Presentation delivered 29th October 2012, at the CoZee workshop in Dundee (see CoZee zoonosis network site for more information: http://www.cozee-zoonosis.net/). [For clarity: our diagnostics work did not at the time form part of the excellent E.coli O104:H4 genome analysis crowd-sourcing consortium work, which can be found at https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki - we talked about it here because it was good work, and without their efforts we couldn't have done what we did]

1. Outbreak of E. coli O104:H4
heralds a new paradigm in
responding to disease threats
Nicola J. Holden
Leighton Pritchard

2. EHEC O104:H4 outbreak, Europe 2011
Unprecedented:
scale of outbreak
(3950 affected, 53 deaths; multiple
import restrictions)
emerging pathogen
(one previous case in S.Korea)
rapid production of sequence data
crowd-sourcing of assembly, and annotation via
GitHub
https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki

3. EHEC O104:H4 outbreak, Europe 2011
Unprecedented:
scale of outbreak
(3950 affected, 53 deaths; multiple
import restrictions)
emerging pathogen
(one previous case in S.Korea)
rapid production of sequence data
crowd-sourcing of assembly and annotation via
collaborative revision control site: GitHub
https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki

4. EHEC O104:H4 outbreak – timeline
 1st May: onset of outbreak
 26th May: strain characteristics (Scheutz et al., 2012 Eurosurveill)
 30th May: diagnostic laboratory information released (Muenster)
 2nd June: first draft assembly available (GitHub)
 9th to 21st June: additional sequences announced
 22nd June: Microbiological characteristics published (Bielaszewska
et al., 2011 LID)
 26th July: official end of the outbreak (RKI)
refs: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki;
RKI; Institute of Hygiene, Muenster

5. EHEC O104:H4 outbreak – timeline

6. EHEC O104:H4 outbreak – timeline
 1st May: onset of outbreak
 26th May: strain characteristics (Scheutz et al., 2012 Eurosurveill)
 30th May: diagnostic laboratory information released (Muenster)
 2nd June: first draft assembly available (GitHub)
 9th to 21st June: additional sequences announced
 22nd June: Microbiological characteristics published (Bielaszewska
et al., 2011 LID)
 26th July: official end of the outbreak (RKI)
refs: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki;
RKI; Institute of Hygiene, Muenster

7. EHEC O104:H4 outbreak – timeline
 27th July: Publication of open-source genomic analysis

8. A changing paradigm?
 Kwan et al. (2011) http://precedings.nature.com/documents/6663/version/1

9. Meanwhile: diagnostics
27th June – 6th July
1. Outbreak isolate-specific, sub-serotype diagnostics
2. Exploit rapid sequencing: work directly from incomplete
and unordered draft genome sequences
3. Rapidly generated (perhaps ahead of the biology?)
4. Validated (good estimates of error rates)
5. Easy to use and distribute
6. Cheap(er than sequencing everything)

10. Meanwhile: diagnostics
27th June – 6th July
1. Outbreak isolate-specific, sub-serotype diagnostics
2. Exploit rapid sequencing: work directly from incomplete
and unordered draft genome sequences
3. Rapidly generated (perhaps ahead of the biology?)
4. Validated (good estimates of error rates)
5. Easy to use and distribute
6. Cheap(er than sequencing everything)
Alignment-free PCR primer design:
no need to identify conserved signature sequences prior to primer
design

11. Alignment-free primer design: strategy
 ‘Positive’ genome set: 11 genome assemblies of 9 EHEC O104:H4
outbreak isolates (GitHub crowdsourcing)
 ‘Negative’ genome set: 31 genomes of E. coli and E. fergusonii
(GenBank)
 Design many (>1000) primers to positive genome set:
target CDS; optimise for qRT; 20 mers; 100 bp amplicons; TA = 58 oC
 Filter primers in silico:
 Exclude sets with predicted productive amplification in negative genomes.
 Screen primers to exclude sets with strong sequence similarity to any of a
larger set of off-target genomes: (GenBank Enterobacteriaceae)

