Promoter Analysis and Expression in Human Retinal Endothelial Cells Amanda L
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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. -
Screening of a Clinically and Biochemically Diagnosed SOD Patient Using Exome Sequencing: a Case Report with a Mutations/Variations Analysis Approach
The Egyptian Journal of Medical Human Genetics (2016) 17, 131–136 HOSTED BY Ain Shams University The Egyptian Journal of Medical Human Genetics www.ejmhg.eg.net www.sciencedirect.com CASE REPORT Screening of a clinically and biochemically diagnosed SOD patient using exome sequencing: A case report with a mutations/variations analysis approach Mohamad-Reza Aghanoori a,b,1, Ghazaleh Mohammadzadeh Shahriary c,2, Mahdi Safarpour d,3, Ahmad Ebrahimi d,* a Department of Medical Genetics, Shiraz University of Medical Sciences, Shiraz, Iran b Research and Development Division, RoyaBioGene Co., Tehran, Iran c Department of Genetics, Shahid Chamran University of Ahvaz, Ahvaz, Iran d Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Received 12 May 2015; accepted 15 June 2015 Available online 22 July 2015 KEYWORDS Abstract Background: Sulfite oxidase deficiency (SOD) is a rare neurometabolic inherited disor- Sulfite oxidase deficiency; der causing severe delay in developmental stages and premature death. The disease follows an auto- Case report; somal recessive pattern of inheritance and causes deficiency in the activity of sulfite oxidase, an Exome sequencing enzyme that normally catalyzes conversion of sulfite to sulfate. Aim of the study: SOD is an underdiagnosed disorder and its diagnosis can be difficult in young infants as early clinical features and neuroimaging changes may imitate some common diseases. Since the prognosis of the disease is poor, using exome sequencing as a powerful and efficient strat- egy for identifying the genes underlying rare mendelian disorders can provide important knowledge about early diagnosis, disease mechanisms, biological pathways, and potential therapeutic targets. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Gene List HTG Edgeseq Immuno-Oncology Assay
Gene List HTG EdgeSeq Immuno-Oncology Assay Adhesion ADGRE5 CLEC4A CLEC7A IBSP ICAM4 ITGA5 ITGB1 L1CAM MBL2 SELE ALCAM CLEC4C DST ICAM1 ITGA1 ITGA6 ITGB2 LGALS1 MUC1 SVIL CDH1 CLEC5A EPCAM ICAM2 ITGA2 ITGAL ITGB3 LGALS3 NCAM1 THBS1 CDH5 CLEC6A FN1 ICAM3 ITGA4 ITGAM ITGB4 LGALS9 PVR THY1 Apoptosis APAF1 BCL2 BID CARD11 CASP10 CASP8 FADD NOD1 SSX1 TP53 TRAF3 BCL10 BCL2L1 BIRC5 CASP1 CASP3 DDX58 NLRP3 NOD2 TIMP1 TRAF2 TRAF6 B-Cell Function BLNK BTLA CD22 CD79A FAS FCER2 IKBKG PAX5 SLAMF1 SLAMF7 SPN BTK CD19 CD24 EBF4 FASLG IKBKB MS4A1 RAG1 SLAMF6 SPI1 Cell Cycle ABL1 ATF1 ATM BATF CCND1 CDK1 CDKN1B NCL RELA SSX1 TBX21 TP53 ABL2 ATF2 AXL BAX CCND3 CDKN1A EGR1 REL RELB TBK1 TIMP1 TTK Cell Signaling ADORA2A DUSP4 HES1 IGF2R LYN MAPK1 MUC1 NOTCH1 RIPK2 SMAD3 STAT5B AKT3 DUSP6 HES5 IKZF1 MAF MAPK11 MYC PIK3CD RNF4 SOCS1 STAT6 BCL6 ELK1 HEY1 IKZF2 MAP2K1 MAPK14 NFATC1 PIK3CG RORC SOCS3 SYK CEBPB EP300 HEY2 IKZF3 MAP2K2 MAPK3 NFATC3 POU2F2 RUNX1 SPINK5 TAL1 CIITA ETS1 HEYL JAK1 MAP2K4 MAPK8 NFATC4 PRKCD RUNX3 STAT1 TCF7 CREB1 FLT3 HMGB1 JAK2 MAP2K7 MAPKAPK2 NFKB1 PRKCE S100B STAT2 TYK2 CREB5 FOS HRAS JAK3 MAP3K1 MEF2C NFKB2 PTEN SEMA4D STAT3 CREBBP GATA3 IGF1R KIT MAP3K5 MTDH NFKBIA PYCARD SMAD2 STAT4 Chemokine CCL1 CCL16 CCL20 CCL25 CCL4 CCR2 CCR7 CX3CL1 CXCL12 CXCL3 CXCR1 CXCR6 CCL11 CCL17 CCL21 CCL26 CCL5 CCR3 CCR9 CX3CR1 CXCL13 CXCL5 CXCR2 MST1R CCL13 CCL18 CCL22 CCL27 CCL7 CCR4 CCRL2 CXCL1 CXCL14 CXCL6 CXCR3 PPBP CCL14 CCL19 CCL23 CCL28 CCL8 CCR5 CKLF CXCL10 CXCL16 CXCL8 CXCR4 XCL2 CCL15 CCL2 CCL24 CCL3 CCR1 CCR6 CMKLR1 CXCL11 CXCL2 CXCL9 CXCR5 -
