Polymorphisms in Leucine-Rich Repeat Genes Are Associated with Autism
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Table 2. Functional Classification of Genes Differentially Regulated After HOXB4 Inactivation in HSC/Hpcs
Table 2. Functional classification of genes differentially regulated after HOXB4 inactivation in HSC/HPCs Symbol Gene description Fold-change (mean ± SD) Signal transduction Adam8 A disintegrin and metalloprotease domain 8 1.91 ± 0.51 Arl4 ADP-ribosylation factor-like 4 - 1.80 ± 0.40 Dusp6 Dual specificity phosphatase 6 (Mkp3) - 2.30 ± 0.46 Ksr1 Kinase suppressor of ras 1 1.92 ± 0.42 Lyst Lysosomal trafficking regulator 1.89 ± 0.34 Mapk1ip1 Mitogen activated protein kinase 1 interacting protein 1 1.84 ± 0.22 Narf* Nuclear prelamin A recognition factor 2.12 ± 0.04 Plekha2 Pleckstrin homology domain-containing. family A. (phosphoinosite 2.15 ± 0.22 binding specific) member 2 Ptp4a2 Protein tyrosine phosphatase 4a2 - 2.04 ± 0.94 Rasa2* RAS p21 activator protein 2 - 2.80 ± 0.13 Rassf4 RAS association (RalGDS/AF-6) domain family 4 3.44 ± 2.56 Rgs18 Regulator of G-protein signaling - 1.93 ± 0.57 Rrad Ras-related associated with diabetes 1.81 ± 0.73 Sh3kbp1 SH3 domain kinase bindings protein 1 - 2.19 ± 0.53 Senp2 SUMO/sentrin specific protease 2 - 1.97 ± 0.49 Socs2 Suppressor of cytokine signaling 2 - 2.82 ± 0.85 Socs5 Suppressor of cytokine signaling 5 2.13 ± 0.08 Socs6 Suppressor of cytokine signaling 6 - 2.18 ± 0.38 Spry1 Sprouty 1 - 2.69 ± 0.19 Sos1 Son of sevenless homolog 1 (Drosophila) 2.16 ± 0.71 Ywhag 3-monooxygenase/tryptophan 5- monooxygenase activation protein. - 2.37 ± 1.42 gamma polypeptide Zfyve21 Zinc finger. FYVE domain containing 21 1.93 ± 0.57 Ligands and receptors Bambi BMP and activin membrane-bound inhibitor - 2.94 ± 0.62 -
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]. -
Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma
Anatomy and Pathology Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma Sarah L. Lake,1 Sarah E. Coupland,1 Azzam F. G. Taktak,2 and Bertil E. Damato3 PURPOSE. To detect deletions and loss of heterozygosity of disease is fatal in 92% of patients within 2 years of diagnosis. chromosome 3 in a rare subset of fatal, disomy 3 uveal mela- Clinical and histopathologic risk factors for UM metastasis noma (UM), undetectable by fluorescence in situ hybridization include large basal tumor diameter (LBD), ciliary body involve- (FISH). ment, epithelioid cytomorphology, extracellular matrix peri- ϩ ETHODS odic acid-Schiff-positive (PAS ) loops, and high mitotic M . Multiplex ligation-dependent probe amplification 3,4 5 (MLPA) with the P027 UM assay was performed on formalin- count. Prescher et al. showed that a nonrandom genetic fixed, paraffin-embedded (FFPE) whole tumor sections from 19 change, monosomy 3, correlates strongly with metastatic death, and the correlation has since been confirmed by several disomy 3 metastasizing UMs. Whole-genome microarray analy- 3,6–10 ses using a single-nucleotide polymorphism microarray (aSNP) groups. Consequently, fluorescence in situ hybridization were performed on frozen tissue samples from four fatal dis- (FISH) detection of chromosome 3 using a centromeric probe omy 3 metastasizing UMs and three disomy 3 tumors with Ͼ5 became routine practice for UM prognostication; however, 5% years’ metastasis-free survival. to 20% of disomy 3 UM patients unexpectedly develop metas- tases.11 Attempts have therefore been made to identify the RESULTS. Two metastasizing UMs that had been classified as minimal region(s) of deletion on chromosome 3.12–15 Despite disomy 3 by FISH analysis of a small tumor sample were found these studies, little progress has been made in defining the key on MLPA analysis to show monosomy 3. -
