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Identification and characterization of effector genes from wheat stripe rust
1. Discovering
the
effector
genes
of
Puccinia
striiformis
f.sp.
tri.ci
John
Rathjen
The
Australian
Na;onal
University
2. Stripe
rust
and
Australian
wheat
produc;on
Annual
losses
Control
cost
GM
Murray
&
JP
Brennan
2009.
Grains
Research
&
Development
Corpora?on.
Australian
Government
3. Stripe
rust
and
Australian
wheat
produc;on
Annual
losses
Control
cost
GM
Murray
&
JP
Brennan
2009.
Grains
Research
&
Development
Corpora?on.
Australian
Government
4. Urediniospores
(2n)
Wheat
Teliospores
(2n)
Dikaryo?c
–
Sexual
host
Two
haploid
nuclei
insignificant
in
Australia
Meiosis
alternate
Basidiospores
host
(1n)
Aeciospores
(2n)
Pycniospores
(1n)
Barberry
hAp://www.apsnet.org/edcenter/intropp/lessons/fungi/Basidiomycetes/Pages/StemRust.aspx
5.
6. P.
striiformis
in
Australia
Psd
BGYR
(2000)
Pst-‐1979
Psp
(~20
strains)
Pst-‐WA
(2002)
Puccinia
striiformis
f.sp.
tri0ci
Barley
grass
yellow
rust
(~6
strains)
Psd–
grows
on
Dactylis
glomerata
(Cocksfoot)
Psp
–
grows
on
Poa
pratensis
(Kentucky
blue
grass)
Stripe
rust
of
Phalaris
spp.,
Bromus
spp.,
“wheat
grass”,
etc,
etc
7. How
can
we
define
effector
genes?
• Generally,
effectors
are
thought
to
be
small
secreted
proteins.
• This
is
sufficient
to
build
a
list
of
such
proteins
if
genomic
sequence
is
available.
• In
some
cases,
amino
acid
mo?fs
such
as
RxLR
or
YxC
are
present…but
don’t
seem
to
be
diagnos?c.
• Another
important
criterion
is
expression
of
candidate
effector
genes
in
planta,
where
that
informa?on
is
available.
8. Puccinia genomics
• Pgt (stem rust) genome (Duplessis et al. 2011) is about 90
Mb, encoding about 17,000 genes – Pgt expected to be
similar.
• This was assembled with a lot of “last-generation
sequencing” which helps with scaffolding and sequence
assembly.
• Transposable elements account for about 45% of the
genome.
• Calling genes from NGS assemblies can be problematic, and
can be difficult to detect expression of fungal genes in
infected tissue (but these are the most interesting genes).
• There are ongoing unresolved problems with the
dikaryotic nature of rusts.
• Broad Institute (Cuomo) has a good Pst assembly in the
pipeline.
9. Perils
and
pi`alls
of
next-‐genera?on
sequencing
(NGS).
• NGS
–
boAom
up
or
‘shotgun’
assembly
of
millions
of
small
sequence
reads,
using
high-‐performance
compu?ng.
Technologies
include:
• Illumina
–
millions
of
very
short
reads
(~100
bp).
• Roche-‐454
–
fewer
numbers
of
longer
reads
(~500
bp).
• Tradi?onal
(Sanger)
sequencing
–
long
reads
800-‐1000
bp.
10. DNA
sequencing;
the
impossible
triangle
NGS
Tradi?onal
Sanger
sequencing
of
physical
con?gs
11. Perils
and
pi`alls
of
next-‐genera?on
sequencing
(NGS).
AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG
13. Detec?ons
of
sequence
polymorphisms
in
small-‐
read
assemblies
X
X
X
X
AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG
C/G
14. Detec?ons
of
sequence
polymorphisms
in
small-‐
read
assemblies
-‐
II
X
X
X
X
X
X
X
X
AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG
T/A
C/G
15. Detec?ons
of
sequence
polymorphisms
in
small-‐
read
assemblies
-‐
II
X
X
X
X
X
X
X
X
AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG
T/A
C/G
T
C
A
C
The
“phase”
problem
T
G
A
G
16. Repeats
and
mul?copy
genes
are
difficult
to
assemble
from
small
reads
Repeats
(transposons…effectors?)
assemble
poorly
or
not
at
all.
This
is
obvious
in
NGS
genome
assemblies.
It’s
a
considerable
problem
for
genomics
of
Puccinia
spp.
