DOCSLIB.ORG

NOD Mice Revealed by Transgenic Complementation in Role of SLAM

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
NOD Mice Revealed by Transgenic Complementation in Role of SLAM Role of SLAM in NKT Cell Development Revealed by Transgenic Complementation in NOD Mice This information is current as Margaret A. Jordan, Julie M. Fletcher, Roby Jose, Shahead of September 26, 2021. Chowdhury, Nicole Gerlach, Janette Allison and Alan G. Baxter J Immunol 2011; 186:3953-3965; Prepublished online 25 February 2011; doi: 10.4049/jimmunol.1003305 Downloaded from http://www.jimmunol.org/content/186/7/3953 References This article cites 82 articles, 44 of which you can access for free at: http://www.jimmunol.org/content/186/7/3953.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on September 26, 2021 • 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 The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2011 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Role of SLAM in NKT Cell Development Revealed by Transgenic Complementation in NOD Mice Margaret A. Jordan,* Julie M. Fletcher,* Roby Jose,* Shahead Chowdhury,* Nicole Gerlach,* Janette Allison,† and Alan G. Baxter* Allelic variation of SLAM expression on CD4+CD8+ thymocytes has been proposed to play a major role in NKT cell development. In this article, this hypothesis is tested by the production of subcongenic mouse strains and Slamf1 transgenic lines. The long isoform of the C57BL/6 allele of Slamf1 was transgenically expressed on CD4+CD8+ thymocytes under control of an hCD2 minigene. NOD.Nkrp1b.Tg(Slamf1)1 mice, which had a 2-fold increase in SLAM protein expression on CD4+CD8+ thymocytes, had a 2-fold increase in numbers of thymic NKT cells. The additional thymic NKT cells in NOD.Nkrp1b.Tg(Slamf1)1 mice were relatively immature, with a similar subset distribution to those of congenic NOD.Nkrp1b.Nkt1 and NOD.Nkrp1b.Slamf1 mice, which also express increased levels of SLAM on CD4+CD8+ thymocytes and produce larger numbers of NKT cells. Transgenic Downloaded from enhancement of SLAM expression also increased IL-4 and IL-17 production in response to TCR-mediated stimulation. Paradox- ically, NOD.Nkrp1b.Tg(Slamf1)2 mice, which had a 7-fold increase in SLAM expression, showed no significant increase in NKT cells numbers; on the contrary, at high transgene copy number, SLAM expression levels correlated inversely with NKT cell numbers, consistent with a contribution to negative selection. These data confirm a role for SLAM in controlling NKT cell development and are consistent with a role in both positive and negative thymic selection of NKT cells. The Journal of Immunology, 2011, 186: 3953–3965. http://www.jimmunol.org/ ype 1 NKT cells are a population of immunoregulatory to the marine sponge-derived glycolipid a-galactosylceramide T cells that can control the strength and character of (a-GalCer) when presented in the context of CD1d (12). This T responses against viruses, bacteria, fungi, parasites, tu- latter characteristic has been used to good advantage within the mors, allografts, and autologous tissues (reviewed in Refs. 1, 2). development of highly specific fluorescent CD1d/a-GalCer tet- Unlike conventional T cells, NKT cells exhibit various NK cell ramers, which have become the gold standard for the flow cyto- characteristics, including CD161c (NK1.1 in mice), and express metric identification of type 1 NKT cells (13, 14). a semi-invariant TCR consisting of an invariant Va24-Ja18 Upon activation, NKT cells rapidly respond with vigorous by guest on September 26, 2021 (Va14-Ja18 in mice) chain coupled to Vb11 (Vb8.2, Vb7, or production of a diverse range of cytokines, including IFN-g, TNF, Vb2 in mice) (3–5). The NKT-associated TCR