Identification of a Novel Deletion Region in 3Q29 Microdeletion Syndrome by Oligonucleotide Array Comparative Genomic Hybridization
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Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
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PROBING THE INTERACTION OF ASPERGILLUS FUMIGATUS CONIDIA AND HUMAN AIRWAY EPITHELIAL CELLS BY TRANSCRIPTIONAL PROFILING IN BOTH SPECIES by POL GOMEZ B.Sc., The University of British Columbia, 2002 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2010 © Pol Gomez, 2010 ABSTRACT The cells of the airway epithelium play critical roles in host defense to inhaled irritants, and in asthma pathogenesis. These cells are constantly exposed to environmental factors, including the conidia of the ubiquitous mould Aspergillus fumigatus, which are small enough to reach the alveoli. A. fumigatus is associated with a spectrum of diseases ranging from asthma and allergic bronchopulmonary aspergillosis to aspergilloma and invasive aspergillosis. Airway epithelial cells have been shown to internalize A. fumigatus conidia in vitro, but the implications of this process for pathogenesis remain unclear. We have developed a cell culture model for this interaction using the human bronchial epithelium cell line 16HBE and a transgenic A. fumigatus strain expressing green fluorescent protein (GFP). Immunofluorescent staining and nystatin protection assays indicated that cells internalized upwards of 50% of bound conidia. Using fluorescence-activated cell sorting (FACS), cells directly interacting with conidia and cells not associated with any conidia were sorted into separate samples, with an overall accuracy of 75%. Genome-wide transcriptional profiling using microarrays revealed significant responses of 16HBE cells and conidia to each other. Significant changes in gene expression were identified between cells and conidia incubated alone versus together, as well as between GFP positive and negative sorted cells. -
Knowledge Management Enviroments for High Throughput Biology
Knowledge Management Enviroments for High Throughput Biology Abhey Shah A Thesis submitted for the degree of MPhil Biology Department University of York September 2007 Abstract With the growing complexity and scale of data sets in computational biology and chemoin- formatics, there is a need for novel knowledge processing tools and platforms. This thesis describes a newly developed knowledge processing platform that is different in its emphasis on architecture, flexibility, builtin facilities for datamining and easy cross platform usage. There exist thousands of bioinformatics and chemoinformatics databases, that are stored in many different forms with different access methods, this is a reflection of the range of data structures that make up complex biological and chemical data. Starting from a theoretical ba- sis, FCA (Formal Concept Analysis) an applied branch of lattice theory, is used in this thesis to develop a file system that automatically structures itself by it’s contents. The procedure of extracting concepts from data sets is examined. The system also finds appropriate labels for the discovered concepts by extracting data from ontological databases. A novel method for scaling non-binary data for use with the system is developed. Finally the future of integrative systems biology is discussed in the context of efficiently closed causal systems. Contents 1 Motivations and goals of the thesis 11 1.1 Conceptual frameworks . 11 1.2 Biological foundations . 12 1.2.1 Gene expression data . 13 1.2.2 Ontology . 14 1.3 Knowledge based computational environments . 15 1.3.1 Interfaces . 16 1.3.2 Databases and the character of biological data . -
Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. -
Table S4. Trophoblast Differentiation-Associated Genes
