Tristetraprolin regulates necroptosis during tonic Toll-like receptor 4 (TLR4) signaling in murine macrophages
Bibliographic record
Abstract
The necrosome is a protein complex required for signaling in cells that results in necroptosis, which is also dependent on tumor necrosis factor receptor (TNF-R) signaling. TNFα promotes necroptosis, and its expression is facilitated by mitogen-activated protein (MAP) kinase–activated protein kinase 2 (MK2) but is inhibited by the RNA-binding protein tristetraprolin (TTP, encoded by the Zfp36 gene). We have stimulated murine macrophages from WT, MyD88 −/− , Trif −/− , MyD88 −/− Trif −/− , MK2 −/− , and Zfp36 −/− mice with graded doses of lipopolysaccharide (LPS) and various inhibitors to evaluate the role of various genes in Toll-like receptor 4 (TLR4)–induced necroptosis. Necrosome signaling, cytokine production, and cell death were evaluated by immunoblotting, ELISA, and cell death assays, respectively. We observed that during TLR4 signaling, necrosome activation is mediated through the adaptor proteins MyD88 and TRIF, and this is inhibited by MK2. In the absence of MK2-mediated necrosome activation, lipopolysaccharide-induced TNFα expression was drastically reduced, but MK2-deficient cells became highly sensitive to necroptosis even at low TNFα levels. In contrast, during tonic TLR4 signaling, WT cells did not undergo necroptosis, even when MK2 was disabled. Of note, necroptosis occurred only in the absence of TTP and was mediated by the expression of TNFα and activation of JUN N-terminal kinase (JNK). These results reveal that TTP plays an important role in inhibiting TNFα/JNK-induced necrosome signaling and resultant cytotoxicity.
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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".