Effects of transportation on physiological indices and metabolomics of the large yellow croaker Larimichthys crocea

doi:10.21203/rs.3.rs-2069900/v1

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Effects of transportation on physiological indices and metabolomics of the large yellow croaker Larimichthys crocea

https://doi.org/10.21203/rs.3.rs-2069900/v1

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Adverse conditions have seriously affected the survival rate of the large yellow croaker (Larimichthys crocea) and the molecular response mechanism needs to be revealed. The aim of this study was to improve the survival rate and alleviate the oxidative stress response of the large yellow croaker during live transport. The experiment involved using different concentrations of MS-222. The transport of live animals is associated with various stressors. After a preliminary experiment, we used an untargeted LC–MS-based metabolomics technique combined with liver biochemical indices assays to examine the changes in L. crocea after 48 h of simulated transport. The results of liver biochemical indices indicated inhibition of gluconeogenesis and pentose phosphate pathway metabolism. In addition, metabolomics analysis identified significantly differentially expressed metabolites between 10 mg/L (T1) and control (C) groups. Furthermore, KEGG results revealed that the pathways of amino acid metabolism, especially the lysine, aspartate and homoserine in the liver were significantly affected. In conclusion, the combination of metabolomics and liver biochemical assays provided a characterization of the response mechanism of L. crocea exposed to simulated transport.

simulated transport

MS-222

metabolomics

liver biochemical indices

large yellow croaker

Live fish transport is a growing concern due to rising living standards and research activities, especially for rare species. As one of the most economically important species of fish, the large yellow croaker (Larimichthys crocea) has a continually increasing consumer demand in China. The texture and flavor of the croaker deteriorate after harvesting. It is difficult to reduce the stress response of the large yellow croakers by traditional methods such as vehicle oxygenated transportation and plastic bag oxygenation transportation. Anaethetized transportation can avoid mechanical damage and reduce the stress response of the large yellow croakers, thus improving the survival rate. Therefore, to meet the recent surge in demand, researchers are seeking solutions to realize efficient transport of live fish. The transport of the large yellow croaker is affected by water flow and collisions with transport devices, factors that can cause a series of neuroendocrine and physiological responses. Respiration and excretion result in deterioration of the water quality, aggravating the stress to the fish and even leading to death. Therefore, it is essential to reduce both transport stress and the duration of exposure. In general, there are two ways to improve the survival rate of live fish during transport: improving the transport methods and reducing the stress response and sensitivity of the fish. The method of chemical anesthesia refers to the use of anesthetics to inhibit the neuroendocrine systems of the fish, thereby reducing total activity, respiration, and metabolism to improve the survival rate during transport. Common anesthetics used in the transport of live fish include Tricaine methane sulfonate (MS-222), eugenol, isoeugenol, and benzocaine, among which MS-222 is the only fish anesthetic approved for use by the US Food and Drug Administration (FDA). MS-222 has good anesthetic effect, short recovery time, high efficiency, and safety without an associated enrichment effect.

When fish are exposed to anesthetics, the mechanism of stress response is a complex activity that is sensitive to environmental stimuli. Concerning neuroendocrine reactions, plasma cortisol (Cor) concentrations remain elevated even days after anaesthetized transport (Bolasina S N, 2011; Juhani et al., 2003; R.A. Weber et al., 2011). The concentration of lactic acid (LACT) in fish increased significantly after anesthetization in several studies (Weber et al., 2011; Iversen et al., 2003). However, Turek (2011) found lower lactic acid levels in an MS-222 group. Liu et al. (2022) found that the fish in the anesthetized group showed a significant increase in serum glucose (GLU) after 12 h. Velíšek et al. (2004) and Weber et al. (2017) found the same trend after 24 h of anesthetization. Velíšek et al. (2004 and 2011) demonstrated increases in the activity of glutamic-oxalacetic transaminase (AST) after 24 hours of anesthetization. In addition, Feng et al. (2010) and Turek (2011) found that lactate dehydrogenase (LDH) levels in fish were not significantly affected by MS-222.

