Senolytics (Dasatinib/Quercetin) Inhibit Hedgehog Interacting Protein (Hhip)-Mediated Tubular Senescence in Diabetic Kidney Disease
Bibliographic record
Abstract
Background: We recently reported that hedgehog interacting protein (Hhip) promotes tubular senescence-associated secretory phenotype in murine diabetic kidney disease (DKD) (Diabetiologia, 2023), the underlying mechanism(s) are not delineated. Here, we asked whether senolytics (dasatinib, D/quercetin, Q) could alleviate Hhip-mediated RPTC senescence in DKD in vivo and in vitro Methods: The low-dose streptozotocin (LDSTZ)-induced diabetes in renal proximal tubules (RPTC)-specific Hhip transgenic (Tg) mice (Hhip-Tg) and their non-Tg littermates at the age of 10-week old were studied. Senolytics (D, 5 mg/kg and Q, 50 mg/kg) in combination were administered by gavage daily for three consecutive days every two weeks from 12 weeks until the mice were 24-weeks old. Vehicle-treated animals served as controls. Primary RPTCs and rat immortalized RPTC cells (IRPTCs) were used for in vitro studies. Results: Diabetic mice displayed typical DKD characteristics (hyperglycemia, increased urinary albumin/creatinine ratio and glomerular filtration rate) and renal dysmorphology (renal hypertrophy, glomerulosclerosis and tubulopathy), and those features were more pronounced in diabetic Hhip-Tg (vs. non-Tg) mice. D/Q senolytics administration ameliorated DKD dysmorphology and renal tubular senescence as measured by heightened β-galactosidase activity in kidneys of diabetic mice. In vitro, excessive Hhip induced by overexpressing Hhip in RPTCs triggered the release of extracellular vesicles carrying Hhip, which facilitated RPTC turnover through accelerated cellular senescence, and fibrotic and apoptotic processes. In contrast, D/Q treatment reversed the effects of increased Hhip in RPTCs. Conclusion: The treatment of senolytic D/Q prevents excessive Hhip-mediated RPTC senescence and DKD-related tubulopathy via the inhibition of Hhip carried by extracellular vehicles in DKD. Funding: Government Support – Non-U.S.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".