DOCSLIB.ORG

Common Genetic Variation and Mammographic Density: Risk Factors for Breast Cancer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Common Genetic Variation and Mammographic Density: Risk Factors for Breast Cancer Common genetic variation and mammographic density: risk factors for breast cancer By Stine Margarethe Biong Cancer Genome Variation Group Department of Genetics Institute for Cancer Research Oslo University Hospital Radiumhospitalet © Stine Margarethe Biong, 2012 Series of dissertations submitted to the Faculty of Medicine, University of Oslo No. 1361 ISBN 978-82-8264-354-2 All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, without permission. Cover: Inger Sandved Anfinsen. Printed in Norway: AIT Oslo AS. Produced in co-operation with Unipub. The thesis is produced by Unipub merely in connection with the thesis defence. Kindly direct all inquiries regarding the thesis to the copyright holder or the unit which grants the doctorate. Table of contents Acknowledgements Aims List of papers 1 Introduction ............................................................................................................................. 1 1.1 The breast .......................................................................................................................... 1 1.1.1 Breast anatomy ........................................................................................................... 1 1.1.2 Breast histology .......................................................................................................... 3 1.1.3 Normal breast development and physiology .............................................................. 6 1.1.4 Steroid hormones ........................................................................................................ 9 1.2 Single nucleotide polymorphisms ................................................................................... 13 1.2.1 SNPs in the estrogen pathway .................................................................................. 14 1.2 Breast cancer .................................................................................................................... 16 1.2.1 Incidence and mortality ............................................................................................ 17 1.2.2 Mammographic screening program ......................................................................... 18 1.2.3 Breast cancer risk ..................................................................................................... 18 1.3 Mammographic density ................................................................................................... 27 1.3.1 Mammographic density classification ...................................................................... 27 1.3.2 Causes of MD variation ............................................................................................ 29 2 Materials and methods ......................................................................................................... 32 2.1 Sample population ........................................................................................................... 32 2.1.1 TMBC ....................................................................................................................... 32 2.1.2 MDG ......................................................................................................................... 32 2.2 Blood samples and plasma analyses ................................................................................ 33 2.2.1 TMBC ....................................................................................................................... 33 2.2.2 MDG ......................................................................................................................... 34 2.3 Biopsy collection ............................................................................................................. 34 2.3.1 MDG ......................................................................................................................... 34 2.4 Mammograms .................................................................................................................. 34 2.4.1 TMBC ....................................................................................................................... 34 2.4.2 MDG ......................................................................................................................... 35 2.4.3 Mammogram assessment ......................................................................................... 35 2.5 In silico analyses: candidate gene and SNP selection ..................................................... 36 2.5.1 Gene selection .......................................................................................................... 36 2.5.2 SNP selection ........................................................................................................... 36 2.6 SNP genotyping ............................................................................................................... 37 2.6.1 Taqman real-time PCR genotyping .......................................................................... 37 2.6.2 Sequenom technology and genotyping .................................................................... 38 2.7 Microarray technology .................................................................................................... 40 2.7.1. Gene expression ....................................................................................................... 40 2.7.2 GWAS ...................................................................................................................... 42 2.8 Statistics ........................................................................................................................... 43 2.8.1 ANOVA ................................................................................................................... 43 2.8.2 Correlation ................................................................................................................ 43 2.8.3 Chi-square and Fishers exact test ............................................................................. 43 2.8.4 Gene expression analysis: SAM ............................................................................... 44 2.8.5 SNP analysis: haplotype estimation ......................................................................... 44 2.8.6 Regression analysis .................................................................................................. 45 2.8.7 FDR multiple testing correction ............................................................................... 47 2.9 Gene ontology databases ................................................................................................. 47 2.9.1 DAVID ..................................................................................................................... 47 3 Results in brief ....................................................................................................................... 48 Paper I ......................................................................................................................................... 48 Paper II ....................................................................................................................................... 49 Paper III ...................................................................................................................................... 51 4 Discussion ............................................................................................................................... 53 4.1 Discussion of main findings ............................................................................................ 53 4.1.1 Involvement of hormone and growth factor pathways in MD ................................. 53 4.1.2 Integrated analyses and complex diseases ............................................................... 57 4.2 Methodological considerations ........................................................................................ 58 4.2.1 Heterogeneity within and between studies. .............................................................. 58 4.2.2 Study selection bias .................................................................................................. 59 4.2.3 Assessment of menopausal status ............................................................................ 59 4.2.4 MD measurement ..................................................................................................... 60 4.2.5 Blood vs Tissue ........................................................................................................ 60 4.2.6 SNP quality control .................................................................................................. 61 4.2.7 SNP analysis ............................................................................................................. 61 4.2.8 Integrated analysis ................................................................................................... 64 5 Main conclusion and future perspectives ........................................................................... 65 5.1 Integrated analyses .......................................................................................................... 65 5.2 Translation into the clinic ................................................................................................ 66 5.3 Over diagnosis ................................................................................................................
