Using hierarchical clustering of secreted protein families to classify and rank candidate effectors of rust fungi.

Abstract

Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that known effector proteins from filamentous pathogens have at least one of the following properties: (i) contain a secretion signal, (ii) are encoded by in planta induced genes, (iii) have similarity to haustorial proteins, (iv) are small and cysteine rich, (v) contain a known effector motif or a nuclear localization signal, (vi) are encoded by genes with long intergenic regions, (vii) contain internal repeats, and (viii) do not contain PFAM domains, except those associated with pathogenicity. We used Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance components.

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Using Hierarchical Clustering of Secreted Protein
Families to Classify and Rank Candidate Effectors of Rust
Fungi
Diane G. O. Saunders1, Joe Win1, Liliana M. Cano1, Les J. Szabo2, Sophien Kamoun1*, Sylvain Raffaele1*
1The Sainsbury Laboratory, Norwich Research Park, Norwich, United Kingdom, 2Cereal Disease Laboratory, Agricultural Research Service, U.S. Department of Agriculture,
St. Paul, Minnesota, United States of America
Abstract
Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici,
the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious
impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules
called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current
knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the
genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative
effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that
known effector proteins from filamentous pathogens have at least one of the following properties: (i) contain a secretion
signal, (ii) are encoded by in planta induced genes, (iii) have similarity to haustorial proteins, (iv) are small and cysteine rich,
(v) contain a known effector motif or a nuclear localization signal, (vi) are encoded by genes with long intergenic regions,
(vii) contain internal repeats, and (viii) do not contain PFAM domains, except those associated with pathogenicity. We used
Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their
likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of
high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of
haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate
effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance
components.
Citation: Saunders DGO, Win J, Cano LM, Szabo LJ, Kamoun S, et al. (2012) Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank
Candidate Effectors of Rust Fungi. PLoS ONE 7(1): e29847. doi:10.1371/journal.pone.0029847
Editor: Jason E. Stajich, University of California Riverside, United States of America
Received November 3, 2011; Accepted December 5, 2011; Published January 6, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This project was funded by the Gatsby Charitable Foundation, a Leverhulme early career fellowship to D.G.O.S. and a Marie Curie IEF Fellowship to S.R.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: sylvain.raffaele@tsl.ac.uk (SR); sophien.kamoun@tsl.ac.uk (SK)
Introduction
Rust fungi are a diverse monophyletic group of obligate plant
pathogens that infect numerous economically important cereal
crops and constitute a serious threat to global food security [1].
Currently, wheat stem rust is of particular concern due to the
emergence of a highly virulent race, Ug99, first detected in
Uganda in 1998 and characterized in 1999 [2]. The Ug99 race
and its variants are estimated to be virulent on over 90% of the
wheat grown globally, presenting a substantial threat to wheat
production [2]. Rust fungi also present a serious threat to the
production of bioenergy and fundamental plant products derived
from the poplar tree. Indeed, poplar plantations are particularly
susceptible to widespread infestation by the leaf rust fungus, with
the threat exacerbated by artificial cultivation methods such as
dense planting and breeding for uniformity, which limits genetic
diversity [3]. Although fungicides can be used to manage rust
fungi, the costs are considerable and often outweigh the benefits,
particularly for developing nations. Therefore, the integration of
new resistance (R) genes through plant breeding programs remains
the main sustainable solution to dealing with these notorious and
destructive plant pathogens.
The plant R proteins form a sophisticated surveillance
mechanism that recognizes pathogen molecules as signatures of
invasion and activates immune responses to halt colonization in
resistant cultivars. However, few R proteins have been character-
ized that are active against rust pathogens. For stem rust R (Sr)
genes, the introduction of the Sr2-complex in high-yielding wheat
cultivars in the 1970s led to the termination of many wheat
breeding programs, limiting the search for new Sr genes [2]. The
Ug99 stem rust race group has overcome most of the key wheat
resistance genes and some of the alien resistance genes that were
previously incorporated, such as Sr31 from rye, Sr38 from Triticum
ventricosum and Sr24 from Agropyron ponticum [4,5,6]. Therefore, the
identification of new resistance genes against these fungi has
become a priority in crop research.
During infection, rust fungi, like many other plant pathogens,
secrete effector proteins from specialized feeding structures known
as haustoria [7]. These structures form invaginations of the plant
plasma membrane, allowing an intimate contact with the plant.
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Once secreted, effectors can act either in the extrahaustorial
matrix, the extracellular space or within the host cell cytoplasm to
promote colonization and pathogenicity [8]. In cases where
effector proteins are recognized by corresponding host R proteins,
they induce an apoptotic cell death known as the hypersensitive
response (HR), and they are considered to have an ‘‘avirulence’’
activity (AVRs).
Only a handful of effectors have been identified from rust fungi.
Understanding the defining features of filamentous plant pathogen
effectors should assist in the identification of additional effectors
from rust fungi, particularly from economically important species.
Effectors from oomycete pathogens that act within the host
cell often contain a conserved host-translocation motif, which is
essential for transport into the host cytoplasm [9]. Some fungal
effectors, including effectors of flax rust fungi, are thought to be
translocated into the host cytoplasm [10,11,12]. However, to date
no universal host-translocation motif has been identified in fungi.
Effectors that remain in the extracellular interface between the
pathogen and the plant, and some host-translocated effectors, are
small cysteine rich proteins (SCRs). They contain intramolecular
disulfide bridges that likely stabilize protein tertiary structure in the
harsh environment such as the plant apoplast. For example, the
Cladosporium fulvum SCR effector protein Avr2 inhibits proteases
within the tomato apoplast to promote virulence on cultivars
lacking the corresponding resistance gene Cf-2 [13]. Some
filamentous pathogen effectors such as Magnaporthe oryzae Pwl
effectors and many oomycete RXLR effectors are repeat-
containing proteins (RCPs) [14,15]. RCPs have been proposed
to be involved in the virulence of Legionella [16], Candida [17],
Fusarium [18] and Phytophthora [19]. Effector genes are known to
occupy unstable regions of genomes such as repeat-rich regions
and centromeres [20]. For instance, effector genes are located
within repeat-rich regions of the genome in the blackleg fungus
Leptosphaeria maculans [21].
Classical strategies for the identification of fungal effectors
include map-based cloning, analysis of fungal secretomes during
infection, identification of HR-inducing pathogen genes, muta-
genesis and screening of expressed sequence tag (EST) libraries
[22]. However, these methods are typically labor-intensive and
can be problematic. The release of the genome sequences of
several rust fungi provides the opportunity to develop a com-
prehensive high throughput computational method for catalogu-
ing their effector repertoires. These effector proteins can then be
used as molecular probes to understand the basic biology of the
plant-pathogen interactions and identify new R proteins [23,24].
