Chk2 Modulates Bmi1-Deficiency-Induced Renal Aging and Fibrosis via Oxidative Stress, DNA Damage, and p53/TGFβ1-Induced Epithelial-Mesenchymal Transition
Bibliographic record
Abstract
Renal aging may lead to fibrosis and dysfunction, yet underlying mechanisms remain unclear.We explored whether deficiency of the Polycomb protein Bmi1 causes renal aging via DNA damage response (DDR) activation, inducing renal tubular epithelial cell (RTEC) senescence and epithelial-mesenchymal transition (EMT).Bmi1 knockout mice exhibited oxidative stress, DDR activation, RTEC senescence, senescence-associated secretory phenotype (SASP), and age-related fibrosis in kidneys.Bmi1 deficiency impaired renal structure and function, increasing serum creatinine/urea, reducing creatinine clearance, and decreasing cortical thickness and glomerular number.However, knockout of the serine-threonine kinase Chk2 alleviated these aging phenotypes.Transcriptomics identified transforming growth factor beta 1 (TGFβ1) upregulation in Bmi1-deficient RTECs, but TGFβ1 was downregulated upon Chk2 knockout.The tumor suppressor protein p53 transcriptionally activated TGFβ1, promoting EMT in RTECs.Bmi1 knockout or oxidative stress (induced with H2O2) increased TGFβ1 expression, and EMT in RTECs and was partly reversed by p53 inhibition.Together, Bmi1 deficiency causes oxidative stress and DDR-mediated RTEC senescence/SASP, thus activating p53 and TGFβ1 to induce EMT and age-related fibrosis.However, blocking DDR (via Chk2 knockout) or p53 ameliorates these changes.Our study reveals mechanisms whereby Bmi1 preserves renal structure and function during aging by suppressing DDR and p53/TGFβ1-mediated EMT.These pathways represent potential targets for detecting and attenuating age-related renal decline.
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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 | 0.001 |
| 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".