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Metabolo-proteomics to discover plant biotic stress resistance genes Ajjamada C. Kushalappa and Raghavendra Gunnaiah Plant Science Department, McGill University, Sainte-Anne-de-Bellevue, Quebec, H9X 3V9, Canada
Plants continuously encounter various environmental stresses and use qualitative and quantitative measures to resist pathogen attack. Qualitative stress responses, based on monogenic inheritance, have been elucidated and successfully used in plant improvement. By contrast, quantitative stress responses remain largely unexplored in plant breeding, due to complex polygenic inheritance, although hundreds of quantitative trait loci for resistance have been identified. Recent advances in metabolomic and proteomic technologies now offer opportunities to overcome the hurdle of polygenic inheritance and identify candidate genes for use in plant breeding, thus improving the global food security. In this review, we describe a conceptual background to the plant–pathogen relationship and propose ten heuristic steps streamlining the application of metabolo-proteomics to improve plant resistance to biotic stress. Study of host–pathogen interaction Plants, in natural and cultivated ecosystems, are exposed to several biotic and abiotic stresses. The resistance to biotic stress can be qualitative or quantitative (Box 1) [1]. The qualitative resistance, because of monogenic inheritance, has been successfully transferred to elite cultivars to improve resistance. The quantitative resistance is generally considered to be durable, non-race-specific, and effective against multiple pathogens. However, owing to complex polygenic inheritance, it has not been well exploited for plant improvement [2]. Hundreds of quantitative trait loci (QTLs) (see Glossary) associated with quantitative resistance have been identified for many plant biotic stresses, such as blast in rice (Oryza sativa) [3], fusarium head blight in wheat (Triticum aestivum) [4] and barley (Hordeum vulgare) [5], powdery mildew in wheat [6], late blight in potato (Solanum tuberosum) [7], and bacterial spot in tomato (Solanum lycopersicum L.) [8], and also with qualitative resistance against pathogen-associated molecular patterns (PAMPs) in soybean (Glycine max) [9]. However, the mechanisms of resistance controlled by these QTLs are largely unknown. These QTLs contain several genes, often colocalizing unfavorable genes; thus, the transfer of QTLs to elite cultivars is often problematic. Accordingly, the identification of resistance genes and elucidation of gene function is crucial to transfer appropriate genes to elite cultivars to improve resistance. In plants the resistance to biotic stress Corresponding author: Kushalappa, A.C. (ajjamada.kushalappa@mcgill.ca). 1360-1385/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tplants.2013.05.002
is governed by hundreds of genes, qualitative or quantitative; therefore, a forward genetics approach is better suited than reverse genetics to discover resistance genes, because forward genetics can significantly increase the probability of discovering a resistance gene based on resistance biochemicals that are closer to the phenotype (Figure 1). Plants resist pathogen attack mainly through production of an array of biochemicals. Metabolomics and proteomics of plant biotic stress, at their homeostasis or perturbed environment status, have been used to extract system information. Targeted biochemical analyses, of host–pathogen interactions, have revealed several mechanisms by which Glossary Biochemicals: are either metabolites or proteins. Metabolites are small biomolecules (<2000 Da) biosynthesized by the plant genome, whereas proteins are encoded by the plant genome. Biotic stress resistance genes: are defined as resistance genes in plants that produce resistance biochemicals (R proteins, RR proteins, RR metabolites) resulting in complete inhibition (R genes; qualitative) or reduction in pathogen biomass or disease severity (RR genes; quantitative). Resistance genes, in combination with trans- and/or cis-acting elements, such as transcription factors, may produce a higher quantity of biochemicals, resulting in higher levels of plant resistance. These resistance genes can be transferred to elite cultivars, should they lack, to enhance resistance in plants to biotic stress. Cisgenics: the genetic modification of a recipient plant with a gene from a sexually compatible donor plant that can be used in conventional breeding. Such a gene includes its introns and is flanked by its native promoter and terminator in the normal sense orientation. Doubled haploid (DH) lines: are homozygous lines developed by induced chromosome doubling of haploids, during gametophytic phases of higher plants, in their ovules and pollens. Metabolomics: the systematic detection and quantification of all metabolites in an organism. Near isogenic lines (NILs): homogeneous lines developed by transferring part of the genome region [single or multiple gene(s) or QTL] from a ‘donor parent’ into the genetic background of a ‘recipient parent’. NILs are nearly identical except for the part of the genome transferred. Proteomics: the systematic identification and quantification of all proteins or peptides in an organism. Quantitative disease resistance: plant–pathogen interaction where genotype resistance is expressed as a reduction in disease severity rather than the absence of disease or as hypersensitive reactions (qualitative disease resistance). Quantitative trait locus (QTL) for resistance to pathogens: genomic locus associated with quantitative resistance (trait with continuous variation). These QTLs, generally, contain several genes. Recombinant inbred lines (RILs): homogeneous and homozygous lines developed through single seed descent from the F2 generation. Resistance-related (RR) biochemicals: are defined as metabolites and proteins with significantly greater abundance in a resistant genotype than in a susceptible genotype, which are directly involved in reducing pathogen progress or disease severity. Proteins that completely inhibit disease are referred as R proteins. Proteins that do not have a direct effect on pathogen or disease severity, such as catalytic proteins of RR metabolites, are not considered as RR proteins. Pathogenesis-related (PR) proteins can be considered as RR proteins, if they fulfill the above requirements. Shotgun proteomics: high-throughput technology to identify proteins based on high resolution mass spectra of their proteolytic peptides, following trypsin digestion, with no prior gel electrophoresis based protein separation. Trends in Plant Science xx (2013) 1–10
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Box 1. Conceptual background to plant–pathogen system relation Before comparing the metabolic and proteomic profiles to phenotype, to decipher the plant resistance mechanisms against biotic stress, it is fundamental to understand the conceptual framework of plant– pathogen system interactions. The variations in resistance observed in plants to biotic stress can be explained based on plant pathological and epidemiological concepts. The plant–pathogen system relationship is a parasitic relationship where the pathogen derives food from the host. Such relationships could be biotrophic, where the pathogen can live and multiply in another living organism or tissue, or necrotrophic, where the pathogen can live, multiply, and feed on dead tissue. Following inoculation, pathogens produce several PAMPs that are perceived by plant receptors (pattern recognition receptors – PRRs), leading to PAMP triggered immunity (PTI) or basal resistance (Figure I). To combat, the pathogens produce effector proteins, which enter into the host cell and interfere with host perception. In response, the host plant resists pathogen invasion by producing effector specific R proteins encoded by R genes, resulting in hypersensitive reaction or effector triggered immunity (ETI), which constitutes qualitative or monogenic resistance [18]. The pathogen continues to attack the host by producing several pathogenicity factors, such as toxins and enzymes. Along with the activation of PTI and/or ETI, several other signaling pathways, inducing phytohormones, are also activated deploying broad biochemical resistance mechanisms to reduce pathogen progress, which constitutes quantitative or polygenic resistance.
