Renal proximal tubule cell state and metabolism are coupled by nuclear receptors
Bibliographic record
Abstract
Abstract Kidney disease development is poorly understood due to the complex cellular interactions of more than 25 different cell types, each with specialized functions. Here, we used single cell RNA-sequencing to resolve cell type-specific and cell fraction changes in kidney disease. Whole kidney RNA-sequencing results were strongly influenced by cell fraction changes, but minimally informative to detect cell type-specific gene expression changes. Cell type-specific differential expression analysis identified proximal tubule cells (PT cells) as the key vulnerable cell type in diseased kidneys. Through unbiased cell trajectory analyses, we show that PT cell differentiation state is altered in disease state. Lipid metabolism (fatty acid oxidation and oxidative phosphorylation) in PT cells showed the strongest and reproducible association with PT cells state. The coupling of cell state and metabolism is established by nuclear receptors such as PPARA and ESRRA that not only control cellular metabolism but also the expression of PT cell-specific genes in mice and patient samples.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".