12. Alignment-free primer design: strategy
 ‘Positive’ genome set: 11 genome assemblies of 9 EHEC O104:H4
outbreak isolates (GitHub crowdsourcing)
 ‘Negative’ genome set: 31 genomes of E. coli and E. fergusonii
(GenBank)
 Design many (>1000) primers to positive genome set:
target CDS; optimise for qRT; 20 mers; 100 bp amplicons; TA = 58 oC
 Filter primers in silico:
 Exclude sets with predicted productive amplification in negative genomes.
 Screen primers to exclude sets with strong sequence similarity to any of a
larger set of off-target genomes: (GenBank Enterobacteriaceae)

13. Automation
https://github.com/widdowquinn/find_differential_primers

14. Alignment-free primer design
Positive
Negative ...
...
...
...
III
II
IV
V
I
1. Process configuration files:
Locations and classes of input
sequence files.
2. Convert to single
(pseudo)chromosomes:
Concatenate draft genome
sequence.
3. Genome feature locations:
From GBK file or predicted from
Prodigal.

15. Primer prediction (on positive set)
Positive
Negative
III
II
IV
V
I
4. Predict primer locations:
> 1000 thermodynamically
plausible primer sets on each
(pseudo)chromosome, using
Primer3.

16. Test cross-amplification in silico
Positive
Negative
III
II
IV
V
I
5. Check cross-amplification:
All primer sets tested against
other organisms, using
PrimerSearch.
6. BLAST screen:
All primers screened for off-
target sequences with BLAST:
7 possible primer sets

17. Classify primers and validation
III
II
IV
V
I
...
...
...
...
...
III IV V +ve -ve
7. Classify primers:
Classified primer sets
according to their ability
to amplify specific
classes of input
sequence.
8. Validate primers:
Primer set validated on
positive and negative
targets in vitro.
5 target sequences:
prophage gp20 (2)
hypothetical CDS (2)
impB (1)

18. Validation
 In silico, diagnostic primers are just another classifier
 Validation on unseen data is critical
 (avoid overfitting, estimation of performance)
 Direct experimental validation of primer candidates (Münster):
 ‘Positive’ set = 21 clinical outbreak isolates
 ‘Negative’ set = 32 HUSEC / EPEC isolates
 Positive control = LB 226692

19. Primer design: validated in vitro
positive negative

20. Alignment-free primer design: summary
 Individual primer sets: 100 % sensitivity; 82–94 % specificity;
9% < FDR < 22%
 Combining primers: 100 % sensitivity and specificity
 A minimal combination of two primer sets discriminated absolutely
between outbreak O104:H4 isolates and non-outbreak E. coli isolates,
including HUSEC 041
 Flexibility in strategy allows for targeted design, e.g. multiplex PCR /
different organisms / large gene families etc..
 Same approach used for
 Resolving Dickeya plant pathogens
 Discriminating between RxLR effectors in Phytophthora infestans

21. Alignment-free primer design: summary
 Bypass the need for:
 multiple genomic alignments
 biological justification for primer choice (maybe even reveal biology…)
 Produce diagnostic primers for any subgroup of organisms (possibly…)
 Limitations
 Scaling issue: PrimerSearch is slow (modular pipeline allows use of
alternative programs)
 Low specificity of primers -> use qPCR
 Very similar organisms may not be distinguished
 Time from genomes to primer sets: 90 hours
 possibility for improvements as collaborative bioinformatics projects
(speed up off-target primer mapping, make into user-friendly tool…)

22. Acknowledgements
nicola.holden@hutton.ac.uk
leighton.pritchard@hutton.ac.uk
Thanks to Nadine Brandt,
Kath Wright and Sean Chapman

23. Sprouted seeds as a source of infections

24. ‘Sproutbreak’ - Jimmy Johns restaurant

25. Colonisation of spinach by VTEC O157:H7
Sakai (vt-)