Next Article in Journal
Growth and Yield Response of Soybean (Glycine max L.) in Relation to Sowing Date in the Southern Region of South Korea
Previous Article in Journal
Deep Learning for Stomatal Opening Recognition in Gynura formosana Kitam Leaves
Previous Article in Special Issue
Transcriptomic Analysis Reveals the Molecular Defense Mechanisms of Poa pratensis Against Powdery Mildew Fungus Blumeria graminis f. sp. Poae
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 11
10.3390/agronomy14112623
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Genomic and Secretome Analysis of Phytophthora capsici Strains: Exploring Pathogenicity and Evolutionary Dynamics

by
Oscar Villanueva
1,2,
Hai D. T. Nguyen
3 and
Walid Ellouze
1,*
1
Agriculture and Agri-Food Canada, London Research and Development Centre, 4902 Victoria Avenue North, Vineland Station, ON L0R 2E0, Canada
2
Department of Biological Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, Canada
3
Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(11), 2623; https://doi.org/10.3390/agronomy14112623
Submission received: 13 October 2024 / Revised: 2 November 2024 / Accepted: 5 November 2024 / Published: 7 November 2024
(This article belongs to the Special Issue Research Progress on Pathogenicity of Fungi in Crops—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Phytophthora capsici is a destructive oomycete pathogen that poses a significant threat to global agriculture by infecting a wide range of economically important crops in the Solanaceae and Cucurbitaceae families. In Canada, the pathogen has been responsible for substantial losses in greenhouse and field-grown crops. Despite extensive worldwide research on P. capsici, little is known about the effector content and pathogenicity of the Canadian isolates. In this study, we sequenced and analyzed the genomes of two Canadian P. capsici strains, namely 55330 and 55898, and conducted a comparative secretome analysis with globally referenced strains LT1534 and LT263. The Canadian strains displayed smaller genomes at 57.3 Mb and 60.2 Mb compared to LT263 at 76 Mb, yet retained diverse effector repertoires, including RxLR and CRN effectors, and exhibited robust pathogenic potential. Our analysis revealed that while the Canadian strains have fewer unique effector clusters compared to LT263, they possess comparable CAZyme profiles, emphasizing their capacity to degrade plant cell walls and promote infection. The differences in effector content likely reflect host adaptation, as P. capsici infects a variety of plant species. This study provides valuable insights into the genetic features of Canadian P. capsici isolates and offers a foundation for future efforts in developing targeted disease-management strategies.

1. Introduction

The oomycete pathogen Phytophthora capsici occurs worldwide and represents a global threat to agriculture, affecting a wide variety of vegetable crops in the Solanaceae and Cucurbitaceae families [1,2]. Initially discovered in Essex County, Ontario, Canada, in 1994, P. capsici has spread to other provinces such as Quebec and British Columbia. This has resulted in losses for growers, in both large-scale greenhouses and agricultural fields [3,4,5,6,7]. The pathogen’s complex infection process, which impacts all parts of the plant [8], is facilitated by the rapid formation of sporangia in saturated soils, releasing zoospores that spread the disease, leading to significant yield reductions and financial losses for farmers [9,10].
During the infection process, P. capsici utilizes a range of effector proteins to infiltrate the host plant, evading its immune defenses and helping infection. These effectors operate by disrupting the plant’s signaling pathways, hindering its ability to detect and combat the pathogen [9,11,12,13,14]. Apoplastic effectors, which include numerous hydrolytic enzymes such as carbohydrate-active enzymes (CAZymes), cutinases, glycoside hydrolases, pectinases, and proteases, break down essential defense-related proteins in the outer space of cells, weakening the plant cell wall structure and facilitating pathogen infiltration [15,16]. In contrast, cytoplasmic effectors such as crinkling and necrosis (CRN) and RxLR effectors, are delivered into plant cells through haustoria [17]. Once inside, they manipulate cellular functions, suppress immune responses, and promote the spread of infection [18]. By disabling the plant’s defenses, these effectors create an environment for P. capsici to thrive and cause disease. They play an important role in weakening both the plant’s initial immune response, triggered by pathogen-associated molecular patterns (PAMPs), and the more specific secondary response initiated by the recognition of pathogen effectors by plant resistance proteins [19]. Recent research has shown variations in effector genes among strains of P. capsici, indicating an adaptive reaction to local environmental conditions [20].
Despite extensive research on Phytophthora effectors, the effector content in Canadian P. capsici strains and their differences from reference genomes remain largely unexplored. In this study, we sequenced, de novo assembled, and annotated the genomes of two Canadian P. capsici strains, 55330 and 55898. We performed a comparative secretome analysis with the cucurbit-infecting strain LT1534 [21] and its recurrent parent LT263 [22]. The reference strain LT263, originally isolated from infected pumpkin in Tennessee, is widely used globally for its virulence on various hosts and for its sexual and asexual fecundity, making it suitable for genetic studies. By focusing on the apoplastic and cytoplasmic effector proteins, we aimed to provide a comprehensive overview of the secretome in these two Canadian strains, offering insights into the genes underlying their pathogenicity. This understanding not only enhanced our comprehension of the complex genomic landscape of P. capsici but also represented a crucial step in characterizing the unique genetic features of Canadian strains. Studying these effector proteins facilitated exploration of host adaptation and enabled direct comparisons of the pathogenic potential across different strains.

2. Materials and Methods

2.1. Pathogenicity Assay

The Canadian P. capsici 55330 and 55898 were generously provided by the plant disease clinic at the University of Guelph and deposited in the Canadian Collection of Fungal Cultures as DAOMC 252527 and DAOMC 252528, respectively. These isolates were obtained from diseased pepper plants (Capsicum annuum) collected from distinct commercial pepper-growing areas in southern Ontario to investigate potential regional differences in their genomic and secretomic profiles.
The pathogenicity of P. capsici 55330 and 55898 was assessed in a greenhouse assay on six-week-old healthy pepper seedlings. Twelve seedlings (4 per strain and 4 controls) were grown in 12-planting-cell-paks seed-starting trays (William Dam Seeds, Dundas, ON, Canada, cat. #8033) filled with sterilized farm soil. The seedlings were inoculated at the roots with 10 mL of a zoospore suspension containing 5 × 10 5 zoospores/mL. The strain culturing, zoospore production, and zoospore suspension preparation for each strain followed the method described by Sholberg et al. [3]. The seedlings were maintained under greenhouse conditions at a day/night temperature of 26/24 °C with a 16 h photoperiod and were watered as needed to maintain soil moisture. The four control seedlings were treated identically but inoculated with sterilized distilled water. The plants were monitored daily for P. capsici infection symptoms, such as wilting.

