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Int J Mol Sci
2022 Dec 07;2324:. doi: 10.3390/ijms232415478.
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Integrative Application of Transcriptomics and Metabolomics Provides Insights into Unsynchronized Growth in Sea Cucumber (Stichopus monotuberculatus).
Ma B
,
Liu Y
,
Pan W
,
Li Z
,
Ren C
,
Hu C
,
Luo P
.
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Ever-increasing consumer demand for sea cucumbers mainly leads to huge damage to wild sea cucumber resources, including Stichopus monotuberculatus, which in turn exerts negative impacts on marine environments due to the lack of ecological functions performed by sea cucumbers. Aquaculture of sea cucumbers is an effective way to meet consumer demand and restore their resources. Unsynchronous growth is a prominent problem in the aquaculture of sea cucumbers which has concealed unelucidated molecular mechanisms until now. In this study, we carried out an integrative analysis of transcriptomics and metabolomics on fast-growing (SMF) and slow-growing (SMS) groups of S. monotuberculatus cultured in the same environmental conditions. The results revealed that a total of 2054 significantly differentially expressed genes (DEGs) were identified, which are mainly involved in fat digestion and absorption, histidine metabolism, arachidonic acid metabolism, and glutathione metabolism. 368 differential metabolites (DMs) were screened out between the SMF group and the SMS group; these metabolites are mainly involved in glycerophospholipid metabolism, purine metabolism, biosynthesis of unsaturated fatty acids, pyrimidine metabolism, arachidonic acid metabolism, and other metabolic pathways. The integrative analysis of transcriptomics and metabolomics of S. monotuberculatus suggested that the SMF group had a higher capacity for lipid metabolism and protein synthesis, and had a more frequent occurrence of apoptosis events, which are likely to be related to coping with environmental stresses. The results of this study provide potential values for the aquaculture of sea cucumbers which may promote their resource enhancement.
2020YFD0901104 National Key Research and Development Program: Blue Granary Scientific and Technological Innovation, 2018YFD0901605 National Key Research and Development Program: Blue Granary Scientific and Technological Innovation
Figure 1. Comparison of the growth performance of S. monotuberculatus in the SMF group and the SMS group. “***” indicates p < 0.001.
Figure 2. Quality analysis of metabolomics data. (A) The PCA scores plot of samples acquired in the positive ion mode. (B) The PCA scores plot of samples acquired in the negative ion mode. (C) The PLS-DA score plot for the positive ion mode. (D) The PLS-DA score plot for the negative ion mode. (E) The PLS-DA validation for the positive ion mode. (F) The PLS-DA validation for the negative ion mode.
Figure 3. Hierarchical clustering analysis for DMs and the metabolomics view map of the significant metabolic pathways. (A) KEGG pathway analysis of metabolites differentially expressed between the SMF group and the SMS group. The vertical coordinates indicate the top 30 KEGG terms significantly enriched by DEMs and the horizontal coordinates indicate the enrichment factor between the two sampled datasets. (B) Hierarchical clustering analysis of DMs between SMF and SMS groups. Red indicates metabolites up-regulated in SMF; blue indicates metabolites up-regulated in SMS.
Figure 4. Transcriptome analysis of S. monotuberculatus in the SMF and SMS groups. (A) Volcano plot of DEGs between the SMF group and the SMS group. (B) The hierarchical clustering analysis of the DEGs between the two groups. (C) GO functions of DEGs between the two groups. (D) KEGG functions of DEGs between the two groups. (E) Comparison of gene expression data from RNA-Seq and qRT-PCR. Data are expressed as the mean ± standard deviation (SD) of three replicates.
Figure 5. Heat map of the correlation between the transcriptome and metabolome of S. monotuberculatus. The columns are genes and the rows are metabolites; they show the relationship between genes and metabolites. Red indicates that genes and metabolites show positive correlation, blue indicates negative correlation between genes and metabolites. “*” indicates a significant correlation between genes and metabolites (p < 0.05). “**” indicates p < 0.1.
Figure 6. An integrative metabolic network map inferred from differential genes and metabolites between the SMF group and the SMS group identified by transcriptome and metabolome integration analysis. DEGs are indicated by boxes, among which red indicates up-regulated genes and blue indicates down-regulated genes in the SMF group. DMs are indicated by circles, among which red indicates up-regulated metabolites and blue indicates down-regulated metabolites in the SMF group. Abbreviations: UTP, Uridine-5’-triphosphate; CTP, Cytidine-5’-triphosphate; dTMP, deoxynucleotide; Nt5e, 5’-nucleotidase; UMP, uridylic acid; GMP, Guanosine monophosphate; AMP, Adenosine 5’-monophosphate; PLA2, phospholipase A2; ARA, Arachidonic acid; 8(S)-HETE, (5Z,9E,11Z,14Z)-(8S)-8-Hydroxyeicosa-5,9,11,14-tetraenoic acid; Lyso PC, 2-Lysolecithin; PLB1, lysophospholipase 1; GGT, gamma-glutamyltranspeptidase; FABP3, fatty acid-binding protein 3; DHA, Docosahexaenoic Acid; EPA, Eicosapentaenoic Acid; CECR1, adenosine deaminase; Asha 2, neutral ceramidase; CGT, ceramide galactosyltransferase; CTSD, cathepsin D; Cyt C, Cytochrome C; Acox3, Acyl-CoA Oxidase 3.
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