Open Thesis.Pdf

Total Page:16

File Type:pdf, Size:1020Kb

Open Thesis.Pdf The Pennsylvania State University The Graduate School College of Medicine A ROLE FOR STANNIN IN CELLULAR SIGNALING A Thesis in Integrative Biosciences by Brian Reese 2005 Brian Reese Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2005 The thesis of Brian Eric Reese was reviewed and approved* by the following: Melvin L. Billingsley Professor of Pharmacology Thesis Advisor Co-Chair of Committee Jong K. Yun Assistant Professor of Pharmacology Co-Chair of Committee Robert J. Milner Professor of Neural and Behavioral Sciences James R. Connor Professor and Vice-Chair of Neurosurgery J. Kyle Krady Assistant Professor of Neural and Behavioral Sciences Anita K. Hopper Professor of Biochemistry and Molecular Biology Co-Director of Integrative Biosciences Graduate Program *Signatures are on file in the Graduate School ABSTRACT Trimethyltin (TMT) is a selective and potent neurotoxicant capable of inducing apoptotic cell death in the hippocampal formation, neocortex, amygdala, and olfactory tubercle. Subtractive hybridization was used in previous studies to uncover any common gene products present in TMT-sensitive tissues that might account for TMT’s selective toxicity. These studies showed that the gene product Stannin (Snn) was preferentially expressed in TMT sensitive tissues, with higher levels of Snn expression correlating with higher regional levels of TMT sensitivity. In addition, using antisense oligonucleotides, Snn was found to be necessary, but not sufficient, for TMT toxicity. Snn is an 88 amino acid, membrane-bound protein with a molecular weight of 9.497 kDa. Snn is found in vertebrate species and is highly conserved across vertebrates, with human and rat Snn showing 98% identity at the amino acid level. Snn is widely expressed in the developing embryo; Snn expression becomes more restricted during maturation to adulthood. In the adult, Snn is expressed in the spleen, immune cells, brain, kidney and lung. Snn has no significant homology to any other known protein. Tumor necrosis factor alpha (TNFα) was shown to induce Snn mRNA expression in human umbilical vein endothelial cells (HUVECs). We used quantitative, real-time PCR (QRT-PCR) to quantify the induction of Snn by TNFα in multiple cell lines. We observed significant increases in Snn mRNA in a time-dependent manner in HUVECs and Jurkat T-cells. To better define a potential mechanism underlying this induction, chemical inhibitors of protein kinase C (PKC) were used to determine if PKC played a role in TNFα-mediated Snn gene expression. The results of these experiments indicated iii that one or more of the βII, δ, γ, or ε isoforms was responsible for TNFα-mediated Snn gene expression. To determine specifically which isoform(s) of PKC were involved, we utilized short interfering RNA technology (siRNA) and found that PKCε was responsible for mediating TNFα-induced Snn gene expression. TMT can induce the expression of TNFα in mixed neuronal/glial cultures and so we hypothesized that TMT exposure would cause an increase in Snn gene expression, potentially as a necessary component of TMT toxicity. Again, QRT-PCR was used and we found that TMT significantly upregulated Snn within 9 hours of exposure. However, blocking TNFα with neutralizing antibodies resulted in only partial protection against TMT. By placing Snn in a defined TNFα-PKCε signaling pathway, several hypotheses arise concerning the function of Snn, such as Snn being part of a cell death or cell survival-signaling pathway. Given the high level of conservation of Snn, generation of high-affinity, Snn-specific antisera has proven difficult. In order to assess a potential role for Snn in the HUVEC response to TNFα, microarray technology was used. We found that knocking Snn down via siRNA significantly altered HUVEC gene expression in response to TNFα. After normalization and statistical analysis, we found that several genes altered by Snn knockdown are involved in the modulation of the cell cycle and cell growth. Specifically, several genes are involved with p53 and Cyclin D1, known G1/S checkpoint proteins. Functional assays showed that Snn knockdown resulted in significantly less HUVEC growth relative to other treatment. Further, analysis via flow iv cytometry indicated that a significant portion of TNFα-, Snn siRNA co-treated HUVECs were halted in the G1 phase of the cell cycle. Together, the data presented outline a signaling pathway leading to enhanced Snn gene expression as well as a potential role for Snn in normal cellular function. A role in modulating the cell cycle would explain the high degree of conservation of Snn across vertebrate evolution as well as the tissue-specific pattern of expression of Snn during different life stages. This work details the first known interaction of Snn in a cellular signaling pathway as well as the first evidence of a potential functional role of Snn in normal cells. v TABLE OF CONTENTS List of Figures..............................................................................................................