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. 2016 May 26;18(3):378–390. doi: 10.1111/mpp.12406

Quantitative proteomics links metabolic pathways to specific developmental stages of the plant‐pathogenic oomycete Phytophthora capsici

Zhili Pang 1,2,†, Vaibhav Srivastava 2,†, Xili Liu 1,✉, Vincent Bulone 2,3,✉
PMCID: PMC6638298  PMID: 27019332

Summary

The oomycete Phytophthora capsici is a plant pathogen responsible for important losses to vegetable production worldwide. Its asexual reproduction plays an important role in the rapid propagation and spread of the disease in the field. A global proteomics study was conducted to compare two key asexual life stages of P. capsici, i.e. the mycelium and cysts, to identify stage‐specific biochemical processes. A total of 1200 proteins was identified using qualitative and quantitative proteomics. The transcript abundance of some of the enriched proteins was also analysed by quantitative real‐time polymerase chain reaction. Seventy‐three proteins exhibited different levels of abundance between the mycelium and cysts. The proteins enriched in the mycelium are mainly associated with glycolysis, the tricarboxylic acid (or citric acid) cycle and the pentose phosphate pathway, providing the energy required for the biosynthesis of cellular building blocks and hyphal growth. In contrast, the proteins that are predominant in cysts are essentially involved in fatty acid degradation, suggesting that the early infection stage of the pathogen relies primarily on fatty acid degradation for energy production. The data provide a better understanding of P. capsici biology and suggest potential metabolic targets at the two different developmental stages for disease control.

Keywords: cysts, mass spectrometry, mycelium, Phytophthora capsici, plant‐pathogenic oomycete, quantitative proteomics

Introduction

The filamentous oomycete Phytophthora capsici is a virulent hemibiotrophic pathogen that causes crown, root and fruit rot in a range of solanaceous and legume hosts, as well as in most cucurbits (Gevens et al., 2008; Gobena et al., 2012; Kamoun et al., 2015; Leonian, 1922). It is ubiquitous and characterized by an explosive epidemiology (Hausbeck and Lamour, 2004; Kamoun et al., 2015; Lamour et al., 2012a, 2012b).

During warm (25–28°C) and wet conditions, P. capsici produces large numbers of asexual spores and rapidly spreads through crops, causing significant losses (Granke et al., 2009). The propagation and survival of P. capsici depend on both rapid asexual reproduction and sexual outcrossing (Lamour and Kamoun, 2009; Quesada‐Ocampo et al., 2011; Risiamo et al., 1991). The sexual life cycle requires interaction between two compatible mating types, designated A1 and A2 (Erwin and Ribeiro, 1996), which results in the production of oospores that can persist in the soil for years, thereby providing a primary source of inoculum in successive crops (Bowers et al., 1990). However, oospores have an indeterminate period of dormancy, generally more than 8 weeks, before germination (Satour and Butler, 1968). This limits their role in the rapid propagation and spread of the disease within an established crop, which is therefore predominantly achieved through asexual propagation. Under favourable environmental conditions, P. capsici produces large numbers of sporangia on the surface of infected tissues, which are easily dislodged by rainfall and irrigation. When immersed in water, the sporangia quickly release 20–40 motile biflagellate zoospores, which swim in search of suitable hosts (Bernhardt and Grogan, 1982). On contact, the spores encyst and adhere to the plant surface, initiating infection. Hyphae are then produced and grow and ramify throughout the host tissue. On maturity, the vegetative mycelium produces new sporangia, which provide the means for dispersal and the establishment of a new infection cycle. Given the importance of the asexual phase in the epidemiology of P. capsici, it is expected that a greater knowledge of the types of proteins associated with the corresponding developmental stages will provide further insight into the growth and infection processes of the pathogen.

Previous proteomics studies of oomycetes have applied both global and more specific approaches using mass spectrometry (MS). For example, a recent study profiled the secretome and extracellular proteome of Phytophthora infestans (Meijer et al., 2014), whereas Ebstrup et al. (2005) identified differentially regulated proteins from the germinating cysts and appressoria of the same pathogen using two‐dimensional gel electrophoresis (2‐DE) and MS. Similar comparative analyses have been made between the hyphae and germinating cysts of Phytophthora pisi and Phytophthora sojae (Hosseini et al., 2015), whereas another study compared the total proteome of P. sojae and Phytophthora ramorum (Savidor et al., 2008). High‐throughput proteomics approaches produce a wealth of data that provide useful information about the basic biology of pathogenic microorganisms. This, in turn, allows the identification of new potential targets of microbial growth inhibitors for the control of devastating diseases.

Here, we have performed a comparative analysis of the proteomes of the mycelium and cysts of P. capsici. The proteins identified were functionally classified, which provides the basis for future research on these developmental stages in P. capsici and closely related species. Many of the proteins enriched in either the mycelium or cysts were found to contribute specific functions in these two life stages. The data provide a better understanding of the metabolic pathways underlying the mycelial and cyst developmental stages of P. capsici and point to potential targets of anti‐oomycete compounds at each of these stages.

Results

Profiling the proteomes of P. capsici mycelium and cysts

Twelve hundred unique proteins were identified from isobaric tags for relative and absolute quantification (iTRAQ) and gel‐based proteomics analyses of P. capsici mycelium and cysts (Table S1, see Supporting Information). Of these, 688 were common to the lists generated independently by iTRAQ and in‐gel approaches (Fig. 1a). The combination of both experimental approaches increased significantly the total number of unique proteins identified. The in‐gel approach led to the identification of 928 unique proteins, 749 from the mycelium and 680 from the cysts, 501 of which were common to both samples (Fig. 1b). Qualitative analysis of the samples used for the iTRAQ experiments allowed the identification of 757, 683 and 724 unique proteins from the three biological replicates BR1, BR2 and BR3, respectively (Fig. 1c). In addition to the 519 proteins that were common to all three biological replicates, each replicate contained proteins that were not identified in the other two samples (Fig. 1c).

Figure 1.

Figure 1

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Qualitative proteomics analysis of the mycelium and cysts of Phytophthora capsici. Venn diagram representing the total number of unique proteins identified from the iTRAQ and in‐gel experiments (a), in‐gel experiments from cysts and mycelium samples (b) and iTRAQ analysis of three biological replicates (BRs) (c).

