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Characterization of metabolomic differences in peanut worm Sipunculus nudus between breeding and nonbreeding seasons
Aquaculture Reports 16 (2020) 100271
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Aquaculture Reports journal homepage: www.elsevier.com/locate/aqrep
Characterization of metabolomic differences in peanut worm Sipunculus nudus between breeding and nonbreeding seasons
T
Chuangye Yang1, Jiawei Zhang1, Ruzhuo Zhong, Zhicheng Guo, Qingheng Wang*, Zhe Zheng Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Sipunculus nudus Metabolomics Oocyte Development
Peanut worm, Sipunculus nudus, is an important economic and ecological species. This study aims to characterize differences in the coelomic fluid of S. nudus between breeding and nonbreeding seasons. Animals were obtained in July and November. The diameter of oocytes was measured and analyzed. Results showed that the number of oocytes formed a primary peak at a diameter of approximately 160 μm in July, while almost all oocytes were below 50 μm in November. A metabolomics approach based on LC–MS and GC–MS was used to investigate differences in coelomic fluid and gain insights into the mechanisms underlying the oocyte development of S. nudus. In the metabolomics assay, 44 and 32 significantly differential metabolites were obtained via LC–MS and GC–MS (VIP > 1 and P < 0.1), respectively. S. nudus changed its metabolic status under different development conditions. The analysis of further integrated key metabolic pathways showed that S. nudus possessed different capabilities for valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoid-quinone biosynthesis between breeding and nonbreeding seasons. S. nudus may exhibit high anabolic ability to store nutrients and hold the high activity of neurotransmitter precursors to regulate oocyte development during the breeding season. This metabolomics study is the first to identify crucial metabolites and key pathways to understand the metabolic mechanism of oocyte development. The results provide theoretical basis for artificial breeding of S. nudus.
1. Introduction Peanut worm, Sipunculus nudus, is widely distributed in the South China Sea region and located in various biotopes, particularly sandy beaches along the intertidal habitats of the seashore (Li et al., 2015). S. nudus is frequently referred to as the “cordyceps of the sea” in China because of its high nutritional and medicinal values (Shen et al., 2004). Moreover, S. nudus is cultured along the beaches without a supplemental artificial diet and with organisms depending on the nutrients from surface sediments (Adrianov and Maiorova, 2010). Therefore, S. nudus is an important economic and ecological species. Up to now, the exploitation and utilization of S. nudus primarily occur in the coastal regions of the Beibu Gulf in China. However, its culture relies entirely on seeds collected from the wild, which may result in rapidly decreasing wild resource because of overexploitation. Therefore, an artificial breeding technology is urgently needed to manage commercial species. The reproductive and development biology of Sipuncula has been studied (Rice, 1988; Lan et al., 2003; Schulze et al., 2007;
Adrianov and Maiorova, 2010). S. nudus has a simple body structure; it possesses the digestive tract and the posterior renal tube that is bathed in coelomic fluid but has no liver and pancreas. The germ cells of S. nudus have a unique developmental pattern with no visible gonads, and dissociated germ cells multiply and develop in coelomic fluid (Andrews, 1889). Coelomic fluid is a complex internal environment system, where many physiological and biochemical processes are completed (Xian and Huang, 2011). The exogenous yolk precursor substance of S. nudus may be secreted from the intestine to coelomic fluid (Wang et al., 2017). Hence, differences in coelomic fluid between breeding and nonbreeding seasons may play an important role in developing germ cells. However, the underlying mechanisms remain unknown. Metabolomics is an effective omic technique used to detect the overall complexity and essential changes in diverse biological systems (Yang et al., 2018a); this field involves the study of chemical processes involving metabolites. Metabolites comprise all compounds in a biological matrix and typically have low molecular weight; examples of metabolites are small peptides, oligonucleotides, sugars, organic acids,
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Corresponding author. E-mail address: [email protected] (Q. Wang). 1 Chuangye Yang and Jiawei Zhang contributed equally to this work. https://doi.org/10.1016/j.aqrep.2019.100271 Received 16 October 2019; Received in revised form 11 December 2019; Accepted 31 December 2019 2352-5134/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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Fig. 1. Frequency of different diameters of coelomic oocyte S. nudus (μm).
Fig. 2. PCA model score scatter plot, OPLS-DA model, and permutation test for groups S and W. The PCA model (A, B, and C), OPLS-DA model (D, E, and F), and permutation test of the OPLS-DA model (G, H and I) were derived from metabolomics profiles. A, D, and G were derived from the LCeMS POS ion mode; B, E, and H were derived from the LCeMS NEG ion mode; and C, F, and I were derived from the. GCeMS.
metabolomic approach. GC–MS is the most widely used because of its high resolution, high detection sensitivity, and numerous open-access spectral libraries (Hao et al., 2018), while cannot detect some thermally unstable metabolites. LC–MS is also very sensitive, and can detect a very wide range of metabolites with high mass accuracy, while lack of comprehensive spectral libraries (Young and Alfaro, 2018). In this study, we used a metabolomics approach based on LC–MS and GC–MS to characterize differences in coelomic fluid between breeding and nonbreeding seasons and to gain insights into the mechanisms underlying the oocyte development of S. nudus. Results provide a reference to procreation regulation and artificial breeding of S. nudus.
ketones, aldehydes, amino acids, lipids, steroids, alkaloids, and xenobiotics (Cappello et al., 2018; Yang et al., 2018b; Venter et al., 2018). Identification and integrative analysis of metabolites are vital for comprehensive characterization of metabolic mechanisms at the molecular and cellular levels under internal or external stimulating conditions to understand the physiological and biochemical status of biosystems and interpret biological principles (Hao et al., 2018). To date, metabolomics using high-throughput approaches, such as 1H-nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC–MS), and liquid chromatography-mass spectrometry (LC–MS), has been applied to the aquaculture industry, particularly in hatchery production (Young et al., 2015, 2016; Yang et al., 2019b), nutrition and diet (Ma et al., 2017; Yang et al., 2019a), growth (Hao et al., 2018, 2019), and disease and immunology (Guo et al., 2015; Li et al., 2016; Zeng et al., 2016). Langenbuch et al. (2006) reported the effects of environmental hypercapnia on S. nudus physiology by using NMR-based 2
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Fig. 3. Hierarchical clustering analysis for the SDMs of LC–MS. The relative metabolite level is depicted according to the color scale. Red indicates upregulation, whereas green indicates downregulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
and incubated for 1 h at −20 °C to precipitate proteins. The solution was centrifuged at 12,000 rpm for 15 min at 4 °C. The supernatant was transferred to LC–MS vials and stored at −80 °C until UHPLC-QE Orbitrap/MS analysis. A sample for quality control (QC) was prepared by mixing an equal aliquot of the supernatants from all of the samples.
2. Materials and methods 2.1. Experimental design S. nudus adults (15.94 ± 3.67 g in mean total weight) were obtained from Zhanjiang Green Bay Aquatic Science and Technology Co., Ltd. (21°20′N, 109°48′E) in July and November 2017. The worms were divided into two experimental groups, namely, S (July) and W (November).
2.3.2. LC–MS analysis LC–MS/MS analyses were performed using an UHPLC system (1290, Agilent Technologies) with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) coupled to Q Exactive (Orbitrap MS, Thermo). The mobile phase A comprised 0.1 % formic acid in water for positive and 5 mmol/ L ammonium acetate in water for negative. The mobile phase B was acetonitrile. The elution gradient was set as follows: 0 min, 1 % B; 1 min, 1 % B; 8 min, 99 % B; 10 min, 99 % B; 10.1 min, 1 % B; 12 min, 1 % B. The flow rate was 0.5 mL/min. The injection volume was 2 μL. A QE mass spectrometer was used to record MS/MS spectra on an information-dependent basis during the LC–MS experiment. In this mode, acquisition software (Xcalibur 4.0.27, Thermo) continuously evaluates the full-scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on the preselected criteria. The ESI source conditions were set as follows: sheath gas flow rate of 45 Arb, Aux gas flow rate of 15 Arb, capillary temperature of 400 °C, full ms resolution of 70000, MS/MS resolution of 17500, collision energy of 20/40/60 eV in NCE model, and spray voltage of 4.0 kV (positive, POS) or −3.6 kV (negative, NEG).
