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Article

Metabolic Response in the Gill Tissue of Juvenile Black-Shelled Pearl Oyster (Pinctada fucata martensii) under Salinity Stress

1
Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China
2
Pearl Research Institute, Guangdong Ocean University, Zhanjiang 524088, China
3
Pearl Breeding and Processing Engineering Technology Research Centre of Guangdong Province, Zhanjiang 524088, China
4
Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Culture, Zhanjiang 524088, China
5
Guangdong Science and Innovation Center for Pearl Culture, Zhanjiang 524088, China
*
Author to whom correspondence should be addressed.
Fishes 2024, 9(9), 366; https://doi.org/10.3390/fishes9090366
Submission received: 2 August 2024 / Revised: 4 September 2024 / Accepted: 7 September 2024 / Published: 22 September 2024

Abstract

Salinity significantly affects shellfish metabolism and growth. In this study, we evaluated the characterization of metabolomic differences in the juvenile black-shelled pearl oyster, Pinctada fucata martensii, under 15‰ (LSG), 25‰ (CG), and 35‰ (HSG) salinity conditions. Non-targeted metabolomics analyses revealed that salinity stress altered the metabolism of pearl oyster. A total of 229 significant differential metabolites (SDMs) were identified between LSG and CG via an in-house MS2 database, 241 SDMs were identified between LSG and HSG, and 50 SDMs were identified between CG and HSG. The pathway analysis showed that 21 metabolic pathways were found between LSG and CG, such as arginine and proline metabolism, glycerophospholipid metabolism, and pentose and glucuronide interconversion. A total of 23 metabolic pathways were obtained between LSG and HSG, such as aspartate, alanine, and glutamate metabolism. Only aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, and biotin metabolism were enriched between CG and HSG. A further integrated analysis suggested that amino acid metabolism might participate in osmoregulation and energy metabolism to respond to salinity stress in P. f. martensii, and the metabolic pathways differed under varying salinity stress conditions. In addition, low salinity stress might promote apoptosis in pearl oysters. Altogether, these results clarify the salinity tolerance mechanism of pearl oysters.
Key Contribution: Our findings clarify the salt tolerance mechanism of pearl oysters and will aid the breeding of pearl oysters with enhanced salt tolerance.

1. Introduction

Pinctada fucata martensii (i.e., Pinctada martensii), a pearl oyster mainly occurring in the tropical and subtropical regions, is primarily cultivated for marine pearl production [1]. P. f. martensii cultivation has been a considerable source of income for China and significantly promoted its marine pearl industry [2]. However, massive mortality events of P. f. martensii have occurred frequently over the last 10 years, and this has been responsible for major economic losses. Several studies conducted on these mass mortality events indicated that salinity, dissolved oxygen, and temperature can inhibit growth, disrupt metabolic homeostasis, alter physiological indexes (cellular immunity, osmotic pressure, and rate of ammonia excretion and oxygen consumption), and even lead to the death of P. f. martensii [2,3,4,5]. These reports suggest that the high P. f. martensii mortality may be closely associated with variations in environmental factors.
Salinity, a crucial ecological factor of marine ecosystems, may be affected by anthropogenic factors, as well as storms, tides, and heavy rainfall. Fluctuations in salinity may affect the growth, osmotic regulation, reproduction, and survival of several marine organisms [6,7]. In bivalves, variations in salinity can affect the biological rhythms, metabolite distribution and metabolism, and osmotic condition. Moreover, severe changes in salinity may affect the physiology of bivalves, causing their death and subsequent economic losses [6,8,9]. Additionally, some studies found that salinity stress can decrease the feeding activity, growth rate, respiration, and filtration ability of marine bivalves [10]. A study found that organisms exhibit low energy consumption and high energy conversion efficiency, consequently showing maximum growth, in isotonic media [11].
The favorable range for the adult stage of pearl oyster (P. martensii) is 18–30‰ [12], and salinity stress can induce physiological and biochemical changes in the pearl oyster. For instance, increases in salinity can decrease the rate of ammonia excretion and oxygen consumption of the pearl oyster [13]. Additionally, short-term low salinity stress can decrease the catalase and lysozyme activities of the pearl oyster, increasing their susceptibility to diseases [2]. Moreover, pearl oysters restore their aquaporin expression to what is observed at the control salinity level (27‰) when cultured for 72 h at low salinity (16‰) and 168 h at high salinity (36‰) [9]. Metabolomics is the study of metabolites (e.g., small peptides, amino acids, and sugars) involved in chemical processes [14], and it can clarify various physiological and biochemical aspects of organisms. Metabolomics has clarified the responses of aquatic animals to environmental stress, particularly in hypoxic stress [5,15,16], microplastic stress [17,18], salinity stress [19,20,21,22], and so on. However, metabolomic changes in pearl oyster P. f. martensii under salinity stress have not yet been characterized. This study explored the changes in gill tissue metabolism of the juvenile black-shelled pearl oyster P. f. martensii against salinity stress to enhance our understanding of their salt tolerance response and provide a scientific basis for its selective breeding.