The true resistance in plants against biotic stress is genetically controlled and it can be complete or partial [1]. The complete resistance in plants is identified based on quality of infection, such as hypersensitive reaction, and is controlled by single genes, or R genes, and thus has been successfully introduced to elite cultivars through breeding [18]. By contrast, partial resistance in plants is identified based on the quantity of infection, such as infection efficiency, latent period, lesion expansion, and amount of sporulation, affecting the monocyclic process of the pathogen [33]. These in turn reduce the rate of disease progress and polycyclic processes in the field. Quantitative resistance is controlled by polygenes, with additive effects. The mechanisms of true resistance can be structural and biochemical, each of which can be constitutive, where the plant produces these compounds during normal growth under homeostasis conditions, or induced, where the plant induces these compounds following pathogen invasion. These biochemicals, proteins or metabolites, inhibit or reduce pathogen biomass or its pathogenicity factors. A comprehensive metaboloproteomics approach can enable visualization of an array of these biochemicals, giving better insight into metabolic pathways involved in plant defense. Some of these biochemicals form structural cell wall components, such as lignin, cellulose, and pectin, which are often not broken down by pathogens, thus, imparting constitutive or induced resistance to pathogen invasion. Both types of resistance can be transferred to elite cultivars through marker-assisted breeding or cisgenics to enhance biotic stress resistance.
Plant breeding for bio c stress resistance PAMPs PRRs
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Figure I. Plant biotic stress resistance breeding. Three types of resistance have been identified: (i) basal resistance: following inoculation, pathogens produce several pathogen-associated molecular patterns (PAMPs) that are perceived by plant receptors (pattern recognition receptors – PRRs), leading to PAMP triggered immunity (PTI); (ii) qualitative resistance (QLR): the pathogen produces effector proteins and the host responds with R proteins, produced by R genes, inducing plant cell death, leading to effector triggered immunity (ETI); (iii) quantitative resistance (QTR): the pathogen produces pathogenicity factors, such as enzymes and toxins, and the host resists attack by producing RR metabolites and RR proteins, produced by RR genes, leading to reduced disease severity. These, especially the R and RR genes, can be transferred to elite cultivars based on marker-assisted breeding (MAB) or cisgenics.
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Figure 1. OMICs of plant resistance to biotic stress. Discovery of resistance genes, starting from metabolome or phenome is forward genetics, and the elucidation of biological mechanisms, controlled by a resistance gene is reverse genetics. Resistance-related metabolites and proteins, produced either constitutively or induced following biotic stress, are biosynthesized by the plant genome.
plants resist biotic stresses [10,11]. However, comprehensive or non-targeted metabolomic and proteomic approaches offer detection of an array of biochemicals that can be correlated to biotic stress resistance, which are governed by monogenes or polygenes. Following discovery, resistance-related (RR) metabolites can be mapped in their metabolic pathways, to better understand their regulation, and the corresponding genes can be identified in biological databases (Table S1 in the supplementary material online). The use of metabolomics to understand plant biological functions [12], plant breeding [13], and to study host–pathogen interaction [14] has been well documented. In parallel, proteomics has been widely used to study host–pathogen interactions [15,16]. In this review, we focus on streamlining different steps involved in the application of metabolomic and proteomic technologies, a novel trend, to decipher the plant mechanisms to pathogen stress resistance, mainly quantitative, and identify candidate genes for use in breeding. Ten heuristic steps We suggest that the application of comprehensive metabolomic and proteomic tools to discover resistance genes and to decipher functions of resistance genes, and to improve resistance in plants against biotic stress can be streamlined into ten major heuristic steps (Figure S1 in the supplementary material online). Step 1. Selection of plant–pathogen systems The study of plant biotic stress is complex, because it involves interaction between two organisms, plant
and pathogen, with distinct metabolic systems. The genetic variability in each leads to variation in their interactions. Depending on the hypotheses, different combinations of plant and pathogen systems can be employed to conduct experiments to explore the system relationship. Selection of host genotypes Quantitative resistance in plants is governed by several genes, each with major and minor effects [2,17], whereas qualitative resistance is governed by single genes [18]. Metabolomic and proteomic profiling of resistant and susceptible genotypes inoculated with different pathogens have identified hundreds of metabolites belonging to different metabolic pathways (Figure 2) [19–21] and proteins belonging to different families [15,16], revealing multiple resistance mechanisms. The use of genetic resources, such as mutant lines [22,23], near isogenic lines (NILs) [24,25], doubled haploid (DH) [26] lines, and recombinant inbred lines (RILs) [27] (Table S2 in the supplementary material online), that vary or segregate for specific traits, allow one to partition the complexity, thus increasing the chance of elucidating resistance mechanisms and identifying candidate genes. Selection of pathogen isolates Pathogens produce different pathogenicity factors such as effector molecules, enzymes, toxins, growth regulators, and polysaccharides to attack plants. The effector molecules produced by pathogens are proteins and are mainly associated with qualitative resistance in plants, especially at 3
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Satellite metabolic pathways: bio c stress resistance
D-glucose
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Alkaloids derived from tryptamine
Tryptophan
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Fructose-6P Alkaloids derived from tyramine
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MEP/DOXP pathway
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t-cinnamate
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Citrate
Lipids Polyke des
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cis-aconitate
(S)-malate
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Ethylene Citruline
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Figure 2. Satellite metabolic pathways, involved in the biosynthesis of resistance-related (RR) metabolites by plants, in response to biotic stress. These RR metabolites are biosynthesized by the catalytic proteins that are coded by the plant genome. The biosynthesis of RR metabolites in a plant are controlled by RR genes; however, the amount produced may be enhanced by regulatory elements.
