Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

Abstract

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

“…The RBC data was integrated with a modified version of the erythrocyte model iAB-RBC-28317, which was previously used for building personalized kinetic models30. The platelet data was integrated with the platelet metabolic model iAT-PLT-63631. The S. cerevisiae data was integrated with the S. cerevisiae metabolic model iMM90432.…”

Systems Analysis of Thrombus Formation

“…The platelet kinome contains 229 kinases and 73 phosphatases. Similarly, a platelet metabolic network 8 has been constructed for platelets. Although platelets are unsuited for direct genetic manipulation, numerous genetically-modified mouse models have been developed to explore platelet phenotypes in vivo.…”

Identifying Cancer Specific Metabolic Signatures Using Constraint-Based Models

“…To assess the performance of mfACHR when compared to gpSampler , flux distributions and convergence speed of both formulations were compared for three different metabolic models: a red blood cell (RBC) model, 36 a platelet model, 22 and the generalized human reconstruction Recon1. 34 These models have 453, 1,008, and 2,473 active reactions respectively, and were sampled for 3·10 4 , 7·10 4 , 3·10 5 steps respectively.…”

“…different lower and upper bounds) affect the sampled reaction flux values. This approach has been used, for example, to model the metabolic exchange between M. tuberculosis and human macrophages, 20 and between different cell types in the human brain; 21 to study aspirin resistance in platelet cells; 22 and to characterize metabolic differences between healthy and cancerous tissues. 23 …”