2.2. Sample collection Coelomic fluid was obtained with a sterile syringe, and an Olympus BX51 optical microscope was used to observe cells. Forty individuals were sacrificed every time, and three times a month. A total of 100–300 cells for each individual were measured, counted, and displayed with histogram. A 5 mL disposable sterile syringe was used to extract coelomic fluid from S. nudus (avoiding the internal organs of the worm). The sample was centrifuged at 3 000 r/min for 5 min at 4 °C. The supernatant was obtained, immediately kept in liquid nitrogen, and stored at −80 °C for metabolomics analysis. 2.3. LC–MS analysis 2.3.1. Metabolite extraction for LC–MS analysis Sample (100 μL) was transferred to an EP tube and added with 300 μL of methanol (containing internal standard 1 μg/mL). The solution was vortexed for 30 s, sonicated for 10 min in an ice-water bath,
2.3.3. Data preprocessing and analysis The raw data were converted into the mzXML format by using ProteoWizard and processed by MAPS software (version 1.0). The 3
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Fig. 4. Hierarchical clustering analysis for the SDMs of GC–MS. The relative metabolite level is depicted according to the color scale. Red indicates upregulation, whereas green indicates downregulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
sample was dried completely in a vacuum concentrator without heating, added with 20 μL of methoxy amination hydrochloride (20 mg/mL in pyridine), and incubated for 30 min at 80 °C. The sample aliquot was added with 30 μL of the bis-(trimethylsilyl)-trifluoroacetamide (BSTFA) regent (1 % trimethylchlorosilane, TMCS, v/v) and incubated for 1.5 h at 70 °C. The QC sample was added with 5 μL of FAMEs (in chloroform) when cooling to room temperature. All samples were analyzed by a gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer (GC-TOF-MS).
preprocessing results generated a data matrix that consisted of retention time, mass-to-charge ratio (m/z), and peak intensity. In-house MS2 database was applied for metabolite identification. The resulting three-dimensional data involving peak number, sample name, and normalized peak area were fed to SIMCA14.1 software package (V14.1, Sartorius Stedim Data Analytics AB, Umea, Sweden) for principal component analysis (PCA) and orthogonal projections to latent structure-discriminate analysis (OPLS-DA). PCA showed the distribution of the origin data. Supervised OPLS-DA was applied to obtain a high level of group separation and identify variables responsible for classification. Sevenfold cross validation was used to estimate the robustness and predictive ability of our model. Permutation test was proceeded to further validate the model. A loading plot was constructed on the basis of OPLS-DA and showed the contribution of variables to differences between the two groups. The first principal component of variable importance in the projection (VIP) was obtained to refine the analysis. If P < 0.1 and VIP > 1 (Chen et al., 2015; Hao et al., 2018), then the variable was defined as a significantly differential metabolite (SDM) between the groups (Saccenti et al., 2014).
2.4.2. GC–MS analysis GC-TOF-MS analysis was performed using an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The system utilized a DB-5MS capillary column coated with 5 % diphenyl cross-linked with 95 % dimethylpolysiloxane (30 m × 250 μm inner diameter, 0.25 μm film thickness; J&W Scientific, Folsom, CA, USA). A 1 μL aliquot of the analyte was injected in splitless mode. Helium was used as the carrier gas, the front inlet purge flow rate was 3 mL min−1, and the gas flow rate through the column was 1 mL min−1. The initial temperature was kept at 50 °C for 1 min, raised to 310 °C at a rate of 10 °C min−1, and kept for 8 min at 310 °C. The injection, transfer line, and ion source temperatures were 280 °C, 280 °C, and 250 °C, respectively. The energy was −70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with the m/z range of 50–500 at a rate of 12.5 spectra per second after a solvent delay of 6.17 min.
2.4. GC–MS analysis 2.4.1. Metabolite extraction for GC–MS analysis Sample (50 μL) was placed in 1.5 mL EP tubes for extraction with 200 μL of methanol and added with 5 μL of adonitol (1 mg/mL of stock in dH2O) as internal standard. The solution was subjected to vortex mixing for 30 s and centrifuged for 15 min at 12,000 rpm and 4 °C. The supernatant (180 μL) was transferred into fresh 1.5 mL EP tubes. About 20 μL was obtained from each sample and pooled as QC sample. The
2.4.3. Data preprocessing and analysis Chroma TOF 4.3X software of LECO Corporation and LECO-Fiehn Rtx5 database were used for raw peak exacting, data baseline filtering 4
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Fig. 5. Metabolomic view map of significant metabolic pathways characterized in coelomic fluid of S. nudus for groups S and W. Significantly changed pathways based on enrichment and topology analysis are shown. The x-axis represents the pathway enrichment, and the y-axis represents the pathway impact. Large sizes and dark colors represent the major pathway enrichment and high pathway impact values, respectively. Table 1 Metabolic pathways identified from the SDMs between groups S and W. Pathway
-ln(p)
Impact
SDMs
Valine, leucine and isoleucine biosynthesis Aminoacyl-tRNA biosynthesis Phenylalanine, tyrosine and tryptophan biosynthesis Butanoate metabolism Ubiquinone and other terpenoid-quinone biosynthesis Galactose metabolism Alanine, aspartate and glutamate metabolism Tyrosine metabolism
2.546 2.459 1.977 1.896 1.772 0.954 0.638 0.494
0.667 0.000 0.500 0.114 0.000 0.081 0.087 0.207
L-Leucine; L-Isoleucine L-Arginine; L-Isoleucine; L-Leucine; L-Tryptophan; L-Tyrosine L-Tyrosine; L-Tryptophan Butanal; Succinic acid semialdehyde L-Tyrosine Alpha-Lactose Succinic acid semialdehyde L-Tyrosine
2.5. Data analysis
and calibration, peak alignment, deconvolution analysis, peak identification, and peak area integration (Kind et al., 2009). Mass spectrum match and retention index match were considered in metabolite identification. Peaks detected in < 50 % of the QC samples or RSD > 30 % in the QC samples were removed (Dunn et al., 2011). The missing values of raw data were filled up by half of the minimum value. Internal standard normalization method was employed in data analysis. The resulting three-dimensional data including peak number, sample name, and normalized peak area were fed to SIMCA14.1 software package (V14.1, Sartorius Stedim Data Analytics AB, Umea, Sweden) for PCA and OPLS-DA. The first principal component of VIP was obtained to refine the analysis. The VIP values exceeding 1 were first selected as changed metabolites. In step 2, the remaining variables were assessed by Student’s t-test (P-value > 0.1). Variables were discarded between the two comparison groups.
Commercial databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/) as well as LC–MS and GC–MS were utilized to search for the pathways of SDMs.
3. Results 3.1. Characterization of oocytes in the coelomic fluid Oocytes in the coelomic fluid of S. nudus between breeding and nonbreeding seasons presented different diameter. In July, the number of oocytes formed a main peak at a diameter of approximately 160 μm (Fig. 1A). In November, almost all oocytes were below 50 μm (Fig. 1B). 5
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leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoidquinone biosynthesis between groups S and W (Table 1).
3.2. Overall data set and metabolic profiles A total of 3155 and 2817 valid peaks for coelomic fluid were obtained in POS and NEG after data preprocessing via LC–MS-based metabolomics, respectively. These valid peaks were matched for 113 (POS) and 40 (NEG) metabolites in the in-house MS2 database. A total of 369 peaks remained after filtering and denoising by GC–MS-based metabolomics. The LECO/Fiehn Rtx5 Metabolomics Library suggested that most of the peaks detected were endogenous metabolites; however, some of the peaks may belong to by-product derivatives. A total of 152 metabolites were identified; 32 of them were identified by mass spectrum matching, with a spectral similarity value (SV) of > 700.
