225. AUTOANTIGEN SPECIFIC TH17 CELLS INFLAME THE KIDNEY IN ANCA-VASCULITIS AND MEDIATE RENAL DAMAGE
Bibliographic record
Abstract
Background: Anti-neutrophil-cytoplasmic-antibody (ANCA)-associated-vasculitis (AAV) is an autoimmune small-vessel-vasculitis. T-cells play a pivotal role in pathogenesis as drivers of autoantibody formation and vasculitic damage. However, the involvement of T-cells in renal vasculitis is understood poorly. It is the aim to this study to investigate the dynamics of renal T-cell inflammation in a rat model of AAV. Methods: Wistar-Kyoto-rats were immunized with myeloperoxidase (MPO) in Freunds Adjuvant to induce AAV. Control rats were immunized with Freunds Adjuvant only. Albuminuria was determined weekly and rats were culled after two, four and six weeks. At the time of harvest, renal T-cells were isolated and characterized by flow cytometry. Antigen-specificity was determined by ELiSPOT. Gene expression was determined by PCR. Selected animals received weekly intraperitoneal injections with a polyclonal anti-IL17A antibody. Results: MPO-rats developed detectable titres of anti-MPO by week two. By week six, all MPO-animals (n = 20) but one developed significant albuminuria. Accordingly, MPO animals showed significant crescent formation as compared to the controls (% of affected glomeruli: 11.4 ±10.5% vs. 0.4 ±0.7%, p < 0.005). From week two on, Th17 and Th22 cells inflamed the kidney as determined by PCR and/or flow cytometry in MPO-rats. The Th17 and Th22 infiltrate was heaviest at week six post-immunization. The intra-renal T-cell response was skewed towards Th17 as compared to the frequency of splenic Th17 cells in MPO-rats (9.1 ±4.3% vs. 1.9 ±0.6%, p < 0.005). The majority of intra- renal Th17 and Th22 cells was MPO-specific. Control rats did not show renal T-cell infiltration. Treatment with neutralizing anti-IL-17 antibody ameliorated renal damage. Conclusion: Th17 and Th22 cells are drivers of renal inflammation in ANCA-vasculitis. IL-17 blockade may have a therapeutic role in renal AAV. Disclosures: This work was funded by the Dr. Werner Jackstädt Foundation to BW.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".