Characterization of Cellular Pathways and Potency of Shiga Toxin on Endothelial Cells

Total Page:16

File Type:pdf, Size:1020Kb

Characterization of Cellular Pathways and Potency of Shiga Toxin on Endothelial Cells Characterization of cellular pathways and potency of Shiga toxin on endothelial cells A dissertation submitted to the Division of Graduate Studies and Research of the University of Cincinnati In partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY (Ph.D.) In the Department of Molecular Genetics, Biochemistry, & Microbiology of the College of Medicine 2015 Kayleigh A. MacMaster B.S., Nazareth College of Rochester, 2008 Committee Chair: Alison A. Weiss, Ph.D. Abstract Shiga toxin-producing E. coli (STEC) are a major cause of food-borne illness in the United States and worldwide. Most STEC cases resolve without complication, however approximately 10% progress to severe disease including hemolytic uremic syndrome (HUS). Shiga toxin (Stx), the main virulence factor of STEC, is an AB5 toxin. The enzymatic A-subunit cleaves the 28S rRNA, inhibiting protein synthesis, while the homopentameric B-subunit binds Stx to the cellular receptor, globotriaosylceramide. Stx has two major antigenic forms, Stx1 and Stx2, and minor subtypes Stx1c and d and Stx2a-h. Epidemiologic studies have found that Stx2 subtype a (Stx2a) is associated with more severe disease than Stx1, and other Stx2 subtypes, although closely related to Stx2a, exhibit discrepancies in disease severity. Despite the fact that infection with Stx2a producing STEC are correlated with more severe disease, there is currently no predictive indicator of which cases will progress to HUS. Endothelial cells are suggested to play a role in HUS; therefore, we investigated whether there is a difference in susceptibility of endothelial cells from different vascular beds to Stx1 and Stx2 subtypes that affects progression to severe disease. Human umbilical vein endothelial cells (HUVECs), glomerular microvascular endothelial cells (GMECs) and cerebral cortex microvascular endothelial cells (BMECs) were fairly insensitive (ED50 > 0.2 µg/ml) to metabolic inhibition by Stx1, Stx2a, Stx2b, Stx2c and Stx2d. Human dermal microvascular endothelial cells (dHMECs) were quite sensitive (ED50 ≤ 1.9 x 10-1 µg/ml) to all toxins except Stx2b. Susceptibility to Stx correlated with the ability of the toxins to bind each cell type which was influenced by expression level of the receptor. In addition to affecting the kidney, 20-30% of HUS cases involve central nervous system dysfunction. The role of Stx in leading to neurological complications is not well understood. Stx must either damage brain endothelial cells or pass through the blood brain barrier (BBB) to ii access susceptible cells. To determine if Stx accesses susceptible cells in the brain by damaging endothelial cells of the BBB, we utilized primary and immortalized cell lines. Neither immortalized microvascular endothelial cells from the cerebral cortex of mice (bEnd.3) nor primary human BMECs were susceptible to Stx2a, suggesting that direct toxicity to endothelial cells is not how Stx weakens the BBB. It is instead likely that inflammation plays a significant role in loss of BBB integrity. In order for Stx to exert toxicity on cells it must bind, undergo endocytosis and retrograde transport to the endoplasmic reticulum and then reach the cytosol. While multiple studies on Stx transport have been reported, most utilize cell lines without direct involvement in human disease. Little is known about trafficking in primary cells pertinent to disease and if it differs from that in cell lines. We used a genome wide siRNA screen to investigate the trafficking pathway of Stx2a in primary renal proximal tubule epithelial cells (RPTECs). The screen both confirmed previously reported cellular components involved in Stx trafficking and identified novel factors. Since there is currently no treatment for HUS, these results