Determining HDAC8 Substrate Specificity by Noah Ariel Wolfson A

Total Page:16

File Type:pdf, Size:1020Kb

Determining HDAC8 Substrate Specificity by Noah Ariel Wolfson A Determining HDAC8 substrate specificity by Noah Ariel Wolfson A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biological Chemistry) in the University of Michigan 2014 Doctoral Committee: Professor Carol A. Fierke, Chair Professor Robert S. Fuller Professor Anna K. Mapp Associate Professor Patrick J. O’Brien Associate Professor Raymond C. Trievel Dedication My thesis is dedicated to all my family, mentors, and friends who made getting to this point possible. ii Table of Contents Dedication ....................................................................................................................................... ii List of Figures .............................................................................................................................. viii List of Tables .................................................................................................................................. x List of Appendices ......................................................................................................................... xi Abstract ......................................................................................................................................... xii Chapter 1 HDAC8 substrates: Histones and beyond ...................................................................... 1 Overview ..................................................................................................................................... 1 HDAC introduction ..................................................................................................................... 2 Known HDAC8 substrates .......................................................................................................... 4 Candidate non-histone HDAC8 Substrates ................................................................................. 8 HDAC8 complex formation ...................................................................................................... 11 Enzyme structure affects substrate specificity .......................................................................... 15 Catalytic Mechanism and Regulation of HDAC8 activity ........................................................ 22 HDAC8 localization .................................................................................................................. 26 Post-translational modification of HDAC8 ............................................................................... 28 Concluding remarks .................................................................................................................. 32 Acknowledgements ................................................................................................................... 33 Bibliography .............................................................................................................................. 33 Chapter 2 An enzyme-coupled assay measuring acetate production for profiling histone deacetylase specificity .................................................................................................................. 45 Introduction ............................................................................................................................... 45 Materials and Methods .............................................................................................................. 47 Reagents................................................................................................................................. 47 Acetyl-CoA synthetase preparation ....................................................................................... 47 HDAC8 expression and purification ..................................................................................... 49 iii Fluor de Lys assay ................................................................................................................. 49 Acetate assay kit .................................................................................................................... 50 Optimized stopped coupled acetate assay ............................................................................. 50 Optimized continuous coupled acetate assay ........................................................................ 52 Fluorescamine assay .............................................................................................................. 52 Results ....................................................................................................................................... 53 Assay ..................................................................................................................................... 53 Optimization of Assay for Measuring HDAC Activity ......................................................... 56 Stopped Assay ....................................................................................................................... 58 Continuous Assay .................................................................................................................. 62 Reactivity of HDAC8 with peptides ...................................................................................... 66 Discussion ................................................................................................................................. 71 Acknowledgements ................................................................................................................... 76 Bibliography .............................................................................................................................. 76 Chapter 3 Profiling small peptide substrates to identify HDAC8 recognition motif(s) ............... 81 Introduction ............................................................................................................................... 81 Materials and Methods .............................................................................................................. 82 Reagents................................................................................................................................. 82 HDAC8 expression and assay ............................................................................................... 82 Improving the assay conditions ............................................................................................. 83 FlexPepBind protocol ............................................................................................................ 84 Modeling the structures of peptide-protein complexes ......................................................... 84 Step 1: Preparation of a coarse starting model of a peptide-HDAC8 complex ..................... 85 Step 2: Optimization of the peptide-HDAC8 complex structure .......................................... 86 Step 2(a) Extensive optimization using Rosetta FlexPepDock ............................................. 86 Step 2(b) Short minimization of starting structure ................................................................ 87 Parameters optimized for HDAC8 FlexPepBind ................................................................... 87 iv Definition of axes for rigid body movements ........................................................................ 89 Addition of an electrostatic term to default scoring function score12 .................................. 90 Step 3: Ranking of the peptide substrates based on the optimized peptide-receptor complex structures ................................................................................................................................ 90 Dataset 1: Training and test set: 361 GXKacZGC peptides .................................................. 91 Initial calibration set: 5 substrates & 5 non-substrates .......................................................... 92 Dataset 2: Validation set: 361 GRKacXZC peptides ............................................................ 92 Statistical tests ....................................................................................................................... 92 Binary distinction between substrates and non-substrates .................................................... 93 Prediction of relative substrate activity ................................................................................. 93 Results ....................................................................................................................................... 93 First round of calibration ....................................................................................................... 93 Analysis of performance in Training and Test sets ............................................................... 94 Searching for novel, non-histone substrates .......................................................................... 95 Experimental validation of de-novo predictions ................................................................... 96 Second round of calibration ................................................................................................. 100 Improved starting structure and score function ................................................................... 101 Second round of Phosphosite screen ................................................................................... 103 Assessing the second set of peptides ..................................................................................
