DOCSLIB.ORG

Mycobacterium Tuberculosis Induced Transcription in Macrophages: the Role of TPL2/ERK Signalling in the Negative Regulation of T

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mycobacterium Tuberculosis Induced Transcription in Macrophages: the Role of TPL2/ERK Signalling in the Negative Regulation of T Mycobacterium tuberculosis induced transcription in macrophages: the role of TPL2/ERK signalling in the negative regulation of type I interferon production and implications for control of tuberculosis John Benson Ewbank August 2012 Division of Immunoregulation MRC National Institute for Medical Research The Ridgeway, Mill Hill London NW7 1AA Submitted to University College London for the Degree of Doctor of Philosophy I, John Benson Ewbank, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis Abstract Abstract Mycobacterium tuberculosis is an important global cause of mortality and morbidity. The major host cell of Mycobacterium tuberculosis is the macrophage, and Mycobacterium tuberculosis is able to subvert the macrophage response in order to survive and replicate. The majority of infected individuals mount an immune response capable of controlling Mycobacterium tuberculosis infection. This requires the cytokines IL-12, TNFα, IL-1 and IFNγ, which promote eradication or control of infection. However, other immune factors, including IL-10 and type I IFN, can inhibit this protective response. In this study we have used microarray analysis to study the temporal response of macrophages to Mycobacterium tuberculosis infection, in an unbiased fashion. In response to Mycobacterium tuberculosis infection, macrophages produced cytokines and chemokines, upregulated genes involved with major histocompatability class I antigen presentation, activated both pro- and anti-apoptotic genes and downregulated many genes involved in cell-division and metabolism. We also observed the early induction of genes regulated by the extracellular-regulated kinase (ERK) MAP kinase pathway, and the upregulation of genes known to be induced by type I IFN, leading us to further investigate the role of these pathways in the macrophage response to Mycobacterium tuberculosis. Both pathways were found to regulate the production of protective and detrimental cytokines in macrophages in response to Mycobacterium tuberculosis infection. In addition, microarray analysis found that these pathways controlled the transcription of numerous genes in response to Mycobacterium tuberculosis infection. Finally, type I IFN was found to inhibit the macrophage response to IFNγ, including IFNγ-mediated killing of Mycobacterium tuberculosis in macrophages, a crucial step in the control of Mycobacterium 1 Abstract tuberculosis infection in vivo. We have therefore identified important regulatory mechanisms in macrophages, which are likely have an important role during Mycobacterium tuberculosis infection in vivo. 2 Acknowledgements Acknowledgements I would first like to thank my principal supervisor, Dr Anne O’Garra, for giving me the opportunity to take on this project in such a prestigious lab. This has allowed me to learn a huge amount about immunology and infection, and given me the opportunity to interact with prominent scientists from all over the world. Anne’s tireless enthusiasm, and her encyclopaedic knowledge of the literature, has helped make this project what it is. All members of the AOG lab have contributed to this project, but special thanks must go to Finlay McNab for a rewarding collaboration on TPL2-ERK signalling, and many helpful suggestions and comments for during the writing of this thesis; Vangelis Stavropoulos for initially training me to work in containment 3, and for providing common sense solutions to a wide range of problems; Paul Redford for constant support, advice and assistance for all things containment 3; Xuemei Wu, and more recently Damian Carragher, for breeding and looking after the various strains of mice required for this project; Christine Graham for assistance with carrying out microarray studies; Chloe Bloom for helpful discussion and suggestions about microarray analysis; Mary Holman for training in the early days; Ashleigh Howes for teaching me Western blotting; and Jon Pitt, Ashleigh Howes and Finlay McNab for last minute proofreading. I would like to thank others at the MRC NIMR, particularly my thesis committee, Douglas Young and Sebastien Gagneux, for helpful discussions and suggestions; Harsha Jani and Abdul Sesay for assistance in carrying out the microarray studies presented in Chapters 4-6; Anna Gibson for her efforts as lab manager, which were beyond the call of duty; and staff at the MRC NIMR animal 3 Acknowledgements facility for breeding and taking care of the mice. The microarray study presented in Chapter 3 was carried out at the Baylor Institute of Immunology Research, with the help and guidance of Damian Chaussabel and Quynh-Anh Nguyen. 