provide possible targets for future therapeutics. iii iv Acknowledgements First and foremost, I would like to acknowledge my advisor, Dr. Alison Weiss, for her guidance and support during my dissertation, and for her encouragement during my scientific development. Second, I would like to acknowledge the members of my dissertation committee, Dr. Bill Miller, Dr. Jay Degen, Dr. Andrew Herr and Dr. Jerry Lingrel, who have provided helpful and constructive discussion and feedback throughout my dissertation. I would also like to recognize members of the Weiss lab who have given guidance, support and input over the years including Dr. Christine Pellino, Dr. Cynthia Fuller, Dr. Scott Millen, Dr. Marsha Gaston, Dr. Sayali Karve, Dr. Suman Pradhan, Crystal Davis and Charles Talbott. Finally, I would like to thank my family and friends for their constant support and encouragement to persevere. I would especially like to thank my parents for their incredible support in everything I choose to pursue. v Table of Contents Abstract…………………………………………………………………………………………..ii Acknowledgements……………………………………………………………………………....v Table of Contents………………………………………………………………………………..vi Figures and Tables………………………………………………………………………………ix Abbreviations…………………………………………………………………………………...xii Chapter I. Introduction: Shiga toxin Review……………………...…………………………..1 I. Shiga toxin-producing Escherichia coli and Shiga toxin background 2 Shiga toxin-producing E. coli and O157:H7 2 Transmission of O157:H7 and progression of Shiga toxin disease 2 Shiga toxin 3 Genetics and regulation of Stx 7 Stx receptor and membrane aspects 7 Endocytosis and retrograde transport of Stx 10 II. Subtypes of Shiga toxin 12 Stx subtypes 12 Interaction of subtypes with receptor 13 III. Role for Shiga toxin in disease 14 Stx and HUS 14 Stx subtypes and progression to severe disease 15 Stx toxicity to endothelial cells and a role for inflammation 18 Effect of Stx on gene expression 20 Contributions of B-subunit signaling on vascular response in HUS 21 vi Animal models 22 IV. Scope of this dissertation 24 Chapter II. Potency of Stx Variants on Endothelial Cells of Different Origins……………25 Abstract 26 Introduction 27 Materials and Methods 30 Results 34 Toxicity of Stx subtypes to primary endothelial cells 34 Inhibition of protein synthesis by Stx subtypes 42 Toxin binding to primary endothelial cells 44 Gb3 content of endothelial cells 44 Discussion 47 Chapter III. Susceptibility of Brain Microvascular Endothelial Cells to Stx2a…………...53 Abstract 54 Introduction 55 Materials and Methods 57 Results 59 Toxicity of Stx2a to cerebral microvascular endothelial cells 59 Gb3 expression on cerebral cortex endothelial cells 63 Discussion 65 vii Chapter IV. siRNA Screen to Identify Novel Components of the Cell Utilized by Shiga Toxin…………………………………………………………………………………………….71 Abstract 72 Introduction 73 Materials and Methods 75 Results and Discussion 78 Transcriptional analysis of RPTECs 78 siRNA screen 78 Pathway and gene ontology enrichment of top candidate hits 82 Stx receptor expression - Glycolipid biosynthesis 82 Cell-surface signaling and endocytosis 83 Intracellular trafficking of Stx 87 Genes associated with damage due to catalytic glycosidase activity of Shiga toxin 92 Intracellular signaling: Inflammation and apoptosis 94 Summary 97 Chapter V. Conclusions and Future Directions……………………………………………...99 Conclusions 100 Future Directions 101 References……………………………………………………………………………………...106 viii Figures Chapter I. Figure 1.1. Stx1 and Stx2a holotoxin crystal structures. 5 Figure 1.2. Surface representation and sequence alignment for Stx1 and Stx2a. 6 Figure 1.3 Intracellular trafficking of Stx. 12 Chapter II. Figure 2.1. Sequence alignments and structural comparison of Stx1 and Stx2 subtypes. 29 Figure 2.2. Potency of Stx to immortalized CDC.HMEC-1 dermal microvascular 35 endothelial cells. Figure 2.3. Inhibition of metabolic activity by Stx in primary endothelial cells. 