2.2. Mycelial Growth, DNA Extraction, and Genome Sequencing

Mycelial cultures of P. capsici 55330 and 55898 were grown in 5% V8® vegetable broth, prepared using V8® original vegetable cocktail juice (Campbell Soup Company, Camden, NJ, USA) with 1 g of CaCO₃ per 100 mL of juice. After centrifugation at 6000× g for 10 min, 50 mL of the supernatant was mixed with 950 mL of the sterile distilled water to complete the broth formulation. The V8® vegetable broth was sterilized at 120 °C for 20 min before inoculation. The strains were incubated for 10 days at 25 °C, after which the mycelia were harvested by filtration through Whatman™ Qualitative filter paper grade 1 (Cytiva, Marlborough, MA, USA), thoroughly rinsed with the sterile distilled water, and freeze-dried using a Labconco FreeZone® 4.5 Liter system (Labconco, Kansas City, MO, USA) for 48 h.
The genomic DNA was extracted from the freeze-dried mycelia using the DNeasy® PowerSoil® Pro Kit (Qiagen, Germantown, MD, USA, cat. #47016), following the manufacturer’s instructions. The DNA quality was verified using a DeNovix spectrophotometer (DeNovix Inc., Wilmington, NC, USA). Sequencing libraries were prepared and sequenced via the Illumina NovaSeq 6000 PE150 platform at Genome Quebec (Montreal, QC, Canada). For strain 55330, an additional genomic DNA library was prepared using the SMRTbell Express Template Prep Kit (PacBio, Menlo Park, CA, USA), and sequencing was performed on a single SMRT cell with the PacBio Sequel II platform at the SickKids sequencing facility (Toronto, ON, Canada).

2.3. Genome Assembly

A hybrid genome assembly strategy was employed for strain 55330. The PacBio continuous long reads (CLR) were first filtered using Filtlong v0.2.1 (https://github.com/rrwick/Filtlong, accessed on 24 January 2024), with reads shorter than 2 kb discarded, the lowest-quality 20% of reads removed, and subsampling down to 4 Gbp of raw sequence data (--min_length 2000--keep_percent 80--target_bases 4000000000). The filtered reads were assembled using wtdbg2 v2.5 [23] with the sequencing technology set as ‘sequel’ (-x sq) and an estimated genome size of 78.7 Mb (-g 78.7m), based on the reference genome of P. capsici isolate LT263 (NCBI, Genome assembly ASM3032425v1, accessed on 11 September 2024). To minimize duplication due to both haplotypes being assembled as separate contigs, the assembled contigs were processed with purge_haplotigs v1.1.2 [24]. The filtered PacBio CLR reads were mapped back to the wtdbg2 assembly, and the read-depth cut-offs were set as -l 5 -m 20 -h 100.
For error correction, the paired-end Illumina reads were first trimmed using the bbduk.sh script from BBMap v38.22 (https://sourceforge.net/projects/bbmap/, accessed on 24 January 2024), applying parameters to remove adapters and trim low-quality bases (qtrim=rl trimq=20 minlength=36 ktrim=r forcetrimleft=15 tossjunk=t), and mapped to the assembly using BWA v0.7.17 [25]. Pilon v1.24 [26] was subsequently used to correct any assembly errors using the aligned Illumina data.
The genome of 55898 was assembled using Illumina short reads (available at ENA: www.ebi.ac.uk/ena/browser/view/SRR18156462, accessed on 24 January 2024). Before assembly, the raw reads were pre-processed with the bbduk.sh script from the BBMap v38.22 toolkit (SourceForge: https://sourceforge.net/projects/bbmap/, accessed on 24 January 2024). This step involved removing the adapter sequences and trimming the low-quality bases under the following parameters: ref=adapters qtrim=rl trimq=20 minlength=36 ktrim=r forcetrimleft=15 tossjunk=t. The cleaned reads were assembled into draft genomes using MEGAHIT v1.2.9 [27], with default k-mer size settings (21, 29, 39, 59, 79, 99, 119, 141).
The resulting contigs from 55330 and 55898 were ordered by length, and those shorter than 1000 bp were excluded. The assembled genomes were submitted to NCBI genomes, where they were automatically screened for contamination. BUSCO v5.4.3 [28] was used to evaluate the genome completeness, and QUAST v5.0.2 [29] provided a detailed assessment of the assembly’s quality metrics.

2.4. Phylogenetic Analysis

To investigate the evolutionary relationships among the P. capsici isolates, a phylogenetic tree was constructed using the Universal Fungal Core Genes (UFCG) [30] pipeline (GitHub—steineggerlab/ufcg). This study involved 28 Phytophthora genomes obtained from NCBI, which included 27 genomes from clade 2, featuring the Canadian strains 55330 and 55898 that were sequenced, assembled, and annotated as part of this research. Phytophthora infestans, representing clade 1, served as the outgroup for the phylogenetic analysis. Fifty core genes were extracted, aligned, and concatenated following the UFCG protocol, allowing for a robust comparison of the evolutionary relationships among the isolates. A Maximum Likelihood phylogenetic analysis of the concatenated gene sequences was conducted using IQ-Tree version 2.2.6 [31], with 1000 bootstrap replicates employed to identify the most reliable maximum-likelihood tree.

2.5. Genome Annotation

The annotation of the decontaminated genome assemblies was conducted using Funannotate v1.8.13 (https://github.com/nextgenusfs/funannotate, accessed on 24 January 2024), following its documentation (https://funannotate.readthedocs.io/en/latest/, accessed on 24 January 2024). Repetitive sequences were masked using the funannotate mask function with Tantan v39 [32]. The coding sequences (CDS) from the P. capsici LT1534-B genome (GenBank record JADEVP000000000.1, https://www.ncbi.nlm.nih.gov/Traces/wgs/JADEVP01, accessed on 7 October 2024) served as transcript evidence for the subsequent gene prediction.
The ab initio gene prediction was carried out through the funannotate predict command. First, the transcript evidence was aligned to the masked assembly with Minimap2 v2.24 [33]. Next, Diamond v2.0.15 [34] and Exonerate v2.4.0 [35] were used to map proteins from the UniProtKB/Swiss-Prot database (March 2022 release) to the assembly. BUSCO v2.0 [36] helped identify conserved gene models to refine the training of ab initio predictors. The gene models were generated using three algorithms: Augustus v3.3.2 [37], SNAP (version 2006-07-28) [38], and GlimmerHMM v3.0.4 [39]. To produce a comprehensive set of gene models, EVidenceModeler (EVM) v1.1.1 [40] combined the outputs of these predictions. Additional parameters were set for the funannotate predict command, including --min_training_models 50, --busco_db alveolata_stramenophiles, and --organism other.
The assembly statistics were evaluated using QUAST v5.0.2 [29], while the completeness of the genome annotation was assessed using BUSCO v5.4.3 [28] in protein mode, with the stramenopiles_odb10 database. Finally, OrthoVenn3 (bioinfotoolkits.net) was used for gene orthology analysis, applying the OrthoMLC algorithm and setting an E-value threshold of 1e−15.

