Neurotrophin‐3 down‐regulates trkA mRNA, NGF high‐affinity binding sites, and associated phenotype in adult DRG neurons
Bibliographic record
Abstract
Neurotrophin-3 (NT-3) binds to multiple trks, not only its initially identified receptor trkC. Recent studies in our laboratory show that NT-3 negatively regulates nociceptive phenotype associated with the trkA subpopulation. Due to the extensive overlap in trkA and trkC expression it is uncertain whether there is a direct influence of NT-3 on trkA in adult sensory neurons. Thus, the aim of this study was to examine whether NT-3 might alter trkA and associated neuronal phenotype outside of the trkC subpopulation. The effect of a seven-day intrathecal infusion of NT-3 on intact, uninjured adult rat dorsal root ganglion neurons was investigated. Serial sections were processed for receptor radioautography or in situ hybridization to identify and colocalize neurons expressing high-affinity nerve growth factor (NGF) binding sites, substance P (SP), trkC, or trkA mRNAs and to examine the influence of NT-3 on these populations. NT-3 does not appear to alter trkC expression, but evokes a notable reduction in trkA, high-affinity NGF binding sites, and SP levels. It is unlikely that signalling by trkC greatly influences this response because the down-regulation of SP occurs most notably in trkA neurons that lack trkC. Moreover, we have shown here that message levels of two trkA isoforms are differentially modulated by NT-3; infusion results in greater down-regulation of the noninsert containing isoform. These findings suggest a clinically relevant role for NT-3 as an antagonist to NGF, but also raise the caution that not just trkC-positive neurons are influenced following exposure to the neurotrophin.
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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 | 0.001 |
| 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".