Load more

Recommended publications
  • UNIVERSITY of CALIFORNIA, SAN DIEGO The
    UNIVERSITY OF CALIFORNIA, SAN DIEGO The Transporter-Opsin-G protein-coupled receptor (TOG) Superfamily A Thesis submitted in partial satisfaction of the requirements for the degree Master of Science in Biology by Daniel Choi Yee Committee in charge: Professor Milton H. Saier Jr., Chair Professor Yunde Zhao Professor Lin Chao 2014 The Thesis of Daniel Yee is approved and it is acceptable in quality and form for publication on microfilm and electronically: _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Chair University of California, San Diego 2014 iii DEDICATION This thesis is dedicated to my parents, my family, and my mentor, Dr. Saier. It is only with their help and perseverance that I have been able to complete it. iv TABLE OF CONTENTS Signature Page ............................................................................................................... iii Dedication ...................................................................................................................... iv Table of Contents ........................................................................................................... v List of Abbreviations ..................................................................................................... vi List of Supplemental Files ............................................................................................ vii List of
    [Show full text]
  • Ovarian Gene Expression in the Absence of FIGLA, an Oocyte
    BMC Developmental Biology BioMed Central Research article Open Access Ovarian gene expression in the absence of FIGLA, an oocyte-specific transcription factor Saurabh Joshi*1, Holly Davies1, Lauren Porter Sims2, Shawn E Levy2 and Jurrien Dean1 Address: 1Laboratory of Cellular and Developmental Biology, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA and 2Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA Email: Saurabh Joshi* - [email protected]; Holly Davies - [email protected]; Lauren Porter Sims - [email protected]; Shawn E Levy - [email protected]; Jurrien Dean - [email protected] * Corresponding author Published: 13 June 2007 Received: 11 December 2006 Accepted: 13 June 2007 BMC Developmental Biology 2007, 7:67 doi:10.1186/1471-213X-7-67 This article is available from: http://www.biomedcentral.com/1471-213X/7/67 © 2007 Joshi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Ovarian folliculogenesis in mammals is a complex process involving interactions between germ and somatic cells. Carefully orchestrated expression of transcription factors, cell adhesion molecules and growth factors are required for success. We have identified a germ-cell specific, basic helix-loop-helix transcription factor, FIGLA (Factor In the GermLine, Alpha) and demonstrated its involvement in two independent developmental processes: formation of the primordial follicle and coordinate expression of zona pellucida genes. Results: Taking advantage of Figla null mouse lines, we have used a combined approach of microarray and Serial Analysis of Gene Expression (SAGE) to identify potential downstream target genes.
    [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]
  • Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
    LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research.
    [Show full text]
  • Goat Anti-Actin-Like 7B Antibody Peptide-Affinity Purified Goat Antibody Catalog # Af1021b
    10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Goat Anti-Actin-like 7B Antibody Peptide-affinity purified goat antibody Catalog # AF1021b Specification Goat Anti-Actin-like 7B Antibody - Product Information Application WB Primary Accession Q9Y614 Other Accession NP_006677, 10880 Reactivity Human Host Goat Clonality Polyclonal Concentration 100ug/200ul Isotype IgG Calculated MW 45234 Goat Anti-Actin-like 7B Antibody - Additional AF1021b (0.03 µg/ml) staining of human Information testis lysate (35 µg protein in RIPA buffer). Primary incubation was 1 hour. Detected by Gene ID 10880 chemiluminescence. Other Names Actin-like protein 7B, Actin-like-7-beta, Goat Anti-Actin-like 7B Antibody - ACTL7B Background Format The protein encoded by this gene is a member 0.5 mg IgG/ml in Tris saline (20mM Tris of a family of actin-related proteins (ARPs) pH7.3, 150mM NaCl), 0.02% sodium azide, which share significant amino acid sequence with 0.5% bovine serum albumin identity to conventional actins. Both actins and ARPs have an actin fold, which is an Storage ATP-binding cleft, as a common feature. The Maintain refrigerated at 2-8°C for up to 6 ARPs are involved in diverse cellular processes, months. For long term storage store at including vesicular transport, spindle -20°C in small aliquots to prevent orientation, nuclear migration and chromatin freeze-thaw cycles. remodeling. This gene (ACTL7B), and related gene, ACTL7A, are intronless, and are located Precautions Goat Anti-Actin-like 7B Antibody is for approximately 4 kb apart in a head-to-head research use only and not for use in orientation within the familial dysautonomia diagnostic or therapeutic procedures.
