Selective expression of connective tissue growth factor in fibroblasts in vivo promotes systemic tissue fibrosis
Bibliographic record
Abstract
OBJECTIVE: Connective tissue growth factor (CTGF) is a cysteine-rich secreted matricellular protein involved in wound healing and tissue repair. Enhanced and prolonged expression of CTGF has been associated with tissue fibrosis in humans. However, questions remain as to whether CTGF expression alone is sufficient to drive fibrosis. This study was undertaken to investigate whether CTGF alone is sufficient to cause fibrosis in intact animals and whether its effects are mediated through activation of transforming growth factor beta (TGFbeta) signaling or through distinct signal transduction pathways. METHODS: We generated mice overexpressing CTGF in fibroblasts under the control of the fibroblast-specific collagen alpha2(I) promoter enhancer. Tissues such as skin, lung, and kidney were harvested for histologic analysis. Mouse embryonic fibroblasts were prepared from embryos (14.5 days postcoitum) for biochemical analysis. RESULTS: Mice overexpressing CTGF in fibroblasts were susceptible to accelerated tissue fibrosis affecting the skin, lung, kidney, and vasculature, most notably the small arteries. We identified a marked expansion of the myofibroblast cell population in the dermis. RNA analysis of transgenic dermal fibroblasts revealed elevated expression of key matrix genes, consistent with a fibrogenic response. CTGF induced phosphorylation of p38, ERK-1/2, JNK, and Akt, but not Smad3, in transgenic mouse fibroblasts compared with wild-type mouse fibroblasts. Transfection experiments showed significantly increased basal activity of the CTGF and serum response element promoters, and enhanced induction of the CTGF promoter in the presence of TGFbeta. CONCLUSION: These results demonstrate that selective expression of CTGF in fibroblasts alone causes tissue fibrosis in vivo through specific signaling pathways, integrating cues from the extracellular matrix into signal transduction pathways to orchestrate pivotal biologic responses relevant to tissue repair and fibrosis.
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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 | 0.000 | 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 | 0.001 |
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".