Mouse Nckipsd Conditional Knockout Project (CRISPR/Cas9)
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Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Unifying the Genomics-Based Classes of Cancer Fusion Gene Partners: Large Cancer Fusion Genes Are Evolutionarily Conserved
CANCER GENOMICS & PROTEOMICS 9: 389-396 (2012) Unifying the Genomics-based Classes of Cancer Fusion Gene Partners: Large Cancer Fusion Genes Are Evolutionarily Conserved LIBIA M. PAVA, DANIEL T. MORTON, REN CHEN and GEORGE BLANCK Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, U.S.A. Abstract. Background: Genes that fuse to cause cancer have fusion of NPM and ALK in anaplastic large-cell lymphoma been studied to determine molecular bases for proliferation, to (3); ABL and BCR in chronic myelogenous leukemia (CML) develop diagnostic tools, and as targets for drugs. To facilitate (4, 5); and C-MYC and IgH in Burkitt’s lymphoma in (6), identification of additional, cancer fusion genes, following among many others. The detection and understanding of the observation of a chromosomal translocation, we have ABL-BCR fusion protein, which stimulates unregulated cell characterized the genomic features of the fusion gene partners. division and leads to leukemia, led to the development of Previous work indicated that cancer fusion gene partners, are Gleevec, a drug able to block the ATP-binding site of the either large or evolutionarily conserved in comparison to the tyrosine kinase domain of ABL-BCR, halting CML (7). This neighboring genes in the region of a chromosomal extraordinary success has led to the hope of designing drugs translocation. These results raised the question of whether targeted against other cancer fusion proteins. large cancer fusion gene partners were also evolutionarily There are about 50,000 unstudied translocations, raising conserved. Methods and Results: We developed two methods the question of whether that information can continue to be for quantifying evolutionary conservation values, allowing the used to facilitate the identification of fusion genes. -
(12) Patent Application Publication (10) Pub. No.: US 2016/0264934 A1 GALLOURAKIS Et Al
US 20160264934A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2016/0264934 A1 GALLOURAKIS et al. (43) Pub. Date: Sep. 15, 2016 (54) METHODS FOR MODULATING AND Publication Classification ASSAYING MI6AIN STEM CELL POPULATIONS (51) Int. Cl. CI2N5/0735 (2006.01) (71) Applicants: THE GENERAL, HOSPITAL AOIN I/02 (2006.01) CORPORATION, Boston, MA (US); CI2O I/68 (2006.01) The Regents of the University of GOIN 33/573 (2006.01) California, Oakland, CA (US) CI2N 5/077 (2006.01) CI2N5/0793 (2006.01) (72) Inventors: Cosmas GIALLOURAKIS, Boston, (52) U.S. Cl. MA (US); Alan C. MULLEN, CPC ............ CI2N5/0606 (2013.01); CI2N5/0657 Brookline, MA (US); Yi XING, (2013.01); C12N5/0619 (2013.01); C12O Torrance, CA (US) I/6888 (2013.01); G0IN33/573 (2013.01); A0IN I/0226 (2013.01); C12N 2501/72 (73) Assignees: THE GENERAL, HOSPITAL (2013.01); C12N 2506/02 (2013.01); C12O CORPORATION, Boston, MA (US); 2600/158 (2013.01); C12Y 201/01062 The Regents of the University of (2013.01); C12Y 201/01 (2013.01) California, Oakland, CA (US) (57) ABSTRACT (21) Appl. No.: 15/067,780 The present invention generally relates to methods, assays and kits to maintain a human stem cell population in an (22) Filed: Mar 11, 2016 undifferentiated state by inhibiting the expression or function of METTL3 and/or METTL4, and mA fingerprint methods, assays, arrays and kits to assess the cell state of a human stem Related U.S. Application Data cell population by assessing mA levels (e.g. mA peak inten (60) Provisional application No. -
Prediction of Age at Onset in Parkinson's Disease Using Objective
