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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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Platelet functional responses and signalling: the molecular relationship. Part 2: receptors.

Small, non-nuclear cells, platelets, are primarily designed to form aggregates when blood vessels are damaged, stopping bleeding. To perform this function, platelets can implement several functional responses induced by various agonists and coordinated by a complex network of intracellular signaling triggered by a dozen of different receptors. This review, the second in a series, describes the known intracellular signaling pathways induced by platelet receptors in response to canonical and rare agonists. Particular focus will be on interaction points and “synergy” of platelet activation pathways and intermediate or “secondary” activation mediators that transmit a signal to functional manifestations.


Different degrees of the platelet activation in hemostasis. Upon weak stimulation, platelets pass into a weakly activated state, in which there is no clustering of platelet integrins and no significant change in the shape of platelets. This weak activation is reversible, and it corresponds to the state of platelets in the outer layers ("coat") of the thrombus. Upon stronger activation, platelet shape significantly changes. Platelets become irreversibly activated and aggregate. The secretion of platelet granules also occurs. At the maximum degree of activation, platelet mitochondria collapse, and platelets pass into a procoagulant state, exposing phosphatidylserine, which significantly accelerates blood plasma coagulation.
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#platelets#intracellular signaling#physiology

Overview of the neutralizing antibody and memory B cell response kinetics in SARS-CoV-2 convalescent and/or mRNA vaccinated individuals.

COVID-19 pandemics triggered by the SARS-CoV-2 virus have caused millions of deaths worldwide and have led to expedited developments of various effective vaccines that, if administered, could prevent and/or circumvent the infection and reduce the death toll. Since the start of the pandemics multiple research groups around the world have been involved in the analysis of immune responses of various human cohorts to the SARS-CoV-2 infection and vaccines. Now, over 1.5 years later, the scientific community has accumulated extensive data about both the development of an immune response to SARS-CoV-2 following infection, as well as its rate of fading off. Kinetic analysis of the immune response generated by vaccines is also emerging, enabling the possibility of making comparisons and predictions. In this review we will focus on the comparing B cell and antibody immune responses to the SARS-CoV-2 infection as opposed to mRNA vaccines for the SARS-CoV-2 S-protein, which have been utilized to immunize hundreds of millions of people and analyzed in multiple studies.

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#B-cells#COVID-19#vaccines#immune response

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

Presence of PI-rich vesicles is required for the PLC ζ activation according to mathematical modeling

Phospholipase C ζ (PLCζ) is an enzyme found in the cytoplasm and acrosome of mammalian spermatozoa. It catalyzes the reaction of phosphatidylinositol-4,5-phosphate hydrolysis into inositol-3-phosphate and diacylglycerol. PLCζ is present in the sperm cell acrosome and cytosol but doesn’t significantly affect its metabolism. However, after the fusion of sperm and egg membranes, its activity increases as it begins to bind membranes of the egg. It is unknown why PLCζ is inactive in spermatozoa or any type of somatic cell.

In this work, the modeling approach explains the reasons for the absence of PLCζ activity in any type of mammalian cells but eggs. A model describing the activity of PLCζ in physiological calcium concentrations was developed. It was shown that the presence of phosphoinositide-rich vesicles is required for the PLC ζ activity in mature mammalian eggs.

Scheme of the full model. (A) Reactions in the oocyte, PLCζ – calcium-free PLCζ , PLCζ_Ca – PLCζ, bound with one calcium ion, PLCζ_2Ca – PLCζ, bound with two calcium ions, PLCζ_3Ca  – with three, PLCζ_4Ca  – with four. PLCζ_m  – PLCζ, bound with cell membrane, PLCζ_(Ca m ) – PLCζ, bound with the cell membrane and one calcium ion, PLCζ_(2Ca m )– PLCζ, bound with the cell membrane and two calcium ions, PLCζ_(3Ca m ) – with the cell membrane and three calcium ions, PLCζ_(4Ca m ) – with the cell membrane and four calcium ions. PIP2 and PIP2_v  – are phosphatidylinositol-4,5 – bis-phosphates on cell and vesicle membrane accordingly. DAG and DAG_v  – diacylglycerol on cell membrane and vesicles accordingly. IP3 – inositol-3-phosphate. PLCζ_v – PLCζ, bound with vesicles, PLCζ_(Ca v ) – PLCζ, bound with vesicles and one calcium ion, PLCζ_(2Ca v ) – PLCζ, bound with vesicles and two calcium ions, PLCζ_(3Ca v ) – with vesicles and three calcium ions, PLCζ_(4Ca v ) – with vesicles and four calcium ions. (B) The sperm cell model is identical to the oocyte model except for the absence of the vesicles.
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#phospholipase Cz#calcium signaling#spermatozoa#oocyte

