agricultural University.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

There are no conflicts of interest.

Appendix A

Table A1. Cumulative mortality and median lethal salinity of grass carp under different salinity stresses within 96 h.
Table A1. Cumulative mortality and median lethal salinity of grass carp under different salinity stresses within 96 h.
Time (h)Salinity (ppt)Cumulative DeathCumulative Mortality %50% Lethal Salinity
(95% Confidence Interval)
24300%16.92 (13.64–21.44)
600%
12310%
182067%
242273%
48300%15.85 (13.92–17.94)
600%
12413%
182273%
242583%
72300%15.43 (14.35–16.54)
600%
12413%
182377%
242790%
96300%13.72 (13.28–15.16)
600%
12827%
182790%
242790%
Table A2. Primer sequence for quantitative reverse transcription–polymerase chain reaction used in this study.
Table A2. Primer sequence for quantitative reverse transcription–polymerase chain reaction used in this study.
Target GenePrimer SequenceSize (bp)Amplification Efficiency (%)Accession No.
β-ActinF 1: CCAAAGCCAACAGGGAAAAG
R 2: GCACAGTGTGGGTGACACCA		15699.7XM_051886219.1
ATP1B1a F: GGCTTATCACACACCCCAC
R: GCTCTCCAGTTCTCCACGA		19596.8XM_051897962.1
ATP1B2aF: CAGTTGGGGTCTGATTCTGTTGT
R: TTATAGGGTGCGAGGAAGTTGTC		24997.2XM_051880029.1
ABCG2bF: AGAGTCTGGGTTATGAGTGC
R: CTGGTAGAAAAACGAGGTGA		23698.7XM_051894188.1
BTKF: CTCCTGACCGACTCTACCCTT
R: CGATTCTTGGAACCTCTTTTG		26599.6XM_051860457.1
hsp70.3F: AGATGAGGCAGTGGCTTATGGT
R: GTGAAGGTCTGGGTCTGTTTGG		10299.6XM_051886299.1
CCL19F: AAGCGGACTTAGCATTGGAC
R: ATAACAGCATCACGGGGACA		11898.5XM_051892863.1
IL-20raF: AGCCCGTACAGGACACAAG
R: GACCCAACAAGCCCATCAG		15798.7XM_051876329.1
TNF-βF: CGGCGTAGGGGGAGTTTATC
R: TTTCCGTGCTCTGGTGGTGT		21099.6XM_051862354.1
PTGS2BF: CAGATAGCTGGAAGGGTGGC
R: AAAGCGTTTCCTGTAGGCGT		12699.7XM_051874699.1
SAFBF: GTGACAACAGGGACTGGGAG
R: ATGTATCCACCTCGGCTTGC		12799.3XM_051908317.1
CIITAF: GTCAGCTGCTGTCCAGTCAT
R: AGCTGCTCTTCAGCTTCACC		17699.2XM_051888588.1
GPTF: CGCCAACGTGAAGAAGGTTG
R: ACACAGTGCCATCACCTGTC		18797.1XM_051914358.1
MHC-IF: AACCTGTCAAGCCCTCTGAT
R: AGTTCTCACAGTCACGTTGTG		13396.9XM_051873036.1
CD22F: GCACTGAAAGTCGAAGTGCC
R: TGACAGTGACTGCAGGTGTC		16398.3XM_051865084.1
IL-8F: ACAACCCTCAATGCATTCCCA
R: CTCGGTTTTGCGACAGTGTG		14498.9XM_051892690.1
1 F: Forward primer (5′-3′). 2 R: Reverse primer (5′-3′).

