Mouse Traf3ip3 Knockout Project (CRISPR/Cas9)

Total Page:16

File Type:pdf, Size:1020Kb

Mouse Traf3ip3 Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Traf3ip3 Knockout Project (CRISPR/Cas9) Objective: To create a Traf3ip3 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Traf3ip3 gene (NCBI Reference Sequence: NM_153137 ; Ensembl: ENSMUSG00000037318 ) is located on Mouse chromosome 1. 16 exons are identified, with the ATG start codon in exon 3 and the TGA stop codon in exon 16 (Transcript: ENSMUST00000043550). Exon 3~8 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a knock-out allele exhibit impaired single positive thymocyte development and increased gamma-delta T cell numbers. Exon 3 starts from the coding region. Exon 3~8 covers 45.81% of the coding region. The size of effective KO region: ~3789 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 5 6 7 8 16 Legends Exon of mouse Traf3ip3 Knockout region Page 2 of 9 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 3 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of Exon 8 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. Page 3 of 9 https://www.alphaknockout.com Overview of the GC Content Distribution (up) Window size: 300 bp Sequence 12 Summary: Full Length(2000bp) | A(27.7% 554) | C(23.05% 461) | T(24.0% 480) | G(25.25% 505) Note: The 2000 bp section upstream of Exon 3 is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Overview of the GC Content Distribution (down) Window size: 300 bp Sequence 12 Summary: Full Length(2000bp) | A(30.05% 601) | C(19.75% 395) | T(28.6% 572) | G(21.6% 432) Note: The 2000 bp section downstream of Exon 8 is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Page 4 of 9 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 2000 1 2000 2000 100.0% chr1 - 193198020 193200019 2000 browser details YourSeq 36 143 219 2000 97.4% chr13 - 114314355 114314878 524 browser details YourSeq 34 192 230 2000 97.3% chr6 + 129279066 129279109 44 browser details YourSeq 31 196 229 2000 97.0% chr3 - 138162419 138162876 458 browser details YourSeq 31 195 237 2000 87.9% chr4 + 106021188 106021228 41 browser details YourSeq 31 196 226 2000 100.0% chr14 + 74358367 74358397 31 browser details YourSeq 30 191 220 2000 100.0% chr9 + 102001666 102001695 30 browser details YourSeq 30 197 226 2000 100.0% chr18 + 38830390 38830419 30 browser details YourSeq 29 190 219 2000 100.0% chr6 - 51491091 51491121 31 browser details YourSeq 29 189 219 2000 90.0% chr11 + 32191351 32191380 30 browser details YourSeq 28 191 218 2000 100.0% chr7 - 24455434 24455461 28 browser details YourSeq 28 190 219 2000 89.7% chr6 - 120107813 120107841 29 browser details YourSeq 28 194 221 2000 100.0% chr6 - 91532105 91532132 28 browser details YourSeq 28 191 218 2000 100.0% chr5 - 133306350 133306377 28 browser details YourSeq 28 192 219 2000 100.0% chr11 - 4822039 4822066 28 browser details YourSeq 28 190 219 2000 89.7% chr8 + 25434366 25434394 29 browser details YourSeq 28 192 219 2000 100.0% chr5 + 151409836 151409863 28 browser details YourSeq 28 190 219 2000 89.7% chr5 + 128658979 128659007 29 browser details YourSeq 28 191 218 2000 100.0% chr17 + 5628641 5628668 28 browser details YourSeq 28 191 218 2000 100.0% chr13 + 101428646 101428673 28 Note: The 2000 bp section upstream of Exon 3 is BLAT searched against the genome. No significant similarity is found. BLAT Search Results (down) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 2000 1 2000 2000 100.0% chr1 - 193192289 193194288 2000 browser details YourSeq 318 1016 1769 2000 92.8% chr11 + 116962111 117131540 169430 browser details YourSeq 275 1378 1771 2000 90.6% chr3 + 88594981 88884426 289446 browser details YourSeq 268 1395 1769 2000 91.5% chr1 + 177698784 178313114 614331 browser details YourSeq 259 1390 1769 2000 91.0% chr15 + 85981177 85990636 9460 browser details YourSeq 243 1397 1768 2000 89.0% chr7 - 140999953 141000527 575 browser details YourSeq 242 1419 1774 2000 89.3% chr9 + 59792070 59792578 509 browser details YourSeq 240 1378 1768 2000 89.1% chr15 - 99007583 99008160 578 browser details YourSeq 220 1430 1769 2000 91.1% chr9 + 54561433 54561902 470 browser details YourSeq 206 1442 1760 2000 87.3% chr5 - 109951991 110012810 60820 browser details YourSeq 180 1481 1763 2000 87.5% chr5 + 114974571 114975225 655 browser details YourSeq 169 1457 1770 2000 82.1% chr10 - 130578123 130578389 267 browser details YourSeq 167 1397 1658 2000 89.3% chr11 - 97189986 97190797 812 browser details YourSeq 161 1551 1769 2000 90.1% chr14 + 34318686 34319230 545 browser details YourSeq 158 1425 1716 2000 89.6% chr4 - 86864115 86864651 537 browser details YourSeq 152 1375 1773 2000 83.0% chr4 - 106771937 106772127 191 browser details YourSeq 151 1464 1772 2000 84.7% chr9 + 53674906 53675212 307 browser details YourSeq 151 1580 1769 2000 88.6% chr5 + 116051549 116051734 186 browser details YourSeq 150 1387 1769 2000 83.9% chr3 + 94888260 94888434 175 browser details YourSeq 148 1549 1769 2000 90.7% chr13 - 93680574 93680911 338 Note: The 2000 bp section downstream of Exon 8 is BLAT searched against the genome. No significant similarity is found. Page 5 of 9 https://www.alphaknockout.com Gene and protein information: Traf3ip3 TRAF3 interacting protein 3 [ Mus musculus (house mouse) ] Gene ID: 215243, updated on 7-Sep-2019 Gene summary Official Symbol Traf3ip3 provided by MGI Official Full Name TRAF3 interacting protein 3 provided by MGI Primary source MGI:MGI:2441706 See related Ensembl:ENSMUSG00000037318 Gene type protein coding RefSeq status VALIDATED Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as T3jam; AI115021; 6030423D04Rik Expression Biased expression in thymus adult (RPKM 26.7), spleen adult (RPKM 17.6) and 7 other tissuesS ee more Orthologs human all Genomic context Location: 1; 1 H6 See Traf3ip3 in Genome Data Viewer Exon count: 16 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 1 NC_000067.6 (193175504..193201546, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 1 NC_000067.5 (195001698..195027740, complement) Chromosome 1 - NC_000067.6 Page 6 of 9 https://www.alphaknockout.com Transcript information: This gene has 13 transcripts Gene: Traf3ip3 ENSMUSG00000037318 Description TRAF3 interacting protein 3 [Source:MGI Symbol;Acc:MGI:2441706] Gene Synonyms 6030423D04Rik Location Chromosome 1: 193,175,453-193,201,703 reverse strand. GRCm38:CM000994.2 About this gene This gene has 13 transcripts (splice variants), 174 