IL-16 Activates Plasminogen-Plasmin System and Promotes Human Eosinophil Migration into Extracellular Matrix via CCR3-Chemokine-Mediated Signaling and by Modulating CD4 Eosinophil Expression
Bibliographic record
Abstract
Increased eosinophil counts are a major feature of asthmatic airways. Eosinophil recruitment requires migration through epithelium and tissue extracellular matrix by activation of proteases. We assessed the capacity of IL-16, a CD4(+) cell chemotactic factor, to induce migration of eosinophils through a reconstituted basement membrane and evaluated the proteases, mediators, and receptors involved in this migration. IL-16 added to lower chambers of Invasion Chambers elicited eosinophil migration through Matrigel. This effect was decreased by inhibition of the plasminogen-plasmin system (Abs against urokinase plasminogen activator receptor or plasminogen depletion), but not by anti-matrix metalloproteinase-9 Abs. Abs against CD4 also inhibited IL-16-induced eosinophil migration. At the baseline level, few eosinophils (4.6% positive cells with a mean fluorescence of 0.9) expressed surface membrane CD4, while most permeabilized eosinophils (68% positive cells with a mean fluorescence of 18) express the CD4 Ag. TNF-pretreatment increased surface membrane CD4(+) expression by 6-fold as previously described, and increased IL-16-induced cell migration by 2.2-fold. Incubation of eosinophils with IL-16 also increased surface membrane CD4 expression by 5.4-fold, supporting the role of CD4 as receptor for IL-16. Abs against CCR3, eotaxin, or RANTES blocked IL-16-induced migration. In conclusion, IL-16 promotes eosinophil migration in vitro, by activating the plasminogen-plasmin system and increasing the membrane expression of its receptor. This effect is initiated via CD4 and mediated via the release of CCR3 ligand chemokines. Interestingly, most eosinophils express intracellular CD4. Hence, IL-16 may play an important role in the recruitment of blood eosinophils to the bronchial mucosa of asthmatics.
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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) | 0.003 | 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".