Haplotype-Specific Gene Expression Profiles in a Telomeric Major Histocompatibility Complex Gene Cluster and Susceptibility to Autoimmune Diseases
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Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
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. -
The Porcine Major Histocompatibility Complex and Related Paralogous Regions: a Review Patrick Chardon, Christine Renard, Claire Gaillard, Marcel Vaiman
The porcine Major Histocompatibility Complex and related paralogous regions: a review Patrick Chardon, Christine Renard, Claire Gaillard, Marcel Vaiman To cite this version: Patrick Chardon, Christine Renard, Claire Gaillard, Marcel Vaiman. The porcine Major Histocom- patibility Complex and related paralogous regions: a review. Genetics Selection Evolution, BioMed Central, 2000, 32 (2), pp.109-128. 10.1051/gse:2000101. hal-00894302 HAL Id: hal-00894302 https://hal.archives-ouvertes.fr/hal-00894302 Submitted on 1 Jan 2000 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Genet. Sel. Evol. 32 (2000) 109–128 109 c INRA, EDP Sciences Review The porcine Major Histocompatibility Complex and related paralogous regions: a review Patrick CHARDON, Christine RENARD, Claire ROGEL GAILLARD, Marcel VAIMAN Laboratoire de radiobiologie et d’etude du genome, Departement de genetique animale, Institut national de la recherche agronomique, Commissariat al’energie atomique, 78352, Jouy-en-Josas Cedex, France (Received 18 November 1999; accepted 17 January 2000) Abstract – The physical alignment of the entire region of the pig major histocompat- ibility complex (MHC) has been almost completed. In swine, the MHC is called the SLA (swine leukocyte antigen) and most of its class I region has been sequenced. -
Genetic Predictors of Response to Anti-Tumor Necrosis Factor Drugs In
Rheumatology Reports 2009; volume 1:e1 Genetic predictors of response of the human immunoglobulin Ig-G1. It works by binding to soluble TNF and neutralizing it Correspondence: Anne Barton, to anti-tumor necrosis factor but can also bind lymphotoxin-α (LTA). Arthritis Research Campaign Epidemiology Unit, drugs in rheumatoid arthritis Adalimumab and infliximab are monoclonal University of Manchester, M13 9PT, UK antibodies directed against membrane-bound E-mail: [email protected] Rachael Tan and Anne Barton and soluble TNFα.1 Whilst DMARDs may slow radiological progression, studies using anti- Key words: genetics, rheumatoid arthritis, ARC Epidemiology Unit, Stopford response, etanercept, infliximab, adalimumab. Building, The University of Manchester, TNF drugs have shown their superiority in suppressing structural change.2 However, anti- Manchester, UK Conflict of interest: the authors reported no potential TNFs are not without their drawbacks. As well conflict of interests. as being expensive, studies have shown links with malignancy and serious infections.3 Received for publication: 24 March 2009. Furthermore, for unknown reasons, up to 30% Revision received: 18 May 2009. Abstract of patients fail to respond to treatment with Accepted for publication: 18 May 2009. anti-TNFs.4 In many countries, these concerns This work is licensed under a Creative Commons limit the wholesale use of these drugs. In the The introduction of anti-tumor necrosis fac- Attribution 3.0 License (by-nc 3.0). tor (anti-TNF) agents has dramatically UK, for example, the National Institute for improved the outlook for many patients with Health and Clinical Excellence (NICE) advises ©Copyright R. Tan and A. -
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 -
What Is the Extent of the Role Played by Genetic Factors in Periodontitis?