binds CD1d, a b2- IL-4, IL-13, IL-10, and IL-17 (15). The contribution of NKT cells microglobulin–dependent integral membrane glycoprotein with to IL-4 production cannot be overstated: they are responsible for homology to MHC classes I and II (6, 7). The Ag-binding cleft of the majority of the IL-4 produced after anti-CD3 i.v. injection, CD1d is highly hydrophobic and presents glycolipids, such as they can produce IL-4 in the absence of IL-4 priming, and they glycosylceramides (8), GPI (8–10), and the lysosomal glyco- can do so within 30 min of TCR ligation (1). NKT cells can para- sphingolipid, iGb3 (11). Type 1 NKT cells are uniformly reactive doxically both suppress and promote cell-mediated immunity; although it is unclear how this dual functionality is regulated, the dominant hypothesis is that functionally different subsets are re- *Comparative Genomics Centre, James Cook University, Townsville, Queensland 4811, Australia; and †St. Vincent’s Institute, Melbourne, Victoria 2480, Australia sponsible for different activities (16). Received for publication October 6, 2010. Accepted for publication January 26, In ontogeny, NKT cells branch from the developmental pathway 2011. of conventional T cells at the CD4+CD8+ double-positive (DP) This work was supported by the Australian National Health and Medical Research stage after generation of the canonical Va14Ja18 TCR. These Council and intramural funding from James Cook University and James Cook Uni- versity Faculty of Medicine, Health and Molecular Sciences. A.G.B. is supported by cells are positively selected by CD1d-expressing DP thymocytes, a Senior Research Fellowship from the Australian National Health and Medical Re- which is in contrast with the selection of conventional T cells search Council. on thymic epithelial cells (17–20). This positive selection event M.A.J. and J.A. designed and produced the transgenic construct and performed requires ligation of both the TCR and a costimulatory molecule, genetic analyses; M.A.J., J.M.F., R.J., and S.C. performed flow cytometry and cel- lular assays; N.G. and J.M.F. performed tissue culture and cytokine assays and such as SLAM, that signals via SAP and the downstream Src generated figures; M.A.J., J.M.F., and N.G. analyzed results, contributed text, and kinase FYN; mice deficient in either SAP or FYN display severe produced figures; and A.G.B. contributed experimental design, analysis, and figures, and wrote the final draft. NKT cell defects (21–25, reviewed in Ref. 16). Address correspondence and reprint requests to Prof. Alan G. Baxter, Comparative The earliest NKT cells identified by binding of CD1d/a-GalCer + 2 2 2 Genomics Centre, Molecular Sciences Building 21, James Cook University, Towns- tetramers are CD4 CD8 NK1.1 and are precursors of the CD4 ville, Queensland 4811, Australia. E-mail address: [email protected] CD82NK1.12 and CD4+NK1.1+ subsets. Expression of NK1.1 is Abbreviations used in this article: a-DP, double-positive; GalCer, glycolipid a-gal- not required for migration to the periphery because the majority of actosylceramide; Klrb, killer cell lectin-like receptor subfamily B; LCR, locus con- 2 2 + trol region; SP, single-positive; SSLP, simple sequence length polymorphism; T1D, recent thymic emigrants are NK1.1 . Both NK1.1 and NK1.1 type 1 diabetes; TESS, Transcription Element Search Software; UTR, untranslated NKT cells can rapidly produce IL-4 and IFN-g on stimulation; region. NK1.1+ NKT cells produce high levels of both cytokines, whereas 2 Copyright Ó 2011 by The American Association of Immunologists, Inc. 0022-1767/11/$16.00 the NK1.1 subset produces high levels of IL-4 and relatively www.jimmunol.org/cgi/doi/10.4049/jimmunol.1003305 3954 ROLE OF SLAM IN NKT CELLS lower levels of IFN-g (17). More recently, a third major subset has versity under specific pathogen-free conditions. The NOD.Nkrp1b strain been identified in lymph nodes. It is CD42NK1.12 and on acti- carries B6-derived alleles at the NK complex on chromosome 6 (from vation secretes large amounts of IL-17 (15). D6mit105 to D6mit135), permitting the use of the NK1.1 marker (32, 33). NOD.Nkrp1b.Nkt1 mice were originally produced by intercrossing NOD. NOD mice are susceptible to multiple autoimmune diseases, Nkrp1b and C57BL/6 mice, and performing serial backcrosses to NOD. including type 1 diabetes (T1D) (26), Sjo¨gren’s syndrome (27), Nkrp1b to N10, before intercrossing and selection of homozygous con- hemolytic anemia (28), systemic lupus erythematosus (29, 30), genic founders, as previously described (39). These studies have been and experimental autoimmune encephalomyelitis (31). The leu- reviewed and approved by the James Cook University institutional Animal Care and Ethics Committee. kocytes of the widely distributed NOD/Lt strain do not react with the NK1.1 Ab PK136, which binds two members of the killer cell Cell suspension preparation lectin-like receptor subfamily B (Klrb) type II transmembrane C- Thymocyte and splenocyte cell suspensions were prepared by gently type lectin receptors, Klrb1c/NKR-P1C and Klrb1b/NKR-P1B. A grinding the organ between frosted microscope slides in FACS buffer (PBS congenic NOD.Nkrp1b line was produced, which expresses NK1.1 containing 2 mM EDTA [Amresco, Solon, OH], 10% [v/v] bovine serum by virtue of carrying the C57BL/6 allele of Klrb1c, to characterize [Invitrogen, Melbourne, Victoria, Australia], and 0.02% [w/v] sodium NK and NKT cell deficiencies in NOD mice (32, 33). azide). Spleen cell suspensions were treated with RBC lysing buffer (Sigma Aldrich, Castle Hill, New South Wales, Australia).
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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,
    [Show full text]
  • Proteomic Bioprofiles and Mechanistic Pathways of Progression to Heart Failure: the HOMAGE Study
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Enlighten Ferreira, J. P. et al. (2019) Proteomic bioprofiles and mechanistic pathways of progression to heart failure: the HOMAGE study. Circulation, 12(5), e005897. (doi:10.1161/CIRCHEARTFAILURE.118.005897) This is the author’s final accepted version. There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it. http://eprints.gla.ac.uk/186516/ Deposited on: 13 May 2019 Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk Proteomic Bioprofiles and Mechanistic Pathways of Progression to Heart Failure: the HOMAGE (Heart OMics in AGEing) study João Pedro Ferreira, MD, PhD1,2* & Job Verdonschot, MD3,4*; Timothy Collier, PhD5; Ping Wang, PhD4; Anne Pizard, PhD1,6; Christian Bär, MD, PhD7; Jens Björkman, PhD8; Alessandro Boccanelli, MD9; Javed Butler, MD, PhD10; Andrew Clark, MD, PhD11; John G. Cleland, MD, PhD12,13; Christian Delles, MD, PhD14; Javier Diez, MD, PhD15,16,17,18; Nicolas Girerd, MD, PhD1; Arantxa González, MD, PhD15,16,17; Mark Hazebroek, MD, PhD3; Anne-Cécile Huby, PhD1; Wouter Jukema, MD, PhD19; Roberto Latini, MD, PhD20; Joost Leenders, MD, PhD21; Daniel Levy, MD, PhD22,23; Alexandre Mebazaa, MD, PhD24; Harald Mischak, MD, PhD25; Florence Pinet, MD, PhD26; Patrick Rossignol, MD, PhD1; Naveed Sattar, MD, PhD27; Peter Sever, MD, PhD28; Jan A. Staessen, MD, PhD29,30; Thomas Thum, MD, PhD7,31; Nicolas Vodovar, PhD24; Zhen-Yu Zhang, MD29; Stephane Heymans, MD, PhD3,32,33** & Faiez Zannad, MD, PhD1** *co-first authors **co-last authors 1 Université de Lorraine, Inserm, Centre d’Investigations Cliniques- Plurithématique 14-33, and Inserm U1116, CHRU, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France.
