DOCSLIB.ORG

Systematic Identification and Quantification of Substrate Specificity Determinants in Human Protein Kinases

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Systematic Identification and Quantification of Substrate Specificity Determinants in Human Protein Kinases Systematic identification and quantification of substrate specificity determinants in human protein kinases (Identificación y cuantificación sistemática de determinantes de la especificidad por sustrato en las proteínas quinasas de humano) Manuel Alejandro Alonso Tarajano ADVERTIMENT. La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions d'ús: La difusió d’aquesta tesi per mitjà del servei TDX (www.tdx.cat) i a través del Dipòsit Digital de la UB (diposit.ub.edu) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX ni al Dipòsit Digital de la UB. No s’autoritza la presentació del seu contingut en una finestra o marc aliè a TDX o al Dipòsit Digital de la UB (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora. ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tdx.cat) y a través del Repositorio Digital de la UB (diposit.ub.edu) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR o al Repositorio Digital de la UB. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR o al Repositorio Digital de la UB (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING. On having consulted this thesis you’re accepting the following use conditions: Spreading this thesis by the TDX (www.tdx.cat) service and by the UB Digital Repository (diposit.ub.edu) has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized nor its spreading and availability from a site foreign to the TDX service or to the UB Digital Repository. Introducing its content in a window or frame foreign to the TDX service or to the UB Digital Repository is not authorized (framing). Those rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it’s obliged to indicate the name of the author. Tesi Doctoral Universitat de Barcelona Facultat de Farmàcia Departament de Bioquímica i Biologia Molecular Programa de Doctorat en Biomedicina 2013 Systematic identification and quantification of substrate specificity determinants in human protein kinases (Identificación y cuantificación sistemática de determinantes de la especificidad por sustrato en las proteínas quinasas de humano) Memòria presentada per Manuel A. Alonso Tarajano, per optar al títol de doctor per la Universitat de Barcelona This thesis was realized under the supervision and tutorship of ICREA research professor Dr. Patrick Aloy Calaf and under the co-supervision of Dr. Roberto Mosca, in the Structural Bioinformatics and Network Biology group of the Institute for Research in Biomedicine (IRB) Barcelona. Manuel A. Alonso Tarajano Dr. Patrick Aloy Calaf Dr. Roberto Mosca Contents Table of Contents iii List of Figures v List of Tables vii List of Acronyms ix 1 Resumen global 1 1.1 Introducción . .1 1.2 Objetivos . .2 1.3 Materiales y Métodos . .2 1.4 Resultados y discusión . .5 1.5 Conclusiones . .9 2 General introduction 11 2.1 Protein phosphorylation . 11 2.2 Protein kinases . 11 2.2.1 Kinases are responsible for protein phosphorylation . 11 2.2.2 The kinase catalytic domain is highly conserved . 12 2.2.3 Phylogeny and diversity of human protein kinases . 12 2.3 Known mechanisms of protein kinases for substrate identification . 15 2.3.1 Role of the kinase catalytic site . 15 2.3.2 Distal docking sites . 15 2.3.3 Regulatory and targeting domains . 16 3 Objectives 19 4 Sequence logos and position-specific scoring matrices 21 4.1 Introduction . 21 4.1.1 Contribution of residues neighboring the phosphorylation sites . 21 4.1.1.1 Residues commonly phosphorylated in eukaryotes . 21 4.1.1.2 Positioning and orientation of the phospho-acceptor residue 21 4.1.1.3 From phosphorylation sequences to phosphorylation motifs . 22 4.1.2 Graphical representation of phosphorylation motifs . 22 4.1.3 Mathematical representation of phosphorylation motifs . 23 4.1.3.1 Probabilistic models . 23 i 4.1.3.2 Prediction of phosphorylation sites . 24 4.2 Materials and methods . 26 4.2.1 Phylogenetic classification of human protein kinases . 26 4.2.2 Phosphorylation data for human protein kinases . 26 4.2.3 Set of ‘unphosphorylated’ human proteins . 