2.6. Identification of Putative Effectors

The predicted proteins from the P. capsici strains were analyzed using InterProScan 5. The proteins showing Pfam domains linked to pathogenicity were identified and classified as potential effectors, following the approach outlined by McGowan and Fitzpatrick [41].
For apoplastic effectors, proteins from P. capsici 55898, 55330, LT263, and LT1564 v11.0 were submitted to the dbCAN3 web server to predict carbohydrate-active enzymes (CAZymes). The protein sequences were processed through HMMER: dbCAN (E-value < 1e−15, coverage > 0.35), DIAMOND: CAZy (E-value < 1e−102), and HMMER: dbCAN-sub (E-value < 1e−15, coverage > 0.35). To further filter the apoplastic proteins, SignalP and TMHMM were used to predict the presence of signal peptides and transmembrane domains, respectively. The proteins predicted to be secreted, lacking transmembrane domains after signal peptide cleavage, were flagged as likely secreted proteins.
The identification of RxLR and CRN effectors, which play key roles in host-cell manipulation, was carried out using the effectR tool (effectR/README.md at master · grunwaldlab/effectR · GitHub). Motifs characteristic of RxLR (RxLR-EER) and CRN (LxFLAK-HVLV) effectors were detected through regular expression-based searches. To confirm their accuracy, the predicted effectors were reanalyzed using InterProScan [42].

3. Results

3.1. Pathogenicity of P. capsici Canadian Strains

The pathogenicity assay demonstrated that both P. capsici 55330 and 55898 were pathogenic on pepper plants, causing root rot, severe wilting, and decline. The pepper seedlings inoculated with 55898 began wilting at 10 days post-inoculation (dpi) and collapsed by 15 dpi, whereas those inoculated with 55330 started wilting at 15 dpi and collapsed by 20 dpi. Examination of the roots from the collapsed plants showed rot symptoms similar to those caused by P. capsici. The pathogen was successfully reisolated from infected tissues, with species identity confirmed by morphological characteristics, thus fulfilling Koch’s postulates. The control plants remained symptom-free, and P. capsici was not recovered from their tissues.

3.2. Genome Assembly and Phylogenetic Analysis

The genomic analysis of the two Canadian P. capsici 55330 and 55898, a cucurbit-infecting strain (LT1534), and its recurrent parent strain (LT263) revealed distinct genomic features (Table 1). Strain 55330 had a genome size of 57.3 Mb, assembled into 1640 contigs with an N50 sequence length of 54.5 Kb and an L50 of 288 sequences, reflecting a high assembly quality. It had 13,474 predicted genes and a GC content of 50.11%, with 99% completeness (BUSCO). Strain 55898, while slightly larger at 60.2 Mb, had a more fragmented assembly with an N50 of 6 Kb and L50 of 2501 sequences. It contained 14,054 predicted genes and also achieved 99% completeness. LT1534, although similar in genome size to the Canadian strains at 56 Mb, exhibited much greater fragmentation, with 10,750 contigs and a lower N50 of 34.6 Kb. In contrast, LT263 had the largest genome at 78.8 Mb, with an N50 of 1.6 Mb, 20,950 predicted genes, and 100% completeness, underscoring its superior assembly quality (Table 1).
The phylogenomic analysis, involving 28 Phytophthora genomes, confirmed that the two Canadian P. capsici (55330 and 55898) belong to subclade 2b, closely related to the reference P. capsici isolates from NCBI (Figure 1).

3.3. Secretome Variation and Functional Classification

In the present study, we assessed the proteome and secretome repertoire of four P. capsici strains: 55330, 55898, LT263, and LT1534. All strains displayed high proteome completeness, with a BUSCO score of 99%, indicating successful annotation of the core oomycete-specific orthologs (Table 1). LT263 had the largest proteome with 20,950 predicted proteins, followed by LT1534 with 14,397, 55898 with 14,288, and 55330 with 13,813. LT263 showed the highest number of predicted secreted proteins with 2224, while LT1534, 55330, and 55898 had 1852, 1831, and 1645, respectively (Table 1). This variation in proteome size and secretome potential points to differences in the organisms’ functional capacities. The predicted secreted proteins were further classified as apoplastic or cytoplasmic and grouped by function, including microbe-associated molecular patterns (MAMPs), carbohydrate-active enzymes (CAZymes), protease inhibitors, necrosis- and ethylene-inducing peptide 1-like proteins (NLPs), phytotoxins (PcF), cutinases, and key effectors like RxLR and CRN (Table 2, Figure S1).

3.3.1. Microbe-Associated Molecular Pattern (MAMP) Profiling

Strain 55330 exhibited the highest number of predicted secreted sterol-binding proteins (140), followed by LT263 (118) and 55898 (110), while LT1534 had the lowest (100). Transglutaminases were most abundant in 55898 (17 predicted proteins), followed by LT263 (15), with 55330 and LT1534 each having 14 predicted secreted proteins.

3.3.2. Apoplastic Effectors Profiling

The total number of genes encoding predicted secreted CAZymes for each strain (Table 2), and the comparative analysis of genes encoding secreted CAZyme sub-families (Figure 2), reveals significant variations in predicted enzyme composition and abundance. LT263 had the highest glycoside hydrolase (GH) count with 137 predicted proteins, followed by 55330 with 119, LT1534 with 111, and 55898 with the lowest at 95. Among the GH families, GH1, GH3, GH12, GH16, and GH28 were well-represented across all strains. LT263 had the highest GH3 count with 16 predicted proteins, while 55330 and 55898 each had 11. LT263 also had the most GH12 with 10 predicted proteins, compared to 7 in 55330, 55898, and LT1534. The GH16 and GH28 families, which degrade beta-glucan and pectin, respectively, showed consistent abundance across the P. capsici strains. Strain 55330 had 11 predicted proteins in GH16, followed by LT263 with 9, and 55898 and LT1534 with 5 and 6, respectively. In GH28, 55330 and LT263 had the highest counts of 12 and 13 predicted proteins, while LT1534 and 55898 had 9 each.
Auxiliary activities (AAs), crucial for assisting in plant cell-wall degradation, were particularly enriched in LT263 (35 predicted proteins) compared to 55330 (26), LT1534 (23), and 55898 (21), indicating potential strain-specific adaptations in substrate utilization [15]. Within the AA families, AA17 was especially abundant in LT263 with 21 predicted proteins, while other AA families, such as AA1, AA2, AA3, and AA7, were consistently present across strains.
Carbohydrate-binding molecules (CBMs) were found in lower numbers, with LT263 having 10 predicted proteins, 55330 with 9, LT1534 with 7, and 55898 with 4. Carbohydrate esterases (CEs), which modify carbohydrates, were most abundant in 55898 (8 predicted proteins), followed by 55330 and LT263 with 7 each, and LT1534 with 6. Glycosyl transferases (GTs), essential for glycosidic bond biosynthesis, were equally abundant in LT263 and 55898 (7 predicted proteins each), while 55330 and LT1534 both had 5. Polysaccharide lyases (PLs), which cleave glycosidic bonds in polysaccharides, were more abundant in LT263 (51 predicted proteins) and 55330 with 35, compared to LT1534 with 26, and 55898 with 17, suggesting that LT263 and 55330 may have a greater ability to degrade plant cell walls [20].
The genomes of P. capsici 55898, 55330, LT263, and LT1534 revealed the presence of various protease inhibitor genes, with counts of 127, 137, 135, and 128, respectively. The analysis identified phytotoxins belonging to the PcF toxin family, known to trigger immune responses in plants, with only strains 55330 and LT263 containing this toxin. For necrosis-inducing proteins (NLPs), the strains exhibited the following predicted protein counts: 168 for 55330, 137 for 55898, 170 for LT263, and 169 for LT1534. Regarding cutinases, which help pathogens breach the plant barrier, strains 55330 and 55898 each had 5 predicted cutinases, while LT263 and LT1534 contained 6 each.