38 Figure 2.4. Upregulation of surface ICAM-1 on HUVECs following stimulation with TNF-α. 41 Figure 2.5. Inhibition of protein synthesis by Stx in primary endothelial cells. 43 Chapter III. Figure 3.1. Metabolic activity of Stx2a-treated cerebral cortex microvascular endothelial 61 cells. Figure 3.2. Upregulation of surface ICAM-1 on BMECs following stimulation with TNF-α. 62 Chapter IV. Figure 4.1. Transfection reagent INTERFERin® does not affect transcription of primary 80 RPTECs. Figure 4.2. Top candidate hits for glycolipid biosynthesis. 85 Figure 4.3. Top candidate hits for cell surface signaling and endocytosis. 86 Figure 4.4. Top candidate hits for intracellular trafficking. 91 ix Figure 4.5. Top candidate hits for damage due to catalytic glycosidase activity. 93 Figure 4.6. Top candidate hits for intracellular signaling. 96 x Tables Chapter II. Table 2.1. Plating densities for endothelial cells. 31 Table 2.2. ED50 values (µg/ml) of Stx1 and Stx2 subtypes for endothelial cells. 40 Table 2.3. Stx1 binding to primary endothelial cells. 45 Table 2.4. Stx2 subtype binding to primary endothelial cells. 45 Table 2.5. Cell surface Gb3 on endothelial cells. 46 Chapter III. Table 3.1. Plating densities for brain endothelial cells. 58 Table 3.2. Cell-surface Gb3 on cerebral cortex microvascular endothelial cells. 64 Chapter IV. Table 4.1. Top candidate hits for siRNA screen in primary renal proximal tubule 81 epithelial cells. xi Abbreviations APC allophycocyanin BBB blood brain barrier BEI Biodefense and Emerging Infectious Diseases Research Resources Repository bEnd.3 murine cerebral cortex microvascular endothelial cells BMEC human cerebral cortex microvascular endothelial cells CFU colony forming units CNS central nervous system CO2 carbon dioxide CXCR4 chemokine (C-X-C) motif receptor 4 CXCR7 chemokine (C-X-C) motif receptor
Recommended publications
  • Genetic Analysis of Retinopathy in Type 1 Diabetes
    Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194).
    [Show full text]
  • Dynamin Functions and Ligands: Classical Mechanisms Behind
    1521-0111/91/2/123–134$25.00 http://dx.doi.org/10.1124/mol.116.105064 MOLECULAR PHARMACOLOGY Mol Pharmacol 91:123–134, February 2017 Copyright ª 2017 by The American Society for Pharmacology and Experimental Therapeutics MINIREVIEW Dynamin Functions and Ligands: Classical Mechanisms Behind Mahaveer Singh, Hemant R. Jadhav, and Tanya Bhatt Department of Pharmacy, Birla Institute of Technology and Sciences Pilani, Pilani Campus, Rajasthan, India Received May 5, 2016; accepted November 17, 2016 Downloaded from ABSTRACT Dynamin is a GTPase that plays a vital role in clathrin-dependent pathophysiology of various disorders, such as Alzheimer’s disease, endocytosis and other vesicular trafficking processes by acting Parkinson’s disease, Huntington’s disease, Charcot-Marie-Tooth as a pair of molecular scissors for newly formed vesicles originating disease, heart failure, schizophrenia, epilepsy, cancer, dominant ’ from the plasma membrane. Dynamins and related proteins are optic atrophy, osteoporosis, and Down s syndrome. This review is molpharm.aspetjournals.org important components for the cleavage of clathrin-coated vesicles, an attempt to illustrate the dynamin-related mechanisms involved phagosomes, and mitochondria. These proteins help in organelle in the above-mentioned disorders and to help medicinal chemists division, viral resistance, and mitochondrial fusion/fission. Dys- to design novel dynamin ligands, which could be useful in the function and mutations in dynamin have been implicated in the treatment of dynamin-related disorders. Introduction GTP hydrolysis–dependent conformational change of GTPase dynamin assists in membrane fission, leading to the generation Dynamins were originally discovered in the brain and identi- of endocytic vesicles (Praefcke and McMahon, 2004; Ferguson at ASPET Journals on September 23, 2021 fied as microtubule binding partners.
