Glycosaminoglycan binding and oligomerization are essential for the <i>in vivo</i> activity of certain chemokines
Bibliographic record
Abstract
During organogenesis, immunosurveillance, and inflammation, chemokines selectively recruit leukocytes by activating seven-transmembrane-spanning receptors. It has been suggested that an important component of this process is the formation of a haptotactic gradient by immobilization of chemokines on cell surface glycosaminoglycans (GAGs). However, this hypothesis has not been experimentally demonstrated in vivo. In the present study we investigated the effect of mutations in the GAG binding sites of three chemokines, monocyte chemoattractant protein-1/CC chemokine ligand (CCL)2, macrophage-inflammatory protein-1beta/CCL4, and RANTES/CCL5, on their ability to recruit cells in vivo. These mutant chemokines retain chemotactic activity in vitro, but they are unable to recruit cells when administered intraperitoneally. Additionally, monomeric variants, although fully active in vitro, are devoid of activity in vivo. These data demonstrate that both GAG binding and the ability to form higher-order oligomers are essential for the activity of particular chemokines in vivo, although they are not required for receptor activation in vitro. Thus, quaternary structure of chemokines and their interaction with GAGs may significantly contribute to the localization of leukocytes beyond migration patterns defined by chemokine receptor interactions.
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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".