DOCSLIB.ORG

Identification of MLL-AF9 Related Target Genes and Micrornas Involved in Leukemogenesis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Identification of MLL-AF9 Related Target Genes and Micrornas Involved in Leukemogenesis Identification of MLL-AF9 related target genes and microRNAs involved in leukemogenesis Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.) an der Fakultät für Biologie der Ludwig-Maximilians-Universität München Durchgeführt am Forschungszentrum des Dr. von Haunerschen Kinderspitals der Ludwig-Maximilians-Universität München vorgelegt von Katrin Kristina Fleischmann München, 3. Mai 2012 Erstgutachterin: Frau Prof. Dr. Elisabeth Weiss Zweitgutachter: Herr Prof. Dr. Michael Schleicher Sondergutachter: Herr Prof. Dr. Adelbert Roscher Tag der Abgabe: 03.05.2012 Tag der mündlichen Prüfung: 17.09.2012 What is a scientist after all? It is a curious man looking through a keyhole, the keyhole of nature, trying to know what's going on. Jacques Yves Cousteau Preamble and Acknowledgments How to explain the change from evolutionary ecology to tumor biology? After my diploma thesis in evolutionary ecology and a major in ecology, I was facing some skepticism while being interviewed for PhD positions in the biomedical field. What was my motivation to study biology? Why do I continue to feel this strong enthusiasm for this field? For me, it was always the grand question “How does life work?” that drove me and which may interconnect almost every aspect within natural sciences. Only when we strive to get answers to this grand question we may reach an understanding as to what happens in disease. I am profoundly grateful to my dear colleagues Dr. Julia von Frowein, Dr. Thomas Magg and Dr. Uta Fuchs who supported me to become one of their colleagues at the research center of the Dr. von Haunerschen Kinderspital. Since then, innumerable great and inspiring discussions allied us. With them and many other splendid colleagues at our research center (among them Kristin Hähnel, Carola Laudano, Rita Meilbeck and many others who may forgive that I can not name all), I shared a vivid exchange of information and cordial coffee breaks. They all made every- day-life at our research center always joyful and worthwhile. I profoundly thank Prof. Dr. Adelbert Roscher for his experimental advice and supervision, many stimulating discussions, for directing my thesis over these years and for his help when it was most crucial. Further great thanks go to: Prof. Dr. Elisabeth Weiss for her friendly support and for representing this thesis at the Faculty of Biology of the Ludwig-Maximilians Universität München. PD Dr. Irene Schmid whose friendly support has always been of great value for my research at the Dr. von Haunerschen Kinderspital and for providing insights into the clinical oncology. PD Dr. Philipp Pagel for bioinformatical collaboration, stimulating discussions and open ears in a truly remarkable cooperation. Prof. Dr. Arndt Borkhardt for the initiation of this work and for supporting me while he was still at our institute. And last but not least to: My dearest Markus, with whom I could share endless happy moments in nature and every-day-life while traveling, climbing and going on ski-randonnée, for taking care of a healthy work-life-balance. My loving parents Sigrid and Prof. Dr. Rudolf Schröck for supporting me in my interests and in every way anyone could wish. My brother and fellow scientist Dr. Florian Schröck for his friendship and interest in my work and for seeing it from a different angle. My family and friends for their friendship and support. This work has been supported by the Graduiertenkolleg 1202 „Oligonucleotides in Cell Biology and Therapy“ from the Deutsche Forschungsgemeinschaft. Index 1 Index 1 Index ..................................................................................................................................... I 2 List of figures...................................................................................................................... V 3 List of tables .................................................................................................................... VII 4 Summary ............................................................................................................................. 1 5 Zusammenfassung .............................................................................................................. 3 6 Introduction ........................................................................................................................ 5 6.1 Genetic aberrations in leukemia ...................................................................................... 5 6.2 The genes MLL, AF9, MLL-AF9 and their functions ...................................................... 5 6.3 The role of MLL-AF9 fusion gene in leukemia ............................................................. 10 6.4 MicroRNAs and their role in hematologic malignancies .............................................. 11 6.5 Aim of this study ........................................................................................................... 14 7 Material and Methods ...................................................................................................... 15 7.1 Cell and molecular biological methods ......................................................................... 15 7.1.1 Cell culture ............................................................................................................ 15 7.1.2 SiRNAs and miRNA mimics ................................................................................ 15 7.1.3 Transfections of siRNAs, miRNA-mimics and shRNA plasmids ........................ 16 7.1.4 Confocal fluorescence microscopy ....................................................................... 