Retrograde influence of muscle fibers on their innervation revealed by a novel marker for slow motoneurons
Bibliographic record
Abstract
Mammalian limb and trunk skeletal muscles are composed of muscle fibers that differ in contractile and molecular properties. They are commonly divided into four categories according to the myosin heavy chain that they express: I, IIA, IIX and IIB, ranging from slowest to fastest. Individual motor axons innervate tens of muscle fibers, nearly all of which are of the same type. The mechanisms accounting for this striking specificity, termed motor unit homogeneity, remain incompletely understood, in part because there have been no markers for motoneuron types. Here we show in mice that the synaptic vesicle protein SV2A is selectively localized in motor nerve terminals on slow (type I and small type IIA) muscle fibers; its close relatives, SV2B and SV2C, are present in all motor nerve terminals. SV2A is broadly expressed at birth; fast motoneurons downregulate its expression during the first postnatal week. An inducible transgene incorporating regulatory elements from the Sv2a gene permits selective labeling of slow motor units and reveals their composition. Overexpression of the transcriptional co-regulator PGC1α in muscle fibers, which converts them to a slow phenotype, leads to an increased frequency of SV2A-positive motor nerve terminals, indicating a fiber type-specific retrograde influence of muscle fibers on their innervation. This retrograde influence must be integrated with known anterograde influences in order to understand how motor units become homogeneous.
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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) | 0.001 | 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".