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Updated: Jan 9, 2026

Quantitative Comparison of cis-Regulatory Element CRE Activities in Transgenic Drosophila melanogaster
Published on: December 19, 2011
Widespread turnover of a conserved cis-regulatory code across 589 grass species
Charles O Hale1, Sheng-Kai Hsu2, Jingjing Zhai2
1Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853 USA.
Comparative genomics reveals a deeply conserved regulatory code in grasses, with thousands of cis-regulatory binding sites evolving while preserving transcription factor preferences, offering insights into trait variation.
Area of Science:
- Evolutionary biology
- Genomics
- Molecular biology
Background:
- Comparative genomics facilitates identifying functional loci for trait variation using non-model organisms.
- Cis-regulatory regions are key drivers of phenotypic evolution, but linking them to specific functions is difficult.
Purpose of the Study:
- To identify conserved cis-regulatory motifs and their evolutionary dynamics across grass species.
- To investigate the relationship between cis-regulatory element evolution and ecological niche transitions.
Main Methods:
- Identification of 514 cis-regulatory motifs enriched in regulatory regions of five grass species.
- Quantification of motif instance occupancy across 589 grass species using new genome assemblies.
- Application of phylogenetic mixed models to associate motif evolution with environmental factors.
Main Results:
- 73% of identified motifs were conserved across all five diverse grass species, indicating a conserved regulatory code.
- Widespread gain and loss of motif instances occurred over evolutionary time, with rapid decline post-divergence but significant ancient conservation.
- Convergent gains of HSF/GARP motifs were linked to temperate environments and an Alpha-N-acetylglucosaminidase gene.
Conclusions:
- Cis-regulatory evolution in grasses follows a "stable motifs, variable binding sites" model, characterized by turnover of individual binding sites.
- Comparative genomics and phylogenetic modeling are powerful tools for uncovering the genetic basis of complex traits and evolutionary adaptations.
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