Transcription Factor Promiscuity Drives Regulatory Rewiring and Evolvability in Gene Networks in Bacteria
Tiffany B Taylor1, Alan M Rice1,2
1Department of Life Sciences, University of Bath, Bath, UK.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 23, 2026
Summary
Researchers studied gene regulatory network (GRN) evolution in Pseudomonas fluorescens, observing transcription factor (TF) rewiring in real time. Key findings reveal predictable evolutionary pathways and the factors influencing TF evolvability.
Area of Science:
- Evolutionary biology
- Genetics
- Microbiology
Background:
- Gene regulatory networks (GRNs) control gene expression and are crucial for cellular function and evolution.
- Understanding how GRNs evolve and adapt is fundamental to deciphering life's complexity.
- Transcription factors (TFs) are key regulators whose functions can evolve, leading to GRN rewiring.
Purpose of the Study:
- To investigate the mechanisms and principles underlying the evolution of gene regulatory networks (GRNs).
- To understand how transcription factor (TF) functions are acquired and how GRNs rewire over evolutionary time.
- To identify factors that make TFs evolvable and predict evolutionary outcomes.
Main Methods:
- Utilized an experimental evolution model system with the soil bacterium Pseudomonas fluorescens.
- Observed transcription factor (TF) rewiring in real time to gain insights into GRN evolution.
- Analyzed hierarchical patterns of TF rewiring, TF recruitment influences, and the role of crosstalk.
Main Results:
- Identified a hierarchical pattern in TF rewiring, with specific regulators acting as 'first responders'.
- Determined that TF expression level and mutational accessibility critically influence recruitment for novel functions.
- Highlighted the role of non-cognate binding (crosstalk) as a source of raw material for adaptive innovation.
Conclusions:
- Evolutionary pathways for GRN rewiring are often constrained and repeatable due to identifiable principles.
- Understanding TF evolvability allows for the prediction and potential direction of evolutionary trajectories.
- Emerging technologies may further transform the study of GRN rewiring and evolvability.
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