bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P4. DISEASE-SPECIFIC NETWORK CONSTRUCTION BY SEED-AND-EXTEND
Ganna Androsova 1* , Reinhard Schneider 1 & Roland Krause 1 .
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg 1 .
* ganna.androsova@uni.lu
INTRODUCTION
Molecular interaction networks are dense structures of
protein interactions, from which we would like to extract
relevant sub-networks specific to the disease of interest.
Such a disease-specific network is often constructed by the
seed-and-extend algorithm, which extracts the relevant
genes from an organism-wide, weighted interaction
network, typically as its first-neighbourhood. Seed-andextend
is suitable when disease biomarkers are poorly
investigated and the knowledge about biomarker
interaction partners is missing or when the interacting
partners are established but the connections are missing
between them.
Our syndrome of interest is the postoperative cognitive
impairment frequently experienced by elderly patients,
characterized by progressive cognitive and sensory decline.
The acute phase of cognitive impairment is postoperative
delirium (POD). The underlying pathophysiological
mechanisms have not been studied in depth due to
mulitifactorial pathogenesis of this postoperative cognitive
impairment. The known POD-related genes can be
integrated into the draft network for exploration on a
systems level.
Here, we investigate how stable the results of such
analysis are when the input set of seed genes is varied, and
what is the role of stringency in the initial selection of the
networks. Ideally, we would like to find the “sweet spot”
that provides a biologically meaningful trade-off between
false-positives and -negatives to be used for such analyses.
METHODS
The list of disease-related genes/proteins was retrieved
from literature studies in the PubMed database.
We extended the seed list with directly linked interactors
by seed-and-extend from protein-protein interaction
network databases. We extracted all interactions between
seeds and connected neighbours, which resulted in the
first-degree network.
Next, we evaluated a biological enrichment of the
extracted network, its topological parameters, overlap with
other diseases and clustered the network into the smaller
sub-networks.
RESULTS & DISCUSSION
The POD network (Figure 1) follows a free-scale
distribution and consists of 541 proteins with 5,242
interactions between them.
FIGURE 1. Postoperative delirium molecular network.
The network was evaluated topologically by degree
assortativity, density, shortest path, eccentricity and other
measures. Pathways enrichment analysis showed
glucocorticoid receptor signalling, immune response, and
dopamine signalling as relevant to POD (Figure 2).
FIGURE 2. Postoperative delirium pathway enrichment analysis.
Top 5 hub proteins included UBC_HUMAN,
GCR_HUMAN, P53_HUMAN, HS90A_HUMAN and
EGFR_HUMAN. Appearance of p53 and other very
frequent genes among top 5 hubs in our but also several
other studies, motivated us to investigate its relevance to
the disease and question the possible data bias. We
compare how size, specificity and completeness of the
input seed list can affect the resulting network and
retrieval of the other disease-related proteins.
48