Abstracts

Poster Abstracts
route reconstruction. For the purpose of reconstructing
putative transmission routes, a
variety of phylogeny building methods have
been developed and can help identify potential
transmission events within a hospital environment.
Method: We sequenced 149 E. faecium
isolates of MLST type ST736 from 106 patients
over 55 months at Westchester Medical
Center, and reconstructed phylogenies on the
samples with various distance based methods.
To construct the phylogenies, we used a variant
calling pipeline to determine likely SNPs
within the samples, and then we applied various
filters to remove inaccurate SNPs, such
as removing SNPs in repetitive regions and
phage regions. Afterwards, a pairwise distance
matrix was built based on the SNP calls, and
finally, we applied various phylogeny building
methods to the distance matrix. The methods
we experimented with were neighbor-joining,
maximum likelihood, and Prim’s (undirected)
and Edmond’s (directed) minimum spanning
tree (MST) algorithms. The results of these
methods were then compared to each other to
determine how robust and accurate our results
may be. Lastly, we also evaluated how frequently
antibiotic resistance increased along
the predicted transmission events in our tree.
Results: The trees we generated mostly have
good concordance with each other often with
more than 80% of subtrees matching between
results. Additionally, we also computed how
frequently antibiotic resistance increased along
transmissions suggested by the MST methods.
Both MST methods showed that antibiotic
resistance generally increased along transmission
edges in the trees, and Edmond’s directed
MST method showed a higher percentage of
the predicted transmission events resulting in
increased daptomycin resistance, with about
3/4 of transmissions indicating an increase in
resistance. As we expect resistance to increase
as transmissions occur, this may indicate that
Edmond’s directed MST produced a more
accurate tree. Conclusion: Our preliminary
results show that Edmond’s directed MST may
produce an accurate phylogeny tree, although
further verification may be needed, as other
methods yield slightly different trees. In the future,
we plan to examine clinical data in order
to help validate the correctness of the transmissions
predicted by our phylogeny trees.
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ANALYSIS OF AN ENTEROVIRUS D68
OUTBREAK THROUGH METAGENOMIC
SEQUENCING
H. Lin 1 , Q. Wan 1 , W. Huang 2 , G. Wang 2 , J.
Zhuge 2 , S. M. Nolan 2 , J. T. Fallon 2 , N. Dimitrova
1 ;
1
Philips Research North America, Briarcliff
Manor, NY, 2 New York Medical College, Valhalla,
NY.
Background: Metagenomic sequencing can be
used to detect the presence of microbial organisms.
Many metagenomic techniques have
been developed to explore and verify microbial
ecology, evolution and diversity, especially for
investigating infectious viruses and bacteria.
By obtaining the population distribution of
classified microbial genomes in certain samples,
metagenomic tools have the potential to
provide insights into the causality of infectious
disease outbreaks. During the Enterovirus D68
outbreak in 2014, 93 nasopharyngeal swab
samples were obtained from patients with
symptoms of respiratory infection and were
sequenced at Westchester Medical Center.
Methods: We used Kraken for the purpose of
classifying reads based on taxonomic orders.
By using Kraken, microbial reads can be identified
and subsequently enable the investigation
of certain infectious disease outbreak. To
obtain the best performance from Kraken, we
build a pipeline to automatically fetch publicly
available reference genomes from NCBI at
user’s demand to keep Kraken’s classification
results up-to-date for detecting pathogens present
within a sample. The pipeline is not only
able to extract reads by viruses, bacteria, and
fungi species, but can also generate aggregate
analysis indicating the most prevalent microorganisms
within any taxonomic group. We also
provide analysis about possible cohabitations
ASM Conference on Rapid Next-Generation Sequencing and Bioinformatic
Pipelines for Enhanced Molecular Epidemiologic Investigation of Pathogens
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