Mouse Piwi Interactome Identifies Binding Mechanism of Tdrkh Tudor Domain to Arginine Methylated Miwi
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Supplementary Table 3: Genes Only Influenced By
Supplementary Table 3: Genes only influenced by X10 Illumina ID Gene ID Entrez Gene Name Fold change compared to vehicle 1810058M03RIK -1.104 2210008F06RIK 1.090 2310005E10RIK -1.175 2610016F04RIK 1.081 2610029K11RIK 1.130 381484 Gm5150 predicted gene 5150 -1.230 4833425P12RIK -1.127 4933412E12RIK -1.333 6030458P06RIK -1.131 6430550H21RIK 1.073 6530401D06RIK 1.229 9030607L17RIK -1.122 A330043C08RIK 1.113 A330043L12 1.054 A530092L01RIK -1.069 A630054D14 1.072 A630097D09RIK -1.102 AA409316 FAM83H family with sequence similarity 83, member H 1.142 AAAS AAAS achalasia, adrenocortical insufficiency, alacrimia 1.144 ACADL ACADL acyl-CoA dehydrogenase, long chain -1.135 ACOT1 ACOT1 acyl-CoA thioesterase 1 -1.191 ADAMTSL5 ADAMTSL5 ADAMTS-like 5 1.210 AFG3L2 AFG3L2 AFG3 ATPase family gene 3-like 2 (S. cerevisiae) 1.212 AI256775 RFESD Rieske (Fe-S) domain containing 1.134 Lipo1 (includes AI747699 others) lipase, member O2 -1.083 AKAP8L AKAP8L A kinase (PRKA) anchor protein 8-like -1.263 AKR7A5 -1.225 AMBP AMBP alpha-1-microglobulin/bikunin precursor 1.074 ANAPC2 ANAPC2 anaphase promoting complex subunit 2 -1.134 ANKRD1 ANKRD1 ankyrin repeat domain 1 (cardiac muscle) 1.314 APOA1 APOA1 apolipoprotein A-I -1.086 ARHGAP26 ARHGAP26 Rho GTPase activating protein 26 -1.083 ARL5A ARL5A ADP-ribosylation factor-like 5A -1.212 ARMC3 ARMC3 armadillo repeat containing 3 -1.077 ARPC5 ARPC5 actin related protein 2/3 complex, subunit 5, 16kDa -1.190 activating transcription factor 4 (tax-responsive enhancer element ATF4 ATF4 B67) 1.481 AU014645 NCBP1 nuclear cap -
Structure and Expression Analyses of SVA Elements in Relation to Functional Genes
pISSN 1598-866X eISSN 2234-0742 Genomics Inform 2013;11(3):142-148 G&I Genomics & Informatics http://dx.doi.org/10.5808/GI.2013.11.3.142 ORIGINAL ARTICLE Structure and Expression Analyses of SVA Elements in Relation to Functional Genes Yun-Jeong Kwon, Yuri Choi, Jungwoo Eo, Yu-Na Noh, Jeong-An Gim, Yi-Deun Jung, Ja-Rang Lee, Heui-Soo Kim* Department of Biological Sciences, College of Natural Sciences, Pusan National University, Busan 609-735, Korea SINE-VNTR-Alu (SVA) elements are present in hominoid primates and are divided into 6 subfamilies (SVA-A to SVA-F) and active in the human population. Using a bioinformatic tool, 22 SVA element-associated genes are identified in the human genome. In an analysis of genomic structure, SVA elements are detected in the 5′ untranslated region (UTR) of HGSNAT (SVA-B), MRGPRX3 (SVA-D), HYAL1 (SVA-F), TCHH (SVA-F), and ATXN2L (SVA-F) genes, while some elements are observed in the 3′UTR of SPICE1 (SVA-B), TDRKH (SVA-C), GOSR1 (SVA-D), BBS5 (SVA-D), NEK5 (SVA-D), ABHD2 (SVA-F), C1QTNF7 (SVA-F), ORC6L (SVA-F), TMEM69 (SVA-F), and CCDC137 (SVA-F) genes. They could contribute to exon extension or supplying poly A signals. LEPR (SVA-C), ALOX5 (SVA-D), PDS5B (SVA-D), and ABCA10 (SVA-F) genes also showed alternative transcripts by SVA exonization events. Dominant expression of HYAL1_SVA appeared in lung tissues, while HYAL1_noSVA showed ubiquitous expression in various human tissues. Expression of both transcripts (TDRKH_SVA and TDRKH_noSVA) of the TDRKH gene appeared to be ubiquitous. -
Two DNA Binding Domains of Mga Act in Combination to Suppress Ectopic Activation
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.21.215079; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Two DNA binding domains of Mga act in combination to suppress ectopic activation of meiosis-related genes in mouse embryonic stem cells Kousuke Uranishi1, Masataka Hirasaki2, Yuka Kitamura1, Yosuke Mizuno3, Masazumi Nishimoto1,3, Ayumu Suzuki1*, Akihiko Okuda1* 1Division of Biomedical Sciences, Research Center for Genomic Medicine, 2Department of Clinical Cancer Genomics, International Medical Center, 3Biomedical Research Center, Saitama Medical University, 1397-1, Yamane Hidaka, Saitama 350-1241, Japan *Corresponding authors Ayumu Suzuki: [email protected] Akihiko Okuda: [email protected] Running title: Roles of two DNA binding domains of Mga in ESCs AUTHOR CONTRIBUTIONS K.U.: performed the experiments, data analysis, and manuscript writing; Y.K., M.H., and Y.M.: performed the experiments and data analysis; M.N.: performed the data analysis, and commented on the manuscript; A.S.: performed the experiments and data analysis, conception and design; A.O.: conception and design, financial support, manuscript writing, and final approval of manuscript. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.21.215079; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. SUMMARY Mouse embryonic stem cells (ESCs) have high potential for meiotic entry, like germ cells. Although the physiological meaning of this potential is not known, it is certain that a rigid safeguarding system is required to prevent ectopic onset of meiosis. -
