Download PDF
CC BY 4.0 · Semin Thromb Hemost 2024; 50(04): 537-551
DOI: 10.1055/s-0043-1774796
Review Article

A Perspective on How Fibrinaloid Microclots and Platelet Pathology May be Applied in Clinical Investigations

Etheresia Pretorius 
1   Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, Matieland, South Africa
2   Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
,
Douglas B. Kell 
1   Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, Matieland, South Africa
2   Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
3   The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Lyngby, Denmark
› Author Affiliations Funding D.B.K. would like to thank the Novo Nordisk Foundation (grant NNF20CC0035580) and Balvi Research Foundation and funding. E.P. would like to thank the NRF of South Africa (grant number 142142), SA MRC (self-initiated research [SIR] grant), Balvi Research Foundation, and Polybio Research Foundation for funding. The content and findings reported and illustrated are the sole deduction, view, and responsibility of the researchers and do not reflect the official position and sentiments of the funders.
› Further Information
   Permissions and Reprints

Abstract

Microscopy imaging has enabled us to establish the presence of fibrin(ogen) amyloid (fibrinaloid) microclots in a range of chronic, inflammatory diseases. Microclots may also be induced by a variety of purified substances, often at very low concentrations. These molecules include bacterial inflammagens, serum amyloid A, and the S1 spike protein of severe acute respiratory syndrome coronavirus 2. Here, we explore which of the properties of these microclots might be used to contribute to differential clinical diagnoses and prognoses of the various diseases with which they may be associated. Such properties include distributions in their size and number before and after the addition of exogenous thrombin, their spectral properties, the diameter of the fibers of which they are made, their resistance to proteolysis by various proteases, their cross-seeding ability, and the concentration dependence of their ability to bind small molecules including fluorogenic amyloid stains. Measuring these microclot parameters, together with microscopy imaging itself, along with methodologies like proteomics and imaging flow cytometry, as well as more conventional assays such as those for cytokines, might open up the possibility of a much finer use of these microclot properties in generative methods for a future where personalized medicine will be standard procedures in all clotting pathology disease diagnoses.

Keywords

microclots - platelet hyperactivation - inflammatory molecules - vascular complications

Conflict of Interest

E.P. is a director of Biocode Technologies, a Stellenbosch University start-up company. D.B.K. has no interests to declare.




Publication History

Article published online:
25 September 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Weisel JW. Fibrinogen and fibrin. Adv Protein Chem 2005; 70: 247-299
    CrossrefPubMedGoogle Scholar
  • 2 Adams RL, Bird RJ. Review article: coagulation cascade and therapeutics update: relevance to nephrology. Part 1: overview of coagulation, thrombophilias and history of anticoagulants. Nephrology (Carlton) 2009; 14 (05) 462-470
    CrossrefPubMedGoogle Scholar
  • 3 Undas A, Ariëns RAS. Fibrin clot structure and function: a role in the pathophysiology of arterial and venous thromboembolic diseases. Arterioscler Thromb Vasc Biol 2011; 31 (12) e88-e99
    CrossrefPubMedGoogle Scholar
  • 4 Wolberg AS. Thrombin generation and fibrin clot structure. Blood Rev 2007; 21 (03) 131-142
    CrossrefPubMedGoogle Scholar
  • 5 Palta S, Saroa R, Palta A. Overview of the coagulation system. Indian J Anaesth 2014; 58 (05) 515-523
    CrossrefPubMedGoogle Scholar
  • 6 Mullin JL, Gorkun OV, Binnie CG, Lord ST. Recombinant fibrinogen studies reveal that thrombin specificity dictates order of fibrinopeptide release. J Biol Chem 2000; 275 (33) 25239-25246
    CrossrefPubMedGoogle Scholar
  • 7 Pechik I, Yakovlev S, Mosesson MW, Gilliland GL, Medved L. Structural basis for sequential cleavage of fibrinopeptides upon fibrin assembly. Biochemistry 2006; 45 (11) 3588-3597
    CrossrefPubMedGoogle Scholar
  • 8 Riedel T, Suttnar J, Brynda E, Houska M, Medved L, Dyr JE. Fibrinopeptides A and B release in the process of surface fibrin formation. Blood 2011; 117 (05) 1700-1706
    CrossrefPubMedGoogle Scholar
  • 9 Lipinski B, Pretorius E. Novel pathway of iron–induced blood coagulation: implications for diabetes mellitus and its complications. Pol Arch Med Wewn 2012; 122 (03) 115-122
    PubMedGoogle Scholar
  • 10 Pretorius E, Vermeulen N, Bester J, Lipinski B, Kell DB. A novel method for assessing the role of iron and its functional chelation in fibrin fibril formation: the use of scanning electron microscopy. Toxicol Mech Methods 2013; 23 (05) 352-359
