Impact of Cold Stress on Hepatopancreas Transcriptomic and Metabolomic in Red Swamp Crayfish 
Due to the significant economic value of red swamp crayfish (RSC), Procambarus clarkii , its global yield has sharply increased from 9.9 thousand tons in 2000 to nearly 2.5 million tons in 2020 . In China, RSC farming is primarily concentrated in the provinces along the middle and lower reaches of the Yangtze River , as the optimal temperature for its growth and development ranges between 21 and 30 °C . Low water temperatures, especially during the overwintering period, are known to be one of the challenges hindering the development of the crayfish aquaculture industry in northern China, particularly in northeastern China. Water temperature is a crucial environmental factor for aquatic animals. Cold stress can affect various aspects of crustaceans and other aquatic ectotherms, including their behavior, growth and development, reproduction, metabolism, and immunity . Crustaceans exposed to low temperatures may exhibit decreased activity and a reduction or cessation of feeding behavior. They may become immobile and can only survive for a few days . Furthermore, low temperatures reduce the speed and quality of embryonic and gonad development, as well as overall growth . Additionally, cold stress induces redox imbalance , lipid peroxidation, DNA damage, and alterations in osmolality . At the molecular level, the expression of genes involved in the unfolded protein response (UPR) pathway, immunity, and apoptosis, is significantly changed under cold stress . Omics technologies (e.g., transcriptomics, metabolomics, and proteomics) provide innovative approaches for exploring biological systems by screening and analyzing large datasets to characterize an organism under a specific condition . To date, these technologies have been employed in several studies investigating the impact of cold stress on different crustaceans, including red claw crayfish ( Cherax quadricarinatu ), giant freshwater prawn ( Macrobrachium rosenbergii ), and Pacific white shrimp ( Litopenaeus vannamei ) . Low-temperature stress could significantly downregulate the expression of numerous immune-related genes in the hepatopancreas and stimulate serine/threonine protein kinase activity , immunity, redox, and lipid metabolism pathways . However, knowledge regarding the mechanisms underlying responses of RSCs to cold stress, particularly at the omics level, remains limited and requires further investigation. The hepatopancreas of crustaceans is a vital organ for coping with adverse stresses, as it plays a significant role in digestion, nutrient absorption and storage, immune defense, and physiological regulation . Therefore, the hepatopancreas is one of the most important tissues for studying stress responses in crustacean species . The primary objective of this study was to identify the key genes and metabolites involved in the response to cold stress and to elucidate molecular mechanisms underlying this response in RSCs by investigating the transcriptome and metabolome of the hepatopancreas in RSCs under cold stress treatment. This study could expand the current knowledge of crustacean responses to cold stress and provide valuable insights for enhancing its cold tolerance in aquaculture practices. 

2.1. Transcriptome Responses Under Cold Stress The sequencing statistics for the six libraries are shown in . The number of raw reads ranged from 45.88 Mb to 57.69 Mb. The clean reads ranged from 45.54 Mb to 57.27 Mb. The values of Q20 and Q30 were above 98.57% and 95.61%, respectively, and the GC content exceeded 43.35% for all samples. The number of differentially expressed genes (DEGs) between T4 and T22 was 787 (460 up and 327 down), as shown in and . GO and KEGG enrichment analyses were performed on DEGs. As for GO enrichment, most DEGs were enriched in a cellular anatomical entity (268) in cellular component, binding (204), and catalytic activity (188) in molecular function, as well as cellular process (131) and metabolic process (86) in biological process ( A). For KEGG enrichment, most DEGs were enriched in signal transduction (54) in environment information processing, transport, and catabolism (37) and cell growth and death (25) in cellular process, endocrine system (36), and immune system (25) in organismal systems, as well as lipid metabolism (22) in metabolism ( B). Pathway enrichment analysis showed that DEGs were significantly enriched in 12 pathways ( p < 0.05), as shown in . They included (1) C-type lectin receptor signaling pathway, (2) fructose and mannose metabolism, (3) valine, leucine, and isoleucine biosynthesis, (4) cell adhesion molecules, (5) caffeine metabolism, (6) sphingolipid metabolism, (7) ferroptosis, (8) arachidonic acid metabolism, (9) nucleotide metabolism, (10) glycolysis/gluconeogenesis, (11) ECM-receptor interaction, and (12) ovarian steroidogenesis. To validate the reliability of the sequencing analysis, quantitative PCR (qPCR) was conducted on 12 DEGs using the 18S rRNA gene as a reference gene. A consistent expression pattern was observed by comparing the qPCR results and the RNA-Seq data, confirming the reliability of RNA-Seq data . 2.2. Metabolome Responses Under Cold Stress Metabolome analysis was performed to investigate the metabolic alteration in the hepatopancreas of RSC under cold stress. Based on the criteria of VIP > 1.5 and p < 0.05, 198 metabolites were identified as differentially expressed metabolites (DEMs) between T4 and T22 , including 27 that were increased and 171 that were decreased ( A). The OPLS-DA analysis based on DEMs demonstrated a strong separation between T4 and T22 (R 2 = 0.999, Q 2 = 0.916), as shown in B. C shows the heatmap of the top 50 DEMs based on their abundance, where greater similarity within groups was observed between groups. HMDB classification revealed that the DEMs were mainly classed into lipids and lipid-like molecules (51, 30.18%), organic acids and derivatives (47, 27.81%), and organoheterocyclic compounds (22, 13.02%; A). KEGG pathway annotation revealed that DEMs were enriched in four pathways ( p < 0.05), including (1) histidine metabolism, (2) glycerophospholipid metabolism, (3) ABC transporters, and (4) glycine, serine, and threonine metabolism ( B). 2.3. Correlation Between Transcriptome and Metabolome The DEGs and DEMs in the significantly enriched KEGG pathways were considered significant differentially expressed genes (SDEGs) and significant differential metabolites (SDEMs), respectively, resulting in a total of 56 SDEGs and 14 SDEMs. The key SDEGs and SDEMs are shown in . The correlation analysis between SDEGs and SDEMs is shown in . The SDEMs were divided into Clusters A and B. Cluster A included betaine, lactose, and maltotriose enriched in the pathway ABC transporter. Cluster B included the seven phospholipid SDEMs and four other SDEMs mainly enriched in pathways including histidine metabolism, glycerophospholipid metabolism, and glycine, serine, and threonine metabolism. The SDEGs significantly correlated with the above SDEMs were mainly concentrated in two clusters (I and II, ). Cluster I mainly included SDEGs enriched in KEGG pathways of fructose and mannose metabolism and glycolysis/gluconeogenesis (three triosephosphate isomerase-like genes) and pathways of cell adhesion molecules and ECM-receptor interaction (integrin beta pat-3-like, integrin alpha-8-like, and neurexin-4-like). Cluster II mainly included DEGs enriched in the C-type lectin receptor signaling pathway (three C-type lectin genes and lectin BRA-3 -like). Briefly, SDEGs in Cluster I mainly positively correlated with SDEMs in Cluster A but negatively correlated with Cluster B, while SDEGs in Cluster II exhibited the opposite pattern. 

