Toll-Like Signaling and the Cytokine IL-6 Regulate Histone Deacetylase Dependent Neuronal Survival
Bibliographic record
Abstract
Histone deacetylase (HDAC) proteins have a role in promoting neuronal survival in vitro, but the mechanism underlying this function has not been identified. Here we provide evidence that components of the neuronal microenvironment, including non-neuronal cells and defined culture media, can mitigate midbrain neuronal cell death induced by HDAC inhibitor treatment. Using microarrays we further identified gene expression changes taking place in non-neuronal cells as a result of HDAC inhibition. This analysis demonstrated that HDAC inhibitor treatment results in the down-regulation of immunity related signaling factors, in particular the Toll-like receptors (TLR). TLR signaling is active in cultured midbrain cells, yet blocking TLR receptors is not sufficient to cause neuronal cell death. In contrast, selective activation of this pathway using TLR ligands can modestly block the effects of HDAC inhibition. Furthermore, we observed that the negative effects of HDAC inhibitor treatment on neuronal survival could be more substantially blocked by the cytokine Interleukin-6 (IL-6), which is a major downstream target of TLR signaling. These data suggest that HDACs function to promote neuronal survival by activating a TLR and IL-6 dependent pathway.
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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) | 0.000 | 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) | 0.001 | 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".