DOCSLIB.ORG

Comparative Analyses of Murine and Human Formyl Peptide Receptor 3

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Comparative Analyses of Murine and Human Formyl Peptide Receptor 3 Aus der Fachrichtung Physiologie – Prof. Dr. Dr. Frank Zufall Theoretische Medizin und Biowissenschaften der Medizinischen Fakultät der Universität des Saarlandes, Homburg Comparative Analyses of Murine and Human Formyl Peptide Receptor 3 Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften der Medizinischen Fakultät der UNIVERSITÄT DES SAARLANDES 2017 vorgelegt von: Hendrik Stempel geb. am: 22.12.1985 in Homburg Es gibt Stunden, in denen der Mensch von aller Unzulänglichkeit befreit ist. Man steht dann auf einem kleinen Flecken eines kleinen Planeten, schaut erstaunt die Schönheit des Ewigen, des in der Tiefe Unergründlichen. Man fühlt, es gibt nicht mehr Werden und Vergehen, es gibt nicht mehr Tod und Leben, sondern nur das Sein. Albert Einstein The following manuscripts emerged from this thesis: Stempel H, Jung M, Pérez-Gómez A, Leinders-Zufall T, Zufall F, Bufe B (2016) Strain- specific loss of formyl peptide receptor 3 in the murine vomeronasal and immune systems. J Biol Chem 291: 9762-9775 Stempel H, Zufall F, Bufe B. Evidence for an Orthologous Function of Mouse and Human Formyl Peptide Receptor 3. In preparation Table of Contents List of Figures ......................................................................................................................... IV List of Tables ........................................................................................................................... VI Abstract ................................................................................................................................. VII Zusammenfassung ............................................................................................................... VIII Abbreviations ........................................................................................................................... X 1 INTRODUCTION ........................................................................................ 1 1.1 Social Recognition and Olfactory Pathogen Recognition ........................................... 1 1.2 The Vomeronasal Organ .............................................................................................. 3 1.2.1 General Function of the Vomeronasal Organ ...................................................... 3 1.2.2 Anatomy of the Vomeronasal Organ.................................................................... 4 1.2.3 Detection Mechanisms in the Vomeronasal Organ .............................................. 6 1.2.3.1 Vmn1r Expressing Vomeronasal Sensory Neurons .......................................... 6 1.2.3.2 Vmn2r Expressing Vomeronasal Sensory Neurons .......................................... 7 1.2.3.3 Formyl Peptide Receptor Expressing Vomeronasal Sensory Neurons ............ 8 1.3 Formyl Peptide Receptors ........................................................................................... 9 1.3.1 General Function of Formyl Peptide Receptors ................................................... 9 1.3.2 Genetics of Formyl Peptide Receptors ............................................................... 11 1.3.3 Tissue Distribution of Formyl Peptide Receptors .............................................. 12 1.3.4 Relationship between Murine and Human Formyl Peptide Receptors .............. 13 1.4 Aims of this Work ..................................................................................................... 14 2 EXPERIMENTAL PROCEDURES ......................................................... 15 2.1 Ligands ...................................................................................................................... 15 2.1.1 Synthetic Ligands ............................................................................................... 15 2.1.2 Natural Ligands .................................................................................................. 16 2.2 Mouse Strains ............................................................................................................ 17 2.3 Molecular Biology ..................................................................................................... 17 2.3.1 Oligonucleotides ................................................................................................. 17 2.3.1.1 Sequencing Primers ........................................................................................ 18 2.3.1.2 PCR Primers ................................................................................................... 18 2.3.2 Polymerase Chain Reaction ............................................................................... 19 2.3.3 PCR Templates ................................................................................................... 19 2.3.4 RNA Isolation .................................................................................................... 20 2.3.5 cDNA Synthesis ................................................................................................. 21 2.3.6 Extraction of Genomic DNA .............................................................................. 21 2.3.7 Purification of PCR Products ............................................................................. 21 2.3.8 Gel Electrophoresis ............................................................................................ 22 2.3.9 RNA Quantification ........................................................................................... 22 2.3.10 Enzymatic DNA Digestion ................................................................................. 22 2.3.11 Expression Vectors ............................................................................................. 