The sequencing statistics for the six libraries are shown in . The number of raw reads ranged from 45.88 Mb to 57.69 Mb. The clean reads ranged from 45.54 Mb to 57.27 Mb. The values of Q20 and Q30 were above 98.57% and 95.61%, respectively, and the GC content exceeded 43.35% for all samples. The number of differentially expressed genes (DEGs) between T4 and T22 was 787 (460 up and 327 down), as shown in and . GO and KEGG enrichment analyses were performed on DEGs. As for GO enrichment, most DEGs were enriched in a cellular anatomical entity (268) in cellular component, binding (204), and catalytic activity (188) in molecular function, as well as cellular process (131) and metabolic process (86) in biological process ( A). For KEGG enrichment, most DEGs were enriched in signal transduction (54) in environment information processing, transport, and catabolism (37) and cell growth and death (25) in cellular process, endocrine system (36), and immune system (25) in organismal systems, as well as lipid metabolism (22) in metabolism ( B). Pathway enrichment analysis showed that DEGs were significantly enriched in 12 pathways ( p < 0.05), as shown in . They included (1) C-type lectin receptor signaling pathway, (2) fructose and mannose metabolism, (3) valine, leucine, and isoleucine biosynthesis, (4) cell adhesion molecules, (5) caffeine metabolism, (6) sphingolipid metabolism, (7) ferroptosis, (8) arachidonic acid metabolism, (9) nucleotide metabolism, (10) glycolysis/gluconeogenesis, (11) ECM-receptor interaction, and (12) ovarian steroidogenesis. To validate the reliability of the sequencing analysis, quantitative PCR (qPCR) was conducted on 12 DEGs using the 18S rRNA gene as a reference gene. A consistent expression pattern was observed by comparing the qPCR results and the RNA-Seq data, confirming the reliability of RNA-Seq data . 

Metabolome analysis was performed to investigate the metabolic alteration in the hepatopancreas of RSC under cold stress. Based on the criteria of VIP > 1.5 and p < 0.05, 198 metabolites were identified as differentially expressed metabolites (DEMs) between T4 and T22 , including 27 that were increased and 171 that were decreased ( A). The OPLS-DA analysis based on DEMs demonstrated a strong separation between T4 and T22 (R 2 = 0.999, Q 2 = 0.916), as shown in B. C shows the heatmap of the top 50 DEMs based on their abundance, where greater similarity within groups was observed between groups. HMDB classification revealed that the DEMs were mainly classed into lipids and lipid-like molecules (51, 30.18%), organic acids and derivatives (47, 27.81%), and organoheterocyclic compounds (22, 13.02%; A). KEGG pathway annotation revealed that DEMs were enriched in four pathways ( p < 0.05), including (1) histidine metabolism, (2) glycerophospholipid metabolism, (3) ABC transporters, and (4) glycine, serine, and threonine metabolism ( B). 

The DEGs and DEMs in the significantly enriched KEGG pathways were considered significant differentially expressed genes (SDEGs) and significant differential metabolites (SDEMs), respectively, resulting in a total of 56 SDEGs and 14 SDEMs. The key SDEGs and SDEMs are shown in . The correlation analysis between SDEGs and SDEMs is shown in . The SDEMs were divided into Clusters A and B. Cluster A included betaine, lactose, and maltotriose enriched in the pathway ABC transporter. Cluster B included the seven phospholipid SDEMs and four other SDEMs mainly enriched in pathways including histidine metabolism, glycerophospholipid metabolism, and glycine, serine, and threonine metabolism. The SDEGs significantly correlated with the above SDEMs were mainly concentrated in two clusters (I and II, ). Cluster I mainly included SDEGs enriched in KEGG pathways of fructose and mannose metabolism and glycolysis/gluconeogenesis (three triosephosphate isomerase-like genes) and pathways of cell adhesion molecules and ECM-receptor interaction (integrin beta pat-3-like, integrin alpha-8-like, and neurexin-4-like). Cluster II mainly included DEGs enriched in the C-type lectin receptor signaling pathway (three C-type lectin genes and lectin BRA-3 -like). Briefly, SDEGs in Cluster I mainly positively correlated with SDEMs in Cluster A but negatively correlated with Cluster B, while SDEGs in Cluster II exhibited the opposite pattern. 

