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Updated: Jun 14, 2026
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
Published on: February 7, 2019
Measuring transcription factor-binding site turnover: a maximum likelihood approach using phylogenies.
Wolfgang Otto1, Peter F Stadler, Francesc López-Giraldéz
1Lehrstuhl für Bioinformatik, Institut für Informatik, Universität Leipzig, Leipzig, Germany.
Genome Biology and Evolution
|March 25, 2010
Summary
Gene expression evolves via cis-regulatory elements (CREs) and transcription factor-binding sites (TFBS). A new stochastic model reveals high TFBS turnover rates, indicating dynamic evolutionary pressures across species. This tool aids in understanding CRE evolution.
Area of Science:
- Evolutionary biology
- Genomics
- Bioinformatics
Background:
- Gene expression evolution is driven by changes in cis-regulatory elements (CREs).
- The function of CREs relies on transcription factor-binding sites (TFBS), which undergo significant turnover even with conserved function.
- Alignment-based studies of CRE evolution are limited to closely related species due to rapid TFBS turnover.
Purpose of the Study:
- To develop and apply a stochastic model for analyzing TFBS turnover rates.
- To identify shifts in selective pressures acting on TFBS across different evolutionary clades.
- To provide a computational tool for studying cis-regulatory element evolution.
Main Methods:
- Implemented a maximum likelihood model to estimate variable TFBS turnover rates across a species tree.
- Applied the model to TFBS in fungal methionine biosynthesis pathways and vertebrate HoxA clusters.
- Utilized the CRETO (Cis-Regulatory Element Turn-Over) software for analysis.
Main Results:
- Estimated high TFBS turnover rates with half-lives typically between 5 and 150 million years.
- Observed significant differences in TFBS turnover rates between clades, particularly for estrogen- and progesterone-response elements in HoxA clusters.
- Detected substantial clade-specific differences in fungal TFBS turnover.
Conclusions:
- Stochastic models of TFBS turnover effectively detect shifts in selective pressures on CREs.
- The high turnover rate explains low sequence similarity in functionally conserved enhancers.
- The CRETO tool facilitates the study of cis-regulatory element evolution and selective pressures.
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