Autophagy regulator ATG7 links lipid metabolism to cell-fate decisions in kidney tubule health and disease
Bibliographic record
Abstract
Abstract Homeostasis in the kidney proximal tubule (PT) requires coordination between metabolism and differentiation, yet the mechanisms governing this balance remain elusive. Here, we integrate model organisms, multiomics profiling, and human genetics to identify the autophagy regulator ATG7 as a key determinant of cell-fate decisions, sustaining PT specialization in health and contributing to dysfunction in disease. In mice, PT-specific deletion of ATG7 reprograms differentiated cells into anabolic, proliferative states, impairing their specialized function and causing kidney tubulopathy. Mechanistically, loss of ATG7-dependent autophagy hinders lipid droplet clearance and restricts fatty-acid oxidation (FAO), leading to energy depletion and functional decline. In zebrafish pronephros, re-expression of wild-type ATG7 restores homeostasis in atg7 mutants, while pharmacological FAO inhibition triggers dysfunction. In humans, ATG7 variants associate with cardio-renal-metabolic traits and increased disease risk, whereas low ATG7 expression correlates with transcriptional signatures of metabolic reprogramming, loss of epithelial markers, and poor prognosis in renal cell carcinoma. These findings establish a conserved genetic paradigm that links autophagy to kidney epithelial cell-fate specialization, with implications for disease, cancer, and metabolic health.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".