Water temperature is a crucial abiotic factor for crustaceans . Therefore, understanding the molecular regulatory mechanisms of cold stress response in RSCs and analyzing their adaptative capacity is essential. Although RSCs are considered a warm-water species, they have a certain degree of tolerance to low temperatures . In this study, RSCs were exposed to cold stress at 4 °C for 96 h, and transcriptomic and metabolomic analyses were conducted using hepatopancreas tissues to evaluate the effects of cold stress. A total of 787 DEGs and 198 DEMs were identified between the cold stress group (T4) and the control group without cold stimulation (T22). These DEGs and DEMs were significantly enriched in pathways related to metabolism, immunity, and other functions. 3.1. Cold Stress-Induced Alterations to Pathways Related to Metabolism Low temperatures can directly affect the metabolism of crustaceans . For example, cold stress altered genes and metabolites associated with carbohydrates, lipids, amino acids, and nucleotide metabolism in the Pacific white shrimp . In this study, DEGs and DEMs between T4 and T22 were also significantly enriched in pathways related to carbohydrate metabolism. In the pathways of glycolysis/gluconeogenesis and fructose and mannose metabolism, fructose-1,6-bisphosphatase (FBP), and fructose-1,6-bisphosphate aldose (FBA) play key roles in gluconeogenesis . In this study, their expressions were significantly downregulated in T4, indicating that the gluconeogenesis process was hindered in RSCs under cold stress. This result is consistent with similar studies on Pacific white shrimp . Since gluconeogenesis is an energy-consuming process, ectothermic organisms are likely to reduce non-essential energy expenditure and prioritize survival at low temperatures . Cold stress had a significant impact on lipid metabolism in this study. Sphingolipid metabolism was changed by cold stress in RSCs, which is consistent with the study in red claw crayfish . Herein, the expression of sphingolipid 4-desaturase ( DES1 ) was significantly upregulated in T4 compared to T22. Studies on red claw crayfish under low-temperature conditions have also identified the overexpression of DES1 and Δ6 desaturase . Similarly, increased desaturase activity has been reported in milkfish ( Chanos chanos ) and Nile tilapia ( Oreochromis niloticus ) exposed to low temperatures . The overexpression of these desaturases would facilitate the conversion of saturated fatty acids (SFAs) to unsaturated fatty acids (UFAs). Since increased UFAs help maintain cell membrane fluidity at low temperatures , this overexpression supports the normal biological functions of RSCs under cold stress. DEGs were also significantly enriched in the arachidonic acid metabolism pathway (enrichment score = 2.587), which has been reported in kuruma shrimp ( Marsupenaeus Japonicus ) and Pacific white shrimp under cold stress . In this pathway, T4 was characterized by the downregulation of cytochrome P450 family 2 genes ( CYP2 ), which was consistent with the study of Pacific white shrimp . In addition to metabolizing arachidonic acid to epoxyeicosatrienoic acids , CYP2 plays a critical role in defending against environmental stress and pathogen infection in aquatic animals . In studies on mud crabs ( Scylla paramamosain ), CYP2 expression was significantly altered by Vibrio parahaemolyticus infection and ammonia stress, and interference with CYP2 led to significantly higher malondialdehyde (MDA) content and increased mortality rate . DEMs from metabolomic analysis were also enriched in the glycerophospholipid metabolism pathway, which is consistent with the study of giant freshwater prawns . Glycerophospholipids are important biological fuels for energy supply . Because crayfish tend to exhibit a fasting response when exposed to low temperatures , we discontinued feeding the RSCs in both T4 and T22 groups during the experiment. The reduction in these glycerophospholipids in T4 indicated that RSCs consumed them to increase energy production and maintain physiological homeostasis under cold stress. Cold stress also altered amino acid metabolism in RSCs. In the DEMs, many tripeptides, mainly including branched-chain amino acids (BCAAs: valine, leucine, and isoleucine) were significantly decreased in the T4 group ( C). A similar decrease in leucine and isoleucine was also detected in the metabolites of kuruma shrimp under cold stress . Meanwhile, overexpression of branched-chain aminotransferase ( BCAT ) in valine, leucine, and isoleucine biosynthesis pathways was detected . BCAT2 , which plays an essential role in the biosynthesis and degradation of branched-chain amino acids , has been reported to be upregulated in the hepatopancreas of Pacific white shrimp under cold stress . We speculate that cold stress induced the decrease in the branched-chain amino acids, and stimulated the overexpression of BCAT2 . DEMs between T4 and T22 were also significantly enriched in KEGG pathways of histidine metabolism and glycine, serine, and threonine metabolism. According to the previous literature, metabolites in the histidine metabolism pathway were also altered in mrigal carp ( Cirrhinus mrigala ) and European seabass ( Dicentrarchus labrax ) . It has been speculated that histidine metabolism may play critical roles in regulating adversity development, such as compensatory growth . In this study, DEGs between T4 and T22 were significantly enriched in the nucleotide metabolism pathway, which has been reported in many fish and crustaceans under cold stress . Nucleotides are of great importance to organisms, as they are involved in many biological processes, including DNA and RNA synthesis, energy storage, and metabolism, and other metabolic regulators . In this study, the expression of xanthine dehydrogenase/oxidase-like ( XDH ), which is involved in hypoxanthine and xanthine degradation, was reduced when RSCs were under cold stress. Other genes, including GMP reductase 2-like ( GMPR ), 5′-nucleotidase ( NT5E ), adenosine kinase ( ADK ), and ectonucleoside triphosphate diphosphohydrolase 5/6 ( ENTPD5_6 ), involved in purine synthesis, degradation, and salvage were upregulated in T4 group. The results indicate that cold stress altered the nucleotide metabolism in RSCs. 3.2. Cold Stress-Induced Alterations to Immune System In addition to metabolism-related pathways, cold stress also impacted immune system pathways in RSCs. Some C-type lectin receptor genes, including DC-SIGN , macrophage C-type lectin ( MCL ), and macrophage inducible C-type lectin ( Mincle ), were significantly downregulated in T4 compared with T22. In the previous literature, the downregulation of the C-type lectin gene has also been found in Pacific white shrimp and Nile tilapia exposed to low temperatures . C-type lectin plays an immunomodulatory role in the innate immunity of crustaceans, including cell adhesion, pathogen recognition, and bacterial clearance . Zhang et al. suggested that the overexpression of a C-type lectin in RSCs could significantly enhance the resistance to the bacteria Vibrio anguillarum . In this study, the downregulation of C-type lectin receptor genes and other immune-related genes, such as CYP2 mentioned previously, indicates that cold stress has a negative impact on immune competence and overall resistance to pathogens. Therefore, aquaculture management strategies, such as water treatment, strict quarantine measures, and clean feed, should be applied to prevent disease-causing agents. 3.3. Cold Stress-Induced Alterations to Other Pathways In the ECM-receptor interaction pathway, RSCs in T4 were characterized by the downregulation of collagen genes ( COL2A , COL4A , and COL9A ). Additionally, collagen abundance in T4 was significantly lower compared to T22, as shown by the metabolomic analysis. The reduction in collagen may indicate tissue damage in the hepatopancreas caused by cold stress. Previous studies have suggested that cold stress can cause morphological damage to the hepatopancreas of fish and crustacean animals  and lead to cell apoptosis . In this study, DEGs were also significantly enriched in the ferroptosis pathway, which has been reported in the Nile tilapia exposed to low temperatures . Ferroptosis is primarily caused by unrestricted lipid peroxidation and subsequent membrane damage . Lipid peroxidation caused by cold stress has been identified in Pacific white shrimp . In this study, long-chain acyl-CoA synthetase ( ACSL4 ) and ferroportin ( FPN ) were significantly upregulated in the T4 group. ACSL4 plays a role in the esterification of CoA to polyunsaturated fatty acids, thereby altering cellular lipid composition and increasing cellular susceptibility to ferroptosis . High ACSL4 expression is considered a marker of ferroptosis . In this study, its overexpression might be related to the FPN , a plasma membrane iron export protein that plays a role in regulating intracellular iron concentration . Previous studies in humans have shown that downregulated FPN induces ferroptosis by increasing iron concentrations and reactive oxygen species (ROS) levels , while upregulated FPN inhibits oxidative stress-induced ferroptosis . In this study, higher expression of FPN suggested that RSCs may possess some capacity to inhibit the ferroptosis induced by cold stress. In conclusion, the transcriptomic and metabolomic of RSC hepatopancreas suggested that cold stress not only significantly affects the metabolism of carbohydrates, lipids, amino acids, and nucleotides in RSCs but also influences their innate immune system and induces ferroptosis. These impacts could significantly affect the functions of the hepatopancreas. The compromised immunity and the induction of ferroptosis may further exacerbate the vulnerability of RSCs under cold stress. Based on these findings, the following measures may enhance the cold tolerance of RSCs. Increasing dietary polyunsaturated fatty acids (PUFAs) and BCAAs may improve the cold tolerance of RSCs. PUFAs not only provide energy but also help maintain cellular physiological functions in cold environments. Previous studies showed that increasing the PUFAs in the diet significantly improved the survival rate of Nile tilapia under low temperatures . Given the overexpression of BACT2 and the reduction in BCAAs in the T4 group, adjusting dietary BCAA proportions may improve the cold tolerance of RSCs. Earlier research demonstrated that optimized leucine improved growth performance in swimming crabs ( Portunus trituberculatus )  and Pacific white shrimp  and increased antioxidant levels in the hepatopancreas of the swimming crabs . Further investigations are necessary to confirm the effects of PUFAs and BCAAs on the cold stress tolerance in RSCs. Additionally, considering the compromised immunity of RSCs in the T4 group, minimizing their exposure to pathogenic factors is crucial. This study expands our knowledge of the response of RSCs to cold stress, which will be valuable for future research on the cold tolerance of RSCs. 

