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RESEARCH ARTICLE
Genome reconstruction of the non-culturable
spinach downy mildew Peronospora effusa by
metagenome filtering
Joe
¨l KleinID
1☯
, Manon NeilenID
1☯
, Marcel van Verk
1,2
, Bas E. Dutilh
3
, Guido Van den
AckervekenID
1
*
1Department of Biology, Plant-Microbe Interactions, Utrecht University, Utrecht, The Netherlands, 2Crop
Data Science, KeyGene, Wageningen, The Netherlands, 3Department of Biology, Theoretical Biology and
Bioinformatics, Utrecht University, Utrecht, The Netherlands
☯These authors contributed equally to this work.
*g.vandenackerveken@uu.nl
Abstract
Peronospora effusa (previously known as P.farinosa f.sp.spinaciae, and here referred to
as Pfs) is an obligate biotrophic oomycete that causes downy mildew on spinach (Spinacia
oleracea). To combat this destructive many disease resistant cultivars have been bred and
used. However, new Pfs races rapidly break the employed resistance genes. To get insight
into the gene repertoire of Pfs and identify infection-related genes, the genome of the first
reference race, Pfs1, was sequenced, assembled, and annotated. Due to the obligate bio-
trophic nature of this pathogen, material for DNA isolation can only be collected from
infected spinach leaves that, however, also contain many other microorganisms. The
obtained sequences can, therefore, be considered a metagenome. To filter and obtain Pfs
sequences we utilized the CAT tool to taxonomically annotate ORFs residing on long
sequences of a genome pre-assembly. This study is the first to show that CAT filtering per-
forms well on eukaryotic contigs. Based on the taxonomy, determined on multiple ORFs,
contaminating long sequences and corresponding reads were removed from the metagen-
ome. Filtered reads were re-assembled to provide a clean and improved Pfs genome
sequence of 32.4 Mbp consisting of 8,635 scaffolds. Transcript sequencing of a range of
infection time points aided the prediction of a total of 13,277 gene models, including 99
RxLR(-like) effector, and 14 putative Crinkler genes. Comparative analysis identified com-
mon features in the predicted secretomes of different obligate biotrophic oomycetes, regard-
less of their phylogenetic distance. Their secretomes are generally smaller, compared to
hemi-biotrophic and necrotrophic oomycete species. We observe a reduction in proteins
involved in cell wall degradation, in Nep1-like proteins (NLPs), proteins with PAN/apple
domains, and host translocated effectors. The genome of Pfs1 will be instrumental in study-
ing downy mildew virulence and for understanding the molecular adaptations by which new
isolates break spinach resistance.
PLOS ONE
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OPEN ACCESS
Citation: Klein J, Neilen M, van Verk M, Dutilh BE,
Van den Ackerveken G (2020) Genome
reconstruction of the non-culturable spinach
downy mildew Peronospora effusa by
metagenome filtering. PLoS ONE 15(5): e0225808.
https://doi.org/10.1371/journal.pone.0225808
Editor: Feng Gao, Tianjin University, CHINA
Received: November 12, 2019
Accepted: April 24, 2020
Published: May 12, 2020
Copyright: ©2020 Klein et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The genome,
including annotations can be obtained from DOI:
https://doi.org/10.17026/dans-xbu-pjsh. The
sequencing and assembly data are also availble on
Genbank under BioProject ID: PRJNA60510,
BioSample accession: SAMN14048960.
Funding: This study was part of a TopSector
Horticulture and Starting Materials (TKI) project
(https://topsectortu.nl/en) in collaboration with four
industrial partners; Enza Zaden (https://www.
enzazaden.com/), Pop Vriend Seeds (https://www.
popvriendseeds.com/), RijkZwaan Breeding B.V.
Introduction
Phytopathogenic oomycetes are eukaryotic microbes that infect a large range of plant species.
Due to their hyphal infection structures they appear fungal-like, however, taxonomically they
belong to the Stramenopiles [1]. The most devastating phytopathogenic oomycetes are found
within the orders of Albuginales,Peronosporales and Pythiales.
The highly radiated Peronosporales order contains species with different lifestyles. The most
infamous species of this order are in the hemi-biotrophic Phytophthora genus. Other species
within the Peronosporales are the obligate biotrophic downy mildews that cause disease while
keeping the plant alive. The relationships between downy mildews and Phytophthora species
have long been unresolved [2]. Until recently, downy mildew species were underrepresented
in studies addressing oomycete phylogeny. This is mainly because the obligate biotrophic
nature of the species makes them hard to work with and they are, therefore, under-sampled
compared to other oomycete phytopathogens.
The first phylogenetic trees based on morphological traits and single gene comparisons [3,
4] classified the downy mildews as a sister clade to the Phytophthora species within the order
of Peronosporales. Recently published studies using multiple gene and full genome compari-
sons, including a number of downy mildew species, suggest that the downy mildews have mul-
tiple independent origins within the Phytophthora genus [2,5,6].
The downy mildew Peronospora effusa (previously known as P.farinosa forma specialis spi-
naciae, and here referred to as Pfs), is the most important pathogen of spinach. Pfs affects the
leaves, severely damaging the harvestable parts of the spinach crop. Under favorable environ-
mental conditions, Pfs infection can progress rapidly resulting in abundant sporulation within
a week post inoculation that is visible as a thick grey ‘furry layer’ of sporangiophores producing
abundant asexual spores [7] Preventing spread of this pathogen is difficult, since only a few
fungicides are effective in chemical control [8]. As a result, the disease can cause severe losses
in this popular crop, and infected fields often completely lose their market value.
During infection, hyphae of Pfs grow intercellularly through the tissue and locally breach
through cell walls to allow the formation of haustoria [9]. These invaginating feeding struc-
tures form a platform for the intimate interaction between plant and pathogen cells, and func-
tion as a site for the exchange of nutrients, signals and proteins. Oomycetes deliver proteins
into plant cells to alter host immunity [10], thereby escaping and suppressing plant immune
responses [11]. These and other molecules are secreted by pathogens to promote the establish-
ment and maintenance of a successful infection in the host are called effectors. Effector pro-
teins can either be functional outside the plant cells (apoplastic effectors) or inside plant cells
(host-translocated effectors). Two types of host translocated are known in oomycetes; the
RXLR and crinkler (CRN) effectors. They are characterized by the presence of a signal peptide,
a conserved domain at the N-terminus and a variable C-terminal part which is responsible for
the function of the effector in the cell [12–14].
Here we describe the sequencing of genomic DNA obtained from Pfs spores collected from
infected spinach plants using a combination of Illumina and PacBio sequencing. Sequencing
of obligate biotrophic species is complicated as the spore washes of infected plant leaves con-
tain many other microorganisms. Bioinformatic filtering on taxonomy using the recently
developed Contig Annotation Tool CAT [15] was deployed to remove the majority of contam-
inating sequences. The obtained assembly of race Pfs1 was used to predict genes and compare
its proteome, in particular its secretome, with that of other oomycete taxa. We show that the
secretomes of obligate biotrophic oomycetes are functionally more similar to each other than
to that of more closely related species with a different lifestyle.
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(https://www.rijkzwaan.com/) and Syngenta
(https://www.syngenta.com/). The grant was
commissioned to GVdA. BED was supported by the
Netherlands Organisation for Scientific Research
(NWO) Vidi grant 864.14.004. Co-author MvV is
currently employed by Keygene NV, but was
employed by the UU at the time of study. The
funders provided financial support for the research,
but did not have any additional role in the study
design, data collection and analysis, decision to
publish, or preparation of the manuscript. The
specific roles of these authors are articulated in the
‘author contributions’ section.
Competing interests: The authors have read the
journal’s policy and the authors of this manuscript
have the following competing interests: MvV is a
paid employee of Keygene NV, but was employed
by the UU at the time of study. Additionally,
funding was provided by a grant commissioned to
GVdA as part of a TopSector Horticulture and
Starting Materials (TKI) project (https://topsectortu.
nl/en) in collaboration with four industrial partners;
Enza Zaden (https://www.enzazaden.com/), Pop
Vriend Seeds (https://www.popvriendseeds.com/),
RijkZwaan Breeding B.V. (https://www.rijkzwaan.
com/) and Syngenta (https://www.syngenta.com/).
BED was supported by the Netherlands
Organisation for Scientific Research (NWO) Vidi
grant 864.14.004. This does not alter our
adherence to PLOS ONE policies on sharing data
and materials. There are no patents, products in
development, or marketed products to declare.
Materials and method
Downy mildew infection
Peronospora effusa race 1 (Pfs1) was provided by the Dutch breeding company Rijk
Zwaan Breeding BV in 2014. As Pfs1 is an obligate biotrophic maintenance was done on
Spinacia oleracea Viroflay plants. Seeds were sown on soil, stratified for two days at 4˚C
and grown under long day condition for two weeks (16h light, 70% humidity, 21˚C).
sporangiophores were washed off infected plant material in 50 ml Falcon tubes. The solu-
tion was filtered through miracloth and the spore concentration was checked under the
microscope. Four-day-old Spinacia oleracea Viroflay plants were infected with Pfs by spray-
ing a spore solution (70 spores/ul) in tap water. Seven days post inoculation, Pfs sporan-
giospores were collected from heavily-infected spinach leaves with tap water, using a soft
brush to prevent plant and soil contamination and used for DNA isolation and genome
sequencing.