Recommended publications
  • 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]
  • Ovulation-Selective Genes: the Generation and Characterization of an Ovulatory-Selective Cdna Library
    531 Ovulation-selective genes: the generation and characterization of an ovulatory-selective cDNA library A Hourvitz1,2*, E Gershon2*, J D Hennebold1, S Elizur2, E Maman2, C Brendle1, E Y Adashi1 and N Dekel2 1Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah 84132, USA 2Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel (Requests for offprints should be addressed to N Dekel; Email: [email protected]) *(A Hourvitz and E Gershon contributed equally to this paper) (J D Hennebold is now at Division of Reproductive Sciences, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, Oregon 97006, USA) Abstract Ovulation-selective/specific genes, that is, genes prefer- (FAE-1) homolog, found to be localized to the inner entially or exclusively expressed during the ovulatory periantral granulosa and to the cumulus granulosa cells of process, have been the subject of growing interest. We antral follicles. The FAE-1 gene is a -ketoacyl-CoA report herein studies on the use of suppression subtractive synthase belonging to the fatty acid elongase (ELO) hybridization (SSH) to construct a ‘forward’ ovulation- family, which catalyzes the initial step of very long-chain selective/specific cDNA library. In toto, 485 clones were fatty acid synthesis. All in all, the present study accom- sequenced and analyzed for homology to known genes plished systematic identification of those hormonally with the basic local alignment tool (BLAST). Of those, regulated genes that are expressed in the ovary in an 252 were determined to be nonredundant.
    [Show full text]
  • Mapping Influenza-Induced Posttranslational Modifications On
    viruses Article Mapping Influenza-Induced Posttranslational Modifications on Histones from CD8+ T Cells Svetlana Rezinciuc 1, Zhixin Tian 2, Si Wu 2, Shawna Hengel 2, Ljiljana Pasa-Tolic 2 and Heather S. Smallwood 1,3,* 1 Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA; [email protected] 2 Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA; [email protected] (Z.T.); [email protected] (S.W.); [email protected] (S.H.); [email protected] (L.P.-T.) 3 Children’s Foundation Research Institute, Memphis, TN 38105, USA * Correspondence: [email protected]; Tel.: +1-(901)-448–3068 Academic Editor: Italo Tempera Received: 10 October 2020; Accepted: 2 December 2020; Published: 8 December 2020 Abstract: T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) to histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis by MS can detect species missed by bottom-up approaches. We used a novel approach of online two-dimensional liquid chromatography-tandem MS with high-resolution reversed-phase liquid chromatography (RPLC), alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. The first online RPLC separation sorted histone families, then RPLC or weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative identifications were assigned by matching proteoform masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for histone-intact protein PTM mapping (HiPTMap) to identify and quantify proteoforms purified from CD8 T cells after in vivo influenza infection.
    [Show full text]
  • HDAC7 Sikens the Heart
    The black sheep of class IIa: HDAC7 SIKens the heart Joshua G. Travers, … , Tianjing Hu, Timothy A. McKinsey J Clin Invest. 2020;130(6):2811-2813. https://doi.org/10.1172/JCI137074. Commentary Class IIa histone deacetylases (HDACs) repress cardiomyocyte hypertrophy through association with the prohypertrophic transcription factor (TF) myocyte enhancer factor-2 (MEF2). The four class IIa HDACs — HDAC4, -5, -7, and -9 — are subject to signal-dependent phosphorylation by members of the Ca2+/calmodulin-dependent protein kinase (CaMK) group. In response to stress, HDAC4, HDAC5, and HDAC9 undergo phosphorylation-induced nuclear export in cardiomyocytes, freeing MEF2 to stimulate progrowth genes; it was generally assumed that HDAC7 is also antihypertrophic. However, in this issue of the JCI, Hsu and colleagues demonstrate that, in sharp contrast to the other class IIa HDACs, HDAC7 is constitutively localized to the cardiomyocyte cytoplasm, where it promotes cardiac hypertrophy. Phosphorylation of HDAC7 by the CaMK group member salt-inducible kinase 1 (SIK1) stabilized the deacetylase, leading to increased expression of c-Myc, which in turn stimulated a pathological gene program. These unexpected findings highlight the SIK1/HDAC7 signaling axis as a promising target for the treatment of cardiac hypertrophy and heart failure. Find the latest version: https://jci.me/137074/pdf The Journal of Clinical Investigation COMMENTARY The black sheep of class IIa: HDAC7 SIKens the heart Joshua G. Travers, Tianjing Hu, and Timothy A. McKinsey Division of Cardiology, Department of Medicine, and Consortium for Fibrosis Research & Translation, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. repress gene expression, block cardiac hypertrophy by associating with the myo- Class IIa histone deacetylases (HDACs) repress cardiomyocyte hypertrophy cyte enhancer factor-2 (MEF2) transcrip- through association with the prohypertrophic transcription factor (TF) tion factor (TF) (11).