Bioinformatics analysis of all identified proteins (1200) indicated that 69 (6%) contained a signal peptide and were therefore likely to be secreted or membrane proteins (Table S1). Gene ontology analysis led to the categorization of the 1200 proteins into six different groups based on their predicted cellular location (Fig. 2a). A higher proportion of proteins were predicted to be membrane bound (23%) and cytoplasmic (15%). Proteins predicted to be associated with intracellular organelles and ribosomes represented 9% and 8% of the total proteins identified, respectively. A smaller proportion of proteins (4%) were predicted to be extracellular. However, the majority of the proteins (41%) could not be assigned any specific cellular location. The 1200 proteins were also classified into 10 functional groups corresponding to different biological processes (Fig. 2b). The largest groups corresponded to proteins involved in protein metabolism (29%) and proteins with an unknown function (27%). The next largest groups contained proteins involved in carbohydrate metabolism (8%), metabolic processes requiring nucleobase‐containing compounds (7%), transport (7%), pathogenesis/response to stress (6%), lipid metabolism (4%), signal transduction (3%) and other miscellaneous processes (9%). Only a very small proportion of the proteins identified (1%) were predicted to be associated with cytoskeleton organization.

Figure 2.

Figure 2

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Predicted cellular location (a) and functional annotation (b) of the proteins identified in the global proteomes of the mycelium and cysts of Phytophthora capsici.

Differential abundance of proteins in the mycelium and cysts

The variation in the intensity of several of the Coomassie blue‐stained sodium dodecylsulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) bands indicated that proteins of different apparent molecular masses were particularly more abundant in either the mycelium or cyst samples (Fig. 3). These results were substantiated by iTRAQ quantitative analysis, which revealed that 73 proteins had significantly different levels of abundance, with 39 being enriched in the mycelium (Table 1) and 34 in the cysts (Table 2).

Figure 3.

Figure 3

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Sodium dodecylsulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) analysis of the proteins extracted from the mycelium and cysts of Phytophthora capsici. Twenty micrograms of protein were loaded in each lane and stained with Coomassie blue. Each lane of the gel was cut into 17 bands, as shown on the left side of the figure, prior to proteomics analysis.

Table 1.

List of the Phytophthora capsici proteins with greater abundance in the mycelium than in the cysts.

P. infestans acc. no. P. capsici acc. no. Description* Score E value Hit identity (%) CC Functional category
PITG_15476 Phyca11|505882 Malate dehydrogenase 1488 0.00E+00 88.99 Cyt Carbohydrate metabolism
PITG_13116 Phyca11|506671 Triose‐phosphate isomerase 1206 4.39E‐158 93.20 Cyt Carbohydrate metabolism
PITG_09402 Phyca11|510172 Phosphoglycerate kinase 1081 4.82E‐141 88.84 Cyt Carbohydrate metabolism
PITG_05636 Phyca11|505958 Transaldolase 1540 0.00E+00 92.51 Cyt Carbohydrate metabolism
PITG_03698 Phyca11|503568 Enolase 2235 0.00E+00 95.59 Cyt Carbohydrate metabolism
PITG_18720 Phyca11|504894 ATP‐citrate synthase 5245 0.00E+00 92.54 Cyt Carbohydrate metabolism
PITG_10032 Phyca11|507227 6‐Phosphogluconate dehydrogenase 2500 0.00E+00 98.36 Mem Carbohydrate metabolism
PITG_01752 Phyca11|506857 Transketolase 3428 0.00E+00 93.94 Mem Carbohydrate metabolism
PITG_00146 Phyca11|509625 Glucose‐6‐phosphate 1‐dehydrogenase 2421 0.00E+00 93.69 Unc Carbohydrate metabolism
PITG_03724 Phyca11|503545 Phosphate di‐kinase 4235 0.00E+00 90.89 Unc Carbohydrate metabolism
PITG_02785 Phyca11|507726 Fructose‐bisphosphate aldolase 1804 0.00E+00 94.97 Unc Carbohydrate metabolism
PITG_02786 Phyca11|507726 Fructose‐bisphosphate aldolase 1815 0.00E+00 95.25 Unc Carbohydrate metabolism
PITG_01938 Phyca11|504523 Glyceraldehyde‐3‐phosphate dehydrogenase 1466 0.00E+00 93.05 Unc Carbohydrate metabolism
PITG_07960 Phyca11|575859 α‐Tubulin 2084 0.00E+00 93.06 Cyt Cytoskeleton
PITG_11069 Phyca11|509055 Villin‐like protein 4817 0.00E+00 65.78 Mem Cytoskeleton
PITG_01407 Phyca11|506353 d‐Isomer‐specific 2‐hydroxyacid dehydrogenase 1487 0.00E+00 86.49 Mem Nucleobase‐containing compound metabolic process
PITG_01408 Phyca11|506353 d‐Isomer‐specific 2‐hydroxyacid dehydrogenase 1619 0.00E+00 93.99 Mem Nucleobase‐containing compound metabolic process
PITG_03552 Phyca11|538675 Histone H4 410 1.93E‐50 100.00 Org Nucleobase‐containing compound metabolic process
PITG_00921 Phyca11|568375 Phospholipase D, Pi‐PLD‐like‐1 2689 0.00E+00 94.54 Unc Nucleobase‐containing compound metabolic process
PITG_15553 Phyca11|509385 Inorganic pyrophosphatase 2856 0.00E+00 95.15 Cyt Other
PITG_00488 Phyca11|530474 Acyl‐coenzyme A‐binding protein 289 1.44E‐31 84.06 Unc Other
PITG_02594 Phyca11|17928 Glutamate decarboxylase 1751 0.00E+00 93.24 Cyt Pathogenesis/response to stress
PITG_12562 Phyca11|529073 Elicitin‐like protein 513 2.59E‐61 83.05 Ext Pathogenesis/response to stress
PITG_18316 Phyca11|503727 Phospholipid hydroperoxide glutathione peroxidase 1952 0.00E+00 92.36 Mem Pathogenesis/response to stress
PITG_12158 Phyca11|509308 Annexin (annexin) family 1377 0.00E+00 86.82 Unc Pathogenesis/response to stress
PITG_11329 Phyca11|107407 Annexin (annexin) family 1742 2.98E‐112 75.32 Mem Pathogenesis/response to stress
PITG_05861 Phyca11|511613 Metalloprotease family m20 m25 m40 1843 0.00E+00 92.86 Unc Protein metabolism
PITG_01072 Phyca11|509774 5‐Methyl‐tetrahydropteroyl‐triglutamate‐homocysteine methyltransferase 3640 0.00E+00 94.20 Unc Protein metabolism
PITG_04611 Phyca11|504366 Protein kinase 1714 0.00E+00 95.13 Unc Protein metabolism
PITG_11719 Phyca11|503391 4‐Hydroxyphenylpyruvate dioxygenase 1939 0.00E+00 92.66 Unc Protein metabolism
PITG_02586 Phyca11|116914 Cytoplasmic dynein 1 heavy chain 1 1095 2.77E‐133 92.80 Unc Signal transduction
PITG_19250 Phyca11|505536 12‐Oxophytodienoate reductase 1651 0.00E+00 83.98 Unc Signal transduction
PITG_14721 Phyca11|506608 12‐Oxophytodienoate reductase 1694 0.00E+00 85.87 Unc Signal transduction
PITG_17921 Phyca11|510618 P‐type ATPase (P‐atpase) superfamily 4957 0.00E+00 95.26 Mem Transport
PITG_00693 Phyca11|535506 Plasma membrane H+‐ATPase 3333 0.00E+00 96.14 Mem Transport
PITG_03719 Phyca11|503558 Voltage‐gated potassium channel subunit β 1779 0.00E+00 96.56 Unc Transport
PITG_02909 Phyca11|507657 Carbohydrate‐binding protein 1782 0.00E+00 81.77 Mem Unclassified
PITG_12486 Phyca11|570704 Methionine‐tRNA ligase 856 1.07E‐109 94.35 Unc Unclassified
PITG_18305 Phyca11|503721 Conserved hypothetical protein 1533 0.00E+00 77.47 Unc Unclassified
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*From P. infestans protein database.

