Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies [Elektronische Ressource] / Michael Hecker. Gutachter: Reinhard Guthke ; Raimund W. Kinne ; Ronald Westra

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• Publié par: friedrich-schiller-universitat_jena
• Publié le: 01 janvier 2011
• Nombre de lectures: 18
• Langue: Deutsch
• Poids de l'ouvrage: 12 Mo

Extrait

Integrative Modeling of
Transcriptional Regulation in Response to
Autoimmune Disease Therapies
Dissertation
zur Erlangung des akademischen Grades
doctor rerum naturalium
(Dr. rer. nat.)
vorgelegt dem Rat der Biologisch-Pharmazeutischen Fakultät
der Friedrich-Schiller-Universität Jena
von
Diplom-Bioinformatiker
Michael Hecker
geboren am 6. April 1982 in ErfurtDie vorgelegte Arbeit, finanziert durch das Bundesministerium für Bildung und Forschung
(Grant 0313692D), wurde am Leibniz-Institut für Naturstoff-Forschung und
Infektionsbiologie e.V. - Hans-Knöll-Institut (HKI) unter der Leitung von PD Dr. Reinhard
Guthke (Abteilung Systembiologie / Bioinformatik) im Zeitraum November 2006 bis Januar
2010 angefertigt.Table of contents I
Table of contents
Abbreviations.................................................................................................................III
1. Introduction......................................................................................................................1
1.1. Gene regulatory network modeling.............................................................................1
1.2. Autoimmune diseases..................................................................................................4
1.3. Objectives and experimental approach........................................................................8
2. Overview of manuscripts...............................................................................................11
3. Manuscript I....................................................................................................................16
Hecker M, Lambeck S, Töpfer S, van Someren E, Guthke R.
Gene regulatory network inference: data integration in dynamic models - a review.
Biosystems 2009, 96(1):86-103.
4. Manuscript II..................................................................................................................35
Hecker M, Goertsches RH, Engelmann R, Thiesen HJ, Guthke R.
Integrative modeling of transcriptional regulation in response to antirheumatic therapy.
BMC Bioinformatics 2009, 10:262.
5. Manuscript III.................................................................................................................54
Goertsches RH, Hecker M, Koczan D, Serrano-Fernández P, Möller S,
Thiesen HJ, Zettl UK.
Long-term genome-wide blood RNA expression profiles yield novel molecular
response candidates for IFN-beta-1b treatment in relapsing remitting MS.
Pharmacogenomics 2010, 11(2):147-161.
6. Manuscript IV.................................................................................................................70
Hecker M, Goertsches RH, Fatum C, Koczan D, Thiesen HJ, Guthke R, Zettl UK.
Network analysis of transcriptional regulation in response to intramuscular
interferon-beta-1a multiple sclerosis treatment.
Pharmacogenomics J. submitted January 25, 2010.
7. Discussion......................................................................................................................104Table of contents II
7.1. Discussion of main results.......................................................................................104
7.2. Discussion of methods.............................................................................................107
7.2.1. Experimental approach......................................................................................107
7.2.2. Microarray data preprocessing..........................................................................108
7.2.3. Integrative network inference............................................................................109
7.2.4. Evaluation of inference performance.................................................................111
7.3. Open issues and outlook..........................................................................................113
7.3.1. Prediction of clinical responses.........................................................................113
7.3.2. Further development of TILAR.........114
7.4. Concluding remarks.................................................................................................116
8. Summary.......................................................................................................................118
9. Zusammenfassung........................................................................................................120
References......................................................................................................................122
Appendix........................................................................................................................127
Danksagung...................................................................................................................141
Ehrenwörtliche Erklärung...........................................................................................142
Tabellarischer Lebenslauf...........................................................................................143Abbreviations III
Abbreviations
ACPA anti-citrullinated protein/peptide antibody
ACR American College of Rheumatology
CDF chip definition file
CSF cerebrospinal fluid
DAS disease activity score
DMARD disease-modifying antirheumatic drug
DREAM dialogue on reverse-engineering assessment and methods
EDSS expanded disability status scale
GO Gene Ontology
GRN gene regulatory network
HLA human leukocyte antigen
IFN-β interferon-beta
IRF IFN regulatory factor
LARS least angle regression
Lasso least absolute shrinkage and selection operator
MAID MA-plot-based signal intensity-dependent fold-change criterion
MHC major histocompatibility complex
MRI magnetic resonance imaging
MS multiple sclerosis
OLS ordinary least squares
PBMC peripheral blood mononuclear cells
PPI protein-protein interaction
RA rheumatoid arthritis
RF rheumatoid factor
RNAP RNA polymerase
ROC recall-precision curve
RPC receiver operating characteristic
TF transcription factor
TFBS transcription factor binding site
TILAR TFBS-integrating least angle regression
TNF-α tumor necrosis factor-alphaIntroduction 1
1. Introduction
At the heart of multicellular life are the complex interactions between genes, proteins and
metabolites. These interactions give rise to the function and behavior of biological systems.
To study and understand such systems as a whole abstractions are needed such as the
concept of networks, in which molecules are represented as nodes and interactions or causal
influences are represented by edges. The reconstruction of biomolecular networks from
experimental data and subsequent network analysis is a challenging and active field of
research. The major focus of the present dissertation is on the inference of gene regulatory
networks (GRNs).
1.1. Gene regulatory network modeling
Gene expression is mainly regulated at the level of DNA transcription by proteins called
transcription factors (TFs). These TFs specifically bind short DNA sequence motifs at the
regulatory region of their target genes. In doing so, they control the recruitment of RNA
polymerase, which reads the DNA and transcribes it into RNA. However, gene regulation is
a far more complex multi-layered process. Any step of gene expression may be modulated,
from the RNA synthesis to the post-translational modification of proteins. Many genes are
(directly oder indirectly) involved in these gene regulatory mechanisms, and therefore it is
reasonable to regard genes as nodes in a network of mutual regulatory interactions.
The introduction of DNA microarrays in the mid-1990s offered the possibility to
simultaneously measure the levels of thousands of RNA transcripts in a single sample of
cells or tissues. Since then, researchers utilized the growing amount of large-scale gene
expression data as input for algorithms to infer, or "reverse-engineer", the regulatory
interaction structure of genes [1-3]. When inferring models of transcriptional regulation
solely from gene expression data, one typically seeks for influences between RNA
transcripts. In this case, the expression levels of each gene are explained by the expression
levels of other genes. By construction, such GRN models do not generally describe physical
interactions as transcripts rather exert their regulatory effects indirectly through the action of
proteins, metabolites and effects on the cell environment (figure 1). Therefore, these models
can be difficult to interpret in terms of real physical interactions, and the implicit description
of hidden regulatory factors may limit the reliability of the inference results.
To overcome these issues and support the network reconstruction it is necessary to integrate
additional biological information. Diverse types of data (e.g. protein-protein and protein-Introduction 2
Figure 1. Gene regulation is carried out by interactions of RNA molecules, proteins and
metabolites. (A) Simplified illustration of an example GRN where gene "3" encodes a
membrane-bound metabolite transporter protein (green shape). The metabolite (blue triangle)
that is imported by this protein binds a TF (orange shape). The activated TF binds the DNA
and together with RNA polymerase (RNAP) initiates the transcri