DNA isolation and genome sequencing
The sporangiospores were freeze-dried, ground and dissolved in CTAB (Cetyltrimethyl
ammonium bromide) extraction buffer, lysed for 30 minutes at 65˚C, followed by a phenol-
chloroform/isoamyl-alcohol, and chloroform/isoamyl-alcohol extraction. DNA was precipi-
tated from the aqueous phase with NaOAc and ice-cold isopropanol. The precipitate was col-
lected by centrifugation, and the resulting pellet washed with ice cold 70% ethanol. DNA was
further purified using a QIAGEN Genomic-tip 20/G, following the standard protocol pro-
vided by the manufacturer. DNA was quantified using a Qubit HS dsDNA assay (Thermo
Fisher Scientific) and sheared using the Covaris S220 ultrasonicator set to 550 bp. The
sequencing library was constructed with the Illumina TruSeq DNA PCR-Free kit. Fragment
size distribution in the library was determined before and after the library preparation using
the Agilent Bioanalyzer 2100 with HS-DNA chip (Agilent Technologies). The library was
sequenced on an Illumina Nextseq machine in high output mode with a 550 bp genomic
insert paired end 150 bp reads. Illumina reads with low quality ends were trimmed (Q<36)
using prinseq-lite [16].
For PacBio sequencing the input DNA was amplified by WGA (Whole Genome Amplifica-
tion) using the Illustra GenomiPhi V2 DNA Amplification (GE Healthcare). The sequencing
library for PacBio was constructed according to the manufacturer protocol. The resulting
library was sequenced on 24 SMRT cells (P6 polymerase and C4 chemistry) using the RSII
sequencer (KeyGene N.V., Wageningen). The obtained PacBio reads were error-corrected
using the FALCON pipeline [17] with the standard settings using the SMRT Portal that is part
of the SMRT analysis software package version 2.3.0 from PacBio [18]. The analysis software
package was installed according to the installation instructions on an Amazon WebService
(AWS) cloud-based computer and operated via its build in GUI.
Taxonomic classification of long reads
The taxonomic origin of each error corrected PacBio read was determined using the CAT
(Contig Annotation Tool) pipeline version 1.0 with default parameters [15]. To do this, CAT
first identifies open reading frames (ORFs) on the long sequences or contigs using Prodigal
[19] and queries them against the NCBI non-redundant (nr) protein database (retrieved
November 2016) using DIAMOND [20]. A benchmarked weighting scheme is then applied
that allows the contig to be classified with high precision [15].
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Genome assembly and identification of repeats
A pre-assembly was made using taxonomically filtered and corrected PacBio sequences and
60% of the Illumina reads using SPAdes version 3.5.0 [21]. The error-corrected PacBio reads
were used as long reads in the assembly, SPAdes was set to use k-mer lengths of: 21, 33, 55, 77,
99, 127 for the assembly and the—careful option was used to minimize the number of mis-
matches in the final contigs. The contigs derived from the pre-assembly were filtered using the
CAT tool (see above), and sequences that were designated as bacterial or non-stramenopile
eukaryotes were collected. The entire set of Illumina sequencing reads were aligned to the col-
lection of removed sequences (annotated as bacterial and non-stramenopile) with Bowtie ver-
sion 2.2.7 using default settings [22]. Illumina reads that aligned to these sequences were
removed from the Illumina data set. The remaining Illumina reads (Illumina filtered), and
PacBio sequences were re-assembled with SPAdes (same settings as the preassembly), which
resulted in a final Pfs1 genome assembly. A custom repeat library for the Pfs1 genome assembly
was generated with RepeatModeler [23]. Repeat regions in the assembled Pfs1 genome were
predicted using RepeatMasker 4.0.7 [23].
Quality evaluation of the assembly
K-mers of length 21 in the filtered Illumina data set were counted with Jellyfish count version
2.0 [24] with settings -C -m 21 -s 1000000000 followed by Jellyfish histo. The histogram was
plotted with GenomeScope [25] to produce a graphical output and an estimate of the genome
size. The coverage of the genome by PacBio sequences was determined by aligning the unfil-
tered error-corrected PacBio reads to the Pfs1 genome assembly using BWA-mem [26] and
selected–x pacbio option. The BBmap pileup [27] script was used to determine the percentage
covered bases by PacBio reads in the final assembly of the Pfs1.
The GC-content per contig larger than 1kb was calculated using a Perl script [28]. GC den-
sity plots were generated in Rstudio version 1.0.143 using GGplot version 3.1 [29]. For com-
parison, the same analysis was done on a selection of other publicly available oomycete
assemblies; Hyaloperonospora (H.) arabidopsidis [30], Peronospora (P.) belbahrii [31], Phy-
tophthora (Ph.) infestans [32], Bremia (B.) lactucae [33], Phytophthora parasitica [34],Phy-
tophthora ramorum (Pr102) [34], Phytophthora sojae [34], Peronospora tabacina (968-S26)
[35] and Plasmopara (Pl.) viticola [5].
Kaiju [36] was used to analyze the taxonomic origin by mapping reads to the NCBI nr
nucleotide database (November 2017). The input for Kaiju was generated using ART [37] set
at 20x coverage with 150 bp Illumina to create artificial sequencing reads from the various
FASTA assembly files of the genomes of different oomycetes.
Genome completeness and gene duplications were analyzed with BUSCO version 3 [38]
with default settings using the protists Ensembl database (May 2018).
RNA sequencing and gene model prediction. RNA of Pfs1 at different stages during the
infection was isolated and sequenced to aid gene model prediction. Infected leaves and cotyle-
dons were harvested every day from three days post infection (dpi) until sporulation (7 dpi).
Besides these infected leaves, spores were harvested, and a subset of these spores were placed
in a petri dish with water and incubated overnight at 16˚ C to allow them to germinate. RNA
was isolated using the RNeasy Plant Mini Kit from Qiagen, and the RNA was analyzed using
the Agilent 2100 bioanalyzer to determine the RNA quality and integrity. The RNA-sequenc-
ing libraries were made with the Illumina TruSeq Stranded mRNA LT kit. Paired-end 150 bp
reads were obtained from the different samples with the Illumina Nextseq 500 machine on
high output mode. RNA-seq reads from all the samples were pooled, aligned to the Pfs1 assem-
bly using Tophat [39], and used as input for gene model prediction using BRAKER1 [40]. The
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obtained gene models for the Pfs1 genome together with the RNA-seq alignment result, the
repeat models, and results obtained from a BLASTp search to the nr NCBI database (January
2017), were loaded into a locally-installed WebApollo [41] instance. Gene models on the 100
largest contigs of the genome were manually curated and all gene models were exported from
WebApollo for further use.
Gene annotation and the identification of functional domains. Bedtools intersect ver-
sion 2.27 was used to determine the overlap between Pfs1 gene models and annotated repeat
elements in the genome. Gene models that had more than 20% overlap with a region marked
as a repeat-containing gene. ANNIE [42] was used to annotate proteins on the Pfs1 genome
based on Pfam domains [43] and homologous sequences in the NCBI-Swissprot database
(accessed Augustus 2017). Sequences that were annotated as transposonsby ANNIE were
removed from the gene set. SignalP 4.1 [44] was used to predict the presence and location of a
signal peptide, the D-cutoff for noTM and TM networks were set at 0.34 to increase sensitivity
[45]. TMHMM version 2 [46] was used to predict the presence of transmembrane helices in
the proteins of Pfs1. To identify proteins that possess one or more WY domains an HMM
model made by Win et al. [47] was used. Protein sequences that possessed a WY domain were
extracted and realigned. This alignment was used to construct a new HMM model using
HMMER version 3.2.1 [48] and queried again against all protein models in the Pfs genome to
obtain the full set of WY domains containing proteins.
Effector identification. Putative effectors residing on the genome of Pfs1 were identified
with a custom- made pipeline [49] constructed using the Perl [50] scripting language. Secreted
proteins were screened for the occurrence of known translocation domains within the first 100
amino acids after the signal peptide. Proteins with a canonical RxLR, or a degenerative RxLR
(xxLR or RxLx) combined with either an EER-like or a WY domain or both where considered
putative RxLR effectors. A degenerative EER domain was allowed to vary from the canonical
EER by at most one position.
Proteins with a canonical LFLAK motif or a degenerative LFLAK and HVL motif in the
first 100 amino acids of the protein sequence. A HMMer profile was constructed based on the
LFLAK or HVL containing proteins. This HMMer profile was used to identify Crinkler effec-
tor candidates lacking the LFLAK or HVL motif.
Proteins with an additional transmembrane domain or a C-terminal ER retention signal
(H/KDEL) were removed. WY domains were identified using hmmsearch version 3.1b2 [51]
with the published Phytophthora HMM model (see above) [52]. Pfs WY-motif containing pro-
tein sequences were realigned and used to construct a Pfs specific WY HMM model using
hmmbuild version 3.1b2 [51]. Based on the Pfs specific HMM model WY-motif containing Pfs
proteins were determined.
The effector prediction for the comparative analysis was done in a similar fashion, except
the published Phytophthora HMM model for RxLR prediction and a published model for CRN
prediction was used [53]. The prediction of effectors using the same model in each species
enabled the comparison.
Comparative gene distance analysis. Based on the gene locations encoded in the GFF file
the 3’ and 5’ intergenic distances between genes on contigs were calculated as a measure of
local gene density. When a gene is located next to beginning or end of a contig, the distance
was taken from the start or end of the gene to the end of the contig. Putative high confidence
RxLR effector sequences that encode for proteins with either an exact canonical RxLR motif or
an RxLR-like motif in combination with one or more WY-motifs were selected for the com-
parison (66 in total). Distances were visualized using a heat map constructed with the GGPlot
geom_hex function [29]. Statistical significance was determined using the Wilcoxon signed-
rank test [54].
