DOCSLIB.ORG

Novel Potential ALL Low-Risk Markers Revealed by Gene

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Novel Potential ALL Low-Risk Markers Revealed by Gene Leukemia (2003) 17, 1891–1900 & 2003 Nature Publishing Group All rights reserved 0887-6924/03 $25.00 www.nature.com/leu BIO-TECHNICAL METHODS (BTS) Novel potential ALL low-risk markers revealed by gene expression profiling with new high-throughput SSH–CCS–PCR J Qiu1,5, P Gunaratne2,5, LE Peterson3, D Khurana2, N Walsham2, H Loulseged2, RJ Karni1, E Roussel4, RA Gibbs2, JF Margolin1,6 and MC Gingras1,6 1Texas Children’s Cancer Center and Department of Pediatrics; 2Human Genome Sequencing Center, Department of Molecular and Human Genetics; 3Department of Internal Medicine; 1,2,3 are all departments of Baylor College of Medicine, Baylor College of Medicine, Houston, TX, USA; and 4BioTher Corporation, Houston, TX, USA The current systems of risk grouping in pediatric acute t(1;19), BCR-ABL t(9;22), and MLL-AF4 t(4;11).1 These chromo- lymphoblastic leukemia (ALL) fail to predict therapeutic suc- somal modifications and other clinical findings such as age and cess in 10–35% of patients. To identify better predictive markers of clinical behavior in ALL, we have developed an integrated initial white blood cell count (WBC) define pediatric ALL approach for gene expression profiling that couples suppres- subgroups and are used as diagnostic and prognostic markers to sion subtractive hybridization, concatenated cDNA sequencing, assign specific risk-adjusted therapies. For instance, 1.0 to 9.9- and reverse transcriptase real-time quantitative PCR. Using this year-old patients with none of the determinant chromosomal approach, a total of 600 differentially expressed genes were translocation (NDCT) mentioned above but with a WBC higher identified between t(4;11) ALL and pre-B ALL with no determi- than 50 000 cells/ml are associated with higher risk group.2 nant chromosomal translocation. The expression of 67 genes was analyzed in different cytogenetic ALL subgroups and B Hyperdiploid and t(12;21) ALL patients are considered low-risk lymphocytes isolated from healthy donors. Three genes, ALL, are treated with less intensive therapy, and have a better 1,3 BACH1, TP53BPL, and H2B/S, were consistently expressed as event-free survival (EFS) after therapy (480% at 5 years). a significant cluster associated with the low-risk ALL sub- Conversely, hypodiploid, t(1;19), t(9;22), and t(4;11) ALL groups. A total of 42 genes were differentially expressed in ALL patients are considered high-risk ALL, and are treated with vs normal B lymphocytes, with no specific association with any more intensive regimens. These patients, with the exception of particular ALL subgroups. The remaining 22 genes were part of t(1;19), are at a higher risk of relapse and have a much lower EFS a specific expression profile associated with the hyperdiploid, 1,3 t(12;21), or t(4;11) subgroups. Using an unsupervised hierarch- after therapy (o40% after 3–5 years). The t(1;19) patients ical cluster analysis, the discriminating power of these specific have an EFS of 70–80% after 5 years that is more similar to low- expression profiles allowed the clustering of patients according than high-risk ALL.1,3 to their subgroups. These genes could help to understand the The present risk grouping based on clinical, cytogenetic, and difference in treatment response and become therapeutical immunophenotypic criteria fails to predict the 10–35% patients targets to improve ALL clinical outcomes. Leukemia (2003) 17, 1891–1900. doi:10.1038/sj.leu.2403073 who will relapse on current therapies. Such inadequacy might Keywords: acute lymphoblastic leukemia; gene expression be overcome with the identification of molecular prognostic profiling; BACH1; TP53BPL; H2B/S; suppression subtractive markers. To identify such markers, it is necessary to establish hybridization/concatenated cDNA sequencing/reverse transcriptase ALL gene expression profiles. Currently, specific gene expres- real-time quantitative PCR sion profiles have been established with the use of microarrays for B-cell precursor ALL subgroups, T-cell ALL, minimal residual disease, and to distinguish between acute myeloid leukemia (AML) and ALL.4–8 A specific gene expression profile that can Introduction accurately predict relapse is beginning to emerge from these studies.5 However, specific markers that can identify low-risk At least six important chromosomal changes have been ALL and be indicative of an effective response to less intensive identified in pediatric B-cell precursor acute lymphoblastic or toxic therapy have yet to be found. leukemia (ALL). They correspond to hyperdiploid (450) and Gene expression profile characterization can be realized with hypodiploid (o45) chromosomal status, or one of the following different techniques that can be grouped under two broad chromosomal translocations: TEL-AML t(12;21), E2A-PBX1 descriptions: sequencing technologies developed for the analy- sis of global expression pattern and technologies developed for 9–15 Correspondence: Dr M-C Gingras, Texas Children’s Hospital, Feigin