18. 454 sequencing of
isolated haustoria
transcriptome
16831 contigs
Contamina;on
removal
14682 contigs
Secreted
proteins
predic;on
Non-‐transmembrane
domains
1299 ORFs-SP
Unique
or
non-‐overlapping
ORFs
515 ORFs-SP
Illumina
Protein
length
≤
300aa
sequencing
418 ORFs-SP
High
expression
100 ORFs-SP Lab tests
19. Prediction of small secreted proteins
(SSPs) from the haustorial transcriptome
433
≤
300
aa
Protein
length
No
memes
98
>
300
aa
No
clusters/tribes
311
≤
4
Cysteines
Cysteine
content
220
>
4
Cysteines
91
have
1
mo?f
,
18
in
the
‘correct’
loca?on
Y/F/WxC
mo?f
42
have
2
mo?ves,
23
correct
loca?on
9
have
3
or
more
mo?ves,
8
correct
loca?on
Invertase
BLASTn
BLASTx
1,3-‐β-‐glucosidase
Pgt
hypothe?cal
protein
74
211
Pepsin
A
e-‐val
≤
10-‐25
Chi?n
deacetylase
Glucose-‐regulated
it
rotein
from
Pgt)
Specific
h p (most
38
29
Previous
SP
from
Pst
e-‐val
>
10-‐25
Not
available
419
291
20. Validation and investigation of effector candidates
AvrM
type-‐III
delivery/
P.
fluorescens
AvrM
75
avrM
24
Agro/AvrM
Narayana
Upadhyaya
and
Diana
Garnica
100
sequenced
and
cloned
in
TOPO
Ø R-‐AvrR
recogni?on
assay
Ø Inhibi?on
of
plant
cell
death
Ø Localisa?on
Ø Influence
on
host
metabolism
21. PST-80 housekeeping genes are not
single allele
Housekeeping
Gene
Copy
Number
10
9
8
7
Copy
number
6
5
4
3
2
1
0
18
39
60
81
221
102
123
144
165
186
207
233
254
275
312
333
368
389
418
453
483
521
295
467
443
511
Boeva
V,
et
al.
(2011)
Control-‐FREEC:
Bioinforma?cs.
2011
Dec
6
22. PST-80 Effector genes are present
with variable copy number
Effector
gene
copy
number
PST_80
Effector
Allele
Number
7
Effector
Allele
Number,
6
6
5
Copy
number
Allele
Number
4
3
2
1
0
1
21
41
61
81
101
121
141
161
181
201
221
241
261
281
326
346
366
415
456
290
486
471
519
308
494
434
Effector
gene,
nominal
ranking
23. Effector copy number variations
between Pst-80 and BGYR
Effector
gAllele
Number
Effector
ene
copy
number
7
6
Axis
umber
5
Copy
nTitle
4
3
2
1
0
1
51
101
151
201
251
301
351
401
451
501
Effector
rAxis
Tnominal)
ank
( itle
24. Copy
nNumber
Allele
umber
0
2
4
6
8
10
12
1
13
25
37
49
61
73
85
97
109
Cantu
et
al.
PLOS
One
(2011)
121
133
145
157
169
181
193
205
217
229
241
253
265
277
289
Effector
Number
301
313
325
337
349
361
Effector
gene
copy
number
Effector
number
(nominal)
373
PST_130
Effector
Allele
Number
385
397
409
421
433
445
457
469
Effector copy number variations
481
493
between Pst-80 and Pst-130 (US)
505
517
Allele
Effector
Number
25. Housekeeping
genes
do
not
show
the
same
degree
of
varia?on
in
copy
number
Conserved
Gene
Copy
Number
BGYR
Control-‐FREEC
predic?on
of
CNVs
Pst-‐80
7
Predicted
Copy
N umber
6
5
4
3
2
1
0
1 51 101 151 201 251 301 351 401 451 501
Gene
Boeva
V,
et
al.
(2011)
Control-‐FREEC:
Bioinforma?cs.
2011
Dec
6
26. Copy
number
varia?on
in
Pst
effectors
• Copy
number
varia?ons
are
readily
apparent
in
Pst
effector
genes,
with
many
single
copy.
• Sequence
polymorphisms
are
also
apparent,
but
these
are
harder
to
annotate
because
of
NGS
assemblies.
• Single-‐copy
effectors
may
allow
the
pathogen
to
mutate
rapidly
to
virulence.
