DOCSLIB.ORG

Analyse Des Klinischen Verlaufs Und Der Differentiellen Genexpression Interferon-Β1a Therapierter Patienten Mit Multipler

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Analyse Des Klinischen Verlaufs Und Der Differentiellen Genexpression Interferon-Β1a Therapierter Patienten Mit Multipler Medizinische Fakultät, Universität Rostock Klinik und Poliklinik für Neurologie Direktor: Prof. Dr. med. Reiner Benecke Analyse des klinischen Verlaufs und der differentiellen Genexpression Interferon-!1a therapierter Patienten mit Multipler Sklerose Inauguraldissertation zur Erlangung des akademischen Grades doctor medicinae (Dr. med.) der Medizinischen Fakultät der Universität Rostock vorgelegt von Christian Wilhelm Fatum geboren am 05.04.1983 in Münster Rostock 2013 Gutachter 1. Gutachter Prof. Dr. med. Uwe K. Zettl Klinik und Poliklinik für Neurologie, Universitätsklinikum Rostock 2. Gutachter Prof. Dr. med. Hayrettin Tumani Klinik und Poliklinik für Neurologie, Universitätsklinikum Ulm 3. Gutachter Prof. Dr. med. Juergen H. Faiss Klinik für Neurologie am Asklepios Fachklinikum Teupitz Datum der Einreichung: 29.10.2012 Datum der Verteidigung: 11.06.2013 Inhaltsverzeichnis 1 Inhaltsverzeichnis Seite 1 Einleitung ............................................................................................. 4 1.1 Epidemiologie ......................................................................................................... 5 1.2 Ätiologie und Pathogenese ...................................................................................... 5 1.3 Klinik ....................................................................................................................... 9 1.3.1 Symptomatik ........................................................................................................... 9 1.3.2 Krankheitsverlauf und Prognose ........................................................................... 10 1.3.3 Diagnostik ............................................................................................................. 16 1.3.4 Therapie ................................................................................................................. 18 1.3.5 Interferon-! Therapie ............................................................................................ 22 1.4 Prädiktive Marker für den Therapieerfolg ............................................................. 24 1.4.1 Molekularbiologische Biomarker .......................................................................... 27 1.4.2 RNA-Profiling ....................................................................................................... 28 2 Fragestellung ..................................................................................... 30 3 Material und Methoden ................................................................... 32 3.1 Aufbau der Studie .................................................................................................. 32 3.2 Patientenpopulation ............................................................................................... 33 3.3 Klinische Beurteilung der Patienten ...................................................................... 35 3.3.1 Response- und Non-Response Kriterien ............................................................... 35 3.3.1.1 Schubfrequenz ....................................................................................................... 35 3.3.1.2 Progression ............................................................................................................ 35 3.3.2 Ermittlung paraklinischer Befunde ....................................................................... 36 3.3.3 Ermittlung des Nebenwirkungsprofils .................................................................. 36 Inhaltsverzeichnis 2 3.4 Genexpressionsanalytik ......................................................................................... 37 3.4.1 Affymetrix GeneChip®-Technologie ................................................................... 37 3.4.2 Genexpressionsanalyse .......................................................................................... 37 3.4.3 Filterung Interferon induzierter und reprimierter Gene ........................................ 39 3.5 Funktionelle Genanalytik und Netzwerkmodellierung ......................................... 41 3.5.1 Biologische Funktionen der gefilterten Gene ........................................................ 41 3.5.2 Analyse der Transkriptionsfaktorbindungsstellen ................................................. 42 3.5.3 Modell des genregulatorischen Netzwerks ........................................................... 43 3.6 Identifikation prädiktiver Marker .......................................................................... 44 4 Ergebnisse .......................................................................................... 46 4.1 Klinik ..................................................................................................................... 46 4.1.1 Klinische und soziodemographische Merkmale der Patientenpopulation ............ 46 4.1.2 Schubfrequenz ....................................................................................................... 48 4.1.3 Krankheitsprogression ........................................................................................... 50 4.1.4 Morphologische Befunde in der Magnetresonanztomographie ............................ 53 4.1.5 Nebenwirkungen der Interferon-! Therapie .......................................................... 56 4.2 Differentielle Genexpression ................................................................................. 