A Thesis Submitted in Partial Fulfilment of the Requirements of the University of Greenwich for the Degree of Doctor of Philosophy

Total Page:16

File Type:pdf, Size:1020Kb

A Thesis Submitted in Partial Fulfilment of the Requirements of the University of Greenwich for the Degree of Doctor of Philosophy Identification of exosomal proteins in primary human bronchial tracheal epithelial cell HBTE and the H358, THP1 and MCF7 cell lines MD Faruk Abdullah Saurav (BPharm, MSc) A thesis submitted in partial fulfilment of the requirements of the University of Greenwich for the Degree of Doctor of Philosophy. February 2018 Declaration I certify that the work contained in this thesis, or any part of it, has not been accepted in substance for any previous degree awarded to me, and is not concurrently being submitted for any degree other than that of Doctor of Philosophy being studied at the University of Greenwich. I also declare that, this work is the result of my own investigations, except where otherwise identified by references and that the contents are not the outcome of any form of research misconduct. Candidate: MD Faruk A Saurav Supervisor: Dr Azizur Rahman i Acknowledgement First of all I would like to thank almighty Allah for providing me to chase my dream. I would like to thank my Supervisor Dr Azizur Rahman for supporting me in every step even if when I was down for providing me enough opportunity to expand my project beyond my initial plan and allowing me to learn new techniques. I would also like to thank my second supervisor Dr. Lauren Pecorino for showing me the right way. I would also like to thank my colleagues who supported me by helping me throughout my project. It would also like to express gratitude to the entire technician team in the link lab, especially John and Cliff for supporting me all the time sometime even out of their way. The best support is the support from family. I want to thank my family members, each one of them. My father, my first hero, who supported me when I fell down, encourages me to get up again. My mother, who prays for me all the time; my brother and sister and as well as my in- laws who always pray for my success. Lastly, I want to thank my lovely wife, Tanzena Akter, without whom my study would not been finished. Her tiring effort to keep in my foot, continuous praying for my wellbeing and the support she gave me was the essential element of my path from beginning to the end. MD Faruk Abdullah Saurav ii Abstract Background: Early detection of cancer is of paramount importance for successful treatment. Unfortunately, this is currently complex and time consuming. New sources of biomarkers are needed to improve. Exosomes are nano sized extracellular vesicles released by almost all cells have gained much interest as a cancer biomarker source due to their ability to transfer genetic materials, stability and ease of availability. Aims: The aim of this study is to investigate the potentials of exosomes as a source of biomarkers of cancer in general and lung cancer in particular. To achieve this, exosomes from three difference cancer cell lines (lung cancer H358, leukaemia THP1 and breast cancer MCF7) and a primary lung cell HBTE were isolated and their protein contents were analysed to establish wheather cancer specific proteins are present. Methods and Materials: Exosomes were isolated and characterized by scanning (SEM) and transmission electron microscopy (TEM), dynamic light scattering (DLS) and Western Blot analysis. The exosome number and protein profile were analysed at different cellular growth stages using Exo-Elisa based on exosomal marker CD63 and mass spectrometry (MS) respectively. LC-MS based proteomic approach has been used to compare the proteomic profile of exosomes from three cancer cells. Finally, comparative proteomic study and gene expression analysis were carried out between exosomes from lung cancer cell (H358) and its counterpart normal cell (HBTE) Results and Discussions: Successful isolation of exosomes from H358, THP1 and MCF7 was confirmed based on their size distribution (ranging from 96.54±28.53nm to 128.06±17.74nm) and by the presence of exosomal markers (CD63, CD81, CD9 and Hsp70). The number of exosomes released/cell was shown to increase with time ranging from 14500 on day 1 to 18000 at day 15. Interestingly, MS analysis revealed that, alpha-2-macroglobulin (A2M) and pregnancy zone protein was present only in stationary phase indicative of the oxidative stress. Comparative proteomic study by LC-MS identified a total of 613 proteins commonly found across three cell lines. A large proportion of membranous proteins were identified including integrins, catenins, cadherins, and cathepsins. Most of which are involved in molecular signalling, cellular growth and transport, supporting the role of exosomes in cellular communication. Several adhesion molecules such as integrins, laminlins, catenins as well as cadherins and cathepsins were also identified as differentially expressed between different types of cancer derived exosomes. Proteomic profiling of HBTE cell derived exosomes revealed 1011 proteins which only partially overlapped with those identified in H358 exosomes. A total of 205 proteins were specific for the cancerous lung cell line derived exosomes. Of particular interest was the identification of CTNNB1, an adhesion molecule known to be present in several other lung cancers, making it an ideal candidate for biomarker for non-small cell lung cancer, bronchioalveloar carcinoma. Gene expression analysis revealed that this protein is expressed at cellular level in both cancerous and normal lung cells, albeit found in a significantly higher level in H358 (p≤0.05). Conclusion: This is the first report to show successful isolation and characterization of exosomes from H358 and HBTE. Comparative proteomic analysis of the newly isolated exosomes not only revealed that exosomes are a good source of lung cancer biomarkers but identified a potential candidate. CTNNB1 was selectively found in lung cancer cell derived exosomes but not in the counterpart normal cell (HBTE) and was selected for further investigation. iii Table of Contents Declaration i Acknowledgements ii Abstract iii Table of Contents iv List of Tables Xii List of Figures xiii List of Abbreviations xvi Chapter 1: ............................................................................................................ 