Cezanne Regulates Inflammatory Responses to Hypoxia in Endothelial Cells by Targeting TRAF6 for Deubiquitination
Bibliographic record
Abstract
RATIONALE: Hypoxia followed by reoxygenation promotes inflammation by activating nuclear factor κB transcription factors in endothelial cells (ECs). This process involves modification of the signaling intermediary tumor necrosis factor receptor-associated factor 6 with polyubiquitin chains. Thus, cellular mechanisms that suppress tumor necrosis factor receptor-associated factor 6 ubiquitination are potential therapeutic targets to reduce inflammation in hypoxic tissues. OBJECTIVE: In this study, we tested the hypothesis that endothelial activation in response to hypoxia-reoxygenation can be influenced by Cezanne, a deubiquitinating enzyme that cleaves ubiquitin from specific modified proteins. METHODS AND RESULTS: Studies of cultured ECs demonstrated that hypoxia (1% oxygen) induced Cezanne via p38 mitogen-activated protein kinase-dependent transcriptional and post-transcriptional mechanisms. Hypoxia-reoxygenation had minimal effects on proinflammatory signaling in unmanipulated ECs but significantly enhanced Lys63 polyubiquitination of tumor necrosis factor receptor-associated factor 6, activation of nuclear factor κB, and expression of inflammatory genes after silencing of Cezanne. Thus, although hypoxia primed cells for inflammatory activation, it simultaneously induced Cezanne, which impeded signaling to nuclear factor κB by suppressing tumor necrosis factor receptor-associated factor 6 ubiquitination. Similarly, ischemia induced Cezanne in the murine kidney in vascular ECs, glomerular ECs, podocytes, and epithelial cells, and genetic deletion of Cezanne enhanced renal inflammation and injury in murine kidneys exposed to ischemia followed by reperfusion. CONCLUSIONS: We conclude that inflammatory responses to ischemia are controlled by a balance between ubiquitination and deubiquitination, and that Cezanne is a key regulator of this process. Our observations have important implications for therapeutic targeting of inflammation and injury during ischemia-reperfusion.
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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".