Low temperatures can directly affect the metabolism of crustaceans . For example, cold stress altered genes and metabolites associated with carbohydrates, lipids, amino acids, and nucleotide metabolism in the Pacific white shrimp . In this study, DEGs and DEMs between T4 and T22 were also significantly enriched in pathways related to carbohydrate metabolism. In the pathways of glycolysis/gluconeogenesis and fructose and mannose metabolism, fructose-1,6-bisphosphatase (FBP), and fructose-1,6-bisphosphate aldose (FBA) play key roles in gluconeogenesis . In this study, their expressions were significantly downregulated in T4, indicating that the gluconeogenesis process was hindered in RSCs under cold stress. This result is consistent with similar studies on Pacific white shrimp . Since gluconeogenesis is an energy-consuming process, ectothermic organisms are likely to reduce non-essential energy expenditure and prioritize survival at low temperatures . Cold stress had a significant impact on lipid metabolism in this study. Sphingolipid metabolism was changed by cold stress in RSCs, which is consistent with the study in red claw crayfish . Herein, the expression of sphingolipid 4-desaturase ( DES1 ) was significantly upregulated in T4 compared to T22. Studies on red claw crayfish under low-temperature conditions have also identified the overexpression of DES1 and Δ6 desaturase . Similarly, increased desaturase activity has been reported in milkfish ( Chanos chanos ) and Nile tilapia ( Oreochromis niloticus ) exposed to low temperatures . The overexpression of these desaturases would facilitate the conversion of saturated fatty acids (SFAs) to unsaturated fatty acids (UFAs). Since increased UFAs help maintain cell membrane fluidity at low temperatures , this overexpression supports the normal biological functions of RSCs under cold stress. DEGs were also significantly enriched in the arachidonic acid metabolism pathway (enrichment score = 2.587), which has been reported in kuruma shrimp ( Marsupenaeus Japonicus ) and Pacific white shrimp under cold stress . In this pathway, T4 was characterized by the downregulation of cytochrome P450 family 2 genes ( CYP2 ), which was consistent with the study of Pacific white shrimp . In addition to metabolizing arachidonic acid to epoxyeicosatrienoic acids , CYP2 plays a critical role in defending against environmental stress and pathogen infection in aquatic animals . In studies on mud crabs ( Scylla paramamosain ), CYP2 expression was significantly altered by Vibrio parahaemolyticus infection and ammonia stress, and interference with CYP2 led to significantly higher malondialdehyde (MDA) content and increased mortality rate . DEMs from metabolomic analysis were also enriched in the glycerophospholipid metabolism pathway, which is consistent with the study of giant freshwater prawns . Glycerophospholipids are important biological fuels for energy supply . Because crayfish tend to exhibit a fasting response when exposed to low temperatures , we discontinued feeding the RSCs in both T4 and T22 groups during the experiment. The reduction in these glycerophospholipids in T4 indicated that RSCs consumed them to increase energy production and maintain physiological homeostasis under cold stress. Cold stress also altered amino acid metabolism in RSCs. In the DEMs, many tripeptides, mainly including branched-chain amino acids (BCAAs: valine, leucine, and isoleucine) were significantly decreased in the T4 group ( C). A similar decrease in leucine and isoleucine was also detected in the metabolites of kuruma shrimp under cold stress . Meanwhile, overexpression of branched-chain aminotransferase ( BCAT ) in valine, leucine, and isoleucine biosynthesis pathways was detected . BCAT2 , which plays an essential role in the biosynthesis and degradation of branched-chain amino acids , has been reported to be upregulated in the hepatopancreas of Pacific white shrimp under cold stress . We speculate that cold stress induced the decrease in the branched-chain amino acids, and stimulated the overexpression of BCAT2 . DEMs between T4 and T22 were also significantly enriched in KEGG pathways of histidine metabolism and glycine, serine, and threonine metabolism. According to the previous literature, metabolites in the histidine metabolism pathway were also altered in mrigal carp ( Cirrhinus mrigala ) and European seabass ( Dicentrarchus labrax ) . It has been speculated that histidine metabolism may play critical roles in regulating adversity development, such as compensatory growth . In this study, DEGs between T4 and T22 were significantly enriched in the nucleotide metabolism pathway, which has been reported in many fish and crustaceans under cold stress . Nucleotides are of great importance to organisms, as they are involved in many biological processes, including DNA and RNA synthesis, energy storage, and metabolism, and other metabolic regulators . In this study, the expression of xanthine dehydrogenase/oxidase-like ( XDH ), which is involved in hypoxanthine and xanthine degradation, was reduced when RSCs were under cold stress. Other genes, including GMP reductase 2-like ( GMPR ), 5′-nucleotidase ( NT5E ), adenosine kinase ( ADK ), and ectonucleoside triphosphate diphosphohydrolase 5/6 ( ENTPD5_6 ), involved in purine synthesis, degradation, and salvage were upregulated in T4 group. The results indicate that cold stress altered the nucleotide metabolism in RSCs. 

