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Funct Integr Genomics DOI 10.1007/s10142-013-0358-8

ORIGINAL PAPER

Quantitative resistance in potato leaves to late blight associated with induced hydroxycinnamic acid amides Kalenahalli N. Yogendra & Doddaraju Pushpa & Kareem A. Mosa & Ajjamada C. Kushalappa & Agnes Murphy & Teresa Mosquera

Received: 2 August 2013 / Revised: 6 December 2013 / Accepted: 22 December 2013 # Springer-Verlag Berlin Heidelberg 2014

Abstract Late blight is a serious economic threat to potato crop, sometimes leading to complete crop loss. The resistance in potato to late blight can be qualitative or quantitative in nature. Qualitative resistance is not durable. Though quantitative resistance is durable, the breeding is challenging due to polygenic inheritance. Several quantitative trait loci (QTLs) have been identified, but the mechanisms of resistance are largely unknown. A nontargeted metabolomics approach was used to identify resistance-related (RR) metabolites in a resistant genotype (F06025), as compared to a susceptible (Shepody) genotype, mock- or pathogen-inoculated. The RR metabolites, which had high fold change in abundance, mainly belonged to phenylpropanoid, flavonoid, fatty acid, and alkaloid chemical groups. The most important phenylpropanoids identified were hydroxycinnamic acid amides, the polyaromatic domain of suberin that is known to be associated with cell wall reinforcement. These metabolites were mapped on to the potato metabolic pathways, and the candidate enzymes and their coding genes were identified. A quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay revealed a higher upregulation of 4-coumarate: CoA ligase (4-CL), tyrosine decarboxylase (TyDC), and tyramine hydroxycinnamoyl Electronic supplementary material The online version of this article (doi:10.1007/s10142-013-0358-8) contains supplementary material, which is available to authorized users. K. N. Yogendra : D. Pushpa : K. A. Mosa : A. C. Kushalappa (*) Plant Science Department, McGill University, Ste.-Anne-de-Bellevue, Quebec H9X 3V9, Canada e-mail: ajjamada.kushalappa@mcgill.ca A. Murphy Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada T. Mosquera Department of Agronomy, National University of Colombia, Bogota, Colombia

transferase (THT) in the pathogen-inoculated resistant genotype than in susceptible. These genes were sequenced in both resistant and susceptible genotypes, and nonsynonymous single-nucleotide polymorphisms (nsSNPs) were found. The application of these genes in potato resistance improvement, following validation, is discussed. Keywords Metabolomics . Quantitative resistance . Potato late blight . Phytophthora infestans . Polygenic resistance . Single-nucleotide polymorphisms

Introduction Biotic and abiotic stresses are the vital causes of yield losses in potato worldwide. Among the biotic stresses, late blight caused by the oomycete Phytophthora infestans is one of the most devastating diseases of potato, ever since the Irish famine in 1845. The disease causes an average annual economic loss that would be sufficient to feed several millions of people (Fisher et al. 2012). The high cost of fungicide application, the evolution of more aggressive isolates resistant to fungicides, the wide diversity of cultivated potatoes, and the occurrence of virulent races of P. infestans make late blight management very challenging (Jacobs et al. 2010). Resistance in potato to late blight can be qualitative or quantitative. Qualitative resistance is a race-specific resistance, controlled by major R genes (Vleeshouwers et al. 2011). To date, 21 functional R genes have been identified and cloned, such as R1 (Ballvora et al. 2002), R2 (Lokossou et al. 2009), R3a (Huang et al. 2005), R3b (Li et al. 2011), and R4 from Solanum demissum (van Poppel et al. 2009), RB from Solanum bulbocastanum (Chen and Halterman 2011), and Rpi from Solanum venturii (Pel et al. 2009; Foster et al. 2009). A majority of the cloned R genes contain a nucleotide-binding (NB) site and leucine-rich repeat (LRR) domain (Jupe et al.


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2012), which lead to programmed cell death through hypersensitive response (HR). However, the R genes’ effect is not durable due to rapid appearance of new races of the pathogen. Even after the breakdown of resistance by new races, some R genes may have a residual resistance adding to quantitative resistance, which are referred as defeated R genes (Tan et al. 2008). The resistance offered by defeated R genes is minimal and often correlated with quantitative resistance. Introduction of multiple broad-spectrum R genes (Rpi), Rpi-sto1 (S. stoloniferum), Rpi-vnt1.1 (S. venturii), and Rpi-blb3 (S. bulbocastanum) into susceptible cultivar Desiree, termed as “Functional gene stacking,” was proposed to enhance the late blight resistance in potato (Zhu et al. 2012). However, gene stacking without knowing the function of the gene may not transfer the trait, as the resistance may be associated with regulatory elements, in which case a set of genes, including the resistance-related (RR) genes, must be transferred (Kushalappa and Gunnaiah 2013). Quantitative disease resistance, however, is controlled by multiple genes or quantitative trait loci (QTLs). The QTL mapping of segregating populations has identified one or more QTLs on all 12 potato chromosomes (Simko 2002). So far, 211 QTLs have been identified, based on 29 QTL maps for resistance to foliage, stem, and tubers, with phenotypic variance of 4–63 % (Danan et al. 2011). However, QTLs with late blight resistance are strongly associated with late maturity, and the genes co-localized at these QTLs and resistance mechanisms governed by any of these QTLs have not been reported (Kou and Wang 2010). Functional genomics approaches, such as transcriptomics, proteomics, and metabolomics, can be applied to decipher the biochemical mechanisms of resistance (Fiehn et al. 2000). A nontargeted metabolomics of potato leaves identified 42 pathogenesis-related (PR) metabolites and 89 RR (abundance higher in resistant than in susceptible) metabolites associated with horizontal resistance against P. infestans (Abunada et al. 2007, 2010). However, these studies were based on gas chromatography and mass spectrometry (GC-MS), which detects only volatile metabolites. Recently, a nontarget metabolomics, based on liquid chromatography and high-resolution mass spectrometry (LC-HRMS), has identified hundreds of RR metabolites against pathogen stress (Bollina et al. 2010; Gunnaiah et al. 2012). RR metabolites identified belonged to several metabolic pathways, including the phenylpropanoid pathway. In plants, phenylpropanoids can function as inducible physical barriers and also as chemical antagonists for the invading pathogens (Dixon et al. 2002). Several candidate genes in the phenylpropanoid pathway were induced against plant pathogens, including tyramine hydroxycinnamoyl transferase (THT) in potato against P. infestans (Schmidt et al. 1999); 4-coumarate: CoA ligase (4-CL) (Cuypers and Hahlbrock 1988; Fritzemeier et al. 1987), tyrosine decarboxylase (TyDC), and phenylalanine ammonia-lyase

