Abstracts

Oral Presentation Abstracts
n S1:3
THREE MONTHS OF SURVEILLANCE OF S.
TYPHIMURIUM AND S. 1,4,[5],12:I:- IN
DENMARK BASED ON WHOLE-GENOME
SEQUENCING AND MLVA TYPING
M. Kjeldsen, P. Gymoese, M. Torpdahl;
Statens Serum Institut, Copenhagen, DEN-
MARK.
Introduction: Salmonella enterica subsp. enterica
Typhimurium (S. Typhimurium) and its
monophasic variant 1,4,[5],12:i:- are zoonotic
pathogens of significance in both humans and
animals worldwide. In Europe, Salmonella
cause the majority of food-borne outbreaks.
Currently, several laboratories primarily use
pulsed-field gel electrophoresis (PFGE) and
Multiple-locus variable-number tandem repeat
analysis (MLVA) for surveillance and outbreak
investigations of Salmonella. Surveillance
studies based on whole-genome sequences
(WGS) shows good results and are promising
alternatives to conventional methods. In this
study, we evaluate SNP analysis in comparison
to MLVA for surveillance of S. Typhimurium
and S. 1,4,[5],12:i:-. Materials and Methods:
We analyzed all S. Typhimurium and S.
1,4,[5],12:i:- human clinical isolates from the
Danish surveillance program from January to
March 2015. This collection comprises of 40
monophasic S. Typhimurium and 66 S. Typhimurium
isolates, hereunder three outbreaks
defined by MLVA-typing and epidemiological
findings. The relatedness of the strains was
examined by core genome SNP analysis, and
results were compared with those of MLVA
and Multi-locus sequence typing (MLST).
Results: WGS analysis on the collection of
106 strains resulted in close to 5900 SNPs.
A clear correlation between SNP and MLST
analysis was observed. S. Typhimurium ST36
was separated by a deep branch from ST19
and ST34. Isolates of ST34 mainly comprised
monophasic variants and were separated by
440 SNPs, indicating a close relationship
within this group. In correspondence with the
MLVA defined S. Typhimurium outbreaks,
the SNP based tree revealed three clusters of
closely related strains with a few SNP differences.
In one of the outbreaks, MLVA included
35 isolates while the SNP analysis added two
potential outbreak isolates to this cluster. In the
ST34 group, SNP analysis dispersed all MLVA
clusters, including the outbreak cluster (of
eight isolates) located within this group; SNP
queried if one of the eight defined outbreak
isolates should be included. Conclusion: Our
results show that strains with identical MLVA
profiles can be either unrelated or closely related
based on SNP distance determined from
WGS. Using WGS analysis for outbreak detection
seems reliable and in addition, it provides
a higher resolution of the strains relationships.
At present, defining an outbreak solely on SNP
differences is problematic, since the number
of SNP differences allowed within a cluster
have to be considered. This study highlights
the challenges with both SNP and MLVA based
cluster detection and emphasizes the importance
of combining molecular methods with
epidemiological data.
n S2:2
LONG READS SEQUENCING FOR BETTER
SHORT READS SNP ANALYSIS
D. Moine 1 , L. Baert 2 , C. Barretto 2 , C. Ngom-
Bru 2 , M. Kasam 1 , C. Fournier 1 , L. Michot 2 , J.
Gimonet 2 , C. Chilton 2 ;
1
Nestlé Institute of Health Sciences, Lausanne,
SWITZERLAND, 2 Nestle Research Center,
Lausanne, SWITZERLAND.
Whole genome sequencing (WGS) is an
emerging tool for foodborne pathogen characterization.
It can help to identify and type
the bacteria for investigative purposes (source
attribution), factory ecology and trend analysis
in the food industry. This novel approach is
ASM Conference on Rapid Next-Generation Sequencing and Bioinformatic
Pipelines for Enhanced Molecular Epidemiologic Investigation of Pathogens
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