To identify R genes by use of effector proteins as probes in
resistant plant cultivars, the complement of effector proteins must
first be characterized. In this study, we took a first step towards
identifying effectors from important rust fungal pathogen species,
by annotating and classifying the secretome of two species. Eight
defining features of effectors were used to classify secreted protein
families. Hierarchical clustering was employed to rank the list of
candidate families revealing secreted protein families with the
highest probability of being effectors. We also highlight eight
candidate effector families that fulfill the most prominent features
of known effectors and that are high priority candidates for follow-
up experimental studies.
Results and Discussion
Defining the effector repertoire of two rust fungi
To identify and classify candidate effectors of the poplar leaf rust
fungus M. larici-populina and the wheat stem rust fungus P. graminis
f. sp. tritici, we constructed a bioinformatic pipeline using current
knowledge of the properties of validated filamentous plant
pathogen effectors. Considering that effector proteins are often
evolutionarily diverse and are rarely similar to characterized
proteins [25], limiting the analysis to sequence similarity searches
to known effectors is insufficient. Rather, we based the iden-
tification and classification of putative effector proteins on an array
of features. The pipeline was organized into three major modules,
(i) filtering, (ii) annotation and (iii) sorting (Figure 1). Using this
pipeline we predicted 1549 secreted proteins from the proteome of
M. larici-populina and 1852 for P. graminis f. sp. tritici. The two
secretomes were combined and used as a template to group
sequences, including non-secreted proteins from the two examined
rust fungal species, into tribes using Markov clustering [26].
Similarity searches were undertaken using the mature protein
sequences, to prevent unspecific clustering due to the N-terminal
signal peptide region. A total of 1222 tribes containing at least one
secreted protein were produced by analysis of the combined
proteomes of M. larici-populina and P. graminis f. sp. tritici. Of these,
435 tribes contained at least three proteins. Pairs and singletons
may contain effector candidates as some displayed similarity to
Melampsora lini haustoria expressed secreted proteins (HESPs), such
as Mellp_37347 (similarity to M. lini AvrL567) and Mellp_124274
(similarity to M. lini AvrP4) and were therefore retained for further
analysis. A total of 6663 proteins were analyzed, of which 2826
contained an identifiable secretion signal. This work therefore
significantly expands the analyses of 1405 small secreted protein
tribes reported previously [27].
De novo motif analysis reveals several conserved cysteine
motifs in secreted proteins of rust fungi
To detect conserved motifs in the secretome of M. larici-populina
and P. graminis f. sp. tritici we used the program MEME [28]. We
identified five positionally constrained motifs (motifs 03, 05, 06, 07
and 08) ranging in abundance from 59 to 107 sites (Figure 2).
None of the motifs of the secretome of rust fungi reached the
frequency of the Phytophthora RXLR or powdery mildew Y/F/
WXC motifs (34.8% and 19%, respectively). The presence of one
or two highly conserved cysteines was a common feature of the five
motifs found.
Motif 03 contained W, M and C residues that were conserved
and organized in a WXXMXXC pattern at median position 67
amino acids after the predicted cleavage site. This motif was
identified in at least two proteins in each of four tribes (tribes 5,
227, 455 and 469) and in a total of 78 proteins. Motif 05 contained
S, Q, C and Y residues in a SXQXCXXY pattern. Seven tribes
(tribes 5, 158, 159, 208, 227, 366 and 455) contained at least 2
proteins with motif 05, which was identified in a total of 107
proteins at a median position of 84 amino acids after the cleavage
site. This motif is also present in the largest tribe of small secreted
protein of M. larici-populina reported in [27]. Motif 05 was the most
abundant motif identified, found in 3.2% of the combined
secretome of M. larici-populina and P. graminis f. sp. tritici. Motif
06 and motif 07 contained two conserved cysteines separated by 4
and 2 residues, respectively. Motif 06 was shared among the
highest number of tribes (9, tribes 5, 76, 158, 209, 237, 362, 369,
455 and 469) and found in 82 proteins at a median position of 56
amino acids after the cleavage site. Motif 07 was shared between 5
tribes (tribes 5, 158, 227, 455 and 482) and found in 59 proteins at
a median position of 119 amino acids after the cleavage site.
Finally, motif 08 contained a conserved YXC pattern that was
preceded by a conserved N residue. This motif was found in 8
tribes (tribes 5, 125, 158, 159, 227, 353, 366 and 562) and a total
of 83 proteins at a median position of 105 amino acids after the
cleavage site.
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All motifs identified contained one or two conserved cysteines
that may function in protein stability in the extracellular space
[22], and several had conserved tyrosine residues, a feature
reported in some host-translocated effectors [29]. Motifs 06 and 08
contained the Y/F/WXC sequence which has been proposed as a
signature for a new class of effectors from haustoria-forming fungi
[30] and has been reported as abundant in the secretome of M.
larici populina and P. graminis f. sp. tritici [27]. These observations are
consistent with the view that some effectors of rust fungi might be
secreted into the apoplast first, where they would be processed and
folded, before uptake by the plant cell [22,31]. However, in spite of
systematic unbiased search efforts such as reported here, clear
translocation motif candidates are still lacking for effectors from
haustoria-forming fungi. It is therefore tempting to speculate that a
specific protein fold, matured in the extracellular space through
the conserved cysteine motifs, might trigger uptake by the plant
cell. As a consequence, conserved motifs seem of little help to
identify effectors of rust fungi, prompting us to consider additional
features of known filamentous plant pathogen effectors.
Lineage-specific orphan protein families are abundant in
the secretome of rust fungi
The genomes of rust fungi contain numerous lineage-specific
expanded protein families [27]. To investigate the distribution of
shared and unique secreted proteins in the secretome of M. larici-
populina and P. graminis f. sp. tritici, we investigated the species
composition of the 435 tribes with three or more proteins in
relation to annotation and number of proteins per tribe. Forty
percent of the tribes with three or more members (174 tribes)
contained proteins from both species (Figure 3A). These tribes
constitute 941 secreted proteins that likely form the core secretome
of rust pathogens. In contrast, 261 tribes were identified as lineage-
specific, with 116 containing proteins specifically from M. larici-
populina (a total of 544 secreted proteins) and 145 containing
proteins only from P. graminis f. sp. tritici (a total of 431 secreted
proteins).