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

STIM1-ORAI1 direct interaction cannot govern store-operated calcium entry (SOCE) in platelets

Store-operated calcium entry (SOCE) plays an important role in platelet function. It is generally assumed that the mechanism of SOCE relies on the direct interaction of STIM1 and ORAI1 proteins with specific STIM1:ORAI1 stoichiometry. However, in platelets, other pathways may take place. Here we aim to investigate the mechanisms of SOCE in platelets. We developed a lattice-based mathematical model that represented STIM1-ORAI1 interactions and applied it to both HEK cells, where SOCE mechanism is well established, and platelets. The model was able to describe STIM1-ORAI1 behavior in HEK cells successfully. We used the same parameters for protein interaction and applied them to platelets. As a result, we demonstrated that the number of STIM1 proteins on ER membrane could not assure the needed stoichiometry to proper SOCE in platelets.

(A) STIM1-ORAI1 interaction for HEK cells. Bound proteins are presented by brown, STIM1 in cluster – green, free proteins - red. (B) STIM1-ORAI1 puncta formation dependance on clustering probability. Dashed lines represent same process for D=0.01 〖μm〗^2/s (C) Typical system state at t=100ms for different clustering probabilities
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#platelets#mathematical modeling#store-operated calcium entry#platelet intracellular signaling

In vitro models of thrombosis and hemostasis

Abnormalities in hemostatic response are responsible for a large number of life-threatening conditions, however, despite many decades of research, today there are no reliable ways to correct hemostasis without significant risks of thrombosis or bleeding. This situation reflects a poor understanding of the key mechanisms that regulate the hemostatic response. To uncover the principles underlying the regulation of hemostasis, both experimental models and theoretical approaches are actively used. This review focuses on current in vitro models of thrombosis and hemostasis and describes key approaches and tools for studying blood coagulation outside the human/animal body. To reconstruct this process, both microfluidic technologies and approaches based on manufacturing artificial vessels using a variety of hydrogels are actively used. In vitro models of thrombosis traditionally mimic non-penetrating damage to the vessel wall and have been used for more than 30 years to uncover the key processes responsible for the formation of arterial thrombi. Models of in vitro hemostasis have been actively developed only in recent years and are focused ono crucial mechanisms governing the formation of hemostatic plugs - clots that stop bleeding upon a penetrating vascular injury. Modern in vitro models of thrombosis and hemostasis are used not only as tools for fundamental research but are also introduced into clinical practice.

Fabrication of PDMS-based flow chamber: a) A master mold is prepared using photolithography. The relief (typically made of photoresist on a silicon wafer, shown in orange) usually contains several patterns to be imprinted on PDMS; b) The relief form (master mold) is poured with the liquid mixture of PDMS base with curing agent; с-d) A part of polymerized PDMS is then сut and extracted from the mold e) inlet and outlet holes are made and the required tubings are connected. The chamber is attached onto the glass or plastic coverslip (gray) using plasma bonding or vacuum-sealing.
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#hemostasis#platelets#microfluidics#hydrogel#whole blood#in vitro models#thrombosis

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

Avoiding common problems with statistical analysis of biological experiments using a simple nested data simulator

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Despite an extensive literature on statistical methods and their proper application to biological data, incorrect analyses remain a critical and widely spread problem in research papers. Inherently hierarchical (nested, clustered) structure of biological measurements is often erroneously neglected, leading to pseudo-replication and false positive results. This, in turn, complicates the correct assessment of statistical power and impairs optimal planning of experiments. In order to attract more attention to this problem and to illustrate the importance of direct account for the nested structure of biological data, in this article we present a simple open-source simulator of two-level normally distributed stochastic data. By defining ‘true’ mean values and ‘true’ intra- and inter-cluster variances of the simulated data, users of the simulator can test various scenarios, appreciate the importance of using correct multi-level analysis and the danger of neglecting the information about the data structure. Here we apply our nested data simulator to highlight some commonly arising mistakes with data analysis and propose a workflow, in which our simulator could be employed to correctly compare two nested groups of experimental data and to optimally plan new experiments in order to increase statistical power when necessary.
Schematic of a typical biological experiment design, generating nested data
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#nested data#statistical analysis#p-value#false positive#false negative#statistical power#simulated data#intra-cluster correlation
Systems Biology and Systems Physiology: regulation of biological networks
The meeting is dedicated to the 75th anniversary of Fazly Ataullakhanov
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Illuminating the nature of complex systems
«Systems Biology and Physiology Reports:Issue #3»
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