Exploratory Aspirin Resistance Trial in Healthy Japanese Volunteers (J-ART) Using Platelet Aggregation As a Measure of Thrombogenicity
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Distinct but Critical Roles for Integrin Aiibb3 in Platelet Lamellipodia Formation on Fibrinogen, Collagen-Related Peptide and T
Distinct but critical roles for integrin aIIbb3 in platelet lamellipodia formation on fibrinogen, collagen-related peptide and thrombin Kelly Thornber1, Owen J. T. McCarty2,3, Steve P. Watson2 and Catherine J. Pears1 1 Department of Biochemistry, University of Oxford, UK 2 Centre for Cardiovascular Sciences, Institute of Biomedical Research, University of Birmingham, UK 3 Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA Keywords Integrins are the major receptor type known to facilitate cell adhesion and aIIbb3; adhesion; integrins; lamellipodia; lamellipodia formation on extracellular matrix proteins. However, collagen- platelets related peptide and thrombin have recently been shown to mediate platelet lamellipodia formation when presented as immobilized surfaces. The aims Correspondence C. Pears, Department of Biochemistry, of this study were to establish if there exists a role for the platelet integrin South Parks Road, University of Oxford, aIIbb3 in this response; and if so, whether signalling from the integrin is Oxford, OX1 3QU, UK required for lamellipodia formation on these surfaces. Real-time analysis Fax: +44 1865 275259 was used to compare platelet morphological changes on surfaces of fibrino- Tel: +44 1865 275737 gen, collagen-related peptide or thrombin in the presence of various E-mail: [email protected] pharmacological inhibitors and platelets from ‘knockout’ mice. We demon- Website: http://www.bioch.ox.ac.uk strate that collagen-related peptide and thrombin stimulate distinct patterns 2+ (Received 11 July 2006, revised 22 August of platelet lamellipodia formation and elevation of intracellular Ca to 2006, accepted 12 September 2006) that induced by the integrin aIIbb3 ligand, fibrinogen. -
Hemoglobin Interaction with Gp1ba Induces Platelet Activation And
ARTICLE Platelet Biology & its Disorders Hemoglobin interaction with GP1bα induces platelet activation and apoptosis: a novel mechanism associated with intravascular hemolysis Rashi Singhal,1,2,* Gowtham K. Annarapu,1,2,* Ankita Pandey,1 Sheetal Chawla,1 Amrita Ojha,1 Avinash Gupta,1 Miguel A. Cruz,3 Tulika Seth4 and Prasenjit Guchhait1 1Disease Biology Laboratory, Regional Centre for Biotechnology, National Capital Region, Biotech Science Cluster, Faridabad, India; 2Biotechnology Department, Manipal University, Manipal, Karnataka, India; 3Thrombosis Research Division, Baylor College of Medicine, Houston, TX, USA, and 4Hematology, All India Institute of Medical Sciences, New Delhi, India *RS and GKA contributed equally to this work. ABSTRACT Intravascular hemolysis increases the risk of hypercoagulation and thrombosis in hemolytic disorders. Our study shows a novel mechanism by which extracellular hemoglobin directly affects platelet activation. The binding of Hb to glycoprotein1bα activates platelets. Lower concentrations of Hb (0.37-3 mM) significantly increase the phos- phorylation of signaling adapter proteins, such as Lyn, PI3K, AKT, and ERK, and promote platelet aggregation in vitro. Higher concentrations of Hb (3-6 mM) activate the pro-apoptotic proteins Bak, Bax, cytochrome c, caspase-9 and caspase-3, and increase platelet clot formation. Increased plasma Hb activates platelets and promotes their apoptosis, and plays a crucial role in the pathogenesis of aggregation and development of the procoagulant state in hemolytic disorders. Furthermore, we show that in patients with paroxysmal nocturnal hemoglobinuria, a chronic hemolytic disease characterized by recurrent events of intravascular thrombosis and thromboembolism, it is the elevated plasma Hb or platelet surface bound Hb that positively correlates with platelet activation. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Supp Table 1.Pdf
