Dissection of the <i>Fgf8</i> regulatory landscape by <i>in vivo</i> CRISPR-editing reveals extensive inter- and intra-enhancer redundancy
Bibliographic record
Abstract
Abstract Developmental genes are often regulated by multiple elements with overlapping activity. Yet, in most cases, the relative function of those elements and their contribution to endogenous gene expression remain uncharacterized. Illustrating this situation, distinct sets of enhancers have been proposed to direct Fgf8 in the limb apical ectodermal ridge (AER) and the midbrain-hindbrain boundary (MHB). Using in vivo CRISPR/Cas9 genome engineering, we functionally dissect this complex regulatory ensemble and demonstrate two distinct regulatory logics. In the AER, the control of Fgf8 expression appears extremely distributed between different enhancers. In contrast, in the MHB, one of the three active enhancers is essential while the other two are dispensable. Further dissection of the essential MHB enhancer revealed another layer of redundancy and identified two sub-parts required independently for Fgf8 expression and formation of midbrain and cerebellar structures. Interestingly, cross-species transgenic analysis of this enhancer suggests changes of the organisation of this essential regulatory node in the vertebrate lineage.
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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) | 0.002 | 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".