Perio Insight 4 Summer 2017 www.efp.org/perioinsight DEBATE EXPERT VIEW FOCUS RESEARCH Editor: Joanna Kamma What is the extent of the role played by genetic factors in periodontitis? Discover latest JCP research hile genetic factors are known to play a role in In a comprehensive overview of current knowledge, The Journal of Clinical Periodontology Wperiodontitis, a lot of research still needs to be they outline what is known today and discuss the (JCP) is the official scientific publication done to understand the mechanisms that are involved. most promising lines for future inquiry. of the European Federation of Indeed, the limited extent to which the genetic They note that the phenotypes of aggressive Periodontology (EFP). It publishes factors associated with periodontitis have been periodontitis (AgP) and chronic periodontitis (CP) may research relating to periodontal and peri- identified is “somewhat disappointing”, say Bruno not be as distinct as previously assumed because they implant diseases and their treatment. G. Loos and Deon P.M. Chin, from the Academic share genetic and other risk factors. The six JCP articles summarised in this Centre for Dentistry in Amsterdam. More on pages 2-5 edition of Perio Insight cover: (1) how periodontitis changes renal structures by oxidative stress and lipid peroxidation; (2) gingivitis and lifestyle influences on high-sensitivity C-reactive protein and interleukin 6 in adolescents; (3) the long-term efficacy of periodontal Researchers call regenerative therapies; (4) tooth loss in generalised aggressive periodontitis; (5) the association between diabetes for global action mellitus/hyperglycaemia and peri- implant diseases; (6) the efficacy of on periodontal collagen matrix seal and collagen sponge on ridge preservation in disease combination with bone allograft. -
Supplementary Table 1. Pain and PTSS Associated Genes (N = 604
Supplementary Table 1. Pain and PTSS associated genes (n = 604) compiled from three established pain gene databases (PainNetworks,[61] Algynomics,[52] and PainGenes[42]) and one PTSS gene database (PTSDgene[88]). These genes were used in in silico analyses aimed at identifying miRNA that are predicted to preferentially target this list genes vs. a random set of genes (of the same length). ABCC4 ACE2 ACHE ACPP ACSL1 ADAM11 ADAMTS5 ADCY5 ADCYAP1 ADCYAP1R1 ADM ADORA2A ADORA2B ADRA1A ADRA1B ADRA1D ADRA2A ADRA2C ADRB1 ADRB2 ADRB3 ADRBK1 ADRBK2 AGTR2 ALOX12 ANO1 ANO3 APOE APP AQP1 AQP4 ARL5B ARRB1 ARRB2 ASIC1 ASIC2 ATF1 ATF3 ATF6B ATP1A1 ATP1B3 ATP2B1 ATP6V1A ATP6V1B2 ATP6V1G2 AVPR1A AVPR2 BACE1 BAMBI BDKRB2 BDNF BHLHE22 BTG2 CA8 CACNA1A CACNA1B CACNA1C CACNA1E CACNA1G CACNA1H CACNA2D1 CACNA2D2 CACNA2D3 CACNB3 CACNG2 CALB1 CALCRL CALM2 CAMK2A CAMK2B CAMK4 CAT CCK CCKAR CCKBR CCL2 CCL3 CCL4 CCR1 CCR7 CD274 CD38 CD4 CD40 CDH11 CDK5 CDK5R1 CDKN1A CHRM1 CHRM2 CHRM3 CHRM5 CHRNA5 CHRNA7 CHRNB2 CHRNB4 CHUK CLCN6 CLOCK CNGA3 CNR1 COL11A2 COL9A1 COMT COQ10A CPN1 CPS1 CREB1 CRH CRHBP CRHR1 CRHR2 CRIP2 CRYAA CSF2 CSF2RB CSK CSMD1 CSNK1A1 CSNK1E CTSB CTSS CX3CL1 CXCL5 CXCR3 CXCR4 CYBB CYP19A1 CYP2D6 CYP3A4 DAB1 DAO DBH DBI DICER1 DISC1 DLG2 DLG4 DPCR1 DPP4 DRD1 DRD2 DRD3 DRD4 DRGX DTNBP1 DUSP6 ECE2 EDN1 EDNRA EDNRB EFNB1 EFNB2 EGF EGFR EGR1 EGR3 ENPP2 EPB41L2 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 EPHX2 ERBB2 ERBB4 EREG ESR1 ESR2 ETV1 EZR F2R F2RL1 F2RL2 FAAH FAM19A4 FGF2 FKBP5 FLOT1 FMR1 FOS FOSB FOSL2 FOXN1 FRMPD4 FSTL1 FYN GABARAPL1 GABBR1 GABBR2 GABRA2 GABRA4 -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PubMed Central Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes Regine Bergholdt1*, Caroline Brorsson1, Kasper Lage2,3,4, Jens Høiriis Nielsen5, Søren Brunak2, Flemming Pociot1,6,7 1 Hagedorn Research Institute and Steno Diabetes Center, Gentofte, Denmark, 2 Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark, 3 Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 4 Broad Institute of MIT and Harvard, Seven Cambridge Center, Cambridge, Massachusetts, United States of America, 5 Department of Medical Biochemistry and Genetics, University of Copenhagen, Copenhagen, Denmark, 6 University of Lund/Clinical Research Centre (CRC), Malmø, Sweden, 7 Department of Biomedical Science, University of Copenhagen, Copenhagen, Denmark Abstract Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have previously identified sets of genes, likely to represent distinct cellular pathways involved in T1D risk. Here we evaluate the candidate genes involved in these putative interaction networks not only at the single gene level, but also in the context of the networks of which they form an integral part. mRNA expression levels for each gene were evaluated and profiling was performed by measuring and comparing constitutive expression in human islets versus cytokine-stimulated expression levels, and for lymphocytes by comparing expression levels among controls and T1D individuals. -