57 4.2.1 Transkriptionelle Änderungen im Blut durch die IFN-!1a Therapie .................... 57 4.2.2 Funktionelle Analyse der exprimierten Gene ........................................................ 59 4.3 Netzwerkanalyse ................................................................................................... 60 4.3.1 Identifikation von Transkriptionsfaktor-Gen Interaktionen .................................. 60 4.3.2 Netzwerk Interferon-modulierter Gene ................................................................. 62 4.4 Assoziation von Expressionsmustern und den klinischen Daten .......................... 65 4.4.1 Grippale Nebenwirkungen und deren assoziierte Gene ........................................ 65 4.4.2 Differentielle Genexpression für die Kriterien EDSS und Schub ......................... 67 4.4.3 Differentielle Genexpression für das Kriterium MRT .......................................... 69 Inhaltsverzeichnis 3 5 Diskussion .......................................................................................... 70 6 Zusammenfassung ............................................................................ 84 7 Literaturverzeichnis ......................................................................... 87 8 Wissenschaftliches Verzeichnis ....................................................... 98 8.1 Abbildungsverzeichnis .......................................................................................... 98 8.2 Tabellenverzeichnis ............................................................................................... 99 8.3 Abkürzungsverzeichnis ....................................................................................... 100 9 Anhang ............................................................................................. 102 A Liste der ermittelten, differentiell exprimierten Gene ......................................... 102 B Graphische Darstellung eines MA-Plots ............................................................. 105 C Ergänzende Erläuterungen zur Methodik der vorliegenden Arbeit ..................... 106 10 Thesen zur Dissertation .................................................................. 107 11 Eidesstattliche Erklärung .............................................................. 110 12 Danksagung ..................................................................................... 111 Einleitung 4 1. Einleitung Die Multiple Sklerose (MS) ist eine immunmediierte Erkrankung des zentralen Nervensys- tems. Sie ist die häufigste nicht-traumatische Ursache für eine schwerwiegende, neurologi- sche Behinderung des jungen Erwachsenen [World Health Organization, WHO 2004]. Die Erkrankung ist durch entzündliche Prozesse an den Myelinscheiden mit Demyelinisierung und Axonschädigung sowie einer Degeneration der zentralen Nervenfasern gekennzeichnet und führt zu diversen neurologischen Defiziten. In ihrer häufigsten Form hat sie einen schubfömig-intermittierenden Verlauf (Kapitel 1.3.2 Krankheitsverlauf und Prognose). Die Ursache der MS ist bisher nicht bekannt, aufgrund dieser Tatsache existieren bisher keine kausalen Behandlungsmöglichkeiten. Genetische Einflüsse, Umweltfaktoren sowie eine auto- immunologische Dysfunktion werden mit der Pathogenese der Erkrankung in Verbindung ge- bracht [Weiner 2009, Compston & Coles 2008, Sospedra & Martin 2005]. Die Therapie stützt sich gegenwärtig auf eine immunmodulatorische Stufentherapie, die im Falle von auftreten- den Symptomen durch eine symptomatische Behandlung ergänzt wird [Rieckmann 2006, Leitlinien zur MS, DGN 2012]. Zudem werden psychosoziale Bewältigungsstrategien, das sogenannte Coping eingesetzt, um dem Patienten einen besseren Umgang mit der Krankheit ermöglichen zu können [Apel et al. 2006]. Die Immunmodulation zielt auf eine Verringerung der Schubfrequenz und Krankheitsprogression ab und versucht die Entstehung neuer Hirnlä- sionen und einer Hirnatrophie zu verhindern. In der Behandlung der schubförmigen MS hat sich rekombinantes Interferon-! weltweit als Standard etabliert. Trotz einer vergleichsweise guten Wirkung hat die Behandlung
Load more

Recommended publications
  • Final Copy 2018 09 25 Gaunt
    This electronic thesis or dissertation has been downloaded from Explore Bristol Research, http://research-information.bristol.ac.uk Author: Gaunt, Jess Title: A Viral Approach to Translatome Profiling of CA1 Neurons During Associative Recognition Memory Formation General rights Access to the thesis is subject to the Creative Commons Attribution - NonCommercial-No Derivatives 4.0 International Public License. A copy of this may be found at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode This license sets out your rights and the restrictions that apply to your access to the thesis so it is important you read this before proceeding. Take down policy Some pages of this thesis may have been removed for copyright restrictions prior to having it been deposited in Explore Bristol Research. However, if you have discovered material within the thesis that you consider to be unlawful e.g. breaches of copyright (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please contact [email protected] and include the following information in your message: •Your contact details •Bibliographic details for the item, including a URL •An outline nature of the complaint Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. A Viral Approach to Translatome Profiling of CA1 Neurons During Associative Recognition Memory Formation Jessica Ruth Gaunt A dissertation submitted to the University of Bristol in accordance with the requirements for award of the degree of Doctor of Philosophy in the Faculty of Health Sciences, Bristol Medical School.