4 Table of contents Table of Contents Abstract ................................................................................................................... 1 Acknowledgements ................................................................................................. 3 Table of Contents .................................................................................................... 5 Table of Figures ..................................................................................................... 11 Table of Tables ...................................................................................................... 14 List of Abbreviations ............................................................................................. 15 Chapter 1. Introduction ........................................................................................... 17 1.1. Tuberculosis and Mycobacterium tuberculosis .............................................. 18 1.1.1. Tuberculosis: a major global health problem ......................................... 18 1.1.2. The outcome of infection with Mtb ........................................................ 19 1.1.3. Different strains of Mtb show increased virulence and may affect disease outcome............................................................................................................. 20 1.2. Early events following Mtb infection ............................................................. 21 1.2.1. The innate immune system ..................................................................... 22 1.2.2. Mtb infects cells of the innate immune system in the lung..................... 23 1.2.3. The interactions between Mtb and macrophages .................................... 24 1.2.3.1. Mtb can avoid macrophage killing mechanisms ........................ 25 1.2.3.2. Recognition of Mtb through PRRs on innate immune cells ....... 26 1.2.3.2.1. Toll-like receptors .......................................................... 26 1.2.3.2.2. Dectin-1 ......................................................................... 28 1.2.3.2.3. DC-SIGN ....................................................................... 29 1.2.3.2.2. NOD2 ............................................................................. 29 1.2.4. Dendritic cells initiate the adaptive immune response to Mtb ................ 30 1.2.5. Apoptosis of innate immune cells promotes immunity to Mtb .............. 33 1.3. Factors involved in protection against Mtb infection ..................................... 34 1.3.1. CD4+ T cells ........................................................................................... 34 1.3.2. The different subsets of effector CD4+ T cells ....................................... 34 1.3.3. IFNγ and cell-mediated immunity is critical for protection against Mtb 35 1.3.3.1. IL-12 is required for control of Mtb infection in mice ............... 36 1.3.3.2. IFNγ activates macrophages to kill intracellular Mtb ................ 37 1.3.3.3. Requirement for the Th1 response in human disease ................. 39 1.3.4. The Th17 response in infection and vaccination .................................... 40 1.3.5. CD8+ T cells ........................................................................................... 41 1.3.6. IL-1α and IL-1β ...................................................................................... 42 5 Table of contents 1.3.7. TNFα in mouse and man ........................................................................ 43 1.3.8. The granuloma in Mtb infection ............................................................. 44 1.4. Factors that regulate the immune response to Mtb ......................................... 46 1.4.1. IL-10 and the suppression of the immune response to Mtb .................... 47 1.4.1.1. IL-10 background ....................................................................... 47 1.4.1.2. The regulation of IL-10 production in innate immune cells ...... 48 1.4.1.3. The role of IL-10 in infectious disease....................................... 49 1.4.1.4. The role of IL-10 in Mtb infection ............................................. 50 1.4.1.4.1. Results from the mouse model ...................................... 51 1.4.1.4.2. IL-10 in human TB ........................................................ 53 1.4.2. Suppression of the immune response to Mtb by Tregs ........................... 53 1.5. Tuberculosis and type I IFN ........................................................................... 54 1.5.1. Background ............................................................................................. 54 1.5.2. The Regulation of type I IFN production ............................................... 55 1.5.3. IFN signalling ........................................................................................