…ix List of Tables ...............................................................................................................…xi List of Abbreviations …………………………………………………………………..xii Acknowledgements.........................................................................................................xiv Chapter 1: Introduction……………………………………………………………………1 Chapter 2: Literature Review……………………………………………………………..4 A. Trimethyltin 1. Organotins as toxicants…………………………………………………4 2. Trimethyltin - a potent, selective neurotoxicant………………………..5 3. Trimethyltin toxicity - cellular mechanisms……………………………6 4. Trimethyltin toxicity - behavioral effects………………………………8 5. Trimethyltin - summary…………………………………………….…..8 B. Stannin 1. The discovery of stannin and characterization of expression…………..9 2. Characterization of the stannin gene…………………………………..10 3. Stannin's role in trimethyltin toxicity……………………………….…13 4. Stannin in cellular signaling…………………………………………...14 C. Tumor necrosis factor-α 1. Tumor necrosis factor-α - origin and plieotropism…………...………17 2. Tumor necrosis factor-α receptor 1: signal transduction and major functions………………………………………………………..17 3. Tumor necrosis factor-α receptor 2: signal transduction and known functions…………………………………………………….…21 D. Protein Kinase C 1. Classes of protein kinase c and activation………………………….…22 2. Functions of protein kinase c…………………………………….……25 3. Protein kinase C - summary………………………………………..….30 Chapter 3: Protein Kinase C Epsilon Regulates TNFα-Induced Stannin Gene Expression……………………………………………………………………31 A. Introduction……………………………………………………………...……31 B. Methods 1. Cell culture………………………………………………………...…..35 2. Reagents…………………………………………………………...…..35 3. Cell viability…………………………………………………………...36 vi 4. RNA isolation/cDNA synthesis………………………………………36 5. Quantitative real-time PCR……………………………………...……37 6. siRNA construction………………………………………………...…37 7. Transfection of siRNA…………………………………………..……39 8. Statistical analysis………………………………………………..…...39 C. Results……………………………………………………………………..…40 1. Tumor necrosis factor-α contributes to trimethyltin toxicity…….…..40 2. Trimethyltin induces stannin gene expression in human umbilical vein endothelial cells……………………………………....40 3. Stannin knockdown rescues HUVECs from trimethyltin toxicity..….41 4. Induction of stannin mRNA by tumor necrosis factor-α………….…47 5. Protein kinase c is required for TNFα-induced stannin mRNA expression…………………………………………………....52 6. Knockdown of protein kinase c epsilon via siRNA prevents TNFα-mediated stannin upregulation………………………………..55 D. Discussion………………………………………………………………..…..59 Chapter 4: Microarray Analysis of Stannin Knockdown in Human Umbilical Vein Endothelial Cells in TNFα Response; Implications for Cell Cycle Control…………………………………………………………………….....65 A. Introduction………………………………………………………………......65 B. Methods 1. Cell culture……………………………………………………………67 2. Microarray fabrication………………………………………………..67 3. Microarray cDNA probe synthesis and indirect labeling with AlexaFluor555 and 647…………………………………………67 4. Gene expression analysis……………………………………………..68 5. Cell Growth…………………………………………………………...69 6. RNA isolation/cDNA synthesis………………………………………69 7. Quantitative real-time PCR…………………………………………...70 8. siRNA construction…………………………………………………...70 9. Transfection of siRNA………………………………………………..71 10. Statistical analysis……………………………………………..….…72 C. Results 1. Stannin knockdown results in significantly altered HUVEC gene expression in response to TNFα……………………………..…73 2. Stannin knockdown significantly alters several genes involved in cell growth……………………………………………………..…..76 vii 3. Loss of stannin gene expression functionally affects HUVEC response to TNFα………………………………………………..…84 4. Knockdown of stannin inhibits the ability of TNFα-treated HUVECs to progress through the cell cycle…………………….….87 D. Discussion……………………………………………………………….…92 Chapter 5: Discussion…………………………………………………………………95 A. Stannin expression is highly regulated in a spatial and temporal manner…95 B. Stannin as a mediator of trimethyltin toxicity……………………………...96 C. Stannin as a component of cell cycle progression…………………………98 D. Overall Conclusions……………………………………………………….106 References……………………………………………………………………………..102 Appendix - Complete list of significantly altered genes from the microarray………..118 viii LIST OF FIGURES 1. Developmental alteration in stannin expression pattern……………………………..12 2. Northern blotting analysis for the expression of 5 novel cytokine-responsive genes in resting and TNFα-activated
Recommended publications
  • S41467-020-18249-3.Pdf
    ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible.
