Unraveling the regulatory dynamics of bidirectional promoters for modulating gene co-expression and metabolic flux in <i>Saccharomyces cerevisiae</i>
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".