DOCSLIB.ORG

The Role of Genetic Variation in Predisposition to Alcohol-Related Chronic Pancreatitis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Role of Genetic Variation in Predisposition to Alcohol-Related Chronic Pancreatitis The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis Thesis submitted in accordance with the requirements of the University of Liverpool for the degree of Doctor in Philosophy by Marianne Lucy Johnstone April 2015 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 Abstract Background Chronic pancreatitis (CP) is a disease of fibrosis of the pancreas for which alcohol is the main causative agent. However, only a small proportion of alcoholics develop chronic pancreatitis. Genetic polymorphism may affect pancreatitis risk. Aim To determine the factors required to classify a chronic pancreatic population and identify genetic variations that may explain why only some alcoholics develop chronic pancreatitis. Methods The most appropriate method of diagnosing CP was assessed using a systematic review. Genetics of different populations of alcohol-related chronic pancreatitics (ACP) were explored using four different techniques: genome-wide association study (GWAS); custom arrays; PCR of variable nucleotide tandem repeats (VNTR) and next generation sequencing (NGS) of selected genes. Results EUS and sMR were identified as giving the overall best sensitivity and specificity for diagnosing CP. GWAS revealed two associations with CP (identified and replicated) at PRSS1-PRSS2_rs10273639 (OR 0.73, 95% CI 0.68-0.79) and X-linked CLDN2_rs12688220 (OR 1.39, 1.28-1.49) and the association was more pronounced in the ACP group (OR 0.56, 0.48-0.64)and OR 2.11, 1.84-2.42). The previously identified VNTR in CEL was shown to have a lower frequency of the normal repeat in ACP than alcoholic liver disease (ALD; OR 0.61, 0.41-0.93). Homozygosity of the normal variant was more common in ALD than ACP (OR 0.53, 0.3-0.96) or Healthy Controls (OR 0.55, 0.3-1.00)). The NGS discovery phase lead on to validation of the 21 most significant SNPs with Sequenom array. This showed significance difference between ACP and ALD in allele frequency of the synonymous SNP, PRSS1_rs6666, (OR 1.99, 1.46-2.72) Conclusion A range of potential exonic and intronic sites have been identified that have association with a predisposition to developing chronic pancreatitis. These findings show that further work is justified to fully assess the interaction of the different polymorphisms and their phenotypic significance in development of the disease. Abstract 2 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 Declaration I declare that this thesis and the research upon which is based is the result of my own work. Whereever I have incorporated the work of others it has been clearly stated. This work has not previously been submitted in any substance for any degree, not is it concurrenly being submitted in candidiature for this or at any other university. Part of this work has been published in: David C Whitcomb, Jessica LaRusch, Alyssa M Krasinskas, Lambertus Klei, Jill P Smith, Randall E Brand, John P Neoptolemos, Markus M Lerch, Matt Tector, Bimaljit S Sandhu, Nalini M Guda, Lidiya Orlichenko, Alzheimer’s Disease Genetics Consortium, Samer Alkaade, Stephen T Amann, Michelle A Anderson, John Baillie, Peter A Banks, Darwin Conwell, Gregory A Coté, Peter B Cotton, James DiSario, Lindsay