Subtype-Specific Plasma Signatures of Platelet-Related Protein Releasate in Acute Pulmonary Embolism

Abstract

Introduction: There is evidence that plasma protein profiles differ in the two subtypes of pulmonary embolism (PE), isolated PE (iPE) and deep vein thrombosis (DVT)-associated PE (DVT-PE), in the acute phase. The aim of this study was to determine specific plasma signatures for proteins related to platelets in acute iPE and DVT-PE compared to isolated DVT (iDVT).

Methods: Within the Genotyping and Molecular Phenotyping of Venous ThromboEmbolism (GMP-VTE) Project, a multicenter prospective cohort study of 693 confirmed VTE cases, a highly sensitive targeted proteomics approach based on dual-antibody proximity extension assay was applied. LASSO-regularized logistic regression analysis selected 33 and 30 of 135 platelet-related candidate proteins in iPE and DVT-PE vs. iDVT, respectively.

Results: All regulated proteins were well associated with six prominently released platelet proteins and the majority showed specificity for iPE and DVT-PE compared to iDVT. While iPE-specific proteins were assigned to be predominantly released via shedding mechanisms and extracellular vesicles, granule secretion was identified as a major release mechanism assigned to DVT-associated PE-specific proteins. Network analysis demonstrated three interconnected clusters of specifically regulated proteins in iPE linked to immunoreceptor signaling, pathogen clearance and chemotaxis, whereas for DVT-associated PE one cluster linked to tissue remodeling and leukocyte trafficking. Machine learning-based analysis reveals specific plasma signatures and differential release mechanisms of proteins related to platelets in acute iPE and DVT-associated PE.

Conclusion: These data suggest that the platelet protein releasate contributes to the differential regulation of plasma proteins in acute PE compared to iDVT, which may be associated with different platelet activation patterns.

Note:

Funding Information: The GMP-VTE project was funded by the German Federal Ministry of Education and Research (BMBF 01EO1003, 01EO1503), internal funds of the Clinical Epidemiology and Systems Medicine (Center for Thrombosis and Hemostasis, Mainz, Germany), and a grant from Bayer AG. G.B. is supported by a PhD fellowship from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 813409.

Declaration of Interests: S.H., C.G., S.S., and K.L. are employees of Bayer AG. The study was sponsored inter alia by Bayer AG. The sponsors had no role in the design or conduct of the research. H.T.C. received research funding outside the present study from Bayer and received outside the present study honoraria for consultation and/or advisory board participation, from Bayer, Alveron, Galapagos, Portola and Alexion. All reimbursements were transferred to the CARIM institute. HtC and HMHS are shareholders with Coagulation Profile, a university spinoff small diagnostic company not involved in the present study. P.S.W. has received research funding outside the present study from Boehringer Ingelheim, Sanofi-Aventis, Bayer Healthcare, Daiichi Sankyo Europe and Novartis and received outside the present study honoraria for lectures or consulting from Boehringer Ingelheim, Bayer HealthCare, Evonik, AstraZeneca and Sanofi-Aventis. P.S.W. is principal investigator of the DIASyM research core (BMBF 161L0217A).

Ethics Approval Statement: The GMP-VTE study encompasses patients from two previously registered prospective cohort studies: VTEval (ClinicalTrials.gov identifier: NCT02156401) and FOCUS Bioseq (German clinical trials registry identifier: DRKS00005939). The studies comply with the Declaration of Helsinki (2013, 7th revision), Good Epidemiological Practices, the locally appointed legal and regulatory requirements, including General Data Protection Regulation (EU 2016/679). All patients signed the written informed consent for biomaterial and blood sampling, genetic analysis and sharing of data with research partners.

Keywords: Platelets, Venous thromboembolism, Pulmonary embolism, Deep vein thrombosis, machine learning, proteomics