Functional Dissection of <i>IME1</i> Transcription Using Quantitative Promoter–Reporter Screening
Bibliographic record
Abstract
Transcriptional regulation is a key mechanism that controls the fate and response of cells to diverse signals. Therefore, the identification of the DNA-binding proteins, which mediate these signals, is a crucial step in elucidating how cell fate is regulated. In this report, we applied both bioinformatics and functional genomic approaches to scrutinize the unusually large promoter of the IME1 gene in budding yeast. Using a recently described fluorescent protein-based reporter screen, reporter-synthetic genetic array (R-SGA), we assessed the effect of viable deletion mutants on transcription of various IME1 promoter-reporter genes. We discovered potential transcription factors, many of which have no perfect consensus site within the IME1 promoter. Moreover, most of the cis-regulatory sequences with perfect homology to known transcription factor (TF) consensus were found to be nonfunctional in the R-SGA analysis. In addition, our results suggest that lack of conservation may not discriminate against a TF regulatory role at a specific promoter. We demonstrate that Sum1 and Sok2, which regulate IME1, bind to nonperfect consensuses within nonconserved regions in the sensu stricto Saccharomyces strains. Our analysis supports the view that although comparative analysis can provide a useful guide, functional assays are required for accurate identification of TF-binding site interactions in complex promoters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".