www.nature.com/scientificreports OPEN Prediction of age at onset in Parkinson’s disease using objective specifc neuroimaging genetics based on a sparse canonical correlation analysis Ji Hye Won1,2, Mansu Kim1,2, Jinyoung Youn3,4* & Hyunjin Park2,5* The age at onset (AAO) is an important determinant in Parkinson’s disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to identify features associated with AAO in PD by applying the objective-specifc neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specifc neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging–genetic, genetic–target, and imaging– target pairs simultaneously. The identifed imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested fve-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging– target and genetic–target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our fndings could have signifcant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth. Parkinson’s disease (PD) is the second most common neurodegenerative disorder and presents with various motor and non-motor symptoms1. -
Supplementary Tables Table S1 Specificities and Efficiency Scores Of
Supplementary Tables Table S1 Specificities and efficiency scores of sgRNAs targeting the reference gene and the CNS-specific promoters sgRNA Sequence 5‘to 3‘ PAMa Positionb MIT Guide CRISPspec Doench Potential Off -Targets Specificity Score Efficiency For Number of Mismatches Score Score (%) 0 1 2 3 RG1 GCGTTACTTC AGG -258 to -239 82 7.394 86 0 0 0 13 ACTGAAGCAG RG2 TAGAGCAGCA GGG -210 to -191 68 7.464 98 0 0 3 14 AGCTGCACAG RG3 ACTGGGGACT AGG -132 to -112 85 8.321 86 0 0 0 5 GTAGTAAGAC CNS1 AGATAATGAG TGG -69 to -82 89 8.362 63 0 0 0 4 CGAGCCGGGA CNS2 CGCAGAGCGA GGG -34 to -56 83 8.320 94 0 0 0 8 GAGACCGCAG CNS3 CGCACACACG AGG +5 to +24 83 8.030 56 0 0 1 7 CAGCCGGCAC *Protospacer Adjacent Motif; RG1-3, reference gene promoter-specific sgRNAs; CNS1-3, CNS-promoter specific sgRNAs. The MIT specificity and the Doench efficienty scores ranges from 0-100 and summarizes off-target effects into a single number; the higher the number the fewer off target effects are anticipated. The CRISPRspec score represents overall whole genome off-targeting; all scores were in the high range, indicating low off-target predictions (rth.dk/resources/crispr/crisproff); please see main text for additional references. Table S2a Effects of mismatches in sgRNAs targeting the CNS promoter on in silico off-targets sgRNA Potential Locus, Position CNS vs Scrambled* RG vs scrambled* Off-target number CNS 1 2 mismatches 0 3 mismatches 4 Intergenic MIR5095-CTCFL; chr 20:56017262(-) Not in file Not in file Intron ASAP1; chr6:131204007(+) 0.145-fold down, p=0.007 0.353-fold down, p =2.4E-11 Intergenic AK124832-CA10; chr 17:49479286(+) Not in file Not in file Intergenic AKAP7-ARG1; chr 6:131676375(+) n.s.; not in file n.s.; not in file CNS 2 2 mismatches 0 3 mismatches 8 Intergenic LYRM2-ANKRD6;chr 6:90300681(-) n.s.; n.s. -
A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression
Supplementary Materials A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression Table S1. Statistically significant DEGs (Adj. p-value < 0.01) derived from meta-analysis for samples irradiated with high doses of HZE particles, collected 6-24 h post-IR not common with any other meta- analysis group. This meta-analysis group consists of 3 DEG lists obtained from DGEA, using a total of 11 control and 11 irradiated samples [Data Series: E-MTAB-5761 and E-MTAB-5754]. Ensembl ID Gene Symbol Gene Description Up-Regulated Genes ↑ (2425) ENSG00000000938 FGR FGR proto-oncogene, Src family tyrosine kinase ENSG00000001036 FUCA2 alpha-L-fucosidase 2 ENSG00000001084 GCLC glutamate-cysteine ligase catalytic subunit ENSG00000001631 KRIT1 KRIT1 ankyrin repeat containing ENSG00000002079 MYH16 myosin heavy chain 16 pseudogene ENSG00000002587 HS3ST1 heparan sulfate-glucosamine 3-sulfotransferase 1 ENSG00000003056 M6PR mannose-6-phosphate receptor, cation dependent ENSG00000004059 ARF5 ADP ribosylation factor 5 ENSG00000004777 ARHGAP33 Rho GTPase activating protein 33 ENSG00000004799 PDK4 pyruvate dehydrogenase kinase 4 ENSG00000004848 ARX aristaless related homeobox ENSG00000005022 SLC25A5 solute carrier family 25 member 5 ENSG00000005108 THSD7A thrombospondin type 1 domain containing 7A ENSG00000005194 CIAPIN1 cytokine induced apoptosis inhibitor 1 ENSG00000005381 MPO myeloperoxidase ENSG00000005486 RHBDD2 rhomboid domain containing 2 ENSG00000005884 ITGA3 integrin subunit alpha 3 ENSG00000006016 CRLF1 cytokine receptor like -
Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein -
Identification of Regions of Positive Selection Using Shared Genomic Segment Analysis
European Journal of Human Genetics (2011) 19, 667–671 & 2011 Macmillan Publishers Limited All rights reserved 1018-4813/11 www.nature.com/ejhg ARTICLE Identification of regions of positive selection using Shared Genomic Segment analysis Zheng Cai*,1, Nicola J Camp2, Lisa Cannon-Albright2,3 and Alun Thomas2 We applied a shared genomic segment (SGS) analysis, incorporating an error model, to identify complete, or near complete, selective sweeps in the HapMap phase II data sets. This method is based on detecting heterozygous sharing across all individuals within a population, to identify regions of sharing with at least one allele in common. We identified multiple interesting regions, many of which are concordant with positive selection regions detected by previous population genetic tests. Others are suggested to be novel regions. Our finding illustrates the utility of SGS as a method for identifying regions of selection, and some of these regions have been proposed to be candidate regions for harboring disease genes. European Journal of Human Genetics (2011) 19, 667–671; doi:10.1038/ejhg.2010.257; published online 9 February 2011 Keywords: identity by state; identity by descent; positive selection INTRODUCTION The composite likelihood ratio (CLR)9 test is a type of derived allele Dense single nucleotide polymorphisms (SNP) data sets provide an test. The CLR test searches for a signature of recent ‘complete’ selective immense opportunity for studying population genetics, allowing the sweep using pooled data from various populations. This test applies development of catalogs for signatures of selection, structural variants, genomic window approach, comparing spatial patterns of allele fre- and haplotype assortment. -
The MLL Recombinome of Acute Leukemias in 2017
OPEN Leukemia (2018) 32, 273–284 www.nature.com/leu ORIGINAL ARTICLE The MLL recombinome of acute leukemias in 2017 C Meyer1, T Burmeister2, D Gröger2, G Tsaur3, L Fechina3, A Renneville4, R Sutton5, NC Venn5, M Emerenciano6, MS Pombo-de-Oliveira6, C Barbieri Blunck6, B Almeida Lopes6, J Zuna7,JTrka7, P Ballerini8, H Lapillonne8, M De Braekeleer9,{, G Cazzaniga10, L Corral Abascal10, VHJ van der Velden11, E Delabesse12, TS Park13,SHOh14, MLM Silva15, T Lund-Aho16, V Juvonen17, AS Moore18, O Heidenreich19, J Vormoor20, E Zerkalenkova21, Y Olshanskaya21, C Bueno22,23,24, P Menendez22,23,24, A Teigler-Schlegel25, U zur Stadt26, J Lentes27, G Göhring27, A Kustanovich28, O Aleinikova28, BW Schäfer29, S Kubetzko29, HO Madsen30, B Gruhn31, X Duarte32, P Gameiro33, E Lippert34, A Bidet34, JM Cayuela35, E Clappier35, CN Alonso36, CM Zwaan37, MM van den Heuvel-Eibrink37, S Izraeli38,39, L Trakhtenbrot38,39, P Archer40, J Hancock40, A Möricke41, J Alten41, M Schrappe41, M Stanulla42, S Strehl43, A Attarbaschi43, M Dworzak43, OA Haas43, R Panzer-Grümayer43, L Sedék44, T Szczepański45, A Caye46, L Suarez46, H Cavé46 and R Marschalek1 Chromosomal rearrangements of the human MLL/KMT2A gene are associated with infant, pediatric, adult and therapy-induced acute leukemias. Here we present the data obtained from 2345 acute leukemia patients. Genomic breakpoints within the MLL gene and the involved translocation partner genes (TPGs) were determined and 11 novel TPGs were identified. Thus, a total of 135 different MLL rearrangements have been identified so far, of which 94 TPGs are now characterized at the molecular level. In all, 35 out of these 94 TPGs occur recurrently, but only 9 specific gene fusions account for more than 90% of all illegitimate recombinations of the MLL gene. -
