ANTI-Β2GLYCOPROTEIN I-INDUCED NEUTROPHIL EXTRACELLULAR TRAPS CAUSE ENDOTHELIAL ACTIVATION
Bibliographic record
Abstract
O001 / #144 Topic:AS03 - Antiphospholipid Syndrome SCIENTIFIC HYBRID SESSION: BASIC TRACK PRESENTATIONS - OUTSTANDING ABSTRACT PRESENTATIONS 23-05-2025 9:00 AM - 10:00 AM Background/Purpose Neutrophil extracellular traps (NETs) formation – NETosis – involvement in antiphospholipid syndrome (APS) pathogenesis is known, but the role of anti-β2glycoprotein I antibodies (aβ2GPI)-induced NETs in triggering a procoagulant and proinflammatory phenotype in endothelial cells (EC) remains to be evaluated. This study investigated whether aβ2GPI-induced NET can activate ECs and whether aβ2GPI-induced NET and phorbol myristate acetate (PMA)-induced NET have different proteomic profiles. Methods Healthy donors (HD) neutrophils were stimulated with aβ2GPI isolated from a pool of primary APS patient sera by affinity chromatography, normal human IgG or PMA. NETs were stained with antineutrophil elastase and DAPI, and the ability of aβ2GPI to bind NETs and inhibit DNA degradation was investigated. Following aβ2GPI, aβ2GPI-induced NET and PMA-induced NET stimuli, we evaluated EC activation investigating ICAM-1 (Intra-Cellular Adhesion Molecule 1), VCAM-1 (Vascular Cell Adhesion Molecule 1) and tissue factor (TF) expression using flow cytometry; and EC dysfunction analyzing extracellular microvesicles (EMVs) release via flow cytometry and NanoSight analysis. Mass spectrometry-based proteomics was performed on aβ2GPI-induced NET and PMA-induced NET. Results Unlike normal IgG, aβ2GPI induced NETosis and bound to NETs by colocalizing with the neutrophil elastase signal at 93.6 % without preventing NET degradation. Compared with unstimulated EC, aβ2GPI-induced NET triggered a robust expression of TF, VCAM and ICAM in EC with a change-fold MFI of 6 (SE 0.1), 4.2 (SE 0.09), 2.3 (SE 0.09). VCAM-1 and ICAM-1 were higher expressed in EA.hy926 treated with aβ2GPI-induced NET than those treated with aβ2GPI (p < 0.0001 in all instances) (Figure 1). aβ2GPI induced a significant increase in EMVs compared to untreated samples and those treated with NETs. Fifty-six proteins were identified, 7 resulted upregulated in aβ2GPI-induced NET and downregulated in PMA-induced ones. GO enrichment analysis revealed that proteins upregulated in aβ2GPI-induced NET were enriched for ubiquitin protein ligase binding and SLC2A4 translocation to the plasma membrane. Notably, triosphosphate isomerase 1 (TPI1), 14-3-3 epsilon protein (YWHAE) and tubulin alpha-1B (TUBA1B) proteins, upregulated in aβ2GPI-induced NET, showed functional relationships among themselves at network analysis that were distinct from other proteins, indicating unique interconnections within some aβ2GPI-induced NET proteins that differentiate them from PMA-induced NET proteins (Figure 2). Figure 1. Endothelial cells activation by aβ2GPI IgG-induced NETs. Figure 2. Proteomic analysis of aβ2GPI and PMA-induced NETs. Conclusions Taken together, these results emphasize that NETs from aβ2GPI and PMA are different in both composition and biological function. In conclusion, our findings describe how aβ2GPI-induced NET amplify endothelial cell activation and TF induction, unveiling a novel mechanism connecting the process of NETosis to thrombotic pathogenesis in APS.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".