(PAL) in potato cell suspension culture treated with P. infestans (Schmidt et al. 1998); and agmatine coumaryl transferase (ACT) against Alternaria brassicicola in Arabidopsis (Muroi et al. 2009). However, the production and role of biochemicals associated with these genes are unclear. Nontargeted metabolomic approach can help in the detection of an array of biochemicals that can be correlated to biotic stress resistance, which are governed by monogenes or polygenes (Kushalappa and Gunnaiah 2013). As plant-pathogen interaction systems are very complex, at the biochemical level, our aim was to focus on the identification of RR metabolites in potato leaves, based on nontargeted metabolomics of two well-characterized genotypes with contrasting levels of resistance, a resistant (F06025) and a susceptible (Shepody) to P. infestans. The genotypes were mockand pathogen-inoculated, and metabolites were identified using LC-HRMS. The RR metabolites with high fold change in abundances were selected as candidate metabolites, which in turn were mapped in metabolic pathways to identify the candidate genes, based on genomic databases. The expressions of these genes were confirmed based on quantitative real-time polymerase chain reaction (qRT-PCR). These RR genes were sequenced in both resistant and susceptible genotypes, and the differences in amino acid substitutions in the nonsynonymous single-nucleotide polymorphisms (nsSNPs) observed were used to explain the variations in resistance. The RR genes in potato against late blight discovered here, based on metabolomics, demonstrated a genotype-specific metabolic pathway regulation in potato to resist P. infestans.

Materials and methods Plant production A resistant genotype F06025 and a susceptible cultivar Shepody were obtained from the Potato Research Centre, Agriculture and Agri-Food Canada, New Brunswick, Canada. The genotype F06025 was derived from AWN86514-2×N06993-13 cross, and Shepody was derived from the cross Bake King×F58050. The Shepody plants are medium-sized, spreading, and light violet with a star-shaped orange-green color flower with a long, medium-deep eyes and white flesh tubers. The sprouted tubers, either one piece of tuber with at least two eyes per piece or one small entire tuber, were planted per pot, in a 16-cm-diameter pot containing PRO-MIX® BX and perlite. Temperature in the greenhouse was maintained at 20± 3 °C, with a 16-h photoperiod, and 70±10 % of relative humidity, throughout the growing period. Plants were fertilized at 15-day intervals with a 250-ml solution of 3 g ml−1 of PlantProd (20-20-20 NPK+trace elements) and 0.3 g ml−1 of micronutrients.


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Pathogen production, inoculation, and incubation P. infestans culture (clonal lineage US-8, A2 mating type, obtained from Dr. H. Platt, AAFC, Charlottetown, PEI) was maintained on potato dextrose agar media. For inoculation, fresh sporangia were produced by inoculating thin potato tuber slices and incubating in petri dishes lined with a moist filter paper at 18 °C. Spore suspensions at a concentration of 1×105 ml−1 sporangia were prepared. Fully expanded leaflets were inoculated with 20 μl of sporangial suspension or mock solution using a Hamilton syringe fitted with an auto dispenser (Gastight 1750DAD W/S, Hamilton, Reno, NV, USA). The inoculated drops were covered with Whatman filter paper punch hole discs (4–5-mm diameter) to prevent spread of spore suspension to other leaves touching them. Immediately after inoculation, the plants were covered with transparent plastic bags sprayed with sterile water on the inside to maintain high humidity to facilitate infection. The covers were removed 72 h postinoculation (hpi). Disease severity assessment The experiment was conducted as a randomized complete block design with three replicates over time. Each experimental unit consisted of five pots, each with two plants and ten leaves inoculated with pathogen, with two inoculations on either side of the midrib. Following inoculation of leaflets, the plants were covered with plastic bags for 72 h. The disease severity was assessed by measuring the lesion diameter, using a digital caliper, at 3-day intervals until 9 dpi. The lesion diameter (millimeters) was used to calculate the area under disease progress curve (AUDPC), from which the relative AUDPC (rAUDPC=AUDPC/maximum AUDPC) was calculated to compare treatments (Campbell and Madden 1990). Metabolite extraction and analysis The experiment was conducted as a completely randomized block design, with two genotypes (F06025 and Shepody) and two inoculations (pathogen and mock), with five replicates, over time. Each replicate consisted of five pots, two plants per pot, and ten leaves per pot, each with two inoculations on either side of the midrib. The inoculated leaflets were harvested at 72 hpi, and discs containing lesion were cut using a 1-cm cork borer. Samples were flash-frozen in liquid nitrogen and stored at −80 °C. The metabolites were extracted using 60 % aqueous methanol with 0.1 % formic acid (De Vos et al. 2007). Metabolites were analyzed in a negative ionization mode using an LC-HRMS system (LC-ESI-LTQ Orbitrap, Thermo Fisher, Waltham, MA, USA) fitted with a relatively polar reverse phase Kinetex column XB-C18 (5 cm×2.1 mm) (Phenomenex, CA, USA) (Bollina et al. 2010). Mass resolution was set at 60,000 at 400m/z. All the samples were first run

