Whole genome sequencing (WGS) is being implemented for surveillance of foodborne pathogens in Denmark as a cost-effective alternative to traditional typing methods. WGS allows for multiple analyses from a single test, including serotyping, virulence profiling, antimicrobial resistance determination, and high-resolution typing for outbreak detection. Validation studies have shown WGS performs comparably or better than existing methods for various pathogens. WGS implementation has improved detection and investigations of outbreaks of Listeria and E. coli in Denmark. International collaboration is key for effective use of WGS in foodborne disease surveillance.
Whole Genome Sequencing (WGS) for surveillance of foodborne infections in Denmark
1. WGS for surveillance of foodborne
infections in Denmark
Eva Møller Nielsen
Head of unit, PhD
Foodborne Infections
Statens Serum Institut
Copenhagen, Denmark
2. WGS for food safety – views from the Danish public health
Small country’s perspective on implementation of WGS
- Cost-effective alternative to classical typing
- Implementation for minimal resources (no extra money)
• Infrastructure, equipment, personnel
Evaluation for use in surveillance
• STEC/VTEC, Salmonella, Listeria
Examples from 2½ years of listeriosis surveillance by WGS
- Detection of outbreaks
- Source tracing and intervention
- Benefits compared to previous methods
3. 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
4. Methods for surveillance
Often many methods used for each isolate, e.g.:
- Serotyping
- Virulence factors
- Antimicrobial resistance
- High-discriminatory molecular typing methods
Next-generation sequencing technology
- Less expensive equipment, easy to use
- Accessible for more laboratories
- WGS of pathogens: costs getting competitive to
traditional typing
- Different typing outputs possible by the development
of bioinformatical analyses based on WGS
5. 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
7. Resources for next-generation sequencing
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
- Bioinformatician hired
2015:
- Two MiSeqs – and need for more capacity
- Three bioinformaticians + more microbiologists have improved skills
8. Whole-genome sequencing
Advantages
- One lab method for all bacteria and all typing needs
- Same overall approach for all bacterial pathogens
- Many different analyses – possible to use different approaches depending on
organism and needs
Analysis still under development
- Validation in each country + international collaboration
- Interpretation of data in relation to epidemiology
- Backward comparability, e.g. serotype, AMR
Interpretation of data for case-definition, relatedness, … (how different is
non-clonal)
Costs, changes in laboratory needs
- Major changes for some labs/staff
9. Validation of WGS for surveillance at SSI
Pathogenic E. coli (VTEC/STEC)
- Development of tools for extracting:
• Virulence profile
• O:H serotype
Listeria
- Retrospective study:
• Variation between epidemiologically linked isolates
- Prospective study:
• Use of WGS in the real-time surveillance (replacing PFGE)
Salmonella
- Outbreak/background isolates
- Validation in comparison to MLVA (high-discriminatory typing)
10. Pathogenic E. coli (verotoxin-producing E.coli)
Expensive and time consuming characterisation:
- Virulence profile → pathogroup, virulence potential, HUS-associated types
- O:H-serotype is useful, e.g. related to expected epidemiology, sources/reservoirs
- High-discriminatory typing needed for outbreaks
Cost-effective to replace this by WGS when sufficiently validated (tools such as virulence
finder and serotype finder developed - genomicepidemiology.org)
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11. E. coli virulence gene database
Database with sequences of 76 E. coli virulence genes and variants of
these
Web-based tool ”VirulenceFinder”
Database now incorporated in our WGS analysis pipeline for routine use
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Joensen et al. 2014. JCM 52:1501-
12. WGS vs. conventional serotyping of E. coli
a In 51 genomes, genes were found by reference mapping, and in 21 genomes, only one gene was used for prediction.
