Analysis of High-Throughput Data - Protein-Protein Interactions, Protein Complexes and RNA Half-life

The development of high-throughput techniques has lead to a
paradigm change in biology from the small-scale analysis of
individual genes and proteins to a genome-scale analysis of
biological systems. Proteins and genes can now be studied in their
interaction with each other and the cooperation within
multi-subunit protein complexes can be investigated. Moreover,
time-dependent dynamics and regulation of these processes and
associations can now be explored by monitoring mRNA changes and
turnover. The in-depth analysis of these large and complex data
sets would not be possible without sophisticated algorithms for
integrating different data sources, identifying interesting
patterns in the data and addressing the high variability and error
rates in biological measurements. In this thesis, we developed such
methods for the investigation of protein interactions and complexes
and the corresponding regulatory processes. In the first part, we
analyze networks of physical protein-protein interactions measured
in large-scale experiments. We show that the topology of the
complete interactomes can be confidently extrapolated despite high
numbers of missing and wrong interactions from only partial
measurements of interaction networks. Furthermore, we find that the
structure and stability of protein interaction networks is not only
influenced by the degree distribution of the network but also
considerably by the suppression or propagation of interactions
between highly connected proteins. As analysis of network topology
is generally focused on large eukaryotic networks, we developed new
methods to analyze smaller networks of intraviral and virus-host
interactions. By comparing interactomes of related herpesviral
species, we could detect a conserved core of protein interactions
and could address the low coverage of the yeast two-hybrid system.
In addition, common strategies in the interaction of the viruses
with the host cell were identified. New affinity purification
methods now make it possible to directly study associations of
proteins in complexes. Due to experimental errors the individual
protein complexes have to be predicted with computational methods
from these purification results. As previously published methods
relied more or less heavily on existing knowledge on complexes, we
developed an unsupervised prediction algorithm which is independent
from such additional data. Using this approach, high-quality
protein complexes can be identified from the raw purification data
alone for any species purification experiments are performed. To
identify the direct, physical interactions within these predicted
complexes and their subcomponent structure, we describe a new
approach to extract the highest scoring subnetwork connecting the
complex and interactions not explained by alternative paths of
indirect interactions. In this way, important interactions within
the complexes can be identified and their substructure can be
resolved in a straightforward way. To explore the regulation of
proteins and complexes, we analyzed microarray measurements of mRNA
abundance, de novo transcription and decay. Based on the
relationship between newly transcribed, pre-existing and total RNA,
transcript half-life can be estimated for individual genes using a
new microarray normalization method and a quality control can be
applied. We show that precise measurements of RNA half-life can be
obtained from de novo transcription which are of superior accuracy
to previously published results from RNA decay. Using such precise
measurements, we studied RNA half-lives in human B-cells and mouse
fibroblasts to identify conserved patterns governing RNA turnover.
Our results show that transcript half-lives are strongly conserved
and specifically correlated to gene function. Although transcript
half-life is highly similar in protein complexes and
\mbox{families}, individual proteins may deviate significantly from
the remaining complex subunits or family members to efficiently
support the regulation of protein complexes or to create
non-redundant roles of functionally similar proteins. These results
illustrate several of the many ways in which high-throughput
measurements lead to a better understanding of biological systems.
By studying large-scale measure\-ments in this thesis, the
structure of protein interaction networks and protein complexes
could be better characterized, important interactions and conserved
strategies for herpes\-viral infection could be identified and
interesting insights could be gained into the regulation of
important biological processes and protein complexes. This was made
possible by the development of novel algorithms and analysis
approaches which will also be valuable for further research on
these topics.