Homotypic Clustering of L1 and B1/Alu Repeats Compartmentalizes the 3D Genome
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Table S1. List of Proteins in the BAHD1 Interactome
Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with -
SOX4-Mediated Repression of Specific Trnas Inhibits Proliferation of Human Glioblastoma Cells
SOX4-mediated repression of specific tRNAs inhibits proliferation of human glioblastoma cells Jianjing Yanga,b,c, Derek K. Smithc,d, Haoqi Nia,b,c,KeWua,b, Dongdong Huanga,b, Sishi Pana,b,c, Adwait A. Sathee, Yu Tangc,d, Meng-Lu Liuc,d, Chao Xinge,f,g, Chun-Li Zhangc,d,1, and Qichuan Zhugea,b,1 aDepartment of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; bZhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; cDepartment of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390; dHamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390; eMcDermott Center of Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390; fDepartment of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390; and gDepartment of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390 Edited by S. Altman, Yale University, New Haven, CT, and approved February 5, 2020 (received for review November 15, 2019) Transfer RNAs (tRNAs) are products of RNA polymerase III (Pol III) indicates that tRNA expression may also be under cell state- and essential for mRNA translation and ultimately cell growth and dependent regulations (12–16). proliferation. Whether and how individual tRNA genes are specif- In this study, we performed a systematic analysis on how ically regulated is not clear. Here, we report that SOX4, a well- NGN2/SOX4-mediated cell-fate reprogramming leads to cell known Pol II-dependent transcription factor that is critical for neuro- cycle exit of human glioblastoma cells. -
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. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
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 -
Virtual Chip-Seq: Predicting Transcription Factor Binding
bioRxiv preprint doi: https://doi.org/10.1101/168419; this version posted March 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Virtual ChIP-seq: predicting transcription factor binding 2 by learning from the transcriptome 1,2,3 1,2,3,4,5 3 Mehran Karimzadeh and Michael M. Hoffman 1 4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 2 5 Princess Margaret Cancer Centre, Toronto, ON, Canada 3 6 Vector Institute, Toronto, ON, Canada 4 7 Department of Computer Science, University of Toronto, Toronto, ON, Canada 5 8 Lead contact: michael.hoff[email protected] 9 March 8, 2019 10 Abstract 11 Motivation: 12 Identifying transcription factor binding sites is the first step in pinpointing non-coding mutations 13 that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is 14 the most common method for identifying binding sites, but performing it on patient samples is 15 hampered by the amount of available biological material and the cost of the experiment. Existing 16 methods for computational prediction of regulatory elements primarily predict binding in genomic 17 regions with sequence similarity to known transcription factor sequence preferences. This has limited 18 efficacy since most binding sites do not resemble known transcription factor sequence motifs, and 19 many transcription factors are not even sequence-specific. 20 Results: 21 We developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new 22 cell types using an artificial neural network that integrates ChIP-seq results from other cell types 23 and chromatin accessibility data in the new cell type. -
UC San Diego UC San Diego Electronic Theses and Dissertations
UC San Diego UC San Diego Electronic Theses and Dissertations Title Regulation of gene expression programs by serum response factor and megakaryoblastic leukemia 1/2 in macrophages Permalink https://escholarship.org/uc/item/8cc7d0t0 Author Sullivan, Amy Lynn Publication Date 2009 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Regulation of Gene Expression Programs by Serum Response Factor and Megakaryoblastic Leukemia 1/2 in Macrophages A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biomedical Sciences by Amy Lynn Sullivan Committee in charge: Professor Christopher K. Glass, Chair Professor Stephen M. Hedrick Professor Marc R. Montminy Professor Nicholas J. Webster Professor Joseph L. Witztum 2009 Copyright Amy Lynn Sullivan, 2009 All rights reserved. The Dissertation of Amy Lynn Sullivan is approved, and it is acceptable in quality and