DOCSLIB.ORG

Gene Expression Profiling in Prepubertal and Adult Male Mice Using Cdna and Oligonucleotide Microarrays

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gene Expression Profiling in Prepubertal and Adult Male Mice Using Cdna and Oligonucleotide Microarrays Graduate Theses, Dissertations, and Problem Reports 2003 Gene expression profiling in prepubertal and adult male mice using cDNA and oligonucleotide microarrays Lisa Marie Tomascik-Cheeseman West Virginia University Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Tomascik-Cheeseman, Lisa Marie, "Gene expression profiling in prepubertal and adult male mice using cDNA and oligonucleotide microarrays" (2003). Graduate Theses, Dissertations, and Problem Reports. 1844. https://researchrepository.wvu.edu/etd/1844 This Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. GENEEXPRESSIONPROFILINGIN PREPUBERTALANDADULTMALEMICEUSING CDNAANDOLIGONUCLEOTIDEMICROARRAYS LisaMarieTomascik-Cheeseman Dissertationsubmittedtothe GeneticsandDevelopmentalBiologyProgram, CollegeofAgriculture,Forestry,andConsumerSciences, atWestVirginiaUniversity inpartialfulfillmentoftherequirements forthedegreeof DoctorofPhilosophy in Genetics JoginderNath,Ph.D.,Co-Chair AndrewJ.Wyrobek,Ph.D.,Co-Chair WalterKaczmarczyk,Ph.D. DanielPanaccione,Ph.D. SharonWenger,Ph.D. Morgantown,WestVirginia 2003 Variations in gene expression are the basis of differences in cell and tissue function, response to DNA damaging agents, susceptibility to genetic disease, and cellular differentiation.Thepurposeofthisdissertationresearchwastocharacterizevariationin basal gene expression among adult mouse tissues for selected stress response, DNA repair and damage control genes and to utilize variation in temporal gene expression patternstoidentifycandidategenesassociatedwithgermcelldifferentiationfrommitosis through meiosis in the prepubertal mouse testis. To accomplish these goals, high throughputanalysesofgeneexpressionwereperformedusingcustomcDNAandrandom oligonucleotidemicroarrays.cDNAmicroarraytechnologywasoptimizedbyevaluating theeffectsofmultiplehybridizationandimageanalysismethodologiesonthemagnitude of background-subtracted hybridization signal intensities. The results showed that hybridizinglowerprobequantitiesinabufferdevelopedatLawrenceLivermoreNational Laboratorytotryptone-blockedmicroarraysimprovedsignalintensities.Inaddition,the error in expression ratio measurements was significantly reduced when microarray images were preprocessed. A custom cDNA microarray comprised of 417 genes and enriched for stress response, DNA repair, and damage control genes was used to investigate basal gene expression differences among adult mouse testis, brain, liver, spleen, and heart. Genes with functions related to stress response exhibited the most variation in expression among tissues whereas DNA repair-associated gene expression variedtheleast.Randomoligonucleotidemicroarrayscomprisedof~10,000geneswere usedtoprofilechangesingeneexpressionduringthefirstwaveofspermatogenesisinthe prepubertalmousetestis.Approximately550genesweredifferentiallyexpressedasmale germcellsdifferentiatedfromspermatogoniatoprimaryspermatocytes.Thesefindings suggestthatthe313unannotatedsequencesand178geneswithknownfunctionsinother biological pathways have spermatogenesis-associated roles. This dissertation research showedthatmicroarraysareausefultoolforquantitatingtheexpressionoflargenumbers ofgenesinparallelundernormalphysiologicalconditionsandduringdifferentiation.It hasalsoprovidedcandidategenesforfutureinvestigationsofthemolecularmechanisms underlying(1)tissue-specificDNAdamage responseand geneticdiseasesusceptibility and(2)cellulardifferentiationduringtheonsetandprogressionofspermatogenesis. Thisdissertationisdedicatedtomyhusband,Matthew, forhisadvice,encouragement,and(mostofall)love. Thankyouforgivingmethecouragetodream. Thisworkisalsodedicatedtomyparents,ThomasandCarol Tomascik, andtomygrandmother,SophieDuzen inhonoroftheirtirelessloveandsupport. Inmemoryofmygrandfather,AndrewDuzen. iv