Metabonomics is a systematic biology research method that emerged subsequent to the development of genomics and proteomics. It has been widely used in early diagnosis of diseases, in metabolic toxicology, and in drug development and other fields (Nicholson et al., 1999; Lindon et al., 2003; Griffin, 2004). Many life activities in cells are related to metabolites, including cell signaling and energy transfer. Metabonomics aims to quantitatively analyze all metabolites in organisms and identify the relevant relationships between metabolites and physiological and pathological changes. Widely targeted metabolomics extracts ions from mass spectrometry fragments from standard samples and analyzes metabolic data to create a local database that can be used in combination with public databases. Using the ion equivalent information of the local database, the metabolic data of biological samples are collected via MRM mode for the qualitative and relative analysis of metabolites. Widely targeted metabolomics combines the advantages of untargeted metabolomics and targeted metabolomics. Based on the targeted detection principle of QQQ triple quadrupole, the quantitative analysis model of MRM, and the self-built reference material database, this technology has the advantages of high throughput, high sensitivity, and broad coverage. For example, metabolomics based on GC/MS has been widely used to study the physiological responses of aquatic organisms to environmental stressors, including pathogen infection, heat, water pollutants, and air exposure (Guo et al., 2014; Ji et al., 2016; Chen et al., 2015). A large number of metabolites and related pathways have been successfully identified, providing data concerning the response mechanisms of various species.

In order to explore the effects of different concentrations of MS-222 on stress responses of the large yellow croaker during transport, liver biochemical indices and widely targeted metabolome analysis were employed to clarify the metabolic characteristics of the large yellow croaker during transport while under anesthesia.

2.1 Preparation of Large Yellow Croaker

Five hundred healthy large yellow croakers (body weight: 23.4 ± 5.3 g, full length: 12.2 ± 0.7 cm) were obtained from Zhoushan Bandao Aquaculture Co., Ltd. (Zhoushan City, Zhejiang Province, China). The fish were acclimated to the lab environment for seven days. Then, 180 large yellow croakers were randomly divided among nine 60 L plastic buckets. Each bucket was equipped with an aerator to ensure sufficient oxygen during the experiment. Seawater was available after chlorine dioxide disinfection and aeration treatment, and MS-222 was used as an anesthetic.

2.2 Experimental Design

In a preliminary experiment, 0, 10, 20, and 30 mg/L anesthesia groups were set to observe the 12 h survival rate, respiratory rate, and changes in dissolved oxygen. The respiratory rate was recorded by the number of gill openings per minute. The survival rate was highest in the 10 mg/L anesthesia group while being lowest in the 30 mg/L anesthesia group. The survival rate in the 30 mg/L anesthesia group was only 40%, and the survival rates in the 0, 10, and 20 mg/L groups were all above 80%. Therefore, in the subsequent experiments, three concentrations were used to carry out 48 h transport experiments: 0, 10, and 20 mg/L.

A simulated transport test was carried out in a 50 L water tank filled with sterilized seawater kept at 16°C ± 0.5°C. Twenty fish in each group were anesthetized with 40 mg/L MS-222 solution for five minutes. The fish were fasted for 24 h before the experiment. When all of the experimental fish were in a deep sedation state, they were transferred to 50 L tanks containing 0 mg/L, 10 mg/L, and 20 mg/L MS-222 seawater, and the water temperature was maintained at 16°C. The simulated transport was carried out by continuous aeration of oxygen. The dissolved oxygen concentration was 6–7.2 mg/L. Dead fish were recorded during the experiment.

2.3 Sample Treatment

At the end of the experiment, five fish from each bucket were sampled. The large yellow croakers were placed on ice for 15 minutes to induce coma and then killed. The livers were excised, washed in cold normal saline (0.8%, w/v), blotted dry, and flash frozen with liquid nitrogen in a mortar. The frozen tissues of five fish from each group were pooled, ground, weighed, dispensed into 2 ml Eppendorf tubes, and stored at − 80°C. Livers were used to measure the levels of cortisol, glucose, lactic acid, glutamic oxalacetic-transaminase, glucose 6-phosphate, and lactate dehydrogenase.

