MAGI3 (NM 152900) Human Mass Spec Standard Product Data
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Genetic Analysis of Retinopathy in Type 1 Diabetes
Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194). -
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. -
Signaling Pathway Activities Improve Prognosis for Breast Cancer Yunlong Jiao1,2,3,4, Marta R
bioRxiv preprint doi: https://doi.org/10.1101/132357; this version posted April 29, 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 4.0 International license. Signaling Pathway Activities Improve Prognosis for Breast Cancer Yunlong Jiao1,2,3,4, Marta R. Hidalgo5, Cankut Çubuk6, Alicia Amadoz5, José Carbonell- Caballero5, Jean-Philippe Vert1,2,3,4, and Joaquín Dopazo6,7,8,* 1MINES ParisTech, PSL Research University, Centre for Computational Biology, 77300 Fontainebleau, France; 2Institut Curie, 75248 Paris Cedex, Franc; 3INSERM, U900, 75248 Paris Cedex, France; 4Ecole Normale Supérieure, Department of Mathematics and their Applications, 75005 Paris, France; 5 Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), 46012 Valencia, Spain; 6Clinical Bioinformatics Research Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013, Sevilla, Spain; 7Functional Genomics Node (INB), FPS, Hospital Virgen del Rocío, 41013 Sevilla, Spain; 8 Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013, Sevilla, Spain *To whom correspondence should be addressed. Abstract With the advent of high-throughput technologies for genome-wide expression profiling, a large number of methods have been proposed to discover gene-based signatures as biomarkers to guide cancer prognosis. However, it is often difficult to interpret the list of genes in a prognostic signature regarding the underlying biological processes responsible for disease progression or therapeutic response. A particularly interesting alternative to gene-based biomarkers is mechanistic biomarkers, derived from signaling pathway activities, which are known to play a key role in cancer progression and thus provide more informative insights into cellular functions involved in cancer mechanism. -
Supplementary Tables S1-S3
Supplementary Table S1: Real time RT-PCR primers COX-2 Forward 5’- CCACTTCAAGGGAGTCTGGA -3’ Reverse 5’- AAGGGCCCTGGTGTAGTAGG -3’ Wnt5a Forward 5’- TGAATAACCCTGTTCAGATGTCA -3’ Reverse 5’- TGTACTGCATGTGGTCCTGA -3’ Spp1 Forward 5'- GACCCATCTCAGAAGCAGAA -3' Reverse 5'- TTCGTCAGATTCATCCGAGT -3' CUGBP2 Forward 5’- ATGCAACAGCTCAACACTGC -3’ Reverse 5’- CAGCGTTGCCAGATTCTGTA -3’ Supplementary Table S2: Genes synergistically regulated by oncogenic Ras and TGF-β AU-rich probe_id Gene Name Gene Symbol element Fold change RasV12 + TGF-β RasV12 TGF-β 1368519_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 42.22 5.53 75.28 1373000_at sushi-repeat-containing protein, X-linked 2 (predicted) Srpx2 19.24 25.59 73.63 1383486_at Transcribed locus --- ARE 5.93 27.94 52.85 1367581_a_at secreted phosphoprotein 1 Spp1 2.46 19.28 49.76 1368359_a_at VGF nerve growth factor inducible Vgf 3.11 4.61 48.10 1392618_at Transcribed locus --- ARE 3.48 24.30 45.76 1398302_at prolactin-like protein F Prlpf ARE 1.39 3.29 45.23 1392264_s_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 24.92 3.67 40.09 1391022_at laminin, beta 3 Lamb3 2.13 3.31 38.15 1384605_at Transcribed locus --- 2.94 14.57 37.91 1367973_at chemokine (C-C motif) ligand 2 Ccl2 ARE 5.47 17.28 37.90 1369249_at progressive ankylosis homolog (mouse) Ank ARE 3.12 8.33 33.58 1398479_at ryanodine receptor 3 Ryr3 ARE 1.42 9.28 29.65 1371194_at tumor necrosis factor alpha induced protein 6 Tnfaip6 ARE 2.95 7.90 29.24 1386344_at Progressive ankylosis homolog (mouse) -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Human-Specific NOTCH-Like Genes in a Region Linked to Neurodevelopmental Disorders Affect Cortical Neurogenesis
