This document summarizes research on the distribution, diversity, and management of Phytophthora species in UK plant nurseries. The objectives were to manage biosecurity risks, generate data to support protocols, identify currently present and potential new threats, and examine diversity across propagation systems, watercourses, and over time. Samples were collected from nurseries and tested, with 40-50% positive for Phytophthora. Metabarcoding identified 58 Phytophthora species including pathogens of concern. Best practices for managing arrival, spread, and dispersal of pathogens in nurseries were discussed. Ongoing work includes accreditation schemes, computational methods, and controlling for contamination.
1. WP1 - Distribution, diversity and
management of Phytophthora in
UK plant nursery systems
PhytoThreats Stakeholder Workshop 13 Nov 2019
David Cooke, Leighton Pritchard, Peter Cock, Peter Thorpe, Eva Randall &
Beatrix Clark – James Hutton Institute
Ana Perez, Sarah Green, Debbie Frederickson Matika - Forest Research
Tim Pettit - University of Worcester
Bethan Purse - CEH
Jane Barbrook - APHA
Alexandra Schlenzig - SASA
Thanks to nurseries for permission to sample
2. Objectives
Managing risk of import and spread of Phytophthora
Generating data in support of Biosecurity protocols
What is already present in UK? – interpreting interceptions
What is the next threat – can we spot it earlier?
Diversity of Phytophthora species
Propagation systems
Ecology in water courses vs surrounding soil
Catchment diversity (time and space)
3. Why are Phytophthora so damaging?
Co-evolved primary pathogens of plants
Evolutionary adaptability
Broad & flexible host range
Flexible breeding systems/form hybrids
Inoculum durability
Chlamydospores
Oospores
Inoculum production
Polycyclic disease – explosive epidemics
Fungicide resistance
13 fungicide groups
Fungistatic = cryptic infection
Environmental adaptability
Arctic, temperate or tropical adapted species
Water always required – achilles heel?
Werres et al., Myc. Res. 2001
4. Yang et al., IMA FUNGUS 2017Blair et al., Fun. Gen. Biol. 2008
170+ species
11 clades
Species names
important/
biosecurity
protocols
Related to
downy
mildews
Phytophthora diversity
5. P. ramorum - history
Twig blight of Rhododendron 1993
in DE & NL
Highly aggressive in horticulture on
Rhododendron, Viburnum etc.
Despite legislation and statutory
action took only 5 years 2003-08 for
broad distribution in UK industry
Unexpected host jump to Larch
Incidence declining - industry
awareness & inspection
Werres et al., 2001
Mycological Research
6. PhytoThreats – Sampling
Objective – improved nursery
management and evidence for
accreditation system
Fine - 15 UK plant nurseries
Range of business types
Detailed sampling by project team 4x
Water and plant material
2800 samples and metadata
Broad - 118 plant nurseries
SASA & APHA inspectors
782 root samples
Community engagement
Open Air Laboratory (OPAL)
Volunteers sampled water (26 samples) Parke & Grünwald
Plant Dis. 2012
10. What is metabarcoding?
Barcoding - means of discriminating organisms
based on differences in short DNA sequence
Species 1 CCACACTGAGCTAAGGCCTTTAA
Species 2 CCACACAGAGGTAAGGCCATTAA
Metabarcoding - massive increase in
throughput due to advancing sequencing
technology and reduced prices per base pair
Oxford Nanopore Technologies
12. Testing outline
Sample Prep
Filter in buffer
Roots freeze-dried
DNA Extraction
Filter – kit
Roots & soil bead beating and kit
PCR (Kappa polymerase)
Round 1 18PH2 & 5.8S1R
Round 2 ITS6 & 5.8S1R
6 synthetic control samples per
plate
Clean-up
AMPure XP ® beads
Add index tags (Nextera ®)
8 cycles PCR
Clean-up
AMPure XP® beads
Quantification and
normalisation
PicoGreen®
Pool samples to single library
96 samples (156K per sample)
192 samples (75K per sample)
Running Illumina MiSeq v2
13. Data analysis pipeline
Illumina QC
Index reading & de-multiplexing
Prepare sequences
Quality trim FASTQ reads
Merge the overlapping reads
Trim primer sequence
Convert to FASTA file
Filter with Phytophthora ITS1 HMM
Name with MD5 checksum & abundance
Setting abundance thresholds
Based on sequence contamination (default 100
reads)
Classifying sequences (steps)
Exact match or 1bp different from;
Curated Phytophthora reference set – returns
species name
Sequenced Phytophthora control isolates –
returns species name
NCBI Peronosporales (including Phytophthora)
download – returns genus only (Phytophthora,
Plasmopora, Bremia, Nothophytophthora etc)
Beyond threshold – sequence with no ID
Linking to sample Metadata – generates;
Sample reports
TSV & Excel spreadsheets
Graphical output
Network analysis to visualise diversity
Leighton Pritchard, Peter Cock
THABI-pict on Github