TableofContents Abstract ii Acknowledgementsvii ListofTables viii ListofFigures x ListofAbbreviations xii Chapter1. Introduction 1 DissertationObjectives 4 Chapter2. ExpressionMicroarrayTechnology ChapterOverview 10 Section2.1 Fluorescentexpressionmicroarraytechnologyreview 2.1.1 Introduction 11 2.1.2 TypesofExpressionMicroarrays 13 2.1.3 ProbeGeneration,Hybridization,andImageCapture 14 2.1.4 MicroarrayDataAcquisitionandAnalysis 20 2.1.5 Summary 21 2.1.6 References 22 Section2.2 Optimizationoftwo-colorfluorescencecDNA microarrayhybridizationsvisualizedwithawhite lightimagingsystem 2.2.1 Abstract 27 2.2.2 Introduction 27 2.2.3 MaterialsandMethods 31 2.2.4 Results 37 2.2.5 Discussion 46 2.2.6 References 50 Section2.3 Accuratequantitationoffluorescencemicroarrays 2.3.1 Abstract 51 2.3.2 Introduction 51 2.3.3 MaterialsandMethods 53 2.3.4 ImageProcessingandAnalysisMethods 58 2.3.5 Results 69 2.3.6 Conclusions 73 2.3.7 Auspices 74 2.3.8 References 75 v Chapter3. DifferentialBasalExpressionofGenesAssociated withStressResponse,DamageControl,andDNA RepairAmongMouseTissues 3.1 Abstract 77 3.2 Introduction 78 3.3 MaterialsandMethods 80 3.4 Results 87 3.5 Discussion 100 3.6 Acknowledgements 106 3.7 References 107 Chapter4. MitoticandMeioticGeneExpressionProfilingof MaleGermCells 4.1 Abstract 114 4.2 Introduction 115 4.3 MaterialsandMethods 117 4.4 Results 121 4.5 Discussion 133 4.6 Acknowledgements 137 4.7 References 138 Chapter5. Conclusions 152 Appendices A Symbols,names,biologicalpathways,I.M.A.G.E. cloneIDs,andcDNAmicroarraydatafor152 geneswithdifferentialexpression amonghealthyadultmousetissues 155 B Allannotatedgenesshowingsignificantdifferences inexpressionduringthetransitionfrommitosisto meiosisintheprepubertalmousetestis 160 C Unannotatedsequenceswithsignificantexpression differencesduringthefirstwaveofspermatogenesis intheprepubertalmouse 167 D Functionalgenomics-relatedwebsites 173 E CurriculumVitae 175 F Publishedresearcharticles 180 vi Acknowledgements Dr. Joginder Nath, Academic Advisor and graduate committee Co-chair: Words can’texpresshowthankfulIamtohavebeenyourstudent.Yourguidancehasenabled metogrowbothpersonallyandprofessionally. Dr.AndrewJ.Wyrobek,ResearchAdvisorandgraduatecommitteeCo-chair:Iam so grateful for the opportunity to be a member of your laboratory. Thank you for constantlychallengingmeandalwaysfindingtimeformyresearch. Dr.WalterKaczmarczyk,Dr.DanielPanaccione,andDr.SharonWenger,graduate committeemembers:Thankyouforreviewingthedissertationandprovidingvaluable comments. Past and present members of the Wyrobek laboratory: Thank you for the spirited scientificconversationsandcamaraderie.Iwouldespeciallyliketorecognize: Dr. Francesco Marchetti: I could never thank you enough for everything you havetaughtme.Iwillalwayscherishourfriendship. Eddie Sloter: I have enjoyed every discussion (scientific and otherwise). You havebeenawonderfulfriend,andIcannotimaginewhatthisexperiencewould havebeenlikewithoutyou. Dr.MatthewA.Coleman:Thanksforalwayskeepingmeontaskbyasking,“Is thatdissertationfinished yet???”Iadmire yourscientificinsightandappreciate yourfriendship. FrancescaHill:Thanksforsharingyourcytogeneticexpertiseandfriendship. Nancy Wrigley: Thank you for the administrative assistance and motherly advice. Dr.DavidO.NelsonandSheaGardner:Iamtrulythankfulforallofyourassistance withthemicroarraystatisticalanalyses. LauraMascioKegelmeyer:ThanksforpatientlyteachingmeeverythingIeverneeded toknowaboutUNIXsystems.Perhapsmoreimportantly,thankyoufortheafternoontea. TheTomascikandCheesemanfamilies:Yourloveandunwaveringsupporthasmade thisdreampossible.IpromisetogetaREALjob…someday. Dr.SaraFrias,AnaClaudiaVelazquez-Wong,DougandLindaHamilton,andthe Jackson family: I will never forget the laughter, smiles, and many words of encouragement.Thankyouforyourfriendship. vii ListofTables Chapter2. Page ExpressionMicroarrayTechnology Section2.2 OptimizationofcDNAmicroarrayhybridizationsvisualized withaXenonlightsource 2.2.1 PrimersusedtoamplifycDNAclonesforthegene-specific hybridizationexperiments 33 2.2.2 Experimentaldesignforthegene-specificandtissuesample hybridizations 35 2.2.3 Comparisonofbackground-subtractedsignalintensities obtainedfromgene-specifichybridizations 40 2.2.4 CharacterizationofthesignalintensitiesobtainedforeachAlexa Fluorfollowingdifferenttissuesamplehybridizationprotocols 43 2.2.5 Relationshipbetweentissuesamplehybridizationprotocoland numberofgeneswithsignalintensityrati≥os1.5 45 Section2.3 Accuratequantitationoffluorescencemicroarrays 2.3.1 Sixgeneswereusedtobuildagroundtruthseriesoverten microarrayslides 56 2.3.2 Errormeasurementsobtainedforthegroundtruthseries followingdifferentimageanalysismethods 71 Chapter3. DifferentialBasalExpressionofGenesAssociatedwithStressResponse, DamageControl,andDNARepairAmongMouseTissues 3.1 Differencesinthegeneexpressionratiosfromreplicate independenthybridizations 91 3.2 DistributionofFratiosfordifferentiallyexpressedgeneswith respecttobiologicalpathway 96 3.3 Differentiallyexpressedstressresponse,DNArepair,and
Load more

Recommended publications
  • Mouse Rabac1 Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Rabac1 Knockout Project (CRISPR/Cas9) Objective: To create a Rabac1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Rabac1 gene (NCBI Reference Sequence: NM_010261 ; Ensembl: ENSMUSG00000003380 ) is located on Mouse chromosome 7. 5 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 5 (Transcript: ENSMUST00000076961). Exon 1~5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 1 starts from about 0.18% of the coding region. Exon 1~5 covers 100.0% of the coding region. The size of effective KO region: ~2657 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 Legends Exon of mouse Rabac1 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of start codon is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of stop codon is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Analysis of Trans Esnps Infers Regulatory Network Architecture
    Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation.
    [Show full text]
  • Computational Genome-Wide Identification of Heat Shock Protein Genes in the Bovine Genome [Version 1; Peer Review: 2 Approved, 1 Approved with Reservations]
    F1000Research 2018, 7:1504 Last updated: 08 AUG 2021 RESEARCH ARTICLE Computational genome-wide identification of heat shock protein genes in the bovine genome [version 1; peer review: 2 approved, 1 approved with reservations] Oyeyemi O. Ajayi1,2, Sunday O. Peters3, Marcos De Donato2,4, Sunday O. Sowande5, Fidalis D.N. Mujibi6, Olanrewaju B. Morenikeji2,7, Bolaji N. Thomas 8, Matthew A. Adeleke 9, Ikhide G. Imumorin2,10,11 1Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta, Nigeria 2International Programs, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, 14853, USA 3Department of Animal Science, Berry College, Mount Berry, GA, 30149, USA 4Departamento Regional de Bioingenierias, Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Queretaro, Mexico 5Department of Animal Production and Health, Federal University of Agriculture, Abeokuta, Nigeria 6Usomi Limited, Nairobi, Kenya 7Department of Animal Production and Health, Federal University of Technology, Akure, Nigeria 8Department of Biomedical Sciences, Rochester Institute of Technology, Rochester, NY, 14623, USA 9School of Life Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa 10School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30032, USA 11African Institute of Bioscience Research and Training, Ibadan, Nigeria v1 First published: 20 Sep 2018, 7:1504 Open Peer Review https://doi.org/10.12688/f1000research.16058.1 Latest published: 20 Sep 2018, 7:1504 https://doi.org/10.12688/f1000research.16058.1 Reviewer Status Invited Reviewers Abstract Background: Heat shock proteins (HSPs) are molecular chaperones 1 2 3 known to bind and sequester client proteins under stress. Methods: To identify and better understand some of these proteins, version 1 we carried out a computational genome-wide survey of the bovine 20 Sep 2018 report report report genome.
    [Show full text]
  • Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice
    Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications 2013 Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice Mihaela Palicev Gunter P. Wagner James P. Noonan Benedikt Hallgrimsson James M. Cheverud Loyola University Chicago, [email protected] Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs Part of the Biology Commons Recommended Citation Palicev, M, GP Wagner, JP Noonan, B Hallgrimsson, and JM Cheverud. "Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice." Genome Biology and Evolution 5(10), 2013. This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. © Palicev et al., 2013. GBE Genomic Correlates of Relationship QTL Involved in Fore- versus Hind Limb Divergence in Mice Mihaela Pavlicev1,2,*, Gu¨ nter P. Wagner3, James P. Noonan4, Benedikt Hallgrı´msson5,and James M. Cheverud6 1Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria 2Department of Pediatrics, Cincinnati Children‘s Hospital Medical Center, Cincinnati, Ohio 3Yale Systems Biology Institute and Department of Ecology and Evolutionary Biology, Yale University 4Department of Genetics, Yale University School of Medicine 5Department of Cell Biology and Anatomy, The McCaig Institute for Bone and Joint Health and the Alberta Children’s Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Canada 6Department of Anatomy and Neurobiology, Washington University *Corresponding author: E-mail: [email protected].
    [Show full text]
  • ACE2 Interaction Networks in COVID-19: a Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors
    Journal of Clinical Medicine Article ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors Zofia Wicik 1,2 , Ceren Eyileten 2, Daniel Jakubik 2,Sérgio N. Simões 3, David C. Martins Jr. 1, Rodrigo Pavão 1, Jolanta M. Siller-Matula 2,4,* and Marek Postula 2 1 Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo Andre 09606-045, Brazil; zofi[email protected] (Z.W.); [email protected] (D.C.M.J.); [email protected] (R.P.) 2 Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, 02-091 Warsaw, Poland; [email protected] (C.E.); [email protected] (D.J.); [email protected] (M.P.) 3 Federal Institute of Education, Science and Technology of Espírito Santo, Serra, Espírito Santo 29056-264, Brazil; [email protected] 4 Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, 1090 Vienna, Austria * Correspondence: [email protected]; Tel.: +43-1-40400-46140; Fax: +43-1-40400-42160 Received: 9 October 2020; Accepted: 17 November 2020; Published: 21 November 2020 Abstract: Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. Methods: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system.
    [Show full text]
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [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]
  • Taf7l Cooperates with Trf2 to Regulate Spermiogenesis
    Taf7l cooperates with Trf2 to regulate spermiogenesis Haiying Zhoua,b, Ivan Grubisicb,c, Ke Zhengd,e, Ying Heb, P. Jeremy Wangd, Tommy Kaplanf, and Robert Tjiana,b,1 aHoward Hughes Medical Institute and bDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, California Institute for Regenerative Medicine Center of Excellence, University of California, Berkeley, CA 94720; cUniversity of California Berkeley–University of California San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA 94720; dDepartment of Animal Biology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104; eState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 210029, People’s Republic of China; and fSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel Contributed by Robert Tjian, September 11, 2013 (sent for review August 20, 2013) TATA-binding protein (TBP)-associated factor 7l (Taf7l; a paralogue (Taf4b; a homolog of Taf4) (9), TBP-related factor 2 (Trf2) (10, of Taf7) and TBP-related factor 2 (Trf2) are components of the core 11), and Taf7l (12, 13). For example, mice bearing mutant or promoter complex required for gene/tissue-specific transcription deficient CREM showed decreased postmeiotic gene expression of protein-coding genes by RNA polymerase II. Previous studies and defective spermiogenesis (14). Mice deficient in Taf4b, reported that Taf7l knockout (KO) mice exhibit structurally abnor- a testis-specific homolog of Taf4, are initially normal but undergo mal sperm, reduced sperm count, weakened motility, and compro- progressive germ-cell loss and become infertile by 3 mo of age −/Y mised fertility.
    [Show full text]
  • Discovering Novel Hearing Loss Genes: Roles for Esrp1 and Gas2 in Inner Ear Development and Auditory Function
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2018 Discovering Novel Hearing Loss Genes: Roles For Esrp1 And Gas2 In Inner Ear Development And Auditory Function Alex Martin Rohacek University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Cell Biology Commons, Developmental Biology Commons, and the Molecular Biology Commons Recommended Citation Rohacek, Alex Martin, "Discovering Novel Hearing Loss Genes: Roles For Esrp1 And Gas2 In Inner Ear Development And Auditory Function" (2018). Publicly Accessible Penn Dissertations. 2843. https://repository.upenn.edu/edissertations/2843 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2843 For more information, please contact [email protected]. Discovering Novel Hearing Loss Genes: Roles For Esrp1 And Gas2 In Inner Ear Development And Auditory Function Abstract Hearing loss is the most common form of congenital birth defect, affecting an estimated 35 million children worldwide. To date, nearly 100 genes have been identified which contribute to a deafness phenotype in humans, however, many cases remain in which a causative mutation has yet to be found. In addition, the exact mechanism by which hearing loss occurs in the presence of many of these mutations is still not understood. This is due, in part, to the complex nature of the development and function of the cochlear duct, the organ of hearing. The cochlea undergoes an intricate morphogenetic development and requires the proper specification and maintenance of dozens of different cell types in order to function correctly. In the mature duct, an interplay between mechanotransducing sensory hair cells, supporting pillar and Dieters' cells, and generation of electrochemical potential by the stria vascularis are necessary to respond to sound stimuli.
