University of California, San Diego

Total Page:16

File Type:pdf, Size:1020Kb

University of California, San Diego UNIVERSITY OF CALIFORNIA, SAN DIEGO The Cholinesterases: A Study in Pharmacogenomics A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biomedical Sciences by Anne Marie Valle Committee in charge: Professor Palmer Taylor, Chair Professor Philip Bourne Professor Mark A. Lawson Professor Daniel T. O’Connor Professor Nicholas J. Schork 2008 Copyright Anne Marie Valle, 2008 All Rights Reserved This Dissertation of Anne Marie Valle is approved, and it is acceptable in quality and form for publication in microfilm: Chair University of California, San Diego 2008 iii To my family: my mother Connie, my husband George, my beautiful children Alethea, Krista, Tammy, Georgie and Carly my wonderful grandchildren Ashley, Kevin, Kristopher, Ezra, Fernando, Diego, Julia, and last but not least Gavin iv TABLE OF CONTENTS Signature Page………………………………..………………………………….……….iii Dedication………………………………………………………………..………….……iv Table of Contents……………………………………………………………….................v List of Abreviations……………………………………………………………………...vii List of Figures……………………………………………………………………….........xi List of Tables and Schemes……………………………………………..………………xiii Acknowledgements…………………………………………………………………........xv Vita and Publications…………………………………………………………………..xviii Abstract of Dissertation……………………………………………………………..…...xx Chapter I Introduction to the Fields of Pharmacogenetics/Pharmacogenomics, Twin Studies, Cholinergic Control of Cardiovascular Function, and the Cholinesterases……………………………………………………………1 A. Pharmacogenetics/Pharmacogenomics and the Promise of Personalized Medicine..….……………………………………………………………...1 B. Twin Studies….………………………………………………………..9 C. Cholinergic Control of Cardiovascular Function.……………..……..13 D. The Cholinesterases.............................................................................22 E. Dissertation Overview………………………………………………..38 Chapter II Butyrylcholinesterase: Association with the Metabolic Syndrome and Identification of Two Gene Loci Affecting Activity…….………………40 A. Abstract………………………………………………………………40 B. Introduction…………………………………………………………..41 C. Participants and Methods…………………………………………….42 D. Results………………………………………………………………..47 E. Discussion…………………………………………………………….54 F. Acknowledgements…………………………………………………...58 v Chapter III A Pharmacogenomic Study: Investigating Naturally Occurring Variations in the Acetylcholinesterase Gene …..........................................................60 A. Abstract………………………………………………………………60 B. Introduction…………………………………………………………..61 C. Material and Methods………………………………………………...63 D. Results………………………………………………………………..75 E. Discussion…………………………………………………………...115 F. Acknowledgements………………………………………………….118 Chapter IV Summary and Closing Remarks……..……………………………….....119 A. The Butyrylcholinesterase Story……………………………………120 B. The Acetylcholinesterase Story……………………………………..125 C. Closing Remarks……………………………………………………126 References …………………………………………………………………………..127 vi LIST OF ABBREVIATIONS 2-PAM pyridinium aldoxime ACh acetylcholine AChE acetylcholinesterase ADR adverse drug response ANOVA analysis of variance ANS autonomic nervous system AV atrioventricular BChE butyrylcholinesterase BMI body mass index BP blood pressure BSA bovine serum albumin cDNA complementary deoxyribonucleic acid CI confidence interval CV cardiovascular ChE cholinesterase CMV cytomegalovirus CNS central nervous system