The ADAM17 sheddase complex regulator iTAP/Frmd8 modulates inflammation and tumor growth
Bibliographic record
Abstract
Abstract The metalloprotease ADAM17 catalyzes the shedding of key signalling molecules from the cell surface, including the inflammatory cytokine TNF (tumour necrosis factor) and activating ligands of the EGFR (epidermal growth factor receptor). ADAM17 exists within an assemblage called the “sheddase complex” containing a rhomboid pseudoprotease (iRhom1 or iRhom2). iRhoms control multiple aspects of ADAM17 biology, including its vesicular trafficking, maturation from its precursor pro-form, activation on the cell surface and specificity for subsets of proteolytic targets. Previous studies from our laboratory and others identified the FERM domain-containing protein Frmd8/iTAP as an iRhom-binding protein. iTAP is required to maintain the cell surface stability of the sheddase complex, thereby preventing the precocious shunting of ADAM17 and iRhom2 to lysosomes and their consequent degradation. As pathophysiological role(s) of iTAP have not been addressed, here we sought to characterize the impact of loss of iTAP on ADAM17-associated phenotypes in mice. Our data show that iTAP KO mice exhibit defects in ADAM17 activity in inflammatory and intestinal epithelial barrier repair functions, but do not exhibit the collateral effects associated with global loss of ADAM17. Furthermore, we show that iTAP promotes cancer cell growth in a cell-autonomous manner, and by modulating the tumor microenvironment. Our work suggests that pharmacological intervention at the level of iTAP may be beneficial to target ADAM17 activity in specific compartments during chronic inflammatory diseases or cancer, avoiding the deleterious impact on vital functions associated with the widespread inhibition of ADAM17 in normal tissues.
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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 | 0.001 |
| 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".