2. Materials and Methods

2.1. Experimental Design

The juvenile black-shelled pearl oyster P. f. martensii (35.84 ± 4.38 mm in average shell length) was used as the study organism [23]. After gently cleaning the shell surface to remove the biofouling, pearl oysters were temporarily reared for 14 d in 300 L cylindrical drums (diameter: 70 cm; height: 81 cm). They were fed Chlorella sp. (20,000 cells/mL) and Platymonas subcordiformis (10,000 cells/mL) in the morning and evening, respectively. The water was aerated continuously, and the temperature and salinity were maintained at 26.5 ± 1.2 °C and 25‰, respectively.
The pearl oysters (N = 900) were distributed into 3 salinity groups: 15‰ (low salinity group, LSG), 25‰ (control group, CG), and 35‰ (high salinity group, HSG). The salinity was slowly adjusted (<3‰ per day) in each plastic bucket to the desired salinity treatment. Sea water diluted with tap water was used for LSG (15‰), and the water of HSG (35‰) was prepared artificially by dissolving sea salt in water. Each group had 3 replicates (100 pearl oysters/replicate). The salinity experiment was performed in 9300 L plastic buckets. During the experiment, pearl oysters were subjected to the same rearing conditions as described above, and the seawater was replaced daily. And water temperature was 25.5–28.0 °C, and dissolved oxygen was >6.00 mg/L. Pearl oysters were sampled after 30 d of salinity stress. Each group comprised 6 randomly dissected gill tissue samples, and samples were frozen in liquid nitrogen and stored at −80 °C until analysis.

2.2. Metabolomics Analysis

2.2.1. Metabolite Extraction and Liquid Chromatography–Mass Spectrometry (LC-MS) Assay

Six gill tissues were collected from each group. The gill tissue samples (25 mg) were weighed at low temperature and added to microcentrifuge tubes, followed by homogenizing beads (3.2 mm, Jingxin Industrial Development Co., Ltd., Shanghai, China) and 500 μL of the extraction solution (2:2:1 v/v, methanol:acetonitrile:water) with isotopically labeled internal standards (Succinic-2,2,3,3-d4 Acid, 2 μmol/L (negative); L-Leucine-d3, 8 μmol/L (negative); L-Glutamic-2.4.4-d3 Acid, 2 μmol/L (negative); 4-Aminobutyric-2,2,3,3,4,4-d6 Acid, 1 μmol/L (positive); Nicotinamide-2,4,5,6-[d4], 0.6 μmol/L (positive); and L-Glutamic-2,4,4-d3 Acid, 12 μmol/L (positive)). Thereafter, the mixture was homogenized (35 Hz) for 4 min in a homogenizer, after vortexing for 30 s, and transferred to a freezing bath for sonication for 5 min; this process was repeated thrice. After incubation at −40 °C for 1 h, the samples were centrifuged for 15 min at 12,000 rpm (13,800× g, radius 8.6 cm) and 4 °C. Equivalent portions of supernatant were added to generate quality control samples.
LC-MS was performed using the Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm × 1.7 μm) along with the Vanquish ultra-high performance liquid chromatograph (Thermo Fisher Scientific, Waltham, MA, USA) with a sample plate temperature of 4 °C and an injection volume of 2 μL. The A and B solvents consisted of ammonia:ammonium acetate (25 mmol/L, 1:1) and acetonitrile, respectively.
Mass spectrometry (primary and secondary) was conducted using the Orbitrap Exploris 120 mass spectrometer, set at the following parameters—MS/MS resolution: 15,000; capillary temperature: 320 °C; aux gas flow rate: 15 Arb; stepped normalized collisional energies: 20/30/40; spray voltage: 3.8 kV (positive) or −3.4 kV (negative); full MS resolution: 60,000; and sheath gas flow rate: 50 Arb. The data were collected using the Xcalibur v4.4 software (Thermo).