    [Show full text]
  • Effect of Cigarette Smoke on DNA Methylation and Lung Function
    de Vries et al. Respiratory Research (2018) 19:212 https://doi.org/10.1186/s12931-018-0904-y RESEARCH Open Access From blood to lung tissue: effect of cigarette smoke on DNA methylation and lung function Maaike de Vries1,2* , Diana A van der Plaat1,2, Ivana Nedeljkovic3, Rikst Nynke Verkaik-Schakel4, Wierd Kooistra2,5, Najaf Amin3, Cornelia M van Duijn3, Corry-Anke Brandsma2,5, Cleo C van Diemen6, Judith M Vonk1,2 and H Marike Boezen1,2 Abstract Background: Genetic and environmental factors play a role in the development of COPD. The epigenome, and more specifically DNA methylation, is recognized as important link between these factors. We postulate that DNA methylation is one of the routes by which cigarette smoke influences the development of COPD. In this study, we aim to identify CpG-sites that are associated with cigarette smoke exposure and lung function levels in whole blood and validate these CpG-sites in lung tissue. Methods: The association between pack years and DNA methylation was studied genome-wide in 658 current smokers with >5 pack years using robust linear regression analysis. Using mediation analysis, we subsequently selected the CpG-sites that were also associated with lung function levels. Significant CpG-sites were validated in lung tissue with pyrosequencing and expression quantitative trait methylation (eQTM) analysis was performed to investigate the association between DNA methylation and gene expression. Results: 15 CpG-sites were significantly associated with pack years and 10 of these were additionally associated with lung function levels. We validated 5 CpG-sites in lung tissue and found several associations between DNA methylation and gene expression.
    [Show full text]
  • Supplemental Information Evolutionary Diversification of Protein-Protein Interactions by Interface Add-Ons
    Supplemental Information Classification: BIOLOGICAL SCIENCES – Biochemistry Evolutionary diversification of protein-protein interactions by interface add-ons Maximilian G. Plach1, Florian Semmelmann1, Florian Busch2, Markus Busch1, Leonhard Heizinger1, Vicki H. Wysocki2, Rainer Merkl1*, Reinhard Sterner1* 1 Institute of Biophysics and Physical Biochemistry University of Regensburg, D-93040 Regensburg, Germany 2 Department of Chemistry and Biochemistry The Ohio State University, 460 West 12th Avenue, OH-43210 Columbus, USA *Corresponding authors: Rainer Merkl: +49-941-3086; [email protected] Reinhard Sterner: +49-941 943 3015; [email protected] SI - 1 SI Materials and Methods Materials Glutamate dehydrogenase was purchased from Roche. Chorismate was purchased from Sigma Aldrich as the barium salt and barium ions were precipitated by the addition of a slight excess of sodium sulfate. All other chemical reagents were purchased from Sigma Aldrich in analytical or HPLC grade and used without further purification. Survey of interface add-ons in heteromeric protein complexes The initial dataset contained 1739 heteromeric bacterial protein complex structures deposited in PDB that were devoid of non-protein macromolecules and had subunit stoichiometries of AB, A2B2, A3B3, A4B4, A6B6, ABC, and A2B2C2. To avoid redundancy, identical proteins crystallized under different experimental conditions were excluded, leaving a subset of 918 complex structures. The InterPro dataset distinguishes protein families, which represent groups of homologous proteins at different levels of functional and structural similarity, and domains, which often occur in numerous non-homologous proteins (1). We thus removed all complex structures whose subunits are only associated with “domain”, “repeat”, and “site” entries and selected those that were assigned to highest-level InterPro families.