Upregulated genes in Hdac8 null cranial neural crest cells fold change Gene Symbol Gene Title 134.39 Stmn4 stathmin-like 4 46.05 Lhx1 LIM homeobox protein 1 31.45 Lect2 leukocyte cell-derived chemotaxin 2 31.09 Zfp108 zinc finger protein 108 27.74 0710007G10Rik RIKEN cDNA 0710007G10 gene 26.31 1700019O17Rik RIKEN cDNA 1700019O17 gene 25.72 Cyb561 Cytochrome b-561 25.35 Tsc22d1 TSC22 domain family, member 1 25.27 4921513I08Rik RIKEN cDNA 4921513I08 gene 24.58 Ofa oncofetal antigen 24.47 B230112I24Rik RIKEN cDNA B230112I24 gene 23.86 Uty ubiquitously transcribed tetratricopeptide repeat gene, Y chromosome 22.84 D8Ertd268e DNA segment, Chr 8, ERATO Doi 268, expressed 19.78 Dag1 Dystroglycan 1 19.74 Pkn1 protein kinase N1 18.64 Cts8 cathepsin 8 18.23 1500012D20Rik RIKEN cDNA 1500012D20 gene 18.09 Slc43a2 solute carrier family 43, member 2 17.17 Pcm1 Pericentriolar material 1 17.17 Prg2 proteoglycan 2, bone marrow 17.11 LOC671579 hypothetical protein LOC671579 17.11 Slco1a5 solute carrier organic anion transporter family, member 1a5 17.02 Fbxl7 F-box and leucine-rich repeat protein 7 17.02 Kcns2 K+ voltage-gated channel, subfamily S, 2 16.93 AW493845 Expressed sequence AW493845 16.12 1600014K23Rik RIKEN cDNA 1600014K23 gene 15.71 Cst8 cystatin 8 (cystatin-related epididymal spermatogenic) 15.68 4922502D21Rik RIKEN cDNA 4922502D21 gene 15.32 2810011L19Rik RIKEN cDNA 2810011L19 gene 15.08 Btbd9 BTB (POZ) domain containing 9 14.77 Hoxa11os homeo box A11, opposite strand transcript 14.74 Obp1a odorant binding protein Ia 14.72 ORF28 open reading -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
A Microfluidic Approach for Evaluating Novel Antithrombotic Targets
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 A Microfluidic Approach For Evaluating Novel Antithrombotic Targets Shu Zhu University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Chemical Engineering Commons Recommended Citation Zhu, Shu, "A Microfluidic Approach For Evaluating Novel Antithrombotic Targets" (2017). Publicly Accessible Penn Dissertations. 2670. https://repository.upenn.edu/edissertations/2670 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2670 For more information, please contact [email protected]. A Microfluidic Approach For Evaluating Novel Antithrombotic Targets Abstract Microfluidic systems allow precise control of the anticoagulation/pharmacology protocols, defined reactive surfaces, hemodynamic flow and optical imaging outines,r and thus are ideal for studies of platelet function and coagulation response. This thesis describes the use of a microfluidic approach to investigate the role of the contact pathway factors XII and XI, platelet-derived polyphosphate, and thiol isomerases in thrombus growth and to evaluate their potential as safer antithrombotic drug targets. The use of low level of corn trypsin inhibitor allowed the study of the contact pathway on collagen/kaolin surfaces with minimally disturbed whole blood sample and we demonstrated the sensitivity of this assay to antithrombotic drugs. On collagen/tissue factor surfaces, we found -
Urokinase Plasminogen Activator: a Potential Thrombolytic Agent for Ischaemic Stroke
Urokinase Plasminogen Activator: A Potential Thrombolytic Agent for Ischaemic Stroke Rais Reskiawan A. Kadir1, Ulvi Bayraktutan1 1Stroke, Division of Clinical Neuroscience, School of Medicine, The University of Nottingham Address for correspondence: Dr. Ulvi Bayraktutan Associate Professor Stroke, Division of Clinical Neuroscience, School of Medicine, The University of Nottingham, Clinical Sciences Building, Hucknall Road, Nottingham NG5 1PB, United Kingdom (UK) Tel: +44-(115) 8231764 Fax: +44-(115) 8231767 E-mail: [email protected] 1 Abstract Stroke continues to be one of the leading causes of mortality and morbidity worldwide. Restoration of cerebral blood flow by recombinant plasminogen activator (rtPA) with or without mechanical thrombectomy is considered the most effective therapy for rescuing brain tissue from ischaemic damage, but this requires advanced facilities and highly skilled professionals, entailing high costs, thus in resource-limited contexts urokinase plasminogen activator (uPA) is commonly used as an alternative. This literature review summarises the existing studies relating to the potential clinical application