Table S4. Trophoblast differentiation-associated genes Gene Stem Chromosomal Affymetrix ID Gene Title Symbol Ave Dif Ave GenBank Location dif/ stem t-test 1390511_at LOC308394 Cgm4 10 1863 BI285801 1 193.51 0.006 1378534_at similar to brain carcinoembryonic antigen LOC308394 10 1746 NM_001025679 1q21 183.10 0.001 1388433_at keratin complex 1, acidic, gene 19 Krt1-19 53 7958 NM_199498 10q32.1 149.07 0.022 1369029_at phospholipid scramblase 1 Plscr1 20 2050 NM_057194 8q31 101.12 0.028 1389856_at carcinoembryonic antigen gene family 4 Cgm4 57 5073 NM_012525 1q21 89.73 0.001 carcinoembryonic antigen-related cell 1368996_at adhesion molecule 3 Ceacam3 202 17879 NM_012702 1q21 88.71 0.001 1392832_at similar to angiopoietin-like 1 LOC684489 17 1398 XM_001068284 --- 83.66 0.001 1377666_at choline dehydrogenase Chdh 26 1571 NM_198731 16p16 60.59 0.003 cytochrome P450, family 11, subfamily a, 1368468_at polypeptide 1 Cyp11a1 196 9216 NM_017286 8q24 47.11 0.000 1376934_x_at similar to brain carcinoembryonic antigen Cgm4 53 2146 BI285801 1 40.22 0.000 stimulated by retinoic acid gene 6 homolog 1390525_a_at (mouse) Stra6 28 1037 NM_001029924 8q24 37.44 0.001 1382690_at carcinoembryonic antigen gene family 4 Cgm4 81 2729 NM_012525 1q21 33.86 0.001 1367809_at prolactin family 4, subfamily a, member 1 Prl4a1 683 22573 NM_017036 17p11 33.05 0.004 calcium channel, voltage-dependent, L type, 1383458_at alpha 1D subunit Cacna1d 26 802 BF403759 16 30.89 0.001 1370852_at spleen protein 1 precursor LOC171573 692 20968 NM_138537 8q21 30.29 0.003 1376036_at transporter -
Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing -
Table SII. Significantly Differentially Expressed Mrnas of GSE23558 Data Series with the Criteria of Adjusted P<0.05 And
Table SII. Significantly differentially expressed mRNAs of GSE23558 data series with the criteria of adjusted P<0.05 and logFC>1.5. Probe ID Adjusted P-value logFC Gene symbol Gene title A_23_P157793 1.52x10-5 6.91 CA9 carbonic anhydrase 9 A_23_P161698 1.14x10-4 5.86 MMP3 matrix metallopeptidase 3 A_23_P25150 1.49x10-9 5.67 HOXC9 homeobox C9 A_23_P13094 3.26x10-4 5.56 MMP10 matrix metallopeptidase 10 A_23_P48570 2.36x10-5 5.48 DHRS2 dehydrogenase A_23_P125278 3.03x10-3 5.40 CXCL11 C-X-C motif chemokine ligand 11 A_23_P321501 1.63x10-5 5.38 DHRS2 dehydrogenase A_23_P431388 2.27x10-6 5.33 SPOCD1 SPOC domain containing 1 A_24_P20607 5.13x10-4 5.32 CXCL11 C-X-C motif chemokine ligand 11 A_24_P11061 3.70x10-3 5.30 CSAG1 chondrosarcoma associated gene 1 A_23_P87700 1.03x10-4 5.25 MFAP5 microfibrillar associated protein 5 A_23_P150979 1.81x10-2 5.25 MUCL1 mucin like 1 A_23_P1691 2.71x10-8 5.12 MMP1 matrix metallopeptidase 1 A_23_P350005 2.53x10-4 5.12 TRIML2 tripartite motif family like 2 A_24_P303091 1.23x10-3 4.99 CXCL10 C-X-C motif chemokine ligand 10 A_24_P923612 1.60x10-5 4.95 PTHLH parathyroid hormone like hormone A_23_P7313 6.03x10-5 4.94 SPP1 secreted phosphoprotein 1 A_23_P122924 2.45x10-8 4.93 INHBA inhibin A subunit A_32_P155460 6.56x10-3 4.91 PICSAR P38 inhibited cutaneous squamous cell carcinoma associated lincRNA A_24_P686965 8.75x10-7 4.82 SH2D5 SH2 domain containing 5 A_23_P105475 7.74x10-3 4.70 SLCO1B3 solute carrier organic anion transporter family member 1B3 A_24_P85099 4.82x10-5 4.67 HMGA2 high mobility group AT-hook 2 A_24_P101651 -
1 Novel Expression Signatures Identified by Transcriptional Analysis
ARD Online First, published on October 7, 2009 as 10.1136/ard.2009.108043 Ann Rheum Dis: first published as 10.1136/ard.2009.108043 on 7 October 2009. Downloaded from Novel expression signatures identified by transcriptional analysis of separated leukocyte subsets in SLE and vasculitis 1Paul A Lyons, 1Eoin F McKinney, 1Tim F Rayner, 1Alexander Hatton, 1Hayley B Woffendin, 1Maria Koukoulaki, 2Thomas C Freeman, 1David RW Jayne, 1Afzal N Chaudhry, and 1Kenneth GC Smith. 