A Farrer, Chris E Forsmark, Marianne Johnstone, Timothy B Gardner, Andres Gelrud, William Greenhalf, Jonathan L Haines, Douglas J Hartman, Robert A Hawes, Christopher Lawrence, Michele Lewis, Julia Mayerle, Richard Mayeux, Nadine M Melhem, Mary E Money, Thiruvengadam Muniraj, Georgios I Papachristou, Margaret A Pericak-Vance, Joseph Romagnuolo, Gerard D Schellenberg, Stuart Sherman, Peter Simon, Vijay P Singh, Adam Slivka, Donna Stolz, Robert Sutton, Frank Ulrich Weiss, C Mel Wilcox, Narcis Octavian Zarnescu, Stephen R Wisniewski, Michael R O’Connell, Michelle L Kienholz, Kathryn Roeder, M Michael Barmada, Dhiraj Yadav & Bernie Devlin Common genetic variants in the CLDN2 and PRSS1-PRSS2 loci alter risk for alcohol-related and sporadic pancreatitis Nature Genetics 2012 Dec;44(12):1349-54 Declaration 3 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 James A Nicholson, William Greenhalf, Richard Jackson, Trevor F Cox, Jane V Butler, Thomas Hanna, Sara Harrison, Christopher J Grocock, Christopher Halloran, Nathan R Howes, Michael G Raraty, Paula Ghaneh, Marianne Johnstone, Sanchoy Sarkar, Howard L Smart, Johnathon C Evans, Robert Sutton, John P Neoptolemos, Martin G Lombard Incidence of Post-ERCP Pancreatitis from Direct Pancreatic Juice Collection in Hereditary Pancreatitis and Familial Pancreatic Cancer before and after the Introduction of Prophylactic Pancreatic Stents and Rectal Diclofenac Pancreas. 2014 Nov 26 James A Nicholson, Marianne Johnstone, William Greenhalf Divisum May be Preserving Pancreatic Function in CFTR Patients-But at a Cost American Journal of Gastroenterology. 2012 Nov;107(11):1758-9 (letter) Marianne Johnstone, Richard Jackson, Thomas Hanna, James A Nicholson, William Greenhalf, Robert Sutton Accuracy of diagnostic tests for chronic pancreatitis: systematic review and meta-analyses GUT submitted 2015 Declaration 4 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 ABSTRACT 2 Decloration 3 Contents 5 Figures 10 Tables 13 Abbreviations 16 Acknowledgements 20 1 CHAPTER 1: INTRODUCTION 22 1.1 Chronic Pancreatitis 22 1.1.1 Epidemiology 23 1.1.2 Pathogenesis of Chronic Pancreatitis 25 1.1.3 Aetiology 31 1.1.4 Risk Factors for Chronic Pancreatitis 34 1.1.5 Diagnosis of Chronic Pancreatitis 35 1.1.6 Consequences of the Development of Chronic Pancreatitis 40 1.1.7 Summary 42 1.2 Alcohol Metabolism 43 1.2.1 Ethanol Metabolism 43 1.2.2 Metabolism of Triglycerides 45 1.2.3 Effects of Ethanol Metabolism 46 1.2.4 Ethanol Metabolism and Alcoholism 48 1.2.5 Summary 49 1.3 Alcoholic Liver Disease 50 1.3.1 Definition of Alcoholic Liver Disease 50 1.3.2 Pathophysiology of Alcoholic Liver Disease 51 1.3.3 Natural History of Alcoholic Liver Disease 51 1.3.4 Diagnosis of Alcoholic Liver Disease 52 Contents 5 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 1.3.5 Chronic Pancreatitis and Alcoholic Liver Disease 52 1.3.6 Summary 55 1.4 Genetics 56 1.4.1 Characterising Genetic Differences 56 1.4.2 Genetics of Chronic Pancreatitis 57 1.4.3 The Genetics of Alcohol Metabolism 64 1.4.4 Genetics of Alcohol Liver Disease 70 1.4.5 Methods for Assessing Genetic Variation 71 1.4.6 Summary 76 2 CHAPTER 2: AIM AND OBJECTIVES 77 2.1 Aim 77 2.2 Objectives 77 3 CHAPTER 3: SYSTEMATIC REVIEW OF THE DIAGNOSIS OF CHRONIC PANCREATITIS 78 3.1 Materials and Methods 78 3.1.1 Data Sources and Search Strategy 78 3.1.2 Study Selection 79 3.1.3 Data Extraction 79 3.1.4 Data Synthesis and