UNIVERSITY of CALIFORNIA, IRVINE Whole Exome Sequencing
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Epigenetic Repression of RARRES1 Is Mediated by Methylation of a Proximal Promoter and a Loss of CTCF Binding
Epigenetic Repression of RARRES1 Is Mediated by Methylation of a Proximal Promoter and a Loss of CTCF Binding Zhengang Peng1,2, Rulong Shen3, Ying-Wei Li1,2, Kun-Yu Teng1,2, Charles L. Shapiro4, Huey-Jen L. Lin1,2,5* 1 Division of Medical Technology, School of Allied Medical Professions, the Ohio State University Medical Center, Columbus, Ohio, United States of America, 2 Molecular Biology and Cancer Genetics Program, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, Ohio, United States of America, 3 Department of Pathology, the Ohio State University Medical Center, Columbus, Ohio, United States of America, 4 Department of Medical Oncology, the Ohio State University Medical Center, Columbus, Ohio, United States of America, 5 Department of Medical Technology, University of Delaware, Newark, Delaware, United States of America Abstract Background: The cis-acting promoter element responsible for epigenetic silencing of retinoic acid receptor responder 1 (RARRES1) by methylation is unclear. Likewise, how aberrant methylation interplays effectors and thus affects breast neoplastic features remains largely unknown. Methodology/Principal Findings: We first compared methylation occurring at the sequences (2664,+420) flanking the RARRES1 promoter in primary breast carcinomas to that in adjacent benign tissues. Surprisingly, tumor cores displayed significantly elevated methylation occurring solely at the upstream region (2664,286), while the downstream element (285,+420) proximal to the transcriptional start site (+1) remained largely unchanged. Yet, hypermethylation at the former did not result in appreciable silencing effect. In contrast, the proximal sequence displayed full promoter activity and methylation of which remarkably silenced RARRES1 transcription. This phenomenon was recapitulated in breast cancer cell lines, in which methylation at the proximal region strikingly coincided with downregulation. -
In Silico Prediction of High-Resolution Hi-C Interaction Matrices
ARTICLE https://doi.org/10.1038/s41467-019-13423-8 OPEN In silico prediction of high-resolution Hi-C interaction matrices Shilu Zhang1, Deborah Chasman 1, Sara Knaack1 & Sushmita Roy1,2* The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to 1234567890():,; experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computa- tional prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg pre- dictions identify topologically associating domains and significant interactions that are enri- ched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome. 1 Wisconsin Institute for Discovery, 330 North Orchard Street, Madison, WI 53715, USA. 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA. *email: [email protected] NATURE COMMUNICATIONS | (2019) 10:5449 | https://doi.org/10.1038/s41467-019-13423-8 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13423-8 he three-dimensional (3D) organization of the genome has Results Temerged as an important component of the gene regulation HiC-Reg for predicting contact count using Random Forests. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d. -
Genome-Wide Sirna Screen for Mediators of NF-Κb Activation
Genome-wide siRNA screen for mediators SEE COMMENTARY of NF-κB activation Benjamin E. Gewurza, Fadi Towficb,c,1, Jessica C. Marb,d,1, Nicholas P. Shinnersa,1, Kaoru Takasakia, Bo Zhaoa, Ellen D. Cahir-McFarlanda, John Quackenbushe, Ramnik J. Xavierb,c, and Elliott Kieffa,2 aDepartment of Medicine and Microbiology and Molecular Genetics, Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115; bCenter for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114; cProgram in Medical and Population Genetics, The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142; dDepartment of Biostatistics, Harvard School of Public Health, Boston, MA 02115; and eDepartment of Biostatistics and Computational Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115 Contributed by Elliott Kieff, December 16, 2011 (sent for review October 2, 2011) Although canonical NFκB is frequently critical for cell proliferation, (RIPK1). TRADD engages TNFR-associated factor 2 (TRAF2), survival, or differentiation, NFκB hyperactivation can cause malig- which recruits the ubiquitin (Ub) E2 ligase UBC5 and the E3 nant, inflammatory, or autoimmune disorders. Despite intensive ligases cIAP1 and cIAP2. CIAP1/2 polyubiquitinate RIPK1 and study, mammalian NFκB pathway loss-of-function RNAi analyses TRAF2, which recruit and activate the K63-Ub binding proteins have been limited to specific protein classes. We therefore under- TAB1, TAB2, and TAB3, as well as their associated kinase took a human genome-wide siRNA screen for novel NFκB activa- MAP3K7 (TAK1). TAK1 in turn phosphorylates IKKβ activa- tion pathway components. Using an Epstein Barr virus latent tion loop serines to promote IKK activity (4). -
Genome-Wide DNA Methylation and Long-Term Ambient Air Pollution
