Platelet RNA Biomarker of Ticagrelor-Responsive Genes Is Associated With Platelet Function and Cardiovascular Events.

BACKGROUND: Identifying patients with the optimal risk:benefit for ticagrelor is challenging. The aim was to identify ticagrelor-responsive platelet transcripts as biomarkers of platelet function and cardiovascular risk. METHODS: Healthy volunteers (n=58, discovery; n=49, validation) were exposed to 4 weeks of ticagrelor with platelet RNA data, platelet function, and self-reported bleeding measured pre-/post-ticagrelor. RNA sequencing was used to discover platelet genes affected by ticagrelor, and a subset of the most informative was summarized into a composite score and tested for validation. This score was further analyzed (1) in CD34+ megakaryocytes exposed to an P2Y12 inhibitor in vitro, (2) with baseline platelet function in healthy controls, (3) in peripheral artery disease patients (n=139) versus patient controls (n=30) without atherosclerosis, and (4) in patients with peripheral artery disease for correlation with atherosclerosis severity and risk of incident major adverse cardiovascular and limb events. RESULTS: Ticagrelor exposure differentially expressed 3409 platelet transcripts. Of these, 111 were prioritized to calculate a Ticagrelor Exposure Signature score, which ticagrelor reproducibly increased in discovery and validation cohorts. Ticagrelor's effects on platelets transcripts positively correlated with effects of P2Y12 inhibition in primary megakaryocytes. In healthy controls, higher baseline scores correlated with lower baseline platelet function and with minor bleeding while receiving ticagrelor. In patients, lower scores independently associated with both the presence and extent of atherosclerosis and incident ischemic events. CONCLUSIONS: Ticagrelor-responsive platelet transcripts are a biomarker for platelet function and cardiovascular risk and may have clinical utility for selecting patients with optimal risk:benefit for ticagrelor use.

Inhibiting the P2Y12 Receptor in Megakaryocytes and Platelets Suppresses Interferon-Associated Responses.

The authors investigated the impact of antiplatelet therapy on the megakaryocyte (MK) and platelet transcriptome. RNA-sequencing was performed on MKs treated with aspirin or P2Y12 inhibitor, platelets from healthy volunteers receiving aspirin or P2Y12 inhibition, and platelets from patients with systemic lupus erythematosus (SLE). P2Y12 inhibition reduced gene expression and inflammatory pathways in MKs and platelets. In SLE, the interferon (IFN) pathway was elevated. In vitro experiments demonstrated the role of P2Y12 inhibition in reducing IFNα-induced platelet-leukocyte interactions and IFN signaling pathways. These results suggest that P2Y12 inhibition may have therapeutic potential for proinflammatory and autoimmune conditions like SLE.

P2Y12 Inhibition Suppresses Proinflammatory Platelet-Monocyte Interactions.

BACKGROUND: Monocyte-platelet aggregates (MPAs) represent the crossroads between thrombosis and inflammation, and targeting this axis may suppress thromboinflammation. While antiplatelet therapy (APT) reduces platelet-platelet aggregation and thrombosis, its effects on MPA and platelet effector properties on monocytes are uncertain. OBJECTIVES: To analyze the effect of platelets on monocyte activation and APT on MPA and platelet-induced monocyte activation. METHODS: Agonist-stimulated whole blood was incubated in the presence of P-selectin, PSGL1, PAR1, P2Y12, GP IIb/IIIa, and COX-1 inhibitors and assessed for platelet and monocyte activity via flow cytometry. RNA-Seq of monocytes incubated with platelets was used to identify platelet-induced monocyte transcripts and was validated by RT-qPCR in monocyte-PR co-incubation ± APT. RESULTS: Consistent with a proinflammatory platelet effector role, MPAs were increased in patients with COVID-19. RNA-Seq revealed a thromboinflammatory monocyte transcriptome upon incubation with platelets. Monocytes aggregated to platelets expressed higher CD40 and tissue factor than monocytes without platelets (p < 0.05 for each). Inhibition with P-selectin (85% reduction) and PSGL1 (87% reduction) led to a robust decrease in MPA. P2Y12 and PAR1 inhibition lowered MPA formation (30 and 21% reduction, p < 0.05, respectively) and decreased monocyte CD40 and TF expression, while GP IIb/IIIa and COX1 inhibition had no effect. Pretreatment of platelets with P2Y12 inhibitors reduced the expression of platelet-mediated monocyte transcription of proinflammatory SOCS3 and OSM. CONCLUSIONS: Platelets skew monocytes toward a proinflammatory phenotype. Among traditional APTs, P2Y12 inhibition attenuates platelet-induced monocyte activation.