    [Show full text]
  • The Role of Testin in Human Cancers
    Pathology & Oncology Research (2019) 25:1279–1284 https://doi.org/10.1007/s12253-018-0488-3 REVIEW The Role of Testin in Human Cancers Aneta Popiel1,2 & Christopher Kobierzycki1 & Piotr Dzięgiel1 Received: 29 November 2017 /Accepted: 10 October 2018 /Published online: 25 October 2018 # The Author(s) 2018 Abstract Testin is a protein expressed in almost all normal human tissues. It locates in the cytoplasm along stress fibers being recruited to focal adhesions. Together with zyxin and vasodilator stimulated protein it forms complexes with various cytoskeleton proteins such as actin, talin and paxilin. They jointly play significant role in cell motility and adhesion. In addition, their involvement in the cell cycle has been demonstrated. Expression of testin protein level correlates positively with percentage of cells in G1 phase, while overexpression can induce apoptosis and decreased colony forming ability. Decreased testin expression associate with loss by cells epithelial morphology and gain migratory and invasive properties of mesenchymal cells. Latest reports indicate that TES is a tumor suppressor gene which can contribute to cancerogenesis but the mechanism of loss TES gene expression is still unknown. Some authors point out hypermethylation of the CpG island as a main factor, however loss of heterozygosity may also play an important role [4, 5]. The altered expression of testin was found in malignant neoplasm, i.a. ovarian, lung, head and neck squamous cell cancer, breast, endometrial, colorectal, prostate and gastric cancers [1–9]. Testin participate in the processes of tumor growth, angiogenesis, and metastasis [10]. Many researchers stated involvement of testin in tumor progression, what suggest its potential usage in immunotherapy [7, 11].
    [Show full text]
  • Predicting Gene Ontology Biological Process from Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R
    Methods Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R. Hvidsten,2 Herman Midelfart,2 Jan Komorowski,2,3,4 and Arne K. Sandvik1 1Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway; 2Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway; 3The Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24 Uppsala, Sweden The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end,supervised learning was used to analyz e microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition,new roles in biological process were hypothesi zed for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition,many biological process roles hypothesi zed for uncharacterized genes were found to agree with assumptions based on homology information.
    [Show full text]
  • DCTN3 Antibody (Internal Region) Peptide-Affinity Purified Goat Antibody Catalog # Af3366a
    10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 DCTN3 Antibody (internal region) Peptide-affinity purified goat antibody Catalog # AF3366a Specification DCTN3 Antibody (internal region) - Product Information Application WB Primary Accession O75935 Other Accession NP_009165.1, NP_077324.1, 11258, 53598 (mouse), 362504 (rat) Reactivity Human, Mouse, Rat Predicted Dog, Cow Host Goat Clonality Polyclonal Concentration 0.5 mg/ml Isotype IgG Calculated MW 21119 AF3366a (0.03 µg/ml) staining of Human Kidney lysate (35 µg protein in RIPA buffer). DCTN3 Antibody (internal region) - Additional Primary incubation was 1 hour. Detected by Information chemiluminescence. Gene ID 11258 Other Names Dynactin subunit 3, Dynactin complex subunit 22 kDa subunit, p22, DCTN3 {ECO:0000312|EMBL:CAG46687.1}, DCTN22 Format 0.5 mg/ml in Tris saline, 0.02% sodium azide, pH7.3 with 0.5% bovine serum albumin Storage Maintain refrigerated at 2-8°C for up to 6 months. For long term storage store at -20°C in small aliquots to prevent freeze-thaw cycles. AF3366a (0.2 µg/ml) staining of Mouse (A) and Rat (B) Skeletal Muscle lysates (35 µg Precautions protein in RIPA buffer). Primary incubation DCTN3 Antibody (internal region) is for was 1 hour. Detected by chemiluminescence. research use only and not for use in diagnostic or therapeutic procedures. DCTN3 Antibody (internal region) - Page 1/2 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Background DCTN3 Antibody (internal region) - Protein Information This antibody is expected to recognize both reported isoforms (NP_009165.1; Name DCTN3 NP_077324.1).
    [Show full text]