Chromatin Patterns Distinguish Breast Tumor Subtypes and Disease Progression in Association 2 with ANP32E Levels
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Transcriptional Repression of IFN Regulatory Factor 7 by MYC Is Critical for Type I IFN Production in Human Plasmacytoid Dendrit
Transcriptional Repression of IFN Regulatory Factor 7 by MYC Is Critical for Type I IFN Production in Human Plasmacytoid Dendritic Cells This information is current as of September 28, 2021. Tae Whan Kim, Seunghee Hong, Yin Lin, Elise Murat, HyeMee Joo, Taeil Kim, Virginia Pascual and Yong-Jun Liu J Immunol 2016; 197:3348-3359; Prepublished online 14 September 2016; doi: 10.4049/jimmunol.1502385 Downloaded from http://www.jimmunol.org/content/197/8/3348 Supplementary http://www.jimmunol.org/content/suppl/2016/09/14/jimmunol.150238 http://www.jimmunol.org/ Material 5.DCSupplemental References This article cites 74 articles, 25 of which you can access for free at: http://www.jimmunol.org/content/197/8/3348.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 28, 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 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 © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Transcriptional Repression of IFN Regulatory Factor 7 by MYC Is Critical for Type I IFN Production in Human Plasmacytoid Dendritic Cells Tae Whan Kim,*,1 Seunghee Hong,*,1 Yin Lin,* Elise Murat,* HyeMee Joo,* Taeil Kim,†,2 Virginia Pascual,* and Yong-Jun Liu*,† Type I IFNs are crucial mediators of human innate and adaptive immunity and are massively produced from plasmacytoid dendritic cells (pDCs). -
Interferon-Α Therapy in Bcr-Abl-Negative Myeloproliferative Neoplasms
Leukemia (2008) 22, 1990–1998 & 2008 Macmillan Publishers Limited All rights reserved 0887-6924/08 $32.00 www.nature.com/leu SPOTLIGHT REVIEW Interferon-a therapy in bcr-abl-negative myeloproliferative neoplasms J-J Kiladjian1,2,3, C Chomienne4 and P Fenaux1 1Assistance PubliqueFHoˆpitaux de Paris, Hoˆpital Avicenne, Service d’He´matologie Clinique, Bobigny, France; 2Assistance PubliqueFHoˆpitaux de Paris, Hoˆpital Saint-Louis, Centre d’Investigation Clinique, Paris, France; 3Universite´ Paris 7, Denis Diderot, Paris, France and 4Assistance PubliqueFHoˆpitaux de Paris, Hoˆpital Saint-Louis, Unite´ de Biologie Cellulaire, Paris, France Interferon (IFN) was the first cytokine discovered 50 years ago, however, did not allow complete purity, and although leukocyte with a wide range of biological properties, including immuno- IFN consisted predominantly of IFN-a, small amounts of other modulatory, proapoptotic and antiangiogenic activities, that rapidly raised interest in its therapeutic use in malignancies. IFN species were also present. Later on, the cloning of IFN-receptor characterization was also pivotal in the discovery recombinant human IFN-a and -b in 1980 allowed the SPOTLIGHT of the JAK/STAT signaling pathway. Among the large IFN production of large amounts of IFN for characterization, family, mainly one of the type I IFN, IFN-a2, is used in therapy. biological activity studies and clinical trials.5–7 It is only 20 Many clinical trials have shown remarkable efficacy of IFN-a in years after their discovery that two IFN species, IFN-a and -b, bcr-abl-negative myeloproliferative neoplasms (MPNs), espe- could be purified satisfactorily using high-performance liquid cially polycythemia vera (PV), and essential thrombocythemia 8 (ET). -
A. Cellular Senescence
Generation of antisense RNAs at convergent gene loci in cells undergoing senescence Maharshi Krishna Deb To cite this version: Maharshi Krishna Deb. Generation of antisense RNAs at convergent gene loci in cells undergo- ing senescence. Human genetics. Université Paul Sabatier - Toulouse III, 2016. English. NNT : 2016TOU30274. tel-03209213 HAL Id: tel-03209213 https://tel.archives-ouvertes.fr/tel-03209213 Submitted on 27 Apr 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 5)µ4& &OWVFEFMPCUFOUJPOEV %0$503"5%&-6/*7&34*5²%&506-064& %ÏMJWSÏQBS Université Toulouse 3 Paul Sabatier (UT3 Paul Sabatier) 1SÏTFOUÏFFUTPVUFOVFQBS DEB Maharshi Krishna -F mercredi 30 mars 2016 5Jtre : Generation of antisense RNAs at convergent gene loci in cells undergoing senescence École doctorale et discipline ou spécialité : ED BSB : Génétique moléculaire 6OJUÏEFSFDIFSDIF CNRS-UMR5088; LBCMCP %JSFDUFVS T EFʾÒTF Dr. TROUCHE Didier Co-Directeur/trice(s) de Thèse : Dr. NICOLAS Estelle 3BQQPSUFVST Prof. GILSON Eric, Dr. LIBRI Domenico, Dr. VERDEL Andre "VUSF T NFNCSF T EVKVSZ Prof. GLEIZES Pierre Emmanuel, President of Jury Dr. TROUCHE Didier, Thesis Supervisor This thesis is dedicated to any patients who may get cured with treatments manifesting from this work. -