    [Show full text]
  • Evidence for Positive Selection and Population Structure at the Human MAO-A Gene
    Evidence for positive selection and population structure at the human MAO-A gene Yoav Gilad*†, Shai Rosenberg‡, Molly Przeworski§, Doron Lancet*, and Karl Skorecki†‡ *Department of Molecular Genetics and the Crown Human Genome Center, The Weizmann Institute of Science, Rehovot 76100, Israel; ‡Rappaport Faculty of Medicine and Research Institute, Technion–Israel Institute of Technology, and Rambam Medical Center, Haifa 31096, Israel; and §Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, United Kingdom Communicated by Eviatar Nevo, University of Haifa, Haifa, Israel, November 19, 2001 (received for review May 15, 2001) We report the analysis of human nucleotide diversity at a genetic locus known to be involved in a behavioral phenotype, the mono- amine oxidase A gene. Sequencing of five regions totaling 18.8 kb and spanning 90 kb of the monoamine oxidase A gene was carried out in 56 male individuals from seven different ethnogeographic groups. We uncovered 41 segregating sites, which formed 46 distinct haplotypes. A permutation test detected substantial pop- ulation structure in these samples. Consistent with differentiation between populations, linkage disequilibrium is higher than ex- pected under panmixia, with no evidence of a decay with distance. The extent of linkage disequilibrium is not typical of nuclear loci and suggests that the underlying population structure may have been accentuated by a selective sweep that fixed different hap- lotypes in different populations, or by local adaptation. In support of this suggestion, we find both a reduction in levels of diversity Fig. 1. Overall genomic structure and sequencing strategy for the MAO-A (as measured by a Hudson–Kreitman–Aguade test with the DMD44 gene.
    [Show full text]
  • An Ontology Driven Knowledge Discovery Framework for Dynamic
    An Ontology Driven Knowledge Discovery Framework for Dynamic Domains: Methodology, Tools and a Biomedical Case. Paulo Gottgtroy A thesis submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) 2010 School of Computing and Mathematical Sciences Primary Supervisor: Prof. Nikola Kasabov Co-supervisor: Prof. Stephen MacDonell i Table of Contents INTRODUCTION ....................................................................................................................... 1 1.1. INTRODUCTION ............................................................................................................. 1 1.2. RESEARCH QUESTIONS .................................................................................................. 5 1.3. RESEARCH METHODOLOGY .......................................................................................... 8 1.3.1. Detailed Research Process ............................................................................... 12 1.4. SCOPE OF THE RESEARCH ............................................................................................ 16 1.4.1. Implementation Environment ........................................................................... 19 1.4.2. Requirements Identification .............................................................................. 23 1.5. SUMMARY .................................................................................................................. 28 CHAPTER 2 RESEARCH LITERATURE REVIEW ......................................................
    [Show full text]
  • Missing Proteins in Chromosome 16
    Missing proteins in Chromosome 16 - Spanish HPP Manuel Fuentes1, Noelia Dasilva1, Felipe Clemente2, María Luisa Hernáez2, Paula Díez1, Maria Gonzalez-Gonzalez1, Alberto Orfao1, Felix Elortza5, Fernando Corrales4, Juan Pablo Albar3, Concha Gil2. 1Cancer Research Center. University of Salamanca-CSIC, IBSAL. Campus Miguel de Unamuno s/n. 37007 Salamanca. Spain. 2ProteoRed-ISCIII. Dpt. Microbiology & Proteomics Unit. University Complutense. Madrid. Spain. 3 ProteoRed-ISCIII. National Center of Biotechnology. CSIC. Madrid. Spain. 4ProteoRed-ISCIII. Center for Applied Medical Research (CIMA), Spain. 5ProteoRed-ISCIII. CIC bioGUNE. Bilbao. Spain. AIM In the scope of the HPP project, there is a special situation for the proteins that had not spectral and/or expression evidence, those are called "Unknown proteins".These proteins must be studied specifically to get enough information to build MRM quantitation methods. The Spanish HPP consortium has developed a specific protocol for unknown proteins in order to get MSMS information for the MRM method. MATERIALS AND METHODS Clones and plasmids All the clones used are from pANT7_cGST clone collection distributed by Plasmid repository at Arizona State University Biodesign Institute. Each clone contains an in-frame fused C-terminal GST tag. Each bacterial clone was grown overnight in 5 mL of Luria broth with 100 µg/mL ampicillin. Plasmid DNA was extracted using Mini- Prep Kit from Promega, following manufacture instructions. All plasmids were sequence to confirm the identity of the insert. DNA isolation and sequence verification Protein IVVT production Proteins were synthesized from plasmid DNA using Human In vitro Protein Expression kit (Thermo) following manufacture s protocol with a few minimal modifications in order to adapt for 1.5 mL eppendorf tubes.
