Literature DB >> 31203476

Adaptive Evolution Patterns in the Pacific Oyster Crassostrea gigas.

Kai Song1, Shiyong Wen2,3, Guofan Zhang4.   

Abstract

Estimation of adaptive evolution rates at the molecular level is important in evolutionary genomics. However, knowledge of adaptive evolutionary patterns in Mollusca is very scarce, especially for oysters. Such information would help clarify how oysters adapt to pathogen-rich and dynamically changing intertidal environments. In this study, we characterized the patterns of adaptive evolution in the Crassostrea gigas genome, using population diversity analysis and congeneric comparison. Our analysis revealed that gene expression patterns were positively associated with adaptive evolution rates, which suggested that positive selection played an important role in gene evolution. The genes with more exons and alternative splicing events had higher adaptive evolution rates. The rates of adaptive evolution in immune-related and stress-response genes were higher than those in other genes, suggesting that these groups of genes experienced strong positive selection. This study represents the first analysis of adaptive evolution rates in oysters and the first comprehensive study of a Mollusca species. These results provide a system-level investigation of association between adaptive evolution rates with some intrinsic genetic factors. They also suggest that adaptation to pathogens and environmental stressors are important forces driving the adaptive evolution of genes.

Entities:  

Keywords:  Adaptive evolution rates; Crassostrea gigas; Gene expression pattern

Mesh:

Year:  2019        PMID: 31203476     DOI: 10.1007/s10126-019-09906-w

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


  54 in total

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Journal:  Mar Biotechnol (NY)       Date:  2017-02-16       Impact factor: 3.619

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Journal:  PLoS One       Date:  2014-11-04       Impact factor: 3.240

9.  Quantifying adaptive evolution in the Drosophila immune system.

Authors:  Darren J Obbard; John J Welch; Kang-Wook Kim; Francis M Jiggins
Journal:  PLoS Genet       Date:  2009-10-23       Impact factor: 5.917

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Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

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  2 in total

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Journal:  Mar Biotechnol (NY)       Date:  2019-11-20       Impact factor: 3.619

  2 in total

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