b Eleven predictions were ambiguous between the two O-processing genes [O118/O151(7), O164/O124, O134/O46,
O90/O127, and O162/O101]
Typing
No. (%) of genomes:
For validation With detected genes With consistent WGS and conventional results
O 601 569a (∼95%) 560b (∼98%)
H 509 508 (∼100%) 504 (∼99%)
Reads
Assembly
Contigs
Gene-finding
best-matching hits
wzx wzyfliC Non-fliC
wzx (O103) + wzy (O103)
= O103
fliC (H21) + flkA (H47)
= H47
Establish in silico O:H serotyping
- wzx, wzy, wzm, wzt genes, representing all 188 O-types
- fliC, flkA, flnA, flmA, fllA genes, representing all 53 H-types
Validation on 682 E. coli
genome sequences + conventional serotype
- Publically available genomes
- Sequencing on MiSeq
BLAST-based serotype prediction
Validation of (O:H) types on ≥3 isolates
Web-tool: genomicepidemiology.org
Joensen et al. 2015. JCM 53:2410
13. O-grouping: WGS vs. phenotyping
86 isolates – Routine surveillance in Denmark 2015
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14. H typing: WGS vs. phenotyping
85 isolates – Routine surveillance in Denmark 2015
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16. Retrospective project: food/human PFGE types 2009-2012
Food 114 isolates, human 159 isolates
45% of human isolates (71/159) has a PFGE pattern seen in this sample of food isolates
Data: DTU and SSI
PFGE types represented by > 2 isolates
17. Validation of methods and interpretation
Intrerpretation of WGS data for case definition in
outbreaks and for linking to probable sources
- Expected variation within outbreaks?
Optimising the analysis pipeline
- SNP-analyses optimised on retrospective data:
mother/child isolates and outbreaks
Confirmed “point-source” outbreak 2009:
• 8 patients with listeriosis within 1 week
• 2 food isolates from catering company (1 mo later)
- Maximum 4 SNP forskel mellem isolater
Some long-term clusters more difficult to interpret
59
104
2
1
1
1
case
food
18. Improved surveillance of listeriosis
Since September 2013: WGS of all clinical isolates
- 7-locus MLST for fast screening to detect possible clusters
- SNP-analysis when isolates of same MLST
Jan 2014: Interview/exposure history for all patients at diagnosis
June 2014: Food isolates undergo WGS and are compared to clinical
isolates (since January 2015: performed at Food Institute)
21. Outbreak summer 2014 (41 cases)
August 2013
Real-time WGS of human isolates
July 7: 5 cases from 2014 in outbreak
July 16: matching food isolate
22. Two outbreaks caused by common fish products
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10 cases 2013-15:
June/July 2015:
New case points at cold smoked fish from supermarket
A as probable source
Identical Lm ST391 found in environmental samples
from Company X
Food Authority: Production stop at Company X until
cleaning and control check
New case 2 weeks later: warm smoked fish from
Company X
10 cases 2013-15:
Sep 2014:
Isolates from cold smoked fish from Company Y identical
to isolates from patients.
Food control intervention
Spring 2015:
New cases – have eaten smoked fish from
supermarkets that sell products from Company Y
Product and environmental samples at Company Y
again positive for the ST-6 clone
23. EFSA project: Listeria WGS – food/human/epi
Main objective is to compare L.monocytogenes isolates collected in the
EU from RTE foods, compartments along the food chain and humans
using whole genome sequencing (WGS) analysis.
EFSA contract after call for tender
SSI, Public Health England, ANSES, Uni. Aberdeen
1000 Listeria isolates will be sequenced (PHE)
- From patients, food, food processing from all Europe
Different bioinformatical approaches for assessing:
- Genetic diversity
- Epidemiological relationship of Lm from sources and human origin considering
the genomic information and the metadata
- Putative markers for the potential to survive/multiply in the food chain and/or
cause disease in humans
- Suitability of WGS as a tool in outbreak investigations
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24. Surveillance at the European level (ECDC, 2012-)
Surveillance of foodborne infections based on isolate typing
- Rapid detection of dispersed international outbreaks
EQAs to ensure comparable methods used in all countries
Pathogens covered:
- Salmonella
- Listeria
- VTEC/STEC
Methods:
- PFGE, MLVA, serotype
- Preparing to include WGS-based typing
ECDC and EFSA databases will be connected (2016)
- Improved linking to sources
25. Benefits and challenges …
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
More clusters for investigations?
- May be, but better defined so less resources on each cluster?
- Prioritisation, when to respond?
International perspectives
- Comparability
- Nomenclature (e.g. wgMLST)
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