    CrossrefPubMedGoogle Scholar
  • 11 Pretorius E, Lipinski B. Differences in morphology of fibrin clots induced with thrombin and ferric ions and its pathophysiological consequences. Heart Lung Circ 2013; 22 (06) 447-449
    CrossrefPubMedGoogle Scholar
  • 12 Swanepoel AC, Visagie A, de Lange Z, Emmerson O, Nielsen VG, Pretorius E. The clinical relevance of altered fibrinogen packaging in the presence of 17β-estradiol and progesterone. Thromb Res 2016; 146: 23-34
    CrossrefPubMedGoogle Scholar
  • 13 Kell DB, Pretorius E. The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen). Integr Biol 2015; 7 (01) 24-52
    CrossrefPubMedGoogle Scholar
  • 14 Pretorius E, Mbotwe S, Bester J, Robinson CJ, Kell DB. Acute induction of anomalous and amyloidogenic blood clotting by molecular amplification of highly substoichiometric levels of bacterial lipopolysaccharide. J R Soc Interface 2016; 13 (122) 20160539
    CrossrefPubMedGoogle Scholar
  • 15 Kell DB, Pretorius E. Proteins behaving badly. Substoichiometric molecular control and amplification of the initiation and nature of amyloid fibril formation: lessons from and for blood clotting. Prog Biophys Mol Biol 2017; 123: 16-41
    CrossrefPubMedGoogle Scholar
  • 16 de Waal GM, Engelbrecht L, Davis T, de Villiers WJS, Kell DB, Pretorius E. Correlative light-electron microscopy detects lipopolysaccharide and its association with fibrin fibres in Parkinson's disease, Alzheimer's disease and type 2 diabetes mellitus. Sci Rep 2018; 8 (01) 16798
    CrossrefPubMedGoogle Scholar
  • 17 Kell DB, Laubscher GJ, Pretorius E. A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications. Biochem J 2022; 479 (04) 537-559
    CrossrefPubMedGoogle Scholar
  • 18 Palhano FL, Lee J, Grimster NP, Kelly JW. Toward the molecular mechanism(s) by which EGCG treatment remodels mature amyloid fibrils. J Am Chem Soc 2013; 135 (20) 7503-7510
    CrossrefPubMedGoogle Scholar
  • 19 Pretorius E, Page MJ, Engelbrecht L, Ellis GC, Kell DB. Substantial fibrin amyloidogenesis in type 2 diabetes assessed using amyloid-selective fluorescent stains. Cardiovasc Diabetol 2017; 16 (01) 141
    CrossrefPubMedGoogle Scholar
  • 20 Pretorius E, Page MJ, Mbotwe S, Kell DB. Lipopolysaccharide-binding protein (LBP) can reverse the amyloid state of fibrin seen or induced in Parkinson's disease. PLoS One 2018; 13 (03) e0192121
    CrossrefPubMedGoogle Scholar
  • 21 Pretorius E, Page MJ, Hendricks L, Nkosi NB, Benson SR, Kell DB. Both lipopolysaccharide and lipoteichoic acids potently induce anomalous fibrin amyloid formation: assessment with novel Amytracker™ stains. J R Soc Interface 2018; 15 (139) 20170941
    CrossrefPubMedGoogle Scholar
  • 22 Pretorius E, Bester J, Page MJ, Kell DB. The potential of LPS-binding protein to reverse amyloid formation in plasma fibrin of individuals with Alzheimer-type dementia. Front Aging Neurosci 2018; 10: 257
    CrossrefPubMedGoogle Scholar
  • 23 Eisenberg D, Jucker M. The amyloid state of proteins in human diseases. Cell 2012; 148 (06) 1188-1203
    CrossrefPubMedGoogle Scholar
  • 24 Serpell LC, Sunde M, Benson MD, Tennent GA, Pepys MB, Fraser PE. The protofilament substructure of amyloid fibrils. J Mol Biol 2000; 300 (05) 1033-1039
    CrossrefPubMedGoogle Scholar
  • 25 Serpell LC. Alzheimer's amyloid fibrils: structure and assembly. Biochim Biophys Acta 2000; 1502 (01) 16-30
    CrossrefPubMedGoogle Scholar
  • 26 Pretorius E, Venter C, Laubscher GJ, Lourens PJ, Steenkamp J, Kell DB. Prevalence of readily detected amyloid blood clots in 'unclotted' Type 2 Diabetes Mellitus and COVID-19 plasma: a preliminary report. Cardiovasc Diabetol 2020; 19 (01) 193
    CrossrefPubMedGoogle Scholar
  • 27 Grobler C, Maphumulo SC, Grobbelaar LM. et al. COVID-19: the rollercoaster of fibrin(Ogen), D-Dimer, Von Willebrand factor, P-selectin and their interactions with endothelial cells, platelets and erythrocytes. Int J Mol Sci 2020; 21 (14) 5168
    CrossrefPubMedGoogle Scholar
  • 28 Laubscher GJ, Lourens PJ, Venter C, Kell DB, Pretorius E. TEG®, microclot and platelet mapping for guiding early management of severe COVID-19 coagulopathy. J Clin Med 2021; 10 (22) 5381
    CrossrefPubMedGoogle Scholar
  • 29 Pretorius E, Vlok M, Venter C. et al. Persistent clotting protein pathology in Long COVID/ post-acute sequelae of COVID-19 (PASC) is accompanied by increased levels of antiplasmin. Cardiovasc Diabetol 2021;20(172). Doi: org/10.1186/s12933-021-01359-7
    PubMedGoogle Scholar
  • 30 Venter C, Bezuidenhout JA, Laubscher GJ. et al. Erythrocyte, platelet, serum ferritin and P-selectin pathophysiology implicated in severe hypercoagulation and vascular complications in COVID-19. Int J Mol Sci 2020; 21 (21) 8234