3.3.3. Cytoplasmic Effectors Profiling

The Canadian strains 55330 and 55898 contained 88 and 85 CRN effectors, respectively (Table 2). Strain 55330 had 27 proteins with complete motifs, 22 without motifs, 26 with only the HVLV motif, and 13 with only the LFLAK motif. Strain 55898 showed the highest number of proteins lacking motifs (34), along with 18 proteins with complete motifs, 21 with HVLV motifs, and 12 with LFLAK motifs (Figure 3). LT263 exhibited the highest number of CRN effectors with 118, while LT1534 had the fewest at 84 (Table 2). Specifically, LT263 contained 48 predicted proteins with complete motifs, 28 with only HVLV motifs, 24 with only LFLAK motifs, and 24 predicted proteins lacking any motifs. LT1534 displayed a more balanced distribution, comprising 24 complete motifs, 30 predicted proteins without motifs, 20 with HVLV motifs only, and 10 with LFLAK motifs only (Figure 3).
An orthology cluster analysis of CRN-predicted effectors identified 21 shared clusters among all strains, with unique clusters in LT263 (3), LT1534 (4), and 55898 (2). Strain 55330 had no unique clusters. Most unique clusters in LT1534 and LT263 contained effectors without motifs, though LT263 also included effectors with complete and HVLV motifs (Figure 4).
In terms of RxLR-predicted effectors, strains 55330 and 55898 had 257 and 251 effectors, respectively (Table 2). Strain 55330 contained 179 complete effectors, 50 without motifs, 19 with only the EER motif, and 9 with only the RxLR motif. In contrast, strain 55898 had 151 complete effectors, 65 without motifs, 16 with only the EER motif, and 19 with only the RxLR motif (Figure 5). LT263 had the highest RxLR-effector count with 294, while LT1534 had the lowest at 236 (Table 2). Across all strains, 109 RxLR-effector orthogroups were identified, with unique orthogroups found in 55330 (3), LT1534 (1), LT263 (6), and none in 55898 (Figure 6).

4. Discussion

The pathogenic potential of Canadian P. capsici 55330 and 55898 was assessed through a comparative genomic and proteomic analysis with the strain LT1534 [21] and its recurrent parent LT263 [22]. LT263 is recognized for its broad virulence and high reproductive capacity, making it a valuable model for genetic studies [9,22].
The de novo genome assembly revealed that the Canadian isolates have smaller genomes compared to LT263, with 55330 at 57.3 Mb and 55898 at 60.2 Mb, suggesting potential differences in pathogenicity and adaptability. Previous studies reported genome size variation in P. capsici, ranging from 40.7 to 92.6 Mb, indicating that genome size alone may not directly correlate with virulence but may reflect different evolutionary adaptations [22,43]. Similar trends are observed in other Phytophthora species, such as P. infestans and P. cactorum, where genome size variation is often linked to shifts in pathogenicity and environmental adaptability [21]. The genome of P. cactorum is 121.5 Mb, larger than P. capsici but smaller than the 240 Mb genome of P. infestans [21]. While genome size alone is not a definitive indicator of virulence, it may reflect evolutionary mechanisms like gene loss and gain, and changes in virulence-associated gene families, enabling rapid adaptability in P. capsici and other oomycetes [1,9,15,44,45]. The role of genome size in evolution is debated, with some suggesting it is primarily influenced by genetic drift, while others argue it can be an adaptive trait affecting phenotypic traits related to growth and development [46].
A phylogenetic analysis positioned the Canadian strains within subclade 2b, confirming their close evolutionary relationship with other P. capsici strains despite geographic isolation [15,47]. A BUSCO analysis showed high completeness across all strains, with BUSCO scores of 99%, indicating that both this study and previous ones effectively captured the complete genomes of these P. capsici strains. Strain LT263 had the highest number of total and secreted proteins. Secretome variability can influence host interactions and adaptability to environmental conditions, reflecting the critical role of secreted effectors in mediating virulence [9].
Sterol-binding proteins, also known as elicitins, are a family of proteins reported to induce hypersensitive cell death and defense-related biochemical changes [13]. The presence of sterol-binding proteins, or elicitins, varied across strains, with strain 55330 showing the highest number (140), suggesting enhanced adaptability and pathogenicity through improved sterol acquisition [13]. Strain LT1534, with lower protein counts (100), may face limitations under certain conditions. Transglutaminase proteins, which strengthen cell walls by cross-linking glutamine and lysine residues, vary among strains, with 55898 showing the highest count (17 predicted proteins), potentially enhancing resistance to plant defenses. The relatively conserved number of transglutaminases across strains suggests their essential role in structural integrity and proteolytic resistance [48]. These findings highlight that while sterol-binding proteins drive strain-specific adaptability and pathogenicity, transglutaminase proteins play a fundamental, conserved role in enhancing resistance to host defenses across P. capsici strains.
CAZymes are crucial for pathogenicity, as they facilitate the breakdown of host cell walls, thereby aiding infection [49]. A comparative analysis of CAZymes across P. capsici strains revealed differences in their potential to degrade plant cell walls, an important factor in pathogenicity. Strain LT263 had a higher overall CAZyme content, particularly in glycoside hydrolases (GHs) and auxiliary activities (AAs), consistent with its known virulence. In particular, the higher counts of GH12 and GH28 in LT263 and 55330 suggest a stronger capacity for degrading cellulose and pectin, key components of plant cell walls. Although the Canadian strains had smaller genomes, they displayed comparable CAZyme diversity, which underscores their potential virulence and adaptability across different environments.
The presence of diverse protease inhibitors indicates a complex interaction between P. capsici and its plant hosts, as the pathogens adapt to counteract host defenses. The finding of PcF toxins in only two strains suggests potential variability in pathogenicity among the strains. The high number of NLP genes across all strains supports their role in promoting necrotrophic behavior. The comparable levels of cutinases across strains imply similar strategies for degrading the plant cuticle, critical for successful infection.
A major finding in our comparative analysis was the diversity in cytoplasmic effectors, such as RxLR and CRN, which play a critical role in virulence and pathogenicity. These variations are consistent with previous reports from comparative analyses of P. capsici, P. cactorum, and other Phytophthora species, which highlighted differences in pathogenicity-related genes, including those encoding secreted proteins with RxLR or Crinkler motifs [21,50]. Gene-family expansions in P. cactorum have been associated with increased detoxification capabilities [21], suggesting that similar mechanisms may contribute to the adaptability of P. capsici. The differences in effector repertoires likely reflect host adaptation, as P. capsici infects a wide range of plant species, and strains may evolve specific effector profiles to enhance colonization of diverse hosts [51].
An orthology analysis revealed shared and unique CRN clusters across strains, with LT263 displaying the highest diversity of effector motifs. This aligns with previous reports highlighting gene-family expansions and the role of effector diversification in increasing the adaptability and host range of aggressive P. capsici isolates [9,52]. The lack of unique CRN clusters in 55330 suggests a more conserved pathogenic strategy compared to LT263, which displayed a more diverse effector motif content, indicating greater flexibility in host–pathogen interactions. The presence of unique RxLR orthogroups in LT263, along with its diverse effector content, may be linked to adaptive evolution, enabling this strain to evade host defenses more effectively and potentially exhibit broader host specificity [53,54].
The pathogenicity assay results, showing differing timelines of wilting and collapse for P. capsici 55330 and 55898, align with the distinct genomic and secretomic profiles of these strains. Strain 55898 induced symptoms in pepper plants more quickly (10 dpi) than strain 55330 (15 dpi), suggesting a difference in virulence. This increased virulence in strain 55898 could be linked to its higher count of transglutaminase proteins (17 vs. 14 in 55330), which strengthen cell-wall defense and potentially increase strain resistance to plant defenses. Additionally, strain 55330 exhibited a higher number of sterol-binding proteins (140 vs. 110 in 55898), which are known to trigger plant immune responses. This higher count might explain the comparatively slower symptom development in plants infected with 55330, as the plant response could delay disease progression. Both strains showed distinct cytoplasmic effectors, such as CRNs, which modulate plant immune responses. Strain 55330 had a slightly higher count of CRN effectors (88 vs. 85 in 55898), with more complete motifs that could contribute to a slower, but persistent, pathogenic effect. In contrast, strain 55898 contained the highest number of proteins lacking CRN motifs, possibly contributing to its faster impact on host plants by evading early recognition. These differences in protein content and effector profiles highlight the functional adaptability of each strain to exploit host plants differently, which reflects their unique pathogenic timelines observed in the greenhouse assays.
The findings in this study align with the broader understanding of how genomic plasticity and effector repertoires drive the evolutionary trajectories of P. capsici strains, influencing their ability to colonize diverse hosts and thrive in varying environmental conditions.