the differential profiling of gene expression. However, each Center, 6621 Fannin Street, MC3-3320, Houston, TX 77030, USA; of these techniques has several limitations. Many of the global Fax: +1 832 825 4846 sequencing approaches have the disadvantage of unacceptable 5The first two authors contributed equally to this work 6 amount of redundant sequencing in order to complete the The last two authors shared the direction of this work characterization. Any of the techniques that require plasmid MCG is supported by a Fleming and Davenport Award and a grant from the Elsa U Pardee Foundation. PG, HL, RAG, and JFM are cloning introduce insert size bias. Microarray approaches are supported by an NIH Grant (USPH 5 U01 CA80200). limited by issues related to gene preselection (it is estimated that Received 15 January 2003; accepted 3 June 2003 only 20% of the entire cell transcriptome is presently Gene expression profiling of pediatric ALL J Qiu et al 1892 represented even on the most comprehensive microarray), conjugated to super-paramagnetic MACS MicroBeads and difficulties with quantification, detection of rare transcripts, applied on a positive selection column-type LS. The eluted and issues of amplification bias especially with small clinical CD3ÀCD14À cells were finally incubated with a CD19+ specimens. Therefore, there is a need for the development of antibody conjugated to super-paramagnetic MACS MicroBeads high-throughput techniques that overcome the above disadvan- and applied twice on an LS column. The purity of the CD19+ tages. cell isolation was over 95% as confirmed by FACS analysis. We have developed a novel approach to facilitate gene expression profiling that combines the selective and normal- RNA isolation ization power of suppression subtractive hybridization (SSH), the high-throughput sequencing capability of concatenated The total cellular RNA was extracted utilizing Ultraspec II cDNA sequencing (CCS), and the quantitative analytical power (Biotecx Laboratories, Houston, TX, USA), a commercial version of reverse transcriptase real-time quantitative PCR (RT-RQ-PCR). of the acid-phenol method.16 RNA integrity was checked The SSH–CCS–RT-RQ-PCR approach was developed using on a formaldehyde agarose gel. mRNA was extracted with pediatric t(4;11) and NDCT ALL subgroups as an experimental Oligotex (Qiagen, Santa Clarita, CA, USA). The total and model. We hypothesized that we could characterize rapidly, mRNA extractions were performed as per the manufacturer’s with low RNA requirement, a large set of differentially expressed instructions. genes and identify unknown markers and expression profiles of ALL subgroups. In addition, the potential for discovery was not limited by preselection of specific target genes. RT-RQ-PCR was Suppression subtractive hybridization used to assess the specificity of the SSH–CCS-generated gene expression profiles, and to expand the study to normal B The Diatchenko and co-workers14,15 PCR-based cDNA subtrac- lymphocytes and other ALL subgroups. This approach allowed tion method was performed using the SMART PCR cDNA us to determine specific gene expression profile for t(4;11), synthesis and PCR-Select subtraction kits (Clontech Labora- t(12;21), hyperdiploid ALL subgroups, and identify general and tories, Palo Alto, CA, USA) as described previously.17 The specific low-risk ALL markers. protocol was initiated with 240 ng of mRNA from two t(4;11) ALL patients (patients A and B) and two NDCT ALL patients (patients C and D). The cDNA was then cut into smaller Materials and methods fragments with RsaI to optimize the hybridization process, and a certain proportion of the resulting fragments (tester) were linked Cell isolation to a set of adaptors as per the manufacturer’s instructions17 (Clontech). One set of hybridizations was performed between Human patient samples were collected under an Institutional patients A and C cDNA, and between patients B and D cDNA. Review Board for Human Subject Research (IRB) for Baylor In this hybridization set, patients C and D cDNA was used in College of Medicine and Affiliated Hospitals approved protocol. excess (drivers), resulting in two differentially expressed Two panels of newly diagnosed patients were used in the gene subtracted t(4;11) cDNA pools. A second set of hybridization expression study: a smaller panel of 19 patients for the general was performed using patients A and B cDNA in excess (drivers), analysis and a broader panel of 38 patients for the specific low- resulting in two differentially expressed subtracted NDCT cDNA vs high-risk study (BACH1, TP53BPL, and H2B/S expression pools. The differentially expressed SSH cDNA fragments from analysis). The patients were categorized into one of the each pool were then amplified twice.17 following pre-B ALL subgroups: four and five patients with hyperdiploidy (55–58 chromosomes), six and 15 patients with the translocation t(12;21), four and six patients with the Concatenation translocation t(4;11), one and five patients with the translocation t(9;22), one and four patients with the translocation t(1;19), and The concatenation strategy is illustrated in Figure 1.