CYP cytochrome P450 CVLM caudal ventrolateral medulla DMEM Dulbecco’s modified Eagles medium DNA deoxyribonucleic acid DTNB 5,5’-dithio-bis(2-nitrobenzoic acid vii DZ dizygotic EPI epinephrine FBS fetal bovine serum G6PD glucose-6-phosphate-dehydrogenase gDNA genomic deoxyribonucleic acid HEK human embryonic kidney HR heart rate HTN hypertension LOD logarithm of odds mAChR muscarinic acetylcholine receptor mRNA messenger ribonucleic acid MZ monozygotic NAT2 N-acetyltransferase-2 nAChR nicotinic acetylcholine receptor NCBI National Center for Biotechnology Information NE norepinephrine NTS nucleus of the solitary tract OMIM Online Mendelian Inheritance in Man OP organophosphates PAS peripheral anionic site PCR polymerase chain reaction PDB Protein Data Bank viii PTC phenylthiocarbamide QTL quantitative trait loci RVLM rostral ventrolateral medulla SA sinoarterial SD standard deviation SNP single nucleotide polymorphism SpDMB (Sp)-3,3dimethylbutyl methylphosphonothiocholine SOLAR Sequential Oligogenic Linkage Analysis Routines TDT transmission disequilibrium test TPMT thiopurine s-metyl transferase UTR untranslated region VMC vasomotor center wt wildtype Amino Acid Residues Ala or A alanine Arg or R arginine Asn or N asparagines Asp or D aspartate Cys or C cysteine Glu or E glutamate Gln or Q glutamine Gly or G glycine ix His or H histidine Ile or I isoleucine Lys or K lysine Leu or L leucine Met or M methionine Phe or F phenylalanine Pro or P proline Ser or S serine Thr or T threonine Trp or W tryptophan Tyr or Y tyrosine Val or V valine x LIST OF FIGURES Chapter I Figure I.1 Contribution of Twins to the Study of Complex Traits and Diseases…...11 Figure I.2 Sympathetic and Parasympathetic Neurotransmitters…………………...14 Figure I.3 Sympathetic and Parasympathetic Regulation of Cardiac Function……..16 Figure I.4 AChE and BChE Gene Structures………………………………………..23 Figure I.5 hAChE and hBChE Crystal Structures…………………………………..27 Figure I.6 Conformation of ChE Proteins with Bound BW284c51…………………28 Figure I.7 ChEs Activity Curves……………………………………………………29 Figure I.8 AChE Inhibition and Reactivation……………………………………….37 Chapter II Figure II.1 Cumulative Sum of Deviations from Overall Mean……………………..48 Figure II.2 Results of Linkage Analysis for the Detection of Loci Affecting Plasma Cholinesterase…………..………………………………………………..53 Chapter III Figure III.1 Frequency Distribution of Cholinesterase Activity……………………...77 Figure III.2 Linear Regression Graphs of MZ and DZ Twin Pairs…………………...78 Figure III.3 SNP Discovery: SNPs Found in the Unrelated Panel Mapped to the AChE gene………………………………………………………………………80 Figure III.4 cSNPs Modeled on mAChE Crystal Structure…………………………..91 Figure III.5 Comparison of wtT547 and D134H Gene Expression…………………..95 Figure III.6 D134H Temperature Sensitivity………………………………………....95 Figure III.7 pS Curves: A = wtT547 B = R3Q C = D134H D = H322N…………..98 Figure III.8 Comparison of pS Curves for wtT547 AChE and Mutants……………...99 Figure III.9 Exponential Decay Curves from Stability Assays……………………...102 xi Figure III.10 Human AChE Structure………………………………………………...104 Figure III.11 Compensating Double Mutant (D134H/R136Q)……………………….105 Figure III.12 Protein Expression Normalized to the wtT547…………………………107 Figure III.13 D134H/R136Q pS Curve (Top Panel) and Summary of pS Curves (Bottom Panel)…………………………………………………………………...108 Figure III.14 Half-Life (t50) Parameter Normalized to the wtT547…………………..109 Figure III.15 Paraoxon Inhibition Curves for wtT547 and D134H Proteins………….112 Figure III.16 Oxime-Assisted Reactivation Curves for the wtT547 and D134H Proteins …...