Draft genome assemblies for tree pathogens Phytophthora pseudosyringae and Phytophthora boehmeriae.

Species of Phytophthora, plant pathogenic eukaryotic microbes, can cause disease on many tree species. Genome sequencing of species from this genus has helped to determine components of their pathogenicity arsenal. Here, we sequenced genomes for two widely distributed species, Phytophthora pseudosyringae and Phytophthora boehmeriae, yielding genome assemblies of 49 and 40 Mb, respectively. We identified more than 270 candidate disease promoting RXLR effector coding genes for each species, and hundreds of genes encoding candidate plant cell wall degrading carbohydrate active enzymes (CAZymes). These data boost genome sequence representation across the Phytophthora genus, and form resources for further study of Phytophthora pathogenesis.

Introduction

Phytophthora species are oomycetes, eukaryotic plant pathogens with a filamentous growth habit that superficially resemble fungi. However, they are stramenopiles, phylogenetically separated from the true fungi, belonging to the SAR group (stramenopiles-alveolata-rhizaria) (Cavalier-Smith 2018). Species of Phytophthora represent significant threats to food security, causing billions of dollars of losses to key crops such as potato, tomato, and soybean (Tyler 2007; Fry ). Phytophthora species also represent some of the greatest threats to tree and forest health (Hansen ). Sampling from diseased trees, forest soils, and water sources has led to the identification of many Phytophthora species that can infect trees (Jung , 2018).

While molecular host-pathogen interactions are intensively researched for a small number of crop pathogenic Phytophthora species, such as Phytophthora infestans (potato late blight) and Phytophthora sojae (soybean root rot), most tree pathogenic species have not received as much attention (Kamoun ). Sudden oak death caused by Phytophthora ramorum, and Jarrah dieback caused by Phytophthora cinnamomi, are well-known examples of tree pathogenic species with global impact (Grünwald ; Fisher ; Hardham and Blackman 2018). Pathogens of woody host plants are present in most, if not all, clades of the Phytophthora genus. The genomes of many tree pathogenic species have been sequenced in recent years (for example Feau ; Studholme , 2019; Vetukuri ), providing resources to predict and compare the pathogenicity factors encoded in these genomes.

Among the most commonly detected Phytophthora species in the United Kingdom in diseased plants and soil is Phytophthora pseudosyringae (Clade 3) (Riddell ). P. pseudosyringae is also present in many other countries (Linzer ; Scanu ; Stamler ; Hansen ; Khaliq ). A draft genome sequence was recently reported for P. pseudosyringae (McGowan ), which provided the first view of the pathogenicity arsenal in this species. The only other genome sequence from a Clade 3 species is from Phytophthora pluvialis (Studholme ). Another globally distributed species is Phytophthora boehmeriae (Clade 10) which not only causes disease on trees such as black wattle (Acacia mearnsii), but also crops such as cotton and chili (Chowdappa ; Santos 2016; Wang ). Phytophthora Clade 10 contains few species (Yang ). While there are multiple genome sequences for different isolates of the tree pathogen P. kernoviae, only one other Clade 10 genome is available (Studholme ).

To build molecular biology resources and enable future gene diversity studies in P. pseudosyringae, we generated a further draft genome sequence assembly for this species. To facilitate more robust evolutionary studies into pathogenicity of Clade 10 species in future, we also generated a genome sequence assembly for P. boehmeriae.

Materials and methods

Phytophthora cultures, DNA preparation, and Illumina sequencing

Cultures of P. pseudosyringae (SCRP734) and P. boehmeriae (SCRP23) from the James Hutton Institute culture collection were maintained on rye agar. Hyphae for both species were grown without shaking in amended lima bean broth (Bruck ) for 72 h at 20°C, harvested by gravity filtration through 70 µm nylon mesh, and immediately frozen in liquid nitrogen until used for DNA extraction. Genomic DNA from each species was extracted from the frozen hyphae using the protocol of Raeder and Broda (1985), followed by RNaseA treatment and DNeasy (Qiagen) column clean-up using the manufacturer’s protocol. DNA quality was assessed by absorbance at 260 and 280 nm (Nanodrop), and agarose gel electrophoresis. Genomic DNA was fragmented for sequencing library construction by physical shearing using a M220 ultrasonicator (Covaris) as recommended. Libraries for whole-genome sequencing were prepared using the Illumina TruSeq DNA PCR-Free kit, following the manufacturer’s protocol (350 bp insert) and standard indexing. Libraries were sequenced (150 bp, paired-end) on a NextSeq 500 (Illumina) as recommended.