to obtain MS-1, and in addition, a few samples were run to obtain MS/MS fragmentations using a normalized collisioninduced dissociation energy of 35 eV. Data were recorded in centroid mode. LC-HRMS output processing The output from LC-HRMS was converted into mzXML and imported to MZmine-2 for mass detection, chromatogram deconvolution, identification of peaks, and retention time alignment across the samples (Pluskal et al. 2010). For the identification of individual peaks, wavelets were used to a signal-to-noise (S/N) threshold of 5, wavelet scales of 0.5 to 5.0 min, and a peak duration of 0.2 to 5.0. RANSAC alignment was used for retention time alignment with a m/z tolerance of 0.001 to 5.0 ppm, a retention time tolerance of 0.5 min, iterations of 10,000, and a threshold value of 0.5 s. The accurate mass and their abundance (relative intensity) were imported to MS Excel; peaks that were not consistent among replicates and those annotated as isotopes and adducts were excluded from further analyses (Bollina et al. 2010). Identification of resistance-related (RR) metabolites The data on intensity of peaks of monoisotopic masses (m/z= mass/charge ratio, subtracted with a proton mass because of negative ionization) were subjected to pairwise Student’s t test analysis (SAS v 9.3). The treatment combinations tested were RM vs SM, RP vs RM, and SP vs SM, where R=resistant, S= susceptible, M=mock, and P=pathogen-inoculated. The peaks significant at P<0.05 were retained (Kushalappa and Gunnaiah 2013). The significant abundances of 756 metabolites, present in all five replicates, were subjected to canonical discriminant analysis using CANDISC procedure (SAS v 9.3.) to classify the treatments. The data dimension was reduced by a nonsupervised principal component analysis, and the principal components were subjected to supervised discriminant analysis to classify the treatments. The canonical discrimination analysis (CAN) scores were used to develop a scatterplot which discriminated the treatments and identified resistance functions (Hamzehzarghani et al. 2005). The loadings of metabolites to CAN vectors were used to interpret the results. The metabolites with significantly higher abundances in R than in S, based on a t test, were considered as RR metabolites. These were further grouped into RR constitutive (RRC= RM>SM) and RR induced (RRI=(RP>RM)>(SP>SM)) metabolites. For these RR metabolites, the fold change (FC) in abundance, relative to susceptible (R/S), was calculated (Kushalappa and Gunnaiah 2013). When a metabolite was induced only in the R (PRr, pathogenesis-related metabolite in resistant) but not in the S (PRs, pathogenesis-related metabolite in susceptible), then the fold change of the RRI metabolite was considered infinity. These RRI metabolites


Funct Integr Genomics

were considered qualitative, and only the fold change in PRr metabolite (RP/RM) was reported. The RR metabolites were putatively identified based on two criteria: (1) accurate mass match (accurate mass error (AME<5 ppm) with metabolites reported in different databases: METLIN, KNApSAcK, Plant Metabolic Network (PMN), LIPIDMAPS, and KEGG and (2) fragmentation pattern match with those in databases or in silico verification (Gunnaiah et al. 2012). The metabolites were mapped on metabolic pathways using a pathway tool omics viewer searched against Arabidopsis thaliana and Solanum tuberosum metabolites (PMN 2013). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) For quantitative reverse transcription polymerase chain reaction (qRT-PCR), total RNA was isolated from leaves inoculated with pathogen or mock solution at 48 hpi, in three replications (20 leaf discs were collected from five plants for each replicate) using the RNeasy Plant Mini Kit (Qiagen). Purified RNA (3 μg from each sample) was reverse-transcribed using the Affinity Script qRTPCR cDNA Synthesis Kit (Agilent Technologies, Stratagene Products Division, La Jolla, CA, USA). For qRT-PCR, PrimerBLAST software (Ye et al. 2012) was used to design primer sets for St 4-CL, St THT, and Sp TyDC, wherein St EF1α was used as a reference gene (Nicot et al. 2005) (Table S1); 25 ng of cDNA was used in a qRT-PCR reaction using the IQ SYBR Green Supermix (BioRad) in a CFX384™ Real-Time System (BioRad, ON, Canada) in a 10-μl reaction volume, respectively, according to the manufacturer’s instructions. There was no amplification of the primer pairs without the cDNA templates. The relative expression values of each gene in P. infestans-inoculated plants compared with mock-inoculated plants were calculated by the following method: first, the expression values of each gene of interest in each sample were normalized to the expression values of Ef-1α in the same sample. This was performed for both pathogen-inoculated and mock-inoculated plants, from which the average value of the three replicates was calculated. Finally, the averaged normalized values for each time point in the pathogen-inoculated samples were related to the averaged normalized values in the mock-inoculated samples. The relative gene expression level was calculated for PRr (higher in pathogen-inoculated resistant genotypes than in mockinoculated plants), PRs (higher in pathogen-inoculated susceptible genotypes than in mock-inoculated plants), and RRI (PRr> PRs) genes (Kushalappa and Gunnaiah 2013) using the 2−ΔΔCT method (Livak and Schmittgen 2001). Cloning and sequencing of late blight candidate genes from the phenylpropanoid pathway The coding region of 4-CL, THT, and TyDC candidate genes was amplified in susceptible and resistant genotypes by PCR