    [Show full text]
  • Gene List HTG Edgeseq Immuno-Oncology Assay
    Gene List HTG EdgeSeq Immuno-Oncology Assay Adhesion ADGRE5 CLEC4A CLEC7A IBSP ICAM4 ITGA5 ITGB1 L1CAM MBL2 SELE ALCAM CLEC4C DST ICAM1 ITGA1 ITGA6 ITGB2 LGALS1 MUC1 SVIL CDH1 CLEC5A EPCAM ICAM2 ITGA2 ITGAL ITGB3 LGALS3 NCAM1 THBS1 CDH5 CLEC6A FN1 ICAM3 ITGA4 ITGAM ITGB4 LGALS9 PVR THY1 Apoptosis APAF1 BCL2 BID CARD11 CASP10 CASP8 FADD NOD1 SSX1 TP53 TRAF3 BCL10 BCL2L1 BIRC5 CASP1 CASP3 DDX58 NLRP3 NOD2 TIMP1 TRAF2 TRAF6 B-Cell Function BLNK BTLA CD22 CD79A FAS FCER2 IKBKG PAX5 SLAMF1 SLAMF7 SPN BTK CD19 CD24 EBF4 FASLG IKBKB MS4A1 RAG1 SLAMF6 SPI1 Cell Cycle ABL1 ATF1 ATM BATF CCND1 CDK1 CDKN1B NCL RELA SSX1 TBX21 TP53 ABL2 ATF2 AXL BAX CCND3 CDKN1A EGR1 REL RELB TBK1 TIMP1 TTK Cell Signaling ADORA2A DUSP4 HES1 IGF2R LYN MAPK1 MUC1 NOTCH1 RIPK2 SMAD3 STAT5B AKT3 DUSP6 HES5 IKZF1 MAF MAPK11 MYC PIK3CD RNF4 SOCS1 STAT6 BCL6 ELK1 HEY1 IKZF2 MAP2K1 MAPK14 NFATC1 PIK3CG RORC SOCS3 SYK CEBPB EP300 HEY2 IKZF3 MAP2K2 MAPK3 NFATC3 POU2F2 RUNX1 SPINK5 TAL1 CIITA ETS1 HEYL JAK1 MAP2K4 MAPK8 NFATC4 PRKCD RUNX3 STAT1 TCF7 CREB1 FLT3 HMGB1 JAK2 MAP2K7 MAPKAPK2 NFKB1 PRKCE S100B STAT2 TYK2 CREB5 FOS HRAS JAK3 MAP3K1 MEF2C NFKB2 PTEN SEMA4D STAT3 CREBBP GATA3 IGF1R KIT MAP3K5 MTDH NFKBIA PYCARD SMAD2 STAT4 Chemokine CCL1 CCL16 CCL20 CCL25 CCL4 CCR2 CCR7 CX3CL1 CXCL12 CXCL3 CXCR1 CXCR6 CCL11 CCL17 CCL21 CCL26 CCL5 CCR3 CCR9 CX3CR1 CXCL13 CXCL5 CXCR2 MST1R CCL13 CCL18 CCL22 CCL27 CCL7 CCR4 CCRL2 CXCL1 CXCL14 CXCL6 CXCR3 PPBP CCL14 CCL19 CCL23 CCL28 CCL8 CCR5 CKLF CXCL10 CXCL16 CXCL8 CXCR4 XCL2 CCL15 CCL2 CCL24 CCL3 CCR1 CCR6 CMKLR1 CXCL11 CXCL2 CXCL9 CXCR5
    [Show full text]
  • SLAM Family Receptor Signaling in Viral Infections: HIV and Beyond
    Review SLAM Family Receptor Signaling in Viral Infections: HIV and Beyond Patrick O’Connell 1 , Andrea Amalfitano 1,2 and Yasser A. Aldhamen 1,* 1 Department of Microbiology and Molecular Genetics, College of Osteopathic Medicine, Michigan State University, East Lansing, MI 48824, USA; [email protected] (P.O.), amalfi[email protected] (A.A.) 2 Department of Pediatrics, College of Osteopathic Medicine, Michigan State University, East Lansing, MI 48824, USA * Correspondence: [email protected] Received: 15 October 2019; Accepted: 13 November 2019; Published: 16 November 2019 Abstract: The signaling lymphocytic activation molecule (SLAM) family of receptors are expressed on the majority of immune cells. These receptors often serve as self-ligands, and play important roles in cellular communication and adhesion, thus modulating immune responses. SLAM family receptor signaling is differentially regulated in various immune cell types, with responses generally being determined by the presence or absence