Neuroprotective effects of resveratrol and epigallocatechin gallate polyphenols are mediated by the activation of protein kinase C gamma
Bibliographic record
Abstract
Polyphenols such as epigallocatechin gallate (EGCG) and resveratrol have received a great deal of attention because they may contribute to the purported neuroprotective action of the regular consumption of green tea and red wine. Many studies, including those published by our group, suggest that this protective action includes their abilities to prevent the neurotoxic effects of beta-amyloid, a protein whose accumulation likely plays a pivotal role in Alzheimer's disease. Moreover, the scavenging activities of polyphenols on reactive oxygen species and their inhibitory action of cyclooxygenase likely explain, at least in part, their antioxidant and anti-inflammatory activities. Besides these well-documented properties, the modulatory action of these polyphenols on intracellular signaling pathways related to cell death/survival (e.g., protein kinase C, PKC) has yet to be investigated in detail. Using rat hippocampal neuronal cells, we aimed to investigate here the effects of EGCG and resveratrol on cell death induced by GF 109203X, a selective inhibitor of PKC. The MTT/resazurin and spectrin assays indicated that EGCG and resveratrol protected against GF 109203X-induced cell death and cytoskeleton degeneration, with a maximal effect at 1 and 3 μM, respectively. Moreover, immunofluorescence data revealed that cells treated with these polyphenols increased PKC gamma (γ) activation and promoted neuronal interconnections. Finally, we found that the protective effects of both polyphenols on the cytoskeleton and synaptic plasticity were mediated by the PKCγ subunit. Taken together, the results suggest that PKC, and more specifically its γ subunit, plays a critical role in the protective action of EGCG and resveratrol on neuronal integrity.
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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 | 0.001 |
| 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".