Histone deacetylase 3 regulates microglial function through histone deacetylation
Bibliographic record
Abstract
Abstract Background As the primary innate immune cells of the brain microglia respond to damage and disease through pro-inflammatory release of cytokines and neuroinflammatory molecules. Histone acetylation is an activating transcriptional mark that regulates gene expression, which is altered in states of disease. Inhibition of histone deacetylase 3 (Hdac3) has been utilized in pre-clinical models of disease to dampen inflammation, but the molecular mechanisms underlying Hdac3’s regulation of inflammatory gene expression in microglia is not well understood. Methods Functional changes in immortalized microglia were characterized using a Hdac3 specific inhibitor RGFP966 in response to an immune challenge lipopolysaccharide (LPS). Flow cytometry and cleavage under tags & release using nucleases (CUT & RUN) were used to investigate global and promoter-specific histone acetylation changes, resulting in altered gene expression. Results Hdac3 inhibition enhanced neuroprotective functions of microglia in response to LPS through reduced nitric oxide release and increased baseline phagocytosis. Inhibition of Hdac3 enhanced histone acetylation globally and at specific gene loci, resulting in the release of gene repression at baseline and enhanced responses to LPS. Conclusion The findings suggest Hdac3 serves as a negative regulator of microglial gene expression, and that inhibition of Hdac3 facilitates the microglial response to inflammation and its subsequent resolution. Together, this work provides new mechanistic insights into therapeutic applications of Hdac3 inhibition which mediate reduced neuroinflammatory insults through microglial response.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".