the initial stages of infection [18,28,29]. At advanced stages of infection, pathogens, both biotrophs and necrotrophs, produce several combinations of these pathogenicity factors. Biochemical profiling of plants inoculated with mutant pathogens, lacking one of these specific pathogenicity factors [30] or effectors [31,32], or with different races [23,25] (Table S2 in the supplementary material online), can reveal the specific mechanisms of resistance in plants. 4
Step 2. Environment influencing inoculation and infection The environment, at inoculation and incubation, is critical for the establishment of infection. Environmental factors comprise physical, biological, and chemical agents [33]. In studying plantâ&#x20AC;&#x201C;pathogen interaction, the ceteris paribus hypothesis should be observed, meaning, in an experiment all variables are equal except for the independent variable(s) studied [34]. Specific environmental conditions
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Review affecting a specific pathosystem, prevailing in a given geographical region, can be simulated under growth chamber or greenhouse conditions to assess resistance and to identify candidate biochemicals against pathogen stress [35]. Although biochemical profiling under field conditions to study gene functions is plausible [36,37], field studies, in an uncontrolled environment, often lead to high variability resulting in inconsistent resistance and/or biochemical profile ranking of genotypes [36]. In spite of this, the field tests of cultivars, with novel stress resistance genes, for performance in a region are critical, to account for location-specific environmental factors. Step 3. Assessment of quantitative resistance in plants against pathogen stress Qualitative resistance is generally expressed as a hypersensitive reaction and is distinct from susceptible disease symptoms, but not quantitative resistance. To explore mechanisms of resistance, it is crucial to accurately assess quantitative resistance in genotypes before correlating them to biochemical profiles. Resistance can be quantified based on the survival of a pathogen at different stages of its monocyclic process [33]: (i) infection efficiency: proportion of infection or disease severity; (ii) lesion expansion: area of lesion or rate of lesion expansion; (iii) latent period: time in days since inoculation until sporulating lesion appearance; and (iv) sporulation: number of spores per unit plant area or rate of process. Resistance can be indirectly measured, by quantifying the pathogen biomass, based on ergosterol [38], ELISA [39], and quantitative real-time PCR [40]. Quantitative resistance under field conditions can be assessed based on disease severity or the area under the disease progress curve (AUDPC) or based on the above techniques [41]. Step 4. Sample collection for biochemical analysis Unlike the genome, the metabolome and proteome are temporally and spatially diversified, in both quality and quantity. Thus, specific organelles, cells, tissues, and organs should be sampled depending on the hypothesis to be tested [24,42]. Laser microdissection has been used to collect specific cell samples [43]. The samples should be immediately frozen in liquid nitrogen and stored at â&#x20AC;&#x201C;808C to quench the metabolism [44]. Intermittent thawing and freezing of samples should be avoided in long-term storage. Although freeze-drying of samples for long-distance transport is recommended, some volatile and semi-volatile metabolites may be lost [45]. Biological variations in the abundance of metabolites among replicates are often high; thus, a minimum of five replicates are needed. The replicates collected over time, and the experimental units consisting of a pool of plants, can make the evaluation more robust or increase reproducibility. Step 5. Extraction and sample preparation for biochemical analysis No one solvent can extract all metabolites and proteins [46]; thus, solvents can be chosen depending on target metabolites. Varying proportions of aqueous methanol and/or chloroform should be optimized to extract polar, semi-polar, and non-polar metabolites that can be analyzed without further derivatization based on liquid chromatography (LC) [46â&#x20AC;&#x201C;48]
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or following derivatization based on gas chromatography (GC) [49,50]. For protein extraction, precipitation using trichloroacetic acid, acetone, or phenol, or a combination of these, can be adapted [51]. Sequential extraction of metabolites, proteins, and RNA can save the cost of sample collection and aid in revealing coexpression of these in complex biochemical networks [52]. Step 6. Mass spectrometry-based metabolomics and proteomics Biochemicals vary in their physical and chemical properties, and thus multiple analytical platforms are needed for comprehensive coverage. Nuclear magnetic resonance is a rapid, high-throughput technology for metabolomics, capable of structural elucidation of unidentified compounds and reproducible quantitation [53â&#x20AC;&#x201C;55]. Although overlapping of signals from complex plant samples has been reduced by linking it to LC, low sensitivity still limits its use. Gas chromatography-mass spectrometry (GC-MS) is limited to volatile metabolites such as simple sugars and amino acids [56], whereas LC-MS detects both volatiles and non-volatiles [19,22,48,57]. Also, LC-MS is widely used in both metabolomics and proteomics [24,52]. MS-based platforms, especially LC-MS, that have high sensitivity, wide dynamic range, and high resolution are more suited for non-targeted analysis [57]. Matrix-assisted laser desorption/ionization (MALDI) imaging has been used to detect metabolites and proteins in specific cells [58]. LC-MS-based shotgun proteomics has been used to analyze proteins in complex mixtures, without prior separation based on gel electrophoresis [59]. The metabolite or peptide separation in LC-MS depends not only on the stationary (column type) and mobile phase (solvents) used but also on the resolution of mass spectrometers used. Electrospray ionization in either positive [60] or negative [19] mode can be used for metabolomics, whereas the use of both increases the number of metabolites detected. Generally positive ionization is used in proteomics. The fragmentation patterns, required for the identification of metabolites and peptides, vary with the collisioninduced dissociation energy used [57,59]. Multiple reaction monitoring of precursor ions of interest can increase possible detection of targeted metabolites and proteins [61]. Step 7. Mass spectral output processing and compound annotation LC-MS data processing, to extract true signals, is the key step in non-targeted biochemical analysis. Several bioinformatic tools are available for LC-MS data processing (Table S2 in the supplementary material online). Most spectral data processing software, such as XCMS [62], involve four basic steps: deconvolution, grouping, alignment across samples, and gap filling. The peaks (m/z) and their intensities, in different samples, are used in statistical analysis. Alternatively, user interactive tools such as MZmine2 [63] and web-based tools such as MetaboAnalyst [64] are more suited for biologists with limited knowledge of MS. The processed outputs from these tools generally contain multiple peaks of the same compound, including isotopes, adducts, neutral loss, and dimers. The processed output in matrix form is imported to an MS-EXCEL spreadsheet and the peaks inconsistent among replicates, 5
