WGS for surveillance of foodborne

infections in Denmark

Eva Møller Nielsen

Head of unit, PhD

Foodborne Infections

Statens Serum Institut

Copenhagen, Denmark

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

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 resistanceSalmonella Typhimurium infections

MLVA types

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

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

Workflow – routine surveillance

6

MiSeq data

Serotype

SNP analysis

Outbreak

investigations

MLST

nomenclature

MLSTAntimicrobial

resistanceVirulence

genes

Risk

assessment

Treatment,

interventions

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

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

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)

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

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-

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. coligenome 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

O-grouping: WGS vs. phenotyping86 isolates – Routine surveillance in Denmark 2015

13

H typing: WGS vs. phenotyping85 isolates – Routine surveillance in Denmark 2015

14

Listeria surveillance by PFGE 2002-2012

Anne Kvistholm Jensen

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

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

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)

Workflow – routine surveillance

19

MiSeq data

MLST

SNP analysis

Outbreak

investigation

QC

QC

QC

MLST

nomenclature

1

3 2

5

4

6 7

10

0

90

80

70

Cluster?

Cluster?

ssi-snp-pipeline at

github.com/PHWGS

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

10

0

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

50

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

10

0

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 SNPs4 patients

19 weeks

1 SNP

ST1

ST-1 isolates (n=12):

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

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

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

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

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