Genome-Wide Analysis in UK Biobank Identifies Four Loci Associated with Mood Instability
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An Insight Into the Promoter Methylation of PHF20L1 and the Gene Association with Metastasis in Breast Cancer
Original papers An insight into the promoter methylation of PHF20L1 and the gene association with metastasis in breast cancer Jose Alfredo Sierra-Ramirez1,B,F, Emmanuel Seseña-Mendez2,E,F, Marycarmen Godinez-Victoria1,B,F, Marta Elena Hernandez-Caballero2,A,C–E 1 Medical School, Graduate Section, National Polytechnic Institute, Mexico City, Mexico 2 Department of Biomedicine, Faculty of Medicine, Meritorious Autonomous University of Puebla (BUAP), Puebla, Mexico A – research concept and design; B – collection and/or assembly of data; C – data analysis and interpretation; D – writing the article; E – critical revision of the article; F – final approval of the article Advances in Clinical and Experimental Medicine, ISSN 1899–5276 (print), ISSN 2451–2680 (online) Adv Clin Exp Med. 2021;30(5):507–515 Address for correspondence Abstract Marta Elena Hernandez-Caballero E-mail: [email protected] Background. Plant homeodomain finger protein 20-like 1 PHF20L1( ) is a protein reader involved in epi- genetic regulation that binds monomethyl-lysine. An oncogenic function has been attributed to PHF20L1 Funding sources This work was supported by the Instituto Politécnico but its role in breast cancer (BC) is not clear. Nacional (SIP20195078) and Benemérita Universidad Objectives. To explore PHF20L1 promoter methylation and comprehensive bioinformatics analysis to improve Autónoma de Puebla PRODEP (BUAP-CA-159). understanding of the role of PHF20L1 in BC. Conflict of interest Materials and methods. Seventy-four BC samples and 16 control samples were converted using sodium None declared bisulfite treatment and analyzed with methylation-specific polymerase chain reaction (PCR). Bioinformatic Acknowledgements analysis was performed in the BC dataset using The Cancer Genome Atlas (TCGA) trough data visualized and We thank Efrain Atenco for assistance with R software. -
Involvement of DPP9 in Gene Fusions in Serous Ovarian Carcinoma
Smebye et al. BMC Cancer (2017) 17:642 DOI 10.1186/s12885-017-3625-6 RESEARCH ARTICLE Open Access Involvement of DPP9 in gene fusions in serous ovarian carcinoma Marianne Lislerud Smebye1,2, Antonio Agostini1,2, Bjarne Johannessen2,3, Jim Thorsen1,2, Ben Davidson4,5, Claes Göran Tropé6, Sverre Heim1,2,5, Rolf Inge Skotheim2,3 and Francesca Micci1,2* Abstract Background: A fusion gene is a hybrid gene consisting of parts from two previously independent genes. Chromosomal rearrangements leading to gene breakage are frequent in high-grade serous ovarian carcinomas and have been reported as a common mechanism for inactivating tumor suppressor genes. However, no fusion genes have been repeatedly reported to be recurrent driver events in ovarian carcinogenesis. We combined genomic and transcriptomic information to identify novel fusion gene candidates and aberrantly expressed genes in ovarian carcinomas. Methods: Examined were 19 previously karyotyped ovarian carcinomas (18 of the serous histotype and one undifferentiated). First, karyotypic aberrations were compared to fusion gene candidates identified by RNA sequencing (RNA-seq). In addition, we used exon-level gene expression microarrays as a screening tool to identify aberrantly expressed genes possibly involved in gene fusion events, and compared the findings to the RNA-seq data. Results: We found a DPP9-PPP6R3 fusion transcript in one tumor showing a matching genomic 11;19-translocation. Another tumor had a rearrangement of DPP9 with PLIN3. Both rearrangements were associated with diminished expression of the 3′ end of DPP9 corresponding to the breakpoints identified by RNA-seq. For the exon-level expression analysis, candidate fusion partner genes were ranked according to deviating expression compared to the median of the sample set. -