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Review isotopes, and adducts can be sieved for further statistical analysis. Metabolite identification Several web-based tools are available for metabolite putative annotation, with links to mass spectral libraries (Table S2 in the supplementary material online). A peak or metabolite can be putatively identified based on three criteria. (i) Accurate mass match with reference compound libraries: each accurate mass from LC-MS is matched with previously identified compounds using various libraries. A metabolite match with an accurate mass error (AME) of >5 ppm can be considered unannotated. (ii) Number of carbons based on isotope ratio: the isotope ratios of carbon (also others) can be used to calculate the number of carbons in the molecular formula [65]. (iii) Fragmentation patterns of peak: fragmentation patterns can be either matched with those published in several databases such as MASSBANK, METLIN, and ReSpecT (Table S2 in the supplementary material online) or with that of the in-house spiked compound library. Fragmentation patterns also can be manually checked using in silico fragmentation tools, such as ChemSketch in IntelliXtract (Figure S2 in the supplementary material online). A complete identification requires fulfillment of the remainder of the seven golden rules [66]. Reverse stable isotope labeling, using labeled carbon and nitrogen sources during plant growth, and then, spiking with non-labeled metabolites, can facilitate large-scale identification of metabolites [67]. Protein identification LC-MS/MS data output from peptide analysis is very complex, as each protein is proteolyzed into more than one peptide before analysis, and each peptide ion produces multiple-charged ions. High resolution MSn can be used to sequence peptides and identify novel proteins, thus annotating gene functions through proteogenomics [68]. Generally, tandem mass spectra are matched to an inventory of mass values generated by in silico fragmented peptides for peptide identification using search engines: MASCOT, SEQUEST, XiTandem, and InSpeCT (Table S2 in the supplementary material online). These search engines are linked to various proteome and genome databases, such as GenBank at NCBI, SWISSPROT, UNIPROT, PPDB, and ProMEX, which can lead to the identification of corresponding genes. Quantification of metabolites and proteins Generally, relative quantification of metabolites and proteins, based on individual peak abundance, is used in treatment comparisons. Absolute quantification can be done by stable isotope labeling [67] or a separate standard curve can be established [69]. Label-free absolute quantification of proteins can be accomplished based on spectral counts, protein abundance index, and absolute protein expression [59]. Step 8. Information extraction â&#x20AC;&#x201C; the discovery of RR metabolites, proteins, and genes Multivariate analyses are powerful tools to classify observations and discover biomarker biochemicals 6
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[14,22]. The abundance of metabolites and proteins or peptides can be subjected to supervised hierarchical cluster analysis and canonical discriminant analysis or to partial least square discriminant analysis to classify observations. Loading of metabolites and proteins gives clues to candidate biochemicals [70â&#x20AC;&#x201C;72]. Multivariate analysis is useful only when the experimental design is simple but not when it is complex [14,73] and, in such cases, the use of t-test or other univariate analyses to identify specific resistance related biochemicals can better explain the underlying biological functions, as detailed below [69,74]. Identification of R proteins and RR biochemicals We have defined R proteins as those that completely inhibit pathogen or disease, and RR biochemicals as those that reduce pathogen progress or disease severity. Host and pathogen system relationships, in different combinations, have been used to identify parameters that can better identify resistance or resistance-related biochemicals: RP versus RM, SP versus SM, RP versus SP, and RM versus SM, where R = resistant, S = susceptible, M = mock inoculated, and P = pathogen inoculated (Figure 3) [19]. They can be resistance or resistance-related constitutive (RC or RRC = RM > SM) or resistance or resistancerelated induced biochemicals [RI or RRI = (RP > RM) > (SP > SM)]. Biochemicals that are qualitatively occurring only in resistant genotypes or in higher fold changes of abundance in a resistant relative to a susceptible genotype are considered to be biomarkers. Various combinations of host and pathogen systems (see Step 1) can be used to identify R and RR biochemicals. Mapping RR metabolites in metabolic pathways and searching proteins in genomic databases to identify R or RR genes We have classified biotic stress resistance genes into R genes that produce R proteins, and RR genes that produce varying amounts of RR biochemicals. The hundreds of RR metabolites identified in resistant plants have been linked to their metabolic pathways to discover their precursors and end products that can further reveal the sets of metabolites involved in resistance. Tools, such as Cytoscape [75], MapMan [76], and Metaboanalyst [64], can be used to map metabolites and proteins on KEGG and PlantCyc pathways (Table S2 in the supplementary material online). Mapping of RR metabolites, on the complex metabolic network, can reveal upregulated pathways: phenylpropanoids, including flavonoid [77], [19,24], fatty acids [78], terpenoid [19], and alkaloids [79,80] (Figure 2 and Table S1 in the supplementary material online). Differentially accumulated biochemicals can be used to discover biological functions of target genes through coexpression analysis [81,82], and also to discover novel R and RR genes [22,24,83]. Metabolic flux analysis or fluxomics [84] can reveal the key RR genes, and associated trans- and/or cisacting elements that induce RR biochemicals. Thus, hundreds of these R or RR genes, and their products, R proteins or RR biochemicals, can be used to explain the mechanisms of resistance against biotic stress in plants.