4. Discussion The seed industry has occupied a leading strategic position in the aquaculture industry chain. Artificial breeding is a key technology utilized in the aquaculture industry of the peanut worm S. nudus. Research on the reproductive biology of S. nudus has become a top priority. Our previous research reported that the development of oocytes can be divided into four stages, namely, small-growth, largegrowth, premature, and mature stages; oocytes at different stages have different diameters (Wang et al., 2005). The oocytes in coelomic fluid develop and become mature with diameter reaching approximately 160 μm (Wang et al., 2017). In the present study, the number of oocytes formed a peak at a diameter of approximately 160 μm in July, which is the breeding period; November is the nonbreeding period. This result is consistent with those of previous studies (Wang et al., 2005; Lan and Yan, 2002). Cutler (1994) reported that the coelomic fluid of S. nudus contains various ions, strontium compounds, enzymes, antibiotics, pigments, cytotoxins, and metabolites. Thus, we surmise that differences in the coelomic fluid between breeding and nonbreeding seasons may influence oocyte development. The metabolic profiling and functional analysis of the key metabolic pathways in the coelomic fluid of S. nudus between breeding and nonbreeding seasons were performed via the proposed LC–MS- and GC–MS-based metabolomics approach. Most of the metabolites with significant differences between the two groups participated in valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoidquinone biosynthesis. Nutrition is important for oocyte development. Protein is an important component of the yolk (Mathieu and Lubet, 1993). In the present study, biosynthesis-related pathways, including valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; and ubiquinone and other terpenoid–quinone biosynthesis, were enriched. This finding suggests that S. nudus exhibited higher synthesis ability in the breeding season. In particular, the levels of L-arginine, L-isoleucine, L-leucine, L-tryptophan, and L-tyrosine, which participated in aminoacyl-tRNA biosynthesis, were higher in group S. Aminoacyl-tRNA biosynthesis can transfer amino acids to ribosomal synthetic proteins (Smith and Hartman, 2015). These results are consistent with a report that free ribosomes are the earliest increased organelles during the development of S. nudus oocytes (Wang et al., 2017). In addition, isoleucine and leucine are branched-chain amino acids and anabolic nutrient signals, which indicate the presence of an ingested protein-containing meal in peripheral tissues and the promotion of protein synthesis (Castillo and GatlinIII, 2018). Leucine is essential for the normal growth and reproductive potential of aquatic animals (National Research Council (NRC, 2011). Tryptophan is an essential amino acid that contains an α-amino group, an α-carboxylic acid group, and a side chain indole, which is involved in various biological functions, including protein synthesis and cellular growth (Nguyen et al., 2019). The analysis results for the upregulated amino acids suggest the higher protein synthesis ability of group S again. Previous studies also revealed that increases in the protein content in the ovaries correspond to increases in the oocyte diameters of oyster (Li et al., 2000). High synthesis protein ability may promote yolk accumulation, which helps develop oocytes in group S. Lipids are important components of oocytes and play an important role in gametogenesis (Marin et al., 2003). Fatty acid content is closely
3.3. Multivariate analysis of metabolite profiles PCA is an unsupervised pattern recognition method that can reveal intrinsic variations within data and reduce data dimensionality. In the PCA score scatter plot, similar datasets are clustered closely, whereas different datasets are placed further apart. The PCA of the LCeMS and GCeMS metabolic profiles is illustrated in Fig. 2 (A, B, and C). The R2X values of the PCA model between S and W were 0.557 (LCeMS POS), 0.570 (LCeMS NEG), and 0.610 (GCeMS). All samples in each score scatter plot were within the 95 % Hotelling’s T-squared ellipse. The metabolic datasets warranted further analysis. OPLS-DA was employed to elucidate different metabolic patterns and maximize the discrimination between the two groups. The OPLS-DA results for S. nudus are displayed in Fig. 2 (D, E, and F). All samples in each score scatter plot of the OPLS-DA model were inside the 95 % Hotelling’s T-squared ellipse. The R2X, R2Y, and Q2 values of the OPLS-DA model of LCeMS POS between S and W were 0.294, 0.994, and 0.739, respectively. The R2X, R2Y, and Q2 values of the OPLS-DA model of LCeMS NEG between S and W were 0.309, 0.998, and 0.799, respectively. The R2X, R2Y, and Q2 values of the OPLS-DA model of GCeMS between S and W were 0.344, 0.964, and 0.628, respectively. Permutation test was conducted for verification to avoid the transition fit of the OPLS-DA model; the results are shown in Fig. 2 (G, H, and I). The permutation test results for the R2Y and Q2 intercepts were 0.97 and −0.54 between S and W (LCeMS POS), 0.96 and −0.48 between S and W (LCeMS NEG), and 0.95 and −0.22 between S and W (GCeMS). The results indicated that the OPLS-DA model had no overfitting and possessed good stability. Hence, the model was suitable to be exploited in subsequent analyses. 3.4. SDMs SDMs were screened from all identified metabolites according to the principle that the P value of the t-test was < 0.1 and the VIP of the OPLS-DA model was > 1. The SDMs were visualized through hierarchical cluster analysis (Figs. 3 and 4). Forty-four SDMs were obtained between groups S and W via LC–MS-based metabolomics (Fig. 3, Supplementary Table 1). Compared with group W, group S had 39 SDMs with higher concentrations. Thirty-two SDMs were obtained between groups S and W by GC–MS-based metabolomics (Fig. 4, Supplementary Table 2). In contrast to group W, group S had 26 SDMs with higher concentrations. 3.5. Characterization and functional analysis of key metabolic pathways The SDMs, including those obtained from LCeMS and GCeMS, were imported into MetaboAnalyst 4.0 to explore different potential metabolic pathways in coelomic fluid between breeding and nonbreeding seasons. As shown in Fig. 5, the bubble plots demonstrate the main influential metabolic pathways, which involved SDMs in coelomic fluid. Sixteen metabolic pathways were found between groups S and W (Supplementary Table 3). On the basis of ln P-value and pathway impact scores, the relevant metabolic pathways were identified as valine, 6
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Declaration of Competing Interest
related to the development of gametes, and large amounts of polyunsaturated fatty acids are required during gametogenesis (Kluytmans et al., 1985). In the present study, lipid metabolism-related metabolic pathways were not enriched. Meanwhile, the contents of fatty acids such as palmitoleic acid (FC = 1.97), oleic acid (FC = 1.61), 17-octadecynoic acid (FC = 1.41), eicosa-5Z, 8Z-dienoic acid (20:2, n-12) (FC = 4.21), and 5, 6-dehydro arachidonic acid (FC = 4.50) were had significantly higher in group S. A similar trend was noted for phospholipids such as PC (16:0) (FC = 2.16), PC (20:5) (FC = 4.43), LysoPC (22:5) (FC = 5.97), LysoPC (15:0) (FC = 2.76), and PC (22:6) (FC = 5.04). The high contents of lipids in the coelomic fluid of S. nudus may imply yolk accumulation, which may promote the development of oocytes in group S. S. nudus possesses high synthesis ability to store nutrients in breeding season. Neurotransmitters also influence the development of germ cells. Tryptophan is not only involved in protein synthesis but also functions as a biochemical precursor for several compounds, including serotine and neurotransmitters (Nguyen et al., 2019). In cells, tryptophan is converted into 5-hydroxytryptophan under the catalysis of tryptophan hydroxylase and then decarboxylated by 5-hydroxytryptophan decarboxylase to form serotonin (Zhang et al., 2019). Serotonin induces vitellogenin levels, allows the breakdown of germinal vesicles and the completion of meiosis, and plays an important role in oocyte maturation and gamete release (Hamida et al., 2004; Tinikul et al., 2014). The present study also indicated that tryptophan participated in phenylalanine, tyrosine, and tryptophan biosynthesis and was significantly upregulated during breeding season. This outcome can partly explain the differences in the reproductive maturity of S. nudus between breeding and nonbreeding seasons. Tyrosine is also a common precursor for important hormones and neurotransmitters, including thyroxine, triiodothyronine, epinephrine, nor-epinephrine, dopamine, and melanin (Zehra and Khan, 2014). Dopamine is a common neurotransmitter that could regulate the synthesis and release of neurohormones. Dopamine receptor 1-like protein is upregulated in the ovaries of mature female Macrobrachium rosenbergii (Jiang et al., 2019). In the present study, tyrosine, a precursor of dopamine, was upregulated in group S. However, in Litopenaeus vannamei, dopamine injection inhibited ovarian maturation (Tinikul et al., 2014). These conflicting results might be related to different stages of ovarian development. A previous study on L. vannamei demonstrated that the concentration of dopamine was the highest in early oocytes and then decreased in late oocytes (Tinikul et al., 2011). Given the activity of neurotransmitter precursors, we speculated their important role in dynamic balance to regulate oocyte development. However, this speculation needs to be verified.