2.2.2. Data Pre-Processing and Metabolite Identification

Raw data in mzXML format were obtained using ProteoWizard (3.0.21229). Thereafter, peaks were identified, extracted, aligned, and integrated using an in-house XCMS kernel-based R package. Lastly, the data were searched against BiotreeDB v2.1, an in-house secondary mass spectrometry database, and the substances were annotated at a 0.3 cutoff value.

2.3. Data Analysis

To ensure that the results highlight the biological significance of the metabolites and decrease the effect of detection system errors, the raw data were subjected to a series of processing steps. This included filtering data with excessive deviation and missing values, normalizing the data, and filling missing values. Subsequently, metabolites were identified and annotated using an in-house secondary mass spectrometry database BiotreeDB at a 0.3 cut-off value. SIMCA16.0.2 was used to perform multivariate analyses of the data. Thereafter, orthogonal projections to latent structures discriminant analysis (OPLS-DA) and principal component analysis (PCA) were conducted. The relative contribution of each principal component was determined by generating their variable importance in projection (VIP) values. Significant differential metabolites (SDMs) were identified according to VIP > 1 and p value < 0.05 (Student’s t-test). The metabolic pathways associated with the identified SDMs were determined using the MetaboAnalyst v4.0 (https://www.metaboanalyst.ca/ (accessed on 10 February 2024)) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.

3. Results

3.1. Gill Tissue Metabolite Profiling of the Juvenile Black-Shelled P. f. martensii Following Salinity Stress

Figure 1A–C show the PCAs of the LC-MS metabolic profiles of the gill tissue of juvenile black-shelled pearl oyster P. f. martensii following salinity stress. The R2X values for LSG (15‰) and CG (25‰), LSG (15‰) and HSG (35‰), and CG (25‰) and HSG (35‰) were 0.709, 0.584, and 0.686, respectively. There were no outliers in the analyzed samples, as indicated by Hotelling’s T-squared ellipse 95% confidence intervals.
The OPLS-DA model scores of the LC-MS metabolic profiles of the juvenile black-shelled pearl oyster P. f. martensii gill tissue following salinity stress are shown in Figure 1D–F. The samples showed no outliers, as indicated by Hotelling’s T-squared ellipse 95% confidence intervals. The values for the R2X, R2Y, and Q2 using the OPLS-DA model between LSG (15‰) and CG (25‰) were 0.538, 0.981, and 0.782, respectively. The values for the R2X, R2Y, and Q2 using the OPLS-DA model between LSG (15‰) and HSG (35‰) were 0.410, 0.997, and 0.788, respectively. The values for the R2X, R2Y, and Q2 using the OPLS-DA model between CG (25‰) and HSG (35‰) were 0.482, 0.987, and 0.450, respectively. This indicates that the OPLS-DA model had high stability, adaptability, and predictability.
Permutation tests (200) were performed to evaluate the statistical significance of the model and avoid its overfitting. R2Y and Q2 values of the random model were obtained by conducting permutation tests after randomly altering the ranking of Y (categorical variable; Figure 1G–I). The R2Y and Q2 intercepts for LSG (15‰) and CG (25‰) were 0.91 and −0.58, respectively; the R2Y and Q2 intercepts for LSG (15‰) and HSG (35‰) were 0.96 and −0.46, respectively; and the R2Y and Q2 intercepts for CG (25‰) and HSG (35‰) were 0.91 and −0.13, respectively. Thus, the OPLS-DA model was not overfit and was suitable and stable for further analyses.

3.2. Identification and Screening of SDMs in the Gill Tissue of the Juvenile Black-Shelled P. f. Martensii under Different Salinity Levels

The SDMs were determined at p < 0.05 and VIP > 1. Red, blue, and gray correspond to up-regulation, down-regulation, and non-significant differences in metabolites in Figure 2. A total of 4093 SDMs were detected in the three comparison groups, of which 1612 were identified between LSG (15‰) and CG (25‰), 1945 were achieved in the LSG (15‰) and HSG (35‰), and 536 were identified in the CG (25‰) and HSG (35‰). Among these, 229 SDMs were identified between LSG (15‰) and CG (25‰) (Figure 3), 241 SDMs were identified between LSG (15‰) and HSG (35‰) (Figure 4), and 50 SDMs were identified between CG (25‰) and HSG (35‰) (Figure 5). Compared to CG (25‰), 148 identified SDMs and 24 identified SDMs had higher concentrations in LSG (15‰) and HSG (35‰), respectively. Compared to LSG (15‰), 84 identified SDMs had higher concentrations in HSG (35‰).