Fully automated platelet differential interference contrast image analysis via deep learning.

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.

Katacine Is a New Ligand of CLEC-2 that Acts as a Platelet Agonist.

BACKGROUND: CLEC-2 is a platelet receptor with an important role in thromboinflammation but a minor role in hemostasis. Two endogenous ligands of CLEC-2 have been identified, the transmembrane protein podoplanin and iron-containing porphyrin hemin, which is formed following hemolysis from red blood cells. Other exogenous ligands such as rhodocytin have contributed to our understanding of the role of CLEC-2. OBJECTIVES: To identify novel CLEC-2 small-molecule ligands to aid therapeutic targeting of CLEC-2. METHODS: ALPHA screen technology has been used for the development of a high-throughput screening (HTS) assay recapitulating the podoplanin-CLEC-2 interaction. Light transmission aggregometry was used to evaluate platelet aggregation. Immunoprecipitation and western blot were used to evaluate direct phosphorylation of CLEC-2 and downstream protein phosphorylation. Autodock vina software was used to predict the molecular binding site of katacine and mass spectrometry to determine the polymeric nature of the ligand. RESULTS AND CONCLUSION: We developed a CLEC-2-podoplanin interaction assay in a HTS format and screened 5,016 compounds from a European Union-open screen library. We identified katacine, a mixture of polymers of proanthocyanidins, as a novel ligand for CLEC-2 and showed that it induces platelet aggregation and CLEC-2 phosphorylation via Syk and Src kinases. Platelet aggregation induced by katacine is inhibited by the anti-CLEC-2 monoclonal antibody fragment AYP1 F(ab)'2. Katacine is a novel nonprotein ligand of CLEC-2 that could contribute to a better understanding of CLEC-2 activation in human platelets.

Combination of network and molecule structure accurately predicts competitive inhibitory interactions.

Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metabolite-protein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.

Interspecies differences in protein expression do not impact the spatiotemporal regulation of glycoprotein VI mediated activation.

BACKGROUND: Accurate protein quantification is a vital prerequisite for generating meaningful predictions when using systems biology approaches, a method that is increasingly being used to unravel the complexities of subcellular interactions and as part of the drug discovery process. Quantitative proteomics, flow cytometry, and western blotting have been extensively used to define human platelet protein copy numbers, yet for mouse platelets, a model widely used for platelet research, evidence is largely limited to a single proteomic dataset in which the total amount of proteins was generally comparatively higher than those found in human platelets. OBJECTIVES: To investigate the functional implications of discrepancies between levels of mouse and human proteins in the glycoprotein VI (GPVI) signalling pathway using a systems pharmacology model of GPVI. METHODS: The protein copy number of mouse platelet receptors was determined using flow cytometry. The Virtual Platelet, a mathematical model of GPVI signalling, was used to determine the consequences of protein copy number differences observed between human and mouse platelets. RESULTS AND CONCLUSION: Despite the small size of mouse platelets compared to human platelets they possessed a greater density of surface receptors alongside a higher concentration of intracellular signalling proteins. Surprisingly the predicted temporal profile of Syk activity was similar in both species with predictions supported experimentally. Super resolution microscopy demonstrates that the spatial distribution of Syk is similar between species, suggesting that the spatial distribution of receptors and signalling molecules in activated platelets, rather than their copy number, is important for signalling pathway regulation.