Whole (meta)genomesequencing in a food safety

perspective

AOAC Europe NMKL NordVal International Symposium, Oslo 3. June 2019

Camilla Sekse1, Gro S. Johannessen1, Bjørn Spilsberg2, Mieke Uyttendaele3 and Arne

Holst-Jensen1

1Department of Animal Health and Food Safety, Norwegian Veterinary Institute, Norway;

2Department of Analysis and Diagnostics, Norwegian Veterinary Institute, Norway;

3Department of Food Technology, Safety and Health, Ghent University, Belgium

Pathogen detection in food

• Bacterial foodborne pathogens (FBP) areoften present in low numbers and heterogenously spread in the food product

• Ability to detect very low level of FBPs in various food sources are important

• Current detection methods often involveone or more enrichment steps, screening (e.g. PCR) followed by isolation

• Low recovery of isolates – PCR positive sample, no isolate identified

Conventional microbial food analyses vs high throughput sequencing

• From phenotypic til molecular methods• Demands for speed and precision

• Technology development

• Still need for cultivation and isolation

• Whole genome sequencing require an isolate

• Metagenomics is culture independent• Often require culture-based verification

a

Stx2

f

Gene Allele IAV

Matrix of interest

Sample

Extracted& purifiednucleic acid

Sequencinglibrary

Sequencereads

BioinformaticsStatistics

1

2

3

4

5

Interpretation& action

6

Protocols (SOPs)Output = input to successive step

Modified from Arne Holst-Jensen, NVI

Whole genome sequencing of isolates– what can we get out of the data

• Strain characterisation;• Virulence and antimicrobial resistance genes

• Serotype (in silico, not phenotype)

• Sequence type (MLST)

• Determine the relatedness between isolates• SNP approach

• Comparison to a reference genome

• Gene-by-gene comparison• cgMLST/wgMLST

Whole genome sequencing of isolates– what can we get out of the data

High discriminatory power

SNP vs cgMLST analyses

SNP analysis

• Reference genome

• Include/exclude mobile elements

• Require bioinformaticscompetence

cgMLST

• Standardised/harmonisedscheme

• Enable global straincomparison

• Implemented in commercial userfriendlysoftware

• Equally discriminatory when calling strain relatednessand epidemiologically concordant for outbreakinvestigations

• Still need for comparison of the the approaches for a wide range of FBP

WGS of isolates - how can we use thedata in a food safety perspective

• Outbreak investigation• Detects outbreaks taking place under the surveillance

radar

• Source finding/source identification• Provides better evidence of links between human cases and

the sources of infection

• Increased power on WGS analysis provides more robust evidence may allow intervention at an early stage

• Prevent false positive association of a food to an outbreak

WGS of isolates - how can we use thedata in a food safety perspective• Surveillance, control and monitoring

• Integrated surveillance; combine data from different parts of thefood chain• Identifying and confirming outbreaks, monitoring disease trends,

identifying risk factors and populations, improving food production and public health practices

• Source tracking during investigation of a contamination event• Sporadic vs recurrent contamination event• Understanding the cause of contamination and focus on priority areas for

interventions• Source attribution; quantifying the relative contribution of different

food sources to human infection• help to prioritise food safety activities that most likely will result in safer

food and reduce the burden of illness• Monitor emergence of virulent clones and antimicrobial resistance• Predictive food microbiology for phenotypic prediction e.g. heat

resistance, biofilm formation, resistance to antimicrobial agents

WGS – interpretation of results

• High sequence similarity means that isolates share a recentcommon ancestor• Low sequence similarity means they don’t

• Use epidemiological and food trace back evidence for support

• No cut-off value for which isolates are closely related• inherent diversity of different bacterial species• different epidemiological contexts• different WGS analysis approach

• In general, 0-20 SNPs/alleles likely that the isolates share a common ancestor and originate from a common source

• In depth characterisation of isolates often requires highly specificspecies competence

Metagenomics

• Study «all» DNA in a sample

• Two approaches;• Amplicon sequencing/metabarcoding/metataxonomics

• Shotgun sequencing/metagenomics

Sekse et al. 2017

Metataxonomics - what can we get outof the data

• More sensitive than shotgun metagenomics

• Identify/characterise microbial populations at varioustaxonomic levels

• Universal phylogenetically informative genetic marker; 16S rRNA gene for bacteria or ITS for fungi

• Can be used for identification of several variants of onespecific gene in a sample

• Cost-effective overview of the taxonomic compositionof a sample

• Will identify familiy or genus, not necessarily species and not pathotype

Metataxonomics - how can we use thedata in a food safety perspective

• Culture independent identification of microbial diversity of• Food/environment over time

• Quality control of fermentation

• Identification of spoilage bacteria in food and environment

• Might detect Salmonella spp., but will not be ableto identify STEC

• Monitoring microbial diversity in food production, storage or food production environment

Shotgun metagenomics - what can weget out of the data

• Independent of a priori knowledge of target sequences

• Limit of detection is high compared to metataxonomics• Need a lot of data to make sure there are at least one copy of all

DNA in the sample

• Identify non-culturable or fastidious microbes

• Permit identification of individual strains

• Can be used for monitor alterations in the microbiome thatmay not be evident from only the composition of themicrobiota

• Prediction of function encoded by the microbial community

• Detection, identification and characterisation of pathogens

Shotgun metagenomics - how can we use thedata in a food safety perspective

• Outbreak investigation• Tracing of microbes; Detection and subtyping of pathogens without

culture• Retrospective studies from outbreaks, strain already identified by culture

• Surveillance and monitoring• Global surveillance of antimicrobial resistance/selected pathogens• Surveillance of a broad range of agents with one lab procedure?• Culture independent identification and characterisation of diversity

of microbial communities• Identify previously uncharacterised microbes or emerging or novel

pathogens• Monitoring microbiomes under different food related conditions

e.g. storage

• Not limited to bacteria – could characterise the microbial communities across domains of life including viruses

Shotgun metagenomics - how can we usethe data in a food safety perspective

• Control purposes in food• Quality control in food products and production lines

• Detection of foodborne pathogens• Detection/identification of virulence/antimicrobial resistance genes• Shift in the microbiome

• Improve understanding of the microbial ecology of food and food processes

• Predicting microbial gene functions

• Improve culture-based enrichment methods• Studying metagenomics in enrichment samples – other bacteria (ie.

Firmicutes) grow well in enrichment for Salmonella in vegetables(Ottosen et al. 2013, Jarvis et al. 2015)• Optimize culture-based methods for detection of the organisms of interest

Metagenomics – interpretation ofresults

• Metagenomics for rapid screening and research in the food industry seems to work well

• For regulatory testing purposes and outbreak investigationspresumptive positive results needs to be confirmed by culture/isolate

• Lack of well-curated and high quality standard database ofgenomic sequence of pathogenic, probiotic and functionalmicrobes• Hindrance to the implemention of metagenomic-based methods for

food safety management

• Lack of bioinformatics pipelines for prediction if thevirulence gene or AMR genes belong to a pathogen or thebackground microbiota

• Identify DNA, not nessecarily expressed genes

Industry:Diversity and changes in microbiome- unbiased pathogen detectionFunctional profile of microbiome- modulating/driving/selecting forces

Persistent contaminants (strain typing)

Pathogenic microbiome profiles- competitive exclusion or promotion- indicators as well as known pathogens

Enforcement control:Diversity and concentration- unbiased pathogen detection- traceability/source trackingFunctional profile of microbiome- modulating/driving/selecting forces

Reemerging or emerging pathotypes?

Pathogenic microbiome profiles- competitive exclusion or promotion

Outbreak investigation:Always patient to food sourceWith clinical reference data:- multiple specific genomic markers- microbiome profile characteristics

Without clinical reference data:- larger outbreak: consensus profiles

can provide putative ref. data- small outbreaks /sporadic cases:

need isolates to provide ref.data

Contexts for FBP detection. Whysequence?

Information, information and information

Arne Holst-Jensen, NVI

Challenges of HTS in food safety perspective

• Representative/relevant samples/sampling• Storage

• Sample preparation• Nucleic acid extraction methods will affect the nature of the

sample, quality and quantity of DNA/RNA

• Large variety of matrices in food – no one-size fits all solution

• Enrichment vs direct sample

• Discriminate live vs dead microorganisms

• Obtain relevant isolates from food samples

• Limit of detection

a

Stx2

f

Gene Allele IAV

Matrix of interest

Sample

Extracted& purifiednucleic acid

Sequencinglibrary

Sequencereads

BioinformaticsStatistics

1

2

3

4

5

Interpretation& action

B

C

D

E

F

A

Thresholds/limits

(Legal/other)

6

Protocols (SOPs)Output = input to successive step

Developers perspective

Specific requirementsthat upstream step must satisfy, as otherwise downstream step cannotprovide fit-for-purpose output

End-users perspective

Arne Holst-Jensen, NVI

Challenges of HTS in food safety perspective

• Lack of standardised bioinformatic pipeline

• No harmonized requirement for detection/identification from HTS

• Data sharing• Trade implications

• Microbial risk assessment in food

The need for standardisation

Harmonisation and validation ofHTS• Lack of international standards for end-to-end

protocols for WGS• Work in progress; ISO; PulseNet International, Global

Microbial Identifier (GMI)

• HTS proficiency testing scheme• From sample preparation to bioinformatic analysis

• GMI

• EU-RL species specific proficiency testing based on WGS data

• Data harmonization; metadata and ontologies/vocabularies

Summary

• HTS – a possible game changer?

• Promising methodology for a range of analysis• Outbreak investigation

• Surveillance, control and monitoring

• Need standardisation of end-to-end protocols

• Need for validation and benchmaking for interpretation of the data in the food safety context

Whole (meta)genome sequencingin a food safety perspective

Questions?