Effects of Histone Deacetylase Inhibitors on the Maintenance of Midbrain Neurons and Glia
Bibliographic record
Abstract
Perturbations of the complex intrinsic and extrinsic factors that contribute to cellular differentiation can have many consequences ranging from dedifferentiation to cell death. The overall objective of my research is to investigate the factors that contribute to the maintenance of mature midbrain neurons and glia. In order to address this objective, I first carried out a detailed immunocytochemical analysis to demonstrate that histone deacetylase inhibitor (HDACI) treatment of differentiated midbrain neurons in culture results in an overall destabilization of neuronal phenotype, which leads to caspase-independent cell death. GFAP positive astrocytes are refractory to the effects of HDACI treatment, suggesting that inhibition of HDACs has differential effects on neurons and glia. HDACI treatment alone was not sufficient to induce neuronal dedifferentiation as evidenced by RT-PCR analysis of stem/progenitor markers, and recovery experiments. Finally, I demonstrate that cortical neurons do not undergo cell death in response to HDACI treatment, suggesting that there may be microenvironmental factors that promote the susceptibility of midbrain neurons to the neurotoxic effects of HDACI. In the second part of this thesis I determined the molecular mechanism that was at least partly responsible for the effects of HDACI treatment on midbrain neurons. Gene expression profiling of HDACI treated midbrain cultures revealed a strong down-regulation of immune related factors. This observation is supported by the loss of microglia in HDACI treated midbrain cultures. I also provide evidence that Toll-like receptor (TLR) signaling, likely through the activation of Interleukin-6 (IL-6) expression, mediates HDAC-dependent neuronal survival. These data provide new evidence that the neuroimmune system is an extrinsic regulator for the homeostasis and survival of neurons.
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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 | 0.001 |
| 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".