5-Iodotubercidin sensitizes cells to RIPK1-dependent necroptosis by interfering with NFκB signaling
Bibliographic record
Abstract
Receptor-interacting protein kinases (RIPK)-1 and -3 play crucial roles in cell fate decisions and are regulated by multiple checkpoint controls. Previous studies have identified IKK1/2- and p38/MK2-dependent checkpoints that phosphorylate RIPK1 at different residues to inhibit its activation. In this study, we investigated TNF-induced death in MAPK-activated protein kinase 2 (MK2)-deficient cells and found that MK2 deficiency or inactivation predominantly leads to necroptotic cell death, even without caspase inhibition. While RIPK1 inhibitors can rescue MK2-deficient cells from necroptosis, inhibiting RIPK3 seems to switch the process to apoptosis. To understand the underlying mechanism of this switch, we screened a library of 149 kinase inhibitors and identified the adenosine analog 5-Iodotubercidin (5-ITu) as the most potent compound that sensitizes MK2-deficient MEFs to TNF-induced cell death. 5-ITu also enhances LPS-induced necroptosis when combined with MK2 inhibition in RAW264.7 macrophages. Further mechanistic studies revealed that 5-ITu induces RIPK1-dependent necroptosis by suppressing IKK signaling in the absence of MK2 activity. These findings highlight the role for the multitarget kinase inhibitor 5-ITu in TNF-, LPS- and chemotherapeutics-induced necroptosis and its potential implications in RIPK1-targeted therapies.
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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".