bioRxiv preprint doi: https://doi.org/10.1101/221226; this version posted November 17, 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 4.0 International license. Human-specific NOTCH-like genes in a region linked to neurodevelopmental disorders affect cortical neurogenesis Authors: Ian T Fiddes1,12, Gerrald A Lodewijk2,12, Meghan Mooring1, Colleen M Bosworth1, Adam D Ewing1#, Gary L Mantalas1,3, Adam M Novak1, Anouk van den Bout2, Alex Bishara4, Jimi L Rosenkrantz1,5, Ryan Lorig-Roach1, Andrew R Field1,3, Maximilian Haeussler1, Lotte Russo2, Aparna Bhaduri6, Tomasz J. Nowakowski6, Alex A. Pollen6, Max L. Dougherty7, Xander Nuttle8, Marie-Claude Addor9, Simon Zwolinski10, Sol Katzman1, Arnold Kriegstein6, Evan E. Eichler7,11, Sofie R Salama1,5,13, Frank MJ Jacobs1,2,13.14*, David Haussler1,5,13,14* Affiliations: 1 UC Santa Cruz Genomics Institute, Santa Cruz, California, United States of America, 2 University of Amsterdam, Swammerdam Institute for Life Sciences, Amsterdam, The Netherlands 3 Molecular, Cell and Developmental Biology, of California Santa Cruz, Santa Cruz, California, United States of America 4 Department of Computer Science and Department of Medicine, Division of Hematology, Stanford University, California, USA 5Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California, United States of America 6The Eli and Edythe Broad -
PB #ISAG2017 1 @Isagofficial #ISAG2017 #ISAG2017
Bioinformatics · Comparative Genomics · Computational Biology Epigenetics · Functional Genomics · Genome Diversity · Geno Genome Sequencing · Immunogenetics · Integrative Geno · Microbiomics · Population Genomics · Systems Biolog Genetic Markers and Selection · Genetics and Dis Gene Editing · Bioinformatics · Comparative Computational Biology · Epigenetics · Fun Genome Diversity · Genome Sequeng Integrative Genomics · Microbiom Population Genomics · Syste Genetic Markers and Sel Genetics and Disease Gene Editing · Bi O’Brien Centre for Science Bioinformati and O’Reilly Hall, University College Dublin, Dublin, Ireland ABSTRACTMINI PROGRAMME BOOK www.isag.us/2017 PB #ISAG2017 1 @isagofficial #ISAG2017 #ISAG2017 Contents ORAL PRESENTATIONS 1 Animal Forensic Genetics Workshop 1 Applied Genetics and Genomics in Other Species of Economic Importance 3 Domestic Animal Sequencing and Annotation 5 Genome Edited Animals 8 Horse Genetics and Genomics 9 Avian Genetics and Genomics 12 Comparative MHC Genetics: Populations and Polymorphism 16 Equine Genetics and Thoroughbred Parentage Testing Workshop 19 Genetics of Immune Response and Disease Resistance 20 ISAG-FAO Genetic Diversity 24 Ruminant Genetics and Genomics 28 Animal Epigenetics 31 Cattle Molecular Markers and Parentage Testing 33 Companion Animal Genetics and Genomics 34 Microbiomes 37 Pig Genetics and Genomics 40 Novel, Groundbreaking Research/Methodology Presentation 44 Applied Genetics of Companion Animals 44 Applied Sheep and Goat Genetics 45 Comparative and Functional Genomics 47 Genetics -
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ORIGINAL ARTICLE PTPN22 Trp620 Explains the Association of Chromosome 1p13 With Type 1 Diabetes and Shows a Statistical Interaction With HLA Class II Genotypes Deborah J. Smyth, Jason D. Cooper, Joanna M.M. Howson, Neil M. Walker, Vincent Plagnol, Helen Stevens, David G. Clayton, and John A. Todd 620 OBJECTIVE—The disease association of the common heterogeneity of rs2476601/Trp disease risk by HLA class II 1858CϾT Arg620Trp (rs2476601) nonsynonymous single nucleo- genotype is consistent with previous studies, and the joint effect tide polymorphism (SNP) of protein tyrosine phosphatase; of the two loci is still greater in the high-risk group. Diabetes 57: nonreceptor type 22 (PTPN22) on chromosome 1p13 has been 1730–1737, 2008 confirmed in type 1 diabetes and also in other autoimmune diseases, including rheumatoid arthritis and Graves’ disease. Some studies have reported additional associated SNPs indepen- o date, there are 10 loci with confirmed evidence dent of rs2476601/Trp620, suggesting that it may not be the sole causal variant in the region and that the relative risk of for association with type 1 