22 2.3.12 DNA Ligation ..................................................................................................... 24 2.3.13 Transformation of Competent Escherichia coli ................................................. 24 2.3.14 Isolation of Plasmid DNA from Bacterial Cultures ........................................... 24 2.3.15 Determining DNA and RNA Concentration ...................................................... 25 2.3.16 DNA Sequencing ................................................................................................ 25 2.3.17 Generation of Bacterial Glycerol Stocks ............................................................ 25 2.3.18 Formyl Peptide Receptor Genes ......................................................................... 25 2.4 HEK293T Cell Culture .............................................................................................. 26 I 2.4.1 HEK293T Cells .................................................................................................. 26 2.4.2 Cell Culture Media ............................................................................................. 26 2.4.3 Cultivating Culture Cells .................................................................................... 26 2.4.4 Transient Transfection of Culture Cells ............................................................. 27 2.4.5 Thawing of Cryopreserved Culture Cells ........................................................... 27 2.4.6 Storage of Culture Cells ..................................................................................... 27 2.5 High-Throughput Calcium Imaging .......................................................................... 28 2.5.1 Cell Population Calcium Imaging ...................................................................... 28 2.5.1.1 Dye Loading of HEK293T Cells for Cell Population Calcium Imaging ........ 28 2.5.1.2 Data Acquisition for Cell Population Calcium Imaging with the FLIPR ...... 28 2.5.1.3 Analysis of FLIPR Experiments ...................................................................... 28 2.5.2 Single Cell Calcium Imaging ............................................................................. 29 2.5.2.1 Dye Loading of HEK293T Cells for Single Cell Calcium Imaging ................ 29 2.5.2.2 Data Acquisition for Single Cell Calcium Imaging with the Bioimager ........ 30 2.5.2.3 Analysis of Bioimager Experiments ................................................................ 30 2.6 Immunocytochemistry ............................................................................................... 31 2.6.1 Preparation of Samples for Immunocytochemistry ............................................ 31 2.6.1.1 Dissociation of Vomeronasal Tissue .............................................................. 31 2.6.1.2 Preparation of Blood Cells ............................................................................. 31 2.6.1.3 Preparation of Bone Marrow Cells ................................................................ 31 2.6.2 Immunostaining Protocol ................................................................................... 32 2.6.2.1 Image Acquisition and Data Analysis for General Immunostainings ............ 32 2.6.3 Antibodies .......................................................................................................... 32 2.6.3.1 Used Antibodies .............................................................................................. 32 2.6.3.2 Generation of Fpr3 Antibodies ....................................................................... 33 2.6.3.3 Peptide-Spot Array Analysis for Antibody Characterization ......................... 34 2.6.3.4 Blocking Peptides ..........................................................................................
Load more

Recommended publications
  • 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]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Regulation of Neuronal Gene Expression and Survival by Basal NMDA Receptor Activity: a Role for Histone Deacetylase 4
    The Journal of Neuroscience, November 12, 2014 • 34(46):15327–15339 • 15327 Cellular/Molecular Regulation of Neuronal Gene Expression and Survival by Basal NMDA Receptor Activity: A Role for Histone Deacetylase 4 Yelin Chen,1 Yuanyuan Wang,1 Zora Modrusan,3 Morgan Sheng,1 and Joshua S. Kaminker1,2 Departments of 1Neuroscience, 2Bioinformatics and Computational Biology, and 3Molecular Biology, Genentech Inc., South San Francisco, California 94080 Neuronal gene expression is modulated by activity via calcium-permeable receptors such as NMDA receptors (NMDARs). While gene expression changes downstream of evoked NMDAR activity have been well studied, much less is known about gene expression changes that occur under conditions of basal neuronal activity. In mouse dissociated hippocampal neuronal cultures, we found that a broad NMDAR antagonist, AP5, induced robust gene expression changes under basal activity, but subtype-specific antagonists did not. While some of the gene expression changes are also known to be downstream of stimulated NMDAR activity, others appear specific to basal NMDARactivity.ThegenesalteredbyAP5treatmentofbasalcultureswereenrichedforpathwaysrelatedtoclassIIahistonedeacetylases (HDACs), apoptosis, and synapse-related signaling. Specifically, AP5 altered the expression of all three class IIa HDACs that are highly expressed in the brain, HDAC4, HDAC5, and HDAC9, and also induced nuclear accumulation of HDAC4. HDAC4 knockdown abolished a subset of the gene expression changes induced by AP5, and led to neuronal death under
    [Show full text]
  • The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
    The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d.
    [Show full text]
  • Análise Integrativa De Perfis Transcricionais De Pacientes Com
    UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas.
