The dependence of chemokine–glycosaminoglycan interactions on chemokine oligomerization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both chemokine oligomerization and binding to glycosaminoglycans (GAGs) are required for their function in cell recruitment. Interactions with GAGs facilitate the formation of chemokine gradients, which provide directional cues for migrating cells. In contrast, chemokine oligomerization is thought to contribute to the affinity of GAG interactions by providing a more extensive binding surface than single subunits alone. However, the importance of chemokine oligomerization to GAG binding has not been extensively quantified. Additionally, the ability of chemokines to form different oligomers has been suggested to impart specificity to GAG interactions, but most studies have been limited to heparin. In this study, several differentially oligomerizing chemokines (CCL2, CCL3, CCL5, CCL7, CXCL4, CXCL8, CXCL11 and CXCL12) and select oligomerization-deficient mutants were systematically characterized by surface plasmon resonance to determine their relative affinities for heparin, heparan sulfate (HS) and chondroitin sulfate-A (CS-A). Wild-type chemokines demonstrated a hierarchy of binding affinities for heparin and HS that was markedly dependent on oligomerization. These results were corroborated by their relative propensity to accumulate on cells and the critical role of oligomerization in cell presentation. CS-A was found to exhibit greater chemokine selectivity than heparin or HS, as it only bound a subset of chemokines; moreover, binding to CS-A was ablated with oligomerization-deficient mutants. Overall, this study definitively demonstrates the importance of oligomerization for chemokine-GAG interactions, and demonstrates diversity in the affinity and specificity of different chemokines for GAGs. These data support the idea that GAG interactions provide a mechanism for fine-tuning chemokine function.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it