RNA Editing at Baseline and Following Endoplasmic Reticulum Stress
RNA Editing at Baseline and Following Endoplasmic Reticulum Stress By Allison Leigh Richards A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Human Genetics) in The University of Michigan 2015 Doctoral Committee: Professor Vivian G. Cheung, Chair Assistant Professor Santhi K. Ganesh Professor David Ginsburg Professor Daniel J. Klionsky Dedication To my father, mother, and Matt without whom I would never have made it ii Acknowledgements Thank you first and foremost to my dissertation mentor, Dr. Vivian Cheung. I have learned so much from you over the past several years including presentation skills such as never sighing and never saying “as you can see…” You have taught me how to think outside the box and how to create and explain my story to others. I would not be where I am today without your help and guidance. Thank you to the members of my dissertation committee (Drs. Santhi Ganesh, David Ginsburg and Daniel Klionsky) for all of your advice and support. I would also like to thank the entire Human Genetics Program, and especially JoAnn Sekiguchi and Karen Grahl, for welcoming me to the University of Michigan and making my transition so much easier. Thank you to Michael Boehnke and the Genome Science Training Program for supporting my work. A very special thank you to all of the members of the Cheung lab, past and present. Thank you to Xiaorong Wang for all of your help from the bench to advice on my career. Thank you to Zhengwei Zhu who has helped me immensely throughout my thesis even through my panic. -
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Molecular BioSystems Accepted Manuscript This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. We will replace this Accepted Manuscript with the edited and formatted Advance Article as soon as it is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. www.rsc.org/molecularbiosystems Page 1 of 29 Molecular BioSystems Mutated Genes and Driver Pathways Involved in Myelodysplastic Syndromes—A Transcriptome Sequencing Based Approach Liang Liu1*, Hongyan Wang1*, Jianguo Wen2*, Chih-En Tseng2,3*, Youli Zu2, Chung-che Chang4§, Xiaobo Zhou1§ 1 Center for Bioinformatics and Systems Biology, Division of Radiologic Sciences, Wake Forest University Baptist Medical Center, Winston-Salem, NC 27157, USA. 2 Department of Pathology, the Methodist Hospital Research Institute, -
Mai Muudatuntuu Ti on Man Mini
MAIMUUDATUNTUU US009809854B2 TI ON MAN MINI (12 ) United States Patent ( 10 ) Patent No. : US 9 ,809 ,854 B2 Crow et al. (45 ) Date of Patent : Nov . 7 , 2017 Whitehead et al. (2005 ) Variation in tissue - specific gene expression ( 54 ) BIOMARKERS FOR DISEASE ACTIVITY among natural populations. Genome Biology, 6 :R13 . * AND CLINICAL MANIFESTATIONS Villanueva et al. ( 2011 ) Netting Neutrophils Induce Endothelial SYSTEMIC LUPUS ERYTHEMATOSUS Damage , Infiltrate Tissues, and Expose Immunostimulatory Mol ecules in Systemic Lupus Erythematosus . The Journal of Immunol @(71 ) Applicant: NEW YORK SOCIETY FOR THE ogy , 187 : 538 - 552 . * RUPTURED AND CRIPPLED Bijl et al. (2001 ) Fas expression on peripheral blood lymphocytes in MAINTAINING THE HOSPITAL , systemic lupus erythematosus ( SLE ) : relation to lymphocyte acti vation and disease activity . Lupus, 10 :866 - 872 . * New York , NY (US ) Crow et al . (2003 ) Microarray analysis of gene expression in lupus. Arthritis Research and Therapy , 5 :279 - 287 . * @(72 ) Inventors : Mary K . Crow , New York , NY (US ) ; Baechler et al . ( 2003 ) Interferon - inducible gene expression signa Mikhail Olferiev , Mount Kisco , NY ture in peripheral blood cells of patients with severe lupus . PNAS , (US ) 100 ( 5 ) : 2610 - 2615. * GeneCards database entry for IFIT3 ( obtained from < http : / /www . ( 73 ) Assignee : NEW YORK SOCIETY FOR THE genecards. org /cgi - bin / carddisp .pl ? gene = IFIT3 > on May 26 , 2016 , RUPTURED AND CRIPPLED 15 pages ) . * Navarra et al. (2011 ) Efficacy and safety of belimumab in patients MAINTAINING THE HOSPITAL with active systemic lupus erythematosus : a randomised , placebo FOR SPECIAL SURGERY , New controlled , phase 3 trial . The Lancet , 377 :721 - 731. * York , NY (US ) Abramson et al . ( 1983 ) Arthritis Rheum . -
WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2012/174282 A2 20 December 2012 (20.12.2012) P O P C T (51) International Patent Classification: David [US/US]; 13539 N . 95th Way, Scottsdale, AZ C12Q 1/68 (2006.01) 85260 (US). (21) International Application Number: (74) Agent: AKHAVAN, Ramin; Caris Science, Inc., 6655 N . PCT/US20 12/0425 19 Macarthur Blvd., Irving, TX 75039 (US). (22) International Filing Date: (81) Designated States (unless otherwise indicated, for every 14 June 2012 (14.06.2012) kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, English (25) Filing Language: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, Publication Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, KR, (30) Priority Data: KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, ME, 61/497,895 16 June 201 1 (16.06.201 1) US MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 61/499,138 20 June 201 1 (20.06.201 1) US OM, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SC, SD, 61/501,680 27 June 201 1 (27.06.201 1) u s SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, 61/506,019 8 July 201 1(08.07.201 1) u s TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
Host Cell Factors Necessary for Influenza a Infection: Meta-Analysis of Genome Wide Studies
Host Cell Factors Necessary for Influenza A Infection: Meta-Analysis of Genome Wide Studies Juliana S. Capitanio and Richard W. Wozniak Department of Cell Biology, Faculty of Medicine and Dentistry, University of Alberta Abstract: The Influenza A virus belongs to the Orthomyxoviridae family. Influenza virus infection occurs yearly in all countries of the world. It usually kills between 250,000 and 500,000 people and causes severe illness in millions more. Over the last century alone we have seen 3 global influenza pandemics. The great human and financial cost of this disease has made it the second most studied virus today, behind HIV. Recently, several genome-wide RNA interference studies have focused on identifying host molecules that participate in Influen- za infection. We used nine of these studies for this meta-analysis. Even though the overlap among genes identified in multiple screens was small, network analysis indicates that similar protein complexes and biological functions of the host were present. As a result, several host gene complexes important for the Influenza virus life cycle were identified. The biological function and the relevance of each identified protein complex in the Influenza virus life cycle is further detailed in this paper. Background and PA bound to the viral genome via nucleoprotein (NP). The viral core is enveloped by a lipid membrane derived from Influenza virus the host cell. The viral protein M1 underlies the membrane and anchors NEP/NS2. Hemagglutinin (HA), neuraminidase Viruses are the simplest life form on earth. They parasite host (NA), and M2 proteins are inserted into the envelope, facing organisms and subvert the host cellular machinery for differ- the viral exterior. -
Inferring Biological Networks from Genome-Wide Transcriptional And