    [Show full text]
  • Genetic Analysis of Indel Markers in Three Loci Associated with Parkinson’S Disease
    RESEARCH ARTICLE Genetic analysis of indel markers in three loci associated with Parkinson's disease Zhixin Huo1, Xiaoguang Luo2, Xiaoni Zhan1, Qiaohong Chu1, Qin Xu1, Jun Yao1, Hao Pang1* 1 Department of Forensic Genetics and Biology, China Medical University, Shenyang North New Area, Shenyang, P.R., China, 2 Department of Neurology, 1st Affiliated Hospital of China Medical University, Shenyang, P.R., China * [email protected] Abstract a1111111111 a1111111111 The causal mutations and genetic polymorphisms associated with susceptibility to Parkin- a1111111111 son's disease (PD) have been extensively described. To explore the potential contribution a1111111111 a1111111111 of insertion (I)/deletion (D) polymorphisms (indels) to the risk of PD in a Chinese population, we performed genetic analyses of indel loci in ACE, DJ-1, and GIGYF2 genes. Genomic DNA was extracted from venous blood of 348 PD patients and 325 age- and sex-matched controls without neurodegenerative disease. Genotyping of the indel loci was performed by fragment length analysis after PCR and DNA sequencing. Our results showed a statistically OPEN ACCESS significant association for both allele X (alleles without 5) vs. 5 (odds ratio = 1.378, 95% con- Citation: Huo Z, Luo X, Zhan X, Chu Q, Xu Q, Yao fidence interval = 1.112±1.708, P = 0.003) and genotype 5/X+X/X vs. 5/5 (odds ratio = J, et al. (2017) Genetic analysis of indel markers in three loci associated with Parkinson's disease. 1.681, 95% confidence interval = 1.174±2.407, P = 0.004) in the GIGYF2 locus; however, PLoS ONE 12(9): e0184269. https://doi.org/ no significant differences were detected for the ACE and DJ-1 indels.
    [Show full text]
  • Identification of Candidate Genes and Pathways Associated with Obesity
    animals Article Identification of Candidate Genes and Pathways Associated with Obesity-Related Traits in Canines via Gene-Set Enrichment and Pathway-Based GWAS Analysis Sunirmal Sheet y, Srikanth Krishnamoorthy y , Jihye Cha, Soyoung Choi and Bong-Hwan Choi * Animal Genome & Bioinformatics, National Institute of Animal Science, RDA, Wanju 55365, Korea; [email protected] (S.S.); [email protected] (S.K.); [email protected] (J.C.); [email protected] (S.C.) * Correspondence: [email protected]; Tel.: +82-10-8143-5164 These authors contributed equally. y Received: 10 October 2020; Accepted: 6 November 2020; Published: 9 November 2020 Simple Summary: Obesity is a serious health issue and is increasing at an alarming rate in several dog breeds, but there is limited information on the genetic mechanism underlying it. Moreover, there have been very few reports on genetic markers associated with canine obesity. These studies were limited to the use of a single breed in the association study. In this study, we have performed a GWAS and supplemented it with gene-set enrichment and pathway-based analyses to identify causative loci and genes associated with canine obesity in 18 different dog breeds. From the GWAS, the significant markers associated with obesity-related traits including body weight (CACNA1B, C22orf39, U6, MYH14, PTPN2, SEH1L) and blood sugar (PRSS55, GRIK2), were identified. Furthermore, the gene-set enrichment and pathway-based analysis (GESA) highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) which were found to be shared among all the traits.
    [Show full text]
  • Mitoxplorer, a Visual Data Mining Platform To
    mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, Cecilia Garcia-Perez, José Villaveces, Salma Gamal, Giovanni Cardone, Fabiana Perocchi, et al. To cite this version: Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, et al.. mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dy- namics and mutations. Nucleic Acids Research, Oxford University Press, 2020, 10.1093/nar/gkz1128. hal-02394433 HAL Id: hal-02394433 https://hal-amu.archives-ouvertes.fr/hal-02394433 Submitted on 4 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Nucleic Acids Research, 2019 1 doi: 10.1093/nar/gkz1128 Downloaded from https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz1128/5651332 by Bibliothèque de l'université la Méditerranée user on 04 December 2019 mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim1,†, Prasanna Koti1,†, Adrien Bonnard2, Fabio Marchiano3, Milena Durrbaum¨ 1, Cecilia Garcia-Perez4, Jose Villaveces1, Salma Gamal1, Giovanni Cardone1, Fabiana Perocchi4, Zuzana Storchova1,5 and Bianca H.
    [Show full text]
  • Exploring the Relationship Between Gut Microbiota and Major Depressive Disorders
    E3S Web of Conferences 271, 03055 (2021) https://doi.org/10.1051/e3sconf/202127103055 ICEPE 2021 Exploring the Relationship between Gut Microbiota and Major Depressive Disorders Catherine Tian1 1Shanghai American School, Shanghai, China Abstract. Major Depressive Disorder (MDD) is a psychiatric disorder accompanied with a high rate of suicide, morbidity and mortality. With the symptom of an increasing or decreasing appetite, there is a possibility that MDD may have certain connections with gut microbiota, the colonies of microbes which reside in the human digestive system. In recent years, more and more studies started to demonstrate the links between MDD and gut microbiota from animal disease models and human metabolism studies. However, this relationship is still largely understudied, but it is very innovative since functional dissection of this relationship would furnish a new train of thought for more effective treatment of MDD. In this study, by using multiple genetic analytic tools including Allen Brain Atlas, genetic function analytical tools, and MicrobiomeAnalyst, I explored the genes that shows both expression in the brain and the digestive system to affirm that there is a connection between gut microbiota and the MDD. My approach finally identified 7 MDD genes likely to be associated with gut microbiota, implicating 3 molecular pathways: (1) Wnt Signaling, (2) citric acid cycle in the aerobic respiration, and (3) extracellular exosome signaling. These findings may shed light on new directions to understand the mechanism of MDD, potentially facilitating the development of probiotics for better psychiatric disorder treatment. 1 Introduction 1.1 Major Depressive Disorder Major Depressive Disorder (MDD) is a mood disorder that will affect the mood, behavior and other physical parts.