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Comparative secretomics. The predicted proteomes of eighteen plant pathogenic oomy-
cetes were obtained from Ensembl and NCBI (S1 Table). Proteins in the collected proteomes
that have a predicted secretion signal [44] (SignalP v.4.1, D-cutoff for SignalP-noTM and TM
networks = 0.34 [45]), no additional transmembrane domain (TMHMM 2.0 [46]) or C-termi-
nal K/HDEL domain were considered secreted. Functional annotations of the secreted pro-
teins were predicted using InterProScan [55] and the CAZymes database [56] using the
dbCAN2 meta server [57].
Phylogenetic analysis. The phylogenetic relationships between the proteomes of the stud-
ied species were inferred using Orthofinder [58]. Orthofinder first identifies ‘orthogroups’ of
proteins that descended from a single ancestral protein. Next it determines pairwise orthologs
between each pair of species. Orthogroups with only one protein of each species were used to
make gene trees using MAFFT [59]. The species tree was inferred from the gene trees using
the distance algorithms of FastMe [60] and visualized using EvolView v2 [61].
Principal component analysis. The total number of InterPro and CAZymes domain per
species was summarized in a counts table. For each domain the number was divided by the
total number of domains for that species. The normalized matrix has been loaded into Phylo-
seq version 1.22.3 [62] with R version 3.4.4 [63] in RStudio [64]. A PCA plot has been made
with the Phyloseq ordinate function on euclidean distance. The PCA plot has been made with
the GGPlot R package [29]. The biplot has been generated with the standard prcomp function
in R with the same normalized matrix. Figures were optimized using Adobe Photoshop
2017.01.1.
Permutational analysis of variance (PERMANOVA). A PERMANOVA using distance
matrices was used to statistically test whether there is a difference between the clades based on
their CAZymes and InterPro domains. PERMANOVA is a non-parametric method for multi-
variate analysis of variance using permutations. The data has been double root transformed
with the vegdist function from the R-package vegan version 2.5–3 [65]. After the transforma-
tion the PERMANOVA has been calculated with the adonis function from the Vegan package.
A total number of 999 permutations have been made to retrieve a representative permutation
result.
Enrichment analysis. A chi-square test with Bonferroni correction was used to identify
under- and over-represented Pfam domains in each group (Hyaloperonospora/Peronospora,
Plasmopara,Albugo) compared to Phytophthora. The actual range was the sum of the proteins
that have a given domain. The expected range was the fraction of proteins with a given domain
that is expected to belong to a species cluster giving the overall ratio of Pfam domains between
species clusters.
Results
An early race 1 isolate, Pfs1, of Peronospora effusa was used to create a reference genome as it
predates resistance breeding in spinach and its infection is effectively stopped by all spinach
resistance genes known to date. Race 1 was first identified in 1824 [66]. Since downy mildews
cannot be grown axenically we isolated asexual sporangiospores by carefully washing highly-
infected leaves of the universally susceptible cultivar Viroflay. Genomic DNA was isolated
from freeze-dried spores and used to construct libraries for PacBio and Illumina sequencing,
resulting in 1.09 million PacBio reads with a N50 of 9,253 bp, and 535 million Illumina reads
of 150 bp. The paired-end Illumina reads were used for a trial assembly using Velvet. Inspec-
tion of the draft assembly showed that many contigs were of bacterial instead of oomycete
origin. This is likely caused by contamination of the isolated Pfs spores with other microorgan-
isms that reside on infected leaves and that are collected in the wash-offs. We, therefore,
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decided to treat the sequences as a metagenome and bioinformatically filter the sequences and
corresponding reads.
Taxonomic filtering
To filter out the sequences that could be classified as contaminants we deployed CAT [15] on
long reads and contigs derived from assemblies. Details on the CAT method are described in
the materials and methods section. In short, CAT utilizes the combined taxonomic annota-
tions of multiple individual ORFs found on each sequence to determine its likely taxonomic
origin. This allows for a robust taxon classification that is based on multiple hits, rather than a
single best hit. An example of the CAT taxonomic classification for two of our sequences (con-
tigs) is visualized in Fig 1.
CAT was first used on the long PacBio reads. As these reads contain about 15% base call
errors on average, they were first error-corrected using the FALCON pipeline. The FALCON
pipeline fixes long PacBio reads by mapping short reads obtained in the same runs. The result-
ing 466,225 PacBio reads had a total length of 1,003 Mb with a N50 of 3,325 bp and were subse-
quently assigned a taxonomic classification using CAT. PacBio reads that were classified as
prokaryotic, or non-stramenopile eukaryotic (e.g. Fungi) were removed, whereas reads with
the assigned taxonomy “stramenopiles” or “unknown” were retained. This resulted in a
cleaned set of 232,846 PacBio reads with a total length of 522 Mb with a N50 of 3,458 bp that
was used for a hybrid pre-assembly. In order to evaluate the effectiveness of the CAT tool in
removing contaminating genomic sequences we analyzed the GC-content of the reads. The
corrected PacBio reads showed two distinct peaks (Fig 2A), whereas oomycete genomes have a
GC band-width around 50%, as shown in S1A Fig for the contigs of the Phytophthora infestans
genome [32]. After CAT filtering a single peak remained with a narrow GC-content distribu-
tion around ~48%, demonstrating that the tool, that does not take into account GC-content
but uses a weighting scheme based on protein sequence similarity, was effective in removing
contaminating sequences (Fig 2B).
Hybrid assembly
A hybrid pre-assembly was generated using the genome assembler SPAdes that can combine
long PacBio with short Illumina reads. The input consisted of all corrected and filtered PacBio
reads together with 60% randomly extracted Illumina reads (321 Million read, 96.3 Gb, to
decrease assembly run time and memory requirements). The pre-assembly consisted of 170,143
contigs with a total length of 176 Mb and an N50 of 6,446 bp, of which only 21,690 contigs were
larger than 1 kb. CAT filtering was applied to the contigs of the pre-assembly, CAT marked
16,518 contigs consisting of 91.5 Mb (52% of total assembled bases) as contaminant sequences.
Next, Illumina reads were aligned to these and Illumina read-pairs of which at least one end
aligned were removed from the data set. A final assembly was generated with the CAT-filtered
PacBio and remaining 77.6 million Illumina reads, resulting in 8,635 scaffolds with a total length
of 32.4 Mb. The assembly size corresponds with the estimate genome size of 36,2 Mb that was
determined based on k-mer count frequency (Table 1) in the filtered Illumina reads.
Filtering results
The effect of filtering with CAT on the pre-assembly is well visualized by plotting the GC-con-
tent of the contigs (Fig 2C), similar as for the PacBio reads. In the pre-assembly many contigs
with a GC-percentage deviating from the 40–55% range are present, indicating that it contains
many contaminating sequences. After filtering, the final assembly shows one major peak of the
expected GC-content at ~48%, with a minor shoulder of slightly higher GC-content (Fig 2D).
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To assess the effectiveness of the taxonomic filtering we used Kaiju [36] as a complementary
tool. Kaiju is typically used for taxonomic classification of sequencing reads in metagenome
analysis but here we used it to determine the effect of taxonomic filtering by CAT. For this,
Fig 1. Taxonomic classification by CAT. Two contigs are depicted and per ORF a single top hit is shown. (A) Contig from the pre-assembly assigned by the CAT tool as
bacterial, ORFs of bacterial origin are colored green, and ORF with no hits to the database are colored white. On this contig most ORFs had a highest blast hit with
Rhodococcus species. The SBmax for this contig is 10982. and the highest SBtaxon is for the Rhodococcus genus at 9660, which is well above the cutoff of 5491 (SBmax �
0.5). The taxonomic origin of this contig was therefore assigned to the genus Rhodococcus, and as a consequence this contig was regarded as non-Pfs and removed. (B)
Contig from the pre-assembly assigned by the CAT tool as an oomycete contig. On this contig all ORFs have a best hit to an oomycete species, and the SBmax is 2328. In
fact, most ORFs have a best hit to species in the Phytophthora genus (SBtaxon: 1184), or the Peronosporales family (SBtaxon: 184). The SBtaxon for the Phytophthora
genus is above the cutoff at 1164 (SBmax �0.5) thus assigning this contig to the Phytophthora genus, and consequently this contig is maintained for the Pfs genome
assembly.
https://doi.org/10.1371/journal.pone.0225808.g001
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Fig 2. Density plot of the GC values of PacBio reads and assembly before and after CAT filtering of sequences.
The yellow bar indicates the region between 40 and 55% GC, based on reads >1 kb. (A) PacBio reads before CAT-
filtering show a bimodal distribution with a presumed peak of contaminating sequences with a GC content of ~40%.
(B) PacBio reads after CAT-filtering show a distribution consisting of a single peak with a GC content around ~46%.
(C). GC content of the Pfs1 contigs from the pre-assembly before filtering shows additional peaks at around 30 and 60
GC%, indicating that there are many contaminant contigs. (D) GC content of the Pfs1 contigs after filtering of the
reads with the CAT tool shows that the additional peaks are no longer present and have thus been successfully filtered
out.
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Table 1. Summary of statistics for the hybrid assembly of the Pfs1 genome.
Pfs1 final Pfs1 size-filtered
Assembly size 32.40 Mb 30.48 Mb
GC content 47.75% 47.80%
Longest scaffold 310.10 kb 310.10 kb
Repeat size 6.93 Mb 6.38 Mb
# Contigs 8,635 3,608
N50 32,837 bp 36,273 bp
# Gene models 13,227 12,630
k-mer estimation
Assembly size 36.18 Mb
Repeat size 8.76 Mb
Read Error Rate 1.04%
Data is provided for the final assembly (Pfs1 final) and size-filtered assembly omitting the contigs smaller than 1 kb
(Pfs1 filtered). In addition, genome information based on k-mer counting of the Illumina reads is provided, giving an
estimate for the predicted genome size and repeat content.