1 Introduction: ........................................................................................................ 1 1.1 Overview of the project: .............................................................................................. 1 1.2 Cancer: ........................................................................................................................ 1 1.3 Cancer Discussed in this study: ................................................................................... 2 1.3.1 Lung Cancer: ........................................................................................................ 3 1.3.2 Breast Cancer: ...................................................................................................... 4 1.3.3 Leukaemia: ........................................................................................................... 5 1.4 Mechanism of Metastasis: ........................................................................................... 7 1.5 Exosome: ................................................................................................................... 10 1.5.1 Role of exosomes in metastasis: ........................................................................ 11 1.6 Composition of Exosome: ......................................................................................... 14 1.6.1 Lipid Composition: ............................................................................................ 14 1.6.2 Exosomal Protein Composition: ........................................................................ 15 1.6.3 Exosomal RNA composition: ............................................................................ 18 1.6.4 Biogenesis of Exosomes: ................................................................................... 19 iv 1.7 Isolation of Exosomes: .............................................................................................. 21 1.7.1 Ultracentrifugation: ............................................................................................ 21 1.7.2 Density Gradient Ultracentrifugation: ............................................................... 23 1.7.3 Size exclusion chromatography: ........................................................................ 23 1.7.4 Ultrafiltration: .................................................................................................... 24 1.7.5 Magnetic Beads:................................................................................................. 24 1.7.6 Precipitation methods: ....................................................................................... 25 1.7.7 Isolation of exosomes by sieving: ...................................................................... 25 1.8 Detection and characterisation of Exosomes: ........................................................... 26 1.8.1 Electron Microscopy: ......................................................................................... 26 1.8.2 Western Blot: ..................................................................................................... 26 1.8.3 Flow Cytometry: ................................................................................................ 27 1.8.4 Atomic force microscopy:.................................................................................. 27 1.8.5 Nano Particle Tracking Analysis (NTA): .........................................................
Recommended publications
  • Bioinformatics Approach for Pattern of Myelin-Specific Proteins And
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Qazvin University of Medical Sciences Repository Biotech Health Sci. 2016 November; 3(4):e38278. doi: 10.17795/bhs-38278. Published online 2016 August 16. Research Article Bioinformatics Approach for Pattern of Myelin-Specific Proteins and Related Human Disorders Samiie Pouragahi,1,2,3 Mohammad Hossein Sanati,4 Mehdi Sadeghi,2 and Marjan Nassiri-Asl3,* 1Department of Molecular Medicine, School of Medicine, Qazvin university of Medical Sciences, Qazvin, IR Iran 2Department of Bioinformatics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, IR Iran 3Department of Pharmacology, Cellular and Molecular Research Center, School of Medicine, Qazvin university of Medical Sciences, Qazvin, IR Iran 4Department of Molecular Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, IR Iran *Corresponding author: Marjan Nassiri-Asl, School of Medicine, Qazvin University of Medical Sciences, Qazvin, IR Iran. Tel: +98-2833336001, Fax: +98-2833324971, E-mail: [email protected] Received 2016 April 06; Revised 2016 May 30; Accepted 2016 June 22. Abstract Background: Recent neuroinformatic studies, on the structure-function interaction of proteins, causative agents basis of human disease have implied that dysfunction or defect of different protein classes could be associated with several related diseases. Objectives: The aim of this study was the use of bioinformatics approaches for understanding the structure, function and relation- ship of myelin protein 2 (PMP2), a myelin-basic protein in the basis of neuronal disorders. Methods: A collection of databases for exploiting classification information systematically, including, protein structure, protein family and classification of human disease, based on a new approach was used.