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Stress resistance gene discovery Resistant (R)
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<
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Figure 3. Stress resistance gene discovery. Resistance-related (RR) biochemicals are metabolites (MET) and proteins that are higher in abundance (indicated by >) in a resistant than in a susceptible genotype, which are directly involved in the reduction of disease severity or pathogen progress in plants. RRC (resistance-related constitutive) represents metabolites and proteins that are constitutively produced by the plant, based on mock inoculation. PR (pathogenesis-related) represents biochemicals that are higher in pathogen-inoculated (P) plants than in mock-inoculated (M) plants. PRr represents PR in a resistant genotype and PRs represents PR in a susceptible genotype. RRI (resistance-related induced) can represent PRr > PRs = [(RP > RM) > (SP > SM)]. Metabolites can be mapped in metabolic pathways to identify enzymes, and both enzymes and RR proteins can be searched in genomic databases to identify RR genes, which in turn can be silenced to identify or confirm gene function. RR (also R) along with regulatory (REG) genes can be transferred to elite cultivars through marker-assisted breeding (MAB) or cisgenics.
Validating the R or RR gene resistance function It is possible that the quantity of RR metabolites produced may be governed by RR genes alone or in combination with genes encoding enzymes of upstream metabolic pathways or regulated by trans- and/or cis-acting regulatory elements such as transcription factors [85,86]. Accordingly, either RR genes or their corresponding regulatory genes are to be considered for validation. The R or RR genes can be validated either by loss-of-function or gain-of-function studies. In the former, target genes can be silenced to prove the resistance mechanisms. Virus-induced gene silencing (VIGS) is a rapid, comparatively cheap, and viable option for most crop species to prove gene function through transient expression of antisense gene constructs for candidate genes [87,88]. For crops that are amenable to tissue culture, genes can be overexpressed (gain-offunction) to prove gene functions [89]. Even though the production of several RR metabolites and proteins have been identified in plants in response to pathogen attack, their link to RR genes and further validation of biological functions are still unknown.
Step 9. New knowledge generation â&#x20AC;&#x201C; mechanisms of resistance in plants to biotic stress Information generated on biochemical profiles of the plantâ&#x20AC;&#x201C;pathogen interaction system can be used to generate new insights by further exploring their roles in plant defense. RR metabolites, and R and RR proteins, explain several mechanisms of resistance (Table S1 in the supplementary material online). Signaling molecules Metabolites such as ethylene [22,90], salicylic acid, jasmonic acid [74,90], and inositol have been associated with plant defense signaling against biotic stress (Figure 2). In addition, several peptides and proteins can also act as signaling molecules or transcription factors regulating downstream biosynthesis of biochemicals [20,90]. Antimicrobial biochemicals and resistance equivalence Many RR metabolites identified, based on non-targeted analysis, have antimicrobial properties [10,77] (Table S1 in the supplementary material online). RR metabolites 7
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Review identified not only varied in their abundance among genotypes but also in their ability to reduce pathogen biomass (LD50 values) and, accordingly, these two mechanisms were combined to calculate resistance equivalence (RE) [19,74,91]. By contrast, R proteins produced by R genes, in response to pathogen produced effector proteins, can lead to complete resistance [18]. PR proteins [16] that directly reduce pathogen biomass or detoxify pathogenicity factors [92], resulting in reduced disease severity, can be called RR proteins. Cell wall enforcement The primary wall of plant cells mainly consists of celluloses, proteins, and pectin. Secondary cell walls are more complex through constitutive deposition of hemicelluloses, pectins, and lignins that make the advancement of the pathogen difficult to impossible [87,93]. The secondary cell wall is also strengthened, following pathogen inoculation, by deposition of lignin, hydroxycinnamic acid amides (HCAAs), flavonoids, suberin, and several other biochemicals [22,24]. Cell wall thickening has been observed in several pathosystems, often lost while breeding for crop yield, and can be exploited for multiple pathogen defense in plants. Step 10. Knowledge application and technology transfer The vast knowledge generated through non-targeted metabolomics and/or proteomics has multiple applications. Candidate resistance metabolites, proteins, and genes identified can be used as biomarkers in breeding [78,94]. RR metabolites identified can be directly used in screening of genotypes, based on flow injection nanospray ionization mass spectrometry [95] and high-performance liquid chromatography [96]. Candidate genes, RR genes, and associated trans- and/or cis-acting elements identified can be used in marker-assisted breeding employing gene specific DNA markers, but is very challenging when several genes are involved [97]. Alternatively, several RR genes identified in a crop plant species can be precisely stacked to an elite cultivar based on cisgenics or genome modification, if the recipient lacks required genes [98,99]. Following gene transfer into elite cultivars, the performance of recipient cultivars should be validated under field conditions. Orthologous genes of RR genes with validated function and significant resistance effect can be identified in other related crop species and utilized in breeding programs with the aim to improve resistance against biotic stress. Concluding remarks and future perspectives In the past decades breakthrough in genetic crop improvement has been mainly on yield, flagged as green revolution. Most likely, the next 50 years will rely on functional genomics to identify genes that enhance crop yield without sacrificing nutritional values but with minimizing loss due to stress. Specifically, advances in metabolomics and proteomics can be applied to improve plant resistance to abiotic and biotic stresses. The ten heuristic steps streamlining the application of metabolo-proteomics to improve plant resistance to stress should enable plant breeders, geneticists, biochemists, bioinformaticians, biologists, and pathologists to take up this challenge to improve 8
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Box 2. Outstanding questions Proteomic and metabolomic profiles cannot explain all the mechanisms of resistance in plants against pathogen attack. Structural resistance involves complex biochemicals and metabolo-proteomic analyses cannot detect them. These polymers have to be broken down to oligomers and monomers that can be detected by mass spectrometry or NMR. In addition, some mechanisms are morphological. All RR metabolites and proteins identified here cannot be linked to respective RR genes. Metabolites can be mapped to their metabolic pathways and searched in biological databases if already known. Unknown enzymes can be identified based on peptide sequences, which in turn can be used to identify RR genes. Further advances in proteomics are needed to correctly identify enzymes. There is much confusion in the literature on qualitative and quantitative resistance genes. Generally, qualitative resistance genes are referred to as R genes, which produce R proteins in response to pathogen effectors. We suggest that the term â&#x20AC;&#x2DC;biotic stress resistance genesâ&#x20AC;&#x2122; should include all genes in plants that govern resistance, explaining mechanisms, to biotic stresses, which result in the inhibition or reduction of disease severity. Advances in forward and reverse genetics, to better understand resistance mechanisms, would lead to a comprehensive definition of resistance genes. In the future, these resistance genes can be classified based on their molecular and/or biological functions.