using potato cDNA (200 ng/μl) as a template and Taq Polymerase (Takara, CA, USA). The PCR cycling protocol was as follows: 95 °C for 3 min–one cycle; 95 °C for 30 s, 56– 57 °C for 30 s, and 72 °C for 90 s–40 cycles; final extension at 72 °C for 7 min. The amplified PCR products were cloned into the pGEM-T Easy Vector (Promega, UK) using the manufacturer’s instructions. The plasmids were isolated using the Zyppy™ Plasmid Miniprep Kit (Zymo Research, CA, USA) and were subjected to DNA sequencing. Sequencing was done on an ABI automated DNA sequencer in Genome Québec, Montreal, Canada. DNA sequences were aligned using BioEdit (http://www.mbio.ncsu.edu/BioEdit/bioedit. html). The DNA sequences were converted to amino acid sequences using the ExPASy translate tool (http://web. expasy.org/translate/). Multiple sequence alignments using the ClustalX tool (Thompson et al. 1997) with the default parameters were performed with the amino acid sequences of resistant and susceptible genotypes and GenBank sequences for 4CL, THT and TyDC candidate genes. Amino acid substitutions were identified between resistant and susceptible genotypes, and their effects on protein stability was predicted by estimating the relative stability changes (ΔΔG value) upon protein mutation using I-Mutant2.0 (Capriotti et al. 2005), where ΔΔG<0 indicates reduced stability of proteins.

Results Disease severity Following pathogen inoculation, brown color circular lesions appeared in resistant (F06025) and susceptible (Shepody) genotypes at 3 dpi. By 9 dpi, lesions expanded covering the entire leaf in susceptible but not in resistant. The AUDPC, calculated based on the lesion diameter, was higher in susceptible (AUDPC=138.77, ratio AUDPC=0.23) than in resistant genotype (47.60, 0.08) (Fig. 1). Differential metabolic profiles of potato genotypes A nontargeted metabolic profiling of potato genotypes, mockand pathogen-inoculated, detected 3,430 peaks. Among the mock-inoculated, a total of 241 metabolites had higher abundance in resistant than in susceptible, and these were designated as RRC metabolites (Table S2). Among the pathogen-inoculated, 110 metabolites were induced in greater abundances in resistant genotype than in susceptible, and these were designated as RRI metabolites (Table S2), of which a total of 26 were induced only in the resistant and, thus, were designated as the qualitative RRI metabolites.


Funct Integr Genomics Fig. 1 Late blight disease severity progress on resistant (F06025) and susceptible (Shepody) potato genotypes. Disease severity was quantified as lesion diameter (millimeters), at 3-day intervals. *P<0.05, **P<0.01, significant difference in lesion diameter in susceptible compared with resistant plants

Classification of observations and treatments based on canonical discriminant analysis Canonical discriminant analysis was used to identify resistance functions based on clustering of observations. The abundances of a total of 756 metabolites, present in all the four treatments, and with significant treatment effects, were subjected to canonical discriminant analysis. The five replicates of each variable were clustered in one group, meaning the experimental error was minimal. The CAN1 vector explained 84 % variance, discriminating the resistant genotype from susceptible genotype. The CAN2 vector explained 13 % variance, discriminating pathogenesis, the separation of pathogen inoculation from mock inoculation (Fig. 2). A total of 138 metabolites had a loading of >90 % for the CAN1 vector that explained the constitutive resistance function in F06025, and 22 metabolites had a loading of >90 % for the CAN2 vector that explained the pathogenesis function (Table S3). But these failed to discriminate induced resistance. Resistance-related constitutive metabolites (RRC=RM>SM) Among the 241 RRC metabolites, 95 were assigned with putative names of identity (Table S2). These metabolites belonged to different chemical groups, and some of the important metabolites with high fold change were as follows: phenyl proponoids: N-caffeoylputrescine, feruloylputrescine, 4-coumaroyl-3hydroxyagmatine, feruloylagmatine, shikimate, coniferin, and 1,2-di-O-sinapoyl-&beta-D-glucose; fatty acid: capric acid and eicosatetraenoic acid; flavonoids: kaempferide 3,7dirhamnoside, isovitexin 2″-O-rhamnoside, retusin 7-Oneohesperidoside, quercetin 3-(6‴-sinapylglucosyl)(1→2)-

galactoside, and aliarin 4′-methyl ether; alkaloids: melicopicine, α-chaconine, and α-solanine; and terpenes: salannin, Nglucosyl nicotinate, and cuminoside B. Resistance-related induced metabolites [RRI=(RP>RM)>(SP>SM)] Out of 110 RRI metabolites, 70 were putatively identified (Tables 1 and S2). These metabolites belonged to different chemical groups, and some of the important and high-foldchange metabolites were as follows: phenylpropanoids: 24 were phenylpropanoids, including feruloylputrescine, pcoumaroyltyramine, N-feruloyltyramine, 4-coumaroyl-3hydroxyagmatine, feruloylagmatine, 4-coumaroylagmatine, terrestriamide, and feruloylserotonin as hydroxycinnamic acid amides (HCAAs) and coumarinate, 1-caffeoyl-4-deoxyquinic acid, p-coumaroyl quinic acid, caffeic acid 3-glucoside, 5-Oferuloylquinic acid, 4-O-beta-D-glucosyl-sinapate, 1-Osinapoyl-beta-D-glucose, and 1,3-dicaffeoylquinic acid as other polyphenols; fatty acids: 14 were fatty acids, including linolenic acid and linoleic acid; flavonoids: ten were flavonoids, including patuletin 3,7-bis(3-acetylrhamnoside), kaempferide 3rhamnoside-7-(6″-succinylglucoside), kaempferol 3-[2‴,3‴,5‴triacetyl-alpha- L-arabinofuranosyl-(1→6)-glucoside], kaempferol 3-sophorotrioside, kaempferol 3-(2″,4″diacetylrhamnoside), and rutin; and alkaloids: six were alkaloids, including melicopicine, solasonine, α-chaconine, and αsolanine. Among the RRI metabolites, the phenylpropanoids had the highest FCs (Table 1) in terrestriamide (FC =5.84), 5-Oferuloylquinic acid (4.80), N-feruloyltyramine (4.32), 4coumaroyl-3-hydroxyagmatine (4.14), feruloylputrescine


