DOCSLIB.ORG

Functional Characterization of B-Cell Receptor Associated Protein 31 in Cancer Cells

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Functional Characterization of B-Cell Receptor Associated Protein 31 in Cancer Cells Dissertation der Fakultät für Biologie der Ludwig‐Maximilians‐Universität München Functional characterization of B-cell receptor associated protein 31 in cancer cells Olga Nagło (geb. Chojnacka) München, 2019 1 Diese Dissertation wurde angefertigt unter der Leitung von Prof. Dr. Angelika Vollmar im Bereich der Pharmazeutischen Biologie an der Ludwig‐Maximilians‐Universität München Erstgutachterin: Prof. Dr. Barbara Conradt Zweitgutachterin: Prof. Dr. Angelika Vollmar Tag der Abgabe: 06.06.2019 Tag der mündlichen Prüfung: 13.08.2019 ERKLÄRUNG Ich versichere hiermit an Eides statt, dass meine Dissertation selbständig und ohne unerlaubte Hilfsmittel angefertigt worden ist. Die vorliegende Dissertation wurde weder ganz, noch teilweise bei einer anderen Prüfungskommission vorgelegt. Ich habe noch zu keinem früheren Zeitpunkt versucht, eine Dissertation einzureichen oder an einer Doktorprüfung teilzunehmen. München, den 06.06.2019 __________________________ Olga Nagło 2 1. TABLE OF CONTENTS 1. TABLE OF CONTENTS ................................................................................................... 3 2. INTRODUCTION ............................................................................................................. 7 2.1 CANCER INCIDENCE AND MORTALITY IN 2018 ............................................................ 7 2.2 HALLMARKS OF CANCER ............................................................................................. 7 2.3 RESISTANCE TO THE COMMON CHEMOTHERAPEUTICS AS A SERIOUS ISSUE ................. 8 2.4 RE-SENSITIZATION OF CANCEROUS CELLS TOWARDS TREATMENT THROUGH INDUCTION OF ER-STRESS ..................................................................................................... 10 2.5 SMALL MOLECULES – T8 AND PS89 .......................................................................... 10 2.6 FUNCTION OF B CELL RECEPTOR - ASSOCIATED PROTEIN (BAP31) PROTEIN ............. 12 3. MATERIALS AND METHODS ..................................................................................... 15 3.1 MATERIALS ............................................................................................................... 15 3.1.1 Cells ..................................................................................................................... 15 3.1.2 Compounds .......................................................................................................... 15 3.1.3 Chemicals and Reagents ...................................................................................... 16 3.1.4 Primary antibodies ............................................................................................... 20 3.1.5 Secondary antibodies ........................................................................................... 21 3.1.6 Technical equipment ............................................................................................ 22 3.1.7 Software ............................................................................................................... 23 3.2 CELL CULTURE .......................................................................................................... 24 3.2.1 Maintenance of cell lines ..................................................................................... 24 3.2.2 Freezing and thawing process .............................................................................. 25 3.2.3 Isolation of Peripheral Blood Mononuclear Cells (PBMCs) ............................... 25 3.2.4 Generation and isolation of patient derived xenograft cells (PDX) ..................... 26 3.2.5 Stimulation with compounds ............................................................................... 26 3.3 PROLIFERATION AND ATTACHMENT ASSAYS ............................................................. 26 3.3.1 Cell-Titer Blue® viability assay .......................................................................... 26 3.3.2 ViCell – counting of the cell number ................................................................... 27 3.3.3 xCELLigence – the real-time analysis of the cellular growth and attachment .... 27 3 3.3.4 Attachment analysis – crystal violet staining ....................................................... 27 3.3.5 Colony formation assay ....................................................................................... 27 3.4 IMMUNOBLOTTING .................................................................................................... 28 3.4.1 Preparation of samples ......................................................................................... 