of two SLAM family adaptor proteins—Ewing’s sarcoma-associated transcript 2 (EAT-2) and SLAM-associated adaptor protein (SAP). In addition to serving as direct regulators of the immune system, certain SLAM family members have also been identified as direct targets for specific microbes and viruses. Here, we will discuss the known roles for these receptors in the setting of viral infection, with special emphasis placed on HIV infection. Because HIV causes such complex dysregulation of the immune system, studies of the roles for SLAM family receptors in this context are particularly exciting. Keywords: HIV; SLAM; SAP; EAT-2; SLAMF7; SLAMF6; innate immunity; adaptive immunity; immune-modulation 1. The SLAM Family of Receptors The signaling lymphocytic activation molecule (SLAM) family of receptors are a set of nine conserved cell-surface glycoproteins present on the cell surface of immune cells (Table1).
    [Show full text]
  • SLAMF1 Antibody (Pab)
    21.10.2014SLAMF1 antibody (pAb) Rabbit Anti-Human/Mouse/Rat Signaling lymphocytic activation molecule family member 1 (CD150, CDw150, IPO -3, SLAM) Instruction Manual Catalog Number PK-AB718-6247 Synonyms SLAMF1 Antibody: Signaling lymphocytic activation molecule family member 1, CD150, CDw150, IPO-3, SLAM Description The signaling lymphocyte-activation molecule family member 1 (SLAMF1) is a novel receptor on T cells that potentiates T cell expansion in a CD28-independent manner. SLAMF1 is predominantly expressed by hematopoietic tissues. Reports suggest that the extracellular domain of SLAMF1 is the receptor for the measles virus and acts as a co-activator on both T and B cells. It is thought to interact with SH2D1A and with PTPN11 via its cytoplasmic domain. Mutations of the SLAM associated gene may be associated with X-linked lympho-proliferative disease (XLP). Quantity 100 µg Source / Host Rabbit Immunogen SLAMF1 antibody was raised against a 15 amino acid synthetic peptide near the amino terminus of human SLAMF1. Purification Method Affinity chromatography purified via peptide column. Clone / IgG Subtype Polyclonal antibody Species Reactivity Human, Mouse, Rat Specificity Two isoforms of SLAMF1 are known to exist; this antibody will recognize both isoforms. SLAMF1 antibody is predicted to not cross-react with other SLAM protein family members. Formulation Antibody is supplied in PBS containing 0.02% sodium azide. Reconstitution During shipment, small volumes of antibody will occasionally become entrapped in the seal of the product vial. For products with volumes of 200 μl or less, we recommend gently tapping the vial on a hard surface or briefly centrifuging the vial in a tabletop centrifuge to dislodge any liquid in the container’s cap.
