The differential regulation of Smad7 in kidney tubule cells by connective tissue growth factor and transforming growth factor‐beta1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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