Abstract Genome-Wide Computational Analysis Of

Total Page:16

File Type:pdf, Size:1020Kb

Abstract Genome-Wide Computational Analysis Of ABSTRACT GENOME-WIDE COMPUTATIONAL ANALYSIS OF CHLAMYDOMONAS REINHARDTII PROMOTERS by Kokulapalan Wimalanathan As a model organism, use of Chlamydomonas is not only limited with biological experiments to understand chloroplast and flagella, but is also extended to biodiesel production. Chlamydomonas promoter regions were extracted based on available RNA-Seq data and community genome annotation, and promoters were used to analyze and detect core and proximal promoter elements. While the evidence suggests only the TATA box (canonical and non-canonical TATA boxes) as the only core promoter element, it also indicates that the TATA box in Chlamydomonas is different than Arabidopsis and human TATA boxes. While some proximal promoter elements discovered show weak similarities to known promoter elements from other species, most are novel elements only present in Chlamydomonas. Most of the proximal promoter elements detected show significant similarities to each other. It is evident from this study that the promoter architecture in Chlamydomonas seems to be simpler compared to animals and plants. GENOME-WIDE COMPUTATIONAL ANALYSIS OF CHLAMYDOMONAS REINHARDTII PROMOTERS A Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science Department of Botany by Kokulapalan Wimalanathan Miami University Oxford, Ohio 2011 Advisor ________________________ (Dr. Chun Liang) Reader ________________________ (Dr. Roger Meicenheimer) Reader ________________________ (Dr. Mufit Ozden) i Table of Contents List of Tables..................................................................................................................................iii List of Figures.................................................................................................................................iv Acknowledgments............................................................................................................................v 1 Chapter 1:Review of Core Promoter Analysis.............................................................................1 1.1 Gene expression...................................................................................................................1 1.1.1 Eukaryotic gene structure.............................................................................................1 1.1.2 Transcription factors and cis-regulatory elements........................................................2 1.1.3 Transcription initiation.................................................................................................2 1.1.4 Different types of promoters based on the distribution of TSS....................................3 1.1.5 Core promoter elements...............................................................................................4 1.1.6 Proximal promoter elements.........................................................................................6 1.1.7 Alternative promoters...................................................................................................7 1.2 Computational methods to identify cis-regulatory elements...............................................9 1.2.1 Enumerative method.....................................................................................................9 1.2.2 Deterministic optimization method............................................................................11 1.2.3 Probabilistic optimization method..............................................................................12 1.2.4 Notations to represent cis-regulatory elements..........................................................12 1.3 References..........................................................................................................................14 1.4 Figures................................................................................................................................21 1.5 Tables.................................................................................................................................28 2 Chapter 2 ................................................................................................................................30 2.1 Abstract..............................................................................................................................30 2.2 Introduction........................................................................................................................31 2.3 Results................................................................................................................................37 2.3.1 Obtaining promoter regions for analysis....................................................................37 2.3.2 LDSS analysis of core promoters of human, Arabidopsis and Chlamydomonas ......37 2.3.3 Comparing and combining octamer clusters to produce putative core promoter elements................................................................................................................................41 2.3.4 Analysis of proximal promoter elements...................................................................42 2.4 Discussion..........................................................................................................................47 2.5 Materials and Methods.......................................................................................................52 2.5.1 Obtaining Valid Promoter Sequences.........................................................................52 2.5.2 LDSS analysis