IL-17 Receptor Signaling Inhibits C/EBPβ by Sequential Phosphorylation of the Regulatory 2 Domain
Bibliographic record
Abstract
Interleukin-17 (IL-17), the hallmark cytokine of T helper 17 (T(H)17) cells, signals through a distinct receptor subclass, yet little is known about the mechanisms involved. IL-17 activates the expression of target genes through the actions of the transcription factors nuclear factor kappaB (NF-kappaB), CAAT enhancer binding protein delta (C/EBPdelta), and C/EBPbeta. The adaptor proteins tumor necrosis factor receptor-associated factor 6 (TRAF6) and Act1 are upstream of NF-kappaB and C/EBPdelta, but the regulation of C/EBPbeta remains undefined. Here, we show that IL-17 signaling led to phosphorylation of two sites in the regulatory 2 domain of C/EBPbeta in a sequential, interdependent fashion. The first was rapid and dependent on extracellular signal-regulated kinase (ERK), whereas the second was dependent on the activity of glycogen synthase kinase 3beta (GSK-3beta). These pathways were mediated by distinct subdomains within IL-17 receptor A (IL-17RA). Whereas phosphorylation of threonine 188 (Thr188) was mediated by the previously identified SEF/IL-17R homology domain-Toll-IL-1R-like loop (SEFIR-TILL), phosphorylation of Thr179 occurred through a newly characterized motif located in the distal tail of IL-17RA. Phosphorylated C/EBPbeta mediated a negative signal, because blocking ERK and GSK-3beta increased expression of IL-17 target genes and a C/EBPbeta-Thr188 mutant enhanced activation of a C/EBP-dependent reporter. Overexpression of GSK-3beta inhibited IL-17-induced activation of a C/EBP-dependent reporter, and Thr179 of C/EBPbeta was not phosphorylated in GSK-3beta-deficient cells. Thus, IL-17 triggered the dual phosphorylation of C/EBPbeta, which inhibited the expression of proinflammatory genes. This detailed dissection is the first for the IL-17-mediated C/EBP pathway and the first known example of a negative signal mediated by IL-17RA.
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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".