Requirement of clusterin expression for prosurvival autophagy in hypoxic kidney tubular epithelial cells
Bibliographic record
Abstract
Cellular autophagy is a prosurvival mechanism in the kidney against ischemia-reperfusion injury (IRI), but the molecular pathways that activate the autophagy in ischemic kidneys are not fully understood. Clusterin (CLU) is a chaperone-like protein, and its expression is associated with kidney resistance to IRI. The present study investigated the role of CLU in prosurvival autophagy in the kidney. Renal IRI was induced in mice by clamping renal pedicles at 32°C for 45 min. Hypoxia in renal tubular epithelial cell (TEC) cultures was induced by exposure to a 1% O2 atmosphere. Autophagy was determined by either light chain 3-BII expression with Western blot analysis or light chain 3-green fluorescent protein aggregation with confocal microscopy. Cell apoptosis was determined by flow cytometric analysis. The unfolded protein response was determined by PCR array. Here, we showed that autophagy was significantly activated by IRI in wild-type (WT) but not CLU-deficient kidneys. Similarly, autophagy was activated by hypoxia in human proximal TECs (HKC-8) and WT mouse primary TECs but was impaired in CLU-null TECs. Hypoxia-activated autophagy was CLU dependent and positively correlated with cell survival, and inhibition of autophagy significantly promoted cell death in both HKC-8 and mouse WT/CLU-expressing TECs but not in CLU-null TECs. Further experiments showed that CLU-dependent prosurvival autophagy was associated with activation of the unfolded protein response in hypoxic kidney cells. In conclusion, these data suggest that activation of prosurvival autophagy by hypoxia in kidney cells requires CLU expression and may be a key cytoprotective mechanism of CLU in the protection of the kidney from hypoxia/ischemia-mediated injury.
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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".