Phytophthora genome assembly and gene prediction

Reads for each species were separately subjected to quality control using trimmomatic (Q15) (Bolger ). FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (last accessed 16 August 2021) was used to assess read quality pre and post-read trimming. CLC assembler (version 4.10.86742) (Qiagen CLC Genomics Workbench) was used for a first pass assembly. This assembly was subject to Blobtools (version 1.0) (Laetsch and Blaxter 2017) analysis to identify contaminant contigs. The contigs were DIAMOND-BLASTp (version 0.9.24.125) (Buchfink ) searched against the NCBI NR database and taxonomically assigned using Blobtools. For final assembly, both genomes were processed through multiple rounds of read filtering/assembly and Blobtools-contamination identification. The final assembly was made with CLC using the contamination filtered reads (as described in Thorpe ), and was passed as trusted contigs for SPAdes (version 3.13.0) (Bankevich ). The resulting SPAdes assembly was screened through Blobplots once more. The final contig assembly was then subjected to scaffolding using SSPACE (version 3.0) (Boetzer ). To reduce false-positive scaffolding, dereplicated reads are needed; the Q15 or greater quality reads already prepared using trimmomatic were dereplicated using PRINSEQ (prinseq-lite-0.20.4) (Schmieder and Edwards 2011). Assemblies were repeat masked using a de novo set of Phytophthora specific repeats (generated here, as described in Thorpe ) and repbase (Bao ). These were modeled from multiple Phytophthora genomes. This was then taken further to predict transposons.

Genomes were soft-masked (lower case) using the identified transposon and repetitive regions, see above, using BEDTools (Quinlan and Hall 2010). Funannotate version 1.5.3 (https://github.com/nextgenusfs/funannotate) (last accessed 16 August 2021) was used to predict genes using the genome only mode. Briefly, Funannotate wraps BUSCO2 (Seppey ) to train Augustus (Hoff and Stanke 2019) which resulted in a gene-level prediction sensitivity and specificity of 0.85 and 0.559, respectively. Genemark-ES (Borodovsky and Lomsadze 2011) was also used to predict genes following DIAMOND-BLAST search against the genome using the Swissprot database (https://www.uniprot.org/statistics/Swiss-Prot) (last accessed 16 August 2021). Moreover, GlimmerHMM (Pertea and Salzberg 2002) and SNAP (Korf 2004) were also used to predict genes. The resulting predictions were then passed into Evidence Modeller (Haas ). The genes were then functionally annotated (putative assignments) using PFAM (https://pfam.xfam.org/) (last accessed 16 August 2021), InterProScan (https://www.ebi.ac.uk/interpro/search/sequence/) (last accessed 16 August 2021) and Eggnog (Huerta-Cepas ).

The completeness of each Phytophthora genome assembly was assessed by analysis of 234 conserved stramenopile genes, with BUSCO (Seppey ) run in long mode.

Ploidy level was estimated by mapping the dereplicated Q15 reads to the final genome using BWA-mem (Li 2013) and assessed using nQuire (Weiß ). nQuire uses several statistical approaches, for example, correlating the SNP distribution with the expected distribution of various known ploidy distributions. The resulting statistical output was then interrogated to determine the most statistically significant ploidy level.