27. Barley grass yellow rust (BGYR) – a
stripe rust that jumped?
wheat
Barley
grass
BGYR
(2000)
Wheat
stripe
(1980)
Stripe
rust
and
BGYR
99+%
iden?cal
in
effector
genes
so
far
sequenced
28. Sequencing summary
• We amplified and sequenced the PCR products of 50 candidate
effector genes from Pst-80 and BGYR and found 99 single
nucleotide polymorphisms (SNPs).
• These were ALWAYS of a particular pattern – twin peak
‘dimorphisms’, rather than clear SNPs (dSNPs).
• 50 of these were'informative' dSNPs - 34 from BGYR, and 16
from Pst-80.
• We amplified and sequenced these alleles from BGYR and
Pst-80.
• When we did this, we found that BGYR ALWAYS shared an allele
with Pst-80, and the alternative allele was divergent.
• We think that this is related to the dikaryotypic nature of P.
striiformis.
31. Model for the origins of BGYR
Pst
BGYR unknown ancestor
Anastamosis +
Heterokaryosis
BGYR
32. Where did BGYR come from?
• One line of evidence suggests that heterokaryosis is
an underlying mechanism for the host jump – but we
need to address the phase problem.
• In the 1950’s, this was proposed as a mechanism to
explain frequent mutation to virulence of stem rust
on wheat.
• We have detected four deleted effector genes, and
will test these for recognition on barley grass by
bacterial delivery.
• Heterokaryosis potentially increases effector
hemizygosity, which could both increase the effective
effector compliment (for virulence) and allow rapid
deletion of recognised effectors.
33. Acknowledgments
• Diana
Garnica
• William
Jackson
• CSIRO
Black
Mountain
• Narayana
Upadhyaya
• Peter
Dodds
• Jeff
Ellis
• Univ
Sydney
CobbiAy
• Colin
Wellings
Robert
Park
• Univ
Exeter,
UK
• David
Studholme
34.
35. Germinated
spores:
Ø Use
lipid
reserves
to
generate
energy
Ø Grow
(DNA
replica?on,
cell
division)
Ø Modify
chi?n
to
avoid
recogni?on
Haustoria:
Ø Take
nutrients
(sugars
and
aminoacids)
from
host
Ø Generate
precursors
of
metabolites
and
energy
Ø Biosynthesise
compounds
necessary
for
the
ul?mate
produc?on
of
spores
Ø Secrete
pathogenicity
factors
(effectors)
36. Many effector genes are single copy
PST_80
Effector
Copy
Number,
Allele
Number
and
SNP
14
80
Number
12
70
Copy,
Allele
and
SNP
Number
60
10
Effec
50
tor
8
Cand
40
idate
6
Copy
30
Num
4
ber
20
2
10
0
0
1
23
45
69
91
113
135
157
179
201
223
245
267
336
409
431
492
290
436
517
502
398
314
358
Effector
Number
37. Copy,
Allele
and
SNP
Number
0
2
4
6
8
10
12
14
16
1
13
25
38
50
62
74
86
98
110
122
134
146
159
172
185
198
211
223
235
247
259
271
283
Effector
Number
295
307
320
332
344
356
368
380
PST_130
Effector
Gene
Variability
392
404
416
428
440
452
464
476
488
500
512
PST_80 effector genes in PST_130
0
20
100
120
have undergone significant modification
40
SNP
Copy
Allele
Effector
60
Effector
80
Effector
Number
Number
Number
38. Mapping
BGYR
genomic
reads
against
500
‘conserved’
Pst
genes
Conserved
Gene
Copy
Number
BGYR
Control-‐FREEC
predic?on
of
CNVs
Pst-‐79
7
Predicted
Copy
N umber
6
5
4
3
2
1
0
1 51 101 151 201 251 301 351 401 451 501
Gene
Boeva
V,
et
al.
(2011)
Control-‐FREEC:
Bioinforma?cs.
2011
Dec
6
39. Mapping
BGYR
genomic
reads
against
500
Pst
effector
candidates
Effector
Candidate
Copy
Number
BGYR
Control-‐FREEC
predic?on
of
CNVs
Pst-‐79
8
6
4
2
0
1 51 101 151 201 251 301 351 401 451 501
Gene
Boeva
V,
et
al.
(2011)
Control-‐FREEC:
Bioinforma?cs.
2011
Dec
6
40. ToxA
cell
death
dependent
on
Tsn1
is
suppressed
by
stripe
rust
infec;on
+ToxA
+H2O
+ToxA
+
stripe
rust
stripe
rust
Diana
Garnica
with
help
from
the
Solomon
lab
