Direct activation of chordoblasts by retinoic acid is required for segmented centra mineralization during zebrafish spine development
Bibliographic record
Abstract
Zebrafish mutants with increased retinoic acid (RA) signaling due to the loss of the RA-inactivating enzyme Cyp26b1 develop a hyper-mineralized spine with gradually fusing vertebral body precursors (centra). However, the underlying cellular mechanisms remained incompletely understood. Here, we show that cells of the notochord epithelium named chordoblasts are sensitive to RA signaling. Chordoblasts are uniformly distributed along the anteroposterior axis and initially generate the continuous collagenous notochord sheath. However, subsequently and iteratively subsets of these cells undergo further RA-dependent differentiation steps, acquire a stellate-like shape, down-regulate collagen 2a1a gene expression, switch on cyp26b1 expression and trigger metameric sheath mineralization. This mineralization fails to appear upon chordoblast-specific cell ablation or RA signal transduction blockade. Together, our data reveal that despite their different developmental origins, chordoblasts display activities and are regulated very similar to osteoblasts, including their RA-induced transitioning from osteoid-producing cells to osteoid-mineralizing ones. Furthermore, our data point to a requirement for locally controlled RA activity within the chordoblast layer in order to generate the segmented vertebral column.
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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".