Novel Targets of Apparently Idiopathic Male Infertility
International Journal of Molecular Sciences Review Molecular Biology of Spermatogenesis: Novel Targets of Apparently Idiopathic Male Infertility Rossella Cannarella * , Rosita A. Condorelli , Laura M. Mongioì, Sandro La Vignera * and Aldo E. Calogero Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy; [email protected] (R.A.C.); [email protected] (L.M.M.); [email protected] (A.E.C.) * Correspondence: [email protected] (R.C.); [email protected] (S.L.V.) Received: 8 February 2020; Accepted: 2 March 2020; Published: 3 March 2020 Abstract: Male infertility affects half of infertile couples and, currently, a relevant percentage of cases of male infertility is considered as idiopathic. Although the male contribution to human fertilization has traditionally been restricted to sperm DNA, current evidence suggest that a relevant number of sperm transcripts and proteins are involved in acrosome reactions, sperm-oocyte fusion and, once released into the oocyte, embryo growth and development. The aim of this review is to provide updated and comprehensive insight into the molecular biology of spermatogenesis, including evidence on spermatogenetic failure and underlining the role of the sperm-carried molecular factors involved in oocyte fertilization and embryo growth. This represents the first step in the identification of new possible diagnostic and, possibly, therapeutic markers in the field of apparently idiopathic male infertility. Keywords: spermatogenetic failure; embryo growth; male infertility; spermatogenesis; recurrent pregnancy loss; sperm proteome; DNA fragmentation; sperm transcriptome 1. Introduction Infertility is a widespread condition in industrialized countries, affecting up to 15% of couples of childbearing age [1]. It is defined as the inability to achieve conception after 1–2 years of unprotected sexual intercourse [2]. -
Endocrine System Local Gene Expression
Copyright 2008 By Nathan G. Salomonis ii Acknowledgments Publication Reprints The text in chapter 2 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC, Dahlquist KD, Doniger SW, Stuart J, Conklin BR, Pico AR. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics. 2007 Jun 24;8:218. The co-authors listed in this publication co-wrote the manuscript (AP and KH) and provided critical feedback (see detailed contributions at the end of chapter 2). The text in chapter 3 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Cotte N, Zambon AC, Pollard KS, Vranizan K, Doniger SW, Dolganov G, Conklin BR. Identifying genetic networks underlying myometrial transition to labor. Genome Biol. 2005;6(2):R12. Epub 2005 Jan 28. The co-authors listed in this publication developed the hierarchical clustering method (KP), co-designed the study (NC, AZ, BC), provided statistical guidance (KV), co- contributed to GenMAPP 2.0 (SD) and performed quantitative mRNA analyses (GD). The text of this dissertation contains a reproduction of a figure from: Yeo G, Holste D, Kreiman G, Burge CB. Variation in alternative splicing across human tissues. Genome Biol. 2004;5(10):R74. Epub 2004 Sep 13. The reproduction was taken without permission (chapter 1), figure 1.3. iii Personal Acknowledgments The achievements of this doctoral degree are to a large degree possible due to the contribution, feedback and support of many individuals. To all of you that helped, I am extremely grateful for your support. -
Integrated Analysis of RNA-Binding Proteins in Human Colorectal Cancer Xuehui Fan, Lili Liu, Yue Shi, Fanghan Guo, Haining Wang, Xiuli Zhao, Di Zhong and Guozhong Li*
Fan et al. World Journal of Surgical Oncology (2020) 18:222 https://doi.org/10.1186/s12957-020-01995-5 RESEARCH Open Access Integrated analysis of RNA-binding proteins in human colorectal cancer Xuehui Fan, Lili Liu, Yue Shi, Fanghan Guo, Haining Wang, Xiuli Zhao, Di Zhong and Guozhong Li* Abstract Background: Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. Methods: Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. -
MAFB Determines Human Macrophage Anti-Inflammatory
MAFB Determines Human Macrophage Anti-Inflammatory Polarization: Relevance for the Pathogenic Mechanisms Operating in Multicentric Carpotarsal Osteolysis This information is current as of October 4, 2021. Víctor D. Cuevas, Laura Anta, Rafael Samaniego, Emmanuel Orta-Zavalza, Juan Vladimir de la Rosa, Geneviève Baujat, Ángeles Domínguez-Soto, Paloma Sánchez-Mateos, María M. Escribese, Antonio Castrillo, Valérie Cormier-Daire, Miguel A. Vega and Ángel L. Corbí Downloaded from J Immunol 2017; 198:2070-2081; Prepublished online 16 January 2017; doi: 10.4049/jimmunol.1601667 http://www.jimmunol.org/content/198/5/2070 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2017/01/15/jimmunol.160166 Material 7.DCSupplemental References This article cites 69 articles, 22 of which you can access for free at: http://www.jimmunol.org/content/198/5/2070.full#ref-list-1 by guest on October 4, 2021 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2017 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
393LN V 393P 344SQ V 393P Probe Set Entrez Gene
393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393 -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19 -
The Pdx1 Bound Swi/Snf Chromatin Remodeling Complex Regulates Pancreatic Progenitor Cell Proliferation and Mature Islet Β Cell
Page 1 of 125 Diabetes The Pdx1 bound Swi/Snf chromatin remodeling complex regulates pancreatic progenitor cell proliferation and mature islet β cell function Jason M. Spaeth1,2, Jin-Hua Liu1, Daniel Peters3, Min Guo1, Anna B. Osipovich1, Fardin Mohammadi3, Nilotpal Roy4, Anil Bhushan4, Mark A. Magnuson1, Matthias Hebrok4, Christopher V. E. Wright3, Roland Stein1,5 1 Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 2 Present address: Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 3 Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 4 Diabetes Center, Department of Medicine, UCSF, San Francisco, California 5 Corresponding author: [email protected]; (615)322-7026 1 Diabetes Publish Ahead of Print, published online June 14, 2019 Diabetes Page 2 of 125 Abstract Transcription factors positively and/or negatively impact gene expression by recruiting coregulatory factors, which interact through protein-protein binding. Here we demonstrate that mouse pancreas size and islet β cell function are controlled by the ATP-dependent Swi/Snf chromatin remodeling coregulatory complex that physically associates with Pdx1, a diabetes- linked transcription factor essential to pancreatic morphogenesis and adult islet-cell function and maintenance. Early embryonic deletion of just the Swi/Snf Brg1 ATPase subunit reduced multipotent pancreatic progenitor cell proliferation and resulted in pancreas hypoplasia. In contrast, removal of both Swi/Snf ATPase subunits, Brg1 and Brm, was necessary to compromise adult islet β cell activity, which included whole animal glucose intolerance, hyperglycemia and impaired insulin secretion. Notably, lineage-tracing analysis revealed Swi/Snf-deficient β cells lost the ability to produce the mRNAs for insulin and other key metabolic genes without effecting the expression of many essential islet-enriched transcription factors. -
Polycomb Directs Timely Activation of Germline Genes in Spermatogenesis
Downloaded from genesdev.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press Polycomb directs timely activation of germline genes in spermatogenesis So Maezawa,1,2,3,9 Kazuteru Hasegawa,1,2,3,9 Masashi Yukawa,3,4,5 Akihiko Sakashita,1,2,3 Kris G. Alavattam,1,2,3 Paul R. Andreassen,3,6 Miguel Vidal,7 Haruhiko Koseki,8 Artem Barski,3,4,5 and Satoshi H. Namekawa1,2,3 1Division of Reproductive Sciences, 2Division of Developmental Biology, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA; 3Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio 49229, USA; 4Division of Allergy and Immunology, 5Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA; 6Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA; 7Department of Cellular and Molecular Biology, Centro de Investigaciones Biológicas, 28040 Madrid, Spain; 8Developmental Genetics Laboratory, RIKEN Center for Allergy and Immunology, Yokohama, Kanagawa 230-0045, Japan During spermatogenesis, a large number of germline genes essential for male fertility are coordinately activated. However, it remains unknown how timely activation of this group of germline genes is accomplished. Here we show that Polycomb-repressive complex 1 (PRC1) directs timely activation of germline genes during spermatogenesis. Inactivation of PRC1 in male germ cells results in the gradual loss of a stem cell population and severe differentiation defects, leading to male infertility. In the stem cell population, RNF2, the dominant catalytic subunit of PRC1, activates transcription of Sall4, which codes for a transcription factor essential for subsequent spermatogenic dif- ferentiation.