Load more

Recommended publications
  • SRC Antibody - N-Terminal Region (ARP32476 P050) Data Sheet
    SRC antibody - N-terminal region (ARP32476_P050) Data Sheet Product Number ARP32476_P050 Product Name SRC antibody - N-terminal region (ARP32476_P050) Size 50ug Gene Symbol SRC Alias Symbols ASV; SRC1; c-SRC; p60-Src Nucleotide Accession# NM_005417 Protein Size (# AA) 536 amino acids Molecular Weight 60kDa Product Format Lyophilized powder NCBI Gene Id 6714 Host Rabbit Clonality Polyclonal Official Gene Full Name V-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) Gene Family SH2D This is a rabbit polyclonal antibody against SRC. It was validated on Western Blot by Aviva Systems Biology. At Aviva Systems Biology we manufacture rabbit polyclonal antibodies on a large scale (200-1000 Description products/month) of high throughput manner. Our antibodies are peptide based and protein family oriented. We usually provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: QTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGG This gene is highly similar to the v-src gene of Rous sarcoma virus. This proto-oncogene may play a role in the Description of Target regulation of embryonic development and cell growth. SRC protein is a tyrosine-protein kinase whose activity can be inhibited by phosphorylation by c-SRC kinase. Mutations in this gene could be involved in the
    [Show full text]
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. 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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis 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 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. 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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
    [Show full text]
  • Targeted Genes and Methodology Details for Neuromuscular Genetic Panels
    Targeted Genes and Methodology Details for Neuromuscular Genetic Panels Reference transcripts based on build GRCh37 (hg19) interrogated by Neuromuscular Genetic Panels Next-generation sequencing (NGS) and/or Sanger sequencing is performed Motor Neuron Disease Panel to test for the presence of a mutation in these genes. Gene GenBank Accession Number Regions of homology, high GC-rich content, and repetitive sequences may ALS2 NM_020919 not provide accurate sequence. Therefore, all reported alterations detected ANG NM_001145 by NGS are confirmed by an independent reference method based on laboratory developed criteria. However, this does not rule out the possibility CHMP2B NM_014043 of a false-negative result in these regions. ERBB4 NM_005235 Sanger sequencing is used to confirm alterations detected by NGS when FIG4 NM_014845 appropriate.(Unpublished Mayo method) FUS NM_004960 HNRNPA1 NM_031157 OPTN NM_021980 PFN1 NM_005022 SETX NM_015046 SIGMAR1 NM_005866 SOD1 NM_000454 SQSTM1 NM_003900 TARDBP NM_007375 UBQLN2 NM_013444 VAPB NM_004738 VCP NM_007126 ©2018 Mayo Foundation for Medical Education and Research Page 1 of 14 MC4091-83rev1018 Muscular Dystrophy Panel Muscular Dystrophy Panel Gene GenBank Accession Number Gene GenBank Accession Number ACTA1 NM_001100 LMNA NM_170707 ANO5 NM_213599 LPIN1 NM_145693 B3GALNT2 NM_152490 MATR3 NM_199189 B4GAT1 NM_006876 MYH2 NM_017534 BAG3 NM_004281 MYH7 NM_000257 BIN1 NM_139343 MYOT NM_006790 BVES NM_007073 NEB NM_004543 CAPN3 NM_000070 PLEC NM_000445 CAV3 NM_033337 POMGNT1 NM_017739 CAVIN1 NM_012232 POMGNT2
    [Show full text]
  • Wo 2010/075007 A2
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date 1 July 2010 (01.07.2010) WO 2010/075007 A2 (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every C12Q 1/68 (2006.01) G06F 19/00 (2006.01) kind of national protection available): AE, AG, AL, AM, C12N 15/12 (2006.01) AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, (21) International Application Number: DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, PCT/US2009/067757 HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (22) International Filing Date: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, 11 December 2009 ( 11.12.2009) ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PE, PG, PH, PL, PT, RO, RS, RU, SC, SD, (25) Filing Language: English SE, SG, SK, SL, SM, ST, SV, SY, TJ, TM, TN, TR, TT, (26) Publication Language: English TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (30) Priority Data: (84) Designated States (unless otherwise indicated, for every 12/3 16,877 16 December 2008 (16.12.2008) US kind of regional protection available): ARIPO (BW, GH, GM, KE, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, ZM, (71) Applicant (for all designated States except US): DODDS, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, W., Jean [US/US]; 938 Stanford Street, Santa Monica, TM), European (AT, BE, BG, CH, CY, CZ, DE, DK, EE, CA 90403 (US).