acc. no., accession number; CC, cellular component; Cyt, cytoplasm; Ext, extracellular compartment; Mem, membrane‐bound; Org, organelles; Unc, unclassified.

Table 2.

List of the Phytophthora capsici proteins with greater abundance in the cysts than in the mycelium.

P. infestans acc. no. P. capsici acc. no. Description* Score E value Hit identity (%) CC Functional category
PITG_10031 Phyca11|534612 Lysosomal α‐mannosidase 1973 0.00E+00 80.67 Ext Carbohydrate metabolism
PITG_00682 Phyca11|118794 Carbonic anhydrase 1486 0.00E+00 94.92 Unc Carbohydrate metabolism
PITG_07027 Phyca11|503263 Isocitrate lyase 2591 0.00E+00 92.18 Unc Carbohydrate metabolism
PITG_07999 Phyca11|575859 α‐Tubulin 2088 0.00E+00 91.01 Cyt Cytoskeleton
PITG_00156 Phyca11|576734 β‐Tubulin 2199 0.00E+00 100.00 Cyt Cytoskeleton
PITG_09955 Phyca11|529555 Acyl‐CoA dehydrogenase family member 9 2929 0.00E+00 95.30 Mem Lipid metabolism
PITG_07197 Phyca11|534354 3‐Ketoacyl‐CoA thiolase 1517 0.00E+00 91.59 Org Lipid metabolism
PITG_13753 Phyca11|118056 Choline/carnitine O‐acyltransferase 2965 0.00E+00 91.71 Unc Lipid metabolism
PITG_02282 Phyca11|502783 Acyl‐CoA dehydrogenase 3670 0.00E+00 92.80 Unc Lipid metabolism
PITG_06693 Phyca11|506742 Trifunctional enzyme subunit α 2980 0.00E+00 89.74 Unc Lipid metabolism
PITG_01208 Phyca11|508178 Trifunctional enzyme subunit β 1998 0.00E+00 95.84 Unc Lipid metabolism
PITG_07024 Phyca11|531975 Inosine‐5′‐monophosphate dehydrogenase 2 2462 0.00E+00 95.64 Cyt Nucleobase‐containing compound metabolic process
PITG_08761 Phyca11|505099 Nucleoside diphosphate kinase B 852 4.76E‐109 93.64 Mem Nucleobase‐containing compound metabolic process
PITG_13575 Phyca11|557455 ATP‐binding cassette (ABC) superfamily 6567 0.00E+00 66.70 Mem Nucleobase‐containing compound metabolic process
PITG_07308 Phyca11|506928 rRNA 2′‐O‐methyltransferase fibrillarin 1243 1.33E‐163 99.59 Org Nucleobase‐containing compound metabolic process
PITG_19178 Phyca11|576174 13‐kDa ribonucleoprotein‐associated protein 561 2.80E‐69 90.62 Rib Nucleobase‐containing compound metabolic process
PITG_09609 Phyca11|13896 Dpy‐30‐like protein 276 8.57E‐30 90.00 Unc Nucleobase‐containing compound metabolic process
PITG_11046 Phyca11|574456 Creatine kinase B‐type 2409 1.65E‐152 60.14 Unc Nucleobase‐containing compound metabolic process
PITG_02968 Phyca11|534866 Choline dehydrogenase 2829 0.00E+00 91.10 Unc Other
PITG_12427 Phyca11|511652 Cytochrome c oxidase subunit mitochondrial‐like 576 3.01E‐71 84.09 Org Pathogenesis/response to stress
PITG_17708 Phyca11|511879 Translationally controlled tumour protein 908 2.91E‐117 95.00 Unc Pathogenesis/response to stress
PITG_06815 Phyca11|539198 NADH dehydrogenase flavoprotein 1 2483 0.00E+00 95.61 Unc Pathogenesis/response to stress
PITG_00850 Phyca11|507542 Thioredoxin‐dependent peroxide reductase 1497 0.00E+00 93.49 Unc Pathogenesis/response to stress
PITG_03098 Phyca11|538943 Carbamoyl‐phosphate synthase 7803 0.00E+00 81.91 Mem Protein metabolism
PITG_22685 Phyca11|541961 Dihydroxy‐acid dehydratase 2585 0.00E+00 84.23 Mem Protein metabolism
PITG_03712 Phyca11|503562 Elongation factor 3 5249 0.00E+00 83.76 Unc Protein metabolism
PITG_08880 Phyca11|563220 ADP‐ribosylation factor family 875 2.92E‐112 93.99 Org Signal transduction
PITG_05884 Phyca11|531039 AP‐2 complex subunit β 4091 0.00E+00 94.02 Cyt Transport
PITG_04335 Phyca11|558677 AP‐2 complex subunit α 4272 0.00E+00 92.44 Cyt Transport
PITG_08785 Phyca11|505109 Conserved hypothetical protein 3677 0.00E+00 77.23 Ext Unclassified
PITG_08793 Phyca11|505109 Conserved hypothetical protein 3801 0.00E+00 73.71 Ext Unclassified
PITG_05360 Phyca11|506120 Conserved hypothetical protein 577 2.92E‐70 84.73 Mem Unclassified
PITG_20397 Phyca11|555281 Conserved hypothetical protein 2192 0.00E+00 96.40 Mem Unclassified
PITG_06241 Phyca11|505003 Conserved hypothetical protein 1579 4.21E‐133 68.78 Unc Unclassified
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*From P. infestans protein database.

acc. no., accession number; CC, cellular component; Cyt, cytoplasm; Ext, extracellular compartment; Mem, membrane‐bound; Org, organelles; Rib, ribosomes; Unc, unclassified.