Funct Integr Genomics

Fig. 2 Canonical discriminant analysis of significant (P<0.05) metabolites from resistant and susceptible potato genotypes following P. infestans or mock inoculation, where RP is a P. infestans-inoculated resistant

genotype, RMis a mock-inoculated resistant genotype, SPis a P. infestansinoculated susceptible genotype, and SM is a mock-inoculated susceptible genotype

(2.04), p-coumaroyl quinic acid (2.03), scopolin (1.64), 1,3dicaffeoylquinic acid (1.60), feruloylagmatine (1.32), and p-coumaroyltyramine (1.28). These polymers are known to be involved in thickening of cell walls and also act as antifungal, antibacterial, and antimicrobial compounds (Table S4).

transcript expressions of TyDC and THT were 3.54and 2.32-fold in resistant genotype (Fig. 4a, b) as opposed to only 0.9-fold for both in susceptible genotype, while for the coding gene of 4-CL, the transcript expression was 3.03-fold in PRr as opposed to 2.09-fold in PRs (Fig. 4c). The RRI transcript expressions of TyDC, THT, and 4-CL were significant with 4.00-, 2.61-, and 1.44-fold, respectively.

In silico analysis of late blight candidate genes from the phenylpropanoid pathway The RRI metabolites, either qualitative or quantitative, with high fold change in abundance following pathogen inoculation were mapped in the metabolic pathway (Fig. 3a) to identify the catalytic enzymes, which were searched in potato and other genomic databases to identify the RR genes. The RRI metabolites with high fold change in abundance were mainly the HCAAs, and the RR genes that encode the enzymes of these RRI were 4-CL, THT, and TyDC (Fig. 3b). Differentially expressed genes in response to P. infestans qRT-PCR was performed to analyze the changes in transcripts of 4-CL, TyDC, and THT in resistant and susceptible genotypes, following pathogen or mock inoculation. The PRr

Difference in coding sequences of RR genes between resistant and susceptible genotypes The DNA of three RR genes, 4-CL, TyDC, and THT, was sequenced, compared to potato database sequences, and the multiple sequence alignment of amino acids was compared. The amino acids from the resistant genotype varied from the susceptible at three positions for 4-CL and at four positions for TyDC (Table 2, Fig. 5). In contrast, the THTcoding sequence was identical in both resistant and susceptible genotypes. The relative free energy, ΔΔG> 0 indicated greater enzyme stability, was calculated for all the amino acid substitutions in 4-CL and TyDC (Table 2). The amino acid substitution at positions 131, 158, and 298 for 4-CL yielded ΔΔG values of −0.95, −0.80, and −0.70, respectively. Likewise,


Funct Integr Genomics Table 1 Resistance-related (RR) phenylproponoid metabolites detected in potato leaf following P. infestans or mock inoculation Observed mass (Da)

Exact mass (Da)

Phenyl propanoids Hydroxycinnamic acid amides 250.1325 250.1317 264.1479 264.1474 276.1593 276.1586 283.1218 283.1208 292.1542 292.1535 306.1700 306.1692 313.1320 313.1314 327.1118 327.1107 352.1412 352.1423 Phenolic compounds 131.0954 131.0946

Name

Fold change

N-Caffeoylputrescine Feruloylputrescine 4-Coumaroylagmatine p-Coumaroyltyramine 4-Coumaroyl-3-hydroxyagmatine Feruloylagmatine N-Feruloyltyramine Terrestriamide Feruloylserotonin

1.54 RRC 2.04 RRI, 4.78 RRC 7.91 PRr 1.28 RRI 4.14 RRI, 3.01 RRC 1.32 RRI, 29.04 RRC 4.32 RRI 5.84 RRI 1.04 RRI

L-Leucine

1.93 RRC 1.53 RRI 1.52 RRI 1.04 RRI 2.44 RRC

136.0532 164.0483 165.0797 174.0536

136.0524 164.0485 165.0790 174.0528

Methyl benzoate Coumarinate L-Phenylalanine Shikimate

192.0641 283.1217 295.1067 300.0844 338.1004 338.1005 338.1006 338.1007 338.1007 342.0957 342.1317 354.0958 356.1113 368.1114 386.122 386.1221 402.1525 416.1035 504.1853

192.0634 283.1208 295.1056 300.0845 338.1002 338.1002 338.1002 338.1002 338.1002 342.0951 342.1315 354.0951 356.1107 368.1107 386.1213 386.1213 402.1526 416.1002 504.1843

L-Quinate

N-Phenylacetylphenylalanine Indican Salicylate &beta-D-glucose ester 1-Caffeoyl-4-deoxyquinic acid 4-Methylumbelliferyl glucoside 4-p-Coumaroylquinic acid 1-Caffeoyl-4-deoxyquinic acid p-Coumaroyl quinic acid Caffeic acid 3-glucoside Coniferin Scopolin 1-O-Feruloyl-beta-D-glucose 5-O-Feruloylquinic acid 4-O-beta-D-Glucosyl-sinapate 1-O-Sinapoyl-beta-D-glucose Benzyl alcohol beta-D-xylopyranosyl(1→6)-beta-D-glucopyranoside O-Carbamoyl-deacetylcephalosporin C Coniferinoside

2.13 RRI 14.86 PRr 2.47 RRC 3.52 RRI 1.06 RRI 1.27 RRI 2.53 PRr 2.69 PRr 2.03 RRI 1.07 RRI 3.31 RRC 1.64 RRI 2.67 PRr 4.80 RRI 1.19 RRI 1.21 RRI 2.94RRC 7.26 RRC 3.61 RRC

516.1277 562.1703 580.31

516.1268 562.1686 580.3121

1,3-Dicaffeoylquinic acid Flavonol 3-O-beta-D-glucosyl-(1→2)-beta-D-glucoside Hordatine B-like compounds

1.60 RRI 12.93 RRC 7.22 RRC

Detailed compound identification is presented in Table S2 Fold change calculation was based on relative intensity of metabolites: RRC=RM/SM, PRr=RP/RM, RRI=(RP/RM)/(SP/SM), and PRr=RP/RM fold change is reported for the metabolites detected only in resistant cultivar as the RRI fold change would be infinity Da daltons, RRC resistance-related constitutive, RRI resistance-related induced, PRr pathogenesis-related metabolite detected in resistant genotype, RP resistant genotype with pathogen inoculation, RM resistant genotype with mock inoculation, SP susceptible genotype with pathogen inoculation, SM susceptible genotype with mock inoculation

at positions 94, 149, and 262, it yielded ΔΔG values of −0.41, −0.84, and −0.33, respectively for TyDC. The decrease in

protein stability leads to a reduced expression of 4-CL and TyDC genes against P. infestans in susceptible cultivar.


