On-farm/pre-harvest control strategies for the reduction

On-farm/pre-harvest control strategies for the reduction of Shiga-toxin producing Escherichia coli

Dr Adrian Cookson

Hopkirk Institute, Palmerston North

Food Assurance & Meat Quality Team, AgResearch

Rationale

Reduced on-farm STEC prevalence

Human infection

Horizontal/vertical transmission

EnvironmentalcontaminationProduct

contamination

Animal infection& colonisation

Aim

To identify and validate through on-farm studies, practical and effective STEC interventions able to be

readily adopted and implemented into current/future dairy (varied) farm systems cheaply with no

deleterious impact on animal health/productivity.

In collaboration with AgResearch experts (food microbiology, environmentalmicrobiology, farm systems, nutritionists, forage feeds, farm system adoption,social scientists) and external stakeholders (government, meat/dairy industry,public health and other NZFSSRC partners)

Focus

• Dairy farming operators

• Operations representing multiple intervention target points

• Pre-partum dams• Young calves• Weaned calves• Heifer replacements• Dairy herd animals

STEC intervention groupings

• Cattle water and feed

• Live animal treatments

• Management practices

Cattle water and feed

Trough cleaning

Supplemental feeds

Ryegrass/clover cultivars

Brassicas, chicory etc.

Live animal treatments

Probiotics Bacteriophage therapy Vaccines

Management practices

Vector control

Feed storage

Off-pasture housing

Heifer replacermanagement

On-farm biosecurity

Calf management

Effluent management

Systems approach to understanding the risk landscape

New Zealand issues/considerations• Intervention effectiveness vs. practicality

• Measure of intervention effectiveness?• STEC O157• Economically important STEC (O157, O26, O45, O103, O111, O121, O145)• All STEC• Generic E. coli

• STEC detection methods?

• Contrasting phenotypes & intervention susceptibilities• Horizontal transmission• Neonatal calf, weaned calf, heifer replacement, dairy cow

• Varying dairy farm practices• Systems 1 to 5• Use of supplemental feeds (maize silage, PKE, brassicas) • Housing of animals, use of HH and SOP

• Climate, geography, rainfall, humidity

Intervention adoption

• STEC infection of cattle• No usual signs of clinical disease• No loss of animal productivity• No restrictions on sale or processing of animal

• No tangible farm level benefits to reduce STEC prevalence• No increased animal productivity• No premium for STEC-free animals• No economic gains

• Farmer education and knowledge• Implementation of ‘general zoonotic mitigation’ programme• Farmer advice for on-farm control of multiple zoonoses• Identification of critical control points• Mutual control points for several pathogens

Next steps

• Engage with and seek feedback on STEC intervention discussion document• AgResearch and NZFSSRC collaborators• Industry stakeholders

• Undertake trials to measure effectiveness of potential interventions on farms recognised as STEC-positive

Acknowledgements

• Funded through AgResearch Core Funded research as part of the Food Provenance & Assurance (FPA) programme.

• Pre-Harvest Food Chain Control component of FPA programme aims to

‘understand and mitigate the risks associated with increased pathogen loading on intensifying dairy farm systems’

‘with the view of developing pre-harvest interventions for reducing contamination further along the food chain’

• FAMQ Team and mEpiLab

Use of gnd to assess Escherichia coli community diversity using culture-

independent methods.Adrian L. Cookson1,2, Angela Reynolds1, Rose Collis1, Patrick J. Biggs2,

Nigel P. French2, and Gale Brightwell1.

1Food & Bio-based Products, AgResearch Ltd, Palmerston North, New Zealand2 mEpiLab, Institute of Veterinary, Animal and Biomedical Sciences (IVABS), Massey University,

Palmerston North, New Zealand

Project Background – E. coli differentiation

• Serology – O, H and K grouping

• Pathotypes – EPEC, STEC, ETEC, EAEC, EIEC, ExPEC, (Nissle 1917, HS)

• Subtyping• Pulsed field gel electrophoresis (PFGE)• Multi locus sequence typing (MLST)• Insertion sequence (IS) typing• Genome sequencing – SNP profiling

E. coli differentiation using pure cultures

Project Background – E. coli diversity

• Previously assessed using culture-based methods1. Faecal samples from beef cattle fed:1

• Roughage & molasses, 30 serotypes (n=10 animals)• Roughage, 21 serotypes (n=11)• Grain, 17 serotypes (n=9)

2. Biotypes from human faecal samples:2

• Range of 1 to 15 (average 5) from healthy humans (n=9) over 6 weeks (range of 10 - 15 samples/volunteer)

Diversity of E. coli using culture-independent methods unknown1Bettelheim et al., 2005. J Appl Micro. 98. 699-7092Apperloo-Renkema et al., 1990. Epi & Inf. 105. 355-61

• To identify a gene for ‘barcoding’ E. coli populations

• To assess its use for community profiling of E. coli from complex matrices (e.g. bovine faecal samples)

Project Aims

Location of barcode targets

• Focus on hot-spots for recombination/horizontal gene transfer

• O antigen biosynthesis gene clusters (O-AGC) prone to recombination• E.g. evolution of STEC O157 from STEC O55• 184 recognised E. coli serogroups based on antigenic

variability – many more untypeable?

