Connective tissue growth factor siRNA modulates mRNA levels for a subset of molecules in normal and TGF‐β1–stimulated porcine skin fibroblasts
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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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".