Transcriptomic Profiling Reveals Mast-Cell Signatures and Active Immune Processes in Chronic ANCA-Associated Glomerulonephritis
Bibliographic record
Abstract
Background: In ANCA-associated glomerulonephritis (AAV-GN), histopathological classifications guide prognosis but the molecular mechanisms underlying different histopathological patterns remain poorly understood. We conducted a retrospective study to integrate transcriptomic signatures with histopathological findings, aiming to refine our understanding of AAV-mediated renal injury, identify potential therapeutic targets and improve prognostication. Methods: We performed targeted RNA expression profiling of 750 immune-related genes using NanoString technology on kidney biopsy specimens from 199 AAV-GN patients selected from the Maine-Anjou and RENVAS registries. Transcriptomic signatures were analyzed in relation to glomerular lesions (normal, crescentic, sclerotic), Berden classification, tubulointerstitial involvement, Renal Risk Score and ANCA Kidney Risk Score. pathway enrichment analyses identified key molecular signals. Immune cell deconvolution was performed to estimate leukocyte subsets. Gene-based signatures for predicting histological features were developed using LASSO regression. Results: Transcriptomic profiles revealed robust immune activation across all histopathological classes. Contrary to the notion of “burnt-out” lesions, sclerotic glomeruli displayed strong expression of immunoinflammatory pathways, including TGF-β, mTOR, and immunometabolism pathways. Mast cell–related genes (e.g., CPA3, TPSAB1/B2) were enriched in advanced lesions (sclerotic glomeruli and interstitial fibrosis). Tubulointerstitial damage showed a gradient of innate and adaptive immune responses. Gene-based signatures accurately predicted histological features (R-squared ≥ 0.88). Conclusion: This study highlights previously unrecognized molecular complexity in AAV-GN, identifying mast cells as potential mediators of chronic kidney damage and showing that even sclerotic lesions harbor immune activation. These findings provide new insights into disease mechanisms and identify novel therapeutic targets for personalized medicine approaches in AAV-GN.
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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 | 0.001 |
| 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) | 0.001 | 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".