Supplementary Data
SUPPLEMENTARY DATA A cyclin D1-dependent transcriptional program predicts clinical outcome in mantle cell lymphoma Santiago Demajo et al. 1 SUPPLEMENTARY DATA INDEX Supplementary Methods p. 3 Supplementary References p. 8 Supplementary Tables (S1 to S5) p. 9 Supplementary Figures (S1 to S15) p. 17 2 SUPPLEMENTARY METHODS Western blot, immunoprecipitation, and qRT-PCR Western blot (WB) analysis was performed as previously described (1), using cyclin D1 (Santa Cruz Biotechnology, sc-753, RRID:AB_2070433) and tubulin (Sigma-Aldrich, T5168, RRID:AB_477579) antibodies. Co-immunoprecipitation assays were performed as described before (2), using cyclin D1 antibody (Santa Cruz Biotechnology, sc-8396, RRID:AB_627344) or control IgG (Santa Cruz Biotechnology, sc-2025, RRID:AB_737182) followed by protein G- magnetic beads (Invitrogen) incubation and elution with Glycine 100mM pH=2.5. Co-IP experiments were performed within five weeks after cell thawing. Cyclin D1 (Santa Cruz Biotechnology, sc-753), E2F4 (Bethyl, A302-134A, RRID:AB_1720353), FOXM1 (Santa Cruz Biotechnology, sc-502, RRID:AB_631523), and CBP (Santa Cruz Biotechnology, sc-7300, RRID:AB_626817) antibodies were used for WB detection. In figure 1A and supplementary figure S2A, the same blot was probed with cyclin D1 and tubulin antibodies by cutting the membrane. In figure 2H, cyclin D1 and CBP blots correspond to the same membrane while E2F4 and FOXM1 blots correspond to an independent membrane. Image acquisition was performed with ImageQuant LAS 4000 mini (GE Healthcare). Image processing and quantification were performed with Multi Gauge software (Fujifilm). For qRT-PCR analysis, cDNA was generated from 1 µg RNA with qScript cDNA Synthesis kit (Quantabio). qRT–PCR reaction was performed using SYBR green (Roche). -
Type I Interferons in Anticancer Immunity
REVIEWS Type I interferons in anticancer immunity Laurence Zitvogel1–4*, Lorenzo Galluzzi1,5–8*, Oliver Kepp5–9, Mark J. Smyth10,11 and Guido Kroemer5–9,12 Abstract | Type I interferons (IFNs) are known for their key role in antiviral immune responses. In this Review, we discuss accumulating evidence indicating that type I IFNs produced by malignant cells or tumour-infiltrating dendritic cells also control the autocrine or paracrine circuits that underlie cancer immunosurveillance. Many conventional chemotherapeutics, targeted anticancer agents, immunological adjuvants and oncolytic 1Gustave Roussy Cancer Campus, F-94800 Villejuif, viruses are only fully efficient in the presence of intact type I IFN signalling. Moreover, the France. intratumoural expression levels of type I IFNs or of IFN-stimulated genes correlate with 2INSERM, U1015, F-94800 Villejuif, France. favourable disease outcome in several cohorts of patients with cancer. Finally, new 3Université Paris Sud/Paris XI, anticancer immunotherapies are being developed that are based on recombinant type I IFNs, Faculté de Médecine, F-94270 Le Kremlin Bicêtre, France. type I IFN-encoding vectors and type I IFN-expressing cells. 4Center of Clinical Investigations in Biotherapies of Cancer (CICBT) 507, F-94800 Villejuif, France. Type I interferons (IFNs) were first discovered more than Type I IFNs in cancer immunosurveillance 5Equipe 11 labellisée par la half a century ago as the factors underlying viral inter Type I IFNs are known to mediate antineoplastic effects Ligue Nationale contre le ference — that is, the ability of a primary viral infection against several malignancies, which is a clinically rel Cancer, Centre de Recherche 1 des Cordeliers, F-75006 Paris, to render cells resistant to a second distinct virus . -
Heatmaps - the Gene Expression Edition
