Utility of the Salmonella in Silico Typing
Resource(SISTR) to outbreak investigations
James Robertson1, Catherine Yoshida1, Peter Kruczkiewicz2, Eduardo N.
Taboada2 and John H. E. Nash3
1 National Microbiology Laboratory @Guelph , Public Health Agency of Canada
2 National Microbiology Laboratory @Lethbridge, Public Health Agency of Canada
3 National Microbiology Laboratory @Toronto, Public Health Agency of Canada

Salmonella is a leading public health concern
 Salmonella is a leading food-borne pathogen both in Canada and around the world
 Globally, there are an estimated 94 million Salmonella infections every year
 Human costs:
• acute illness
• loss of life (155,000 deaths)
 Societal costs:
• health care costs
• lost productivity
• legal costs
• impact to food industry
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Potential Sources

Challenges in Salmonella typing and epidemiology
 Small number of highly prevalent/globally distributed serovars account for most
outbreaks (e.g. Enteritidis, Typhimurium)
 Epidemiologicaly unrelated isolates within same serovar  difficult to
investigate
 Additional subtyping resolution within a serovar needed (e.g. phage typing)
 Increasing use of genotypic methods (i.e. molecular typing)
 Driven by need for methods with higher discriminatory power
 A number of different approaches have been applied to molecular typing of
Salmonella
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GATCGATCGATCG
GATCAATCGATCG
MLST cgMLST wgSNP’sSerotyping
Discriminatory Power
Low Low-Mid Mid-High High
• Based on reaction
of antibodies to
surface antigens
• Broad usage and
common
nomenclature in
use since the
1930’s
• Multi-Locus Sequence Typing:
developed by Maiden et al. (1998)
• Indexes genetic variation in 7 core (i.e.
“housekeeping”) genes
• cgMLST extends this principle to 100’s
to 1000’s of loci
• Provides a portable naming scheme
which correlates with historical
serotypes
• Utilizes individual
SNP’s and gives
very high
resolution
• Results are not
portable to other
public health
professionals

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• Initial dataset of 4330 genomes
• 94.6% concordance between predicted
and reported serovar
• in silico serovar predictions based on O
and H antigens
• cgMLST refinement of serovar
assignment and analysis
• Uses minimally processed genome
assemblies
• Very fast ~30 seconds to process a
genome

What does SISTR do?
In silico analysis of WGS data
 assembly statistics
 serovar prediction
 in silico typing (MLST,
cgMLST)
 AMR prediction
Comparative genomic analyses
 cgMLST
 accessory gene content
 core SNPs
Epidemiologic analysis
 geospatial distribution
 temporal distribution
 source association
https://lfz.corefacility.ca/sistr-app/

SISTR cgMLST
• Current cgMLST scheme in SISTR based on 330 core
genes with high “assignability” (i.e. very low levels of
“missing” data)
• Will include international Salmonella cgMLST scheme (i.e.
once it is developed!)
• cgMLST information is used to:
– Assess quality of WGS data  complete, partial,
missing loci
– Supplement genoserotyping predictions
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Testing the accuracy of SISTR
• ~45,000 Salmonella genomes were downloaded from the
SRA
• Raw reads were assembled using FLASH and Spades
• Assemblies were loaded into SISTR and the serovar
predictions were compared between predicted and
reported (where available)
• Assemblies were checked for contamination using Kraken
• Quality was assessed using Quast
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Recovery rates of 330 cgMLST genes from Assembled
SRA genomes
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41781
1393
1905
Number of Genomes with
Complete 330
Number of Genomes with >300
Genes
Number of Genomes with <300
Genes
N=45,079

SISTR Accuracy
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2347
29884
N=32,321
• 93.7% Overall concordance with serovar
specified
Discordant
Concordant

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• Two outbreaks of Salmonella Enteriditis were retrospectively sequenced
• Examined the feasibility of WGS to outbreak investigations
• Compared results of traditional molecular and microbial tests to WGS

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SISTR (cgMLST) PARSNP (core SNP)
SNP Tree (Wuyts et al 2015)
• All three methods produce concordant
trees.
• cgMLST has a tendency to overgroup

Outbreak Clustering Categories
B
A
C
B
A+C
B
C
A
A
Correct Incorrectly Split
Over-grouped
A+B
A+C
Incorrectly Split and grouped

Concordance between cgMLST and SNP trees
Study Correct Over-grouped Split Combination Serovar(s)
1 1 1 0 0 Enteriditis
2 2 3 0 0 Enteriditis
3 5 1 0 0 Enteriditis,Typhimurium,
Derby
4 2 7 0 0 Enteriditis
5 2 0 0 0 Enteriditis
6 5 2 0 0 Enteriditis
Total 18 13 0 0
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Conclusions
• SISTR is a a robust and accurate platform for Salmonella in silico
typing with 93.7% concordance between specified serovar and
predicted serovar
• The prototype 330 gene cgMLST scheme is readily retrievable from
HTS assemblies of varying quality levels.
• The current scheme provides coarse grain separation of Salmonella
genetic lineages that will be useful in outbreak analysis
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Acknowledgements
Team:
 Ed Taboada, Peter Kruczkiewicz, Catherine Yoshida, John Nash
Research partners:
 Public Health Agency of Canada:
 OIE Laboratory for Salmonellosis – National Microbiology Lab (NML) @
Guelph
 Genomics Core and Bioinformatics Core – NML @ Winnipeg
 Public Health Genomics team – NML @ Winnipeg
 IRIDA project team
 Animal Health Veterinary Laboratory Agency – UK
 Austrian Institute of Technology – Austria
Funding:
 Genomics Research and Development Initiative
 Genome Canada (IRIDA project)
 Public Health Agency of Canada