English

Anastasia Sveshnikova

MSU, Faculty of physics

Hubs and Webs in Platelet Intracellular Signalling

In this issue of Systems Biology and Physiology Reports A.A. Martyanov and M.A. Panteleev suggested a review on platelet intracellular signalling network, which is a second part in the discussion on the molecular relationships between platelet activation and responses [1]. The review contains seven thousands words and two hundred references and yet it is not complete, as there are still unclear parts in platelet signalling, especially in its inhibition [2-4]. In an effort to comprehend the platelet activation pattern, I drew a signalling scheme based on the review and data from other authors [2, 3].

Scheme of platelet intracellular signalling with focus on cytosolic calcium as a “Hub”. Cytosolic calcium concentration is given in the shades of red. Almost all types of platelet receptor agonists lead to activation of phospholipase C (PLC), followed by calcium release from intracellular stores (DTS). Calcium concentration is rapidly reduced by calcium-dependent ATPases (SERCA and PMCA). Also, it could be reduced by binding with some buffering proteins, including its effector-proteins (indicated with red dots). Platelet mitochondria also can function as a calcium buffer and, simultaneously, be regulated by calcium concentration. Direct activation is shown by solid green arrows, direct inhibition - by solid red arrows. Indirect interactions are shown by dashed lines. Abbreviations. AC – adenylate cyclase, α2AAR - α2A-adrenergic receptor, CDGEF - CalDAGGEFI, COX – cyclooxygenase, DAG - diacylglycerol, DTS - dense tubular system, Fbg – fibrinogen, IP3R - receptor for inositol-1,4,5-trisphosphate (IP3), Mit - a mitochondrion, mPTP - mitochondrial permeability transition pore, NCLX - mitochondrial sodium/calcium exchanger, OCS - open canalicular system, P2Y - purinergic receptor, PAR - protease-activated receptor, PIP2 - phosphoinositol-4,5-bisphosphate, PIP3 - phosphoinositol-3,4,5-trisphosphate, PI(P)n – phosphoinositides, PKA – protein kinase A, PKG – protein kinase G, PKC – protein kinase C, PL – phospholipid, PLA2 – phospholipase A2, PMCA - plasma membrane calcium ATPase, PR - PGI2 receptor, P-Tyr – phosphorylated tyrosine residue, SERCA - sarcoplasmic/endoplasmic reticulum calcium ATPase, sGC – soluble guanylate cyclase, TR - thromboxane A2 (TxA2) receptor, TRPC - transient receptor potential channel, UNI - mitochondrial uniporter, vWF – von Willebrand Factor, Y-Pase – tyrosine phosphatase.
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A minimal mathematical model of neutrophil pseudopodium formation during chemotaxis

The directed movement of neutrophils is provided by the rapid polymerization of actin with the formation of a protrusion growing forward. In our previous work we observed impaired neutrophil movement for patients with Wiskott-Aldrich syndrome (WAS) compared to healthy donors.

In this work, we set out to explain the impairment of neutrophil chemotaxis in patients by observation and computer modeling of the linear growth rates of the anterior pseudopodia. The neutrophil chemotaxis was observed by means of low-angle fluorescent microscopy in parallel-plate flow chambers. The computational model was constructed as a network-like 2D stochastic polymerization of actin guided by the proximity of cell membrane with branching governed by Arp2/3 and WASP proteins.

The observed linear velocity of neutrophil pseudopodium formation was 0.22 ± 0.04 μm/s for healthy donors and 0.23 ± 0.08 μm/s for WAS patients. The model described the velocity of the pseudopodium formation for healthy donors well. For the description of WAS patients data, a variation of branching velocity (governed by WASP) by an order of magnitude was applied, which did not significantly alter the linear protrusion growth velocity.

We conclude that the proposed mathematical model of neutrophil pseudopodium formation could describe the experimental data well, but the data on overall neutrophil movement could not be explained by the velocities of the pseudopodium growth.

Scheme of the computational model. (A) The scheme of stochastic events and species included in the model. A single F-actin filament is assumed to be straight and to be divided into segments. Each segment can be considered to be an actin monomer. New G-actin molecules can attach to and detach from the filament “barbed” end. It is assumed that the child filament begins to grow from the middle between two segments of F-actin at the angle of 70o.  If there is a branch growing from the F-actin segments, they are considered occupied and no branching can occur there. (B) The spatial restrictions on the filament growth and branching. The filaments can branch if the distance from the cell membrane is lesser than D. Filaments can grow if the distance from the cell membrane is lesser than H, where H > D.
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#cytoskeleton#neutrophils#actin#chemotaxis#Wiskott-Aldrich syndrome

A strong correlation exists between platelet consumption and platelet hyperactivation in COVID-19 patients. Pilot study of the patient cohort from CCH RAS Hospital (Troitsk).

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It is known that in COVID-19, hypercoagulation and sometimes thrombocytopenia are related to disease severity. There is also controversial data on platelet participation in COVID-19 pathology. We aimed to determine the degree of platelet hyperactivation in COVID-19 patients. Whole blood flow cytometry with Annexin-V and lactadherin staining ("PS+ platelets") was utilized. Additionally, a stochastic mathematical model of platelet production and consumption was developed. Here we demonstrated that the percentage of PS+ platelets in COVID-19 patients was twofold that of healthy donors. There was a significant correlation between the amount of PS+ platelets and the percentage of lung damage in patients. No connection was found between platelet senescence and hospital therapy or patients' chronic diseases, except for chronic lung disease. Although no thrombocytopenia was observed in patients, the observed increase in platelet size (FSC-A parameter in flow cytometry) could indicate that platelet age is decreased in patients. The developed computational model of platelet turnover confirms the possibility of intense platelet consumption without noticeable changes in platelet count. We conclude that the observed platelet hyperactivation in COVID-19 could be caused by platelet activation in circulation, leading to platelet consumption without significant thrombocytopenia.

Computational model of platelet production in the presence of COVID-19 induced thrombosis. A – Detailed scheme of the model (most sensitive reactions are highlighted in red). B – Dependence of the average platelet count (green curve and dots) and platelet size (red curve and dots) from the platelet consumption index in the model. Platelet number and size in the absence of consumption lie in the areas, highlighted by green and red rectangles correspondingly. C – Platelet size distribution in the absence (green bars) and the presence (red bars) of consumption (with consumption index set to 2). Whiskers on all plots represent SD.
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#COVID-19#platelets#coagulation#inflammation#hyperactivation

Platelet functional responses and signalling: the molecular relationship. Part 1: responses.

Blood platelets are small anucleated cells whose main function is to form a plug upon vascular damage to stop bleeding. This role involves a number of functional responses induced by different agonists and coordinated by an intricate network of signal transduction pathways. Understanding this network is vital from both basic research point of view and for the purposes of drug target identification in thrombosis and hemostasis. This review series will focus on the regulation of platelet signalling, on tracking the molecular relationship between receptor activation and functional responses, and on the networking aspects of these pathways. The present paper, first one out of two, focuses on the description of platelet functional responses and of the conditions for their triggering.
Platelet functional responses within arterial thrombus.
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#hemostasis#platelet activation#platelet intracellular signaling#thrombosis#thrombus formation