Adaptive Evolution Patterns in the Pacific Oyster Crassostrea gigas.


Journal

Marine biotechnology (New York, N.Y.)
ISSN: 1436-2236
Titre abrégé: Mar Biotechnol (NY)
Pays: United States
ID NLM: 100892712

Informations de publication

Date de publication:
Oct 2019
Historique:
received: 18 03 2019
accepted: 27 05 2019
pubmed: 17 6 2019
medline: 6 2 2020
entrez: 17 6 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31203476
doi: 10.1007/s10126-019-09906-w
pii: 10.1007/s10126-019-09906-w
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

614-622

Subventions

Organisme : National Natural Science Foundation of China
ID : 11701546
Organisme : National Natural Science Foundation of China
ID : 31530079

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Auteurs

Kai Song (K)

School of Mathematics and Statistics, Qingdao University, Qingdao, 266071, Shandong, China. songkai1987@126.com.

Shiyong Wen (S)

College of Veterinary Medicine, Inner Mongolia Agricultural University, Huhhot, 010018, China.
Dezhou State-owned Assets Supervision and Administration Commission, Dezhou,, 253000, China.

Guofan Zhang (G)

Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, Shandong, China. gzhang@qdio.ac.cn.

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Classifications MeSH