Fibroblast growth factors 2, 4, and 8 exert both negative and positive effects on limb, frontonasal, and mandibular chondrogenesis via MEK‐ERK activation
Bibliographic record
Abstract
Fibroblast growth factors (FGFs) and their receptors play fundamental roles regulating growth, morphogenesis, and cartilage formation in embryonic limbs and facial primordia. However, the intracellular pathways that transduce FGF signals during the differentiation of pluripotent mesenchymal cells into chondrocytes are currently unknown. Our present study demonstrates that FGF8, 4, and 2 treatments exert both inhibitory and stimulatory effects on cartilage differentiation in micromass cultures prepared from mesenchymal cells of the chick embryo wing bud, frontonasal mass, and mandibular arch through activation of the MEK-ERK mitogen-activated protein kinase (MAPK) cascade. In cultures of stage 23/24 and stage 28/29 wing bud mesenchyme, as well as stage 24/25 and stage 28/29 frontonasal cells, FGF treatments depressed cartilage matrix production and decreased transcript levels for three cartilage-specific genes: col2a1, aggrecan, and sox9. Conversely, FGF treatment increased cartilage differentiation in cultures of stage 24/25 and stage 28/29 mandibular mesenchyme. In all cell types, FGF treatment elevated endogenous ERK phosphorylation. Moreover, both the stimulatory effects of FGFs on mandibular chondrogenesis, as well as the inhibitory effects of FGFs on wing mesenchyme and stage 24/25 frontonasal cells, were completely blocked when cultures were treated with MEK inhibitor U0126 or transfected with dominant negative ERK2. Thus, MEK-ERK activation is an essential component of the signal transduction pathway that mediates both positive and negative effects of FGFs 8, 4, and 2 on chondrogenesis in embryonic limb, mandibular, and early-stage frontonasal mesenchyme cells. Interestingly, the effects of FGF on late-stage frontonasal cells appear to be relayed by an ERK-independent system.
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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) | 0.001 | 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) | 0.002 | 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".