• Representative O-AGC sequenced1

• Development of serogroup-specific PCRs2

1Iguchi et al., 2015. DNA Res. 22. 101-72Iguchi et al., 2015. J Clin Microbiol. 53. 2427-32

gnd – 6-phosphogluconate dehydrogenase• Housekeeping gene often associated with O-AGC in

Enterobacteriaceae

• Third enzyme reaction of pentose phosphate pathway

• Described as passive hitch-hiker1 with existing O-AGC variants

• Variability noted in prior work through MLEE2, RFLP3, sequencing1,4

1Nelson & Selander, 1994. PNAS. 91. 10227-31 2Selander & Levin, 1980. Science 210. 545-72Dykhuizen & Green, 1991. J Bact. 173. 7257-68 4Gilmour et al., 2007. J Med Micro. 56. 620-8

gnd sequence analysis• Alignment made of >1000 E. coli gnd DNA sequences from PATRIC, GenBank,

IMG etc.

• Degenerate PCR primers designed for gnd amplicon sequencing (Illumina)• Designed to be sequenced with good overlap on 2 x 250 bp MiSeq run

• gnd database created including 300 unique combinations of E. coli serotype and gnd sequence• Covers all 184 serogroups and 35 untypeable or rough strains

Method sensitivity and specificity• gnd amplicons obtained from all E. coli test cultures

• Separate O-AGC groups may have same gnd sequence (e.g. O17 and O44: >99.9% similar)1

• O157 – gnd sequence variation differentiates STEC from non-STEC2

• gnd amplicons from STEC Super Six serogroups are not always distinguishable from corresponding stx-negative strains

• Multiple gnd sequences associated with diverse lineages of same serogroup indicative of separate O-AGC recombination events (e.g. O91, O104, O128)

1Iguchi et al., 2015. DNA Res. 22. 101-72Tarr et al., 2000. J Bact. 182. 6183-91

• Animal study (n=23) to assess role of bifidobacteria on calf (3-4 days) health• Treatment group orally dosed daily with 2 x bifidobacteria (14 days)• RAMS and faecal samples taken from calves at 17-18 days of age• Barcoded gnd amplicons generated from DNA extracts

• Faeces (23) • mTSB pre-enrichment (23)• mTSB post-enrichment – boiled lysate (23)• mTSB post-enrichment – kit (23)• Synthetic libraries (4)

• MiSeq (2 x 250bp PE) analysis of gnd amplicon libraries

• 96 libraries undergoing analyses

Detailed sequencing study

4-5 colonies (MAC plates)/animal

gnd applications

• Identify temporal changes in community structure• E.g. before/during/after interventions• Identification of E. coli that may ‘exclude’ STEC7

• Detection of industry/clinically important strains in complex samples using culture-independent methods

• Targeted, culture-independent approach for E. coli isolation

• Alternative to serology• Successfully adopted to serotype STEC

Acknowledgements

• Patrick Biggs – MiSeq data analysis & bioinformatics • Rose Collis – PCR, Sanger sequencing• Angie Reynolds – gnd library preparation

• New Zealand Genomics Ltd at Massey University

• Rose Collis was the recipient of an AgResearch Core-funded Summer Studentship (2014-2015)

• This work was funded through AgResearch Core (Curiosity) Funding

gnd as a tool for serological analysis

O serogroups # known isolates

Virulence Profile Source Enrichment

O3/O21 2 stx1, ehxA Dairy cow Pre & Post

O149:(H10) 2 stx2 Calf Pre & PostO91/O96 3 stx2 Calf Pre & Post

O165:(NM) 1 stx1, stx2, eae, ehxA Calf PreNo match* 1 stx2 Calf PreO182/O119 2 stx1, eae, ehxA Dairy cow Post

O84 1 stx1, eae, ehxA Dairy cow Post

* Single base pair variation compared to O104 gnd sequence from database

serogroup 9_112_S9counts 33_112_S33counts 57_112_S57counts 81_112_S81counts4428 5656 4537 3298

O45B 37.127 34.689 6.436 5.397O46B 11.089 12.712 2.689 1.698O123A 9.124 7.249 2.358 3.032O160 8.220 8.080 1.036 1.031O90A 4.810 5.110 0.926 0.970O15B 3.410 6.100 0.683 0.879O2D 3.049 2.475 0.353 0.515id0021813 2.936 3.041 0.683 0.334O176A 2.778 2.670 76.681 79.351O38B 1.829 2.086 0.661 0.243O153A 1.581 1.255 1.940 1.243id0002942 0.813 0.778 0.000 0.000OND 0.700 0.972 0.000 0.030O13 0.632 0.566 0.022 0.000O113 0.632 1.167 0.088 0.030id0036825 0.407 0.371 0.066 0.000O32 0.384 0.636 0.044 0.030O17 0.384 0.283 0.242 0.212id0018949 0.248 0.035 0.000 0.000O43 0.248 0.159 0.022 0.000id0027660 0.248 0.000 0.022 0.000O14 0.226 0.460 0.044 0.030id0017529 0.226 0.088 0.022 0.000O174C 0.203 0.424 0.000 0.000O91E 0.203 0.159 0.088 0.091id0031132 0.203 0.265 0.000 0.152O187 0.181 0.106 0.000 0.000id0035009 0.158 0.088 0.000 0.000O15 0.158 0.071 0.000 0.000id0017737 0.158 0.265 0.022 0.000id0030274 0.158 0.018 1.234 0.940O156C 0.136 0.177 0.000 0.030O62 0.136 0.106 0.000 0.030id0015622 0.136 0.000 0.000 0.000id0026225 0.136 0.018 0.000 0.030