Funct Integr Genomics

Fig. 3 a Satellite metabolic pathway of potato Phytophthora, the resistance-related (RR) metabolites detected in potato-inoculated with P. infestans or mock solution. b Part of the phenylpropanoid pathway showing resistance-related metabolites and their catalyzing enzymes involved in their biosynthesis, in a resistant potato (compounds in italics are detected in the study): HCAAs hydroxycinnamic acid amides, PAL phenyl

alanine ammonia lyase, C4H cinnamate 4-hydroxylase, 4-CL 4coumarate: CoA ligase, ACT agmatine coumaryl transferase, TyDC tyrosine decarboxylase, THT tyramine hydroxycinnamoyl transferase, SHT spermidine hydroxycinnamoyl transferase, and PHT putrescence hydroxycinnamoyl transferase

Discussion

flavonoids, and alkaloids. Some of these were complex polymers and conjugates, which were not detected in our previous studies based on GC-MS analysis (Abunada et al. 2007, 2010). However, we were able to detect them based on LCHRMS (Bollina et al. 2010, 2011; Gunnaiah et al. 2012; Kumaraswamy et al. 2011a, b). The most striking RRI metabolites were from the phenylpropanoid pathway, especially the HCAAs. In wheat, a confocal microscopy study revealed cell

In the present study, we identified 241 RRC and 110 RRI metabolites in a resistant potato genotype against P. infestans. Most of these metabolites also had high loadings to the CAN1 vector which explained both constitutive and induced resistance functions. These RR metabolites belonged to four important chemical groups: phenylpropanoids, fatty acids,


Funct Integr Genomics Fig. 4 Relative transcript expression, in resistant genotype relative to susceptible, following P. infestans and mock inoculation, at 48 hpi using qRT-PCR in comparing to reference gene Ef-1Îą: a tyrosine decarboxylase, b tyramine hydroxycinnamoyl transferase, and c 4-coumarate: CoA ligase. RP is a resistant genotype with P. infestans inoculation, RM is a resistant genotype with mock inoculation, SP is a susceptible genotype with P. infestans inoculation, and SM is a susceptible genotype with mock inoculation. ,*P<0.05, ** P<0.01, significant difference in expression level in RP compared to SP

wall thickening in rachis following Fusarium graminearum inoculation, which prevented further spead of pathogen within spike (Gunnaiah et al. 2012). The RRI metabolites with high fold change, especially the HCAAs, were mapped on a satellite metabolic pathway of potato Phytophthora (Fig. 3a) to relate them to their precursors and to possible polymers biosynthesized from these compounds. The catalytic enzymes of these RRI metabolites were searched in the genomic databases to identify the RR genes involved in their biosynthesis.

Since hundreds of metabolites additively contribute at varying levels to quantitative resistance, only those metabolites with high fold change and with known resistance mechanisms were emphasized. The applications of these metabolites and genes in breeding is discussed. Alternatively, all the RR metabolites identified here, following validation in other genotypes, can also be used in screening for genotypes with relatively higher levels of resistance to late blight (Kushalappa and Gunnaiah 2013).


Funct Integr Genomics Table 2 Amino acid sequences of RR proteins from resistant and susceptible genotypes and potato database sequences Candidate genes

Amino acid position

Potato database

Resistant cultivar F06025

Susceptible cultivar Shepody

ΔΔG value (kcal/mol)

4-CL

131 158 298 94 149 150 262

I A H P P N A

I A H P P N A

V V D L L I S

−0.95 −0.80 −0.70 −0.41 −0.84 0.35 −0.33

TyDc

4-CL 4-coumarate: CoA ligase, TyDC tyrosine decarboxylase, A alanine, D aspartic acid, H histidine, I isoleucine, L leucine, N asparagine, P proline, S serine, V valine, ΔΔG relative free energy=ΔG (new protein)−ΔG (wild protein) in kilocalories per mole

Resistance to lesion expansion through cell wall enforcement by depositing phenylpropanoids Most of the qualitative and high-fold-change RRI metabolites, observed here, belonged to the phenylpropanoid pathway. The most remarkable were the HCAAs, including feruloylputrescine, p-coumaroyltyramine, N-feruloyltyramine, 4-coumaroyl-3-hydroxyagmatine, feruloylagmatine, 4coumaroylagmatine, terrestriamide, and feruloylserotonin (Fig. 3a). The HCAAs constitute the polyaromatic domain of suberin, which is a complex, intractable biopolymer deposited appoplastically between the primary cell wall and plasmalemma (Graca 2010). These polymers increase the cell wall thickness, limiting the spread of pathogen and also act as antifungal, antimicrobial, and antibacterial compounds. Previous studies have demonstrated the synthesis and integration of HCAAs into cell walls as an initial response of potato tubers to fungal attack (Clarke 1982) and four feruloyl amides: N-transferuloyloctopamine, N-cis-feruloyloctopamine, N-transferuloyltyramine, and N-cis-feruloyltyramine have been identified in potato tuber against scab caused by Streptomyces scabies Fig. 5 Amino acid sequences of RR proteins from resistant and susceptible genotype and potato database sequences. a 4-coumarate: CoA ligase and b tyrosine decarboxylase. RP is a resistant genotype with P. infestans inoculation and SP is a susceptible genotype with P. infestans inoculation