No conflicts of interest, financial or otherwise, are declared by the authors. Acknowledgements This work was supported by the Science and Technology Department, Guangdong Province (Grant number: 2017A030303075 and 2016A020209010), the Guangdong Ocean University Student's Plan for Innovation and Entrepreneurship (Grant number: 201810566049), the Graduate Education Innovation Program of Guangdong Ocean University (Grant number: 201836). Metabolomics analysis was assisted by Biotree Biotech Co., Ltd. (Shanghai, China). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.aqrep.2019.100271. References Adrianov, A.V., Maiorova, A.S., 2010. Reproduction and development of common species of peanut worms (Sipuncula) from the sea of Japan. Russ. J. Mar. Biol. 36 (1), 1–15. Andrews, E.A., 1889. The reproductive organs of Phascolosoma gouldii. Zool. Anz. 12, 140–142. Cappello, T., Giannetto, A., Parrino, V., Maisano, M., Oliva, S., Marcoet, G.D., Guerriero, G., Mauceri, A., Fasulo, S., 2018. Baseline levels of metabolites in different tissues of mussel Mytilus galloprovincialis (Bivalvia: Mytilidae). Comp. Biochem. Phys. D. 26, 32–39. Castillo, S., GatlinIII, D.M., 2018. Imbalanced dietary levels of branched-chain amino acids affect growth performance and amino acid utilization of juvenile red drum Sciaenops ocellatus. Aquaculture 497, 17–23. Chen, H.H., Tseng, Y.J., Wang, S.Y., Tsai, Y.S., Chang, C.S., Kuo, T.C., Yao, W.J., Shieh, C.C., Wu, C.H., Kuo, P.H., 2015. The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity. Int. J. Obesity 39 (8), 1241–1248. Cutler, E.B., 1994. The Sipuncula: Their Systematics, Biology, and Evolution. Cornell University Press, New York, pp. 268–274. Dunn, W.B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J.D., Halsall, A., Haselden, J.N., Nicholls, A.W., Wilson, I.D., Kell, D.B., 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083. Guo, C., Peng, B., Song, M., Wu, C.W., Yang, M.J., Zhang, J.Y., Li, H., 2015. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47 (2), 664–673. Hamida, L., Medhioub, M.N., Cochard, J.C., Pennec, M.L., 2004. Evaluation of the effects of serotonin (5-HT) on oocyte competence in Ruditapes decussatus (Bivalvia, Veneridae). Aquaculture 239 (1-4), 413–420. Hao, R.J., Wang, Z.M., Yang, C.Y., Deng, Y.W., Zheng, Z., Wang, Q.H., Du, X.D., 2018. Metabolomic responses of juvenile pearl oyster Pinctada maxima, to different growth performances. Aquaculture 491, 258–265. Hao, R.J., Du, X.D., Yang, C.Y., Deng, Y.W., Zheng, Z., Wang, Q.H., 2019. Integrated application of transcriptomics and metabolomics provides insights into unsynchronized growth in pearl oyster Pinctada fucata martensii. Sci. Total Environ. 666, 46–56. Jiang, Q., Min, Y., Yang, H., Wan, W.L., Zhang, X.J., 2019. De novo transcriptome analysis of eyestalk reveals ovarian maturation related genes in Macrobrachium rosenbergii. Aquaculture 505, 280–288. Kind, T., Wohlgemuth, G., Lee, D.Y., Lu, Y., Palazoglu, M., Shahbaz, S., Fiehn, O., 2009. Fiehnlib-mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81 (24), 10038–10048. Kluytmans, J.H., Boot, J.H., Oudejans, R.C.H.M., Zandee, D.I., 1985. Fatty acid synthesis in relation to gametogenesis in the mussel Mytilus edulis L. Comp. Biochem. Phys. B. 81 (4), 959–963. Lan, G.B., Yan, B., Liao, S.M., 2003. A study on embryonic and larval developments of Sipunculus nudus. Journal of Tropical Oceanography 22 (6), 70–76 (in Chinese with English abstract). Lan, G.B., Yan, B., 2002. The reproductive biology of peanut worm, Sipunculus nudus. J. Fish. China 26 (6), 503–509 (in Chinese with English abstract). Langenbuch, M., Bock, C., Leibfritz, D., Pörtnera, H.O., 2006. Effects of environmental hypercapnia on animal physiology: A 13C NMR study of protein synthesis rates in the marine invertebrate Sipunculus nudus. Comp. Biochem. Phys. A. 144 (4), 479–484. Li, J.W., Zhu, C.B., Guo, Y.J., Xie, X.Y., Huang, G.Q., Chen, S.W., 2015. Experimental study of bioturbation by Sipunculus nudus in a polyculture system. Aquaculture 437, 175–181. Li, T.Y., Li, E., Suo, Y.T., Xu, Z.X., Jia, Y.Y., Qin, J.G., Chen, L.Q., Gu, Z.M., 2016. Energy metabolism and metabolomics response of pacific white shrimp Litopenaeus vannamei
5. Conclusions S. nudus changes its metabolic status with different development performances. The analysis of further integrated key metabolic pathways showed that S. nudus possessed different capabilities for valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoidquinone biosynthesis between breeding and nonbreeding seasons. Therefore, S. nudus may exhibit high anabolic ability to store nutrients and hold the high activity of neurotransmitter precursors to regulate oocyte development during the breeding season. This metabolomics study is the first to identify crucial metabolites and key pathways and understand the metabolic mechanism of different levels of oocyte development. The results provide theoretical basis for the artificial breeding technology of S. nudus.
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Haliotis midae. Aquaculture 493, 144–152. Wang, Q.H., Du, X.D., Huang, H.Y., Qin, H.G., 2005. Development of germ cells and reproductive cycle of Sipunculus nudus L in Zhanjiang. J. Zhanjiang Ocean Univ. 25 (1), 5–9 (in Chinese with English abstract). Wang, Q.H., Zhang, J.W., Hao, R.J., Huang, R.L., Du, X.D., 2017. Ultrastructure of oogenesis in coelomic fluid of Sipunculus nudus. Oceanologia et Limnologia Sinica 48 (1), 57–66 (in Chinese with English abstract). Xian, X.J., Huang, R.L., 2011. Electropherograms analysis and determination of soluble proteins in the coelomic fluid of Sipunculus nudus. Guangdong Agricultural Sciences 1, 144–145 (in Chinese with English abstract). Yang, C.Y., Hao, R.J., Du, X.D., Deng, Y.W., Sun, R.J., Wang, Q.H., 2018a. Metabolomics responses of pearl oysters (Pinctada fucata martensii) fed a formulated diet indoors and cultured with natural diet outdoors. Front. Physiol. 9, 944. Yang, C.Y., Hao, R.J., Du, X.D., Wang, Q.H., Deng, Y.W., Sun, R.J., Zheng, Z., 2018b. GC–TOF/MS-based metabolomics studies on the effect of protein sources in formulated diet for pearl oyster Pinctada fucata martensii. Aquaculture 486, 139–147. Yang, C.Y., Hao, R.J., Du, X.D., Wang, Q.H., Deng, Y.W., Sun, R.J., 2019a. Response to different dietary carbohydrate and protein levels of pearl oysters (Pinctada fucata martensii) as revealed by GC-TOF/MS-based metabolomics. Sci. Total Environ. 650, 2614–2623. Yang, C.Y., Du, X.D., Hao, R.J., Wang, Q.H., Deng, Y.W., Sun, R.J., 2019b. Effect of vitamin D3 on immunity and antioxidant capacity of pearl oyster Pinctada fucata martensii after transplantation: insights from LC–MS-based metabolomics analysis. Fish Shellfish Immunol. 94, 271–279. Young, T., Alfaro, A.C., Villasbôas, S., 2015. Identification of candidate biomarkers for quality assessment of hatchery-reared mussel larvae via GC/MS-based metabolomics. N. Z. J. Mar. Freshw. Res. 49 (1), 87–95. Young, T., Alfaro, A.C., Villas-Bôas, S.G., 2016. Metabolic profiling of mussel larvae: effect of handling and culture conditions. Aquac. Int. 24 (3), 843–856. Young, T., Alfaro, A.C., 2018. Metabolomic strategies for aquaculture research: a primer. Rev. Aquacult. 10, 26–56. Zehra, S., Khan, M.A., 2014. Dietary phenylalanine requirement and tyrosine replacement value for phenylalanine for fingerling Catla catla (Hamilton). Aquaculture 433, 256–265. Zeng, Z.H., Du, C.C., Liu, S.R., Li, H., Peng, X.X., Peng, B., 2016. Glucose enhances tilapia against Edwardsiella tarda, infection through metabolome reprogramming. Fish Shellfish Immunol. 61, 34–43. Zhang, C., Zhang, Q., Song, X.Z., Pang, Y.Y., Song, Y.M., Wang, Y.Y., He, L., Lv, J.H., Cheng, Y.X., Yang, X.Z., 2019. L-tryptophan promotes the cheliped regeneration of Chinese mitten crab (Eriocheir sinensis) through melatonin, serotonin and dopamine involvement. Aquaculture 511, 734205.
