The role of beta2-glycoprotein I-reactive T cells in antiphospholipid syndrome
Bibliographic record
Abstract
Antiphospholipid syndrome (APS) is an autoimmune disorder characterized by the presence of autoantibodies to phospholipid (PL)-binding proteins, such as beta2-glycoprotein I (beta2GPI), and clinical manifestations including thrombosis and/or recurrent pregnancy loss. Beta2GPI-reactive T cells have been shown to be activated in patients with APS, but the mechanism responsible for this activation remains unclear. Recent studies have proposed that exposure of a cryptic epitope on beta2GPI, as a consequence of binding to PL, leads to the activation of beta2GPI-autoreactive T cells in APS patients. To test this hypothesis, we evaluated the development of beta2GPI-reactive T cells in a murine model of aPL production. C57BL/6 mice were immunized repeatedly with human beta2GPI in the presence of lipopolysaccharide (LPS) to induce aPL production. High levels of circulating aPL were observed as early as the second immunization, but splenic T cell reactivity to beta2GPI was not detectable in vitro until after the fourth immunization. Splenic T cells from mice producing high levels of aPL proliferated in response to native human beta2GPI, alone or bound to anionic PL, but PL-bound beta2GPI appeared to be a more potent antigen. Beta2GPI-reactive T cells produced IL-2 and IFN-gamma, but not IL-4 or IL-10, suggesting a TH1 bias of this T cell response. These results demonstrate that T cell reactivity to beta2GPI can develop in nonautoimmune individuals repeatedly exposed to this antigen in a proinflammatory context (e.g., LPS). Our data further suggest that the beta2GPI-reactive T cells induced in this model have a TH1 bias and may be more reactive to a PL-dependent epitope on beta2GPI than to native beta2GPI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".