of uPA in ischaemic stroke patients. In translational studies of ischaemic stroke, uPA has been shown to promote nerve regeneration and reduce infarct volume and neurological deficits. Clinical trials employing uPA as a thrombolytic agent have replicated these favourable outcomes and reported consistent increases in recanalisation, functional improvement, and cerebral haemorrhage rates, similar to those observed with rtPA. Single-chain zymogen pro-urokinase (pro-uPA) and rtPA appear to be complementary and synergistic in their action, suggesting that their co-administration may improve the efficacy of thrombolysis without affecting the overall risk of haemorrhage. Large clinical trials examining the efficacy of uPA or the combination of pro-uPA and rtPA are desperately required to unravel whether either therapeutic approach may be a safe first-line treatment option for patients with ischaemic stroke. -
Biomechanical Thrombosis: the Dark Side of Force and Dawn of Mechano- Medicine
Open access Review Stroke Vasc Neurol: first published as 10.1136/svn-2019-000302 on 15 December 2019. Downloaded from Biomechanical thrombosis: the dark side of force and dawn of mechano- medicine Yunfeng Chen ,1 Lining Arnold Ju 2 To cite: Chen Y, Ju LA. ABSTRACT P2Y12 receptor antagonists (clopidogrel, pras- Biomechanical thrombosis: the Arterial thrombosis is in part contributed by excessive ugrel, ticagrelor), inhibitors of thromboxane dark side of force and dawn platelet aggregation, which can lead to blood clotting and A2 (TxA2) generation (aspirin, triflusal) or of mechano- medicine. Stroke subsequent heart attack and stroke. Platelets are sensitive & Vascular Neurology 2019;0. protease- activated receptor 1 (PAR1) antag- to the haemodynamic environment. Rapid haemodynamcis 1 doi:10.1136/svn-2019-000302 onists (vorapaxar). Increasing the dose of and disturbed blood flow, which occur in vessels with these agents, especially aspirin and clopi- growing thrombi and atherosclerotic plaques or is caused YC and LAJ contributed equally. dogrel, has been employed to dampen the by medical device implantation and intervention, promotes Received 12 November 2019 platelet thrombotic functions. However, this platelet aggregation and thrombus formation. In such 4 Accepted 14 November 2019 situations, conventional antiplatelet drugs often have also increases the risk of excessive bleeding. suboptimal efficacy and a serious side effect of excessive It has long been recognized that arterial bleeding. Investigating the mechanisms of platelet thrombosis -
Confirmation of Pathogenic Mechanisms by SARS-Cov-2–Host
Messina et al. Cell Death and Disease (2021) 12:788 https://doi.org/10.1038/s41419-021-03881-8 Cell Death & Disease ARTICLE Open Access Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2–host interactome Francesco Messina 1, Emanuela Giombini1, Chiara Montaldo1, Ashish Arunkumar Sharma2, Antonio Zoccoli3, Rafick-Pierre Sekaly2, Franco Locatelli4, Alimuddin Zumla5, Markus Maeurer6,7, Maria R. Capobianchi1, Francesco Nicola Lauria1 and Giuseppe Ippolito 1 Abstract In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; clinical outcomes. -
MALE Protein Name Accession Number Molecular Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean H Mean PDAC Mean T-Test PDAC Vs. H T-Test
MALE t-test t-test Accession Molecular H PDAC PDAC vs. PDAC vs. Protein Name Number Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean Mean Mean H CP PDAC/H PDAC/CP - 22 kDa protein IPI00219910 22 kDa 7 5 4 8 1 0 6 6 1 0.1126 0.0456 0.1 0.1 - Cold agglutinin FS-1 L-chain (Fragment) IPI00827773 12 kDa 32 39 34 26 53 57 36 30 55 0.0309 0.0388 1.8 1.5 - HRV Fab 027-VL (Fragment) IPI00827643 12 kDa 4 6 0 0 0 0 5 0 0 - 0.0574 - 0.0 - REV25-2 (Fragment) IPI00816794 15 kDa 8 12 5 7 8 9 10 6 8 0.2225 0.3844 1.3 0.8 A1BG Alpha-1B-glycoprotein precursor IPI00022895 54 kDa 115 109 106 112 111 100 112 109 105 0.6497 0.4138 1.0 0.9 A2M Alpha-2-macroglobulin precursor IPI00478003 163 kDa 62 63 86 72 14 18 63 79 16 0.0120 0.0019 0.2 0.3 ABCB1 Multidrug resistance protein 1 IPI00027481 141 kDa 41 46 23 26 52 64 43 25 58 0.0355 0.1660 2.4 1.3 ABHD14B Isoform 1 of Abhydrolase