to sulfide toxicity. Aquat. Toxicol. 183, 28–37. Li, Q., Osada, M., Mori, K., 2000. Seasonal biochemical variations in Pacific oyster gonadal tissue during sexual maturation. Fish. Sci. 66 (3), 502–508. Ma, Q.Q., Chen, Q., Shen, Z.H., Li, D.L., Han, T., Qin, J.G., Chen, L.Q., Du, Z.Y., 2017. The metabolomics responses of Chinese mitten-hand crab (Eriocheir sinensis) to different dietary oils. Aquaculture 479, 188–199. Marin, M.G., Moschino, V., Deppieri, M., Lucchetta, L., 2003. Variations in gross biochemical composition, energy value and condition index of T. Philippinarum from the Lagoon of Venice. Aquaculture 219 (1-4), 859–871. Mathieu, M., Lubet, P., 1993. Storage tissue metabolism and reproduction in marine bivalves-a brief review. Invert. Reprod. Develop. 23 (2–3), 123–129. National Research Council (NRC), 2011. Nutrient Requirements of Fish and Shrimp. National Academy Press, Washington, DC, pp. 57–101. Nguyen, L., Salem, S.M.R., Salze, G.P., Dinh, H., Davis, D.A., 2019. Tryptophan requirement in semi-purified diets of juvenile Nile tilapia Oreochromis niloticus. Aquaculture 502, 258–267. Rice, M.E., 1988. Observation on the development and metamorphosis of Siphonosoma cumanense with comparative remarks on Sipunculus nudus. B. mar. sci. 42 (1), 1–15. Saccenti, E., Hoefsloot, H.C.J., Smilde, A.K., Westerhuis, J.A., Hendriks, M.M.W.B., 2014. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics 10 (3), 361–374. Schulze, A., Cutler, E.B., Giribet, G., 2007. Phylogeny of sipunculan worms: a combined analysis of four gene regions and morphology. Mol. Phylogenet. Evol. 42 (1), 171–192. Shen, X.R., Jiang, D.W., Jia, F.X., Chen, M.H., 2004. Study on anti-senescence effect of Sipunculus nudus preparation. Chinese Journal of Marine Drugs 1, 30–32 (in Chinese with English abstract). Smith, T.F., Hartman, H., 2015. The evolution of Class II Aminoacyl-tRNA synthetases and the first code. FEBS Lett. 589 (23), 3499–3507. Tinikul, Y., Poljaroen, J., Kornthong, N., Chotwiwatthanakun, C., Anuracpreeda, P., Poomtong, T., Hanna, P.J., Sobhon, P., 2011. Distribution and changes of serotonin and dopamine levels in the central nervous system and ovary of the Pacific white shrimp, Litopenaeus vannamei, during ovarian maturation cycle. Cell Tissue Res. 345 (1), 103–124. Tinikul, Y., Poljaroen, J., Tinikul, R., Anuracpreeda, P., Chotwiwatthanakun, C., Senin, N., Poomtong, T., Hanna, P.J., Sobhon, P., 2014. Effects of gonadotropin-releasing hormones and dopamine on ovarian maturation in the Pacific white shrimp, Litopenaeus vannamei, and their presence in the ovary during ovarian development. Aquaculture 420-421, 79–88. Venter, L., Vosloo, A., Loots, D.T., Mienie, L.J., Rensburg, P.J.J.V., Lindeque, J.Z., 2018. Characterising the metabolic differences related to growth variation in farmed
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Characterization of metabolomic differences in peanut worm Sipunculus nudus between breeding and nonbreeding seasons
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Chuangye Yang1, Jiawei Zhang1, Ruzhuo Zhong, Zhicheng Guo, Qingheng Wang*, Zhe Zheng Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Sipunculus nudus Metabolomics Oocyte Development
Peanut worm, Sipunculus nudus, is an important economic and ecological species. This study aims to characterize differences in the coelomic fluid of S. nudus between breeding and nonbreeding seasons. Animals were obtained in July and November. The diameter of oocytes was measured and analyzed. Results showed that the number of oocytes formed a primary peak at a diameter of approximately 160 μm in July, while almost all oocytes were below 50 μm in November. A metabolomics approach based on LC–MS and GC–MS was used to investigate differences in coelomic fluid and gain insights into the mechanisms underlying the oocyte development of S. nudus. In the metabolomics assay, 44 and 32 significantly differential metabolites were obtained via LC–MS and GC–MS (VIP > 1 and P < 0.1), respectively. S. nudus changed its metabolic status under different development conditions. The analysis of further integrated key metabolic pathways showed that S. nudus possessed different capabilities for valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoid-quinone biosynthesis between breeding and nonbreeding seasons. S. nudus may exhibit high anabolic ability to store nutrients and hold the high activity of neurotransmitter precursors to regulate oocyte development during the breeding season. This metabolomics study is the first to identify crucial metabolites and key pathways to understand the metabolic mechanism of oocyte development. The results provide theoretical basis for artificial breeding of S. nudus.
1. Introduction Peanut worm, Sipunculus nudus, is widely distributed in the South China Sea region and located in various biotopes, particularly sandy beaches along the intertidal habitats of the seashore (Li et al., 2015). S. nudus is frequently referred to as the “cordyceps of the sea” in China because of its high nutritional and medicinal values (Shen et al., 2004). Moreover, S. nudus is cultured along the beaches without a supplemental artificial diet and with organisms depending on the nutrients from surface sediments (Adrianov and Maiorova, 2010). Therefore, S. nudus is an important economic and ecological species. Up to now, the exploitation and utilization of S. nudus primarily occur in the coastal regions of the Beibu Gulf in China. However, its culture relies entirely on seeds collected from the wild, which may result in rapidly decreasing wild resource because of overexploitation. Therefore, an artificial breeding technology is urgently needed to manage commercial species. The reproductive and development biology of Sipuncula has been studied (Rice, 1988; Lan et al., 2003; Schulze et al., 2007;
Adrianov and Maiorova, 2010). S. nudus has a simple body structure; it possesses the digestive tract and the posterior renal tube that is bathed in coelomic fluid but has no liver and pancreas. The germ cells of S. nudus have a unique developmental pattern with no visible gonads, and dissociated germ cells multiply and develop in coelomic fluid (Andrews, 1889). Coelomic fluid is a complex internal environment system, where many physiological and biochemical processes are completed (Xian and Huang, 2011). The exogenous yolk precursor substance of S. nudus may be secreted from the intestine to coelomic fluid (Wang et al., 2017). Hence, differences in coelomic fluid between breeding and nonbreeding seasons may play an important role in developing germ cells. However, the underlying mechanisms remain unknown. Metabolomics is an effective omic technique used to detect the overall complexity and essential changes in diverse biological systems (Yang et al., 2018a); this field involves the study of chemical processes involving metabolites. Metabolites comprise all compounds in a biological matrix and typically have low molecular weight; examples of metabolites are small peptides, oligonucleotides, sugars, organic acids,
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Corresponding author. E-mail address: [email protected] (Q. Wang). 1 Chuangye Yang and Jiawei Zhang contributed equally to this work. https://doi.org/10.1016/j.aqrep.2019.100271 Received 16 October 2019; Received in revised form 11 December 2019; Accepted 31 December 2019 2352-5134/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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Fig. 1. Frequency of different diameters of coelomic oocyte S. nudus (μm).
