Whole genome sequencing –

sink or swim…opportunities and challenges for veterinarians

working in veterinary public health

Lesley LarkinPublic Health England

12 October 2019

Overview

1. Introduction

• Public Health England

• Whole genome

sequencing

2. How we are using

WGS – examples

3. Impact, challenges and

opportunities for

veterinarians working

in public health

4. Key conclusions

Source: google images

Introduction – Public Health England

Infectious disease surveillance and

control:

➢ Reference microbiology services

➢ FW&E laboratory services

➢ National epidemiology team in London

➢ Regional teams – epidemiologists and

Health Protection Teams

Functions:

✓ National and regional surveillance for

infectious diseases

✓ Outbreak detection and investigation

(PHE lead)

✓ Research and production of guidance

Introduction - whole genome sequencing

• PHE processes and analyses

approximately 1,000 bacterial

genome sequences each week.

• Routine sequencing and use for

surveillance purposes:

– Mycobacteria

– Salmonella

– Listeria

– Shigatoxin producing E. coli

– Campylobacter

etc

Assessing genetic similarity between

genomes

• Bacteria DNA broken down into fragments and markers

attached (short read sequencing)

• Short reads lined up against a reference genome and

variants compared between strains

• A SNP or single nucleotide polymorphism is a one

base difference in the bacteria’s DNA compared to a

reference genomeA G T C G C G T A T G T C T G A C C C

A G T C G C A T G T A T

C G C G T A T A T G A C C C

A G T C G C G A T G A C C C

G T A T G T A

T C G C G T G T A T G A

A G T C G C G A T G A C

G C G T A T

T G T A T

C G T A T G

Reference genome

Isolate genome pieces

Single nucleotide polymorphism (SNP)

Two matching SNP addresses indicate that the isolates are ‘~genetically identical’

5 SNP level corresponds to typical outbreak diversity

SNP threshold 250 100 50 25 10 5 0

Isolate 1 1 2 18 158 199 222 243

Isolate 2 1 2 18 158 199 222 243

Isolate 3 1 2 18 158 199 222 100

Isolate 4 1 2 3 178 200 245 289

The ‘SNP address’

SNP address indicates how closely related genetically an

isolate is to other isolates in the database

Phylogeny Trees

To fully assess the genetic relationship between

isolates in a cluster needs a phylogeny tree.

Salmonella NGS at PHE

Maximum likelihood phylogeny Minimum spanning tree

5 SNPs clusters → likely to be

epidemiologically linked/ share a

common exposure

0 SNPs clusters → strongest

evidence of being part of an outbreak

with a common exposure or with a

direct person-to-person transmission

Outbreak detection with WGS

t50= 276

• US Shell Eggs

• PT8/PT13a

• Recent Emergence in EU

• Invasive?

• MDR

• “Global Epidemic”

• PT4, PT1, PT14B

• Reduced susceptibility to

ciprofloxacin

• Peak in early 90’s – 20k

isolates a year in England!

Salmonella Enteritidis

Population Structure

Acknowledgements: Tim Dallman and Hassan Hartman

Travel associated

Salmonella Enteritidis

Acknowledgements: Tim Dallman and Hassan Hartman

Key points

• WGS is a highly discriminatory typing method –comparing the genomes of the bacteria

• Provides phylogenetic information → genetic diversity, wider phylogenetic context, MRCA etc

• The 5-SNP threshold corresponds to typical outbreak diversity.

→ = same ‘source of contamination’ (≠ same vehicle of infection)

• WGS is still only a microbiological typing technique –and the results must be interpreted carefully.

