Genomic Organization of the Siglec Gene Locus on Chromosome 19Q13.4 and Cloning of Two New Siglec Pseudogenes
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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. -
The Genomic Organization and Expression Pattern of the Low-Affinity Fc Gamma Receptors (Fcγr) in the Göttingen Minipig
Immunogenetics (2019) 71:123–136 https://doi.org/10.1007/s00251-018-01099-1 ORIGINAL ARTICLE The genomic organization and expression pattern of the low-affinity Fc gamma receptors (FcγR) in the Göttingen minipig Jerome Egli1 & Roland Schmucki1 & Benjamin Loos1 & Stephan Reichl1 & Nils Grabole1 & Andreas Roller1 & Martin Ebeling1 & Alex Odermatt2 & Antonio Iglesias1 Received: 9 August 2018 /Accepted: 24 November 2018 /Published online: 18 December 2018 # The Author(s) 2018 Abstract Safety and efficacy of therapeutic antibodies are often dependent on their interaction with Fc receptors for IgG (FcγRs). The Göttingen minipig represents a valuable species for biomedical research but its use in preclinical studies with therapeutic antibodies is hampered by the lack of knowledge about the porcine FcγRs. Genome analysis and sequencing now enabled the localization of the previously described FcγRIIIa in the orthologous location to human FCGR3A. In addition, we identified nearby the gene coding for the hitherto undescribed putative porcine FcγRIIa. The 1′241 bp long FCGR2A cDNA translates to a 274aa transmembrane protein containing an extracellular region with high similarity to human and cattle FcγRIIa. Like in cattle, the intracellular part does not contain an immunoreceptor tyrosine-based activation motif (ITAM) as in human FcγRIIa. Flow cytometry of the whole blood and single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) of Göttingen minipigs revealed the expression profile of all porcine FcγRs which is compared to human and mouse. The new FcγRIIa is mainly expressed on platelets making the minipig a good model to study IgG-mediated platelet activation and aggregation. In contrast to humans, minipig blood monocytes were found to express inhibitory FcγRIIb that could lead to the underestimation of FcγR-mediated effects of monocytes observed in minipig studies with therapeutic antibodies. -
Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea. -
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, -
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 -
Flow Reagents Single Color Antibodies CD Chart
CD CHART CD N° Alternative Name CD N° Alternative Name CD N° Alternative Name Beckman Coulter Clone Beckman Coulter Clone Beckman Coulter Clone T Cells B Cells Granulocytes NK Cells Macrophages/Monocytes Platelets Erythrocytes Stem Cells Dendritic Cells Endothelial Cells Epithelial Cells T Cells B Cells Granulocytes NK Cells Macrophages/Monocytes Platelets Erythrocytes Stem Cells Dendritic Cells Endothelial Cells Epithelial Cells T Cells B Cells Granulocytes NK Cells Macrophages/Monocytes Platelets Erythrocytes Stem Cells Dendritic Cells Endothelial Cells Epithelial Cells CD1a T6, R4, HTA1 Act p n n p n n S l CD99 MIC2 gene product, E2 p p p CD223 LAG-3 (Lymphocyte activation gene 3) Act n Act p n CD1b R1 Act p n n p n n S CD99R restricted CD99 p p CD224 GGT (γ-glutamyl transferase) p p p p p p CD1c R7, M241 Act S n n p n n S l CD100 SEMA4D (semaphorin 4D) p Low p p p n n CD225 Leu13, interferon induced transmembrane protein 1 (IFITM1). p p p p p CD1d R3 Act S n n Low n n S Intest CD101 V7, P126 Act n p n p n n p CD226 DNAM-1, PTA-1 Act n Act Act Act n p n CD1e R2 n n n n S CD102 ICAM-2 (intercellular adhesion molecule-2) p p n p Folli p CD227 MUC1, mucin 1, episialin, PUM, PEM, EMA, DF3, H23 Act p CD2 T11; Tp50; sheep red blood cell (SRBC) receptor; LFA-2 p S n p n n l CD103 HML-1 (human mucosal lymphocytes antigen 1), integrin aE chain S n n n n n n n l CD228 Melanotransferrin (MT), p97 p p CD3 T3, CD3 complex p n n n n n n n n n l CD104 integrin b4 chain; TSP-1180 n n n n n n n p p CD229 Ly9, T-lymphocyte surface antigen p p n p n -
Sialic Acids and Their Influence on Human NK Cell Function
cells Review Sialic Acids and Their Influence on Human NK Cell Function Philip Rosenstock * and Thomas Kaufmann Institute for Physiological Chemistry, Martin-Luther-University Halle-Wittenberg, Hollystr. 1, D-06114 Halle/Saale, Germany; [email protected] * Correspondence: [email protected] Abstract: Sialic acids are sugars with a nine-carbon backbone, present on the surface of all cells in humans, including immune cells and their target cells, with various functions. Natural Killer (NK) cells are cells of the innate immune system, capable of killing virus-infected and tumor cells. Sialic acids can influence the interaction of NK cells with potential targets in several ways. Different NK cell receptors can bind sialic acids, leading to NK cell inhibition or activation. Moreover, NK cells have sialic acids on their surface, which can regulate receptor abundance and activity. This review is focused on how sialic acids on NK cells and their target cells are involved in NK cell function. Keywords: sialic acids; sialylation; NK cells; Siglecs; NCAM; CD56; sialyltransferases; NKp44; Nkp46; NKG2D 1. Introduction 1.1. Sialic Acids N-Acetylneuraminic acid (Neu5Ac) is the most common sialic acid in the human organism and also the precursor for all other sialic acid derivatives. The biosynthesis of Neu5Ac begins in the cytosol with uridine diphosphate-N-acetylglucosamine (UDP- Citation: Rosenstock, P.; Kaufmann, GlcNAc) as its starting component [1]. It is important to understand that sialic acid T. Sialic Acids and Their Influence on formation is strongly linked to glycolysis, since it results in the production of fructose-6- Human NK Cell Function. Cells 2021, phosphate (F6P) and phosphoenolpyruvate (PEP). -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Chain Locus Heavy Genomic Organization of the Variable