5. Conclusions

The comparative genomic and proteomic analysis of Canadian Phytophthora capsici strains 55330 and 55898 revealed important differences in the genome size, effector diversity, and gene-family expansions, influencing their pathogenicity and adaptability. Although smaller than global strains such as LT263, the Canadian strains exhibited diverse effector repertoires and CAZyme profiles, demonstrating adaptability to infect various plant hosts. In particular, 55898 induced rapid wilting in pepper plants, likely due to its higher transglutaminase content, which may enhance host cell-wall stability against defense responses, while 55330’s higher sterol-binding proteins may activate host immune responses, delaying disease progression. These strain-specific pathogenic strategies underscore the potential for targeted disease control by focusing on effectors and enzymes essential for infection. This study sets the stage for future research in gene silencing and fungicide development targeting these key pathogenic genes, advancing strategies for precise, effective P. capsici management.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14112623/s1, Figure S1: Density plot displaying intergenic distances (5′ and 3′) for cytoplasmic, apoplastic, and MAMP effector candidates in Phytophthora capsici strains 55330, 55898, LT1534v11.0, and LT263. The plots illustrate the distribution of gene locations, emphasizing specific subsets of effector candidates, including complete RxLR and CRN effectors, apoplastic effectors, and MAMP candidates.

Author Contributions

Conceptualization, O.V. and W.E.; data curation, O.V., H.D.T.N. and W.E.; formal analysis, H.D.T.N. and W.E.; funding acquisition, W.E.; investigation, O.V. and W.E.; methodology, O.V., H.D.T.N. and W.E.; project administration, W.E.; resources, W.E.; software, O.V., H.D.T.N. and W.E.; supervision, W.E.; validation, O.V., H.D.T.N. and W.E.; visualization, O.V. and W.E.; writing—original draft, O.V.; writing—review and editing, H.D.T.N. and W.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Agriculture and Agri-Food Canada under grant number J-002328, awarded to Ellouze.

Data Availability Statement

The datasets generated and analyzed in this study are publicly available in the NCBI repository, (https://www.ncbi.nlm.nih.gov, accessed on 7 October 2024), under BioProject: PRJNA810702; BioSample ID: SAMN26278237 and SAMN26274322; WGS project: JAVLHG01 and JAVLHH01; GenBank assembly: GCA_032158065.1 and GCA_032161705.1; genome assembly: ASM3215806v1 and ASM3216170v1; and Sequence Read Archive (SRA): SRX21773689, SRX14302110, and SRX14303976.