Genome size and heterozygosity were predicted using Genomescope v.2.0 (Ranallo-Benavidez ) using kmers of k = 21. Briefly, using the resulting bam files generated above, kmers were counted using Jellyfish (Marcais and Kingsford 2011), count m = 21, and converted into a histogram using Jellyfish histo. The resulting histogram was uploaded to the Genomscope2.0 web interface (http://qb.cshl.edu/genomescope/genomescope2.0/) (last accessed 16 August 2021).

Secreted proteins, RXLR effectors, and CAZymes

The catalog of secreted proteins (presence of a signal peptide and absence of a transmembrane domain) for each Phytophthora genome was predicted by SignalP v4.1 (Nielsen 2017) and Phobius (Käll ).

To identify genes encoding secreted RXLR effectors, we adopted an inclusive strategy. RXLR effector genes were predicted using three strategies, described in Whisson , Win , and Bhattacharjee . The resulting gene lists were then combined to form a final nonredundant catalog of genes encoding candidate RXLR effectors.

Intergenic distances (bp) were calculated for all genes, and separately for the BUSCO and RXLR effector coding genes. Intergenic distances at 5’ and 3’ ends of genes were then plotted against each other to identify if there was a bias in the intergenic distances for the RXLR effector coding genes.

Candidate secreted crinkle and necrosis (CRN) effectors were predicted using a regular expression search for L[FY]LA[RK] in the predicted proteins from both species sequenced (https://github.com/peterthorpe5/reg_exp_finder/blob/master/crinkler_reg.py) (last accessed 16 August 2021). The positive hits were then aligned using Muscle (Edgar 2004) and inputted to hmmbuild. The resulting Hidden Markov Model was used to interrogate the predicted proteins using hmmsearch (1e−10 cut off), to yield the candidate CRNs. A further regular expression search only for the LXLFLAK motif characteristic of CRN effectors (Schornack ) was also performed (https://github.com/test1932/Regex-script-s-) (last accessed 16 August 2021). Returned sequences were assessed by SignalP v5.0 for the presence of a signal peptide.

Additional regular expression searches were performed for proteins containing the PEP13 elicitor (VWNQPVRGFKVXE) (Brunner ), the GHRHDWE peptide characteristic of necrosis and ethylene-inducing 1-like (NLP) proteins (Chen ), and HXGPCEXXXDD found in a class of candidate secreted effector proteins in P. palmivora (Evangelisti ).

Carbohydrate active (CAZy) proteins were predicted through the dbCAN2 web server, using hmmer, DIAMOND-BLAST, and HotPep tools (Zhang ).

Alien Index calculation to detect candidate horizontal gene transfer events

Potential horizontal gene transfer (HGT) events in the evolutionary history of the Phytophthora species sequenced here were identified through calculation of the Alien Index (AI) for each gene (Gladyshev ; Flot ), as described by Thorpe . Differences to the published method were that DIAMOND-BLASTP results identified as nonstramenopile were considered as candidate HGT events, and the strict AI threshold value for a gene to be considered as an HGT event was 30.

BLAST similarity and dN/dS analysis of RXLR effectors

Predicted RXLR effector protein sequences and predicted proteomes were searched against the NCBI NR database. Percentage identity, length of alignment, and bitscore were recorded and binned for each query sequence. Percentage identity, alignment, and bitscore were plotted against number of sequences in each bin.

For comparison with the RXLR effectors from the species sequenced in this report, genome assemblies (including versions) of 27 Phytophthora species were used to source RXLR effectors for dN/dS calculations (Supplementary Table S1). As a negative control, the set of genes identified from BUSCO analysis for the species assembled here were analyzed.

Analysis of selection on RXLR and BUSCO proteins was carried out by Orthofinder (Emms and Kelly 2019) and separately a Reciprocal Best BLAST Hit (RBBH) clustering network, as described in Thorpe . Briefly, RBBH searches were performed between all species. The results were then clustered using MCL (version 14-137) (Dongen 2000) using inflation value I = 6, which resulted in an RBBH clustering network. dN/dS analysis was performed as described in Thorpe et al. (2016) with slight modification. Briefly, amino acid sequences for each cluster were aligned using Muscle (Edgar 2004) and badseq_remover.pl (https://github.com/dukecomeback/bad-sequence-remover) (last accessed 16 August 2021) was used to remove sequences too divergent from each other. Then the alignment was refined using Muscle. The aligned amino acid sequence was back translated to its original nucleotide sequence, preserving its alignment information. The nucleotide aligned clusters were then filtered for the most informative coding regions, and indels removed using trimAI (no_gaps) (Capella-Gutiérrez ). On clusters with three or more members, Codonphyml (Gil ) was used to perform dN/dS analysis. Detailed method and scripts can be found at the github page listed in the data availability statement.

