Type I Interferon Signature Distinguishes ANCA-Associated Vasculitis Phenotypes and Predicts Kidney Prognosis
Bibliographic record
Abstract
Background: ANCA-associated vasculitis (AAV) causes severe multisystemic organ damage. The main phenotypes, microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA), share similarities but differ in clinical presentation and outcome. To uncover their molecular differences, we performed transcriptomic profiling of kidney tissue, then focused on type I interferon (IFN-I) pathway activation in kidney and blood and its clinical implications. Methods: We analyzed two independent cohorts (Maine-Anjou and RENVAS registries) of 193 AAV patients with glomerulonephritis. NanoString nCounter transcriptomic profiling, qPCR, MxA immunohistochemistry, and serum inflammatory molecules quantification were conducted. Comparative analyses of MPA vs. GPA (and MPO-AAV vs. PR3-AAV) were validated using independent public datasets. Results: Kidney transcriptomics revealed significant upregulation of the IFN-I pathway in MPA/MPO-AAV compared to GPA/PR3-AAV and controls. qPCR, immunohistochemistry, and analysis of external datasets confirmed these findings. IFN-I activation correlated with increased kidney fibrosis, independently of kidney function. High renal IFN-I signatures were linked to lower kidney survival, independently of kidney function and pathological scores. MPA kidneys also exhibited higher mast cell and T cell infiltration. Systemic analyses showed elevated IFNα and interferon-related inflammatory molecules in AAV, driven by a stronger IFN-I gene signature in MPA. Conclusion: This study identifies a distinct IFN-I signature in MPA/MPO-AAV, underscoring its potential role in AAV heterogeneity and kidney pathology. IFN-I emerges as a potential prognostic biomarker and therapeutic target in AAV, particularly for MPA. Further studies are needed to clarify its mechanisms and explore IFN-I modulation in clinical trials.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".