References

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Figure 1. The survival curve of grass carp under different salinity stress in 7 days.
Figure 1. The survival curve of grass carp under different salinity stress in 7 days.
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Figure 2. The differences in ion levels and osmolality in the serum of grass carp between the control group and the salinity-treated groups. The data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01.
Figure 2. The differences in ion levels and osmolality in the serum of grass carp between the control group and the salinity-treated groups. The data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01.
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Figure 3. Transcriptomic analysis of the effects of salinity stimulation on the intestine and kidney of grass carp. (A,B) Principal component analysis of RNA-Seq data from different sample groups. (C,D) Venn diagrams showing the number of differentially expressed genes in the abovementioned groups.
Figure 3. Transcriptomic analysis of the effects of salinity stimulation on the intestine and kidney of grass carp. (A,B) Principal component analysis of RNA-Seq data from different sample groups. (C,D) Venn diagrams showing the number of differentially expressed genes in the abovementioned groups.
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Figure 4. Each dot in the volcano plot represents an individual gene: blue dots represent downregulated DEGs (lower), red dots represent upregulated DEGs (upper), and gray dots represent genes that are not differentially expressed (nosig). (A–C) Volcano plots of the control and salinity groups’ intestinal gene libraries show differences in gene expression in terms of fold change (FC) and significance (Padjust), and (D–F) volcano plots of the control and salinity groups’ kidney gene libraries show differences in gene expression in terms of fold change (FC) and significance (Padjust). (A,D) stands for 0 ppt vs. 3 ppt, (B,E) stands for 0 ppt vs. 6 ppt, (C,F) stands for 0 ppt vs. 12 ppt.
Figure 4. Each dot in the volcano plot represents an individual gene: blue dots represent downregulated DEGs (lower), red dots represent upregulated DEGs (upper), and gray dots represent genes that are not differentially expressed (nosig). (A–C) Volcano plots of the control and salinity groups’ intestinal gene libraries show differences in gene expression in terms of fold change (FC) and significance (Padjust), and (D–F) volcano plots of the control and salinity groups’ kidney gene libraries show differences in gene expression in terms of fold change (FC) and significance (Padjust). (A,D) stands for 0 ppt vs. 3 ppt, (B,E) stands for 0 ppt vs. 6 ppt, (C,F) stands for 0 ppt vs. 12 ppt.
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Figure 5. KEGG enrichment analysis of differentially expressed genes (DEGs) in transcriptome data. (A) KEGG analysis of co-regulated DEGs in the intestine and kidney after salinity stress. (B,C) KEGG analysis of specifically regulated DEGs in the intestine and kidney, respectively, after salinity stress.
Figure 5. KEGG enrichment analysis of differentially expressed genes (DEGs) in transcriptome data. (A) KEGG analysis of co-regulated DEGs in the intestine and kidney after salinity stress. (B,C) KEGG analysis of specifically regulated DEGs in the intestine and kidney, respectively, after salinity stress.
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Figure 6. The heatmap of co-regulated differentially expressed genes in the intestine and kidney after salinity stress. The expression profiles of co-regulated differentially expressed genes in antigen processing and presentation (A), glycolysis/gluconeogenesis (B), and apoptosis (C) are displayed in heatmap format. Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
Figure 6. The heatmap of co-regulated differentially expressed genes in the intestine and kidney after salinity stress. The expression profiles of co-regulated differentially expressed genes in antigen processing and presentation (A), glycolysis/gluconeogenesis (B), and apoptosis (C) are displayed in heatmap format. Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
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Figure 7. The heatmap of the expression patterns of differentially expressed genes (DEGs) specifically regulated in the intestine after salinity stress. Expression profiles of DEGs in cell adhesion molecules (A), PPAR signaling pathway (B), and mineral absorption (C). Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
Figure 7. The heatmap of the expression patterns of differentially expressed genes (DEGs) specifically regulated in the intestine after salinity stress. Expression profiles of DEGs in cell adhesion molecules (A), PPAR signaling pathway (B), and mineral absorption (C). Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
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Figure 8. The heatmap of the expression patterns of differentially expressed genes (DEGs) specifically regulated in the kidney after salinity stress. Expression profiles of DEGs in the TNF signaling pathway (A), antigen processing and presentation (B), B cell receptor signaling pathway (C), cell adhesion molecules (D), and NOD-like receptor signaling pathway (E). Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
