Competition for DNA Binding Between Paralogous Transcription Factors Determines Their Genomic Occupancy and Regulatory Functions
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SUPPLEMENTAL MATERIALS Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator Adrian L. Sanborn,1,2,* Benjamin T. Yeh,2 Jordan T. Feigerle,1 Cynthia V. Hao,1 Raphael J. L. Townshend,2 Erez Lieberman Aiden,3,4 Ron O. Dror,2 Roger D. Kornberg1,*,+ 1Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA 2Department of Computer Science, Stanford University, Stanford, CA 94305, USA 3The Center for Genome Architecture, Baylor College of Medicine, Houston, USA 4Center for Theoretical Biological Physics, Rice University, Houston, USA *Correspondence: [email protected], [email protected] +Lead Contact 1 A B Empty GFP Count Count Count VP16 AD mCherry mCherry GFP C G Count 2 AD tiles 10 Random sequences 2 12 10 10 Gcn4 AD 8 1 10 1 6 10 Z-score Activation 4 Count 2 0 Activation, second library 0 10 0 10 Gal4 Gln3 Hap4 Ino2 Ino2 Msn2 Oaf1 Pip2 VP16 VP64 r = 0.952 Pho4 AD 147 104 112 1 108 234 995 944 0 1 2 Synonymously encoded control ADs 10 10 10 Activation, first library Count H 102 GFP 101 D E Empty VP16 activation 100 80% Mean positional VP16 AD 0 200 400 600 800 1000 1200 60% Position in Adr1 protein 40% By FACS GFP By sequencing I 20% 50% 0% Percent of cells 1 2 3 4 5 6 7 8 mCherry mCherry 25% 80% Gcn4 AD 60% Empty VP16 0% 40% AD at position with Percent of proteins 0.0 0.2 0.4 0.6 0.8 1.0 20% Position on protein, normalized 0% Percent of cells 100% 1 2 3 4 5 6 7 8 100% 80% 75% 75% Pho4 AD / mCherry ratio GFP 60% GFP GFP 50% 50% 40% 20% 25% 25% C-term ADs 0% ADs Percent of Percent of cells Other ADs 0% 1 2 3 4 5 6 7 8 0% 1 2 F 25 50 75 100 125 10 10 Gcn4 hydrophobic mutants (cumulative distribution) GFP bin number 1 AD length Activation J 1.0 0 0.8 0.6 0.4 −1 (log2 change vs wild-type) 0.2 Frequency of aTF expression 5 10 15 TFs without ADs falling within sorted mCh range Hydrophobic content 0.0 TFs with ADs Cumulative fraction of TFs (kcal/mol) 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of genes regulated by TF that are activating Figure S1. -
Transcriptome Profiling of Pediatric Core Binding Factor AML
RESEARCH ARTICLE Transcriptome Profiling of Pediatric Core Binding Factor AML Chih-Hao Hsu1, Cu Nguyen1, Chunhua Yan1, Rhonda E. Ries2, Qing-Rong Chen1, Ying Hu1, Fabiana Ostronoff2, Derek L. Stirewalt2, George Komatsoulis1, Shawn Levy3, Daoud Meerzaman1☯, Soheil Meshinchi2☯* 1 Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, United States of America, 2 Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America, 3 Hudson Alpha Institute for Biotechnology, Huntsville, AL, United States of America ☯ These authors contributed equally to this work. * [email protected] Abstract The t(8;21) and Inv(16) translocations disrupt the normal function of core binding factors alpha (CBFA) and beta (CBFB), respectively. These translocations represent two of the most com- OPEN ACCESS mon genomic abnormalities in acute myeloid leukemia (AML) patients, occurring in approxi- Citation: Hsu C-H, Nguyen C, Yan C, Ries RE, Chen mately 25% pediatric and 15% of adult with this malignancy. Both translocations are Q-R, Hu Y, et al. (2015) Transcriptome Profiling of associated with favorable clinical outcomes after intensive chemotherapy, and given the per- Pediatric Core Binding Factor AML. PLoS ONE 10(9): ceived mechanistic similarities, patients with these translocations are frequently referred to as e0138782. doi:10.1371/journal.pone.0138782 having CBF-AML. It remains uncertain as to whether, collectively, these translocations are Editor: Ken Mills, Queen's University Belfast, mechanistically the same or impact different pathways in subtle ways that have both biological UNITED KINGDOM and clinical significance. Therefore, we used transcriptome sequencing (RNA-seq) to investi- Received: June 15, 2015 gate the similarities and differences in genes and pathways between these subtypes of pediat- Accepted: September 3, 2015 ric AMLs. -
Locating Mammalian Transcription Factor Binding Sites: a Survey of Computational and Experimental Techniques
Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Review Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques Laura Elnitski,1,4 Victor X. Jin,2 Peggy J. Farnham,2 and Steven J.M. Jones3 1Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland 20878, USA; 2Genome and Biomedical Sciences Facility, University of California–Davis, Davis, California 95616-8645, USA; 3Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada V5Z-4E6 Fields such as genomics and systems biology are built on the synergism between computational and experimental techniques. This type of synergism is especially important in accomplishing goals like identifying all functional transcription factor binding sites in vertebrate genomes. Precise detection of these elements is a prerequisite to deciphering the complex regulatory networks that direct tissue specific and lineage specific patterns of gene expression. This review summarizes approaches for in silico, in vitro, and in vivo identification of transcription factor binding sites. A variety of techniques useful for localized- and high-throughput analyses are discussed here, with emphasis on aspects of data generation and verification. [Supplemental material is available online at www.genome.org.] One documented goal of the National Human Genome Research and other regulatory elements is mediated by DNA/protein in- Institute (NHGRI) is the identification of all functional noncod- teractions. Thus, one major step in the characterization of the ing elements in the human genome (ENCODE Project Consor- functional elements of the human genome is the identification tium 2004). -
Specificity Landscapes Unmask Submaximal Binding Site Preferences of Transcription Factors
Specificity landscapes unmask submaximal binding site preferences of transcription factors Devesh Bhimsariaa,b,1, José A. Rodríguez-Martíneza,2, Junkun Panc, Daniel Rostonc, Elif Nihal Korkmazc, Qiang Cuic,3, Parameswaran Ramanathanb, and Aseem Z. Ansaria,d,4 aDepartment of Biochemistry, University of Wisconsin–Madison, Madison, WI 53706; bDepartment of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI 53706; cDepartment of Chemistry, University of Wisconsin–Madison, Madison, WI 53706; and dThe Genome Center of Wisconsin, University of Wisconsin–Madison, Madison, WI 53706 Edited by Michael Levine, Princeton University, Princeton, NJ, and approved September 24, 2018 (received for review July 13, 2018) We have developed Differential Specificity and Energy Landscape Here, we report the development of Differential Specificity (DiSEL) analysis to comprehensively compare DNA–protein inter- and Energy Landscapes (DiSEL) to compare experimental actomes (DPIs) obtained by high-throughput experimental plat- platforms, computational methods, and interactomes of TFs, forms and cutting edge computational methods. While high-affinity especially those factors that bind identical consensus motifs. Our DNA binding sites are identified by most methods, DiSEL uncovered results reveal that (i) most high-throughput experimental plat- nuanced sequence preferences displayed by homologous transcription forms reliably identify high-affinity motifs but yield less reliable factors. Pairwise analysis of 726 DPIs uncovered homolog-specific dif- information on submaximal sites; (ii) with few exceptions, com- ferences at moderate- to low-affinity binding sites (submaximal sites). putational methods model DPIs with a focus on high-affinity DiSEL analysis of variants of 41 transcription factors revealed that sites; (iii) submaximal sites improve the annotation of biologi- many disease-causing mutations result in allele-specific changes in cally relevant binding sites across genomes; (iv) among members binding site preferences. -
Chromatin Dynamics in The