Results and discussion

Genome assembly

Illumina sequencing yielded 118,669,161 and 168,739,903 raw reads for P. pseudosyringae and P. boehmeriae, respectively. Our 48.9 Mb assembly for P. pseudosyringae is broadly similar in size to that reported for a different isolate by McGowan , but the N50 value of 76 kb for the assembly reported here is threefold greater (Table 1). The P. boehmeriae genome assembly, at 40.0 Mb, is similar in size to other sequenced Clade 10 Phytophthora species (Sambles ; Studholme ).

Table 1

Genome assembly and analysis statistics for P. pseudosyringae and P. boehmeriae

	P. pseudosyringae	P. boehmeriae
Culture accession	SCRP734	SCRP23
Host plant	Fagus sylvatica (European beech)	Gossypium hirsutum (cotton)
Country and year	Italy, 2003	China, 1998
Assembled genome size	48,944,789	39,965,592
Predicted haploid genome size (k-mer)	52,169,164	46,223,661
Estimated coverage	440×	828×
GC content	0.546	0.509
N50	76,110	55,354
Mean Scaffold Size	17,656	13,277
Longest Scaffold	415,940	354,378
Number of scaffolds greater than length 200 bp	2,772	3,010
% Heterozygosity	0.01%	0.03%
Predicted genes	15,624	12,121
Secreted proteins	1,599	1,459
RXLR effectors	279	380
CAZy proteins	565	503

Our estimation of heterozygosity in the isolates of the two species sequenced here showed low levels for P. boehmeriae and P. pseudosyringae (0.01 and 0.03%, respectively). Both P. pseudosyringae and P. boehmeriae are homothallic (inbreeding), and the observed low levels of heterozygosity are consistent with this.

Genome sizes estimated from sequence reads were 52.1 and 46.2 Mb for P. pseudosyringae and P. boehmeriae, respectively. These estimates suggest that our assemblies for P. pseudosyringae and P. boehmeriae are largely representative of the genome content. Discrepancy in genome size estimates and assembly sizes may be resolved through use of longer read sequencing technologies and flow cytometry (Cui ; Lee ; van Poucke ). Genome sizes of Phytophthora species, estimated by flow cytometry, were typically larger than the genome assembly sizes from sequencing (McGowan and Fitzpatrick 2017; van Poucke ). Flow cytometry estimated a haploid genome size for P. pseudosyringae ranging from 72 to 86 Mb (van Poucke ); no data are available for P. boehmeriae.

The repetitive DNA content in Phytophthora genomes can be highly variable, ranging from 74% for P. infestans (Haas ) to 15% in P. plurivora (Vetukuri ). These repeats encompass a diverse range of DNA sequences, but are primarily made up of mobile elements, either intact or degraded. We estimated the proportion of mobile element repeats in the two genome assemblies reported here at 22.4% (10.9 Mb) for P. pseudosyringae and 13.3% (5.3 Mb) for P. boehmeriae.

Phytophthora species are at least diploid during asexual stages and may have elevated ploidy. We used nQuire to estimate ploidy from sequence data, where the smallest ΔlogL is accepted as the ploidy level. We found the greatest support for tetraploidy in P. pseudosyringae (ΔlogL = 1534) and P. boehmeriae (ΔlogL = 603.8).