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genome assemblies of Pfs1 and other oomycetes were divided into artificial short reads. The
taxonomic distributions generated by Kaiju provide a clear picture of the removal of contami-
nating sequences from the Pfs1 genome data (Fig 3). Whereas the pre-assembly mostly con-
tained artificial reads with an assigned bacterial taxonomy, this was reduced to 14% in the final
assembly. The percentage of >80% of oomycete-assigned reads in the Pfs1 final assembly is
similar to what we observe for the high-quality genome assemblies of P.infestans and P.sojae,
pathogens that can be grown axenically, i.e. free of contaminating other microbes (Fig 3).
Genome statistics
To assess the quality of the assembly we re-aligned the Illumina reads to the contigs and found
a large variation in coverage between the contigs smaller than 1 kb and the larger contigs, sug-
gesting that these small contigs contain a high number of repeats or assembly errors. In addi-
tion, the CAT pipeline depends on classification of individual ORFs on contigs, so it’s
accuracy may be expected to improve with contig length. Therefore, several small contigs
could possibly be derived from microbes other than Pfs. Removing contigs smaller than 1 kb
(5027 contigs) resulted in a small reduction of 1.9 Mb in genome length, slightly reducing the
assembly size to 30.5 Mb, but resulting in a 58% reduction in the number of contigs. The
remaining 3608 contigs, larger than 1 kb, had an N50 of 36,273 bp. The statistics of the size-fil-
tered assembly are further detailed in Table 1.
To assess the gene space completeness of our assembly in comparison to other oomycete
genomes we used BUSCO that identifies single core orthologs that are conserved in a certain
lineage. Here, we used the protist Ensembl database as the protist lineage encompasses the
Fig 3. Taxonomic classification of reads in assemblies of different oomycetes. Kaiju bar plot showing the
percentage of reads assigned to three taxonomical classes; Oomycetes, Fungi and Bacteria and other non-oomycetes. In
error corrected PacBio reads 42.64% are assigned to oomycetes, after filtering with CAT 88.09% of the reads are
assigned to oomycetes. For the pre-assembly (96.3 Gb), only 5% of the artificial reads is assigned to oomycetes. For the
Pfs1 final assembly (32.4 Mb), 88.6% of the reads are assigned to oomycetes. This is comparable to other oomycetes
that can be axenically grown on plates, indicating that the remaining non-oomycete-assigned sequences are most likely
a result of an incorrect classification in the database.
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oomycetes and other Stramenopila. According to the BUSCO analysis the gene space in our
final assembly is 88.9% complete with only 0.5% fragmented genes and 0.5% duplicates. This
gene space completeness score is similar to that of other downy mildew genomes, but slightly
lower than of genomes of Phytophthora species (S2 Table). Furthermore, the low number of
duplicates suggests that there is a low incidence of erroneous assembly of haplotypes, suggest-
ing that the obtained Pfs assembly represents most of the single-copy gene space of the Pfs
genome [38].
Repeat content
In addition to a genome size estimate, the k-mer analysis estimated a repeat content of ~8.8
Mb. This is slightly higher than the observed repeat content in the final assembly of ~6.9 Mb
(~6.4 Mb in the size-filtered assembly) (Table 1). The difference between the estimated repeat
size and the repeat content in the assembly (1.87 Mb) is most likely caused by long repetitive
elements that are hard to assemble. Repeatmasker [23] identified a total of 13,089 repeat ele-
ments of which most are part of the Gypsy and Copia superfamily. We also identified 562
LINEs (Long interspersed nuclear elements) and only 16 SINE (short interspersed nuclear ele-
ments), which belong to the class I transposon (retrotransposons). Other repeat elements con-
sisted of 2297 simple repeats, 298 Low complexity regions, 391 different types of DNA
transposons (Table 2), and several (278) other minor repeat types; full details can be found in
S3 Table.
When we compare the genome assembly size of Pfs (30.5 Mb) to other sequenced oomycete
genomes such as those of Ph.infestans (240 Mb), H.arabidopsidis (100 Mb), Pl.halstedii (75.3
Mb) or the relatively small genome of P.tabacina (63.1 Mb), Pfs has a strikingly compact
genome (S4 Table). The repeat content (21%) is also low compared to that of other oomycetes,
e.g. Ph.infestans (74%), H.arabidopsidis (43%), Pl.halstedii (39% Mbp) and more comparable
to P.tabacina (24%).
Pfs gene prediction
RNA sequencing. Gene prediction is greatly aided by transcript sequence information.
We, therefore, isolated and sequenced mRNA from Pfs spores and Pfs-infected spinach leaves
at several time points during the infection. For this, leaves were harvested daily starting from 3
days post inoculation (dpi) until 7 dpi when sporulation was observed. In addition, mRNA
was also isolated from sporangiospores and germlings grown from spores that were incubated
in water overnight. The 7 different samples ensure a broad sampling of transcripts to facilitate
gene identification. Illumina transcript sequences (659 million) were aligned to the assembled
Pfs genome which resulted in ~100 million aligned read pairs. Most of the other reads map to
the spinach genome but were not further analyzed.
Table 2. Total number and size of major repeat types identified in the Pfs1 genome assembly.
Repeat type Count % of total count Total length (bp)
LTR 9247 70,65 6532069
LINE 562 4,29 201127
Simple repeat 2297 17,55 97983
DNA repeats/TE 391 2,99 46677
Rolling Circle TE 97 0,74 26123
The percentage of total count is based on the total number of repeat types identified in the assembly which can be
found in S3 Table.
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Predicted proteins. The aligned transcript read pairs served as input for the BRAKER1
[40] pipeline to generate a Pfs specific training set for gene model prediction. This was then
used to predict 13227 gene models on the final assembly. The corresponding protein models
were annotated using ANNIE [42] and provided putative annotations for 7297 Pfs proteins (S5
Table). We found that 12630 protein models reside on contigs larger than 1 kb and are thus
contained in the size-filtered assembly. In addition, we found that 2983 gene models had 20%
or more overlap with a repeat that was identified by RepeatMasker [23], another 952 protein
models were annotated by ANNIE as transposable elements. When analyzing protein models
that reside on small contigs (<1 kb) we observe that most of them (61%) have a significant
overlap with a repeat region and are marked by ANNIE as transposons. The number of gene
models found in the assembly of Pfs1 is strikingly low in comparison to that in Ph.infestans
(17,792), H.arabidopsidis (14,321), Pl.halstedii (15,469) and more similar to P.tabacina
(11,310).
Secretome and host-translocated effectors. For the identification of the Pfs secretome as
well as of candidate host-translocated RxLR and Crinkler effectors we choose to start with the
proteins encoded by the initial 13,227 gene set. This reduced the risk of missing effectors that
are encoded on smaller contigs (<1 kb). SignalP [44] prediction identified 783 proteins with a
N-terminal signal peptide. Of these, 231 were found to have an additional transmembrane
domain (as determined by TMHMM [46] analysis) leaving 557 proteins. In addition, five of
these carried a C-terminal H/KDEL motif that functions as an ER retention signal. The result-
ing set of 552 secreted proteins, ~ 4% of the Pfs1 proteome, was used for secretome
comparison.
Previous research showed that some effectors of the lettuce downy mildew Bremia lactucae
have a single transmembrane domain in addition to the signal peptide [67]. Therefore, we
chose to predict the host-translocated effectors not only from the secretome but also from the
set of proteins with a signal peptide and an additional transmembrane domain. A total of 99
putative RxLR or RXLR-like proteins and 14 putative Crinkler effectors were identified (S2
and S3). Ten putative RxLR effector proteins were found to have a single transmembrane
domain. Also, five putative RxLR effectors were found on contigs smaller than 1 kb (S6 Table).
Of the 99 RxLR effectors, 64 had a canonical RxLR domain, while 35 had a degenerative RxLR
domain combined with an EER-like and/or WY domain [68]. The number of host-translo-
cated effectors in Pfs is significantly smaller compared to that of Phytophthora species (eg. 563
RxLR and 385 effector genes in the genomes of P.infestans [32] and P.sojae [69] respectively).
Crinkler effectors are charaterized by the N-terminal five amino acid “LFLAK” domain [14].
Five of the identified putative Crinkler effectors had a canonical LFLAK domain. The others
had a degenerative LFLAK combined with an HVL domain or were identified using the cus-
tom made Crinkler HMM.
Genomic distribution of effectors. It was previously described for the potato late blight
pathogen P.infestans that effectors often reside in genomic regions with a relatively large
repeat content compared the rest of genome [70]. To test this in Pfs, the distance between
neighboring genes was measured to estimate the genomic context of the 13277 Pfs1 genes in
general and for 66 selected RxLR effector (canonical RxlR and degenerative RxLR with WY-
motifs) genes specifically. To get a good overview of the intergenic distances we plotted the 3’
and 5’ values for all the genes in the Pfs1 genome on a log10 scaled heat map (Fig 4).
The genome of Pfs1 is highly gene dense and effectors show a modest but significant (Wil-
coxon rank sum test, p = 1.914e
-11
) enrichment in the gene-spare regions of the genome (Fig
4). The median 3’ and 5’ combined spacing for all genes is 925 bp, while for the selected effec-
tor genes it is 2976 bp. However, the difference in gene density between the effectors and core
genes is not as strong as in the P.infestans two-speed genome [32].
