Presented at the GMI (Global Microbial Identifier) satellite meeting, sponsored by the UK Department for Environment, Food and Rural Affairs (DEFRA), organised by the Food and Environment Research Agency (FERA), Bedern Hall, York, 10th September 2014.

1. Highly Discriminatory
Diagnostic Primer Design
From Whole Genome Data
Leighton Pritchard1;3;4, Sonia Humphris2;3, Nicola Holden2;3;4 and Ian Toth2;3;4
1Information and Computational Sciences,
2Cellular and Molecular Sciences,
3Centre for Human and Animal Pathogens in the Environment,
4Dundee Eector Consortium,
The James Hutton Institute, Invergowrie, Dundee, Scotland, DD2 5DA

2. Table of Contents
Introduction
The Insidious Dickeya Menace
Primer Design
Standard qPCR Primer Design
qPCR Primer Design From Whole Genomes
Results
Dickeya Diagnostic Primer Performance
E. coli Diagnostic Primer Performance
Primer Design Software
Acknowledgements
Without Whom. . .

3. Dickeya spp.
Dickeya spp.1 are virulent enterobacterial soft-rotting
pathogens of ornamental and crop plants
Eight species now assigned.
Quarantine organism (zero tolerance in Scotland)
1
formerly Erwinia chrysanthemi

4. Dickeya on crops and ornamentals
2 3
2
Landesanst. f. P
anzenbau und P
anzenschutz, Mainz Archive
3
Florida Division of Plant Industry Archive

5. Dickeya spp. are a threat in Europe
D. dianthicola is established across Europe
D. solani is an emerging, encroaching threat

6. Why Dickeya qPCR diagnostics?
To legislate for, or quarantine contaminated materials, one
has to be able to identify the pathogen
qPCR is still cheaper, quicker and easier than bacterial
genome sequencing (for now, anyway. . . )
No qPCR primers existed to distinguish among Dickeya spp.
Having sequenced 25 Dickeya isolates, we were approached to
develop diagnostic primers at the species/isolate level

7. Table of Contents
Introduction
The Insidious Dickeya Menace
Primer Design
Standard qPCR Primer Design
qPCR Primer Design From Whole Genomes
Results
Dickeya Diagnostic Primer Performance
E. coli Diagnostic Primer Performance
Primer Design Software
Acknowledgements
Without Whom. . .

8. qPCR In a Nutshell

9. Standard qPCR Primer Design
The goal is to identify a region to be ampli

10. ed that is:
suciently similar in all target organisms to be ampli

11. ed by
your primer/oligo set
suciently dierent (or absent) in all o-target organisms
that it is not ampli

12. ed by your primer/oligo set
This is harder to do manually, the more similar the target and
o-target organisms
Frequent choices:
intergenic transcribed spacers (ITS)
ribosomal DNA
housekeeping or virulence genes

13. qPCR Primer Design Problems
Which permutation of
protocol choice?
Is ampli

14. ed region
diagnostic?
Are primers/oligos speci

15. c
in a sample?
Are primers/oligos ecient
across positives (SNPs)?

16. A Brute Force Approach
1. Design large numbers of primers to (draft) genomes from the
target groups
2. Test cross-hybridisation of primer sets in silico against target
and o-target groups
3. Screen primers against broader set of o-target sequences
4. Classify primer sets according to in silico speci

17. city
5. Evaluate speci

18. city against unseen panel of target/o-target
organisms

19. qPCR Primer Design: 1
1. If needed, convert drafts to (pseudo)chromosomes and
identify CDS
2. De

20. ne target and related o-target groups
3. De

21. ne classes within target groups
targets
o-targets
classication
V
IV
III
II
I
genomes

22. qPCR Primer Design: 2
1. Bulk predict primer sets on all chromosomes (Primer3)
2. Design only thermodynamically plausible primers
3. Over 1000 primer sets per chromosome
targets
o-targets
classication
V
IV
III
II
I
genomes

23. qPCR Primer Design: 3
1. Predict cross-ampli

24. cation in silico (primersearch)
2. Classify primers by cross-ampli

25. cation pro

26. le
3. Additional screen against o-target database (BLAST)
targets
o-targets
classication
V
IV
III
II
I
genomes
I
II
III
IV
V

27. qPCR Primer Design: 4
1. Select diagnostic primer sets
2. Evaluate in vitro against panel of previously unseen isolates
of known class
3. Report performance metrics
targets
classication
o-targets
V
IV
III
II
I
primer sets validation gels
I
II
III
IV
V
III IV V +ve -ve
III IV V +ve -ve
III IV V +ve -ve
III IV V +ve -ve
II
V
I
III

28. Table of Contents
Introduction
The Insidious Dickeya Menace
Primer Design
Standard qPCR Primer Design
qPCR Primer Design From Whole Genomes
Results
Dickeya Diagnostic Primer Performance
E. coli Diagnostic Primer Performance
Primer Design Software
Acknowledgements
Without Whom. . .

29. Dickeya primer evaluation
Primers designed to 29 sequenced Dickeya isolates
Evaluated against panel of 70 unseen isolates
100% sensitivity; 0-4% FDR 4
4
Pritchard et al. (2013) Plant Path. 62: 587-596. doi:10.1111/j.1365-3059.2012.02678.x

30. E. coli diagnostic primers
Summer 2011, E. coli EHEC O104:H4 outbreak
Unprecedented scale: 3950 aected, 53 deaths
Rapid production of sequence data, crowd-sourcing5
6
5
https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki
6
Kwan et al. (2011) http://precedings.nature.com/documents/6663/version/1

31. E. coli primer evaluation
Primers designed to nine crowdsourced draft outbreak E. coli
O104:H4 assemblies
21 clinical outbreak, 32 HUSEC/EPEC isolates
Combined primers speci

32. c at sub-serotype level
100% sensitivity, 9-22% FDR for individual primers; 100% speci

33. city and sensitivity for paired primers 7
7
Pritchard et al. (2012) PLoS One 7: e34498. doi:10.1371/journal.pone.0034498

34. find differential primers.py
Software freely available at GitHub8
Installs as Python script, takes con

35. g

36. le, and runs from
command-line (or Makefile)
8
https://github.com/widdowquinn/find_differential_primers

37. Table of Contents
Introduction
The Insidious Dickeya Menace
Primer Design
Standard qPCR Primer Design
qPCR Primer Design From Whole Genomes
Results
Dickeya Diagnostic Primer Performance
E. coli Diagnostic Primer Performance
Primer Design Software
Acknowledgements
Without Whom. . .

38. Acknowledgements
James Hutton Institute
Nicola Holden
Sonia Humphris
Ian Toth
Emma Campbell
GitHub
Benjamin Leopold
Michael Robeson
FERA
Valerie Bertrand
John Elphinstone
Neil Parkinson
SASA
Gerry Saddler
University of Munster
Martina Bielaszewska
Helge Karch