Journal Article Ppublish
PubMed: 40503683 DOI: 10.1093/nar/gkaf511
Unraveling the regulatory dynamics of bidirectional promoters for modulating gene co-expression and metabolic flux in Saccharomyces cerevisiae.
Nucleic Acids Res 53(11)
Authors: Zimo Jin, Yueming Dong, Abdul Muntakim Rafi, Md Mohsin Patwary, Catherine Xu, Morten H Raadam, Carl G de Boer, Codruta Ignea
Affiliations
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, QCH3A 0C3, Canada.
- School of Biomedical Engineering, University of British Columbia, 6088 University Boulevard, Vancouver, BC Canada V6T 1Z3, Canada.
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, QCH3A 0C3, Canada. Centre de recherche en biologie structurale, McGill University, 3649 Promenade Sir William Osler, Montreal, QCH3G 0B1, Canada.
Abstract
Bidirectional promoters (BDPs) hold great promise for applications in synthetic biology by enabling co-expression of multiple genes with minimized promoter size. However, the lack of well-characterized BDPs along with an incomplete understanding of their regulatory mechanisms limits broader applications. Here, we conducted genome-wide screening and characterization of 749 BDP candidates containing a single shared nucleosome-depleted region in yeast Saccharomyces cerevisiae. A pronounced asymmetry in BDP strength was observed using both transcriptomic and fluorescence reporter analyses. We demonstrated that these unbalanced BDP strengths could be utilized for fine-tuning metabolic flux in yeast, achieving yields comparable to or exceeding those of commonly used constitutive or inducible promoters for terpenoid production under the examined conditions. Using in silico mutagenesis guided by the DREAM-CNN yeast cis-regulatory AI prediction model, we identified conserved activator-binding hotspots within the central region of 63.8% of identified BDP candidates. Disruption of these hotspots in six selected BDPs significantly reduced promoter strength in both orientations, suggesting that these AI-predicted motifs are indeed critical for the functionality of BDPs. Overall, this study provides a comprehensive framework for BDP identification and engineering, leveraging AI-guided models to advance rational synthetic promoter design, thus paving the way for precise genetic control in synthetic biology.
MeSH
Genes, Saccharomyces Cerevisiae, Yeasts, Promoter Regions, Genetic, Saccharomyces, Synthetic Biology, Nucleic Acids, Artificial Intelligence