CCN3/CCN2 regulation and the fibrosis of diabetic renal disease
Bibliographic record
Abstract
Prior work in the CCN field, including our own, suggested to us that there might be co-regulatory activity and function as part of the actions of this family of cysteine rich cytokines. CCN2 is now regarded as a major pro-fibrotic molecule acting both down-stream and independent of TGF-beta1, and appears causal in the disease afflicting multiple organs. Since diabetic renal fibrosis is a common complication of diabetes, and a major cause of end stage renal disease (ESRD), we examined the possibility that CCN3 (NOV), might act as an endogenous negative regulator of CCN2 with the capacity to limit the overproduction of extracellular matrix (ECM), and thus prevent, or ameliorate fibrosis. We demonstrate, using an in vitro model of diabetic renal fibrosis, that both exogenous treatment with CCN3 and transfection with the over-expression of the CCN3 gene in mesangial cells markedly down-regulates CCN2 activity and blocks ECM over-accumulation stimulated by TGF-beta1. Conversely, TGF-beta1 treatment reduces endogenous CCN3 expression and increases CCN2 activity and matrix accumulation, indicating an important, novel yin/yang effect. Using the db/db mouse model of diabetic nephropathy, we confirm the expression of CCN3 in the kidney, with temporal localization that supports these in vitro findings. In summary, the results corroborate our hypothesis that one function of CCN3 is to regulate CCN2 activity and at the concentrations and conditions used down-regulates the effects of TGF-beta1, acting to limit ECM turnover and fibrosis in vivo. The findings suggest opportunities for novel endogenous-based therapy either by the administration, or the upregulation of CCN3.
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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".