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Microinjection for Transgenesis and Genome Editing in Threespine Sticklebacks
Published on: May 13, 2016
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Different contributions of local- and distant-regulatory changes to transcriptome divergence between stickleback
Asano Ishikawa1, Makoto Kusakabe2,3, Kohta Yoshida1
1Division of Ecological Genetics, National Institute of Genetics, Shizuoka, Japan.
Evolution; International Journal of Organic Evolution
|January 12, 2017
Summary
Local regulatory changes drive transcriptome evolution in sticklebacks, while distant changes may act as constraints. Understanding gene expression regulation is key to adaptation in natural populations.
Area of Science:
- Evolutionary biology
- Genomics
- Population genetics
Background:
- Differential gene expression is crucial for phenotypic evolution and adaptation.
- The roles of local versus distant regulatory changes in transcriptome evolution are not well understood.
Purpose of the Study:
- Investigate the genetic architecture of transcriptome divergence between marine and stream stickleback ecotypes.
- Determine the relative contributions of local and distant regulatory elements to adaptation.
Main Methods:
- Expression quantitative trait loci (eQTL) analysis in threespine sticklebacks.
- Genome scan analysis to identify regions of high genetic differentiation.
- Comparison of eQTLs across different salinity conditions.
Main Results:
- Identified both local and distant eQTLs, including regulatory hotspots.
- Local eQTLs generally aligned with the direction of ecotype divergence and overlapped with differentiated genomic regions.
- Distant eQTLs often showed opposite expression effects and rarely overlapped with differentiated regions, suggesting a potential constraint on evolution.
- Nearly half of eQTL hotspots were environment-specific.
Conclusions:
- Local regulatory changes are major drivers of transcriptome evolution and adaptation in sticklebacks.
- Distant regulatory elements may constrain transcriptome evolution.
- Analyzing genetic architecture across multiple environments is essential for predicting adaptive responses to selection.
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