Fig. 2. PCA model score scatter plot, OPLS-DA model, and permutation test for groups S and W. The PCA model (A, B, and C), OPLS-DA model (D, E, and F), and permutation test of the OPLS-DA model (G, H and I) were derived from metabolomics profiles. A, D, and G were derived from the LCeMS POS ion mode; B, E, and H were derived from the LCeMS NEG ion mode; and C, F, and I were derived from the. GCeMS.
metabolomic approach. GC–MS is the most widely used because of its high resolution, high detection sensitivity, and numerous open-access spectral libraries (Hao et al., 2018), while cannot detect some thermally unstable metabolites. LC–MS is also very sensitive, and can detect a very wide range of metabolites with high mass accuracy, while lack of comprehensive spectral libraries (Young and Alfaro, 2018). In this study, we used a metabolomics approach based on LC–MS and GC–MS to characterize differences in coelomic fluid between breeding and nonbreeding seasons and to gain insights into the mechanisms underlying the oocyte development of S. nudus. Results provide a reference to procreation regulation and artificial breeding of S. nudus.
ketones, aldehydes, amino acids, lipids, steroids, alkaloids, and xenobiotics (Cappello et al., 2018; Yang et al., 2018b; Venter et al., 2018). Identification and integrative analysis of metabolites are vital for comprehensive characterization of metabolic mechanisms at the molecular and cellular levels under internal or external stimulating conditions to understand the physiological and biochemical status of biosystems and interpret biological principles (Hao et al., 2018). To date, metabolomics using high-throughput approaches, such as 1H-nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC–MS), and liquid chromatography-mass spectrometry (LC–MS), has been applied to the aquaculture industry, particularly in hatchery production (Young et al., 2015, 2016; Yang et al., 2019b), nutrition and diet (Ma et al., 2017; Yang et al., 2019a), growth (Hao et al., 2018, 2019), and disease and immunology (Guo et al., 2015; Li et al., 2016; Zeng et al., 2016). Langenbuch et al. (2006) reported the effects of environmental hypercapnia on S. nudus physiology by using NMR-based 2
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Fig. 3. Hierarchical clustering analysis for the SDMs of LC–MS. The relative metabolite level is depicted according to the color scale. Red indicates upregulation, whereas green indicates downregulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
and incubated for 1 h at −20 °C to precipitate proteins. The solution was centrifuged at 12,000 rpm for 15 min at 4 °C. The supernatant was transferred to LC–MS vials and stored at −80 °C until UHPLC-QE Orbitrap/MS analysis. A sample for quality control (QC) was prepared by mixing an equal aliquot of the supernatants from all of the samples.
2. Materials and methods 2.1. Experimental design S. nudus adults (15.94 ± 3.67 g in mean total weight) were obtained from Zhanjiang Green Bay Aquatic Science and Technology Co., Ltd. (21°20′N, 109°48′E) in July and November 2017. The worms were divided into two experimental groups, namely, S (July) and W (November).
2.3.2. LC–MS analysis LC–MS/MS analyses were performed using an UHPLC system (1290, Agilent Technologies) with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) coupled to Q Exactive (Orbitrap MS, Thermo). The mobile phase A comprised 0.1 % formic acid in water for positive and 5 mmol/ L ammonium acetate in water for negative. The mobile phase B was acetonitrile. The elution gradient was set as follows: 0 min, 1 % B; 1 min, 1 % B; 8 min, 99 % B; 10 min, 99 % B; 10.1 min, 1 % B; 12 min, 1 % B. The flow rate was 0.5 mL/min. The injection volume was 2 μL. A QE mass spectrometer was used to record MS/MS spectra on an information-dependent basis during the LC–MS experiment. In this mode, acquisition software (Xcalibur 4.0.27, Thermo) continuously evaluates the full-scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on the preselected criteria. The ESI source conditions were set as follows: sheath gas flow rate of 45 Arb, Aux gas flow rate of 15 Arb, capillary temperature of 400 °C, full ms resolution of 70000, MS/MS resolution of 17500, collision energy of 20/40/60 eV in NCE model, and spray voltage of 4.0 kV (positive, POS) or −3.6 kV (negative, NEG).
2.2. Sample collection Coelomic fluid was obtained with a sterile syringe, and an Olympus BX51 optical microscope was used to observe cells. Forty individuals were sacrificed every time, and three times a month. A total of 100–300 cells for each individual were measured, counted, and displayed with histogram. A 5 mL disposable sterile syringe was used to extract coelomic fluid from S. nudus (avoiding the internal organs of the worm). The sample was centrifuged at 3 000 r/min for 5 min at 4 °C. The supernatant was obtained, immediately kept in liquid nitrogen, and stored at −80 °C for metabolomics analysis. 2.3. LC–MS analysis 2.3.1. Metabolite extraction for LC–MS analysis Sample (100 μL) was transferred to an EP tube and added with 300 μL of methanol (containing internal standard 1 μg/mL). The solution was vortexed for 30 s, sonicated for 10 min in an ice-water bath,
2.3.3. Data preprocessing and analysis The raw data were converted into the mzXML format by using ProteoWizard and processed by MAPS software (version 1.0). The 3
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Fig. 4. Hierarchical clustering analysis for the SDMs of GC–MS. The relative metabolite level is depicted according to the color scale. Red indicates upregulation, whereas green indicates downregulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
sample was dried completely in a vacuum concentrator without heating, added with 20 μL of methoxy amination hydrochloride (20 mg/mL in pyridine), and incubated for 30 min at 80 °C. The sample aliquot was added with 30 μL of the bis-(trimethylsilyl)-trifluoroacetamide (BSTFA) regent (1 % trimethylchlorosilane, TMCS, v/v) and incubated for 1.5 h at 70 °C. The QC sample was added with 5 μL of FAMEs (in chloroform) when cooling to room temperature. All samples were analyzed by a gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer (GC-TOF-MS).
preprocessing results generated a data matrix that consisted of retention time, mass-to-charge ratio (m/z), and peak intensity. In-house MS2 database was applied for metabolite identification. The resulting three-dimensional data involving peak number, sample name, and normalized peak area were fed to SIMCA14.1 software package (V14.1, Sartorius Stedim Data Analytics AB, Umea, Sweden) for principal component analysis (PCA) and orthogonal projections to latent structure-discriminate analysis (OPLS-DA). PCA showed the distribution of the origin data. Supervised OPLS-DA was applied to obtain a high level of group separation and identify variables responsible for classification. Sevenfold cross validation was used to estimate the robustness and predictive ability of our model. Permutation test was proceeded to further validate the model. A loading plot was constructed on the basis of OPLS-DA and showed the contribution of variables to differences between the two groups. The first principal component of variable importance in the projection (VIP) was obtained to refine the analysis. If P < 0.1 and VIP > 1 (Chen et al., 2015; Hao et al., 2018), then the variable was defined as a significantly differential metabolite (SDM) between the groups (Saccenti et al., 2014).
2.4.2. GC–MS analysis GC-TOF-MS analysis was performed using an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The system utilized a DB-5MS capillary column coated with 5 % diphenyl cross-linked with 95 % dimethylpolysiloxane (30 m × 250 μm inner diameter, 0.25 μm film thickness; J&W Scientific, Folsom, CA, USA). A 1 μL aliquot of the analyte was injected in splitless mode. Helium was used as the carrier gas, the front inlet purge flow rate was 3 mL min−1, and the gas flow rate through the column was 1 mL min−1. The initial temperature was kept at 50 °C for 1 min, raised to 310 °C at a rate of 10 °C min−1, and kept for 8 min at 310 °C. The injection, transfer line, and ion source temperatures were 280 °C, 280 °C, and 250 °C, respectively. The energy was −70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with the m/z range of 50–500 at a rate of 12.5 spectra per second after a solvent delay of 6.17 min.