28 3.4.2 Preparation of samples for the cytosol/mitochondrial fractionation .................... 28 3.4.3 Preparation of samples for the cytosol/nuclear fractionation. ............................. 29 3.4.4 Determination of protein concentration ............................................................... 30 3.4.5 Sodium dodecyl sulfate – polyacrylamide gel electrophoresis (SDS-PAGE) ..... 31 3.4.6 Blotting process ................................................................................................... 32 3.4.7 Protein detection .................................................................................................. 33 3.5 FLOW CYTOMETRY .................................................................................................... 33 3.5.1 Analysis of apoptosis ........................................................................................... 33 3.5.2 Measurement of cytosolic calcium level .............................................................. 34 3.5.3 Measurement of Reactive Oxygen Species (ROS) level ..................................... 35 3.5.4 Identification of apoptosis in the CD34+ cells .................................................... 36 3.6 TRANSIENT TRANSFECTION OF CELLS ........................................................................ 36 3.6.1 Transfection with siRNA ..................................................................................... 36 3.6.2 Transfection with plasmid.................................................................................... 37 3.7 GENOME EDITING USING THE CRISPR-CAS9 TECHNIQUE ......................................... 37 3.7.1 Design of targeting components. ......................................................................... 37 3.7.2 Cloning of oligos into Cas9 plasmid .................................................................... 39 3.7.3 Transformation of plasmids into E. Coli.............................................................. 41 3.7.4 Determining Genome Targeting Efficiency using T7 Endonuclease I ................ 41 3.7.5 Transfection and selection of clones. ................................................................... 42 3.7.6 2.8. The polymerase chain reaction (PCR) .......................................................... 42 3.8 MIGRATION OF CANCER CELLS .................................................................................. 43 3.8.1 The Boyden Chambers ......................................................................................... 43 3.8.2 Wound healing assay ........................................................................................... 43 3.9 LUCIFERASE DOUBLE REPORTER GENE ASSAY - SPLICING ....................................... 43 3.10 IMMUNOSTAINING AND CONFOCAL MICROSCOPY ...................................................... 44 3.11 RECYCLING OF THE TRANSFERRIN RECEPTOR IN CANCER CELLS. .............................. 44 3.12 FORMATION OF LIPOSOMES CONTAINING PHOSPHATIDYLINOSITOL (PI) MIX ............. 45 3.13 RESCUE OF THE SORAPHEN A TREATMENT EFFECT ................................................... 45 4 3.14 IDENTIFICATION OF BAP31 INTERACTION PARTNERS APPLYING YEAST 2 HYBRID APPROACH ............................................................................................................................. 46 4. AIMS OF THE STUDY .................................................................................................. 47 5. RESULTS ........................................................................................................................ 48 5.1 INVOLVEMENT OF BAP31 IN THE CHEMO-SENSITIZATION OF VARIOUS LEUKEMIA TYPES TOWARDS COMMONLY USED CHEMOTHERAPEUTICS ................................................... 48 5.1.1 Combination of various cytostatics with PS89 enhances their anti-cancer properties in different leukemia types but not in hematopoietic cells. ............................ 48 5.1.2 Caspase-8 and BAP31 activation constitutes a platform for the induction of apoptosis in leukemia cells upon the treatment with the combination of PS89 and cytostatic. ......................................................................................................................... 52 5.1.3 The pro-apoptotic cross-talk between Endoplasmic Reticulum (ER) and mitochondria. ................................................................................................................... 54 5.1.4 The cytochrome c release induces an activation of the death protease. ............... 57 5.1.5 The summary of the effect of BAP31 activation on the chemo-sensitization of leukemic cells towards cytostatic treatment. ................................................................... 58 5.2 THE FUNCTION OF BAP31 IN HEPATOCELLULAR CARCINOMA................................... 59 5.2.1 Generation of the BAP31 deficient cell line. ......................................................