3.3. Metabolic Pathway Enrichment Analysis of the SDMs in the Gill Tissue of the Juvenile Black-Shelled Pearl Oysters Following Salinity Stress

The effects of salinity on juvenile black-shelled P. f. martensii gill tissue metabolism were determined by conducting pathway enrichment analysis of the SDMs using the MetaboAnalyst and KEGG databases. Twenty-one metabolic pathways were found between groups LSG (15‰) and CG (25‰), and enriched metabolic pathways included arginine and proline metabolism, glycerophospholipid metabolism, methane metabolism, fructose and mannose metabolism, and pentose and glucuronide interconversion (Figure 6A). Twenty-three metabolic pathways were detected between LSG (15‰) and HSG (35‰), and enriched metabolic pathways included methane metabolism and pentose and glucuronide interconversion (Figure 6B). Lastly, only aminoacyl-tRNA biosynthesis, biotin metabolism, and cysteine and methionine metabolism were enriched between CG (25‰) and HSG (35‰) (Figure 6C).

4. Discussion

The osmoregulation of shellfish is a complex process involving different tissues; within these tissues, various metabolic processes take place, including osmotic fluid transfer and lipid and energy substance metabolism. Since metabolites play distinct roles in different metabolic pathways, the metabolic profiling of the juvenile black-shelled P. f. martensii gill tissue at different salinity levels can provide insights into the osmoregulatory mechanisms of juvenile pearl oysters under salinity stress.
The regulation of inorganic ions (i.e., rapid release or accumulation of Na+, K+, and Cl) and intracellular free amino acids (FAAs) is essential for osmoregulation [24,25]. FAAs are important organic osmoregulators that account for approximately 30% of all osmoregulators in bivalves, and they have been shown to be involved in the osmoregulation of several marine mollusks, including Sinonovacula constricta [26], Crassostrea gigas [27], and Meretrix Iusoria [28], under salinity stress. Our results show that the pearl oysters in the LSG group (15‰) had a higher level of aspartate and a lower level of metabolites (L-leucine, L-isoleucine, valyl-glycine, and proline) compared to CG (25‰). Additionally, the level of metabolites (N-acetyl-L-aspartate and L-aspartic acid) were significantly lower in the LSG group (15‰) compared to HSG (35‰), while the level of L-methionine was significantly higher in the HSG group (35‰) compared to CG (25‰). These results suggest that they are key osmoregulatory FAAs. Taurine, proline, alanine, glycine, and glutamate are the key osmolytic FAAs in C. gigas [29] and C. hongkongensis [30]. Different FAAs may be used to maintain osmotic equilibrium in different bivalves [31]. Isoleucine is involved in energy production and immunity [32]. Proline plays a key role in protein protection, ROS detoxification, and cellular osmoregulation [33]. A study found that the gills of razor clams (Sinonovacula constricta) have higher proline and isoleucine levels at 35‰ salinity than at 20‰ salinity [23]. Reports suggest that environmental stress can affect proline accumulation in mollusks [16]. We found that low salinity stress significantly decreased the proline concentration, indicating that the electrons generated by proline metabolism might facilitate adenosine triphosphate production under stress [33]. Aspartic acid is used in protein biosynthesis, and changes in the aspartic acid content can affect metabolic function [34]. Higher levels of aspartic acid were also found in Litopenaeus vannamei under sulfide exposure [35]. This indicates that amino acid metabolism might regulate osmoregulation in P. f. martensii, while the metabolic pathways of the amino acids involved differed under different salinity levels.
Osmoregulation is a highly energy-demanding process, and adequate energy is needed to maintain normal metabolic functions under stress. In this current study, levels of metabolites (glucose 6-phosphate, mannose 6-phosphate, fructose 6-phosphate, fructose 1-phosphate, and mannose 1-phosphate) increased in the LSG group (15‰) compared to CG (25‰), while they were no significant differences between the CG group (25‰) and HSG (35‰). These metabolites mainly participated in fructose and mannose metabolism and starch and sucrose metabolism, suggesting that pearl oysters conserve more energy to respond to low salinity stress. Carbohydrate metabolism provides most of the energy required for ion and osmotic regulation, which has been reported for tongue sole Cynoglossus semilaevis [36]. In addition to their ability to maintain osmotic homeostasis, amino acids may also promote ATP production in several tissues by acting as oxidative substrates, thus playing a crucial role in energy metabolism during osmoregulation [37,38]. A study on C. semilaevis found higher valine, glycine, glutamate, and leucine levels in the gills and higher plasma glutamate, methionine, and aspartic acid levels at high salinity (S50) than at low salinity (S30) and control (S0) conditions, respectively [36]. Leucine, as a major energy source in various organisms, regulates the energy metabolism of fish [32]. In Salmo gairdnerii R., 35–40% of leucine is oxidized under salinity stress, while the rest is converted into proteins [32,39]. Our results show that the LSG group’s (15‰) lower level of L-leucine suggests pearl oysters may consume L-leucine to increase energy consumption to respond to hypo-salinity stress.
Sphingolipids play key roles in cell differentiation, growth, and apoptosis. These biomarkers promote sphingomyelin production via hydrolase activity [40]. Cell membranes primarily comprise lipids [41]; phosphatidylcholine (PC) is abundant in phospholipid bilayers. Our study found significant higher levels of PC metabolites (LysoPC(22:5(7Z,10Z,13Z,16Z,19Z)), PC(38:2), LysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)), Phosphorylcholine, PC(34:3), LPC(18:4), Glycerophosphocholine, PC(15:0/18:3(9Z,12Z,15Z)), 1,2-Dieicosenoyl-sn-glycero-3-phosphocholine, and choline) in the LSG group (15‰) compared to CG (25‰). The content of sphingomyelin (SM(d18:1/18:0)) also increased, suggesting that low salt stress up-regulates sphingolipid metabolism. This increase in the SM content might have stemmed from ceramide production, which affects cell viability and promotes apoptosis. Consistently, another study found a significant up-regulation of several glycerophospholipids and phospholipids in Mercenaria mercenaria after 5 d under low salt stress, which facilitated the stabilizing of its cell structure and restoring membrane permeability [31], consistent with our findings. Another study revealed that low salinity stress affects the glycerophospholipid metabolism of C. hongkongensis [42]. Cellular swelling and lysis might occur in response to osmotic disturbance [43]. Apoptosis was also observed when the oyster C. hongkongensis [44] and pearl oyster P. maxima [21] were exposed to low salinity. Together, these results suggest that low salinity stress induces apoptosis in pearl oysters.