14. Pipeline
output
1 column per sample
Shaded by location
Red= >0 reads
Checksum Species Seq. Samples Reads
15. Host plants sampled
2869 root samples collected from 163 genera of plants (top 25
shown below)
Forestry and horticultural species depending on the nursery
128 genera
16. Phytophthora PCR test results
40-50% of nursery samples +ve for Phytophthora
n = 691 n=2310 n=24
18. Phytophthora test results vs nursery
practices
The proportion of Phytophthora +ve results per nursery from 20 to almost 70%
A generally predictable relationship between +ve rate and observed plant health
status & nursery management practices
Key objective to improve management practices & feedback provided to each
manager
20. Broad scale +ve proportion by host
Average of 6.3 samples per nursery
21. Metabarcoding output
35 M barcode reads from 800
samples
71% of reads of known
Phytophthora species
7% unclassified Phytophthora
species
10% downy mildews
12% other unknown –
Phytophthora & downy
mildew species (beyond threshold)
22. Metabarcode output - Phytophthora
Barcodes consistent with 58 Phytophthora species detected
P. gonapodyides and other clade 6 taxa abundant – generally
considered native and a less pathogenic ‘root nibbler’
abundant in rivers in Europe
P. cryptogea, cambivora, plurivora, cactorum & nicotianae
abundant - commonly found pathogenic species on many
hosts in nursery industry
26. Reporting to nurseries
Metadata compiled to text reports
Positive – awaiting sequencing
Sequenced twice (B & R)
Downy mildews – unexpected host
Puddle with many species
More work to do on final reporting
27. Quarantine pathogen findings
# nurseries
# samples Fine Broad
P. ramorum 8 2 2
P. kernoviae 0 0 0
P. austrocedri 17 3 2
P. lateralis 10 2 1
28. Other species of concern
P. cinnamomi
Exceptionally wide host range
UK generally considered too cold for an impact
Widespread on range of hosts and especially samples from southern
England
P. quercina
Probably native to Europe
Implicated in fine root damage and progressive decline of oaks (Jung et
al., publications)
Finding in a sizeable proportion of Quercus plants
P. agathidicida/castaneae/cocois
Related clade 5 taxa only reported from Australasia, Hawaii and Africa.
Hosts: Agathus, Castanea, Coconut
Found in puddles in >1 nursery in southern England
29. Examples of new UK host/pathogen
reports
Man in’t Weld et al., 2015
P. terminalis
Reported on Pachysandra terminalis plants
in NL
Multiple plants infected – single nursery
P. occultans
Reported on Buxus sempervirens species in
NL and BE
Buxus sampled from truck – single nursery
30. Best practice management
Pathogen arrival
Plant material (quarantine?)
Irrigation water
Potting mix
Surrounding plants
Vehicles/feet - mud
Pathogen spread on-site
Hygiene – from potting shed to discard pile
Water management/flow – pot to pot, bed to bed, mypex vs frames
Fungicides – manage disease or disguise symptoms?
Training – trained staff member taking action
Pathogen dispersal off-site
Sale – quality control
Run-off
Discard pile/surrounding vegetation
31. Ongoing work/challenges
Detection tool development
Sensitivity of 1 attogram (10-18g) a blessing and a curse
A lot of bleach and gloves used!
Accounting for and preventing field and lab contamination
Careful use of synthetic barcode controls
Computational pipeline development
Coping with reads beyond 1-2 bp thresholds
Species boundaries
Visualising output
Final PCR testing and reporting ongoing
Meeting nursery managers, APHA and SASA
32. Ongoing work/challenges
Discussions on nursery accreditation
Plant Healthy
Meeting UK Plant Health and WP3 teams
Looking at wider global surveys and estimating risk
according to Phytophthora clade, host preferences
and centre of origin
Thanks to Tree Health and Plant Biosecurity
Initiative for funding
33. Final thoughts/conclusions
Metabarcoding a powerful targeted method to explore
microbial diversity in new ways
Classifier developed
Interpret with caution. When confident – data to GenBank
Expanded primer sets - wider oomycetes groupings
Sample bank of eDNA samples offers huge potential
Supports plant biosecurity protocols and nursery
accreditation – Plant Healthy scheme – 2019
Experiments now needed to advance biology/ecology
34. Metabarcoding – Technical variation
4 synthetic DNA control sequences synthesised
PCR and Illumina barcoded alongside real samples
1000s sequence variants generated – mostly low abundance
Six control samples per plate & any cross contamination used to set read
thresholds per batch/plate
Leighton Pritchard
1000 ag
100 ag
10 ag
1 ag
Sequence identity
0.00 0.05 0.10 0.15
35. Preparing sequence data
• Quality trim the FASTQ reads (pairs where either read
becomes too short are discarded).
