Regulation of BMP-dependent chondrogenesis in early limb mesenchyme by TGFβ signals
Bibliographic record
Abstract
In the developing axial skeleton, sequential sonic hedgehog (SHH) and bone morphogenetic protein (BMP) signals are required for specification of a chondrogenic fate in presomitic tissue. A similar paradigm is thought to operate in the limb, but the signals involved are unclear. To investigate the nature of these signals, we examined BMP action in mesenchymal populations derived from the early murine limb bud (approximately embryonic day 10.5). These populations exhibited a graded response to BMPs, in which early limb mesenchymal cells (from the distal hind limb) displayed an anti-chondrogenic response, whereas BMPs promoted chondrogenesis in more mature cell populations (from the proximal fore limb). Under these conditions, multiple Gata genes were induced by BMPs and the extent of induction correlated with BMP anti-chondrogenic activity. A screen of limb-bud-expressed ligands revealed that prior short-term exposure to transforming growth factor beta1 (TGFbeta1) ameliorated the anti-chondrogenic response to BMP. Furthermore, brief activation of the TGFbeta pathway was found to be necessary for subsequent induction of chondrogenesis by BMPs. Our findings indicate that, similar to axial skeletogenesis, induction of chondrogenesis in the appendicular skeleton is a two-step process. However, the programs differ in the transient signals driving chondrogenic responsiveness to BMPs, with SHH operating in the former and TGFbeta activation in the latter.
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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".