Gene prediction, and BUSCO v2 estimation of genome completeness

We predicted 15,624 genes for P. pseudosyringae and 12,121 genes for P. boehmeriae. For P. pseudosyringae, this is a similar gene number to that previously predicted by McGowan . The completeness of the genome assemblies was estimated using a set of 234 conserved stramenopile genes (BUSCO v2; Table 2). This showed a high degree of gene representation for P. pseudosyringae (92.3%) and P. boehmeriae (90.2%). The assemblies for P. pseudosyringae and P. boehmeriae had no duplicated or fragmented BUSCO genes (Table 2).

Table 2

BUSCO analysis of P. pseudosyringae and P. boehmeriae genome assemblies

	P. pseudosyringae	P. boehmeriae
Complete	216 (92.3%)	211 (90.2%)
Complete, single copy	216	211
Completed, duplicated	0	0
Fragmented	0	0
Missing	18 (7.7%)	23 (9.8%)

Gene duplication was also assessed for both species, identifying single copy, dispersed copies, proximal duplications, tandem duplications, and segmental duplications. P. boehmeriae and P. pseudosyringae have similar proportions of single-copy and duplicated genes (Figure 1; Supplementary Table S2).

Figure 1

Classes of gene duplications in P. boehmeriae and P. pseudosyringae. Single copy genes are shown in blue, dispersed copies in orange, proximal duplications in grey, and tandem duplications in yellow. Segmental duplications represent 0.1% or less of gene duplications and are not shown here. Genomes of P. boehmeriae and P. pseudosyringae have similar proportions of single copy and different classes of duplications.

Pathogenicity genes

Phytophthora species are known to deploy a diverse array of secreted proteins to facilitate infection (Schornack ; McGowan and Fitzpatrick 2017). We predicted signal peptides for a total of 1,599 and 1,459 proteins from P. pseudosyringae and P. boehmeriae, respectively.

A major component of the pathogenicity arsenal of Phytophthora species are a large group of secreted proteins that are translocated into plant cells to exert their function. They are characterized by a conserved RXLR peptide motif (arginine-any amino acid-leucine-arginine) within the N-terminal 50 amino acids (Whisson ; Wang ). We adopted an inclusive strategy for identifying candidate RXLR effector coding genes, incorporating results from three different search strategies. We identified 279 and 380 candidate RXLR effectors from P. pseudosyringae and P. boehmeriae, respectively (Supplementary Table S3). The candidate RXLR effector number for P. pseudosyringae is higher than that determined by McGowan due to the more inclusive prediction strategy used here. The list of RXLR effector candidates will likely include some false positive predictions, but these can be eliminated using future transcriptome experiments of plant infection; RXLR effectors are typically up-regulated during infection (Haas ; Jupe ). From our RXLR predictions, we identified 118 and 276 proteins possessing both complete RXLR and downstream EER motifs from P. pseudosyringae and P. boehmeriae, respectively. Both of these motifs have been demonstrated to have a role in translocating effectors into plant cells (Whisson ; Dou ), and the RXLR motif represents a protease cleavage site (Wawra ).

In other Phytophthora genomes, RXLR effectors predominantly reside in gene poor, repeat rich regions, which is reflected in larger intergenic distances (Haas ; Vetukuri ). We also determined the 5’ and 3’ intergenic distances from RXLR effector coding genes from P. pseudosyringae and P. boehmeriae and plotted them against all genes from the genomes, and against the set of genes identified in BUSCO analysis. Only genes for which both 5’ and 3’ intergenic distances could be calculated were included in plots. This revealed that, similar to other examined Phytophthora species, the intergenic distances for RXLR effector coding genes in these species are greater than those for the core set of genes (Figure 2; Supplementary Table S4).

Figure 2

Plots of 5’ against 3’ intergenic distances (log10) for genes from P. pseudosyringae and P. boehmeriae. Richness of gene density for intergenic distances is represented by color scale ranging from blue (low) to red (high). Genes encoding RXLR effector proteins are shown as black triangles; BUSCO genes are shown as yellow dots. Only genes for which both 5’ and 3’ intergenic distances could be calculated are shown.

