Cathepsins Limit Macrophage Necroptosis through Cleavage of Rip1 Kinase
Bibliographic record
Abstract
It has recently been shown that programmed necrosis, necroptosis, may play a key role in the development of inflammation. Deciphering the regulation of this pathway within immune cells may therefore have implications in pathology associated with inflammatory diseases. We show that treatment of macrophages with the pan caspase inhibitor (zVAD-FMK) results in both increased phosphorylation and decreased cleavage of receptor interacting protein kinase-1 (Rip1), leading to necroptosis that is dependent on autocrine TNF signaling. Stimulation of cells with TLR agonists such as LPS in the presence of zVAD-FMK also induced Rip1-phosphorylation via a TNFR-independent mechanism. Further examination of Rip1 expression under these stimulatory conditions revealed a regulatory cleavage of Rip1 in macrophages that is not apparently attributable to caspase-8. Instead, we provide novel evidence that cysteine family cathepsins, which are highly abundant in myeloid cells, can also cleave Rip1 kinase. Using small interfering RNA knockdown, specific cathepsin inhibitors, and cell-free cleavage assays, we demonstrate that cysteine cathepsins B and S can directly cleave Rip1. Finally, we demonstrate that only through combined inhibition of cathepsins and caspase-8 could a potent induction of macrophage necroptosis be achieved. These data reveal a novel mechanism of regulation of necroptosis by cathepsins within macrophage cells.
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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) | 0.001 | 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".