orthologues, 2 paralogues, is a member of 1 Ensembl protein family and is associated with 10 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Traf3ip3- ENSMUST00000043550.10 2122 513aa ENSMUSP00000040977.4 Protein coding CCDS15634 G3X949 TSL:1 201 GENCODE basic APPRIS P2 Traf3ip3- ENSMUST00000192020.5 1854 513aa ENSMUSP00000141698.1 Protein coding CCDS15634 G3X949 TSL:5 212 GENCODE basic APPRIS P2 Traf3ip3- ENSMUST00000160302.3 675 147aa ENSMUSP00000142321.1 Protein coding - A0A0A6YY84 CDS 5' 204 incomplete TSL:2 Traf3ip3- ENSMUST00000162480.2 447 97aa ENSMUSP00000141669.1 Protein coding - A0A0A6YWS1 CDS 5' 210 incomplete TSL:5 Traf3ip3- ENSMUST00000159666.1 360 17aa ENSMUSP00000141938.1 Protein coding - A0A0A6YXD0 CDS 5' 202 incomplete TSL:3 Traf3ip3- ENSMUST00000161367.1 340 113aa ENSMUSP00000141358.1 Protein coding - A0A0A6YW19 CDS 5' and 3' 208 incomplete TSL:3 APPRIS ALT2 Traf3ip3- ENSMUST00000194278.5 2235 185aa ENSMUSP00000141661.1 Nonsense mediated - A0A0A6YWR4 TSL:5 213 decay Traf3ip3- ENSMUST00000161403.7 3372 No - Retained intron - - TSL:1 209 protein Traf3ip3- ENSMUST00000160332.7 1590 No - Retained intron - - TSL:1 205 protein Traf3ip3- ENSMUST00000159805.7 1289 No - Retained intron - - TSL:1 203 protein Traf3ip3- ENSMUST00000161151.2 706 No - lncRNA - - TSL:2 207 protein Traf3ip3- ENSMUST00000161145.7 496 No - lncRNA - - TSL:5 206 protein Traf3ip3- ENSMUST00000162905.1 373 No - lncRNA - - TSL:5 211 protein Page 7 of 9 https://www.alphaknockout.com 46.25 kb Forward strand 193.17Mb 193.18Mb 193.19Mb 193.20Mb 193.21Mb Genes Irf6-201 >protein coding A130010J15Rik-204 >protein coding Gm15872-201 >lncRNA
Recommended publications
  • Supp Material.Pdf
    Simon et al. Supplementary information: Table of contents p.1 Supplementary material and methods p.2-4 • PoIy(I)-poly(C) Treatment • Flow Cytometry and Immunohistochemistry • Western Blotting • Quantitative RT-PCR • Fluorescence In Situ Hybridization • RNA-Seq • Exome capture • Sequencing Supplementary Figures and Tables Suppl. items Description pages Figure 1 Inactivation of Ezh2 affects normal thymocyte development 5 Figure 2 Ezh2 mouse leukemias express cell surface T cell receptor 6 Figure 3 Expression of EZH2 and Hox genes in T-ALL 7 Figure 4 Additional mutation et deletion of chromatin modifiers in T-ALL 8 Figure 5 PRC2 expression and activity in human lymphoproliferative disease 9 Figure 6 PRC2 regulatory network (String analysis) 10 Table 1 Primers and probes for detection of PRC2 genes 11 Table 2 Patient and T-ALL characteristics 12 Table 3 Statistics of RNA and DNA sequencing 13 Table 4 Mutations found in human T-ALLs (see Fig. 3D and Suppl. Fig. 4) 14 Table 5 SNP populations in analyzed human T-ALL samples 15 Table 6 List of altered genes in T-ALL for DAVID analysis 20 Table 7 List of David functional clusters 31 Table 8 List of acquired SNP tested in normal non leukemic DNA 32 1 Simon et al. Supplementary Material and Methods PoIy(I)-poly(C) Treatment. pIpC (GE Healthcare Lifesciences) was dissolved in endotoxin-free D-PBS (Gibco) at a concentration of 2 mg/ml. Mice received four consecutive injections of 150 μg pIpC every other day. The day of the last pIpC injection was designated as day 0 of experiment.
