The Kinase Activity of Rip1 Is Not Required for Tumor Necrosis Factor-α-induced IκB Kinase or p38 MAP Kinase Activation or for the Ubiquitination of Rip1 by Traf2
Bibliographic record
Abstract
The death domain kinase Rip1 is recruited to the tumor necrosis factor receptor type 1 and mediates the IkappaB kinase and p38 MAP kinase pathways. In response to tumor necrosis factor-alpha (TNF-alpha), we find Rip1 phosphorylated and ubiquitinated, suggesting that Rip1 phosphorylation may stimulate its ubiquitination. To address the contribution of the kinase activity of Rip1 to its ubiquitination and to TNF-alpha signaling, we introduced wild type Rip1 and a kinase-inactive form of Rip1, Rip1D138N, into rip1-/- murine embryonic fibroblast cells by retroviral infection. TNF-alpha-induced ubiquitination of Rip1 is observed in Rip1D138N cells, supporting the argument that Rip1 autophosphorylation is not required for Rip1 ubiquitination. TNF-alpha-induced Ikk and p38 MAP kinase activation is normal, and the Rip1D138N cells are resistant to TNF-alpha-induced cell death, indicating that the kinase activity of Rip1 is not required to mediate its antiapoptotic functions. In the absence of Traf2, TNF-alpha-induced ubiquitination of Rip1 is impaired, suggesting that Traf2 may be the E3 ubiquitin ligase responsible for the TNF-alpha-dependent, ubiquitination of Rip1. Finally, recruitment of the ubiquitinated Tak1 complex is dependent on the presence of Rip1, suggesting that Rip1 ubiquitination rather than its phosphorylation is critical in signaling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".