Chromosome Heatmap in CDK Patients As Defined by Multiregional Sequencing on Illumina Miseq Platform
ORIGINAL ARTICLE European Journal of Medical and Health Sciences www.ejmed.org Chromosome HeatMap in CDK Patients as Defined by Multiregional Sequencing on Illumina MiSeq Platform Mohammad F. Fazaludeen, Aymen A. Warille, Mohd Ibrahim Alaraj, Edem Nuglozeh ABSTRACT Renal failure and kidney disease are major concerns worldwide and are commonly coupled to diseases like hypertension, diabetes, obesity, and Published Online: November 16, 2020 hypercholesterolemia. We undertook this study to explore the scope of ISSN: 2593-8339 genetic spectrum underlying the physiopathology of end-stage renal disease (ESRD) using whole exome sequencing (WES) on genomic DNA (gDNA) DOI: 10.24018/ejmed.2020.2.6.525 from 12 unrelated patients in younger ages. We have performed WES on 12 patients in stage of ESRD and analyze the FASTQ data through GATK Mohammad F. Fazaludeen pipeline. Here, we report for the first time a novel approach of establishing A. I. Virtanen Institute for Molecular the severity and the magnitude of a disease on different chromosomes and Sciences, University of Eastern Finland, associated karyotypes using chromosome Heatmap. The chromosome Heat Finland. Aymen A. Warille will provide us with a road map to narrow down mutations selection Department of Histology and leading us to SNPs characterization. Our preliminary results presented in Embryology, Faculty of Medicine, the form of chromosomes HeatMap prelude our ongoing works which Ondokuz Mayis University, Turkey. consist in identifying and characterizing new genes involved in the problem Mohd Ibrahim Alaraj of renal diseases, results that depict the magnitude of the uncovered genes Department of Pharmacology, Faculty of mutations and their biological implications related to the genome of these Medicine, University of Hail, Saudi patients. -
Transcription Mediated Insulation and Interference Direct Gene Cluster
1 Transcription mediated insulation and interference direct gene 2 cluster expression switches 3 Tania Nguyen1, Harry Fischl1#, Françoise S. Howe1#, Ronja Woloszczuk1#, Ana Serra 4 Barros1*, Zhenyu Xu2*, David Brown1, Struan C. Murray1, Simon Haenni1, James M. 5 Halstead1, Leigh O’Connor1, Gergana Shipkovenska1, Lars M. Steinmetz2 and Jane Mellor1§ 6 1Department of Biochemistry, South Parks Road, Oxford, OX1 3QU, UK 7 2European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, 8 Germany 9 10 . 11 # , * Equal contribution 12 § Corresponding author: [email protected] 13 14 Competing interests statement: JM is an advisor to Oxford Biodynamics Ltd and Sibelius Ltd and sits 15 on the board of Chronos Therapeutics Ltd. OBD supported this work but have no commercial 16 interest in the study design, data collection and interpretation, or the decision to submit the work 17 for publication. 1 18 Abstract 19 In yeast, many tandemly arranged genes show peak expression in different phases of the 20 metabolic cycle (YMC) or in different carbon sources, indicative of regulation by a bi- 21 modal switch, but it is not clear how these switches are controlled. Using native 22 elongating transcript analysis (NET-seq), we show that transcription itself is a component 23 of bi-modal switches, facilitating reciprocal expression in gene clusters. HMS2, encoding a 24 growth-regulated transcription factor, switches between sense- or antisense-dominant 25 states that also coordinate up- and down-regulation of transcription at neighbouring 26 genes. Engineering HMS2 reveals alternative mono-, di- or tri-cistronic and antisense 27 transcription units (TUs), using different promoter and terminator combinations, that 28 underlie state-switching.