The differential regulation of Smad7 in kidney tubule cells by connective tissue growth factor and transforming growth factor‐beta1
Bibliographic record
Abstract
AIMS: Smad7 is an inhibitory Smad that regulates transforming growth factor-beta (TGF-beta) signaling. Connective tissue growth factor (CTGF) is recognized as a potent downstream mediator of the fibrogenic effects of TGF-beta1. SMAD binding sites have been identified in both TGF-beta and CTGF promoters. The effect of CTGF on Smad7 expression and its role in the regulation of Smad7 induced by TGF-beta1 in renal tubular cells is unknown. METHODS: Human model of proximal tubular cells (HK-2 cells) was used and confirmed using a diabetic rat model. RT-PCR was performed to measure Smad7, TGF-beta1 and Smad2 and ELISA was performed to measure active TGF-beta1. CTGF or TGF-beta1 was silenced in HK-2 cells using siRNA methodology. RESULTS: TGF-beta1 induced Smad7 in a time-dependent manner, peaking at 30 min (P<0.0005) but sustained up to 24 hrs (p<0.005). Conversely, CTGF reduced Smad7, which was maximal at 24 hrs (p<0.05). This was supported by our in vivo data demonstrating that CTGF protein significantly increased while Smad7 mRNA level was reduced in a diabetic rat model. The basal expression level of Smad7 decreased in TGF-beta1 silenced cells compared to cells transfected with non-specific siRNA (p<0.0005). The basal expression level of Smad7 increased in CTGF silenced cells (p<0.05), which was increased by TGF-beta1 (p<0.005). Both mRNA and protein levels of TGF-beta1 decreased in CTGF silenced cells (p<0.05 and p<0.005 respectively) accompanied by reduction in Smad2 mRNA level in CTGF silenced cells. CONCLUSIONS: Smad7 is induced rapidly by TGF-beta1 limiting the response to TGF-beta1. CTGF likely plays a key role in promoting TGF-beta1 activity by decreasing the availability of Smad7 and increasing Smad2.
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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) | 0.002 | 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".