    [Show full text]
  • Prospects for Using Expression Patterns of Paramyxovirus Receptors As Biomarkers for Oncolytic Virotherapy
    cancers Review Prospects for Using Expression Patterns of Paramyxovirus Receptors as Biomarkers for Oncolytic Virotherapy Olga V. Matveeva 1,* and Svetlana A. Shabalina 2,* 1 Sendai Viralytics LLC, 23 Nylander Way, Acton, MA 01720, USA 2 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA * Correspondence: [email protected] (O.V.M.); [email protected] (S.A.S.) Received: 27 October 2020; Accepted: 1 December 2020; Published: 5 December 2020 Simple Summary: Some non-pathogenic viruses that do not cause serious illness in humans can efficiently target and kill cancer cells and may be considered candidates for cancer treatment with virotherapy. However, many cancer cells are protected from viruses. An important goal of personalized cancer treatment is to identify viruses that can kill a certain type of cancer cells. To this end, researchers investigate expression patterns of cell entry receptors, which viruses use to bind to and enter host cells. We summarized and analyzed the receptor expression patterns of two paramyxoviruses: The non-pathogenic measles and the Sendai viruses. The receptors for these viruses are different and can be proteins or lipids with attached carbohydrates. This review discusses the prospects for using these paramyxovirus receptors as biomarkers for successful personalized virotherapy for certain types of cancer. Abstract: The effectiveness of oncolytic virotherapy in cancer treatment depends on several factors, including successful virus delivery to the tumor, ability of the virus to enter the target malignant cell, virus replication, and the release of progeny virions from infected cells. The multi-stage process is influenced by the efficiency with which the virus enters host cells via specific receptors.
    [Show full text]
  • Supplementary Information.Pdf
    Supplementary Information Whole transcriptome profiling reveals major cell types in the cellular immune response against acute and chronic active Epstein‐Barr virus infection Huaqing Zhong1, Xinran Hu2, Andrew B. Janowski2, Gregory A. Storch2, Liyun Su1, Lingfeng Cao1, Jinsheng Yu3, and Jin Xu1 Department of Clinical Laboratory1, Children's Hospital of Fudan University, Minhang District, Shanghai 201102, China; Departments of Pediatrics2 and Genetics3, Washington University School of Medicine, Saint Louis, Missouri 63110, United States. Supplementary information includes the following: 1. Supplementary Figure S1: Fold‐change and correlation data for hyperactive and hypoactive genes. 2. Supplementary Table S1: Clinical data and EBV lab results for 110 study subjects. 3. Supplementary Table S2: Differentially expressed genes between AIM vs. Healthy controls. 4. Supplementary Table S3: Differentially expressed genes between CAEBV vs. Healthy controls. 5. Supplementary Table S4: Fold‐change data for 303 immune mediators. 6. Supplementary Table S5: Primers used in qPCR assays. Supplementary Figure S1. Fold‐change (a) and Pearson correlation data (b) for 10 cell markers and 61 hypoactive and hyperactive genes identified in subjects with acute EBV infection (AIM) in the primary cohort. Note: 23 up‐regulated hyperactive genes were highly correlated positively with cytotoxic T cell (Tc) marker CD8A and NK cell marker CD94 (KLRD1), and 38 down‐regulated hypoactive genes were highly correlated positively with B cell, conventional dendritic cell
    [Show full text]
  • KRAS Mutations Are Negatively Correlated with Immunity in Colon Cancer
    www.aging-us.com AGING 2021, Vol. 13, No. 1 Research Paper KRAS mutations are negatively correlated with immunity in colon cancer Xiaorui Fu1,2,*, Xinyi Wang1,2,*, Jinzhong Duanmu1, Taiyuan Li1, Qunguang Jiang1 1Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China 2Queen Mary College, Medical Department, Nanchang University, Nanchang, Jiangxi, People's Republic of China *Equal contribution Correspondence to: Qunguang Jiang; email: [email protected] Keywords: KRAS mutations, immunity, colon cancer, tumor-infiltrating immune cells, inflammation Received: March 27, 2020 Accepted: October 8, 2020 Published: November 26, 2020 Copyright: © 2020 Fu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT The heterogeneity of colon cancer tumors suggests that therapeutics targeting specific molecules may be effective in only a few patients. It is therefore necessary to explore gene mutations in colon cancer. In this study, we obtained colon cancer samples from The Cancer Genome Atlas, and the International Cancer Genome Consortium. We evaluated the landscape of somatic mutations in colon cancer and found that KRAS mutations, particularly rs121913529, were frequent and had prognostic value. Using ESTIMATE analysis, we observed that the KRAS-mutated group had higher tumor purity, lower immune score, and lower stromal score than the wild- type group. Through single-sample Gene Set Enrichment Analysis and Gene Set Enrichment Analysis, we found that KRAS mutations negatively correlated with enrichment levels of tumor infiltrating lymphocytes, inflammation, and cytolytic activities.