of core promoters of three species.....................................................53 2.5.3 Comparing and combining octamer clusters to form octamer groups .....................55 2.5.4 Analysis of proximal promoter elements...................................................................56 2.6 Acknowledgments..............................................................................................................58 2.7 References..........................................................................................................................59 2.8 Figures................................................................................................................................68 2.9 Tables.................................................................................................................................81 ii List of Tables Table 1.1: The position and consensus of the core promoter elements in plants and animals.......28 Table 1.2: IUPAC single character codes for nucleic acid sequences............................................29 Table 2.1: Basic statistics of upstream promoter analysis.............................................................81 Table 2.2: The 14 KEGG motif groups and their component KEGG motifs.................................82 Table 2.3: Functional annotation of GO motifs detected in GO gene groups................................83 Table 2.4: Functional annotation of KEGG motifs detected in KEGG gene groups.....................84 iii List of Figures Figure 1.1: Gene expression, structural gene components and pre-initiation complex.................21 Figure 1.2: Different types of promoters based on TSS distribution.............................................22 Figure 1.3: Core promoter elements commonly found in plants and animals...............................23 Figure 1.4: A simple example of how to use the LDSS method. ..................................................24 Figure 1.5: Creating sequence logos..............................................................................................25 Figure 1.6: Information represented in a sequence logo................................................................26 Figure 1.7: Several ways commonly used to represent consensus motif models..........................27 Figure 2.1: The Major core promoter elements present in animals and plants..............................68 Figure 2.2: Examples of LDSS-positive and LDSS-negative octamers in Chlamydomonas.......69 Figure 2.3: LDSS heatmap graphs for Arabidopsis, human, and Chlamydomonas......................70 Figure 2.4: Sequence logos from Arabidopsis LDSS octamer clusters.........................................71 Figure 2.5: Sequence logos from human LDSS octamer clusters.................................................72 Figure 2.6: Sequence logos from Chlamydomonas LDSS octamer clusters. ...............................73 Figure 2.7: Sequence logos from combined putative core promoter elements..............................74 Figure 2.8: GO motifs....................................................................................................................75 Figure 2.9: Representative motifs from each KEGG motif group.................................................76 Figure 2.10: Overall method used to analyze promoters...............................................................77 Figure 2.11: LDSS Parameters.......................................................................................................78 Figure 2.12: Valid Promoters.........................................................................................................79 Figure 2.13: MEME Minimal Motif Format..................................................................................80 iv Acknowledgments I heartily thank my advisor, Dr. Chun Liang, whose encouragement, guidance, and support has helped me conduct this research in a meaningful
Recommended publications
  • Involvement of REST Corepressor 3 in Prognosis of Human Hepatitis B
    Acta Pharmacologica Sinica (2011) 32: 1019–1024 npg © 2011 CPS and SIMM All rights reserved 1671-4083/11 $32.00 www.nature.com/aps Original Article Involvement of REST corepressor 3 in prognosis of human hepatitis B Ji-hua XUE, Min ZHENG, Xiao-wei XU, Shan-shan WU, Zhi CHEN, Feng CHEN* State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Aim: To examine the potential correlation between serum REST corepressor 3 (RCOR3) level and the outcome of patients with hepa-­­ titis B. Methods: Concanavalin A (ConA)-induced mouse hepatitis model was used. The mRNA level of RCOR3 in mouse liver was measured using GeneChip array and real-time PCR. One hundred seventy-seven patients with hepatitis B and 34 healthy individuals were catego- rized into six groups including mild chronic hepatitis, moderate chronic hepatitis B, severe hepatitis B (SHB), cirrhosis, hepatocellular carcinoma (HCC) and healthy control. Serum levels of human RCOR3 were measured using ELISA. Results: In the mouse hepatitis model, the mRNA level of RCOR3 in liver was reduced early after exposure to ConA, then increased after 6 h of exposure. There was no significant difference in the serum RCOR3 level between the mild chronic hepatitis B and the con- trol groups. The serum RCOR3 level was significantly increased in the moderate chronic hepatitis B group, but significantly reduced in SHB, cirrhosis and HCC groups, as compared with the control group. Moreover, the serum RCOR3 levels in SHB, cirrhosis and liver cancer patients were significantly lower than those in the patients with moderate chronic hepatitis B and with mild chronic hepatitis B.