Analysis 79 3.1.5 Quality Assessment 80 3.1.6 Statistical Analysis 80 3.2 Results 84 3.2.1 Population 84 3.2.2 Gold Standards 84 3.2.3 Index Tests 85 3.2.4 Variation Over Time 85 Contents 6 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 3.2.5 Comparison of Specific Test 85 3.2.6 Publication bias 86 3.3 Discussion 100 4 CHAPTER 4: GENOME-WIDE ASSOCIATION STUDY 104 4.1 Discussion 104 5 CHAPTER 5: NEXT GENERATION SEQUENCING 106 5.1 Materials and Methods 106 5.1.1 Patients and samples 106 5.1.2 DNA Preparation 112 5.1.3 DNA Quality Control 113 5.1.4 Selection of Genes of Interest 114 5.1.5 Sequence Capture (Haloplex) 115 5.1.6 Ion Torrent™ 117 5.1.7 Data Output from Ion Torrent™ 117 5.1.8 Data Analysis from Ion Torrent™ 118 5.1.9 Linkage Disequilibrium 120 5.1.10 Concordance of Results between Modalities 121 5.1.11 Sequenom Validation 121 5.1.12 Haplotype Analysis 122 5.2 Results 123 5.2.1 Patients 123 5.2.2 Next Generation Sequencing Analysis 124 5.2.3 Known Chronic Pancreatitis Associated Variant Analysis 126 5.2.4 Known Variants in Genes of Alcohol Metabolism 131 5.2.5 Next Generation Sequencing SNPs of Interest 135 5.2.6 Biological Effects of SNPs of Interest 138 Contents 7 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 5.2.7 Linkage Disequilibrium 139 5.2.8 Tagging SNPs 139 5.2.9 Sensitivity Sub-analysis of Next Generation Sequencing Samples 142 5.2.10 Internal Validation - Next Generation Sequencing compared with Genome-wide Association Study 145 5.2.11 Next Generation Sequencing Validation with Sequenom (Stage 1) 146 5.2.12 Next Generation Sequencing Validation with Sequenom (Stage 2) 147 5.2.13 Next Generation Sequencing Haplotype Analysis 152 5.3 Discussion 154 5.3.1 Previous Identified Variants 154 5.3.2 Significant SNPs identified in the NGS analysis 156 5.3.3 Quality of Next Generation Sequencing Results 165 5.3.4 Statistical Analysis 167 6 CHAPTER 6: VARIABLE NUCULEOTIDE TANDAM REPEAT IN CARBOXYL-ESTER LIPASE 168 6.1 Materials and Methods 168 6.1.1 Patients 168 6.1.2 Association of Carboxyl-Ester Lipase Variable Nucleotide Tandem Repeat 170 6.2 Results 171 6.3 Discussion 176 7 CHAPTER 7: OVERALL DISCUSSION 178 7.1 Patients 178 7.1.1 Definition of Alcohol Excess 178 7.1.2 Defining the Presence or Absence of Pancreatitis 178 7.1.3 Control Groups 179 7.1.4 Heterogeneity between Groups 180 Contents 8 The Role of Genetic Variation in Predisposition to Alcohol-related Chronic Pancreatitis 2015 7.1.5 Bias in Sample Source 180 7.2 Synonymous SNPs 181 8 CHAPTER 8: CONCLUSION 182 9 CHAPTER 9: REFERENCES 184 10 CHAPTER 10: APPENDICES 210 10.1 Common Alleles of Genes of Alcohol Metabolism 210 10.1.1 Alcohol
Load more

Recommended publications
  • Supplemental Information to Mammadova-Bach Et Al., “Laminin Α1 Orchestrates VEGFA Functions in the Ecosystem of Colorectal Carcinogenesis”
    Supplemental information to Mammadova-Bach et al., “Laminin α1 orchestrates VEGFA functions in the ecosystem of colorectal carcinogenesis” Supplemental material and methods Cloning of the villin-LMα1 vector The plasmid pBS-villin-promoter containing the 3.5 Kb of the murine villin promoter, the first non coding exon, 5.5 kb of the first intron and 15 nucleotides of the second villin exon, was generated by S. Robine (Institut Curie, Paris, France). The EcoRI site in the multi cloning site was destroyed by fill in ligation with T4 polymerase according to the manufacturer`s instructions (New England Biolabs, Ozyme, Saint Quentin en Yvelines, France). Site directed mutagenesis (GeneEditor in vitro Site-Directed Mutagenesis system, Promega, Charbonnières-les-Bains, France) was then used to introduce a BsiWI site before the start codon of the villin coding sequence using the 5’ phosphorylated primer: 5’CCTTCTCCTCTAGGCTCGCGTACGATGACGTCGGACTTGCGG3’. A double strand annealed oligonucleotide, 5’GGCCGGACGCGTGAATTCGTCGACGC3’ and 5’GGCCGCGTCGACGAATTCACGC GTCC3’ containing restriction site for MluI, EcoRI and SalI were inserted in the NotI site (present in the multi cloning site), generating the plasmid pBS-villin-promoter-MES. The SV40 polyA region of the pEGFP plasmid (Clontech, Ozyme, Saint Quentin Yvelines, France) was amplified by PCR using primers 5’GGCGCCTCTAGATCATAATCAGCCATA3’ and 5’GGCGCCCTTAAGATACATTGATGAGTT3’ before subcloning into the pGEMTeasy vector (Promega, Charbonnières-les-Bains, France). After EcoRI digestion, the SV40 polyA fragment was purified with the NucleoSpin Extract II kit (Machery-Nagel, Hoerdt, France) and then subcloned into the EcoRI site of the plasmid pBS-villin-promoter-MES. Site directed mutagenesis was used to introduce a BsiWI site (5’ phosphorylated AGCGCAGGGAGCGGCGGCCGTACGATGCGCGGCAGCGGCACG3’) before the initiation codon and a MluI site (5’ phosphorylated 1 CCCGGGCCTGAGCCCTAAACGCGTGCCAGCCTCTGCCCTTGG3’) after the stop codon in the full length cDNA coding for the mouse LMα1 in the pCIS vector (kindly provided by P.
    [Show full text]
  • Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
    Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism
    [Show full text]
  • Regulation of Phosphoinositide Levels in the Retina by Protein Tyrosine Phosphatase 1B and Growth Factor Receptor-Bound Protein 14
    biomolecules Article Regulation of Phosphoinositide Levels in the Retina by Protein Tyrosine Phosphatase 1B and Growth Factor Receptor-Bound Protein 14 Raju V. S. Rajala 1,2,3,4,* , Austin McCauley 1,4, Rahul Rajala 3,5 , Kenneth Teel 1,4 and Ammaji Rajala 1,4 1 Department of Ophthalmology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; [email protected] (A.M.); [email protected] (K.T.); [email protected] (A.R.) 2 Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA 3 Department of Cell Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; [email protected] 4 Dean McGee Eye Institute, Oklahoma City, OK 73104, USA 5 Cardiovascular Biology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA * Correspondence: [email protected]; Tel.: +1-405-271-8255; Fax: +1-405-271-8128 Abstract: Protein tyrosine kinases and protein phosphatases play a critical role in cellular regulation. The length of a cellular response depends on the interplay between activating protein kinases and deactivating protein phosphatases. Protein tyrosine phosphatase 1B (PTP1B) and growth factor receptor-bound protein 14 (Grb14) are negative regulators of receptor tyrosine kinases. However, in the retina, we have previously shown that PTP1B inactivates insulin receptor signaling, whereas phosphorylated Grb14 inhibits PTP1B activity. In silico docking of phosphorylated Grb14 and PTP1B Citation: Rajala, R.V.S.; McCauley, indicate critical residues in PTP1B that may mediate the interaction. Phosphoinositides (PIPs) are A.; Rajala, R.; Teel, K.; Rajala, A. acidic lipids and minor constituents in the cell that play an important role in cellular processes.