    [Show full text]
  • Noelia Díaz Blanco
    Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr.
    [Show full text]
  • Structure and Expression Analyses of SVA Elements in Relation to Functional Genes
    pISSN 1598-866X eISSN 2234-0742 Genomics Inform 2013;11(3):142-148 G&I Genomics & Informatics http://dx.doi.org/10.5808/GI.2013.11.3.142 ORIGINAL ARTICLE Structure and Expression Analyses of SVA Elements in Relation to Functional Genes Yun-Jeong Kwon, Yuri Choi, Jungwoo Eo, Yu-Na Noh, Jeong-An Gim, Yi-Deun Jung, Ja-Rang Lee, Heui-Soo Kim* Department of Biological Sciences, College of Natural Sciences, Pusan National University, Busan 609-735, Korea SINE-VNTR-Alu (SVA) elements are present in hominoid primates and are divided into 6 subfamilies (SVA-A to SVA-F) and active in the human population. Using a bioinformatic tool, 22 SVA element-associated genes are identified in the human genome. In an analysis of genomic structure, SVA elements are detected in the 5′ untranslated region (UTR) of HGSNAT (SVA-B), MRGPRX3 (SVA-D), HYAL1 (SVA-F), TCHH (SVA-F), and ATXN2L (SVA-F) genes, while some elements are observed in the 3′UTR of SPICE1 (SVA-B), TDRKH (SVA-C), GOSR1 (SVA-D), BBS5 (SVA-D), NEK5 (SVA-D), ABHD2 (SVA-F), C1QTNF7 (SVA-F), ORC6L (SVA-F), TMEM69 (SVA-F), and CCDC137 (SVA-F) genes. They could contribute to exon extension or supplying poly A signals. LEPR (SVA-C), ALOX5 (SVA-D), PDS5B (SVA-D), and ABCA10 (SVA-F) genes also showed alternative transcripts by SVA exonization events. Dominant expression of HYAL1_SVA appeared in lung tissues, while HYAL1_noSVA showed ubiquitous expression in various human tissues. Expression of both transcripts (TDRKH_SVA and TDRKH_noSVA) of the TDRKH gene appeared to be ubiquitous.
    [Show full text]
  • Genome-Wide Association Study Identifies 44 Independent Genomic Loci for Self-Reported Adult Hearing Difficulty in the UK Biobank Cohort
    bioRxiv preprint doi: https://doi.org/10.1101/549071; this version posted February 14, 2019. 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-NC-ND 4.0 International license. Genome-wide association study identifies 44 independent genomic loci for self-reported adult hearing difficulty in the UK Biobank cohort Helena RR. Wells1,2, Maxim B. Freidin1, Fatin N. Zainul Abidin2, Antony Payton3, Piers Dawes4, Kevin J. Munro4,5, Cynthia C. Morton4,5,6, David R. Moore4,7, #*Sally J Dawson2, #*Frances MK. Williams1 1Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London 2UCL Ear Institute, University College London 3Division of Informatics, Imaging & Data Sciences, The University of Manchester 4Manchester Centre for Audiology and Deafness, The University of Manchester 5Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre 6Departments of Obstetrics and Gynecology and of Pathology, Brigham and Women’s Hospital, Harvard Medical School 7Cincinnati Children's Hospital Medical Centre, Department of Otolaryngology, University of Cincinnati College of Medicine #Joint senior authors *Corresponding authors 1 bioRxiv preprint doi: https://doi.org/10.1101/549071; this version posted February 14, 2019. 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-NC-ND 4.0 International license. Age-related hearing impairment (ARHI) is the most common sensory impairment in the aging population; a third of individuals are affected by disabling hearing loss by the age of 651.