• Merge the overlapping paired FASTQ reads into single
sequences (pairs which do not overlap are discarded,
for example from unexpectedly long fragments, or not
enough left after quality trimming).
• Primer trim (reads without both primers are discarded).
• Convert into a non-redundant FASTA file, with the
sequence name recording the abundance (discarding
sequences of low abundance).
• Filter with Hidden Markov Models (HMMs) of ITS1 and
our four synthetic controls (non-matching sequences
are discarded).
36. Edit-graphs
Nodes (dots) are unique sequences
Nodes scaled by number of samples found in
Nodes coloured if exact sequence in NCBI
Solid black lines – one bp different
Dashed line – two bp different
Dotted line – three bp different
This is a zoomed out view of the largest
clusters from all the nursery data
38. Self—contained sequence cluster,
ITS1 shared by P. agathidica and P. castaneae.
Much of this grey-halo likely PCR artefacts, half
these nodes are seen in a single sample.
(Seen in synthetic controls too)
42. Unknown, found in multiple nurseries,
NCBI BLAST suggests novel Phytopythium
43. Complex cluster,
P. rubi (top left),
P. cambivora (rop right),
mixture bottom left,
unknown bottom right
44. Most complex cluster,
P. gonapodyides (central),
P. megasperma (top)
P. chlamydospora (top right)
P. lacustris (bottom left)
45. Interpreting sequence space
• Connected components often single species, but there
are some complicated hair-balls for species complexes
(using up to 3bp edits)
• Seem to be some novel species in here (grey clusters)
• One base pair difference is reasonable/cautious for
species match
• Backed up by single isolate control plates
• Allows for PCR artefacts
• Two or three base pair difference seems safe at genus
level?
• Plan some downy-mildew etc. controls
46. Software tool THAPBI PICT
• https://github.c
om/peterjc/tha
pbi-pict
• Illumina FASTQ
input through
to classification
and reports
• Can run on a
laptop
(Linux/macOS)
47.
48. Basic pipeline in THAPBI PICT
Input files
Output filesIntermediate files
FASTQ
(forward
)
FASTQ
(reverse
)
prepare
classif
y
TSV
(per sample)
ITS1
Databas
e
FASTA
(per sample)
summary
edit-
graph
XGMML graph
for Cytoscape
TXT, TSV, and
Excel reports
49. Sample metadata in THAPBI PICT
Input files
Output filesIntermediate files
FASTQ
(forward
)
FASTQ
(reverse
)
prepare
classif
y
TSV
(per sample)
ITS1
Databas
e
FASTA
(per sample)
summary
edit-
graph
XGMML graph
for Cytoscape
TXT, TSV, and
Excel reports
Sample
metadata
50. Default classification algorithm
• Trims FASTA sequences to ITS1 only using HMM
• Compares to DB of sequences (trimmed the same
way)
• Takes perfect DB match(es), failing that anything
1bp away
• Reports any species level match(es), failing that
genus level
• Database content is also vital to classifier
performance!
51. Current database contents
NCBI
search
CuratedSingle isolate input files
Intermediate files
FASTQ
(forward
)
FASTQ
(reverse
)
prepare curated-
import
ITS1
Databas
e
FASTA
(per sample)
ncbi-
import
seq-
import
FASTA
(genus)
TSV
(species)
FASTA
(species)
52. Alternative rejected classification
algorithms
• As before, but only consider perfect matches
• Best equal BLAST match(es), subject to a minimum
score threshold (to reject spurious distant matches)
• Cluster each sample’s sequences plus database
entries with SWARM, assign any DB species to
entire cluster
• Use SWARM, but first check for a perfect match in
the DB
53. Alternative potential classification
algorithms
Current approach validated only on Phytophthora, need
single isolate controls from other genera.
• One base pair method, but do not trim to HMM
matches
• One base pair method, but do not apply HMM filter
• One base pair method, but if no matches allow 2bp
genus level
• Cluster all observed sequences plus database entries
with SWARM
• Use the edit-graph, e.g. automatically assign same
genus to clusters
54. Tool generality
• Riddell et al (2019) dataset, public gardens and
arboretums
• Same protocol as here (but without synthetic controls)
• Redekar et al (2019) dataset, Ohio irrigation water
• They used different primers, amplify more non-Phytophthora
• Tool defaults and DB seems to work nicely
• East African nematode ITS1 dataset
• Primers for Globodera and Heterodera, i.e. different ITS1
region
• Ran without HMM filter/trim
• Much less diverse sample, Globodera rostochiensis
everywhere
55. Papers planned
• Need for and lessons from synthetic controls
• Beware PCR artefacts
• Illumina amplicon sequencing at best semi-quantified
• Need minimum abundance thresholds
• Software paper (THAPBI PICT)
• Include use on other project data
• Nursery data paper
• Could include environmental factors (host trees) and
management
• Environmental monitoring paper
• Hope to culture some of the novel Phytophthora data hints at