    [Show full text]
  • Silencer Elements Controlling the B29 (Ig␤) Promoter Are Neither Promoter- Nor Cell-Type-Specific
    Proc. Natl. Acad. Sci. USA Vol. 94, pp. 12314–12319, November 1997 Biochemistry Silencer elements controlling the B29 (Igb) promoter are neither promoter- nor cell-type-specific CINDY SUE MALONE*, SIDNE A. OMORI*†, AND RANDOLPH WALL*‡ *Molecular Biology Institute and Department of Microbiology and Immunology, University of California, Los Angeles, School of Medicine, Los Angeles, CA 90095 Communicated by David S. Eisenberg, University of California, Los Angeles, CA, July 11, 1997 (received for review May 9, 1997) ABSTRACT The murine B29 (Igb) promoter is B cell combinatorial activities of this cassette of different transcrip- specific and contains essential SP1, ETS, OCT, and Ikaros tion factors (11, 20). motifs. Flanking 5* DNA sequences inhibit B29 promoter The current study focuses on the B29 regulatory region 59 of activity, suggesting this region contains silencer elements. the murine minimal B29 promoter. Deletion analyses of the Two adjacent 5* DNA segments repress transcription by the B29 gene 59 flanking region revealed two adjacent regions with murine B29 promoter in a position- and orientation- significantly lower transcriptional activity than the minimal independent manner, analogous to known silencers. Both promoter, suggesting the presence of silencer elements. Si- these 5* segments also inhibit transcription by several heter- lencers are functionally defined cis-acting regulatory DNA ologous promoters in B cells, including mb-1, c-fos, and human elements that down-regulate gene transcription. They gener- B29. These 5* segments also inhibit transcription by the c-fos ally exhibit activity in either orientation, may be either posi- promoter in T cells suggesting they are not B cell-specific tion-dependent or -independent, and may or may not affect elements.
    [Show full text]
  • Robijn Bruinsma
    Opportunities for Theory in Biological Physics. 1) Chromosome Control. 2) The Polyglutamine Problem. 3) Transcription Initiation Complex. 4) Ribosomal Proofreading. 5) Focal Adhesion Sites. *DNA/DNA interaction: Aqueous electrostatics beyond mean-field theory. (Oosawa) *DNA/nucleosome interaction: electrostatic attraction versus bending stiffness. (Manning) *Micromechanics (M.Wang) Nucleus: 23 chromosomes (1m DNA in micron-sized nucleus) Gene regulation by compaction. “Chromosome painting”: 3D-FISH Statics: 3-D Reconstruction of Nucleus. DNA-DNA mean spacing: 30-40 Angstrom. Close-packing is close (Cremer) Expanded Chromosome Condensed Loop (active genes) inactive genes Decondensed, active genes Inter-chromatin Compartment Active gene: on surface. Late replicating gene Nucleus is fully accessible to protein transport. 3-D Fish: Chromosome Dynamics (20 minute intervals) Chromosomal “Diffusion” Chromosomal Volume and Surface Area vs time. Statics: How is the “open” architecture of the nucleus maintained and controlled under the osmotic pressure of de-condensed, active DNA sections. Equation of State of DNA bundles is known. Dynamics: Chromosome dynamics driven by DNA condensation/de-conden- sation events triggered by local gene expression:”gene noise”. *Can we deduce temporal and spatial correlation functions for gene noise from the motion of the chromosomes by fluctuation analysis and relate it to gene activity? *Chromosome “micro-rheology”? The Polyglutamine Problem Nine neuro-degenerative diseases are associated with (CAG)N triplet repeats: Huntingdon’s, spinal dystrophy, ataxia …. CAG is the code for the amino-acid glutamine. C. Elegans worm GFP (CAG)N N=19 Homogeneous N=82: Toxic Aggregates Impaired motility Proteosome action N=82 (x 40) inhibited. Aggregates: N > 35-40 In vitro polyglutamine homopolymer aggregation (N=37) Aggregation Kinetics (Wetzel): Chen, Songming et al.
