Modulation of Nucleosomal DNA Accessibility Via Charge-Altering
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Functional Roles of Bromodomain Proteins in Cancer
cancers Review Functional Roles of Bromodomain Proteins in Cancer Samuel P. Boyson 1,2, Cong Gao 3, Kathleen Quinn 2,3, Joseph Boyd 3, Hana Paculova 3 , Seth Frietze 3,4,* and Karen C. Glass 1,2,4,* 1 Department of Pharmaceutical Sciences, Albany College of Pharmacy and Health Sciences, Colchester, VT 05446, USA; [email protected] 2 Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA; [email protected] 3 Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT 05405, USA; [email protected] (C.G.); [email protected] (J.B.); [email protected] (H.P.) 4 University of Vermont Cancer Center, Burlington, VT 05405, USA * Correspondence: [email protected] (S.F.); [email protected] (K.C.G.) Simple Summary: This review provides an in depth analysis of the role of bromodomain-containing proteins in cancer development. As readers of acetylated lysine on nucleosomal histones, bromod- omain proteins are poised to activate gene expression, and often promote cancer progression. We examined changes in gene expression patterns that are observed in bromodomain-containing proteins and associated with specific cancer types. We also mapped the protein–protein interaction network for the human bromodomain-containing proteins, discuss the cellular roles of these epigenetic regu- lators as part of nine different functional groups, and identify bromodomain-specific mechanisms in cancer development. Lastly, we summarize emerging strategies to target bromodomain proteins in cancer therapy, including those that may be essential for overcoming resistance. Overall, this review provides a timely discussion of the different mechanisms of bromodomain-containing pro- Citation: Boyson, S.P.; Gao, C.; teins in cancer, and an updated assessment of their utility as a therapeutic target for a variety of Quinn, K.; Boyd, J.; Paculova, H.; cancer subtypes. -
Enhanced Representation of Natural Product Metabolism in Uniprotkb
H OH metabolites OH Article Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB Marc Feuermann 1,* , Emmanuel Boutet 1,* , Anne Morgat 1 , Kristian B. Axelsen 1, Parit Bansal 1, Jerven Bolleman 1 , Edouard de Castro 1, Elisabeth Coudert 1, Elisabeth Gasteiger 1,Sébastien Géhant 1, Damien Lieberherr 1, Thierry Lombardot 1,†, Teresa B. Neto 1, Ivo Pedruzzi 1, Sylvain Poux 1, Monica Pozzato 1, Nicole Redaschi 1 , Alan Bridge 1 and on behalf of the UniProt Consortium 1,2,3,4,‡ 1 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 Michel-Servet, CH-1211 Geneva 4, Switzerland; [email protected] (A.M.); [email protected] (K.B.A.); [email protected] (P.B.); [email protected] (J.B.); [email protected] (E.d.C.); [email protected] (E.C.); [email protected] (E.G.); [email protected] (S.G.); [email protected] (D.L.); [email protected] (T.L.); [email protected] (T.B.N.); [email protected] (I.P.); [email protected] (S.P.); [email protected] (M.P.); [email protected] (N.R.); [email protected] (A.B.); [email protected] (U.C.) 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 3 Protein Information Resource, University of Delaware, 15 Innovation Way, Suite 205, Newark, DE 19711, USA 4 Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NorthWest, Suite 1200, Washington, DC 20007, USA * Correspondence: [email protected] (M.F.); [email protected] (E.B.); Tel.: +41-22-379-58-75 (M.F.); +41-22-379-49-10 (E.B.) † Current address: Centre Informatique, Division Calcul et Soutien à la Recherche, University of Lausanne, CH-1015 Lausanne, Switzerland. -
Quantitative Proteomics Methods for the Analysis of Histone Post-Translational Modifications
Université de Montréal Quantitative Proteomics Methods for the Analysis of Histone Post-translational Modifications par Nebiyu Ali Abshiru Département de Chimie Faculté des arts et des sciences Thèse présentée à la Faculté des études supérieures et postdoctorales en vue de l’obtention du grade de philosophiae doctor (Ph.D.) en chimie Septembre 2015 ©Nebiyu Ali Abshiru, 2015 i Résumé Les histones sont des protéines nucléaires hautement conservées chez les cellules des eucaryotes. Elles permettent d’organiser et de compacter l’ADN sous la forme de nucléosomes, ceux-ci representant les sous unités de base de la chromatine. Les histones peuvent être modifiées par de nombreuses modifications post-traductionnelles (PTMs) telles que l’acétylation, la méthylation et la phosphorylation. Ces modifications jouent un rôle essentiel dans la réplication de l’ADN, la transcription et