In addition to metabolism-related pathways, cold stress also impacted immune system pathways in RSCs. Some C-type lectin receptor genes, including DC-SIGN , macrophage C-type lectin ( MCL ), and macrophage inducible C-type lectin ( Mincle ), were significantly downregulated in T4 compared with T22. In the previous literature, the downregulation of the C-type lectin gene has also been found in Pacific white shrimp and Nile tilapia exposed to low temperatures . C-type lectin plays an immunomodulatory role in the innate immunity of crustaceans, including cell adhesion, pathogen recognition, and bacterial clearance . Zhang et al. suggested that the overexpression of a C-type lectin in RSCs could significantly enhance the resistance to the bacteria Vibrio anguillarum . In this study, the downregulation of C-type lectin receptor genes and other immune-related genes, such as CYP2 mentioned previously, indicates that cold stress has a negative impact on immune competence and overall resistance to pathogens. Therefore, aquaculture management strategies, such as water treatment, strict quarantine measures, and clean feed, should be applied to prevent disease-causing agents. 

In the ECM-receptor interaction pathway, RSCs in T4 were characterized by the downregulation of collagen genes ( COL2A , COL4A , and COL9A ). Additionally, collagen abundance in T4 was significantly lower compared to T22, as shown by the metabolomic analysis. The reduction in collagen may indicate tissue damage in the hepatopancreas caused by cold stress. Previous studies have suggested that cold stress can cause morphological damage to the hepatopancreas of fish and crustacean animals  and lead to cell apoptosis . In this study, DEGs were also significantly enriched in the ferroptosis pathway, which has been reported in the Nile tilapia exposed to low temperatures . Ferroptosis is primarily caused by unrestricted lipid peroxidation and subsequent membrane damage . Lipid peroxidation caused by cold stress has been identified in Pacific white shrimp . In this study, long-chain acyl-CoA synthetase ( ACSL4 ) and ferroportin ( FPN ) were significantly upregulated in the T4 group. ACSL4 plays a role in the esterification of CoA to polyunsaturated fatty acids, thereby altering cellular lipid composition and increasing cellular susceptibility to ferroptosis . High ACSL4 expression is considered a marker of ferroptosis . In this study, its overexpression might be related to the FPN , a plasma membrane iron export protein that plays a role in regulating intracellular iron concentration . Previous studies in humans have shown that downregulated FPN induces ferroptosis by increasing iron concentrations and reactive oxygen species (ROS) levels , while upregulated FPN inhibits oxidative stress-induced ferroptosis . In this study, higher expression of FPN suggested that RSCs may possess some capacity to inhibit the ferroptosis induced by cold stress. In conclusion, the transcriptomic and metabolomic of RSC hepatopancreas suggested that cold stress not only significantly affects the metabolism of carbohydrates, lipids, amino acids, and nucleotides in RSCs but also influences their innate immune system and induces ferroptosis. These impacts could significantly affect the functions of the hepatopancreas. The compromised immunity and the induction of ferroptosis may further exacerbate the vulnerability of RSCs under cold stress. Based on these findings, the following measures may enhance the cold tolerance of RSCs. Increasing dietary polyunsaturated fatty acids (PUFAs) and BCAAs may improve the cold tolerance of RSCs. PUFAs not only provide energy but also help maintain cellular physiological functions in cold environments. Previous studies showed that increasing the PUFAs in the diet significantly improved the survival rate of Nile tilapia under low temperatures . Given the overexpression of BACT2 and the reduction in BCAAs in the T4 group, adjusting dietary BCAA proportions may improve the cold tolerance of RSCs. Earlier research demonstrated that optimized leucine improved growth performance in swimming crabs ( Portunus trituberculatus )  and Pacific white shrimp  and increased antioxidant levels in the hepatopancreas of the swimming crabs . Further investigations are necessary to confirm the effects of PUFAs and BCAAs on the cold stress tolerance in RSCs. Additionally, considering the compromised immunity of RSCs in the T4 group, minimizing their exposure to pathogenic factors is crucial. This study expands our knowledge of the response of RSCs to cold stress, which will be valuable for future research on the cold tolerance of RSCs. 