    CrossrefPubMedGoogle Scholar
  • 31 Akbarialiabad H, Taghrir MH, Abdollahi A. et al. Long COVID, a comprehensive systematic scoping review. Infection 2021; 49 (06) 1163-1186
    CrossrefPubMedGoogle Scholar
  • 32 Davis HE, Assaf GS, McCorkell L. et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 2021; 38: 101019
    CrossrefPubMedGoogle Scholar
  • 33 Proal AD, VanElzakker MB. Long COVID or post-acute sequelae of COVID-19 (PASC): an overview of biological factors that may contribute to persistent symptoms. Front Microbiol 2021; 12: 698169
    CrossrefPubMedGoogle Scholar
  • 34 Grobbelaar LM, Venter C, Vlok M. et al. SARS-CoV-2 spike protein S1 induces fibrin(ogen) resistant to fibrinolysis: implications for microclot formation in COVID-19. Biosci Rep 2021; 41 (08) BSR20210611
    CrossrefPubMedGoogle Scholar
  • 35 Pretorius E, Vlok M, Venter C. et al. Persistent clotting protein pathology in Long COVID/Post-Acute Sequelae of COVID-19 (PASC) is accompanied by increased levels of antiplasmin. Cardiovasc Diabetol 2021; 20 (01) 172
    CrossrefPubMedGoogle Scholar
  • 36 Olumuyiwa-Akeredolu OO, Page MJ, Soma P, Pretorius E. Platelets: emerging facilitators of cellular crosstalk in rheumatoid arthritis. Nat Rev Rheumatol 2019; 15 (04) 237-248
    CrossrefPubMedGoogle Scholar
  • 37 Pretorius E. Platelets as potent signaling entities in type 2 diabetes mellitus. Trends Endocrinol Metab 2019; 30 (08) 532-545
    CrossrefPubMedGoogle Scholar
  • 38 Chu SG, Becker RC, Berger PB. et al. Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis. J Thromb Haemost 2010; 8 (01) 148-156
    CrossrefPubMedGoogle Scholar
  • 39 Fintel DJ. Oral antiplatelet therapy for atherothrombotic disease: overview of current and emerging treatment options. Vasc Health Risk Manag 2012; 8: 77-89
    CrossrefPubMedGoogle Scholar
  • 40 Guo L, Rondina MT. The era of thromboinflammation: platelets are dynamic sensors and effector cells during infectious diseases. Front Immunol 2019; 10: 2204
    CrossrefPubMedGoogle Scholar
  • 41 Mancuso ME, Santagostino E. Platelets: much more than bricks in a breached wall. Br J Haematol 2017; 178 (02) 209-219
    CrossrefPubMedGoogle Scholar
  • 42 Ziegler M, Wang X, Peter K. Platelets in cardiac ischaemia/reperfusion injury: a promising therapeutic target. Cardiovasc Res 2019; 115 (07) 1178-1188
    CrossrefPubMedGoogle Scholar
  • 43 Grobbelaar LM, Kruger A, Venter C. et al. Relative hypercoagulopathy of the SARS-CoV-2 beta and delta variants when compared to the less severe omicron variants is related to TEG parameters, the extent of fibrin amyloid microclots, and the severity of clinical illness. Semin Thromb Hemost 2022; 48 (07) 858-868
    Article in Thieme ConnectPubMedGoogle Scholar
  • 44 Kell DB, Pretorius E. No effects without causes: the iron dysregulation and dormant microbes hypothesis for chronic, inflammatory diseases. Biol Rev Camb Philos Soc 2018; 93 (03) 1518-1557
    CrossrefPubMedGoogle Scholar
  • 45 Kell DB, Pretorius E. To what extent are the terminal stages of sepsis, septic shock, SIRS, and multiple organ dysfunction syndrome actually driven by a toxic prion/amyloid form of fibrin?. Semin Thromb Hemost 2018; 44: 224-238
    Article in Thieme ConnectPubMedGoogle Scholar
  • 46 Fajgenbaum DC, June CH. Cytokine storm. N Engl J Med 2020; 383 (23) 2255-2273
    CrossrefPubMedGoogle Scholar
  • 47 Williams RJ. Biochemical Individuality. New York: John Wiley; 1956
    Google Scholar
  • 48 Kleiner G, Marcuzzi A, Zanin V, Monasta L, Zauli G. Cytokine levels in the serum of healthy subjects. Mediators Inflamm 2013; 2013: 434010
    CrossrefPubMedGoogle Scholar
  • 49 Li Y, Oosting M, Smeekens SP. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 2016; 167 (04) 1099-1110.e14
    CrossrefPubMedGoogle Scholar
  • 50 Wu D, Dinh TL, Bausk BP, Walt DR. Long-term measurements of human inflammatory cytokines reveal complex baseline variations between individuals. Am J Pathol 2017; 187 (12) 2620-2626
    CrossrefPubMedGoogle Scholar
  • 51 Schirmer M, Kumar V, Netea MG, Xavier RJ. The causes and consequences of variation in human cytokine production in health. Curr Opin Immunol 2018; 54: 50-58
    CrossrefPubMedGoogle Scholar
  • 52 Koelman L, Pivovarova-Ramich O, Pfeiffer AFH, Grune T, Aleksandrova K. Cytokines for evaluation of chronic inflammatory status in ageing research: reliability and phenotypic characterisation. Immun Ageing 2019; 16: 11
    CrossrefPubMedGoogle Scholar
  • 53 Schirmer M, Smeekens SP, Vlamakis H. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 2016; 167 (04) 1125-1 -136.e8
    CrossrefPubMedGoogle Scholar
  • 54 Liu C, Chu D, Kalantar-Zadeh K, George J, Young HA, Liu G. Cytokines: from clinical significance to quantification. Adv Sci (Weinh) 2021; 8 (15) e2004433
    CrossrefPubMedGoogle Scholar