    [Show full text]
  • Role of Genomic Variants in the Response to Biologics Targeting Common Autoimmune Disorders
    Role of genomic variants in the response to biologics targeting common autoimmune disorders by Gordana Lenert, PhD The thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Master of Science Ottawa-Carleton Joint Program in Bioinformatics Carleton University Ottawa, Canada © 2016 Gordana Lenert Abstract Autoimmune diseases (AID) are common chronic inflammatory conditions initiated by the loss of the immunological tolerance to self-antigens. Chronic immune response and uncontrolled inflammation provoke diverse clinical manifestations, causing impairment of various tissues, organs or organ systems. To avoid disability and death, AID must be managed in clinical practice over long periods with complex and closely controlled medication regimens. The anti-tumor necrosis factor biologics (aTNFs) are targeted therapeutic drugs used for AID management. However, in spite of being very successful therapeutics, aTNFs are not able to induce remission in one third of AID phenotypes. In our research, we investigated genomic variability of AID phenotypes in order to explain unpredictable lack of response to aTNFs. Our hypothesis is that key genetic factors, responsible for the aTNFs unresponsiveness, are positioned at the crossroads between aTNF therapeutic processes that generate remission and pathogenic or disease processes that lead to AID phenotypes expression. In order to find these key genetic factors at the intersection of the curative and the disease pathways, we combined genomic variation data collected from publicly available curated AID genome wide association studies (AID GWAS) for each disease. Using collected data, we performed prioritization of genes and other genomic structures, defined the key disease pathways and networks, and related the results with the known data by the bioinformatics approaches.
    [Show full text]
  • STRIPAK Complexes in Cell Signaling and Cancer
    Oncogene (2016), 1–9 © 2016 Macmillan Publishers Limited All rights reserved 0950-9232/16 www.nature.com/onc REVIEW STRIPAK complexes in cell signaling and cancer Z Shi1,2, S Jiao1 and Z Zhou1,3 Striatin-interacting phosphatase and kinase (STRIPAK) complexes are striatin-centered multicomponent supramolecular structures containing both kinases and phosphatases. STRIPAK complexes are evolutionarily conserved and have critical roles in protein (de) phosphorylation. Recent studies indicate that STRIPAK complexes are emerging mediators and regulators of multiple vital signaling pathways including Hippo, MAPK (mitogen-activated protein kinase), nuclear receptor and cytoskeleton remodeling. Different types of STRIPAK complexes are extensively involved in a variety of fundamental biological processes ranging from cell growth, differentiation, proliferation and apoptosis to metabolism, immune regulation and tumorigenesis. Growing evidence correlates dysregulation of STRIPAK complexes with human diseases including cancer. In this review, we summarize the current understanding of the assembly and functions of STRIPAK complexes, with a special focus on cell signaling and cancer. Oncogene advance online publication, 15 February 2016; doi:10.1038/onc.2016.9 INTRODUCTION in the central nervous system and STRN4 is mostly abundant in Recent proteomic studies identified a group of novel multi- the brain and lung, whereas STRN3 is ubiquitously expressed in 5–9 component complexes named striatin (STRN)-interacting phos- almost all tissues. STRNs share a
    [Show full text]
  • Supplementary Table 1
    Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7
    [Show full text]
  • Algorithms to Integrate Omics Data for Personalized Medicine
    ALGORITHMS TO INTEGRATE OMICS DATA FOR PERSONALIZED MEDICINE by MARZIEH AYATI Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Thesis Adviser: Mehmet Koyut¨urk Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY August, 2018 Algorithms to Integrate Omics Data for Personalized Medicine Case Western Reserve University Case School of Graduate Studies We hereby approve the thesis1 of MARZIEH AYATI for the degree of Doctor of Philosophy Mehmet Koyut¨urk 03/27/2018 Committee Chair, Adviser Date Department of Electrical Engineering and Computer Science Mark R. Chance 03/27/2018 Committee Member Date Center of Proteomics Soumya Ray 03/27/2018 Committee Member Date Department of Electrical Engineering and Computer Science Vincenzo Liberatore 03/27/2018 Committee Member Date Department of Electrical Engineering and Computer Science 1We certify that written approval has been obtained for any proprietary material contained therein. To the greatest family who I owe my life to Table of Contents List of Tables vi List of Figures viii Acknowledgements xxi Abstract xxiii Abstract xxiii Chapter 1. Introduction1 Chapter 2. Preliminaries6 Complex Diseases6 Protein-Protein Interaction Network6 Genome-Wide Association Studies7 Phosphorylation 10 Biweight midcorrelation 10 Chapter 3. Identification of Disease-Associated Protein Subnetworks 12 Introduction and Background 12 Methods 15 Results and Discussion 27 Conclusion 40 Chapter 4. Population Covering Locus Sets for Risk Assessment in Complex Diseases 43 Introduction and Background 43 iv Methods 47 Results and Discussion 59 Conclusion 75 Chapter 5. Application of Phosphorylation in Precision Medicine 80 Introduction and Background 80 Methods 83 Results 89 Conclusion 107 Chapter 6.
