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Evolution of a Eukaryotic Transcription Factor's co-TF Dependence Involves Multiple Intrinsically Disordered Regions Affecting Activation and Autoinhibition.

Lindsey F SnyderEmily M O'BrienJia ZhaoJinye LiangYuning ZhangWei ZhuThomas J CassierNicholas J SchnickerXu ZhouRaluca GordânBin Z He
Published in: bioRxiv : the preprint server for biology (2024)
Combinatorial control by multiple transcription factors (TFs) is a hallmark of eukaryotic gene regulation. Despite its prevalence and crucial roles in enhancing specificity and integrating information, the mechanisms behind why eukaryotic TFs depend on one another, and whether such interdependence evolves, are not well understood. We exploit natural variation in co-TF dependence in the yeast phosphate starvation (PHO) response to address this question. In the model yeast Saccharomyces cerevisiae , the main TF, Pho4, relies on the co-TF Pho2 to regulate ∼28 genes. In a related yeast pathogen, Candida glabrata , its Pho4 exhibits significantly reduced Pho2 dependence and has an expanded target set of ∼70 genes. Biochemical analyses showed C. glabrata Pho4 (CgPho4) binds to the same consensus motif with 3-4-fold higher affinity than ScPho4 does. A machine-learning-based prediction and yeast one-hybrid assay identified two Intrinsically Disordered Regions (IDRs) in CgPho4 that boost the activity of the main activation domain but showed little to no activity on their own. We also found evidence for autoinhibition behind the co-TF dependence in ScPho4. An IDR in ScPho4 next to its DNA binding domain was found to act as a double-edged sword: it both allows for enhanced activity with Pho2, and inhibits Pho4's activity without Pho2. This study provides a detailed molecular picture of how co-TF dependence is mediated and how its evolution, mainly driven by IDR divergence, can lead to significant rewiring of the regulatory network.
Keyphrases
  • saccharomyces cerevisiae
  • transcription factor
  • dna binding
  • machine learning
  • candida albicans
  • risk factors
  • cell wall
  • gene expression
  • artificial intelligence
  • clinical practice
  • single cell
  • network analysis