Angiopoietin-1 deficiency increases renal capillary rarefaction and tubulointerstitial fibrosis in mice
Bibliographic record
Abstract
Presence of tubulointerstitial fibrosis is predictive of progressive decline in kidney function, independent of its underlying cause. Injury to the renal microvasculature is a major factor in the progression of fibrosis and identification of factors that regulate endothelium in fibrosis is desirable as they might be candidate targets for treatment of kidney diseases. The current study investigates how loss of Angipoietin-1 (Angpt1), a ligand for endothelial tyrosine-kinase receptor Tek (also called Tie2), affects tubulointerstitial fibrosis and renal microvasculature. Inducible Angpt1 knockout mice were subjected to unilateral ureteral obstruction (UUO) to induce fibrosis, and kidneys were collected at different time points up to 10 days after obstruction. Staining for aSMA showed that Angpt1 deficient kidneys had significantly more fibrosis compared to wildtype mice 3, 6, and 10 days after UUO. Further investigation 3 days after UUO showed a significant increase of Col1a1 and vimentin in Angpt1 deficient mice, as well as increased gene expression of Tgfb1, Col1a1, Fn1, and CD44. Kidney injury molecule 1 (Kim1/Havcr1) was significantly more increased in Angpt1 deficient mice 1 and 3 days after UUO, suggesting a more severe injury early in the fibrotic process in Angpt1 deficient mice. Staining for endomucin showed that capillary rarefaction was evident 3 days after UUO and Angpt1 deficient mice had significantly less capillaries 6 and 10 days after UUO compared to UUO kidneys in wildtype mice. RNA sequencing revealed downregulation of several markers for endothelial cells 3 days after UUO, and that Angpt1 deficient mice had a further downregulation of Emcn, Plvap, Pecam1, Erg, and Tek. Our results suggest that loss of Angpt1 is central in capillary rarefaction and fibrogenesis and propose that manipulations to maintain Angpt1 levels may slow down fibrosis progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".