The LDH activity was measured using biochemical kits (Keming Biotechnology Co., Ltd., Suzhou, China). Cor level was determined with an ELISA kit (Preferred biotechnology Co., Ltd., Shanghai China), and the levels of GLU, LACT, AST, and G6P were assayed with commercial kits (Preferred Biotechnology Co., Ltd., Shanghai China).

2.4 Metabolite Detection Analysis

Liver samples from the 10 mg/L group (highest survival rate) and the control group were further used for widely targeted metabolomics.

One hundred milligram samples ground with liquid nitrogen were placed into EP tubes; 500 µL of 80% methanol solution containing 0.1% formic acid was added, and the samples were shaken to precipitate proteins. The samples were centrifuged at 15000 rpm, 4°C for 10 min, and the supernatant collected. The supernatant was diluted with a 1/2 volume of water. Then, the supernatant was placed in a centrifuge tube and centrifuged at 15000 rpm, 4°C for 20 min, and the supernatant was collected. Samples from C and T1 groups were mixed in the same amount, and quality control (QC) samples were prepared to evaluate system stability over the entire experiment before testing. After sample pretreatment, samples were sent to Nanjing Genepioneer Biotechnologies Co., Ltd. for liquid chromatography-tandem mass spectrometry (LC/MS) analysis.

All chromatographic separations were performed using an ultra-performance liquid chromatography (UPLC) system. For positive ion mode, a C8 column (100 mm × 2.1 mm) was used for the reversed phase separation. The column oven was maintained at 50°C. The flow rate was 0.35 mL/min, and the mobile phase consisted of solvent A (water + 0.1% formic acid) and solvent B (acetonitrile + 0.1% formic acid). Gradient elution conditions were set as follows: 0—1 min, 5% phase B; 1—24 min, 5%—100% phase B; 24—28 min, 100% phase B; 28—28.1 min, 100—5% phase B, 28.1—30 min, 5% phase B. For negative ion mode, a T3 column (10 cm × 2.1 mm) was used for the reversed phase separation. The column oven was maintained at 50°C. The flow rate was 0.35 mL/min, and the mobile phase consisted of solvent A (water + 6.5 mM ammonium bicarbonate) and solvent B (water + 6.5 mM ammonium bicarbonate + 95% methanol). Gradient elution conditions were set as follows: 0—1 min, 2% phase B; 1—18 min, 2–100% phase B; 18—22 min, 100% phase B; 22—22.1 min, 100–5% phase B, 22.1—25 min, 5% phase B. Samples were analyzed using a SCIEX QTRAP® 6500 + mass spectrometer. Parameters were set as follows: For positive ion mode, Curtain Gas, 35; Collision Gas, Medium; Ion Spray Voltage, 5500; Temperature, 500; Ion Source Gas 1, 55; Ion Source Gas 2, 55. For negative ion mode, the parameters were Curtain Gas, 35; Collision Gas, Medium; Ion Spray Voltage, − 4500; Temperature, 500; Ion Source Gas 1, 55; Ion Source Gas 2, 55

2.5 Data Processing and Statistical Analysis

After the data had been preprocessed by scaling, SIMCA (V14.1, MKS Data Analytics Solutions, Umea, Sweden) software was used for data analysis and mining that consisted of principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA). Data were pretreated before PCA and OPLS-DA using mean-centering without scaling (Ctr) and mean-centering and scaled to unit variance (UV), respectively.