    [Show full text]
  • CHD7 Represses the Retinoic Acid Synthesis Enzyme ALDH1A3 During Inner Ear Development
    CHD7 represses the retinoic acid synthesis enzyme ALDH1A3 during inner ear development Hui Yao, … , Shigeki Iwase, Donna M. Martin JCI Insight. 2018;3(4):e97440. https://doi.org/10.1172/jci.insight.97440. Research Article Development Neuroscience CHD7, an ATP-dependent chromatin remodeler, is disrupted in CHARGE syndrome, an autosomal dominant disorder characterized by variably penetrant abnormalities in craniofacial, cardiac, and nervous system tissues. The inner ear is uniquely sensitive to CHD7 levels and is the most commonly affected organ in individuals with CHARGE. Interestingly, upregulation or downregulation of retinoic acid (RA) signaling during embryogenesis also leads to developmental defects similar to those in CHARGE syndrome, suggesting that CHD7 and RA may have common target genes or signaling pathways. Here, we tested three separate potential mechanisms for CHD7 and RA interaction: (a) direct binding of CHD7 with RA receptors, (b) regulation of CHD7 levels by RA, and (c) CHD7 binding and regulation of RA-related genes. We show that CHD7 directly regulates expression of Aldh1a3, the gene encoding the RA synthetic enzyme ALDH1A3 and that loss of Aldh1a3 partially rescues Chd7 mutant mouse inner ear defects. Together, these studies indicate that ALDH1A3 acts with CHD7 in a common genetic pathway to regulate inner ear development, providing insights into how CHD7 and RA regulate gene expression and morphogenesis in the developing embryo. Find the latest version: https://jci.me/97440/pdf RESEARCH ARTICLE CHD7 represses the retinoic acid synthesis enzyme ALDH1A3 during inner ear development Hui Yao,1 Sophie F. Hill,2 Jennifer M. Skidmore,1 Ethan D. Sperry,3,4 Donald L.
    [Show full text]
  • Oncjuly3 6..6
    Oncogene (1999) 18, 4137 ± 4143 ã 1999 Stockton Press All rights reserved 0950 ± 9232/99 $12.00 http://www.stockton-press.co.uk/onc Tax protein of HTLV-1 inhibits CBP/p300-mediated transcription by interfering with recruitment of CBP/p300 onto DNA element of E-box or p53 binding site Takeshi Suzuki1, Masami Uchida-Toita1 and Mitsuaki Yoshida*,1 1Department of Cellular and Molecular Biology, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan Tax protein of human T-cell leukemia virus type 1 Tax was originally identi®ed as a transcriptional (HTLV-1) is a potent transcriptional regulator which can activator for viral gene expression and then was shown activate or repress speci®c cellular genes and has been to activate a wide variety of cellular genes (Yoshida et proposed to contribute to leukemogenic processes in al., 1995). Tax was also demonstrated to inhibit adult T-cell leukemia. The molecular mechanism of Tax- expression of several genes. In addition to the mediated trans-activation has been well investigated. transcriptional deregulation, we found that Tax binds However, trans-repression by Tax remains to be studied to p16ink4a protein, a cyclin-dependent kinase (CDK) in detail, although it is known to require a speci®c DNA inhibitor, and suppresses its inhibitory activity pre- element such as E-box or p53 binding site. Examining venting the cell from undergoing growth arrest (Suzuki possible mechanisms of trans-repression, we found that et al., 1996). These pleiotropic functions of Tax aect co-expression of E47 and p300 activated E-box multiple regulatory processes of cells and are believed dependent transcription and this activation was eciently to play roles at least in the initial stage of repressed by Tax.
