Inflammatory Cytokines IL-32 and IL-17 Have Common Signaling Intermediates despite Differential Dependence on TNF-Receptor 1
Bibliographic record
Abstract
Cytokines IL-32 and IL-17 are emerging as critical players in the pathophysiology of immune-mediated chronic inflammatory diseases. It has been speculated that the molecular mechanisms governing IL-32- and IL-17-mediated cellular responses are differentially dependent on the TNF pathway. In this study, kinome analysis demonstrated that following stimulation with cytokine IL-32, but not IL-17, there was increased phosphorylation of a peptide target corresponding to TNF-R1. Consistent with this observation, blocking TNF-R1 resulted in a suppression of IL-32-induced downstream responses, indicating that IL-32-mediated activity may be dependent on TNF-R1. In contrast, blocking TNF-R1 did not affect IL-17-induced downstream responses. Kinome analysis also implicated p300 (transcriptional coactivator) and death-associated protein kinase-1 (DAPK-1) as signaling intermediates for both IL-32 and IL-17. Phosphorylation of p300 and DAPK-1 upon stimulation with either IL-32 or IL-17 was confirmed by immunoblots. The presence of common targets was supported by results demonstrating similar downstream responses induced in the presence of IL-32 and IL-17, such as transcriptional responses and the direct activation of NF-κB. Furthermore, knockdown of p300 and DAPK-1 altered downstream responses induced by IL-32 and IL-17, and impacted certain cellular responses induced by TNF-α and IL-1β. We hypothesize that p300 and DAPK-1 represent nodes where the inflammatory networks of IL-32 and IL-17 overlap, and that these proteins would affect both TNF-R1-dependent and -independent pathways. Therefore, p300 and DAPK-1 are viable potential therapeutic targets for chronic inflammatory 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) | 0.000 | 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 | 0.001 |
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".