KLF4 protein stability regulated by interaction with pluripotency transcription factors overrides transcriptional control
Bibliographic record
Abstract
Embryonic stem (ES) cells are regulated by a network of transcription factors that maintain the pluripotent state. Differentiation relies on down-regulation of pluripotency transcription factors disrupting this network. While investigating transcriptional regulation of the pluripotency transcription factor Kruppel-like factor 4 ( Klf4 ), we observed that homozygous deletion of distal enhancers caused a 17-fold decrease in Klf4 transcript but surprisingly decreased protein levels by less than twofold, indicating that posttranscriptional control of KLF4 protein overrides transcriptional control. The lack of sensitivity of KLF4 to transcription is due to high protein stability (half-life >24 h). This stability is context-dependent and is disrupted during differentiation, as evidenced by a shift to a half-life of <2 h. KLF4 protein stability is maintained through interaction with other pluripotency transcription factors (NANOG, SOX2, and STAT3) that together facilitate association of KLF4 with RNA polymerase II. In addition, the KLF4 DNA-binding and transactivation domains are required for optimal KLF4 protein stability. Posttranslational modification of KLF4 destabilizes the protein as cells exit the pluripotent state, and mutations that prevent this destabilization also prevent differentiation. These data indicate that the core pluripotency transcription factors are integrated by posttranslational mechanisms to maintain the pluripotent state and identify mutations that increase KLF4 protein stability while maintaining transcription factor function.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".