α‐Dystroglycan is essential for the induction of Egr3, a transcription factor important in muscle spindle formation
Bibliographic record
Abstract
Muscle spindle fibers are specialized stretch receptors that allow the perception and coordination of limb movement. The differentiation of these specialized structures is initiated by signals derived from the in growing Ia sensory neurons during development. While the direct molecular signaling mechanisms between sensory neurons and developing muscle at nascent spindle fibers have been well documented in past studies the roles of muscle basal lamina components on this process have not previously been described. As such, our initial experiments addressed potential roles for agrin (AGRN) and laminin (LN) in the expression of the transcription factor Egr3. Levels of Egr3 were monitored using immunoblot analysis and both basal lamina molecules proved effective in inducing Erg3 expression. Previous work had established neuregulin (NRG) as a critical signaling component in spindle fiber development so blocking experiments with NRG and ErbB inhibitors were then used to determine if LN-induced Egr3 expression was occurring as a result of NRG-ErbB signaling and not via other, novel pathway. Inhibiting signaling through this pathway did indeed reduce the expression of Egr3. Finally, we looked at alpha-dystrogylcan, a shared receptor for AGRN and LN at neuromuscular junctions. Using a alpha-dystroglycan (alpha-DG) silenced muscle cell line and an anti-alpha-DG antibody we attempted to block basal lamina/alpha-DG interactions. Again, and in both instances, Egr3 expression was significantly decreased. Taken together, analysis of the results from these experiments revealed that indeed AGRN, LN, and alpha-DG influence Egr3 levels and therefore may play an important role in spindle fiber differentiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".