Abstracts

Poster Abstracts
cost reduction of next generation sequencing
has allowed researchers to migrate from
analyzing distinct loci in only few prototypical
isolates to whole genome sequencing typing
(WGST) of large bacterial collections. The
growing sequence databases fortified with
strain-associated metadata require efficient
and well-integrated bioinformatics tools that
will explore and harvest the information
content of WGS data to enhance the traceability
of microbes in support of informed and
timely countermeasures. Particularly single
nucleotide polymorphism (SNP) discovery
and typing often surpass labor-intensive molecular
typing schemes in offering both robust
long- and short-term high-resolution variant
markers. Methods: Our group has developed
a bioinformatics SNP Discovery and Validation
Pipeline (SNPDV) implemented on the
open source platform Galaxy coded in Python
that makes use of the wealth of sequence data
for the whole genome sequence typing of
bacterial pathogens. Its modular architecture
guarantees high flexibility and scalability for
SNP discovery and validation and can be run
in serial, multiprocessor, or threaded version
depending on available server capabilities.
This pipeline allows to rapidly type strains of
unknown provenance by testing allelic states
in already established SNPs panels, or for unbiased
de novo discovery. Results: Given the
clonal nature of outbreaks, we have successfully
deployed this pipeline in the investigation
of the 2010 Haiti cholera and 2006 spinach
outbreaks, and diverse other enteric and
emerging pathogens, such as Yersinia pestis,
Bacillus spp., Vibrio spp., and Acinetobacter
baumannii, achieving improved phylogenetic
accuracy and resolution. Discussion: Following
the concept of genomic epidemiology
the gathered phylogenomic data have major
public health relevance in utilizing sequencebased
information for improved surveillance,
strain attribution, and source identification
to contain outbreaks. Canonical SNPs can be
implemented in efficient typing assays offering
robust phylogenetic signals for outbreak inand
exclusion that surpass classical technologies.
The phylogenomic framework is a further
critical resource to precisely cluster pathogens
into subgroups distinguished by different genotypic,
epidemiological, and phenotypic traits,
such as whether particular genotypes reflect
highly pathogenic subpopulations. Future
developments are directed towards the cloud
implementation of this pipeline in collaboration
with the UTSA Cloud and Big Data Laboratory
(NSF Cloud).
n 12
COMBINING LABORATORY AND
EPIDEMIOLOGICAL DATA FOR OUTBREAK
INVESTIGATION AND PUBLIC HEALTH
SURVEILLANCE: LESSONS FROM AN
OUTBREAK OF HIV-1 INFECTION LINKED TO
INJECTION DRUG USE OF OXYMORPHONE _
INDIANA, 2015
E. M. Campbell 1 , R. R. Galang 1 , J. Gentry 2 , P.
J. Peters 1 , S. J. Blosser 2 , E. L. Chapman 2 , C.
Conrad 2 , J. W. Duwve 2 , L. Ganova-Raeva 3 , W.
Heneine 1 , D. Hillman 2 , H. Jia 1 , L. Lui 2 , J. Lovchik
2 , A. Perez 2 , P. Peyrani 4 , P. Pontones 2 , S.
Ramachandran 3 , J. C. Roseberry 2 , M. Sandoval
2 , A. Shankar 1 , H. Thai 3 , G. Xia 3 , Y. Khudyakov
3 , W. H. Switzer 1 ;
1
Division of HIV/AIDS Prevention, National
Center for HIV/AIDS, Viral Hepatitis, STD,
and TB Prevention, CDC, Atlanta, GA, 2 Indiana
State Department of Health, Indianapolis,
IN, 3 Division of Viral Hepatitis, National Center
for HIV/AIDS, Viral Hepatitis, STD, and
TB Prevention, CDC, Atlanta, GA, 4 Division of
Infectious Diseases, University of Louisville,
Louisville, KY.
In January 2015, a cluster of HIV-1 infections
was detected in rural Indiana among persons
who reported injecting the prescription opioid,
oxymorphone. As of May, HIV-1 infection
was diagnosed in 153 individuals. Molecular
analyses of HIV-1 and HCV sequences were
combined with epidemiological data via a
novel bioinformatics pipeline to infer the
timing of HIV transmission relative to HCV
and to explore risk factors associated with
46
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