26 4.2.4 Generation of sequence logos . 27 4.2.5 Position-specific scoring matrices . 27 4.2.5.1 Generating PSSMs . 27 4.2.5.2 Selection of a score threshold . 27 4.2.5.3 Statistical significance of the PSSMs . 28 4.2.5.4 Performance of the PSSMs . 29 4.2.6 Quantification of the kinase specificity encoded in the PSSMs . 29 4.2.6.1 Sequence motifs most commonly recognized by kinases . 29 4.3 Results and discussion . 30 4.3.1 Motifs recognized by kinases and kinases families . 30 4.3.2 Strong specificity-determinant residues from kinase families . 30 4.3.2.1 Proline-directed kinase families . 30 4.3.2.2 Glutamine-directed kinase families . 32 4.3.2.3 Basophilic kinase families . 34 4.3.2.4 Acidophilic kinase families . 39 4.3.3 Statistical analysis of the PSSMs . 42 4.3.3.1 Independent kinases . 42 4.3.3.2 Kinase families . 48 4.4 Concluding remarks . 54 5 Contribution of adaptor and scaffold proteins 57 5.1 Introduction . 57 5.1.1 Adaptors and scaffolds are multidomain spatio-temporal regulators . 57 5.1.1.1 Adaptors and scaffolds of the MAPK/ERK cascade . 58 5.1.1.2 The IQGAP scaffolds regulate cytoskeleton dynamics . 60 5.1.2 Adaptors and scaffolds contribute to kinase substrate specificity . 62 5.1.2.1 The biological roles of A-kinase anchoring proteins . 63 5.1.2.2 mAKAP assembles a signalosome at the nuclear envelope . 64 5.1.3 Computational identification of signaling scaffold proteins . 64 5.2 Materials and methods . 67 5.2.1 Protein-protein interactions for human . 67 5.2.2 Statistical enrichment of Pfam families . 67 5.2.3 Statistical enrichment of Gene Ontology terms . 67 5.2.4 Automatic collection of known adaptor/scaffold proteins . 67 5.2.5 Gold standard set of kinase–adaptor/scaffold pairs . 67 5.2.6 Identification of potential adaptors/scaffolds from substrates partners 68 5.2.6.1 Data . 68 5.2.6.2 Collection of PIN for randomization . 68 5.2.6.3 Generating background distributions . 68 5.2.6.4 Testing the classification method . 69 5.2.6.5 Algorithm for the identification of potential adaptors/scaffolds 69 5.2.6.6 Evaluating the performance of the method . 69 5.2.6.7 Co-annotation of substrates and potential adaptors/scaffolds 69 5.2.7 Identification of known adaptors/scaffolds interacting with signifi- cantly large sets of substrates . 70 5.2.7.1 Data . 70 5.2.7.2 Generating background distributions . 70 5.2.7.3 Estimating the statistical significance . 71 5.2.7.4 Statistical likeliness substrate-adaptor/scaffold interactions . 71 5.2.8 Identification of kinases sharing significantly large sets of substrates . 71 5.2.8.1 Data . 71 5.2.8.2 Generating background distributions . 72 5.2.8.3 Estimating the statistical significance . 72 5.3 Results and discussion . 73 5.3.1 Set of known adaptors/scaffolds collected for human kinases . 73 5.3.2 Gold standard set of known adaptors/scaffolds . 76 5.3.3 Potential adaptors/scaffolds identified from substrates partners . 77 5.3.4 Adaptors/scaffolds binding to significantly large numbers of substrates 86 5.3.5 Association to adaptors/scaffolds diminish cross-specificity of kinases 88 5.4 Concluding remarks . 90 6 General discussion 91 6.1 Analysis of phosphorylation sites and their adjacent residues . 91 6.1.1 Sequence logos . 91 6.1.2 Specificity-determinant residues . 91 6.1.3 Position-specific scoring matrices . 92 6.2 Analysis of the association of kinases to adaptors and scaffolds . 93 6.2.1 Identification of adaptors and scaffolds . 93 6.2.2 Colocalization of adaptors and scaffolds with substrates . 94 6.2.3 Adaptors and scaffolds diminish kinase cross-specificity . 95 7 General conclusions 97 Bibliography 101 Appendices 111 A1 SDRs from kinase families . 113 A2 Sequence logos from kinase families . 118 A3 List of known adaptors/scaffolds . 119 A4 List of potential adaptors/scaffolds . 125 A5 Cellular compartment annotation of potential adaptors/scaffolds . 127 A6 Submitted article . 130 List of Figures 2.1 Protein phosphorylation reaction. 12 2.2 Eukaryotic kinase catalytic domain. 13 2.3 Modular domain composition of protein kinases. 14 2.4 Distal docking sites in MAPKs. 16 4.1 Frequencies of the SDRs from two families of Pro-directed kinases. 33 4.2 Frequencies of SDRs from four families of basophilic kinases.