2.4. GC–MS analysis 2.4.1. Metabolite extraction for GC–MS analysis Sample (50 μL) was placed in 1.5 mL EP tubes for extraction with 200 μL of methanol and added with 5 μL of adonitol (1 mg/mL of stock in dH2O) as internal standard. The solution was subjected to vortex mixing for 30 s and centrifuged for 15 min at 12,000 rpm and 4 °C. The supernatant (180 μL) was transferred into fresh 1.5 mL EP tubes. About 20 μL was obtained from each sample and pooled as QC sample. The
2.4.3. Data preprocessing and analysis Chroma TOF 4.3X software of LECO Corporation and LECO-Fiehn Rtx5 database were used for raw peak exacting, data baseline filtering 4
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Fig. 5. Metabolomic view map of significant metabolic pathways characterized in coelomic fluid of S. nudus for groups S and W. Significantly changed pathways based on enrichment and topology analysis are shown. The x-axis represents the pathway enrichment, and the y-axis represents the pathway impact. Large sizes and dark colors represent the major pathway enrichment and high pathway impact values, respectively. Table 1 Metabolic pathways identified from the SDMs between groups S and W. Pathway
-ln(p)
Impact
SDMs
Valine, leucine and isoleucine biosynthesis Aminoacyl-tRNA biosynthesis Phenylalanine, tyrosine and tryptophan biosynthesis Butanoate metabolism Ubiquinone and other terpenoid-quinone biosynthesis Galactose metabolism Alanine, aspartate and glutamate metabolism Tyrosine metabolism
2.546 2.459 1.977 1.896 1.772 0.954 0.638 0.494
0.667 0.000 0.500 0.114 0.000 0.081 0.087 0.207
L-Leucine; L-Isoleucine L-Arginine; L-Isoleucine; L-Leucine; L-Tryptophan; L-Tyrosine L-Tyrosine; L-Tryptophan Butanal; Succinic acid semialdehyde L-Tyrosine Alpha-Lactose Succinic acid semialdehyde L-Tyrosine
2.5. Data analysis
and calibration, peak alignment, deconvolution analysis, peak identification, and peak area integration (Kind et al., 2009). Mass spectrum match and retention index match were considered in metabolite identification. Peaks detected in < 50 % of the QC samples or RSD > 30 % in the QC samples were removed (Dunn et al., 2011). The missing values of raw data were filled up by half of the minimum value. Internal standard normalization method was employed in data analysis. The resulting three-dimensional data including peak number, sample name, and normalized peak area were fed to SIMCA14.1 software package (V14.1, Sartorius Stedim Data Analytics AB, Umea, Sweden) for PCA and OPLS-DA. The first principal component of VIP was obtained to refine the analysis. The VIP values exceeding 1 were first selected as changed metabolites. In step 2, the remaining variables were assessed by Student’s t-test (P-value > 0.1). Variables were discarded between the two comparison groups.
Commercial databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/) as well as LC–MS and GC–MS were utilized to search for the pathways of SDMs.
3. Results 3.1. Characterization of oocytes in the coelomic fluid Oocytes in the coelomic fluid of S. nudus between breeding and nonbreeding seasons presented different diameter. In July, the number of oocytes formed a main peak at a diameter of approximately 160 μm (Fig. 1A). In November, almost all oocytes were below 50 μm (Fig. 1B). 5
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leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoidquinone biosynthesis between groups S and W (Table 1).
3.2. Overall data set and metabolic profiles A total of 3155 and 2817 valid peaks for coelomic fluid were obtained in POS and NEG after data preprocessing via LC–MS-based metabolomics, respectively. These valid peaks were matched for 113 (POS) and 40 (NEG) metabolites in the in-house MS2 database. A total of 369 peaks remained after filtering and denoising by GC–MS-based metabolomics. The LECO/Fiehn Rtx5 Metabolomics Library suggested that most of the peaks detected were endogenous metabolites; however, some of the peaks may belong to by-product derivatives. A total of 152 metabolites were identified; 32 of them were identified by mass spectrum matching, with a spectral similarity value (SV) of > 700.
4. Discussion The seed industry has occupied a leading strategic position in the aquaculture industry chain. Artificial breeding is a key technology utilized in the aquaculture industry of the peanut worm S. nudus. Research on the reproductive biology of S. nudus has become a top priority. Our previous research reported that the development of oocytes can be divided into four stages, namely, small-growth, largegrowth, premature, and mature stages; oocytes at different stages have different diameters (Wang et al., 2005). The oocytes in coelomic fluid develop and become mature with diameter reaching approximately 160 μm (Wang et al., 2017). In the present study, the number of oocytes formed a peak at a diameter of approximately 160 μm in July, which is the breeding period; November is the nonbreeding period. This result is consistent with those of previous studies (Wang et al., 2005; Lan and Yan, 2002). Cutler (1994) reported that the coelomic fluid of S. nudus contains various ions, strontium compounds, enzymes, antibiotics, pigments, cytotoxins, and metabolites. Thus, we surmise that differences in the coelomic fluid between breeding and nonbreeding seasons may influence oocyte development. The metabolic profiling and functional analysis of the key metabolic pathways in the coelomic fluid of S. nudus between breeding and nonbreeding seasons were performed via the proposed LC–MS- and GC–MS-based metabolomics approach. Most of the metabolites with significant differences between the two groups participated in valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; butanoate metabolism; tyrosine metabolism; alanine, aspartate, and glutamate metabolism; galactose metabolism; and ubiquinone and other terpenoidquinone biosynthesis. Nutrition is important for oocyte development. Protein is an important component of the yolk (Mathieu and Lubet, 1993). In the present study, biosynthesis-related pathways, including valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; aminoacyl-tRNA biosynthesis; and ubiquinone and other terpenoid–quinone biosynthesis, were enriched. This finding suggests that S. nudus exhibited higher synthesis ability in the breeding season. In particular, the levels of L-arginine, L-isoleucine, L-leucine, L-tryptophan, and L-tyrosine, which participated in aminoacyl-tRNA biosynthesis, were higher in group S. Aminoacyl-tRNA biosynthesis can transfer amino acids to ribosomal synthetic proteins (Smith and Hartman, 2015). These results are consistent with a report that free ribosomes are the earliest increased organelles during the development of S. nudus oocytes (Wang et al., 2017). In addition, isoleucine and leucine are branched-chain amino acids and anabolic nutrient signals, which indicate the presence of an ingested protein-containing meal in peripheral tissues and the promotion of protein synthesis (Castillo and GatlinIII, 2018). Leucine is essential for the normal growth and reproductive potential of aquatic animals (National Research Council (NRC, 2011). Tryptophan is an essential amino acid that contains an α-amino group, an α-carboxylic acid group, and a side chain indole, which is involved in various biological functions, including protein synthesis and cellular growth (Nguyen et al., 2019). The analysis results for the upregulated amino acids suggest the higher protein synthesis ability of group S again. Previous studies also revealed that increases in the protein content in the ovaries correspond to increases in the oocyte diameters of oyster (Li et al., 2000). High synthesis protein ability may promote yolk accumulation, which helps develop oocytes in group S. Lipids are important components of oocytes and play an important role in gametogenesis (Marin et al., 2003). Fatty acid content is closely