Acknowledgments

The authors sincerely thank Shannon Xuechan Shan from the plant disease clinic at the University of Guelph, for generously providing the two Canadian P. capsici strains. We are also deeply thankful to Antonet Svircev, Agriculture and Agri-Food Canada, Vineland Station, ON, for her invaluable feedback and thorough review of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lamour, K.H.; Stam, R.; Jupe, J.; Huitema, E. The oomycete broad-host-range pathogen Phytophthora capsici. Mol. Plant Pathol. 2012, 13, 329–337. [Google Scholar] [CrossRef] [PubMed]
  2. Kamoun, S.; Furzer, O.; Jones, J.D.G.; Judelson, H.S.; Ali, G.S.; Dalio, R.J.D.; Roy, S.G.; Schena, L.; Zambounis, A.; Panabières, F.; et al. The Top 10 oomycete pathogens in molecular plant pathology. Mol. Plant Pathol. 2015, 16, 413–434. [Google Scholar] [CrossRef] [PubMed]
  3. Sholberg, P.L.; Walker, M.C.; O’Gorman, D.T.; Jesperson, G.D. First report of Phytophthora capsici on cucurbits and peppers in British Columbia. Can. J. Plant Pathol. 2007, 29, 153–158. [Google Scholar] [CrossRef]
  4. Abad, Z.G.; Burgess, T.I.; Redford, A.J.; Bienapfl, J.C.; Srivastava, S.; Mathew, R.; Jennings, K. IDphy: An International Online Resource for Molecular and Morphological Identification of Phytophthora. Plant Dis. 2023, 107, 987–998. [Google Scholar] [CrossRef] [PubMed]
  5. Quesada-Ocampo, L.M.; Parada-Rojas, C.H.; Hansen, Z.; Vogel, G.; Smart, C.; Hausbeck, M.K.; Carmo, R.M.; Huitema, E.; Naegele, R.P.; Kousik, C.S.; et al. Phytophthora capsici: Recent Progress on Fundamental Biology and Disease Management 100 Years After Its Description. Annu. Rev. Phytopathol. 2023, 61, 185–208. [Google Scholar] [CrossRef]
  6. Anderson, T.R.; Garton, R. First report of blight of field peppers caused by Phytophthora capsici in Ontario. Plant Dis. 2000, 84, 705. [Google Scholar] [CrossRef]
  7. Gilbert, G.; Lacroix, M.; Hamel, D. Diseases diagnosed on commercial crops submitted to the MAPAQ diagnostic laboratory in 2000. Can. Plant Dis. Surv. 2001, 81, 40–58. [Google Scholar]
  8. French-Monar, R.D.; Jones, J.B.; Ozores-Hampton, M.; Roberts, P.D. Survival of Inoculum of Phytophthora capsici in Soil Through Time Under Different Soil Treatments. Plant Dis. 2007, 91, 593–598. [Google Scholar] [CrossRef]
  9. Lamour, K.H.; Mudge, J.; Gobena, D.; Hurtado-Gonzales, O.P.; Schmutz, J.; Kuo, A.; Miller, N.A.; Rice, B.J.; Raffaele, S.; Cano, L.M.; et al. Genome sequencing and mapping reveal loss of heterozygosity as a mechanism for rapid adaptation in the vegetable pathogen Phytophthora capsici. Mol. Plant Microbe Interact. 2012, 25, 1350–1360. [Google Scholar] [CrossRef]
  10. Barchenger, D.W.; Lamour, K.H.; Bosland, P.W. Challenges and strategies for breeding resistance in Capsicum annuum to the multifarious pathogen, Phytophthora capsici. Front. Plant Sci. 2018, 9, 628. [Google Scholar] [CrossRef]
  11. Stam, R.; Jupe, J.; Howden, A.J.; Morris, J.A.; Boevink, P.C.; Hedley, P.E.; Huitema, E. Identification and characterisation CRN effectors in Phytophthora capsici shows modularity and functional diversity. PLoS ONE 2013, 8, e59517. [Google Scholar] [CrossRef]
  12. Raffaele, S.; Kamoun, S. Genome evolution in filamentous plant pathogens: Why bigger can be better. Nat. Rev. Microbiol. 2012, 10, 417–430. [Google Scholar] [CrossRef] [PubMed]
  13. Kamoun, S. A catalogue of the effector secretome of plant pathogenic oomycetes. Annu. Rev. Phytopathol. 2006, 44, 41–60. [Google Scholar] [CrossRef] [PubMed]
  14. Tyler, B.M. Towards a systems-level understanding of oomycete-plant interactions. In Genome-Enabled Analysis of Plant-Pathogen Interactions; Wolpert, T., Shiraishi, T., Collmer, A., Akimitsu, K., Glazebrook, J., Eds.; The American Phytopathological Society: St. Paul, MN, USA, 2017; pp. 161–174. [Google Scholar]
  15. McGowan, J.; Fitzpatrick, D.A. Recent advances in oomycete genomics. In Advances in Genetics; Kumar, D., Ed.; Academic Press: Cambridge, MA, USA, 2020; Volume 105, pp. 175–228. [Google Scholar]
  16. Wang, Y.; Wang, Y. Trick or treat: Microbial pathogens evolved apoplastic effectors modulating plant susceptibility to infection. Mol. Plant-Microbe Interact. 2018, 31, 6–12. [Google Scholar] [CrossRef]
  17. Bozkurt, T.O.; Kamoun, S. The plant-pathogen haustorial interface at a glance. J. Cell. Sci. 2020, 133, jcs237958. [Google Scholar] [CrossRef]
  18. Morgan, W.; Kamoun, S. RXLR effectors of plant pathogenic oomycetes. Curr. Opin. Microbiol. 2007, 10, 332–338. [Google Scholar] [CrossRef]
  19. Kourelis, J.; van der Hoorn, R.A.L. Defended to the nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function. Plant Cell 2018, 30, 285–299. [Google Scholar] [CrossRef]
  20. Lee, J.-H.; Siddique, M.I.; Kwon, J.-K.; Kang, B.-C. Comparative genomic analysis reveals genetic variation and adaptive evolution in the pathogenicity-related genes of Phytophthora capsici. Front. Microbiol. 2021, 12, 694136. [Google Scholar] [CrossRef]
  21. Szadkowski, E.; Lagnel, J.; Touhami, N.; Sayeh, A.; Lopez-Roques, C.; Bouchez, O.; Lefebvre, V. Phytophthora capsici genome assembly for two isolates using long-read Oxford Nanopore Technology sequencing. Microbiol. Resour. Announc. 2023, 12, e00196-00123. [Google Scholar] [CrossRef]
  22. Cui, C.; Herlihy, J.H.; Bombarely, A.; McDowell, J.M.; Haak, D.C. Draft assembly of Phytophthora capsici from long-read sequencing uncovers complexity. Mol. Plant-Microbe Interact. 2019, 32, 1559–1563. [Google Scholar] [CrossRef]
  23. Ruan, J.; Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 2020, 17, 155–158. [Google Scholar] [CrossRef] [PubMed]
  24. Roach, M.J.; Schmidt, S.A.; Borneman, A.R. Purge Haplotigs: Allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinform. 2018, 19, 460. [Google Scholar] [CrossRef] [PubMed]
  25. Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed]
  26. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, Q.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9, e112963. [Google Scholar] [CrossRef] [PubMed]
  27. Li, D.; Liu, C.M.; Luo, R.; Sadakane, K.; Lam, T.W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef]
  28. Manni, M.; Berkeley, M.R.; Seppey, M.; Zdobnov, E.M. BUSCO: Assessing Genomic Data Quality and Beyond. Curr. Protoc. 2021, 1, e323. [Google Scholar] [CrossRef]