Although the gene ontology analysis failed to ascribe locations for many of the proteins from both the mycelium (46%) and cyst (41%) samples (Fig. 4a,b), it did reveal that the two samples had slightly different profiles, with a higher proportion of the mycelium (23%) relative to the cyst (15%) proteins being located in the cytoplasm, and a greater number of the cyst proteins being extracellular (9%) or associated with organelles (12%) compared with those of the mycelium (3% and 3%, respectively). The two samples also exhibited differences in the function of the enriched proteins (Figs 4c,d and 5). Most notably, the mycelium samples contained a much higher proportion of proteins associated with carbohydrate metabolism (33%) than did the cyst samples (9%). In contrast, the cyst samples contained a higher proportion of proteins associated with lipid metabolism (17%), compared with the mycelium. These data indicate that the two types of sample differ in their primary energy metabolism. The proteins associated with carbohydrate metabolism in the mycelium and those involved in lipid metabolism in the cysts were selected for pathway enrichment analysis. The results showed that the most enriched proteins were involved in glycolysis/gluconeogenesis (Fig. S1, Table S3, see Supporting Information), the pentose phosphate pathway (Fig. S2, Table S3, see Supporting Information), the tricarboxylic acid (TCA) (or citric acid) cycle (Fig. S3, Table S3, see Supporting Information) and fatty acid catabolism (Fig. S4, Table S3, see Supporting Information).

Figure 4.

Figure 4

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Predicted cellular location (a, b) and functional annotation (c, d) of the differentially expressed proteins from the mycelium and cysts of P. capsici. (a and c) proteins with greater abundance in the mycelium; (b and d) proteins with greater abundance in the cysts. The asterisks mark the functional categories of proteins that present significantly different levels of expression between the mycelium and cysts (p < 0.05; Chi‐square test).

Figure 5.

Figure 5

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Heat map illustrating the relative abundance of the significantly enriched proteins in the mycelium (red) and cysts (green) in all three biological replicates. Missing values are shown in black.

Quantitative polymerase chain reaction (PCR) analyses

The relative levels of expression of the genes that encode the enriched proteins identified by iTRAQ analysis were determined using quantitative real‐time PCR. Although the expression levels of glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) and malate dehydrogenase (MDH) did not differ between the mycelium and cysts, the mRNA levels of the other genes associated with glycolysis/gluconeogenesis (Fig. 6a), the pentose phosphate pathway (Fig. 6b) and the TCA cycle (Fig. 6c) corroborated the data from the iTRAQ experiments, with higher levels of expression in the mycelium than in the cysts. The genes associated with the fatty acid degradation pathway, i.e. the trifunctional enzyme subunit β (TP‐β), 3‐ketoacyl‐CoA thiolase (thiolase I) and acyl‐CoA dehydrogenase family member 9 (ACAD9), exhibited higher levels of expression in the cysts than in the mycelium (Fig. 6d), in good agreement with the quantitative proteomics data. However, in contrast with the data from the iTRAQ experiments, the expression levels of the trifunctional enzyme subunit α (TP‐α) and choline/carnitine O‐acyltransferase (ChAT) were higher in the mycelium than in the cysts (Fig. 6d).

Figure 6.

Figure 6

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Expression profiles of selected genes from Phytophthora capsici. Expression levels of genes associated with: (a) glycolysis/gluconeogenesis; (b) the pentose phosphate pathway; (c) the tricarboxylic acid (TCA) (or citric acid) cycle; (d) fatty acid degradation. Relative expression levels were determined by comparison with the 40S ribosomal protein S3a (WS21) and mago nashi RNA‐binding protein homologue. They were calculated using Biogazelle qbase+. Error bars represent the standard deviation based on three biological replicates. ACAD, acyl‐CoA dehydrogenase; ACAD9, acyl‐CoA dehydrogenase family member 9; aldolase, fructose‐bisphosphate aldolase; ChAT, choline/carnitine O‐acyltransferase; GAPDH, glyceraldehyde‐3‐phosphate dehydrogenase; G6PDH, glucose‐6‐phosphate 1‐dehydrogenase; MDH, malate dehydrogenase; 6PGD, 6‐phosphogluconate dehydrogenase; PGK, phosphoglycerate kinase; thiolase I, 3‐ketoacyl‐CoA thiolase; TP‐α, trifunctional enzyme subunit α; TP‐β, trifunctional enzyme subunit β; TPI, triose‐phosphate isomerase.

Discussion

The asexual propagation of P. capsici is responsible for regular disease outbreaks that cause significant crop losses worldwide (Granke et al., 2009). Here, we have used a proteomics approach to identify proteins specifically associated with the two asexual life stages of P. capsici to gain further insights into the candidate proteins that might be involved in vegetative growth and the early stages of infection. The combination of iTRAQ‐liquid chromatography‐tandem MS (iTRAQ‐LC‐MS/MS) with a gel‐based qualitative analysis led to the identification of a total of 1200 unique proteins associated with the mycelium and cysts of P. capsici. Approximately 6% (69 proteins) of the proteins were predicted to contain signal peptides (Table S1), which is similar to the proportion of secreted proteins (8%) estimated by analysis of the Phytophthora genome (Haas et al., 2009; Jiang et al., 2006; Tyler et al., 2006). iTRAQ analysis revealed that 73 of the proteins exhibited significantly different levels of abundance between the mycelium and cyst samples. In the discussion that follows, the enriched proteins have been divided into two groups: those that showed higher abundance in the mycelium, which were considered to be candidate proteins for vegetative growth (Table 1), and those that showed higher abundance in the cysts, which were considered to be candidate proteins for early infection (Table 2).

Candidate proteins associated with vegetative growth (Group I)

Compared with the situation in the cysts (Figs 4d and 5), a higher proportion of the proteins that exhibited increased levels of abundance in the mycelium were associated with carbohydrate metabolism, protein metabolism, transport, pathogenesis/response to stress and signal transduction (Figs 4c and 5). Of the proteins for which a function could be assigned based on available annotations, the largest group was associated with carbohydrate metabolism. Similar results have been obtained in previous studies of other Phytophthora pathogens, including P. sojae (Hosseini et al., 2015; Savidor et al., 2008), P. pisi (Hosseini et al., 2015) and P. ramorum (Savidor et al., 2008). In our work, quantitative reverse transcription‐polymerase chain reaction (RT‐PCR) analysis further confirmed the increased levels of expression of several genes associated with carbohydrate metabolism. The assignment of these proteins to metabolic pathways revealed that they are essentially involved in glycolysis/gluconeogenesis (Fig. S1), the pentose phosphate pathway (Fig. S2) and the TCA cycle (Fig. S3). The up‐regulation of proteins associated with gluconeogenesis suggests that P. capsici utilizes carbohydrates from its host environment for vegetative growth during the necrotrophic phase of the infection. However, the up‐regulation of proteins involved in glycolysis, the pentose phosphate pathway and TCA cycle could also reflect the conversion of nutrient reserves to usable energy to drive the biosynthetic processes needed for infection or growth and development of P. capsici. In addition, isoforms of GAPDH, which are associated with glycolysis, have been reported in the proteome of virulent strains of Botrytis cinerea, leading to the hypothesis that they might also act as virulence factors (Bhadauria et al., 2010). Similarly, another of the up‐regulated proteins is MDH, which is a ubiquitous regulatory enzyme of energy metabolism via the TCA cycle. MDH catalyses the reversible conversion of oxaloacetate, a precursor of oxalic acid, which is reported to be a pathogenicity factor of the ascomycete plant pathogen B. cinerea. Furthermore, it has also been reported that the acidification of the environment by oxalic acid stimulates the production of the botrydial and dihydrobotrydial toxins by B. cinerea (Fernández‐Acero et al., 2006). The important changes associated with energy metabolism observed in the mycelium were accompanied by the increased expression of proteins involved in protein anabolism, for example the up‐regulation of 5‐methyl‐tetrahydropteroyltriglutamate‐homocysteine methyltransferase, which catalyses the biosynthesis of the essential amino acid methionine.

