G9a Inhibition Promotes Neuroprotection through GMFB Regulation in Alzheimer’s Disease
Bibliographic record
Abstract
Epigenetic alterations are a fundamental pathological hallmark of Alzheimer's disease (AD). Herein, we show the upregulation of G9a and H3K9me2 in the brains of AD patients. Interestingly, treatment with a G9a inhibitor (G9ai) in SAMP8 mice reversed the high levels of H3K9me2 and rescued cognitive decline. A transcriptional profile analysis after G9ai treatment revealed increased gene expression of glia maturation factor β (GMFB) in SAMP8 mice. Besides, a H3K9me2 ChIP-seq analysis after G9a inhibition treatment showed the enrichment of gene promoters associated with neural functions. We observed the induction of neuronal plasticity and a reduction of neuroinflammation after G9ai treatment, and more strikingly, these neuroprotective effects were reverted by the pharmacological inhibition of GMFB in mice and cell cultures; this was also validated by the RNAi approach generating the knockdown of GMFB/Y507A.10 in Caenorhabditis elegans. Importantly, we present evidence that GMFB activity is controlled by G9a-mediated lysine methylation as well as we identified that G9a directly bound GMFB and catalyzed the methylation at lysine (K) 20 and K25 in vitro. Furthermore, we found that the neurodegenerative role of G9a as a GMFB suppressor would mainly rely on methylation of the K25 position of GMFB, and thus G9a pharmacological inhibition removes this methylation promoting neuroprotective effects. Then, our findings confirm an undescribed mechanism by which G9a inhibition acts at two levels, increasing GMFB and regulating its function to promote neuroprotective effects in age-related cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".