Using regular expression and HMM searches to predict candidate secreted CRN effectors, we predicted only a single candidate in P. pseudosyringae, and two in P. boehmeriae (Supplementary Table S5). This was surprising, as most Phytophthora genomes sequenced to date encode numerous candidate CRN effectors (McGowan and Fitzpatrick 2017). Many predicted CRN-family proteins do not possess predicted signal peptides (McGowan and Fitzpatrick 2017) and here we found that none of the three CRN candidates possessed a predicted signal peptide. McGowan manually curated 90 CRN candidates in P. pseudosyringae, but only 37 were considered to be secreted effector proteins. Our findings suggest that CRN proteins are not major contributors to pathogenicity in the two species sequenced.

Phytophthora species are also known to possess multiple genes encoding proteins that can cause plant cell death, either by cytotoxicity or through elicitation of plant immune responses. Regular expression searches for two families of these proteins revealed four candidate PEP13 elicitor containing proteins in P. pseudosyringae and P. boehmeriae (Supplementary Table S5). For cytotoxic NLP1-like proteins, seven candidates were identified in P. pseudosyringae (six predicted secreted), and 14 in P. boehmeriae (12 secreted) (Supplementary Table S5).

Evangelisti identified a class of 42 predicted secreted proteins from P. palmivora that contained a conserved HXGPCEXXXDD peptide motif. Searches of the predicted proteomes of the two species sequenced here identified 34 proteins containing this motif from P. pseudosyringae (29 predicted secreted), and 42 from P. boehmeriae (36 secreted) (Supplementary Table S5). That the motif is conserved in similar numbers of proteins in these species from different clades of the genus, and a high proportion are predicted to be secreted, suggests that they may be a class of candidate effector proteins important for Phytophthora pathogenicity.

Another significant set of genes involved in pathogenicity encode carbohydrate active (CAZy) proteins, especially those with potential enzymatic activity for digesting plant cell walls, allowing pathogen ingress. Phytophthora genomes are predicted to encode hundreds of CAZymes, often present as gene families of closely related members (Ospina-Giraldo ; Brouwer ; McGowan and Fitzpatrick 2017). We used three CAZy prediction tools within dbCAN2 to identify candidate CAZy proteins and included all positive returned sequences as candidates, since Phytophthora proteins may be more divergent than many CAZy proteins represented in databases. We predicted 565 and 503 CAZy proteins for P. pseudosyringae and P. boehmeriae, respectively. Of particular interest are the lytic polysaccharide monoxygenases (within the auxiliary activity grouping), glycoside hydrolases, polysaccharide lyases, and carbohydrate esterases (Supplementary Table S6). The number of proteins predicted for each CAZy family was broadly similar in the two Phytophthora species sequenced, but with some differences that may reflect the pathogenic niche for each species. For example, P. pseudosyringae possesses single secreted lytic cellulose monooxygenase (AA16) and secreted cellulose binding (CBM1) proteins, while P. boehmeriae possesses four of each. Similarly, P. boehmeriae possesses a single secreted glycoside hydrolase family 10 protein, while P. pseudosyringae possesses four.

Evidence for HGT

During their evolution, the oomycetes may have acquired genes from other kingdoms, with genes potentially transferred horizontally from fungi and bacteria to oomycetes (Morris ; Richards ; McCarthy and Fitzpatrick 2016). We carried out a strict analysis of potential HGT events in the two genomes presented here, using a high AI threshold value (Supplementary Table S7). Our approach identified four potential HGT events in P. pseudosyringae, all but one from eukaryotic sources. These encompassed proteins with similarity to a TPR/SEL1 repeat protein (PHYPSEUDO_011742), a glycoside hydrolase family 63 (PHYPSEUDO_013022), an alpha/beta hydrolase (PHYPSEUDO_013053), and an M54 peptidase (PHYPSEUDO_014249). A single virus (Catovirus) to oomycete HGT event was identified for P. boehmeriae (mRNA capping enzyme; PHYBOEH_005573). Whether these candidate HGTs contribute significantly to pathogenicity remains to be determined experimentally.

