A-Disintegrin and Metalloproteinase (ADAM) 17 Enzymatically Degrades Interferon-gamma
Bibliographic record
Abstract
Interferon-gamma (IFN-γ) is a pleiotropic cytokine that exerts anti-tumor and anti-osteoclastogenic effects. Although transcriptional and post-transcriptional regulation of IFN-γ is well understood, subsequent modifications of secreted IFN-γ are not fully elucidated. Previous research indicates that some cancer cells escape immune surveillance and metastasize into bone tissue by inducing osteoclastic bone resorption. Peptidases of the a-disintegrin and metalloproteinase (ADAM) family are implicated in cancer cell proliferation and tumor progression. We hypothesized that the ADAM enzymes expressed by cancer cells degrades IFN-γ and attenuates IFN-γ-mediated anti-tumorigenic and anti-osteoclastogenic effects. Recombinant ADAM17 degraded IFN-γ into small fragments. The addition of ADAM17 to the culture supernatant of stimulated mouse splenocytes decreased IFN-γ concentration. However, ADAM17 inhibition in the stimulated mouse T-cells prevented IFN-γ degradation. ADAM17-expressing human breast cancer cell lines MCF-7 and MDA-MB-453 also degraded recombinant IFN-γ, but this was attenuated by ADAM17 inhibition. Degraded IFN-γ lost the functionality including the inhibititory effect on osteoclastogenesis. This is the first study to demonstrate the extracellular proteolytic degradation of IFN-γ by ADAM17. These results suggest that ADAM17-mediated degradation of IFN-γ may block the anti-tumorigenic and anti-osteoclastogenic effects of IFN-γ. ADAM17 inhibition may be useful for the treatment of attenuated cancer immune surveillance and/or bone metastases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".