The ERK MAPK Pathway Is Essential for Skeletal Development and Homeostasis
Bibliographic record
Abstract
Mitogen-activated protein kinases (MAPKs) are a family of protein kinases that function as key signal transducers of a wide spectrum of extracellular stimuli, including growth factors and pro-inflammatory cytokines. Dysregulation of the extracellular signal-regulated kinase (ERK) MAPK pathway is associated with human skeletal abnormalities including Noonan syndrome, neurofibromatosis type 1, and cardiofaciocutaneous syndrome. Here, we demonstrate that ERK activation in osteoprogenitors is required for bone formation during skeletal development and homeostasis. Deletion of Mek1 and Mek2, kinases upstream of ERK MAPK, in osteoprogenitors (Mek1OsxMek2−/−), resulted in severe osteopenia and cleidocranial dysplasia (CCD), similar to that seen in humans and mice with impaired RUNX2 function. Additionally, tamoxifen-induced deletion of Mek1 and Mek2 in osteoprogenitors in adult mice (Mek1Osx-ERTMek2−/−) significantly reduced bone mass. Mechanistically, this corresponded to decreased activation of osteoblast master regulators, including RUNX2, ATF4, and β-catenin. Finally, we identified potential regulators of osteoblast differentiation in the ERK MAPK pathway using unbiased phospho-mass spectrometry. These observations demonstrate essential roles of ERK activation in osteogenesis and bone formation.
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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 | 0.001 |
| 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".