Liver Sinusoidal Endothelial Cells — Gatekeepers of Hepatic Immunity
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Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
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. -
Pro-Inflammatory Tnfα and IL-1Β Differentially Regulate the Inflammatory Phenotype of Brain Microvascular Endothelial Cells Simon J
O’Carroll et al. Journal of Neuroinflammation (2015) 12:131 JOURNAL OF DOI 10.1186/s12974-015-0346-0 NEUROINFLAMMATION RESEARCH Open Access Pro-inflammatory TNFα and IL-1β differentially regulate the inflammatory phenotype of brain microvascular endothelial cells Simon J. O’Carroll1,2, Dan Ting Kho1,3, Rachael Wiltshire1, Vicky Nelson1,3, Odunayo Rotimi1,2, Rebecca Johnson1,3, Catherine E. Angel4 and E. Scott Graham1,3* Abstract Background: The vasculature of the brain is composed of endothelial cells, pericytes and astrocytic processes. The endothelial cells are the critical interface between the blood and the CNS parenchyma and are a critical component of the blood-brain barrier (BBB). These cells are innately programmed to respond to a myriad of inflammatory cytokines or other danger signals. IL-1β and TNFα are well recognised pro-inflammatory mediators, and here, we provide compelling evidence that they regulate the function and immune response profile of human cerebral microvascular endothelial cells (hCMVECs) differentially. Methods: We used xCELLigence biosensor technology, which revealed global differences in the endothelial response between IL-1β and TNFα. xCELLigence is a label-free impedance-based biosensor, which is ideal for acute or long-term comparison of drug effects on cell behaviour. In addition, flow cytometry and multiplex cytokine arrays were used to show differences in the inflammatory responses from the endothelial cells. Results: Extensive cytokine-secretion profiling and cell-surface immune phenotyping confirmed that the immune response of the hCMVEC to IL-1β was different to that of TNFα. Interestingly, of the 38 cytokines, chemokines and growth factors measured by cytometric bead array, the endothelial cells secreted only 13. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
9-Azido Analogs of Three Sialic Acid Forms for Metabolic Remodeling Of
Supporting Information 9-Azido Analogs of Three Sialic Acid Forms for Metabolic Remodeling of Cell-Surface Sialoglycans Bo Cheng,†,‡ Lu Dong,†,§ Yuntao Zhu,†,‡ Rongbing Huang,†,‡ Yuting Sun,†,‖ Qiancheng You,†,‡ Qitao Song,†,§ James C. Paton, ∇ Adrienne W. Paton,∇ and Xing Chen*,†,‡,§,⊥,# †College of Chemistry and Molecular Engineering, ‡Beijing National Laboratory for Molecular Sciences, §Peking−Tsinghua Center for Life Sciences,‖Academy for Advanced Interdisciplinary Studies, ⊥Synthetic and Functional Biomolecules Center, and #Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing 100871, China ∇Research Centre for Infectious Diseases, Department of Molecular and Biomedical Science, University of Adelaide, Adelaide SA 5005, Australia Page S1 Table of Contents: Scheme S1.……………………………………………………….........……………. S3 Figure S1……………………………………………………..………..……………. S3 Figure S2……………………………………………………..………..…………… S4 Figure S3……………………………………………………..………..…………… S4 Figure S4……………………………………………………..………..…………… S5 Figure S5……………………………………………………..………..…………… S6 Figure S6……………………………………………………..………..…………….S7 Figure S7……………………………………………………..………..…………….S8 Figure S8……………………………………………………..………..…………….S9 Experimental Procedures……………………………….…........…………....S10-S27 Table S1………………………………………………..………..…………….S28-S48 Supporting Reference……………………………………………….......………...S49 Page S2 Scheme S1. Synthesis of 9AzNeu5Gc Figure S1: a, b, c, d) Representative scatter plots (FSC vs. SSC) and histograms of flow cytometry analysis -
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Mft•] ~;;I~ [I) I~ t?l3 ·ilr!f·S; [,j ~ M Hepatobiliary Imaging Update Maggie Chester and Jerry Glowniak Veterans Affairs Medical Center and Oregon Health Sciences University, Portland, Oregon and the gallbladder ejection fraction (EF) after the injection This is the first article in a four-part series on interventional of cholecystokinin (CCK) (Kinevac®, Squibb Diagnostics, nuclear medicine. Upon completion, the nuclear medicine New Brunswick, NJ). A brief description of the hepatic ex technologist should be able to (1) list the advantages of using traction fraction (HEF) was given; the technique used quan interventional hepatic imaging, (2) identify the benefit in tifies hepatocyte function more accurately than does excretion calculating HEF, and (3) utilize the HEF calculation method when appropriate. half-time. Since publication of the previous article (5), the HEF has become more widely used as a measure of hepatocyte function, and nearly all the major nuclear medicine software vendors include programs for calculating