Most tribes shared between species (,80%, 138 tribes) could be
annotated by similarity to known proteins, whereas lineage-specific
tribes could rarely be annotated. Only 19.5% of lineage-specific
tribes were annotated, with 15 M. larici-populina-specific and 36
P. graminis f. sp. tritici-specific tribes (Figure 3B). This is consistent
with the distinction between a shared core secretome and lineage-
specific protein innovations. The distribution of number of
proteins in tribes was shifted towards higher number for core
secretome tribes (Figure 3C). Approximately one half of the
secretome of M. larici-populina and P. graminis f. sp. tritici constitute
the putative core secreted protein set, grouped into shared
annotated tribes. The remaining half contained tribes of non-
annotated lineage-specific secreted proteins, which are likely to be
enriched in effector candidates.
Figure 1. Bioinformatic pipeline for the clustering of secreted
protein families and classification and ranking of effector
candidates. The pipeline is composed of six major steps delimited by
boxes. Step 1 (Secretome prediction) identifies secreted proteins from
the predicted proteomes. A total of 1549 and 1852 secreted proteins
were predicted from M. larici-populina and P. graminis f. sp. tritici
proteomes, respectively. Step 2 (Markov clustering) groups secreted
and non-secreted proteins according to sequence similarities to
secreted proteins. A total of 435 secreted protein families (tribes) of
at least 3 proteins were defined from the proteomes of the two rust
fungi. Step 3 (Functional annotation) implements tribe annotations
based on sequence homology searches. Step 4 (De novo motif search)
searches for conserved motifs. Step 5 (Effector features annotation) uses
the current knowledge of effector features to annotate individual
members of secreted protein families. Step 6 (Hierarchical tribe
clustering) ranks and classifies the tribes based on their content in
proteins with effector features to provide a priority list for functional
validation studies. Tools (programs and databases) used are indicated in
red. HESP, haustoria expressed secreted protein; AVR, effector protein
with defined avirulence activity; NLS, nuclear localization signal; FIR,
flanking intergenic region; RCPs, repeat containing proteins; SCRs, small
cysteine rich proteins.
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Figure 2. De novo motif searches in the secretome of M. larici-populina and P. graminis f. sp. tritici reveal conserved cysteine rich
motifs. (A) Amino acid position of 25 motifs in the secretome tribes of rust fungi reported by MEME. Arrows highlight positionally constrained motifs
that are abundant in the secreted protein tribes. Position is given after the signal peptide cleavage site when applicable. Grey shading indicates the
expected position for putative host-translocation motifs (amino acids 50 to 150). Box plots show median position (bar) first and third quartiles (box),
first values outside 1.5 the interquartile range (IQR) (whiskers) and outliers (dots), coloured according to the number of tribes containing at least two
proteins harbouring the motif. Motifs are classified by decreasing IQR from top to bottom. (B) Sequence logos of motifs with the highest positional
constraint, distribution over the largest number of tribes and greatest number of individual proteins (sites) containing the motif.
doi:10.1371/journal.pone.0029847.g002
Figure 3. Comparison between core and lineage-specific secretome tribes of at least three proteins in rust fungi. (A) Core tribes
(containing proteins from both species of rust fungi) represent forty percent (174 out of 435) of the secretome tribes containing three or more
proteins. (B) Core secretome tribes of at least three proteins are enriched in proteins annotated by homology searches whereas lineage-specific tribes
often remained non-annotated. (C) Size distribution of core secretome tribes of at least three proteins was shifted towards larger tribes compared to
lineage-specific tribes. The same conventions as in Figure 2 were used in the boxplots.
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The secretome of rust fungi is enriched in forty-seven
PFAM domains
To document biological functions specifically enriched in the
secretome of rust fungi, we mapped PFAM domains on the
proteomes of the two species. We then applied the filtering module
and tested for enrichment of each domain among secreted proteins
versus non-secreted proteins. We identified 47 PFAM domains
significantly enriched in the secretome of rust fungi, including 36
PFAM-A domains and 11 PFAM-B domains that lack annotation
(File S1). The enriched PFAM-A domains were distributed
among 45 protein tribes of rust fungi containing three or more
members. Of these, 43 tribes (96%) contained proteins from both
species (File S1). Tribe 234 was the only tribe specific to M. larici-
populina and contained proteins with the PF01670 (xyloglucan-
specific endo-beta-glucanase) PFAM domain. Tribe 422 contain-
ing the PF01161 (phosphatidylethanolamine binding protein)
PFAM domain was specific to P. graminis f. sp. tritici. We
hypothesize that proteins in tribes containing members from both
species may perform core biological functions of secreted proteins
and functions that may be unrelated to host specificity. In
accordance, twenty-one PFAM domains (,60% of the enriched
PFAM-A) corresponded to typical secreted enzymes, such as
proteases, plant cell wall degrading enzymes, phospholipases, and
detoxification enzymes (Table S1).
Five domains (,14% of the enriched PFAM-A) had previously
been reported as involved in pathogenicity (Table 1). These
include CFEM (PF05730, tribe 179), an eight-cysteine-containing
domain unique to fungi [32], also identified in the Melampsora
secretome [33]. The developmentally regulated MAPK interacting
protein (DRMIP) domain (PF10342, tribe 132) found in fungal
serine/threonine-rich membrane-anchored proteins homologous
to HESP-379 from M. lini [34]. One member of the DRMIP
family from the fungus Lentinula edodes that interacts with the kinase
LeMAPK and is proposed to be involved in cell differentiation
during the development of L. edodes [35]. The cysteine-rich
secretory proteins, antigen 5, and pathogenesis-related 1 protein,
CAP protein family (domain PF00188, tribe 83), are proposed to
be calcium chelating serine proteases [36]. These proteins can
function as proteases or protease inhibitors, ion channel regulators,
tumour suppressors or pro-oncogenic proteins, and in cell-cell
adhesion [37]. The thaumatin domain (PF00314, tribes 163 and
394) is found in pathogenesis-related (PR) proteins with antifungal
activity. They are also involved in systematically acquired
resistance and stress responses in plants, via unknown mechanisms
[38]. In plant TLP-Ks, a thaumatin-like protein (TLP) domain is
associated with a protein kinase domain and TLP-Ks were
proposed to act as receptor-like kinases during defence responses
[39], in particular against M. larici-populina [40]. Thaumatin-like
secreted proteins of rust fungi may alter this plant-signalling
pathway and have been reported in the Melampsora secretome [33].
The rare lipoprotein A domain (PF03330, tribe 52) adopts a
double-psi beta-barrel fold, which is also found in the cerrato-
palatin (CP) fungal elicitor protein [41]. CP induces a defence
response in the plant and is therefore considered a pathogen-
associated molecular pattern (PAMP) that has been proposed to be
involved in polysaccharide recognition [41].
