Dynamic BMP signaling regulates sclerotome induction and lineage diversification in zebrafish
Bibliographic record
Abstract
The sclerotome is an embryonic structure that gives rise to various supportive tissues, including the axial skeleton and connective tissues. Despite its significance, the mechanisms underlying sclerotome induction and diversification during embryonic development remain poorly understood. Sclerotome progenitors exhibit transient bmp4 expression and an active response to BMP signaling. Using BMP gain- and loss-of-function tools, we demonstrate that BMP signaling is both necessary and sufficient for sclerotome induction. Furthermore, through mosaic expression of a dominant-negative tool, we show that BMP signaling induces sclerotome fate in a cell-autonomous manner. Interestingly, different populations of sclerotome-derived cells have distinct BMP signaling requirements. Sclerotome-derived notochord-associated cells in the trunk lack any BMP response, and sustained BMP signaling inhibits their differentiation into tenocytes. By contrast, sclerotome-derived fin mesenchymal cells in the fin fold require high levels of BMP signaling for proper morphogenesis. Our findings suggest that dynamic regulation of BMP signaling is crucial for the induction of the sclerotome and the subsequent diversification of sclerotome-derived lineages in zebrafish.
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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".