17 7.1.5 Cell diameter and proliferation ............................................................................. 18 7.1.6 Flow cytometric measurements ............................................................................. 19 7.1.6.1 Transfection efficiency ................................................................................... 19 7.1.6.2 Monitoring of stable transfected cells ............................................................. 19 7.1.6.3 Cell cycle analysis ........................................................................................... 19 7.1.6.4 Apoptosis detection ......................................................................................... 20 7.1.7 RNA extraction and determination of RNA concentration, purity and integrity .. 21 7.1.8 Reverse transcriptase quantitative real-time PCR ................................................. 21 7.1.9 Gene expression profiling ..................................................................................... 22 7.1.10 MicroRNA profiling and confirmatory techniques ............................................... 23 7.1.10.1 Quantitative microRNA detection via TaqMan miRNA Low Density Array and single assay qRT-PCR .................................................................. 23 7.1.10.2 Semiquantitative microRNA detection via microarrays ................................ 23 I Index 7.2 Biochemical methods .................................................................................................... 24 7.2.1 Western blot .......................................................................................................... 24 7.2.2 TaqMan Protein Assay ........................................................................................... 25 7.3 Biostatistical methods ................................................................................................... 27 7.3.1 Gene expression profiling data analysis ................................................................. 27 7.3.2 Quantitative LDA miRNA profiling data analysis ................................................ 29 7.3.3 Semiquantitative microarray miRNA profiling data analysis ................................ 30 7.3.4 Correlation analysis of array and qRT-PCR data ................................................... 31 7.3.5 Functional disease ontology analysis ..................................................................... 31 7.3.6 Functional gene ontology analysis ......................................................................... 31 7.3.7 MiRNA target prediction ....................................................................................... 32 7.4 Prioritization of likely candidate genes ......................................................................... 32 8 Results ................................................................................................................................ 35 8.1 Experimental design and siRNA selection .................................................................... 35 8.1.1 Transfection method and efficiency ....................................................................... 35 8.1.2 Design and selection of efficient siRNAs against MLL-AF9 in THP1 cells .......... 37 8.2 MLL-AF9 knockdown ................................................................................................... 39 8.2.1 Experimental conditions ........................................................................................ 39 8.2.2 Validation of MLL-AF9 knockdown ...................................................................... 39 8.3 Cellular phenotype and functional endpoints of MLL-AF9 knockdown ....................... 43 8.4 MLL-AF9 knockdown dependent effects on gene expression ...................................... 47 8.4.1 Quality control of gene expression profiling results .............................................
Load more

Recommended publications
  • ABSTRACT ANGSTADT, ANDREA Y. Evaluation of the Genomic
    ABSTRACT ANGSTADT, ANDREA Y. Evaluation of the Genomic Aberrations in Canine Osteosarcoma and Their Resemblance to the Human Counterpart. (Under the direction of Dr. Matthew Breen). In the last decade the domestic dog has emerged as an ideal biomedical model of complex genetic diseases such as cancers. Cancer in the dog occurs spontaneously and several studies have concluded that human and canine cancers have similar characteristics such as presentation of disease, rate of metastases, genetic dysregulation, and survival rates. Furthermore, in the genomic era the dog genome was found more homologous in sequence conservation to humans than mice, making it a valuable model organism for genetic study in addition to pathophysiological analysis. Osteosarcoma (OS), the most commonly diagnosed malignant bone tumor in humans and dogs, is one such cancer that would benefit from comparative genomic analysis. In humans, OS is a rare cancer diagnosed in fewer than 1,000 people per year in the USA, while in the domestic dog population the annual number of new cases is estimated to far exceed 10,000. This high rate of disease occurrence in dogs provides a unique opportunity to study the genomic imbalances in canine OS and their translational value to human OS as a means to identify important alterations involved in disease etiology. OS in humans is characterized by extremely complex karyotypes which contain both structural changes (translocations and/or rearrangements) and DNA copy number changes. Metaphase and array comparative genomic hybridization (aCGH) has assisted in uncovering the genetic imbalances that are associated with human OS phenotype. In dog OS, previous low-resolution (10-20Mb) aCGH analysis identified a wide range of recurrent copy number aberrations (CNAs), indicative of a similar level of genomic instability to human OS.