plant resistance, thus significantly contributing to global food security. Future challenges in the application of metabolo-proteomics in plant stress resistance improvement are multifold (Box 2). Metabolite identification is still a major hurdle and annotation of more metabolites can hasten progress in this area [60]. Furthermore, proteomic analysis suffers from the inability to directly analyze proteins (top-down proteomics), because of their large size and unavailability of precise libraries. Model and crop plant genomic databases have significantly advanced the discovery of candidate genes. Production of mutants, NILs, RILs, and DH populations in crop plants will hasten RR gene identification. Genetic control of quantitative resistance, however, is complex. The quantity of RR metabolites produced may depend not only on allelic variation of an RR gene controlling its production but also on regulating trans- and/or cisacting elements [85,86]. The transfer of a set of significant effect RR genes, along with regulatory genes, through marker-assisted breeding is very challenging. However, transfer of RR genes from one genotype to another within sexually compatible plant species, through cisgenics, as opposed to transgenic approaches, should be more acceptable to the public, because no foreign DNA is incorporated [98]. Gene deletion or replacement of the RR gene through genome editing technologies is also promising. Acknowledgments We thank the Natural Sciences and Engineering Research Council of Canada, Le Ministe`re de lâ&#x20AC;&#x2122;Agriculture, des PeË&#x2020;cheries et de lâ&#x20AC;&#x2122;Alimentation du Que´bec, Canada, and the International Development Research Center, and the Canadian International Development Agency (Food Security Program) for their financial support.
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.tplants. 2013.05.002.
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Review References 1 Agrios, G. (2005) Genetics of plant disease. In Plant Pathology (5th edn), pp. 125–174, Elsevier Academic Press 2 Poland, J.A. et al. (2009) Shades of gray: the world of quantitative disease resistance. Trends Plant Sci. 14, 21–29 3 Ballini, E. et al. (2008) A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance. Mol. Plant Microbe Interact. 21, 859–868 4 Buerstmayr, H. et al. (2009) QTL mapping and marker-assisted selection for Fusarium head blight resistance in wheat: a review. Plant Breed. 128, 1–26 5 Massman, J. et al. (2011) Genome-wide association mapping of Fusarium head blight resistance in contemporary barley breeding germplasm. Mol. Breed. 27, 439–454 6 Aghnoum, R. et al. (2010) Basal host resistance of barley to powdery mildew: connecting quantitative trait loci and candidate genes. Mol. Plant Microbe Interact. 23, 91–102 7 Danan, S. et al. (2011) Construction of a potato consensus map and QTL meta-analysis offer new insights into the genetic architecture of late blight resistance and plant maturity traits. BMC Plant Biol. 11, 16 8 Hutton, S.F. et al. (2010) Identification of QTL associated with resistance to bacterial spot race T4 in tomato. Theor. Appl. Genet. 121, 1275–1287 9 Valde´s-Lo´pez, O. et al. (2011) Identification of quantitative trait loci controlling gene expression during the innate immunity response of soybean. Plant Physiol. 157, 1975–1986 10 Ahuja, I. et al. (2012) Phytoalexins in defense against pathogens. Trends Plant Sci. 17, 73–90 11 Vidhyasekaran, P. (2008) Fungal Pathogenesis in Plants and Crops: Molecular Biology and Host Defense Mechanisms. CRC Press 12 Schauer, N. and Fernie, A.R. (2006) Plant metabolomics: towards biological function and mechanism. Trends Plant Sci. 11, 508–516 13 Fernie, A.R. and Schauer, N. (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 25, 39–48 14 Allwood, J.W. et al. (2008) Metabolomic technologies and their application to the study of plants and plant–host interactions. Physiol. Plant 132, 117–135 15 Quirino, B. et al. (2010) Proteomic approaches to study plant–pathogen interactions. Phytochemistry 71, 351–362 16 Rampitsch, C. and Bykova, N.V. (2012) Proteomics and plant disease: advances in combating a major threat to the global food supply. Proteomics 12, 673–690 17 St Clair, D.A. (2010) Quantitative disease resistance and quantitative resistance loci in breeding. Annu. Rev. Phytopathol. 48, 247–268 18 Spoel, S.H. and Dong, X. (2012) How do plants achieve immunity? Defence without specialized immune cells. Nat. Rev. Immunol. 12, 89–100 19 Bollina, V. et al. (2010) Mass spectrometry-based metabolomics application to identify quantitative resistance-related metabolites in barley against Fusarium head blight. Mol. Plant Pathol. 11, 769–782 20 Wang, T. et al. (2010) Identification of seed proteins associated with resistance to pre-harvested aflatoxin contamination in peanut (Arachis hypogaea L). BMC Plant Biol. 10, 267 21 Geddes, J. et al. (2008) Differential expression of proteins in response to the interaction between the pathogen Fusarium graminearum and its host, Hordeum vulgare. Proteomics 8, 545–554 22 Lloyd, A.J. et al. (2011) Metabolomic approaches reveal that cell wall modifications play a major role in ethylene-mediated resistance against Botrytis cinerea. Plant J. 67, 852–868 23 Consonni, C. et al. (2010) Tryptophan-derived metabolites are required for antifungal defense in the Arabidopsis mlo2 mutant. Plant Physiol. 152, 1544–1561 24 Gunnaiah, R. et al. (2012) Integrated metabolo-proteomic approach to decipher the mechanisms by which wheat QTL (Fhb1) contributes to resistance against Fusarium graminearum. PLoS ONE 7, e40695 25 Liao, M. et al. (2009) Identification of elicitor-responsive proteins in rice leaves by a proteomic approach. Proteomics 9, 2809–2819 26 Sharma, N. et al. (2008) Towards identifying Brassica proteins involved in mediating resistance to Leptosphaeria maculans: a proteomics-based approach. Proteomics 8, 3516–3535 27 Mohammadi, M. et al. (2011) Proteomic profiling of two maize inbreds during early gibberella ear rot infection. Proteomics 11, 3675–3684