Load more

Recommended publications
  • Aurora Kinase a in Gastrointestinal Cancers: Time to Target Ahmed Katsha1, Abbes Belkhiri1, Laura Goff3 and Wael El-Rifai1,2,4*
    Katsha et al. Molecular Cancer (2015) 14:106 DOI 10.1186/s12943-015-0375-4 REVIEW Open Access Aurora kinase A in gastrointestinal cancers: time to target Ahmed Katsha1, Abbes Belkhiri1, Laura Goff3 and Wael El-Rifai1,2,4* Abstract Gastrointestinal (GI) cancers are a major cause of cancer-related deaths. During the last two decades, several studies have shown amplification and overexpression of Aurora kinase A (AURKA) in several GI malignancies. These studies demonstrated that AURKA not only plays a role in regulating cell cycle and mitosis, but also regulates a number of key oncogenic signaling pathways. Although AURKA inhibitors have moved to phase III clinical trials in lymphomas, there has been slower progress in GI cancers and solid tumors. Ongoing clinical trials testing AURKA inhibitors as a single agent or in combination with conventional chemotherapies are expected to provide important clinical information for targeting AURKA in GI cancers. It is, therefore, imperative to consider investigations of molecular determinants of response and resistance to this class of inhibitors. This will improve evaluation of the efficacy of these drugs and establish biomarker based strategies for enrollment into clinical trials, which hold the future direction for personalized cancer therapy. In this review, we will discuss the available data on AURKA in GI cancers. We will also summarize the major AURKA inhibitors that have been developed and tested in pre-clinical and clinical settings. Keywords: Aurora kinases, Therapy, AURKA inhibitors, MNL8237, Alisertib, Gastrointestinal, Cancer, Signaling pathways Introduction stage [9-11]. Furthermore, AURKA is critical for Mitotic kinases are the main proteins that coordinate ac- bipolar-spindle assembly where it interacts with Ran- curate mitotic processing [1].
    [Show full text]
  • Hearing Aging Is 14.1±0.4% GWAS-Heritable
    medRxiv preprint doi: https://doi.org/10.1101/2021.07.05.21260048; this version posted July 7, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging Alan Le Goallec1,2+, Samuel Diai1+, Théo Vincent1, Chirag J. Patel1* 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA 2Department of Systems, Synthetic and Quantitative Biology, Harvard University, Cambridge, MA, 02118, USA +Co-first authors *Corresponding author Contact information: Chirag J Patel [email protected] Abstract With the aging of the world population, age-related hearing loss (presbycusis) and other hearing disorders such as tinnitus become more prevalent, leading to reduced quality of life and social isolation. Unveiling the genetic and environmental factors leading to age-related auditory disorders could suggest lifestyle and therapeutic interventions to slow auditory aging. In the following, we built the first machine learning-based hearing age predictor by training models to predict chronological age from hearing test results (root mean squared error=7.10±0.07 years; R-Squared=31.4±0.8%). We defined hearing age as the prediction outputted by the model on unseen samples, and accelerated auditory aging as the difference between a participant’s hearing age and age. We then performed a genome wide association study [GWAS] and found that accelerated hearing aging is 14.1±0.4% GWAS-heritable.