Heatmaps - the gene expression edition Jeff Oliver 20 July, 2020 An application of heatmap visualization to investigate differential gene expression. Learning objectives 1. Manipulate data into a ‘tidy’ format 2. Visualize data in a heatmap 3. Become familiar with ggplot syntax for customizing plots Heatmaps for differential gene expression Heatmaps are a great way of displaying three-dimensional data in only two dimensions. But how can we easily translate tabular data into a format for heatmap plotting? By taking advantage of “data munging” and graphics packages, heatmaps are relatively easy to produce in R. Getting started We are going to start by isolating different types of information by imposing structure in our file managment. That is, we are going to put our input data in one folder and any output such as plots or analytical results in a different folder. We can use the dir.create to create these two folders: dir.create("data") dir.create("output") For this lesson, we will use a subset of data on a study of gene expression in cells infected with the influenza virus (doi: 10.4049/jimmunol.1501557). The study infected human plasmacytoid dendritic cells with the influenza virus, and compared gene expression in those cells to gene expression in uninfected cells. Thegoal was to see how the flu virus affected the function of these immune system cells. The data for this lesson is available at: http://tinyurl.com/flu-expression-data or https://jcoliver.github.io/ learn-r/data/GSE68849-expression.csv. Download this comma separated file and put it in the data folder. -
Identification of Differentially Methylated Cpg
biomedicines Article Identification of Differentially Methylated CpG Sites in Fibroblasts from Keloid Scars Mansour A. Alghamdi 1,2 , Hilary J. Wallace 3,4 , Phillip E. Melton 5,6 , Eric K. Moses 5,6, Andrew Stevenson 4 , Laith N. Al-Eitan 7,8 , Suzanne Rea 9, Janine M. Duke 4, 10 11 4,9,12 4, , Patricia L. Danielsen , Cecilia M. Prêle , Fiona M. Wood and Mark W. Fear * y 1 Department of Anatomy, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia; [email protected] 2 Genomics and Personalized Medicine Unit, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia 3 School of Medicine, The University of Notre Dame Australia, Fremantle 6959, Australia; [email protected] 4 Burn Injury Research Unit, School of Biomedical Sciences, Faculty of Health and Medical Sciences, The University of Western Australia, Perth 6009, Australia; andrew@fionawoodfoundation.com (A.S.); [email protected] (J.M.D.); fi[email protected] (F.M.W.) 5 Centre for Genetic Origins of Health and Disease, Faculty of Health and Medical Sciences, The University of Western Australia, Perth 6009, Australia; [email protected] (P.E.M.); [email protected] (E.K.M.) 6 School of Pharmacy and Biomedical Sciences, Faculty of Health Science, Curtin University, Perth 6102, Australia 7 Department of Applied Biological Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan; [email protected] 8 Department of Biotechnology and Genetic Engineering, Jordan University of Science and Technology, Irbid 22110, -
ANP32B Deficiency Impairs Proliferation and Suppresses Tumor Progression by Regulating AKT Phosphorylation
Citation: Cell Death and Disease (2016) 7, e2082; doi:10.1038/cddis.2016.8 OPEN & 2016 Macmillan Publishers Limited All rights reserved 2041-4889/16 www.nature.com/cddis ANP32B deficiency impairs proliferation and suppresses tumor progression by regulating AKT phosphorylation S Yang1,6, L Zhou2,6, PT Reilly3, S-M Shen1,PHe1, X-N Zhu1, C-X Li1, L-S Wang4, TW Mak5, G-Q Chen*,1 and Y Yu*,1 The acidic leucine-rich nuclear phosphoprotein 32B (ANP32B) is reported to impact normal development, with Anp32b-knockout mice exhibiting smaller size and premature aging. However, its cellular and molecular mechanisms, especially its potential roles in tumorigenesis, remain largely unclear. Here, we utilize 'knockout' models, RNAi silencing and clinical cohorts to more closely investigate the role of this enigmatic factor in cell proliferation and cancer phenotypes. We report that, compared with Anp32b wild- type (Anp32b+/+) littermates, a broad panel of tissues in Anp32b-deficient (Anp32b− / −) mice are demonstrated hypoplasia. Anp32b− / − mouse embryo fibroblast cell has a slower proliferation, even after oncogenic immortalization. ANP32B knockdown also significantly inhibits in vitro and in vivo growth of cancer cells by inducing G1 arrest. In line with this, ANP32B protein has higher expression in malignant tissues than adjacent normal tissues from a cohort of breast cancer patients, and its expression level positively correlates with their histopathological grades. Moreover, ANP32B deficiency downregulates AKT phosphorylation, which involves its regulating effect on cell growth. Collectively, our findings suggest that ANP32B is an oncogene and a potential therapeutic target for breast cancer treatment. Cell Death and Disease (2016) 7, e2082; doi:10.1038/cddis.2016.8; published online 4 February 2016 The acidic leucine-rich nuclear phosphoprotein 32 kDa lethality and reduced body weight,22–25 indicating a greater (ANP32) protein family are characterized by a N-terminal importance of Anp32b in normal development. -