    [Show full text]
  • Thiadiazole Derivatives As Anticancer Agents
    Pharmacological Reports (2020) 72:1079–1100 https://doi.org/10.1007/s43440-020-00154-7 REVIEW Thiadiazole derivatives as anticancer agents Monika Szeliga1 Received: 15 June 2020 / Revised: 13 August 2020 / Accepted: 20 August 2020 / Published online: 3 September 2020 © The Author(s) 2020 Abstract In spite of substantial progress made toward understanding cancer pathogenesis, this disease remains one of the leading causes of mortality. Thus, there is an urgent need to develop novel, more efective anticancer therapeutics. Thiadiazole ring is a versatile scafold widely studied in medicinal chemistry. Mesoionic character of this ring allows thiadiazole-containing compounds to cross cellular membrane and interact strongly with biological targets. Consequently, these compounds exert a broad spectrum of biological activities. This review presents the current state of knowledge on thiadiazole derivatives that demonstrate in vitro and/or in vivo efcacy across the cancer models with an emphasis on targets of action. The infuence of the substituent on the compounds’ activity is depicted. Furthermore, the results from clinical trials assessing thiadiazole- containing drugs in cancer patients are summarized. Keywords Thiadiazole derivatives · Cancer · Anticancer therapy · Clinical trials Introduction antiparasitic, anti-infammatory and anticancer activities [2]. Due to the mesoionic nature, thiadiazoles are able to According to the most recent data provided by the Interna- cross the cellular membranes. Their relatively good lipo- tional Agency for Research on Cancer (IARC), 18.1 mil- solubility is most likely attributed to the presence of the sul- lion new cases and 9.6 million cancer deaths were regis- phur atom [3]. The thiadiazole-containing drugs, including tered worldwide in 2018 [1].
    [Show full text]
  • Nitric Oxide Mediated Redox Regulation of Protein Homeostasis T Irmgard Tegeder
    Cellular Signalling 53 (2019) 348–356 Contents lists available at ScienceDirect Cellular Signalling journal homepage: www.elsevier.com/locate/cellsig Nitric oxide mediated redox regulation of protein homeostasis T Irmgard Tegeder Institute of Clinical Pharmacology, Goethe University Hospital Frankfurt, Theodor Stern Kai 7, 60590 Frankfurt, Germany ARTICLE INFO ABSTRACT Keywords: Nitric oxide is a versatile diffusible signaling molecule, whose biosynthesis by three NO synthases (NOS) is tightly regulated Nitric oxide at transcriptional and posttranslational levels, availability of co-factors, and calcium binding. Above normal levels of NO Chaperone have beneficial protective effects for example in the cardiovascular system, but also contribute to the pathophysiology inthe Ubiquitin context of inflammatory diseases, and to aging and neurodegeneration in the nervous system. The effect specificity relieson Aging the functional and spatial specificity of the NOS isoenzymes, and on the duality of two major signaling mechanisms (i) Proteostasis activation of soluble guanylycylase (sGC)-dependent cGMP production and (ii) direct S-nitrosylation of redox sensitive cy- Nitrosylation steines of susceptible proteins. The present review summarizes the functional implications of S-nitrosylation in the context of proteostasis, and focuses on two NO target proteins, heat shock cognate of 70 kDa (Hsc70/HSPA8) and the ubiquitin 2 ligase (UBE2D), because both are modified on functionally critical cysteines and are key regulators of chaperone mediated and assisted autophagy and proteasomal protein degradation. SNO modifications of these candidates are associated with protein accumulations and adoption of a senescent phenotype of neuronal cells suggesting that S-nitrosylations of protein homeo- static machineries contribute to aging phenomena. 1. Introduction PKG1, [15] leading to smooth muscle relaxation via regulation of intracellular calcium stores [16] and actin-myosin dynamics.
