p75NTR antagonists attenuate photoreceptor cell loss in murine models of retinitis pigmentosa
Bibliographic record
Abstract
Abstract ProNGF signaling through p75 NTR has been associated with neurodegenerative disorders. Retinitis pigmentosa (RP) comprises a group of inherited retinal dystrophies that causes progressive photoreceptor cell degeneration and death, at a rate dependent on the genetic mutation. There are more than 300 mutations causing RP, and this is a challenge to therapy. Our study was designed to explore a common mechanism for p75 NTR in the progression of RP, and assess its potential value as a therapeutic target. The proNGF/p75 NTR system is present in the dystrophic retina of the rd10 RP mouse model. Compared with wild-type (WT) retina, the levels of unprocessed proNGF were increased in the rd10 retina at early degenerative stages, before the peak of photoreceptor cell death. Conversely, processed NGF levels were similar in rd10 and WT retinas. ProNGF remained elevated throughout the period of photoreceptor cell loss, correlating with increased expression of α 2 -macroglobulin, an inhibitor of proNGF processing. The neuroprotective effect of blocking p75 NTR was assessed in organotypic retinal cultures from rd10 and RhoP mouse models. Retinal explants treated with p75 NTR antagonists showed significantly reduced photoreceptor cell death, as determined by the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay and by preservation of the thickness of the outer nuclear layer (ONL), where photoreceptor nuclei are located. This effect was accompanied by decreased retinal-reactive gliosis and reduced TNF α secretion. Use of p75 NTR antagonist THX-B (1,3-diisopropyl-1-[2-(1,3-dimethyl-2,6-dioxo-1,2,3,6-tetrahydro-purin-7-yl)-acetyl]-urea) in vivo in the rd10 and RhoP mouse models, by a single intravitreal or subconjunctival injection, afforded neuroprotection to photoreceptor cells, with preservation of the ONL. This study demonstrates a role of the p75 NTR /proNGF axis in the progression of RP, and validates these proteins as therapeutic targets in two different RP models, suggesting utility irrespective of etiology.
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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".