    [Show full text]
  • Aberrant Methylation Underlies Insulin Gene Expression in Human Insulinoma
    ARTICLE https://doi.org/10.1038/s41467-020-18839-1 OPEN Aberrant methylation underlies insulin gene expression in human insulinoma Esra Karakose1,6, Huan Wang 2,6, William Inabnet1, Rajesh V. Thakker 3, Steven Libutti4, Gustavo Fernandez-Ranvier 1, Hyunsuk Suh1, Mark Stevenson 3, Yayoi Kinoshita1, Michael Donovan1, Yevgeniy Antipin1,2, Yan Li5, Xiaoxiao Liu 5, Fulai Jin 5, Peng Wang 1, Andrew Uzilov 1,2, ✉ Carmen Argmann 1, Eric E. Schadt 1,2, Andrew F. Stewart 1,7 , Donald K. Scott 1,7 & Luca Lambertini 1,6 1234567890():,; Human insulinomas are rare, benign, slowly proliferating, insulin-producing beta cell tumors that provide a molecular “recipe” or “roadmap” for pathways that control human beta cell regeneration. An earlier study revealed abnormal methylation in the imprinted p15.5-p15.4 region of chromosome 11, known to be abnormally methylated in another disorder of expanded beta cell mass and function: the focal variant of congenital hyperinsulinism. Here, we compare deep DNA methylome sequencing on 19 human insulinomas, and five sets of normal beta cells. We find a remarkably consistent, abnormal methylation pattern in insu- linomas. The findings suggest that abnormal insulin (INS) promoter methylation and altered transcription factor expression create alternative drivers of INS expression, replacing cano- nical PDX1-driven beta cell specification with a pathological, looping, distal enhancer-based form of transcriptional regulation. Finally, NFaT transcription factors, rather than the cano- nical PDX1 enhancer complex, are predicted to drive INS transactivation. 1 From the Diabetes Obesity and Metabolism Institute, The Department of Surgery, The Department of Pathology, The Department of Genetics and Genomics Sciences and The Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
    [Show full text]
  • DNA Methylation Signatures of Early Childhood Malnutrition Associated with Impairments in Attention and Cognition
    Biological Archival Report Psychiatry DNA Methylation Signatures of Early Childhood Malnutrition Associated With Impairments in Attention and Cognition Cyril J. Peter, Laura K. Fischer, Marija Kundakovic, Paras Garg, Mira Jakovcevski, Aslihan Dincer, Ana C. Amaral, Edward I. Ginns, Marzena Galdzicka, Cyralene P. Bryce, Chana Ratner, Deborah P. Waber, David Mokler, Gayle Medford, Frances A. Champagne, Douglas L. Rosene, Jill A. McGaughy, Andrew J. Sharp, Janina R. Galler, and Schahram Akbarian ABSTRACT BACKGROUND: Early childhood malnutrition affects 113 million children worldwide, impacting health and increasing vulnerability for cognitive and behavioral disorders later in life. Molecular signatures after childhood malnutrition, including the potential for intergenerational transmission, remain unexplored. METHODS: We surveyed blood DNA methylomes (483,000 individual CpG sites) in 168 subjects across two generations, including 50 generation 1 individuals hospitalized during the first year of life for moderate to severe protein-energy malnutrition, then followed up to 48 years in the Barbados Nutrition Study. Attention deficits and cognitive performance were evaluated with the Connors Adult Attention Rating Scale and Wechsler Abbreviated Scale of Intelligence. Expression of nutrition-sensitive genes was explored by quantitative reverse transcriptase polymerase chain reaction in rat prefrontal cortex. RESULTS: We identified 134 nutrition-sensitive, differentially methylated genomic regions, with most (87%) specific for generation 1. Multiple neuropsychiatric risk genes, including COMT, IFNG, MIR200B, SYNGAP1, and VIPR2 showed associations of specific methyl-CpGs with attention and IQ. IFNG expression was decreased in prefrontal cortex of rats showing attention deficits after developmental malnutrition. CONCLUSIONS: Early childhood malnutrition entails long-lasting epigenetic signatures associated with liability for attention and cognition, and limited potential for intergenerational transmission.