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Comparative analysis of orthologs
Eighteen phytopathogenic oomycete species, that represent a diverse taxonomic range and dif-
ferent lifestyles, were chosen for a comparative analysis with Pfs (Table 3). The objective of the
comparison is to see whether the biotrophic lifestyle of downy mildew species, like Pfs, is
reflected in the secretome. For the analysis, the secretome of Pfs was compared to that of
closely related Phytophthora (hemibiotrophic), Plasmopara (biotrophic) and more distantly
related Pythium (necrotrophic) and Albugo (biotrophic) species. First, the predicted proteins
of each species were used to create a multigene phylogenetic tree to infer their taxonomic rela-
tionships using Orthofinder. In total, 86.9% (267,813) of all proteins were assigned to 14,484
orthogroups. Of those, 2383 had proteins from all species in the dataset of which 152 groups
contained proteins corresponding to single copy proteins in each species. These single-copy
Fig 4. Genome spacing of predicted genes of Pfs1.The distance between neighbouring genes was depicted by plotting the 50
and 30intergenic distances (on a log10 scale) for each if the 13,227 predicted genes. The scale bar represents the number of genes
in each bin, shown as a color-coded hexagonal heat map in which red indicates a gene dense and blue a gene-poor region. The
locations of putative Pfs effectors genes are indicated with white dots.
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orthologous proteins of each species were used to infer a Maximum-likelihood species tree
(Fig 5).
The resulting tree shows that Pfs clusters with H.arabidopsidis (Hpa), P.tabacina (Pta) and
P.belbahrii (Pbe). The closest relative of Pfs, in this study, based on single-copy orthologs is
Table 3. Predicted secretomes of 18 oomycete species used in this study.
Predicted proteins Secretome % secreted
P.effusa 13227 552 4,2
P.belbahrii 9049 494 4,7
H.arabidopsidis 14321 999 7
P.tabacina 18447 798 4,3
Pl.halstedii 15498 1071 6,9
Pl.viticola 12201 1850 15,2
Ph.infestans 18138 1885 10,4
Ph.parasitica 27942 2250 8,1
Ph.sojae 26584 2337 8,8
Ph.capsici 19805 1433 7,2
Py.arrhenomanes 13805 913 6,6
Py.aphanidermatum 12312 928 7,5
Py.irregulare 13805 961 7
Py.iawyamai 15249 1067 7
Py.vexans 11958 863 7,2
Py.ultimum 15322 1071 7
A.candida 13310 888 6,8
A.laibachii 13804 679 4,9
The total number of predicted proteins, those with a signal peptide (SP), proteins with SP but without additional transmembrane domains (TM), and the number of
proteins with SP, no TM, and no C-terminal KDEL sequence are shown. In the final column the percentage of the proteome that is predicted to be secreted is
highlighted.
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Fig 5. Maximum likelihood tree of 18 plant infecting oomycete species based on core othologous proteins. The
tree was inferred from 152 single copy ortholog groups in which all species in the comparison where represented.
Branch numbers represent bootstrap values of N = 12171 trees. Five taxonomic clusters were defined for further
analysis; Hyaloperonospora/Peronospora (green), Plasmopara (red), Phytophthora (blue), Pythium (grey) and Albugo
(green). The obligate biotrophic clades are highlighted using green circle.The fish infecting species Saprolegnia
parasitica, was used as an outgroup.
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the downy mildew of tobacco Pta, followed by the basil-infecting Pbe. Based on the tree, Hpa
is more divergent from the former three downy mildew species within the Hyaloperonospora/
Peronospora clade. The Plasmopara downy mildew species are in a different clade that is more
closely related to the Phytophthora species used in this study. The separation between the Pero-
nospora lineage and the Phytophthora/Plasmopara lineages is well supported with a bootstrap
value of 0.75. This clustering pattern is in line with the recent studies that suggest that the
downy mildew species are not monophyletic within the Peronosporales [2,71]. The Phy-
tophthora species, although belonging to three different Phytophthora clades, are more closely
related to each other than to the other species in this study. Phytopythium vexans appears as a
sister group to the Phytophthora/Peronospora lineage, which is in line with a recently published
multi gene phylogeny [72]. The other five species of Pythium form two clusters, as previously
observed [72]. The two Albugo species form a cluster that is separated from the other clades
with maximum bootstrap support.
Based on the core ortholog protein tree, we grouped the species into five phylogenetically-
related clades; Hyaloperonospora/Peronospora,Plasmopara,Phytophthora,Pythium and
Albugo for further analysis of the secretomes. Three of these clades only have obligate bio-
trophic species (Hyaloperonospora/Peronospora,Plasmopara and Albugo), whereas the Phy-
tophthora cluster consists of hemi-biotrophs and Pythium cluster of necrotrophic species.
(Phyto)Pythium vexans was included in the Pythium cluster. The fish-infecting oomycete
Saprolegnia parasitica served as an outgroup for the phylogenetic tree and is not used for fur-
ther comparison.
Secretome comparison. For each species, the total number of proteins and the subset that
is predicted to be secreted (signal peptide, no additional transmembrane domains, no ER
retention signal) is shown in Table 3.Phytophthora species generally have a larger proteome
than downy mildew species and secrete a larger percentage of the predicted proteins. The Phy-
tophthora species in this study are predicted to secrete 1976 proteins on average, whereas the
Plasmopara and Peronospora species secrete an average of 1461 and 703 proteins, respectively.
Carbohydrate active enzymes and Pfam domains. The secretome content was compared
between species by looking at the carboydrate-active enzymes (CAZymes) and Pfam domains.
CAZymes are, amongst others, involved in degrading and modifying plant cell walls, which is
an important part of the infection process. The Pfam domain database represents a broad col-
lection of protein families, including RxLR effectors, with diverse functions.
A total of 95 different CAZyme domains were found in the combined secretomes of the 18
oomycete species. The total number of CAZymes per species ranges from 35 in A.laibachii to
336 in P.sojae, and was lower in obligate biotrophic species (35–193) compared to Phy-
tophthora species (197–336) (S7 Table). A total of 1354 different Pfam domains were found in
the combined secretomes of the oomycetes analyzed. The number of domains identified ran-
ged from 304 in Al.candida to 1710 in Ph.parasitica. The total number as well as the relative
number of Pfam domains in secretomes of obligate biotrophic species was lower in obligate
biotrophic species compared to Phytophthora and Pythium (S8 Table).
The presence and numbers of CAZyme and Pfam domains were compared between species
using a Principal Component Analysis (PCA), a statistical reduction technique that determines
what variables contribute most to the variation observed in a data set. We report the relative
abundance of each CAZyme/Pfam domain to the total number of secreted Pfam/CAZyme
domains per species, to account for the large variation in absolute numbers of proteins
between the species (Fig 6). A PCA based on the absolute numbers can be found in S4 Fig,
which shows a similar pattern. The species clusters as depicted in Fig 6 were confirmed using a
PERMANOVA (p <0.001).
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The CAZymes-based PCA supports the separate clusters of Albugo,Phytophthora and
Pythium species as found in the core ortholog tree (Fig 5). Remarkably, neither the Hyalopero-
nospora/Peronospora nor the Plasmopara species form a clear cluster, although the clustering
is significant (PERMANOVA p <0.001). The variation along PC1 (Hyaloperonospora/Pero-
nospora) and PC2 (Plasmopara) indicates that the secreted CAZyme domains vary largely
between the species in these groups, despite their close phylogenetic relationship and same life-
style. The secreted CAZymes of the two Plasmopara species appear more similar to those of
the Hyaloperonospora/Peronospora species than to the Phytophthora species, which is different
from the results of the core ortholog protein comparison as shown in the phylogenetic tree
(Fig 5). To exclude the effect of the more distantly-related species on the separation between
the downy mildew and Phytophthora species, the PCA was performed on the set without the
Pythium and Albugo species (Fig 6B). The pattern, as observed in the total set, is maintained
when the more distantly related species are excluded from the analysis.
To look further into the properties of the secreted CAZymes we highlighted literature-
curated domains of phytopathogenic oomycetes that are known to modify the main plant cell
wall components; lignin, cellulose and hemicellulose [69] (S7 Fig). We found that the secre-
tomes differ more in terms of the absolute number of plant cell wall-degrading enzymes than
in the relative occurrence of the different corresponding CAZyme catalytic activities per spe-
cies. Secretomes of obligate biotrophic and hemibiotrophic/necrotrophic oomycetes have
secreted proteins with similar functions (like breakdown of cellulose, pectin, hemicellulose
etc.) but the numbers and diversity of those proteins in obligate biotrophic species are
reduced.
The Pfam-based PCA shows a clear separation between lifestyles (Fig 6C and 6D). The Phy-
tophthora species cluster together and separate from all other species along PC1 (25,3%). The
Pythium species form a cluster that separates clearly from the other species along PC2 (20,2%).
All biotrophic species, including both groups of downy mildews and the Albugo species, cluster
together. Within the obligate biotrophic cluster the phylogenetic groups (Hyaloperonospora/
Peronospora,Plasmopara,Albugo) as found in the core ortholog tree are still present but the
differences are minor. To exclude the effect of the more distantly related species on the separa-
tion between the obligate biotrophs, the PCA was also performed without Pythium and Albugo
species (Fig 7B). The pattern observed in Fig 6C is maintained when the more distantly related
species are excluded from the analysis (Fig 6D).
The repertoires of Pfam domains in the different groups of obligate biotrophs (Hyalopero-
nospora/Peronospora,Plasmopara and Albugo) are more similar than would be expected based
on their taxonomic relationship. This could be the result of convergent evolution towards the
obligate biotrophic lifestyle. Plasmopara and Hyaloperonospora/Peronospora CAZyme repe-
toires are similar as well, but the Albugo species have a different CAZyme profile.
