Mutual Regulation of Src Family Kinases and the Neurotrophin Receptor TrkB
Bibliographic record
Abstract
The neurotrophin receptor tyrosine kinase TrkB is critical to diverse biological processes. We investigated the interplay of Src family kinases (SFKs) and TrkB to better understand mechanisms of TrkB signaling in physiological and pathological conditions. We compared and contrasted the role of SFKs in TrkB signaling following activation of TrkB by two mechanisms, its transactivation by zinc, and its activation by its prototypic neurotrophin ligand, brain-derived neurotrophic factor (BDNF). Using biochemical, pharmacological, and chemical genetic studies of cultured rodent neurons, we found that zinc promotes preferential phosphorylation of Tyr-705/Tyr-706 of TrkB by a SFK-dependent but TrkB kinase-independent mechanism, a signaling event critical for transactivation of TrkB by zinc. By contrast, SFK activity is not essential for BDNF-mediated activation of TrkB, yet SFK activity is increased as a consequence of TrkB activation by BDNF. Moreover, BDNF-induced phosphorylation of Tyr-705/Tyr-706 of TrkB was inhibited by SFK inhibitors, implicating a regulatory role of SFKs in TrkB activation by BDNF. In sum, SFKs are activated by TrkB and, in turn, SFKs can promote TrkB activation. We propose models depicting the mutual regulation of SFKs and TrkB following activation of TrkB by zinc and BDNF.
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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".