A Conserved Gene Family Encodes Transmembrane Proteins with Fibronectin, Immunoglobulin and Leucine-Rich Repeat Domains (FIGLER) Delicia L
A conserved gene family encodes transmembrane proteins with fibronectin, immunoglobulin and leucine-rich repeat domains (FIGLER) Delicia L. Munfus, University of Alabama at Birmingham Christopher L. Haga, University of Alabama at Birmingham Peter D. Burrows, University of Alabama at Birmingham Max Cooper, Emory University Journal Title: BMC Biology Volume: Volume 5, Number 1 Publisher: BioMed Central | 2007-09-13, Pages 36-36 Type of Work: Article | Final Publisher PDF Publisher DOI: 10.1186/1741-7007-5-36 Permanent URL: https://pid.emory.edu/ark:/25593/rqdhh Final published version: http://dx.doi.org/10.1186/1741-7007-5-36 Copyright information: © 2007 Munfus et al; licensee BioMed Central Ltd. This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/). Accessed October 6, 2021 6:19 AM EDT BMC Biology BioMed Central Research article Open Access A conserved gene family encodes transmembrane proteins with fibronectin, immunoglobulin and leucine-rich repeat domains (FIGLER) Delicia L Munfus†1,2, Christopher L Haga†1,2, Peter D Burrows1,2,3 and Max D Cooper*1,2,4,5 Address: 1Division of Developmental and Clinical Immunology, University of Alabama at Birmingham, Birmingham, AL 35294-3300, USA, 2Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294-3300, USA, 3Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294-3300,, 4Department of Medicine, University of Alabama at Birmingham, Birmingham, -
Genetic Drivers of Pancreatic Islet Function
| INVESTIGATION Genetic Drivers of Pancreatic Islet Function Mark P. Keller,*,1 Daniel M. Gatti,†,1 Kathryn L. Schueler,* Mary E. Rabaglia,* Donnie S. Stapleton,* Petr Simecek,† Matthew Vincent,† Sadie Allen,‡ Aimee Teo Broman,§ Rhonda Bacher,§ Christina Kendziorski,§ Karl W. Broman,§ Brian S. Yandell,** Gary A. Churchill,†,2 and Alan D. Attie*,2 *Department of Biochemistry, §Department of Biostatistics and Medical Informatics, and **Department of Horticulture, University of Wisconsin–Madison, Wisconsin 53706-1544, †The Jackson Laboratory, Bar Harbor, Maine 06409, and ‡Maine School of Science and Mathematics, Limestone, Maine 06409, ORCID IDs: 0000-0002-7405-5552 (M.P.K.); 0000-0002-4914-6671 (K.W.B.); 0000-0001-9190-9284 (G.A.C.); 0000-0002-0568-2261 (A.D.A.) ABSTRACT The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for a-cells, and another specific for d-cells. -
Epigenetic Alterations of Chromosome 3 Revealed by Noti-Microarrays in Clear Cell Renal Cell Carcinoma
Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 735292, 9 pages http://dx.doi.org/10.1155/2014/735292 Research Article Epigenetic Alterations of Chromosome 3 Revealed by NotI-Microarrays in Clear Cell Renal Cell Carcinoma Alexey A. Dmitriev,1,2 Evgeniya E. Rudenko,3 Anna V. Kudryavtseva,1,2 George S. Krasnov,1,4 Vasily V. Gordiyuk,3 Nataliya V. Melnikova,1 Eduard O. Stakhovsky,5 Oleksii A. Kononenko,5 Larissa S. Pavlova,6 Tatiana T. Kondratieva,6 Boris Y. Alekseev,2 Eleonora A. Braga,7,8 Vera N. Senchenko,1 and Vladimir I. Kashuba3,9 1 Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia 2 P.A. Herzen Moscow Oncology Research Institute, Ministry of Healthcare of the Russian Federation, Moscow 125284, Russia 3 Institute of Molecular Biology and Genetics, Ukrainian Academy of Sciences, Kiev 03680, Ukraine 4 Mechnikov Research Institute for Vaccines and Sera, Russian Academy of Medical Sciences, Moscow 105064, Russia 5 National