2.6 Metabolite Identification and Pathway Analysis

For identification of differentially expressed metabolic ions, volcano plots from univariate analysis were drawn based on fold change (FC) of > 1.2 or < 0.83 and analysis of variance yielding a value of p < 0.05 (C vs T1). For multivariate analysis, PCA and OPLS-DA were used to identify the significantly different metabolites. We screened the differentially expressed metabolites in serum using variable projection importance value scores of > 1 and a t-test yielding p < 0.05. The differential metabolites were selected by multivariate and univariate statistical analyses using HMDB (https://hmdb.ca/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The significantly different metabolites were further screened using enrichment and topological analyses to identify the key pathways that were correlated with the metabolite differences. We subsequently mapped a metabolism network of the differential metabolites to better demonstrate the relationships between the differential metabolites in the serum.

3.1 Analysis of liver biochemical indices

The Cor level of the 20 mg/L (T2 group) increased after simulated transport (Fig. 1.a). There was no significant difference in Cor levels between the control group (C group) and 10 mg/L (the T1 group). The LACT level in serum gradually increased along with the increase in the concentration of MS-222 (Fig. 1.b). The levels of GLU, AST, G6P, and LDH decreased to different degrees with the increase in the concentration of MS-222 (Fig. 1.c-f).

3.2 Quality Control of Raw Data

The total ion chromatogram (TIC) plot from the UPLC system takes the time point as the abscissa and the total intensity of all ions in the mass spectrum at each time point as the ordinate. TIC of QC macroscopically reflects the separation of all metabolites, and TIC results for positive (Fig. 2.a) and negative ion modes (Fig. 2.b) reveals the differences in metabolites of QC samples. The metabolic profiles showed the overlap in intensity of each sample at the same retention time, indicating the slight degree of variation from instrument fluctuation and demonstrating the high reliability of the acquired data.

Six experimental samples were normalized with total ion current (TIC). There were 796 characteristic peaks (430 in positive ion mode and 366 in negative ion mode) retained after the raw data were pretreated.

The QC samples were clustered and reproducible (Fig. 3a). The Pearson correlation coefficients between every two of QC samples were close to 1 (Fig. 3b). As in the QC samples, the proportion of characteristic peaks with RSD < 30% exceeded 70% (Fig. 3c). This demonstrated the high reliability of the acquired data.

3.3 Metabolic Profiles and Data Analysis

The PCA results of the metabolites of the C and T1 groups are presented in Figs. 4a and 4b, respectively, in order to show the differences in metabolites using 95% confidence intervals. The supervised discriminant statistical analysis methods of PLS-DA (Figs. 4c and 4d) and OPLS-DA (Figs. 4e and 4f) were further applied to eliminate the influence of irrelevant factors on the experimental data. To avoid over-fitting and random effects, permutations of 200 tests in the OPLS-DA model were separately performed to enhance the predictive ability (Figs. 4g and 4h). Then, R2Y and Q2 were used to evaluate the validity of the model. Samples from the C and T1 groups were significantly separated, and the difference was located within the 95% confidence interval. When the replacement retention decreased, the proportions of Y variables R2 and Q2 decreased gradually, indicating good model robustness with no over-fitting phenomena. The distinguishing efficacy of the PCA, PLS-DA, and OPLS-DA models is presented in Table 1. The OPLS-DA model exhibited better fitness and higher predictability.

Table 1

The effects of model pattern and data acquiring types on the model fitness.
Model	Type	pre	R2X(cum)	R2Y(cum)	Q2(cum)
PCA-X	Positive ion	2	0.525
	Negative ion	2	0.689
PLS-DA	Positive ion	2	0.477	0.996	0.0674
	Negative ion	2	0.676	0.983	0.693
OPLS-DA	Positive ion	1 + 1 + 0	0.477	0.996	0.266
	Negative ion	1 + 1 + 0	0.676	0.983	0.468

