Colchicine attenuates renal injury in a model of hypertensive chronic kidney disease
Bibliographic record
Abstract
Hypertension is a risk factor for chronic kidney disease, particularly when associated with impaired renal autoregulation and thereby increased intraglomerular pressure (Pgc). Elevated Pgc can be modeled in vitro by exposing glomerular mesangial cells to mechanical strain. We previously showed that RhoA mediates strain-induced matrix production. Here, we show that RhoA activation is dependent on an intact microtubule network. Upregulation of the profibrotic cytokine connective tissue growth factor (CTGF) by mechanical strain is dependent on RhoA activation and inhibited by microtubule disruption. We tested the effects of the microtubule depolymerizing agent colchicine in 5/6 nephrectomized rats, a model of chronic kidney disease driven by elevated Pgc. Colchicine inhibited glomerular RhoA activation and attenuated both glomerular sclerosis and interstitial fibrosis without affecting systemic blood pressure. Upregulation of the matrix proteins collagen I and fibronectin, as well as CTGF, was attenuated by colchicine. Activity of the profibrotic cytokine TGF-β, as assessed by Smad3 phosphorylation, was also inhibited by colchicine. Microtubule disruption significantly decreased renal infiltration of lymphocytes and macrophages. Our studies thus indicate that colchicine modifies hypertensive renal fibrosis. Its protective effects are likely mediated by inhibition of RhoA signaling and renal infiltration of inflammatory cells. Already well-established in clinical practice for other indications, prevention of hypertension-associated renal fibrosis may represent a new potential use for colchicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".