Gene expression correlated with delay in shell formation in larval Pacific oysters (Crassostrea gigas) exposed to experimental ocean acidification provides insights into shell formation mechanisms ...

• Main Authors: De Wit, Pierre, Durland, Evan, Ventura, Alexander, Langdon, Chris
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: figshare 2018
• Subjects: Biophysics; Biochemistry; Genetics; FOS: Biological sciences; Molecular Biology; Environmental Sciences not elsewhere classified; Chemical Sciences not elsewhere classified; Ecology; Biological Sciences not elsewhere classified; Developmental Biology; Inorganic Chemistry; FOS: Chemical sciences; Pacific; Crassostrea gigas; Ocean acidification
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.4013740.v1; https://springernature.figshare.com/collections/Gene_expression_correlated_with_delay_in_shell_formation_in_larval_Pacific_oysters_Crassostrea_gigas_exposed_to_experimental_ocean_acidification_provides_insights_into_shell_formation_mechanisms/4013740/1

Abstract Background Despite recent work to characterize gene expression changes associated with larval development in oysters, the mechanism by which the larval shell is first formed is still largely unknown. In Crassostrea gigas, this shell forms within the first 24Â h post fertilization, and it has been demonstrated that changes in water chemistry can cause delays in shell formation, shell deformations and higher mortality rates. In this study, we use the delay in shell formation associated with exposure to CO2-acidified seawater to identify genes correlated with initial shell deposition. Results By fitting linear models to gene expression data in ambient and low aragonite saturation treatments, we are able to isolate 37 annotated genes correlated with initial larval shell formation, which can be categorized into 1) ion transporters, 2) shell matrix proteins and 3) protease inhibitors. Clustering of the gene expression data into co-expression networks further supports the result of the linear models, and ...