RGMB and neogenin control cell differentiation in the developing olfactory epithelium
Bibliographic record
Abstract
Cellular interactions are key for the differentiation of distinct cell types within developing epithelia, yet the molecular mechanisms engaged in these interactions remain poorly understood. In the developing olfactory epithelium (OE), neural stem/progenitor cells give rise to odorant-detecting olfactory receptor neurons (ORNs) and glial-like sustentacular (SUS) cells. Here, we show in mice that the transmembrane receptor neogenin (NEO1) and its membrane-bound ligand RGMB control the balance of neurons and glial cells produced in the OE. In this layered epithelium, neogenin is expressed in progenitor cells, while RGMB is restricted to adjacent newly born ORNs. Ablation of Rgmb via gene-targeting increases the number of dividing progenitor cells in the OE and leads to supernumerary SUS cells. Neogenin loss-of-function phenocopies these effects observed in Rgmb(-/-) mice, supporting the proposal that RGMB-neogenin signaling regulates progenitor cell numbers and SUS cell production. Interestingly, Neo1(-/-) mice also exhibit increased apoptosis of ORNs, implicating additional ligands in the neogenin-dependent survival of ORNs. Thus, our results indicate that RGMB-neogenin-mediated cell-cell interactions between newly born neurons and progenitor cells control the ratio of glia and neurons produced in the OE.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".