Load more

Recommended publications
  • Inhibition of Breast and Brain Cancer Cell Growth by Bccipα, An
    Oncogene (2001) 20, 336 ± 345 ã 2001 Nature Publishing Group All rights reserved 0950 ± 9232/01 $15.00 www.nature.com/onc Inhibition of breast and brain cancer cell growth by BCCIPa,an evolutionarily conserved nuclear protein that interacts with BRCA2 Jingmei Liu1, Yuan Yuan1,2, Juan Huan2 and Zhiyuan Shen*,1 1Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center; 915 Camino de Salud, NE. Albuquerque, New Mexico, NM 87131, USA; 2Graduate Program of Molecular Genetics, College of Medicine, University of Illinois at Chicago, 900 S. Ashland Ave. Chicago, Illinois, IL 60607, USA BRCA2 is a tumor suppressor gene involved in mammary mouse BRCA2. It is expected that important functions tumorigenesis. Although important functions have been of BRCA2 reside in these conserved domains. Based on assigned to a few conserved domains of BRCA2, little is the functional analysis of the conserved BRCA2 known about the longest internal conserved domain domains, several models have been proposed for the encoded by exons 14 ± 24. We identi®ed a novel protein, role of BRCA2 in tumor suppression. designated BCCIPa, that interacts with part of the An N-terminus conserved domain in exon 3 (amino internal conserved region of human BRCA2. Human acids 48 ± 105) has been implicated in transcriptional BCCIP represents a family of proteins that are regulation of gene expression (Milner et al., 1997; evolutionarily conserved, and contain three distinct Nordling et al., 1998). Deletion of this region has been domains: an N-terminus acidic domain (NAD) of 30 ± identi®ed in breast cancers (Nordling et al., 1998).
    [Show full text]
  • Cytoplasmic Expression of Epithelial Cell Transforming Sequence 2 in Lung Adenocarcinoma and Its Implications for Malignant Progression
    Laboratory Investigation (2019) 99:551–567 https://doi.org/10.1038/s41374-018-0142-4 ARTICLE Cytoplasmic expression of epithelial cell transforming sequence 2 in lung adenocarcinoma and its implications for malignant progression 1 2 3 1 2 2 Zeinab Kosibaty ● Yoshihiko Murata ● Yuko Minami ● Tomoko Dai ● Junko Kano ● Ryota Matsuoka ● 2 2 Noriyuki Nakano ● Masayuki Noguchi Received: 8 March 2018 / Revised: 14 August 2018 / Accepted: 20 August 2018 / Published online: 12 December 2018 © United States & Canadian Academy of Pathology 2018 Abstract Epithelial cell transforming sequence 2 (ECT2), a guanine nucleotide exchange factor, is predominantly localized in the nucleus of non-transformed cells and functions to regulate cytokinesis. ECT2 is also localized in the cytoplasm of cancer cells. Aberrant cytoplasmic expression of ECT2 is thought to drive tumor growth and invasion. In this study, we investigated the cytoplasmic expression of ECT2 and its prognostic and biological significance in lung adenocarcinoma. Western blotting of cellular fractions from the nucleus and cytoplasm was performed to determine the subcellular localization of ECT2 in lung adenocarcinoma cell lines. The cytoplasmic expression of ECT2 in 167 lung fi 1234567890();,: 1234567890();,: adenocarcinomas was evaluated by immunohistochemistry and its clinical signi cance was examined using Kaplan–Meier curves and Cox regression analysis. Scraping cytology specimens of 13 fresh lung adenocarcinomas were used to assess the subcellular localization of ECT2 and its phosphorylation at Thr790 (P-ECT2(T790)). We found that ECT2 was localized in both the nucleus and cytoplasm of lung adenocarcinoma cell lines and tumor tissues. Cytoplasmic expression of ECT2 was detected by immunohistochemistry in 83 (50%) of the lung adenocarcinomas, and was found to increase during cancer progression.
