Interaction of β2-Glycoprotein I with Lipopolysaccharide Leads to Toll-like Receptor 4 (TLR4)-dependent Activation of Macrophages
Bibliographic record
Abstract
β(2)-Glycoprotein I (β(2)GPI) is an abundant plasma protein that binds to the surface of cells and particles expressing negatively charged lipids, but its physiological role remains unknown. Antibodies to β(2)GPI are found in patients with anti-phospholipid syndrome, a systemic autoimmune disease associated with vascular thrombosis and pregnancy morbidity. Although it has been suggested that anti-β(2)GPI antibodies activate endothelial cells and monocytes by signaling through TLR4, it is unclear how anti-β(2)GPI antibodies and/or β(2)GPI interact with TLR4. A number of mammalian proteins (termed "endogenous Toll-like receptor (TLR) ligands") have been reported to bind to TLR4, but, in most cases, subsequent studies have shown that LPS interaction with these proteins is responsible for TLR activation. We hypothesized that, like other endogenous TLR ligands, β(2)GPI interacts specifically with LPS and that this interaction is responsible for apparent TLR4 activation by β(2)GPI. Here, we show that both LPS and TLR4 are required for β(2)GPI to bind to and activate macrophages. Untreated β(2)GPI stimulated TNF-α production in TLR4-sufficient (but not TLR4-deficient) macrophages. In contrast, neither polymyxin B-treated nor delipidated β(2)GPI stimulated TNF-α production. Furthermore, β(2)GPI bound to LPS in a specific and dose-dependent manner. Finally, untreated β(2)GPI bound to the surface of TLR4-sufficient (but not TLR4-deficient) macrophages. Polymyxin B treatment of β(2)GPI abolished macrophage binding. Our findings suggest a potential new biological activity for β(2)GPI as a protein that interacts specifically with LPS and point to the need to evaluate newly discovered endogenous TLR ligands for potential interactions with LPS.
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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 | 0.000 | 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 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".