INFERRING BIOLOGICAL NETWORKS FROM GENOME-WIDE TRANSCRIPTIONAL AND FITNESS DATA By WAZEER MOHAMMAD VARSALLY A thesis submitted to The University of Birmingham for the degree of Doctor of Philosophy College of Life and Environmental Sciences School of Biosciences The University of Birmingham July 2013 I University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT In the last 15 years, the increased use of high throughput biology techniques such as genome-wide gene expression profiling, fitness profiling and protein interactomics has led to the generation of an extraordinary amount of data. The abundance of such diverse data has proven to be an essential foundation for understanding the complexities of molecular mechanisms and underlying pathways within a biological system. One approach of extrapolating biological information from this wealth of data has been through the use of reverse engineering methods to infer biological networks. This thesis demonstrates the capabilities and applications of such methodologies in identifying functionally enriched network modules in the yeast species Saccharomyces cerevisiae and Schizosaccharomyces pombe. This study marks the first time a mutual information based network inference approach has been applied to a set of specific genome-wide expression and fitness compendia, as well as the integration of these multi- level compendia. -
Supplemental Table 1
Supplemental Data Supplemental Table 1. Genes differentially regulated by Ad-KLF2 vs. Ad-GFP infected EC. Three independent genome-wide transcriptional profiling experiments were performed, and significantly regulated genes were identified. Color-coding scheme: Up, p < 1e-15 Up, 1e-15 < p < 5e-10 Up, 5e-10 < p < 5e-5 Up, 5e-5 < p <.05 Down, p < 1e-15 As determined by Zpool Down, 1e-15 < p < 5e-10 Down, 5e-10 < p < 5e-5 Down, 5e-5 < p <.05 p<.05 as determined by Iterative Standard Deviation Algorithm as described in Supplemental Methods Ratio RefSeq Number Gene Name 1,058.52 KRT13 - keratin 13 565.72 NM_007117.1 TRH - thyrotropin-releasing hormone 244.04 NM_001878.2 CRABP2 - cellular retinoic acid binding protein 2 118.90 NM_013279.1 C11orf9 - chromosome 11 open reading frame 9 109.68 NM_000517.3 HBA2;HBA1 - hemoglobin, alpha 2;hemoglobin, alpha 1 102.04 NM_001823.3 CKB - creatine kinase, brain 96.23 LYNX1 95.53 NM_002514.2 NOV - nephroblastoma overexpressed gene 75.82 CeleraFN113625 FLJ45224;PTGDS - FLJ45224 protein;prostaglandin D2 synthase 21kDa 74.73 NM_000954.5 (brain) 68.53 NM_205545.1 UNQ430 - RGTR430 66.89 NM_005980.2 S100P - S100 calcium binding protein P 64.39 NM_153370.1 PI16 - protease inhibitor 16 58.24 NM_031918.1 KLF16 - Kruppel-like factor 16 46.45 NM_024409.1 NPPC - natriuretic peptide precursor C 45.48 NM_032470.2 TNXB - tenascin XB 34.92 NM_001264.2 CDSN - corneodesmosin 33.86 NM_017671.3 C20orf42 - chromosome 20 open reading frame 42 33.76 NM_024829.3 FLJ22662 - hypothetical protein FLJ22662 32.10 NM_003283.3 TNNT1 - troponin T1, skeletal, slow LOC388888 (LOC388888), mRNA according to UniGene - potential 31.45 AK095686.1 CONFLICT - LOC388888 (na) according to LocusLink. -
WO 2016/040794 Al 17 March 2016 (17.03.2016) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2016/040794 Al 17 March 2016 (17.03.2016) P O P C T (51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, C12N 1/19 (2006.01) C12Q 1/02 (2006.01) BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, C12N 15/81 (2006.01) C07K 14/47 (2006.01) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, (21) International Application Number: KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, PCT/US20 15/049674 MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (22) International Filing Date: PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, 11 September 2015 ( 11.09.201 5) SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of regional protection available): ARIPO (BW, GH, (30) Priority Data: GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, 62/050,045 12 September 2014 (12.09.2014) US TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, (71) Applicant: WHITEHEAD INSTITUTE FOR BIOMED¬ DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, ICAL RESEARCH [US/US]; Nine Cambridge Center, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, Cambridge, Massachusetts 02142-1479 (US). -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number.