Genome-wide analyses of Shavenbaby target genes reveals distinct features of enhancer organization
Bibliographic record
Abstract
BACKGROUND: Developmental programs are implemented by regulatory interactions between Transcription Factors (TFs) and their target genes, which remain poorly understood. While recent studies have focused on regulatory cascades of TFs that govern early development, little is known about how the ultimate effectors of cell differentiation are selected and controlled. We addressed this question during late Drosophila embryogenesis, when the finely tuned expression of the TF Ovo/Shavenbaby (Svb) triggers the morphological differentiation of epidermal trichomes. RESULTS: We defined a sizeable set of genes downstream of Svb and used in vivo assays to delineate 14 enhancers driving their specific expression in trichome cells. Coupling computational modeling to functional dissection, we investigated the regulatory logic of these enhancers. Extending the repertoire of epidermal effectors using genome-wide approaches showed that the regulatory models learned from this first sample are representative of the whole set of trichome enhancers. These enhancers harbor remarkable features with respect to their functional architectures, including a weak or non-existent clustering of Svb binding sites. The in vivo function of each site relies on its intimate context, notably the flanking nucleotides. Two additional cis-regulatory motifs, present in a broad diversity of composition and positioning among trichome enhancers, critically contribute to enhancer activity. CONCLUSIONS: Our results show that Svb directly regulates a large set of terminal effectors of the remodeling of epidermal cells. Further, these data reveal that trichome formation is underpinned by unexpectedly diverse modes of regulation, providing fresh insights into the functional architecture of enhancers governing a terminal differentiation program.
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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 | 0.000 |
| 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".