p75 Neurotrophin receptor is implicated in the ability of neurotrophin‐3 to negatively modulate activated ERK1/2 signaling in TrkA‐expressing adult sensory neurons
Bibliographic record
Abstract
Neurotrophin-3 (NT-3) can negatively modulate trkA and associated phenotype in intact sensory neurons, while positively regulating trkC and associated phenotype. How NT-3 effects this response is not clear. Whether NT-3 exerts a differential influence on levels of activated ERK1/2 signaling in trkA- versus trkC-mRNA-positive subpopulations of neurons and the role that the common neurotrophin receptor, p75NTR, plays in this response was assessed by examining alterations in the levels of phospho-ERK1/2 immunofluorescence signal over nuclei of sensory neurons expressing trkA alone, trkC alone, or both trkA and trkC mRNA. NT-3 intrathecal infusion differentially modulated nuclear phospho-ERK1/2 levels detected over neurons expressing trkA alone or trkC alone. Levels were significantly decreased over nuclei of neurons expressing trkA alone and significantly increased over the nuclei of neurons expressing trkC alone. Neurons expressing both trkA and trkC or neurons expressing neither trkA nor trkC had no significant alteration in phospho-ERK1/2. Antisense oligonucleotides directed against p75NTR were infused intrathecally with or without NT-3 infusion to examine the impact of suppressing p75NTR expression on the ability of NT-3 to diminish phospho-ERK1/2 signaling in neurons expressing only trkA. NT-3 did not significantly attenuate levels of phospho-ERK1/2 when p75NTR expression was suppressed by antisense infusion, despite being able to do so when NT-3 was infused alone. In conclusion, NT-3's ability to negatively modulate ERK1/2 signaling in a p75-dependent manner in sensory neurons that express trkA to the exclusion of trkC provides a feasible mechanism by which it negatively modulates other aspects of nociceptive phenotype in these neurons.
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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".