    [Show full text]
  • Blueprint Genetics ENO3 Single Gene Test
    ENO3 single gene test Test code: S00654 Phenotype information Glycogen storage disease Panels that include the ENO3 gene Glycogen Storage Disorder Panel Comprehensive Metabolism Panel Hypoglycemia, Hyperinsulinism and Ketone Metabolism Panel Metabolic Myopathy and Rhabdomyolysis Panel Test Strengths The strengths of this test include: CAP accredited laboratory CLIA-certified personnel performing clinical testing in a CLIA-certified laboratory Powerful sequencing technologies, advanced target enrichment methods and precision bioinformatics pipelines ensure superior analytical performance Careful construction of clinically effective and scientifically justified gene panels Our Nucleus online portal providing transparent and easy access to quality and performance data at the patient level Our publicly available analytic validation demonstrating complete details of test performance ~2,000 non-coding disease causing variants in our clinical grade NGS assay for panels (please see ‘Non-coding disease causing variants covered by this test’) Our rigorous variant classification scheme Our systematic clinical interpretation workflow using proprietary software enabling accurate and traceable processing of NGS data Our comprehensive clinical statements Test Limitations This test does not detect the following: Complex inversions Gene conversions Balanced translocations Mitochondrial DNA variants Repeat expansion disorders unless specifically mentioned Non-coding variants deeper than ±20 base pairs from exon-intron boundary unless otherwise indicated (please see above non-coding variants covered by the test). This test may not reliably detect the following: Low level mosaicism (variant with a minor allele fraction of 14.6% is detected with 90% probability) Stretches of mononucleotide repeats Indels larger than 50bp Single exon deletions or duplications Variants within pseudogene regions/duplicated segments The sensitivity of this test may be reduced if DNA is extracted by a laboratory other than Blueprint Genetics.
    [Show full text]
  • Implications in Parkinson's Disease
    Journal of Clinical Medicine Review Lysosomal Ceramide Metabolism Disorders: Implications in Parkinson’s Disease Silvia Paciotti 1,2 , Elisabetta Albi 3 , Lucilla Parnetti 1 and Tommaso Beccari 3,* 1 Laboratory of Clinical Neurochemistry, Department of Medicine, University of Perugia, Sant’Andrea delle Fratte, 06132 Perugia, Italy; [email protected] (S.P.); [email protected] (L.P.) 2 Section of Physiology and Biochemistry, Department of Experimental Medicine, University of Perugia, Sant’Andrea delle Fratte, 06132 Perugia, Italy 3 Department of Pharmaceutical Sciences, University of Perugia, Via Fabretti, 06123 Perugia, Italy; [email protected] * Correspondence: [email protected] Received: 29 January 2020; Accepted: 20 February 2020; Published: 21 February 2020 Abstract: Ceramides are a family of bioactive lipids belonging to the class of sphingolipids. Sphingolipidoses are a group of inherited genetic diseases characterized by the unmetabolized sphingolipids and the consequent reduction of ceramide pool in lysosomes. Sphingolipidoses include several disorders as Sandhoff disease, Fabry disease, Gaucher disease, metachromatic leukodystrophy, Krabbe disease, Niemann Pick disease, Farber disease, and GM2 gangliosidosis. In sphingolipidosis, lysosomal lipid storage occurs in both the central nervous system and visceral tissues, and central nervous system pathology is a common hallmark for all of them. Parkinson’s disease, the most common neurodegenerative movement disorder, is characterized by the accumulation and aggregation of misfolded α-synuclein that seem associated to some lysosomal disorders, in particular Gaucher disease. This review provides evidence into the role of ceramide metabolism in the pathophysiology of lysosomes, highlighting the more recent findings on its involvement in Parkinson’s disease. Keywords: ceramide metabolism; Parkinson’s disease; α-synuclein; GBA; GLA; HEX A-B; GALC; ASAH1; SMPD1; ARSA * Correspondence [email protected] 1.
