Cross-species network and transcript transfer

Metabolic processes, signal transduction, gene regulation, as well
as gene and protein expression are largely controlled by biological
networks. High-throughput experiments allow the measurement of a
wide range of cellular states and interactions. However, networks
are often not known in detail for specific biological systems and
conditions. Gene and protein annotations are often transferred from
model organisms to the species of interest. Therefore, the question
arises whether biological networks can be transferred between
species or whether they are specific for individual contexts. In
this thesis, the following aspects are investigated: (i) the
conservation and (ii) the cross-species transfer of eukaryotic
protein-interaction and gene regulatory (transcription factor-
target) networks, as well as (iii) the conservation of
alternatively spliced variants. In the simplest case, interactions
can be transferred between species, based solely on the sequence
similarity of the orthologous genes. However, such a transfer often
results either in the transfer of only a few interactions
(medium/high sequence similarity threshold) or in the transfer of
many speculative interactions (low sequence similarity threshold).
Thus, advanced network transfer approaches also consider the
annotations of orthologous genes involved in the interaction
transfer, as well as features derived from the network structure,
in order to enable a reliable interaction transfer, even between
phylogenetically very distant species. In this work, such an
approach for the transfer of protein interactions is presented
(COIN). COIN uses a sophisticated machine-learning model in order
to label transferred interactions as either correctly transferred
(conserved) or as incorrectly transferred (not conserved). The
comparison and the cross-species transfer of regulatory networks is
more difficult than the transfer of protein interaction networks,
as a huge fraction of the known regulations is only described in
the (not machine-readable) scientific literature. In addition,
compared to protein interactions, only a few conserved regulations
are known, and regulatory elements appear to be strongly
context-specific. In this work, the cross-species analysis of
regulatory interaction networks is enabled with software tools and
databases for global (ConReg) and thousands of context-specific
(CroCo) regulatory interactions that are derived and integrated
from the scientific literature, binding site predictions and
experimental data. Genes and their protein products are the main
players in biological networks. However, to date, the aspect is
neglected that a gene can encode different proteins. These
alternative proteins can differ strongly from each other with
respect to their molecular structure, function and their role in
networks. The identification of conserved and species-specific
splice variants and the integration of variants in network models
will allow a more complete cross-species transfer and comparison of
biological networks. With ISAR we support the cross-species
transfer and comparison of alternative variants by introducing a
gene-structure aware (i.e. exon-intron structure aware) multiple
sequence alignment approach for variants from orthologous and
paralogous genes. The methods presented here and the appropriate
databases allow the cross-species transfer of biological networks,
the comparison of thousands of context-specific networks, and the
cross-species comparison of alternatively spliced variants. Thus,
they can be used as a starting point for the understanding of
regulatory and signaling mechanisms in many biological systems.