Deficiency of neutrophil gelatinase-associated lipocalin elicits a hemophilia-like bleeding and clotting disorder in mice
Bibliographic record
Abstract
ABSTRACT: Coagulation is related to inflammation, but the key pathway, especially innate immune system and coagulation regulation, is not well understood and need to be further explored. Here, we demonstrated that neutrophil gelatinase-associated lipocalin (NGAL), an innate immune inflammatory mediator, is upregulated in patients with thrombosis. Furthermore, it contributes to the initiation and amplification of coagulation, hemostasis, and thrombosis. This occurs by enhancing tissue factor expression on the cell surface, potentiating various clotting factors such as thrombin, kallikrein, factor XIa (FXIa), and FVIIa, promoting thrombin-induced platelet aggregation, and inhibiting antithrombin. NGAL knockout led to strikingly prolonged clot reaction time and kinetic time in thromboelastography analysis, along with reduced thrombus generation angle and lower thrombus maximum amplitude, which were in line with remarkably prolonged activated partial thromboplastin time and prothrombin time. In several mouse hemostasis and thrombosis models, NGAL overexpression or IV administration promoted coagulation and hemostasis and aggravated thrombosis, whereas NGAL knockout or treatment with anti-NGAL monoclonal antibody significantly prolonged bleeding time and alleviated thrombus formation. Notably, NGAL knockout prolonged mouse tail bleeding time or artery occlusion time to over 40 or 60 minutes, respectively, resembling uncontrollable bleeding and clotting disorder seen in hemophilic mice. Furthermore, anti-NGAL monoclonal antibody treatment markedly reduced the formation of blood clots in inflammation-induced thrombosis models. Collectively, these findings unveil a previously unidentified role of NGAL in the processes of coagulation, hemostasis, and thrombosis, as well as the cross talk between innate immunity, inflammation, and coagulation. Thus, modulating NGAL levels could potentially help balance thrombotic and hemorrhagic risks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".