Consistent with vegetative growth and the associated increased carbohydrate metabolism in the mycelium, our data also revealed an up‐regulation of transport proteins that facilitate the uptake of nutrients (Latijnhouwers et al., 2003). A key protein enriched in the mycelium is the plasma membrane H+‐ATPase, which generates a proton gradient across the cell membrane to provide the energy required for the transport of nutrients (glucose/fructose, amino acids) from the environment (Szabo and Bushnell, 2001).

Several of the proteins with increased abundance during mycelial growth were found to be directly associated with pathogenesis and response to stress. A typical example is an elicitin‐like protein, which is a member of the elicitin superfamily of proteins. This type of protein is structurally related to extracellular proteins that induce hypersensitive cell death and other biochemical changes associated with the defence response (Bonnet et al., 1996; Kamoun et al., 1993; Ricci et al., 1989; Van't Slot and Knogge, 2002). Previous studies have indicated that the elicitin‐like proteins from P. capsici exhibit phospholipase activity (Nespoulous et al., 1999), suggesting a general lipid binding/processing role for the various members of the elicitin family in this species (Osman et al., 2001). In addition, it has been suggested that such elicitins are essential to Phytophthora species, which are unable to synthesize sterols, possibly by playing a role in sterol assimilation from the environment (Kamoun, 2006). Another up‐regulated protein is phospholipid hydroperoxide glutathione peroxidase, which is known to provide protection against reactive oxygen species. The occurrence of phospholipid hydroperoxide glutathione peroxidase has also been reported during the in vitro vegetative growth of P. parasitica (Panabières et al., 2005). In addition, two annexins exhibited increased levels of abundance in the mycelium samples of P. capsici. The conserved annexin domains of these proteins contain a typical type‐2 calcium‐binding motif [GxGT‐(38 residues)‐E] (Benz and Hofmann, 1996), and the presence of these proteins in the cell walls of Phytophthora species suggests that they play an important role in the adhesion of the pathogen to the hosts and infection (Meijer et al., 2006).

Two orthologous 12‐oxophytodienoate reductase proteins (Phyca11|505536, Phyca11|506608) were also found to show increased abundance in the mycelium, and might be important during hyphal vegetative growth. blast searches revealed that the amino acid sequences of these two proteins showed a high degree of similarity (∼50%) with the 12‐oxophytodienoate reductase homologues found in Arabidopsis thaliana, which have been associated with jasmonate biosynthesis and plant responses to a variety of stimuli (Müssig et al., 2000; Schaller et al., 2000). The expression of 12‐oxophytodienoate reductase in P. capsici might therefore contribute to its virulence by influencing the stress responses of its host plants.

Another protein that might influence the vegetative growth of the mycelium is phospholipase D, which is known to be expressed in other Phytophthora species (Hosseini et al., 2015; Meijer et al., 2011, 2014; Savidor et al., 2008). Phospholipase D is a member of the phospholipase superfamily, and is responsible for the hydrolysis of phosphatidylcholine to produce soluble choline and the signal molecule phosphatidic acid. Proteins belonging to the phospholipase superfamily participate in various cellular processes, including phospholipid metabolism and signal transduction (Cazzolli et al., 2006; Meijer et al., 2005). Although the precise function of phospholipase D in the biology of Phytophthora diseases has not been determined, its consistently high abundance during vegetative growth suggests that it may have a conserved function in Phytophthora, perhaps facilitating biochemical processes important for the proliferation of the mycelium within the host tissue.

Candidate proteins specifically involved in cyst development (Group II)

Thirty‐four differentially expressed proteins were up‐regulated in the cysts (Table 2). However, compared with mycelium (Figs 4c and 5), a higher proportion of these proteins could not be functionally annotated (Figs 4d and 5), reflecting the fact that many of the proteins associated with cyst development have yet to be characterized in P. capsici and other related organisms.

The single largest functional category for the proteins that could be annotated was lipid metabolism, which accounted for 17% of the differentially expressed proteins. This indicates that lipid metabolism is one of the most distinctive processes that quantitatively distinguishes the mycelium and the cysts. The up‐regulated proteins associated with lipid metabolism included ACAD9, thiolase I, ChAT, acyl‐CoA dehydrogenase, TP‐α and TP‐β. The increased expression of TP‐β, thiolase I and ACAD9 was confirmed by quantitative real‐time RT‐PCR analysis. Interestingly, however, higher mRNA expression levels of TP‐α and ChAT were measured in the mycelium samples, which might have been caused by regulation at the post‐transcriptional level (Brockmann et al., 2007). Metabolic pathway enrichment analysis revealed that the enriched proteins were involved in fatty acid degradation (Fig. S4), which produces greater amounts of energy than does carbohydrate catabolism. Previous studies have also shown that proteins involved in fatty acid degradation are up‐regulated in the germinating cysts of other Phytophthora species (Ebstrup et al., 2005; Hosseini et al., 2015; Savidor et al., 2008), which suggests that fatty acid degradation via the β‐oxidation pathway serves as the primary source of energy in the cysts, ‘fuelling’ germination and penetration of the hosts. The up‐regulation of isocitrate lyase provides further confirmation of this hypothesis, as this protein is a key enzyme that catalyses the cleavage of isocitrate to succinate and glyoxylate as part of the glyoxylate cycle. The glyoxylate cycle is involved in the conversion of acetyl‐CoA from fatty acid degradation to succinate for the synthesis of carbohydrates, and is required for the virulence of fungi (Lorenz and Fink, 2001).

