A Snapshot of the Physical and Functional Wiring of the Eps15 Homology Domain Network in the Nematode
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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. -
A Yeast Phenomic Model for the Gene Interaction Network Modulating
Louie et al. Genome Medicine 2012, 4:103 http://genomemedicine.com/content/4/12/103 RESEARCH Open Access A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis Raymond J Louie3†, Jingyu Guo1,2†, John W Rodgers1, Rick White4, Najaf A Shah1, Silvere Pagant3, Peter Kim3, Michael Livstone5, Kara Dolinski5, Brett A McKinney6, Jeong Hong2, Eric J Sorscher2, Jennifer Bryan4, Elizabeth A Miller3* and John L Hartman IV1,2* Abstract Background: The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically. Methods: To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a ‘phenomic’ model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
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 -
WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T (51) International Patent Classification: David [US/US]; 13539 N . 95th Way, Scottsdale, AZ C12Q 1/68 (2006.01) 85260 (US). (21) International Application Number: (74) Agent: AKHAVAN, Ramin; Caris Science, Inc., 6655 N . PCT/US20 12/0425 19 Macarthur Blvd., Irving, TX 75039 (US). (22) International Filing Date: (81) Designated States (unless otherwise indicated, for every 14 June 2012 (14.06.2012) kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, English (25) Filing Language: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, Publication Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, KR, (30) Priority Data: KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, ME, 61/497,895 16 June 201 1 (16.06.201 1) US MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 61/499,138 20 June 201 1 (20.06.201 1) US OM, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SC, SD, 61/501,680 27 June 201 1 (27.06.201 1) u s SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, 61/506,019 8 July 201 1(08.07.201 1) u s TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in -
Supplementary Material Contents
Supplementary Material Contents Immune modulating proteins identified from exosomal samples.....................................................................2 Figure S1: Overlap between exosomal and soluble proteomes.................................................................................... 4 Bacterial strains:..............................................................................................................................................4 Figure S2: Variability between subjects of effects of exosomes on BL21-lux growth.................................................... 5 Figure S3: Early effects of exosomes on growth of BL21 E. coli .................................................................................... 5 Figure S4: Exosomal Lysis............................................................................................................................................ 6 Figure S5: Effect of pH on exosomal action.................................................................................................................. 7 Figure S6: Effect of exosomes on growth of UPEC (pH = 6.5) suspended in exosome-depleted urine supernatant ....... 8 Effective exosomal concentration....................................................................................................................8 Figure S7: Sample constitution for luminometry experiments..................................................................................... 8 Figure S8: Determining effective concentration ......................................................................................................... -
Genetic Identification of Brain Cell Types Underlying Schizophrenia
bioRxiv preprint doi: https://doi.org/10.1101/145466; this version posted June 2, 2017. 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. Genetic identification of brain cell types underlying schizophrenia Nathan G. Skene 1 †, Julien Bryois 2 †, Trygve E. Bakken3, Gerome Breen 4,5, James J Crowley 6, Héléna A Gaspar 4,5, Paola Giusti-Rodriguez 6, Rebecca D Hodge3, Jeremy A. Miller 3, Ana Muñoz-Manchado 1, Michael C O’Donovan 7, Michael J Owen 7, Antonio F Pardiñas 7, Jesper Ryge 8, James T R Walters 8, Sten Linnarsson 1, Ed S. Lein 3, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Patrick F Sullivan 2,6 *, Jens Hjerling- Leffler 1 * Affiliations: 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 3 Allen Institute for Brain Science, Seattle, Washington 98109, USA. 4 King’s College London, Institute of Psychiatry, Psychology and Neuroscience, MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK. 5 National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, UK. 6 Departments of Genetics, University of North Carolina, Chapel Hill, NC, 27599-7264, USA. 7 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK. -
Molecular Signatures Differentiate Immune States in Type 1 Diabetes Families
