Disarming FcγRIIA: Separating Immunothrombosis From Protective Immunity.
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Biomedical subjects
Publications and source records attributed to Elizabeth E Gardiner.
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Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by platelet destruction and dysfunction associated with anti-platelet antibodies. This study evaluated the relationship between total anti-platelet antibody burden, measured using a modified platelet immunofluorescence test (PIFT), platelet functional responses and clinical features in chronic ITP. Thirty-one patients with chronic ITP and 20 healthy controls were included. Bleeding severity was assessed, and platelet function was analysed in peripheral blood by measuring P-selectin expression, PAC-1 binding (antibody against active conformation of GPIIb/IIIa) and reactive oxygen species (ROS) generation at baseline and following agonist stimulation. Relative platelet-reactive antibody burden was assessed using a ratio-based PIFT assay. ITP patients demonstrated significantly higher antibody burden compared with controls. Increased platelet-reactive immunoglobulin G (IgG) signals were associated with reduced platelet responsiveness to agonist stimulation. Antibody burden correlated with bleeding severity (p < 0.01) but not with platelet count. Non-responders exhibited significantly higher antibody levels than responders. receiver operator characteristic (ROC) analysis demonstrated discrimination between responder groups at a PIFT cut-off ≥3.85 (area under the curve [AUC] 0.877, sensitivity 80%; specificity 87%). Patients above this threshold showed attenuated platelet functional responses. Taken together, this study concluded that quantitative assessment of total antibody burden against platelets using modified PIFT is associated with platelet dysfunction, bleeding severity and treatment response status in chronic ITP.
BACKGROUND: Triggering receptor expressed on myeloid cells-like transcript 1 (TLT-1), a platelet-specific α-granule protein, is implicated in hemostasis, but its regulation remains unclear. Platelet dysfunction contributes to trauma-induced coagulopathy (TIC) and thrombotic complications in trauma or mechanical circulatory support (MCS); however, underlying mechanisms remain poorly understood. OBJECTIVES: This study investigated the molecular mechanisms underlying soluble TLT (sTLT)-1 release and its role as a biomarker of platelet dysfunction in patients with severe trauma or receiving MCS. METHODS: TLT-1 dynamics on platelets exposed to glycoprotein (GP)VI ligand, coagulation, or shear stress in vitro were evaluated by ELISA and immunoblotting. sTLT-1 was measured in plasma from trauma or MCS-treated patients and healthy donors. Associations with TIC, injury severity, and mortality were assessed. RESULTS: Proteolysis of TLT-1 to release a 10- to 17-kDa fragment was metalloproteinase dependent and blocked by ADAM10 and ADAM17 inhibition. Unlike GPVI, platelet TLT-1 exposure increased following PAR-1 activation. sTLT-1 was elevated in trauma patients compared with controls and correlated with TIC (P < .05) and injury severity (P < .01). Receiver-operating characteristic analysis demonstrated discriminatory performance for TIC (area under the curve, 0.78; P = .011), with a Youden cutoff of 1.180 ng/mL yielding 89% sensitivity and 73% specificity. Platelet TLT-1 was basally expressed, mobilized 2.5-fold with activation, and shed in response to GPVI ligation and plasma recalcification. Shear-exposed platelets and plasma from MCS-treated patients exhibited elevated sTLT-1 levels. CONCLUSION: Unlike GPVI, TLT-1 increased on activated platelets and was regulated by ADAM10 and ADAM17. TLT-1 release is triggered by shear stress, GPVI ligands or activated factor X. Plasma sTLT-1 was associated with trauma severity and TIC.
SUMMARY: Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. AVAILABILITY AND IMPLEMENTATION: A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.