Cancer Institute, Kiev 03022, Ukraine 6 N.N. Blokhin Russian Cancer Research Center, Russian Academy of Medical Sciences, Moscow 115478, Russia 7 Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, Moscow 125315, Russia 8 Research Center of Medical Genetics, Russian Academy of Medical Sciences, Moscow 115478, Russia 9 DepartmentofMicrobiology,TumorandCellBiology,KarolinskaInstitute,17177Stockholm,Sweden Correspondence should be addressed to Alexey A. Dmitriev; alex [email protected] Received 19 February 2014; Revised 10 April 2014; Accepted 17 April 2014; Published 22 May 2014 Academic Editor: Carole Sourbier Copyright © 2014 Alexey A. Dmitriev 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. -
RKIP Regulates Differentiation- Related Features in Melanocytic Cells
Cancers 2020. 12 S1 of S14 Supplementary Materials: RKIP Regulates Differentiation- Related Features in Melanocytic Cells Cristina Penas, Aintzane Apraiz, Iraia Muñoa, Yoana Arroyo-Berdugo, Javier Rasero, Pilar A. Ezkurra, Veronica Velasco, Nerea Subiran, Anja K. Bosserhoff, Santos Alonso, Aintzane Asumendi and Maria D. Boyano Figure S1. Representative images of manually scored RKIP staining categories in FFPE biopsies from patients. At the feft: Hematoxylin-Eosin staining (H-E). At the right: Immunohistochemistry (IHC) of RKIP for increasing Breslow Index. (a-b) Strong staining for intradermal nevus and in situ melanoma, respectively; (c) weak staining for a melanoma with Breslow Index between 1 and 2 mm; (d-e) negative staining for melanomas thicker than 2 mm. Cancers 2020. 12 S2 of S14 Figure S2. RNA Sequencing data analysis. Each row of the figure represents an over-represented gene signature generated by analyzing the up-regulated and down-regulated genes from a differential expression analysis of RKIP Knockdown melanocytes HEMn-LP. Results from the EnrichR Website with a p-value > 0.05. Figure S3. RNA Sequencing data quality. (a) Read counts normalization per sample; (b) Spearman correlation coefficient between replicates; (c) Principal component analysis for replicates. Cancers 2020. 12 S3 of S14 Figure S4. Details of Western Blot from Figure 2 related with RKIP expression in several cell lines. (a) Blots for RKIP and Tubulin in primary and metastatic melanoma and in three primary melanocytes HEMn-DP (dark pigmented), HEMn-LP (light pigmented) and HEM2710. On the right, original blot for each analyzed protein. Highlighted with a red square the information included in the Figure 3 (horizontal rotation). -
Identifying Lineage Relationships in Human T Cell Populations
Identifying lineage relationships in human T cell populations by Celia Lara Menckeberg A thesis submitted to The University of Birmingham for the degree of DOCTOR OF PHILOSOPHY School of Immunity and Infection College of Medical and Dental Sciences The University of Birmingham December 2010 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. ii ABSTRACT CD4+ and CD8+ T cell populations can be divided into subpopulations based on expression of surface markers CCR7 and CD45RA. The resulting populations are referred to as naive, central memory, effector memory and effector memory RA+ (EMRA). The aim of this study was to identify potential lineage relationships between these subpopulations for both CD4+ and CD8+ T cells through microarray analysis. The genes found to distinguish between these subpopulations include many molecules with known functions in T cell differentiation, including CCR7, CD45RA, granzymes, L-selectin and TNF receptors. Several genes from the tetraspanin family of proteins were found to be differentially expressed at mRNA and protein level; suggesting a possible role for these genes in CD4+ and CD8+ T cell activation, migration and lysosomal function. -
