From takahē and microarrays to the Reverend Bayes

mEpiLab

Nigel French, Patrick Biggs and Jonathan Marshall

IDReC • An interdisciplinary

research centre at Massey University.

• IDReC brings together 6 research groups each with knowledge and expertise in different aspects of infectious disease research.

IDReC

Epidemiology and molecular epidemiology

Population genetics and

disease ecology

Mathematical modelling and

statistics Public health

Microbiology

IDReC’s Six Principal Investigators Professor Nigel French IDReC Director mEpiLab – Palmerston North Molecular epidemiology and veterinary public health

Professor Paul Rainey Rainey Lab, Auckland Max Planck Institute, Germany Evolutionary genetics

Professor Martin Hazelton Statistics and Bioinformatics Group – Palmerston North Statistics and spatial modelling

Professor Tim Carpenter Epicentre – Palmerston North Veterinary epidemiology and economics

Professor Mick Roberts Institute of Information & Mathematical Sciences - Auckland Mathematical biology and infectious disease modelling

Professor Jeroen Douwes Centre for Public Health Research - Wellington Public health and epidemiology

Scientific Advisory Group

Molecular Epidemiology and Public Health Laboratory

– Epidemiology, evolution and control of infectious diseases

• Particularly zoonotic diseases • Campylobacter, Salmonella, E. coli, Leptospira,

Cryptosporidium, Giardia, and multidrug resistant bacteria

– Located in the Hopkirk Research Institute

Zoonoses and Emerging Infectious Diseases

• ~60% of ~1500 human infectious diseases are zoonotic

• ~75% of human emerging infectious diseases are zoonotic

• 33% of zoonoses are transmissible between humans

• Many zoonoses don’t affect their animal host

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Zoonoses in perspective

• Emerging and re-emerging zoonoses – Global pandemics, high mortality (SARS, H5N1,

H1N1, Nipah…..)

– Major burden to health services and economy

• Outbreaks grab attention, but many persisting – Rabies kills 55,000 per year

Single and multi-host pathogens

• Most infectious agents are multi-host – Particularly emerging ones – Subset of these are the zoonoses

• Single host agents relatively rare – Subset of these are the solely human to

human spread agents (anthroponoses) – Evolved once population reached critical size

Emerging Infectious Disease: why the increase?

• Human risk factors – Population density,

urbanisation and growth – Increased global travel – Poverty – Changing dietary habits

• Animal risk factors – Farm animals and

wildlife • Habitat destruction • Global distribution of food • Climate change

Zoonoses figure very prominently in New Zealand. Source: ESR Ltd

Pathways and pathogens

E. coli O157 ~ 150 cases

Salmonella DT160 ~ 250 cases

Campylobacter ~ 8,000 cases

Cryptosporidium ~ 800 cases

Giardia ~ 1000 cases Leptospira ~ 100 cases

Molecular epidemiology: tools • Microbiology • Molecular biology

– PCR – Sequencing – Microarray

• Genomics and bioinformatics • Modelling

– Space and time – Case-control / risk factor – Evolution – Source attribution

Campylobacteriosis

Source: Olsen et al Campylobacter. 3rd ed. Washington DC: ASM Press; 2008 .

Campylobacter from space... Google Earth 2000-10 110,000 cases

C. Jewell

Manawatu sentinel site 2005-2012 • Identify genotypes common to

particular sources • Modelling (reservoir attribution)

Campylobacter reservoir attribution

• Multilocus sequence

typing • Population genetics

approach • Evolutionary modelling • Used to find out source

of human infections Human

Wild bird

Chicken Sheep

Cow

Water

Reservoir attribution (Mullner et al, Sears et al)

Est $40M saving per annum to NZ economy

Control of Campylobacter in poultry

Changing epidemiology presents new challenges

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Poultry intervention

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Seasonality and dairy density

Urban

High dairy rural Young children During calving season Direct contact

Hand mouth behaviour Epidemiology changed

Different set of policy and decision makers Different stakeholders

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VTEC

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E. coli O157 VTEC/STEC (mainly O157)

Major impact on export trade Super eight

P. Jaros H. Irshad B. Govan

A. Cookson A. Reynolds

Ecosystem goods and services: Water supply and purification

Important for public health

…and recreation

Water-borne protozoan infections ~ 1800 cases per year in NZ Major global issue

Water quality and safety

Second order properties

+Deb Prattley

Alex Grinberg Mark Stevenson

Rima Shrestha Ben Phiri

Eve Pleydell Julanda Al Malwy Pete Lockhart

Anthony Pita

Whole genome: 80% of sample to be sequenced

Sequenced on Illumina MiSeq system

16S rRNA V4 region PCR amplification

18S rRNA PCR amplification; region to be determined

DNA extracted from water samples

Metagenomics

Leptospirosis: an occupationally acquired zoonosis in NZ • 81 notified cases in 2010

