Involvement of a disintegrin and metalloproteinase 10 and 17 in shedding of tumor necrosis factor-α
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".