……………………………………………………………………...114 Chapter IV Figure IV.1 Ideogram of Genes Located Around GATA12G02…………………….122 xii LIST OF TABLES AND SCHEMES Chapter I Table I.1 Key Discoveries of the 1950s……………………………………………..4 Table I.2 Genetic Basis of Variability in Drug Response…………………………...8 Table I.3 Twin Studies and Their Applications……………………………………12 Table I.4 In vivo Studies Implicating a Role for the Cholinergic Control of Cardiovascular Function…………………………………………………20 Chapter II Table II.1 Effects of Method Variation, Sex and Sex-Specific Age Variation on Plasma Cholinesterase Activity…………………………………………..47 Table II.2 Correlation between Plasma Cholinesterase Activity (adjustment for method variation) and Variables Related to Cardiovascular Risk……….50 Table II.3 Pairwise Similarity of Twin Plasma Cholinesterase and Models for Fitting Data………………………………………………………………………51 Chapter III Table III.1 Mutagenesis Primers with Corresponding Template Sequence……….....70 Table III.2 Correlation Analysis of Cholinesterase Enzymatic Activity…………….81 Table III.3 SNP Results: Resequencing of AChE in the Unrelated Panel…………..82 Table III.4 Minor Allelic Frequencies in the Unrelated Panel (Contig NT_007933.13 Reverse Complement)…………………………………………………....84 Table III.5 Haplotype Reconstruction using the Nonsynonymous cSNPs…………..85 Table III.6 Haplotype (HAP) Reconstruction using SNPs 5, 8, 9, 12, 16…………...86 Table III.7A Heritability and Association Study (Complete Twin Panel)…………….88 Table III.7B Heritability and Association Study (Eur-Am Panel)………………….....89 Table III.8 Vector pcDNA3 + hAChE cDNA (7224 bp)…………………………....93 Table III.9 Kinetic Parameters for wtT547 and Mutant Protein Enzymes................100 Table III.10 Relative Stability of the Mutant Protein Enzymes……………………...110 xiii Chapter IV Table IV.1 Links to Identified Genes around GATA126G02 on the UCSC Genome Browser…………………………………………………………………123 LIST OF SCHEMES Chapter I Scheme I.1 Scheme and Equation I.1………………………………………………...31 Scheme I.2 Scheme and Equation I.2………………………………………………...32 Chapter III Scheme III.1 Scheme and Equation III.1……………………………………………....96 Scheme III.2 Scheme and Equation III.2……………………………………………...111 Scheme III.3 Scheme and Equation III.3……………………………………………...113 xiv ACKNOWLEDGEMENTS First and foremost I would like to give my thanks and gratitude to my mentor and dissertation chair Dr. Palmer Taylor not only for accepting me into his lab but most importantly, for having the patience and understanding to help me surmount my learning curve and lack of confidence. I feel very fortunate to have worked
Recommended publications
  • The Ciliated Cell Transcriptome A
    THE CILIATED CELL TRANSCRIPTOME A DISSERTATION SUBMITTED TO THE DEPARTMENT OF BIOLOGICAL SCIENCES AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Ramona Hoh March 2010 ! © 2010 by Ramona Amy Hoh. All Rights Reserved. Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/ This dissertation is online at: http://purl.stanford.edu/sk794dv5857 ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Timothy Stearns, Primary Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Mark Krasnow I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Maxence Nachury I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. William Nelson Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.