    [Show full text]
  • PRODUCT SPECIFICATION Anti-C12orf43
    Anti-C12orf43 Product Datasheet Polyclonal Antibody PRODUCT SPECIFICATION Product Name Anti-C12orf43 Product Number HPA046148 Gene Description chromosome 12 open reading frame 43 Clonality Polyclonal Isotype IgG Host Rabbit Antigen Sequence Recombinant Protein Epitope Signature Tag (PrEST) antigen sequence: AWGLEQRPHVAGKPRAGAANSQLSTSQPSLRHKVNEHEQDGNELQTTPEF RAHVAKKLGALLDSFITISEAAKEPAKAKVQKVALEDDGFRLFFTSVPGG REKEESPQPR Purification Method Affinity purified using the PrEST antigen as affinity ligand Verified Species Human Reactivity Recommended IHC (Immunohistochemistry) Applications - Antibody dilution: 1:50 - 1:200 - Retrieval method: HIER pH6 WB (Western Blot) - Working concentration: 0.04-0.4 µg/ml ICC-IF (Immunofluorescence) - Fixation/Permeabilization: PFA/Triton X-100 - Working concentration: 0.25-2 µg/ml Characterization Data Available at atlasantibodies.com/products/HPA046148 Buffer 40% glycerol and PBS (pH 7.2). 0.02% sodium azide is added as preservative. Concentration Lot dependent Storage Store at +4°C for short term storage. Long time storage is recommended at -20°C. Notes Gently mix before use. Optimal concentrations and conditions for each application should be determined by the user. For protocols, additional product information, such as images and references, see atlasantibodies.com. Product of Sweden. For research use only. Not intended for pharmaceutical development, diagnostic, therapeutic or any in vivo use. No products from Atlas Antibodies may be resold, modified for resale or used to manufacture commercial products without prior written approval from Atlas Antibodies AB. Warranty: The products supplied by Atlas Antibodies are warranted to meet stated product specifications and to conform to label descriptions when used and stored properly. Unless otherwise stated, this warranty is limited to one year from date of sales for products used, handled and stored according to Atlas Antibodies AB's instructions.
    [Show full text]
  • Mrna-Lncrna Co-Expression Network Analysis Reveals the Role of Lncrnas in Immune Dysfunction During Severe SARS-Cov-2 Infection
    viruses Article mRNA-lncRNA Co-Expression Network Analysis Reveals the Role of lncRNAs in Immune Dysfunction during Severe SARS-CoV-2 Infection Sumit Mukherjee 1 , Bodhisattwa Banerjee 2 , David Karasik 2 and Milana Frenkel-Morgenstern 1,* 1 Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel; [email protected] 2 Musculoskeletal Genetics Laboratory, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel; [email protected] (B.B.); [email protected] (D.K.) * Correspondence: [email protected]; Tel.: +972-72-264-4901 Abstract: The recently emerged SARS-CoV-2 virus is responsible for the ongoing COVID-19 pan- demic that has rapidly developed into a global public health threat. Patients severely affected with COVID-19 present distinct clinical features, including acute respiratory disorder, neutrophilia, cy- tokine storm, and sepsis. In addition, multiple pro-inflammatory cytokines are found in the plasma of such patients. Transcriptome sequencing of different specimens obtained from patients suffering from severe episodes of COVID-19 shows dynamics in terms of their immune responses. However, those host factors required for SARS-CoV-2 propagation and the underlying molecular mechanisms responsible for dysfunctional immune responses during COVID-19 infection remain elusive. In the present study, we analyzed the mRNA-long non-coding RNA (lncRNA) co-expression network derived from publicly available SARS-CoV-2-infected transcriptome data of human lung epithelial Citation: Mukherjee, S.; Banerjee, B.; cell lines and bronchoalveolar lavage fluid (BALF) from COVID-19 patients. Through co-expression Karasik, D.; Frenkel-Morgenstern, M.