    [Show full text]
  • RASSF1A Interacts with and Activates the Mitotic Kinase Aurora-A
    Oncogene (2008) 27, 6175–6186 & 2008 Macmillan Publishers Limited All rights reserved 0950-9232/08 $32.00 www.nature.com/onc ORIGINAL ARTICLE RASSF1A interacts with and activates the mitotic kinase Aurora-A L Liu1, C Guo1, R Dammann2, S Tommasi1 and GP Pfeifer1 1Division of Biology, Beckman Research Institute, City of Hope Cancer Center, Duarte, CA, USA and 2Institute of Genetics, University of Giessen, Giessen, Germany The RAS association domain family 1A (RASSF1A) gene tumorigenesis and carcinogen-induced tumorigenesis is located at chromosome 3p21.3 within a specific area of (Tommasi et al., 2005; van der Weyden et al., 2005), common heterozygous and homozygous deletions. RASS- supporting the notion that RASSF1A is a bona fide F1A frequently undergoes promoter methylation-asso- tumor suppressor. However, it is not fully understood ciated inactivation in human cancers. Rassf1aÀ/À mice how RASSF1A is involved in tumor suppression. are prone to both spontaneous and carcinogen-induced The biochemical function of the RASSF1A protein is tumorigenesis, supporting the notion that RASSF1A is a largely unknown. The homology of RASSF1A with the tumor suppressor. However, it is not fully understood how mammalian Ras effector novel Ras effector (NORE)1 RASSF1A is involved in tumor suppression pathways. suggests that the RASSF1A gene product may function Here we show that overexpression of RASSF1A inhibits in signal transduction pathways involving Ras-like centrosome separation. RASSF1A interacts with Aurora-A, proteins. However, recent data indicate that RASSF1A a mitotic kinase. Surprisingly, knockdown of RASS- itself binds to RAS only weakly and that binding to F1A by siRNA led to reduced activation of Aurora-A, RAS may require heterodimerization of RASSF1A and whereas overexpression of RASSF1A resulted in in- NORE1 (Ortiz-Vega et al., 2002).
    [Show full text]
  • Therapeutic Implications for Ovarian Cancer Emerging from the Tumor Cancer Genome Atlas
    Review Article Therapeutic implications for ovarian cancer emerging from the Tumor Cancer Genome Atlas Claire Verschraegen, Karen Lounsbury, Alan Howe, Marc Greenblatt University of Vermont Cancer Center, Burlington, VT 05405, USA Correspondence to: Claire Verschraegen, MD. University of Vermont Cancer Center, 89 Beaumont Avenue, Given E 214, Burlington, VT 05405, USA. Email: [email protected] Abstract: With increasing insights into the molecular landscape of ovarian cancer, subtypes are emerging that might require differential targeted therapies. While the combination of a platinum and a taxane remains the standard of care, newer therapies, specifically targeted to molecular anomalies, are rapidly being tested in various cancers. A major effort to better understand ovarian cancer occurred through the Cancer Atlas Project. The Catalogue of Somatic Mutations in Cancer (COSMIC) is a database that collates mutation data and associated information extracted from the primary literature. The information from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), which systematically analyzed hundreds of ovarian cancer, are included in COSMIC, sometimes with discordant results. In this manuscript, the published data (mainly from TCGA) on somatic high grade papillary serous ovarian cancer (HGSOC) mutations has been used as the basis to propose a more granular approach to ovarian cancer treatment, already a reality for tumors such as lung and breast cancers. TP53 mutations are the most common molecular anomaly in HGSOC, and lead to genomic instability, perhaps through the FOXM1 node. Normalizing P53 has been a therapeutic challenge, and is being extensively studied. BRCAness is found an about 50% of HGSOC and can be targeted with poly (ADP-ribose) polymerase (PARP) inhibitors, such as olaparib, recently approved for ovarian cancer treatment.