    [Show full text]
  • Identification of the Long, Edited Dsrnaome of LPS-Stimulated Immune Cells
    Downloaded from genome.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Identification of the long, edited dsRNAome of LPS-stimulated immune cells Matthew G. Blango and Brenda L. Bass* Department of Biochemistry, University of Utah, Salt Lake City, Utah 84112, USA * Correspondence: [email protected] Phone: 801-581-4884 Running title: dsRNAome of mammalian immune cells Keywords: dsRNA, RNA binding protein, ADAR, Inosine, RNA editing, LPS 1 Downloaded from genome.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract Endogenous double-stranded RNA (dsRNA) must be intricately regulated in mammals to prevent aberrant activation of host inflammatory pathways by cytosolic dsRNA binding proteins. Here we define the long, endogenous dsRNA repertoire in mammalian macrophages and monocytes during the inflammatory response to bacterial lipopolysaccharide. Hyperediting by adenosine deaminases that act on RNA (ADAR) enzymes was quantified over time using RNA-seq data from activated mouse macrophages to identify 342 Editing Enriched Regions (EERs), indicative of highly structured dsRNA. Analysis of publicly available datasets for samples of human peripheral blood monocytes resulted in discovery of 3,438 EERs in the human transcriptome. Human EERs had predicted secondary structures that were significantly more stable than those of mouse EERs and were located primarily in introns, whereas nearly all mouse EERs were in 3’UTRs. Seventy-four mouse EER-associated genes contained an EER in the orthologous human gene, although nucleotide sequence and position were only rarely conserved. Among these conserved EER-associated genes were several TNFα-signaling genes, including Sppl2a and Tnfrsf1b, important for processing and recognition of TNFα, respectively.
    [Show full text]
  • Identification of the Long, Edited Dsrnaome of LPS-Stimulated Immune Cells
    Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Identification of the long, edited dsRNAome of LPS-stimulated immune cells Matthew G. Blango and Brenda L. Bass Department of Biochemistry, University of Utah, Salt Lake City, Utah 84112, USA Endogenous double-stranded RNA (dsRNA) must be intricately regulated in mammals to prevent aberrant activation of host inflammatory pathways by cytosolic dsRNA binding proteins. Here, we define the long, endogenous dsRNA reper- toire in mammalian macrophages and monocytes during the inflammatory response to bacterial lipopolysaccharide. Hyperediting by adenosine deaminases that act on RNA (ADAR) enzymes was quantified over time using RNA-seq data from activated mouse macrophages to identify 342 Editing Enriched Regions (EERs), indicative of highly structured dsRNA. Analysis of publicly available data sets for samples of human peripheral blood monocytes resulted in discovery of 3438 EERs in the human transcriptome. Human EERs had predicted secondary structures that were significantly more stable than those of mouse EERs and were located primarily in introns, whereas nearly all mouse EERs were in 3′ UTRs. Seventy-four mouse EER-associated genes contained an EER in the orthologous human gene, although nucleotide sequence and position were only rarely conserved. Among these conserved EER-associated genes were several TNF alpha-signaling genes, including Sppl2a and Tnfrsf1b, important for processing and recognition of TNF alpha, respectively. Using publicly available data and experimental validation, we found that a significant proportion of EERs accumulated in the nucleus, a strategy that may prevent aberrant activation of proinflammatory cascades in the cytoplasm.