Page 1 of 65 Diabetes Molecular signatures differentiate immune states in Type 1 diabetes families Yi-Guang Chen1, Susanne M. Cabrera1, Shuang Jia1, Mary L. Kaldunski1, Joanna Kramer1, Sami Cheong2, Rhonda Geoffrey1, Mark F. Roethle1, Jeffrey E. Woodliff3, Carla J. Greenbaum4, Xujing Wang5, and Martin J. Hessner1 1The Max McGee National Research Center for Juvenile Diabetes, Children's Research Institute of Children's Hospital of Wisconsin, and Department of Pediatrics at the Medical College of Wisconsin Milwaukee, WI 53226, USA. 2The Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA. 3Flow Cytometry & Cell Separation Facility, Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA. 4Diabetes Research Program, Benaroya Research Institute, Seattle, WA, 98101, USA. 5Systems Biology Center, the National Heart, Lung, and Blood Institute, the National Institutes of Health, Bethesda, MD 20824, USA. Corresponding author: Martin J. Hessner, Ph.D., The Department of Pediatrics, The Medical College of Wisconsin, Milwaukee, WI 53226, USA Tel: 011-1-414-955-4496; Fax: 011-1-414-955-6663; E-mail: [email protected]. Running title: Innate Inflammation in T1D Families Word count: 3999 Number of Tables: 1 Number of Figures: 7 1 For Peer Review Only Diabetes Publish Ahead of Print, published online April 23, 2014 Diabetes Page 2 of 65 ABSTRACT Mechanisms associated with Type 1 diabetes (T1D) development remain incompletely defined. Employing a sensitive array-based bioassay where patient plasma is used to induce transcriptional responses in healthy leukocytes, we previously reported disease-specific, partially IL-1 dependent, signatures associated with pre and recent onset (RO) T1D relative to unrelated healthy controls (uHC). -
ID AKI Vs Control Fold Change P Value Symbol Entrez Gene Name *In
ID AKI vs control P value Symbol Entrez Gene Name *In case of multiple probesets per gene, one with the highest fold change was selected. Fold Change 208083_s_at 7.88 0.000932 ITGB6 integrin, beta 6 202376_at 6.12 0.000518 SERPINA3 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3 1553575_at 5.62 0.0033 MT-ND6 NADH dehydrogenase, subunit 6 (complex I) 212768_s_at 5.50 0.000896 OLFM4 olfactomedin 4 206157_at 5.26 0.00177 PTX3 pentraxin 3, long 212531_at 4.26 0.00405 LCN2 lipocalin 2 215646_s_at 4.13 0.00408 VCAN versican 202018_s_at 4.12 0.0318 LTF lactotransferrin 203021_at 4.05 0.0129 SLPI secretory leukocyte peptidase inhibitor 222486_s_at 4.03 0.000329 ADAMTS1 ADAM metallopeptidase with thrombospondin type 1 motif, 1 1552439_s_at 3.82 0.000714 MEGF11 multiple EGF-like-domains 11 210602_s_at 3.74 0.000408 CDH6 cadherin 6, type 2, K-cadherin (fetal kidney) 229947_at 3.62 0.00843 PI15 peptidase inhibitor 15 204006_s_at 3.39 0.00241 FCGR3A Fc fragment of IgG, low affinity IIIa, receptor (CD16a) 202238_s_at 3.29 0.00492 NNMT nicotinamide N-methyltransferase 202917_s_at 3.20 0.00369 S100A8 S100 calcium binding protein A8 215223_s_at 3.17 0.000516 SOD2 superoxide dismutase 2, mitochondrial 204627_s_at 3.04 0.00619 ITGB3 integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) 223217_s_at 2.99 0.00397 NFKBIZ nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, zeta 231067_s_at 2.97 0.00681 AKAP12 A kinase (PRKA) anchor protein 12 224917_at 2.94 0.00256 VMP1/ mir-21likely ortholog -
Mapping Transmembrane Binding Partners for E-Cadherin Ectodomains
SUPPLEMENTARY INFORMATION TITLE: Mapping transmembrane binding partners for E-cadherin ectodomains. AUTHORS: Omer Shafraz 1, Bin Xie 2, Soichiro Yamada 1, Sanjeevi Sivasankar 1, 2, * AFFILIATION: 1 Department of Biomedical Engineering, 2 Biophysics Graduate Group, University of California, Davis, CA 95616. *CORRESPONDING AUTHOR: Sanjeevi Sivasankar, Tel: (530)-754-0840, Email: [email protected] Figure S1: Western blots a. EC-BioID, WT and Ecad-KO cell lysates stained for Ecad and tubulin. b. HRP-streptavidin staining of biotinylated proteins eluted from streptavidin coated magnetic beads incubated with cell lysates of EC-BioID with (+) and without (-) exogenous biotin. c. C-BioID, WT and Ecad-KO cell lysates stained for Ecad and tubulin. d. HRP-streptavidin staining of biotinylated proteins eluted from streptavidin coated magnetic beads incubated with cell lysates of C-BioID with (+) and without (-) exogenous biotin. (+) Biotin (-) Biotin Sample 1 Sample 2 Sample 3 Sample 4 Sample 1 Sample 2 Sample 3 Sample 4 Percent Percent Percent Percent Percent Percent Percent Percent Gene ID Coverage Coverage Coverage Coverage Coverage Coverage Coverage Coverage CDH1 29.6 31.4 41.1 36.5 10.8 6.7 28.8 29.1 DSG2 26 14.6 45 37 0.8 1.9 1.6 18.7 CXADR 30.2 26.2 32.7 27.1 0.0 0.0 0.0 6.9 EFNB1 24.3 30.6 24 30.3 0.0 0.0 0.0 0.0 ITGA2 16.5 22.2 30.1 33.4 1.1 1.1 5.2 7.2 CDH3 21.8 9.7 20.6 25.3 1.3 1.3 0.0 0.0 ITGB1 11.8 16.7 23.9 20.3 0.0 2.9 8.5 5.8 DSC3 9.7 7.5 11.5 13.3 0.0 0.0 2.6 0.0 EPHA2 23.2 31.6 31.6 30.5 0.8 0.0 0.0 5.7 ITGB4 21.8 27.8 33.1 30.7 0.0 1.2 3.9 4.4 ITGB3 23.5 22.2 26.8 24.7 0.0 0.0 5.2 9.1 CDH6 22.8 18.1 28.6 24.3 0.0 0.0 0.0 9.1 CDH17 8.8 12.4 20.7 18.4 0.0 0.0 0.0 0.0 ITGB6 12.7 10.4 14 17.1 0.0 0.0 0.0 1.7 EPHB4 11.4 8.1 14.2 16.3 0.0 0.0 0.0 0.0 ITGB8 5 10 15 17.6 0.0 0.0 0.0 0.0 ITGB5 6.2 9.5 15.2 13.8 0.0 0.0 0.0 0.0 EPHB2 8.5 4.8 9.8 12.1 0.0 0.0 0.0 0.0 CDH24 5.9 7.2 8.3 9 0.0 0.0 0.0 0.0 Table S1: EC-BioID transmembrane protein hits.