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
28 Deslandes, L. and Rivas, S. (2012) Catch me if you can: bacterial effectors and plant targets. Trends Plant Sci. 17, 644–655 29 de Jonge, R. et al. (2011) How filamentous pathogens co-opt plants: the ins and outs of fungal effectors. Curr. Opin. Plant Biol. 14, 400–406 30 Kumaraswamy, G. et al. (2011) Differential metabolic response of barley genotypes, varying in resistance, to trichothecene-producing and-nonproducing (tri5 ) isolates of Fusarium graminearum. Plant Pathol. 61, 509–521 31 Whisson, S. et al. (2011) Exploiting knowledge of pathogen effectors to enhance late blight resistance in potato. Potato Res. 54, 325–340 32 Li, T. et al. (2013) Designer TAL effectors induce disease susceptibility and resistance to Xanthomonas oryzae pv. Oryzae in rice. Mol. Plant http://dx.doi.org/10.1093/mp/sst1034 33 Zadoks, J.C. and Schein, R.D. (1979) Epidemiology and Plant Disease Management. Oxford University Press 34 Yang, L. et al. (2011) Proteomic analysis of grapevine stem in response to Xylella fastidiosa inoculation. Physiol. Mol. Plant Pathol. 75, 90–99 35 Gibon, Y. and Rolin, D. (2012) Aspects of experimental design for plant metabolomics experiments and guidelines for growth of plant material. In Plant Metabolomics: Methods and Protocols (Hardy, N.W. and Hall, R.D., eds), pp. 13–30, Humana Press 36 Barros, E. et al. (2010) Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant Biotechnol. J. 8, 436–451 37 Catchpole, G.S. et al. (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc. Natl. Acad. Sci. U.S.A. 102, 14458–14462 38 Lau, A.P.S. et al. (2006) Ergosterol as a biomarker for the quantification of the fungal biomass in atmospheric aerosols. Atmos. Environ. 40, 249–259 39 Tian, S. et al. (2005) Accurate assessment of wheat and triticale cultivar resistance to Septoria tritici and Stagonospora nodorum infection by biotin/avidin ELISA. Plant Dis. 89, 1229–1234 40 Parisi, O. et al. (2011) Development of a quick quantitative real-time PCR for the in vivo detection and quantification of peach latent mosaic viroid. Plant Dis. 95, 137–142 41 Sankaran, S. et al. (2010) A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1–13 42 Pechanova, O. et al. (2011) Proteomic analysis of the maize rachis: potential roles of constitutive and induced proteins in resistance to Aspergillus flavus infection and aflatoxin accumulation. Proteomics 11, 114–127 43 Amantonico, A. et al. (2010) Analytical techniques for single-cell metabolomics: state of the art and trends. Anal. Bioanal. Chem. 398, 2493–2504 ´ lvarez-Sa´nchez, B. et al. (2010) Metabolomics analysis I. Selection of 44 A biological samples and practical aspects preceding sample preparation. TrAC Trends Anal. Chem. 29, 111–119 45 Oikawa, A. et al. (2011) Effects of freeze-drying of samples on metabolite levels in metabolome analyses. J. Sep. Sci. 34, 3561–3567 46 Kim, H.K. and Verpoorte, R. (2010) Sample preparation for plant metabolomics. Phytochem. Anal. 21, 4–13 47 De Vos, R.C.H. et al. (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2, 778–791 48 Theodoridis, G. et al. (2012) LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation. Metabolomics 8, 175–185 49 Lisec, J. et al. (2006) Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat. Protoc. 1, 387–396 50 Fiehn, O. et al. (2000) Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 51 Wang, W. et al. (2008) Optimizing protein extraction from plant tissues for enhanced proteomics analysis. J. Sep. Sci. 31, 2032–2039 52 Weckwerth, W. (2009) Metabolomics: integrating the metabolome and the proteome for systems biology. Annu. Plant Rev. 35, 258–289 53 Kim, H.K. et al. (2011) NMR-based plant metabolomics: where do we stand, where do we go? Trends Biotechnol. 29, 267–275 54 Choi, Y.H. et al. (2006) NMR metabolomics to revisit the tobacco mosaic virus infection in Nicotiana tobaccum leaves. J. Nat. Prod. 69, 742–748 9
TRPLSC-1067; No. of Pages 10
Review 55 Figueiredo, A. et al. (2008) Transcriptional and metabolic profiling of grape (Vitis vinifera L.) leaves unravel possible innate resistance against pathogenic fungi. J. Exp. Bot. 59, 3371–3381 56 Cevallos-Cevallos, J.M. et al. (2012) GC-MS metabolomic differentiation of selected citrus varieties with different sensitivity to citrus huanglongbing. Plant Physiol. Biochem. 