diabetes: the major rs2476601/Trp620 is greater in lower risk by HLA class II geno- histocompatibility complex (MHC) HLA class I types than in the highest risk class II risk category. Tand II genes (1,2), insulin (3,4), the CTLA4 locus (5,6), protein tyrosine phosphatase;nonreceptor type 22 RESEARCH DESIGN AND METHODS—We resequenced ␣ Ͼ (PTPN22; 7), interleukin-2 receptor-2 chain (IL2RA) PTPN22 and used these and other data to provide 150 SNPs to (8–10), interferon induced with helicase C domain 1 evaluate the association of the PTPN22 gene and its flanking chromosome region with type 1 diabetes in a minimum of 2,000 (IFIH1) (11), and the regions on chromosomes 12q24, case subjects and 2,400 control subjects. -
PTEN-Opathies and Precision Medicine
25 8 Endocrine-Related L Yehia and C Eng PTEN-opathies and precision 25:8 T121–T140 Cancer medicine THEMATIC REVIEW 65 YEARS OF THE DOUBLE HELIX One gene, many endocrine and metabolic syndromes: PTEN-opathies and precision medicine Lamis Yehia1 and Charis Eng1,2,3,4 1Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA 2Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA 3Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA 4Germline High Risk Cancer Focus Group, CASE Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA Correspondence should be addressed to C Eng: [email protected] This paper is part of a thematic review section celebrating 65 Years of the Double Helix. The guest editors for this section were Charis Eng, William Foulkes and Jérôme Bertherat. C Eng was not involved in the review or editorial process for this paper, on which she is listed as an author. Abstract An average of 10% of all cancers (range 1–40%) are caused by heritable mutations Key Words and over the years have become powerful models for precision medicine practice. f PTEN Furthermore, such cancer predisposition genes for seemingly rare syndromes have turned f PTEN hamartoma tumor out to help explain mechanisms of sporadic carcinogenesis and often inform normal syndrome development. The tumor suppressor PTEN encodes a ubiquitously expressed phosphatase f PTEN-opathies that counteracts the PI3K/AKT/mTOR cascade – one of the most critical growth-promoting f molecular diagnosis signaling pathways. Clinically, individuals with germline PTEN mutations have diverse f cancer risk assessment phenotypes and fall under the umbrella term PTEN hamartoma tumor syndrome (PHTS). -
Premature Polyadenylation of MAGI3 Produces a Dominantly-Acting Oncogene in Human Breast Cancer Thomas K. Ni1,2,* and Charlotte
1 Premature Polyadenylation of MAGI3 Produces a Dominantly-Acting Oncogene in Human 2 Breast Cancer 3 4 5 Thomas K. Ni1,2,* and Charlotte Kuperwasser1,2,* 6 7 8 Affiliations: 9 1 Department of Developmental, Chemical and Molecular Biology, Tufts University, 150 10 Harrison Ave, Boston, MA 02111, USA 11 2 Molecular Oncology Research Institute, Tufts Medical Center, 800 Washington St, Boston, MA 12 02111, USA 13 * Correspondence to: 14 [email protected] 15 [email protected] 16 17 18 Competing Interests Statement: 19 The authors declare that there are no conflicts of interest. 1 20 Abstract 21 Genetic mutation, chromosomal rearrangement and copy number amplification are common 22 mechanisms responsible for generating gain-of-function, cancer-causing alterations. Here we 23 report a new mechanism by which premature cleavage and polyadenylation (pPA) of RNA can 24 produce an oncogenic protein. We identify a pPA event at a cryptic intronic poly(A) signal in 25 MAGI3, occurring in the absence of local exonic and intronic mutations. The altered mRNA 26 isoform, called MAGI3pPA, produces a truncated protein that acts in a dominant-negative manner 27 to prevent full-length MAGI3 from interacting with the YAP oncoprotein, thereby relieving YAP 28 inhibition and promoting malignant transformation of human mammary epithelial cells. We 29 additionally find evidence for recurrent expression of MAGI3pPA in primary human breast tumors 30 but not in tumor-adjacent normal tissues. Our results provide an example of how pPA contributes 31 to cancer by generating a truncated mRNA isoform that encodes an oncogenic, gain-of-function 32 protein. -