Analysis of selection on RXLR effectors and BUSCOs

The RXLR effectors encoded by Phytophthora genomes are considered to be evolving at a greater rate than core ortholog proteins (Win ). This may be reflected in characteristics such as lower levels of sequence identity with orthologous proteins and shorter length of sequence similarity. When the RXLR effector complements and total predicted proteomes from both genomes in this report were searched against the NCBI NR database, the RXLR effectors exhibited lower levels of sequence identity, lower alignment lengths, and lower bitscores (Figure 3; Supplementary Figure S1). The total predicted proteome showed the opposite trend to the RXLR effectors, with higher levels of sequence identity (Figure 3; Supplementary Figure S1).

Figure 3

Percentage identity of P. boehmeriae (top) and P. pseudosyringae (bottom) proteins when BLASTP searched against GenBank non-redundant sequences. Percentage identity (x-axis) is plotted against number in each bin (y-axis) for RXLR effector proteins, and entire predicted proteome.

The more rapid rate of evolution of RXLR effectors has been reflected in a dN/dS score greater than 1.0 (Win ). We evaluated dN/dS scores for the two genome assemblies reported here, using both Orthofinder and a reciprocal best blast hit (RBBH) clustering network, the latter being a stricter method. For comparison, we also performed this analysis on the BUSCO orthologs identified when analyzing gene representation in the two genomes. Using Orthofinder, only one gene in the BUSCO gene set had a dN/dS value greater than 1, signifying positive selection (PHYPSEUDO_008773; PITH domain protein; dN/dS = 1.6). Using RBBH, no genes in the BUSCO set showed evidence for positive selection. Using Orthofinder and all RXLR coding genes from both genomes, we found 238 RXLR coding genes were present in clusters with at least two other genes, of which 54 genes showed evidence of positive selection (dN/dS > 1.0) (Supplementary Table S8). RXLR effectors from P. pseudosyringae were most highly represented (44) among those exhibiting positive selection, with over four-fold fewer from P. boehmeriae (10). The RBBH strategy revealed only three RXLR genes with evidence for positive selection, two from P. pseudosyringae (PHYPSEUDO_006359 and PHYPSEUDO_007604) and one from P. boehmeriae (PHYBOEH_007729). In other Phytophthora species, the number of candidate RXLR effectors exhibiting a signature of positive selection has ranged from as few as one in P. plurivora (Vetukuri ) to greater than 20 in P. ramorum (Win ). The effectors with the greatest level of positive selection are candidates for further studies into their function in disease development.

Conclusions

Genomes from two Phytophthora species that can infect trees have been sequenced and assembled. Our assembly for P. pseudosyringae is the most complete. Less fragmented genome assemblies may be achieved in the future by using longer read sequencing. The P. pseudosyringae strain used here is the second for this species to be sequenced and will begin to provide insights into gene diversity in this globally prevalent Phytophthora species. The genome sequence for P. boehmeriae will add to the genome sequencing coverage in Clade 10, which includes the important tree pathogen P. kernoviae, and provide a resource for evolutionary studies within this clade and the genus. The candidate pathogenicity proteins identified here will provide a basis for further experimental research, such as transcriptomic analyses and effector function assays, to gain a deeper understanding of Phytophthora pathogenesis on trees.

Data availability

Data in this publication have been deposited at NCBI GenBank: P. pseudosyringae (BioProject PRJNA702035; raw data SRX10106440; assembly JAGDFM000000000), and P. boehmeriae (BioProject PRJNA702033; raw data SRX10106096; assembly JAGDFL000000000). Assembled genomes, predicted transcriptomes and proteomes, and annotations are also publicly available at https://doi.org/10.5281/zenodo.4554917 (last accessed 16 August 2021). Scripts used to analyze the data are publicly available at https://github.com/peterthorpe5/genomes_tree_phyto_pathogens (last accessed 16 August 2021) and https://github.com/test1932/Regex-script-s- (last accessed 16 August 2021).

Supplementary material is available at G3 online.

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