    [Show full text]
  • Transcrip\Onal and Epigene\C Changes During Heart Disease
    786/110 Transcrip)onal and Epigene)c Changes during Heart Disease Danish Sayed Unique Features of Heart • Involuntary, rhythmic, cyclic contractions • Terminally differentiated, postnatal myocytes increase in size not numbers for growth • Number of non myocytes (e.g. fibroblasts) is more (60-70%) compared to number of myocytes (~30%). Neonate Heart Postnatal Growth “Physiological” Adult Heart - Exercise - Hypertension - Pregnancy - Aortic Stenosis - Sarcomeric Gene mutation - Myocardial Infarction - Dilated Cardiomyopathy Physiological Pathological Hypertrophy Hypertrophy Dilatation and Failure Compensatory Decompensation Overload • Increase in cardiomyocyte size • Chamber dilatation and mass, resulting in enlarged heart • Decreased Ventricular Wall • Increase in generalized gene Thickness and Increased expression, superimposed with wall tension significant increase in specialized genes, fetal gene • Altered Ca+2 handling program • Increased wall thickness • Increased myocardial • Switch to glucose metabolism apoptosis for energy Compromised Maintained Cardiac Cardiac function and Function and Output Output Modulators of Cardiac Hypertrophy L-Type Ca+2 Ang II, ET-1 Channel α-Adrenergic β-Adrenergic Mechanical Ca+2 Insulin-like Growth Stretch + Factor Na Gq Gs P Ca+2 PKA PLC Adenylyl Integrin Cyclase Ca+2 RAS-GTP Na+ PI3K IP3 DAG cAMP Calcineurine FAK AKT Ca+2 Rho PKC PKA Ras mTOR Rac GSK3β 4EBP1 Rock MLCK NFAT MAPK eIF2B p70S6K eIF4E ME ME ME ME GATA4 GATA4 MEF2 Sarcomere SRF Translation AC AC Transcription HDAC Histone Acetylation HAT Modificatn. Methylation HMT Chromatin Demethylases Remodeling Phosphorylation DNA Methylation Transcriptional Modificatn. General Factors Transcription factors TFIIA, TFIIB, TFIID… Promoter Specific Factors GATA family, NFATc family Activity Recruitment Gene Regulation RNA Polymerase II Dynamics Initiation Elongation Translation factors e.g.
    [Show full text]
  • Repressor to Activator Switch by Mutations in the First Zn Finger of The
    Proc. Nat!. Acad. Sci. USA Vol. 88, pp. 7086-7090, August 1991 Biochemistry Repressor to activator switch by mutations in the first Zn finger of the glucocorticoid receptor: Is direct DNA binding necessary? (interleukin 1 indudbility/dexamethasone modulation/DNA-binding domain mutants/interleukin 6 and c-fos promoters) ANURADHA RAY, K. STEVEN LAFORGE, AND PRAVINKUMAR B. SEHGAL* The Rockefeller University, New York, NY 10021 Communicated by Igor Tamm, May 20, 1991 (receivedfor review March 15, 1991) ABSTRACT Transfection ofHeLa cells with cDNA vectors previous experiments in HeLa cells transfected with cDNA expressing the wild-type human glucocorticoid receptor (GR) vectors constitutively expressing the wild type (wt) but not enabled dexamethasone to strongly repress cytokine- and sec- the inactive carboxyl-terminal truncation mutants of GR, we ond messenger-induced expression of cotransfected chimeric observed that dexamethasone (Dex) strongly repressed the reporter genes containing transcription regulatory DNA ele- induction by interleukin 1 (IL-1), tumor necrosis factor, ments from the human interleukin 6 (IL-6) promoter. Deletion phorbol ester, or forskolin of IL-6-chloramphenicol acetyl- of the DNA-binding domain or of the second Zn finger or a transferase (CAT) constructs containing IL-6 DNA from point mutation in the Zn catenation site in the second finger -225 to +13. Dex also repressed induction of IL-6- blocked the ability of GR to mediate repression of the IL-6 thymidine kinase (TK)-CAT chimeric constructs containing promoter.
    [Show full text]
  • UNIT 6 from DNA to Protein: Gene Expression PART 2 Hillis Textbook
    UNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11 REVIEW: Signals that Start and Stop Transcription and Translation BUT, HOW DO CELLS CONTROL WHICH GENES ARE EXPRESSED AND WHEN? First of all, There is a difference between regulation in a prokaryote and a eukaryote…. OPERONS - PROKARYOTES Prokaryotes conserve energy by making proteins only when needed. The most efficient gene regulation is at the level of transcription. A gene cluster with a single promoter is an operon. An operator is a short stretch of DNA near the promoter that controls transcription of the structural genes. 1. Inducible operon—turned off unless needed In inducible systems—a metabolic substrate (inducer) interacts with a regulatory protein (repressor); the repressor cannot bind and allows transcription. Usually control CATABOLIC REACTIONS 2. Repressible operon—turned on unless not needed In repressible systems—a metabolic product (co-repressor) binds to regulatory protein, which then binds to the operator and blocks transcription. Usually control ANABOLIC REACTIONS. LAC OPERON - INDUCIBLE A compound that induces protein synthesis is an inducer. When the enzymes are induced, metabolism will take place. LAC OPERON – INDUCIBLE E. coli must adapt quickly to supply of food (lactose is a dissacharide example) Uptake and metabolism of lactose involves three important -galactoside enzymes -galactoside is a type of glycosidic bond between monosaccharides… if this is present, LACTOSE is present. If E. coli is grown with glucose but no lactose present, no enzymes for lactose conversion are produced. If lactose is predominant and glucose is low, E. coli synthesizes all three enzymes. If lactose is removed, synthesis stops.