    [Show full text]
  • 1 Supporting Information for a Microrna Network Regulates
    Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia.
    [Show full text]
  • The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer
    cancers Article The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer Risk and Carriers Share a Common Ancestor Andreia Brandão 1 , Paula Paulo 1, Sofia Maia 1, Manuela Pinheiro 1, Ana Peixoto 2, Marta Cardoso 1, Maria P. Silva 1, Catarina Santos 2, Rosalind A. Eeles 3,4 , Zsofia Kote-Jarai 3, 5,6 7, 8,9 10 Kenneth Muir , UKGPCS Collaborators y, Johanna Schleutker , Ying Wang , Nora Pashayan 11,12 , Jyotsna Batra 13,14, APCB BioResource 13,14, Henrik Grönberg 15, David E. Neal 16,17,18, Børge G. Nordestgaard 19,20 , Catherine M. Tangen 21, Melissa C. Southey 22,23,24, Alicja Wolk 25,26, Demetrius Albanes 27, Christopher A. Haiman 28, Ruth C. Travis 29, Janet L. Stanford 30,31, Lorelei A. Mucci 32, Catharine M. L. West 33, Sune F. Nielsen 19,20, Adam S. Kibel 34, Olivier Cussenot 35,36, Sonja I. Berndt 27, Stella Koutros 27, Karina Dalsgaard Sørensen 37,38 , Cezary Cybulski 39 , Eli Marie Grindedal 40, Jong Y. Park 41 , Sue A. Ingles 42, Christiane Maier 43, Robert J. Hamilton 44,45, Barry S. Rosenstein 46,47, 48,49,50 7, Ana Vega , The IMPACT Study Steering Committee and Collaborators z, Manolis Kogevinas 51,52,53,54, Fredrik Wiklund 15 , Kathryn L. Penney 55, Hermann Brenner 56,57,58 , Esther M. John 59, Radka Kaneva 60, Christopher J. Logothetis 61, Susan L. Neuhausen 62, Kim De Ruyck 63, Azad Razack 64, Lisa F. Newcomb 30,65, Canary PASS Investigators 30,65, Davor Lessel 66 , Nawaid Usmani 67,68, Frank Claessens 69, Manuela Gago-Dominguez 70,71 , Paul A.
    [Show full text]
  • Physiological and Pathophysiological Regulation of the Ryanodine Receptor in Skeletal Muscle
    Physiological and pathophysiological regulation of the ryanodine receptor in skeletal muscle Alisa Umanskaya Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2015 © 2015 Alisa Umanskaya All rights reserved Abstract Physiological and pathophysiological regulation of ryanodine receptor in skeletal muscle Alisa Umanskaya Ryanodine receptor calcium release channels are essential for skeletal muscle contraction, as they mediate the release of calcium ions from intracellular stores into the cytosol. The data presented in this dissertation demonstrate the evolutionarily conserved mechanisms of skeletal muscle ryanodine receptor regulation in the physiological and pathophysiological states. Adrenergic stimulation causes increased skeletal muscle force, however, despite the well- established role of this physiological response, the molecular mechanism is not known. Here we present a mechanism whereby phosphorylation of a single amino acid on the ryanodine receptor is a key signal in the physiological stress-induced inotropic response in mouse skeletal muscle. Therefore acute post-translational modifications of ryanodine receptor channels are important for healthy muscle contraction. Conversely, chronic stress-induced post-translational modifications result in poorly functioning murine ryanodine receptor channels that contribute to skeletal muscle dysfunction in age- dependent skeletal muscle weakness and Muscular Dystrophies. Finally, we present data that demonstrates striking evolutionary conservation in ryanodine receptor regulation in the physiological and pathophysiological states between mice and C. elegans. This work has broad implications for understanding the underlying mechanisms of skeletal muscle contraction and important disorders that affect human health. Furthermore, this works presents ryanodine receptor channels as a viable therapeutic target for age-related skeletal muscle weakness, Muscular Dystrophies, and also implicates C.
