Peripheral Nerve Single Cell Analysis Identifies Mesenchymal Ligands That Promote Axonal Growth
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ENSG Gene Encodes Effector TCR Pathway Costimulation Inhibitory/Exhaustion Synapse/Adhesion Chemokines/Receptors
ENSG Gene Encodes Effector TCR pathway Costimulation Inhibitory/exhaustion Synapse/adhesion Chemokines/receptors ENSG00000111537 IFNG IFNg x ENSG00000109471 IL2 IL-2 x ENSG00000232810 TNF TNFa x ENSG00000271503 CCL5 CCL5 x x ENSG00000139187 KLRG1 Klrg1 x ENSG00000117560 FASLG Fas ligand x ENSG00000121858 TNFSF10 TRAIL x ENSG00000134545 KLRC1 Klrc1 / NKG2A x ENSG00000213809 KLRK1 Klrk1 / NKG2D x ENSG00000188389 PDCD1 PD-1 x x ENSG00000117281 CD160 CD160 x x ENSG00000134460 IL2RA IL-2 receptor x subunit alpha ENSG00000110324 IL10RA IL-10 receptor x subunit alpha ENSG00000115604 IL18R1 IL-18 receptor 1 x ENSG00000115607 IL18RAP IL-18 receptor x accessory protein ENSG00000081985 IL12RB2 IL-12 receptor x beta 2 ENSG00000186810 CXCR3 CXCR3 x x ENSG00000005844 ITGAL CD11a x ENSG00000160255 ITGB2 CD18; Integrin x x beta-2 ENSG00000156886 ITGAD CD11d x ENSG00000140678 ITGAX; CD11c x x Integrin alpha-X ENSG00000115232 ITGA4 CD49d; Integrin x x alpha-4 ENSG00000169896 ITGAM CD11b; Integrin x x alpha-M ENSG00000138378 STAT4 Stat4 x ENSG00000115415 STAT1 Stat1 x ENSG00000170581 STAT2 Stat2 x ENSG00000126561 STAT5a Stat5a x ENSG00000162434 JAK1 Jak1 x ENSG00000100453 GZMB Granzyme B x ENSG00000145649 GZMA Granzyme A x ENSG00000180644 PRF1 Perforin 1 x ENSG00000115523 GNLY Granulysin x ENSG00000100450 GZMH Granzyme H x ENSG00000113088 GZMK Granzyme K x ENSG00000057657 PRDM1 Blimp-1 x ENSG00000073861 TBX21 T-bet x ENSG00000115738 ID2 ID2 x ENSG00000176083 ZNF683 Hobit x ENSG00000137265 IRF4 Interferon x regulatory factor 4 ENSG00000140968 IRF8 Interferon -
Strategies to Increase ß-Cell Mass Expansion
This electronic thesis or dissertation has been downloaded from the King’s Research Portal at https://kclpure.kcl.ac.uk/portal/ Strategies to increase -cell mass expansion Drynda, Robert Lech Awarding institution: King's College London The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without proper acknowledgement. END USER LICENCE AGREEMENT Unless another licence is stated on the immediately following page this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ You are free to copy, distribute and transmit the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author (but not in any way that suggests that they endorse you or your use of the work). Non Commercial: You may not use this work for commercial purposes. No Derivative Works - You may not alter, transform, or build upon this work. Any of these conditions can be waived if you receive permission from the author. Your fair dealings and other rights are in no way affected by the above. Take down policy If you believe that this document breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Strategies to increase β-cell mass expansion A thesis submitted by Robert Drynda For the degree of Doctor of Philosophy from King’s College London Diabetes Research Group Division of Diabetes & Nutritional Sciences Faculty of Life Sciences & Medicine King’s College London 2017 Table of contents Table of contents ................................................................................................. -
Recombinant Human FLRT1 Catalog Number: 2794-FL
Recombinant Human FLRT1 Catalog Number: 2794-FL DESCRIPTION Source Mouse myeloma cell line, NS0derived human FLRT1 protein Ile21Pro524, with a Cterminal 6His tag Accession # Q9NZU1 Nterminal Sequence Ile21 Analysis Predicted Molecular 56.3 kDa Mass SPECIFICATIONS SDSPAGE 7080 kDa, reducing conditions Activity Measured by the ability of the immobilized protein to support the adhesion of Neuro2A mouse neuroblastoma cells. Recombinant Human FLRT1 immobilized at 2.5 μg/mL, 100 μL/well, will meidate > 50% Neuro2A cell adhesion. Optimal dilutions should be determined by each laboratory for each application. Endotoxin Level <0.10 EU per 1 μg of the protein by the LAL method. Purity >95%, by SDSPAGE under reducing conditions and visualized by silver stain. Formulation Lyophilized from a 0.2 μm filtered solution in PBS. See Certificate of Analysis for details. PREPARATION AND STORAGE Reconstitution Reconstitute at 200 μg/mL in sterile PBS. Shipping The product is shipped at ambient temperature. Upon receipt, store it immediately at the temperature recommended below. Stability & Storage Use a manual defrost freezer and avoid repeated freezethaw cycles. l 12 months from date of receipt, 20 to 70 °C as supplied. l 1 month, 2 to 8 °C under sterile conditions after reconstitution. l 3 months, 20 to 70 °C under sterile conditions after reconstitution. BACKGROUND FLRT1 is one of three FLRT (fibronectin, leucine rich repeat, transmembrane) glycoproteins expressed in distinct areas of the developing brain and other tissues (1, 2). The 90 kDa type I transmembrane (TM) human FLRT1 is synthesized as a 646 amino acid (aa) precursor with a 20 aa signal sequence, a 504 aa extracellular domain (ECD), a 21 aa TM segment and a 101 aa cytoplasmic region. -
