TAK1 is involved in the autophagy process in retinal pigment epithelial cells
Bibliographic record
Abstract
Autophagy is an evolutionarily conserved mechanism for degrading long-lived or malfunctioning proteins and organelles, such as those resulting from oxidative stress. Several publications have demonstrated the importance of the autophagy process in the pathophysiology of dry age-related macular degeneration (AMD). Still, the mechanism underlying this process and its involvement in dry AMD are not fully characterized. Investigating the autophagy process in retinal pigment epithelial (RPE) cells, we identified transforming growth factor β activated kinase 1 (TAK1) as a key player in the process. We found increased TAK1 phosphorylation in ARPE-19 and D407 cells treated with different inducers of autophagy, such as oxidative stress and rapamycin. Moreover, utilizing TAK1 specific inhibitor prior to oxidative stress or rapamycin treatment, we found significant reduction in LC3A/B-II expression. These results point at the involvement of TAK1 in the regulation of autophagy in RPE cells. This study suggests that aberrant activity of this kinase impairs autophagy and subsequently leads to alterations in the vitality of RPE cells. Proper activity of TAK1 may be essential for efficient autophagy, and crucial for the ability of RPE cells to respond to stress and dispose of damaged organelles, thus preventing or delaying retinal pathologies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".