    [Show full text]
  • Family-Based Quantitative Trait Meta-Analysis Implicates
    bioRxiv preprint doi: https://doi.org/10.1101/460972; this version posted November 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Family-based quantitative trait meta-analysis implicates rare noncoding 2 variants in DENND1A in pathogenesis of polycystic ovary syndrome 3 4 5 Matthew Dapas1, Ryan Sisk1, Richard S. Legro2, Margrit Urbanek1,3,4, Andrea Dunaif5,¶*, M. 6 Geoffrey Hayes1,3,6¶* 7 8 9 1 Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, 10 Northwestern University Feinberg School of Medicine, Chicago, IL 11 2 Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, PA 12 3 Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 13 4 Center for Reproductive Science, Northwestern University Feinberg School of Medicine, Chicago, IL 14 5 Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, 15 New York, NY 16 6 Department of Anthropology, Northwestern University, Evanston, IL 17 18 19 ¶ These authors jointly supervised this work. 20 * Corresponding author 21 22 23 24 25 26 27 E-mail: [email protected] (MGH) 28 [email protected] (AD) 29 30 31 32 bioRxiv preprint doi: https://doi.org/10.1101/460972; this version posted November 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Region Based Gene Expression Via Reanalysis of Publicly Available Microarray Data Sets
    University of Louisville ThinkIR: The University of Louisville's Institutional Repository Electronic Theses and Dissertations 5-2018 Region based gene expression via reanalysis of publicly available microarray data sets. Ernur Saka University of Louisville Follow this and additional works at: https://ir.library.louisville.edu/etd Part of the Bioinformatics Commons, Computational Biology Commons, and the Other Computer Sciences Commons Recommended Citation Saka, Ernur, "Region based gene expression via reanalysis of publicly available microarray data sets." (2018). Electronic Theses and Dissertations. Paper 2902. https://doi.org/10.18297/etd/2902 This Doctoral Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact [email protected]. REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Submitted To the J. B. Speed School of Engineering in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science and Engineering Department of Computer Engineering and Computer Science University of Louisville Louisville, Kentucky May 2018 Copyright 2018 by Ernur Saka All rights reserved REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Approved On April 20, 2018 by the following Committee __________________________________ Dissertation Director Dr.
    [Show full text]
  • Limitations of the Rhesus Macaque Draft Genome Assembly and Annotation
    University of Nebraska Medical Center DigitalCommons@UNMC Journal Articles: Genetics, Cell Biology & Anatomy Genetics, Cell Biology & Anatomy 2012 Limitations of the Rhesus Macaque Draft Genome Assembly and Annotation Xiongfei Zhang University of Nebraska Medical Center Joel Goodsell University of Nebraska Medical Center, [email protected] Robert B. Norgren University of Nebraska Medical Center, [email protected] Follow this and additional works at: https://digitalcommons.unmc.edu/com_gcba_articles Part of the Medical Anatomy Commons, Medical Cell Biology Commons, and the Medical Genetics Commons Recommended Citation Zhang, Xiongfei; Goodsell, Joel; and Norgren, Robert B., "Limitations of the Rhesus Macaque Draft Genome Assembly and Annotation" (2012). Journal Articles: Genetics, Cell Biology & Anatomy. 42. https://digitalcommons.unmc.edu/com_gcba_articles/42 This Article is brought to you for free and open access by the Genetics, Cell Biology & Anatomy at DigitalCommons@UNMC. It has been accepted for inclusion in Journal Articles: Genetics, Cell Biology & Anatomy by an authorized administrator of DigitalCommons@UNMC. For more information, please contact [email protected]. Zhang et al. BMC Genomics 2012, 13 :206 http://www.biomedcentral.com/1471-2164/13/206 CORRESPONDENCE Open Access Limitations of the rhesus macaque draft genome assembly and annotation Xiongfei Zhang, Joel Goodsell and Robert B Norgren, Jr. * Abstract Finished genome sequences and assemblies are available for only a few vertebrates. Thus, investigators studying many species must rely on draft genomes. Using the rhesus macaque as an example, we document the effects of sequencing errors, gaps in sequence and misassemblies on one automated gene model pipeline, Gnomon. The combination of draft genome with automated gene finding software can result in spurious sequences.
    [Show full text]