    [Show full text]
  • PARSANA-DISSERTATION-2020.Pdf
    DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
    [Show full text]
  • Supplementary Table 1. Down-Regulation of Oligodendrocyte Genes in Schizophrenia
    Supplementary Table 1. Down-regulation of oligodendrocyte genes in schizophrenia Schizophrenia ID Accession Symbol product fold change P value oligodendrocyte transcription factors 36018_at AJ001183 SOX10 SRY (sex determining region Y)-box 10 -1.60 0.001 40624_at U48250 OLIG2 oligodendrocyte lineage transcription factor 2 -1.30 0.019 32187_at AB028973 MYT1 myelin transcription factor 1 1.12 0.571 33539_at W28567 MYEF2 myelin expression factor 2 -1.04 0.887 oligodendrocyte-expressed genes 38558_at M29273 MAG myelin associated glycoprotein -1.76 0.004 41158_at M54927 PLP1 proteolipid protein 1 -1.50 0.011 38499_s_at D28113 MOBP myelin-associated oligodendrocyte basic protein -1.59 0.017 35903_at M63623 OMG oligodendrocyte myelin glycoprotein -1.15 0.022 38653_at D11428 PMP22 peripheral myelin protein 22 -1.28 0.058 38051_at X76220 MAL mal, T-cell differentiation protein -1.11 0.065 612_s_at M19650 CNP 2',3'-cyclic nucleotide 3' phosphodiesterase -1.17 0.072 32538_at S95936 TF transferrin -1.48 0.098 32612_at X04412 GSN gelsolin (amyloidosis, Finnish type) -1.23 0.118 39598_at X04325 GJB1 gap junction protein, beta 1, 32kDa -1.10 0.416 37867_at Z48051 MOG myelin oligodendrocyte glycoprotein 1.39 0.600 35328_at AF055023 NF1 neurofibromin 1 -1.03 0.973 35817_at M13577 MBP myelin basic protein 1.02 1 other oligodendrocyte-related genes 41346_at AJ007583 LARGE like-glycosyltransferase -1.02 0.786 32719_at L41827 NRG1 neuregulin 1 1.07 0.792 1585_at M34309 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 -1.10 0.294 40387_at U80811
    [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]
  • Basic Protein Detect Circulating Antibodies in Ataxic Horses Siobhan P Ellison Tom J Kennedy Austin Li
    Neuritogenic Peptides Derived from Equine Myelin P2 Basic Protein Detect Circulating Antibodies in Ataxic Horses Siobhan P Ellison Tom J Kennedy Austin Li Corresponding Author: Siobhan P. Ellison, DVM PhD 15471 NW 112th Ave Reddick, Fl 32686 Phone: 352-591-3221 Fax: 352-591-4318 e-mail: [email protected] KEY WORDS: Need Keywords nosis of EPM. No cross-reactivity between the antigens was observed. An evaluation of the agreement between the assays (McNe- ABSTRACT mar’s test) suggests as CRP values increase, the likelihood of a positive MPP ELISA also Polyneuritis equi is an immune-mediated increases. Clinical signs of EPM may be neurodegenerative condition in horses that is due to an immune-mediated polyneuropathy related to circulating demyelinating anti- that involves complex in vivo interactions bodies against equine myelin basic protein with the IL6 pathway because MPP antibod- 2 (MP ). The present study examined the 2 ies and elevated CRP concentrations were presence of circulating demyelinating anti- detected in some horses with S. neurona bodies against neuritogenic peptides of MP 2 sarcocystosis. in sera from horses suspected of equine pro- tozoal encephalomyelitis (EPM), a neurode- INTRODUCTION generative condition in horses that may be Polyneuritis equi is a neurodegenerative immune-mediated. The goals of this study condition in horses that is related to circulat- were to develop serum ELISA tests that may ing demyelinating antibodies against equine identify neuroinflammatory conditions in myelin basic protein 2 (MP2). The clinical horses with EPM and indirectly relate the signs of polyneuritis equi (PE) are simi- pathogenesis of inflammation to IL6 by se- lar to equine protozoal myeloencephalitis rum C-reactive protein (CRP) concentration.