    [Show full text]
  • Pnas.201413825SI.Pdf
    Supporting Information Impens et al. 10.1073/pnas.1413825111 13 15 13 15 SI Methods beling) (Silantes Gmbh), or C6 N2 L-lysine HCl and C6 N4 L- Plasmids. pSG5-His6-SUMO1 plasmid encodes the N-terminal arginine HCl (heavy labeling) (Silantes Gmbh). L-Lysine HCl was His6-tagged mature Small ubiquitin modifier 1 (SUMO1) isoform added at its normal concentration in DMEM (146 mg/L), but the (kind gift of A. Dejean, Institut Pasteur, Paris). The pSG5-His6- concentration of L-arginine HCl was reduced to 25 mg/L (30% of SUMO1 T95R mutat was derived from this plasmid using PCR the normal concentration in DMEM) to prevent metabolic con- mutagenesis. pSG5-His6-SUMO2 was obtained by inserting the version of arginine to proline (4). Cells were kept for at least six cDNA corresponding to the human mature SUMO2 isoform population doublings to ensure complete incorporation of the la- with an N-terminal His6 tag in the pSG5 vector (Stratagene). beled lysine and arginine. 2 The pSG5-His6-SUMO2 T91R mutant was derived from this For transfections, cells were seeded in 75-cm flasks or in 6- or plasmid by PCR mutagenesis. N-terminally HA-tagged human 24-well plates at a density of 2.7 × 106 cells per flask or 3 × 105 or cDNA of ZBTB20 (Zinc finger and BTB domain containing 0.5 × 105 cells per well, respectively. The next day cells were 20) isoform 2 (UniProt identifier Q9HC78-2), HMBOX1 (Ho- transfected with Lipofectamine LTX reagents (Invitrogen) (20 μg meobox containing protein 1) isoform 1 (HMBOX1A) (UniProt of DNA per flask, 3.5 μg per well in the six-well plates, or 0.75 μg identifier Q6NT76-1), NACC1 (Nucleus accumbens-associated per well in the 24-well plates) for 48 h.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Produktinformation
    Produktinformation Diagnostik & molekulare Diagnostik Laborgeräte & Service Zellkultur & Verbrauchsmaterial Forschungsprodukte & Biochemikalien Weitere Information auf den folgenden Seiten! See the following pages for more information! Lieferung & Zahlungsart Lieferung: frei Haus Bestellung auf Rechnung SZABO-SCANDIC Lieferung: € 10,- HandelsgmbH & Co KG Erstbestellung Vorauskassa Quellenstraße 110, A-1100 Wien T. +43(0)1 489 3961-0 Zuschläge F. +43(0)1 489 3961-7 [email protected] • Mindermengenzuschlag www.szabo-scandic.com • Trockeneiszuschlag • Gefahrgutzuschlag linkedin.com/company/szaboscandic • Expressversand facebook.com/szaboscandic SANTA CRUZ BIOTECHNOLOGY, INC. MAGE-D4/MAGE-D4B CRISPR/Cas9 KO Plasmid (h): sc-418394 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and MAGE-D4/MAGE-D4B CRISPR/Cas9 KO Plasmid (h) is recommended for the CRISPR-associated protein (Cas9) system is an adaptive immune response disruption of gene expression in human cells. defense mechanism used by archea and bacteria for the degradation of foreign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1.
