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

6.
Workflow – routine surveillance
6
MiSeq data
Serotype
SNP analysis
Outbreak
investigations
MLST
nomenclature
MLST
Antimicrobial
resistance
Virulence
genes
Risk
assessment
Treatment,
interventions

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)
10

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
11
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
13

14.
H typing: WGS vs. phenotyping
85 isolates – Routine surveillance in Denmark 2015
14

15.
Listeria surveillance by PFGE 2002-2012
Anne Kvistholm Jensen

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)

19.
Workflow – routine surveillance
19
MiSeq data
MLST
SNP analysis
Outbreak
investigation
QC
QC
QC
MLST
nomenclature
1
3 2
5
4
6 7
100
90
80
70
Cluster?
Cluster?
ssi-snp-pipeline at
github.com/PHWGS

20.
MLST & SNP of clinical isolates (Jan 2013 to April 2014)
MLST tree, all isolates (n=64):2013-14 WGS surv for EMN (64 entries)
MLST
100
95
90
85
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
391
391
391
391
155
155
399
399
399
399
399
7
7
120
120
8
8
8
403
403
451
451
451
398
37
37
37
37
6
6
6
6
1
1
1
1
1
1
1
1
1
1
1
1
224
224
224
224
224
59
59
Key
20130820
20140920
20140999
20130728
20130883
20140905
20130980
20130620
20140982
20130806
20130815
20130657
20130731
20130740
20130812
20140931
20130737
20130716
20130801
20130798
20140912
20130633
20130656
20130794
20130624
20130687
20130621
20130775
20130814
20130851
20130852
20130788
20130829
20140997
20130702
20130711
20140901
20140951
20130632
20130670
20130718
20140930
20130576
20130580
20130661
20130694
20130695
20130715
20130741
20130762
20130797
20140940
20140942
20140989
20130786
20130836
20130863
20130873
20141000
20130572
20130979
20130579
20130799
20130774
Patient-nr
918
934
939
899
925
927
937
870
935
914
916
890
900
901
915
930
902
897
913
911
928
886
887
909
872
891
871
906
917
921
922
908
919
941
894
895
926
933
885
889
898
929
865
867
888
892
893
896
903
904
910
931
932
938
907
920
923
924
940
864
936
866
912
905
100
90
80
70
Patient A
Patient A
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
85 SNPs
4 patients
19 weeks
1 SNP
ST1
ST-1 isolates (n=12):

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
22
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
23

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)
25