<i>Mek1</i> <i>Y130C</i> mice recapitulate aspects of the human Cardio-Facio-Cutaneous syndrome
Bibliographic record
Abstract
The RAS/MAPK signaling pathway is one of the most investigated pathways due to its established role in numerous cellular processes and its implication in cancer. Germline mutations in genes encoding members of the RAS/MAPK pathway also cause severe developmental syndromes gathered under the name of RASopathies. These syndromes share overlapping characteristics including craniofacial dysmorphology, cardiac malformations, cutaneous abnormalities and developmental delay. The cardio-facio-cutaneous syndrome (CFC) is a rare RASopathy associated with mutations in BRAF, KRAS, MEK1 and MEK2. MEK1 and MEK2 mutations are found in about 25% of the CFC patients and the MEK1Y130C substitution is the most common one. However, little is known about the origins and mechanisms responsible for the development of CFC. To our knowledge, no mouse model carrying RASopathy-linked Mek1 or Mek2 gene mutations have been reported. To investigate the molecular and developmental consequences of the Mek1Y130C mutation, we generated a mouse line carrying this mutation. Analysis of mice from a Mek1 allelic series revealed that the Mek1Y130C allele expresses both wild-type and Y130C mutant forms of MEK1. However, despite reduced levels of MEK1 protein and the lower abundance of MEK1 Y130C protein than wt, Mek1Y130C mutants showed increased ERK activation in response to growth factors, supporting a role for MEK1 Y130C in the hyperactivation of RAS/MAPK pathway leading to CFC syndrome. Mek1Y130C mutant mice exhibited pulmonary artery stenosis, cranial dysmorphia and neurological anomalies, including increased numbers of GFAP+ astrocytes and Olig2+ oligodendrocytes in regions of the cerebral cortex. These data indicate that the Mek1Y130C mutation recapitulates major aspects of CFC syndrome, providing a new animal model to investigate the physiopathology of this RASopathy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".