High-Throughput Bioinformatics Approaches to Understand Gene Expression Regulation in Head and Neck Tumors
High-throughput bioinformatics approaches to understand gene expression regulation in head and neck tumors by Yanxiao Zhang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in The University of Michigan 2016 Doctoral Committee: Associate Professor Maureen A. Sartor, Chair Professor Thomas E. Carey Assistant Professor Hui Jiang Professor Ronald J. Koenig Associate Professor Laura M. Rozek Professor Kerby A. Shedden c Yanxiao Zhang 2016 All Rights Reserved I dedicate this thesis to my family. For their unfailing love, understanding and support. ii ACKNOWLEDGEMENTS I would like to express my gratitude to Dr. Maureen Sartor for her guidance in my research and career development. She is a great mentor. She patiently taught me when I started new in this field, granted me freedom to explore and helped me out when I got lost. Her dedication to work, enthusiasm in teaching, mentoring and communicating science have inspired me to feel the excite- ment of research beyond novel scientific discoveries. I’m also grateful to have an interdisciplinary committee. Their feedback on my research progress and presentation skills is very valuable. In particular, I would like to thank Dr. Thomas Carey and Dr. Laura Rozek for insightful discussions on the biology of head and neck cancers and human papillomavirus, Dr. Ronald Koenig for expert knowledge on thyroid cancers, Dr. Hui Jiang and Dr. Kerby Shedden for feedback on the statistics part of my thesis. I would like to thank all the past and current members of Sartor lab for making the lab such a lovely place to stay and work in. -
Rabbit Anti-Phospho-CD18-SL10462R-FITC
SunLong Biotech Co.,LTD Tel: 0086-571- 56623320 Fax:0086-571- 56623318 E-mail:[email protected] www.sunlongbiotech.com Rabbit Anti-Phospho-CD18 SL10462R-FITC Product Name: Anti-Phospho-CD18 (Thr758)/FITC Chinese Name: FITC标记的磷酸化整合素β2/Integrin β2抗体 CD18 (Phospho Thr758); CD18 (Phospho-Thr758); CD18 (Phospho T758); p-CD18 (T758); p-CD18 (Thr758); Integrin beta 2; 95 subunit beta; CD 18; CD18; Cell surface adhesion glycoprotein LFA 1/CR3/P150,959 beta subunit precursor); Cell surface adhesion glycoproteins LFA 1/CR3/p150,95 subunit beta; Cell surface adhesion glycoproteins LFA-1/CR3/p150; Complement receptor C3 beta subunit; Complement Alias: receptor C3 subunit beta; Integrin beta chain beta 2; Integrin beta-2; Integrin, beta 2 (complement component 3 receptor 3 and 4 subunit); ITB2_HUMAN; ITGB2; LAD; LCAMB; Leukocyte associated antigens CD18/11A, CD18/11B, CD18/11C; Leukocyte cell adhesion molecule CD18; LFA 1; LFA1; Lymphocyte function associated antigen 1; MAC 1; MAC1; MF17; MFI7; OTTHUMP00000115278; OTTHUMP00000115279; OTTHUMP00000115280; OTTHUMP00000115281; OTTHUMP00000115282. Organism Species: Rabbit Clonality: Polyclonal React Species: Human,Mouse,Rat,Chicken,Dog,Pig,Cow,Horse,Rabbit,Sheep,Guinea Pig, Flow-Cyt=1:50-200ICC=1:50-200IF=1:50-200www.sunlongbiotech.com Applications: not yet tested in other applications. optimal dilutions/concentrations should be determined by the end user. Molecular weight: 82kDa Form: Lyophilized or Liquid Concentration: 1mg/ml KLH conjugated synthesised phosphopeptide derived from human CD18 around the immunogen: phosphorylation site of Thr758 Lsotype: IgG Purification: affinity purified by Protein A Storage Buffer: 0.01M TBS(pH7.4) with 1% BSA, 0.03% Proclin300 and 50% Glycerol. Store at -20 °C for one year. -