    [Show full text]
  • Histone Isoform H2A1H Promotes Attainment of Distinct Physiological
    Bhattacharya et al. Epigenetics & Chromatin (2017) 10:48 DOI 10.1186/s13072-017-0155-z Epigenetics & Chromatin RESEARCH Open Access Histone isoform H2A1H promotes attainment of distinct physiological states by altering chromatin dynamics Saikat Bhattacharya1,4,6, Divya Reddy1,4, Vinod Jani5†, Nikhil Gadewal3†, Sanket Shah1,4, Raja Reddy2,4, Kakoli Bose2,4, Uddhavesh Sonavane5, Rajendra Joshi5 and Sanjay Gupta1,4* Abstract Background: The distinct functional efects of the replication-dependent histone H2A isoforms have been dem- onstrated; however, the mechanistic basis of the non-redundancy remains unclear. Here, we have investigated the specifc functional contribution of the histone H2A isoform H2A1H, which difers from another isoform H2A2A3 in the identity of only three amino acids. Results: H2A1H exhibits varied expression levels in diferent normal tissues and human cancer cell lines (H2A1C in humans). It also promotes cell proliferation in a context-dependent manner when exogenously overexpressed. To uncover the molecular basis of the non-redundancy, equilibrium unfolding of recombinant H2A1H-H2B dimer was performed. We found that the M51L alteration at the H2A–H2B dimer interface decreases the temperature of melting of H2A1H-H2B by ~ 3 °C as compared to the H2A2A3-H2B dimer. This diference in the dimer stability is also refected in the chromatin dynamics as H2A1H-containing nucleosomes are more stable owing to M51L and K99R substitu- tions. Molecular dynamic simulations suggest that these substitutions increase the number of hydrogen bonds and hydrophobic interactions of H2A1H, enabling it to form more stable nucleosomes. Conclusion: We show that the M51L and K99R substitutions, besides altering the stability of histone–histone and histone–DNA complexes, have the most prominent efect on cell proliferation, suggesting that the nucleosome sta- bility is intimately linked with the physiological efects observed.
    [Show full text]
  • Targeted Genes and Methodology Details for Neuromuscular Genetic Panels
    Targeted Genes and Methodology Details for Neuromuscular Genetic Panels Reference transcripts based on build GRCh37 (hg19) interrogated by Neuromuscular Genetic Panels Next-generation sequencing (NGS) and/or Sanger sequencing is performed Motor Neuron Disease Panel to test for the presence of a mutation in these genes. Gene GenBank Accession Number Regions of homology, high GC-rich content, and repetitive sequences may ALS2 NM_020919 not provide accurate sequence. Therefore, all reported alterations detected ANG NM_001145 by NGS are confirmed by an independent reference method based on laboratory developed criteria. However, this does not rule out the possibility CHMP2B NM_014043 of a false-negative result in these regions. ERBB4 NM_005235 Sanger sequencing is used to confirm alterations detected by NGS when FIG4 NM_014845 appropriate.(Unpublished Mayo method) FUS NM_004960 HNRNPA1 NM_031157 OPTN NM_021980 PFN1 NM_005022 SETX NM_015046 SIGMAR1 NM_005866 SOD1 NM_000454 SQSTM1 NM_003900 TARDBP NM_007375 UBQLN2 NM_013444 VAPB NM_004738 VCP NM_007126 ©2018 Mayo Foundation for Medical Education and Research Page 1 of 14 MC4091-83rev1018 Muscular Dystrophy Panel Muscular Dystrophy Panel Gene GenBank Accession Number Gene GenBank Accession Number ACTA1 NM_001100 LMNA NM_170707 ANO5 NM_213599 LPIN1 NM_145693 B3GALNT2 NM_152490 MATR3 NM_199189 B4GAT1 NM_006876 MYH2 NM_017534 BAG3 NM_004281 MYH7 NM_000257 BIN1 NM_139343 MYOT NM_006790 BVES NM_007073 NEB NM_004543 CAPN3 NM_000070 PLEC NM_000445 CAV3 NM_033337 POMGNT1 NM_017739 CAVIN1 NM_012232 POMGNT2