Lee et al. Clinical Epigenetics (2019) 11:37 https://doi.org/10.1186/s13148-019-0635-z RESEARCH Open Access Genome-wide DNA methylation and long- term ambient air pollution exposure in Korean adults Mi Kyeong Lee1 , Cheng-Jian Xu2,3,4, Megan U. Carnes5, Cody E. Nichols1, James M. Ward1, The BIOS consortium, Sung Ok Kwon6, Sun-Young Kim7*†, Woo Jin Kim6*† and Stephanie J. London1*† Abstract Background: Ambient air pollution is associated with numerous adverse health outcomes, but the underlying mechanisms are not well understood; epigenetic effects including altered DNA methylation could play a role. To evaluate associations of long-term air pollution exposure with DNA methylation in blood, we conducted an epigenome- wide association study in a Korean chronic obstructive pulmonary disease cohort (N = 100 including 60 cases) using Illumina’s Infinium HumanMethylation450K Beadchip. Annual average concentrations of particulate matter ≤ 10 μmin diameter (PM10) and nitrogen dioxide (NO2) were estimated at participants’ residential addresses using exposure prediction models. We used robust linear regression to identify differentially methylated probes (DMPs) and two different approaches, DMRcate and comb-p, to identify differentially methylated regions (DMRs). Results: After multiple testing correction (false discovery rate < 0.05), there were 12 DMPs and 27 DMRs associated with PM10 and 45 DMPs and 57 DMRs related to NO2. DMP cg06992688 (OTUB2) and several DMRs were associated with both exposures. Eleven DMPs in relation to NO2 confirmed previous findings in Europeans; the remainder were novel. Methylation levels of 39 DMPs were associated with expression levels of nearby genes in a separate dataset of 3075 individuals. -
Neuromuscular Disorders Neurology in Practice: Series Editors: Robert A
Neuromuscular Disorders neurology in practice: series editors: robert a. gross, department of neurology, university of rochester medical center, rochester, ny, usa jonathan w. mink, department of neurology, university of rochester medical center,rochester, ny, usa Neuromuscular Disorders edited by Rabi N. Tawil, MD Professor of Neurology University of Rochester Medical Center Rochester, NY, USA Shannon Venance, MD, PhD, FRCPCP Associate Professor of Neurology The University of Western Ontario London, Ontario, Canada A John Wiley & Sons, Ltd., Publication This edition fi rst published 2011, ® 2011 by Blackwell Publishing Ltd Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing program has been merged with Wiley’s global Scientifi c, Technical and Medical business to form Wiley-Blackwell. Registered offi ce: John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial offi ces: 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK 111 River Street, Hoboken, NJ 07030-5774, USA For details of our global editorial offi ces, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell The right of the author to be identifi ed as the author of this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. -
Noelia Díaz Blanco
Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr. -
Genetic and Epigenetic Profiling of Human Prostate Cancer Cell-Subsets
Genetic and Epigenetic Profiling of Human Prostate Cancer Cell-Subsets Alberto John Taurozzi PhD University of York Biology September 2016 Abstract Perturbation of androgen signalling drives progression of human prostate cancer (CaP) to castration-resistant prostate cancer (CRPC). Additionally, CaP is initiated and maintained by cancer stem cells (CSC)s which are analogous to normal prostate stem cells (SC)s. This study presents a qPCR assay to detect androgen receptor gene amplification (GAAR), which is the most common mechanism of castration resistance (>30%). Also, the epigenetic regulation and function of two SC-silenced genes with tumour-suppressive activity (Latexin (LXN) and Retinoic Acid Receptor Responder 1 (RARRES1)) were interrogated using micro-ChIP, transcriptional profiling and mass spectrometry. Traditionally, GAAR is detected using FISH which is labour-intensive and semi- quantitative, limiting clinical applicability. The mechanism of action of LXN or RARRES1 in CaP is unknown, and epigenetic regulation by DNA methylation has been ruled-out in primary CaP. The qPCR assay can detect GAAR in minor cell populations (~1%) within a heterogeneous sample and also quantifies X chromosome aneuploidy (XCA) - a predictor of poor- prognosis in CaP. GAAR and XCA were detected in near-patient xenografts derived from CRPC-tissue indicating that these abnormalities are present in cells capable of in vivo tumour-reconstitution. Micro-ChIP analysis of fractionated primary CaP cultures identified bivalent chromatin at LXN and RARRES1 promoters. Transcriptomic profiling failed to reveal significant changes in gene expression after transduction with LXN or RARRES1. However, an interactome for LXN and RARRES1 was successfully generated in PC3 cells. Additionally, confocal microscopy of mVenus-tagged LXN revealed a pan-cellular distribution which is reflected in the interactome. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase