Selective inhibitors of the TrkC.T1 receptor reduce retinal inflammation and delay neuronal death in a model of retinitis pigmentosa
Bibliographic record
Abstract
Abstract The heterogeneity of receptor isoforms can cause an apparent paradox where each isoform can promote different or even opposite biological pathways. One example is the neurotrophin receptor TrkC. The trkC mRNA translates a full-length receptor tyrosine kinase (TrkC-FL) whose activation by the growth factor NT3 promotes neuronal survival. In some diseases, the trkC mRNA is spliced to a kinase-truncated isoform (TrkC.T1) whose activation by NT3 up-regulates tumor necrosis factor alpha (TNF-α) causing neurotoxicity. Since TrkC.T1 expression is significantly increased at the onset of neurodegeneration, we hypothesized that in disease TrkC.T1-mediated toxicity prevails over TrkC-FL-mediated survival. To study this, we developed small molecules that selectively antagonize NT3-driven TrkC.T1 neurotoxicity without compromising TrkC-FL survival. In a genetic mouse model of retinitis pigmentosa, therapeutic administration of TrkC.T1 antagonists prevents elevation of TNF-α and reduces photoreceptor neuronal death. This work demonstrates the importance of accounting for functional and structural heterogeneity in receptor–ligand interactions, illustrates chemical biology strategies to develop isoform-selective agents, validates TrkC.T1 as a druggable target, and expands the therapeutic concept of reducing neurotoxicity as a strategy to achieve neuroprotection.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".