bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P67. IDENTIFICATION OF ANTIBIOTIC RESISTANCE MECHANISMS USING
A NETWORK-BASED APPROACH
Bram Weytjens 1,2,3,4 , Dries De Maeyer 1,2,,3,4 & Kathleen Marchal 1,2,4 *.
Dept. of Information Technology (INTEC, iMINDS), UGent, Ghent, 9052, Belgium 1 ; Dept. of Plant Biotechnology and
Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium 2 ; Dept. of Microbial and Molecular
Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium 3 , Bioinformatics Institute Ghent, Ghent
University, Ghent B-9000, Belgium 4 . * kathleen.marchal@intec.ugent.be
Antibiotic resistance is a growing public health concern as the effectiveness of multiple types of antibiotics is decreasing.
To prevent and combat the further spread of antibiotic resistance in bacteria there is the need to better understand the
relationship between genetic alterations and the (molecular) phenotype of antibiotic resistant strains. As several (-omics)
experiments regarding the attainment of antibiotic resistance by bacteria have already been performed and are publicly
available, we re-analysed a laboratory evolution experiment by Suzuki et al. (Suzuki, 2014) in order to demonstrate the
power of a network-based approach in identifying mutations and molecular pathways driving the resistance phenotype.
INTRODUCTION
While network-based approaches are no longer new in
high-throughput (-omics) analysis, they are not yet widely
used in standard analysis pipelines. We analysed a dataset
consisting of multiple E. coli MDS42 strains, each
independently evolved in the presence of a specific
antibiotic (10 in total). By adapting PheNetic (De Maeyer.
2013), an algorithm which connects genetic alterations to
their differentially expressed genes over a genome-wide
interaction network, we were able to automatically
identify mutations in genes which are known to induce
antibiotic resistance.
METHODS
For every strain whole-genome sequencing data and
microarray data (eQTL data) was available. By finding the
most probable connections between the mutations of every
strain and the strain’s respective expression data over a
biological network, PheNetic was able to not only uncover
potential driver genes and molecular pathways for the
resistance phenotype but also to prioritize the identified
mutations based on the likelihood that they are truly
driving the resistance phenotype. Such network-based
approach has following advantages:
Integration of interactomics (network), genomics
and interactomics data
Multiple related datasets can be analyzed together
FIGURE 1: Part of Amikacin resistance network.
RESULTS & DISCUSSION
In the case of Amikacin resistance (figure 1) we were able
to uncover a gain-of-function mutation in cpxA, a gene of
a two-component signal transduction mechanisms which is
known to be involved in amikacin resistance for two
strains out of four. For the other two strains, deleterious
cyoB mutations were found, which is known to lead to
intracellular oxidized copper and eventually multidrug
resistance. These genes were furthermore ranked highest
by PheNetic.
REFERENCES
Suzuki S et al. Nat Commun 5, 5792 (2014).
De Maeyer D et al. Mol Biosyst 9: 1594-1603 (2013).
111