Kinetics of Chemokine–Glycosaminoglycan Interactions Control Neutrophil Migration into the Airspaces of the Lungs
Bibliographic record
Abstract
Chemokine-glycosaminoglycan (GAG) interactions are thought to result in the formation of tissue-bound chemokine gradients. We hypothesized that the binding of chemokines to GAGs would increase neutrophil migration toward CXC chemokines instilled into lungs of mice. To test this hypothesis we compared neutrophil migration toward recombinant human CXCL8 (rhCXCL8) and two mutant forms of CXCL8, which do not bind to heparin immobilized on a sensor chip. Unexpectedly, when instilled into the lungs of mice the CXCL8 mutants recruited more neutrophils than rhCXCL8. The CXCL8 mutants appeared in plasma at significantly higher concentrations and diffused more rapidly across an extracellular matrix in vitro. A comparison of the murine CXC chemokines, KC and MIP-2, revealed that KC was more effective in recruiting neutrophils into the lungs than MIP-2. KC appeared in plasma at significantly higher concentrations and diffused more rapidly across an extracellular matrix in vitro than MIP-2. In kinetic binding studies, KC, MIP-2, and rhCXCL8 bound heparin differently, with KC associating and dissociating more rapidly from immobilized heparin than the other chemokines. These data suggest that the kinetics of chemokine-GAG interactions contributes to chemokine function in tissues. In the lungs, it appears that chemokines, such as CXCL8 or MIP-2, which associate and disassociate slowly from GAGs, form gradients relatively slowly compared with chemokines that either bind GAGs poorly or interact with rapid kinetics. Thus, different types of chemokine gradients may form during an inflammatory response. This suggests a new model, whereby GAGs control the spatiotemporal formation of chemokine gradients and neutrophil migration in tissue.
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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 | 0.001 |
| 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 | 0.001 | 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".