Literature DB >> 27704223

Gene Co-Expression Network Analysis Reveals the Correlation Patterns Among Genes in Euryhaline Adaptation of Crassostrea gigas.

Xuelin Zhao1, Hong Yu1, Lingfeng Kong1, Qi Li2.   

Abstract

The Pacific oyster Crassostrea gigas is a dominant aquaculture species in many intertidal zones throughout the Pacific and Atlantic Oceans and can tolerate a wide range of salinity. Studying the gene expression profiles of oyster gills had found differentially expressed genes (DEGs) involved in salinity tolerance. A systematic study of cellular response to salinity stress may provide insights into the mechanism of acquired salinity tolerance. Here, weighted gene co-expression network analysis (WGCNA) was carried out using RNA-seq data from gill transcriptome in response to different salinity. A total of 25,463 genes were parsed into 22 gene modules, of which 5 gene modules were identified as salinity-related modules. Brown module was the only one significantly correlated with salinity and free amino acids (FAAs) contents, which was associated with cellular metabolism, biosynthesis of amino acids, oxidation reduction, electron transport, nitrogen compound metabolism, and others. The enriched pathways in brown module were mainly about FAAs metabolism. The other four modules were significantly correlated with certain FAAs, and were over-represented in certain salinity. These results indicated that C. gigas triggered different FAAs in different salinity stress. This study represents the first RNA-seq gene network analysis in oysters responding to different salinity stresses. These results provide a systems-level framework to help understand the complexity of cellular process in response to osmotic stress and show the function and regulated genes of different FAAs at the molecular level.

Entities:  

Keywords:  Crassostrea gigas; Osmotic stress; Weighted gene co-expression network analysis

Mesh:

Substances:

Year:  2016        PMID: 27704223     DOI: 10.1007/s10126-016-9715-7

Source DB:  PubMed          Journal:  Mar Biotechnol (NY)        ISSN: 1436-2228            Impact factor:   3.619


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