    [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]
  • UCSD MOLECULE PAGES Doi:10.6072/H0.MP.A002549.01 Volume 1, Issue 2, 2012 Copyright UC Press, All Rights Reserved
    UCSD MOLECULE PAGES doi:10.6072/H0.MP.A002549.01 Volume 1, Issue 2, 2012 Copyright UC Press, All rights reserved. Review Article Open Access WAVE2 Tadaomi Takenawa1, Shiro Suetsugu2, Daisuke Yamazaki3, Shusaku Kurisu1 WASP family verprolin-homologous protein 2 (WAVE2, also called WASF2) was originally identified by its sequence similarity at the carboxy-terminal VCA (verprolin, cofilin/central, acidic) domain with Wiskott-Aldrich syndrome protein (WASP) and N-WASP (neural WASP). In mammals, WAVE2 is ubiquitously expressed, and its two paralogs, WAVE1 (also called suppressor of cAMP receptor 1, SCAR1) and WAVE3, are predominantly expressed in the brain. The VCA domain of WASP and WAVE family proteins can activate the actin-related protein 2/3 (Arp2/3) complex, a major actin nucleator in cells. Proteins that can activate the Arp2/3 complex are now collectively known as nucleation-promoting factors (NPFs), and the WASP and WAVE families are a founding class of NPFs. The WAVE family has an amino-terminal WAVE homology domain (WHD domain, also called the SCAR homology domain, SHD) followed by the proline-rich region that interacts with various Src-homology 3 (SH3) domain proteins. The VCA domain located at the C-terminus. WAVE2, like WAVE1 and WAVE3, constitutively forms a huge heteropentameric protein complex (the WANP complex), binding through its WHD domain with Abi-1 (or its paralogs, Abi-2 and Abi-3), HSPC300 (also called Brick1), Nap1 (also called Hem-2 and NCKAP1), Sra1 (also called p140Sra1 and CYFIP1; its paralog is PIR121 or CYFIP2). The WANP complex is recruited to the plasma membrane by cooperative action of activated Rac GTPases and acidic phosphoinositides.
    [Show full text]
  • Cell Division Symmetry Control and Cancer Stem Cells
    AIMS Molecular Science, 7(2): 82–98. DOI: 10.3934/molsci.2020006 Received: 15 February 2020 Accepted: 26 April 2020 Published: 06 May 2020 http://www.aimspress.com/journal/Molecular Review Cell division symmetry control and cancer stem cells Sreemita Majumdar and Song-Tao Liu* Department of Biological Sciences, University of Toledo, Toledo, OH 43606, USA * Correspondence: Email: [email protected]; Tel: +14195307853. Table S1. Genes encoding polarity and fate-determinant proteins involved in asymmetric cell division. C. elegans1 D. melanogaster 1 Mammals1 Description2 Associated with/ Interactors 3 Cellular Localization (mammalian cell)4 Serine/threonine protein microtubule-associated protein cell membrane, peripheral and lateral, par-1 par-1 MARK1/2/3/4 kinase MAPT/TAU cytoplasm, dendrite RING, Lipid binding par-2 - - domain PDZ for membrane, cell junction, adherens junction, cell cortex, par-3 baz PARD3 Oligomerization domain at actin, PARD6 endomembrane system, NTD Continued on next page 2 C. elegans1 D. melanogaster 1 Mammals1 Description2 Associated with/ Interactors 3 Cellular Localization (mammalian cell)4 Serine/threonine-protein nucleus, mitochondria, cytoplasm, par-4 Lkb1 STK11/LKB1 STRAD complex kinase membrane 14-3-3 domain binding par-5 14-3-3 YWHAB phosphoserine/ adapter to many proteins cytoplasm phosphothreonine motif cell membrane, centriolar satellite, actin par-6 par-6 PARD6A/B/G PB1, CRIB, PDZ PARD3 cytoskeleton,centrosome, cytoplasm ,ruffles PARD3, and a PARD6 protein PB1, AGC-Kinase (PARD6A, PARD6B or PARD6G) pkc-3 aPKC PRKCI/Z domain, DAG binding, cytoplasm, nucleus, membrane and a GTPase protein (CDC42 or Zinc finger domain RAC1), LLGL1,ECT2 LRR and PDZ protein Cadherin, Scrib-APC-beta-catenin nucleoplasm, basolateral plasma membrane, let-413 scrib SCRIB family.
