Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies
Bibliographic record
Abstract
Diabetic kidney disease (DKD) occurs in ∼40% of patients with diabetes and causes kidney failure, cardiovascular disease, and premature death. We analyzed the response of a murine DKD model to five treatment regimens using single-cell RNA sequencing (scRNA-seq). Our atlas of ∼1 million cells revealed a heterogeneous response of all kidney cell types both to DKD and its treatment. Both monotherapy and combination therapies targeted differing cell types and induced distinct and non-overlapping transcriptional changes. The early effects of sodium-glucose cotransporter-2 inhibitors (SGLT2i) on the S1 segment of the proximal tubule suggest that this drug class induces fasting mimicry and hypoxia responses. Diabetes downregulated the spliceosome regulator serine/arginine-rich splicing factor 7 (Srsf7) in proximal tubule that was specifically rescued by SGLT2i. In vitro proximal tubule knockdown of Srsf7 induced a pro-inflammatory phenotype, implicating alternative splicing as a driver of DKD and suggesting SGLT2i regulation of proximal tubule alternative splicing as a potential mechanism of action for this drug class.
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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) | 0.001 | 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".