Abstracts

Poster Abstracts
utilize EDGE at an OCONUS DoD facility has
already taken place, with successful processing
of locally-generated data using a recently
locally-installed MiSeq, and demonstrated
reachback capability using CONUS support.
n 103
SUPERPHY: PREDICTIVE GENOMICS FOR
THE PATHOGEN ESCHERICHIA COLI
M. D. Whiteside 1 , C. R. Laing 1 , A. Manji 1 , J.
Masih 1 , P. Kruczkiewicz 1 , E. N. Taboada 1 , V. P.
J. Gannon 1
1
CANADA - Laboratory for Foodborne Zoonoses,
Public Health Agency of Canada
Introduction: Predictive genomics is the
translation of raw genome sequence data into
an assessment of the phenotypes exhibited by
the organism. For bacterial pathogens, these
phenotypes can range from environmental
survivability, to the severity of human disease
associated with them. Significant progress has
been made in the development of generic tools
for genomic analyses that are broadly applicable
to all microorganisms; however, a fundamental
missing component is the ability to
analyze genomic data in the context of organism-specific
phenotypic knowledge, which has
been accumulated from decades of research
and can provide a meaningful interpretation
of genome sequence data. Implementation:
In this study, we present SuperPhy, an online
predictive genomics platform (http://lfz.corefacility.ca/superphy/)
for Escherichia coli.
The platform integrates the analyses tools and
genome sequence data for all publicly available
E. coli genomes and facilitates the upload
of new genome sequences from users under
public or private settings. SuperPhy provides
real-time analyses of thousands of genome
sequences with results that are understandable
and useful to a wide community, including
those in the fields of clinical medicine, epidemiology,
ecology, and evolution. SuperPhy
includes identification of: 1) virulence and
antimicrobial resistance determinants 2) statistical
associations between genotypes, biomarkers,
geospatial distribution, host, source,
and phylogenetic clade; 3) the identification
of biomarkers for groups of genomes on the
based presence / absence of specific genomic
regions and single-nucleotide polymorphisms
and 4) in silico Shiga-toxin subtype. Conclusions:
SuperPhy is a predictive genomics platform
that attempts to provide an essential link
between the vast amounts of genome information
currently being generated and phenotypic
knowledge in an organism-specific context.
n 104
MOLECULAR SURVEILLANCE OF A.
BAUMANNII IN A REGIONAL WASTE
STABILISATION POND IN AUSTRALIA
1, 2
Maxim Sheludchenko, 2 Mohammad Katouli
and 1 Helen Stratton
1
Smart Water Research Centre, Griffith University,
Southport, Queensland and 2 Genecology
Research Centre, University of the Sunshine
Coast, Sippy Downs, Queensland, Australia
A.baumannii is an increasingly important
pathogen responsible for many nosocomial
infections. Survival of these bacteria in the
environment, especially in waste stabilization
ponds (WSPs) have not been investigated before
although some reports indicate isolation
of these A. baumannii from filaments of active
sludge of wastewaters and paleosol contaminated
by waste leakage. Identification of A.
baumannii from the environmental is normally
done by growing samples on selective culture
media but this requires the use of molecular
techniques for confirmation. Furthermore,
the ability of these bacteria to grow on some
selective media may also cause misinterpretations
of the data. In this study we report such
an event and show that the use of molecular
techniques especially 16S rRNA sequencing
helped identification and surveillance of these
bacteria in a WSP. Between October 2013 and
September 2014 we surveyed the die off of a
number of pathogens in the maturation pond of
a WSP in a regional area of Australia. Samples
cultivated on mCCDA agar containing selec-
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