Abstract 256: Activation of Cell Surface GRP78 by Anti-GRP78 Autoantibodies Accelerates Lesion Development by Promoting Endothelial Cell Activation
Bibliographic record
Abstract
Damage to the endothelium is an important contributor to the initiation and progression of atherosclerosis. GRP78 is a molecular chaperone localized to the endoplasmic reticulum (ER) in healthy cells that functions to assist in the correct folding of newly synthesized proteins and to prevent aggregation of folding intermediates. In addition, GRP78 is also present as a transmembrane protein on the surface of lesion-resident endothelial cells. Surface GRP78 is known to act as a membrane signaling receptor in cancer cells and is activated by anti-GRP78 autoantibodies isolated from the serum of cancer patients. We have demonstrated previously that high levels of anti-GRP78 autoantibodies accelerate lesion development in apoE -/- mice. However, the role of anti-GRP78 autoantibody activation of cell surface GRP78 on endothelial cells and how this contributes to atherogenesis is unknown. The objective of this study is to identify factors that mediate surface GRP78 expression on endothelial cells and to determine the mechanism by which surface GRP78 activation on endothelial cells contributes to atherogenesis. Here we demonstrate that induction of ER stress by tunicamycin increased surface GRP78 expression in cultured human aortic endothelial cells. We also show that activation of surface GRP78 on endothelial cells by anti-GRP78 autoantibodies significantly increases gene expression of the adhesion molecules ICAM-1 and VCAM-1. Pretreatment of these cells with a calcium chelator or an inhibitor of NF-B activation attenuated the anti-GRP78 autoantibody-induced promotion of ICAM-1 and VCAM-1. Our results suggest that signaling through cell surface GRP78 can activate intracellular pathways that contribute to endothelial cell activation, an important initiating event in atherogenesis. These findings further our understanding of the role of anti-GRP78 autoantibodies and the activation of surface GRP78 in endothelial cell function and lesion development. Furthermore, inhibiting the interaction of anti-GRP78 autoantibodies with surface GRP78 could present a novel therapeutic strategy to modulate lesion growth and thereby reduce the risk for atherosclerosis and cardiovascular 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) | 0.013 | 0.003 |
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".