    [Show full text]
  • Supporting Information
    Supporting Information Figure S1. The functionality of the tagged Arp6 and Swr1 was confirmed by monitoring cell growth and sensitivity to hydeoxyurea (HU). Five-fold serial dilutions of each strain were plated on YPD with or without 50 mM HU and incubated at 30°C or 37°C for 3 days. Figure S2. Localization of Arp6 and Swr1 on chromosome 3. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position of Tel 3L, Tel 3R, CEN3, and the RP gene are shown under the panels. Figure S3. Localization of Arp6 and Swr1 on chromosome 4. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) in the whole chromosome region are compared. The position of Tel 4L, Tel 4R, CEN4, SWR1, and RP genes are shown under the panels. Figure S4. Localization of Arp6 and Swr1 on the region including the SWR1 gene of chromosome 4. The binding of Arp6- FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position and orientation of the SWR1 gene is shown. Figure S5. Localization of Arp6 and Swr1 on chromosome 5. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position of Tel 5L, Tel 5R, CEN5, and the RP genes are shown under the panels. Figure S6. Preferential localization of Arp6 and Swr1 in the 5′ end of genes. Vertical bars represent the binding ratio of proteins in each locus.
    [Show full text]
  • Yeast Genome Gazetteer P35-65
    gazetteer Metabolism 35 tRNA modification mitochondrial transport amino-acid metabolism other tRNA-transcription activities vesicular transport (Golgi network, etc.) nitrogen and sulphur metabolism mRNA synthesis peroxisomal transport nucleotide metabolism mRNA processing (splicing) vacuolar transport phosphate metabolism mRNA processing (5’-end, 3’-end processing extracellular transport carbohydrate metabolism and mRNA degradation) cellular import lipid, fatty-acid and sterol metabolism other mRNA-transcription activities other intracellular-transport activities biosynthesis of vitamins, cofactors and RNA transport prosthetic groups other transcription activities Cellular organization and biogenesis 54 ionic homeostasis organization and biogenesis of cell wall and Protein synthesis 48 plasma membrane Energy 40 ribosomal proteins organization and biogenesis of glycolysis translation (initiation,elongation and cytoskeleton gluconeogenesis termination) organization and biogenesis of endoplasmic pentose-phosphate pathway translational control reticulum and Golgi tricarboxylic-acid pathway tRNA synthetases organization and biogenesis of chromosome respiration other protein-synthesis activities structure fermentation mitochondrial organization and biogenesis metabolism of energy reserves (glycogen Protein destination 49 peroxisomal organization and biogenesis and trehalose) protein folding and stabilization endosomal organization and biogenesis other energy-generation activities protein targeting, sorting and translocation vacuolar and lysosomal
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
    medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses 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 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened.
    [Show full text]
  • Pathological Relationships Involving Iron and Myelin May Constitute a Shared Mechanism Linking Various Rare and Common Brain Diseases
    Rare Diseases ISSN: (Print) 2167-5511 (Online) Journal homepage: http://www.tandfonline.com/loi/krad20 Pathological relationships involving iron and myelin may constitute a shared mechanism linking various rare and common brain diseases Moones Heidari, Sam H. Gerami, Brianna Bassett, Ross M. Graham, Anita C.G. Chua, Ritambhara Aryal, Michael J. House, Joanna F. Collingwood, Conceição Bettencourt, Henry Houlden, Mina Ryten , John K. Olynyk, Debbie Trinder, Daniel M. Johnstone & Elizabeth A. Milward To cite this article: Moones Heidari, Sam H. Gerami, Brianna Bassett, Ross M. Graham, Anita C.G. Chua, Ritambhara Aryal, Michael J. House, Joanna F. Collingwood, Conceição Bettencourt, Henry Houlden, Mina Ryten , John K. Olynyk, Debbie Trinder, Daniel M. Johnstone & Elizabeth A. Milward (2016) Pathological relationships involving iron and myelin may constitute a shared mechanism linking various rare and common brain diseases, Rare Diseases, 4:1, e1198458, DOI: 10.1080/21675511.2016.1198458 To link to this article: http://dx.doi.org/10.1080/21675511.2016.1198458 © 2016 The Author(s). Published with View supplementary material license by Taylor & Francis Group, LLC© Moones Heidari, Sam H. Gerami, Brianna Bassett, Ross M. Graham, Anita C.G. Chua, Ritambhara Aryal, Michael J. House, Joanna Accepted author version posted online: 22 Submit your article to this journal JunF. Collingwood, 2016. Conceição Bettencourt, PublishedHenry Houlden, online: Mina 22 Jun Ryten, 2016. for the UK Brain Expression Consortium (UKBEC), John K. Olynyk, Debbie Trinder, Daniel M. Johnstone,Article views: and 541 Elizabeth A. Milward. View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=krad20 Download by: [University of Newcastle, Australia] Date: 17 May 2017, At: 19:57 RARE DISEASES 2016, VOL.
