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野生种群十年转录组学研究:我们对其生态学和进化有哪些了解?

Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

作者信息

Alvarez Mariano, Schrey Aaron W, Richards Christina L

机构信息

Department of Integrative Biology, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33620, USA.

出版信息

Mol Ecol. 2015 Feb;24(4):710-25. doi: 10.1111/mec.13055. Epub 2015 Jan 21.

Abstract

Molecular ecology has moved beyond the use of a relatively small number of markers, often noncoding, and it is now possible to use whole-genome measures of gene expression with microarrays and RNAseq (i.e. transcriptomics) to capture molecular response to environmental challenges. While transcriptome studies are shedding light on the mechanistic basis of traits as complex as personality or physiological response to catastrophic events, these approaches are still challenging because of the required technical expertise, difficulties with analysis and cost. Still, we found that in the last 10 years, 575 studies used microarrays or RNAseq in ecology. These studies broadly address three questions that reflect the progression of the field: (i) How much variation in gene expression is there and how is it structured? (ii) How do environmental stimuli affect gene expression? (iii) How does gene expression affect phenotype? We discuss technical aspects of RNAseq and microarray technology, and a framework that leverages the advantages of both. Further, we highlight future directions of research, particularly related to moving beyond correlation and the development of additional annotation resources. Measuring gene expression across an array of taxa in ecological settings promises to enrich our understanding of ecology and genome function.

摘要

分子生态学已经超越了使用相对较少数量的标记(通常为非编码标记)的阶段,现在可以利用微阵列和RNA测序(即转录组学)对基因表达进行全基因组测量,以捕捉对环境挑战的分子反应。虽然转录组研究正在揭示诸如个性或对灾难性事件的生理反应等复杂性状的机制基础,但由于所需的技术专长、分析困难和成本问题这些方法仍然具有挑战性。尽管如此,我们发现,在过去10年里,有575项研究在生态学中使用了微阵列或RNA测序。这些研究大致涉及反映该领域进展的三个问题:(i)基因表达存在多少变异以及其结构如何?(ii)环境刺激如何影响基因表达?(iii)基因表达如何影响表型?我们讨论了RNA测序和微阵列技术的技术方面,以及一个利用两者优势的框架。此外,我们强调了未来的研究方向,特别是超越相关性以及开发更多注释资源方面。在生态环境中测量一系列分类群的基因表达有望丰富我们对生态学和基因组功能的理解。

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