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Record W2168850679 · doi:10.4049/jimmunol.176.2.721

Cutting Edge: Tissue Inhibitor of Metalloproteinase 3 Regulates TNF-Dependent Systemic Inflammation

2006· article· en· W2168850679 on OpenAlexaff
David Smookler, Fazilat F. Mohammed, Zamaneh Kassiri, Gordon S. Duncan, Tak W. Mak, Rama Khokha

Bibliographic record

VenueThe Journal of Immunology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsTumor necrosis factor alphaInnate immune systemReceptorMetalloproteinaseInflammationTissue inhibitor of metalloproteinaseSystemic inflammationDownregulation and upregulationImmunityChemistryImmune systemImmunologyBiologyMatrix metalloproteinaseCell biologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Host response to infectious agents must be rapid and powerful. One mechanism is the release of presynthesized membrane-bound TNF. TNF shedding is mediated by TNF-alpha converting enzyme, which is selectively inhibited by the tissue inhibitor of metalloproteinase 3 (TIMP3). We show that loss of TIMP3 impacts innate immunity by dysregulating cleavage of TNF and its receptors. Cultured timp3-/- macrophages release more TNF in response to LPS than wild-type macrophages. In timp3-/- mice, LPS causes serum levels of TNF and its receptors to rise more rapidly and remain higher compared with wild-type mice. The altered kinetics of ligand and receptor shedding enhances TNF signaling in timp3-/- mice, indicated by elevated serum IL-6. Physiologically, timp3-/- mice are more susceptible to LPS-induced mortality. Ablation of the TNF receptor gene p55 (Tnfrsf1a) or treatment with a synthetic metalloproteinase inhibitor rescues timp3-/- mice. Thus, TIMP3 is essential for normal innate immune function.

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)0.001
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 score0.012
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.211
Teacher spread0.207 · 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

Citations148
Published2006
Admission routes1
Has abstractyes

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