    [Show full text]
  • Multidrug Transporter MRP4/ABCC4 As a Key Determinant of Pancreatic
    www.nature.com/scientificreports OPEN Multidrug transporter MRP4/ ABCC4 as a key determinant of pancreatic cancer aggressiveness A. Sahores1, A. Carozzo1, M. May1, N. Gómez1, N. Di Siervi1, M. De Sousa Serro1, A. Yanef1, A. Rodríguez‑González2, M. Abba3, C. Shayo2 & C. Davio1* Recent fndings show that MRP4 is critical for pancreatic ductal adenocarcinoma (PDAC) cell proliferation. Nevertheless, the signifcance of MRP4 protein levels and function in PDAC progression is still unclear. The aim of this study was to determine the role of MRP4 in PDAC tumor aggressiveness. Bioinformatic studies revealed that PDAC samples show higher MRP4 transcript levels compared to normal adjacent pancreatic tissue and circulating tumor cells express higher levels of MRP4 than primary tumors. Also, high levels of MRP4 are typical of high-grade PDAC cell lines and associate with an epithelial-mesenchymal phenotype. Moreover, PDAC patients with high levels of MRP4 depict dysregulation of pathways associated with migration, chemotaxis and cell adhesion. Silencing MRP4 in PANC1 cells reduced tumorigenicity and tumor growth and impaired cell migration. Transcriptomic analysis revealed that MRP4 silencing alters PANC1 gene expression, mainly dysregulating pathways related to cell-to-cell interactions and focal adhesion. Contrarily, MRP4 overexpression signifcantly increased BxPC-3 growth rate, produced a switch in the expression of EMT markers, and enhanced experimental metastatic incidence. Altogether, our results indicate that MRP4 is associated with a more aggressive phenotype in PDAC, boosting pancreatic tumorigenesis and metastatic capacity, which could fnally determine a fast tumor progression in PDAC patients. Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human malignancies, due to its late diag- nosis, inherent resistance to treatment and early dissemination 1.
    [Show full text]
  • Genome-Wide Transcriptional Sequencing Identifies Novel Mutations in Metabolic Genes in Human Hepatocellular Carcinoma DAOUD M
    CANCER GENOMICS & PROTEOMICS 11 : 1-12 (2014) Genome-wide Transcriptional Sequencing Identifies Novel Mutations in Metabolic Genes in Human Hepatocellular Carcinoma DAOUD M. MEERZAMAN 1,2 , CHUNHUA YAN 1, QING-RONG CHEN 1, MICHAEL N. EDMONSON 1, CARL F. SCHAEFER 1, ROBERT J. CLIFFORD 2, BARBARA K. DUNN 3, LI DONG 2, RICHARD P. FINNEY 1, CONSTANCE M. CULTRARO 2, YING HU1, ZHIHUI YANG 2, CU V. NGUYEN 1, JENNY M. KELLEY 2, SHUANG CAI 2, HONGEN ZHANG 2, JINGHUI ZHANG 1,4 , REBECCA WILSON 2, LAUREN MESSMER 2, YOUNG-HWA CHUNG 5, JEONG A. KIM 5, NEUNG HWA PARK 6, MYUNG-SOO LYU 6, IL HAN SONG 7, GEORGE KOMATSOULIS 1 and KENNETH H. BUETOW 1,2 1Center for Bioinformatics and Information Technology, National Cancer Institute, Rockville, MD, U.S.A.; 2Laboratory of Population Genetics, National Cancer Institute, National Cancer Institute, Bethesda, MD, U.S.A.; 3Basic Prevention Science Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, U.S.A; 4Department of Biotechnology/Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, U.S.A.; 5Department of Internal Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea; 6Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, Korea; 7Department of Internal Medicine, College of Medicine, Dankook University, Cheon-An, Korea Abstract . We report on next-generation transcriptome Worldwide, liver cancer is the fifth most common cancer and sequencing results of three human hepatocellular carcinoma the third most common cause of cancer-related mortality (1). tumor/tumor-adjacent pairs.
    [Show full text]
  • Supplementary Data
    Supplementary Fig. 1 A B Responder_Xenograft_ Responder_Xenograft_ NON- NON- Lu7336, Vehicle vs Lu7466, Vehicle vs Responder_Xenograft_ Responder_Xenograft_ Sagopilone, Welch- Sagopilone, Welch- Lu7187, Vehicle vs Lu7406, Vehicle vs Test: 638 Test: 600 Sagopilone, Welch- Sagopilone, Welch- Test: 468 Test: 482 Responder_Xenograft_ NON- Lu7860, Vehicle vs Responder_Xenograft_ Sagopilone, Welch - Lu7558, Vehicle vs Test: 605 Sagopilone, Welch- Test: 333 Supplementary Fig. 2 Supplementary Fig. 3 Supplementary Figure S1. Venn diagrams comparing probe sets regulated by Sagopilone treatment (10mg/kg for 24h) between individual models (Welsh Test ellipse p-value<0.001 or 5-fold change). A Sagopilone responder models, B Sagopilone non-responder models. Supplementary Figure S2. Pathway analysis of genes regulated by Sagopilone treatment in responder xenograft models 24h after Sagopilone treatment by GeneGo Metacore; the most significant pathway map representing cell cycle/spindle assembly and chromosome separation is shown, genes upregulated by Sagopilone treatment are marked with red thermometers. Supplementary Figure S3. GeneGo Metacore pathway analysis of genes differentially expressed between Sagopilone Responder and Non-Responder models displaying –log(p-Values) of most significant pathway maps. Supplementary Tables Supplementary Table 1. Response and activity in 22 non-small-cell lung cancer (NSCLC) xenograft models after treatment with Sagopilone and other cytotoxic agents commonly used in the management of NSCLC Tumor Model Response type
    [Show full text]
  • DEGS2 Polymorphism Associated with Cognition in Schizophrenia Is Associated with Gene Expression in Brain
    OPEN Citation: Transl Psychiatry (2015) 5, e550; doi:10.1038/tp.2015.45 www.nature.com/tp ORIGINAL ARTICLE DEGS2 polymorphism associated with cognition in schizophrenia is associated with gene expression in brain K Ohi1,2, G Ursini1,MLi1, JH Shin1,TYe1, Q Chen1,RTao1, JE Kleinman1, TM Hyde1,3,4, R Hashimoto2,5 and DR Weinberger1,3,4,6,7 A genome-wide association study of cognitive deficits in patients with schizophrenia in Japan found association with a missense genetic variant (rs7157599, Asn8Ser) in the delta(4)-desaturase, sphingolipid 2 (DEGS2) gene. A replication analysis using Caucasian samples showed a directionally consistent trend for cognitive association of a proxy single-nucleotide polymorphism (SNP), rs3783332. Although the DEGS2 gene is expressed in human brain, it is unknown how DEGS2 expression varies during human life and whether it is affected by psychiatric disorders and genetic variants. To address these questions, we examined DEGS2 messenger RNA using next-generation sequencing in postmortem dorsolateral prefrontal cortical tissue from a total of 418 Caucasian samples including patients with schizophrenia, bipolar disorder and major depressive disorder. DEGS2 is expressed at very low levels prenatally and increases gradually from birth to adolescence and consistently expressed across adulthood. Rs3783332 genotype −3 was significantly associated with the expression across all subjects (F3,348 = 10.79,P= 1.12 × 10 ), particularly in control subjects − 4 (F1,87 = 13.14, P = 4.86 × 10 ). Similar results were found with rs715799 genotype. The carriers of the risk-associated minor allele at both loci showed significantly lower expression compared with subjects homozygous for the non-risk major allele and this was a consistent finding across all diagnostic groups.
    [Show full text]