Small molecules targeting GRP78 mitigate anti-GRP78 autoantibody–mediated tissue factor procoagulant activity in cultured endothelial cells
Bibliographic record
Abstract
Background The 78-kDa glucose-regulated protein (GRP78) expressed on the cell surface (csGRP78) has been reported to regulate tissue factor procoagulant activity (TF PCA) in lesion-resident endothelial cells (ECs), which is further enhanced by circulating anti-GRP78 autoantibodies that bind to the Leu98-Leu115 epitope in GRP78. Objectives Determine the effects of the engagement of the anti-GRP78 autoantibody to cell surface GRP78 on endothelial cells and the underlying mechanisms that impact TF PCA. Methods Immunofluorescent staining was used to determine the presence of csGRP78 in TNFα-treated endothelial cells. An established TF PCA assay was used to evaluate human ECs following treatment with anti-GRP78 autoantibodies. The Fura 2-AM assay was used to quantify changes in intracellular Ca 2+ levels. Small molecules predicted to bind csGRP78 were identified using artificial intelligence. ELISAs were used to assess the ability of these GRP78 binders to mitigate TF activity and interfere with the autoantibody/csGRP78 complex. Results In TNFα-treated ECs, anti-GRP78 autoantibodies increased TF PCA. This observation was further enhanced by ER-stress-induced elevation of csGRP78 levels. Anti-GRP78 autoantibody treatment increased intracellular Ca 2+ levels. Sequestering the anti-GRP78 autoantibody with a conformational peptide or blocking with heparin molecules attenuated anti-GRP78 autoantibody-induced TF PCA. We identified B07*, a GRP78 binder that diminished anti-GRP78 autoantibody-induced TF PCA on ECs. Conclusions These findings show how anti-GRP78 autoantibodies enhance TF PCA that contributes to thrombosis and identify novel GRP78 binders that represent a potential novel therapeutic strategy for treating and managing atherothrombotic 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".