    [Show full text]
  • The Neuron-Restrictive Silencer Element: a Dual Enhancer͞silencer Crucial for Patterned Expression of a Nicotinic Receptor Gene in the Brain
    Proc. Natl. Acad. Sci. USA Vol. 94, pp. 5906–5911, May 1997 Neurobiology The neuron-restrictive silencer element: A dual enhancerysilencer crucial for patterned expression of a nicotinic receptor gene in the brain ALAIN BESSIS*, NICOLAS CHAMPTIAUX,LAURENT CHATELIN, AND JEAN-PIERRE CHANGEUX† Neurobiologie Mole´culaire,UA CNRS D1284, De´partementdes Biotechnologies, Institut Pasteur 25y28 rue du Dr Roux, 75724 Paris Cedex 15 Contributed by Jean-Pierre Changeux, March 24, 1997 ABSTRACT The neuron-restrictive silencer element transcriptional repressing activity on promoters that carry the (NRSE) has been identified in several neuronal genes and NRSE sequence (15, 21, 23). confers neuron specificity by silencing transcription in non- In this work, we have analyzed the function of NRSE in the neuronal cells. NRSE is present in the promoter of the promoter of the mouse gene encoding the nAChR b2-subunit neuronal nicotinic acetylcholine receptor b2-subunit gene as well as the importance of its location within the promoter. that determines its neuron-specific expression in the nervous In transgenic mice, we show that upon mutation of NRSE, the system. Using transgenic mice, we show that NRSE may either pattern of expression of the b2-subunit gene promoter dra- silence or enhance transcription depending on the cellular matically changes but remains restricted to the nervous system. context within the nervous system. In vitro in neuronal cells, We confirm that NRSE in vitro can behave either as an NRSE activates transcription of synthetic promoters when enhancer or as a silencer depending both on the primary located downstream in the 5* untranslated region, or at less structure of the promoter and the type of cell line.
    [Show full text]
  • Identification of a Family of Camp Response Element-Binding Protein Coactivators by Genome-Scale Functional Analysis in Mammalian Cells
    Identification of a family of cAMP response element-binding protein coactivators by genome-scale functional analysis in mammalian cells Vadim Iourgenko*†, Wenjun Zhang*†, Craig Mickanin*, Ira Daly*, Can Jiang*, Jonathan M. Hexham*, Anthony P. Orth‡, Loren Miraglia‡, Jodi Meltzer*, Dan Garza*, Gung-Wei Chirn*, Elizabeth McWhinnie*, Dalia Cohen*, Joanne Skelton*, Robert Terry*, Yang Yu*, Dale Bodian*, Frank P. Buxton*, Jian Zhu*, Chuanzheng Song*, and Mark A. Labow*§ *Department of Functional Genomics, Novartis Institute for Biomedical Research, 100 Technology Square, Cambridge, MA 02139; and ‡Genomics Institute, Novartis Research Foundation, 10675 John Jay Hopkins Drive, Suite F117, San Diego, CA 92121 Edited by Peter K. Vogt, The Scripps Research Institute, La Jolla, CA, and approved July 30, 2003 (received for review May 8, 2003) This report describes an unbiased method for systematically de- of screening data and further experiments demonstrated that the termining gene function in mammalian cells. A total of 20,704 IL-8 promoter contained an unrecognized cAMP response predicted human full-length cDNAs were tested for induction of element (CRE)-like element that was activated by a protein, the IL-8 promoter. A number of genes, including those for cyto- termed transducer of regulated cAMP response element-binding kines, receptors, adapters, kinases, and transcription factors, were protein (CREB) TORC1, which is the founding member of a identified that induced the IL-8 promoter through known regula- conserved family of CREB coactivators. Thus, screening of tory sites. Proteins that acted through a cooperative interaction arrayed cDNAs represents an unbiased approach for identifica- between an AP-1 and an unrecognized cAMP response element tion of gene function and elucidation of pathways that regulate (CRE)-like site were also identified.
