Connective tissue growth factor siRNA modulates mRNA levels for a subset of molecules in normal and TGF‐β1–stimulated porcine skin fibroblasts
Why this work is in the frame
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Bibliographic record
Abstract
Previous studies in a pig model of skin wound healing showed a coordinate expression of transforming growth factor-beta (TGF-beta) and connective tissue growth factor (CTGF), and exposure of porcine skin fibroblasts in vitro to recombinant human CTGF significantly up-regulated mRNA levels for a number of molecules. Therefore, based on recent reports that small interfering RNA (siRNA; double-stranded RNA) can effect silencing of the expression of gene(s), this approach has now been used with CTGF-specific siRNA to better understand the function of this growth factor in regulating matrix homeostasis and repair. Normal skin fibroblasts from Yorkshire pigs were treated with 0.1-0.8 microM CTGF siRNA, TGF-beta, or TGF-beta plus CTGF siRNA for 12-48 hours. Total RNA was isolated and quantified, and then mRNA levels for specific molecules were analyzed by reverse transcription-polymerase chain reaction. Protein levels for CTGF and HSP47 were assessed by Western-blot analysis. CTGF siRNA transfection led to significant decreases in mRNA and protein levels for CTGF in both a dose- and time-dependent manner. mRNA levels for types I and III procollagen, decorin, HSP47, tissue inhibitor of metalloproteinase -1, -2, -3, and basic fibroblast growth factor were also significantly and uniquely decreased following exposure of cells to CTGF siRNA. Addition of TGF-beta to the cells led to increases in CTGF mRNA levels that were blocked by CTGF siRNA. CTGF siRNA exposure also significantly and selectively down-regulated TGF-beta-mediated increases in mRNA levels for types I and III procollagen. The results indicate that CTGF can regulate extracellular matrix molecule, growth factor, and proteinase inhibitor gene expression, and that some of the TGF-beta effects on skin fibroblasts are via a CTGF-dependent pathway.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it