5. Conclusions

Changes in the levels of gill metabolites in black-shelled juvenile pearl oysters were examined under different salinity conditions. Several metabolites, such as free amino acids, lipids, and energy-related substances, responded to salt stress. These findings indicate that amino acid metabolism might participate in osmoregulation and energy metabolism in P. f. martensii; however, the metabolic pathways of the amino acids involved differed under varying salinity stress conditions. In addition, low salinity stress may aggravate apoptosis in pearl oysters.

Author Contributions

Conceptualization, J.L.; methodology, C.Q., C.Y., F.L. and Y.L.; writing—original draft preparation, C.Q. and J.L.; writing—review and editing, J.L.; project administration, Y.D.; funding acquisition, Y.D. and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key Research and Development Program of China (Grant No. 2022YFD2401204), National Natural Science Foundation of China (Grant No. 32102817), Department of Education of Guangdong Province (Grant No. 2020ZDZX1045 and 2021KCXTD026), and the Earmarked Fund for CARS-49.

Institutional Review Board Statement

The pearl oyster P. f. martensii is a lower invertebrate, and therefore, the study was not subject to ethical approval.

Informed Consent Statement

This study does not involve human research.

Data Availability Statement

Data will be made available upon request.

Acknowledgments

We are very grateful to the Marine Pearl Science and Technology Backyard in Leizhou of Guangdong for collecting samples. The authors would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript. Metabolomics analysis was assisted by Biotree Biotech Co., Ltd. (Shanghai, China).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. General metabolites changes in the gill tissue of blac-shelled pearl oysters P. f. martensii following salinity stress. (A) PCA, (D) OPLS-DA, and (G) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of LSG (15‰) and CG (25‰). (B) PCA, (E) OPLS-DA, and (H) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of LSG (15‰) and HSG (35‰). (C) PCA, (F) OPLS-DA, and (I) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of CG (25‰) and HSG (35‰).
Figure 1. General metabolites changes in the gill tissue of blac-shelled pearl oysters P. f. martensii following salinity stress. (A) PCA, (D) OPLS-DA, and (G) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of LSG (15‰) and CG (25‰). (B) PCA, (E) OPLS-DA, and (H) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of LSG (15‰) and HSG (35‰). (C) PCA, (F) OPLS-DA, and (I) OPLS-DA permutation test were derived from the LC/MS metabolomics profiles of CG (25‰) and HSG (35‰).
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Figure 2. Volcano plots of SDMs in the gill tissue of black-shelled pearl oysters P. f. martensii following salinity stress. (A) is the volcano plot for groups LSG (15‰) and CG (25‰), (B) is the volcano plot for groups LSG (15‰) and HSG (35‰), and (C) is the volcano plot for groups CG (25‰) and HSG (35‰). Each point indicates a metabolite, and the VIP value is correlated with the size of the point. Red and blue indicate significant up- and down-regulation, respectively; gray indicates non-significant differences in metabolites.
Figure 2. Volcano plots of SDMs in the gill tissue of black-shelled pearl oysters P. f. martensii following salinity stress. (A) is the volcano plot for groups LSG (15‰) and CG (25‰), (B) is the volcano plot for groups LSG (15‰) and HSG (35‰), and (C) is the volcano plot for groups CG (25‰) and HSG (35‰). Each point indicates a metabolite, and the VIP value is correlated with the size of the point. Red and blue indicate significant up- and down-regulation, respectively; gray indicates non-significant differences in metabolites.
