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Record W4402600892 · doi:10.1038/s42003-024-06882-3

Activation of the NLRP1B inflammasome by caspase-8

2024· article· en· W4402600892 on OpenAlexafffund
J. Meade, Sarah Stuart, Jana Neiman-Zenevich, Christian Krustev, Stephen E. Girardin, Jeremy Mogridge

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsInflammasomeChemistryCaspase 1Cell biologyBiologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Cleavage of the innate immune receptor NLRP1B by various microbial proteases causes the proteasomal degradation of its N-terminal fragment and the subsequent release of a C-terminal fragment that forms an inflammasome. We reported previously that metabolic stress caused by intracellular bacteria triggers NLRP1B activation, but the mechanism by which this occurs was not elucidated. Here we demonstrate that TLR4 signaling in metabolically stressed macrophages promotes the formation of a TRIF/RIPK1/caspase-8 complex. Caspase-8 activity, induced downstream of this TLR4 pathway or through a distinct TNF receptor pathway, causes cleavage and activation of NLRP1B, which facilitates the maturation of both pro-caspase-1 and pro-caspase-8. Thus, our findings indicate that caspase-8 and NLRP1B generate a positive feedback loop that amplifies cell death processes and promotes a pro-inflammatory response through caspase-1. The ability of NLRP1B to detect caspase-8 activity suggests that this pattern recognition receptor may play a role in the defense against a variety of pathogens that induce apoptosis. Metabolic stress is shown to impair IKKβ function, which leads to the activation of pro-caspase-8 downstream of TLR4 engagement. Caspase-8 cleaves and activates the NLRP1B inflammasome, thereby enhancing a cell death signal.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.233

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 science0.0010.000
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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