Tissue-bound hyaluronan molecular weight as a regulator of dendritic cell immune potency
Bibliographic record
Abstract
Abstract Hyaluronic acid (HA) is a major glycosaminoglycan found in the extracellular matrix (ECM) and exhibits immunoregulatory properties depending on its molecular weight (MW). However, the impact of tissue bound HA on dendritic cell (DC) functions is not well understood due to the varying distribution of HA MW under different physiological and pathological conditions. To investigate DCs in defined biosystems, we used three-dimensional (3D) collagen matrices modified with HA of specific MW, while maintaining similar microstructure and HA levels. Using these matrices, we examined the influence of HA on cytokine binding and observed distinct properties depending on the presence and MW of HA, suggesting modulation of cytokine availability by the different MW of HA. Our studies on DC immune potency revealed that low molecular weight HA (LMW-HA; 8-15 kDa) enhances immature DC (iDC) differentiation and antigen uptake, while medium (MMW-HA; 500-750 kDa) and high molecular weight HA (HMW-HA; 1250-1500 kDa) increase cytokine secretion in matured DCs (mDCs). Interestingly, the modulation of DCs surface marker expression and cytokine secretion by different MW of HA appeared to be independent of CD44. However, we found that cytokine secretion of DCs was dependent on the CD44 receptor regardless of the presence or absence of HA in the matrix. Additionally, we observed reduced migratory capacity of iDCs and mDCs when cultured on MMW- and HMW-HA matrices, and this effect was dependent on CD44. In summary, our findings provide new insights into the MW-dependent effects of tissue-bound HA on DCs, opening avenues for the design of DC-modulating materials to enhance DC-based therapy.
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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".