    [Show full text]
  • The Macrocyclizing Protease Butelase 1 Remains Auto-Catalytic and Reveals the Structural Basis for Ligase Activity
    bioRxiv preprint doi: https://doi.org/10.1101/380295; this version posted October 21, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Amy M. James1,2,†,‡, Joel Haywood1,2,†, Julie Leroux1,2, Katarzyna Ignasiak1, Alysha G. Elliott3, Jason W. Schmidberger1, Mark F. Fisher1,2, Samuel G. Nonis1,2, Ricarda Fenske2, Charles S. Bond1, and Joshua S. Mylne1,2,* The University of Western Australia, 1 School of Molecular Sciences & 2 The ARC Centre of Excellence in Plant Energy Biology, 35 Stirling Highway, Crawley, Perth 6009, Australia; 3 The University of Queensland, Institute for Molecular Bioscience, St Lucia, Brisbane, QLD 4072, Australia † Equal author contribution ‡ Current address: University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK * Address correspondence to [email protected]. The macrocyclizing protease butelase 1 remains auto- catalytic and reveals the structural basis for ligase activity Short title: Cleavage and ligation by butelase 1 bioRxiv preprint doi: https://doi.org/10.1101/380295; this version posted October 21, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Plant asparaginyl endopeptidases (AEPs) are expressed as inactive zymogens that perform seed storage protein maturation upon cleavage dependent auto-activation in the low pH environment of storage vacuoles.
    [Show full text]
  • UC Irvine UC Irvine Electronic Theses and Dissertations
    UC Irvine UC Irvine Electronic Theses and Dissertations Title Generation and application of a chimeric model to examine human microglia responses to amyloid pathology Permalink https://escholarship.org/uc/item/38g5s0sj Author Hasselmann, Jonathan Publication Date 2021 Supplemental Material https://escholarship.org/uc/item/38g5s0sj#supplemental Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Generation and application of a chimeric model to examine human microglia responses to amyloid pathology DISSERTATION Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY In Biological Sciences By Jonathan Hasselmann Dissertation Committee: Associate Professor Mathew Blurton-Jones, Chair Associate Professor Kim Green Professor Brian Cummings 2021 Portions of the Introduction and Chapter 2 © 2020 Wiley Periodicals, Inc. Chapter 1 and a portion of Chapter 2 © 2019 Elsevier Inc. All other materials © 2021 Jonathan Hasselmann DEDICATION To Nikki and Isla: During this journey, I have been fortunate enough to have the love and support of two of the strongest ladies I have ever met. I can say without a doubt that I would not have made it here without the two of you standing behind me. I love you both with all my heart and this achievement belongs to the two of you just as much as it belongs to me. Thank you for always having my back. ii TABLE OF CONTENTS LIST OF FIGURES .......................................................................................................................v
    [Show full text]
  • Analysis of DNA Methylation Sites Used for Forensic Age Prediction and Their Correlation with Human Aging
    Silva DSBS, et al., J Forensic Leg Investig Sci 2021, 7: 054 DOI: 10.24966/FLIS-733X/100054 HSOA Journal of Forensic, Legal & Investigative Sciences Research Article Analysis of DNA Methylation Introduction DNA samples left behind in crime scenes are often used for Sites used for Forensic Age identification purposes by comparing them to reference samples or to a forensic database. In cases where there is no match, investigators may Prediction and their Correlation turn to alternative approaches using advanced technologies to gain valuable leads on phenotypic traits (externally visible characteristics) with Human Aging of the person who had left the DNA material. Previous studies have found that phenotypes can be predicted by using information obtained from different DNA markers, such as single nucleotide Silva DSBS1* and Karantenislis G2 polymorphisms (SNPs), insertion/deletion polymorphisms (InDel), and epigenetic markers [1-11]. It is well established that epigenetic 1Department of Chemistry, Hofstra University, Hempstead-NY, USA modifications are major regulators in the translation of genotype 2John F Kennedy High School, Bellmore-NY, USA to phenotype. Epigenetic regulation encompasses various levels of gene expression and involves modifications of DNA, histones, RNA and chromatin, with functional consequences for the human Abstract genome [12-14]. DNA methylation is the most well-characterized epigenetic modification. It is usually associated with transcriptional Phenotype-related features, such as age, are linked to repression and it involves the addition of a methyl group (– CH3) in genetic components through regulatory pathways and epigenetic the cytosine of CpG dinucleotides. Differences in DNA methylation modifications, such as DNA methylation, are major regulators in levels in specific regions of the genome can affect the expression of the translation of genotype to phenotype.