– underdiagnosed – 75 reported occupation

• 41 farmers • 14 meat industry

– Half hospitalised

RWNZ, Massey and leptospirosis.... a one health approach

Fang Fang, Anou Dreyfus, Emilie Vallee, Alison Harland Peter Wilson, Julie Collins-Emerson, Anne Midwinter

Wildlife and Emerging Infectious Disease

• Wildlife important source – Human encroachment,

urbanisation – Habitat destruction – Climate change

Nipah virus

Salmonella DT160

Conservation, translocation and wildlife health • Major challenges

– Declining habitats – Small, fragmented populations

• Low immune repertoire • Immunologically naive

– Translocation • Host and its parasites • Transmission of infectious agents • Treatments may reduce biodiversity • Stress host and alter immune system

Supershedders and superspreaders

Kyle Richardson and Dan Tompkins

Population biology and evolution of zoonotic pathogens in NZ

• Low diversity • Unique genotypes • Population structure shaped by:

– Hitchhiking – immigration, people and animals • Isolation, recent colonisation by man

– Subsequent evolution

EID 7, 767

Campylobacter: Mechanisms of evolution

• Recombination more important than mutation – Natural transformation – Transduction

• Using MLST and whole genome data

C. jejuni C. coli

EM algorithm

Next (Now) generation sequencing

• Human and poultry isolates, same genotype, time and space.

• 83 genes differed, 55 with amino acid differences.

• 96.7% were imported via recombination. • Recombination much more important

than mutation for generating divergence.

ST 474 flaA 14

Non-homologous recombination

2011

Livestock and wildlife populations Barbara Binney

Cattle

Modelling demographic history

Tim Vaughan Alexei Drummond Paul Fearnhead Dennis Prangle

Ruminant

Campylobacter source attribution: water Most bacteria from water in Manawatu associated with wildlife – even in dairy catchments

Water birds

Sheep Cattle

New Zealand water rail lineages

Nearest to common ancestor

NJ tree glnA allele

Phil Carter, ESR

7,000yrs

Divergence time of “C. aotearoa” and other NZ lineages

100yrs

500yrs

10yrs

Analysis in BEAST

Dr Patrick Biggs Genomic epidemiology and public health

Bacterial sequence typing • Goal is to use typing methods of bacteria to inform molecular

and/or genomic epidemiology • Molecular bacterial typing methods

– “Generation 1”: MLST 1990’s – “Generation 2”: rMLST 2010’s

• MLST (multilocus sequence typing) – Used sequencing technology of the time to sequence portions of

housekeeping genes

• rMLST (ribosomal MLST) – Uses high throughput sequencing of whole genomes to analyse many

more genomic loci – Higher resolution

MLST in Campylobacter jejuni/coli • Both species share same

MLST scheme – Other schemes for other

bacteria

• PCR highly conserved genes

• 7 genes used • Used to define:

– ST = sequence type – unique pattern of 7 genes

– CC = clonal complex – group of related STs

The PubMLST website • Lists all genomes analysed by Prof Martin Maiden’s

group in Oxford, UK • Other sites exist for other bacteria • For each scheme,

this shows: – Allele definitions,

number and amplification conditions

– Isolates for that scheme

Ribosomal MLST (rMLST) • Proposed in 2012 as typing scheme to integrate

genealogy and typing – Synthetic – needs whole genome sequence for

analysis • Use of the 53 genes encoding bacterial ribosome

subunits (rps) – Present in all bacteria – Distributed around chromosome – Under stabilising selection

• More resolution than: – MLST approaches – 16S rRNA approaches

Microbiology (2012) 158, 1005-1015

rMLST for Campylobacter jejuni/coli • Only 52 of the 53 genes

found – No rpmD gene

• Few genes distributed in clusters around genome

• Genomes uploaded and analysed on a remote copy (UK) of the BIGSdb database set up for our project in collaboration with Prof Martin Maiden’s group in Oxford, UK

rMLST for 62 C. jejuni/coli genomes

• SplitsTree image based on allele numbers (n =52)

• Maximum distance is therefore 52

Host type: Env water/wild birds Livestock/human gut

The core genome • Genes present in ALL

strains are core genes – A proxy for analysing

evolution

• Genes present in a subset of strains are accessory genes – Virulence? – Niche adaptation?