    [Show full text]
  • Genetic Basis of Simple and Complex Traits with Relevance to Avian Evolution
    Genetic basis of simple and complex traits with relevance to avian evolution Małgorzata Anna Gazda Doctoral Program in Biodiversity, Genetics and Evolution D Faculdade de Ciências da Universidade do Porto 2019 Supervisor Miguel Jorge Pinto Carneiro, Auxiliary Researcher, CIBIO/InBIO, Laboratório Associado, Universidade do Porto Co-supervisor Ricardo Lopes, CIBIO/InBIO Leif Andersson, Uppsala University FCUP Genetic basis of avian traits Nota Previa Na elaboração desta tese, e nos termos do número 2 do Artigo 4º do Regulamento Geral dos Terceiros Ciclos de Estudos da Universidade do Porto e do Artigo 31º do D.L.74/2006, de 24 de Março, com a nova redação introduzida pelo D.L. 230/2009, de 14 de Setembro, foi efetuado o aproveitamento total de um conjunto coerente de trabalhos de investigação já publicados ou submetidos para publicação em revistas internacionais indexadas e com arbitragem científica, os quais integram alguns dos capítulos da presente tese. Tendo em conta que os referidos trabalhos foram realizados com a colaboração de outros autores, o candidato esclarece que, em todos eles, participou ativamente na sua conceção, na obtenção, análise e discussão de resultados, bem como na elaboração da sua forma publicada. Este trabalho foi apoiado pela Fundação para a Ciência e Tecnologia (FCT) através da atribuição de uma bolsa de doutoramento (PD/BD/114042/2015) no âmbito do programa doutoral em Biodiversidade, Genética e Evolução (BIODIV). 2 FCUP Genetic basis of avian traits Acknowledgements Firstly, I would like to thank to my all supervisors Miguel Carneiro, Ricardo Lopes and Leif Andersson, for the demanding task of supervising myself last four years.
    [Show full text]
  • 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.
    [Show full text]
  • Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79
    www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g.
    [Show full text]
  • Systems Genetics of Sensation Seeking. Price E
    The Jackson Laboratory The Mouseion at the JAXlibrary Faculty Research 2019 Faculty Research 3-2019 Systems genetics of sensation seeking. Price E. Dickson Tyler A Roy Kathryn A. McNaughton Troy Wilcox Padam Kumar See next page for additional authors Follow this and additional works at: https://mouseion.jax.org/stfb2019 Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons Authors Price E. Dickson, Tyler A Roy, Kathryn A. McNaughton, Troy Wilcox, Padam Kumar, and Elissa J Chesler Received: 20 May 2018 Revised: 9 September 2018 Accepted: 11 September 2018 DOI: 10.1111/gbb.12519 ORIGINAL ARTICLE Systems genetics of sensation seeking Price E. Dickson | Tyler A. Roy | Kathryn A. McNaughton | Troy D. Wilcox | Padam Kumar | Elissa J. Chesler Center for Systems Neurogenetics of Addiction, The Jackson Laboratory, Bar Sensation seeking is a multifaceted, heritable trait which predicts the development of substance Harbor, Maine use and abuse in humans; similar phenomena have been observed in rodents. Genetic correla- Correspondence tions among sensation seeking and substance use indicate shared biological mechanisms, but Price E. Dickson, The Jackson Laboratory, the genes and networks underlying these relationships remain elusive. Here, we used a systems 600 Main Street, Bar Harbor, ME 04609 Email: [email protected] genetics approach in the BXD recombinant inbred mouse panel to identify shared genetic mech- Elissa J. Chesler, The Jackson Laboratory, anisms underlying substance use and preference for sensory stimuli, an intermediate phenotype 600 Main Street, Bar Harbor, ME 04609 of sensation seeking. Using the operant sensation seeking (OSS) paradigm, we quantified prefer- Email: [email protected] ence for sensory stimuli in 120 male and 127 female mice from 62 BXD strains and the Funding information C57BL/6J and DBA/2J founder strains.