    [Show full text]
  • Wo 2010/056982 A2 I
    (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 20 May 2010 (20.05.2010) WO 2010/056982 A2 (51) International Patent Classification: 2300 Eye St., N.W., Suite 712, Washington, DC 20037 C12Q 1/68 (2006.01) C12N 15/11 (2006.01) (US). (21) International Application Number: (72) Inventor; and PCT/US2009/064370 (75) Inventor/Applicant (for US only): HU, Valerie, Wailin [US/US]; 16610 Leopard Terrace, Rockville, MD 20854 (22) International Filing Date: (US). 13 November 2009 (13.1 1.2009) (74) Agent: KHALILIAN, Houri; Law Offices of Khalilian (25) Filing Language: English Sira, LLC, 9100 Persimmon Tree Road, Potomac, MD (26) Publication Language: English 20854 (US). (30) Priority Data: (81) Designated States (unless otherwise indicated, for every 61/1 15,1 84 17 November 2008 (17.1 1.2008) US kind of national protection available): AE, AG, AL, AM, 61/171,5 10 22 April 2009 (22.04.2009) US AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, (71) Applicant (for all designated States except US): THE DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, GEORGE WASHINGTON UNIVERSITY [US/US]; HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, [Continued on next page] (54) Title: COMPOSITIONS AND METHODS FOR IDENTIFYING AUTISM SPECTRUM DISORDERS (57) Abstract: The compositions and methods described are directed to gene chips having a plurality of different oligonucleotides with specificity for genes associated with autism spectrum disorders.
    [Show full text]
  • Investigating the Effect of Chronic Activation of AMP-Activated Protein
    Investigating the effect of chronic activation of AMP-activated protein kinase in vivo Alice Pollard CASE Studentship Award A thesis submitted to Imperial College London for the degree of Doctor of Philosophy September 2017 Cellular Stress Group Medical Research Council London Institute of Medical Sciences Imperial College London 1 Declaration I declare that the work presented in this thesis is my own, and that where information has been derived from the published or unpublished work of others it has been acknowledged in the text and in the list of references. This work has not been submitted to any other university or institute of tertiary education in any form. Alice Pollard The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives license. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the license terms of this work. 2 Abstract The prevalence of obesity and associated diseases has increased significantly in the last decade, and is now a major public health concern. It is a significant risk factor for many diseases, including cardiovascular disease (CVD) and type 2 diabetes. Characterised by excess lipid accumulation in the white adipose tissue, which drives many associated pathologies, obesity is caused by chronic, whole-organism energy imbalance; when caloric intake exceeds energy expenditure. Whilst lifestyle changes remain the most effective treatment for obesity and the associated metabolic syndrome, incidence continues to rise, particularly amongst children, placing significant strain on healthcare systems, as well as financial burden.