    [Show full text]
  • High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors
    RESEARCH ARTICLE High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors Antonio F. Di Narzo1,2, Shannon E. Telesco3, Carrie Brodmerkel3, Carmen Argmann1,2, Lauren A. Peters2,4, Katherine Li3, Brian Kidd1,2, Joel Dudley1,2, Judy Cho1,2, Eric E. Schadt1,2, Andrew Kasarskis1,2, Radu Dobrin3*, Ke Hao1,2* 1 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, 2 Icahn Institute of Genomics and Multiscale Biology, Icahn School of a1111111111 Medicine at Mount Sinai, New York, New York, United States of America, 3 Janssen R&D, LLC, Spring a1111111111 House, Pennsylvania, United States of America, 4 Graduate School of Biomedical Sciences, Icahn School of a1111111111 Medicine at Mount Sinai, New York, New York, United States of America a1111111111 a1111111111 * [email protected] (RD); [email protected] (KH) Abstract OPEN ACCESS To date, no large scale, systematic description of the blood serum proteome has been per- Citation: Di Narzo AF, Telesco SE, Brodmerkel C, formed in inflammatory bowel disease (IBD) patients. By using microarray technology, a Argmann C, Peters LA, Li K, et al. (2017) High- more complete description of the blood proteome of IBD patients is feasible. It may help to Throughput Characterization of Blood Serum achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 Proteomics of IBD Patients with Respect to Aging and Genetic Factors. PLoS Genet 13(1): e1006565. proteins in IBD patients of European descent (84 Crohn's Disease (CD) subjects and 88 doi:10.1371/journal.pgen.1006565 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein Editor: Gregory S.
    [Show full text]
  • Research Article Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets
    Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 170289, 10 pages http://dx.doi.org/10.1155/2014/170289 Research Article Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets Fang Liu,1 Yaning Feng,1 Zhenye Li,2 Chao Pan,1 Yuncong Su,1 Rui Yang,1 Liying Song,1 Huilong Duan,1 and Ning Deng1 1 Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China 2 General Hospital of Ningxia Medical University, Yinchuan 750004, China Correspondence should be addressed to Ning Deng; [email protected] Received 30 March 2014; Accepted 12 May 2014; Published 2 June 2014 Academic Editor: Degui Zhi Copyright © 2014 Fang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis.
    [Show full text]
  • Genome-Wide Association Studies of Smooth Pursuit and Antisaccade Eye Movements in Psychotic Disorders: findings from the B-SNIP Study
    OPEN Citation: Transl Psychiatry (2017) 7, e1249; doi:10.1038/tp.2017.210 www.nature.com/tp ORIGINAL ARTICLE Genome-wide association studies of smooth pursuit and antisaccade eye movements in psychotic disorders: findings from the B-SNIP study R Lencer1, LJ Mills2, N Alliey-Rodriguez3, R Shafee4,5,AMLee6, JL Reilly7, A Sprenger8, JE McDowell9, SA McCarroll4, MS Keshavan10, GD Pearlson11,12, CA Tamminga13, BA Clementz9, ES Gershon3, JA Sweeney13,14 and JR Bishop6,15 Eye movement deviations, particularly deficits of initial sensorimotor processing and sustained pursuit maintenance, and antisaccade inhibition errors, are established intermediate phenotypes for psychotic disorders. We here studied eye movement measures of 849 participants from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study (schizophrenia N = 230, schizoaffective disorder N = 155, psychotic bipolar disorder N = 206 and healthy controls N = 258) as quantitative phenotypes in relation to genetic data, while controlling for genetically derived ancestry measures, age and sex. A mixed-modeling genome-wide association studies approach was used including ~ 4.4 million genotypes (PsychChip and 1000 Genomes imputation). Across participants, sensorimotor processing at pursuit initiation was significantly associated with a single nucleotide polymorphism in IPO8 (12p11.21, P =8×10− 11), whereas suggestive associations with sustained pursuit maintenance were identified with SNPs in SH3GL2 (9p22.2, P =3×10− 8). In participants of predominantly African ancestry, sensorimotor processing was also significantly associated with SNPs in PCDH12 (5q31.3, P = 1.6 × 10 − 10), and suggestive associations were observed with NRSN1 (6p22.3, P = 5.4 × 10 −8) and LMO7 (13q22.2, P = 7.3x10−8), whereas antisaccade error rate was significantly associated with a non-coding region at chromosome 7 (P = 6.5 × 10− 9).