Abstracts from the 50Th European Society of Human Genetics Conference: Oral Presentations
European Journal of Human Genetics (2019) 26:3–112 https://doi.org/10.1038/s41431-018-0249-5 ABSTRACT Abstracts from the 50th European Society of Human Genetics Conference: Oral Presentations Copenhagen, Denmark, May 27–30, 2017 Published online: 1 October 2018 © European Society of Human Genetics 2018 The ESHG 2017 marks the 50th Anniversary of the first ESHG Conference which took place in Copenhagen in 1967. Additional information about the event may be found on the conference website: https://2017.eshg.org/ Sponsorship: Publication of this supplement is sponsored by the European Society of Human Genetics. All authors were asked to address any potential bias in their presentation and to declare any competing financial interests. These disclosures are listed at the end of each presentation. Contributions of up to EUR 10 000 (ten thousand euros, or equivalent value in kind) per year per company are considered "modest". Contributions above EUR 10 000 per year are considered "significant". Plenary Sessions By 1988 it had become clear that something more was needed if the ESHG was to become a significant force in 1234567890();,: 1234567890();,: developing a European human genetics community. Revo- PL1 lutionary moves culminated at the meeting in Leuven in 50 years of ESHG 1991 where a rotating president and officers were elected, and statutes adopted formally incorporating the society PL1.1 under Belgian law. The society’s journal, the European A brief history of how we got here Journal of Human Genetics, was established shortly after- wards and the modern ESHG was born. We now have an A. Read annual turnover of over 2 million euros, professional administration through Jerome del Picchia and his team at Manchester, United Kingdom the Vienna Medical Academy, and an important voice in European and international developments in human genet- This year the European Society of Human Genetics ics. -
E Mouse Genome Informatics Online Resource: Worksheet Outline
!e Mouse Genome Informatics online resource: Open a browser and go to www.informatics.jax.org to begin. If necessary for assistance while completing this worksheet, or for future reference, see the “Introduction to mouse genetics” and “How to use MGI” in the Getting Started box below topic speci"c search tools on the home page. For assistance with completing Section 6 questions (the Human-Mouse: Disease Connection), or for fu- ture reference, see “Take a tour of the Human-Mouse Disease Connection” on the HMDC home page. !e HMDC home page can be accessed by clicking the link from the MGI homepage, or bookmarked directly at http://diseasemodel.org. For assistance with completing Section 7 questions (MouseMine) or for future reference, see the Help link in the top right corner of MouseMine pages. MouseMine can be accessed via Batch Data on the MGI homepage, or bookmarked directly at www.mousemine.org. Answers to questions are on the "nal page. Contact: [email protected] [email protected] Worksheet Outline Section 1: What does this gene do? Section 2: Does a (knockout/conditional allele/reporter) mouse exist for this gene? How do I obtain it? Section 3: Where is this gene expressed? Section 4: How can I "nd SNPs between two inbred mouse strains within a gene or region? Section 5: How can I "nd a list of alleles annotated to a disease or phenotype? Section 6: How can I prioritize a gene list using mouse phenotype or disease associations? Section 7: How can I determine if my gene set is enriched for GO (ontology:function/biological process/cell component) terms? Mammalian phenotype terms? Human disease terms? Section 8: I read a paper with a mouse described in it, how can I "nd that mouse in MGI? Section 1.