Neural Development
31 October 2007 NEURAL DEVELOPMENT www.neuraldevelopment.com Analysis of Lrrn1 expression and its relationship to neuromeric boundaries during chick neural development Laura C Andreae et al. Neural Development 2007, 2:22 http://www.neuraldevelopment.com/content/2/1/22 Neural Development BioMed Central Research article Open Access Analysis of Lrrn1 expression and its relationship to neuromeric boundaries during chick neural development LauraCAndreae1,2, Daniela Peukert1, Andrew Lumsden1 and Jonathan D Gilthorpe*1 Address: 1MRC Centre for Developmental Neurobiology, King's College London, New Hunt's House, Guy's Campus, London, UK, SE1 1UL and 2Department of Neurophysiology, National Institute for Medical Research, The Ridgeway, Mill Hill, London, UK, NW7 1AA Email: Laura C Andreae - [email protected]; Daniela Peukert - [email protected]; Andrew Lumsden - [email protected]; Jonathan D Gilthorpe* - [email protected] * Corresponding author Published: 31 October 2007 Received: 26 March 2007 Accepted: 31 October 2007 Neural Development 2007, 2:22 doi:10.1186/1749-8104-2-22 This article is available from: http://www.neuraldevelopment.com/content/2/1/22 © 2007 Andreae 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: The Drosophila leucine-rich repeat proteins Tartan (TRN) and Capricious (CAPS) mediate cell affinity differences during compartition of the wing imaginal disc. This study aims to identify and characterize the expression of a chick orthologue of TRN/CAPS and examine its potential function in relation to compartment boundaries in the vertebrate central nervous system. -
Transcriptomic and Epigenomic Characterization of the Developing Bat Wing
ARTICLES OPEN Transcriptomic and epigenomic characterization of the developing bat wing Walter L Eckalbar1,2,9, Stephen A Schlebusch3,9, Mandy K Mason3, Zoe Gill3, Ash V Parker3, Betty M Booker1,2, Sierra Nishizaki1,2, Christiane Muswamba-Nday3, Elizabeth Terhune4,5, Kimberly A Nevonen4, Nadja Makki1,2, Tara Friedrich2,6, Julia E VanderMeer1,2, Katherine S Pollard2,6,7, Lucia Carbone4,8, Jeff D Wall2,7, Nicola Illing3 & Nadav Ahituv1,2 Bats are the only mammals capable of powered flight, but little is known about the genetic determinants that shape their wings. Here we generated a genome for Miniopterus natalensis and performed RNA-seq and ChIP-seq (H3K27ac and H3K27me3) analyses on its developing forelimb and hindlimb autopods at sequential embryonic stages to decipher the molecular events that underlie bat wing development. Over 7,000 genes and several long noncoding RNAs, including Tbx5-as1 and Hottip, were differentially expressed between forelimb and hindlimb, and across different stages. ChIP-seq analysis identified thousands of regions that are differentially modified in forelimb and hindlimb. Comparative genomics found 2,796 bat-accelerated regions within H3K27ac peaks, several of which cluster near limb-associated genes. Pathway analyses highlighted multiple ribosomal proteins and known limb patterning signaling pathways as differentially regulated and implicated increased forelimb mesenchymal condensation in differential growth. In combination, our work outlines multiple genetic components that likely contribute to bat wing formation, providing insights into this morphological innovation. The order Chiroptera, commonly known as bats, is the only group of To characterize the genetic differences that underlie divergence in mammals to have evolved the capability of flight. -
