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Record W1967304829 · doi:10.1139/o09-015

Involvement of a disintegrin and metalloproteinase 10 and 17 in shedding of tumor necrosis factor-α

2009· article· en· W1967304829 on OpenAlexvenueno aff
Atsuhiko Hikita, Nobuho Tanaka, Shoji Yamane, Yasuko Ikeda, Hiroshi Furukawa, Shigeto Tohma, Ryuji Suzuki, Sakae Tanaka, Hiroyuki Mitomi, Naoshi Fukui

Bibliographic record

VenueBiochemistry and Cell Biology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsnot available
Fundersnot available
KeywordsADAM10DisintegrinTumor necrosis factor alphaMetalloproteinaseCell biologyChemistryBiologyMolecular biologyMatrix metalloproteinaseImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Tumor necrosis factor-alpha (TNF-alpha) is initially synthesized as a membrane-bound protein and converted into a soluble form by proteolytic cleavage. Although a disintegrin and metalloproteinase 17 (ADAM17) is considered to be the primary sheddase for TNF-alpha, it is not known whether ADAM17 is solely responsible for that process in any type of cells. To identify the TNF-alpha sheddase(s) in varieties of cells, we performed experiments using a unique screening system and observed that ADAM9, ADAM10, ADAM17, and ADAM19 were capable of cleaving TNF-alpha. We then performed RNA interference experiments and confirmed that ADAM10 and ADAM17 were in fact involved in TNF-alpha shedding in 293A cells. In mouse macrophages, ADAM17 was confirmed to be the primary sheddase, but the involvement of ADAM10 was also demonstrated. In NIH3T3 cells, ADAM10 could be more important in the shedding than ADAM17. In mouse vascular endothelial cell line UVfemale2, ADAM10 and ADAM17 were equally involved in TNF-alpha shedding, whereas ADAM17 was a major sheddase in human osteoarthritic chondrocytes. From these observations and others, we concluded that both ADAM10 and ADAM17 can be a TNF-alpha sheddase and that their significance could be determined by their expression levels and the abundance of tissue inhibitor of metalloproteinases.

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 score0.004
Threshold uncertainty score0.510

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 head0.009
GPT teacher head0.234
Teacher spread0.224 · 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

Citations56
Published2009
Admission routes1
Has abstractyes

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