Finally, some of the secretome-enriched domains we identified
are atypical for secreted proteins (Table 1). The putative stress-
responsive nuclear envelope protein domain (PF10281, tribe 551)
is related to hydrophilins such as LEA1 that prevent aggregation
of structurally compromised proteins [42]. The phosphatidyleth-
anolamine-binding protein domain (PF01161, tribes 199, 248
and 422) is involved in lipid binding, serine protease inhibition
and the regulation of several signalling pathways such as the
MAP kinase pathway. Immunoglobulin-like domains such as the
domain of unknown function DUF3129 (PF11327, tribe 57)
could be involved in cell-cell recognition or cell-surface receptor
signalling. Proteins containing the ML domain (PF02221, tribes
363 and 415) have been implicated in lipid recognition,
particularly in the recognition of pathogen related products such
as lipopolysaccharide (LPS) binding and signalling. LPS and
glycoproteins have been detected in the neck region of haustoria
[43]. These atypical PFAM domains enriched in secretome
proteins might represent specific functions of the secreted proteins
of rust fungi.
In general, known effector proteins rarely contain PFAM
domains [22,44,45]. We assessed nineteen AVR effectors for the
Table 1. Non-enzymatic PFAM domains significantly enriched in the secretome of rust fungi.
Pfam Description Enrich-ment1
p-value2
TotalSecreted3
Haustoria4
Tribes5
Proteins with documented or suggested role in plant-pathogen interactions
PF00188 Cysteine-rich secretory protein family5.4 1.94E-031775 83 (x14)
PF00314 Thaumatin family 6.6 1.06E-02 1053 163 (x5), 394 (x3)
PF03330 Rare lipoprotein A (RlpA)-like double-psi beta-barrel6.14.81E-1026126 52 (x14)
PF05730 CFEM fungal specific cysteine rich domain10.18.62E213
17 13 16160, 179 (x4)
PF10342Developmentally Regulated MAPK Interacting Protein 7.92.26E-10 159 13 132 (x2)
Atypical secretome functions
PF01161 Phosphatidylethanolamine-binding protein6.6 1.04E207
1895 199 (x4), 248 (x4),
422 (x3)
PF02221ML domain - MD-2-related lipid recognition domain11.0 3.02E207
650 363, 415 (x2)
PF10281Putative stress-responsive nuclear envelope protein 6.0 4.43E202
1155 551 (x5)
PF11327Protein of unknown function (DUF3129) 9.28.62E213
23164 57 (x12)
1Enrichment: Number of PFAM hits in secretome over number of hits in non secreted proteins;
2p-value for enrichment in secretome;
3number of domains in secretome;
4number of domains in haustorial proteins;
5tribes containing at least two instances of the domain with number of instances in parenthesis.
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presence of PFAM domains (Figure 4A). C. fulvum Avr4 had a
significant hit to the Chitin Binding Module PFAM domain
(PF03067) and Ecp6 to the LysM PFAM domain (PF01476),
however these domains were not enriched in the secretome of rust
fungi. The remaining validated AVR effectors considered in this
study do not have significant similarities to known PFAM
domains. Therefore, we considered that one effector property is
the absence of a PFAM domain, with the exception of five
domains that are associated with pathogenicity and enriched in the
secretome of rust fungi (Table 1). Using this criterion, nearly 80%
(5294) of the proteins analyzed did not harbour a PFAM domain
(Figure 4B). A total of 1108 tribes contained at least one protein
with no PFAM domain, of which 141 contained proteins from
both rust fungal species (Figure 4C).
M. larici-populina and P. graminis f. sp. tritici in planta
induced genes
All known AVRs from M. lini, L. maculans, C. fulvum, and P.
infestans are expressed in planta [22,46]. We used published
transcriptome data [27] to identify proteins that are encoded by
genes induced at least 2-fold in planta when compared to resting
urediniospores. We identified a total of 1308 proteins (19.6% of
proteins analyzed), encoded by genes induced in planta at 96 hours
post-inoculation (Figure 4B). These proteins were distributed
among 847 tribes, of which 137 contained proteins from both
species of rust fungi (Figure 4C).
M. larici-populina and P. graminis f. sp. tritici proteins with
similarity to haustorial ESTs
All five characterized M. lini AVRs were originally identified
from cDNAsequencesprepared
(Figure 4A). We therefore searched for proteins of the secretome
of rust fungi showing similarity to available rust fungi haustorial
ESTs from P. triticina, P. striiformis f. sp. tritici [47], M. larici-populina
[33], and M. lini [34]. We identified 2445 proteins with similarity
to haustoria expressed secreted proteins (HESPs) or fungal AVRs
(Figure 4B). These proteins were distributed across 905 tribes, of
which 149 contained proteins from both species of rust fungi
(Figure 4C). Notably, some putative haustorial proteins showed
similarity to the known flax rust pathogen AVR proteins AvrL567,
AvrP123, AvrM and AvrP4 (File S2).
from purifiedhaustoria
The secretome of rust fungi contains proteins with motifs
common to filamentous plant pathogen effectors
We used nuclear localization signals (NLS) and effector
signature motifs [48,49,50,51], such as the Y/F/WxC motif found
in M. lini AvrL567 and C. fulvum Avr2 and Avr4, as a criterion for
mining effectors from the secretome of M. larici-populina and
P. graminis f. sp. tritici. We identified 1769 proteins with either a
reported effector motif or an NLS (Figure 4B). The most
abundant motif was Y/F/WxC [30] that was identified in 999
secreted proteins distributed across 340 tribes. In total, proteins
with effector motifs or NLS were distributed across 483 tribes, of
which 144 contained proteins from both species of rust fungi
(Figure 4C).
Some genes of the secretome of rust fungi show
unusually long intergenic distances
To identify candidate effectors based on the length of their
flanking intergenic regions (FIRs) we calculated 59 and 39 FIRs for
every gene in the M. larici-populina and P. graminis f. sp. tritici
genomes. We sorted genes into two-dimensional data bins for
each genome, as described earlier [46] (Figure S1A). This
Figure 4. Distribution of effector features in the M. larici-
populina and P. graminis f. sp. tritici secretome proteins and
tribes. (A) Percentage of known avirulence effectors (AVRs) from M. lini,
P. infestans, L. maculans and C. fulvum showing each effector property.
A red cross indicates no match; N.A., not available. (B) Number of
proteins grouped in secretome tribes showing each one of eight
effector properties. Numbers on charts refers to total number of
proteins. (C) The distribution of eight effector features in core and
lineage-specific secretome tribes of rust fungi. *Five PFAM domains
associated with pathogenicity and enriched in secretome tribes of rust
fungi (Table 1) and PFAM-B domains were permitted.