  • 55 Li Y, Oosting M, Deelen P. et al. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat Med 2016; 22 (08) 952-960
    CrossrefPubMedGoogle Scholar
  • 56 Mudd PA, Crawford JC, Turner JS. et al. Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm. Sci Adv 2020; 6 (50) eabe3024
    CrossrefPubMedGoogle Scholar
  • 57 Del Valle DM, Kim-Schulze S, Huang HH. et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med 2020; 26 (10) 1636-1643
    CrossrefPubMedGoogle Scholar
  • 58 Guo J, Wang S, Xia H. et al. Cytokine signature associated with disease severity in COVID-19. Front Immunol 2021; 12: 681516
    CrossrefPubMedGoogle Scholar
  • 59 Wang SY, Takahashi T, Pine AB. et al. Challenges in interpreting cytokine data in COVID-19 affect patient care and management. PLoS Biol 2021; 19 (08) e3001373
    CrossrefPubMedGoogle Scholar
  • 60 Tjan LH, Furukawa K, Nagano T. et al. Early differences in cytokine production by severity of coronavirus disease 2019. J Infect Dis 2021; 223 (07) 1145-1149
    CrossrefPubMedGoogle Scholar
  • 61 Montoya JG, Holmes TH, Anderson JN. et al. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc Natl Acad Sci U S A 2017; 114 (34) E7150-E7158
    CrossrefPubMedGoogle Scholar
  • 62 Turner S, Naidoo CA, Usher TJ. et al. Increased levels of inflammatory and endothelial biomarkers in blood of long COVID patients point to thrombotic endothelialitis. Semin Thromb Hemost 2023; 50 (02) 288-294
    PubMedGoogle Scholar
  • 63 Kohane IS. HEALTH CARE POLICY. Ten things we have to do to achieve precision medicine. Science 2015; 349 (6243) 37-38
    CrossrefPubMedGoogle Scholar
  • 64 Kell DB, Pretorius E. Are fibrinaloid microclots a cause of autoimmunity in Long Covid and other post-infection diseases?. Biochem J 2023; 480 (15) 1217-1240
    CrossrefPubMedGoogle Scholar
  • 65 Bateman RM, Ellis CG, Suematsu M, Walley KR. S-nitrosoglutathione acts as a small molecule modulator of human fibrin clot architecture. PLoS One 2012; 7 (08) e43660
    CrossrefPubMedGoogle Scholar
  • 66 Bezuidenhout J, Venter C, Roberts T. et al. The atypical fibrin fibre network in rheumatoid arthritis and its relation to autoimmunity, inflammation and thrombosis. bioRxiv 2020; May 30: 2020-05
    PubMedGoogle Scholar
  • 67 Turner S, Khan MA, Putrino D, Woodcock A, Kell DB, Pretorius E. Long COVID: pathophysiological factors and abnormalities of coagulation. Trends Endocrinol Metab 2023; 34 (06) 321-344
    CrossrefPubMedGoogle Scholar
  • 68 Pretorius E, Oberholzer HM, van der Spuy WJ, Meiring JH. The changed ultrastructure of fibrin networks during use of oral contraception and hormone replacement. J Thromb Thrombolysis 2010; 30 (04) 502-506
    CrossrefPubMedGoogle Scholar
  • 69 Swanepoel AC, Lindeque BG, Swart PJ, Abdool Z, Pretorius E. Estrogen causes ultrastructural changes of fibrin networks during the menstrual cycle: a qualitative investigation. Microsc Res Tech 2014; 77 (08) 594-601
    CrossrefPubMedGoogle Scholar
  • 70 Page MJ, Thomson GJA, Nunes JM. et al. Serum amyloid A binds to fibrin(ogen), promoting fibrin amyloid formation. Sci Rep 2019; 9 (01) 3102
    CrossrefPubMedGoogle Scholar
  • 71 Ryu JK, Sozmen EG, Dixit K. et al. SARS-CoV-2 spike protein induces abnormal inflammatory blood clots neutralized by fibrin immunotherapy. bioRxiv 2021; Oct 31: 2021-10
    PubMedGoogle Scholar
  • 72 Tetz G, Pinho M, Pritzkow S, Mendez N, Soto C, Tetz V. Bacterial DNA promotes Tau aggregation. Sci Rep 2020; 10 (01) 2369
    CrossrefPubMedGoogle Scholar
  • 73 Tetz G, Tetz V. Bacterial extracellular DNA promotes β-amyloid aggregation. Microorganisms 2021; 9 (06) 1301
    CrossrefPubMedGoogle Scholar
  • 74 Kruger A, Vlok M, Turner S. et al. Proteomics of fibrin amyloid microclots in long COVID/post-acute sequelae of COVID-19 (PASC) shows many entrapped pro-inflammatory molecules that may also contribute to a failed fibrinolytic system. Cardiovasc Diabetol 2022; 21 (01) 190
    CrossrefPubMedGoogle Scholar
  • 75 Nasica-Labouze J, Nguyen PH, Sterpone F. et al. Amyloid β protein and Alzheimer's disease: when computer simulations complement experimental studies. Chem Rev 2015; 115 (09) 3518-3563
    CrossrefPubMedGoogle Scholar
  • 76 Serpell LC, Benson M, Liepnieks JJ, Fraser PE. Structural analyses of fibrinogen amyloid fibrils. Amyloid 2007; 14 (03) 199-203
    CrossrefPubMedGoogle Scholar
  • 77 Kell DB, Pretorius E. The potential role of ischaemia-reperfusion injury in chronic, relapsing diseases such as rheumatoid arthritis, long COVID, and ME/CFS: evidence, mechanisms, and therapeutic implications. Biochem J 2022; 479 (16) 1653-1708
    CrossrefPubMedGoogle Scholar
  • 78 Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. BioEssays 2004; 26 (01) 99-105
    CrossrefPubMedGoogle Scholar
  • 79 Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020; 477 (23) 4559-4580