    [Show full text]
  • The Rac Gtpase in Cancer: from Old Concepts to New Paradigms Marcelo G
    Published OnlineFirst August 14, 2017; DOI: 10.1158/0008-5472.CAN-17-1456 Cancer Review Research The Rac GTPase in Cancer: From Old Concepts to New Paradigms Marcelo G. Kazanietz1 and Maria J. Caloca2 Abstract Rho family GTPases are critical regulators of cellular func- mislocalization of Rac signaling components. The unexpected tions that play important roles in cancer progression. Aberrant pro-oncogenic functions of Rac GTPase-activating proteins also activity of Rho small G-proteins, particularly Rac1 and their challenged the dogma that these negative Rac regulators solely regulators, is a hallmark of cancer and contributes to the act as tumor suppressors. The potential contribution of Rac tumorigenic and metastatic phenotypes of cancer cells. This hyperactivation to resistance to anticancer agents, including review examines the multiple mechanisms leading to Rac1 targeted therapies, as well as to the suppression of antitumor hyperactivation, particularly focusing on emerging paradigms immune response, highlights the critical need to develop ther- that involve gain-of-function mutations in Rac and guanine apeutic strategies to target the Rac pathway in a clinical setting. nucleotide exchange factors, defects in Rac1 degradation, and Cancer Res; 77(20); 5445–51. Ó2017 AACR. Introduction directed toward targeting Rho-regulated pathways for battling cancer. Exactly 25 years ago, two seminal papers by Alan Hall and Nearly all Rho GTPases act as molecular switches that cycle colleagues illuminated us with one of the most influential dis- between GDP-bound (inactive) and GTP-bound (active) forms. coveries in cancer signaling: the association of Ras-related small Activation is promoted by guanine nucleotide exchange factors GTPases of the Rho family with actin cytoskeleton reorganization (GEF) responsible for GDP dissociation, a process that normally (1, 2).
    [Show full text]
  • Defining Functional Interactions During Biogenesis of Epithelial Junctions
    ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
    [Show full text]
  • Systems Analysis Implicates WAVE2&Nbsp
    JACC: BASIC TO TRANSLATIONAL SCIENCE VOL.5,NO.4,2020 ª 2020 THE AUTHORS. PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY-NC-ND LICENSE (http://creativecommons.org/licenses/by-nc-nd/4.0/). PRECLINICAL RESEARCH Systems Analysis Implicates WAVE2 Complex in the Pathogenesis of Developmental Left-Sided Obstructive Heart Defects a b b b Jonathan J. Edwards, MD, Andrew D. Rouillard, PHD, Nicolas F. Fernandez, PHD, Zichen Wang, PHD, b c d d Alexander Lachmann, PHD, Sunita S. Shankaran, PHD, Brent W. Bisgrove, PHD, Bradley Demarest, MS, e f g h Nahid Turan, PHD, Deepak Srivastava, MD, Daniel Bernstein, MD, John Deanfield, MD, h i j k Alessandro Giardini, MD, PHD, George Porter, MD, PHD, Richard Kim, MD, Amy E. Roberts, MD, k l m m,n Jane W. Newburger, MD, MPH, Elizabeth Goldmuntz, MD, Martina Brueckner, MD, Richard P. Lifton, MD, PHD, o,p,q r,s t d Christine E. Seidman, MD, Wendy K. Chung, MD, PHD, Martin Tristani-Firouzi, MD, H. Joseph Yost, PHD, b u,v Avi Ma’ayan, PHD, Bruce D. Gelb, MD VISUAL ABSTRACT Edwards, J.J. et al. J Am Coll Cardiol Basic Trans Science. 2020;5(4):376–86. ISSN 2452-302X https://doi.org/10.1016/j.jacbts.2020.01.012 JACC: BASIC TO TRANSLATIONALSCIENCEVOL.5,NO.4,2020 Edwards et al. 377 APRIL 2020:376– 86 WAVE2 Complex in LVOTO HIGHLIGHTS ABBREVIATIONS AND ACRONYMS Combining CHD phenotype–driven gene set enrichment and CRISPR knockdown screening in zebrafish is an effective approach to identifying novel CHD genes.
