TGF-β Signaling and the Epithelial-Mesenchymal Transition during Palatal Fusion
Bibliographic record
Abstract
Signaling by transforming growth factor (TGF)-β plays an important role in development, including in palatogenesis. The dynamic morphological process of palatal fusion occurs to achieve separation of the nasal and oral cavities. Critically and specifically important in palatal fusion are the medial edge epithelial (MEE) cells, which are initially present at the palatal midline seam and over the course of the palate fusion process are lost from the seam, due to cell migration, epithelial-mesenchymal transition (EMT), and/or programed cell death. In order to define the role of TGF-β signaling during this process, several approaches have been utilized, including a small interfering RNA (siRNA) strategy targeting TGF-β receptors in an organ culture context, the use of genetically engineered mice, such as Wnt1-cre/R26R double transgenic mice, and a cell fate tracing through utilization of cell lineage markers. These approaches have permitted investigators to distinguish some specific traits of well-defined cell populations throughout the palatogenic events. In this paper, we summarize the current understanding on the role of TGF-β signaling, and specifically its association with MEE cell fate during palatal fusion. TGF-β is highly regulated both temporally and spatially, with TGF-β3 and Smad2 being the preferentially expressed signaling molecules in the critical cells of the fusion processes. Interestingly, the accessory receptor, TGF-β type 3 receptor, is also critical for palatal fusion, with evidence for its significance provided by Cre-lox systems and siRNA approaches. This suggests the high demand of ligand for this fine-tuned signaling process. We discuss the new insights in the fate of MEE cells in the midline epithelial seam (MES) during the palate fusion process, with a particular focus on the role of TGF-β signaling.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".