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Depuration is a vital stage to ensure the safety of oyster consumption, and salinity had a great impact on the environmental adaptability of oysters, but the underlying molecular mechanism was poorly understood during depuration stage. Here, Crassostrea gigas was depurated for 72 h at different salinity (26, 29, 32, 35, 38 g/L, corresponding to ±20%, ±10% salinity fluctuation away from oyster's production area) and then analyzed by using transcriptome, proteome, and metabolome combined with bioinformatics techniques. The transcriptome showed that the salinity stress led to 3185 differentially expressed genes and mainly enriched in amino acid metabolism, carbohydrate metabolism, lipid metabolism, etc. A total of 464 differentially expressed proteins were screened by the proteome, and the number of up-regulated expression proteins was less than the down-regulated, indicating that the salinity stress would affect the regulation of metabolism and immunity in oysters. 248 metabolites significantly changed in response to depuration salinity stress in oysters, including phosphate organic acids and their derivatives, lipids, etc. The results of integrated omics analysis indicated that the depuration salinity stress induced abnormal metabolism of the citrate cycle (TCA cycle), lipid metabolism, glycolysis, nucleotide metabolism, ribosome, ATP-binding cassette (ABC) transport pathway, etc. By contrast with Pro-depuration, more radical responses were observed in the S38 group. Based on the results, we suggested that the 10% salinity fluctuation was suitable for oyster depuration and the combination of multi-omics analysis could provide a new perspective for the analysis of the mechanism changes. Copyright © 2023 Elsevier Ltd. All rights reserved.

Citation

Lipin Chen, Hongwei Zhang, Haohao Shi, Zhaojie Li, Changhu Xue. Application of multi-omics combined with bioinformatics techniques to assess salinity stress response and tolerance mechanisms of Pacific oyster (Crassostrea gigas) during depuration. Fish & shellfish immunology. 2023 Jun;137:108779

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PMID: 37120087

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