domain-containing proteinIPI00063827 14B 22 kDa 19 15 19 17 15 9 17 18 12 0.2502 0.3306 0.7 0.7 ABP1 Isoform 1 of Amiloride-sensitive amine oxidase [copper-containing]IPI00020982 precursor85 kDa 1 5 8 8 0 0 3 8 0 0.0001 0.2445 0.0 0.0 ACAN aggrecan isoform 2 precursor IPI00027377 250 kDa 38 30 17 28 34 24 34 22 29 0.4877 0.5109 1.3 0.8 ACE Isoform Somatic-1 of Angiotensin-converting enzyme, somaticIPI00437751 isoform precursor150 kDa 48 34 67 56 28 38 41 61 33 0.0600 0.4301 0.5 0.8 ACE2 Isoform 1 of Angiotensin-converting enzyme 2 precursorIPI00465187 92 kDa 11 16 20 30 4 5 13 25 5 0.0557 0.0847 0.2 0.4 ACO1 Cytoplasmic aconitate hydratase IPI00008485 98 kDa 2 2 0 0 0 0 2 0 0 - 0.0081 - 0.0 -
The Human in Vivo Biomolecule Corona Onto Pegylated Liposomes
RevisedView metadata, Manuscript citation and similar papers at core.ac.uk brought to you by CORE provided by Nottingham Trent Institutional Repository (IRep) 1 2 3 4 5 6 The human in vivo biomolecule corona onto PEGylated 7 8 liposomes: a proof-of-concept clinical study 9 10 11 Marilena Hadjidemetriou1, Sarah McAdam2, Grace Garner2, Chelsey Thackeray3, David Knight4, Duncan 12 Smith5, Zahraa Al-Ahmady1, Mariarosa Mazza1, Jane Rogan2, Andrew Clamp3 and Kostas Kostarelos1* 13 14 15 16 17 1Nanomedicine Lab, Faculty of Biology, Medicine & Health, AV Hill Building, The University of Manchester, Manchester, United Kingdom; 2 18 Manchester Cancer Research Centre Biobank, The Christie NHS Foundation Trust, CRUK Manchester Institute, Manchester, United Kingdom 3Institute of Cancer Sciences and The Christie NHS Foundation Trust, Manchester Cancer Research Centre (MCRC), 19 University of Manchester, Manchester, United Kingdom 20 4Bio-MS Facility, Michael Smith Building, The University of Manchester, Manchester, United Kingdom; 21 5xCRUK Manchester Institute, The University of Manchester, Manchester, United Kingdom 22 23 24 25 26 27 28 29 30 31 _______________________________________ 32 * Correspondence should be addressed to: [email protected] 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 1 63 64 65 1 2 3 4 5 Abstract 6 7 The self-assembled layered adsorption of proteins onto nanoparticle (NP) surfaces, once in contact 8 with biological fluids, has been termed the ‘protein corona’ and it is gradually seen as a determinant 9 10 factor for the overall biological behavior of NPs. -
Related Cardiometabolic and Renal Biomarkers in Human Plasma And
www.nature.com/scientificreports OPEN Comparative analysis of obesity- related cardiometabolic and renal biomarkers in human plasma and serum Meenu Rohini Rajan1,2,16, Matus Sotak 1,2,16, Fredrik Barrenäs1,2,3, Tong Shen4, Kamil Borkowski4, Nicholas J. Ashton2,5,6,7, Christina Biörserud9, Tomas L. Lindahl10, Sofa Ramström10,11, Michael Schöll2,5,8, Per Lindahl1, Oliver Fiehn 4, John W. Newman 4,12,13, Rosie Perkins 1, Ville Wallenius9, Stephan Lange 1,14 & Emma Börgeson 1,2,15,17* The search for biomarkers associated with obesity-related diseases is ongoing, but it is not clear whether plasma and serum can be used interchangeably in this process. Here we used high-throughput screening to analyze 358 proteins and 76 lipids, selected because of their relevance to obesity- associated diseases, in plasma and serum from age- and sex-matched lean and obese humans. Most of the proteins/lipids had similar concentrations in plasma and serum, but a subset showed signifcant diferences. Notably, a key marker of cardiovascular disease PAI-1 showed a diference in concentration between the obese and lean groups only in plasma. Furthermore, some biomarkers showed poor correlations between plasma and serum, including PCSK9, an important regulator of cholesterol homeostasis. Collectively, our results show that the choice of biofuid may impact study outcome when screening for obesity-related biomarkers and we identify several markers where this will be the case. Obesity-related illness is an increasingly important global health issue that places a tremendous economic burden on society1. Te negative health efects of prolonged obesity are partly fuelled by chronic low-grade infammation, which contributes to cardiometabolic and kidney pathophysiology2–4.