Necroptotic cell binding of β<sub>2</sub>‐glycoprotein I provides a potential autoantigenic stimulus in systemic lupus erythematosus
Bibliographic record
Abstract
Abstract Systemic lupus erythematosus (SLE) is characterized by the development of autoantibodies against diverse self‐antigens with damage to multiple organs. Immunization with the SLE autoantigen β2‐glycoprotein I (β2GPI) and lipopolysaccharide (LPS), a known trigger of necroptosis, induces a murine model of SLE. We hypothesized that necroptotic cells, like apoptotic cells, provide a “scaffold” of cellular self‐antigens, but, unlike apoptotic cells, necroptotic cells do so in a proinflammatory and immunogenic context. We demonstrate that β2GPI indeed binds to necroptotic cells and serves as a target for anti‐β2GPI autoantibodies. We further demonstrate that necroptotic, but not apoptotic, cells promote antigenic presentation of β2GPI to CD4 T cells by dendritic cells. Finally, we show that β2GPI/LPS‐immunized mice deficient in RIPK3 (receptor‐interacting serine/threonine‐protein kinase 3) or MLKL (mixed lineage kinase domain like), and consequently unable to undergo necroptosis, have reduced SLE autoantibody production and pathology. RIPK3−/− mice had low levels of SLE autoantibodies and no renal pathology, while MLKL−/− mice produced low levels of SLE autoantibodies initially, but later developed levels comparable with wild type (WT) mice and pathology intermediate to that of WT and RIPK3−/− mice. Serum cytokine levels induced by LPS tended to be lower in RIPK3−/− and MLKL−/− mice than in WT mice, suggesting that impaired proinflammatory cytokine production may impact the initiation of autoantibody production in both strains. Our data suggest that self‐antigen (i.e. β2GPI) presented in the context of necroptosis and proinflammatory signals may be sufficient to overcome immune tolerance and induce SLE.
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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 | 0.000 | 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 | 0.000 |
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".