3.4 Differential Metabolite Analysis

A variable importance in the projection (VIP) score from the OPLS-DA model > 1 and p < 0.05 were set as the criteria for differential metabolite screening. Based on the multivariant analysis, the significantly different metabolites between C and T1 groups were identified, and the parameters of compound name, VIP, P-value, fold change, and variation of these metabolites are presented in Table 2. In total, 38 differential metabolites (23 in positive ion mode and 15 in negative ion mode) were identified. In positive ion mode, the expression levels of eight metabolites decreased significantly: L-lysine, DL-citrulline, L-pipecolate, pipecolinic acid, D-glutamine, L-cystathionine and 1,4-naphthoquinone. In contrast, in the same group, the expression levels of 15 metabolites increased significantly: benzoic acid, thiamine phosphate, 3-methoxytyramine, azelaic acid, glycine, L-aspartic acid, hypoxanthine, urea, 19-nortestosterone, cyprodinil, N7-methylguanosine and spermidine (Table 2). In negative ion mode, the metabolites of D-mannose were significantly upregulated. Significant reductions were observed in the metabolites of L-homoserine, hydroxyphenyl lactic acid, glycerol 3-phosphate, citraconic acid, galactaric acid, methylmalonate, and succinic acid. In addition, the metabolites of 6-phosphogluconic acid trisodium salt, L-beta-imidazole lactic acid, methyl benzoate, N-lauryl diethanolamine, (R)-3-hydroxyisobutyric acid, 9,10-EODE, hydroxyphenyl lactic acid, lysope 14:0, lysope 16:0, nelarabine, and beta-hydroxyisobutyrate were excluded for being classified as artificial chemical compounds. This result could be used as the basis for a functional analysis of metabolic pathways. The differentially expressed metabolites were analyzed using the KEGG database, and functional pathways were identified.

Table 2

Significantly changed metabolites in *L. crocea* liver between C and T1 groups.
Ion mode	Compounds name	VIP	P value	Fold Change	variation
Positive	Benzoic Acid	1.8232	0.0225	1.5499	up
	thiamine phosphate	1.8596	0.0423	1.4155	up
	L-Lysine	1.9105	0.0293	0.6628	down
	3-Methoxytyramine	1.9059	0.0126	1.1073	up
	L-beta-Imidazole lactic acid	2.0592	0.0007	0.7087	down
	Azelaic acid	1.9879	0.0204	1.0830	up
	Glycine	1.8845	0.0391	1.5822	up
	N-Lauryl diethanolamine	1.7398	0.0366	1.2123	up
	L-aspartic Acid	1.8814	0.0284	2.0322	up
	DL-Citrulline	1.8448	0.0326	0.7198	down
	L-Pipecolate	1.8814	0.0210	0.7003	down
	Hypoxanthine	1.9090	0.0141	1.2887	up
	6-Phosphogluconic Acid Trisodium Salt	1.8289	0.0242	1.2413	up
	1,4-Naphthoquinone	1.8130	0.0464	0.7255	down
	Pipecolinic Acid	1.9922	0.0072	0.7057	down
	Urea	1.8790	0.0319	1.6253	up
	D-Glutamine	1.9339	0.0310	0.7000	down
	19-Nortestosterone	1.8049	0.0258	1.2407	up
	Cyprodinil	1.9913	0.0061	1.1505	up
	N7-Methylguanosine	1.9794	0.0389	2.6764	up
	Spermidine	1.8792	0.0131	1.2128	up
	L-Cystathionine	1.9669	0.0229	0.7671	down
	Methyl Benzoate	1.8564	0.0305	1.3324	up
Negative	beta-Hydroxyisobutyrate	2.0493	0.0227	1.7472	up
	Nelarabine	1.9517	0.0286	1.7064	up
	L-Homoserine	2.1145	0.0228	0.5687	down
	Lysope 16:0	1.9247	0.0297	0.6422	down
	Hydroxyphenyl lactic acid	2.1718	0.0000	0.3382	down
	Dl-P-Hydroxyphenyl lactic acid	2.1806	0.0001	0.3832	down
	9,10-EODE	2.1349	0.0042	0.5238	down
	(R)-3-Hydroxyisobutyric acid	1.9961	0.0274	1.7170	up
	D-Mannose	2.0208	0.0199	1.6612	up
	Glycerol 3-phosphate	2.0645	0.0110	0.5049	down
	Citraconic acid	1.9235	0.0481	0.5115	down
	Galactaric acid	2.1289	0.0328	0.7230	down
	Methylmalonate	2.1260	0.0177	0.5863	down
	Succinic acid	2.1531	0.0190	0.5838	down
	Lysope 14:0	1.8089	0.0467	0.6744	down