We conclude that a different composition and abundance in secreted Pfam domains is
clearly associated with obligate biotrophy, suggesting it is the result of convergent evolution
towards an obligate lifestyle.
To look further into the properties of the secreted CAZymes we highlighted literature-
curated domains of phytopathogenic oomycetes that are known to modify the main plant cell
wall components; lignin, cellulose and hemicellulose [73] (S7 Fig). We found that the secre-
tomes differ more in terms of the absolute number of plant cell wall-degrading enzymes than
in the relative occurrence of the different corresponding CAZyme catalytic activities per spe-
cies. Secretomes of obligate biotrophic and hemibiotrophic/necrotrophic oomycetes have
secreted proteins with similar functions (like breakdown of cellulose, pectin, hemicellulose
etc.) but the numbers and diversity of those proteins in obligate biotrophic species are
reduced.
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Five Pfam domains contribute largely to the difference between obligate
biotrophs and others
The Pfam domains that contribute to the variance in PC1 and PC2 were (Fig 6C and 6D) iden-
tified using a biplot. In a biplot, the variables are presented as vectors, with their length reflect-
ing their contribution. Many of the domains contribute to the differences between the
biological groups, but seven of them stand out (Fig 7. and Table 4).
Two Pfam domains that have a higher relative abundance in Phytophthora contribute
strongly to the separation between Phytophthora and the other species. The first, PF16810,
Fig 6. Principal component analysis (PCA) of variation in the relative abundance of secreted CAZymes and Pfam
domains. The variation in secreted CAZyme (AB) and Pfam (CD) domains along PC1 and PC2 is depicted in the
figure. The PCAs include all of the 18 species (AC) or the Peronospora,Plasmopara and Phytophthora species only
(BD). The PERMANOVA test shows that the grouping based on the CAZyme and Pfam domains is significant
(P <0.001). Species are grouped by color based on the classes that were defined in the phylogenetic tree (Fig 5).
Phytophthora (blue), Peronospora (yellow), Plasmopara (red), Albugo (green) and Pythium (grey). Abbr. PFS;
Peronospora (P.) effusa,PBE; P.belbahrii,PTA; P.tabacina,HPA,Hyaloperonospora arabidopsidis,PHA; Plasmopara
(Pl.) halstedii,PVI; Pl.vitiocola,PIN; Phytophthora (Ph.) infestans,PSO; Ph.sojae,PCA; Ph.capsici,PPA; Ph.parasitica,
ACA; Albugo (A) candida,ALA; A.laibachii,PUL; Pythium (Py.) ultimum,PAR; Py.arrhenomanes,PAP; Py.
Aphanidermatum,PIR; Py.irregulare,PIW; Py.Iawyamai,PVE; Phytopythium vexans.
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represents a RxLR protein family with a conserved core α-helical fold (WY-fold). Some of the
proteins that this domain was based on have a known avirulence activity [52], i.e. they are rec-
ognized by plant resistance proteins. On average, 82 PF16810 domains were identified in Phy-
tophthora species compared to 1.3 in Peronospora, 1.0 in Plasmopara and none in Albugo
species. Using HMMer searches, many more WY-fold proteins can be identified in
Fig 7. Pfam domains that strongly contribute to the variation in the relative abundance between species. Although
many domains contribute to the variation, PF16810, PF05630, PF08238, PF14295, PF00090, PF00254 and PF00082 are
the domains that contribute most, as evidenced by the length of their vectors in the biplot.
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Table 4. Pfam domains that contribute most to the variation between species in the PCA.
Hpa/Peronospora Plasmopara Phytophthora Pythium Albugo
Pfs Pta Pbe Hpa Pha Pvi Pin Ppa Pso Pca Par Pap Pir Piw Pve Pul Aca Ala
PF16810 RxLR 3 0 0 2 2 0 92 90 104 41 0 0 0 0 0 0 0 0
PF05630 NPP1 7 18 2 10 15 10 31 54 59 42 4 3 3 2 5 7 0 0
PF08238 Sel1 repeat 16 39 14 7 10 23 14 20 22 16 27 30 27 24 10 25 6 13
PF14295 PAN/Apple 1 1 2 0 3 0 39 36 31 22 64 60 35 33 21 33 1 5
PF00082 Subtilase 1 1 1 1 5 20 5 4 2 0 10 21 19 17 9 26 2 2
PF00090 Thrombosp. 0 0 0 0 0 12 14 11 40 12 0 26 21 22 12 23 0 0
PF00254 FKBP 1 13 1 1 1 2 1 1 1 1 2 1 0 2 1 1 0 1
Numbers represent the number of domains per secretome per species. Domains that are relatively less abundant are blue, domains that occur in relatively high numbers
are yellow.
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Plasmopara and Hyaloperonospora/Peronospora downy mildew species. However, these pro-
teins do not match to the PF16810 Pfam domain that is based on a larger protein sequence as
the HMM.
The second, PF05630, is a necrosis-inducing protein domain (NPP1) that is based on a pro-
tein of Ph.parasitica [74]. This domain is conserved in proteins belonging to the family of
Nep1-like proteins (NLPs) that occur in bacteria, fungi and oomycetes [75]. Infiltration of
cytotoxic NLPs in eudicot plant species results in cytolysis and cell death, visible as necrosis
[76]. Phytophthora species are known to have high numbers of recently expanded NLP genes
in their genomes, encoding both cytotoxic and non-cytotoxic NLPs [75]. H.arabidopsidis and
other obligate biotrophs tend to have lower numbers and only encode non-cytotoxic NLPs
[75,77].
Domain PF08238 contributes to the distance between the Phytophthora and obligate bio-
trophic species and is relatively more abundant in the biotrophs (PC1). PF08238 is a Sel1
repeat domain that is found in bacterial as well as eukaryotic species. Proteins with Sel1 repeats
are suggested to be involved in protein or carbohydrate recognition and ER-associated protein
degradation in eukaryotes [78]. No function of proteins with a PF08238 domain is known for
oomycete or fungal pathogens.
The distance between Pythium and the obligate biotrophic species along PC2 is largely
caused by differences in four domains that are commonly reported in oomycete secretomes
[71]. The first, PF14295, a PAN/Apple domain, is known to be associated with carbohydrate-
binding module (CBM)-containing proteins that recognize and bind saccharide ligands in Ph.
parasitica. Loss of these genes, as in the biotrophs, may facilitate the evasion of host recogni-
tion as some CBM proteins are known to induce plant defense [79]. Second, PF00082, is a sub-
tilase domain, which is found in a family of serine proteases. Secreted serine proteases are
ubiquitous in secretomes of plant pathogens [80]. Secreted proteases from fungal species have
been shown to enhance infection success by degrading plant derived antimicrobial proteins
[81]. A third is PF00090, a Thrombospondin type 1 domain that is present in large numbers in
the secretome of Phytophthora and Pythium species but is absent from the secretomes of Hya-
loperonospora/Peronospora species and Plasmopara halstedii. The function of proteins with
this domain in oomycetes or plants is unknown. Finally, PF00254 contributes to the separation
along PC1, which seems mainly caused by 13 occurences of the domain in the secretome of P.
tabacina versus 2 or less in the secretomes of the other oomycete species.
Over and under-representation of Pfam domains in obligate biotrophic species. Statis-
tical analysis of enrichment of Pfam domains, to identify under- and over-represented
domains in each group (Hyaloperonospora/Peronospora,Plasmopara,Albugo) compared to
Phytophthora, confirmed the pattern that was shown in the biplot. In total, 60 Pfam domains
were found to be differentially abundant in obligate biotrophic species clusters compared to
Phytophthora (Table 5). All of the seven Pfam domains that contributed most to the separation
between phylogenetic groups in the PCA (Fig 7 and Table 4) were also found to be differen-
tially abundant in at least one obligate biotrophic cluster compared to Phytophthora in the
enrichment analysis.
Previous studies identified Pfam domains that are associated with virulence in other phyto-
pathogenic oomycete species like Pythium,Plasmopara,Peronospora and Phytophthora [82].
The occurrence of these known virulence-associated domains in the Pfs proteome is summa-
rized in S5 Fig. We found that obligate biotrophic species have a lower total, as well as relative,
number of secreted proteins with virulence-associated domains compared to the other oomy-
cete species.
Host-translocated effectors. The RxLR effector models in the Pfam database (PF16810
and PF16829) mentioned above cover only a small fraction of the predicted RxLR effectors in
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Table 5. Over and under-representation of Pfam domains in the secretomes of Hyaloperonospora/Peronospora (HP),Plasmopara (Pl) and Albugo (Al) compared to
Phytophthora species.