    [Show full text]
  • Diagnosing Platelet Secretion Disorders: Examples Cases
    Diagnosing platelet secretion disorders: examples cases Martina Daly Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield Disclosures for Martina Daly In compliance with COI policy, ISTH requires the following disclosures to the session audience: Research Support/P.I. No relevant conflicts of interest to declare Employee No relevant conflicts of interest to declare Consultant No relevant conflicts of interest to declare Major Stockholder No relevant conflicts of interest to declare Speakers Bureau No relevant conflicts of interest to declare Honoraria No relevant conflicts of interest to declare Scientific Advisory No relevant conflicts of interest to declare Board Platelet granule release Agonists (FIIa, Collagen, ADP) Signals Activation Shape change Membrane fusion Release of granule contents Platelet storage organelles lysosomes a granules Enzymes including cathepsins Adhesive proteins acid hydrolases Clotting factors and their inhibitors Fibrinolytic factors and their inhibitors Proteases and antiproteases Growth and mitogenic factors Chemokines, cytokines Anti-microbial proteins Membrane glycoproteins dense (d) granules ADP/ATP Serotonin histamine inorganic polyphosphate Platelet a-granule contents Type Prominent components Membrane glycoproteins GPIb, aIIbb3, GPVI Clotting factors VWF, FV, FXI, FII, Fibrinogen, HMWK, FXIII? Clotting inhibitors TFPI, protein S, protease nexin-2 Fibrinolysis components PAI-1, TAFI, a2-antiplasmin, plasminogen, uPA Other protease inhibitors a1-antitrypsin, a2-macroglobulin
    [Show full text]
  • Final Copy 2018 09 25 Gaunt
    This electronic thesis or dissertation has been downloaded from Explore Bristol Research, http://research-information.bristol.ac.uk Author: Gaunt, Jess Title: A Viral Approach to Translatome Profiling of CA1 Neurons During Associative Recognition Memory Formation General rights Access to the thesis is subject to the Creative Commons Attribution - NonCommercial-No Derivatives 4.0 International Public License. A copy of this may be found at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode This license sets out your rights and the restrictions that apply to your access to the thesis so it is important you read this before proceeding. Take down policy Some pages of this thesis may have been removed for copyright restrictions prior to having it been deposited in Explore Bristol Research. However, if you have discovered material within the thesis that you consider to be unlawful e.g. breaches of copyright (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please contact [email protected] and include the following information in your message: •Your contact details •Bibliographic details for the item, including a URL •An outline nature of the complaint Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. A Viral Approach to Translatome Profiling of CA1 Neurons During Associative Recognition Memory Formation Jessica Ruth Gaunt A dissertation submitted to the University of Bristol in accordance with the requirements for award of the degree of Doctor of Philosophy in the Faculty of Health Sciences, Bristol Medical School.
    [Show full text]
  • Membrane Tension Buffering by Caveolae: a Role in Cancer?