Supp Material.Pdf
Legends for Supplemental Figures and Tables Figure S1. Expression of Tlx during retinogenesis. (A) Staged embryos were stained for β- galactosidase knocked into the Tlx locus to indicate Tlx expression. Tlx was expressed in the neural blast layer in the early phase of neural retina development (blue signal). (B) Expression of Tlx in neural retina was quantified using Q-PCR at multiple developmental stages. Figure S2. Expression of p27kip1 and cyclin D1 (Ccnd1) at various developmental stages in wild-type or Tlx-/- retinas. (A) Q-PCR analysis of p27kip1 mRNA expression. (B) Western blotting analysis of p27kip1 protein expression. (C) Q-PCR analysis of cyclin D1 mRNA expression. Figure S3. Q-PCR analysis of mRNA expression of Sf1 (A), Lrh1 (B), and Atn1 (C) in wild-type mouse retinas. RNAs from testis and liver were used as controls. Table S1. List of genes dysregulated both at E15.5 and P0 Tlx-/- retinas. Gene E15.5 P0 Cluste Gene Title Fold Fold r Name p-value p-value Change Change nuclear receptor subfamily 0, group B, Nr0b1 1.65 0.0024 2.99 0.0035 member 1 1 Pou4f3 1.91 0.0162 2.39 0.0031 POU domain, class 4, transcription factor 3 1 Tcfap2d 2.18 0.0000 2.37 0.0001 transcription factor AP-2, delta 1 Zic5 1.66 0.0002 2.02 0.0218 zinc finger protein of the cerebellum 5 1 Zfpm1 1.85 0.0030 1.88 0.0025 zinc finger protein, multitype 1 1 Pten 1.60 0.0155 1.82 0.0131 phospatase and tensin homolog 2 Itgb5 -1.85 0.0063 -1.85 0.0007 integrin beta 5 2 Gpr49 6.86 0.0001 15.16 0.0001 G protein-coupled receptor 49 3 Cmkor1 2.60 0.0007 2.72 0.0013 -
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. -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
NIH Public Access Author Manuscript Science
NIH Public Access Author Manuscript Science. Author manuscript; available in PMC 2014 September 08. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Science. 2014 January 31; 343(6170): 506–511. doi:10.1126/science.1247363. Exome Sequencing Links Corticospinal Motor Neuron Disease to Common Neurodegenerative Disorders A full list of authors and affiliations appears at the end of the article. # These authors contributed equally to this work. Abstract Hereditary spastic paraplegias (HSPs) are neurodegenerative motor neuron diseases characterized by progressive age-dependent loss of corticospinal motor tract function. Although the genetic basis is partly understood, only a fraction of cases can receive a genetic diagnosis, and a global view of HSP is lacking. By using whole-exome sequencing in combination with network analysis, we identified 18 previously unknown putative HSP genes and validated nearly all of these genes functionally or genetically. The pathways highlighted by these mutations link HSP to cellular transport, nucleotide metabolism, and synapse and axon development. Network analysis revealed a host of further candidate genes, of which three were mutated in our cohort. Our analysis links HSP to other neurodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease. Hereditary spastic paraplegias (HSPs) are a group of genetically heterogeneous neurodegenerative disorders with prevalence between 3 and 10 per 100,000 individuals (1). Hallmark features are axonal degeneration and progressive lower limb spasticity resulting from a loss of corticospinal tract (CST) function. HSP is classified into two broad categories, uncomplicated and complicated, on the basis of the presence of additional clinical features such as intellectual disability, seizures, ataxia, peripheral neuropathy, skin abnormalities, and visual defects. -
Edinburgh Research Explorer
Edinburgh Research Explorer International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list Citation for published version: Davenport, AP, Alexander, SPH, Sharman, JL, Pawson, AJ, Benson, HE, Monaghan, AE, Liew, WC, Mpamhanga, CP, Bonner, TI, Neubig, RR, Pin, JP, Spedding, M & Harmar, AJ 2013, 'International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings with cognate ligands', Pharmacological reviews, vol. 65, no. 3, pp. 967-86. https://doi.org/10.1124/pr.112.007179 Digital Object Identifier (DOI): 10.1124/pr.112.007179 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Pharmacological reviews Publisher Rights Statement: U.S. Government work not protected by U.S. copyright General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 1521-0081/65/3/967–986$25.00 http://dx.doi.org/10.1124/pr.112.007179 PHARMACOLOGICAL REVIEWS Pharmacol Rev 65:967–986, July 2013 U.S. -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
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. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,