Title: Computationally Driven Discovery in Coagulation
Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa. more »« less
Abstract Disseminated intravascular coagulation (DIC) is a pathological coagulopathy associated with infection that increases mortality. In DIC, excessive thrombin generation causes symptoms from formation of microthrombi to multiorgan failure; bleeding risks can also be a concern because of clotting factor consumption. Different clinical events lead to DIC, including sepsis, trauma, and shock. Treatments for thrombotic episodes or bleeding presentation in DIC oppose each other, thus creating therapeutic dilemmas in management. The objective of this study was to develop fibrin-specific core-shell nanogels (FSNs) loaded with tissue-type plasminogen activator (tPA) to treat the microcirculatory complications of DIC, which would facilitate targeted clot dissolution to manage microthrombi and the potential consumptive coagulopathy that causes bleeding. FSNs enhance formation of actively polymerizing clots by crosslinking fibrin fibers, but they can also target preexisting microthrombi and, when loaded with tPA, facilitate targeted delivery to lyse the microthrombi. We hypothesized that this dual action would simultaneously address bleeding and microthrombi with DIC to improve outcomes. In vivo, tPA-FSNs decreased the presentation of multiorgan microthrombi, recovered platelet counts, and improved bleeding outcomes in a DIC rodent model. When incorporated with human DIC patient plasma, tPA-FSNs restored clot structure and clot growth under flow. Together, these data demonstrate that a fibrinolytic agent loaded into fibrin-targeting nanogels could improve DIC outcomes.
Leiderman, Karin; Sindi, Suzanne S.; Monroe, Dougald M.; Fogelson, Aaron L.; Neeves, Keith B.
(, Seminars in Thrombosis and Hemostasis)
null
(Ed.)
Abstract Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.
Kelley, Michael A.; Leiderman, Karin
(, PLOS Computational Biology)
Vavylonis, Dimitrios
(Ed.)
Thrombin is an enzyme produced during blood coagulation that is crucial to the formation of a stable clot. Thrombin cleaves soluble fibrinogen into fibrin, which polymerizes and forms an insoluble, stabilizing gel around the growing clot. A small fraction of circulating fibrinogen is the variant γ A / γ ′, which has been associated with high-affinity thrombin binding and implicated as a risk factor for myocardial infarctions, deep vein thrombosis, and coronary artery disease. Thrombin is also known to be strongly sequestered by polymerized fibrin for extended periods of time in a way that is partially regulated by γ A / γ ′. However, the role of γ A / γ ′-thrombin interactions during fibrin polymerization is not fully understood. Here, we present a mathematical model of fibrin polymerization that considered the interactions between thrombin, fibrinogen, and fibrin, including those with γ A / γ ′. In our model, bivalent thrombin-fibrin binding greatly increased thrombin residency times and allowed for thrombin-trapping during fibrin polymerization. Results from the model showed that early in fibrin polymerization, γ ′ binding to thrombin served to localize the thrombin to the fibrin(ogen), which effectively enhanced the enzymatic conversion of fibrinogen to fibrin. When all the fibrin was fully generated, however, the fibrin-thrombin binding persisted but the effect of fibrin on thrombin switched quickly to serve as a sink, essentially removing all free thrombin from the system. This dual role for γ ′-thrombin binding during polymerization led to a paradoxical decrease in trapped thrombin as the amount of γ ′ was increased. The model highlighted biochemical and biophysical roles for fibrin-thrombin interactions during polymerization and agreed well with experimental observations.
Administration of FVIII-Expressing Human Placental Cells to Juvenile Sheep Yields Multi-Organ Engraftment, Therapeutic Plasma FVIII Levels and Alter Immune Signaling Pathways to Evade FVIII Inhibitor Induction 63rd ASH Annual Meeting and Exposition, December 11-14, 2021, Georgia World Congress Center, Atlanta, GA Program: Oral and Poster Abstracts Session: 801. Gene Therapies: Poster III Hematology Disease Topics & Pathways: Bleeding and Clotting, Biological, Translational Research, Hemophilia, Genetic Disorders, Immune Mechanism, Diseases, Gene Therapy, Therapies, Adverse Events, Biological Processes, Transplantation Monday, December 13, 2021, 6:00 PM-8:00 PM We have previously reported that normal juvenile sheep that received weekly intravenous (IV) infusions of human (n=3) or an expression/secretion-optimized, bioengineered human/porcine hybrid (ET3) FVIII protein (n=3) for 5 weeks (20 IU/kg) developed anti-FVIII inhibitory antibodies (10-116 BU, and IgG titers of 1:20–1:245) by week 3 of infusion. By contrast, the IV infusion, or IP administration, of human placental mesenchymal cells (PLC) transduced with a lentiviral vector encoding a myeloid codon-optimized ET3 transgene (PLC-mcoET3) to produce high levels of ET3 protein (4.9-6IU/10^6 cells/24h) enabled the delivery of FVIII without eliciting antibodies, despite using PLC-mcoET3 doses that provided ~20-60 IU/kg ET3 each 24h to mirror the amount of FVIII protein infused. In addition, we showed that the route of PLC-mcoET3 administration (IP vs IV) did not impact the resultant plasma FVIII levels, with animals in these two groups exhibiting mean increases in FVIII activity (quantified by aPTT) of 30.9% and 34.2%, respectively, at week 15 post-treatment. Here, we investigated whether the sites and levels of PLC-mcoET3 engraftment were dependent upon the route of administration and performed s sheep-specific multiplexed transcriptomic analysis (NanoString) to define the immune signaling pathways that thwarted FVIII/ET3 protein immune response when ET3 was delivered through PLC. Tissue samples were collected from various organs at euthanasia and RT-qPCR performed using primers specific to the mcoET3 transgene, to the human housekeeping transcript GAPDH, and to sheep GAPDH, to quantify PLC-mcoET3 tissue engraftment, and normalize the results. RT-qPCR demonstrated PLC-mcoET3 engrafted, in both IP and IV groups, in all the organs evaluated (liver, lung, lymph nodes, thymus, and spleen). Animals that received PLC-mcoET3 via the IP route displayed higher overall levels of engraftment than their IV counterparts. The spleen was the preferential organ of engraftment for both IP and IV groups (IP:2.41±1.97%; IV: 0.64±0.54%). The IP group exhibited significantly higher engraftment in the left lobe of the liver (IP: 1.36±0.35%; IV: 0.041±0.022%), which was confirmed by immunohisto-chemistry (IHC) with an antibody to the human nuclear antigen Ku80 and ImageJ analysis (IP:5.24±3.36%; IV: 0±0). Of note is that the IP route resulted in higher levels of engraftment in the thymus, while IV infusion yielded higher levels of PLC-mcoET3 in lymph nodes. Analysis of H&E-stained tissues demonstrated they were devoid of any abnormal histologic changes and exhibited no evidence of hyperplasia or neoplasia, supporting the safety of the cell platform, irrespective of the route of administration. To date, NanoString analysis of PBMC collected at day 0, week 1, and week 5 post-infusion demonstrated that animals who received FVIII protein had upregulation of UBA5 and BATF, genes involved in antigen processing and Th17 signaling pathways, respectively. Although both IV and IP recipients of PLC-mcoET3 also had an increase in BATF, the IV group exhibited upregulation of BTLA, a gene involved in immune-tolerance, and downregulation of NOTCH and DDL1, involved in T cell differentiation, as well as MAPK12 and PLCG1, genes involved in proinflammatory cytokine regulation and T signaling within the Th17 signature. In IP recipients, BTLA, NOTCH, and DLL1 were all downregulated. Since ET3-reactive Th1 cells were not present in any of the treated animals, it is possible that the Th17 cells are responsible for the inhibitory antibodies seen in the juvenile sheep treated with FVIII/ET3 protein, while in animals receiving PLC-mcoET3, downregulation of genes involved in T cell differentiation and proinflammatory cytokine signaling keeps the immune system in check to avoid an immune response. Disclosures: Doering: Expression Therapeutics: Divested equity in a private or publicly-traded company in the past 24 months. Spencer: Expression Therapeutics: Divested equity in a private or publicly-traded company in the past 24 months.
Mao, Yating; Tan, Mingyang; Kohs, Tia C.; Sylman, Joanna L.; Ngo, Anh T.; Puy, Cristina; McCarty, Owen J.; Walker, Travis W.
(, Applied Rheology)
Abstract The maintenance of hemostasis to ensure vascular integrity is dependent upon the rapid conversion of zymogen species of the coagulation cascade to their enzymatically active forms. This process culminates in the generation of the serine protease thrombin and polymerization of fibrin to prevent vascular leak at sites of endothelial cell injury or loss of cellular junctions. Thrombin generation can be initiated by the extrinsic pathway of coagulation through exposure of blood to tissue factor at sites of vascular damage, or alternatively by the coagulation factor (F) XII activated by foreign surfaces with negative charges, such as glass, through the contact activation pathway. Here, we used transient particle tracking microrheology to investigate the mechanical properties of fibrin in response to thrombin generation downstream of both coagulation pathways. We found that the structural heterogeneity of fibrin formation was dependent on the reaction kinetics of thrombin generation. Pharmacological inhibition of FXII activity prolonged the time to form fibrin and increased the degree of heterogeneity of fibrin, resulting in fibrin clots with reduced mechanical properties. Taken together, this study demonstrates a dependency of the physical biology of fibrin formation on activation of the contact pathway of coagulation.
Link, Kathryn G., Stobb, Michael T., Monroe, Dougald M., Fogelson, Aaron L., Neeves, Keith B., Sindi, Suzanne S., and Leiderman, Karin.
"Computationally Driven Discovery in Coagulation". Arteriosclerosis, Thrombosis, and Vascular Biology (). Country unknown/Code not available. https://doi.org/10.1161/ATVBAHA.120.314648.https://par.nsf.gov/biblio/10251743.
@article{osti_10251743,
place = {Country unknown/Code not available},
title = {Computationally Driven Discovery in Coagulation},
url = {https://par.nsf.gov/biblio/10251743},
DOI = {10.1161/ATVBAHA.120.314648},
abstractNote = {Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa.},
journal = {Arteriosclerosis, Thrombosis, and Vascular Biology},
author = {Link, Kathryn G. and Stobb, Michael T. and Monroe, Dougald M. and Fogelson, Aaron L. and Neeves, Keith B. and Sindi, Suzanne S. and Leiderman, Karin},
editor = {null}
}
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