    [Show full text]
  • Supplemental Table 1. Complete Gene Lists and GO Terms from Figure 3C
    Supplemental Table 1. Complete gene lists and GO terms from Figure 3C. Path 1 Genes: RP11-34P13.15, RP4-758J18.10, VWA1, CHD5, AZIN2, FOXO6, RP11-403I13.8, ARHGAP30, RGS4, LRRN2, RASSF5, SERTAD4, GJC2, RHOU, REEP1, FOXI3, SH3RF3, COL4A4, ZDHHC23, FGFR3, PPP2R2C, CTD-2031P19.4, RNF182, GRM4, PRR15, DGKI, CHMP4C, CALB1, SPAG1, KLF4, ENG, RET, GDF10, ADAMTS14, SPOCK2, MBL1P, ADAM8, LRP4-AS1, CARNS1, DGAT2, CRYAB, AP000783.1, OPCML, PLEKHG6, GDF3, EMP1, RASSF9, FAM101A, STON2, GREM1, ACTC1, CORO2B, FURIN, WFIKKN1, BAIAP3, TMC5, HS3ST4, ZFHX3, NLRP1, RASD1, CACNG4, EMILIN2, L3MBTL4, KLHL14, HMSD, RP11-849I19.1, SALL3, GADD45B, KANK3, CTC- 526N19.1, ZNF888, MMP9, BMP7, PIK3IP1, MCHR1, SYTL5, CAMK2N1, PINK1, ID3, PTPRU, MANEAL, MCOLN3, LRRC8C, NTNG1, KCNC4, RP11, 430C7.5, C1orf95, ID2-AS1, ID2, GDF7, KCNG3, RGPD8, PSD4, CCDC74B, BMPR2, KAT2B, LINC00693, ZNF654, FILIP1L, SH3TC1, CPEB2, NPFFR2, TRPC3, RP11-752L20.3, FAM198B, TLL1, CDH9, PDZD2, CHSY3, GALNT10, FOXQ1, ATXN1, ID4, COL11A2, CNR1, GTF2IP4, FZD1, PAX5, RP11-35N6.1, UNC5B, NKX1-2, FAM196A, EBF3, PRRG4, LRP4, SYT7, PLBD1, GRASP, ALX1, HIP1R, LPAR6, SLITRK6, C16orf89, RP11-491F9.1, MMP2, B3GNT9, NXPH3, TNRC6C-AS1, LDLRAD4, NOL4, SMAD7, HCN2, PDE4A, KANK2, SAMD1, EXOC3L2, IL11, EMILIN3, KCNB1, DOK5, EEF1A2, A4GALT, ADGRG2, ELF4, ABCD1 Term Count % PValue Genes regulation of pathway-restricted GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, SMAD protein phosphorylation 9 6.34 1.31E-08 ENG pathway-restricted SMAD protein GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, phosphorylation
    [Show full text]
  • A General Binding Mechanism for All Human Sulfatases by the Formylglycine-Generating Enzyme
    A general binding mechanism for all human sulfatases by the formylglycine-generating enzyme Dirk Roeser*, Andrea Preusser-Kunze†, Bernhard Schmidt†, Kathrin Gasow*, Julia G. Wittmann*, Thomas Dierks‡, Kurt von Figura†, and Markus Georg Rudolph*§ *Department of Molecular Structural Biology, University of Go¨ttingen, Justus-von-Liebig-Weg 11, D-37077 Go¨ttingen, Germany; †Department of Biochemistry II, Heinrich-Du¨ker-Weg 12, University of Go¨ttingen, D-37073 Go¨ttingen, Germany; and ‡Department of Biochemistry I, Universita¨tsstrasse 25, University of Bielefeld, D-33615 Bielefeld, Germany Edited by Carolyn R. Bertozzi, University of California, Berkeley, CA, and approved November 8, 2005 (received for review September 1, 2005) The formylglycine (FGly)-generating enzyme (FGE) uses molecular tases, suggesting a general binding mechanism of substrate sulfa- oxygen to oxidize a conserved cysteine residue in all eukaryotic tases by FGE. sulfatases to the catalytically active FGly. Sulfatases degrade and The details of how O2-dependent cysteine oxidation is mediated remodel sulfate esters, and inactivity of FGE results in multiple by FGE are unknown. As a first step toward the elucidation of the sulfatase deficiency, a fatal disease. The previously determined FGE molecular mechanism of FGly formation, we have previously crystal structure revealed two crucial cysteine residues in the active determined crystal structures of FGE in various oxidation states site, one of which was thought to be implicated in substrate (8). FGE adopts a novel fold with surprisingly little regular sec- 2ϩ binding. The other cysteine residue partakes in a novel oxygenase ondary structure and contains two structural Ca ions and two mechanism that does not rely on any cofactors.