(King and Calhoun 2005). Likewise, HCAAs derived from tyramine: p-coumaroyltyramine and feruloyltyramine and from dopamine: p-coumaroyldopamine and feruloyldopamine (Zacarés et al. 2007), noradrenaline (cis/trans N-pcoumaroylnoradrenaline, and cis/trans N-feruloylnoradrenline), and octopamine (cis/trans N-p-coumaroyloctopamine and cis/ trans N-feruloyloctopamine), which act as antibacterial and antioxidant compounds, were reported in tomato infected with the bacterial pathogen Pseudomonas syringae ( L ó p ez - G r e s a e t a l . 20 11) . H C A A s , s u c h a s pcoumaroylputrescine, feruloylputrescine, cinnamoyltyramine, c i s- p- c o u m a r o y l a g m a t i n e , f e r u l o y l a g m a t i n e , pcoumaroylserotonin, caffeoylserotonin, and feruloylserotonin, have also been proved to increase cell wall thickness, limiting the F. graminearum movement, especially in rachis of wheat near isogenic lines (NILs) with QTL-Fhb1 (Gunnaiah et al. 2012). Serotonin and its HCAAs, p-coumaroylserotonin and feruloylserotonin, were also accumulated in Bipolaris oryzaeinfected leaves of rice (Ishihara et al. 2008). Similarly, feruloyl-3′-methoxytyramine, feruloyltyramine, and pcoumaroyltyramine were detected in the cell walls of epidermal onion cells at the site of Botrytis allii penetration (McLusky et al. 1999). HCAAs were induced by ethylene gene, while the mutant failed to induce in Arabidopsis against the necrotrophic pathogen Botrytis cinerea (Lloyd et al. 2011). Resistance due to antimicrobial flavonoids Several kaempferol and related metabolites and their conjugates were identified here as RRI and RRC metabolites. The most abundant were kaempferide 3,7-dirhamnoside, isovitexin 2″-O-rhamnoside, retusin 7-O-neohesperidoside, quercetin 3-(6‴-sinapylglucosyl)(1→2)-galactoside, and aliarin 4′-methyl ether as RRC metabolites and patuletin 3,7-bis(3acetylrhamnoside), kaempferide 3-rhamnoside-7-(6″succinylglucoside), saponarin, and rutin as RRI metabolites. These are produced in the downstream phenylpropanoid pathway, and naringenin is the key precursor (Vogt 2010). Catechin, rutin, and flavonol glycoside P2 were considered to be involved


Funct Integr Genomics

in the defense of the potato cultivar Defender to P. infestans isolates US-8 and US-11 (Henriquez et al. 2012), and rutin acts as an antimicrobial compound, reducing Verticillium dahliae infection in potato plants (El-Hadrami et al. 2011). Kaempferol and its glucosylated forms were involved in barley resistance to F. graminearum (Bollina et al. 2010, 2011; Kumaraswamy et al. 2011a). Resistance due to antimicrobial fatty acids The fatty acids capric acid, linolenic acid, linoleic acid, and eicosatetraenoic acid were identified as RRI metabolites. The capric acid had the lowest LD50 value among the RR metabolites identified in barley against Gibberella zeae (Bollina et al. 2010). The polyunsaturated 18 carbon fatty acids linolenic and linoleic acids are not only antimicrobial but also are oxidized to form oxilipins, viz., jasmonic acid and its conjugates (Blee 2002; Walters et al. 2001). These act as signalling molecules inducing plant defense; however, the active molecule, methyl jasmonate or jasmonoyl isoleucine, was not detected in our study (Tsitsigiannis and Keller 2007; Gunnaiah et al. 2012). Resistance due to antimicrobial alkaloids Alkaloids are the diverse group of plant secondary metabolites, involved in plant defense against herbivores and pathogens (Facchini 2001). We have identified here melicopicine, solasonine, α-chaconine, and α-solanine as RRI metabolites. α-Chaconine and α-solanine have been reported as the most abundant (95 %) glycoalkaloids in the potato tuber (Stapleton et al. 1991), and also, these act as phytoalexins against insects and fungi (Cantwell 1996; Valkonen et al. 1996). Glycoalkaloids, α-chaconine, and α-solanine were associated with resistance to P. infestans and Erwinia carotovora in six progenies obtained from crosses of S. tuberosum and accessions of Solanum andigena, Solanum berthaultii, Solanum phureja, and Solanum vernei (Andrivon et al. 2003). In this study, we have not extracted and analyzed all the metabolites present in the potato late blight system. The use of solvents other than aqueous methanol and other analytical platforms can detect additional metabolites (Kushalappa and Gunnaiah 2013). RR genes in potato against late blight In our study, the most important catalytic enzymes, which produced RR metabolites, identified were 4-coumarate: CoA ligase, tyrosine decarboxylase, and tyramine hydroxycinnamoyl transferase (Fig. 3b). The compounds biosynthesized by these enzymes coded by RR genes can significantly increase potato cell wall fortification in response to P. infestans attack. The possible resistance mechanisms of these genes are discussed below.