    [Show full text]
  • Table S3. RAE Analysis of Well-Differentiated Liposarcoma
    Table S3. RAE analysis of well-differentiated liposarcoma Model Chromosome Region start Region end Size q value freqX0* # genes Genes Amp 1 145009467 145122002 112536 0.097 21.8 2 PRKAB2,PDIA3P Amp 1 145224467 146188434 963968 0.029 23.6 10 CHD1L,BCL9,ACP6,GJA5,GJA8,GPR89B,GPR89C,PDZK1P1,RP11-94I2.2,NBPF11 Amp 1 147475854 148412469 936616 0.034 23.6 20 PPIAL4A,FCGR1A,HIST2H2BF,HIST2H3D,HIST2H2AA4,HIST2H2AA3,HIST2H3A,HIST2H3C,HIST2H4B,HIST2H4A,HIST2H2BE, HIST2H2AC,HIST2H2AB,BOLA1,SV2A,SF3B4,MTMR11,OTUD7B,VPS45,PLEKHO1 Amp 1 148582896 153398462 4815567 1.5E-05 49.1 152 PRPF3,RPRD2,TARS2,ECM1,ADAMTSL4,MCL1,ENSA,GOLPH3L,HORMAD1,CTSS,CTSK,ARNT,SETDB1,LASS2,ANXA9, FAM63A,PRUNE,BNIPL,C1orf56,CDC42SE1,MLLT11,GABPB2,SEMA6C,TNFAIP8L2,LYSMD1,SCNM1,TMOD4,VPS72, PIP5K1A,PSMD4,ZNF687,PI4KB,RFX5,SELENBP1,PSMB4,POGZ,CGN,TUFT1,SNX27,TNRC4,MRPL9,OAZ3,TDRKH,LINGO4, RORC,THEM5,THEM4,S100A10,S100A11,TCHHL1,TCHH,RPTN,HRNR,FLG,FLG2,CRNN,LCE5A,CRCT1,LCE3E,LCE3D,LCE3C,LCE3B, LCE3A,LCE2D,LCE2C,LCE2B,LCE2A,LCE4A,KPRP,LCE1F,LCE1E,LCE1D,LCE1C,LCE1B,LCE1A,SMCP,IVL,SPRR4,SPRR1A,SPRR3, SPRR1B,SPRR2D,SPRR2A,SPRR2B,SPRR2E,SPRR2F,SPRR2C,SPRR2G,LELP1,LOR,PGLYRP3,PGLYRP4,S100A9,S100A12,S100A8, S100A7A,S100A7L2,S100A7,S100A6,S100A5,S100A4,S100A3,S100A2,S100A16,S100A14,S100A13,S100A1,C1orf77,SNAPIN,ILF2, NPR1,INTS3,SLC27A3,GATAD2B,DENND4B,CRTC2,SLC39A1,CREB3L4,JTB,RAB13,RPS27,NUP210L,TPM3,C1orf189,C1orf43,UBAP2L,HAX1, AQP10,ATP8B2,IL6R,SHE,TDRD10,UBE2Q1,CHRNB2,ADAR,KCNN3,PMVK,PBXIP1,PYGO2,SHC1,CKS1B,FLAD1,LENEP,ZBTB7B,DCST2, DCST1,ADAM15,EFNA4,EFNA3,EFNA1,RAG1AP1,DPM3 Amp 1
    [Show full text]
  • Single-Cell RNA Landscape of Intratumoral Heterogeneity and Immunosuppressive Microenvironment in Advanced Osteosarcoma
    ARTICLE https://doi.org/10.1038/s41467-020-20059-6 OPEN Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma Yan Zhou1,11, Dong Yang2,11, Qingcheng Yang2,11, Xiaobin Lv 3,11, Wentao Huang4,11, Zhenhua Zhou5, Yaling Wang 1, Zhichang Zhang2, Ting Yuan2, Xiaomin Ding1, Lina Tang 1, Jianjun Zhang1, Junyi Yin1, Yujing Huang1, Wenxi Yu1, Yonggang Wang1, Chenliang Zhou1, Yang Su1, Aina He1, Yuanjue Sun1, Zan Shen1, ✉ ✉ ✉ ✉ Binzhi Qian 6, Wei Meng7,8, Jia Fei9, Yang Yao1 , Xinghua Pan 7,8 , Peizhan Chen 10 & Haiyan Hu1 1234567890():,; Osteosarcoma is the most frequent primary bone tumor with poor prognosis. Through RNA- sequencing of 100,987 individual cells from 7 primary, 2 recurrent, and 2 lung metastatic osteosarcoma lesions, 11 major cell clusters are identified based on unbiased clustering of gene expression profiles and canonical markers. The transcriptomic properties, regulators and dynamics of osteosarcoma malignant cells together with their tumor microenvironment particularly stromal and