Towards the identification of regulatory networks using statistical and information theoretical methods on the mammalian transcriptome [Elektronische Ressource] / vorgelegt von Dominik Ralph Lionel Lutter

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• Biologie

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• Publié par: universitat_regensburg
• Publié le: 01 janvier 2009
• Nombre de lectures: 12
• Langue: English
• Poids de l'ouvrage: 4 Mo

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Towards the identi cation of regulatory networks
using statistical and information theoretical
methods on the mammalian transcriptome
Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften
(Dr. rer. nat.) der Fakultat III { Biologie und Vorklinische Medizin der Universitat
Regensburg
vorgelegt von
Dominik Ralph Lionel Lutter aus Nairobi
April 20092Das Promotionsgesuch wurde eingereicht am: 3.06.2009.
Die mundlic he Prufung fand am 9.11.2009 statt.
Die Arbeit wurde angeleitet von Prof. Dr. Elmar W. Lang.
Prufungsaussc hu :
Prof. Dr. Dr. Hans R. Kalbitzer (Vorsitzender)
Prof. Dr. Elmar W. Lang (1. Gutachter)
Prof. Dr. Dr. Fabian J. Theis (2.hter)
Prof. Dr. Reinhard Sterner (Weiterer Prufer)4‘Just look down there’ said Denny.
‘That seemingly endless convoy,
trailing along the dried up valley below,
look for all the world like ants.’
‘They ARE ants’ said his companion Minnie,
‘And so are we’.
And it was true.
They were both ants,
perched on the edge of a clod of earth
no more than six inches high.
‘Oh’, sighed Denny sadly,
‘I forgot’.
Robert Wyatt { Comicopera
56Summary
All life known consists of cells. Every cell contains DNA. DNA is just a code. A
code existent of four simple letters A, T, G and C. But the sequence composed of
these letters contains nearly all information needed to form a complete organism
as complex as a human being out of a single fertilized egg cell. And every single
cell | up to a few exceptions | of one organism contains exactly the same DNA
sequence as the fertilized egg, the genetic information. This genetic information
belonging to a cell or organism is called a genome. This code is executed by the
genes whereas a gene may contain structural, signalling or regulatory information.
Our comprehension of the genetic machinery regulating the expression of thou-
sands of di erent genes controlling cell di erentiation or responding to various
external signals is still highly incomplete. Furthermore, recently discovered reg-
ulatory mechanisms like those mediated by microRNAs expand our knowledge
but also add an additional layer of complexity. Since all genes are primarily
transcribed into RNA, the genetic activity of gene di erential expression can be
estimated by measuring the RNA expression. Several techniques to measure large
scale gene expression on the basis of RNA have been developed. In this work,
data generated with the microarray technology, one of the most commonly used
methods, were analyzed towards extracting novel biological regulatory structures.
In the following several aspects on the analysis of these large gene expression
data will be discussed. Since this is nowadays a common task, a lot has been writ-
ten about various methods in all its particulars, but often from a more technical
or statistical point of view. However, the aim of a biologist planning and carrying
out a microarray experiment lies on the acquisition of novel biological ndings.
In fact, there is still a gap between the experimentalists and the methods devel-
oping community. The experimentalists are often not too familiar with the latest
fancy method based on modern statistics as it is used in e.g. information the-
ory whereas the developing community normally does not deal extensively with
current biological questions. Therefore, the author of this work tries to give an
7additional view on the eld of microarray analysis and the applicability of diverse
methods. Hence, the focus is to discuss commonly used methods towards their
usage, the underlying biological assumptions and the possible interpretations,
pros and cons. Furthermore, beyond ordinary di erential gene expression analy-
ses, this work also concentrates on an unbiased search for hidden information in
gene expression patterns.
In the rst section of chapter 1, a general overview about the main biological
principles is given. The term transcriptome and its composition of several RNA
types will be introduced. Furthermore the mechanism controlling gene expression
will be presented. The chapter further explains the basic principles of microarray
technology and also discusses the advantages and limitations of this method.
Finally, by means of two di erent biological models, commonly used and a few
more specialized and less popular analysis methods will be presented. In doing
so, less emphasis is given on a complete and detailed mathematical description,
but more on a general applicability and the biological outcome of these tools.
Chapter 2 extensively discusses the usage of a blind source separation tech-
nique, independent component analysis (ICA), on a two class microarray dataset.
Monocytes extracted from human donors were di erentiated into macrophages
using M-CSF (Macrophage Colony-Stimulating Factor). By applying ICA to the
data, so called expression modes or sub-modes could be extracted. According to
referring biological annotations, these sub-modes were then combined to meta
modes and elaborately discussed. In this way, several known biological signalling
pathways as well as regulatory mechanism involved in monocyte di erentiation
could be reconstructed. Furthermore, a novel biological nding, the remaining
proliferative potential of macrophages could also be identi ed. The results of
this investigation were already published by the author [Lutter et al., 2008].
In chapter 3 again ICA was used, but in this case applied to time-dependent
microarray data, and results were compared to a very common analysis method,
hierarchical clustering. Time-dependent data was derived from human mono-
cytes infected with the intracellular pathogen F. tularensis. Using the clustering
approach, groups of genes referring to distinct timepoints were identi ed, and
a temporal behaviour of genetic immune response could be reconstructed. In
parallel, ICA was used to decompose the data into expression modes (analo-
gously to chapter 2). These modes were then mapped on the experimental time
8course. Compared to the clustering results, the ICA-based reconstructed immune
response was more detailed and temporal activity of distinct genes could be re-
solved more precisely. These ndings were also published by the author [Lutter
et al., 2009].
In the following chapter 4, three di erent microarray datasets were used to
con rm a suggested regulatory mechanism. The observation that about 50%
of all microRNAs in humans and mice are intronic and therefore coupled with
the expression of protein coding genes, so-called host genes, allowed for the use
of established large-scale gene expression measurement techniques to approxi-
mate microRNA expression. Since a single microRNA can regulate up to dozens
of other protein-coding genes, the hypothesis that this expressional linkage in-
cludes an additional functional component was investigated. Using the ordinary
clustering algorithm ‘hierarchical clustering’ and an approach based on gene an-
notations, this hypothesis could be basically con rmed. The main results were
already outlined in a manuscript, which is currently under review.
Finally, in the last chapter, a short summary of the previous ones is given and
a conclusion is drawn. A short outlook about further developments within the
eld of large gene expression data analysis is given and brie y discussed.
Taken together, the main contributions of this thesis are:
This work provides an overview of the biology of gene expression and a
discussion of the major analysis methods with a focus on applications.
Based on a two-class microarray experiment, the outcome of an independent
component analysis is investigated with respect to its biological relevance
[Lutter et al., 2008].
By separating time dependent microarray data into independent compo-
nents, a method is presented that reconstructs a temporal regulatory net-
work with high biological impact [Lutter et al., 2009].
A regulatory motif of conserved microRNA functionality is con rmed, al-
lowing for an expansion of the interpretation of gene expression data [manuscript
currently under review].
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