The synergistic effects of NGF and IGF‐1 on neurite growth in adult sensory neurons: convergence on the PI 3‐kinase signaling pathway
Bibliographic record
Abstract
Nerve growth factor (NGF) and insulin-like growth factor-1 (IGF-1) play an important role in promoting axonal growth from dorsal root ganglion (DRG) neurons. Adult DRG neurons exhibit neurotrophin-independent survival, providing an excellent system with which to study trophic factor effects on neurite growth in the absence of significant survival effects. Using young adult rat DRG neurons we have demonstrated a synergistic effect of NGF plus IGF (N + I), compared with either factor alone, in promoting neurite growth. Not only does the presence of NGF and IGF-1 enhance neurite initiation, it also significantly augments the extent of neurite branching and elongation. We have also examined potential mechanism(s) underlying this synergistic effect. Immunoblotting experiments of classical growth factor intermediary signalling pathways (PI 3-K-Akt-GSK-3 and Ras-Raf-MAPK) were performed using phospho-specific antibodies to assess activation state. We found that activation of Akt and MAPK correlated with neurite elongation and branching. However, using pharmacological inhibitors, we observed that a PI 3-K pathway involving both Akt and GSK-3 appeared to be more important for neurite extension and branching than MAPK-dependent signalling. In fact, inhibition of activation of MAPK with U0126 resulted in increased neuritic branching, possibly as a result of the concomitant increase observed in phospho-Akt. Furthermore, inhibition of GSK3 (which is negatively regulated by phosphorylation on S9/S21) also resulted in increased growth. Our data point to signalling convergence upon the PI 3-K-Akt-GSK-3 pathway that underlies the NGF plus IGF synergism. In addition, to our knowledge, this is the first report in primary neurons that inhibition of GSK3 results in an enhanced neurite growth.
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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) | 0.001 | 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".