immune cells are characterized. The transdifferentiation of malignant osteoblastic cells from malignant chondroblastic cells is revealed by analyses of inferred copy-number variation and trajectory. A proinflammatory FABP4+ macrophages infiltration is noticed in lung metastatic osteosarcoma lesions. Lower osteoclasts infiltration is observed in chondroblastic, recurrent and lung metastatic osteosarcoma lesions compared to primary osteoblastic osteosarcoma lesions. Importantly, TIGIT blockade enhances the cytotoxicity effects of the primary CD3+ T cells with high proportion of TIGIT+ cells against osteo- sarcoma. These results present a single-cell atlas, explore intratumor heterogeneity, and provide potential therapeutic targets for osteosarcoma. 1 Oncology Department of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China.
    [Show full text]
  • STAT5A and STAT5B—Twins with Different Personalities In
    cancers Review STAT5A and STAT5B—Twins with Different Personalities in Hematopoiesis and Leukemia , Barbara Maurer * y, Sebastian Kollmann y, Judith Pickem, Andrea Hoelbl-Kovacic and Veronika Sexl Institute of Pharmacology and Toxicology, University of Veterinary Medicine, 1210 Vienna, Austria; [email protected] (S.K.); [email protected] (J.P.); [email protected] (A.H.-K.); [email protected] (V.S.) * Correspondence: [email protected]; Tel.: +43-125-077-2940 Equal first author contribution. y Received: 30 September 2019; Accepted: 1 November 2019; Published: 4 November 2019 Abstract: The transcription factors STAT5A and STAT5B have essential roles in survival and proliferation of hematopoietic cells—which have been considered largely redundant. Mutations of upstream kinases, copy number gains, or activating mutations in STAT5A, or more frequently in STAT5B, cause altered hematopoiesis and cancer. Interfering with their activity by pharmacological intervention is an up-and-coming therapeutic avenue. Precision medicine requests detailed knowledge of STAT5A’s and STAT5B’s individual functions. Recent evidence highlights the privileged role for STAT5B over STAT5A in normal and malignant hematopoiesis. Here, we provide an overview on their individual functions within the hematopoietic system. Keywords: STAT5A; STAT5B; hematopoietic stem cells; STAT5BN642H; STAT5 mouse models; BCR–ABL; leukemia; hematopoiesis 1. Introduction The transcription factors signal transducer and activator of transcription 5A (STAT5A) and 5B (STAT5B) are part of the highly conserved Janus kinase (JAK)/STAT signaling pathway. They fulfill critical functions in processes like proliferation, differentiation, survival, and senescence. Triggers for JAK activation come from the stimulation of upstream membrane receptors which respond to cytokines or growth factors.
    [Show full text]