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Comparative proteomics reveals unexpected quantitative phosphorylation differences linked to platelet activation state.

G J SchmidtC M ReumillerH ErcanU ReschE ButtS HeberZ LiutkevičiūteJosé BasílioJohannes A SchmidA AssingerB JilmaMaria Zellner
Published in: Scientific reports (2019)
There is a need to assess platelet activation in patients with thrombotic disorders. P-selectin and activated integrin αIIbβ3 are usually quantified by flow cytometry to measure platelet activation. Monitoring changes in vasodilator-stimulated phosphoprotein (VASP) phosphorylation is an established method to determine the platelet-reactivity status. To study disruptions of platelet reactivity more comprehensively, we compared the human non-secretory platelet proteome after in-vitro -activation and -inhibition with their respective untreated controls using unbiased fluorescence two-dimensional differential in-gel electrophoresis. The non-secretory platelet proteome was more severely affected during inhibition than during activation. Strikingly, while VASP reached a 1.3-fold increase in phosphorylation levels in inhibited platelets, other protein kinase A targets showed several-fold stronger inhibition-induced phosphorylation levels, including LIM and SH3 domain protein 1 (6.7-fold), Src kinase-associated phosphoprotein 2 (4.6-fold), and Ras-related protein Rap1b (4.1-fold). Moreover, phosphorylation of integrin-linked protein kinase (ILK) and pleckstrin (PLEK) species was associated with P-selectin surface expression. The discrimination power between activation and inhibition was more pronounced for dephosphorylated ILK (3.79 Cohen's d effect size) and phosphorylated PLEK (3.77) species than for P-selectin (2.35). These data reveal new insights into the quantitative changes of the platelet reactivity proteome and suggest powerful alternatives to characterise their activation and inactivation potential.
Keyphrases
  • protein kinase
  • flow cytometry
  • mass spectrometry
  • gene expression
  • poor prognosis
  • machine learning
  • oxidative stress
  • risk assessment
  • dna methylation
  • big data
  • long non coding rna
  • small molecule
  • quantum dots