The SISTR resource provides accurate in silico typing of Salmonella genomes from whole genome sequencing data. It can predict serovars and perform core genome MLST (cgMLST) analysis on assemblies in under 30 seconds. Testing on over 45,000 Salmonella genomes found 93.7% concordance between predicted and reported serovars. SISTR's cgMLST scheme provides useful separation of genetic lineages and has been shown to cluster outbreak isolates correctly, aiding outbreak investigations.

1. 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

2. 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
2

3. 3
Potential Sources

4. 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
4

5. 5
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

7. 7
• 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

8. 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/

9. 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
9

10. 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
10

11. Recovery rates of 330 cgMLST genes from Assembled
SRA genomes
11
41781
1393
1905
Number of Genomes with
Complete 330
Number of Genomes with >300
Genes
Number of Genomes with <300
Genes
N=45,079

12. SISTR Accuracy
12
2347
29884
N=32,321
• 93.7% Overall concordance with serovar
specified
Discordant
Concordant

13. 13
• 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

14. 14

15. 15

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18. 18
SISTR (cgMLST) PARSNP (core SNP)
SNP Tree (Wuyts et al 2015)
• All three methods produce concordant
trees.
• cgMLST has a tendency to overgroup

19. 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

20. 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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21. 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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22. 22
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