Antibody Repertoire Development in Fetal and Neonatal Piglets. XI. The Relationship of Variable Heavy Chain Gene Usage and the Genomic Organization of the Variable Heavy This information is current as Chain Locus of September 27, 2021. Tomoko Eguchi-Ogawa, Nancy Wertz, Xiu-Zhu Sun, Francois Puimi, Hirohide Uenishi, Kevin Wells, Patrick Chardon, Gregory J. Tobin and John E. Butler J Immunol published online 5 March 2010 Downloaded from http://www.jimmunol.org/content/early/2010/03/05/jimmun ol.0903616 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2010/03/05/jimmunol.090361 Material 6.DC1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 27, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts Errata An erratum has been published regarding this article. Please see next page or: /content/190/3/1383.full.pdf The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published March 5, 2010, doi:10.4049/jimmunol.0903616 The Journal of Immunology Antibody Repertoire Development in Fetal and Neonatal Piglets. -
Human Lectins, Their Carbohydrate Affinities and Where to Find Them
biomolecules Review Human Lectins, Their Carbohydrate Affinities and Where to Review HumanFind Them Lectins, Their Carbohydrate Affinities and Where to FindCláudia ThemD. Raposo 1,*, André B. Canelas 2 and M. Teresa Barros 1 1, 2 1 Cláudia D. Raposo * , Andr1 é LAQVB. Canelas‐Requimte,and Department M. Teresa of Chemistry, Barros NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829‐516 Caparica, Portugal; [email protected] 12 GlanbiaLAQV-Requimte,‐AgriChemWhey, Department Lisheen of Chemistry, Mine, Killoran, NOVA Moyne, School E41 of ScienceR622 Co. and Tipperary, Technology, Ireland; canelas‐ [email protected] NOVA de Lisboa, 2829-516 Caparica, Portugal; [email protected] 2* Correspondence:Glanbia-AgriChemWhey, [email protected]; Lisheen Mine, Tel.: Killoran, +351‐212948550 Moyne, E41 R622 Tipperary, Ireland; [email protected] * Correspondence: [email protected]; Tel.: +351-212948550 Abstract: Lectins are a class of proteins responsible for several biological roles such as cell‐cell in‐ Abstract:teractions,Lectins signaling are pathways, a class of and proteins several responsible innate immune for several responses biological against roles pathogens. such as Since cell-cell lec‐ interactions,tins are able signalingto bind to pathways, carbohydrates, and several they can innate be a immuneviable target responses for targeted against drug pathogens. delivery Since sys‐ lectinstems. In are fact, able several to bind lectins to carbohydrates, were approved they by canFood be and a viable Drug targetAdministration for targeted for drugthat purpose. delivery systems.Information In fact, about several specific lectins carbohydrate were approved recognition by Food by andlectin Drug receptors Administration was gathered for that herein, purpose. plus Informationthe specific organs about specific where those carbohydrate lectins can recognition be found by within lectin the receptors human was body. -
Supplemental Material Table II. Genes Downregulated After Stimulation with SEI and SEB
Supplemental material Table II. Genes downregulated after stimulation with SEI and SEB. SEI SEB Gene Gene Title NCBI Accession probe set ID mean FDR mean FDR Symbola No fold fold changeb changeb HEY1c hairy/enhancer-of-split related with YRPW NM_012258 218839_at -6.2 < 0.0001 -3.9 0.0015 motif 1 ST3GAL6c ST3 beta-galactoside alpha-2,3- AI989567 213355_at -6.1 < 0.0001 -4.2 0.0005 sialyltransferase 6 CXCL6 chemokine (C-X-C motif) ligand 6 NM_002993 206336_at -5.7 < 0.0001 -4.8 0.0010 (granulocyte chemotactic protein 2) NT5DC2 5'-nucleotidase domain containing 2 NM_022908 218051_s_at -5.6 < 0.0001 -2.6 0.0490 MFI2 antigen p97 (melanoma associated) identified BC001875 223723_at -5.3 < 0.0001 -3.7 0.0036 by monoclonal antibodies 133.2 and 96.5 VCANc versican BF590263 204619_s_at -4.6 0.0001 -5.2 0.0003 --- Transcribed locus AI494347 240393_at -4.5 0.0003 -2.6 0.0413 NOG Noggin AL575177 231798_at -4.5 0.0002 -2.6 0.0413 HNMTc histamine N-methyltransferase BC005907 211732_x_at -4.2 0.0006 -3.2 0.0093 LOC285181 hypothetical protein LOC285181 AA002166 1561334_at -4.1 0.0005 -2.9 0.0212 OLIG1 oligodendrocyte transcription factor 1 AL355743 228170_at -3.9 0.0002 -3.8 0.0021 TFPI Tissue factor pathway inhibitor (lipoprotein- AL080215 215447_at -3.8 0.0011 -3.2 0.0092 associated coagulation inhibitor) C5AR1 complement component 5a receptor 1 NM_001736 220088_at -3.8 0.0012 -2.7 0.0418 --- --- AA805633 230175_s_at -3.7 0.0011 -2.8 0.0326 SIGLEC9 sialic acid binding Ig-like lectin 9 AF247180 210569_s_at -3.7 0.0011 -3.1 0.0136 KCNE3 potassium voltage-gated -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................