A member of the ATP‐binding cassette (ABC) superfamily, assigned to the category nucleobase‐containing compound metabolic process, was found to be up‐regulated in the cysts. Proteins belonging to this family have previously been associated with detoxification (Torto‐Alalibo et al., 2007), and it is possible that a similar role in the plant–pathogen interaction of P. capsici could contribute to the success of infection. In addition, ABC proteins have also been implicated in the secretion of phytotoxins (Chen et al., 2011; Connolly et al., 2005), which would also increase the virulence of P. capsici. Other proteins associated with the nucleobase‐containing compound metabolic process included rRNA 2′‐O‐methyltransferase fibrillarin, 13‐kDa ribonucleoprotein‐associated protein and creatine kinase B‐type protein. Both the rRNA 2′‐O‐methyltransferase fibrillarin and 13‐kDa ribonucleoprotein‐associated protein are known to be involved in ribosomal RNA processing (Bonnerot et al., 2003; Monaghan et al., 2009), whereas the creatine kinase B‐type protein catalyses the transfer of a phosphoryl group from phosphocreatine to ADP to form creatine and ATP, a process that buffers the levels of ATP in cell types with high energy demands (Kim and Judelson, 2003). A similar role could be played by this protein during cyst germination, which is expected to require high levels of energy. In addition, elongation factor 3, which is involved in protein biosynthesis, and the cytoskeleton proteins α‐tubulin and β‐tubulin, were also found to be up‐regulated in the cysts, indicating that these proteins could play an important role in the production of large amounts of cytoskeleton proteins, as well as other proteins necessary for cyst development.

Several other proteins that could be pathogenicity factors were also found to be up‐regulated in the cysts. For example, lysosomal α‐mannosidase, which is involved in the breakdown of complex glycans derived from glycoproteins (De Gasperi et al., 1992), has been observed to be up‐regulated in the germinating cysts of P. ramorum and P. sojae (Savidor et al., 2008). This suggests that it could have a conserved function, allowing Phytophthora pathogens to utilize host glycoproteins. In addition, carbonic anhydrase has been reported to be a candidate virulence factor in P. infestans based on bioinformatics analysis (Raffaele et al., 2010).

In summary, the data presented here provide evidence for a more specific involvement of carbohydrate and fatty acid metabolic pathways in the vegetative growth of the mycelium and development of cysts, respectively. In the mycelium, many of the up‐regulated proteins were found to be related to energy production via glycolysis, the pentose phosphate pathway and the TCA cycle, which drive the biosynthesis of, for example, amino acids, for vegetative growth. A second group of candidate proteins associated with the adaptation and survival of the pathogen were also identified, including an elicitin‐like protein, phospholipid hydroperoxide glutathione peroxidase, two annexins, 12‐oxophytodienoate reductase proteins and phospholipase D. As opposed to the mycelium, the cysts appeared to obtain their energy essentially from fatty acid degradation, ‘fuelling’ germination and penetration of the hosts. In addition, several candidate pathogenicity factors were identified, including an ABC superfamily protein, lysosomal α‐mannosidase and carbonic anhydrase. Their involvement in the early stages of infection remains to be demonstrated experimentally.

Experimental Procedures

Cultures of P. capsici and extraction of proteins from mycelium and cysts

Cultures of P. capsici isolate Hd11 were established as described previously (Pang et al., 2013) and maintained on potato dextrose agar (PDA). The mycelium was collected from 3‐day‐old cultures (potato dextrose broth) of 100 mL grown at 25 ºC in the dark, whereas the asexual sporangia and zoospores were obtained by growing P. capsici on V8 agar plates under 12‐h light/12‐h dark cycles over a period of 7 days (Pang et al., 2014). The zoospores were stimulated to encyst by vigorous shaking for 1–2 min. The mycelium and cysts were harvested and stored at −80 ºC until further use.

For protein extraction, the mycelial and cyst cells were disrupted using a pestle and mortar or glass beads, respectively. Lysis buffer was added to each of the samples [250 µL of 100 mm triethylammonium bicarbonate (TEAB), 4% sodium deoxycholate (SDC) and 1 mm ethylenediaminetetraacetic acid (EDTA)], which were subsequently incubated at 80 ºC for 5 min and chilled on ice. Low‐temperature sonication (Branson® Sonifier 250, BRANSON Ultrasonics Corporation, Danbury, CT, USA) was used to assist cell lysis. The samples were then centrifuged at 100 000 g for 30 min to remove cell debris. The supernatant was collected and the protein concentration was determined using the Bradford assay (Bradford, 1976).

Sample preparation for qualitative and quantitative proteomics

A preliminary analysis of the qualitative differences between the protein profiles of the mycelium and cysts of P. capsici was conducted by SDS‐PAGE. Samples of the mycelium and cysts containing 20 µg of total protein were analysed on 10% SDS‐polyacrylamide gels. After staining with Coomassie blue (Thermo Scientific, Waltham, MA), each lane of the gel was cut into 17 bands of similar volume from the top to the bottom of the gel, and the proteins were subjected to in‐gel proteolysis and analysed by MS, as described previously (Srivastava et al., 2013).

For quantitative proteomics, mycelial and cyst lysates, prepared as described above (aliquots containing 100 µg of protein), were diluted in 0.05 m TEAB containing 1% sodium deoxycholate (SDC). The proteins were reduced, alkylated and hydrolysed in the presence of trypsin (Srivastava et al., 2013). The resulting peptides were labelled with iTRAQ reagents (114–117; AB SCIEX, Foster City, CA, USA), following the manufacturer's instructions. The iTRAQ tags used for the technical and biological replicates are shown in Fig. 7. The labelled peptides from each biological replicate were combined (Fig. 7), dried and re‐suspended in 10 mm ammonium formate, pH 3.0, containing 10% acetonitrile (loading buffer). The mixtures were then loaded onto 1‐mL Nuvia™ HR‐S cartridges (Bio‐Rad, Munich, Germany), which were prepared according to the manufacturer's instructions, using a syringe pump. After washing the cartridges with loading buffer, the peptides were eluted at a rate of 0.2 mL/min by the sequential addition of 1.5‐mL ammonium formate salt plugs (50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350 and 400 mm in 20% acetonitrile) at pH 3.0. Each fraction was dried, desalted using C18 Spin Columns (Thermo Scientific) and analysed by MS.

Figure 7.

Figure 7

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Schematic representation of the experimental workflow. BR, biological replicate; iTRAQ, isobaric tags for relative and absolute quantification; RP, reverse phase; SCX, strong cation‐exchange chromatography; SDS‐PAGE, sodium dodecylsulfate‐polyacrylamide gel electrophoresis.

