Inflammation and Regulatory T Cell Genes Are Differentially Expressed in Peripheral Blood Mononuclear Cells of Parkinson's
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The Ligands for Human Igg and Their Effector Functions
antibodies Review The Ligands for Human IgG and Their Effector Functions Steven W. de Taeye 1,2,*, Theo Rispens 1 and Gestur Vidarsson 2 1 Sanquin Research, Dept Immunopathology and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, 1066 CX Amsterdam, The Netherlands; [email protected] 2 Sanquin Research, Dept Experimental Immunohematology and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, 1066 CX Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected] Received: 26 March 2019; Accepted: 18 April 2019; Published: 25 April 2019 Abstract: Activation of the humoral immune system is initiated when antibodies recognize an antigen and trigger effector functions through the interaction with Fc engaging molecules. The most abundant immunoglobulin isotype in serum is Immunoglobulin G (IgG), which is involved in many humoral immune responses, strongly interacting with effector molecules. The IgG subclass, allotype, and glycosylation pattern, among other factors, determine the interaction strength of the IgG-Fc domain with these Fc engaging molecules, and thereby the potential strength of their effector potential. The molecules responsible for the effector phase include the classical IgG-Fc receptors (FcγR), the neonatal Fc-receptor (FcRn), the Tripartite motif-containing protein 21 (TRIM21), the first component of the classical complement cascade (C1), and possibly, the Fc-receptor-like receptors (FcRL4/5). Here we provide an overview of the interactions of IgG with effector molecules and discuss how natural variation on the antibody and effector molecule side shapes the biological activities of antibodies. The increasing knowledge on the Fc-mediated effector functions of antibodies drives the development of better therapeutic antibodies for cancer immunotherapy or treatment of autoimmune diseases. -
C8cc08685k1.Pdf
Electronic Supplementary Material (ESI) for ChemComm. This journal is © The Royal Society of Chemistry 2018 Supporting Information Minimalist Linkers Suitable for Irreversible Inhibitors in Simultaneous Proteome Profiling, Live-Cell Imaging and Drug Screening Cuiping Guo,Yu Chang, Xin Wang, Chengqian Zhang, Piliang Hao*, Ke Ding and Zhengqiu Li* School of Pharmacy, Jinan University, Guangzhou, China 510632 *Corresponding author ([email protected]) 1. General Information All chemicals were purchased from commercial vendors and used without further purification, unless indicated otherwise. All reactions requiring anhydrous conditions were carried out under argon or nitrogen atmosphere using oven-dried glassware. AR-grade solvents were used for all reactions. Reaction progress was monitored by TLC on pre-coated silica plates (Merck 60 F254 nm, 0.25 µm) and spots were visualized by UV, iodine or other suitable stains. Flash column chromatography was carried out using silica gel (Qingdao Ocean). All NMR spectra (1H-NMR, 13C-NMR) were recorded on Bruker 300 MHz/400 MHz NMR spectrometers. Chemical shifts were reported in parts per million (ppm) referenced with respect to appropriate internal standards or residual solvent peaks (CDCl3 = 7.26 ppm, DMSO-d6 = 2.50 ppm). The following abbreviations were used in reporting spectra, br s (broad singlet), s (singlet), d (doublet), t (triplet), q (quartet), m (multiplet), dd (doublet of doublets). Mass spectra were obtained on Agilent LC-ESI-MS system. All analytical HPLC were carried out on Agilent system. Water with 0.1% TFA and acetonitrile with 0.1% TFA were used as eluents and the flow rate was 0.5 mL/min. -
CENPT (NM 025082) Human 3' UTR Clone – SC200995 | Origene
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for SC200995 CENPT (NM_025082) Human 3' UTR Clone Product data: Product Type: 3' UTR Clones Product Name: CENPT (NM_025082) Human 3' UTR Clone Vector: pMirTarget (PS100062) Symbol: CENPT Synonyms: C16orf56; CENP-T; SSMGA ACCN: NM_025082 Insert Size: 140 bp Insert Sequence: >SC200995 3’UTR clone of NM_025082 The sequence shown below is from the reference sequence of NM_025082. The complete sequence of this clone may contain minor differences, such as SNPs. Blue=Stop Codon Red=Cloning site GGCAAGTTGGACGCCCGCAAGATCCGCGAGATTCTCATTAAGGCCAAGAAGGGCGGAAAGATCGCCGTG TAACAATTGGCAGAGCTCAGAATTCAAGCGATCGCC AGTGGCAACTCTGTCTTCCCTGCCCAGTAGTGGCCAGGCTTCAACACTTTCCCTGTCCCCACCTGGGGA CTCTTGCCCCCACATATTTCTCCAGGTCTCCTCCCCACCCCCCCAGCATCAATAAAGTGTCATAAACAG AA ACGCGTAAGCGGCCGCGGCATCTAGATTCGAAGAAAATGACCGACCAAGCGACGCCCAACCTGCCATCA CGAGATTTCGATTCCACCGCCGCCTTCTATGAAAGG Restriction Sites: SgfI-MluI OTI Disclaimer: Our molecular clone sequence data has been matched to the sequence identifier above as a point of reference. Note that the complete sequence of this clone is largely the same as the reference sequence but may contain minor differences , e.g., single nucleotide polymorphisms (SNPs). RefSeq: NM_025082.4 Summary: The centromere is a specialized chromatin domain, present throughout the cell cycle, that acts as a platform on which the transient assembly of the kinetochore occurs during mitosis. All active centromeres are characterized by the presence of long arrays of nucleosomes in which CENPA (MIM 117139) replaces histone H3 (see MIM 601128). CENPT is an additional factor required for centromere assembly (Foltz et al., 2006 [PubMed 16622419]).[supplied by OMIM, Mar 2008] Locus ID: 80152 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. -