    [Show full text]
  • A Bootstrap-Based Regression Method for Comprehensive Discovery of Differential Gene Expressions: an Application to the Osteoporosis Study
    A bootstrap-based regression method for comprehensive discovery of differential gene expressions: an application to the osteoporosis study Yan Lu1,2, Yao-Zhong Liu3, Peng–Yuan Liu2, Volodymyr Dvornyk4, and Hong–Wen Deng1,3 1. College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing 100044, P. R. China 2. Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA 3. Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics, Tulane University School of Public Health, New Orleans, LA 70112, USA 4. School of Biological Sciences, University of Hong Kong, Pokfulam Rd., Pokfulam, Hong Kong, P.R. China Running title: Bootstrap-based regression method for microarray analysis Key words: microarray, bootstrap, regression, osteoporosis Corresponding author: Hong–Wen Deng, Ph. D. Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics, Tulane University School of Public Health, 1440 Canal Street, Suite 2001, New Orleans, LA 70112 Tel: 504-988-1310 Email: [email protected] 1 Abstract A common purpose of microarray experiments is to study the variation in gene expression across the categories of an experimental factor such as tissue types and drug treatments. However, it is not uncommon that the studied experimental factor is a quantitative variable rather than categorical variable. Loss of information would occur by comparing gene-expression levels between groups that are factitiously defined according to the quantitative threshold values of an experimental factor. Additionally, lack of control for some sensitive clinical factors may bring serious false positive or negative findings. In the present study, we described a bootstrap-based regression method for analyzing gene expression data from the non-categorical microarray experiments.
    [Show full text]
  • Supplementary File 2A Revised
    Supplementary file 2A. Differentially expressed genes in aldosteronomas compared to all other samples, ranked according to statistical significance. Missing values were not allowed in aldosteronomas, but to a maximum of five in the other samples. Acc UGCluster Name Symbol log Fold Change P - Value Adj. P-Value B R99527 Hs.8162 Hypothetical protein MGC39372 MGC39372 2,17 6,3E-09 5,1E-05 10,2 AA398335 Hs.10414 Kelch domain containing 8A KLHDC8A 2,26 1,2E-08 5,1E-05 9,56 AA441933 Hs.519075 Leiomodin 1 (smooth muscle) LMOD1 2,33 1,3E-08 5,1E-05 9,54 AA630120 Hs.78781 Vascular endothelial growth factor B VEGFB 1,24 1,1E-07 2,9E-04 7,59 R07846 Data not found 3,71 1,2E-07 2,9E-04 7,49 W92795 Hs.434386 Hypothetical protein LOC201229 LOC201229 1,55 2,0E-07 4,0E-04 7,03 AA454564 Hs.323396 Family with sequence similarity 54, member B FAM54B 1,25 3,0E-07 5,2E-04 6,65 AA775249 Hs.513633 G protein-coupled receptor 56 GPR56 -1,63 4,3E-07 6,4E-04 6,33 AA012822 Hs.713814 Oxysterol bining protein OSBP 1,35 5,3E-07 7,1E-04 6,14 R45592 Hs.655271 Regulating synaptic membrane exocytosis 2 RIMS2 2,51 5,9E-07 7,1E-04 6,04 AA282936 Hs.240 M-phase phosphoprotein 1 MPHOSPH -1,40 8,1E-07 8,9E-04 5,74 N34945 Hs.234898 Acetyl-Coenzyme A carboxylase beta ACACB 0,87 9,7E-07 9,8E-04 5,58 R07322 Hs.464137 Acyl-Coenzyme A oxidase 1, palmitoyl ACOX1 0,82 1,3E-06 1,2E-03 5,35 R77144 Hs.488835 Transmembrane protein 120A TMEM120A 1,55 1,7E-06 1,4E-03 5,07 H68542 Hs.420009 Transcribed locus 1,07 1,7E-06 1,4E-03 5,06 AA410184 Hs.696454 PBX/knotted 1 homeobox 2 PKNOX2 1,78 2,0E-06
    [Show full text]
