Recombinant N–Terminal Slit2 Inhibits TGF-β–Induced Fibroblast Activation and Renal Fibrosis
Bibliographic record
Abstract
Fibrosis and inflammation are closely intertwined injury pathways present in nearly all forms of CKD for which few safe and effective therapies exist. Slit glycoproteins signaling through Roundabout (Robo) receptors have been described to have anti-inflammatory effects through regulation of leukocyte cytoskeletal organization. Notably, cytoskeletal reorganization is also required for fibroblast responses to TGF-β Here, we examined whether Slit2 also controls TGF-β-induced renal fibrosis. In cultured renal fibroblasts, which we found to express Slit2 and Robo-1, the bioactive N-terminal fragment of Slit2 inhibited TGF-β-induced collagen synthesis, actin cytoskeletal reorganization, and Smad2/3 transcriptional activity, but the inactive C-terminal fragment of Slit2 did not. In mouse models of postischemic renal fibrosis and obstructive uropathy, treatment with N-terminal Slit2 before or after injury inhibited the development of renal fibrosis and preserved renal function, whereas the C-terminal Slit2 had no effect. Our data suggest that administration of recombinant Slit2 may be a new treatment strategy to arrest chronic injury progression after ischemic and obstructive renal insults by not only attenuating inflammation but also, directly inhibiting renal fibrosis.
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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".