Distinct functional motifs within the IL-17 receptor regulate signal transduction and target gene expression
Bibliographic record
Abstract
IL-17 is the founding member of a novel family of proinflammatory cytokines that defines a new class of CD4+ effector T cells, termed "Th17." Mounting evidence suggests that IL-17 and Th17 cells cause pathology in autoimmunity, but little is known about mechanisms of IL-17RA signaling. IL-17 through its receptor (IL-17RA) activates genes typical of innate immune cytokines, such as TNFalpha and IL-1beta, despite minimal sequence similarity in their respective receptors. A previous bioinformatics study predicted a subdomain in IL-17-family receptors with homology to a Toll/IL-1R (TIR) domain, termed the "SEFIR domain." However, the SEFIR domain lacks motifs critical for bona fide TIR domains, and its functionality was never verified. Here, we used a reconstitution system in IL-17RA-null fibroblasts to map functional domains within IL-17RA. We demonstrate that the SEFIR domain mediates IL-17RA signaling independently of classic TIR adaptors, such as MyD88 and TRIF. Moreover, we identified a previously undescribed"TIR-like loop" (TILL) required for activation of NF-kappaB, MAPK, and up-regulation of C/EBPbeta and C/EBPdelta. Mutagenesis of the TILL domain revealed a site analogous to the LPS(d) mutation in TLR4, which renders mice insensitive to LPS. However, a putative salt bridge typically found in TIR domains appears to be dispensable. We further identified a C-terminal domain required for activation of C/EBPbeta and induction of a subset IL-17 target genes. This structure-function analysis of a IL-17 superfamily receptor reveals important differences in IL-17RA compared with IL-1/TLR receptors.
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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) | 0.002 | 0.001 |
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".