TGF-β<sub>1</sub> induces IL-8 and MCP-1 through a connective tissue growth factor-independent pathway
Bibliographic record
Abstract
Transforming growth factor-beta(1) (TGF-beta(1)) functions as an important immunomodulatory cytokine in human kidney. Evidence suggests that connective tissue growth factor (CTGF) is an important downstream mediator of the profibrotic effects of TGF-beta(1). However, the role of CTGF in TGF-beta(1)-induced chemokine production remains unknown. This study was undertaken to determine whether CTGF is involved in mediating TGF-beta(1)-induced chemokine production in renal proximal tubular (HK-2) cells. Interleukin-8 (IL-8) and macrophage chemoattractant protein-1 (MCP-1) were measured. TGF-beta(1) induced an increase in IL-8 and MCP-1 (both P < 0.05) compared with control levels. CTGF was effectively silenced using small interference RNA (siRNA) in HK-2 cells. RT-PCR and real-time PCR confirmed a 94% reduction in CTGF mRNA. In the CTGF-silenced cells, TGF-beta(1)-stimulated IL-8 and MCP-1 secretion was not altered compared with control cells. Similarly, basal secretion of IL-8 and MCP-1 was not changed in CTGF-silenced cells. The direct effect of CTGF (20, 200, and 400 ng/ml) on IL-8 and MCP-1 was assessed at 24-, 48-, and 72-h time points and no stimulation was observed. Our studies further demonstrate that in the CTGF gene-silenced cells, CTGF partially mediates TGF-beta(1)-induced fibronectin and collagen IV secretion. These data suggest that TGF-beta(1) induced IL-8 and MCP-1 via CTGF-independent pathway. TGF-beta mediates both fibrosis and chemokine production in the proximal tubule of the kidney. However, CTGF plays a more specific role as a downstream mediator of TGF-beta(1)-induced fibrosis.
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.
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 | 0.001 |
| 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".