1.
WGS for surveillance of foodborne infections in Denmark
Benefits and potential drawbacks
Eva Møller Nielsen
Head of unit, PhD
Foodborne Infections
Statens Serum Institut
Copenhagen, Denmark

2.
WGS - Benefits and drawbacks
Performance
- Confidence in clusters and links
- Variety of outputs based on one laboratory procedure
- Flexibility – not pathogen specific, avoid batches
- No phenotype
- Education and experience needed for analysis and interpretation
Costs
- Cost-effective alternative to classical typing
- Flexibility – same person can analyse different pathogens
- Expensive to establish (equipment, education) – but can be shared
across departments

3.
WGS for food safety – views from the Danish public health
Implementation of WGS for surveillance of infections in Denmark
Small country’s perspective on implementation of WGS
Building on established and functioning surveillance system
Implementation for minimal extra resources (no extra funding)
• Infrastructure, equipment, personnel
• Limited parallel use of old and new methods
WGS implemented for routine surveillance in 2013
- Starting with WGS of all Listeria, replacing other methods
- Outbreaks of all foodborne pathogens
- STEC/VTEC surveillance from 2015

4.
Laboratory-based surveillance of human infections
Real-time typing/characterisation of isolates from patients:
- Detect clusters
- Outbreak investigations/ case definition
- Linking to sources/reservoirs
- Determine virulence potential
- Antimicrobial resistance
Salmonella Typhimurium infections
MLVA types

5.
In-house resources for WGS
2011-2012:
- Batches of project isolates were sequenced by external facilities
- Limited bioinformatics competences in our department
2013:
- Purchase of MiSeq – shared by all microbiology groups at SSI
- Bioinformatician hired
2016:
- Two MiSeqs – and need for more capacity
- Three bioinformaticians + more microbiologists have improved skills
Same laboratory staff performing WGS and classical methods
Same microbiologists/epidemiologists doing surveillance and taking action

6.
Large outbreak: confidence in link to food source (41 cases)
August 2013
Real-time WGS of human isolates
July 7: 5 cases from 2014 in outbreak
July 16: matching food isolate

7.
Clear case definition
100
90
80
70
Cluster
Cluster
Cluster
Cluster
Date
2014 Jan
2013 Marts
2013 Juli
2014 Marts
2013 Jan
2013 Jan
2013 April
2013 April
2013 Aug
2013 Sept
2013 Juni
2014 Jan
ST-1 isolates (n=12):
SNP analysis of WGS data of Listeria isolates belonging to ST-1

8.
More clusters detected
Listeriosis:
75 patients part of cluster
68 patients sporadic

9.
Linking “sporadic” cases + linking to food facility
10 cases 2013-15 (specific clone of ST-6):
Sep 2014:
Isolates from cold smoked fish from Company A identical to isolates from patients.
Food control intervention
Spring 2015:
New cases – have eaten smoked fish from supermarkets that sell products from Company A
Product and environmental samples at Company A were again positive for the ST-6 clone

10.
SNP tree: Outbreak related and non-related ST-6 isolates
Squares: isolates related to the outbreak
Circles: isolates not part of the outbreak
Maximum parsimony tree:
All ST6 isolates from the years 2013-15

11.
From a variety of laboratory methods to WGS
Mix of lab-techniques
serotyping, antimicrobial resistance, PCR, PFGE, MLVA, sequencing
Whole-genome-sequencing
Analysis of sequence data for different purposes (typing, virulence,…)
”Backward comparability” for some characteristics

12.
Backward comparability
Pathogenic E. coli (e.g. STEC/VTEC)
Expensive and time consuming characterisation:
- PCR or hybridisation: virulence profile → pathogroup, virulence potential
- Classical O:H-serotyping with antisera → expected epidemiology, sources/reservoirs
- PFGE or MLVA: high-discriminatory typing → outbreak detection and investigations
Cost-effective to replace these methods by WGS
- Virulence genes can be extracted
- O:H-serotype can be predicted
- SNP-analysis gives a very high discrimination for cluster detection
12

13.
Workflow – routine surveillance
13
Sequence
data
Serotype
SNP analysis
Outbreak
investigations
MLST
nomenclature
MLST
Antimicrobial
resistance
Virulence
genes
Risk
assessment
Treatment,
interventions

14.
Performance
Defining clusters/outbreaks
- More confident definition of clusters/outbreaks
- Better case definition
- Interpretation of data (- as for all typing methods)
- Re-define “rules” for a cluster (time span, similarity)
Improved source tracing
- More certain microbiological evidence for linking to sources
- Potential for correlation to time/evolution
Analysis still under development
- Validation
- Interpretation of data in relation to epidemiology
- International comparability
14

15.
Costs, flexibility
Costs
- Investments in equipment
- Expensive reagents
- Education of staff
Workflow, flexibility
- One lab method for all bacteria and all typing needs
- Same overall approach for all bacterial pathogens
• organism-specific data analysis if relevant, e.g. for backward comparability
- Cost-effective if replacing several classical methods
Changes
- Need for other skills among laboratory staff
- Need for bioinformatics
- Organisation of lab work and data analysis (less organism specific)
- More money spend on reagents/kits, less on personnel?

16.
Perspective for using WGS (Denmark/Europe)
Partly implemented in Denmark and other European countries
- Public health, routine surveillance of foodborne infections
- Food/veterinary labs
- Will be implemented with different pace in different countries
- Parallel use of different methods (same situation as with other methods)
European-level surveillance of foodborne infections
- Surveillance system for rapid detection of dispersed international outbreaks
• Presently based on isolate typing by PFGE and MLVA
• Plans for using WGS
- Joint human-food databases about to be established
International perspectives
- Comparability
- Nomenclature