Coagulation abnormalities and increased risk of atherothrombosis are common in patients with chronic kidney diseases (CKD). Mechanisms that alter renal hemostasis and lead to thrombotic events are not fully understood. Here we show that activation of protease activated receptor-2 (PAR2) on human kidney tubular epithelial cells (HTECs), induces tissue factor (TF) synthesis and secretion that enhances blood clotting. PAR-activating coagulation-associated protease (thrombin), as well as specific PAR2 activators (matriptase, trypsin, or synthetic agonist 2f-LIGRLO-NH2 (2F), induced TF synthesis and secretion that were potently inhibited by PAR2 antagonist, I-191. Thrombin-induced TF was also inhibited by a PAR1 antagonist, Vorapaxar. Peptide activators of PAR1, PAR3, and PAR4 failed to induce TF synthesis. Differential centrifugation of the 2F-conditoned medium sedimented the secreted TF, together with the exosome marker ALG-2 interacting protein X (ALIX), indicating that secreted TF was associated with extracellular vesicles. 2F-treated HTEC conditioned medium significantly enhanced blood clotting, which was prevented by pre-incubating this medium with an antibody for TF. In summary, activation of PAR2 on HTEC stimulates synthesis and secretion of TF that induces blood clotting, and this is attenuated by PAR2 antagonism. Thrombin-induced TF synthesis is at least partly mediated by PAR1 transactivation of PAR2. These findings reveal how underlying hemostatic imbalances might increase thrombosis risk and subsequent chronic fibrin deposition in the kidneys of patients with CKD and suggest PAR2 antagonism as a potential therapeutic strategy for intervening in CKD progression.
Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).
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