    Cancer and Metastasis Reviews (2020) 39:505–517 https://doi.org/10.1007/s10555-020-09899-2 Membrane tension buffering by caveolae: a role in cancer? Vibha Singh1 & Christophe Lamaze1 Published online: 30 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Caveolae are bulb-like invaginations made up of two essential structural proteins, caveolin-1 and cavins, which are abundantly present at the plasma membrane of vertebrate cells. Since their discovery more than 60 years ago, the function of caveolae has been mired in controversy. The last decade has seen the characterization of new caveolae components and regulators together with the discovery of additional cellular functions that have shed new light on these enigmatic structures. Early on, caveolae and/ or caveolin-1 have been involved in the regulation of several parameters associated with cancer progression such as cell migration, metastasis, angiogenesis, or cell growth. These studies have revealed that caveolin-1 and more recently cavin-1 have a dual role with either a negative or a positive effect on most of these parameters. The recent discovery that caveolae can act as mechanosensors has sparked an array of new studies that have addressed the mechanobiology of caveolae in various cellular functions. This review summarizes the current knowledge on caveolae and their role in cancer development through their activity in membrane tension buffering. We propose that the role of caveolae in cancer has to be revisited through their response to the mechanical forces encountered by cancer cells during tumor mass development. Keywords Caveolae . Cancer . Mechanosensing . Mechanotransdcution . Membrane tension .
    [Show full text]
  • A Novel Computational Algorithm for Predicting Immune Cell Types Using Single-Cell RNA Sequencing Data
    A novel computational algorithm for predicting immune cell types using single-cell RNA sequencing data By Shuo Jia A hesis submitted to the Faculty of Graduate Studies of The University of Manitoba n partial fulfillment of the requirements of the degree of MASTER OF SCIENCE Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg, Manitoba, Canada Copyright © 2020 by Shuo Jia Abstract Background: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional 1 convolutional neural network (1D-CNN).
    [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]
  • Differences for Ectopic Versus Eutopic Cells
    556 RBMO VOLUME 39 ISSUE 4 2019 ARTICLE Chemosensitivity and chemoresistance in endometriosis – differences for ectopic versus eutopic cells BIOGRAPHY Andres Salumets is Professor of Reproductive Medicine at the University of Tartu, and Scientific Head at the Competence Centre on Health Technologies, Tartu, Estonia. He has been involved in assisted reproduction for 20 years, first as an embryologist and later as a researcher. His major interests are endometriosis, endometrial biology and implantation. Darja Lavogina1,2,*, Külli Samuel1, Arina Lavrits1,3, Alvin Meltsov1, Deniss Sõritsa1,4,5, Ülle Kadastik6, Maire Peters1,4, Ago Rinken2, Andres Salumets1,4,7, 8 KEY MESSAGE Akt/PKB inhibitor GSK690693, CK2 inhibitor ARC-775, MAPK pathway inhibitor sorafenib, proteasome inhibitor bortezomib, and microtubule-depolymerizing toxin MMAE showed higher cytotoxicity in eutopic cells. In contrast, 10 µmol/l of the anthracycline toxin doxorubicin caused cellular death in ectopic cells more effectively than in eutopic cells, underlining the potential of doxorubicin in endometriosis research. ABSTRACT Research question: Endometriosis is a common gynaecological disease defined by the presence of endometrium-like tissue outside the uterus. This complex disease, often accompanied by severe pain and infertility, causes a significant medical and socioeconomic burden; hence, novel strategies are being sought for the treatment of endometriosis. Here, we set out to explore the cytotoxic effects of a panel of compounds to find toxins with different efficiency in eutopic versus ectopic cells, thus highlighting alterations in the corresponding molecular pathways. Design: The effect on cellular viability of 14 compounds was established in a cohort of paired eutopic and ectopic endometrial stromal cell samples from 11 patients.