Nano‐LC‐MS/MS analysis of samples subjected to iTRAQ labelling

Reverse‐phase LC‐electrospray ionization‐MS/MS analysis of the peptide samples subjected to iTRAQ labelling was performed using a nanoACQUITY ultra‐performance liquid chromatography (UPLC) system (Waters, Milford, MA, USA) coupled to a Q‐TOF mass spectrometer (Xevo Q‐TOF, Waters). Before analysis, the purified peptide fractions corresponding to each salt plug were re‐suspended in 0.1% trifluoroacetic acid (TFA), loaded onto a C18 trap column (Symmetry 180 µm × 20 mm, 5 µm; Waters) and washed with 0.1% (v/v) formic acid at 10 µL/min for 10 min. The samples eluted from the trap column were then separated on a C18 analytical column (75 µm × 200 mm, 1.7 µm; Waters) at a rate of 225 nL/min using 0.1% formic acid as solvent A and 0.1% formic acid in acetonitrile as solvent B. The proportion of solvent B varied as follows: 0.1%–8% B (0–5 min), 8%–25% B (5–185 min), 25%–45% B (185–201 min), 45%–90% B (201–205 min), 90% B (205–213 min) and 90%–0.1% B (213–215 min). The eluting peptides were sprayed into the mass spectrometer with the capillary and cone voltages set to 2.2 kV and 45 V, respectively. The five most abundant signals from a survey scan (400–1300 m/z range; scan time, 1 s) were then selected by charge state, and the appropriate collision energy was applied for sequential MS/MS fragmentation (50–1800 m/z range; scan time, 1 s).

Data processing and protein identification and quantification

Our in‐house Automated Proteomics Pipeline, which automates the processing of proteomics tasks, such as peptide identification, validation and quantification from LC‐MS/MS data, and allows easy integration of many separate proteomics tools (Malm et al., 2014), was used to analyse the MS data. The raw MS data file was first analysed using the Mascot Distiller software (version 2.4.3.2, Matrix Science, London, UK) and the resulting mgf files were converted into the mzML file format using msconvert (Kessner et al., 2008). The P. infestans protein database (http://www.broadinstitute.org/annotation/genome/phytophthora_infestans/MultiHome.html; 18 141 entries) was then searched using several algorithms in parallel, i.e. MS‐GF+ (Kim et al., 2010) v1.0 (v8299), MyriMatch (Tabb et al., 2007) (version 2.1.120), Comet (Eng et al., 2013) (version 2013.01 rev.0) and X!Tandem (Craig and Beavis, 2004) (version 2011.12.01.1; LabKey, Insilicos, ISB, Seattle, WA, USA). The following settings were used for the searches: trypsin‐specific digestion with two missed cleavages allowed; peptide tolerance of 200 ppm; fragment tolerance of 0.5 Da; methylthio on cysteine (Cys) and iTRAQ 4‐plex for peptide N‐t and lysine (Lys) used as fixed modifications; oxidized methionine (Met) and tyrosine (Tyr) for iTRAQ 4‐plex analysis in variable mode. The results from all search engines were validated by PeptideProphet (Keller et al., 2002). Protein quantification was performed from the intensities of the iTRAQ reporter ions, which were extracted using the TPP tool Libra (Li et al., 2003) (TPP v4.6 OCCUPY rev 3) after the isotopic correction factors provided by the manufacturer of the iTRAQ reagent had been applied. The iTRAQ channels were normalized using the sum of all the reporter ion intensities from each iTRAQ channel and equalizing each channel's contribution by dividing the individual reporter ion intensities by the corresponding channel‐specific correction factor. The pep.xml files obtained from PeptideProphet were combined using iProphet (Shteynberg et al., 2011) and the protein lists were assembled using ProteinProphet (Nesvizhskii et al., 2003). The final protein ratios were calculated using LibraProteinRatioParser (Li et al., 2003), and a concatenated target‐decoy database search strategy showed that the rate of false positives was less than 1% for all searches.

Sequences with a peptide probability cut‐off of 0.95 were exported for each protein. Peptides matching two or more proteins (shared peptides) were excluded from the analysis, together with proteins that had no unique peptides (i.e. identified by shared peptides only). In each biological replicate, proteins identified by at least one unique peptide were considered to be identified, and those identified by two or more unique peptides were used for quantitative analysis. The mycelium to cyst ratio of each protein was calculated for each of the three biological replicates (Table S1) and log2 transformed to obtain a normal distribution (Ross et al., 2004) before statistical analysis of the quantitative data was conducted. The values were normalized to the median logarithm values, and the global means and standard deviations were calculated for each biological replicate. Proteins with average ratios outside ±1 standard deviations from the global mean in at least two of the three biological replicates were considered to be significantly enriched (Ross et al., 2004). MultiExperiment Viewer (MeV version 4.9.0) (Saeed et al., 2006) was used to construct the relative protein abundance heat map.

The MS proteomics data have been deposited at the ProteomeXchange Consortium (Vizcaíno et al., 2014) via the PRIDE partner repository with the dataset identifier PXD002603.

Bioinformatics analysis

The P. infestans protein database was used in the first instance for protein identification because the analysis, annotation and curation of the genome of this species are much advanced compared with those of P. capsici. In addition, a high level of sequence identity has been shown to occur between the two genomes (Lamour et al., 2012a). The sequences of all proteins identified using the P. infestans database were used for blast searches against the P. capsici database. The accession numbers, e‐values, scores and percentage hit identities of the resulting top hit matches from P. capsici were extracted and are listed in Tables 1, 2 and S1. Gene ontology annotation of the identified proteins was performed using Blast2GO (Conesa and Götz, 2008), and conserved domains were analysed using the National Center for Biotechnology Information (NCBI) conserved domain database (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). The presence of signal peptides was predicted using SignalP (version 4.1) with the default settings (Petersen et al., 2011), and transmembrane domains were predicted using HMMTOP 2.0 (Tusnady and Simon, 2001). The sorting and grouping of the identified proteins was performed based on the data resulting from these analyses, in combination with literature searches. Enrichment analysis of candidate proteins from different metabolic pathways and metabolic reconstruction were conducted based on the information available in the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database (www.genome.jp/kegg/pathway.html).

RNA extraction and quantitative RT‐PCR analysis

Total RNA was obtained from frozen P. capsici mycelium and cyst samples by grinding them in liquid nitrogen and extracting the RNA using an RNeasy® kit (Qiagen, Hilden, Sweden) according to the manufacturer's instructions. Contaminating DNA was removed from the RNA samples using a TURBO DNA‐free™ kit (Ambion, Austin, Texas, USA) and first‐strand cDNA synthesis was performed from 1 µg of total RNA using the Maxima First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA). The primers used for the analyses, including primers for control genes and primers for genes involved in glycolysis/gluconeogenesis, the pentose phosphate pathway, the TCA cycle and the fatty acid degradation pathway, are listed in Table S2 (see Supporting Information). qRT‐PCR analyses were performed using a CFX96 real‐time PCR detection system from Bio‐Rad, Munich, Germany. Reaction mixtures had a final volume of 10 µL and consisted of 5 µL of 2 × iQ SYBR Green Supermix (Bio‐Rad, Singapore), 0.5 µm of each primer and 2 µL of cDNA that had been diluted 15‐fold. The PCR was processed using the following program: 95°C for 3 min, followed by 40 cycles of 10 s at 95°C, 10 s at 60°C and 10 s at 72°C. Melting curves were generated at the end of the experiment to check the specificity of the PCR products. The raw data from three individual biological replicates were analysed using Biogazelle's qbasePLUS software version 3.0 (Hellemans et al., 2007). Relative expression levels were calculated by normalizing the data to the geometric mean of two reference genes [40S ribosomal protein S3a, WS21 (Yan and Liou, 2006), and mago nashi RNA‐binding protein homologue (Vetukuri et al., 2011)], which were selected from an expression stability analysis of five reference genes using geNorm (D'haene et al., 2012) (Table S2). The PCR efficiency for each gene was calculated using Real‐time PCR Miner (Zhao and Fernald, 2005), which confirmed that they were all in the 85%–100% range.

