Human cathelicidin <scp>LL</scp>‐37 and its derivative <scp>IG</scp>‐19 regulate interleukin‐32‐induced inflammation
Bibliographic record
Abstract
Human cathelicidin LL-37 protects against infections and endotoxin-induced inflammation. In a recent study we have shown that IG-19, an LL-37-derived peptide, protects in a murine model of arthritis. Cytokine interleukin-32 (IL-32) is elevated and directly associated with the disease severity of inflammatory arthritis. Therefore, in this study we examined the effects of LL-37 and IG-19 on IL-32-induced responses in human peripheral blood-derived mononuclear cells (PBMC) and macrophages. We showed that CD14(+) monocytes are the primary cells that produce pro-inflammatory tumour necrosis factor-α (TNF-α) following stimulation of PBMC with IL-32. We demonstrated that LL-37 and IG-19 significantly suppress IL-32-induced production of pro-inflammatory cytokines, e.g. TNF-α and IL-1β, without altering chemokine production. In contrast, LL-37 and IG-19 enhance the production of the anti-inflammatory cytokine IL-1RA. Further mechanistic studies revealed that LL-37 and IG-19 suppress IL-32-mediated phosphorylation of Fyn (Y420) Src kinase. In contrast, IL-32-mediated phosphorylation of AKT-1 (T308) and MKP-1 (S359) is not suppressed by the peptides. LL-37 and IG-19 alone induce the phosphorylation of MKP-1 (S359), which is a known negative regulator of inflammation. Furthermore, the peptides induce the activity of p44/42 mitogen-activated protein kinase, which is known to phosphorylate MKP-1 (S359). This is the first study to demonstrate the regulation of IL-32-induced inflammation by LL-37 and its derivative peptide IG-19. The mechanistic results from this study suggest that regulation of immune-mediated inflammation by these peptides may be controlled by the dual phosphatase MKP-1. We speculate that LL-37 and its derivatives may contribute to the control of immune-mediated 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".