    [Show full text]
  • SUPPLEMENTARY INFORMATION Genome-Wide Association Study
    SUPPLEMENTARY INFORMATION Genome-wide association study and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes Franceschini, Giambartolomei et al. Supplementary Note 1 Study Descriptions This study includes data from the CHARGE and UCLEB Consortia. For all studies, each participant provided written informed consent. The Institutional Review Board at the parent institution for each respective study approved the study protocols. CHARGE Consortium The Aging Gene-Environment Susceptibility-Reykjavik Study (AGES) cohort originally comprised a random sample of 30,795 men and women born in 1907–1935 and living in Reykjavik in 1967.1 A total of 19,381 individuals attended, resulting in 71% recruitment rate. The study sample was divided into six groups by birth year and birth date within month. One group was designated for longitudinal follow-up and was examined in all stages. One group was designated a control group and was not included in examinations until 1991. Other groups were invited to participate in specific stages of the study. Between 2002 and 2006, the AGES-Reykjavik study re-examined 5764 survivors of the original cohort who had participated before in the Reykjavik Study. The AGES Reykjavik Study GWAS was approved by the National Bioethics Committee (00-063-V8+1) and the Data Protection Authority. The Atherosclerosis Risk in Communities Study (ARIC) is a multi-center prospective investigation of atherosclerotic disease in a predominantly bi-racial population. 2 Men and women aged 45-64 years at baseline were recruited from 4 communities: Forsyth County, North Carolina; Jackson, Mississippi; suburban areas of Minneapolis, Minnesota; and Washington County, Maryland.
    [Show full text]
  • Cavernous Malformations of the Nervous System
    Cavernous Malformations of the Nervous System Cavernous Malformations of the Nervous System Edited by Daniele Rigamonti Johns Hopkins University cambridge university press Cambridge University Press has no responsibility for the Cambridge, New York, Melbourne, Madrid, Cape Town, persistence or accuracy of URLs for external or third-party Singapore, São Paulo, Delhi, Tokyo, Mexico City internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will Cambridge University Press remain, accurate or appropriate. The Edinburgh Building, Cambridge CB2 8RU, UK Every effort has been made in preparing this book to provide Published in the United States of America by Cambridge accurate and up-to-date information which is in accord with University Press, New York accepted standards and practice at the time of publication. Although case histories are drawn from actual cases, every www.cambridge.org effort has been made to disguise the identities of the individuals involved. Nevertheless, the authors, editors and Information on this title: www.cambridge.org/ publishers can make no warranties that the information 9780521764278 contained herein is totally free from error, not least because clinical standards are constantly changing through research © Cambridge University Press 2011 and regulation. The authors, editors and publishers therefore disclaim all liability for direct or consequential damages This publication is in copyright. Subject to statutory resulting from the use of material contained in this book. exception Readers are strongly advised to pay careful attention to and to the provisions of relevant collective licensing information provided by the manufacturer of any drugs or agreements, equipment that they plan to use.
    [Show full text]
  • Understanding Regulatory Mechanisms Underlying Stem Cells Helps to Identify Cancer Biomarkers
    Understanding Regulatory Mechanisms Underlying Stem Cells Helps to Identify Cancer Biomarkers A dissertation submitted towards the degree Doctor of Engineering (Dr.-Ing) of the Faculty of Mathematics and Computer Science of Saarland University by Maryam Nazarieh Saarbrücken, June 2018 i iii Day of Colloquium Jun 28, 2018 Dean of the Faculty Prof. Dr. Sebastian Hack Chair of the Committee Prof. Dr. Hans-Peter Lenhof Reporters First reviewer Prof. Dr. Volkhard Helms Second reviewer Prof. Dr. Dr. Thomas Lengauer Academic Assistant Dr. Christina Backes Acknowledgements Firstly, I would like to thank Prof. Volkhard Helms for offering me a position at his group and for his supervision and support on the SFB 1027 project. I am grateful to Prof. Thomas Lengauer for his helpful comments. I am thankful to Prof. Andreas Wiese for his contribution and discussion. I would like to thank Prof. Jan Baumbach that allowed me to spend a training phase in his group during my PhD preparatory phase and the collaborative work which I performed with his PhD student Rashid Ibragimov where I proposed a heuristic algorithm based on the characteristics of protein-protein interaction networks for solving the graph edit dis- tance problem. I would like to thank Graduate School of Computer Science and Center for Bioinformatics at Saarland University, especially Prof. Raimund Seidel and Dr. Michelle Carnell for giving me an opportunity to carry out my PhD studies. Furthermore, I would like to thank to Prof. Helms for enhancing my experience by intro- ducing master students and working as their advisor for successfully accomplishing their master projects.