Tyrosine decarboxylase (TyDC) In our study, the tyrosine decarboxylase (TyDC, Gene ID: PGSC0003DMT400038501)-coding gene expression was RRI=4.00-fold in resistant, relative to susceptible genotype (Fig. 4a). TyDC converts L-tyrosine into tyramine, in the phenylpropanoid pathway (Schmidt et al. 1998). TyDC contains a conserved putative pyridoxal phosphate-binding site DAAYAG, indicating that this enzyme belongs to the family of amino acid decarboxylases (Facchini and Luca 1994). The RR gene coding TyDC is located on chromosome 3 of potato (PGSC 2013), which is involved in the biosynthesis of tyramine-derived HCAAs, and it increases cell wall stability, leading to the formation of a cell wall barrier against ingressing pathogen. Treatment of cell suspension culture of the potato culitvar Desiree with an elicitor from P. infestans induced higher accumulation of TyDC and PAL at 5–10 hpi, followed by the production of tyramine-derived HCAAs (Schmidt et al. 1998). Likewise, TyDC along with PAL, cinnamoyl CoA reductase (CCR), cinnamyl alcohol dehydrogenase (CAD), and UDP glycosyltransferase (UGT) was highly expressed in flax plants infected with Fusarium oxysporum (Kostyn et al. 2012). The overexpression of TyDC in canola (Facchini et al. 1999) and tobacco plants led to the accumulation of higher levels of soluble tyramine (Guillet et al. 2000) and elevated production of cell wallbound tyramine, as well as increased cell wall fortification (Facchini et al. 1999). Tyramine hydroxycinnamoyl transferase (THT) In our study, the hydroxycinnamoyl-Co A: tyramine hydroxycinnamoyl transferase (THT, GenBank: JX896425.1) gene expression was RRI=2.61-fold in resistant as compared to susceptible cultivar, following inoculation with P. infestans (Fig. 4b). THT is an important enzyme for the biosynthesis of hydroxycinnamic acid tyramine amides. It catalyzes the conversion of hydroxycinnamoyl Co-A thiol esters in combination with tyramine to their respective hydroxycinnamic acid tyramine amides. THT was first discovered in tobacco leaves infected with tobacco mosaic virus (TMV) (Negrel and Martin 1984), and the coding gene was located on chromosome 10 of potato (PGSC 2013). Potato THT belonged to the class of acetyltransferases, which contains a conserved acetyl-CoAbinding site, RKLGMGS, responsible for its catalytic activity (Schmidt et al. 1999). Two genes encoding THT, THT1, and THT7-1, were overexpressed 5.0- and 3.8-fold, enhancing resistance to Ralstonia solanacearum infection in tomato (Ishihara et al. 2012). The cDNA clones encoding THT have been characterized from tobacco, and it was also involved in cell wall strengthening through synthesis of lignin-like compounds (Farmer et al. 1999). Similarly, THT-encoding gene has been cloned and characterized in Capsicum annum and was involved in cell wall thickening in plants with response to UV-C and wounding (Back et al. 2001). The overexpression


Funct Integr Genomics

of genes encoding THT and TyDC induced accumulation of tyramine-derived HCA amides in transgenic tobacco (Hagel and Facchini 2005; Guillet and De Luca 2005). 4-Coumarate: CoA ligase (4-CL) In our study, the 4coumarate: CoA ligase (4-CL, GenBank: AF150686.1)-coding gene expression was 1.44-fold in resistant genotype relative to susceptible (Fig. 4c). 4-CL is a key enzyme in the phenylpropanoid metabolic pathway. 4-CL has characteristic features of the conserved AMP-binding domain PYSSG TTGLPKGV, the GEICIRGR motif (Stuible and Kombrink 2001), and the conserved VPP and PVL domains (Schneider et al. 2003). It catalyzes the conversion of 4-coumaric acid and other substituted cinnamic acid such as caffeic acid and ferulic acid into hydroxycinnamoyl-CoA thiol esters which are used for the synthesis of flavonoids, lignin, polyphenol, coumarin, and suberin (Douglas 1996; Ehlting et al. 1999). It is physically located on chromosome 3 of potato at 66.001 cM (Gebhardt and Valkonen 2001; Meyer et al. 2005). The earlier studies reported 4-CL induction following infection by P. infestans (Cuypers et al. 1988; Fritzemeier et al. 1987), and silencing of 4-CL reduced lignin content in switch grass (Xu et al. 2011). These results suggest that the interaction between potato and P. infestans induces HCAA accumulation, as a physical barrier to prevent pathogen spread within a plant. Effect of amino acid substitutions on RR gene expression and late blight resistance The differential expression of RR genes in resistant genotype in relative to susceptible is due to nsSNPs, leading to amino acid substitutions that can affect the accumulation of HCAAs in potato leaves. The amino acid substitutions of I131V, A158V, and H298D in 4-CL and P94L, P149L, and A262S in TyDC in the susceptible cultivar were predicted to affect protein function and stability (Capriotti et al. 2005). Amino acid substitution of alanine to valine in 4-CL has reflective energetic effects of tertiary interactions by affecting helical structure of protein (Gregoret and Sauer 1998). Similarly, amino acid substitutions were detected in the highly conserved regions of the protein that adversely affect protein structure and function in TyDC (Ashkenazy et al. 2010). These differences are due to allelic differences between the resistant and susceptible genotypes, which correlated with differential expression of RR genes and also possibly due to polymorphism in the promoter region or other cis-acting regulatory sequences present in the upstream of the coding region, which need to be further investigated.

Conclusion In this study, we have identified several RR metabolites and only those with significant effects belonging to

phenylpropanoids, especially the HCAAs, were further explored for variation in gene expression, allelic variation in gene sequence, and to select candidate genes. Silencing of these candidate genes, encoding the biosynthesis of RR metabolites, through posttranscriptional gene silencing (PTGS) via virusinduced gene silencing (VIGS) strategy can prove the role of these RR genes in late blight resistance. Following validation, the RR metabolites or the genes involved in the biosynthesis of most significant RR metabolites can be used as potential biomarkers in breeding to improve quantitative resistance in potato against late blight. Acknowledgments This work was carried out with the aid of a grant from the International Development Research Center, Ottawa, Canada and with the financial support of the Department of Foreign Affairs, Trade and Development Canada.

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2014 fig quantitative resistance in potato leaves to late blight associated kushalappa and mosquera