    CrossrefPubMedGoogle Scholar
  • 80 Shrivastava AD, Swainston N, Samanta S, Roberts I, Wright Muelas M, Kell DB. MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra. Biomolecules 2021; 11 (12) 1793
    CrossrefPubMedGoogle Scholar
  • 81 Meng F, Yoo J, Chung HS. Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42. Proc Natl Acad Sci U S A 2022; 119 (12) e2116736119
    CrossrefPubMedGoogle Scholar
  • 82 Collet JP, Park D, Lesty C. et al. Influence of fibrin network conformation and fibrin fiber diameter on fibrinolysis speed: dynamic and structural approaches by confocal microscopy. Arterioscler Thromb Vasc Biol 2000; 20 (05) 1354-1361
    CrossrefPubMedGoogle Scholar
  • 83 Bezuidenhout J, Venter C, Roberts T. et al. Detection of citrullinated fibrin in plasma clots of RA patients and its relation to altered structural clot properties, disease-related inflammation and prothrombotic tendency. Front Immunol 2020; 11: 577523
    CrossrefPubMedGoogle Scholar
  • 84 Furukawa Y, Kaneko K, Nukina N. Tau protein assembles into isoform- and disulfide-dependent polymorphic fibrils with distinct structural properties. J Biol Chem 2011; 286 (31) 27236-27246
    CrossrefPubMedGoogle Scholar
  • 85 Sarell CJ, Stockley PG, Radford SE. Assessing the causes and consequences of co-polymerization in amyloid formation. Prion 2013; 7 (05) 359-368
    CrossrefPubMedGoogle Scholar
  • 86 Hu R, Zhang M, Chen H, Jiang B, Zheng J. Cross-seeding interaction between β-amyloid and human islet amyloid polypeptide. ACS Chem Neurosci 2015; 6 (10) 1759-1768
    CrossrefPubMedGoogle Scholar
  • 87 Morales R, Moreno-Gonzalez I, Soto C. Cross-seeding of misfolded proteins: implications for etiology and pathogenesis of protein misfolding diseases. PLoS Pathog 2013; 9 (09) e1003537
    CrossrefPubMedGoogle Scholar
  • 88 Ono K, Takahashi R, Ikeda T, Yamada M. Cross-seeding effects of amyloid β-protein and α-synuclein. J Neurochem 2012; 122 (05) 883-890
    CrossrefPubMedGoogle Scholar
  • 89 Zhang M, Hu R, Chen H. et al. Polymorphic cross-seeding amyloid assemblies of amyloid-β and human islet amyloid polypeptide. Phys Chem Phys 2015; 17 (35) 23245-23256
    CrossrefPubMedGoogle Scholar
  • 90 Hammarström P, Lindgren M, Nilsson KPR. Fluorescence spectroscopy as a tool to characterize amyloid oligomers and fibrils. In: Otzen DE. ed, Amyloid Fibrils and Prefibrillar Aggregates: Molecular and Biological Properties. Weinheim: Wiley-VCH; 2013: 211-243
    Google Scholar
  • 91 Rasmussen J, Mahler J, Beschorner N. et al. Amyloid polymorphisms constitute distinct clouds of conformational variants in different etiological subtypes of Alzheimer's disease. Proc Natl Acad Sci U S A 2017; 114 (49) 13018-13023
    CrossrefPubMedGoogle Scholar
  • 92 Stepanchuk AA, Heyne B, Stys PK. Complex photophysical properties of K114 make for a versatile fluorescent probe for amyloid detection. ACS Chem Neurosci 2021; 12 (07) 1273-1280
    CrossrefPubMedGoogle Scholar
  • 93 Stepanchuk AA, Barber PA, Lashley T, Joseph JT, Stys PK. Quantitative detection of grey and white matter amyloid pathology using a combination of K114 and CRANAD-3 fluorescence. Neurobiol Dis 2021; 161: 105540
    CrossrefPubMedGoogle Scholar
  • 94 Leite JP, Gales L. Fluorescence properties of the amyloid indicator dye thioflavin T in constrained environments. Dyes Pigments 2019; 160: 64-70
    CrossrefPubMedGoogle Scholar
  • 95 Sulatskaya AI, Turoverov KK, Kuznetsova IM. Spectral properties and factors determining high quantum yield of thioflavin T incorporated in amyloid fibrils. Spectrosc Int J 2010; 24 (1–2): 169-172
    CrossrefPubMedGoogle Scholar
  • 96 Sulatskaya AI, Rychkov GN, Sulatsky MI. et al. New evidence on a distinction between Aβ40 and Aβ42 amyloids: thioflavin t binding modes, clustering tendency, degradation resistance, and cross-seeding. Int J Mol Sci 2022; 23 (10) 5513
    CrossrefPubMedGoogle Scholar
  • 97 Stepanenko OV, Sulatsky MI, Mikhailova EV. et al. Trypsin induced degradation of amyloid fibrils. Int J Mol Sci 2021; 22 (09) 4828
    CrossrefPubMedGoogle Scholar
  • 98 Pretorius E, Venter C, Laubscher GJ. et al. Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/ Post-Acute Sequelae of COVID-19 (PASC). Cardiovascular Diabetology 2022; Aug 6; 21 (01) 148
    CrossrefPubMedGoogle Scholar
  • 99 Kell DB. Genotype-phenotype mapping: genes as computer programs. Trends Genet 2002; 18 (11) 555-559
    CrossrefPubMedGoogle Scholar
  • 100 Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edition. Berlin: Springer-Verlag; 2009
    CrossrefGoogle Scholar
  • 101 Davey HM, Davey CL. Multivariate data analysis methods for the interpretation of microbial flow cytometric data. Adv Biochem Eng Biotechnol 2011; 124: 183-209
    PubMedGoogle Scholar
  • 102 Broadhurst D, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006; 2 (04) 171-196