Load more

Recommended publications
  • Behavioural Brain Research 217 (2011) 271–281
    Behavioural Brain Research 217 (2011) 271–281 Contents lists available at ScienceDirect Behavioural Brain Research journal homepage: www.elsevier.com/locate/bbr Research report The telomeric part of the human chromosome 21 from Cstb to Prmt2 is not necessary for the locomotor and short-term memory deficits observed in the Tc1 mouse model of Down syndrome Arnaud Duchon a, Stéphanie Pothion b, Véronique Brault a, Andrew J. Sharp c, Victor L.J. Tybulewicz d, Elizabeth M.C. Fisher e, Yann Herault a,b,f,∗ a Institut de Génétique Biologie Moléculaire et Cellulaire, Translational Medicine and Neuroscience Program, IGBMC, CNRS, INSERM, Université de Strasbourg, UMR7104, UMR964, 1 rue Laurent Fries, 67404 Illkirch, France b Transgenese et Archivage Animaux Modèles, TAAM, CNRS, UPS44, 3B rue de la Férollerie 45071 Orléans, France c Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, Room 14-75B, Box 1498, New York, NY 10029, USA d MRC National Institute for Medical Research, Mill Hill, London NW7 1AA, UK e UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK f Institut Clinique de la Souris, ICS, 1 rue Laurent Fries, 67404 Illkirch, France article info abstract Article history: Trisomy 21 or Down syndrome (DS) is the most common form of human aneuploid disorder. Increase Received 5 September 2010 in the copy number of human chromosome 21 genes leads to several alterations including mental retar- Received in revised form 6 October 2010 dation, heart and skeletal dysmorphologies with additional physiological defects. To better understand Accepted 17 October 2010 the genotype and phenotype relationships, several mouse models have been developed, including the Available online 31 October 2010 transchromosomic Tc1 mouse, which carries an almost complete human chromosome 21, that displays several locomotor and cognitive alterations related to DS.
    [Show full text]
  • In Silico Prediction of High-Resolution Hi-C Interaction Matrices
    ARTICLE https://doi.org/10.1038/s41467-019-13423-8 OPEN In silico prediction of high-resolution Hi-C interaction matrices Shilu Zhang1, Deborah Chasman 1, Sara Knaack1 & Sushmita Roy1,2* The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to 1234567890():,; experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computa- tional prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg pre- dictions identify topologically associating domains and significant interactions that are enri- ched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome. 1 Wisconsin Institute for Discovery, 330 North Orchard Street, Madison, WI 53715, USA. 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA. *email: [email protected] NATURE COMMUNICATIONS | (2019) 10:5449 | https://doi.org/10.1038/s41467-019-13423-8 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13423-8 he three-dimensional (3D) organization of the genome has Results Temerged as an important component of the gene regulation HiC-Reg for predicting contact count using Random Forests.
    [Show full text]
  • An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Netwo
    Washington University School of Medicine Digital Commons@Becker Open Access Publications 2014 An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis Sriram Devanathan Washington University School of Medicine in St. Louis Timothy Whitehead Washington University School of Medicine in St. Louis George G. Schweitzer Washington University School of Medicine in St. Louis Nicole Fettig Washington University School of Medicine in St. Louis Attila Kovacs Washington University School of Medicine in St. Louis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Recommended Citation Devanathan, Sriram; Whitehead, Timothy; Schweitzer, George G.; Fettig, Nicole; Kovacs, Attila; Korach, Kenneth S.; Finck, Brian N.; and Shoghi, Kooresh I., ,"An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis." PLoS One.9,7. e101900. (2014). https://digitalcommons.wustl.edu/open_access_pubs/3326 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Sriram Devanathan, Timothy Whitehead, George G. Schweitzer, Nicole Fettig, Attila Kovacs, Kenneth S. Korach, Brian N. Finck, and Kooresh I. Shoghi This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/3326 An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Network Analysis Sriram Devanathan1, Timothy Whitehead1, George G. Schweitzer2, Nicole Fettig1, Attila Kovacs3, Kenneth S.
    [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]
  • Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
    Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen,
    [Show full text]
  • A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L
    www.nature.com/scientificreports OPEN A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L. Patel-Murray1, Miriam Adam2, Nhan Huynh2, Brook T. Wassie2, Pamela Milani2 & Ernest Fraenkel 2,3* High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with benefcial efects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these efects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specifc assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on efectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that afect a specifc target or phenotype1–3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug can- didates failed to earn FDA approval, with toxicity or a lack of efcacy being common reasons for their clinical trial termination4,5.