l’assemblage de la chromatine. L’abondance de ces modifications peut varier de facon significative lors du developpement des maladies incluant plusieurs types de cancer. Par exemple, la perte totale de la triméthylation sur H4K20 ainsi que l’acétylation sur H4K16 sont des marqueurs tumoraux spécifiques a certains types de cancer chez l’humain. Par conséquent, l’étude de ces modifications et des événements determinant la dynamique des leurs changements d’abondance sont des atouts importants pour mieux comprendre les fonctions cellulaires et moléculaires lors du développement de la maladie. De manière générale, les modifications des histones sont étudiées par des approches biochimiques telles que les immuno-buvardage de type Western ou les méthodes d’immunoprécipitation de la chromatine (ChIP). Cependant, ces approches présentent plusieurs inconvénients telles que le manque de spécificité ou la disponibilité des anticorps, leur coût ou encore la difficulté de les produire et de les valider. -
Research Resources for Nuclear Receptor Signaling Pathways Neil J
Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. The final version may differ from this version. MOL #103713 Research resources for nuclear receptor signaling pathways Neil J. McKenna Department of Molecular and Cellular Biology and Nuclear Receptor Signaling Atlas (NURSA) Bioinformatics Resource, Downloaded from Baylor College of Medicine, Houston, TX, 77030, USA molpharm.aspetjournals.org at ASPET Journals on September 27, 2021 1 Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. The final version may differ from this version. MOL #103713 Running title: Research resources for NR signaling pathways Corresponding author: Neil J McKenna Room M620 Baylor College of Medicine One Baylor Plaza Downloaded from Houston, TX, 77030, USA t: 713-798-7490 molpharm.aspetjournals.org f: 713-798-6822 e: [email protected] Number of text pages: 21 at ASPET Journals on September 27, 2021 Number of tables: 1 Number of figures: 1 Number of references: 56 Number of words in Abstract: 124 Review: 3613 List of non-standard abbreviations: 17βE2, 17β-estradiol; AB, Allen Brain Atlas; BG, BIOGRID; BGS, BioGPS; CoR, coregulator; CTD, Comparative Toxicogenomics Database; DAV, DAVID; DB, DrugBank; EDC, endocrine disrupting chemical; EG, Entrez Gene; EM, Edinburgh Mouse; ENC, ENCODE; ENR, ENRICHR; ENS, Ensembl; EX, Expression Atlas; GC, GeneCards; GSEA, GeneSet Enrichment Analysis; GtoP, IUPHAR Guide To Pharmacology; 2 Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. -
Uniprot: the Universal Protein Knowledgebase in 2021 the Uniprot Consortium1,2,3,4,*
D480–D489 Nucleic Acids Research, 2021, Vol. 49, Database issue Published online 25 November 2020 doi: 10.1093/nar/gkaa1100 UniProt: the universal protein knowledgebase in 2021 The UniProt Consortium1,2,3,4,* 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK, 2Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA, 3Protein Information Resource, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, Suite 147, 590 Avenue 1743, Newark, DE 19713, USA and 4SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland Received September 15, 2020; Revised October 21, 2020; Editorial Decision October 22, 2020; Accepted November 02, 2020 ABSTRACT tomated systems. The UniRef databases cluster sequence sets at various levels of sequence identity and the UniProt The aim of the UniProt Knowledgebase is to provide Archive (UniParc) delivers a complete set of known se- users with a comprehensive, high-quality and freely quences, including historical obsolete sequences. UniProt accessible set of protein sequences annotated with additionally integrates, interprets, and standardizes data functional information. In this article, we describe from multiple selected resources to add biological knowl- significant updates that we have made over the last edge and associated metadata to protein records and acts two years to the resource. The number of sequences as a central hub from which users can link out to 180 in UniProtKB has risen to approximately 190 million, other resources. In recognition of the quality of our data, despite continued work to reduce sequence redun- and the service we provide, UniProt was recognised as dancy at the proteome level. -
The Chromosome-Centric Human Proteome Project for Cataloging Proteins Encoded in the Genome