Downloaded 18 July 2014 with a 1% False Discovery Rate (FDR)
UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer Permalink https://escholarship.org/uc/item/0t47b9ws Author Spiciarich, David Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Chemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Professor Matthew B. Francis Professor Amy E. Herr Fall 2017 Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer © 2017 by David Spiciarich Abstract Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich Doctor of Philosophy in Chemistry University of California, Berkeley Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Changes in glycosylation have long been appreciated to be part of the cancer phenotype; sialylated glycans are found at elevated levels on many types of cancer and have been implicated in disease progression. However, the specific glycoproteins that contribute to cell surface sialylation are not well characterized, specifically in bona fide human cancer. Metabolic and bioorthogonal labeling methods have previously enabled enrichment and identification of sialoglycoproteins from cultured cells and model organisms. The goal of this work was to develop technologies that can be used for detecting changes in glycoproteins in clinical models of human cancer. -
Primepcr™Assay Validation Report
PrimePCR™Assay Validation Report Gene Information Gene Name acidic (leucine-rich) nuclear phosphoprotein 32 family, member E Gene Symbol Anp32e Organism Mouse Gene Summary Description Not Available Gene Aliases 2810018A15Rik, AI047746, AI326868, CPD1, LANP-L, LANPL, mLANP-L RefSeq Accession No. NC_000069.6, NT_039240.8 UniGene ID Mm.218657 Ensembl Gene ID ENSMUSG00000015749 Entrez Gene ID 66471 Assay Information Unique Assay ID qMmuCIP0034318 Assay Type Probe - Validation information is for the primer pair using SYBR® Green detection Detected Coding Transcript(s) ENSMUST00000165307, ENSMUST00000015893, ENSMUST00000168106, ENSMUST00000170125 Amplicon Context Sequence GGGGAAAGGAGGAGGACATGGAGATGAAGAAGAAGATTAACATGGAGTTGAAG AACAGAGCCCCGGAGGAGGTGACAGAGTTAGTCCTCGATAATTGCTTGTGTGTC AATGGGGAAATCGAAGGCCTGAA Amplicon Length (bp) 100 Chromosome Location 3:95929580-95933967 Assay Design Intron-spanning Purification Desalted Validation Results Efficiency (%) 100 R2 0.9995 cDNA Cq 18.9 cDNA Tm (Celsius) 81 gDNA Cq 42.75 Specificity (%) 100 Information to assist with data interpretation is provided at the end of this report. Page 1/4 PrimePCR™Assay Validation Report Anp32e, Mouse Amplification Plot Amplification of cDNA generated from 25 ng of universal reference RNA Melt Peak Melt curve analysis of above amplification Standard Curve Standard curve generated using 20 million copies of template diluted 10-fold to 20 copies Page 2/4 PrimePCR™Assay Validation Report Products used to generate validation data Real-Time PCR Instrument CFX384 Real-Time PCR Detection System Reverse Transcription Reagent iScript™ Advanced cDNA Synthesis Kit for RT-qPCR Real-Time PCR Supermix SsoAdvanced™ SYBR® Green Supermix Experimental Sample qPCR Mouse Reference Total RNA Data Interpretation Unique Assay ID This is a unique identifier that can be used to identify the assay in the literature and online. -
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 -
Species Specific Differences in Use of ANP32 Proteins by Influenza a Virus
Edinburgh Research Explorer Species specific differences in use of ANP32 proteins by influenza A virus Citation for published version: Long, JS, Idoko-Akoh, A, Mistry, B, Goldhill, DH, Staller , E, Schreyer, J, Ross, C, Goodbourn, S, Shelton, H, Skinner, MA, Sang, H, McGrew, M & Barclay, WS 2019, 'Species specific differences in use of ANP32 proteins by influenza A virus', eLIFE, vol. N/A, 2019;8:e45066. https://doi.org/10.7554/eLife.45066 Digital Object Identifier (DOI): 10.7554/eLife.45066 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: eLIFE Publisher Rights Statement: This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 11. Oct. 2021