    [Show full text]
  • Role of Stromal Caveolin-1 (CAV1) Levels in Breast Cancer Angiogenesis
    Universidad Autónoma de Madrid Programa de Doctorado en Biociencias Moleculares Role of stromal Caveolin-1 (CAV1) levels in breast cancer angiogenesis Alberto Díez Sánchez Madrid, 2018 0 1 Departamento de Bioquímica Facultad de Medicina Universidad Autónoma de Madrid Role of stromal Caveolin-1 (CAV1) levels in breast cancer angiogenesis Doctorando: Alberto Díez Sánchez, Licenciado en Biotecnología Director: Miguel Ángel del Pozo Barriuso, MD, PhD. Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) Madrid, 2018 1 2 CERTIFICADO DEL DIRECTOR DE TESIS El doctor Miguel Ángel del Pozo Barriuso CERTIFICA que el doctorando Alberto Díez Sánchez ha desarrollado y concluido su trabajo de tesis doctoral “Role of stromal Caveolin-1 (CAV1) levels in breast cancer angiogenesis” bajo su supervisión, en el Centro Nacional de Investigaciones Cardiovasculares (CNIC). Y para que así conste lo firma en Madrid, a 10 de Julio de 2018, Fdo. Dr. Miguel Ángel del Pozo Barriuso Centro Nacional de Investigaciones Cardiovasculares (CNIC) 3 4 ACKNOWLEDGMENTS It is said that scientific knowledge is built on top of the shoulder of giants, in more practical terms, I consider all these people below my personal giants. First ones I encountered, were my parents and grandparents, everything I have achieved has been done on top of their previous efforts, to them I dedicate my most sincere gratitude for teaching this once lazy kid the value of effort. Next, I have to thank all those high-school teachers and university professors that during my education have been able to spark in me the sense of amazement derived from understanding how nature works.
    [Show full text]
  • Download The
    ANNOTATION OF THE HUMAN ODONTOBLAST CELL LAYER AND DENTAL PULP PROTEOMES AND N-TERMINOMES by Simon Abbey B.Sc., Queen’s University, 1989 D.M.D., The University of British Columbia, 1997 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Craniofacial Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2017 © Simon Abbey, 2017 Abstract The proteome and N-terminome of the human odontoblast cell layer was identified for the first time by shotgun proteomic and terminal amine isotopic labeling of substrates (TAILS) N-terminomic analyses, respectively, and compared with that of human dental pulp stroma from 3rd molar teeth. After reverse-phase liquid chromatography-tandem mass spectrometry, >170,000 spectra from the shotgun and TAILS analyses were matched by four search engines to 4,888 and 12,063 peptides in the odontoblast cell layer and pulp stroma, respectively. Using the Trans-Proteomic Pipeline, I identified 895 and 2,423 unique proteins in these tissues at an FDR of ≤ 1 %. In the odontoblast cell layer proteome I found proteomic evidence for dentin sialophosphoprotein, which is cleaved into dentin phosphoprotein and dentin sialoprotein, proteins that are important in dentin mineralization. Further, 222 proteins of the odontoblast cell layer were not found in the pulp, suggesting many of these proteins are synthesized preferentially by odontoblasts. I also found minor differences in the odontoblast cell layer between the dental pulp proteomes of older and younger donors. The human dental pulp stroma proteome was expanded by 974 new proteins, not previously identified among the 4,123 proteins identified in our previous dental pulp study (Eckhard et al., 2015).
    [Show full text]