Annual Report 2015
McGill University Department of Microbiology and Immunology 2015 Annual Report Submitted by J. Madrenas, MD, PhD, FCAHS Canada Research Chair in Human Immunology Professor and Chairman On behalf of Primary members of the Department of Microbiology and Immunology Section I Introduction A. Executive Summary 2015 was an exciting year in the life of the Department of Microbiology and Immunology at McGill. During 2015, we had an External Cyclical Review of the Department, the first review of its kind for (at least) the last 22 years. This process identified key strengths and challenges for our unit, and coalesced different strategic initiatives in consultation with the leadership in the Faculty of Medicine and the Provost office. In 2015, the department welcomed Dr. Corinne Maurice arriving from Harvard University. Corinne started as a tenure track assistant professor on January 1. Her arrival was the fourth recruitment in the context of departmental faculty renewal in the last three years. In addition, we completed the selection process to recruit a Virologist to the Department: the selected candidate was Dr. Jacques Archambault, who will be joining on August 1, 2016 as a Full Professor with tenure. We successfully completed the promotion to Full Professors of Drs. Ciriacco Piccirillo and Donald Sheppard and the 3 yr. reappointment process for Dr. Selena Sagan, and initiated the promotion to Associate Professor with tenure for Dr. Jorg Fritz (a process successfully completed in May 2016). In Research, our members were very successful in attracting high calibre awards. Dr. Mark Wainberg was a 2016 Canadian Medical Hall of Fame inductee. Dr. -
Gene Knockdown of CENPA Reduces Sphere Forming Ability and Stemness of Glioblastoma Initiating Cells
Neuroepigenetics 7 (2016) 6–18 Contents lists available at ScienceDirect Neuroepigenetics journal homepage: www.elsevier.com/locate/nepig Gene knockdown of CENPA reduces sphere forming ability and stemness of glioblastoma initiating cells Jinan Behnan a,1, Zanina Grieg b,c,1, Mrinal Joel b,c, Ingunn Ramsness c, Biljana Stangeland a,b,⁎ a Department of Molecular Medicine, Institute of Basic Medical Sciences, The Medical Faculty, University of Oslo, Oslo, Norway b Norwegian Center for Stem Cell Research, Department of Immunology and Transfusion Medicine, Oslo University Hospital, Oslo, Norway c Vilhelm Magnus Laboratory for Neurosurgical Research, Institute for Surgical Research and Department of Neurosurgery, Oslo University Hospital, Oslo, Norway article info abstract Article history: CENPA is a centromere-associated variant of histone H3 implicated in numerous malignancies. However, the Received 20 May 2016 role of this protein in glioblastoma (GBM) has not been demonstrated. GBM is one of the most aggressive Received in revised form 23 July 2016 human cancers. GBM initiating cells (GICs), contained within these tumors are deemed to convey Accepted 2 August 2016 characteristics such as invasiveness and resistance to therapy. Therefore, there is a strong rationale for targeting these cells. We investigated the expression of CENPA and other centromeric proteins (CENPs) in Keywords: fi CENPA GICs, GBM and variety of other cell types and tissues. Bioinformatics analysis identi ed the gene signature: fi Centromeric proteins high_CENP(AEFNM)/low_CENP(BCTQ) whose expression correlated with signi cantly worse GBM patient Glioblastoma survival. GBM Knockdown of CENPA reduced sphere forming ability, proliferation and cell viability of GICs. We also Brain tumor detected significant reduction in the expression of stemness marker SOX2 and the proliferation marker Glioblastoma initiating cells and therapeutic Ki67. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Identification and Characterization of Tissue-Resident Memory T Cells in Humans Brahma Vencel Kumar
