Glomerular Autoimmune Multicomponents of Human Lupus Nephritis In Vivo
Bibliographic record
Abstract
Renal targets of autoimmunity in human lupus nephritis (LN) are unknown. We sought to identify autoantibodies and glomerular target antigens in renal biopsy samples from patients with LN and determine whether the same autoantibodies can be detected in circulation. Glomeruli were microdissected from biopsy samples of 20 patients with LN and characterized by proteomic techniques. Serum samples from large cohorts of patients with systemic lupus erythematosus (SLE) with and without LN and other glomerulonephritides were tested. Glomerular IgGs recognized 11 podocyte antigens, with reactivity varying by LN pathology. Notably, IgG2 autoantibodies against α-enolase and annexin AI were detected in 11 and 10 of the biopsy samples, respectively, and predominated over other autoantibodies. Immunohistochemistry revealed colocalization of α-enolase or annexin AI with IgG2 in glomeruli. High levels of serum anti-α-enolase (>15 mg/L) IgG2 and/or anti-annexin AI (>2.7 mg/L) IgG2 were detected in most patients with LN but not patients with other glomerulonephritides, and they identified two cohorts: patients with high anti-α-enolase/low anti-annexin AI IgG2 and patients with low anti-α-enolase/high anti-annexin AI IgG2. Serum levels of both autoantibodies decreased significantly after 12 months of therapy for LN. Anti-α-enolase IgG2 recognized specific epitopes of α-enolase and did not cross-react with dsDNA. Furthermore, nephritogenic monoclonal IgG2 (clone H147) derived from lupus-prone MRL-lpr/lpr mice recognized human α-enolase, suggesting homology between animal models and human LN. These data show a multiantibody composition in LN, where IgG2 autoantibodies against α-enolase and annexin AI predominate in the glomerulus and can be detected in serum.
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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 | 0.000 |
| 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 | 0.000 |
| 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".