Figure 8. The heatmap of the expression patterns of differentially expressed genes (DEGs) specifically regulated in the kidney after salinity stress. Expression profiles of DEGs in the TNF signaling pathway (A), antigen processing and presentation (B), B cell receptor signaling pathway (C), cell adhesion molecules (D), and NOD-like receptor signaling pathway (E). Groups A, B, C, and D represent 0, 3, 6, and 12 ppt groups, respectively.
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Figure 9. The expression of selected differentially expressed genes in qRT-PCR and RNA-seq validated the intestinal transcriptome results of grass carp exposed to 0 ppt, 3 ppt, 6 ppt, and 12 ppt salinity. qRT-PCR expression levels were normalized by the change in β-actin values, and the data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01, and *** means p < 0.001.
Figure 9. The expression of selected differentially expressed genes in qRT-PCR and RNA-seq validated the intestinal transcriptome results of grass carp exposed to 0 ppt, 3 ppt, 6 ppt, and 12 ppt salinity. qRT-PCR expression levels were normalized by the change in β-actin values, and the data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01, and *** means p < 0.001.
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Figure 10. The expression of selected differentially expressed genes in qRT-PCR and RNA-seq validated the kidney transcriptome results of grass carp exposed to 0 ppt, 3 ppt, 6 ppt, and 12 ppt salinity. qRT-PCR expression levels were normalized by the change in β-actin values, and the data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01, *** means p < 0.001, and ns means no significant difference.
Figure 10. The expression of selected differentially expressed genes in qRT-PCR and RNA-seq validated the kidney transcriptome results of grass carp exposed to 0 ppt, 3 ppt, 6 ppt, and 12 ppt salinity. qRT-PCR expression levels were normalized by the change in β-actin values, and the data are shown as mean ± SD (n = 3). Differences in the groups labeled * means p < 0.05, ** means p < 0.01, *** means p < 0.001, and ns means no significant difference.
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Table 1. qPCR reaction system.
Table 1. qPCR reaction system.
ComponentVolume (μL)
2×Polarsignal®qPCR mix with low rox5
Forward primer 10.3
Reverse primer 10.3
cDNA 21
ddH2O3.4
Total10
1 primer concentration: 10 μM; 2 cDNA concentration: 200 ng/μL.
Table 2. Summary of transcriptome sequencing data and qualify filtering.
Table 2. Summary of transcriptome sequencing data and qualify filtering.
SamplesRaw Reads (M)Clean Reads (M)Clean Data (bpG)Clean Reads Ratio (%)Q20
(%)		Q30
(%)		GC Content
(%)		Total Mapping (%)Uniquely Mapping (%)
I0A45.1244.246.6498.0597.2591.9347.6895.0990.73
I0B44.844.036.698.2897.2491.9848.3195.0590.69
I0C45.4444.086.6197.0197.1991.7147.3295.1490.78
I3A45.1244.36.6598.1897.2391.8148.6595.790.3
I3B45.1244.236.6398.0397.4792.5548.2495.6290.26
I3C45.1244.266.6498.0997.3192.0548.5895.7690.26
I6A45.1244.076.6197.6797.5392.7847.1295.9291.59
I6B45.1244.266.6498.0997.4892.6647.4095.8891.54
I6C43.0342.086.3197.7997.5192.5647.6095.8791.53
I12A45.1244.16.6297.7497.3392.1747.1296.7492.23
I12B44.844.016.698.2497.3592.1847.4096.6192.17
I12C45.1244.256.6498.0797.4892.5647.6096.8392.34
K0A45.1244.26.6397.9697.0691.3848.6594.6690.6
K0B44.844.066.6198.3597.1991.8148.2494.7190.63
K0C44.844.116.6298.4697.0691.4748.5894.8990.78
K3A45.4444.246.6497.3697.3992.3947.1295.190.97
K3B44.8943.936.5997.8696.9490.847.4095.0290.91
K3C45.1244.086.6197.797.2692.0748.6595.1690.99
K6A4844.216.6392.197.4492.8748.2495.890.59
K6B48.0844.126.6291.7697.4592.8848.5895.8490.55
K6C47.0444.126.620.025397.3492.4647.1295.9490.75
K12A45.1244.156.620.025197.291.8847.4096.4592.37
K12B46.0844.126.620.025197.0791.5648.5896.2792.21
K12C45.7644.136.620.025397.0391.3247.1296.4792.4
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Ruan, L.; Wei, B.; Liu, Y.; Mu, R.; Li, H.; Wei, S. Transcriptome Analysis Revealed the Immune and Metabolic Responses of Grass Carp (Ctenopharyngodon idellus) Under Acute Salinity Stress. Fishes 2025, 10, 380. https://doi.org/10.3390/fishes10080380

AMA Style

Ruan L, Wei B, Liu Y, Mu R, Li H, Wei S. Transcriptome Analysis Revealed the Immune and Metabolic Responses of Grass Carp (Ctenopharyngodon idellus) Under Acute Salinity Stress. Fishes. 2025; 10(8):380. https://doi.org/10.3390/fishes10080380

Chicago/Turabian Style

Ruan, Leshan, Baocan Wei, Yanlin Liu, Rongfei Mu, Huang Li, and Shina Wei. 2025. "Transcriptome Analysis Revealed the Immune and Metabolic Responses of Grass Carp (Ctenopharyngodon idellus) Under Acute Salinity Stress" Fishes 10, no. 8: 380. https://doi.org/10.3390/fishes10080380

APA Style

Ruan, L., Wei, B., Liu, Y., Mu, R., Li, H., & Wei, S. (2025). Transcriptome Analysis Revealed the Immune and Metabolic Responses of Grass Carp (Ctenopharyngodon idellus) Under Acute Salinity Stress. Fishes, 10(8), 380. https://doi.org/10.3390/fishes10080380

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