A RHAMM Mimetic Peptide Blocks Hyaluronan Signaling and Reduces Inflammation and Fibrogenesis in Excisional Skin Wounds
Bibliographic record
Abstract
Hyaluronan is activated by fragmentation and controls inflammation and fibroplasia during wound repair and diseases (eg, cancer). Hyaluronan-binding peptides were identified that modify fibrogenesis during skin wound repair. Peptides were selected from 7- to 15mer phage display libraries by panning with hyaluronan-Sepharose beads and assayed for their ability to block fibroblast migration in response to hyaluronan oligosaccharides (10 kDa). A 15mer peptide (P15-1), with homology to receptor for hyaluronan mediated motility (RHAMM) hyaluronan binding sequences, was the most effective inhibitor. P15-1 bound to 10-kDa hyaluronan with an affinity of K(d) = 10(-7) and appeared to specifically mimic RHAMM since it significantly reduced binding of hyaluronan oligosaccharides to recombinant RHAMM but not to recombinant CD44 or TLR2,4, and altered wound repair in wild-type but not RHAMM(-/-) mice. One topical application of P15-1 to full-thickness excisional rat wounds significantly reduced wound macrophage number, fibroblast number, and blood vessel density compared to scrambled, negative control peptides. Wound collagen 1, transforming growth factor β-1, and α-smooth muscle actin were reduced, whereas tenascin C was increased, suggesting that P15-1 promoted a form of scarless healing. Signaling/microarray analyses showed that P15-1 blocks RHAMM-regulated focal adhesion kinase pathways in fibroblasts. These results identify a new class of reagents that attenuate proinflammatory, fibrotic repair by blocking hyaluronan oligosaccharide signaling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".