Research Article Epigenetic Alterations of Chromosome 3 Revealed by Noti-Microarrays in Clear Cell Renal Cell Carcinoma
Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 735292, 9 pages http://dx.doi.org/10.1155/2014/735292 Research Article Epigenetic Alterations of Chromosome 3 Revealed by NotI-Microarrays in Clear Cell Renal Cell Carcinoma Alexey A. Dmitriev,1,2 Evgeniya E. Rudenko,3 Anna V. Kudryavtseva,1,2 George S. Krasnov,1,4 Vasily V. Gordiyuk,3 Nataliya V. Melnikova,1 Eduard O. Stakhovsky,5 Oleksii A. Kononenko,5 Larissa S. Pavlova,6 Tatiana T. Kondratieva,6 Boris Y. Alekseev,2 Eleonora A. Braga,7,8 Vera N. Senchenko,1 and Vladimir I. Kashuba3,9 1 Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia 2 P.A. Herzen Moscow Oncology Research Institute, Ministry of Healthcare of the Russian Federation, Moscow 125284, Russia 3 Institute of Molecular Biology and Genetics, Ukrainian Academy of Sciences, Kiev 03680, Ukraine 4 Mechnikov Research Institute for Vaccines and Sera, Russian Academy of Medical Sciences, Moscow 105064, Russia 5 National Cancer Institute, Kiev 03022, Ukraine 6 N.N. Blokhin Russian Cancer Research Center, Russian Academy of Medical Sciences, Moscow 115478, Russia 7 Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, Moscow 125315, Russia 8 Research Center of Medical Genetics, Russian Academy of Medical Sciences, Moscow 115478, Russia 9 DepartmentofMicrobiology,TumorandCellBiology,KarolinskaInstitute,17177Stockholm,Sweden Correspondence should be addressed to Alexey A. Dmitriev; alex [email protected] Received 19 February 2014; Revised 10 April 2014; Accepted 17 April 2014; Published 22 May 2014 Academic Editor: Carole Sourbier Copyright © 2014 Alexey A. Dmitriev 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. -
Epigenetic Mechanisms Are Involved in the Oncogenic Properties of ZNF518B in Colorectal Cancer
Epigenetic mechanisms are involved in the oncogenic properties of ZNF518B in colorectal cancer Francisco Gimeno-Valiente, Ángela L. Riffo-Campos, Luis Torres, Noelia Tarazona, Valentina Gambardella, Andrés Cervantes, Gerardo López-Rodas, Luis Franco and Josefa Castillo SUPPLEMENTARY METHODS 1. Selection of genomic sequences for ChIP analysis To select the sequences for ChIP analysis in the five putative target genes, namely, PADI3, ZDHHC2, RGS4, EFNA5 and KAT2B, the genomic region corresponding to the gene was downloaded from Ensembl. Then, zoom was applied to see in detail the promoter, enhancers and regulatory sequences. The details for HCT116 cells were then recovered and the target sequences for factor binding examined. Obviously, there are not data for ZNF518B, but special attention was paid to the target sequences of other zinc-finger containing factors. Finally, the regions that may putatively bind ZNF518B were selected and primers defining amplicons spanning such sequences were searched out. Supplementary Figure S3 gives the location of the amplicons used in each gene. 2. Obtaining the raw data and generating the BAM files for in silico analysis of the effects of EHMT2 and EZH2 silencing The data of siEZH2 (SRR6384524), siG9a (SRR6384526) and siNon-target (SRR6384521) in HCT116 cell line, were downloaded from SRA (Bioproject PRJNA422822, https://www.ncbi. nlm.nih.gov/bioproject/), using SRA-tolkit (https://ncbi.github.io/sra-tools/). All data correspond to RNAseq single end. doBasics = TRUE doAll = FALSE $ fastq-dump -I --split-files SRR6384524 Data quality was checked using the software fastqc (https://www.bioinformatics.babraham. ac.uk /projects/fastqc/). The first low quality removing nucleotides were removed using FASTX- Toolkit (http://hannonlab.cshl.edu/fastxtoolkit/).