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Record W4417260898 · doi:10.1038/s41418-025-01650-0

cFLIP suppresses caspase-1- and MLKL-independent perinatal lethality driven by auto-processing impaired caspase-8 D387A

2025· article· en· W4417260898 on OpenAlexaff
Kim Newton, Katherine E. Wickliffe, Allie Maltzman, Debra L. Dugger, Juan G. Reyes, Natasha Bacarro, Søren Warming, Neha Rohatgi, Rohit Reja, Joshua D. Webster, Vishva M. Dixit

Bibliographic record

VenueCell Death and Differentiation · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsRoche (Canada)
FundersGenentech
KeywordsFADDApoptosisRIPK1Signal transducing adaptor proteinFas ligandSignal transductionTumor necrosis factor alphaDeath domainProgrammed cell deathTRADD

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.227
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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