    [Show full text]
  • W O 2019/079360 a L 25 April 2019 (25.04.2019) W 1P O PCT
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date W O 2019/079360 A l 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: (72) Inventors; and G01N 33/48 (2006.01) G01N 33/53 (2006.01) (71) Applicants: KEAN, Leslie [US/US]; c/o 818 Stewart St, Suite 603, Seattle, Washington 98101 (US). COLONNA, (21) International Application Number: Lucrezia [US/US]; c/o 818 Stewart St, Suite 603, Seattle, PCT/US2018/056166 Washington 98101 (US). CARROLL, Shaina [US/US]; (22) International Filing Date: c/o 77 Massachusetts Avenue, Cambridge, Massachusetts 16 October 2018 (16. 10.2018) 02139 (US). (25) Filing Language: English (72) Inventors: SHALEK, Alexander K.; c/o 77 Massachu¬ setts Avenue, Cambridge, Massachusetts 02139 (US). ZIE- (26) Publication Language: English GLER, Carly; c/o 77 Massachusetts Avenue, Cambridge, (30) Priority Data: Massachusetts 02139 (US). 62/573,015 16 October 2017 (16. 10.2017) US (74) Agent: SCHER, Michael B. et al.; Johnson, Marcou & (71) Applicants: MASSACHUSETTS INSTITUTE OF Isaacs, LLC, P.O. Bo 691, Hoschton, Georgia 30548 (US). TECHNOLOGY [US/US]; 77 Massachusetts Av¬ (81) Designated States (unless otherwise indicated, for every enue, Cambridge, Massachusetts 02139 (US). SEAT¬ kind of national protection available): AE, AG, AL, AM, TLE CHILDREN'S HOSPITAL DBA SEATTLE AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CHILDREN'S RESEARCH INSTITUTE [US/US]; 818 CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, Stewart St, Suite 603, Seattle, Washington 98101 (US).
    [Show full text]
  • STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma
    STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma Jennifer Hardee*,§, Zhengqing Ouyang*,1,2,3, Yuping Zhang*,4 , Anshul Kundaje*,†, Philippe Lacroute*, Michael Snyder*,5 *Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305; §Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520; and †Department of Computer Science, Stanford University School of Engineering, Stanford, CA 94305 1The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030 2Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269 3Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, CT 06030 4Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06520 5Corresponding author: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305. Email: [email protected] DOI: 10.1534/g3.113.007674 Figure S1 STAT3 immunoblotting and immunoprecipitation with sc-482. Western blot and IPs show a band consistent with expected size (88 kDa) of STAT3. (A) Western blot using antibody sc-482 versus nuclear lysates. Lanes contain (from left to right) lysate from K562 cells, GM12878 cells, HeLa S3 cells, and HepG2 cells. (B) IP of STAT3 using sc-482 in HeLa S3 cells. Lane 1: input nuclear lysate; lane 2: unbound material from IP with sc-482; lane 3: material IP’d with sc-482; lane 4: material IP’d using control rabbit IgG. Arrow indicates the band of interest. (C) IP of STAT3 using sc-482 in K562 cells. Lane 1: input nuclear lysate; lane 2: material IP’d using control rabbit IgG; lane 3: material IP’d with sc-482.
    [Show full text]