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representation showed an overall expansion of M. larici-populina
intergenic regions compared to P. graminis f. sp. tritici, with a
significant proportion of genes having FIRs significantly longer
than 10 Kb. However, the patterns produced by this method did
not reveal groups of genes with dramatically extended FIRs
compared to median values, in contrast to L. maculans [21] or P.
infestans [46] where effector genes often have long FIRs.
To determine whether secretome genes showed a distinctive
pattern in the distribution of the length of their FIRs we analyzed
the enrichment of secretome genes along the bins, as a ratio of the
frequency in a bin compared to the frequency in the whole
genome (Figure S1B). We found that globally, genes with FIRs
less than ,800 bp tended to be depleted for secretome genes
whereas genes with at least one FIR longer than ,10 Kb were
enriched (Figure S1B), particularly in the M. larici-populina
genome. Therefore, we considered having a FIR longer than
10 Kb as a criterion for selecting candidate effector genes. Tribes
containing a high proportion of genes with FIR.10 Kb tended to
be small, non-annotated and contained a high proportion of genes
encoding secreted proteins (Figure S1C), which was expected
for candidate effector tribes. We identified 772 secretome genes
with FIR.10 Kb (Figure 4B), distributed across 320 tribes
(Figure 4B). One hundred and fourteen of these tribes contained
proteins from both species rust of rust fungi. In the absence of
genome sequences for the corresponding species, information on
FIRs for M. lini and C. fulvum AVRs could not be calculated.
However, all P. infestans and L. maculans AVR genes have at least
one intergenic region longer than 10 Kb (Figure 4A). In contrast
to what has been reported for P. infestans, B. graminis and L.
maculans, long intergenic distances do not seem to be a common
feature of effector genes of rust fungi. Nevertheless, some genes of
the secretome of rust fungi showed unusually long intergenic
distances and may, therefore, correspond to a specific subset of
fast-evolving effector candidates of rust fungi.
Families of small secreted cysteine-rich proteins in the
secretome of rust fungi
Filamentous plant pathogen effectors are often small cysteine-
rich (SCR) proteins [22]. Based on the published examples in
Table S2, known SCR effectors are typically less than 150 amino
acids long and have a cysteine content higher than 3%. To identify
candidate SCR effector tribes in the secretome of M. larici-populina
and P. graminis f. sp. tritici we calculated the cysteine content and
length of each protein. Tribes containing at least one secreted
protein shorter than 150 amino acids and with a cysteine content
higher than 3% were considered as SCR tribes. We found a total
of 638 SCRs proteins in the secretome of M. larici-populina and
P. graminis f. sp. tritici (Figure 4B), which contributed to 287 tribes
(Figure 4C). Remarkably, only 22 tribes containing SCRs
included proteins from both species of rust fungi (Figure 4C),
suggesting a high divergence of cysteine patterns in these proteins.
The presence of SCR tribes in these secretome is consistent with
the results from de novo motif searches that identified conserved
cysteine motifs shared across several tribes (Figure 2).
The secretome of M. larici-populina but not P. graminis f.
sp. tritici includes repeat-containing proteins (RCPs)
To identify tribes containing RCPs in the secretome of M. larici-
populina and P. graminis f. sp. tritici we used the T-REKS algorithm
[52] to systematically search for tandem repeats in proteins from
secretome tribes. We found a total of 493 RCPs (Figure 4B),
which contributed to 134 tribes (Figure 4C). Surprisingly, all
RCPs belonged to the M. larici-populina proteome. Although some
M. larci-populina RCPs were grouped in tribes with proteins from
bothspecies,theP.graminis proteinsinthesetribeswerenot classified
as RCPs. An M. larici-populina gene encoding a pentatricopeptide
repeat-containing protein was identified previously as specifically
induced in the sporogenous area of an infected poplar leaf [53],
supporting the importance of RCPs during the infection process.
Ranking candidate effectors by hierarchical clustering of
tribes
We noted a variable and complex distribution of the effector
properties we analyzed across proteins and tribes. A given effector
property may match only a subset of proteins within a tribe, while
multiple effector properties may match a single protein in a tribe.
Therefore, tribe content in relation to matching effector properties
can be considered as a set of quantitative data amenable to
classification by methods such as hierarchical clustering, which in
biology is typically used for classification of gene expression data.
To reduce bias due to the variable size of tribes, we associated an
e-value to each effector property for every tribe examined. The e-
value corresponds to the likelihood of obtaining at least the same
number of proteins with the given property by chance (see
methods). These e-values were log-converted into a score
(Figure 5A), and a combined score was calculated as the sum
of scores associated to each effector property. The median
combined score for tribes of three or more proteins was 6.342.
A total of 213 tribes out of 1222 tribes examined had a combined
score $6.342 (Figure 5B). Tribes that scored below this cut-off
were omitted from further analysis, as they were frequently smaller
tribes (pairs and singletons) more prone to harbouring effector
properties by chance. In addition, considering that being secreted
is a property that should be found in all effectors, we excluded 25
tribes (remaining was 188 tribes) with a secretion signal score of 0
out of the 213 that passed the combined score threshold. Next, we
used hierarchical clustering to classify the 188 secretome tribes
that passed the score threshold, based on the score associated to
each of the eight effector properties (File S2). A hierarchical tree
was built for the tribe variable, whereas the order of effector
properties was not optimized by the algorithm but set manually by
priority for enrichment in (i) presence of a secretion signal, (ii)
in planta induced genes, (iii) similarity to haustorial proteins, (iv)
presence of an effector motif or a NLS, (v) SCR proteins, (vi)
RCPs, (vii) long FIR genes, (viii) proteins without PFAM domains
(with the exception of the five PFAM domains associated with
pathogenicity and PFAM-B domains that do not have functional
annotations). The optimized hierarchical tree of tribes obtained
after 1000 bootstrap rounds produced eight delimited clusters of
tribes (Figure 6, File S3).
The hierarchical clustering approach allowed us to classify and
sort a complex data set. Ranking tribes based on the prevalence of
effector features highlighted tribes in clusters I, III, IV and V as the
most likely to contain effectors because they fulfil most of the high
value criteria. Tribes in cluster II contained mostly annotated
proteins expressed in haustoria and likely contain proteins involved
in core haustoria biological processes. Generally, tribes in clusters
VI, VII and VIII contain a low number of secreted proteins that
grouped with a large proportion of non-secreted proteins and are
less likely to be bona fide families of secreted proteins.