3.5 Clustering and correlation analysis of the identified differential metabolites

Cluster analysis was used to compare the variation of identified differential metabolites between C and T1 groups. As shown in Fig. 5.a, in positive ions mode, except the artificial chemical compounds of 6-phosphogluconic acid trisodium Salt, L-beta-imidazole lactic acid, methyl benzoate and N-Lauryl diethanolamine, benzoic acid, thiamine phosphate, 3-methoxytyramine, azelaic acid, glycine, L-aspartic acid, hypoxanthine, urea, 19-nortestosterone, cyprodinil, N7-methylguanosine and spermidine were nested into one cluster and were significantly upregulated, whereas the metabolites of L-lysine, L-beta-imidazole lactic acid, DL-citrulline, L-pipecolate, pipecolinic acid, D-glutamine, L-cystathionine and 1,4-naphthoquinone were clustered together and were significantly downregulated after L. crocea was exposed to simulated transport. As illustrated in Fig. 5.b, except for the artificial chemical compounds (R)-3-hydroxyisobutyric acid, 9,10-EODE, hydroxyphenyl lactic acid, lysope 14:0, lysope 16:0, nelarabine, and beta-hydroxyisobutyrate, significant downregulation of L-homoserine, hydroxyphenyl lactic acid, glycerol 3-phosphate, citraconic acid, galactaric acid, methylmalonate, and succinic acid was observed, while D-mannose dramatically increased in the liver of L. crocea in the T1 group. This result indicated that the metabolism of lipids, amino acids, and nucleotides were involved in the simulated transport response.

In addition, the correlation analysis further illustrated the interactions of differential metabolites (Figs. 5c and d). The metabolites of benzoic acid, thiamine phosphate, 3-methoxytyramine, azelaic acid, glycine, L-aspartic acid, hypoxanthine, urea, 19-nortestosterone, cyprodinil, N7-methylguanosine, spermidine, and methyl benzoate exhibited significantly positive correlations, indicating their involvement in positive regulation of amino acid metabolism of L. crocea exposed to simulated transport. Moreover, significantly positive correlations between DL-citrulline, L-pipecolate, pipecolinic acid, D-glutamine, L-cystathionine, and 1,4-naphthoquinone were observed, suggesting the positive regulation of biosynthesis of amino acids. In negative ion mode, the metabolites of L-homoserine, hydroxyphenyl lactic acid, glycerol 3-phosphate, citraconic acid, galactaric acid, methylmalonate, and succinic acid exhibited significant positive correlations indicating their involvement in lipid and amino acid metabolism.