3.3. Multivariate analysis of metabolite profiles PCA is an unsupervised pattern recognition method that can reveal intrinsic variations within data and reduce data dimensionality. In the PCA score scatter plot, similar datasets are clustered closely, whereas different datasets are placed further apart. The PCA of the LCeMS and GCeMS metabolic profiles is illustrated in Fig. 2 (A, B, and C). The R2X values of the PCA model between S and W were 0.557 (LCeMS POS), 0.570 (LCeMS NEG), and 0.610 (GCeMS). All samples in each score scatter plot were within the 95 % Hotelling’s T-squared ellipse. The metabolic datasets warranted further analysis. OPLS-DA was employed to elucidate different metabolic patterns and maximize the discrimination between the two groups. The OPLS-DA results for S. nudus are displayed in Fig. 2 (D, E, and F). All samples in each score scatter plot of the OPLS-DA model were inside the 95 % Hotelling’s T-squared ellipse. The R2X, R2Y, and Q2 values of the OPLS-DA model of LCeMS POS between S and W were 0.294, 0.994, and 0.739, respectively. The R2X, R2Y, and Q2 values of the OPLS-DA model of LCeMS NEG between S and W were 0.309, 0.998, and 0.799, respectively. The R2X, R2Y, and Q2 values of the OPLS-DA model of GCeMS between S and W were 0.344, 0.964, and 0.628, respectively. Permutation test was conducted for verification to avoid the transition fit of the OPLS-DA model; the results are shown in Fig. 2 (G, H, and I). The permutation test results for the R2Y and Q2 intercepts were 0.97 and −0.54 between S and W (LCeMS POS), 0.96 and −0.48 between S and W (LCeMS NEG), and 0.95 and −0.22 between S and W (GCeMS). The results indicated that the OPLS-DA model had no overfitting and possessed good stability. Hence, the model was suitable to be exploited in subsequent analyses. 3.4. SDMs SDMs were screened from all identified metabolites according to the principle that the P value of the t-test was < 0.1 and the VIP of the OPLS-DA model was > 1. The SDMs were visualized through hierarchical cluster analysis (Figs. 3 and 4). Forty-four SDMs were obtained between groups S and W via LC–MS-based metabolomics (Fig. 3, Supplementary Table 1). Compared with group W, group S had 39 SDMs with higher concentrations. Thirty-two SDMs were obtained between groups S and W by GC–MS-based metabolomics (Fig. 4, Supplementary Table 2). In contrast to group W, group S had 26 SDMs with higher concentrations. 3.5. Characterization and functional analysis of key metabolic pathways The SDMs, including those obtained from LCeMS and GCeMS, were imported into MetaboAnalyst 4.0 to explore different potential metabolic pathways in coelomic fluid between breeding and nonbreeding seasons. As shown in Fig. 5, the bubble plots demonstrate the main influential metabolic pathways, which involved SDMs in coelomic fluid. Sixteen metabolic pathways were found between groups S and W (Supplementary Table 3). On the basis of ln P-value and pathway impact scores, the relevant metabolic pathways were identified as valine, 6
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Declaration of Competing Interest
related to the development of gametes, and large amounts of polyunsaturated fatty acids are required during gametogenesis (Kluytmans et al., 1985). In the present study, lipid metabolism-related metabolic pathways were not enriched. Meanwhile, the contents of fatty acids such as palmitoleic acid (FC = 1.97), oleic acid (FC = 1.61), 17-octadecynoic acid (FC = 1.41), eicosa-5Z, 8Z-dienoic acid (20:2, n-12) (FC = 4.21), and 5, 6-dehydro arachidonic acid (FC = 4.50) were had significantly higher in group S. A similar trend was noted for phospholipids such as PC (16:0) (FC = 2.16), PC (20:5) (FC = 4.43), LysoPC (22:5) (FC = 5.97), LysoPC (15:0) (FC = 2.76), and PC (22:6) (FC = 5.04). The high contents of lipids in the coelomic fluid of S. nudus may imply yolk accumulation, which may promote the development of oocytes in group S. S. nudus possesses high synthesis ability to store nutrients in breeding season. Neurotransmitters also influence the development of germ cells. Tryptophan is not only involved in protein synthesis but also functions as a biochemical precursor for several compounds, including serotine and neurotransmitters (Nguyen et al., 2019). In cells, tryptophan is converted into 5-hydroxytryptophan under the catalysis of tryptophan hydroxylase and then decarboxylated by 5-hydroxytryptophan decarboxylase to form serotonin (Zhang et al., 2019). Serotonin induces vitellogenin levels, allows the breakdown of germinal vesicles and the completion of meiosis, and plays an important role in oocyte maturation and gamete release (Hamida et al., 2004; Tinikul et al., 2014). The present study also indicated that tryptophan participated in phenylalanine, tyrosine, and tryptophan biosynthesis and was significantly upregulated during breeding season. This outcome can partly explain the differences in the reproductive maturity of S. nudus between breeding and nonbreeding seasons. Tyrosine is also a common precursor for important hormones and neurotransmitters, including thyroxine, triiodothyronine, epinephrine, nor-epinephrine, dopamine, and melanin (Zehra and Khan, 2014). Dopamine is a common neurotransmitter that could regulate the synthesis and release of neurohormones. Dopamine receptor 1-like protein is upregulated in the ovaries of mature female Macrobrachium rosenbergii (Jiang et al., 2019). In the present study, tyrosine, a precursor of dopamine, was upregulated in group S. However, in Litopenaeus vannamei, dopamine injection inhibited ovarian maturation (Tinikul et al., 2014). These conflicting results might be related to different stages of ovarian development. A previous study on L. vannamei demonstrated that the concentration of dopamine was the highest in early oocytes and then decreased in late oocytes (Tinikul et al., 2011). Given the activity of neurotransmitter precursors, we speculated their important role in dynamic balance to regulate oocyte development. However, this speculation needs to be verified.
No conflicts of interest, financial or otherwise, are declared by the authors. Acknowledgements This work was supported by the Science and Technology Department, Guangdong Province (Grant number: 2017A030303075 and 2016A020209010), the Guangdong Ocean University Student's Plan for Innovation and Entrepreneurship (Grant number: 201810566049), the Graduate Education Innovation Program of Guangdong Ocean University (Grant number: 201836). Metabolomics analysis was assisted by Biotree Biotech Co., Ltd. (Shanghai, China). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.aqrep.2019.100271. References Adrianov, A.V., Maiorova, A.S., 2010. Reproduction and development of common species of peanut worms (Sipuncula) from the sea of Japan. Russ. J. Mar. Biol. 36 (1), 1–15. Andrews, E.A., 1889. The reproductive organs of Phascolosoma gouldii. Zool. Anz. 12, 140–142. Cappello, T., Giannetto, A., Parrino, V., Maisano, M., Oliva, S., Marcoet, G.D., Guerriero, G., Mauceri, A., Fasulo, S., 2018. Baseline levels of metabolites in different tissues of mussel Mytilus galloprovincialis (Bivalvia: Mytilidae). Comp. Biochem. Phys. D. 26, 32–39. Castillo, S., GatlinIII, D.M., 2018. Imbalanced dietary levels of branched-chain amino acids affect growth performance and amino acid utilization of juvenile red drum Sciaenops ocellatus. Aquaculture 497, 17–23. Chen, H.H., Tseng, Y.J., Wang, S.Y., Tsai, Y.S., Chang, C.S., Kuo, T.C., Yao, W.J., Shieh, C.C., Wu, C.H., Kuo, P.H., 2015. The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity. Int. J. Obesity 39 (8), 1241–1248. Cutler, E.B., 1994. The Sipuncula: Their Systematics, Biology, and Evolution. Cornell University Press, New York, pp. 268–274. Dunn, W.B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J.D., Halsall, A., Haselden, J.N., Nicholls, A.W., Wilson, I.D., Kell, D.B., 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083. Guo, C., Peng, B., Song, M., Wu, C.W., Yang, M.J., Zhang, J.Y., Li, H., 2015. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47 (2), 664–673. Hamida, L., Medhioub, M.N., Cochard, J.C., Pennec, M.L., 2004. Evaluation of the effects of serotonin (5-HT) on oocyte competence in Ruditapes decussatus (Bivalvia, Veneridae). Aquaculture 239 (1-4), 413–420. Hao, R.J., Wang, Z.M., Yang, C.Y., Deng, Y.W., Zheng, Z., Wang, Q.H., Du, X.D., 2018. Metabolomic responses of juvenile pearl oyster Pinctada maxima, to different growth performances. Aquaculture 491, 258–265. Hao, R.J., Du, X.D., Yang, C.Y., Deng, Y.W., Zheng, Z., Wang, Q.H., 2019. Integrated application of transcriptomics and metabolomics provides insights into unsynchronized growth in pearl oyster Pinctada fucata martensii. Sci. Total Environ. 666, 46–56. Jiang, Q., Min, Y., Yang, H., Wan, W.L., Zhang, X.J., 2019. De novo transcriptome analysis of eyestalk reveals ovarian maturation related genes in Macrobrachium rosenbergii. Aquaculture 505, 280–288. Kind, T., Wohlgemuth, G., Lee, D.Y., Lu, Y., Palazoglu, M., Shahbaz, S., Fiehn, O., 2009. Fiehnlib-mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81 (24), 10038–10048. Kluytmans, J.H., Boot, J.H., Oudejans, R.C.H.M., Zandee, D.I., 1985. Fatty acid synthesis in relation to gametogenesis in the mussel Mytilus edulis L. Comp. Biochem. Phys. B. 81 (4), 959–963. Lan, G.B., Yan, B., Liao, S.M., 2003. A study on embryonic and larval developments of Sipunculus nudus. Journal of Tropical Oceanography 22 (6), 70–76 (in Chinese with English abstract). Lan, G.B., Yan, B., 2002. The reproductive biology of peanut worm, Sipunculus nudus. J. Fish. China 26 (6), 503–509 (in Chinese with English abstract). Langenbuch, M., Bock, C., Leibfritz, D., Pörtnera, H.O., 2006. Effects of environmental hypercapnia on animal physiology: A 13C NMR study of protein synthesis rates in the marine invertebrate Sipunculus nudus. Comp. Biochem. Phys. A. 144 (4), 479–484. Li, J.W., Zhu, C.B., Guo, Y.J., Xie, X.Y., Huang, G.Q., Chen, S.W., 2015. Experimental study of bioturbation by Sipunculus nudus in a polyculture system. Aquaculture 437, 175–181. Li, T.Y., Li, E., Suo, Y.T., Xu, Z.X., Jia, Y.Y., Qin, J.G., Chen, L.Q., Gu, Z.M., 2016. Energy metabolism and metabolomics response of pacific white shrimp Litopenaeus vannamei
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