  29. Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013, 29, 1072–1075. [Google Scholar] [CrossRef]
  30. Kim, D.; Gilchrist, C.L.M.; Chun, J.; Steinegger, M. UFCG: Database of universal fungal core genes and pipeline for genome-wide phylogenetic analysis of fungi. Nucleic Acids Res. 2022, 51, D777–D784. [Google Scholar] [CrossRef]
  31. Minh, B.Q.; Schmidt, H.A.; Chernomor, O.; Schrempf, D.; Woodhams, M.D.; von Haeseler, A.; Lanfear, R. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol. Biol. Evol. 2020, 37, 1530–1534. [Google Scholar] [CrossRef]
  32. Frith, M.C. A new repeat-masking method enables specific detection of homologous sequences. Nucleic Acids Res. 2011, 39, e23. [Google Scholar] [CrossRef]
  33. Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 2018, 34, 3094–3100. [Google Scholar] [CrossRef] [PubMed]
  34. Buchfink, B.; Reuter, K.; Drost, H.G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 2021, 18, 366–368. [Google Scholar] [CrossRef] [PubMed]
  35. Slater, G.S.; Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 2005, 6, 31. [Google Scholar] [CrossRef] [PubMed]
  36. Simão, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef] [PubMed]
  37. Stanke, M.; Diekhans, M.; Baertsch, R.; Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 2008, 24, 637–644. [Google Scholar] [CrossRef]
  38. Johnson, A.D.; Handsaker, R.E.; Pulit, S.L.; Nizzari, M.M.; O’Donnell, C.J.; de Bakker, P.I.W. SNAP: A web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008, 24, 2938–2939. [Google Scholar] [CrossRef]
  39. Majoros, W.H.; Pertea, M.; Salzberg, S.L. TigrScan and GlimmerHMM: Two open source ab initio eukaryotic gene-finders. Bioinformatics 2004, 20, 2878–2879. [Google Scholar] [CrossRef]
  40. Haas, B.J.; Salzberg, S.L.; Zhu, W.; Pertea, M.; Allen, J.E.; Orvis, J.; White, O.; Buell, C.R.; Wortman, J.R. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008, 9, R7. [Google Scholar] [CrossRef]
  41. McGowan, J.; Fitzpatrick, D.A. Genomic, network, and phylogenetic analysis of the oomycete effector arsenal. mSphere 2017, 2, 10–1128. [Google Scholar] [CrossRef]
  42. Jones, P.; Binns, D.; Chang, H.-Y.; Fraser, M.; Li, W.; McAnulla, C.; McWilliam, H.; Maslen, J.; Mitchell, A.; Nuka, G.; et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 2014, 30, 1236–1240. [Google Scholar] [CrossRef]
  43. Reyes-Tena, A.; Huguet-Tapia, J.C.; Lamour, K.H.; Goss, E.M.; Rodríguez-Alvarado, G.; Vázquez-Marrufo, G.; Santillán-Mendoza, R.; Fernández-Pavía, S.P. Genome sequence data of six isolates of Phytophthora capsici from Mexico. Mol. Plant-Microbe Interact. 2019, 32, 1267–1269. [Google Scholar] [CrossRef] [PubMed]
  44. Vercauteren, A.; Boutet, X.; D’hondt, L.; Van Bockstaele, E.; Maes, M.; Leus, L.; Chandelier, A.; Heungens, K. Aberrant genome size and instability of Phytophthora ramorum oospore progenies. Fungal Genet. Biol. 2011, 48, 537–543. [Google Scholar] [CrossRef] [PubMed]
  45. Villanueva, O.; Ellouze, W. First report of a Canadian isolate of Phytopythium vexans causing root rot disease on apple and peach under laboratory conditions. New Dis. Rep. 2023, 48, e12195. [Google Scholar] [CrossRef]
  46. Blommaert, J. Genome size evolution: Towards new model systems for old questions. Proc. R. Soc. B Biol. Sci. 2020, 287, 20201441. [Google Scholar] [CrossRef] [PubMed]
  47. Yang, X.; Tyler, B.M.; Hong, C. An expanded phylogeny for the genus Phytophthora. IMA Fungus 2017, 8, 355–384. [Google Scholar] [CrossRef]
  48. Serafini-Fracassini, D.; Del Duca, S. Transglutaminases: Widespread cross-linking enzymes in plants. Ann. Bot. 2008, 102, 145–152. [Google Scholar] [CrossRef]
  49. Lyu, X.; Shen, C.; Fu, Y.; Xie, J.; Jiang, D.; Li, G.; Cheng, J. Comparative genomic and transcriptional analyses of the carbohydrate-active enzymes and secretomes of phytopathogenic fungi reveal their significant roles during infection and development. Sci. Rep. 2015, 5, 15565. [Google Scholar] [CrossRef]
  50. Armitage, A.D.; Lysøe, E.; Nellist, C.F.; Lewis, L.A.; Cano, L.M.; Harrison, R.J.; Brurberg, M.B. Bioinformatic characterisation of the effector repertoire of the strawberry pathogen Phytophthora cactorum. PLoS ONE 2018, 13, e0202305. [Google Scholar] [CrossRef]
  51. Li, Q.; Chen, Y.; Wang, J.; Zou, F.; Jia, Y.; Shen, D.; Zhang, Q.; Jing, M.; Dou, D.; Zhang, M. A Phytophthora capsici virulence effector associates with NPR1 and suppresses plant immune responses. Phytopathol. Res. 2019, 1, 6. [Google Scholar] [CrossRef]
  52. Yang, M.; Duan, S.; Mei, X.; Huang, H.; Chen, W.; Liu, Y.; Guo, C.; Yang, T.; Wei, W.; Liu, X.; et al. The Phytophthora cactorum genome provides insights into the adaptation to host defense compounds and fungicides. Sci. Rep. 2018, 8, 6534. [Google Scholar] [CrossRef]
  53. Li, T.; Ai, G.; Fu, X.; Liu, J.; Zhu, H.; Zhai, Y.; Pan, W.; Shen, D.; Jing, M.; Xia, A.; et al. A Phytophthora capsici RXLR effector manipulates plant immunity by targeting RAB proteins and disturbing the protein trafficking pathway. Mol. Plant Pathol. 2022, 23, 1721–1736. [Google Scholar] [CrossRef] [PubMed]
  54. Wang, W.; Jiao, F. Effectors of Phytophthor a pathogens are powerful weapons for manipulating host immunity. Planta 2019, 250, 413–425. [Google Scholar] [CrossRef] [PubMed]
Agronomy 14 02623 g001
Figure 1. Maximum-likelihood phylogenetic tree generated based on concatenated alignment of 50 core genes shared between the 28 Phytophthora spp. genomes sourced from NCBI. The two P. capsici genomes from this study are highlighted in bold. The tree was rooted to P. infestans T30-4. The numbers at each node represent bootstrap support, expressed as percentages.
Figure 1. Maximum-likelihood phylogenetic tree generated based on concatenated alignment of 50 core genes shared between the 28 Phytophthora spp. genomes sourced from NCBI. The two P. capsici genomes from this study are highlighted in bold. The tree was rooted to P. infestans T30-4. The numbers at each node represent bootstrap support, expressed as percentages.
Agronomy 14 02623 g001
Agronomy 14 02623 g002
Figure 2. Distribution, abundance, and diversity of CAZyme families across P. capsici strains. Glycoside hydrolases (GHs), glycosyl transferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), auxiliary activities (AAs), and carbohydrate-binding modules (CBMs).
