Act1 Adaptor Protein Is an Immediate and Essential Signaling Component of Interleukin-17 Receptor
Bibliographic record
Abstract
Interleukin (IL)-17, the founding member of the IL-17 cytokine family, is the hallmark of a novel subset of CD4+ T cells that is regulated by TGFbeta, IL-6, and IL-23. IL-17 plays an important role in promoting tissue inflammation in host defense against infection and in autoimmune diseases. Although IL-17 has been reported to regulate the expression of proinflammatory cytokines, chemokines, and matrix metalloproteinases, the signaling mechanism of IL-17 receptor has not been understood. An earlier study found that IL-17 activates NF-kappaB and MAPK pathways and requires TRAF6 to induce IL-6. However, it is unknown what molecule(s) directly associates with IL-17 receptor to initiate the signaling. We demonstrate here that IL-17 receptor family shares sequence homology in their intracellular region with Toll-IL-1 receptor (TIR) domains and with Act1, a novel adaptor previously reported as an NF-kappaB activator. MyD88 and IRAK4, downstream signaling components of TIR, are not required for IL-17 signaling. On the other hand, Act1 and IL-17 receptor directly associate likely via homotypic interaction. Deficiency of Act1 in fibroblast abrogates IL-17-induced cytokine and chemokine expression, as well as the induction of C/EBPbeta, C/EBPdelta, and IkappaBzeta. Also, absence of Act1 results in a selective defect in IL-17-induced activation of NF-kappaB pathway. These results thus indicate Act1 as a membrane-proximal adaptor of IL-17 receptor with an essential role in induction of inflammatory genes. Our study not only for the first time reveals an immediate signaling mechanism downstream of an IL-17 family receptor but also has implications in therapeutic treatment of various immune diseases.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".