Calcitonin gene‐related peptide regulation of glial cell‐line derived neurotrophic factor in differentiated rat myotubes
Bibliographic record
Abstract
Glial cell-line derived neurotrophic factor (GDNF) is the most potent trophic factor for motoneuron survival and neuromuscular junction formation. GDNF is upregulated in injured or denervated skeletal muscle and returns to normal levels following reinnervation. However, the mechanism by which GDNF is regulated in denervated muscle is not well understood. The nerve-derived neurotransmitter calcitonin gene-related peptide (CGRP) is upregulated following neuromuscular injury and is subsequently released from motoneurons at the neuromuscular junction. CGRP also promotes nerve regeneration, but the mechanism is not well understood. The current study investigates whether this increase in CGRP regulates GDNF, thus playing a key role in promoting regeneration of injured nerves. This study demonstrates that CGRP increases GDNF secretion without affecting its transcription or translation. Rat L6 myoblasts were differentiated into myotubes and subsequently treated with CGRP. GDNF mRNA expression levels were quantified by quantitative real-time reverse transcription-polymerase chain reaction, and secreted GDNF was quantified in the conditioned medium by ELISA. CGRP treatment increased secreted GDNF protein without altering GDNF mRNA levels. The translational inhibitor cycloheximide did not affect CGRP-induced GDNF secreted protein levels, whereas the secretional inhibitor brefeldin A blocked the CGRP-induced increase in GDNF. This study highlights the importance of injury-induced upregulation of CGRP by exposing its ability to increase GDNF levels and demonstrates a secretional mechanism for regulation of this key regeneration-promoting neurotrophic factor.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".