Phospholipid-binding proteins differ in their capacity to induce autoantibodies and murine systemic lupus erythematosus
Bibliographic record
Abstract
We have previously shown that immunization of nonautoimmune mice with the phospholipid-binding protein β2-glycoprotein I (β2GPI), in combination with lipopolysaccharide (LPS), induces a murine model of systemic lupus erythematosus (SLE), with sequential emergence of autoantibodies and glomerulonephritis. Here, we determine whether the paradigm for induction of murine SLE extends to other phospholipid-binding proteins. Mice were immunized with a phospholipid-binding protein (prothrombin (PT), protein S, or β2GPI), or a nonphospholipid-binding protein (glu-plasminogen), in the presence of LPS. The breadth and degree of the autoantibody response, and the frequency of glomerulonephritis, varied among the three proteins, with β2GPI being the most effective in inducing SLE-like disease. The phospholipid-binding proteins also differed in the pattern of serum cytokines they elicited. The most apparent difference between β2GPI and the other phospholipid-binding proteins was in their ability to bind to LPS: β2GPI bound to LPS, while PT and protein S did not. Our data suggest that binding to phospholipid(s) is a necessary, but not sufficient, condition for full induction of murine SLE. We propose that other properties, such as physiologic function, avidity for anionic phospholipids, and degree of interaction with other cell surface and/or circulating molecules (particularly LPS) may determine the range and severity of disease.
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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".