    CrossrefPubMedGoogle Scholar
  • 103 Kell DB, King RD. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol 2000; 18 (03) 93-98
    CrossrefPubMedGoogle Scholar
  • 104 Day JP, Kell DB, Griffith GW. Differentiation of Phytophthora infestans sporangia from other airborne biological particles by flow cytometry. Appl Environ Microbiol 2002; 68 (01) 37-45
    CrossrefPubMedGoogle Scholar
  • 105 Kenny LC, Dunn WB, Ellis DI. et al. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 2005; 1 (03) 227-234
    CrossrefPubMedGoogle Scholar
  • 106 Kenny LC, Broadhurst DI, Dunn W. et al; Screening for Pregnancy Endpoints Consortium. Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension 2010; 56 (04) 741-749
    CrossrefPubMedGoogle Scholar
  • 107 Kingma DP, Welling M. An Introduction to Variational Autoencoders. Arxiv 2019
    CrossrefGoogle Scholar
  • 108 Girin L, Leglaive S, Bie X. et al. Dynamical Variational Autoencoders: A Comprehensive Review. Arxiv 2020
    Google Scholar
  • 109 Samanta S, O'Hagan S, Swainston N, Roberts TJ, Kell DB. VAE-Sim: a novel molecular similarity measure based on a variational autoencoder. Molecules 2020; 25 (15) 3446
    CrossrefPubMedGoogle Scholar
  • 110 Vaswani A, Shazeer N, Parmar N. et al. Attention Is All You Need. Arxiv 2017
    Google Scholar
  • 111 Lin T, Wang Y, Liu X, Qiu X. A Survey of Transformers. Arxiv 2021
    Google Scholar
  • 112 Wolf T, Debut L, Sanh V. et al. Transformers: State-of-the-art Natural Language Processing. . Proceedings of the 2020 EMNLP (Systems Demonstrations) 2019: 38-45
    PubMedGoogle Scholar
  • 113 Zhang H, Song H, Li S, Zhou M, Song D. A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models. Arxiv 2022
    Google Scholar
  • 114 Liu Y, Zhang Y, Wang Y. et al. A Survey of Visual Transformers. Arxiv 2021
    Google Scholar
  • 115 He K, Gan C, Li Z. et al. Transformers in Medical Image Analysis: A Review. Arxiv 2022
    Google Scholar
  • 116 Shamshad F, Khan S, Zamir SW. et al. Transformers in Medical Imaging: A Survey. Arxiv 2022
    Google Scholar
  • 117 Shrivastava AD, Kell DB. FragNet, a contrastive learning-based transformer model for clustering, interpreting, visualising and navigating chemical space. Molecules 2021; 26 (07) 2065
    CrossrefPubMedGoogle Scholar
  • 118 Dunn WB, Broadhurst D, Begley P. et al; Human Serum Metabolome (HUSERMET) Consortium. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 2011; 6 (07) 1060-1083
    CrossrefPubMedGoogle Scholar
  • 119 Wright Muelas M, Roberts I, Mughal F, O'Hagan S, Day PJ, Kell DB. An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines. Metabolomics 2020; 16 (10) 107
    CrossrefPubMedGoogle Scholar
  • 120 Roberts I, Wright Muelas M, Taylor JM. et al. Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome. Metabolomics 2021; 18 (01) 6
    CrossrefPubMedGoogle Scholar
  • 121 Anderson NL, Anderson NG. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 1998; 19 (11) 1853-1861
    CrossrefPubMedGoogle Scholar
  • 122 Rosendahl P, Plak K, Jacobi A. et al. Real-time fluorescence and deformability cytometry. Nat Methods 2018; 15 (05) 355-358
    CrossrefPubMedGoogle Scholar
  • 123 Inwald DP, McDowall A, Peters MJ, Callard RE, Klein NJ. CD40 is constitutively expressed on platelets and provides a novel mechanism for platelet activation. Circ Res 2003; 92 (09) 1041-1048
    CrossrefPubMedGoogle Scholar
  • 124 Dewitte A, Tanga A, Villeneuve J. et al. New frontiers for platelet CD154. Exp Hematol Oncol 2015; 4: 6
    CrossrefPubMedGoogle Scholar
  • 125 Henn V, Steinbach S, Büchner K, Presek P, Kroczek RA. The inflammatory action of CD40 ligand (CD154) expressed on activated human platelets is temporally limited by coexpressed CD40. Blood 2001; 98 (04) 1047-1054
    CrossrefPubMedGoogle Scholar
  • 126 Chapman LM, Aggrey AA, Field DJ. et al. Platelets present antigen in the context of MHC class I. J Immunol 2012; 189 (02) 916-923
    CrossrefPubMedGoogle Scholar
  • 127 Placke T, Örgel M, Schaller M. et al. Platelet-derived MHC class I confers a pseudonormal phenotype to cancer cells that subverts the antitumor reactivity of natural killer immune cells. Cancer Res 2012; 72 (02) 440-448
    CrossrefPubMedGoogle Scholar
  • 128 Vallance TM, Zeuner MT, Williams HF, Widera D, Vaiyapuri S. Toll-like receptor 4 signalling and its impact on platelet function, thrombosis, and haemostasis. Mediators Inflamm 2017; 2017: 9605894
    CrossrefPubMedGoogle Scholar
  • 129 Qiao J, Al-Tamimi M, Baker RI, Andrews RK, Gardiner EE. The platelet Fc receptor, FcγRIIa. Immunol Rev 2015; 268 (01) 241-252
    CrossrefPubMedGoogle Scholar
  • 130 Arman M, Krauel K. Human platelet IgG Fc receptor FcγRIIA in immunity and thrombosis. J Thromb Haemost 2015; 13 (06) 893-908
    CrossrefPubMedGoogle Scholar