    [Show full text]
  • Transcription in Archaea
    Proc. Natl. Acad. Sci. USA Vol. 96, pp. 8545–8550, July 1999 Evolution Transcription in Archaea NIKOS C. KYRPIDES* AND CHRISTOS A. OUZOUNIS†‡ *Department of Microbiology, University of Illinois at Urbana-Champaign, B103 Chemistry and Life Sciences, MC 110, 407 South Goodwin Avenue, Urbana, IL 61801; and †Computational Genomics Group, Research Programme, The European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge Outstation, Wellcome Trust Genome Campus, Cambridge CB10 1SD, United Kingdom Edited by Norman R. Pace, University of California, Berkeley, CA, and approved May 11, 1999 (received for review December 21, 1998) ABSTRACT Using the sequences of all the known transcrip- enzyme was found to have a complexity similar to that of the tion-associated proteins from Bacteria and Eucarya (a total of Eucarya (consisting of up to 15 components) (17). Subsequently, 4,147), we have identified their homologous counterparts in the the sequence similarity between the large (universal) subunits of four complete archaeal genomes. Through extensive sequence archaeal and eukaryotic polymerases was demonstrated (18). comparisons, we establish the presence of 280 predicted tran- This discovery was followed by the first unambiguous identifica- scription factors or transcription-associated proteins in the four tion of transcription factor TFIIB in an archaeon, Pyrococcus archaeal genomes, of which 168 have homologs only in Bacteria, woesei (19). Since then, we have witnessed a growing body of 51 have homologs only in Eucarya, and the remaining 61 have evidence confirming the presence of key eukaryotic-type tran- homologs in both phylogenetic domains. Although bacterial and scription initiation factors in Archaea (5, 20). Therefore, the eukaryotic transcription have very few factors in common, each prevailing view has become that Archaea and Eucarya share a exclusively shares a significantly greater number with the Ar- transcription machinery that is very different from that of Bac- chaea, especially the Bacteria.
    [Show full text]
  • A STAT Protein Domain That Determines DNA Sequence Recognition Suggests a Novel DNA-Binding Domain
    Downloaded from genesdev.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press A STAT protein domain that determines DNA sequence recognition suggests a novel DNA-binding domain Curt M. Horvath, Zilong Wen, and James E. Darnell Jr. Laboratory of Molecular Cell Biology, The Rockefeller University, New York, New York 10021 Statl and Stat3 are two members of the ligand-activated transcription factor family that serve the dual functions of signal transducers and activators of transcription. Whereas the two proteins select very similar (not identical) optimum binding sites from random oligonucleotides, differences in their binding affinity were readily apparent with natural STAT-binding sites. To take advantage of these different affinities, chimeric Statl:Stat3 molecules were used to locate the amino acids that could discriminate a general binding site from a specific binding site. The amino acids between residues -400 and -500 of these -750-amino-acid-long proteins determine the DNA-binding site specificity. Mutations within this region result in Stat proteins that are activated normally by tyrosine phosphorylation and that dimerize but have greatly reduced DNA-binding affinities. [Key Words: STAT proteins; DNA binding; site selection] Received January 6, 1995; revised version accepted March 2, 1995. The STAT (signal transducers and activators if transcrip- Whereas oligonucleotides representing these selected se- tion) proteins have the dual purpose of, first, signal trans- quences exhibited slight binding preferences, the con- duction from ligand-activated receptor kinase com- sensus sites overlapped sufficiently to be recognized by plexes, followed by nuclear translocation and DNA bind- both factors. However, by screening different natural ing to activate transcription (Darnell et al.
    [Show full text]