Histones link inflammation and thrombosis through the induction of Weibel-Palade Body exocytosis
Bibliographic record
Abstract
Damage-associated molecular patterns (DAMPs), including molecules like DNA and histones, are released into the blood following cell death. DAMPs promote a procoagulant phenotype through enhancement of thrombin generation and platelet activation thereby contributing to immunothrombosis. Weibel-Palade bodies (WPBs) are dynamic endothelial cell organelles that contain procoagulant and proinflammatory mediators, such as von Willebrand factor (VWF) and are released in response to cell stresses. VWF mediates platelet adhesion and aggregation, and has been implicated as a procoagulant component of the innate immune response. Objective: To determine the influence of histones and DNA on WPB release and characterize their association in models of inflammation. Methods: We treated C57BL/6J mice and cultured endothelial cells with histones (unfractionated, lysine or arginine-rich) and measured WPB exocytosis. We used inhibitors to determine a mechanism of histone-induced WPB release in vitro. We characterized the release of DAMPs and WPBs in response to acute and chronic inflammation in human and murine models. Results and conclusions: Histones, but not DNA, induced the release of VWF (1.46-fold) from WBPs and caused thrombocytopenia (0.74-fold), which impaired arterial thrombus formation in mice. Histones induced WPB release from endothelial cells in a caspase, calcium and charge-dependent manner and promoted platelet capture in a flow chamber model of VWF-platelet string formation. DAMPs and WPB-released proteins were elevated during inflammation and were positively correlated in chronic inflammation. These studies showed that DAMPs can regulate VWF levels and function by inducing its release from endothelial WPBs. This DAMPs-WPB axis may propagate immunothrombosis associated with inflammation. https://amazonsale.io/eportfolios/1098/CircadiYin_Amazon/CircadiYin_Amazonhttps://amazonsale.io/eportfolios/1099/CircadiYin_Amazon_UK/CircadiYin_Amazon_UKhttps://amazonsale.io/eportfolios/1100/CircadiYin_Ingredients/CircadiYin_Ingredientshttps://amazonsale.io/eportfolios/1101/CircadiYin_Ingredients_List/CircadiYin_Ingredients_Listhttps://amazonsale.io/eportfolios/1102/CircadiYin_UK/CircadiYin_UKhttps://amazonsale.io/eportfolios/1103/CircadiYin_Australia/CircadiYin_Australiahttps://amazonsale.io/eportfolios/1104/CircadiYin_Canada/CircadiYin_Canadahttps://amazonsale.io/eportfolios/1105/CircadiYin_NZ/CircadiYin_NZhttps://amazonsale.io/eportfolios/1106/CircadiYin_South_Africa/CircadiYin_South_Africahttps://amazonsale.io/eportfolios/1107/CircadiYin_Walmart/CircadiYin_Walmarthttps://amazonsale.io/eportfolios/1108/CircadiYin_Reviews_UK/CircadiYin_Reviews_UKhttps://amazonsale.io/eportfolios/1109/CircadiYin_Reddit_Reviews/CircadiYin_Reddit_Reviewshttps://amazonsale.io/eportfolios/1110/CircadiYin_Review_Australia/CircadiYin_Review_Australiahttps://amazonsale.io/eportfolios/1111/CircadiYin_Supplement_Ingredients/CircadiYin_Supplement_Ingredientshttps://amazonsale.io/eportfolios/1112/LeptoFix_Amazon/LeptoFix_Amazonhttps://amazonsale.io/eportfolios/1113/LeptoFix_Amazon_UK/LeptoFix_Amazon_UKhttps://amazonsale.io/eportfolios/1114/LeptoFix_Ingredients/LeptoFix_Ingredients
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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) | 0.001 | 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".