The 12 tribes in cluster I contain a high number of RCPs. Tribe
383 contains proteins with similarity to M. oryzae Pwl1 and tribe 46
contains proteins with similarity to Pep1, a secreted effector from
Ustilago maydis [54]. Tribes 144 and 208 had the highest combined
score in this cluster.
Cluster III contains 8 tribes rich in in planta induced genes. Most
of these tribes also obtained good scores for the presence of a
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Figure 5. Distribution of scores for effector candidates in secretome tribes from M. larici-populina and P. graminis f. sp. tritici. (A) The
distribution of scores (equivalent to -log of e-value) for individual effector properties among tribes given as a boxplot (same conventions as in
Figures 2 and 3). The median value is indicated in red. (B) Distribution of combined scores (sum of scores for individual effector properties) among the
1222 tribes analyzed. A combined score threshold $6.342 (median value for tribes of 3 or members) and secretion signal (sec.) score .0 was used to
select 188 tribes for hierarchical clustering. *Five PFAM domains associated with pathogenicity and enriched in secretome tribes of rust fungi (Table 1)
and PFAM-B domains were permitted.
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secretion signal. Tribe 432 contains homologs of M. lini AvrP4.
Tribes 184 and 190 obtained the highest combined score in this
cluster.
The 30 tribes in cluster IV contain a high percentage of secreted
proteins that consist mostly of SCR proteins. Compared to
proteins in Cluster V, they have a lower incidence of similarity to
haustorial proteins, indicating that they may not be secreted from
haustoria. Of the SCR-containing tribes, tribe 34 is similar to C.
fulvum chitin-binding Avr4 effector, tribe 287 is similar to AvrP4
from M. lini, tribes 372 and 380 show similarity to uncharacterized
M. lini HESPs, and tribe 5 corresponds to the largest SCR tribe
from M. larici-populina reported in [27]. Tribe 5 and 34 obtained
the highest combined score in this cluster.
Cluster V consists of 29 tribes that contain a high proportion of
predicted secreted proteins and proteins with similarity to
haustorial proteins. They corresponded to the HESP tribes of
the M. larici-populina and P. graminis f. sp. tritici proteomes. Tribe
110 in cluster II contains proteins similar to Pwl2, an Avr effector
from M. oryzae [15] and C. fulvum Ecp6 LysM domain virulence
effector. Tribes 123 and 408 contain proteins similar to M. oryzae
Pwl4 and Pwl3 respectively. Tribe 228 contains with similarity to
C. fulvum Six1 (Avr3) and tribe 381 proteins similar to C. fulvum
Ecp2. Some of these tribes also contain a large number of in planta
induced genes suggesting they are good effector candidates. Tribes
63 and 110 had the highest combined score in this cluster.
Clusters II, VI, VII and VIII have a lower probability of
including effector candidates. In addition to the 14 tribes in cluster
II, likely involved in core haustoria biological processes, three
clusters (VI, VII and VIII) contain tribes with generally low scores
for the most important effector properties, in particular a low
content in proteins predicted to be secreted. The 28 tribes in cluster
VIII have high scores for proteins with similarity to haustorial
proteins, and the absence of annotated proteins, but low scores for
the number of proteins predicted to be secreted. Similarly, the 52
tribes in cluster VII have low scores for their content in secreted
proteins, in proteins encoded by in planta induced genes and in
proteins with similarity to haustorial proteins. Although they had
highscorefor the presenceof effector motifs or NLS, the 10 tribes of
cluster VI have low scores for their content in proteins predicted to
be secreted, encoded by in planta induced genes, and with few
exceptions for proteins with similarity to haustorial proteins. For
these reasons, we decided to rank clusters VI, VII and VIII with a
lower priority. Nevertheless, five tribes in cluster VIII (tribes 78,
108, 71,148and 107), eight tribesinclusterVII(tribes 84, 125, 114,
200, 203, 202, 36 and 259) and three tribes in cluster VI (tribes 91,
249, and 307) contained proteins with similarity to fungal effectors
or reported M. lini HESPs, and might therefore constitute in-
teresting effector candidates. The unexpected clustering of these
tribes might result from inaccurate gene models, preventing
accurate signal peptide prediction, unspecific aggregation in a
tribe, or spurious similarity to effectors.
A selection of remarkable candidate effector tribes of
rust fungi
To identify the tribes with the highest likelihood of containing
effector proteins, we selected the two tribes with the highest
combined score from the four most promising clusters identified
above (Clusters I, III, IV and V) (Table 2). We propose these
eight tribes as including high-priority candidate effectors and we
examined them in more detail.
Tribe 144 had the highest combined score (23.4) in cluster I. It
contains 9 RCPs with Glycine-rich repeats, 8 proteins predicted to
be secreted and 8 with similarity to haustorial proteins. Tribe 208
obtained the second highest combined score from cluster I (15.5),
because it contains 6 proteins with similarity to haustorial proteins
and 5 Glycine-rich RCPs. It only contains 3 secreted proteins, and
3 encoded by in planta induced genes, suggesting that few copies of
genes from this family may be effectors. These two RCP tribes are
specific to M. larici-populina.
Tribe 190 had the highest score in cluster III (17.9). It contains
seven P. graminis f. sp. tritici proteins, all predicted to be secreted
and induced in planta. Six of these have features of SCR proteins
and genes encoding tribe 190 proteins are clustered within the P.
graminis f. sp. tritici genome. Tribe 184 had the second highest score
in cluster III (17.4), it contains seven M. larici-populina proteins, five
of which were predicted to be secreted, six induced in planta, and
six with SCR features.
The highest scoring tribe in cluster IV was tribe 5 (210.3), a
large tribe (92 proteins) of M. larici-populina-specific SCR proteins.
Among those, 67 were predicted to be secreted, 13 induced in
planta at least 2-fold and 7 are similar to haustorial proteins. Tribe
5 proteins are also similar to F. oxysporum Six2 and Six3 cysteine-
rich effectors. This tribe corresponds to the small secreted protein
family described previously in Duplessis et al. [27]. Tribe 34 had
the second highest score (58.8) in cluster IV. It contains 38 M.
larici-populina proteins, 20 of which were predicted to be secreted
and 32 have SCR features. Four proteins from tribe 34 are similar
to C. fulvum Avr4 cysteine-rich effector.
Tribe 63 has the highest combined score in Cluster V (31.3).