    [Show full text]
  • Statistical Methods for High-Dimensional Networked Data Analysis
    Statistical Methods for High-Dimensional Networked Data Analysis by Yan Zhou A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biostatistics) in The University of Michigan 2015 Doctoral Committee: Professor Peter X. K. Song, Chair Professor Matthias Kretzler Assistant Professor Xiaoquan William Wen Professor Ji Zhu c Yan Zhou 2015 All Rights Reserved To my family ii ACKNOWLEDGEMENTS I would like to acknowledge many people for their help throughout my graduate study. I would first like to express my deepest gratitude to my advisor Dr. Peter X.- K. Song, for sharing his knowledge, encouraging me to work hard and constantly try to improve my work, and giving me the freedom to explore a diverse set of projects. He is the one who first sparked my interest in networked data analysis. I am not only respectful for his immense knowledge, motivation, and enthusiasm towards research, but also appreciate his invaluable guidance and enthusiastic encouragement during many difficult moments in my research. I could not have imagined having a better advisor and mentor for my Ph.D. study. I would also like to thank my other thesis committee members Dr. Ji Zhu, Dr. Xiaoquan Willian Wen and Dr. Matthias Kretzler for their help and advices that made me more productive than what I could achieve on my own. All enlightening discussions I had with them have lead to better contents of this dissertation. In addition, I would like to acknowledge my collaborators, Dr. Pei Wang, Dr. Betsy Lozoff and Dr. Fengji Geng, who offered me excellent opportunities and con- structive suggestions to apply my statistical knowledge to solve real-world problems, which greatly strengthened the scientific background of my dissertation research.
    [Show full text]
  • Supplementary Table 1. All Differentially Expressed Genes from Microarray Screening Analysis
    Supplementary Table 1. All differentially expressed genes from microarray screening analysis. FCa (Gal-KD Gene Symbol Gene Name p-Value versus Vector) Up-regulated genes HAPLN1 hyaluronan and proteoglycan link protein 1 10.49 0.0027085 THBS1 thrombospondin 1 9.20 0.0022192 ODC1 ornithine decarboxylase 1 6.38 0.0055776 TGM2 transglutaminase 2 4.76 0.0015627 IL7R interleukin 7 receptor 4.75 0.0017245 SERINC2 serine incorporator 2 4.51 0.0014919 ITM2C integral membrane protein 2C 4.32 0.0044644 SERPINB7 serpin peptidase inhibitor, clade B, member 7 4.18 0.0081136 tumor necrosis factor receptor superfamily, member TNFRSF10D 4.01 0.0085561 10d TAGLN transgelin 3.90 0.0099963 LRRN4 leucine rich repeat neuronal 4 3.82 0.0046513 TGFB2 transforming growth factor beta 2 3.51 0.0035017 CPA4 carboxypeptidase A4 3.43 0.0008452 EPB41L3 erythrocyte membrane protein band 4.1-like 3 3.34 0.0025309 NRG1 neuregulin 1 3.28 0.0079724 F3 coagulation factor III (thromboplastin, tissue factor) 3.27 0.0038968 POLR3G polymerase III polypeptide G 3.26 0.0070675 SEMA7A semaphorin 7A 3.20 0.0087335 NT5E 5-nucleotidase 3.17 0.0036353 CAMK2N1 calmodulin-dependent protein kinase II inhibitor 1 3.07 0.0090141 TIMP3 TIMP metallopeptidase inhibitor 3 3.03 0.0047953 SERPINE1 serpin peptidase inhibitor, clade E 2.97 0.0053652 MALL mal, T-cell differentiation protein-like 2.88 0.0078205 DDAH1 dimethylarginine dimethylaminohydrolase 1 2.86 0.0002895 WDR3 WD repeat domain 3 2.85 0.0058842 WNT5A Wnt Family Member 5A 2.81 0.0043796 GPR1 G protein-coupled receptor 1 2.81 0.0021313
    [Show full text]
  • Effects and Mechanisms of Eps8 on the Biological Behaviour of Malignant Tumours (Review)