4.1. Cold Stress Treatment and Sample Collection The RSCs were purchased in October 2023 from Shenyang Yurun Agricultural Products Wholesale Market, sourced from a grower in Qianjiang, Hubei. Prior to the experiments, RSCs were temporarily cultured in the aquatic animal lab of Shenyang Agricultural University under continuous aeration at 22 °C for one week. The crayfish were fed a commercial diet twice daily at 9:00 and 18:00, and one-third of the water was replaced daily. Dead crayfish, feed residues, and feces were cleaned up in a timely manner. The RSCs (mean weight 17.57 ± 4.97 g; mean length: 8.37 ± 1.27 cm) were then randomly divided into two groups, with 20 RSCs in each group. To prevent mortality caused by a sharp temperature drop, one group was transferred to an incubator, where the temperature was gradually decreased from 22 °C to 4 °C in 12 h. The crayfish were then maintained at 4 °C for 96 h (a preliminary experiment indicated a mortality rate of approximately 50% after 96 h; T4). The other group was raised at 22 °C for 96 h (T22). To eliminate the effects of food intake, feeding was stopped for both T4 and T22 because the cessation of feeding behavior was observed in T4. Six live RSCs were randomly collected from each group. The shell surfaces were rinsed with sterile water and then wiped using 75% ethanol. The hepatopancreas were immediately collected in a sterile environment. Each hepatopancreas was placed in a 2 mL cryogenic vial, submerged in liquid nitrogen, and stored at −80 °C. In each group, three of the hepatopancreas samples were used for transcriptomic sequencing, while the rest were used for metabolomic analysis. 4.2. Transcriptome Analysis 4.2.1. RNA Extraction, Library Preparation, and Sequencing Total RNA was extracted from hepatopancreas samples using QIAzol Lysis Reagent (Qiagen, Germantown, MD, USA) following the manufacturer’s instructions. Then, RNA quality was determined by 5300 Bioanalyser (Agilent) and quantified using the NanoDrop ND-2000 (Thermo Fisher Scientific, Wilmington, DE, USA). RNA purification, reverse transcription, library construction, and sequencing were carried out at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer’s protocols. RNA Purification Kit (Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd., Shanghai, China) was used for RNA purification following the manufacturer’s instructions. For RNA-seq transcriptome library preparation, 1 μg of total RNA was processed following Illumina ® Stranded mRNA Prep, Ligation (Illumina, San Diego, CA, USA). Messenger RNA was isolated through the polyA selection method with oligo (dT) beads and fragmented using a fragmentation buffer. Double-stranded cDNA synthesis was conducted using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) with random hexamer primers. The resulting cDNA underwent end-repair, phosphorylation, and adapter ligation according to library construction steps. Target fragments of 300 bp were selected on 2% Low-range Ultra Agarose, and the library was amplified by 15 cycles of PCR using Phusion DNA polymerase (NEB). After quantification using a Qubit 4, sequencing was performed on the NovaSeq X Plus platform (Illumina, San Diego, CA, USA) with the NovaSeq Reagent Kit (Illumina, San Diego, CA, USA). 4.2.2. Quality Control and Read Mapping The raw paired-end reads were trimmed and quality controlled by fastp  with default settings. The resulting clean reads were then individually aligned to the reference genome  in orientation mode using HISAT2 . For each sample, the mapped reads were assembled using a reference-based approach by StringTie . 4.2.3. Differentially Expression Analysis and Functional Enrichment To identify differential expression genes (DEGs) between T4 and T22, the expression levels of each transcript were determined using the transcripts per million (TPM) reads method. Gene abundances were quantified using RSEM . Differential expression analysis was conducted with DESeq2  and DEGseq . Genes were considered significantly differentially expressed if they met the criteria of p < 0.05 and |log2FC| ≧ 1. Functional enrichment, including GO and KEGG analysis, was performed to identify significant enrichment of DEGs in GO terms and metabolic pathways using p < 0.05. GO enrichment and KEGG pathway analysis were performed using Goatools and Python scipy software, respectively. 4.2.4. Verification of the RNA-seq Results To assess the reliability of the RNA-seq results, 12 genes were randomly selected from DEGs and validated using quantitative PCR (qPCR). Primers were designed using Primer-BLAST ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ , accessed on 29 January 2025) , and synthesized by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) The 20 μL reaction mixture contained 10 ng of DNA template, 1 μL of 10 μM forward and reverse primers, and 10 μL of ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). QPCR was performed on the QuantStudio3 system (Thermo Fisher Scientific, Foster City, CA, USA). The qPCR began with a pre-incubation phase at 95 °C for 30 s. DNA amplification was performed over 40 cycles under the following conditions: denaturation at 95 °C for 10 s, annealing at 60° for 30 s, and the specificity of amplicons was confirmed through melt curve and agarose electrophoresis. The primers’ details are provided in . The relative expression levels were normalized by the reference gene [ 18S rRNA gene (18S)]  through the 2 −ΔΔCt method . 4.3. Metabolomic Analysis 4.3.1. Metabolite Extraction Hepatopancreas samples (50 mg) were placed in a 2 mL centrifuge tube with a 6 mm diameter grinding bead. To extract metabolites, 400 μL of extraction solution [methanol/water = 4:1 ( v : v )] containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was added. Samples were ground for 6 min (−10 °C, 50 Hz) using the Wonbio-96c frozen tissue grinder (Shanghai Wanbo Biotechnology Co., Ltd., Shanghai, China), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). After 30 min at −20 °C for 30 min, the samples were centrifuged (15 min at 4 °C, 13,000 g), and the supernatant was transferred to the injection vial for LC-MS analysis. 4.3.2. Quality Control Sample To ensure system conditioning and quality control, a pooled quality control (QC) sample was created by mixing equal volumes of all samples. The QC samples were processed and analyzed in the same way as the test samples. These QC samples represented the entire dataset and were injected at regular intervals to monitor the consistency and stability of the analysis. 4.3.3. LC-MS Analysis The LC-MS analysis of the samples was performed using a Thermo UHPLC-Q Exactive HF-X system (Thermo Fisher Scientific, San Jose, CA, USA) at Majorbio Bio-pharm Technology Co. Ltd. (Shanghai, China). The mobile phases included solvent A, which was 0.1% formic acid in a water mixture (95:5, v / v ), and solvent B, composed of 0.1% formic acid in acetonitrile/isopropanol (47.5:47.5:5, v / v ). The flow rate was set at 0.40 mL/min, with a column temperature of 40 °C and an injection volume of 3 μL. 4.3.4. Data Analyses The raw data from the LC/MS analysis were preprocessed using Progenesis QI software (Waters Corporation, Milford, MA, USA). Metabolite identification was carried out by searching databases, including the HMDB ( http://www.hmdb.ca/ ), Metlin ( https://metlin.scripps.edu/ ), and Majorbio Database. The resulting data matrix was subsequently uploaded to the Majorbio cloud platform ( https://cloud.majorbio.com ) for further analysis. Data filtering was applied to retain only metabolites detected in at least 80% of any sample group. Missing values were replaced with the minimum value, and metabolic features were normalized by summation. Sample mass spectrum peak intensities were normalized using the sum normalization method. Variables showing a relative standard deviation (RSD) greater than 30% in the quality control (QC) samples were excluded. The final data matrix was created through log10 transformation. Orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted using the R package "ropls" (Version 1.6.2). Significant differences in metabolites were determined based on the variable importance in projection (VIP) score from the OPLS-DA model and the p values from Student’s t test. Metabolites with VIP > 1.5 and p < 0.05 were considered significantly different metabolites (DEMs). These DEMs were classified using the HMDB database and mapped to biochemical pathways through enrichment and pathway analysis using the KEGG database ( p < 0.05). The “scipy.stats” Python package ( https://docs.scipy.org/doc/scipy/ , accessed on 29 January 2025) was employed for pathway enrichment, using Fisher’s exact test to determine statistically significant results. All analyses were conducted via the Majorbio I-Sanger Cloud Platform . 4.4. Correlation Between Transcriptome and Metabolome The Spearman correlation coefficients between SDEGs and SDEMs were calculated and visualized by the corrplot package in R (1.6.2). 

The RSCs were purchased in October 2023 from Shenyang Yurun Agricultural Products Wholesale Market, sourced from a grower in Qianjiang, Hubei. Prior to the experiments, RSCs were temporarily cultured in the aquatic animal lab of Shenyang Agricultural University under continuous aeration at 22 °C for one week. The crayfish were fed a commercial diet twice daily at 9:00 and 18:00, and one-third of the water was replaced daily. Dead crayfish, feed residues, and feces were cleaned up in a timely manner. The RSCs (mean weight 17.57 ± 4.97 g; mean length: 8.37 ± 1.27 cm) were then randomly divided into two groups, with 20 RSCs in each group. To prevent mortality caused by a sharp temperature drop, one group was transferred to an incubator, where the temperature was gradually decreased from 22 °C to 4 °C in 12 h. The crayfish were then maintained at 4 °C for 96 h (a preliminary experiment indicated a mortality rate of approximately 50% after 96 h; T4). The other group was raised at 22 °C for 96 h (T22). To eliminate the effects of food intake, feeding was stopped for both T4 and T22 because the cessation of feeding behavior was observed in T4. Six live RSCs were randomly collected from each group. The shell surfaces were rinsed with sterile water and then wiped using 75% ethanol. The hepatopancreas were immediately collected in a sterile environment. Each hepatopancreas was placed in a 2 mL cryogenic vial, submerged in liquid nitrogen, and stored at −80 °C. In each group, three of the hepatopancreas samples were used for transcriptomic sequencing, while the rest were used for metabolomic analysis. 