3.6 Metabolic pathway analysis of the identified differential metabolites

The significantly differential metabolites were subjected to KEGG pathway analysis, and the most relevant pathways were identified based on p < 0.05. The bubble plot (Fig. 6) shows enrichment of differential metabolites in signaling pathways. We identified 20 metabolic pathways with significant differences between the C and T1 groups. The metabolite pipecolinic acid was involved in lysine degradation, tropane, piperidine, pyridine alkaloid biosynthesis, biosynthesis of alkaloids derived from ornithine, lysine and nicotinic acid, metabolic pathways, and biosynthesis of secondary metabolites; glycine was involved in primary bile acid biosynthesis, purine metabolism, glycine, serine and threonine metabolism, lysine degradation, phosphonate and phosphinate metabolism, cyanoamino acid metabolism, glutathione metabolism, glyoxylate and dicarboxylate metabolism, methane metabolism, thiamine metabolism, porphyrin and chlorophyll metabolism, aminoacyl-tRNA biosynthesis, biosynthesis of various secondary metabolites - part 3, biosynthesis of plant secondary metabolites, metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, carbon metabolism, biosynthesis of amino acids, vancomycin resistance, ABC transporters, biofilm formation - Escherichia coli, neuroactive ligand-receptor interaction, synaptic vesicle cycle, protein digestion and absorption, mineral absorption, and central carbon metabolism in cancer. L-aspartic acid was involved in arginine biosynthesis, alanine, L-aspartic acid, and glutamate metabolism, glycine, serine, and threonine metabolism, monobactam biosynthesis, cysteine and methionine metabolism, lysine biosynthesis, histidine metabolism, beta-alanine metabolism, cyanoamino acid metabolism, carbon fixation in photosynthetic organisms, nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, aminoacyl-tRNA biosynthesis, biosynthesis of various secondary metabolites - part 3, biosynthesis of plant secondary metabolites, biosynthesis of alkaloids derived from ornithine, lysine, and nicotinic acid, biosynthesis of plant hormones, metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, carbon metabolism, 2-Oxocarboxylic acid metabolism, biosynthesis of amino acids, ABC transporters, two-component system, bacterial chemotaxis, neuroactive ligand-receptor interaction, protein digestion and absorption, and central carbon metabolism in cancer. D-glutamine was involved in D-glutamine and D-glutamate metabolism and metabolic pathways, among others. This enriched KEEG pathway analysis showed that important components of metabolic pathways, including the urea cycle, amino acid metabolism, and lipid metabolism, were affected by simulated transport.

The affected metabolic pathways were further integrated. As shown in Fig. 7, simulated transport significantly affected material metabolism. The significant reduction in the levels of DL-citrulline and the accumulation of L-aspartic acid suggested the activation of amino acid metabolism and the arginine pathway when fish were exposed to simulated transport for 48 h. L-aspartic acid is a metabolite of arginine and plays a vital role in the urea cycle. The increases in the quantities of primary and secondary metabolites of L-aspartic acid (L-arginine succinic acid, fumarate, and arginine) revealed the promotion of the citrate cycle and urination in L. crocea exposed to simulated transport. In addition, the elevation of L-aspartic acid and glycine indicated the activation of glycolysis. These results suggested that L. crocea rely on the glycolysis pathway to compensate for the energy depletion when exposed to simulated transport. In addition, the metabolites of L-lysine regulate lysine biosynthesis, while the metabolites of L-lysine, DL-citrulline, and cystathionine are involved in the regulation of amino acid biosynthesis.

Generally, DL-citrulline, L-aspartic acid, glycine, and L-lysine are potential biomarkers involved in amino acid metabolism (arginine biosynthesis and serine and threonine metabolism). L-aspartic acid is an important precursor of CoA. D-glutamine is a nonessential amino acid but performs many important physiological functions; for example, it can regulate metabolism and immune status. Simulated transport provokes a response toward amino acid metabolism. In addition, the metabolite profiles (Table 2) were related with lipid metabolism. According to the above results, the levels of some amino acids in fish undergoing simulated transport were significantly altered, suggesting that amino acid metabolism was affected. L-lysine and D-glutamine levels increased significantly, and the glycine level decreased significantly in theT1 group (Table 2), indicating a substantial influence of simulated transport on amino acid metabolism.

