C0535: Coagulation Factor XII Protease Domain Crystal Structure Reveals a Novel Zymogen Triad

Abstract

Background: Impaired fibrinolytic activity is associated with an increased risk for venous thromboembolism (VTE). Both activated thrombin activatable fibrinolysis inhibitor (TAFI) and active plasminogen activator inhibitor-1 (PAI-1) attenuate fibrinolysis. Activated TAFI cleaves C-terminal lysine residues of fibrin thereby hampering plasmin generation whereas active PAI-1 inhibits plasminogen activator through complex formation. Aim: Analyze the role of fibrinolysis in patients diagnosed with VTE by comparing TAFI and PAI-1 plasma levels with healthy controls (HC). Methods: Plasma of 102 patients diagnosed with VTE was collected after the VTE event in a time range of 10 to 2868 days with a median of 163 days. Plasma parameters (intact TAFI, activation peptide of TAFI (AP, marker for the extent of TAFI activation), total PAI-1 and active PAI-1) of VTE patients were measured using in-house developed ELISAs and compared with plasma parameters of 113 HC. VTE patients had either a deep vein thrombosis (DVT), a pulmonary embolism (PE) or both (DVT+PE). Results: Median [interquartile range] active PAI-1 and AP concentrations were significantly increased in VTE patients compared to HC (20.8 ng/ml [9.6–37.8] vs. 6.2 ng/ml [3.6–9.7], p vs. 314ng/ml [257–376], p = 0.0027, respectively). The type of VTE (PE, DVT or PE+DVT) had no impact on PAI-1 and/or TAFI levels. Neither did the days between diagnosis of the VTE and plasma sampling. ROC analyses revealed that active PAI-1 was the best parameter to discriminate VTE patients from healthy controls (Area Under Curve (AUC) = 0.84, 95% confidence interval 0.79 to 0.90). Conclusions: Preliminary data show thatactive PAI-1 may function as a good biomarker to distinguish VTE patients from healthy subjects. In a next step the association of other cardiovascular risk factors such as recurrent VTE, age, body mass index, immobilization, contraceptive use, . . . , with PAI-1 and TAFI levels will be analyzed using multivariate analyses.