Dissecting Wnt/β-catenin signaling during gastrulation using RNA interference in mouse embryos
Bibliographic record
Abstract
Differential gene regulation integrated in time and space drives developmental programs during embryogenesis. To understand how the program of gastrulation is regulated by Wnt/beta-catenin signaling, we have used genome-wide expression profiling of conditional beta-catenin mutant embryos. Known Wnt/beta-catenin target genes, known components of other signaling pathways, as well as a number of uncharacterized genes were downregulated in these mutants. To further narrow down the set of differentially expressed genes, we used whole-mount in situ screening to associate gene expression with putative domains of Wnt activity. Several potential novel target genes were identified by this means and two, Grsf1 and Fragilis2, were functionally analyzed by RNA interference (RNAi) in completely embryonic stem (ES) cell-derived embryos. We show that the gene encoding the RNA-binding factor Grsf1 is important for axial elongation, mid/hindbrain development and axial mesoderm specification, and that Fragilis2, encoding a transmembrane protein, regulates epithelialization of the somites and paraxial mesoderm formation. Intriguingly, the knock-down phenotypes recapitulate several aspects of Wnt pathway mutants, suggesting that these genes are components of the downstream Wnt response. This functional genomic approach allows the rapid identification of functionally important components of embryonic development from large datasets of putative targets.
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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 | 0.001 | 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) | 0.001 | 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".