• Microbiological (WGS) results + epidemiology → outbreak detection/investigation/management

Outbreak

investigation

examples…

Gary Larson, google images

Whole genome sequencing…

• The unparalleled sensitivity this method provides over previous phenotypic methods, combined with epidemiological data, provides greater power to:

1. Detect and define outbreaks more easily

2. Collect better evidence for trace back, identifying vehicle and source → greater confidence in source attribution

3. Assess/monitor effectiveness of control measures

4. Improve our understanding of pathogen populations and infection transmission routes

Example 1: Outbreak detection,

case ascertainment and

increased strength of association

in investigations

Salmonella Enteritidis PT8/t5:2684

‘High definition’ case ascertainment

Cluster of Salmonella Enteritidis cases 5-SNP

1.2.3.18.2190.2684.%

• 31 cases reported May to September 2016, nationally

distributed, no travel reported

• 76% children <10yrs age

Increased strength of associations

Outcome of case interviews = 7/9 cases bought items from

East/Central European delicatessen shops:

➢ 5/9 cases (56%) consumed sausages

➢ 5/9 (56%) consumed cold sliced pork/ham

But also – ‘name ontology analysis’…

http://worldnames.publicprofiler.org/Main.aspx and http://onomap.org/

Key points

1. Improved ability to detect outbreaks, especially clusters of

cases that are:

• small

• geographically dispersed

• a common serovar/PT combinations

2. High resolution strain discrimination:

• More accurate case definition and case ascertainment

• Link cases that have had an exposure to a ‘common

source of contamination’ with much greater accuracy →

find commonality between cases more easily –

time/person/place and reported exposures (higher odds

ratios)

3. Time to outbreak resolution ~ faster using WGS.

Example 2: Source attribution

Salmonella Typhimurium DT104/ t5:459

Acknowledgements t5:459 IMT

S. Typhimurium t5:459

➢ 5-SNP cluster 60.11.15.16.458.459.%

➢ First investigated by local teams in 2016, national

investigation 2017

➢ 179 cases reported Aug 2014 - Sept 2018

➢ Two ‘temporal events’

S. Typhimurium DT104/t5:459

➢Phylogenetic analysis → ‘common

strain’, related to DT104 seen to

date in England and Wales

➢Mostly urban population, many

reporting eating halal meat and

shopping at halal butchers

➢Hypothesis = epidemiology +

phylogeny ≈ consumption UK

produced meat

2017 cases

2017 cases

2014-16 cases

2017 cases

2015-16 cases

Farms

Overall…

• Multiple food vehicles of infection through multiple

supply chains likely involved!

• Seasonal factors in livestock movements and

husbandry driving seasonal pattern?

• Amplification in specific supply chains - likely that a

combination of pre and post slaughter factors were

increasing total risk and driving transmission.

• Total number livestock farms identified with outbreak

strain 2014 – 2018 = 32/57

• Likely that persistently circulating on farms →

environmental contamination and perpetration via

animal movements…

How to handle this?

• Scale of contamination and timelines → very

difficult to decide on risk management actions to

take.

• Livestock movement data for the initial 9 positive

farms → identified a further 73 premises in the

network (January 2015 – June 2017) and 26,389

animal movements (sheep, cattle, pigs and goats)

Presentation title - edit in Header and Footer

How to handle this?

• Feasible and proportionate

control options?

✓ Enhanced surveillance on

livestock in the affected region

✓ Farm visits, sampling and expert

advice on control.

✓ Identification of linked catering

/retail premises and

slaughterhouses for hygiene

inspections/enhanced hygiene

measures

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Monitoring controls

28

Three week moving average number of cases of

Salmonella Typhimurium RDNC/t5:3225 by report

date and week July 2017 – August 2019 (n= 365 cases)

➢ Controls applied at 6 farms and 9 slaughterhouses and

proactive press releases with consumer advice

➢ New epidemic clone – possible wild bird transmission Acknowledgements: Paul Crook and t5:3225 IMT

Key points

1. We are still not able to resolve some of our large Salmonella outbreaks (even with WGS!)

2. Salmonella epidemiology in humans and animals is complex. Likely that a combination of pre and post slaughter factors were increasing total risk and driving transmission of these outbreak strains.

3. ‘Trace forward’ difficult → specific supply chain(s) could not be identified to target hygiene measures. This is a common problem and our tools for epidemiological and food chain analyses are lagging behind

4. One Health approach - now more reason than ever before to develop platforms for real time sharing of WGS and epidemiological data for joint analyses and collaborative control action → animals + food + human.