form for publication on microfilm and electronically: ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ Chair University of California, San Diego 2009 iii DEDICATION To my husband, Shane, for putting up with me through all of the long hours, last minute late nights, and for not letting me quit no matter how many times my projects fell apart. To my son, Tyler, for always making me smile and for making every day an adventure. To my gifted colleagues, for all of the thought-provoking discussions, technical help and moral support through the roller- coaster ride that has been my graduate career. To my family and friends, for all of your love and support. I couldn’t have done it without you! iv EPIGRAPH If at first you don’t succeed, try, try, again. -
Integrative Clinical Sequencing in the Management of Refractory Or
Supplementary Online Content Mody RJ, Wu Y-M, Lonigro RJ, et al. Integrative Clinical Sequencing in the Management of Children and Young Adults With Refractory or Relapsed CancerJAMA. doi:10.1001/jama.2015.10080. eAppendix. Supplementary appendix This supplementary material has been provided by the authors to give readers additional information about their work. © 2015 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 SUPPLEMENTARY APPENDIX Use of Integrative Clinical Sequencing in the Management of Pediatric Cancer Patients *#Rajen J. Mody, M.B.B.S, M.S., *Yi-Mi Wu, Ph.D., Robert J. Lonigro, M.S., Xuhong Cao, M.S., Sameek Roychowdhury, M.D., Ph.D., Pankaj Vats, M.S., Kevin M. Frank, M.S., John R. Prensner, M.D., Ph.D., Irfan Asangani, Ph.D., Nallasivam Palanisamy Ph.D. , Raja M. Rabah, M.D., Jonathan R. Dillman, M.D., Laxmi Priya Kunju, M.D., Jessica Everett, M.S., Victoria M. Raymond, M.S., Yu Ning, M.S., Fengyun Su, Ph.D., Rui Wang, M.S., Elena M. Stoffel, M.D., Jeffrey W. Innis, M.D., Ph.D., J. Scott Roberts, Ph.D., Patricia L. Robertson, M.D., Gregory Yanik, M.D., Aghiad Chamdin, M.D., James A. Connelly, M.D., Sung Choi, M.D., Andrew C. Harris, M.D., Carrie Kitko, M.D., Rama Jasty Rao, M.D., John E. Levine, M.D., Valerie P. Castle, M.D., Raymond J. Hutchinson, M.D., Moshe Talpaz, M.D., ^Dan R. Robinson, Ph.D., and ^#Arul M. Chinnaiyan, M.D., Ph.D. CORRESPONDING AUTHOR (S): # Arul M. -
Identification of Regulatory Elements from Nascent Transcription Using Dreg
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press Identification of regulatory elements from nascent transcription using dREG Zhong Wang1, Tinyi Chu1,2, Lauren A. Choate1, and Charles G. Danko1,3,* 1 Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853. 2 Graduate field of Computational Biology, Cornell University, Ithaca, NY 14853. 3 Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853. * Address correspondence to: Charles G. Danko, Ph.D. Baker Institute for Animal Health Cornell University Hungerford Hill Rd. Ithaca, NY 14853 Phone: (607) 256-5620 E-mail: [email protected] Abstract: Our genomes encode a wealth of transcription initiation regions (TIRs) that can be identified by their distinctive patterns of actively elongating RNA polymerase. We previously introduced dREG to identify TIRs using PRO-seq data. Here we introduce an efficient new implementation of dREG that uses PRO-seq data to identify both uni- and bidirectionally transcribed TIRs with 70% improvements in accuracy, 3-4-fold higher resolution, and >100-fold increases in computational efficiency. Using a novel strategy to identify TIRs based on their statistical confidence reveals extensive overlap with orthogonal assays, yet also reveals thousands of additional weakly-transcribed TIRs that were not identified by H3K27ac ChIP-seq or DNase-seq. Novel TIRs discovered by dREG were often associated with RNA polymerase III initiation, bound by pioneer transcription factors, or located in broad domains marked by repressive chromatin modifications. Our results suggest that transcription initiation can be a powerful tool for expanding the catalog of functional elements. -
Heparanase Overexpression Induces Glucagon Resistance and Protects
