Functional redundancy of the kinases MEK1 and MEK2: Rescue of the <i>Mek1</i> mutant phenotype by <i>Mek2</i> knock-in reveals a protein threshold effect
Bibliographic record
Abstract
The mammalian genome contains two mitogen-activated protein kinase (MAPK) kinase (MEK)-encoding genes, Mek1 and Mek2. MEKs phosphorylate and activate the two extracellular signal-regulated kinase (ERK) isoforms ERK1 and ERK2. Mek1(-/-) embryos die due to placental defects, whereas Mek2(-/-) mice survive with a normal life span and fertility, suggesting that MEK1 has functions not shared by MEK2. However, most Mek1(+/-)Mek2(+/-) embryos also die from placental defects, indicating that both Mek genes contribute to placental development. To assess the functional specificity of the Mek1 and Mek2 genes, we produced a Mek1 knock-in allele in which the Mek2 coding sequences were placed under the control of Mek1 regulatory sequences (Mek1(2) allele). Mek1(2/2) mice were viable with no apparent phenotype, indicating rescue by MEK2 and functional redundancy between the two MEK proteins. However, Mek1(2/-) embryos with Mek2 in only one of the Mek1 alleles and the other Mek1 allele null died from abnormal placenta, suggesting a dosage effect. Analysis of mice from a Mek1 Mek2 allelic series revealed that the occurrence of the placenta phenotype correlated with the amount of MEK protein independently of which MEK isoform was produced. Thus, although MEK1 and MEK2 can substitute for each other, a minimum amount of MEK is critical for placenta development and embryo survival.
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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".