CCN1 expression by fibroblasts is required for bleomycin-induced skin fibrosis
Bibliographic record
Abstract
The microenvironment contributes to the excessive connective tissue deposition that characterizes fibrosis. Members of the CCN family of matricellular proteins are secreted by fibroblasts into the fibrotic microenvironment; however, the role of endogenous CCN1 in skin fibrosis is unknown. Mice harboring a fibroblast-specific deletion for CCN1 were used to assess if CCN1 contributes to dermal homeostasis, wound healing, and skin fibrosis. Mice with a fibroblast-specific CCN1 deletion showed progressive skin thinning and reduced accumulation of type I collagen; however, the overall mechanical property of skin (Young's modulus) was not significantly reduced. Real time-polymerase chain reaction analysis revealed that CCN1-deficient skin displayed reduced expression of mRNAs encoding enzymes that promote collagen stability (including prolyl-4-hydroxylase and PLOD2), although expression of COL1A1 mRNA was unaltered. CCN1-deficent skin showed reduced hydroxyproline levels. Electron microscopy revealed that collagen fibers were disorganized in CCN1-deficient skin. CCN1-deficient mice were resistant to bleomycin-induced skin fibrosis, as visualized by reduced collagen accumulation and skin thickness suggesting that deposition/accumulation of collagen is impaired in the absence of CCN1. Conversely, CCN1-deficient mice showed unaltered wound closure kinetics, suggesting de novo collagen production in response to injury did not require CCN1. In response to either wounding or bleomycin, induction of α-smooth muscle actin-positive myofibroblasts was unaffected by loss of CCN1. CCN1 protein was overexpressed by dermal fibroblasts isolated from lesional (i.e., fibrotic) areas of patients with early onset diffuse scleroderma. Thus, CCN1 expression by fibroblasts, being essential for skin fibrosis, is a viable anti-fibrotic target.
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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 | 0.001 |
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".