Identification and characterization of tissue-resident memory T cells in humans Brahma Vencel Kumar Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2018 ©2017 Brahma Vencel Kumar All rights reserved ABSTRACT Identification and characterization of tissue-resident memory T cells in humans Brahma Vencel Kumar Memory T cells are critical for maintaining lifelong immunity by protecting against reinfection with previously encountered pathogens. In recent years, a subset of memory T cells termed tissue-resident memory T cells (TRM) has emerged as the primary mediator of protection at many tissue sites. Numerous studies in mice have demonstrated that TRM accelerate pathogen clearance compared with other subsets of memory T cells. The defining characteristic of TRM is that they are retained within tissues and do not circulate in the blood. The lack of TRM in blood has proved to be a barrier for investigating the role of TRM in healthy humans. As a result, there are many outstanding questions about TRM biology in humans, including which phenotypic markers identify TRM, if TRM represent a unique memory subset, as well as defining transcriptional and functional characteristics of this subset. Through a unique collaboration with the local organ procurement agency, we obtained samples from >15 tissue sites from healthy organ donors of all ages. We found that the surface marker CD69 was expressed by memory CD4 + and CD8 + T cells in multiple tissues including spleen and other lymphoid tissues, lung, and intestines, but not in blood, suggesting that this marker may identify TRM in human tissues. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
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. -
Divergence of Human and Mouse Brain Transcriptome Highlights Alzheimer Disease Pathways
Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways Jeremy A. Millera,b, Steve Horvathc,d, and Daniel H. Geschwindb,c,1 aInterdepartmental Program for Neuroscience, Departments of cHuman Genetics and dBiostatistics, and bProgram in Neurogenetics and Neurobehavioral Genetics, Department of Neurology and Semel Institute, University of California, Los Angeles, CA 90095-1769 Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved June 1, 2010 (received for review December 10, 2009) Because mouse models play a crucial role in biomedical research works by merging data from many microarray studies, and then related to the human nervous system, understanding the similari- applying weighted gene coexpression network analysis (WGCNA) ties and differences between mouse and human brain is of (4–7). WGCNA elucidates the higher-order relationships between fundamental importance. Studies comparing transcription in hu- genes based on their coexpression relationships, delineating modules man and mouse have come to varied conclusions, in part because of of biologically related genes and permitting a robust view of their relatively small sample sizes or underpowered methodologies. transcriptome organization (3, 5, 8, 9). Within groups of highly coexpressed genes (“modules”) that comprise the core functional To better characterize gene expression differences between mouse fi and human, we took a systems-biology approach by using weighted units of the transcriptional network, WGCNA also identi es gene coexpression network analysis on more than 1,000 micro- the most highly connected, or most central genes within each module, referred to as “hubs.” We find that both gene expression arrays from brain. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
Atrial Fibrillation (ATRIA) Study
European Journal of Human Genetics (2014) 22, 297–306 & 2014 Macmillan Publishers Limited All rights reserved 1018-4813/14 www.nature.com/ejhg REVIEW Atrial fibrillation: the role of common and rare genetic variants Morten S Olesen*,1,2,4, Morten W Nielsen1,2,4, Stig Haunsø1,2,3 and Jesper H Svendsen1,2,3 Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting 1–2% of the general population. A number of studies have demonstrated that AF, and in particular lone AF, has a substantial genetic component. Monogenic mutations in lone and familial AF, although rare, have been recognized for many years. Presently, mutations in 25 genes have been associated with AF. However, the complexity of monogenic AF is illustrated by the recent finding that both gain- and loss-of-function mutations in the same gene can cause AF. Genome-wide association studies (GWAS) have indicated that common single-nucleotide polymorphisms (SNPs) have a role in the development of AF. Following the first GWAS discovering the association between PITX2 and AF, several new GWAS reports have identified SNPs associated with susceptibility of AF. To date, nine SNPs have been associated with AF. The exact biological pathways involving these SNPs and the development of AF are now starting to be elucidated. Since the first GWAS, the number of papers concerning the genetic basis of AF has increased drastically and the majority of these papers are for the first time included in a review. In this review, we discuss the genetic basis of AF and the role of both common and rare genetic variants in the susceptibility of developing AF.