Pfam Name Interpro
#
HP Pl Al
PF16810 RxLR protein, Avirulence activity IPR031825 8,30E-24 2,70E-16 3,90E-07
PF14295 PAN domain IPR003609 1,80E-07 1,80E-04 16,913
PF00090 Thrombospondin type 1 IPR000884 4,50E-05 77,4987 1,98225
PF05630 Necrosis inducing protein (NPP1) IPR008701 1,60E-01 1,36788 2,13E-03
PF08238 Sel1 repeat IPR006597 1,70E-07 4,61559 1,07208
PF00254 FKBP-type cis-trans isomerase IPR001179 1,80E-04
PF00050 Kazal-type serine protease inhibitor IPR002350 2,05E-03
PF07974 EGF-like domain IPR013111 1,23E-02
PF13456 Reverse transcriptase-like IPR002156 2,60E-10
PF00300 Histidine phosphatase superfamily IPR013078 7,74E-03
PF00665 Integrase core domain IPR001584 1,07E-02
PF00571 CBS domain IPR000644 1,66E-02
PF00089 Trypsin IPR001254 1,10E-12
PF01833 IPT/TIG domain IPR002909 8,00E-12
PF00082 Subtilase family IPR000209 3,10E-10
PF01341 Glycosyl hydrolases family 6 IPR016288 3,30E-05
PF00182 Chitinase class I IPR000726 2,60E-04
PF01670 Glycosyl hydrolase family 12 IPR002594 1,09E-03
PF03184 DDE superfamily endonuclease IPR004875 2,40E-06
PF09818 Predicted ATPase of the ABC class IPR019195 4,60E-06
PF00169 PH domain IPR001849 8,10E-06
PF01764 Lipase (class 3) IPR002921 1,30E-04
PF00026 Eukaryotic aspartyl protease IPR033121 2,30E-04
PF13405 EF-hand domain IPR002048 3,70E-04
PF15924 ALG11 mannosyltransferase IPR031814 3,70E-04
PF01546 Peptidase family M20/M25/M40 IPR002933 3,70E-04
PF07687 Peptidase dimerisation domain IPR011650 3,70E-04
PF03870 RNA polymerase Rpb8 IPR005570 3,70E-04
PF13041 PPR repeat family IPR002885 3,70E-04
PF00443 Ubiquitin carboxyl-terminal hydrolase IPR001394 1,38E-03
PF10152 Subunit CCDC53 of WASH complex IPR019309 1,63E-03
PF00041 Fibronectin type III domain IPR003961 2,29E-03
PF07727 Reverse transcriptase IPR013103 6,09E-03
PF04130 Spc97 / Spc98 family IPR007259 2,15E-02
PF01753 MYND finger IPR002893 2,15E-02
PF03577 Peptidase family C69 IPR005322 2,15E-02
PF03388 Legume-like lectin family IPR005052 3,02E-02
PF03133 Tubulin-tyrosine ligase family IPR004344 3,02E-02
PF13181 Tetratricopeptide repeat IPR019734 3,02E-02
PF01156 Nucleoside hydrolase IPR001910 3,02E-02
PF06367 Diaphanous FH3 Domain IPR010472 3,02E-02
PF04910 Transcriptional repressor TCF25 IPR006994 3,02E-02
PF00044 Glyceraldehyde 3-ph. dehydrogenase IPR020828 3,02E-02
PF02800 Glyceraldehyde 3-ph. dehydrogenase IPR020829 3,02E-02
PF01428 AN1-like Zinc finger IPR000058 3,02E-02
PF00766 Electron transfer FAD-binding domain IPR014731 3,02E-02
(Continued)
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secretomes of phytopathogenic oomycetes. We predicted the total number of host-translocated
effectors for each secretome using a Perl regex script and HMM searches (see methods),
including RxLR effectors without WY domains and CRN effectors (Fig 8). RxLR effector pro-
teins were more abundant in Phytophthora compared to the obligate biotrophic species. On
average 399 RxLR effector proteins were found in Phytophthora whereas Plasmopara and Hya-
loperonospora/Peronospora had 79 and 90. The same pattern is evident for CRN effectors. The
average number of CRN proteins in Hyaloperonospora/Peronospora is 11, while Plasmopara
has 12 and Phytopthora 56. We conclude that downy mildew species (Hyaloperonospora/Pero-
nospora and Plasmopara) have fewer host-translocated effectors compared to Phytophthora
species.
Discussion
Taxonomic filtering
The ability to sequence full genomes at high pace and relatively low cost has aided research in
phytopathology dramatically. Over the past few years, the genomes of many phytopathogenic
oomycetes have been sequenced and their genomes revealed an arsenal of protein coding
genes with a putative virulence role. However, technical difficulties restricted the sequencing
and assembly of genomes of obligate biotrophic oomycetes that cannot be cultured axenically.
Obligate biotrophic species can only grow on living host tissue so when collecting spores for
DNA isolation DNA of other microbes and the host plant will inevitably contaminate the sam-
ple, which complicates the genome assembly. In this paper we use a metagenome filtering
method resulting in the assembly of a relatively clean genome sequence of the obligate bio-
trophic downy mildew of spinach, Peronospora effusa.
To get a clean assembly, sequence that are derived from different species were filtered out
and removed. Several methods were considered to identify and filter contigs or reads that were
likely contaminants in our data. Initially we considered to filter contigs or reads based on their
GC content, since this differs between genomes of oomycetes [83] and many other microbes
Table 5. (Continued)
Pfam Name Interpro
#
HP Pl Al
PF01012 Electron transfer flavoprotein domain IPR014730 3,02E-02
PF03690 UPF0160 (uncharacterized) IPR003226 3,02E-02
PF13307 Helicase C-terminal domain IPR006555 3,02E-02
PF08683 Microtubule-binding calmodulin-reg IPR014797 3,02E-02
PF01846 FF domain IPR002713 3,02E-02
PF13418 Galactose oxidase 3,02E-02
PF03776 MinE IPR005527 3,02E-02
PF13815 Iguana/Dzip1-like DAZ-interacting IPR032714 3,02E-02
PF04851 Type III restriction enzyme IPR006935 3,02E-02
PF13831 PHD-finger 3,02E-02
PF04045 Arp2/3 complex, p34-Arc IPR007188 3,02E-02
PF08144 CPL (NUC119) domain IPR012959 3,02E-02
PF00659 POLO box duplicated region IPR000959 3,02E-02
PF08450 SMP-30/Gluconolaconase/LRE-like IPR013658 4,76E-02
Over (green) and under (blue)-representation was tested relative to the expected distribution of each Pfam domain. The abundance of each domain was compared
between the species clusters using a Chi-square test with Bonferroni correction. Bonferroni corrected p-values are shown in the table.
#
The InterPro domain code corresponding to each Pfam domain is provided.
https://doi.org/10.1371/journal.pone.0225808.t005
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[84]. However, some bacterial species have a GC content similar to that of Pfs, e.g. E.coli with
a GC content of 51.7% [84]. In addition, the GC content is not constant over the genome, so
filtering based on this could potentially remove valuable parts of the genome.
Alternatively, reads of non-oomycete origin could be identified by mapping them to data-
bases with sequences of known taxonomy. For example, a database containing only oomycete
or bacterial genomes. This is not ideal as the databases are incomplete and are likely to contain
annotation errors. In addition, it could lead to the removal of novel parts of the downy mildew
Fig 8. Predicted (a) RxLR and (b) CRN effectors in the secretome of Hyaloperonospora/Peronospora,Plasmopara and Phytophthora species. The predicted
effectors are classified into four (RxLR) or five (CRN) categories, based on the additional domains they possess. Please note that the number of Pfs effectors is
slightly different from the numbers reported before (S2 and S3 Figs). For this comparison we used HMM models that were previously published rather than the
models trained for Pfs (S2 and S3 Figs).
https://doi.org/10.1371/journal.pone.0225808.g008
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genome that are not present in other oomycetes, and which would hamper the study of valu-
able species-specific parts of your genome.
The filtering we applied with the CAT tool does not classify a contig based on a single hit.
Instead it determines the taxonomic origin of each ORF on an assembled contig or corrected
PacBio read, providing a robust classification [15]. In our Pfs study, after filtering with the
CAT-pipeline of the error-corrected PacBio reads, 50% remained, and were used in the assem-
bly. Of the sequenced (unfiltered) Illumina reads, 56% could be aligned to the final assembly.
This indicates that roughly half of our sequencing reads originated from other sources besides
Pfs. Notably, while the classifications in the original CAT paper were only benchmarked on
prokaryotic sequences [15], our study shows that the tool also performs well for classifying
eukaryotic contigs. Thus, CAT may also be promising for classification of eukaryotes including
oomycetes in metagenomic datasets, provided that long contigs, or corrected PacBio or Nano-
pore sequencing reads are available.
It should be noted that sequences of unknown taxonomy were maintained for the assembly,
making it possible that these are still contaminants. When we compare the taxonomic distribu-
tions generated by Kaiju of the pre-assembly and final assembly, we see a dramatic reduction
of sequences of bacterial origin (Fig 3). The oomycete content according to Kaiju and the over-
all GC content of the final assembly is similar to that of genome assemblies of axenically-
grown oomycetes. We can therefore conclude that the CAT filtering method, allowed the suc-
cessful removal of sequences of non-oomycete origin.
Hybrid assembly
Most oomycete genomes sequenced to date were found to contain long repeat regions [85]
that cannot be resolved using only a short-read technology such as Illumina. Long reads can
potentially sequence over long repeats, and contribute to the contiguity of a genome assembly
[86]. Therefore, our Illumina data was complemented with long read PacBio sequences in an
attempt to close gaps between contigs. Although the inclusion of PacBio reads in our assembly
improved the contiguity, the final result still consists of a large number of contigs, indicating
that our PacBio reads were unable to span many repeat regions. Besides biological reasons for
the large number of contigs, there could also be a technical reason. Prior to PacBio sequencing
whole genome amplification (WGA) with random primers was performed as the initial
sequencing attempt with non-amplified DNA barely yielded sequencing reads. WGA might
create a bias, where some parts of the oomycete genome may be under-represented in the Pac-
Bio data.
The genome of Pfs1
The assembled Pfs1 genome size is 32.4 Mb divided over of 8,635 contigs. The genome is
highly gene dense and contains in total 13,227 genes. Overall, the BUSCO analysis showed that
this assembly contains most of the gene-space. Many of the 8,635 contigs were smaller than 1
kb. However, the CAT filtering method performs best on relatively large contigs containing
multiple ORFs. Therefore, small contigs could still contain sequences derived from other
organisms. The removal of these small contigs results in only a small genome size reduction
(1.9 Mb) and loss of gene models (597), but significantly reduces the number of contigs (by
5,027). When we also account for genes that have a significant overlap (>20%) with repeats in
the genome (3983 gene models), or that were annotated as transposable elements (36 gene
models that did not had an overlap with a repeat region) we come to 8,976 high-confidence
gene models.
