<i>Frizzled</i> gene expression and negative regulation of canonical WNT–β-catenin signaling in mouse F9 teratocarcinoma cells
Bibliographic record
Abstract
Mouse F9 cells differentiate into primitive endoderm (PrE) following the activation of the canonical WNT-β-catenin pathway. The upregulation of Wnt6 and activation of β-catenin-TCF-LEF-dependent transcription is known to accompany differentiation, but the Frizzled (FZD) receptor responsible for transducing the WNT6 signal is not known. Eight of the 10 Fzd genes were found to be expressed in F9 cells, with Fzd7 being the most highly expressed, and chosen for further analysis. To alter steady-state Fzd7 levels and test the effect this has on differentiation, siRNA and overexpression approaches were used to knock-down and ectopically express the Fzd7 message, respectively. siRNA knock-down of Fzd7 resulted in reduced DAB2 levels, and the overexpression activated a TCF-LEF reporter, but neither approach affected differentiation. Our focus turned to how canonical WNT6 signaling was attenuated to allow PrE cells to form parietal endoderm (PE). Dkk1, encoding a WNT antagonist, was examined and results showed that its expression increased in F9 cells treated with retinoic acid (RA) or overexpressing Wnt6. F9 cells overexpressing human DKK1 or treated with DKK1-conditioned medium and then treated with RA failed to differentiate, indicating that a negative feedback loop involving WNT6 and DKK1 attenuates canonical WNT-β-catenin signaling, thereby allowing PE cells to differentiate.
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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 | 0.001 |
| 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".