Protein Kinase Cδ Suppresses Autophagy to Induce Kidney Cell Apoptosis in Cisplatin Nephrotoxicity
Bibliographic record
Abstract
Nephrotoxicity is a major adverse effect in cisplatin chemotherapy, and renoprotective approaches are unavailable. Recent work unveiled a critical role of protein kinase C δ (PKC δ ) in cisplatin nephrotoxicity and further demonstrated that inhibition of PKC δ not only protects kidneys but enhances the chemotherapeutic effect of cisplatin in tumors; however, the underlying mechanisms remain elusive. Here, we show that cisplatin induced rapid activation of autophagy in cultured kidney tubular cells and in the kidneys of injected mice. Cisplatin also induced the phosphorylation of mammalian target of rapamycin (mTOR), p70S6 kinase downstream of mTOR, and serine/threonine-protein kinase ULK1, a component of the autophagy initiating complex. In vitro , pharmacologic inhibition of mTOR, directly or through inhibition of AKT, enhanced autophagy after cisplatin treatment. Notably, in both cells and kidneys, blockade of PKC δ suppressed the cisplatin-induced phosphorylation of AKT, mTOR, p70S6 kinase, and ULK1 resulting in upregulation of autophagy. Furthermore, constitutively active and inactive forms of PKC δ respectively enhanced and suppressed cisplatin-induced apoptosis in cultured cells. In mechanistic studies, we showed coimmunoprecipitation of PKC δ and AKT from lysates of cisplatin-treated cells and direct phosphorylation of AKT at serine-473 by PKC δ in vitro . Finally, administration of the PKC δ inhibitor rottlerin with cisplatin protected against cisplatin nephrotoxicity in wild-type mice, but not in renal autophagy–deficient mice. Together, these results reveal a pathway consisting of PKC δ , AKT, mTOR, and ULK1 that inhibits autophagy in cisplatin nephrotoxicity. PKC δ mediates cisplatin nephrotoxicity at least in part by suppressing autophagy, and accordingly, PKC δ inhibition protects kidneys by upregulating autophagy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".