4.2.1. RNA Extraction, Library Preparation, and Sequencing Total RNA was extracted from hepatopancreas samples using QIAzol Lysis Reagent (Qiagen, Germantown, MD, USA) following the manufacturer’s instructions. Then, RNA quality was determined by 5300 Bioanalyser (Agilent) and quantified using the NanoDrop ND-2000 (Thermo Fisher Scientific, Wilmington, DE, USA). RNA purification, reverse transcription, library construction, and sequencing were carried out at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer’s protocols. RNA Purification Kit (Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd., Shanghai, China) was used for RNA purification following the manufacturer’s instructions. For RNA-seq transcriptome library preparation, 1 μg of total RNA was processed following Illumina ® Stranded mRNA Prep, Ligation (Illumina, San Diego, CA, USA). Messenger RNA was isolated through the polyA selection method with oligo (dT) beads and fragmented using a fragmentation buffer. Double-stranded cDNA synthesis was conducted using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) with random hexamer primers. The resulting cDNA underwent end-repair, phosphorylation, and adapter ligation according to library construction steps. Target fragments of 300 bp were selected on 2% Low-range Ultra Agarose, and the library was amplified by 15 cycles of PCR using Phusion DNA polymerase (NEB). After quantification using a Qubit 4, sequencing was performed on the NovaSeq X Plus platform (Illumina, San Diego, CA, USA) with the NovaSeq Reagent Kit (Illumina, San Diego, CA, USA). 4.2.2. Quality Control and Read Mapping The raw paired-end reads were trimmed and quality controlled by fastp  with default settings. The resulting clean reads were then individually aligned to the reference genome  in orientation mode using HISAT2 . For each sample, the mapped reads were assembled using a reference-based approach by StringTie . 4.2.3. Differentially Expression Analysis and Functional Enrichment To identify differential expression genes (DEGs) between T4 and T22, the expression levels of each transcript were determined using the transcripts per million (TPM) reads method. Gene abundances were quantified using RSEM . Differential expression analysis was conducted with DESeq2  and DEGseq . Genes were considered significantly differentially expressed if they met the criteria of p < 0.05 and |log2FC| ≧ 1. Functional enrichment, including GO and KEGG analysis, was performed to identify significant enrichment of DEGs in GO terms and metabolic pathways using p < 0.05. GO enrichment and KEGG pathway analysis were performed using Goatools and Python scipy software, respectively. 4.2.4. Verification of the RNA-seq Results To assess the reliability of the RNA-seq results, 12 genes were randomly selected from DEGs and validated using quantitative PCR (qPCR). Primers were designed using Primer-BLAST ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ , accessed on 29 January 2025) , and synthesized by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) The 20 μL reaction mixture contained 10 ng of DNA template, 1 μL of 10 μM forward and reverse primers, and 10 μL of ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). QPCR was performed on the QuantStudio3 system (Thermo Fisher Scientific, Foster City, CA, USA). The qPCR began with a pre-incubation phase at 95 °C for 30 s. DNA amplification was performed over 40 cycles under the following conditions: denaturation at 95 °C for 10 s, annealing at 60° for 30 s, and the specificity of amplicons was confirmed through melt curve and agarose electrophoresis. The primers’ details are provided in . The relative expression levels were normalized by the reference gene [ 18S rRNA gene (18S)]  through the 2 −ΔΔCt method . 

Total RNA was extracted from hepatopancreas samples using QIAzol Lysis Reagent (Qiagen, Germantown, MD, USA) following the manufacturer’s instructions. Then, RNA quality was determined by 5300 Bioanalyser (Agilent) and quantified using the NanoDrop ND-2000 (Thermo Fisher Scientific, Wilmington, DE, USA). RNA purification, reverse transcription, library construction, and sequencing were carried out at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer’s protocols. RNA Purification Kit (Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd., Shanghai, China) was used for RNA purification following the manufacturer’s instructions. For RNA-seq transcriptome library preparation, 1 μg of total RNA was processed following Illumina ® Stranded mRNA Prep, Ligation (Illumina, San Diego, CA, USA). Messenger RNA was isolated through the polyA selection method with oligo (dT) beads and fragmented using a fragmentation buffer. Double-stranded cDNA synthesis was conducted using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) with random hexamer primers. The resulting cDNA underwent end-repair, phosphorylation, and adapter ligation according to library construction steps. Target fragments of 300 bp were selected on 2% Low-range Ultra Agarose, and the library was amplified by 15 cycles of PCR using Phusion DNA polymerase (NEB). After quantification using a Qubit 4, sequencing was performed on the NovaSeq X Plus platform (Illumina, San Diego, CA, USA) with the NovaSeq Reagent Kit (Illumina, San Diego, CA, USA). 

The raw paired-end reads were trimmed and quality controlled by fastp  with default settings. The resulting clean reads were then individually aligned to the reference genome  in orientation mode using HISAT2 . For each sample, the mapped reads were assembled using a reference-based approach by StringTie . 

To identify differential expression genes (DEGs) between T4 and T22, the expression levels of each transcript were determined using the transcripts per million (TPM) reads method. Gene abundances were quantified using RSEM . Differential expression analysis was conducted with DESeq2  and DEGseq . Genes were considered significantly differentially expressed if they met the criteria of p < 0.05 and |log2FC| ≧ 1. Functional enrichment, including GO and KEGG analysis, was performed to identify significant enrichment of DEGs in GO terms and metabolic pathways using p < 0.05. GO enrichment and KEGG pathway analysis were performed using Goatools and Python scipy software, respectively. 

To assess the reliability of the RNA-seq results, 12 genes were randomly selected from DEGs and validated using quantitative PCR (qPCR). Primers were designed using Primer-BLAST ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ , accessed on 29 January 2025) , and synthesized by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) The 20 μL reaction mixture contained 10 ng of DNA template, 1 μL of 10 μM forward and reverse primers, and 10 μL of ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). QPCR was performed on the QuantStudio3 system (Thermo Fisher Scientific, Foster City, CA, USA). The qPCR began with a pre-incubation phase at 95 °C for 30 s. DNA amplification was performed over 40 cycles under the following conditions: denaturation at 95 °C for 10 s, annealing at 60° for 30 s, and the specificity of amplicons was confirmed through melt curve and agarose electrophoresis. The primers’ details are provided in . The relative expression levels were normalized by the reference gene [ 18S rRNA gene (18S)]  through the 2 −ΔΔCt method . 