Anti-ICAM4 Monoclonal Antibody (DCABH-11966) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
Anti-ICAM4 monoclonal antibody (DCABH-11966) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Antigen Description This gene encodes the Landsteiner-Wiener (LW) blood group antigen(s) that belongs to the immunoglobulin (Ig) superfamily, and that shares similarity with the intercellular adhesion molecule (ICAM) protein family. This ICAM protein contains 2 Ig-like C2-type domains and binds to the leukocyte adhesion LFA-1 protein. The molecular basis of the LW(A)/LW(B) blood group antigens is a single aa variation at position 100; Gln-100=LW(A) and Arg-100=LW(B). Alternative splicing results in multiple transcript variants encoding distinct isoforms. Immunogen A synthetic peptide of human ICAM4 is used for rabbit immunization. Isotype IgG Source/Host Rabbit Species Reactivity Human Purification Protein A Conjugate Unconjugated Applications Western Blot (Transfected lysate); ELISA Size 1 ea Buffer In 1x PBS, pH 7.4 Preservative None Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. GENE INFORMATION Gene Name ICAM4 intercellular adhesion molecule 4 (Landsteiner-Wiener blood group) [ Homo sapiens ] Official Symbol ICAM4 Synonyms ICAM4; intercellular adhesion molecule 4 (Landsteiner-Wiener blood group); intercellular adhesion molecule 4 (LW blood group) , intercellular adhesion molecule 4, Landsteiner Wiener blood group , Landsteiner Wiener blood group , LW; intercellular adhesion molecule 4; CD242; CD242 antigen; LW blood group protein; Landsteiner-Wiener blood group -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
Genetic Testing Policy Number: PG0041 ADVANTAGE | ELITE | HMO Last Review: 04/11/2021
Genetic Testing Policy Number: PG0041 ADVANTAGE | ELITE | HMO Last Review: 04/11/2021 INDIVIDUAL MARKETPLACE | PROMEDICA MEDICARE PLAN | PPO GUIDELINES This policy does not certify benefits or authorization of benefits, which is designated by each individual policyholder terms, conditions, exclusions and limitations contract. It does not constitute a contract or guarantee regarding coverage or reimbursement/payment. Paramount applies coding edits to all medical claims through coding logic software to evaluate the accuracy and adherence to accepted national standards. This medical policy is solely for guiding medical necessity and explaining correct procedure reporting used to assist in making coverage decisions and administering benefits. SCOPE X Professional X Facility DESCRIPTION A genetic test is the analysis of human DNA, RNA, chromosomes, proteins, or certain metabolites in order to detect alterations related to a heritable or acquired disorder. This can be accomplished by directly examining the DNA or RNA that makes up a gene (direct testing), looking at markers co-inherited with a disease-causing gene (linkage testing), assaying certain metabolites (biochemical testing), or examining the chromosomes (cytogenetic testing). Clinical genetic tests are those in which specimens are examined and results reported to the provider or patient for the purpose of diagnosis, prevention or treatment in the care of individual patients. Genetic testing is performed for a variety of intended uses: Diagnostic testing (to diagnose disease) Predictive -
Rna-Sequencing Applications: Gene Expression Quantification and Methylator Phenotype Identification