53, 69–76 57 Allwood, J.W. and Goodacre, R. (2010) An introduction to liquid chromatography–mass spectrometry instrumentation applied in plant metabolomic analyses. Phytochem. Anal. 21, 33–47 58 Kaspar, S. et al. (2011) MALDI-imaging mass spectrometry – an emerging technique in plant biology. Proteomics 11, 1840–1850 59 Matros, A. et al. (2011) Recent progress in liquid chromatographybased separation and label-free quantitative plant proteomics. Phytochemistry 72, 963–974 60 Matsuda, F. et al. (2008) MS/MS spectral tag-based annotation of non-targeted profile of plant secondary metabolites. Plant J. 57, 555–577 61 Boja, E.S. and Rodriguez, H. (2012) Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics 12, 1093–1110 62 Smith, C.A. et al. (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 63 Pluskal, T. et al. (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 64 Xia, J. and Wishart, D.S. (2011) Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 65 Iijima, Y. et al. (2008) Metabolite annotations based on the integration of mass spectral information. Plant J. 54, 949–962 66 Kind, T. and Fiehn, O. (2007) Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105 67 Hegeman, A.D. et al. (2007) Stable isotope assisted assignment of elemental compositions for metabolomics. Anal. Chem. 79, 6912–6921 68 Castellana, N. and Bafna, V. (2010) Proteogenomics to discover the full coding content of genomes: a computational perspective. J. Proteomics 73, 2124–2135 69 Bollina, V. et al. (2011) Identification of metabolites related to mechanisms of resistance in barley against Fusarium graminearum, based on mass spectrometry. Plant Mol. Biol. 77, 355–370 70 Hamzehzarghani, H. et al. (2008) Metabolite profiling coupled with statistical analyses for potential high-throughput screening of quantitative resistance to Fusarium head blight in wheat. Can. J. Plant Pathol. 30, 24–36 71 Hoehenwarter, W. et al. (2008) A rapid approach for phenotypescreening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment. Proteomics 8, 4214–4225 72 Goodacre, R. et al. (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3, 231–241 73 Johnson, H.E. et al. (2007) The application of MANOVA to analyse Arabidopsis thaliana metabolomic data from factorially designed experiments. Metabolomics 3, 517–530 74 Kumaraswamy, G.K. et al. (2011) Metabolomics technology to phenotype resistance in barley against Gibberella zeae. Eur. J. Plant Pathol. 130, 29–43 75 Smoot, M.E. et al. (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432 76 Usadel, B. et al. (2009) A guide to using MapMan to visualize and compare omics data in plants: a case study in the crop species, maize. Plant Cell Environ. 32, 1211–1229
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
77 Ballester, A.R. et al. (2013) Citrus phenylpropanoids and defence against pathogens. Part I: metabolic profiling in elicited fruits. Food Chem. 136, 178–185 78 Batovska, D.I. et al. (2008) Preliminary study on biomarkers for the fungal resistance in Vitis vinifera leaves. J. Plant Physiol. 165, 791–795 79 Machado, A.R.T. et al. (2012) Metabolic profiling in the roots of coffee plants exposed to the coffee root-knot nematode, Meloidogyne exigua. Eur. J. Plant Pathol. 134, 431–441 80 Sana, T.R. et al. (2010) Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae. Metabolomics 6, 451–465 81 Quanbeck, S.M. et al. (2012) Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of ‘‘unknown function’’. Front. Plant Sci. 3, 15 82 Tohge, T. and Fernie, A.R. (2010) Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function. Nat. Protoc. 5, 1210–1227 83 Matsuda, F. et al. (2012) Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J. 70, 624–636 84 Winter, G. and Kro¨mer, J.O. (2013) Fluxomics – connecting ‘omics’ analysis and phenotypes. Environ. Microbiol. http://dx.doi.org/ 10.1111/1462-2920.12064 85 Pandey, S.P. and Somssich, I.E. (2009) The role of WRKY transcription factors in plant immunity. Plant Physiol. 150, 1648–1655 86 Marchive, C. et al. (2013) Over-expression of VvWRKY1 in grapevines induces expression of jasmonic acid pathway-related genes and confers higher tolerance to the downy mildew. PLoS ONE 8, e54185 87 Bhuiyan, N.H. et al. (2009) Role of lignification in plant defense. Plant Signal. Behav. 4, 158–159 88 Scofield, S.R. and Nelson, R.S. (2009) Resources for virus-induced gene silencing in the grasses. Plant Physiol. 149, 152–157 89 Muroi, A. et al. (2009) Accumulation of hydroxycinnamic acid amides induced by pathogen infection and identification of agmatine coumaroyltransferase in Arabidopsis thaliana. Planta 230, 517–527 90 Ding, L. et al. (2011) Resistance to hemi-biotrophic F. graminearum infection is associated with coordinated and ordered expression of diverse defense signaling pathways. PLoS ONE 6, e19008 91 Boutigny, A.L. et al. (2009) Ferulic acid, an efficient inhibitor of type B trichothecene biosynthesis and Tri gene expression in Fusarium liquid cultures. Mycol. Res. 113, 746–753 92 Poppenberger, B. et al. (2003) Detoxification of the Fusarium mycotoxin deoxynivalenol by a UDP-glucosyltransferase from Arabidopsis thaliana. J. Biol. Chem. 278, 47905–47914 93 Naoumkina, M.A. et al. (2010) Genome-wide analysis of phenylpropanoid defence pathways. Mol. Plant Pathol. 11, 829–846 94 Kumaraswamy, K.G. et al. (2011) Mass spectrometry based metabolomics to identify potential biomarkers for resistance in barley against Fusarium head blight (Fusarium graminearum). J. Chem. Ecol. 37, 846–856 95 Parker, D. et al. (2009) Metabolomic analysis reveals a common pattern of metabolic re-programming during invasion of three host plant species by Magnaporthe grisea. Plant J. 59, 723–737 96 Aldini, G. et al. (2011) An integrated high resolution mass spectrometric and informatics approach for the rapid identification of phenolics in plant extract. J. Chromatogr. A 1218, 2856–2864 97 Kou, Y. and Wang, S. (2010) Broad-spectrum and durability: understanding of quantitative disease resistance. Curr. Opin. Plant Biol. 13, 181–185 98 Jacobsen, E. and Schouten, H. (2009) Cisgenesis: an important subinvention for traditional plant breeding companies. Euphytica 170, 235–247 99 Shukla, V.K. et al. (2009) Precise genome modification in the crop species Zea mays using zinc-finger nucleases. Nature 459, 437–441