Figure 2. Distribution, abundance, and diversity of CAZyme families across P. capsici strains. Glycoside hydrolases (GHs), glycosyl transferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), auxiliary activities (AAs), and carbohydrate-binding modules (CBMs).
Agronomy 14 02623 g002
Agronomy 14 02623 g003
Figure 3. CRN motif content across P. capsici strains. CRN motif categories were defined as follows: “complete” contains both motifs of interest (LFLAK + HVLV), “LFLAK only” includes effectors with just the LFLAK motif, and “HVLV only” includes those with just the HVLV motif. The “no motifs” category refers to sequences that matched the HMM profile but lacked either of the motifs of interest.
Figure 3. CRN motif content across P. capsici strains. CRN motif categories were defined as follows: “complete” contains both motifs of interest (LFLAK + HVLV), “LFLAK only” includes effectors with just the LFLAK motif, and “HVLV only” includes those with just the HVLV motif. The “no motifs” category refers to sequences that matched the HMM profile but lacked either of the motifs of interest.
Agronomy 14 02623 g003
Agronomy 14 02623 g004
Figure 4. CRN-protein orthology analysis of P. capsici strains. (a) Venn diagram showing the number of shared CRN-protein clusters across different P. capsici strains. (b) Homology network representing the CRN clusters unique to each P. capsici strain. (c) Classification of CRN-effector motifs within the unique clusters identified in each P. capsici strain.
Figure 4. CRN-protein orthology analysis of P. capsici strains. (a) Venn diagram showing the number of shared CRN-protein clusters across different P. capsici strains. (b) Homology network representing the CRN clusters unique to each P. capsici strain. (c) Classification of CRN-effector motifs within the unique clusters identified in each P. capsici strain.
Agronomy 14 02623 g004
Agronomy 14 02623 g005
Figure 5. RxLR motif content across P. capsici strains. RxLR motif categories were defined as follows: “complete” contains both motifs of interest (RxLR and EER), “RxLR only” includes effectors with just the RxLR motif, and “EER only” includes those with just the EER motif. The “no motifs” category refers to sequences that matched the HMM profile but lacked either of the motifs of interest.
Figure 5. RxLR motif content across P. capsici strains. RxLR motif categories were defined as follows: “complete” contains both motifs of interest (RxLR and EER), “RxLR only” includes effectors with just the RxLR motif, and “EER only” includes those with just the EER motif. The “no motifs” category refers to sequences that matched the HMM profile but lacked either of the motifs of interest.
Agronomy 14 02623 g005
Agronomy 14 02623 g006
Figure 6. RxLR-protein orthology analysis of P. capsici strains. (a) Venn diagram showing the number of shared RxLR-protein clusters across different P. capsici strains. (b) Homology network representing the RxLR clusters unique to each P. capsici strain. (c) Classification of RxLR-effector motifs within the unique clusters identified in each P. capsici strain.
Figure 6. RxLR-protein orthology analysis of P. capsici strains. (a) Venn diagram showing the number of shared RxLR-protein clusters across different P. capsici strains. (b) Homology network representing the RxLR clusters unique to each P. capsici strain. (c) Classification of RxLR-effector motifs within the unique clusters identified in each P. capsici strain.
Agronomy 14 02623 g006
Table 1. Assembly and annotation statistics of P. capsici genomes.
Table 1. Assembly and annotation statistics of P. capsici genomes.
P. capsici Strains5533055898LT 263LT 1534 v11.0
Total size (Mb)57.660.278.856
Number of contigs1640159511710,750
Contig N5054.5 Kb6 Kb1.6 Mb34.6 Kb
Contig L50288250116396
Longest contig (bp)292,67694,0786,028,800387,890
GC percent50.1150.1151.0050.5
Coverage52.0×127.0×200.0×35.0×
BUSCO genome completeness99%99%100%100%
BUSCO duplication1%0%0%1%
BUSCO protein completeness99%99%99%99%
Total number of proteins13,81314,28820,95014,397
Secreted proteins1831164522241852
GenBank WGS accessionJAVLHG01JAVLHH01JAFEIO01ADVJ01
Table 2. Total number of predicted effector gene candidates in P. capsici strains. The numbers represent genes encoding predicted secreted proteins.
Table 2. Total number of predicted effector gene candidates in P. capsici strains. The numbers represent genes encoding predicted secreted proteins.
Category FamilyNumber of Proteins per Strain
5589855330LT263LT1534 v11.0
MAMP Sterol-binding proteins110140118100
Tranglucomicase proteins17141514
Apoplastic effectorsCAZymes 1Glycoside hydrolases (GHs)95119137111
Auxiliary activities (AAs)21263523
Carbohydrate-binding molecules (CBMs)49107
Carbohydrate esterases (CEs)8776
Glycosyl transferase (GTs)7575
Polysaccharide lyases (PLs)17355126
Protease/inhibitorGlucanase49525652
Kazal64716461
Cathepsin8888
Cystatin6677
OthersPhytotoxins (PcF)0110
Necrose-inducing proteins (NLPs)137168170169
Cutinases5566
Cytoplasmic effectors RxLR 2251257294236
CRN 2858811884
1 CAZYme families obtained from dbCAN. 2 Manually curated.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Villanueva, O.; Nguyen, H.D.T.; Ellouze, W. Comparative Genomic and Secretome Analysis of Phytophthora capsici Strains: Exploring Pathogenicity and Evolutionary Dynamics. Agronomy 2024, 14, 2623. https://doi.org/10.3390/agronomy14112623

AMA Style

Villanueva O, Nguyen HDT, Ellouze W. Comparative Genomic and Secretome Analysis of Phytophthora capsici Strains: Exploring Pathogenicity and Evolutionary Dynamics. Agronomy. 2024; 14(11):2623. https://doi.org/10.3390/agronomy14112623

Chicago/Turabian Style

Villanueva, Oscar, Hai D. T. Nguyen, and Walid Ellouze. 2024. "Comparative Genomic and Secretome Analysis of Phytophthora capsici Strains: Exploring Pathogenicity and Evolutionary Dynamics" Agronomy 14, no. 11: 2623. https://doi.org/10.3390/agronomy14112623

APA Style

Villanueva, O., Nguyen, H. D. T., & Ellouze, W. (2024). Comparative Genomic and Secretome Analysis of Phytophthora capsici Strains: Exploring Pathogenicity and Evolutionary Dynamics. Agronomy, 14(11), 2623. https://doi.org/10.3390/agronomy14112623

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop