Comparative Analysis of Human Chromosome 7Q21 and Mouse
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Analyses of Allele-Specific Gene Expression in Highly Divergent
ARTICLES Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance James J Crowley1,10, Vasyl Zhabotynsky1,10, Wei Sun1,2,10, Shunping Huang3, Isa Kemal Pakatci3, Yunjung Kim1, Jeremy R Wang3, Andrew P Morgan1,4,5, John D Calaway1,4,5, David L Aylor1,9, Zaining Yun1, Timothy A Bell1,4,5, Ryan J Buus1,4,5, Mark E Calaway1,4,5, John P Didion1,4,5, Terry J Gooch1,4,5, Stephanie D Hansen1,4,5, Nashiya N Robinson1,4,5, Ginger D Shaw1,4,5, Jason S Spence1, Corey R Quackenbush1, Cordelia J Barrick1, Randal J Nonneman1, Kyungsu Kim2, James Xenakis2, Yuying Xie1, William Valdar1,4, Alan B Lenarcic1, Wei Wang3,9, Catherine E Welsh3, Chen-Ping Fu3, Zhaojun Zhang3, James Holt3, Zhishan Guo3, David W Threadgill6, Lisa M Tarantino7, Darla R Miller1,4,5, Fei Zou2,11, Leonard McMillan3,11, Patrick F Sullivan1,5,7,8,11 & Fernando Pardo-Manuel de Villena1,4,5,11 Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. -
MEG8 Regulates Tissue Factor Pathway Inhibitor 2 (TFPI2) Expression in the Endothelium
MEG8 regulates Tissue Factor Pathway Inhibitor 2 (TFPI2) expression in the endothelium Veerle Kremer VU Medical Center, Amsterdam UMC Diewertje I. Bink VU Medical Center, Amsterdam UMC Laura Stanicek VU Medical Center, Amsterdam UMC Eva van Ingen Leiden University Medical Center Sarah Hilderink VU Medical Center, Amsterdam UMC Anne Yaël Nossent Leiden University Medical Center Reinier A. Boon ( [email protected] ) VU Medical Center, Amsterdam UMC Research Article Keywords: human umbilical vein endothelial cells (HUVECs), Tissue Factor Pathway Inhibitor 2 (TFPI2) Posted Date: August 13th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-800527/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/19 Abstract A large portion of the genome is transcribed into non-coding RNA, which does not encode protein. Many long non-coding RNAs (lncRNAs) have been shown to be involved in important regulatory processes such as genomic imprinting and chromatin modication. The 14q32 locus contains many non-coding RNAs such as Maternally Expressed Gene 8 (MEG8). We observed an induction of this gene in ischemic heart disease. We investigated the role of MEG8 specically in endothelial function as well as the underlying mechanism. We hypothesized that MEG8 plays an important role in cardiovascular disease via epigenetic regulation of gene expression. Experiments were performed in human umbilical vein endothelial cells (HUVECs). In vitro silencing of MEG8 resulted in impaired angiogenic sprouting. More specically, total sprout length was reduced as was proliferation, while migration was unaffected. We performed RNA sequencing to assess changes in gene expression after loss of MEG8. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
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. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
G Protein-Coupled Receptors
www.aladdin-e.com Address:800 S Wineville Avenue, Ontario, CA 91761,USA Website:www.aladdin-e.com Email USA: [email protected] Email EU: [email protected] Email Asia Pacific: [email protected] G PROTEIN-COUPLED RECEPTORS Overview: The completion of the Human Genome Project allowed the identification of a large family of proteins with a common motif of seven groups of 20–24 hydrophobic amino acids arranged as a-helices. Approximately 800 of these seven transmembrane (7TM) receptors have been identified of which over 300 are non-olfactory receptors (see Fredriksson et al., 2003; Lagerstrom and Schioth, 2008). Subdivision on the basis of sequence homology allows the definition of rhodopsin, secretin, adhesion, glutamate and Frizzled receptor families. NC-IUPHAR recognizes Classes A, B, and C, which equate to the rhodopsin, secretin, and glutamate receptor families. The nomenclature of 7TM receptors is commonly used interchangeably with G protein-coupled receptors (GPCR), although the former nomenclature recognises signalling of 7TM receptors through pathways not involving G proteins. For example, adiponectin and membrane progestin receptors have some sequence homology to 7TM receptors but signal independently of G proteins and appear to reside in membranes in an inverted fashion compared to conventional GPCR. Additionally, the NPR-C natriuretic peptide receptor (see Page S195) has a single transmembrane domain structure, but appears to couple to G proteins to generate cellular responses. The 300+ non-olfactory GPCR are the targets for the majority of drugs in clinical usage (Overington et al., 2006), although only a minority of these receptors are exploited therapeutically. -
G Protein-Coupled Receptors
G PROTEIN-COUPLED RECEPTORS Overview:- The completion of the Human Genome Project allowed the identification of a large family of proteins with a common motif of seven groups of 20-24 hydrophobic amino acids arranged as α-helices. Approximately 800 of these seven transmembrane (7TM) receptors have been identified of which over 300 are non-olfactory receptors (see Frederikson et al., 2003; Lagerstrom and Schioth, 2008). Subdivision on the basis of sequence homology allows the definition of rhodopsin, secretin, adhesion, glutamate and Frizzled receptor families. NC-IUPHAR recognizes Classes A, B, and C, which equate to the rhodopsin, secretin, and glutamate receptor families. The nomenclature of 7TM receptors is commonly used interchangeably with G protein-coupled receptors (GPCR), although the former nomenclature recognises signalling of 7TM receptors through pathways not involving G proteins. For example, adiponectin and membrane progestin receptors have some sequence homology to 7TM receptors but signal independently of G-proteins and appear to reside in membranes in an inverted fashion compared to conventional GPCR. Additionally, the NPR-C natriuretic peptide receptor has a single transmembrane domain structure, but appears to couple to G proteins to generate cellular responses. The 300+ non-olfactory GPCR are the targets for the majority of drugs in clinical usage (Overington et al., 2006), although only a minority of these receptors are exploited therapeutically. Signalling through GPCR is enacted by the activation of heterotrimeric GTP-binding proteins (G proteins), made up of α, β and γ subunits, where the α and βγ subunits are responsible for signalling. The α subunit (tabulated below) allows definition of one series of signalling cascades and allows grouping of GPCRs to suggest common cellular, tissue and behavioural responses. -
Mouse Ppp1r9a Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Ppp1r9a Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Ppp1r9a conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Ppp1r9a gene (NCBI Reference Sequence: NM_181595 ; Ensembl: ENSMUSG00000032827 ) is located on Mouse chromosome 6. 16 exons are identified, with the ATG start codon in exon 2 and the TGA stop codon in exon 16 (Transcript: ENSMUST00000035813). Exon 3 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Ppp1r9a gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-2F8 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a knock-out allele exhibit defects in dopamine-mediated neuromodulation, deficient long-term potentiation at corticostriatal synapses, increased spontaneous excitatory post-synaptic current frequency, and enhanced locomotor activationin response to cocaine treatment. Exon 3 starts from about 42.59% of the coding region. The knockout of Exon 3 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 139091 bp, and the size of intron 3 for 3'-loxP site insertion: 11389 bp. The size of effective cKO region: ~633 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 3 16 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Ppp1r9a Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
The Arf1p Gtpase-Activating Protein Glo3p Executes Its Regulatory Function Through a Conserved Repeat Motif at Its C-Terminus