the HEF. Scintigraphic assessment of hepatobiliary function began in In this article, we will describe new observations and meth the 1950s with the introduction of iodine-131 C31 1) Rose ods used in hepatobiliary imaging. The following topics will bengal (1). Due to the poor imaging characteristics of 1311, be discussed: ( 1) the use of morphine as an aid in the diagnosis numerous attempts were made to find a technetium-99m 99 of acute cholecystitis, (2) the rim sign in the diagnosis of acute ( mTc) labeled hepatobiliary agent (2). The most useful of cholecystitis, and (3) methods for calculating the HEF. the several 99mTc-labeled agents that were investigated were the iminodiacetic acid (IDA) analogs, which were introduced MORPHINE-AUGMENTED CHOLESCINTIGRAPHY in the mid 1970s (3). -
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G C A T T A C G G C A T genes Article A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets Deeksha Doddahonnaiah 1,†, Patrick J. Lenehan 1,† , Travis K. Hughes 1, David Zemmour 1, Enrique Garcia-Rivera 1 , A. J. Venkatakrishnan 1, Ramakrishna Chilaka 2, Apoorv Khare 2, Akhil Kasaraneni 2, Abhinav Garg 2, Akash Anand 2, Rakesh Barve 2, Viswanathan Thiagarajan 2 and Venky Soundararajan 1,2,* 1 nference, One Main Street, Cambridge, MA 02142, USA; [email protected] (D.D.); [email protected] (P.J.L.); [email protected] (T.K.H.); [email protected] (D.Z.); [email protected] (E.G.-R.); [email protected] (A.J.V.) 2 nference Labs, Bengaluru, Karnataka 560017, India; [email protected] (R.C.); [email protected] (A.K.); [email protected] (A.K.); [email protected] (A.G.); [email protected] (A.A.); [email protected] (R.B.); [email protected] (V.T.) * Correspondence: [email protected] † These authors contributed equally. Abstract: Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been Citation: Doddahonnaiah, D.; adequately explored. Here we deployed an NLP framework to objectively quantify associations Lenehan, P.J.; Hughes, T.K.; between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type Zemmour, D.; Garcia-Rivera, E.; terms across over 26 million biomedical documents. -
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int. -
Suppression of Hepatocyte CYP1A2 Expression by Kupffer Cells Via Ahr Pathway: the Central Role of Proinflammatory Cytokines
339-346 29/6/06 12:40 Page 339 INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE 18: 339-346, 2006 339 Suppression of hepatocyte CYP1A2 expression by Kupffer cells via AhR pathway: The central role of proinflammatory cytokines RONGQIAN WU1, XIAOXUAN CUI1, WEIFENG DONG1, MIAN ZHOU1, H. HANK SIMMS2 and PING WANG1 1Department of Surgery, North Shore University Hospital and Long Island Jewish Medical Center, Manhasset, NY 11030, USA Received February 6, 2006; Accepted March 23, 2006 Abstract. The hepatic cytochrome P-450 (CYP) enzyme such downregulation. Inhibition of proinflammatory cytokines system provides a major aspect of liver function, yet alterations by curcumin may provide a novel approach to modulate the of CYP in sepsis remain largely unknown. Although we have hepatic CYP function in sepsis. recently shown that CYP1A2, one of the major isoforms of CYP in rats, is downregulated in sepsis, the underlying mech- Introduction anism and possible therapeutic approaches warrant further investigation. The aim of this study was to determine whether Sepsis is the leading cause of death in non-cardiac intensive Kupffer cells (KCs) play any role in suppressing CYP1A2 in care units with >210,000 people succumbing to overwhelming the hepatocytes (HCs) and if so, how to modulate CYP1A2 infection (or the resultant multiple organ failure) in the US expression in sepsis. To study this, primary KCs and HCs annually (1). Although experimental studies using cell and were cultured separately or together with or without transwells. animal models have greatly improved our understanding of Cells and supernatant samples were collected after various the pathophysiology of sepsis, there remains a remarkable stimulations. -
Hepatocyte Growth Factor Signaling Pathway As a Potential Target in Ductal Adenocarcinoma of the Pancreas