Example 3: improved understanding

of transmission pathways and

source attribution

STEC O157:H7 t10:1009

Acknowledgements: Amy Mikhail and Claire Jenkins

• August – October 2015: 47 cases of STEC O157 PT8

reported 5-SNP cluster 18.35.380.765.1009.1526.%

• Cases nationally distributed

• Adults = 88%; females = 69%

• Exposure frequency analysis ESQs – pre-packed salad

81%

• Case-case study identified pre-packed salad from

supermarket A as the primary exposure (OR 54, 95% CI

11- 247)

STEC O157 was not

detected in any of the

food samples tested

Phylogeny → evidence of a domestic

source …

Key points

1. WGS provided insight into the likely route of

transmission and further evidence of a domestic source

2. Animal movement data (ARAMS) was used for the first

time in an STEC outbreak investigation to map sheep

movements → epidemiological links were established

3. Salad outbreak investigations are challenging and rarely

manage to detect the pathogen in the food

4. Conclusion was that ‘timely and targeted veterinary and

environmental sampling should be considered during

outbreaks of STEC especially where RTE vegetables

are implicated’. Presentation title - edit in Header and Foote

Impact, challenges and

opportunities for veterinarians

Salmonella reservoirs & transmission routes

37Presentation title - edit in Header and Footer

Cattle

Pigs

Poultry

Household

pets

Seafood

Water

Environm

ent

Beef

Pork

Poultry meat

Beef

Pork

Poultry meat

Seafood

Eggs

Beef

Pork

Poultry meat

Farm Processing Retail Consumer

Consumption

Slaughter

Travel abroad

Illness

Humans

Egg layers

Egg products

Person-

to-

person

Tra

nsm

issio

n r

oute

s

Wildlife

Direct contact

(Pires et al., 2009).

Challenges and opportunities

1. Understand what WGS results can

tell us = same source of

contamination (≠ same vehicle of

infection).

2. Interpret with care! Microbiology

plus epidemiology

3. Knowledge gaps on the distribution

of Salmonella clones in animals and

the environment – those available

for WGS represent a small piece of

the jigsaw

Challenges and opportunities

5. Cannot apply next generation sequencing

technology effectively if don’t understand the

populations to which we are applying it (human

and animal)! So vets have important role to

play.

✓ Understanding of disease transmission and risk

factors in animal populations

✓ Understanding of animal production systems

✓ Understanding of surveillance limitations in livestock

populations – imperfect sampling of the population

plays a role in interpretation

Challenges and opportunities

6. Cannot implement the most effective and

proportionate risk management measures to

control zoonotic foodborne diseases if don’t

understand the populations to which we are

applying interventions. So vets have important

role to play.

✓ Understanding what is practically possible for risk

management

✓ Understanding where the most effective interventions

can be made → primary production and further down

the food chain

Challenges and opportunities

3. What is an ‘outbreak’? Now need to prioritise more carefully

and target efforts on strains causing significant public health

impact - timely cross sector communication, collaboration

and information sharing is critical.

4. Sampling at primary production and early stages of food

chain more sensitive than sampling end product. Now with

high discriminatory power of WGS, more demand for

targeted veterinary and environmental sampling to inform

outbreak investigations

5. Playing ‘catch-up’ – need better tools for epidemiological

and food chain analyses to maximise utility of WGS

In conclusion…

➢ WGS technology is now

revolutionising our ability to

detect and control zoonotic

pathogens. It requires

some understanding!

➢ Vets have an increasingly important role to play:

✓ It is critical to identify the source and control at

source for resolution of foodborne pathogen

outbreaks

✓ Cannot maximise use of WGS technology

effectively if don’t understand the populations

and food production systems to which we are

applying it

Acknowledgements

Public Health England

• Jacquelyn McCormick

• Amy Mikhail

• Claire Jenkins

• Tim Dallman

• Hassan Hartman

• Gauri Godbole

• Marie Chattaway

• Satheesh Nair

• PHE Field Services

Others

• Incident Management

Teams

• Food Standards Agency

• Local Authorities

• Animal and Plant Health

Agency

44

Thank you for

listening

Gary Larson – google images