Table 2. All Probe Sets with Fold Changes Higher Than Set Thresholds but P > 0.001 Affy No
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Influencers on Thyroid Cancer Onset: Molecular Genetic Basis
G C A T T A C G G C A T genes Review Influencers on Thyroid Cancer Onset: Molecular Genetic Basis Berta Luzón-Toro 1,2, Raquel María Fernández 1,2, Leticia Villalba-Benito 1,2, Ana Torroglosa 1,2, Guillermo Antiñolo 1,2 and Salud Borrego 1,2,* 1 Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain; [email protected] (B.L.-T.); [email protected] (R.M.F.); [email protected] (L.V.-B.); [email protected] (A.T.); [email protected] (G.A.) 2 Centre for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain * Correspondence: [email protected]; Tel.: +34-955-012641 Received: 3 September 2019; Accepted: 6 November 2019; Published: 8 November 2019 Abstract: Thyroid cancer, a cancerous tumor or growth located within the thyroid gland, is the most common endocrine cancer. It is one of the few cancers whereby incidence rates have increased in recent years. It occurs in all age groups, from children through to seniors. Most studies are focused on dissecting its genetic basis, since our current knowledge of the genetic background of the different forms of thyroid cancer is far from complete, which poses a challenge for diagnosis and prognosis of the disease. In this review, we describe prevailing advances and update our understanding of the molecular genetics of thyroid cancer, focusing on the main genes related with the pathology, including the different noncoding RNAs associated with the disease. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
1 the Neuroprotective Transcription Factor ATF5 Is Decreased And
The neuroprotective transcription factor ATF5 is decreased and sequestered into polyglutamine inclusions in Huntington’s disease Ivó H. Hernández1,2,3, Jesús Torres-Peraza1,2,5, María Santos-Galindo1,2, Eloísa Ramos-Morón4, María R. Fernández-Fernández1,2, María J. Pérez-Álvarez1,2,3, Antonio Miranda-Vizuete4 and José J. Lucas1,2* 1 Centro de Biología Molecular Severo Ochoa (CBM”SO”) CSIC/UAM, Madrid, Spain. 2 Instituto de Salud Carlos III, Networking Research Center on Neurodegenerative Diseases (CIBERNED), Spain. 3 Departamento de Biología, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain. 4 Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain 5 Present Address: Gerència d’Atenció Primària del Servei de Salut de les Illes Balears (IB-SALUT), Palma, Spain *Corresponding author: José J. Lucas [email protected] http://www.cbm.uam.es/lineas/lucasgroup.htm provided by Digital.CSIC View metadata, citation and similar papers at core.ac.uk CORE brought to you by 1 Abstract Activating transcription factor-5 (ATF5) is a stress-response transcription factor induced upon different cell stressors like fasting, amino-acid limitation, cadmium or arsenite. ATF5 is also induced, and promotes transcription of anti-apoptotic target genes like MCL1, during the unfolded protein response (UPR) triggered by endoplasmic reticulum stress. In the brain, high ATF5 levels are found in gliomas and also in neural progenitor cells, which need to decrease their ATF5 levels for differentiation into mature neurons or glia. This initially led to believe that ATF5 is not expressed in adult neurons. More recently, we reported basal neuronal ATF5 expression in adult mouse brain and its neuroprotective induction during UPR in a mouse model of status epilepticus. -
Enzymatic Encoding Methods for Efficient Synthesis Of
(19) TZZ__T (11) EP 1 957 644 B1 (12) EUROPEAN PATENT SPECIFICATION (45) Date of publication and mention (51) Int Cl.: of the grant of the patent: C12N 15/10 (2006.01) C12Q 1/68 (2006.01) 01.12.2010 Bulletin 2010/48 C40B 40/06 (2006.01) C40B 50/06 (2006.01) (21) Application number: 06818144.5 (86) International application number: PCT/DK2006/000685 (22) Date of filing: 01.12.2006 (87) International publication number: WO 2007/062664 (07.06.2007 Gazette 2007/23) (54) ENZYMATIC ENCODING METHODS FOR EFFICIENT SYNTHESIS OF LARGE LIBRARIES ENZYMVERMITTELNDE KODIERUNGSMETHODEN FÜR EINE EFFIZIENTE SYNTHESE VON GROSSEN BIBLIOTHEKEN PROCEDES DE CODAGE ENZYMATIQUE DESTINES A LA SYNTHESE EFFICACE DE BIBLIOTHEQUES IMPORTANTES (84) Designated Contracting States: • GOLDBECH, Anne AT BE BG CH CY CZ DE DK EE ES FI FR GB GR DK-2200 Copenhagen N (DK) HU IE IS IT LI LT LU LV MC NL PL PT RO SE SI • DE LEON, Daen SK TR DK-2300 Copenhagen S (DK) Designated Extension States: • KALDOR, Ditte Kievsmose AL BA HR MK RS DK-2880 Bagsvaerd (DK) • SLØK, Frank Abilgaard (30) Priority: 01.12.2005 DK 200501704 DK-3450 Allerød (DK) 02.12.2005 US 741490 P • HUSEMOEN, Birgitte Nystrup DK-2500 Valby (DK) (43) Date of publication of application: • DOLBERG, Johannes 20.08.2008 Bulletin 2008/34 DK-1674 Copenhagen V (DK) • JENSEN, Kim Birkebæk (73) Proprietor: Nuevolution A/S DK-2610 Rødovre (DK) 2100 Copenhagen 0 (DK) • PETERSEN, Lene DK-2100 Copenhagen Ø (DK) (72) Inventors: • NØRREGAARD-MADSEN, Mads • FRANCH, Thomas DK-3460 Birkerød (DK) DK-3070 Snekkersten (DK) • GODSKESEN, -
Feedback Regulation Between Initiation and Maturation Networks Orchestrates the Chromatin Dynamics of Epidermal Lineage
