DZNep inhibits H3K27me3 deposition and delays retinal degeneration in the rd1 mice
Bibliographic record
Abstract
Abstract Retinitis pigmentosa (RP) is a group of inherited retinal degenerative diseases causing progressive loss of photoreceptors. Numerous gene mutations are identified to be related with RP, but epigenetic modifications may also be involved in the pathogenesis. Previous studies suggested that both DNA methylation and histone acetylation regulate photoreceptor cell death in RP mouse models. However, the role of histone methylation in RP has never been investigated. In this study, we found that trimethylation of several lysine sites of histone H3, including lysine 27 (H3K27me3), increased in the retinas of rd1 mice. Histone methylation inhibitor DZNep significantly reduced the calpain activity, delayed the photoreceptor loss, and improved ERG response of rd1 retina. RNA-sequencing indicated that DZNep synergistically acts on several molecular pathways that regulate photoreceptor survival in rd1 retina, including PI3K-Akt and photoreceptor differentiation pathways, revealing the therapeutic potential of DZNep for RP treatment. PI3K-Akt pathway and H3K27me3 form a feedback loop in rd1 retina, thus PI3K inhibitor LY294002 reduces phosphorylation of Ezh2 at serine 21 and enhances H3K27me3 deposition, and inhibiting H3K27me3 by DZNep can activate PI3K-Akt pathway by de-repressing gene expression of PI3K subunits Pik3r1 and Pik3r3. These findings suggest that histone methylation, especially H3K27me3 deposition is a novel mechanism and therapeutic target for retinal degenerative diseases, similar to H3K27me3-mediated ataxia-telangiectasia in Atm −/− mouse.
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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) | 0.000 | 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) | 0.002 | 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".