Cortisol is one of the most important indicators of acute stress responses. Bolasina (2011) reported that capture, transport, high stocking density, and routine husbandry practices have the capacity to cause stress in flounder leading to higher cortisol levels. As a chronic stressor, rearing density leads to marked hierarchical interactions. Crowding, as a common aquaculture-related stressor, may lead to deteriorating health of fish. Barnett et al. (1998) demonstrated that in general terms, recovery from an acute stress takes place within 48 h or even less if the stress duration is only a few minutes. In this study with L. crocea, the T1 group showed no significant difference in cortisol levels compared to the C group as well as a lower serum glucose content, probably because L. crocea in the T1 group had reestablished their homeostasis and glucose was being used to meet the energy demand due to 72-h fasting (24 h initially and 48-h transport). Moreover, other secondary responses (LACT, AST, G6P, and LDH levels) were recorded. Olsen reported that anesthetics can induce hypoxia and increase the serum lactic acid concentration. In this study, the accumulation of serum lactic acid after anesthesia may indicate an increased reliance on anaerobic metabolism as fish respond to stressors. The decreases in the levels of AST, G6P, and LDH in serum indicate that MS-222 does not induce damage to tissues of L. crocea. Additionally, G6P is a vital enzyme controlling the rate and direction of pentose phosphate pathway metabolism. The lower G6P levels may indicate suppression of glycolysis. In addition, as the terminal enzyme of the glycolysis pathway, LDH reversibly converts pyruvate into lactate. The reduction of LDH activity indicates the inhibition of gluconeogenesis during simulated transport. The concentrations of GLU, G6P, and LDH decreased, whereas lactic acid had an elevated concentration, indicating that L. crocea were recovering from anesthetization.

The increases in the levels of L-aspartate and aspartate together with the reductions in the levels of L-lysine and L-homoserine illustrated that amino acid metabolism was involved in the response mechanism of L. crocea when exposed to simulated transport. Endogenous spermidine leads to autophagy of cells. Eisenberg et al. (2009) reported that spermidine administration inhibited oxidative stress in ageing mice and that enhanced autophagy was crucial for enhanced longevity. This may indicate that spermidine suppresses oxidative stress by promoting the biosynthesis of proteins and preventing their degradation. In addition, the change in cystathione is one of the signs of metabolic activity. Moreover, Westerink et al. (2010) reported that 3-methoxytyramine is closely related to dopamine (DA) metabolism. The dopamine neuronal excitation leads to the release of 3-methoxytyramine and functions by activating DA receptors; 3-methoxytyramine facilitates the regulation of physical movement and pituitary secretion function as well as participating in mental and emotional activities. Thiamine phosphate is the coenzyme form of vitamin B1. Vitamin B1 plays an important role in maintaining normal nerve conduction as well as heart and digestive system activities. The increase in thiamine phosphate was consistent with the view that L. crocea were recovering from anesthetization.

When exposed to simulated transport, the liver biochemical indices and metabolome of L. crocea were significantly altered. A total of 38 differential metabolites were identified, and functional analysis indicted that the significantly different metabolites were mostly involved in the metabolism of amino acids that influence the survival rate of L. crocea. Among biochemical processes, coexisting differential metabolites could represent regulatory indicators of anesthesia transport. The results of the present study offer novel insights into the response of L. crocea to simulated transport and provide information for the regulation of live transport management.

Ethical Approval

All experimental and sampling procedures were conducted according to the Guidelines of the Animal Care of the Zhejiang Ocean University and were approved by the Ethics Committee of the university.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors' contributions

Yang Gao conceived the project; Yangchen Zhou conducted the project, wrote and revised the manuscript; Zhangjie Chu, Xiaolong Yin and Weiye Li participated in the discussions and provided valuable advice.

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This study was financially supported by national key research and development project (2019YFD0900901)，the project of science and technology of Zhoushan (2019C21016, 2021C31003), 2020 Second Municipal Modern Fisheries Development Project, Document No. 21 of the Zhoushan Treasury Bureau (2020), Cooperative extension plan of major agricultural technologies in Zhejiang province (2020XTTGSC04).

Availability of data and materials

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Effects of transportation on physiological indices and metabolomics of the large yellow croaker Larimichthys crocea

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Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Preparation of Large Yellow Croaker

2.2 Experimental Design

2.3 Sample Treatment

2.4 Metabolite Detection Analysis

2.5 Data Processing and Statistical Analysis

2.6 Metabolite Identification and Pathway Analysis

3 Results

3.1 Analysis of liver biochemical indices

3.2 Quality Control of Raw Data

3.3 Metabolic Profiles and Data Analysis

3.4 Differential Metabolite Analysis

3.5 Clustering and correlation analysis of the identified differential metabolites

3.6 Metabolic pathway analysis of the identified differential metabolites

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

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