CORRESPONDENCE The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome To the Editor: utility for biological and disease studies. Table 1 Features of salient genes on The Chromosome-Centric Human With development of new tools for in- chromosomes 13 and 17 Proteome Project (C-HPP) aims to define depth characterization of the transcriptome Genea AST nsSNPs the full set of proteins encoded in each and proteome, the HPP is well positioned Chromosome 13 chromosome through development of a to have a strategic role in addressing the BRCA2 3 54 standardized approach for analyzing the complexity of human phenotypes. With this RB1 2 3 massive proteomic data sets currently being in mind, the HUPO has organized national IRS2 1 3 generated from dedicated efforts of national chromosome teams that will collaborate and international teams. The initial goal with well-established laboratories building Chromosome 17 of the C-HPP is to identify at least one complementary proteotypic peptides, BRCA1 24 24 representative protein encoded by each of antibodies and informatics resources. ERBB2 6 13 the approximately 20,300 human genes1,2. An important C-HPP goal is to encourage TP53 14 5 aEnsembl protein and AST information can be found at The proteins will be characterized for tissue capture and open sharing of proteomic http://www.ensembl.org/Homo_sapiens/. localization and major isoforms, including data sets from diverse samples to enhance AST, alternative splicing transcript; nsSNP, nonsyno- mous single-nucleotide polyphorphism assembled from post-translational modifications (PTMs), a gene- and chromosome-centric display data from the 1000 Genomes Projects. -
The Determinants of Nucleosome Patterns and the Impact of Phosphate Starvation on Nucleosome Patterns and Gene Expression in Rice
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 3-14-2018 The etD erminants of Nucleosome Patterns and the Impact of Phosphate Starvation on Nucleosome Patterns and Gene Expression in Rice Qi Zhang Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Agronomy and Crop Sciences Commons, Bioinformatics Commons, Genetics Commons, Genomics Commons, and the Plant Breeding and Genetics Commons Recommended Citation Zhang, Qi, "The eD terminants of Nucleosome Patterns and the Impact of Phosphate Starvation on Nucleosome Patterns and Gene Expression in Rice" (2018). LSU Doctoral Dissertations. 4502. https://digitalcommons.lsu.edu/gradschool_dissertations/4502 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. THE DETERMINANTS OF NUCLEOSOME PATTERNS AND THE IMPACT OF PHOSPHATE STARVATION ON NUCLEOSOME PATTERNS AND GENE EXPRESSION IN RICE A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Biological Sciences by Qi Zhang B.S., Sichuan Agricultural University, 2012 May 2018 ACKNOWLEDGEMENTS The journey towards the completion of the degree hasn’t been easy, and I would like to express my heartfelt gratitude to everyone who has been around me, listening to me, and supporting me. -
Machine Learning & Learning Machines
A Brief History • Bootstrapping • Bagging • Boosting (Schapire 1989) • Adaboost (Schapire 1995) What’s So Good About Adaboost • Improves classification accuracy • Can be used with many different classifiers • Commonly used in many areas • Simple to implement • Not prone to over-fitting Ex. “How May I Help You?” easy to find “rules of thumb” that are “often” correct • e.g.: “IF ‘card’ occurs in utterance THEN predict ‘Calling Card’ ” • hard to find single highly accurate prediction rule Bootstrap Estimation • Repeatedly draw n samples from D • For each set of samples, estimate a statistic • The bootstrap estimate is the mean of the individual estimates • Used to estimate a statistic (parameter) and its variance Bagging - Aggregate Bootstrapping • For i = 1 .. M – Draw n*<n samples from D with replacement – Learn classifier Ci • Final classifier is a vote of C1 .. CM • Increases classifier stability/reduces variance Boosting (Schapire 1989) • Randomly select n1 < n samples from D without replacement to obtain D1 – Train weak learner C1 • Select n2 < n samples from D with half of the samples misclassified by C1 to obtain D2 – Train weak learner C2 • Select all samples from D that C1 and C2 disagree on – Train weak learner C3 • Final classifier is vote of weak learners Adaboost - Adaptive Boosting • Instead of sampling, re-weight – Previous weak learner has only 50% accuracy over new distribution • Can be used to learn weak classifiers • Final classification based on weighted vote of weak classifiers Adaboost Terms • Learner = Hypothesis = Classifier • Weak Learner: < 50% error over any distribution • Strong Classifier: thresholded linear combination of weak learner outputs Basic idea of boosting technique In general Boosting Bagging Single Tree. -
How Many Human Proteoforms Are There?