The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 8-2013 RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION Guoshuai Cai Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Computational Biology Commons, and the Medicine and Health Sciences Commons Recommended Citation Cai, Guoshuai, "RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION" (2013). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 386. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/386 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION -
Cep-2020-00633.Pdf
Clin Exp Pediatr Vol. 64, No. 5, 208–222, 2021 Review article CEP https://doi.org/10.3345/cep.2020.00633 Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis Seoung Wan Nam, MD, PhD1,*, Kwang Seob Lee, MD2,*, Jae Won Yang, MD, PhD3,*, Younhee Ko, PhD4, Michael Eisenhut, MD, FRCP, FRCPCH, DTM&H5, Keum Hwa Lee, MD, MS6,7,8, Jae Il Shin, MD, PhD6,7,8, Andreas Kronbichler, MD, PhD9 1Department of Rheumatology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea; 2Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; 3Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Korea; 4Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea; 5Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, UK; 6Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea; 7Division of Pediatric Nephrology, Severance Children’s Hospital, Seoul, Korea; 8Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea; 9Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria 1,3) The publication of genetic epidemiology meta-analyses has analyses have redundant duplicate topics and many errors. increased rapidly, but it has been suggested that many of the Although there has been an impressive increase in meta-analyses statistically significant results are false positive. In addition, from China, particularly those on genetic associa tions, most most such meta-analyses have been redundant, duplicate, and claimed candidate gene associations are likely false-positives, erroneous, leading to research waste. In addition, since most suggesting an urgent global need to incorporate genome-wide claimed candidate gene associations were false-positives, cor- data and state-of-the art statistical inferences to avoid a flood of rectly interpreting the published results is important. -
Comparative Transcriptomics of Primary Cells in Vertebrates
Edinburgh Research Explorer Comparative transcriptomics of primary cells in vertebrates Citation for published version: Alam, T, Agrawal, S, Severin, J, Young, R, Andersson, R, Amer, E, Hasegawa, A, Lizio, M, Ramilowski, J, Abugessaisa, I, Ishizu, Y, Noma, S, Tarui, H, Taylor, MS, Lassmann, T, Itoh, M, Kasukawa, T, Kawaji, H, Marchionni, L, Sheng, G, Forrest, ARR, Khachigian, LM, Hayashizaki, Y, Carninci, P & De Hoon, M 2020, 'Comparative transcriptomics of primary cells in vertebrates', Genome Research. https://doi.org/10.1101/gr.255679.119 Digital Object Identifier (DOI): 10.1101/gr.255679.119 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Genome Research Publisher Rights Statement: is article, published inGenome Research, is avail-able under a Creative Commons License (Attribution 4.0 Internatio General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 30. Sep. 2021 1 1 Comparative transcriptomics of primary cells in vertebrates 2 Tanvir Alam1, Saumya Agrawal2, Jessica Severin2, Robert S. Young3,4, Robin Andersson5, Erik 3 Arner2, Akira Hasegawa2, Marina Lizio2, Jordan Ramilowski2, Imad Abugessaisa2, Yuri Ishizu6, 4 Shohei Noma2, Hiroshi Tarui7, Martin S.