    [Show full text]
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
    [Show full text]
  • Microarray Bioinformatics and Its Applications to Clinical Research
    Microarray Bioinformatics and Its Applications to Clinical Research A dissertation presented to the School of Electrical and Information Engineering of the University of Sydney in fulfillment of the requirements for the degree of Doctor of Philosophy i JLI ··_L - -> ...·. ...,. by Ilene Y. Chen Acknowledgment This thesis owes its existence to the mercy, support and inspiration of many people. In the first place, having suffering from adult-onset asthma, interstitial cystitis and cold agglutinin disease, I would like to express my deepest sense of appreciation and gratitude to Professors Hong Yan and David Levy for harbouring me these last three years and providing me a place at the University of Sydney to pursue a very meaningful course of research. I am also indebted to Dr. Craig Jin, who has been a source of enthusiasm and encouragement on my research over many years. In the second place, for contexts concerning biological and medical aspects covered in this thesis, I am very indebted to Dr. Ling-Hong Tseng, Dr. Shian-Sehn Shie, Dr. Wen-Hung Chung and Professor Chyi-Long Lee at Change Gung Memorial Hospital and University of Chang Gung School of Medicine (Taoyuan, Taiwan) as well as Professor Keith Lloyd at University of Alabama School of Medicine (AL, USA). All of them have contributed substantially to this work. In the third place, I would like to thank Mrs. Inge Rogers and Mr. William Ballinger for their helpful comments and suggestions for the writing of my papers and thesis. In the fourth place, I would like to thank my swim coach, Hirota Homma.
    [Show full text]
  • 556 Positive Significant Genes Computed Quantities Input
    Significant Genes List Input Parameters Imputation Engine Data Type Data in log scale? Number of Permutations Blocked Permutation? RNG Seed (Delta, Fold Change) (Upper Cutoff, Lower Cutoff) Computed Quantities Computed Exchangeability Factor S0 S0 percentile False Significant Number (Median, 90 percentile) False Discovery Rate (Median, 90 percentile) Pi0Hat 556 Positive Significant Genes Row Gene Name 5288 AGI_HUM1_OLIGO_A_23_P170096 6496 AGI_HUM1_OLIGO_A_23_P210021 9658 AGI_HUM1_OLIGO_A_23_P43444 9730 AGI_HUM1_OLIGO_A_23_P44466 12489 AGI_HUM1_OLIGO_A_23_P81507 666 AGI_HUM1_OLIGO_A_23_P108599 2085 AGI_HUM1_OLIGO_A_23_P127727 8831 AGI_HUM1_OLIGO_A_23_P32499 2483 AGI_HUM1_OLIGO_A_23_P132736 5424 AGI_HUM1_OLIGO_A_23_P17808 11383 AGI_HUM1_OLIGO_A_23_P66543 8456 AGI_HUM1_OLIGO_A_23_P2762 5455 AGI_HUM1_OLIGO_A_23_P1823 11724 AGI_HUM1_OLIGO_A_23_P71067 692 AGI_HUM1_OLIGO_A_23_P108932 4216 AGI_HUM1_OLIGO_A_23_P155504 13553 AGI_HUM1_OLIGO_A_23_P96529 5370 AGI_HUM1_OLIGO_A_23_P171284 10443 AGI_HUM1_OLIGO_A_23_P53946 5573 AGI_HUM1_OLIGO_A_23_P1981 3624 AGI_HUM1_OLIGO_A_23_P147869 9215 AGI_HUM1_OLIGO_A_23_P3766 10396 AGI_HUM1_OLIGO_A_23_P53257 9179 AGI_HUM1_OLIGO_A_23_P37251 8146 AGI_HUM1_OLIGO_A_23_P257762 6728 AGI_HUM1_OLIGO_A_23_P212720 6322 AGI_HUM1_OLIGO_A_23_P208059 6707 AGI_HUM1_OLIGO_A_23_P212499 12483 AGI_HUM1_OLIGO_A_23_P8142 7995 AGI_HUM1_OLIGO_A_23_P255925 6898 AGI_HUM1_OLIGO_A_23_P214681 8593 AGI_HUM1_OLIGO_A_23_P29472 8259 AGI_HUM1_OLIGO_A_23_P259192 13318 AGI_HUM1_OLIGO_A_23_P93015 10290 AGI_HUM1_OLIGO_A_23_P51861 9965 AGI_HUM1_OLIGO_A_23_P47709
    [Show full text]
  • Blood Transcriptome Based Biomarkers for Human Circadian
    RESEARCH ARTICLE Blood transcriptome based biomarkers for human circadian phase Emma E Laing1*†, Carla S Mo¨ ller-Levet2†, Norman Poh3†, Nayantara Santhi4, Simon N Archer4, Derk-Jan Dijk4* 1Department of Microbial Sciences, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom; 2Bioinformatics Core Facility, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom; 3Department of Computer Science, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom; 4Surrey Sleep Research Centre, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom Abstract Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain’s circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA- based predictor of melatonin phase which requires few samples. Transcriptome data were collected under normal, sleep-deprivation and abnormal sleep-timing conditions to assess robustness of the predictor. Partial least square regression (PLSR), applied to the transcriptome, *For correspondence: e.laing@ identified a set of 100 biomarkers primarily related to glucocorticoid signaling and immune surrey.ac.uk (EEL); d.j.dijk@surrey. function. Validation showed that PLSR-based predictors outperform published blood-derived 2 ac.uk (D-JD) circadian phase predictors. When given one sample as input, the R of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R2 was 0.90.