  • 131 Estevez B, Du X. New concepts and mechanisms of platelet activation signaling. Physiology (Bethesda) 2017; 32 (02) 162-177
    PubMedGoogle Scholar
  • 132 Speth C, Rambach G, Würzner R. et al. Complement and platelets: mutual interference in the immune network. Mol Immunol 2015; 67 (01) 108-118
    CrossrefPubMedGoogle Scholar
  • 133 Cha JK, Jeong MH, Jang JY. et al. Serial measurement of surface expressions of CD63, P-selectin and CD40 ligand on platelets in atherosclerotic ischemic stroke. A possible role of CD40 ligand on platelets in atherosclerotic ischemic stroke. Cerebrovasc Dis 2003; 16 (04) 376-382
    CrossrefPubMedGoogle Scholar
  • 134 Israels SJ, McMillan-Ward EM, Easton J, Robertson C, McNicol A. CD63 associates with the alphaIIb beta3 integrin-CD9 complex on the surface of activated platelets. Thromb Haemost 2001; 85 (01) 134-141
    Article in Thieme ConnectPubMedGoogle Scholar
  • 135 Wichaiyo S, Lax S, Montague SJ. et al. Platelet glycoprotein VI and C-type lectin-like receptor 2 deficiency accelerates wound healing by impairing vascular integrity in mice. Haematologica 2019; 104 (08) 1648-1660
    CrossrefPubMedGoogle Scholar
  • 136 Nieswandt B, Pleines I, Bender M. Platelet adhesion and activation mechanisms in arterial thrombosis and ischaemic stroke. J Thromb Haemost 2011; 9 (Suppl. 01) 92-104
    CrossrefPubMedGoogle Scholar
  • 137 Nieswandt B, Watson SP. Platelet-collagen interaction: is GPVI the central receptor?. Blood 2003; 102 (02) 449-461
    CrossrefPubMedGoogle Scholar
  • 138 Moroi M, Jung SM. Platelet glycoprotein VI: its structure and function. Thromb Res 2004; 114 (04) 221-233
    CrossrefPubMedGoogle Scholar
  • 139 Schönberger T, Ziegler M, Borst O. et al. The dimeric platelet collagen receptor GPVI-Fc reduces platelet adhesion to activated endothelium and preserves myocardial function after transient ischemia in mice. Am J Physiol Cell Physiol 2012; 303 (07) C757-C766
    CrossrefPubMedGoogle Scholar
  • 140 Suzuki-Inoue K, Fuller GL, García A. et al. A novel Syk-dependent mechanism of platelet activation by the C-type lectin receptor CLEC-2. Blood 2006; 107 (02) 542-549
    CrossrefPubMedGoogle Scholar
  • 141 Fuller GL, Williams JA, Tomlinson MG. et al. The C-type lectin receptors CLEC-2 and Dectin-1, but not DC-SIGN, signal via a novel YXXL-dependent signaling cascade. J Biol Chem 2007; 282 (17) 12397-12409
    CrossrefPubMedGoogle Scholar
  • 142 Merten M, Thiagarajan P. P-selectin expression on platelets determines size and stability of platelet aggregates. Circulation 2000; 102 (16) 1931-1936
    CrossrefPubMedGoogle Scholar
  • 143 Andrews RK, Gardiner EE, Shen Y, Whisstock JC, Berndt MC. Glycoprotein Ib-IX-V. Int J Biochem Cell Biol 2003; 35 (08) 1170-1174
    CrossrefPubMedGoogle Scholar
  • 144 Gardiner EE, Karunakaran D, Shen Y, Arthur JF, Andrews RK, Berndt MC. Controlled shedding of platelet glycoprotein (GP)VI and GPIb-IX-V by ADAM family metalloproteinases. J Thromb Haemost 2007; 5 (07) 1530-1537
    CrossrefPubMedGoogle Scholar
  • 145 Pennings GJ, Kritharides L. CD147 in cardiovascular disease and thrombosis. Semin Thromb Hemost 2014; 40 (07) 747-755
    Article in Thieme ConnectPubMedGoogle Scholar
  • 146 Muramatsu T. Basigin (CD147), a multifunctional transmembrane glycoprotein with various binding partners. J Biochem 2016; 159 (05) 481-490
    CrossrefPubMedGoogle Scholar
  • 147 Schmidt R, Bültmann A, Fischel S. et al. Extracellular matrix metalloproteinase inducer (CD147) is a novel receptor on platelets, activates platelets, and augments nuclear factor kappaB-dependent inflammation in monocytes. Circ Res 2008; 102 (03) 302-309
    CrossrefPubMedGoogle Scholar
  • 148 Pennings GJ, Yong AS, Kritharides L. Expression of EMMPRIN (CD147) on circulating platelets in vivo. J Thromb Haemost 2010; 8 (03) 472-481
    CrossrefPubMedGoogle Scholar
  • 149 Canobbio I, Balduini C, Torti M. Signalling through the platelet glycoprotein Ib-V-IX complex. Cell Signal 2004; 16 (12) 1329-1344
    CrossrefPubMedGoogle Scholar
  • 150 Yong A, Pennings G, Wong C. et al. Intracoronary upregulation of platelet extracellular matrix metalloproteinase inducer (CD147) in coronary disease. Int J Cardiol 2013; 166 (03) 716-721
    CrossrefPubMedGoogle Scholar
  • 151 Berger G, Hartwell DW, Wagner DD. P-Selectin and platelet clearance. Blood 1998; 92 (11) 4446-4452
    CrossrefPubMedGoogle Scholar
  • 152 Blann AD. Soluble P-selectin: the next step. Thromb Res 2014; 133 (01) 3-4
    CrossrefPubMedGoogle Scholar
  • 153 Au AE, Josefsson EC. Regulation of platelet membrane protein shedding in health and disease. Platelets 2017; 28 (04) 342-353
    CrossrefPubMedGoogle Scholar
  • 154 Panzer S, Rosales S, Gisslinger H. et al. Plasma levels of P-selectin are determined by platelet turn-over and the P-selectin Thr715Pro polymorphism. Thromb Res 2008; 121 (04) 573-579
