Hedgehog-Gli Pathway Activation during Kidney Fibrosis
Bibliographic record
Abstract
The Hedgehog (Hh) signaling pathway regulates tissue patterning during development, including patterning and growth of limbs and face, but whether Hh signaling plays a role in adult kidney remains undefined. In this study, using a panel of hedgehog-reporter mice, we show that the two Hh ligands (Indian hedgehog and sonic hedgehog ligands) are expressed in tubular epithelial cells. We report that the Hh effectors (Gli1 and Gli2) are expressed exclusively in adjacent platelet-derived growth factor receptor-β-positive interstitial pericytes and perivascular fibroblasts, suggesting a paracrine signaling loop. In two models of renal fibrosis, Indian Hh ligand was upregulated with a dramatic activation of downstream Gli effector expression. Hh-responsive Gli1-positive interstitial cells underwent 11-fold proliferative expansion during fibrosis, and both Gli1- and Gli2-positive cells differentiated into α-smooth muscle actin-positive myofibroblasts. In the pericyte-like cell line 10T1/2, hedgehog ligand triggered cell proliferation, suggesting a possible role for this pathway in the regulation of cell cycle progression of myofibroblast progenitors during the development of renal fibrosis. The hedgehog antagonist IPI-926 abolished Gli1 induction in vivo but did not decrease kidney fibrosis. However, the transcriptional induction of Gli2 was unaffected by IPI-926, suggesting the existence of smoothened-independent Gli activation in this model. This study is the first detailed description of paracrine hedgehog signaling in adult kidney, which indicates a possible role for hedgehog-Gli signaling in fibrotic chronic kidney disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".