TRANSCRIPTION AND CHROMATIN DYNAMICS IN THE NOTCH SIGNALLING RESPONSE Zoe Pillidge Churchill College Department of Physiology, Development and Neuroscience University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy September 2018 TRANSCRIPTION AND CHROMATIN DYNAMICS IN THE NOTCH SIGNALLING RESPONSE During normal development, different genes are expressed in different cell types, often directed by cell signalling pathways and the pre-existing chromatin environment. The highly-conserved Notch signalling pathway is involved in many cell fate decisions during development, activating different target genes in different contexts. Upon ligand binding, the Notch receptor itself is cleaved, allowing the intracellular domain to travel to the nucleus and activate gene expression with the transcription factor known as Suppressor of Hairless (Su(H)) in Drosophila melanogaster. It is remarkable how, with such simplicity, the pathway can have such diverse outcomes while retaining precision, speed and robustness in the transcriptional response. The primary goal of this PhD has been to gain a better understanding of this process of rapid transcriptional activation in the context of the chromatin environment. To learn about the dynamics of the Notch transcriptional response, a live imaging approach was used in Drosophila Kc167 cells to visualise the transcription of a Notch-responsive gene in real time. With this technique, it was found that Notch receptor cleavage and trafficking can take place within 15 minutes to activate target gene expression, but that a ligand-receptor interaction between neighbouring cells may take longer. These experiments provide new data about the dynamics of the Notch response which could not be obtained with static time-point experiments. -
Intrinsic Specificity of DNA Binding and Function of Class II Bhlh
INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS APPROVED BY SUPERVISORY COMMITTEE Jane E. Johnson Ph.D. Helmut Kramer Ph.D. Genevieve Konopka Ph.D. Raymond MacDonald Ph.D. INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS by BRADFORD HARRIS CASEY DISSERTATION Presented to the Faculty of the Graduate School of Biomedical Sciences The University of Texas Southwestern Medical Center at Dallas In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY The University of Texas Southwestern Medical Center at Dallas Dallas, Texas December, 2016 DEDICATION This work is dedicated to my family, who have taught me pursue truth in all forms. To my grandparents for inspiring my curiosity, my parents for teaching me the value of a life in the service of others, my sisters for reminding me of the importance of patience, and to Rachel, who is both “the beautiful one”, and “the smart one”, and insists that I am clever and beautiful, too. Copyright by Bradford Harris Casey, 2016 All Rights Reserved INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS Publication No. Bradford Harris Casey The University of Texas Southwestern Medical Center at Dallas, 2016 Jane E. Johnson, Ph.D. PREFACE Embryonic development begins with a single cell, and gives rise to the many diverse cells which comprise the complex structures of the adult animal. Distinct cell fates require precise regulation to develop and maintain their functional characteristics. Transcription factors provide a mechanism to select tissue-specific programs of gene expression from the shared genome. -
Core Binding Factor Β Is Required for Group 2 Innate Lymphoid Cell Activation
Cutting Edge: Core Binding Factor β Is Required for Group 2 Innate Lymphoid Cell Activation This information is current as Xiaofei Shen, Mingwei Liang, Xiangyu Chen, Muhammad of September 24, 2021. Asghar Pasha, Shanti S. D'Souza, Kelsi Hidde, Jennifer Howard, Dil Afroz Sultana, Ivan Ting Hin Fung, Longyun Ye, Jiexue Pan, Gang Liu, James R. Drake, Lisa A. Drake, Jinfang Zhu, Avinash Bhandoola and Qi Yang J Immunol 2019; 202:1669-1673; Prepublished online 6 Downloaded from February 2019; doi: 10.4049/jimmunol.1800852 http://www.jimmunol.org/content/202/6/1669 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2019/02/05/jimmunol.180085 Material 2.DCSupplemental References This article cites 20 articles, 10 of which you can access for free at: http://www.jimmunol.org/content/202/6/1669.full#ref-list-1 Why The JI? Submit online. by guest on September 24, 2021 • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2019 by The American Association of Immunologists, Inc. All rights reserved. -
An Activation-Specific Role for Transcription Factor TFIIB in Vivo (Sua7͞pho4͞pho5͞transcriptional Control)