    [Show full text]
  • Downloaded from [266]
    Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes.
    [Show full text]
  • The Role of Arginine Methylation of Hnrnpul1 in the DNA Damage Response Pathway Gayathri Gurunathan
    The role of arginine methylation of hnRNPUL1 in the DNA damage response pathway Gayathri Gurunathan Faculty of Medicine Division of Experimental Medicine McGill University, Montreal, Quebec, Canada August 2014 A Thesis Submitted to McGill University in Partial Fulfillment of the Requirements for the Degree of Master of Science © Gayathri Gurunathan 2014 Abstract Post-translational modifications play a key role in mediating the DNA damage response (DDR). It is well-known that serine/threonine phosphorylation is a major post-translational modification required for the amplification of the DDR; however, less is known about the role of other modifications, such as arginine methylation. It is known that arginine methylation of the DDR protein, MRE11, by protein arginine methyltransferase 1 (PRMT1) is essential for the response, as the absence of methylation of MRE11 in mice leads to hypersensitivity to DNA damage agents. Herein, we identify hnRNPUL1 as a substrate of PRMT1 and the methylation of hnRNPUL1 is required for DNA damage signalling. I show that several RGG/RG sequences of hnRNPUL1 are methylated in vitro by PRMT1. Recombinant glutathione S-transferase (GST) proteins harboring hnRNPUL1 RGRGRG, RGGRGG and a single RGG were efficient in vitro substrates of PRMT1. Moreover, I performed mass spectrometry analysis of Flag-hnRNPUL1 and identified the same sites methylated in vivo. PRMT1-depletion using RNA interference led to the hypomethylation of hnRNPUL1, consistent with PRMT1 being the only enzyme in vivo to methylate these sequences. We replaced the arginines with lysine in hnRNPUL1 (Flag- hnRNPUL1RK) such that this mutant protein cannot be methylated by PRMT1. Indeed Flag- hnRNPUL1RK was undetected using specific dimethylarginine antibodies.
    [Show full text]
  • Accepted By:- Signature Redacted Department of Biology Graduate Co-Chair: Michael T
    EXPLORING THE FUNCTIONAL CONSERVATION OF MUSCLEBLIND (MBL) PROTEINS by MASSACH USETTS INSTITUTE Julia C. Oddo d o ECHNOLOGY B.S. Molecular, Cellular, and Developmental Biology, SE 1 7 2015 Minor Biochemistry 2011 LIB RARIES SUBMITTED TO THE DEPARTMENT OF BIOLOGY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE IN BIOLOGY AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY SEPTEMBER 2015 C 2015 Massachusetts Institute of Technology. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Signature of Auth or:_ Department of Biology May 22, 2015 Certified by: Signature redacte Thesis Supervisor: Chris B. Burge Professor of Biology and Biological Engineering Director Program CSB Certified by: Signature redactec Thesis Co-Supervisor: Eric T. Wang Research Fellow Accepted by:- Signature redacted Department of Biology Graduate Co-Chair: Michael T. Hemann Associate Professor of Biology 1 EXPLORING THE FUNCTIONAL CONSERVATION OF MUSCLEBLIND (MBL) PROTEINS by Julia C. Oddo Submitted to the Department of Biology on September 8th, 2015 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biology ABSTRACT Muscleblind (Mbl) is an evolutionarily conserved family of proteins involved in many aspects of RNA metabolism, including alternative splicing. Disruption of Muscleblind in several animals lends to a variety of defects and disease, including the multi-systemic disorder Myotonic Dystrophy (DM). Though much is known about the involvement of Muscleblind in DM, there is much basic knowledge of the protein's function to be discovered.
    [Show full text]
  • Epigenetic Arginine Methylation in Breast Cancer: Emerging Therapeutic Strategies
    62 3 Journal of Molecular S-C M Wang et al. Epigenetic PRMT signalling in 62:3 R223–R237 Endocrinology breast cancer REVIEW Epigenetic arginine methylation in breast cancer: emerging therapeutic strategies Shu-Ching M Wang, Dennis H Dowhan and George E O Muscat Cell Biology and Molecular Medicine Division, The University of Queensland, Institute for Molecular Bioscience, St Lucia, Australia Correspondence should be addressed to S-C M Wang or G E O Muscat: [email protected] or [email protected] Abstract Breast cancer is a heterogeneous disease, and the complexity of breast carcinogenesis Key Words is associated with epigenetic modification. There are several major classes of epigenetic f breast cancer enzymes that regulate chromatin activity. This review will focus on the nine mammalian f epigenetic signalling protein arginine methyltransferases (PRMTs) and the dysregulation of PRMT expression f protein arginine and function in breast cancer. This class of enzymes catalyse the mono- and (symmetric methyltransferases and asymmetric) di-methylation of arginine residues on histone and non-histone target f arginine methylation proteins. PRMT signalling (and R methylation) drives cellular proliferation, cell invasion and metastasis, targeting (i) nuclear hormone receptor signalling, (ii) tumour suppressors, (iii) TGF-β and EMT signalling and (iv) alternative splicing and DNA/chromatin stability, influencing the clinical and survival outcomes in breast cancer. Emerging reports suggest that PRMTs are also implicated in the development of drug/endocrine resistance providing another prospective avenue for the treatment of hormone resistance and associated metastasis. The complexity of PRMT signalling is further underscored by the degree of alternative splicing and the scope of variant isoforms (with distinct properties) within each PRMT family member.
    [Show full text]
  • Mapping of Diabetes Susceptibility Loci in a Domestic Cat Breed with an Unusually High Incidence of Diabetes Mellitus
    G C A T T A C G G C A T genes Article Mapping of Diabetes Susceptibility Loci in a Domestic Cat Breed with an Unusually High Incidence of Diabetes Mellitus 1,2, 3, 4 5 Lois Balmer y , Caroline Ann O’Leary y, Marilyn Menotti-Raymond , Victor David , Stephen O’Brien 6,7, Belinda Penglis 8, Sher Hendrickson 9, Mia Reeves-Johnson 3, 3 10 3 3,11, Susan Gottlieb , Linda Fleeman , Dianne Vankan , Jacquie Rand z and 1, , Grant Morahan * z 1 Centre for Diabetes Research, Harry Perkins Institute for Medical Research, University of Western Australia, Nedlands 6009, Australia; [email protected] 2 School of Medical and Health Sciences, Edith Cowan University, Joondalup, Perth 6027, Australia 3 School of Veterinary Science, the University of Queensland, Gottan 4343, Australia; [email protected] (C.A.O.); [email protected] (M.R.-J.); [email protected] (S.G.); [email protected] (D.V.); [email protected] (J.R.) 4 Laboratory of Genomic Diversity, Center for Cancer Research (FNLCR), Frederick, MD 21702, USA; [email protected] 5 Laboratory of Basic Research, Center for Cancer Research (FNLCR), National Cancer Institute, Frederick, MD 21702, USA; [email protected] 6 Laboratory of Genomics Diversity, Center for Computer Technologies, ITMO University, 197101 St. Petersburg, Russia; [email protected] 7 Guy Harvey Oceanographic Center, Halmos College of Arts and Sciences, Nova Southeastern University, Ft Lauderdale, FL 33004, USA 8 IDEXX Laboratories, East Brisbane 4169, Australia; [email protected] 9 Department of Biology, Shepherd University, Shepherdstown, WV 25443, USA; [email protected] 10 Animal Diabetes Australia, Melbourne 3155, Australia; l.fl[email protected] 11 American College of Veterinary Internal Medicine, University of Zurich, 8006 Zurich, Switzerland * Correspondence: [email protected] These authors contributed equally to this work.
    [Show full text]
  • Supplementary Materials
    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
    [Show full text]