    [Show full text]
  • Genes in Eyecare Geneseyedoc 3 W.M
    Genes in Eyecare geneseyedoc 3 W.M. Lyle and T.D. Williams 15 Mar 04 This information has been gathered from several sources; however, the principal source is V. A. McKusick’s Mendelian Inheritance in Man on CD-ROM. Baltimore, Johns Hopkins University Press, 1998. Other sources include McKusick’s, Mendelian Inheritance in Man. Catalogs of Human Genes and Genetic Disorders. Baltimore. Johns Hopkins University Press 1998 (12th edition). http://www.ncbi.nlm.nih.gov/Omim See also S.P.Daiger, L.S. Sullivan, and B.J.F. Rossiter Ret Net http://www.sph.uth.tmc.edu/Retnet disease.htm/. Also E.I. Traboulsi’s, Genetic Diseases of the Eye, New York, Oxford University Press, 1998. And Genetics in Primary Eyecare and Clinical Medicine by M.R. Seashore and R.S.Wappner, Appleton and Lange 1996. M. Ridley’s book Genome published in 2000 by Perennial provides additional information. Ridley estimates that we have 60,000 to 80,000 genes. See also R.M. Henig’s book The Monk in the Garden: The Lost and Found Genius of Gregor Mendel, published by Houghton Mifflin in 2001 which tells about the Father of Genetics. The 3rd edition of F. H. Roy’s book Ocular Syndromes and Systemic Diseases published by Lippincott Williams & Wilkins in 2002 facilitates differential diagnosis. Additional information is provided in D. Pavan-Langston’s Manual of Ocular Diagnosis and Therapy (5th edition) published by Lippincott Williams & Wilkins in 2002. M.A. Foote wrote Basic Human Genetics for Medical Writers in the AMWA Journal 2002;17:7-17. A compilation such as this might suggest that one gene = one disease.
    [Show full text]
  • 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,
    [Show full text]
  • Enzyme DHRS7
    Toward the identification of a function of the “orphan” enzyme DHRS7 Inauguraldissertation zur Erlangung der Würde eines Doktors der Philosophie vorgelegt der Philosophisch-Naturwissenschaftlichen Fakultät der Universität Basel von Selene Araya, aus Lugano, Tessin Basel, 2018 Originaldokument gespeichert auf dem Dokumentenserver der Universität Basel edoc.unibas.ch Genehmigt von der Philosophisch-Naturwissenschaftlichen Fakultät auf Antrag von Prof. Dr. Alex Odermatt (Fakultätsverantwortlicher) und Prof. Dr. Michael Arand (Korreferent) Basel, den 26.6.2018 ________________________ Dekan Prof. Dr. Martin Spiess I. List of Abbreviations 3α/βAdiol 3α/β-Androstanediol (5α-Androstane-3α/β,17β-diol) 3α/βHSD 3α/β-hydroxysteroid dehydrogenase 17β-HSD 17β-Hydroxysteroid Dehydrogenase 17αOHProg 17α-Hydroxyprogesterone 20α/βOHProg 20α/β-Hydroxyprogesterone 17α,20α/βdiOHProg 20α/βdihydroxyprogesterone ADT Androgen deprivation therapy ANOVA Analysis of variance AR Androgen Receptor AKR Aldo-Keto Reductase ATCC American Type Culture Collection CAM Cell Adhesion Molecule CYP Cytochrome P450 CBR1 Carbonyl reductase 1 CRPC Castration resistant prostate cancer Ct-value Cycle threshold-value DHRS7 (B/C) Dehydrogenase/Reductase Short Chain Dehydrogenase Family Member 7 (B/C) DHEA Dehydroepiandrosterone DHP Dehydroprogesterone DHT 5α-Dihydrotestosterone DMEM Dulbecco's Modified Eagle's Medium DMSO Dimethyl Sulfoxide DTT Dithiothreitol E1 Estrone E2 Estradiol ECM Extracellular Membrane EDTA Ethylenediaminetetraacetic acid EMT Epithelial-mesenchymal transition ER Endoplasmic Reticulum ERα/β Estrogen Receptor α/β FBS Fetal Bovine Serum 3 FDR False discovery rate FGF Fibroblast growth factor HEPES 4-(2-Hydroxyethyl)-1-Piperazineethanesulfonic Acid HMDB Human Metabolome Database HPLC High Performance Liquid Chromatography HSD Hydroxysteroid Dehydrogenase IC50 Half-Maximal Inhibitory Concentration LNCaP Lymph node carcinoma of the prostate mRNA Messenger Ribonucleic Acid n.d.