    [Show full text]
  • Splicing Regulatory Factors in Breast Cancer Hallmarks and Disease Progression
    www.oncotarget.com Oncotarget, 2019, Vol. 10, (No. 57), pp: 6021-6037 Review Splicing regulatory factors in breast cancer hallmarks and disease progression Esmee Koedoot1, Liesanne Wolters1, Bob van de Water1 and Sylvia E. Le Dévédec1 1Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, The Netherlands Correspondence to: Sylvia E. Le Dévédec, email: [email protected] Keywords: hallmarks of cancer; breast cancer; alternative splicing; splice factors; RNA sequencing Received: April 23, 2019 Accepted: August 29, 2019 Published: October 15, 2019 Copyright: Koedoot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT By regulating transcript isoform expression levels, alternative splicing provides an additional layer of protein control. Recent studies show evidence that cancer cells use different splicing events to fulfill their requirements in order to develop, progress and metastasize. However, there has been less attention for the role of the complex catalyzing the complicated multistep splicing reaction: the spliceosome. The spliceosome consists of multiple sub-complexes in total comprising 244 proteins or splice factors and 5 associated RNA molecules. Here we discuss the role of splice factors in the oncogenic processes tumors cells need to fulfill their oncogenic properties (the so-called the hallmarks of cancer). Despite the fact that splice factors have been investigated only recently, they seem to play a prominent role in already five hallmarks of cancer: angiogenesis, resisting cell death, sustaining proliferation, deregulating cellular energetics and invasion and metastasis formation by affecting major signaling pathways such as epithelial-to-mesenchymal transition, the Warburg effect, DNA damage response and hormone receptor dependent proliferation.
    [Show full text]
  • 14321 NAE1/APPBP1 (D9I4Z) Rabbit Mab
    Revision 1 C 0 2 - t NAE1/APPBP1 (D9I4Z) Rabbit mAb a e r o t S Orders: 877-616-CELL (2355) [email protected] 1 Support: 877-678-TECH (8324) 2 3 Web: [email protected] 4 www.cellsignal.com 1 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source/Isotype: UniProt ID: Entrez-Gene Id: WB H M R Mk Endogenous 60 Rabbit IgG Q13564 8883 Product Usage Information 2. Huang, D.T. et al. (2005) Mol Cell 17, 341-50. 3. Liakopoulos, D. et al. (1998) EMBO J 17, 2208-14. Application Dilution 4. Gong, L. and Yeh, E.T. (1999) J Biol Chem 274, 12036-42. 5. Wada, H. et al. (2000) J Biol Chem 275, 17008-15. Western Blotting 1:1000 6. Sakata, E. et al. (2007) Nat Struct Mol Biol 14, 167-8. 7. Kawakami, T. et al. (2001) EMBO J 20, 4003-12. Storage 8. Podust, V.N. et al. (2000) Proc Natl Acad Sci USA 97, 4579-84. 9. Wu, K. et al. (2002) J Biol Chem 277, 516-27. Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA, 50% 10. Amir, R.E. et al. (2002) J Biol Chem 277, 23253-9. glycerol and less than 0.02% sodium azide. Store at –20°C. Do not aliquot the antibody. 11. Herrmann, J. et al. (2007) Circ Res 100, 1276-91. 12. Walden, H. et al. (2003) Mol Cell 12, 1427-37.
    [Show full text]
  • Is APP Key to the Appbp1 Pathway?
