Negative Regulation of TGFβ Signaling by Stem Cell Antigen-1 Protects against Ischemic Acute Kidney Injury
Bibliographic record
Abstract
Acute kidney injury, often caused by an ischemic insult, is associated with significant short-term morbidity and mortality, and increased risk of chronic kidney disease. The factors affecting the renal response to injury following ischemia and reperfusion remain to be clarified. We found that the Stem cell antigen-1 (Sca-1), commonly used as a stem cell marker, is heavily expressed in renal tubules of the adult mouse kidney. We evaluated its potential role in the kidney using Sca-1 knockout mice submitted to acute ischemia reperfusion injury (IRI), as well as cultured renal proximal tubular cells in which Sca-1 was stably silenced with shRNA. IRI induced more severe injury in Sca-1 null kidneys, as assessed by increased expression of Kim-1 and Ngal, rise in serum creatinine, abnormal pathology, and increased apoptosis of tubular epithelium, and persistent significant renal injury at day 7 post IRI, when recovery of renal function in control animals was nearly complete. Serum creatinine, Kim-1 and Ngal were slightly but significantly elevated even in uninjured Sca-1-/- kidneys. Sca-1 constitutively bound both TGFβ receptors I and II in cultured normal proximal tubular epithelial cells. Its genetic loss or silencing lead to constitutive TGFβ receptor-mediated activation of canonical Smad signaling even in the absence of ligand and to KIM-1 expression in the silenced cells. These studies demonstrate that by normally repressing TGFβ-mediated canonical Smad signaling, Sca-1 plays an important in renal epithelial cell homeostasis and in recovery of renal function following ischemic acute kidney injury.
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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) | 0.001 | 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".