Activity of microRNAs and transcription factors in Gene Regulatory Networks

In biological research, diverse high-throughput techniques enable
the investigation of whole systems at the molecular level. The
development of new methods and algorithms is necessary to analyze
and interpret measurements of gene and protein expression and of
interactions between genes and proteins. One of the challenges is
the integrated analysis of gene expression and the associated
regulation mechanisms. The two most important types of regulators,
transcription factors (TFs) and microRNAs (miRNAs), often cooperate
in complex networks at the transcriptional and post-transcriptional
level and, thus, enable a combinatorial and highly complex
regulation of cellular processes. For instance, TFs activate and
inhibit the expression of other genes including other TFs whereas
miRNAs can post-transcriptionally induce the degradation of
transcribed RNA and impair the translation of mRNA into proteins.
The identification of gene regulatory networks (GRNs) is mandatory
in order to understand the underlying control mechanisms. The
expression of regulators is itself regulated, i.e. activating or
inhibiting regulators in varying conditions and perturbations.
Thus, measurements of gene expression following targeted
perturbations (knockouts or overexpressions) of these regulators
are of particular importance. The prediction of the activity states
of the regulators and the prediction of the target genes are first
important steps towards the construction of GRNs. This thesis deals
with these first bioinformatics steps to construct GRNs. Targets of
TFs and miRNAs are determined as comprehensively and accurately as
possible. The activity state of regulators is predicted for
specific high-throughput data and specific contexts using
appropriate statistical approaches. Moreover, (parts of) GRNs are
inferred, which lead to explanations of given measurements. The
thesis describes new approaches for these tasks together with
accompanying evaluations and validations. This immediately defines
the three main goals of the current thesis: 1. The development of a
comprehensive database of regulator-target relation. Regulators and
targets are retrieved from public repositories, extracted from the
literature via text mining and collected into the miRSel database.
In addition, relations can be predicted using various published
methods. In order to determine the activity states of regulators
(see 2.) and to infer GRNs (3.) comprehensive and accurate
regulator-target relations are required. It could be shown that
text mining enables the reliable extraction of miRNA, gene, and
protein names as well as their relations from scientific free
texts. Overall, the miRSel contains about three times more
relations for the model organisms human, mouse, and rat as compared
to state-of-the-art databases (e.g. TarBase, one of the currently
most used resources for miRNA-target relations). 2. The prediction
of activity states of regulators based on improved target sets. In
order to investigate mechanisms of gene regulation, the
experimental contexts have to be determined in which the respective
regulators become active. A regulator is predicted as active based
on appropriate statistical tests applied to the expression values
of its set of target genes. For this task various gene set
enrichment (GSE) methods have been proposed. Unfortunately, before
an actual experiment it is unknown which genes are affected. The
missing standard-of-truth so far has prevented the systematic
assessment and evaluation of GSE tests. In contrast, the trigger of
gene expression changes is of course known for experiments where a
particular regulator has been directly perturbed (i.e. by knockout,
transfection, or overexpression). Based on such datasets, we have
systematically evaluated 12 current GSE tests. In our analysis
ANOVA and the Wilcoxon test performed best. 3. The prediction of
regulation cascades. Using gene expression measurements and given
regulator-target relations (e.g. from the miRSel database) GRNs are
derived. GSE tests are applied to determine TFs and miRNAs that
change their activity as cellular response to an overexpressed
miRNA. Gene regulatory networks can constructed iteratively. Our
models show how miRNAs trigger gene expression changes: either
directly or indirectly via cascades of miRNA-TF, miRNA-kinase-TF as
well as TF-TF relations. In this thesis we focus on measurements
which have been obtained after overexpression of miRNAs.
Surprisingly, a number of cancer relevant miRNAs influence a common
core of TFs which are involved in processes such as proliferation
and apoptosis.