PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is a publisher's version. For additional information about this publication click this link. http://hdl.handle.net/2066/100868 Please be advised that this information was generated on 2017-12-06 and may be subject to change. Deciphering cellular responses to pathogens using genomics data Iziah Edwin Sama Deciphering cellular responses to pathogens using genomics data This research was performed at the Centre for Molecular and Biomolecular Informatics (CMBI), Nijmegen Centre of Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. Funding: This work was supported by the VIRGO consortium, an Innovative Cluster approved by the Netherlands Genomics Initiative and partially funded by the Dutch Government (BSIK 03012), The Netherlands. ISBN 978-90-9027062-3 © 2012 Iziah Edwin Sama All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, by print or otherwise, without permission in writing from the author Front Cover Image: A metaphorical illustration of the complexity in a host cell (the field), wherein fundamental moieties like proteins interact with each other (the network) in response to various pathogenic stimuli triggering respective cellular responses (the sub-fields demarcated by different line colors). The background is a picture of an indoors multi-sports field .The network is a protein-protein interaction network (HsapiensPPI of chapter 3) in which nodes represent proteins and edges between nodes indicate physical association. (Concept by Iziah Edwin Sama) Cover design and lay-out: In Zicht Grafisch Ontwerp, Arnhem Printed by: Ipskamp Drukkers, Enschede II Deciphering cellular responses to pathogens using genomics data Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. mr. S.C.J.J. Kortmann, volgens besluit van het college van decanen in het openbaar te verdedigen op vrijdag 30 november 2012 om 12:30 uur precies door Iziah Edwin Sama geboren op 10 februari 1976 te Kombone, Kameroen III Promotor Prof. dr. Martijn A. Huynen Manuscriptcommissie Prof. dr. Robert W. Sauerwein (Voorzitter) Dr. Elena Marchiori Prof. dr. Willem J. Stiekema (UvA) IV Deciphering cellular responses to pathogens using genomics data Doctoral thesis to obtain the degree of doctor from Radboud University Nijmegen on the authority of the Rector Magnificus prof.dr.S.C.J.J Kortmann, according to the decision of the Council of Deans to be defended in public on Friday, November 30, 2012 at 12:30 hours by Iziah Edwin Sama Born on February 10, 1976 in Kombone (Cameroon) V Supervisor Prof. dr. Martijn A. Huynen Doctoral Thesis Committee Prof. dr. Robert W. Sauerwein (Chairman) Dr. Elena Marchiori Prof. dr. Willem J. Stiekema (University of Amsterdam) VI Contents Chapter 1 General Introduction 11 1. Genomics perspectives to host-pathogen interactions 13 1.1 Transcriptomics 13 1.2 MicroRNAs 14 1.3 Transcription factor binding sites 15 1.4 Proteomics 16 1.5 Protein-protein interaction networks 16 2. Immune response 18 3. Outline of thesis 18 References 23 Chapter 2 Quantitative proteome profiling of respiratory virus-infected lung 27 epithelial cells Abstract 28 Introduction 29 Materials and Methods 30 Results and Discussion 33 Conclusions 53 Acknowledgements 53 References 54 Chapter 3 Measuring the physical cohesiveness of proteins using physical 59 interaction enrichment Abstract 60 Introduction 61 Methods 63 Results 70 Discussion 73 Acknowledgements 75 References 76 Chapter 4 Transcriptomic assessment of cellular response to intracellular 79 pathogens reveals an iron-depletion profile Abstract 80 Introduction 81 Materials and Methods 83 Results 89 Discussion 98 Acknowledgement 100 References 101 VII VIII Contents Chapter 5 MicroRNA genes preferentially expressed in dendritic cells 105 contain sites for conserved transcription factor binding motifs in their promoters Abstract 106 Background 107 Methods 108 Results 112 Discussion 124 Conclusions 127 Acknowledgements 127 References 128 Chapter 6 Transcriptome kinetics of circulating neutrophils during human 133 experimental endotoxemia Abstract 134 Introduction 135 Methods 135 Results 139 Discussion 148 Acknowledgements 151 References 152 Chapter 7 General Discussion 155 1. Summary of findings 157 2. Future perspectives 158 3. Conclusion 164 References 165 Summary 171 Samenvatting 175 Acknowledgement 179 Appendix 1: 181 Appendix 2: 186 Appendix 3: 204 Curriculum Vitae (English) 222 Curriculum Vitae (Nederlands) 223 List of Publications 224 IX 1 General Introduction General Introduction 1 1. Genomics perspectives to host-pathogen interactions The central dogma in molecular biology posits that, at a very basic level of most