    CrossrefPubMedGoogle Scholar
  • 155 Kim OV, Nevzorova TA, Mordakhanova ER. et al. Fatal dysfunction and disintegration of thrombin-stimulated platelets. Haematologica 2019; 104 (09) 1866-1878
    CrossrefPubMedGoogle Scholar
  • 156 Huynh KC, Stoldt VR, Scharf RE. Contribution of distinct platelet integrins to binding, unfolding, and assembly of fibronectin. Biol Chem 2013; 394 (11) 1485-1493
    CrossrefPubMedGoogle Scholar
  • 157 Xu XR, Carrim N, Neves MA. et al. Platelets and platelet adhesion molecules: novel mechanisms of thrombosis and anti-thrombotic therapies. Thromb J 2016; 14 (Suppl. 01) 29
    PubMedGoogle Scholar
  • 158 Gao W, Shi P, Chen X. et al. Clathrin-mediated integrin αIIbβ3 trafficking controls platelet spreading. Platelets 2018; 29 (06) 610-621
    CrossrefPubMedGoogle Scholar
  • 159 Durrant TN, van den Bosch MT, Hers I. Integrin αIIbβ3 outside-in signaling. Blood 2017; 130 (14) 1607-1619
    CrossrefPubMedGoogle Scholar
  • 160 Coller BS. αIIbβ3: structure and function. J Thromb Haemost 2015; 13 (Suppl 1, Suppl 1) S17-S25
    CrossrefPubMedGoogle Scholar
  • 161 Marjoram RJ, Li Z, He L. et al. α2β1 integrin, GPVI receptor, and common FcRγ chain on mouse platelets mediate distinct responses to collagen in models of thrombosis. PLoS One 2014; 9 (11) e114035
    CrossrefPubMedGoogle Scholar
  • 162 Sebastiano M, Momi S, Falcinelli E, Bury L, Hoylaerts MF, Gresele P. A novel mechanism regulating human platelet activation by MMP-2-mediated PAR1 biased signaling. Blood 2017; 129 (07) 883-895
    CrossrefPubMedGoogle Scholar
  • 163 Duvernay MT, Temple KJ, Maeng JG. et al. Contributions of protease-activated receptors PAR1 and PAR4 to thrombin-induced GPIIbIIIa activation in human platelets. Mol Pharmacol 2017; 91 (01) 39-47
    CrossrefPubMedGoogle Scholar
  • 164 Kahn ML, Nakanishi-Matsui M, Shapiro MJ, Ishihara H, Coughlin SR. Protease-activated receptors 1 and 4 mediate activation of human platelets by thrombin. J Clin Invest 1999; 103 (06) 879-887
    CrossrefPubMedGoogle Scholar
  • 165 Shakhidzhanov SS, Shaturny VI, Panteleev MA, Sveshnikova AN. Modulation and pre-amplification of PAR1 signaling by ADP acting via the P2Y12 receptor during platelet subpopulation formation. Biochim Biophys Acta 2015; 1850 (12) 2518-2529
    CrossrefPubMedGoogle Scholar
  • 166 Amann M, Ferenc M, Valina CM. et al. Validation of a P2Y12-receptor specific whole blood platelet aggregation assay. Platelets 2016; 27 (07) 668-672
    CrossrefPubMedGoogle Scholar
  • 167 Cunningham MR, Nisar SP, Mundell SJ. Molecular mechanisms of platelet P2Y(12) receptor regulation. Biochem Soc Trans 2013; 41 (01) 225-230
    CrossrefPubMedGoogle Scholar
  • 168 Hensch NR, Karim ZA, Pineda J, Mercado N, Alshbool FZ, Khasawneh FT. P2Y12 antibody inhibits platelet activity and protects against thrombogenesis. Biochem Biophys Res Commun 2017; 493 (02) 1069-1074
    CrossrefPubMedGoogle Scholar
  • 169 Oestreich JH, Ferraris SP, Steinhubl SR, Akers WS. Pharmacodynamic interplay of the P2Y(1), P2Y(12), and TxA(2) pathways in platelets: the potential of triple antiplatelet therapy with P2Y(1) receptor antagonism. Thromb Res 2013; 131 (02) e64-e70
    CrossrefPubMedGoogle Scholar
  • 170 Chakraborty R, Bhullar RP, Dakshinamurti S, Hwa J, Chelikani P. Inverse agonism of SQ 29,548 and ramatroban on thromboxane A2 receptor. PLoS One 2014; 9 (01) e85937
    CrossrefPubMedGoogle Scholar
  • 171 Amano H, Ito Y, Eshima K. et al. Thromboxane A2 induces blood flow recovery via platelet adhesion to ischaemic regions. Cardiovasc Res 2015; 107 (04) 509-521
    CrossrefPubMedGoogle Scholar
  • 172 Nyström S, Hammarström P. Amyloidogenesis of SARS-CoV-2 spike protein. J Am Chem Soc 2022; 144 (20) 8945-8950
    CrossrefPubMedGoogle Scholar
  • 173 Bester J, Pretorius E. Effects of IL-1β, IL-6 and IL-8 on erythrocytes, platelets and clot viscoelasticity. Sci Rep 2016; 6: 32188
    CrossrefPubMedGoogle Scholar
  • 174 Pretorius E, du Plooy J, Soma P, Gasparyan AY. An ultrastructural analysis of platelets, erythrocytes, white blood cells, and fibrin network in systemic lupus erythematosus. Rheumatol Int 2014; 34 (07) 1005-1009
    CrossrefPubMedGoogle Scholar
  • 175 Olumuyiwa-Akeredolu OO, Soma P, Buys AV, Debusho LK, Pretorius E. Characterizing pathology in erythrocytes using morphological and biophysical membrane properties: relation to impaired hemorheology and cardiovascular function in rheumatoid arthritis. Biochim Biophys Acta Biomembr 2017; 1859 (12) 2381-2391
    CrossrefPubMedGoogle Scholar
  • 176 Bester J, Soma P, Kell DB, Pretorius E. Viscoelastic and ultrastructural characteristics of whole blood and plasma in Alzheimer-type dementia, and the possible role of bacterial lipopolysaccharides (LPS). Oncotarget 2015; 6 (34) 35284-35303
    CrossrefPubMedGoogle Scholar