    Open Access Full Text Article Austin Alzheimer’s and Parkinson’s Disease A Austin Publishing Group Review Article Cycle on Wheels: Is APP Key to the AppBp1 Pathway? Chen Y1,2*, Neve RN4, Zheng H3, Griffin WST1,2, Barger SW1,2 and Mrak RE5 Abstract 1Department of Geriatrics, University of Arkansas for Alzheimer’s disease (AD) is the gradual loss of the cognitive function due Medical Sciences, USA to neuronal death. Currently no therapy is available to slow down, reverse 2Department of Neurobiology and Developmental or prevent the disease. Here we analyze the existing data in literature and Sciences, University of Arkansas for Medical Sciences, hypothesize that the physiological function of the Amyloid Precursor Protein USA (APP) is activating the AppBp1 pathway and this function is gradually lost during 3Huffington Center on Aging, Baylor College of Medicine, the progression of AD pathogenesis. The AppBp1 pathway, also known as the USA neddylation pathway, activates the small ubiquitin-like protein nedd8, which 4Department of Brain and Cognitive Sciences, covalently modifies and switches on Cullin ubiquitin ligases, which are essential Massachusetts Institute of Technology, USA in the turnover of cell cycle proteins. Here we discuss how APP may activate the 5Department of Pathology, University of Toledo Health AppBp1 pathway, which downregulates cell cycle markers and protects genome Sciences Campus, USA integrity. More investigation of this mechanism-driven hypothesis may provide *Corresponding author: Chen Y, Department insights into disease treatment and prevention strategies. of Geriatrics and Department of Neurobiology and Keywords: APP; Alzheimer’s disease; Ubiquitination; Neddylation; Cell Developmental Sciences, University of Arkansas for cycle Medical Sciences, Little Rock, AR 72205, USA Received: September 02, 2014; Accepted: September 29, 2014; Published: September 30, 2014 Abbreviations at least 18 proteins have been identified to bind AICD [25-27].
    [Show full text]
  • 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.
    [Show full text]
  • Plant Mitogen-Activated Protein Kinase Signaling Cascades Guillaume Tena*, Tsuneaki Asai†, Wan-Ling Chiu‡ and Jen Sheen§
    392 Plant mitogen-activated protein kinase signaling cascades Guillaume Tena*, Tsuneaki Asai†, Wan-Ling Chiu‡ and Jen Sheen§ Mitogen-activated protein kinase (MAPK) cascades have components that link sensors/receptors to target genes emerged as a universal signal transduction mechanism that and other cellular responses. connects diverse receptors/sensors to cellular and nuclear responses in eukaryotes. Recent studies in plants indicate that In the past few years, it has become apparent that mitogen- MAPK cascades are vital to fundamental physiological functions activated protein kinase (MAPK) cascades play some of the involved in hormonal responses, cell cycle regulation, abiotic most essential roles in plant signal transduction pathways stress signaling, and defense mechanisms. New findings have from cell division to cell death (Figure 1). MAPK cascades revealed the complexity and redundancy of the signaling are evolutionarily conserved signaling modules with essen- components, the antagonistic nature of distinct pathways, and tial regulatory functions in eukaryotes, including yeasts, the use of both positive and negative regulatory mechanisms. worms, flies, frogs, mammals and plants. The recent enthu- siasm for plant MAPK cascades is backed by numerous Addresses studies showing that plant MAPKs are activated by hor- Department of Molecular Biology, Massachusetts General Hospital, mones, abiotic stresses, pathogens and pathogen-derived Department of Genetics, Harvard Medical School, Wellman 11, elicitors, and are also activated at specific stages during the 50 Blossom Street, Boston, Massachusetts 02114, USA cell cycle [2]. Until recently, studies of MAPK cascades in *e-mail: [email protected] †e-mail: [email protected] plants were focused on cDNA cloning [3,4] and used a ‡e-mail: [email protected] MAPK in-gel assay, MAPK and tyrosine-phosphate anti- §e-mail: [email protected] bodies, and kinase inhibitors to connect signals to MAPKs Current Opinion in Plant Biology 2001, 4:392–400 [2].
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]