Page 1 of 85 Diabetes Heparanase overexpression induces glucagon resistance and protects animals from chemically-induced diabetes Dahai Zhang1, Fulong Wang1, Nathaniel Lal1, Amy Pei-Ling Chiu1, Andrea Wan1, Jocelyn Jia1, Denise Bierende1, Stephane Flibotte1, Sunita Sinha1, Ali Asadi2, Xiaoke Hu2, Farnaz Taghizadeh2, Thomas Pulinilkunnil3, Corey Nislow1, Israel Vlodavsky4, James D. Johnson2, Timothy J. Kieffer2, Bahira Hussein1 and Brian Rodrigues1 1Faculty of Pharmaceutical Sciences, UBC, 2405 Wesbrook Mall, Vancouver, BC, Canada V6T 1Z3; 2Department of Cellular & Physiological Sciences, Life Sciences Institute, UBC, 2350 Health Sciences Mall, Vancouver, BC, Canada V6T 1Z3; 3Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dalhousie University, 100 Tucker Park Road, Saint John, NB, Canada E2L 4L5; 4Cancer and Vascular Biology Research Center, Rappaport Faculty of Medicine, Technion, Haifa, Israel 31096 Running Title: Heparanase overexpression and the pancreatic islet Corresponding author: Dr. Brian Rodrigues Faculty of Pharmaceutical Sciences University of British Columbia, 2405 Wesbrook Mall, Vancouver, B.C., Canada V6T 1Z3 TEL: (604) 822-4758; FAX: (604) 822-3035 E-mail: [email protected] Key Words: Heparanase, heparan sulfate proteoglycan, glucose homeostasis, glucagon resistance, pancreatic islet, STZ Word Count: 4761 Total Number of DiabetesFigures: Publish 6 Ahead of Print, published online October 7, 2016 Diabetes Page 2 of 85 Abstract Heparanase, a protein with enzymatic and non-enzymatic properties, contributes towards disease progression and prevention. In the current study, a fortuitous observation in transgenic mice globally overexpressing heparanase (hep-tg) was the discovery of improved glucose homeostasis. We examined the mechanisms that contribute towards this improved glucose metabolism. Heparanase overexpression was associated with enhanced GSIS and hyperglucagonemia, in addition to changes in islet composition and structure. -
Genome-Wide Meta-Analysis Identifies Six Novel Loci Associated With
Molecular Psychiatry (2015) 20, 647–656 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption The Coffee and Caffeine Genetics Consortium, MC Cornelis1,2, EM Byrne3,117, T Esko4,5,6,7,117, MA Nalls8,117, A Ganna9, N Paynter10, KL Monda11, N Amin12, K Fischer4, F Renstrom13, JS Ngwa14, V Huikari15, A Cavadino16, IM Nolte17, A Teumer18,KYu19, P Marques-Vidal20, R Rawal21, A Manichaikul22, MK Wojczynski23, JM Vink24, JH Zhao25, G Burlutsky26, J Lahti27,28, V Mikkilä29,30, RN Lemaitre31, J Eriksson32, SK Musani33, T Tanaka34, F Geller35, J Luan25, J Hui36,37,38,39, R Mägi4, M Dimitriou40, ME Garcia41, W-K Ho42, MJ Wright43, LM Rose10, PKE Magnusson9, NL Pedersen9, D Couper44, BA Oostra45, A Hofman12, MA Ikram12,46,47, HW Tiemeier12,48, AG Uitterlinden12,49, FJA van Rooij12, I Barroso50,51, I Johansson52, L Xue14, M Kaakinen15,53,54, L Milani4, C Power16, H Snieder17, RP Stolk17, SE Baumeister55, R Biffar56,FGu19, F Bastardot57, Z Kutalik58,59,60, DR Jacobs Jr61, NG Forouhi25, E Mihailov4, L Lind62, C Lindgren63, K Michaëlsson64, A Morris63, M Jensen2, K-T Khaw42, RN Luben42, JJ Wang26, S Männistö65, M-M Perälä65, M Kähönen66, T Lehtimäki67, J Viikari68, D Mozaffarian1,2,69,70, K Mukamal71, BM Psaty31,72,73,74, A Döring75, AC Heath76, GW Montgomery43, N Dahmen77, T Carithers78, KL Tucker79, L Ferrucci34, HA Boyd35, M Melbye35, JL Treur24, D Mellström32, JJ Hottenga24, I Prokopenko63,80, A Tönjes81,82, -
Sequence and Chromatin Determinants of Cell-Type Specific
Sequence and chromatin determinants of cell-type specific transcription factor binding: supplementary data Aaron Arvey1, Phaedra Agius1, William Stafford Noble2, and Christina Leslie1∗ 1Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY 2Department of Genome Sciences, University of Washington, Seattle, WA March 15, 2012 Table S1: List of all TF ChIP-seq experiments analyzed in the study. File name Cell TF wgEncodeYaleChIPseqRawDataRep1Helas3Ap2alpha Helas3 AP2A1 wgEncodeYaleChIPseqRawDataRep2Helas3Ap2alpha Helas3 AP2A1 wgEncodeYaleChIPseqRawDataRep1Helas3Ap2gamma Helas3 TFAP2C wgEncodeYaleChIPseqRawDataRep2Helas3Ap2gamma Helas3 TFAP2C wgEncodeYaleChIPseqRawDataRep1K562Atf3 K562 ATF3 wgEncodeYaleChIPseqRawDataRep2K562Atf3 K562 ATF3 wgEncodeYaleChIPseqRawDataRep1Helas3Baf155Musigg Helas3 SMARCC1 wgEncodeYaleChIPseqRawDataRep2Helas3Baf155Musigg Helas3 SMARCC1 wgEncodeYaleChIPseqRawDataRep1Helas3Baf170Musigg Helas3 SMARCC2 wgEncodeHudsonalphaChipSeqRawDataRep1Gm12878Batf Gm12878 BATF wgEncodeHudsonalphaChipSeqRawDataRep2Gm12878Batf Gm12878 BATF wgEncodeHudsonalphaChipSeqRawDataRep1Gm12878Bcl11a Gm12878 BCL11A wgEncodeHudsonalphaChipSeqRawDataRep2Gm12878Bcl11a Gm12878 BCL11A wgEncodeHudsonalphaChipSeqRawDataRep1Gm12878Bcl3Pcr1xBcl3 Gm12878 BCL3 wgEncodeHudsonalphaChipSeqRawDataRep2Gm12878Bcl3Pcr1xBcl3 Gm12878 BCL3 wgEncodeYaleChIPseqRawDataRep1Helas3Bdp1 Helas3 BDP1 wgEncodeYaleChIPseqRawDataRep2Helas3Bdp1 Helas3 BDP1 wgEncodeYaleChIPseqRawDataRep1K562Bdp1 K562 BDP1 wgEncodeYaleChIPseqRawDataRep2K562Bdp1 K562