bioRxiv preprint doi: https://doi.org/10.1101/349308; this version posted June 18, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Li et al., p. 1 Feedback Regulation between Initiation and Maturation Networks Orchestrates the Chromatin Dynamics of Epidermal Lineage Commitment Lingjie Li1,3,4, Yong Wang2,4,7,8*, Jessica L. Torkelson1,3*, Gautam Shankar1, Jillian M. Pattison1,3, Hanson H. Zhen1,3, Zhana Duren2,4,7, Fengqin Fang5, Sandra P. Melo1, Samantha N. Piekos1,3, Jiang Li1, Eric J. Liaw1, Lang Chen7, Rui Li1,4, Marius Wernig6, Wing H. Wong2,4, Howard Y. Chang1,4, Anthony E. Oro1,3,9 1 Program in Epithelial Biology and Department of Dermatology 2 Department of Statistics and Biomedical Data Science 3 Center for Definitive and Curative Medicine 4 Center for Personal Dynamic Regulome 5 Division of Immunology and Rheumatology, Department of Medicine, 6 Institute for Stem Cell Biology and Regenerative Medicine, Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. 7 CEMS, NCMIS, MDIS, Academy of Mathematics & Systems Science, Chinese Academy of Sciences, Beijing,100080, China 8 Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China *These authors made equal and independent contributions. 9 Correspondence to Lead Contact: Anthony E. Oro at [email protected] bioRxiv preprint doi: https://doi.org/10.1101/349308; this version posted June 18, 2018. -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
Genotyping of Breech Flystrike Resource – Update
Project No: ON-00515 Contract No: PO4500010753 AWI Project Manager: Bridget Peachey Contractor Name: CSIRO Agriculture and Food Prepared by: Dr Sonja Dominik Publication Date: July 2019 Genotyping of breech flystrike resource – update Published by Australian Wool Innovation Limited, Level 6, 68 Harrington Street, THE ROCKS, NSW, 2000 This publication should only be used as a general aid and is not a substitute for specific advice. To the extent permitted by law, we exclude all liability for loss or damage arising from the use of the information in this publication. AWI invests in research, development, innovation and marketing activities along the global supply chain for Australian wool. AWI is grateful for its funding, which is primarily provided by Australian woolgrowers through a wool levy and by the Australian Government which provides a matching contribution for eligible R&D activities © 2019 Australian Wool Innovation Ltd. All rights reserved. Contents Executive Summary .................................................................................................................... 3 1 Introduction/Hypothesis .................................................................................................... 5 2 Literature Review ............................................................................................................... 6 3 Project Objectives .............................................................................................................. 8 4 Success in Achieving Objectives ........................................................................................ -
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. -
Generated by SRI International Pathway Tools Version 25.0, Authors S
An online version of this diagram is available at BioCyc.org. Biosynthetic pathways are positioned in the left of the cytoplasm, degradative pathways on the right, and reactions not assigned to any pathway are in the far right of the cytoplasm. Transporters and membrane proteins are shown on the membrane. Periplasmic (where appropriate) and extracellular reactions and proteins may also be shown. Pathways are colored according to their cellular function. Gcf_000238675-HmpCyc: Bacillus smithii 7_3_47FAA Cellular Overview Connections between pathways are omitted for legibility. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d. -
Determining HDAC8 Substrate Specificity by Noah Ariel Wolfson A
Determining HDAC8 substrate specificity by Noah Ariel Wolfson A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biological Chemistry) in the University of Michigan 2014 Doctoral Committee: Professor Carol A. Fierke, Chair Professor Robert S. Fuller Professor Anna K. Mapp Associate Professor Patrick J. O’Brien Associate Professor Raymond C. Trievel Dedication My thesis is dedicated to all my family, mentors, and friends who made getting to this point possible. ii Table of Contents Dedication ....................................................................................................................................... ii List of Figures .............................................................................................................................. viii List of Tables .................................................................................................................................. x List of Appendices ......................................................................................................................... xi Abstract ......................................................................................................................................... xii Chapter 1 HDAC8 substrates: Histones and beyond ...................................................................... 1 Overview ..................................................................................................................................... 1 HDAC introduction