    [Show full text]
  • Ncomms4885.Pdf
    ARTICLE Received 4 Feb 2014 | Accepted 14 Apr 2014 | Published 30 May 2014 DOI: 10.1038/ncomms4885 Microcephaly disease gene Wdr62 regulates mitotic progression of embryonic neural stem cells and brain size Jian-Fu Chen1,2, Ying Zhang1, Jonathan Wilde1, Kirk C. Hansen3, Fan Lai4 & Lee Niswander1 Human genetic studies have established a link between a class of centrosome proteins and microcephaly. Current studies of microcephaly focus on defective centrosome/spindle orientation. Mutations in WDR62 are associated with microcephaly and other cortical abnormalities in humans. Here we create a mouse model of Wdr62 deficiency and find that the mice exhibit reduced brain size due to decreased neural progenitor cells (NPCs). Wdr62 depleted cells show spindle instability, spindle assembly checkpoint (SAC) activation, mitotic arrest and cell death. Mechanistically, Wdr62 associates and genetically interacts with Aurora A to regulate spindle formation, mitotic progression and brain size. Our results suggest that Wdr62 interacts with Aurora A to control mitotic progression, and loss of these interactions leads to mitotic delay and cell death of NPCs, which could be a potential cause of human microcephaly. 1 Howard Hughes Medical Institute, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado 80045, USA. 2 Department of Genetics, Department of Biochemistry & Molecular Biology, University of Georgia, Athens, Georgia 30602, USA. 3 Biochemistry & Molecular Genetics, University of Colorado Denver, Aurora, Colorado 80045, USA. 4 The Wistar Institute, 3601 Spruce Street, Philadelphia, Pennsylvania 19104, USA. Correspondence and requests for materials should be addressed to J-F.C. (email: [email protected]) or to L.N.
    [Show full text]
  • Covalent Aurora a Regulation by the Metabolic Integrator Coenzyme A
    bioRxiv preprint doi: https://doi.org/10.1101/469585; this version posted November 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Covalent Aurora A regulation by the metabolic integrator coenzyme A Yugo Tsuchiya1#, Dominic P Byrne2#, Selena G Burgess3#, Jenny Bormann4, Jovana Bakovic1, Yueyan Huang1, Alexander Zhyvoloup1, Sew Peak-Chew5, Trang Tran6, Fiona Bellany6, Alethea Tabor6, AW Edith Chan7, Lalitha Guruprasad8, Oleg Garifulin9, Valeriy Filonenko9, Samantha Ferries10, Claire E Eyers10, John Carroll4^, Mark Skehel5, Richard Bayliss3*, Patrick A Eyers2* and Ivan Gout1,9* 1Department of Structural and Molecular Biology, University College London, London WC1E 6BT, UK 2Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK 3School of Molecular and Cellular Biology, Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds LS2 9JT, UK 4Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK 5MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK 6Department of Chemistry, University College London, London WC1E 6BT, UK 7Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK 8School of Chemistry, University of Hyderabad, Hyderabad 500 046, India 9Department of Cell
    [Show full text]
  • Centre for Arab Genomic Studies a Division of Sheikh Hamdan Award for Medical Sciences
    Centre for Arab Genomic Studies A Division of Sheikh Hamdan Award for Medical Sciences The atalogue for ransmission enetics in rabs C T G A CTGA Database A-Kinase Anchor Protein 6 Alternative Names cardiac and skeletal muscle and several regions of AKAP6 the brain. A-Kinase Anchor Protein, 100-KD AKAP100 Epidemiology in the Arab World Saudi Arabia Record Category Monies et al. (2017) described the genomic Gene locus landscape of Saudi Arabia based on the findings of 1000 diagnostic panels and exomes. One patient, a WHO-ICD 3-year-old female from a consanguineous family, N/A to gene loci presented with intellectual disability and precocious puberty. Reflex WES helped identify a novel Incidence per 100,000 Live Births heterozygous mutation (c.1874A>T, p.Y625F) in N/A to gene loci exon 4 of the patient’s AKAP6 gene, which was predicted to be deleterious. Through an internal OMIM Number matchmaking effort, the authors identified another 604691 patient with intellectual disability with a de-novo truncating variant (c.1572_1573del, Mode of Inheritance p.Lys525Glufs30*) in AKAP6. These two findings N/A to gene loci help suggest the possibility of AKAP6 being a bona-fide disease gene for intellectual disability in Gene Map Locus humans. 