PERSPECTIVE PUBLISHED ONLINE: 14 FEBRUARY 2018 | DOI: 10.1038/NCHEMBIO.2576 How many human proteoforms are there? Ruedi Aebersold1, Jeffrey N Agar2, I Jonathan Amster3 , Mark S Baker4 , Carolyn R Bertozzi5, Emily S Boja6, Catherine E Costello7, Benjamin F Cravatt8 , Catherine Fenselau9, Benjamin A Garcia10, Ying Ge11,12, Jeremy Gunawardena13, Ronald C Hendrickson14, Paul J Hergenrother15, Christian G Huber16 , Alexander R Ivanov2, Ole N Jensen17, Michael C Jewett18, Neil L Kelleher19* , Laura L Kiessling20 , Nevan J Krogan21, Martin R Larsen17, Joseph A Loo22 , Rachel R Ogorzalek Loo22, Emma Lundberg23,24, Michael J MacCoss25, Parag Mallick5, Vamsi K Mootha13, Milan Mrksich18, Tom W Muir26, Steven M Patrie19, James J Pesavento27 , Sharon J Pitteri5 , Henry Rodriguez6, Alan Saghatelian28, Wendy Sandoval29, Hartmut Schlüter30 , Salvatore Sechi31, Sarah A Slavoff32, Lloyd M Smith12,33, Michael P Snyder24, Paul M Thomas19 , Mathias Uhlén34, Jennifer E Van Eyk35, Marc Vidal36, David R Walt37, Forest M White38, Evan R Williams39, Therese Wohlschlager16, Vicki H Wysocki40, Nathan A Yates41, Nicolas L Young42 & Bing Zhang42 Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous ques- tions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry–based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
Mouse Model of Endemic Burkitt Translocations Reveals the Long-Range Boundaries of Ig-Mediated Oncogene Deregulation
Mouse model of endemic Burkitt translocations reveals the long-range boundaries of Ig-mediated oncogene deregulation Alexander L. Kovalchuka,1, Camilo Ansarah-Sobrinhob,1,Ofir Hakimc,1, Wolfgang Reschb, Helena Tolarováb, Wendy Duboisd, Arito Yamaneb, Makiko Takizawab, Isaac Kleinf, Gordon L. Hagerc, Herbert C. Morse IIIa, Michael Potterd,1, Michel C. Nussenzweige,f,2, and Rafael Casellasb,g,2 aLaboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852; bGenomics and Immunity, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892; cLaboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892; dLaboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892; eLaboratory of Molecular Immunology, and fHoward Hughes Medical Institute, The Rockefeller University, New York, NY 10065; and gCenter of Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892 Edited by Klaus Rajewsky, Max-Delbrück-Center for Molecular Medicine, Berlin, Germany, and approved May 22, 2012 (received for review January 5, 2012) Human Burkitt lymphomas are divided into two main clinical human Burkitt lymphomas (BLs), mouse plasmacytomas (PCTs), variants: the endemic form, affecting African children infected with and rat immunocytomas (15, 16). malaria and the Epstein-Barr virus, and the sporadic form, distrib- Human BLs are divided into two main clinical variants: the uted across the rest of the world. However, whereas sporadic endemic form, affecting African children infected with malaria translocations decapitate Myc from 5′ proximal regulatory ele- and the Epstein-Barr virus, and the sporadic form, distributed ments, most endemic events occur hundreds of kilobases away across the rest of the world (17). -
Learning Chroma\N States from Chip-‐Seq Data
Learning Chroman States from ChIP-seq data Luca Pinello GC Yuan Lab Outline • Chroman structure, histone modificaons and combinatorial paerns • How to segment the genome in chroman states • How to use ChromHMM step by step • Further references 2 Epigene-cs and chroman structure • All (almost) the cells of our body share the same genome but have very different gene expression programs…. 3 h?p://jpkc.scu.edu.cn/ywwy/zbsw(E)/edetail12.htm The code over the code • The chroman structure and the accessibility are mainly controlled by: 1. Nucleosome posioning, 2. DNA methylaon, 3. Histone modificaons. 4 Histone Modificaons Specific histone modificaons or combinaons of modificaons confer unique biological func-ons to the region of the genome associated with them: • H3K4me3: promoters, gene acva.on • H3K27me3: promoters, poised enhancers, gene silencing • H2AZ: promoters • H3K4me1: enhancers • H3K36me3: transcribed regions • H3K9me3: gene silencing • H3k27ac: acve enhancers 5 Examples of *-Seq Measuring the genome genome fragmentation assembler DNA DNA reads “genome” ChIP-seq to measure histone data fragments Measuring the regulome (e.g., protein-binding of the genome) Chromatin Immunopreciptation genomic (ChIP) + intervals fragmentation Protein - peak caller bound by DNA bound DNA reads proteins REVIEWS fragments a also informative, as this ratio corresponds to the fraction ChIP–chip of nucleosomes with the particular modification at that location, averaged over all the cells assayed. One of the difficulties in conducting a ChIP–seq con- trol experiment is the large amount of sequencing that ChIP–seq may be necessary. For input DNA and bulk nucleosomes, many of the sequenced tags are spread evenly across the genome.