Supporting information

Additional Supporting Information may be found in the online version of this article at the publisher's website:

Table S1. List of unique proteins and their corresponding peptides obtained from in‐gel and solution (iTRAQ) proteomics analysis.

Click here for additional data file. (1.5MB, xlsx)

Table S2. List of oligonucleotide primer pairs used for reverse transcription‐polymerase chain reaction (RT‐PCR) analysis.

Click here for additional data file. (13.7KB, xlsx)

Table S3. List of identified proteins involved in glycolysis/gluconeogenesis, the pentose phosphate pathway, the tricarboxylic acid (TCA) (or citric acid) cycle and the fatty acid degradation pathway.

Click here for additional data file. (15.1KB, xlsx)

Fig S1. Glycolysis/gluconeogenesis pathway. All of the proteins presented were identified by proteomics and the enzymes marked in red were found to be up‐regulated in the mycelium of Phytophthora capsici. These include fructose‐bisphosphate aldolase (Phyca11|507726), triose‐phosphate isomerase (TPI; Phyca11|506671), glyceraldehyde‐3‐phosphate dehydrogenase (Phyca11|504523), phosphoglycerate kinase (Phyca11|510172) and enolase (Phyca11|503568).

Click here for additional data file. (1.2MB, tif)

Fig S2. Pentose phosphate pathway. The figure represents the enzymes that were identified by proteomics analysis. The proteins marked in red were found to be up‐regulated in the mycelium of Phytophthora capsici, including glucose‐6‐phosphate 1‐dehydrogenase (Phyca11|509625), transketolase (Phyca11|506857), transaldolase (Phyca11|505958) and 6‐phosphogluconate dehydrogenase (Phyca11|507227).

Click here for additional data file. (947.3KB, tif)

Fig S3. Tricarboxylic acid (TCA) cycle (or citric acid cycle). All of the enzymes presented were identified in this work. Malate dehydrogenase (Phyca11|505882) and ATP‐citrate synthase (Phyca11|504894) were found to be up‐regulated in the mycelium of Phytophthora capsici (red).

Click here for additional data file. (978.9KB, tif)

Fig S4. Fatty acid degradation via the β‐oxidation pathway. All of the proteins presented were identified by proteomics analysis and assigned to the catabolism of fatty acid. The proteins up‐regulated in the cysts of Phytophthora capsici are shown in red and correspond to choline/carnitine O‐acyltransferase (Phyca11|118056), acyl‐CoA dehydrogenase (Phyca11|502783, Phyca11|529555), trifunctional enzyme subunit β (Phyca11|508178), trifunctional enzyme subunit α (Phyca11|506742) and 3‐ketoacyl‐CoA thiolase (Phyca11|534354).

Click here for additional data file. (1.5MB, tif)

Acknowledgements

This work was partially funded by the National Science Foundation of China (31272061), the Special Fund for Agro‐scientific Research in the Public Interest (201303023) and the Swedish Science Council FORMAS (grant # 2009‐515 to VB). ZP was supported by the China Scholarship Council programme.

Contributor Information

Xili Liu, Email: seedling@cau.edu.cn.

Vincent Bulone, Email: bulone@kth.se.

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Associated Data

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Supplementary Materials

Additional Supporting Information may be found in the online version of this article at the publisher's website:

Table S1. List of unique proteins and their corresponding peptides obtained from in‐gel and solution (iTRAQ) proteomics analysis.

Click here for additional data file. (1.5MB, xlsx)

Table S2. List of oligonucleotide primer pairs used for reverse transcription‐polymerase chain reaction (RT‐PCR) analysis.

Click here for additional data file. (13.7KB, xlsx)

Table S3. List of identified proteins involved in glycolysis/gluconeogenesis, the pentose phosphate pathway, the tricarboxylic acid (TCA) (or citric acid) cycle and the fatty acid degradation pathway.

Click here for additional data file. (15.1KB, xlsx)

Fig S1. Glycolysis/gluconeogenesis pathway. All of the proteins presented were identified by proteomics and the enzymes marked in red were found to be up‐regulated in the mycelium of Phytophthora capsici. These include fructose‐bisphosphate aldolase (Phyca11|507726), triose‐phosphate isomerase (TPI; Phyca11|506671), glyceraldehyde‐3‐phosphate dehydrogenase (Phyca11|504523), phosphoglycerate kinase (Phyca11|510172) and enolase (Phyca11|503568).

Click here for additional data file. (1.2MB, tif)

Fig S2. Pentose phosphate pathway. The figure represents the enzymes that were identified by proteomics analysis. The proteins marked in red were found to be up‐regulated in the mycelium of Phytophthora capsici, including glucose‐6‐phosphate 1‐dehydrogenase (Phyca11|509625), transketolase (Phyca11|506857), transaldolase (Phyca11|505958) and 6‐phosphogluconate dehydrogenase (Phyca11|507227).

Click here for additional data file. (947.3KB, tif)

Fig S3. Tricarboxylic acid (TCA) cycle (or citric acid cycle). All of the enzymes presented were identified in this work. Malate dehydrogenase (Phyca11|505882) and ATP‐citrate synthase (Phyca11|504894) were found to be up‐regulated in the mycelium of Phytophthora capsici (red).

Click here for additional data file. (978.9KB, tif)

Fig S4. Fatty acid degradation via the β‐oxidation pathway. All of the proteins presented were identified by proteomics analysis and assigned to the catabolism of fatty acid. The proteins up‐regulated in the cysts of Phytophthora capsici are shown in red and correspond to choline/carnitine O‐acyltransferase (Phyca11|118056), acyl‐CoA dehydrogenase (Phyca11|502783, Phyca11|529555), trifunctional enzyme subunit β (Phyca11|508178), trifunctional enzyme subunit α (Phyca11|506742) and 3‐ketoacyl‐CoA thiolase (Phyca11|534354).

Click here for additional data file. (1.5MB, tif)

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