Orphan nuclear receptors Err2 and 3 promote a feature-specific terminal differentiation program underlying gamma motor neuron function and proprioceptive movement control
Bibliographic record
Abstract
Abstract Motor neurons are commonly thought of as mere relays between the central nervous system and the movement apparatus, yet, in mammals about one-third of them function exclusively as regulators of muscle proprioception. How these gamma motor neurons acquire properties to function differently from the muscle force-producing alpha motor neurons remains unclear. Here, we found that upon selective loss of the orphan nuclear receptors Err2 and Err3 (Err2/3) in mice, gamma motor neurons acquire characteristic structural (e.g. synaptic wiring), but not functional (e.g. physiological firing rates) properties necessary for regulating muscle proprioception, thus disrupting gait and precision movements in vivo . Moreover, Err2/3 operate via transcriptional activation of neural activity modulators, one of which (Kcna10) promoted gamma motor neuron functional properties. Our work identifies a long-sought mechanism specifying gamma motor neuron properties necessary for proprioceptive movement control, which implies a ‘feature-specific’ terminal differentiation program implementing neuron subtype-specific functional but not structural properties. Summary The transcription factors Err2 and 3 promote functional properties in a subset of motor neurons necessary for executing precise movements.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".