4.3.1. Metabolite Extraction Hepatopancreas samples (50 mg) were placed in a 2 mL centrifuge tube with a 6 mm diameter grinding bead. To extract metabolites, 400 μL of extraction solution [methanol/water = 4:1 ( v : v )] containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was added. Samples were ground for 6 min (−10 °C, 50 Hz) using the Wonbio-96c frozen tissue grinder (Shanghai Wanbo Biotechnology Co., Ltd., Shanghai, China), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). After 30 min at −20 °C for 30 min, the samples were centrifuged (15 min at 4 °C, 13,000 g), and the supernatant was transferred to the injection vial for LC-MS analysis. 4.3.2. Quality Control Sample To ensure system conditioning and quality control, a pooled quality control (QC) sample was created by mixing equal volumes of all samples. The QC samples were processed and analyzed in the same way as the test samples. These QC samples represented the entire dataset and were injected at regular intervals to monitor the consistency and stability of the analysis. 4.3.3. LC-MS Analysis The LC-MS analysis of the samples was performed using a Thermo UHPLC-Q Exactive HF-X system (Thermo Fisher Scientific, San Jose, CA, USA) at Majorbio Bio-pharm Technology Co. Ltd. (Shanghai, China). The mobile phases included solvent A, which was 0.1% formic acid in a water mixture (95:5, v / v ), and solvent B, composed of 0.1% formic acid in acetonitrile/isopropanol (47.5:47.5:5, v / v ). The flow rate was set at 0.40 mL/min, with a column temperature of 40 °C and an injection volume of 3 μL. 4.3.4. Data Analyses The raw data from the LC/MS analysis were preprocessed using Progenesis QI software (Waters Corporation, Milford, MA, USA). Metabolite identification was carried out by searching databases, including the HMDB ( http://www.hmdb.ca/ ), Metlin ( https://metlin.scripps.edu/ ), and Majorbio Database. The resulting data matrix was subsequently uploaded to the Majorbio cloud platform ( https://cloud.majorbio.com ) for further analysis. Data filtering was applied to retain only metabolites detected in at least 80% of any sample group. Missing values were replaced with the minimum value, and metabolic features were normalized by summation. Sample mass spectrum peak intensities were normalized using the sum normalization method. Variables showing a relative standard deviation (RSD) greater than 30% in the quality control (QC) samples were excluded. The final data matrix was created through log10 transformation. Orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted using the R package "ropls" (Version 1.6.2). Significant differences in metabolites were determined based on the variable importance in projection (VIP) score from the OPLS-DA model and the p values from Student’s t test. Metabolites with VIP > 1.5 and p < 0.05 were considered significantly different metabolites (DEMs). These DEMs were classified using the HMDB database and mapped to biochemical pathways through enrichment and pathway analysis using the KEGG database ( p < 0.05). The “scipy.stats” Python package ( https://docs.scipy.org/doc/scipy/ , accessed on 29 January 2025) was employed for pathway enrichment, using Fisher’s exact test to determine statistically significant results. All analyses were conducted via the Majorbio I-Sanger Cloud Platform . 

Hepatopancreas samples (50 mg) were placed in a 2 mL centrifuge tube with a 6 mm diameter grinding bead. To extract metabolites, 400 μL of extraction solution [methanol/water = 4:1 ( v : v )] containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was added. Samples were ground for 6 min (−10 °C, 50 Hz) using the Wonbio-96c frozen tissue grinder (Shanghai Wanbo Biotechnology Co., Ltd., Shanghai, China), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). After 30 min at −20 °C for 30 min, the samples were centrifuged (15 min at 4 °C, 13,000 g), and the supernatant was transferred to the injection vial for LC-MS analysis. 

To ensure system conditioning and quality control, a pooled quality control (QC) sample was created by mixing equal volumes of all samples. The QC samples were processed and analyzed in the same way as the test samples. These QC samples represented the entire dataset and were injected at regular intervals to monitor the consistency and stability of the analysis. 

The LC-MS analysis of the samples was performed using a Thermo UHPLC-Q Exactive HF-X system (Thermo Fisher Scientific, San Jose, CA, USA) at Majorbio Bio-pharm Technology Co. Ltd. (Shanghai, China). The mobile phases included solvent A, which was 0.1% formic acid in a water mixture (95:5, v / v ), and solvent B, composed of 0.1% formic acid in acetonitrile/isopropanol (47.5:47.5:5, v / v ). The flow rate was set at 0.40 mL/min, with a column temperature of 40 °C and an injection volume of 3 μL. 

The raw data from the LC/MS analysis were preprocessed using Progenesis QI software (Waters Corporation, Milford, MA, USA). Metabolite identification was carried out by searching databases, including the HMDB ( http://www.hmdb.ca/ ), Metlin ( https://metlin.scripps.edu/ ), and Majorbio Database. The resulting data matrix was subsequently uploaded to the Majorbio cloud platform ( https://cloud.majorbio.com ) for further analysis. Data filtering was applied to retain only metabolites detected in at least 80% of any sample group. Missing values were replaced with the minimum value, and metabolic features were normalized by summation. Sample mass spectrum peak intensities were normalized using the sum normalization method. Variables showing a relative standard deviation (RSD) greater than 30% in the quality control (QC) samples were excluded. The final data matrix was created through log10 transformation. Orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted using the R package "ropls" (Version 1.6.2). Significant differences in metabolites were determined based on the variable importance in projection (VIP) score from the OPLS-DA model and the p values from Student’s t test. Metabolites with VIP > 1.5 and p < 0.05 were considered significantly different metabolites (DEMs). These DEMs were classified using the HMDB database and mapped to biochemical pathways through enrichment and pathway analysis using the KEGG database ( p < 0.05). The “scipy.stats” Python package ( https://docs.scipy.org/doc/scipy/ , accessed on 29 January 2025) was employed for pathway enrichment, using Fisher’s exact test to determine statistically significant results. All analyses were conducted via the Majorbio I-Sanger Cloud Platform . 

The Spearman correlation coefficients between SDEGs and SDEMs were calculated and visualized by the corrplot package in R (1.6.2). 

In this study, we conducted a comprehensive evaluation of the transcriptome and metabolome of the hepatopancreas of RSCs exposed to cold stress at 4 °C (T4) and 22 °C (T22: control) for 96 h. The results indicated that cold stress caused significant changes in gene expression and metabolic levels in RSCs. A total of 787 DEGs and 198 DEMs were identified between T4 and T22. DEGs were significantly enriched in KEGG pathways related to carbohydrate, lipid, amino acid, and nucleotide metabolism, immunity, and signaling, while DEMs were significantly enriched in pathways associated with lipid and amino acid metabolism, and membrane transport. Additionally, cold stress negatively impacted the innate immune system and induced ferroptosis, which significantly affected hepatopancreas functions. These findings could provide a better understanding of the molecular mechanisms of RSCs’ responses to cold stress and could benefit its aquaculture practices. 