Human Genetics of Mycobacterial Disease
Mammalian Genome (2018) 29:523–538 https://doi.org/10.1007/s00335-018-9765-4 Human genetics of mycobacterial disease Monica Dallmann‑Sauer1,2,3 · Wilian Correa‑Macedo1,2,4 · Erwin Schurr1,2,3,4 Received: 30 March 2018 / Accepted: 23 July 2018 / Published online: 16 August 2018 © The Author(s) 2018 Abstract Mycobacterial diseases are caused by members of the genus Mycobacterium, acid-fast bacteria characterized by the presence of mycolic acids within their cell walls. Claiming almost 2 million lives every year, tuberculosis (TB) is the most common mycobacterial disease and is caused by infection with M. tuberculosis and, in rare cases, by M. bovis or M. africanum. The second and third most common mycobacterial diseases are leprosy and buruli ulcer (BU), respectively. Both diseases affect the skin and can lead to permanent sequelae and deformities. Leprosy is caused by the uncultivable M. leprae while the etiological agent of BU is the environmental bacterium M. ulcerans. After exposure to these mycobacterial species, a majority of individuals will not progress to clinical disease and, among those who do, inter-individual variability in disease manifestation and outcome can be observed. Susceptibility to mycobacterial diseases carries a human genetic component and intense efforts have been applied over the past decades to decipher the exact nature of the genetic factors controlling disease susceptibility. While for BU this search was mostly conducted on the basis of candidate genes association studies, genome-wide approaches have been widely applied for TB and leprosy. In this review, we summarize some of the findings achieved by genome-wide linkage, association and transcriptome analyses in TB disease and leprosy and the recent genetic findings for BU susceptibility. -
(12) Patent Application Publication (10) Pub. No.: US 2011/0098188 A1 Niculescu Et Al
US 2011 0098188A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2011/0098188 A1 Niculescu et al. (43) Pub. Date: Apr. 28, 2011 (54) BLOOD BOMARKERS FOR PSYCHOSIS Related U.S. Application Data (60) Provisional application No. 60/917,784, filed on May (75) Inventors: Alexander B. Niculescu, Indianapolis, IN (US); Daniel R. 14, 2007. Salomon, San Diego, CA (US) Publication Classification (51) Int. Cl. (73) Assignees: THE SCRIPPS RESEARCH C40B 30/04 (2006.01) INSTITUTE, La Jolla, CA (US); CI2O I/68 (2006.01) INDIANA UNIVERSITY GOIN 33/53 (2006.01) RESEARCH AND C40B 40/04 (2006.01) TECHNOLOGY C40B 40/10 (2006.01) CORPORATION, Indianapolis, IN (52) U.S. Cl. .................. 506/9: 435/6: 435/7.92; 506/15; (US) 506/18 (57) ABSTRACT (21) Appl. No.: 12/599,763 A plurality of biomarkers determine the diagnosis of psycho (22) PCT Fled: May 13, 2008 sis based on the expression levels in a sample Such as blood. Subsets of biomarkers predict the diagnosis of delusion or (86) PCT NO.: PCT/US08/63539 hallucination. The biomarkers are identified using a conver gent functional genomics approach based on animal and S371 (c)(1), human data. Methods and compositions for clinical diagnosis (2), (4) Date: Dec. 22, 2010 of psychosis are provided. Human blood Human External Lines Animal Model External of Evidence changed in low vs. high Lines of Evidence psychosis (2pt.) Human postmortem s Animal model brai brain data (1 pt.) > Cite go data (1 p. Biomarker For Bonus 1 pt. Psychosis Human genetic 2 N linkage? association A all model blood data (1 pt.) data (1 p.