1Cambridge Institute for Medical Research and Department of Medicine, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK 2Roslin Institute, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK Correspondence should be addressed to Dr Paul Lyons or Prof Kenneth Smith, Department of Medicine, Cambridge Institute for Medical Research, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK. Telephone: +44 1223 762642, Fax: +44 1223 762640, E-mail: [email protected] or [email protected] Key words: Gene expression, autoimmune disease, SLE, vasculitis Word count: 2,906 The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in Annals of the Rheumatic Diseases and any other BMJPGL products to exploit all subsidiary rights, as set out in their licence (http://ard.bmj.com/ifora/licence.pdf). http://ard.bmj.com/ on September 29, 2021 by guest. Protected copyright. 1 Copyright Article author (or their employer) 2009. -
Fernanda Caroline Dos Santos Belo Horizonte 2019
UNIVERSIDADE FEDERAL DE MINAS GERAIS Instituto de Ciências Biológicas Programa de Pós-graduação em Genética Fernanda Caroline dos Santos CONTRIBUIÇÃO DE POLIMORFISMOS FUNCIONAIS EM GENES DO SISTEMA MONOAMINÉRGICO PARA A ANSIEDADE MATEMÁTICA EM CRIANÇAS EM IDADE ESCOLAR Belo Horizonte 2019 ii Fernanda Caroline Dos Santos CONTRIBUIÇÃO DE POLIMORFISMOS FUNCIONAIS EM GENES DO SISTEMA MONOAMINÉRGICO PARA A ANSIEDADE MATEMÁTICA Tese apresentada ao Programa de Pós-Graduação em Genética da Universidade Federal de Minas Gerais, como requisito parcial para obtenção do grau de Doutor em Genética. Área de Concentração: Genômica e Bioinformática Orientadora: Profa. Dra. Maria Raquel Santos Carvalho Belo Horizonte 2019 iii iv v Aos meus pais, Soraia e Maikson, que com amor e simplicidade me ensinaram os verdadeiros valores da vida. Tudo o que sou hoje, eu devo a eles Ao meu irmão, Filipe, pelo amor, amizade, companheirismo e torcida Aos meus avós, Yeda, Nilo e Wilma, por todo incentivo, por sempre acreditar em mim e por terem me criado com tanto amor Às crianças com dificuldade de aprendizagem e às suas famílias Dedico. vi “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more so that we may fear less.” - Marie Skłodowska-Curie “Don't let anyone rob you of your imagination, your creativity, or your curiosity. It's your place in the world; it's your life. Go on and do all you can with it, and make it the life you want to live.” - Mae Jemison "Courage is like — it’s a habitus, a habit, a virtue: you get it by courageous acts. -
Table S3a Table
Table S3a C2 KEGG Geneset Genesets enriched and upregulated in responders (FDR <0.25) Genesets enriched and upregulated in non-responders (FDR <0.25) HSA04610_COMPLEMENT_AND_COAGULATION_CASCADES HSA00970_AMINOACYL_TRNA_BIOSYNTHESIS HSA04640_HEMATOPOIETIC_CELL_LINEAGE HSA05050_DENTATORUBROPALLIDOLUYSIAN_ATROPHY HSA04060_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION HSA04514_CELL_ADHESION_MOLECULES HSA04650_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY HSA04630_JAK_STAT_SIGNALING_PATHWAY HSA03320_PPAR_SIGNALING_PATHWAY HSA04080_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION HSA00980_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 HSA00071_FATTY_ACID_METABOLISM HSA04660_T_CELL_RECEPTOR_SIGNALING_PATHWAY HSA04612_ANTIGEN_PROCESSING_AND_PRESENTATION HSA04662_B_CELL_RECEPTOR_SIGNALING_PATHWAY HSA04920_ADIPOCYTOKINE_SIGNALING_PATHWAY HSA00120_BILE_ACID_BIOSYNTHESIS HSA04670_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION HSA00641_3_CHLOROACRYLIC_ACID_DEGRADATION HSA04020_CALCIUM_SIGNALING_PATHWAY HSA04940_TYPE_I_DIABETES_MELLITUS HSA04512_ECM_RECEPTOR_INTERACTION HSA00010_GLYCOLYSIS_AND_GLUCONEOGENESIS HSA02010_ABC_TRANSPORTERS_GENERAL HSA04664_FC_EPSILON_RI_SIGNALING_PATHWAY HSA04710_CIRCADIAN_RHYTHM HSA04510_FOCAL_ADHESION HSA04810_REGULATION_OF_ACTIN_CYTOSKELETON HSA00410_BETA_ALANINE_METABOLISM HSA01040_POLYUNSATURATED_FATTY_ACID_BIOSYNTHESIS HSA00532_CHONDROITIN_SULFATE_BIOSYNTHESIS HSA04620_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY HSA04010_MAPK_SIGNALING_PATHWAY HSA00561_GLYCEROLIPID_METABOLISM HSA00053_ASCORBATE_AND_ALDARATE_METABOLISM HSA00590_ARACHIDONIC_ACID_METABOLISM