2604 Research Article The Arf1p GTPase-activating protein Glo3p executes its regulatory function through a conserved repeat motif at its C-terminus Natsuko Yahara1,*, Ken Sato1,2 and Akihiko Nakano1,3,‡ 1Molecular Membrane Biology Laboratory, RIKEN Discovery Research Institute and 2PRESTO, Japan Science and Technology Agency, Hirosawa, Wako, Saitama 351-0198, Japan 3Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan *Present address: Department of Biochemistry, University of Geneva, Sciences II, Geneva, Switzerland ‡Author for correspondence (e-mail: [email protected]) Accepted 21 March 2006 Journal of Cell Science 119, 2604-2612 Published by The Company of Biologists 2006 doi:10.1242/jcs.02997 Summary ADP-ribosylation factors (Arfs), key regulators of ArfGAP, Gcs1p, we have shown that the non-catalytic C- intracellular membrane traffic, are known to exert multiple terminal region of Glo3p is required for the suppression of roles in vesicular transport. We previously isolated eight the growth defect in the arf1 ts mutants. Interestingly, temperature-sensitive (ts) mutants of the yeast ARF1 gene, Glo3p and its homologues from other eukaryotes harbor a which showed allele-specific defects in protein transport, well-conserved repeated ISSxxxFG sequence near the C- and classified them into three groups of intragenic terminus, which is not found in Gcs1p and its homologues. complementation. In this study, we show that the We name this region the Glo3 motif and present evidence overexpression of Glo3p, one of the GTPase-activating that the motif is required for the function of Glo3p in vivo. proteins of Arf1p (ArfGAP), suppresses the ts growth of a particular group of the arf1 mutants (arf1-16 and arf1-17). -
Comparative Analysis of Human Chromosome 7Q21 and Mouse
Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Comparative analysis of human chromosome 7q21 and mouse proximal chromosome 6 reveals a placental-specific imprinted gene, TFPI2/Tfpi2, which requires G9a and Eed for allelic-silencing. David Monk1, Alexandre Wagschal2, Philippe Arnaud 2, Pari Muller3, Layla Parker- Katiraee4, Déborah Bourc’his5, Stephen W Scherer4, Robert Feil2, Philip Stanier1, Gudrun E Moore1. 1 Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK. 2 Institute of Molecular Genetics, CNRS UMR-5535 and University of Montpellier-II, 1919, route de Mende, 34293 Montpellier, France. 3 University of Oxford, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK. 4 Center for Applied Genomics, The Hospital for Sick Children, Toronto, M5G 1L7, Canada. 5 Inserm U741, Paris 7 Univserity, F-75251 Paris Cedex 05, France. Corresponding author: Dr David Monk [email protected] Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK Tel: +44 207 905 2865 Fax: +44 207 905 2832 Running title: Epigenetic analysis of the imprinted Peg10 domain Key words: Imprinting, epigenetics, histone methylation, placenta, G9a, Eed Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press ABTRACT: Genomic imprinting is a developmentally important mechanism that involving both differential DNA methylation and allelic histone modifications. Through detailed comparative characterization a large imprinted domain was re-define mapping to chromosome 7q21 in humans, and proximal chromosome 6 in mice. This domain is organized around a maternally methylated CpG island comprising the promoters of the adjacent PEG10 and SGCE imprinted genes. -
Plasma Cells in Vitro Generation of Long-Lived Human
Downloaded from http://www.jimmunol.org/ by guest on September 24, 2021 is online at: average * The Journal of Immunology , 32 of which you can access for free at: 2012; 189:5773-5785; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication November 2012; doi: 10.4049/jimmunol.1103720 http://www.jimmunol.org/content/189/12/5773 In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco, Sophie Stephenson, Matthew A. Care, Darren Newton, Nicholas A. Barnes, Adam Davison, Andy Rawstron, David R. Westhead, Gina M. Doody and Reuben M. Tooze J Immunol cites 65 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2012/11/16/jimmunol.110372 0.DC1 This article http://www.jimmunol.org/content/189/12/5773.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 24, 2021. The Journal of Immunology In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco,*,1 Sophie Stephenson,*,1 Matthew A.