    824 ONCOLOGY REPORTS 45: 824-834, 2021 Effects and mechanisms of Eps8 on the biological behaviour of malignant tumours (Review) KAILI LUO1, LEI ZHANG2, YUAN LIAO1, HONGYU ZHOU1, HONGYING YANG2, MIN LUO1 and CHEN QING1 1School of Pharmaceutical Sciences and Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming, Yunnan 650500; 2Department of Gynecology, Yunnan Tumor Hospital and The Third Affiliated Hospital of Kunming Medical University; Kunming, Yunnan 650118, P.R. China Received August 29, 2020; Accepted December 9, 2020 DOI: 10.3892/or.2021.7927 Abstract. Epidermal growth factor receptor pathway substrate 8 1. Introduction (Eps8) was initially identified as the substrate for the kinase activity of EGFR, improving the responsiveness of EGF, which Malignant tumours are uncontrolled cell proliferation diseases is involved in cell mitosis, differentiation and other physiological caused by oncogenes and ultimately lead to organ and body functions. Numerous studies over the last decade have demon- dysfunction (1). In recent decades, great progress has been strated that Eps8 is overexpressed in most ubiquitous malignant made in the study of genes and signalling pathways in tumours and subsequently binds with its receptor to activate tumorigenesis. Eps8 was identified by Fazioli et al in NIH-3T3 multiple signalling pathways. Eps8 not only participates in the murine fibroblasts via an approach that allows direct cloning regulation of malignant phenotypes, such as tumour proliferation, of intracellular substrates for receptor tyrosine kinases (RTKs) invasion, metastasis and drug resistance, but is also related to that was designed to study the EGFR signalling pathway. Eps8 the clinicopathological characteristics and prognosis of patients.
    [Show full text]
  • Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals
    animals Review Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals Mohammadreza Mohammadabadi 1 , Farhad Bordbar 1,* , Just Jensen 2 , Min Du 3 and Wei Guo 4 1 Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 77951, Iran; [email protected] 2 Center for Quantitative Genetics and Genomics, Aarhus University, 8210 Aarhus, Denmark; [email protected] 3 Washington Center for Muscle Biology, Department of Animal Sciences, Washington State University, Pullman, WA 99163, USA; [email protected] 4 Muscle Biology and Animal Biologics, Animal and Dairy Science, University of Wisconsin-Madison, Madison, WI 53558, USA; [email protected] * Correspondence: [email protected] Simple Summary: Skeletal muscle mass is an important economic trait, and muscle development and growth is a crucial factor to supply enough meat for human consumption. Thus, understanding (candidate) genes regulating skeletal muscle development is crucial for understanding molecular genetic regulation of muscle growth and can be benefit the meat industry toward the goal of in- creasing meat yields. During the past years, significant progress has been made for understanding these mechanisms, and thus, we decided to write a comprehensive review covering regulators and (candidate) genes crucial for muscle development and growth in farm animals. Detection of these genes and factors increases our understanding of muscle growth and development and is a great help for breeders to satisfy demands for meat production on a global scale. Citation: Mohammadabadi, M.; Abstract: Farm-animal species play crucial roles in satisfying demands for meat on a global scale, Bordbar, F.; Jensen, J.; Du, M.; Guo, W.
    [Show full text]