Proc. Natl. Acad. Sci. USA Vol. 96, pp. 2764–2769, March 1999 Biochemistry An activation-specific role for transcription factor TFIIB in vivo (SUA7yPho4yPHO5ytranscriptional control) WEI-HUA WU AND MICHAEL HAMPSEY* Department of Biochemistry, Division of Nucleic Acids Enzymology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ 08854-5635 Edited by Robert G. Roeder, The Rockefeller University, New York, NY, and approved January 20, 1999 (received for review November 9, 1998) ABSTRACT A yeast mutant was isolated encoding a sin- a study of PIC formation and transcriptional activity demon- gle amino acid substitution [serine-53 3 proline (S53P)] in strated that PIC assembly occurs by at least two steps and that transcription factor TFIIB that impairs activation of the the TATA box and TFIIB also can affect transcription subse- PHO5 gene in response to phosphate starvation. This effect is quent to PIC assembly (11). Thus, processes other than factor activation-specific because S53P did not affect the uninduced recruitment are potential activator targets. level of PHO5 expression, yet is not specific to PHO5 because TFIIB is recruited to the PIC by the acidic activator VP16 Adr1-mediated activation of the ADH2 gene also was impaired and the proline-rich activator CTF1 (3, 12). VP16 directly by S53P. Pho4, the principal activator of PHO5, directly targets TFIIB (3) and this interaction is required for activation interacted with TFIIB in vitro, and this interaction was (13, 14). Interestingly, VP16 induces a conformational change impaired by the S53P replacement. Furthermore, Pho4 in- in TFIIB that disrupts the intramolecular interaction between duced a conformational change in TFIIB, detected by en- the N- and C-terminal domains in solution (15). -
The HLH-6 Transcription Factor Regulates C
The HLH-6 Transcription Factor Regulates C. elegans Pharyngeal Gland Development and Function Ryan B. Smit1, Ralf Schnabel2, Jeb Gaudet1,3* 1 Genes and Development Research Group, Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada, 2 Institut fu¨r Genetik, Technische Universita¨t Braunschweig, Braunschweig, Germany, 3 Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada Abstract The Caenorhabditis elegans pharynx (or foregut) functions as a pump that draws in food (bacteria) from the environment. While the ‘‘organ identity factor’’ PHA-4 is critical for formation of the C. elegans pharynx as a whole, little is known about the specification of distinct cell types within the pharynx. Here, we use a combination of bioinformatics, molecular biology, and genetics to identify a helix-loop-helix transcription factor (HLH-6) as a critical regulator of pharyngeal gland development. HLH-6 is required for expression of a number of gland-specific genes, acting through a discrete cis-regulatory element named PGM1 (Pharyngeal Gland Motif 1). hlh-6 mutants exhibit a frequent loss of a subset of glands, while the remaining glands have impaired activity, indicating a role for hlh-6 in both gland development and function. Interestingly, hlh-6 mutants are also feeding defective, ascribing a biological function for the glands. Pharyngeal pumping in hlh-6 mutants is normal, but hlh-6 mutants lack expression of a class of mucin-related proteins that are normally secreted by pharyngeal glands and line the pharyngeal cuticle. An interesting possibility is that one function of pharyngeal glands is to secrete a pharyngeal lining that ensures efficient transport of food along the pharyngeal lumen. -
Supplemental Information For
Supplemental Information for: Gene Expression Profiling of Pediatric Acute Myelogenous Leukemia Mary E. Ross, Rami Mahfouz, Mihaela Onciu, Hsi-Che Liu, Xiaodong Zhou, Guangchun Song, Sheila A. Shurtleff, Stanley Pounds, Cheng Cheng, Jing Ma, Raul C. Ribeiro, Jeffrey E. Rubnitz, Kevin Girtman, W. Kent Williams, Susana C. Raimondi, Der-Cherng Liang, Lee-Yung Shih, Ching-Hon Pui & James R. Downing Table of Contents Section I. Patient Datasets Table S1. Diagnostic AML characteristics Table S2. Cytogenetics Summary Table S3. Adult diagnostic AML characteristics Table S4. Additional T-ALL characteristics Section II. Methods Table S5. Summary of