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Core genome estimation from 13 NZ E. coli O157:H7 genomes

Accessory Character Core

Core genome for 76 C. jejuni/coli genomes

• Core genome NeighborNet using 548 genes with length range < 20 aa and variable sites.

• 148,046 aa sites with 36,528 variable (24.7%).

rMLST for Escherichia coli

• All 53 genes present • Significant clusters

of genes in the genome

• 13 O157:H7 genomes analysed: – 7 bovine – 6 human

rMLST for 13 NZ E. coli genomes • rMLST

NeighborNet image from 13 New Zealand E. coli O157:H7 genomes, with 21 reference genomes (3 non-O157 and 18 O157)

Core genome for 13 NZ E. coli genomes

• Core genome NeighborNet using 2216 genes with length range < 20 aa and variable sites present.

• 734,563 aa sites with 19,530 variable (2.65%). • In addition, 178,620 aa sites in a further 758 genes are invariant.

Integration of genotype with phenotype for E. coli

• Use of the Omnilog system

• Brown SBI type 5 with stx2c

• Sucrose non-fermenter

• Other studies (Lee et al. AEM 2012) have shown stx2c strains to be ‘stress sensitive’

Tessy George

Dr Jonathan Marshall

epiclustR

A Bayesian model for detecting outbreaks of disease from

notification data

NZ campylobacteriosis cases

NZ campylobacteriosis cases

epiclustR goals

• Model the underlying temporal trend.

• Model the underlying spatial trend.

• Identify potential ‘outbreaks’ as short periods of localised increased risk over and above these trends.

• Produce output for visualising these ‘outbreaks’ suitable for surveillance.

epiclustR models the expected number of cases within a particular area at a particular time using The rate is then split into time, space and space-time effects

epiclustR statistical model

epiclustR statistical model The random effects Rt and Ui ‘soak up’ the primary temporal and spatial variation. Rt Change in risk this week is similar to the change in risk last week. Ui Risk in a spatial location is similar to the risk in neighbouring locations. What is left over are the short periods of localized increased risk.

The influence of affluence

epiclustR detecting potential outbreaks

• Group fine spatial units into coarser regions.

• Associate a binary variable with each region, indicating whether a localized outbreak occurred.

• Prior information for these variables may be specified according to disease behaviour.

• The model output for these variables gives the probability of an outbreak.

epiclustR output

epiclustR output

epiclustR output

Raw milk cluster

Raw milk cluster

Raw milk cluster

epiclustR summary

• Identifies underlying spatial and temporal trends

• Identifies potential localised outbreaks over and above this trend.

• Produces output that can be visualised in a number of ways.

• May be a useful supplement to the current NZ disease surveillance tools.

Conclusion • Molecular epidemiology and public health – we work on:

• Understanding epidemiology of major zoonoses • Pathogen evolution (emerging infectious disease) • Control and prevention • Food safety • Protecting export markets • Ecosystem health (water supply) • Conservation

• ... Under ‘One Health’ model • In collaboration with ESR, AgResearch, Landcare, University of

Otago and members of the Allan Wilson Centre

Acknowledgements • mEpiLab

– Julie Collins-Emerson, Anne Midwinter, Jonathan Marshall, Eve Pleydell, Deb Prattley, Patrick Biggs

– Rebecca Pattison, Rukhshana Akhter, Errol Kwan, Lynn Rogers, Isabel Li, Sarah Moore, Angie Reynolds, Neville Haack

– mEpiLab PhD students – Tessy George

• Martin Hazelton, Chris Jewell • Brett Gartrell, Laryssa Howe • ESR - Phil Carter, Sharla McTavish, Elaine Moriarty , Stephen On,

ERL team • AgResearch –Adrian Cookson, Gale Brightwell, Helen Withers, Bryce

Buddle • University of Oxford: Martin Maiden, Danny Wilson, Sam Sheppard • MidCentral Public Health, Tui Shadbolt • Petra Mullner • MedLab Central • Landcare: Dan Tompkins • MPI – Peter van de Logt, Steve Hathaway, Donald Campbell, Roger

Cook • University of Otago: A/Prof Michael Baker, Simon Hales, Aparna Lal,

Ann Sears • VUW: Nicky Nelson • University of Tasmania: Barbara Holland • Lancaster University: Dennis Prangle and Paul Fearnhead • University of Warwick: Simon Spencer

Funding