    [Show full text]
  • Leveraging Pleiotropy to Discover and Interpret GWAS Results for Sleep
    bioRxiv preprint doi: https://doi.org/10.1101/832162; this version posted November 7, 2019. 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 4.0 International license. Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits Sebastian Akle,1,2,3,24 Sung Chun,2,3,4,24 Athanasios Teodosiadis,3 Brian E. Cade,5,6,7 Heming Wang,5,6,7 Tamar Sofer,5,8 Daniel S. Evans,9 Katie L. Stone,9 Sina A. Gharib,10,11 Sutapa Mukherjee,12,13 Lyle J. Palmer,14 David Hillman,15,16 Jerome I. Rotter,17 John A. Stamatoyannopoulos,3,18 Susan Redline,5,6,19 Chris Cotsapas,3,20,21,22,25,* Shamil R. Sunyaev2,3,23,25,* 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; 2Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA; 3Altius Institute for Biomedical Sciences, Seattle, WA, USA; 4Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA, USA; 5Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA; 6Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; 7Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; 8Department of Medicine, Harvard Medical School, Boston, MA, USA; 9California Pacific Medical Center Research Institute, San Francisco, CA, USA; 10Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, WA, USA; 11Computational Medicine Core at Center for Lung Biology, University of Washington, Seattle, WA, USA; 12Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia; 13Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia; 14School of Public Health, University of Adelaide, Adelaide, South Australia, Australia; bioRxiv preprint doi: https://doi.org/10.1101/832162; this version posted November 7, 2019.
    [Show full text]
  • Junl;G.~~ Assistant Professor Department of Mechanical Engineering Rice University
    RICE UNIVERSITY Novel Exogenous Agents for Improving Articular Cartilage Tissue Engineering by Donald Joseph Responte A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE Doctor of Philosophy APPROVED, THESIS COMMITTEE: Kyriacos thanas u, PH. ., Thesis Director Distinguis ed Professor Department of Biomedical Engineering University of California, Davis Antonios Mikos, Ph.D., Committee Chair Louis Calder Professor Department of Bioengineering Rice University Junl;G.~~ Assistant Professor Department of Mechanical Engineering Rice University HOUSTON, TEXAS OCTOBER 2011 ii ABSTRACT Novel Exogenous Agents for Improving Articular Cartilage Tissue Engineering by Donald Joseph Responte This thesis demonstrated the effects of exogenous stimuli on engineered articular cartilage constructs and elucidated mechanisms underlying the responses to these agents. In particular, a series of studies detailed the effects of chondroitinase-ABC (C-ABC), hyaluronic acid (HA), and TGF-~1 on the biochemical and biomechanical properties of self-assembled articular cartilage. Work with C-ABC showed that this catabolic agent can be employed to improve the tensile properties of constructs. When constructs were cultured for 6 weeks, treating with C-ABC at 2 weeks enhanced the tensile stiffness. Furthermore, treating at 2 and 4 weeks synergistically increased tensile properties and allowed compressive stiffness to recover to control levels. Another study showed that combining C-ABC and TGF-~1 synergistically enhanced the biochemical and biomechanical properties of neotissue. Microarray analysis demonstrated that TGF-~1 increased MAPK signaling in self-assembled neocartilage whereas C­ ABC had minimal effects on gene expression. SEM analysis showed that C-ABC increased collagen fibril diameter and fibril density, indicating that C-ABC potentially acts via a biophysical mechanism.
    [Show full text]