JOP. J Pancreas (Online) 2017 Nov 30; 18(6):448-457. REVIEW ARTICLE Hepatocyte Growth Factor Signaling Pathway as a Potential Target in Ductal Adenocarcinoma of the Pancreas Samra Gafarli, Ming Tian, Felix Rückert Department of Surgery, Medical Faculty Mannheim, University of Heidelberg, Germany ABSTRACT Hepatocyte growth factor is an important cellular signal pathway. The pathway regulates mitogenesis, morphogenesis, cell migration, invasiveness and survival. Hepatocyte growth factor acts through activation of tyrosine kinase receptor c-Met (mesenchymal epithelial transition factor) as the only known ligand. Despite the fact that hepatocyte growth factor is secreted only by mesenchymal origin cells, the targets of this multifunctional pathway are cells of mesenchymal as well as epithelial origin. Besides its physiological role recent evidences suggest that HGF/c-Met also plays a role in tumor pathophysiology. As a “scatter factor” hepatocyte growth factor stimulates cancer cell migration, invasion and subsequently promote metastases. Hepatocyte growth factor further is involved in desmoplastic reaction and consequently indorse chemo- and radiotherapy resistance. Explicitly, this pathway seems to mediate cancer cell aggressiveness and to correlate with poor prognosis and survival rate. Pancreatic Ductal Adenocarcinoma is a carcinoma with high aggressiveness and metastases rate. Latest insights show that the HGF/c-Met signal pathway might play an important role in pancreatic ductal adenocarcinoma pathophysiology. In the present review, we highlight the role of HGF/c-Met pathway in pancreatic ductal adenocarcinoma with focus on its effect on cellular pathophysiology and discuss its role as a potential therapeutic target in pancreatic ductal adenocarcinoma. INTRODUCTION activation causes auto-phosphorylation of c-Met and subsequent activation of downstream signaling pathways Hepatocyte growth factor (HGF) is a multifunctional such as mitogen-activated protein kinases (MAPKs), gene. -
The Immunomodulatory CEA Cell Adhesion Molecule 6 (CEACAM6/Cd66c) Is a Candidate Receptor for the Influenza a Virus
bioRxiv preprint doi: https://doi.org/10.1101/104026; this version posted January 30, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 The immunomodulatory CEA cell adhesion molecule 6 (CEACAM6/CD66c) is a 2 candidate receptor for the influenza A virus 3 Shah Kamranur Rahmana *, Mairaj Ahmed Ansarib, Pratibha Gaurc, Imtiyaz Ahmada, 4 Chandrani Chakravartya,d, Dileep Kumar Vermaa, Sanjay Chhibbere, Naila Nehalf, 5 Shanmugaapriya Sellathanbyd, Dagmar Wirthc, Gulam Warisb and Sunil K. Lala,g # 6 7 Virology Group, International Centre for Genetic Engineering & Biotechnology, New Delhi, 8 Indiaa. 9 Department of Microbiology and Immunology, H. M. Bligh Cancer Research Laboratories, 10 Rosalind Franklin University of Medicine and Science, Chicago Medical School, North 11 Chicago, Illinois, USAb. 12 Helmholtz Centre for Infection Research, Braunschweig, Germanyc. 13 Department of Biomedical Science, Bharathidasan University, Trichy, Indiad. 14 Microbiology Department, Panjab University, Chandigarh, Indiae. 15 Career Institute of Medical & Dental Sciences and Hospital, Lucknow, Indiaf. 16 School of Science, Monash University, Selangor DE, Malaysiag. 17 18 Running Head: Protein receptor for Influenza A Virus 19 20 # Corresponding author: Professor of Microbiology, School of Science, Monash University, 21 47500 Bandar Sunway, Selangor DE, Malaysia. 22 Email: [email protected]; Telephone: (+603) 551 59606 23 24 * Current address: Department of Pathogen Molecular Biology, London School of Hygiene & 25 Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom. 26 1 bioRxiv preprint doi: https://doi.org/10.1101/104026; this version posted January 30, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Hepatic Toxicity
Hepatic Toxicity MSc in Molecular Pathology and Toxicology 2001 Andy Smith MRC Toxicology Unit THE LIVER • The liver constitutes about 5% of the body mass of a rodent or human. • It has many functions eg. Carbohydrate storage and metabolism Synthesis of fibrinogen and albumin etc. Fat metabolism Synthesis of bile acids Metabolism of hormones Formation of urea from amino acids • Blood supply is about 20% arterial-80% venous. • May contain 10-15% of blood volume INFLUENCE OF TOXIC CHEMICALS ON THE LIVER • The liver is the most common site of damage in laboratory animals administered drugs and other chemicals. There are many reasons including the fact that the liver is the first major organ to be exposed to ingested chemicals due to its portal blood supply. • Although chemicals are delivered to the liver to be metabolized and excreted, this can frequently lead to activation and liver injury. • Study of the liver has been and continues to be important in understanding fundamental molecular mechanisms of toxicity as well as in assessment of risks to humans. VENA CAVA HEPATIC VEIN LIVER HEPATIC PORTAL VEIN BILE DUCT SMALL AND LARGE INTESTINE LOBULE PORTAL VEIN BLOOD FLOW HEPATIC ARTERY III I BILE FLOW BILE DUCT CENTRAL VEIN PORTAL TRIAD TYPES OF LIVER CELLS Hepatocytes- Not all the same; depends on lobular site Zone I Higher in respiratory enzymes (periportal) Zone III Higher in cytochrome P450 (centrilobular) Endothelial cells Bile duct cells Oval cells- Possibly stem cells Kupffer cells- Phagocytic cells Important role in inflammation Ito cells- Fat storing or stellate cells TYPES OF HEPATIC INJURY OR RESPONSES Each of the different cell types may respond to a toxic insult.