filtered probe sets Table S6. MLL-PTD primers Additional Statistical Methods Section III. Genetic Subtype Discriminating Genes Figure S1. Unsupervised Heirarchical clustering Figure S2. Heirarchical clustering with class discriminating genes Table S7. Top 100 probe sets selected by SAM for t(8;21)[AML1-ETO] Table S8. Top 100 probe sets selected by SAM for t(15;17) [PML-RARα] Table S9. Top 63 probe sets selected by SAM for inv(16) [CBFβ-MYH11] Table S10. Top 100 probe sets selected by SAM for MLL chimeric fusion genes Table S11. Top 100 probe sets selected by SAM for FAB-M7 Table S12. Top 100 probe sets selected by SAM for CBF leukemias (whole dataset) Section IV. MLL in combined ALL and AML dataset Table S13. Top 100 probe sets selected by SAM for MLL chimeric fusions irrespective of blast lineage (whole dataset) Table S14. Class discriminating genes for cases with an MLL chimeric fusion gene that show uniform high expression, irrespective of blast lineage Section V. -
DNA Binding Site of the Growth Factor-Inducible Protein Zif268
Proc. Natl. Acad. Sci. USA Vol. 86, pp. 8737-8741, November 1989 Biochemistry DNA binding site of the growth factor-inducible protein Zif268 ("zinc fingers"/transcription factor/cell growth) BARBARA CHRISTY AND DANIEL NATHANS* Howard Hughes Medical Institute and the Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205 Contributed by Daniel Nathans, August 31, 1989 ABSTRACT Zif268, a zinc finger protein whose mRNA is The recombinant plasmid was transfected into E. coli strain rapidly activated in cells exposed to growth factors or other BL21(DE3) and the transfected cells were grown as described signaling agents, is thought to play a role in regulating the (22) to an optical density of about 0.6 before induction of T7 genetic program induced by extracellular ligands. We report RNA polymerase with 0.4 mM isopropyl B-D-thiogalacto- that Zif268 has one of the characteristics of a transcriptional pyranoside (IPTG). Bacterial extracts used for DNA binding regulator, namely, sequence-specific binding to DNA. Zif268 were prepared as described (23). After solubilization of synthesized in Escherichia coli bound to two sites upstream of proteins with 4 M urea the preparation was dialyzed against the zif268 gene and to sites in the promoter regions of other 20 mM Tris, pH 7.7/50 mM KCI/10 mM MgCl2/i mM genes. The nucleotide sequences responsible for binding were EDTA/10 ,uM ZnSO4/20% (vol/vol) glycerol/1 mM dithio- defined by DNase I footprinting, by methylation interference threitol/0.2 mM phenylmethylsulfonyl fluoride/1 mM so- experiments, and by use of synthetic oligonucleotides. -
Steffensen Et Al. Supplement 2A
Liver Wild-type Knockout 1 2 4 1 2 4 1 1 1 C C C C C C W W W K K K IMAGE:640919 expressed sequence AA960558 IMAGE:523974 RIKEN cDNA 1810004N01 gene IMAGE:1211217 eukaryotic translation initiation factor 4E binding protein 2 IMAGE:314741 beta-site APP cleaving enzyme IMAGE:1038420 silica-induced gene 41 IMAGE:1229655 soc-2 suppressor of clear homolog C. elegans IMAGE:1150065 Unknown IMAGE:1038486 RIKEN cDNA 2700038M07 gene IMAGE:524351 amyloid beta A4 precursor protein IMAGE:819912 thymus expressed acidic protein IMAGE:779426 RIKEN cDNA 5230400G24 gene IMAGE:945643 ESTs IMAGE:850544 Mpv17 transgene, kidney disease mutant IMAGE:537568 expressed sequence AA675315 IMAGE:1107584 yes-associated protein, 65 kDa IMAGE:961363 expressed sequence AU015422 IMAGE:775218 expressed sequence AI265322 IMAGE:792656 protein phosphatase 1B, magnesium dependent, beta isoform IMAGE:1038592 ESTs, Weakly similar to A43932 mucin 2 precursor, intestinal [H.sapiens] IMAGE:1224917 huntingtin interacting protein 2 IMAGE:751186 ESTs IMAGE:865151 Trk-fused gene IMAGE:523016 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 5 IMAGE:765039 FBJ osteosarcoma oncogene B IMAGE:1003995 expressed sequence AI844632 IMAGE:903863 RIKEN cDNA 1300006L01 gene IMAGE:934094 ESTs IMAGE:988962 expressed sequence C77245 IMAGE:1023308 ESTs, Weakly similar to S71512 hypothetical protein T2 - mouse [M.musculus] IMAGE:865317 eukaryotic translation initiation factor 3 IMAGE:720445 ribosomal protein S6 IMAGE:1005417 expressed sequence AU024550