This tribe was specific to P. graminis f. sp. tritici and contains 21
proteins of which 19 have signal peptides and 20 have high
similarity to uncharacterized HESPs. Seven members of this tribe
were upregulated higher than 2-fold during infection suggesting
that they may play a role at the plant-pathogen interface,
potentially as effectors. Tribe 110 had the second highest
combined score (24.2) in cluster II. Out of 12 proteins in this
tribe, 10 have a secretion signal, 9 were induced in planta, and all
have homology to haustorial proteins. In particular, homologs of
M. lini HESP-C49, C. fulvum Ecp6 and M. oryzae Pwl2 were
found in this tribe. Whereas most top-scoring tribes highlighted
here are lineage-specific, tribe 110 contains 6 proteins from
M. larici-populina and 6 from P. graminis f. sp. tritici.
Conclusions
In this study, we report a bioinformatic pipeline aimed
specifically at finding effector genes from two agriculturally
important rust fungi whose genome sequences have recently been
released. Our pipeline revealed a list of candidate effector genes
that constitute a valuable resource for accelerating the discovery
and deployment of genetic resistance to rust fungi. To date, only a
few attempts have been made to comprehensively characterize the
secretome of rust fungi. Joly et al. [33] analyzed the secretome of
Figure 6. Hierarchical clustering of the secretome reveals clusters of secreted protein families as high priority effector candidates. A
complete hierarchical cluster tree of the 188 secretome tribes with combined score $6.342 and secretion signal score .0. The tribe identifiers are
indicated at the tip the branches of the boostrap support tree. For each tribe, the number of proteins is indicated on the left of the clustering image
and the combined score on the right. When proteins in a tribe show similarity (10e25BlastP e-value threshold) to fungal AVRs and M. lini HESPs, these
are indicated along the score bars. We distinguished eight clusters. The number of tribes in a cluster is indicated in parenthesis. AVR, avirulence
protein; HESP, haustoria expressed secreted protein; FIR, flanking intergenic region; NLS, nuclear localization signal; RCP, repeat containing protein;
SCR, small cysteine rich protein.
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Table 2. A selection of 8 tribes of candidate effectors of rust fungi.
HCL
cluster
Tribe
n6
Comb.
Score1
Size2
Secreted3
Haus-toria4
Ind. in
planta5
Species
distribution
Annotation
Similarity
Comments
I
144
23.4
9
8
(2.7)
8
(2.7)
1
(0.1)
M. larici only
9 Gly-rich RCPs (10.3),
2 long FIRs (0.5)
Uncharacterized
P. triticina HESPs
I
208
15.5
6
3
(0.4)
6
(4.4)
3
(0.4)
M. larici only
5 Gly-rich RCPs (4.9)
Uncharacterized
P. triticina HESPs
III
190
17.9
7
6
(1.9)
1
(0)
7
(5.1)
P. graminis only
6 SCRs (5.3)
NA
. Genes clustered in P. graminis genome
III
184
17.4
7
5
(1.2)
0
(0)
6
(3.5)
M. larici only
6 SCRs (5.3), 3 with
long FIRs (1.4)
NA
IV
5
210.3
92
62
(8.7)
7
(0)
13
(0)
M. larici only
90 classified as SCRs (90.8),
12 with long FIRs (0.4)
F. oxysporum Six2,
Six3, M. oryzae Avr-Pita
Described in Duplessis et al
.2011.2genesinTop100inducedingerminatingurediniosporesa
IV
34
58.8
38
20
(1.4)
4
(0)
3
(0)
M. larici only
32 SCRs (26.7), 7 with
long FIRs (0.8)
C. fulvum Avr4
V
63
31.3
21
19
(6.3)
20
(7.6)
7
(1.0)
P. graminis only
NA
NA
. 4 genes in Top100 induced in infected wheatb; 1 gene in
Top100 induced in germinating urediniosporesc
V
110
24.2
12
10
(2.9)
12
(5.6)
9
(4.3)
6 M. larici
6 P. graminis
3 SCRs (1.0), 3 genes with
long FIRs (0.8), 1 RCP (0.2)
M. lini HESP-C49, C. fulvum
Ecp6, M. oryzae Pwl2
. 2 M. larici genes in Top100 induced in infected poplar leavesd;
3 P. graminis genes in Top100 induced in infected wheatb
1Combined score;
2Number of proteins in tribe;
3number of secreted proteins in tribe;
4number of haustorial proteins in tribe;
5number of proteins encoded by in planta induced genes. Values in parenthesis show scores obtained for each property. FIR, Flanking Intergenic Regions; NA; Not Applicable; RCP, Repeat-Containing Protein; SCR, Small Cysteine
Rich protein. Tables from Duplessis et al. 2011 cited for gene expression:
aTable S11;
bTable S14;
cTable S15;
dTable S10.
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four Melampsora species and identified thirteen protein families
showing evidence of positive selection [33]. The Joly et al. [33]
study provides an additional selection criterion for effector mining
that complements our analysis. More recently, Duplessis et al. [27]
probed the genome sequence of M. larici-populina and P. graminis f.
sp. tritici for small secreted proteins [27]. Unlike the analyses by
Duplessis et al [27], we processed secreted proteins of all sizes and
based our protein clustering on the combined secretome of both
species. We also omitted signal peptide sequences from our
clustering analysis to classify the secretome based on the functional
domains of the candidate effectors. We included non-secreted
proteins in the Markov clustering approach in order to detect false
positives and to use tribe enrichment in secreted proteins as a
ranking criterion. Finally, we developed an original hierarchical
clustering method to integrate multiple effector mining criteria to
classify and rank tribes based on their likelihood of being genuine
effectors. Hierarchical clustering of effector features resulted in a
priority list that should prove valuable for follow-up wet lab
experiments. These could include functional expression of the
candidate genes in resistant plant genotypes to identify effectors
with avirulence activity.
Methods
Secretome prediction and annotation
Predicted proteomes of M. larici-populina and P. graminis f. sp.
tritici were obtained from [55] and [56] respectively. The
secretomes were defined using PexFinder [57]. Transmembrane
domain containing proteins and proteins with mitochondrial signal
peptides were removed using TMHMM and TargetP, respective-
ly. Automated BlastP-based annotation was performed on proteins
included in the secretome tribes of rust fungi using Blast2GO [58]
with default parameters. In addition, a database including P.
striiformis f. sp. tritici haustoria ESTs [59], P. triticina haustoria ESTs
[47], M. lini HESPs [34], fungal AVRs [22], and M. larici-populina
haustoria ESTs [33] was constructed. A BlastP analysis of proteins
included in the secretome of rust fungi was conducted using this
haustorial EST database, with an e-value cutoff of 1025. We
searched each protein for the effector motifs [L/I]xAR [60,61],
[R/K]CxxCx12H [60], RxLR [9], [Y/F/W]xC [30], YxSL[R/
K] [62] and G[I/F/Y][A/L/S/T]R [34] between amino acids 10
to 110 using Perl scripts. Nuclear localisation signals were
predicted with PredictNLS [63]. Protein internal repeats were
predicted using T-Reks [52]. Disulfide bridges were predicted
using Disulfind [64].
