Literature DB >> 25604587

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

Mariano Alvarez1, Aaron W Schrey, Christina L Richards.   

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.
© 2015 John Wiley & Sons Ltd.

Keywords:  RNAseq; ecological transcriptome; gene expression; microarrays; transcriptomics

Mesh:

Year:  2015        PMID: 25604587     DOI: 10.1111/mec.13055

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  57 in total

1.  Applying a gene-suite approach to examine the physiological status of wild-caught walleye (Sander vitreus).

Authors:  Jennifer D Jeffrey; Hunter Carlson; Dale Wrubleski; Eva C Enders; Jason R Treberg; Ken M Jeffries
Journal:  Conserv Physiol       Date:  2020-12-15       Impact factor: 3.079

2.  Increased expression diversity buffers the loss of adaptive potential caused by reduction of genetic diversity in new unfavourable environments.

Authors:  Wei Liu; Lifang Kang; Qin Xu; Chengcheng Tao; Juan Yan; Tao Sang
Journal:  Biol Lett       Date:  2019-01-31       Impact factor: 3.703

3.  Dissecting the contributions of time and microbe density to variation in immune gene expression.

Authors:  Ann T Tate; Andrea L Graham
Journal:  Proc Biol Sci       Date:  2017-07-26       Impact factor: 5.349

4.  Natural history collections-based research: progress, promise, and best practices.

Authors:  Bryan S McLean; Kayce C Bell; Jonathan L Dunnum; Bethany Abrahamson; Jocelyn P Colella; Eleanor R Deardorff; Jessica A Weber; Amanda K Jones; Fernando Salazar-Miralles; Joseph A Cook
Journal:  J Mammal       Date:  2015-11-24       Impact factor: 2.416

5.  Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes.

Authors:  Clare H Scott Chialvo; Ronglin Che; David Reif; Alison Motsinger-Reif; Laura K Reed
Journal:  Metabolomics       Date:  2016-09-20       Impact factor: 4.290

6.  Dissecting the Transcriptional Patterns of Social Dominance across Teleosts.

Authors:  Suzy C P Renn; Cynthia F O'Rourke; Nadia Aubin-Horth; Eleanor J Fraser; Hans A Hofmann
Journal:  Integr Comp Biol       Date:  2016-12       Impact factor: 3.326

7.  Fitness Effects of Cis-Regulatory Variants in the Saccharomyces cerevisiae TDH3 Promoter.

Authors:  Fabien Duveau; William Toubiana; Patricia J Wittkopp
Journal:  Mol Biol Evol       Date:  2017-11-01       Impact factor: 16.240

Review 8.  Peromyscus transcriptomics: Understanding adaptation and gene expression plasticity within and between species of deer mice.

Authors:  Jason Munshi-South; Jonathan L Richardson
Journal:  Semin Cell Dev Biol       Date:  2016-08-12       Impact factor: 7.727

9.  Nonrandom RNAseq gene expression associated with RNAlater and flash freezing storage methods.

Authors:  Courtney N Passow; Thomas J Y Kono; Bethany A Stahl; James B Jaggard; Alex C Keene; Suzanne E McGaugh
Journal:  Mol Ecol Resour       Date:  2018-12-21       Impact factor: 7.090

Review 10.  Opportunities and limitations of reduced representation bisulfite sequencing in plant ecological epigenomics.

Authors:  Ovidiu Paun; Koen J F Verhoeven; Christina L Richards
Journal:  New Phytol       Date:  2018-08-19       Impact factor: 10.151

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