cFLIP suppresses caspase-1- and MLKL-independent perinatal lethality driven by auto-processing impaired caspase-8 D387A
Bibliographic record
Abstract
Abstract Death ligands, including FAS ligand (FASL) and tumor necrosis factor (TNF), trigger apoptosis by promoting caspase-8 dimerization and activation. Impaired FAS signaling causes unconventional lymphocytes to accumulate, resulting in lymphadenopathy. Although autoprocessing of caspase-8 is considered important for apoptosis, autoprocessing-deficient Casp8 D387A/D387A mice do not develop lymphadenopathy. We show that this is because heterodimers of caspase-8 D387A and cFLIP, besides suppressing MLKL-driven necroptosis, can also induce apoptosis. Interestingly, caspase-8 D387A elicited MLKL- and caspase-1-independent intestinal atrophy and perinatal lethality in mice lacking cFLIP. Caspase-8 D387A interacted with FADD and RIPK1 in the intestine, where there was aberrant cleavage of N4BP1 and caspase-3, plus enhanced NF-κB signaling. Eliminating FADD, the adaptor protein that promotes caspase-8 oligomerization, prevented this perinatal lethality. Collectively, our results suggest that cFLIP forms heterodimers with caspase-8 D387A to promote apoptosis in some contexts, while limiting the activity of caspase-8 D387A homodimers in others.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".