Markov clustering
Signal peptide regions were removed from all secreted proteins
and then the truncated proteins used in a similarity search against
a combined database of the two proteomes of rust fungi (e-value
1025). Proteins were clustered using TribeMCL [65] following
methods described in [46].
PFAM enrichment analysis
PFAM domains were mapped on proteins of the secretome
tribes using the PFAM batch search server [66]. Domain hits with
e-values higher than 1025were ignored. We considered domains
as enriched when their frequency (number of domains per protein)
in the secreted proteins was higher than their frequency among
non-secreted proteins; domains were considered as depleted
otherwise. Statistical significance of the enrichment ratios was
assessed using a Chi-square test with Bonferroni correction.
Enrichment and depletion were considered significant when p-
value,0.01.
De novo motif searches
De novo protein motif search was performed on proteins in
secretome tribes using MEME [28]. The program was set to
report the 25 most robust motifs of 4 to 10 amino-acids, occurring
zero or once per sequence, among the proteins belonging to tribes
of three or more members. These motifs were classified based on
the dispersion of their position along the protein sequence, and the
number of tribes in which they were found. Motifs showing
reduced dispersion (interquartile range for motif position ,10
amino acids) and found in at least two proteins in each of at least 3
different tribes were considered as conserved and reported.
Scoring and tribes ranking
An e-value was associated to the tribes for each of the eight
effector properties analyzed. This e-value was calculated based on
the probability of randomly grouping the same number of proteins
with a given effector property as a tribe, from the complete set of
analyzed proteins. This probability was calculated as follows. Let k
be the number of proteins matching a given effector property in a
tribe; n be the size of that tribe; K be the total number of proteins
matching a given effector property among grouped proteins and N
be the total number of proteins grouped in a tribe. The number of
tribes of size n with k proteins matching an effector property is the
number of k-combinations from a set of n elements, given by the
formula:
A~
n!
k! n{k
ðÞ!
equivalent to V0vkƒn
A~ P
k{1
i~0
n{i
iz1
and A=1 for k=0. The probability of having exactly k proteins
matching a given effector property in a tribe of n proteins is BxC,
where B is the probability of having k proteins matching a given
effector property and C is the probability of having n2k proteins
not matching that property.
So V0vkƒn
B~ P
k{1
i~0
K{i
N{i
And B=1 for k=0
And V0ƒkƒn{1
C~ P
n{1
i~k
N{i{Kzk
N{i
Therefore the likelihood of having a tribe of n proteins with k
proteins matching a given effector property is P(k)=AxBxC. We
defined the e-value associated with a given effector property and a
given tribe as
e(k)~
X
k
i~0
P(i)
the corresponding score as 2log10(e(k)), and the combined score as
the sum of scores obtained for each effector property by a given
tribe. These scores were calculated using homemade scripts in R.
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Hierarchical clustering
The hierarchical clustering analysis was conducted using MEV4
[67]. The 177 secretome tribes with score $6 were considered as
‘genes’, each of the eight effector properties (being secreted,
induced in planta, having similarity to haustorial proteins, SCR
properties, RCP properties and no PFAM annotation) were
considered as ‘samples’. The score associated to each property for
proteins in tribes were considered as ‘intensity’ values. The tribe
hierarchical tree was optimized using 1000 bootstrap runs with
Pearson correlation coefficient as distance value, and average
linkage between groups. The priority of effector properties used for
clustering was set manually and ranked as described in the text.
Supporting Information
Figure S1
define the threshold for long FIR genes. (A) Distribution
of P. graminis f. sp. tritici and M. larici-populina genes according to the
length of their FIRs. Genes were sorted into two-dimensional data
bins for each genome and number of genes is shown by a colour
code. Crosses indicate median value for the two genomes
combined; dotted circles are given as a reference to compare the
two genomes; arrows point toward areas of the graph illustrating
an overall expansion of M. larici-populina intergenic regions
compared to P. graminis f. sp. tritici. (B) Same diagrams as in A
showing the ratio of the frequency of secretome genes in a bin
compared to frequency in the whole genome. Genes with FIRs less
than ,800 bp tend to be depleted in secretome genes (green
dotted line) whereas genes with at least one FIR longer than
,10 Kb tended to be enriched in secretome genes (purple dotted
line). (C) Distribution of secretome tribes according to their
content in secreted proteins (Y-axis) and in proteins encoded by
genes with at least one FIR longer than 10 Kb (X-axis). Size of
bubbles corresponds to size of the tribes.
(PDF)
Analysis of genome architecture used to
Table S1
enriched in the secretome of rust fungi. Table providing
enrichment fold, p-value of a chi-squared test for enrichment in
secretome, number of total and secreted proteins and list of tribes
containing the PFAM domains.
(DOC)
Typical secreted enzyme PFAM domains
Table S2
proteins (SCRs) used to define SCR features, with
associated references. Microsoft Excel worksheet.
(XLS)
List of known small-secreted cysteine rich
File S1
Ontology terms detected in the proteomes of rust fungi
and secretome enrichment analysis. Microsoft Excel
Workbook containing thirteen worksheets with the complete list
of PFAM domain and Gene Ontology terms found in the
secretome of rust fungi, enrichment values, P-value of chi-square
test for enrichment with Bonferroni correction, and list of tribes
containing the domains/ontologies. ‘‘Combined’’ secretome refers
to analyses performed on the proteomes of M. larici-populina and P.
graminis f. sp. tritici merged together. Analyses performed on
separate proteomes from the two species are also provided.
(XLS)
Complete list of PFAM domains and Gene
File S2
and features matching. Microsoft Excel workbook containing
(i) the list of proteins included in the tribe analysis with full
annotation including effector properties, and (ii) the list of tribes
with the number of proteins matching effector properties they
contain.
(XLS)
Complete list of tribes with full annotation data
File S3
presented in Figure 4.
(TXT)
Details of the complete hierarchical cluster tree
Acknowledgments
We thank Brande Wulff for critical reading of the manuscript, Se ´bastien
Duplessis for sharing valuable dataset, Cristobal Uauy and members of the
Kamoun lab for discussions and useful suggestions.
Author Contributions
Conceived and designed the experiments: DGOS SK SR. Performed the
experiments: DGOS JW LMC SR. Analyzed the data: DGOS JW LMC
SR. Wrote the paper: DGOS JW SR SK. Provided gene expression data:
LJS. Commented on the manuscript before submission: DGOS JW LMC
LJS SK SR.
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