Signaling motifs in the IL-17 receptor (94.2)
Bibliographic record
Abstract
Abstract IL-17 and its receptor IL-17RA have received much attention as the defining cytokine of a new class of T helper cells termed Th17. Mounting evidence suggests that IL-17 causes pathology in autoimmunity, but surprisingly little is known about mechanisms of IL-17 signal transduction. Although produced by T cells, IL-17 activates genes typical of innate immune cytokines such as TNFα and IL-1β, despite little sequence similarity in their respective receptors. A prior bioinformatics study predicted a subdomain in IL-17-family receptors with homology to a Toll/IL-1R (TIR) domain, termed “SEFIR.” However, the SEFIR domain lacks motifs critical for bona fide TIR domains, and its functionality has never been verified. Here, we used a reconstitution system in IL-17RA-null fibroblasts to map functional domains within IL-17RA. We demonstrate for the first time that the SEFIR domain mediates signaling, independently of TIR adaptors such as MyD88 and TRIF. Moreover, we identified a novel “TIR-like loop” (TILL) also required for activation of NF-κB, NF-κB-dependent genes and upregulation of C/EBPβ and C/EBPδ. We further identified a distinct domain required for activation of C/EBPβ and induction of a subset IL-17 target genes. This is the first structure-function analysis of any IL-17R superfamily receptor, and reveals unique structural and functional properties in IL-17 signaling.
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 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) | 0.002 | 0.002 |
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".