  • Lysosomal Hydrolases: Conversion of Acidic to Basic Forms by Neuraminidase
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Volume 13, number 1 FEBS LETTERS February 1971 LYSOSOMAL HYDROLASES: CONVERSION OF ACIDIC TO BASIC FORMS BY NEURAMINIDASE Alfred GOLDSTONE, Philip KONECNY and Harold KOENIG Neurology Service, VA Research Hospital; and Departments of Neurology and Biochemistry, Northwestern University Medical School, Chicago, Illinois, USA Received 21 December 1970 1. Introduction intervals to avoid overheating). The lysosomal lysate was ultracentrifuged at 100,000 g for 30 min to give A number of hydrolytic enzymes of lysosomal a soluble enzyme fraction. This fraction also contains origin occur in multiple forms which differ in such an acidic lipoprotein [4]. The insoluble residue was physicochemical properties as solubility, heat and pH suspended in 0.05 M acetate buffer pH 5.0 with 0.2% stability, pH optima and electrophoretic mobility [I]. Triton X-100 and resonicated to release the bound ly- Recent studies in this laboratory indicate that essen- sosomal enzymes together with additional acidic lipo- tially all the enzyme proteins of rat kidney and liver protein ([4] and A. Goldstone and H. Koenig, unpu- lysosomes are glycoproteins, and that at least some of blished observations). All operations were carried out these glycoproteins contain sialic acid [2-41. The at 4’. acidic form of lysosomal N-acetylhexosaminidase from Aliquots of the soluble fraction containing about human spleen [ 51 and kidney [6] is converted into 2 mg of protein were incubated with neuraminidase the basic form by treatment with neuraminidase. We from Cl. perfringens (Type Vl enzyme supplied by now confirm this finding for rat kidney and show Sigma), 0.1 mg/ml, or V.
    [Show full text]
  • Genome-Wide Transcriptional Sequencing Identifies Novel Mutations in Metabolic Genes in Human Hepatocellular Carcinoma DAOUD M
    CANCER GENOMICS & PROTEOMICS 11 : 1-12 (2014) Genome-wide Transcriptional Sequencing Identifies Novel Mutations in Metabolic Genes in Human Hepatocellular Carcinoma DAOUD M. MEERZAMAN 1,2 , CHUNHUA YAN 1, QING-RONG CHEN 1, MICHAEL N. EDMONSON 1, CARL F. SCHAEFER 1, ROBERT J. CLIFFORD 2, BARBARA K. DUNN 3, LI DONG 2, RICHARD P. FINNEY 1, CONSTANCE M. CULTRARO 2, YING HU1, ZHIHUI YANG 2, CU V. NGUYEN 1, JENNY M. KELLEY 2, SHUANG CAI 2, HONGEN ZHANG 2, JINGHUI ZHANG 1,4 , REBECCA WILSON 2, LAUREN MESSMER 2, YOUNG-HWA CHUNG 5, JEONG A. KIM 5, NEUNG HWA PARK 6, MYUNG-SOO LYU 6, IL HAN SONG 7, GEORGE KOMATSOULIS 1 and KENNETH H. BUETOW 1,2 1Center for Bioinformatics and Information Technology, National Cancer Institute, Rockville, MD, U.S.A.; 2Laboratory of Population Genetics, National Cancer Institute, National Cancer Institute, Bethesda, MD, U.S.A.; 3Basic Prevention Science Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, U.S.A; 4Department of Biotechnology/Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, U.S.A.; 5Department of Internal Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea; 6Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, Korea; 7Department of Internal Medicine, College of Medicine, Dankook University, Cheon-An, Korea Abstract . We report on next-generation transcriptome Worldwide, liver cancer is the fifth most common cancer and sequencing results of three human hepatocellular carcinoma the third most common cause of cancer-related mortality (1). tumor/tumor-adjacent pairs.
    [Show full text]
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
    [Show full text]