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Record W4315928781 · doi:10.4049/jimmunol.2200465

Cutting Edge: Negative Regulation of Inflammasome Activation by TRAF1 Can Limit Gout

2023· article· en· W4315928781 on OpenAlexafffund
Ali Mirzaesmaeili, Safoura Zangiabadi, Jonathan Raspanti, Ali Akram, Robert D. Inman, Ali A. Abdul‐Sater

Bibliographic record

VenueThe Journal of Immunology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity Health NetworkArthritis SocietyUniversity of TorontoYork University
FundersCanadian Institutes of Health ResearchArthritis SocietyGovernment of Canada
KeywordsInflammasomeGoutProinflammatory cytokineNALP3Knockout mouseSecretionInflammationArthritisRegulatorCaspase 1PathogenesisCytokineImmunologyMedicineCancer researchChemistryReceptorInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Secretion of IL-1β, a potent cytokine that plays a key role in gout pathogenesis, is regulated by inflammasomes. TRAF1 has been linked to heightened risk to inflammatory arthritis. In this article, we show that TRAF1 negatively regulates inflammasome activation to limit caspase-1 and IL-1β secretion in human and mouse macrophages. TRAF1 reduces linear ubiquitination and subsequent oligomerization of the adapter protein, ASC. i.p. injection of monosodium urate crystals resulted in increased inflammatory cell infiltrates and IL-1β production in Traf1 knockout mice compared with wild type littermates. In a model of monosodium urate crystal-induced gout, Traf1 knockout mice exhibited more swelling in the knee joints, increased infiltration of inflammatory cells, and higher expression of proinflammatory cytokines. In summary, this study identifies TRAF1 as a key regulator of IL-1β production and a potential therapeutic target for inflammasome-driven diseases such as gout.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.007
GPT teacher head0.225
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2023
Admission routes2
Has abstractyes

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