    [Show full text]
  • Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands That Promote Axonal Growth
    Research Article: New Research Development Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands that Promote Axonal Growth Jeremy S. Toma,1 Konstantina Karamboulas,1,ª Matthew J. Carr,1,2,ª Adelaida Kolaj,1,3 Scott A. Yuzwa,1 Neemat Mahmud,1,3 Mekayla A. Storer,1 David R. Kaplan,1,2,4 and Freda D. Miller1,2,3,4 https://doi.org/10.1523/ENEURO.0066-20.2020 1Program in Neurosciences and Mental Health, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada, 2Institute of Medical Sciences University of Toronto, Toronto, Ontario M5G 1A8, Canada, 3Department of Physiology, University of Toronto, Toronto, Ontario M5G 1A8, Canada, and 4Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada Abstract Peripheral nerves provide a supportive growth environment for developing and regenerating axons and are es- sential for maintenance and repair of many non-neural tissues. This capacity has largely been ascribed to paracrine factors secreted by nerve-resident Schwann cells. Here, we used single-cell transcriptional profiling to identify ligands made by different injured rodent nerve cell types and have combined this with cell-surface mass spectrometry to computationally model potential paracrine interactions with peripheral neurons. These analyses show that peripheral nerves make many ligands predicted to act on peripheral and CNS neurons, in- cluding known and previously uncharacterized ligands. While Schwann cells are an important ligand source within injured nerves, more than half of the predicted ligands are made by nerve-resident mesenchymal cells, including the endoneurial cells most closely associated with peripheral axons. At least three of these mesen- chymal ligands, ANGPT1, CCL11, and VEGFC, promote growth when locally applied on sympathetic axons.
    [Show full text]
  • Dnasp V5. Tutorial
    DnaSP v5. Tutorial Copyright © 2016 by Julio Rozas et al. & Universitat de Barcelona. All Rights Reserved. DnaSP v5. Tutorial Table of contents Contents ........................................................................................................... 4 Introduction ...................................................................................................... 6 What DnaSP can do ........................................................................................ 6 System Requirements ..................................................................................... 7 File Menu / Input and Output .............................................................................. 9 Input Data Files Formats ............................................................................... 10 FASTA Format ......................................................................................... 10 MEGA Format .......................................................................................... 11 NBRF/PIR Format ..................................................................................... 12 NEXUS Format ......................................................................................... 12 PHYLIP Format ........................................................................................ 15 HapMap3 Phased Haplotypes Format .......................................................... 16 Multiple Data Files ........................................................................................ 17 Unphase/Genotype
    [Show full text]
  • Mouse Snn Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Snn Knockout Project (CRISPR/Cas9) Objective: To create a Snn knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Snn gene (NCBI Reference Sequence: NM_009223 ; Ensembl: ENSMUSG00000037972 ) is located on Mouse chromosome 16. 2 exons are identified, with the ATG start codon in exon 2 and the TGA stop codon in exon 2 (Transcript: ENSMUST00000089011). Exon 2 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 0.38% of the coding region. Exon 2 covers 100.0% of the coding region. The size of effective KO region: ~264 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 Legends Exon of mouse Snn Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of start codon is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of stop codon is aligned with itself to determine if there are tandem repeats.
    [Show full text]