    [Show full text]
  • An Overview of the Role of Hdacs in Cancer Immunotherapy
    International Journal of Molecular Sciences Review Immunoepigenetics Combination Therapies: An Overview of the Role of HDACs in Cancer Immunotherapy Debarati Banik, Sara Moufarrij and Alejandro Villagra * Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, 800 22nd St NW, Suite 8880, Washington, DC 20052, USA; [email protected] (D.B.); [email protected] (S.M.) * Correspondence: [email protected]; Tel.: +(202)-994-9547 Received: 22 March 2019; Accepted: 28 April 2019; Published: 7 May 2019 Abstract: Long-standing efforts to identify the multifaceted roles of histone deacetylase inhibitors (HDACis) have positioned these agents as promising drug candidates in combatting cancer, autoimmune, neurodegenerative, and infectious diseases. The same has also encouraged the evaluation of multiple HDACi candidates in preclinical studies in cancer and other diseases as well as the FDA-approval towards clinical use for specific agents. In this review, we have discussed how the efficacy of immunotherapy can be leveraged by combining it with HDACis. We have also included a brief overview of the classification of HDACis as well as their various roles in physiological and pathophysiological scenarios to target key cellular processes promoting the initiation, establishment, and progression of cancer. Given the critical role of the tumor microenvironment (TME) towards the outcome of anticancer therapies, we have also discussed the effect of HDACis on different components of the TME. We then have gradually progressed into examples of specific pan-HDACis, class I HDACi, and selective HDACis that either have been incorporated into clinical trials or show promising preclinical effects for future consideration.
    [Show full text]
  • Table 2. Significant
    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
    [Show full text]
  • 35 Disorders of Purine and Pyrimidine Metabolism
    35 Disorders of Purine and Pyrimidine Metabolism Georges van den Berghe, M.- Françoise Vincent, Sandrine Marie 35.1 Inborn Errors of Purine Metabolism – 435 35.1.1 Phosphoribosyl Pyrophosphate Synthetase Superactivity – 435 35.1.2 Adenylosuccinase Deficiency – 436 35.1.3 AICA-Ribosiduria – 437 35.1.4 Muscle AMP Deaminase Deficiency – 437 35.1.5 Adenosine Deaminase Deficiency – 438 35.1.6 Adenosine Deaminase Superactivity – 439 35.1.7 Purine Nucleoside Phosphorylase Deficiency – 440 35.1.8 Xanthine Oxidase Deficiency – 440 35.1.9 Hypoxanthine-Guanine Phosphoribosyltransferase Deficiency – 441 35.1.10 Adenine Phosphoribosyltransferase Deficiency – 442 35.1.11 Deoxyguanosine Kinase Deficiency – 442 35.2 Inborn Errors of Pyrimidine Metabolism – 445 35.2.1 UMP Synthase Deficiency (Hereditary Orotic Aciduria) – 445 35.2.2 Dihydropyrimidine Dehydrogenase Deficiency – 445 35.2.3 Dihydropyrimidinase Deficiency – 446 35.2.4 Ureidopropionase Deficiency – 446 35.2.5 Pyrimidine 5’-Nucleotidase Deficiency – 446 35.2.6 Cytosolic 5’-Nucleotidase Superactivity – 447 35.2.7 Thymidine Phosphorylase Deficiency – 447 35.2.8 Thymidine Kinase Deficiency – 447 References – 447 434 Chapter 35 · Disorders of Purine and Pyrimidine Metabolism Purine Metabolism Purine nucleotides are essential cellular constituents 4 The catabolic pathway starts from GMP, IMP and which intervene in energy transfer, metabolic regula- AMP, and produces uric acid, a poorly soluble tion, and synthesis of DNA and RNA. Purine metabo- compound, which tends to crystallize once its lism can be divided into three pathways: plasma concentration surpasses 6.5–7 mg/dl (0.38– 4 The biosynthetic pathway, often termed de novo, 0.47 mmol/l). starts with the formation of phosphoribosyl pyro- 4 The salvage pathway utilizes the purine bases, gua- phosphate (PRPP) and leads to the synthesis of nine, hypoxanthine and adenine, which are pro- inosine monophosphate (IMP).