14q12 References Description Monies D, Abouelhoda M, AlSayed M, Alhassnan The AKAP6 gene encodes a protein belonging to Z, Alotaibi M, Kayyali H, Al-Owain M, Shah A, the A-Kinase Anchor Proteins (AKAPs) family. Rahbeeni Z, Al-Muhaizea MA, Alzaidan HI, Members of this family are responsible for binding Cupler E, Bohlega S, Faqeih E, Faden M, Alyounes to the regulatory subunit of the holoenzyme Protein B, Jaroudi D, Goljan E, Elbardisy H, Akilan A, Kinase A (PKA) and anchoring it to the nuclear Albar R, Aldhalaan H, Gulab S, Chedrawi A, Al membrane or sarcoplasmic reticulum.
    [Show full text]
  • Myogenin Controls Via AKAP6 Non-Centrosomal Microtubule
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.11.421263; this version posted December 11, 2020. 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 Myogenin controls via AKAP6 non-centrosomal microtubule 2 organizing center formation at the nuclear envelope 3 4 Robert Becker1, Silvia Vergarajauregui1, Florian Billing1, Maria Sharkova1, 5 Eleonora Lippolis2, Kamel Mamchaoui3, Fulvia Ferrazzi2,4,5,6, Felix B. Engel1,6,* 6 7 1Experimental Renal and Cardiovascular Research, Department of Nephropathology, 8 Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 9 91054 Erlangen, Germany 10 2Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 11 91054 Erlangen, Germany 12 3Sorbonne Universités UPMC Université Paris 06, INSERM U974, CNRS FRE3617, 13 Center for Research in Myology, GH Pitié Salpêtrière, 47 Boulevard de l’Hôpital, 75013 14 Paris, France 15 4Department of Nephropathology, Institute of Pathology, Friedrich-Alexander- 16 Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany 17 5Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 18 91054 Erlangen, Germany 19 6Muscle Research Center Erlangen (MURCE), 91054 Erlangen, Germany 20 21 Short title: Myogenin controls ncMTOC formation 22 23 *Corresponding author: 24 Felix B. Engel, Universitätsklinikum Erlangen, Schwabachanlage 12 (TRC), 91054 25 Erlangen, Germany, [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.11.421263; this version posted December 11, 2020. 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.
    [Show full text]
  • A Genome-Wide Association Study of Idiopathic Dilated Cardiomyopathy in African Americans
    Journal of Personalized Medicine Article A Genome-Wide Association Study of Idiopathic Dilated Cardiomyopathy in African Americans Huichun Xu 1,* ID , Gerald W. Dorn II 2, Amol Shetty 3, Ankita Parihar 1, Tushar Dave 1, Shawn W. Robinson 4, Stephen S. Gottlieb 4 ID , Mark P. Donahue 5, Gordon F. Tomaselli 6, William E. Kraus 5,7 ID , Braxton D. Mitchell 1,8 and Stephen B. Liggett 9,* 1 Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA; [email protected] (A.P.); [email protected] (T.D.); [email protected] (B.D.M.) 2 Center for Pharmacogenomics, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; [email protected] 3 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA; [email protected] 4 Division of Cardiovascular Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA; [email protected] (S.W.R.); [email protected] (S.S.G.) 5 Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC 27708, USA; [email protected] (M.P.D.); [email protected] (W.E.K.) 6 Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; [email protected] 7 Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27701, USA 8 Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration
    [Show full text]