Function-Blocking RHAMM Peptides Attenuate Fibrosis and Promote Antifibrotic Adipokines in a Bleomycin-Induced Murine Model of Systemic Sclerosis
Bibliographic record
Abstract
Systemic sclerosis a chronic, fibrotic disorder associated with high disease-specific mortality and morbidity. Cutaneous manifestations include dermal thickening and obliteration of dermal adipose tissue. Accumulation of low-molecular-weight hyaluronan, which signals through the receptor for hyaluronan-mediated motility, RHAMM, leads to progressive fibrosis and is correlated with increased severity of systemic sclerosis. The purpose of this study is to test the efficacy of two function-blocking RHAMM peptides, NPI-110 and NPI-106, in reducing skin fibrosis in a bleomycin-induced mouse model of systemic sclerosis. NPI-110 reduced visible measures of fibrosis (dermal thickness and collagen production, deposition, and organization) and profibrotic gene expression (Tgfb1, c-Myc, Col1a1, Col3a1). NPI-110 treatment also increased the expression of the antifibrotic adipokines perilipin and adiponectin. Both RHAMM peptides strongly reduced dermal RHAMM expression, predicting that dermal fibroblasts are peptide targets. Transcriptome and cell culture analyses using Rhamm-/- and Rhamm-rescued dermal fibroblasts reveal a TGFβ1/RHAMM/MYC signaling axis that promotes fibrogenic gene expression and myofibroblast differentiation. RHAMM function‒blocking peptides suppress this signaling and prevent TGFβ1-induced myofibroblast differentiation. These results suggest that inhibiting RHAMM signaling will offer a treatment method for cutaneous fibrosis in systemic sclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".