    [Show full text]
  • Supporting Online Material
    1 2 3 4 5 6 7 Supplementary Information for 8 9 Fractalkine-induced microglial vasoregulation occurs within the retina and is altered early in diabetic 10 retinopathy 11 12 *Samuel A. Mills, *Andrew I. Jobling, *Michael A. Dixon, Bang V. Bui, Kirstan A. Vessey, Joanna A. Phipps, 13 Ursula Greferath, Gene Venables, Vickie H.Y. Wong, Connie H.Y. Wong, Zheng He, Flora Hui, James C. 14 Young, Josh Tonc, Elena Ivanova, Botir T. Sagdullaev, Erica L. Fletcher 15 * Joint first authors 16 17 Corresponding author: 18 Prof. Erica L. Fletcher. Department of Anatomy & Neuroscience. The University of Melbourne, Grattan St, 19 Parkville 3010, Victoria, Australia. 20 Email: [email protected] ; Tel: +61-3-8344-3218; Fax: +61-3-9347-5219 21 22 This PDF file includes: 23 24 Supplementary text 25 Figures S1 to S10 26 Tables S1 to S7 27 Legends for Movies S1 to S2 28 SI References 29 30 Other supplementary materials for this manuscript include the following: 31 32 Movies S1 to S2 33 34 35 36 1 1 Supplementary Information Text 2 Materials and Methods 3 Microglial process movement on retinal vessels 4 Dark agouti rats were anaesthetized, injected intraperitoneally with rhodamine B (Sigma-Aldrich) to label blood 5 vessels and retinal explants established as described in the main text. Retinal microglia were labelled with Iba-1 6 and imaging performed on an inverted confocal microscope (Leica SP5). Baseline images were taken for 10 7 minutes, followed by the addition of PBS (10 minutes) and then either fractalkine or fractalkine + candesartan 8 (10 minutes) using concentrations outlined in the main text.
    [Show full text]
  • The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc Oncogenesis
    The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis By Yuting Sun This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy at the University of New South Wales Children’s Cancer Institute Australia for Medical Research School of Women’s and Children’s Health, Faculty of Medicine University of New South Wales Australia August 2014 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Sun First name: Yuting Other name/s: Abbreviation for degree as given in the University calendar: PhD School : School of·Women's and Children's Health Faculty: Faculty of Medicine Title: The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis. Abstract 350 words maximum: (PLEASE TYPE) N-Myc Induces neuroblastoma by regulating the expression of target genes and proteins, and N-Myc protein is degraded by Fbxw7 and NEDD4 and stabilized by Aurora A. The class lla histone deacetylase HDAC5 suppresses gene transcription, and blocks myoblast and leukaemia cell differentiation. While histone H3 lysine 4 (H3K4) trimethylation at target gene promoters is a pre-requisite for Myc· induced transcriptional activation, WDRS, as a histone H3K4 methyltransferase presenter, is required for H3K4 methylation and transcriptional activation mediated by a histone H3K4 methyltransferase complex. Here, I investigated the roles of HDAC5 and WDR5 in N-Myc overexpressing neuroblastoma. I have found that N-Myc upregulates HDAC5 protein expression, and that HDAC5 represses NEDD4 gene expression, increases Aurora A gene expression and consequently upregulates N-Myc protein expression in neuroblastoma cells.
    [Show full text]
  • Anticancer Effects of NSC‑631570 (Ukrain) in Head and Neck Cancer Cells: in Vitro Analysis of Growth, Invasion, Angiogenesis and Gene Expression
    282 ONCOLOGY REPORTS 43: 282-295, 2020 Anticancer effects of NSC‑631570 (Ukrain) in head and neck cancer cells: In vitro analysis of growth, invasion, angiogenesis and gene expression RUTH HERRMANN1, JOSEPH SKAF2, JEANETTE ROLLER1, CHRISTINE POLEDNIK1, ULRIKE HOLZGRABE2 and MARIANNE SCHMIDT1 1Department of Otorhinolaryngology, University of Würzburg, D-97080 Würzburg; 2Institute of Pharmacy and Food Chemistry, University of Würzburg, D-97074 Würzburg, Germany Received September 17, 2018; Accepted September 30, 2019 DOI: 10.3892/or.2019.7416 Abstract. NSC-631570 (Ukrain) is an aqueous extract of laminin). Microarray analysis revealed the downregulation of Chelidonium majus, a herbaceous perennial plant, one of two genes encoding key regulators, including EGFR, AKT2, JAK1, species in the genus Chelidonium, which has been demonstrated STAT3 and ß-catenin (CTNNB1), all of which are involved in to selectively kill tumor cells without affecting non-malignant cell proliferation, migration, angiogenesis, apoptosis as well as cells. In the present study, the components of NSC-631570 the radiation- and chemo-resistance of HNSCC. The strongest were examined by combined liquid chromatography/mass upregulation occurred for cytochrome P450 1A1 (CYP1A1) spectroscopy (LC-MS) and the effects of NSC-631570 on and 1B1 (CYP1B1), involved in the metabolism of xenobiotics. HNSCC cell lines, as well as primary cells, were analyzed Upregulation of CYP1A1 was at least partially caused by chel- with respect to growth, apoptosis, invasion, angiogenesis erythrine and allocryptopine, as shown by RT-qPCR in two and gene expression. LC-MS identified chelerythrine and HNSCC cell lines. In addition, NSC-631570 showed a high allocryptopine as the major alkaloids of the extract.
    [Show full text]