Zebrafish Mnx proteins specify one motoneuron subtype and suppress acquisition of interneuron characteristics
Bibliographic record
Abstract
BACKGROUND: Precise matching between motoneuron subtypes and the muscles they innervate is a prerequisite for normal behavior. Motoneuron subtype identity is specified by the combination of transcription factors expressed by the cell during its differentiation. Here we investigate the roles of Mnx family transcription factors in specifying the subtypes of individually identified zebrafish primary motoneurons. RESULTS: Zebrafish has three Mnx family members. We show that each of them has a distinct and temporally dynamic expression pattern in each primary motoneuron subtype. We also show that two Mnx family members are expressed in identified VeLD interneurons derived from the same progenitor domain that generates primary motoneurons. Surprisingly, we found that Mnx proteins appear unnecessary for differentiation of VeLD interneurons or the CaP motoneuron subtype. Mnx proteins are, however, required for differentiation of the MiP motoneuron subtype. We previously showed that MiPs require two temporally-distinct phases of Islet1 expression for normal development. Here we show that in the absence of Mnx proteins, the later phase of Islet1 expression is initiated but not sustained, and MiPs become hybrids that co-express morphological and molecular features of motoneurons and V2a interneurons. Unexpectedly, these hybrid MiPs often extend CaP-like axons, and some MiPs appear to be entirely transformed to a CaP morphology. CONCLUSIONS: Our results suggest that Mnx proteins promote MiP subtype identity by suppressing both interneuron development and CaP axon pathfinding. This is, to our knowledge, the first report of transcription factors that act to distinguish CaP and MiP subtype identities. Our results also suggest that MiP motoneurons are more similar to V2 interneurons than are CaP motoneurons.
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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 | 0.000 | 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) | 0.002 | 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".