    [Show full text]
  • 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
    [Show full text]
  • Serum Albumin OS=Homo Sapiens
    Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo
    [Show full text]
  • Whole Exome Sequencing Analyses Reveal Gene–Microbiota Interactions
    Inflammatory bowel disease ORIGINAL RESEARCH Whole exome sequencing analyses reveal gene– Gut: first published as 10.1136/gutjnl-2019-319706 on 10 July 2020. Downloaded from microbiota interactions in the context of IBD Shixian Hu ,1,2 Arnau Vich Vila ,1,2 Ranko Gacesa,1,2 Valerie Collij,1,2 Christine Stevens,3 Jack M Fu,4,5,6 Isaac Wong,4,5 Michael E Talkowski,4,5,6,7,8 Manuel A Rivas,9 Floris Imhann,1,2 Laura Bolte,1,2 Hendrik van Dullemen,1 Gerard Dijkstra ,1 Marijn C Visschedijk,1 Eleonora A Festen,1 Ramnik J Xavier,10,11 Jingyuan Fu,2,12 Mark J Daly,3 Cisca Wijmenga,2 Alexandra Zhernakova,2 Alexander Kurilshikov,2 Rinse K Weersma 1 ► Additional material is ABSTRact published online only. To view Objective Both the gut microbiome and host genetics Significance of this study please visit the journal online are known to play significant roles in the pathogenesis (http:// dx. doi. org/ 10. 1136/ What is already known about this subject? gutjnl- 2019- 319706). of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain ► Gene–microbiome interactions are important in For numbered affiliations see the pathogenesis of IBD. end of article. underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD. ► Multiple genetic and epidemiological factors have been identified to be associated to Correspondence to Design To evaluate the impact of host genetics on Professor Rinse K Weersma; the gut microbiota of patients with IBD, we combined changes in gut microbiome homeostasis in both r.
    [Show full text]
  • Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
    University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed.
    [Show full text]
  • Supplementary Materials
    Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented.
    [Show full text]
  • Investigating Novel Regulators of Golgi Membrane Tubulation
    INVESTIGATING NOVEL REGULATORS OF GOLGI MEMBRANE TUBULATION A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Kevin Dinh Ha August 2012 © 2012 Kevin Dinh Ha INVESTIGATING NOVEL REGULATORS OF GOLGI MEMBRANE TUBULATION Kevin Dinh Ha, Ph.D. Cornell University 2012 The Golgi complex serves as a vital organelle from which proteins and membrane lipids are modified, sorted, and trafficked to various destinations. Mutations that cause defects in structural maintenance or membrane trafficking at the Golgi are commonly linked to neurodegeneration, metabolic disease, and reproductive disorders. Both structural maintenance and membrane trafficking rely on cooperative efforts of coated vesicles and membrane tubules. Although extensive information is available for membrane coated vesicle traffic, knowledge of membrane tubules remains comparably deficient. Understanding the regulatory mechanisms behind membrane tubules may help elucidate how Golgi tubule biogenesis can respond to varying physiological stimuli such as increased secretory loads. I utilized an siRNA library against all known and purported human kinases, or the kinome, in a high throughput, microscopy-based screen that identified proteins involved in Brefeldin A (BFA)-induced Golgi membrane tubulation. This screen successfully identified siRNAs that significantly inhibited or enhanced the effects of BFA-induced Golgi tubulation. Among the identified hits, I further characterized two inhibitory siRNA that targeted Protein- Associating with the Carboxyl-terminal domain of Ezrin (PACE1) and diacylglycerol kinase γ (DGK-γ), and determined that they play important roles in maintaining intact Golgi ribbon structures through regulating Golgi membrane tubule biogenesis. I found that these proteins also facilitate Golgi reassembly and anterograde membrane trafficking of both soluble and transmembrane proteins, further buttressing the importance of membrane tubules in multiple, cellular processes.
    [Show full text]