    [Show full text]
  • Methylation of Leukocyte DNA and Ovarian Cancer
    Fridley et al. BMC Medical Genomics 2014, 7:21 http://www.biomedcentral.com/1755-8794/7/21 RESEARCH ARTICLE Open Access Methylation of leukocyte DNA and ovarian cancer: relationships with disease status and outcome Brooke L Fridley1*, Sebastian M Armasu2, Mine S Cicek2, Melissa C Larson2, Chen Wang2, Stacey J Winham2, Kimberly R Kalli3, Devin C Koestler1,4, David N Rider2, Viji Shridhar5, Janet E Olson2, Julie M Cunningham5 and Ellen L Goode2 Abstract Background: Genome-wide interrogation of DNA methylation (DNAm) in blood-derived leukocytes has become feasible with the advent of CpG genotyping arrays. In epithelial ovarian cancer (EOC), one report found substantial DNAm differences between cases and controls; however, many of these disease-associated CpGs were attributed to differences in white blood cell type distributions. Methods: We examined blood-based DNAm in 336 EOC cases and 398 controls; we included only high-quality CpG loci that did not show evidence of association with white blood cell type distributions to evaluate association with case status and overall survival. Results: Of 13,816 CpGs, no significant associations were observed with survival, although eight CpGs associated with survival at p < 10−3, including methylation within a CpG island located in the promoter region of GABRE (p = 5.38 x 10−5, HR = 0.95). In contrast, 53 CpG methylation sites were significantly associated with EOC risk (p <5 x10−6). The top association was observed for the methylation probe cg04834572 located approximately 315 kb upstream of DUSP13 (p = 1.6 x10−14). Other disease-associated CpGs included those near or within HHIP (cg14580567; p =5.6x10−11), HDAC3 (cg10414058; p = 6.3x10−12), and SCR (cg05498681; p = 4.8x10−7).
    [Show full text]
  • Molecular Processes During Fat Cell Development Revealed by Gene
    Open Access Research2005HackletVolume al. 6, Issue 13, Article R108 Molecular processes during fat cell development revealed by gene comment expression profiling and functional annotation Hubert Hackl¤*, Thomas Rainer Burkard¤*†, Alexander Sturn*, Renee Rubio‡, Alexander Schleiffer†, Sun Tian†, John Quackenbush‡, Frank Eisenhaber† and Zlatko Trajanoski* * Addresses: Institute for Genomics and Bioinformatics and Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of reviews Technology, Petersgasse 14, 8010 Graz, Austria. †Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, 1030 Vienna, Austria. ‡Dana- Farber Cancer Institute, Department of Biostatistics and Computational Biology, 44 Binney Street, Boston, MA 02115. ¤ These authors contributed equally to this work. Correspondence: Zlatko Trajanoski. E-mail: [email protected] Published: 19 December 2005 Received: 21 July 2005 reports Revised: 23 August 2005 Genome Biology 2005, 6:R108 (doi:10.1186/gb-2005-6-13-r108) Accepted: 8 November 2005 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2005/6/13/R108 © 2005 Hackl et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which deposited research permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene-expression<p>In-depthadipocytecell development.</p> cells bioinformatics were during combined fat-cell analyses with development de of novo expressed functional sequence annotation tags fo andund mapping to be differentially onto known expres pathwayssed during to generate differentiation a molecular of 3 atlasT3-L1 of pre- fat- Abstract Background: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development.
    [Show full text]
  • Animal Models to Study Bile Acid Metabolism T ⁎ Jianing Li, Paul A
    BBA - Molecular Basis of Disease 1865 (2019) 895–911 Contents lists available at ScienceDirect BBA - Molecular Basis of Disease journal homepage: www.elsevier.com/locate/bbadis ☆ Animal models to study bile acid metabolism T ⁎ Jianing Li, Paul A. Dawson Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Emory University, Atlanta, GA 30322, United States ARTICLE INFO ABSTRACT Keywords: The use of animal models, particularly genetically modified mice, continues to play a critical role in studying the Liver relationship between bile acid metabolism and human liver disease. Over the past 20 years, these studies have Intestine been instrumental in elucidating the major pathways responsible for bile acid biosynthesis and enterohepatic Enterohepatic circulation cycling, and the molecular mechanisms regulating those pathways. This work also revealed bile acid differences Mouse model between species, particularly in the composition, physicochemical properties, and signaling potential of the bile Enzyme acid pool. These species differences may limit the ability to translate findings regarding bile acid-related disease Transporter processes from mice to humans. In this review, we focus primarily on mouse models and also briefly discuss dietary or surgical models commonly used to study the basic mechanisms underlying bile acid metabolism. Important phenotypic species differences in bile acid metabolism between mice and humans are highlighted. 1. Introduction characteristics such as small size, short gestation period and life span, which facilitated large-scale laboratory breeding and housing, the Interest in bile acids can be traced back almost three millennia to availability of inbred and specialized strains as genome sequencing the widespread use of animal biles in traditional Chinese medicine [1].
    [Show full text]