Listeria whole-genome-sequencingEFSA Project

AnsesStatens Serum InstitutPublic Health EnglandUniversity of Aberdeen

Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE)

foods: activity 3, the comparison of isolates from different compartments along the food chain, and from humans using whole genome sequencing

(WGS) analysis.

OC/EFSA/BIOCONTAM/2014/01

Consortium partners – key persons

Statens Serum Institut:- Eva Møller Nielsen, project leader

- Jonas Larsson

- Kristoffer Kiil

Anses- Sophie Roussell

- Laurent Guillier

- Yannick Blanchard

Public Health England- Kathie Grant

- Tim Dallman

- Corinne Amar

University of Aberdeen- Kenn Forbes

- Norval Strachan

Main objective of EFSA tender

Main objective:The 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.

Specific objective 1

Specific objective 1: to carry out the molecular characterisation of a selection of L. monocytogenes isolates from different sources, i.e. RTE foods, compartments along the food chain (e.g. food producing animals, food processsing environment), and humans employing WGS analysis.

• Database with relevant and available metadata will be established• 1000 L.m strains will be whole-genome sequenced using Illumina

platforms (HiSeq, MiSeq, NextSeq). • The majority will be performed at PHE, high-throughput sequencing

facility. • QC-pipeline, assembly

Typing based on WGS

Phylogenetic analysis (SNP-trees), overall framework to assess the diversity in sources

Gene based typing- MLST (7-locus; established nomenclature)- Extended MLST (25-locus; nomenclature possible)- Core-genome MLST (>1700 loci)

These analyses form the basis for the further epidemiological analysis in objectives 2 and 3.

Specific objective 2

Specific objective 2: to analyse the WGS typing data of the selected L. monocytogenes isolates with three goals:

i. to explore the genetic diversity of L. m within and between the different sources and human origin;

ii. to assess the epidemiological relationship of L. monocytogenes from the different sources and of human origin considering the genomic information and the metadata available for each isolate;

iii. to identify the presence of putative markers conferring the potential to survive/multiply in the food chain and/or cause disease in humans (e.g. virulence and antimicrobial resistance).

Genetic diversity

• SNP-trees highlighting the source, geography and time of isolation

• Diversity and frequency of types depending on source/geography/time, frequency of overlapping types between sources and human origins.

• Type distributions in the various sources, the genotype diversity and genetic relatedness: diversity index, geographical ditribution, etc• Types defined at the level of MLST • WGS types defined at higher discriminatory level when suitable

• extended MLST (SSI method)• cgMLST (method by Institut Pasteur & co)

Listeria phylogeny WGS

Maximum likelihood SNP phylogeny of 195 strains of Listeria monocytogenesfrom PHE, SSI, publicly available (Tim Dallman; PHE)

Listeria phylogeny (MLST)

Putative markers for potential to survive/multiply in the food chain and/or cause disease in humans

Allele based association: Allele information from wgMLST used for identification of loci or specific alleles that are over- or under-represented in specific sources/reservoirs or in human infections.

Genome-wide association: Identify genes showing evidence of association with a particular phenotype (e.g. clinical listeriosis)

Antibiotic resistance genes: Presence/absence of known AMR genes.

Specific objective 3: Outbreaks

To perform a retrospective analysis of outbreak strains (i.e. using a subset of epidemiologically linked human and food isolates) to investigate the suitability of WGS as a tool in outbreak investigations.- provide an evaluation on the advantages and limitations of WGS for

investigating outbreaks of food-borne listeriosis

Analysis of WGS data from outbreak strains and similar background isolates - explore the diversity between epidemiologically linked isolates- phylogenetic relationship of the isolates using SNP and wgMLST data- evaluating the thresholds for cluster definitions- demonstrating how WGS methods work for well-described outbreaks.

Selection of isolates

Human clinical isolates, sporadic 250Outbreaks (5-8) 100BLS isolates 349- Fish 293- Meat 49- Cheese 7

Supplementary food isolates 236Selected processing plants 100(raw, environment, food)

Selection of strains related to food sold in many EU-countriesMeat and meat products (France) Cheeses and cheese plants over several years (Italy, UK)Fish processing industry – fish and processing environment (Scotland)

Food and food chain isolates

Baseline survey: 707 L. monocytogenes isolated from - 72 RTE meat products, - 619 fish products (310 at sampling and 309 at end of shelf life stages)- 16 cheeses

Other isolates, provided by consortium or collaborators. - Supplement to BLS- Other food sources- Food processing chains

Selection of strains related to food sold in many EU-countries:Fish processing industry – fish and processing environment (Scotland)Meat and meat products (France, 2010-2014) Cheeses and cheese plants over several years (Sardinia, Italia)

293 RTE fish strains

49 RTE

meat 7

cheese

SelectioncriteriaOF1

Baseline surveystrains

+151 RTE

meat strains

Other foods

+ 85 chesse

strains

MLST OG ST + SNP?Whole-genome-sequencing (WGS), SNPs, MLST

MLST – approx 3500 basepairs WGS – MLST + SNP analysis

7 selected regions comparison of isolates

SNP: 2.8 mio bp = 98% of the genome

1

3 2

5

4

6 7

1 52 3 4 6 7Pat 1

Pat 2

Pat 3

Pat 4

Pat 5

MLST: 3500 bp = 0.13% of the genome

MLST – cgMLST – wgMLST

Extended MLST (xtMLST)

Relatively few additional loci increase discriminatory power dramatically

Simpel nomenclature possible- String of e.g. 25 numbers

25-locus MLST developed on retrospetive data- Selected outbreak and sporadic cases 2002-2012

Evaluated on independent surveillance data - 2013-2014 human infections

MLST+100 CORE GENES

MLST DI=0,914

MLST+1 DI=0,9352

MLST+2 DI=0,9369

MLST+3 DI=0,9459

MLST+20 DI=0,9748

MLST+45 DI=0,9818

MLST+100 DI=0,9826

xtMST tree: coloured by 7-locus MLST (n=264)

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xtMST tree: coloured by SNP cluster

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Numbers: allele differences

xtMST tree: coloured by molecular serotype

Two-year project

Contract signed October 7, 2014Selection of isolates- Database, including information on isolates

Collection of isolates, agreements with isolate providersPerforming WGS from March 2015- First data analysis April-May 2015

WGS, final set including QC and re-runs- Final data set for further analysis ready by July 2015

Data analysis, objectives 2 and 3:- August 2015 to spring 2016

Report to EFSA by September 7, 2016.

Data sharing and collaborations

Isolate providers:- Sequence data in return- Set their data into context- Publications

EFSA owns data and data analysis