known living things lies deoxyribonucleic acid (DNA) which consists of sequential segmental nucleotide units called genes, interspersed by other nucleotide sequences for structural and regulatory purposes. As and exception, the genes of RNA viruses are in ribonucleic acid (RNA) as they do not have DNA. Genes are our hereditary material and are also the units that encode complex biological products like proteins of the immune system and various types of RNA, e.g. microRNAs. We refer to the collection of all genetic material in an organism as its genome. The human genome was completely sequenced 10 years ago1, 2 and it has since then been used to investigate disease processes in a host-cell at a genomic level by, for example, uncovering host genes that are up- or down-regulated in expression because of an infection. To study such events more systematically, biologists examine biological information at various genomics levels, some of which I shall discuss below. 1.1 Transcriptomics The process of expressing genes from DNA is known as transcription and the protein coding precursors of transcribed genes are known as messenger RNAs (mRNAs). In this “omics” age, when several genes can be simultaneously investigated using technologies like DNA microarrays or RNA-seq to generate the so-called trancriptomics datasets from cells, reporting and interpreting huge numbers of genes against the background of what is known in the scientific literature is not a simple task. In infection biology for example, whole-genome transcriptomics (transcriptome) data, can be generated from a host-cell, in order to observe genes or group of genes that are responsive to infectious disease agents. Such efforts have yielded good results in several studies. For example, using transcriptomics data from high-density oligonucleotide microarrays, Bao and colleagues used alveolar epithelial cells (A549 cells) to globally uncover cytokines and chemokines that are induced in expression upon human metapneumovirus (HMPV) infection of these cells3, 4; thus providing an important resource for HMPV biology which I shall use in this thesis, and that will likely inspire other studies on HMPV pathogenesis. Another transcriptomics study related to infection concerns the respiratory syncytial virus (RSV) infection. Using mouse models, Schuurhof and colleagues examined differential host transcription profiles generated during secondary immune responses to RSV compared to primary infection to explain the phenomenon of vaccine-enhanced disease. They discovered that 5 days after challenge, the chemokine, inflammation and interferon response genes were expressed at higher levels during primary immune responses, while the immunoglobulin gene expression was higher during secondary immune responses. Furthermore, formalin-inactivated RSV vaccination with RSV challenge generated vaccine-enhanced disease and resulted in a transcription profile similar to 13 that of a primary immune response instead of a secondary response. In addition, Th2 gene expression was specifically induced in mice that were vaccinated with formalin- inactivated RSV. Schuurhof and colleagues concluded that their findings support the hypothesis that vaccine-enhanced disease is mediated by prolonged innate immune responses and Th2 polarization, in the absence of replication of the virus5. 1.2 MicroRNAs Not all genes are protein-encoding. Some genes encode microRNAs (miRNAs) that are important in post-transcriptional gene regulation. MiRNAs can stall protein translation by binding to the 3’ untranslated region (UTR) of mRNAs of their so-called target genes. Furthermore, it has been shown that targeted mRNAs are mainly degraded upon miRNA binding; leading to low concentrations of the mRNA targets6-8. MicroRNAs are currently being recognized as important regulatory factors in several biological processes such as the control of differentiation and maturation of innate-immune cells that are challenged with pathogenic material like LPS9-12. Identifying miRNAs and their target genes is important in unraveling the gene network underpinning immune responses to pathogens. There are a number of bioinformatics- based algorithms aimed at predicting target genes for miRNAs. These include PicTar13 and TargetScan14. These prediction programs take genomic factors like the phylogenetic conservation of both the miRNA and the target genes amongst multiple animal species into account in order to ascertain the reliability of the predictions. In the context of evolutionary biology, strong conservation of a miRNA and its target gene suggests a functional partnership of the pair. Experimental identification of