    [Show full text]
  • DNA Methylation Changes in Down Syndrome Derived Neural Ipscs Uncover Co-Dysregulation of ZNF and HOX3 Families of Transcription
    Laan et al. Clinical Epigenetics (2020) 12:9 https://doi.org/10.1186/s13148-019-0803-1 RESEARCH Open Access DNA methylation changes in Down syndrome derived neural iPSCs uncover co- dysregulation of ZNF and HOX3 families of transcription factors Loora Laan1†, Joakim Klar1†, Maria Sobol1, Jan Hoeber1, Mansoureh Shahsavani2, Malin Kele2, Ambrin Fatima1, Muhammad Zakaria1, Göran Annerén1, Anna Falk2, Jens Schuster1 and Niklas Dahl1* Abstract Background: Down syndrome (DS) is characterized by neurodevelopmental abnormalities caused by partial or complete trisomy of human chromosome 21 (T21). Analysis of Down syndrome brain specimens has shown global epigenetic and transcriptional changes but their interplay during early neurogenesis remains largely unknown. We differentiated induced pluripotent stem cells (iPSCs) established from two DS patients with complete T21 and matched euploid donors into two distinct neural stages corresponding to early- and mid-gestational ages. Results: Using the Illumina Infinium 450K array, we assessed the DNA methylation pattern of known CpG regions and promoters across the genome in trisomic neural iPSC derivatives, and we identified a total of 500 stably and differentially methylated CpGs that were annotated to CpG islands of 151 genes. The genes were enriched within the DNA binding category, uncovering 37 factors of importance for transcriptional regulation and chromatin structure. In particular, we observed regional epigenetic changes of the transcription factor genes ZNF69, ZNF700 and ZNF763 as well as the HOXA3, HOXB3 and HOXD3 genes. A similar clustering of differential methylation was found in the CpG islands of the HIST1 genes suggesting effects on chromatin remodeling. Conclusions: The study shows that early established differential methylation in neural iPSC derivatives with T21 are associated with a set of genes relevant for DS brain development, providing a novel framework for further studies on epigenetic changes and transcriptional dysregulation during T21 neurogenesis.
    [Show full text]
  • Supplementary Figure 1
    Supplementary Table 1 siRNA Oligonucleotide Sequences not Used for IGFBP-3 Knockdown siRNA Sequence nucleotides Source GCUACAAAGUUGACUACGA 686-704 ON-TARGET Plus SMART pool sequences GAAAUGCUAGUGAGUCGGA 536-554 ON-TARGET Plus SMART pool sequences GCACAGAUACCCAGAACUU 713-731 ON-TARGET Plus SMART pool sequences GAAUAUGGUCCCUGCCGUA 757-775 ON-TARGET Plus SMART pool sequences UAUCGAGAAUAGGAAAACC 1427-1445 siDESIGN center GCAGCCUCUCCCAGGCUACA 940-958 siDESIGN center GCAUAAGCUCUUUAAAGGCA 1895-1913 siDESIGN center UGCCUGGAUUCCACAGCUU 44-62 siDESIGN center AAGCAGCGTGCCCCGGUUG 106-124 siDESIGN center AAAGGCAAAGCUUUAUUUU 1908-1926 siDESIGN center Oligonucleotide sequences used for siRNA oligonucleotides tested to induce IGFBP-3 knockdown. Sequences 1-4 were from ON-TARGET Plus SMART pool sequences (Cat. # L-004777-00-0005, Dharmacon, Lafayette, CO). Sequences 5-10 were generated in our laboratory using the siDESIGN center from the Dharmacon website (www.dharmacon.com) by inputting the Genbank accession number NM_000598 (IGFBP-3). Supplementary Table 2 Transcripts Activated by NKX3.1 in PC-3 Cells PC-3 cells were stably transfected with the pcDNA3.1 empty vector or NKX3.1 expression vector and mRNA from two clones of each cell type was isolated for microarray analysis on the Affymetrix U-133 expression array. Analyses of results from each pair of clones of the same genotype that did not match up were discarded to ensure clonal variation was not a factor. 984 genes were found to be up- or down-regulated more that 1.4 fold in the NKX3.1 expressing PC-3 cells, in comparison to the PC-3 control cells. The 6th and 9th most activated probe sets were for human growth hormone-dependent insulin-like growth factor-binding protein, now known as IGFBP-3.
    [Show full text]