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The genomes of Pfs race 13 and 14 have recently been published [87,88], with a similar
genome size (32.1 Mb, and 30.8 Mb respectively) and gene content (~ 8000 gene models) com-
pared to our Pfs1 genome assembly. Contrary to our assembly method, the input data for
those genome assemblies were filtered by alignment to an oomycete and bacterial database to
discard reads that do not belong to the oomycete genus. This filtering method could potentially
lead to the incorporation of bacterial sequences that are not in the public databases. Besides,
the positive filtering for oomycete scaffolds against NCBI nt database could have resulted in
the loss of Pfs specific genome sequences. In addition, by filtering reads based on a database
containting bacterial and fungal sequences, part of the Pfs genome yielded by horizontal gene
transfer (HGT) may be discarded [89]. The CAT-tool overcomes this issue by determining the
overall taxonomy of larger contigs based on multiple genes.
Peronospora species have reduced genomes
Recent sequencing of Peronospora species shows that they have remarkably small and compact
genomes (32.3–63.1 Mb) compared to Phytophthora (82–240 Mb) species [32,35,87,90]. The
k-mer analysis predicts the Pfs1 genome to be 36.2 Mb containing 8.8 Mb of repeats (24%).
The predicted genome size of Pfs R13 and R14 based on k-mer analysis is 44.1–41.2 mb
(repeats; 24–22%) [87]. The increased genome size of Phytophthora is attributed to an ancestral
whole genome duplication in the lineage leading to Phytophthora and to an increase in the
proportion of repetitive non-coding DNA [32,91]. The duplication event has been proposed
to have taken place after the speciation of H.arabidopsidis [92]. However new multigene phy-
logenies show that the Peronospora lineage has speciated after the divergence of Phytophthora
clade 7 from clade 1 and 2. Notably, these three clades all contain species with duplicated
genomes [2,5,6,93]. This would suggest that an ancestral whole genome duplication before
this speciation point would also apply to Peronospora, and would mean that duplication can-
not account for the difference in genome size. The availability now of genomes of three Pero-
nospora species for comparisons asks for a reevaluation of the timing of the duplication and
subsequent speciation events.
Biologically, the question of how Peronospora species can be host-specific and obligate bio-
trophic while maintaining only a small and compact genome is interesting. It is argued that
the trend in filamentous phytopathogens is towards large genomes with repetitive stretches to
enhance genome plasticity [91]. Plasticity may enable host jumps and adaptations that favor
the species for survival over species with small, less flexible genomes [91]. The reduced
genomes of Peronospora species show an opposing trend that cannot be attributed to their
obligate biotrophic lifestyle alone, as it is not evident in Plasmopara species (75 Mb– 9 2 Mb)
[5,94]. Sequencing of multiple isolates of the same Peronospora species may shed light on
genome plasticity at the species level.
Secretome reflects biotrophic lifestyle
Evolving biotrophy. The biotrophic lifestyle has emerged on several independent occa-
sions in the evolution of filamentous plant pathogens, in several branches of the tree of life.
Convergent evolution is thought to be the main driving factor behind the development of bio-
trophy in such distantly related organisms [95]. However, it was shown that horizontal gene
transfer can also occur between fungi and oomycetes, resulting in 21 fungal proteins in the
secretome of H.arabidopsidis. Out of these 21 proteins, 13 were predicted to secreted, indicat-
ing that horizontal gene transfer may affect a species pathogenicity and interaction with the
host [96,97].
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It was proposed that the critical step for adopting biotrophy in filamentous phytopathogens
is the ability to create and maintain functional haustoria [98]. To do so, a species needs to be
able to avoid host recognition or suppress the host defense response. A proposed mechanism
for avoidance of host recognition is the loss of proteins involved in cell wall degradation, as
evidenced by the reduction of cell wall degrading enzymes in mutualistic species compared to
biotrophs [99]. In this and other studies, we find a reduction of the number of cell wall degrad-
ing enzymes in obligate biotrophic species compared to hemi-biotrophic Phytophthora species
(S5 Fig) [30]. This is true for all three obligate biotrophic groups in this study (Hyaloperonos-
pora/Peronospora,Plasmopara and Albugo) although the difference is less clear in Plasmopara.
Possibly this reduction is the result of a similar selection pressure to reduce recognition by the
host plant in the biotrophic species, where the hemi-biotrophic nature of the interaction
between host and Phytophthora allows for slightly less caution in recognition avoidance.
The other mechanism of establishing a strong interaction is suppression or avoidance of the
host defense response. Biotrophic infections are often accompanied by co-infection of species
that are unable to infect the plant in the absence of the biotroph, indicating efficient defense
suppression [98,100]. We found enhanced numbers of secreted serine proteases (PF00082)
(suppression) and reduced numbers of proteins with PAN/Apple domains that are known to
be recognized by the plant immune system.
While the expansion of host translocated RxLR effectors is evident in both hemi-biotrophic
and biotrophic species, their numbers are smaller in secretomes of obligate biotrophs. CRN
effectors are especially reduced in secretomes of biotrophic species. As opposed to RxLR effec-
tors, CRNs are an ancient class of effectors that are known to induce cell death. Obligate bio-
trophic species presumably lost them as they are not beneficial for their survival.
In this study we first showed that the CAT tool performs well for taxonomic filtering of
eukaryotic contigs. We provided a clean reference genome of a race 1 isolate of the spinach
infecting downy mildew, Pfs1. In a comparative approach, we found that the secretomes of the
obligate biotrophic oomycetes are more similar to each other than to more closely related
hemi-biotrophic species when comparing the presence and absence of functional domains,
including the host translocated effectors. We conclude that adaptation to biotrophy is reflected
in the secretome of oomycete species.
Supporting information
S1 Fig. GC plot of various oomycete assemblies on contigs larger than 1kb.
(TIF)
S2 Fig. RxLR (-like) motifs observed in the putative RxLR effectors identified in the
genome of Pfs1.For each (degenerate) RxLR motif the presence of a WY domain (orange),
EER-like (green) domain or both (purple) is shown.
(TIF)
S3 Fig. CRN (-like) motifs observed in the putative CRN effectors identified in the genome
of Pfs1.For each (degenerate) CRN protein the presence of an HVL domain (orange), identi-
fied with an CRN HMM model (red) or both (green).
(TIF)
S4 Fig. PCA on absolute numbers of secreted CAZyme domains.
(TIF)
S5 Fig. Secreted cell wall degrading proteins (CAZymes). Numbers (a) of literature curated
plant cell wall degrading enzymes per species. (b) The same data represented as fraction of the
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total number cell wall degrading protein domains per species.
(TIF)
S6 Fig. PCA on absolute numbers of secreted Pfam domains.
(TIF)
S7 Fig. Secreted pathogenicity associated Pfam domains. Occurrence of Pfam domains
known to be involved in pathogenicity within the secretome of each species. Figure (a) shows
the absolute number of Pfam domains, while (b) shows the number relative to the total num-
ber of Pfam domains per species.
(TIF)
S1 Table. Species used for comparative secretomics.
(XLSX)
S2 Table. Comparison of conserved eukaryotic genes for different oomycetes and the Pfs1
assembly using BUSCO.
(XLSX)
S3 Table. Repeat elements in the Pfs1 genome. Repeat elements identified in the Pfs1
genome, for each repeat type the total numbers and percentage are shown. In addition, also a
detailed annotation for each repeat element is provided.
(XLSX)
S4 Table. Genome sizes and repeat content of different assembled oomycete genomes.
(XLSX)
S5 Table. Putative annotations of the Pfs proteins as obtained with ANNIE. In addition, the
presence of a N-terminal signal peptide for secretion, WY motif, TM motif and overlap with a
repeat region are listed for each protein coding gene.
(XLSX)
S6 Table. Overview of the host translocated effectors (RxLR and CRN) identified in the
genome of Pfs1.Also, their respective functional domains and locations are listed per effector.
Selected effectors that were used in the gene intergenic distance analysis are listed in the sec-
ond tab.
(XLSX)
S7 Table. Secreted CAZyme domains per species.
(XLSX)
S8 Table. Secreted Pfam domains per species.
(XLSX)
Acknowledgments
We thank Ronnie de Jonge (Utrecht University) for useful input for the orthology analysis,
Bjorn Wouterse for helping out with the comparative and statistical analysis, and the Utrecht
Sequencing Facility for providing sequencing service and data. Utrecht Sequencing Facility is
subsidized by the University Medical Center Utrecht, Hubrecht Institute, Utrecht University
and The Netherlands X-omics Initiative (NWO).
Author Contributions
Conceptualization: Joe¨l Klein, Manon Neilen, Bas E. Dutilh, Guido Van den Ackerveken.
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Formal analysis: Joe¨l Klein, Manon Neilen, Marcel van Verk.
Funding acquisition: Guido Van den Ackerveken.
Project administration: Guido Van den Ackerveken.
Supervision: Guido Van den Ackerveken.
Visualization: Joe¨l Klein.
Writing – original draft: Joe¨l Klein, Manon Neilen.
Writing – review & editing: Joe¨l Klein, Manon Neilen, Marcel van Verk, Bas E. Dutilh, Guido
Van den Ackerveken.
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PLOS ONE
Genome reconstruction of Peronospora effusa by metagenome filtering
PLOS ONE | https://doi.org/10.1371/journal.pone.0225808 May 12, 2020 32 / 32