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Figure 3. Hierarchical clustering analysis of SDMs in gill tissue in LSG (15‰) and CG (25‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
Figure 3. Hierarchical clustering analysis of SDMs in gill tissue in LSG (15‰) and CG (25‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
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Figure 4. Hierarchical clustering analysis of SDMs in gill tissue in LSG (15‰) and HSG (35‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
Figure 4. Hierarchical clustering analysis of SDMs in gill tissue in LSG (15‰) and HSG (35‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
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Figure 5. Hierarchical clustering analysis of SDMs in the gill tissue of black-shelled pearl oysters in CG (25‰) and HSG (35‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
Figure 5. Hierarchical clustering analysis of SDMs in the gill tissue of black-shelled pearl oysters in CG (25‰) and HSG (35‰). Colors indicate the relative metabolite level. Red = up-regulated; blue = down-regulated.
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Figure 6. Metabolic pathway enrichment analysis of SDMs in the gill tissue following salinity stress. (A) is the bubble chart for groups LSG (15‰) and CG (25‰), (B) is the bubble chart for groups LSG (15‰) and HSG (35‰), and (C) is the bubble chart for groups CG (25‰) and HSG (35‰). Each bubble corresponds to a metabolic pathway. Larger bubbles on the right side of the plot have larger effects. Darker bubbles on the top part of the plots have larger -log p values (i.e., greater enrichment).
Figure 6. Metabolic pathway enrichment analysis of SDMs in the gill tissue following salinity stress. (A) is the bubble chart for groups LSG (15‰) and CG (25‰), (B) is the bubble chart for groups LSG (15‰) and HSG (35‰), and (C) is the bubble chart for groups CG (25‰) and HSG (35‰). Each bubble corresponds to a metabolic pathway. Larger bubbles on the right side of the plot have larger effects. Darker bubbles on the top part of the plots have larger -log p values (i.e., greater enrichment).
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MDPI and ACS Style

Qin, C.; Lu, F.; Li, J.; Liao, Y.; Yang, C.; Deng, Y. Metabolic Response in the Gill Tissue of Juvenile Black-Shelled Pearl Oyster (Pinctada fucata martensii) under Salinity Stress. Fishes 2024, 9, 366. https://doi.org/10.3390/fishes9090366

AMA Style

Qin C, Lu F, Li J, Liao Y, Yang C, Deng Y. Metabolic Response in the Gill Tissue of Juvenile Black-Shelled Pearl Oyster (Pinctada fucata martensii) under Salinity Stress. Fishes. 2024; 9(9):366. https://doi.org/10.3390/fishes9090366

Chicago/Turabian Style

Qin, Chengru, Fenglan Lu, Junhui Li, Yongshan Liao, Chuangye Yang, and Yuewen Deng. 2024. "Metabolic Response in the Gill Tissue of Juvenile Black-Shelled Pearl Oyster (Pinctada fucata martensii) under Salinity Stress" Fishes 9, no. 9: 366. https://doi.org/10.3390/fishes9090366

APA Style

Qin, C., Lu, F., Li, J., Liao, Y., Yang, C., & Deng, Y. (2024). Metabolic Response in the Gill Tissue of Juvenile Black-Shelled Pearl Oyster (Pinctada fucata martensii) under Salinity Stress. Fishes, 9(9), 366. https://doi.org/10.3390/fishes9090366

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