Molecular Plant Advance Access published online on July 7, 2009
Molecular Plant, doi:10.1093/mp/ssp045
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Analysis of Transcriptome Changes Induced by Ptr ToxA in Wheat Provides Insights into the Mechanisms of Plant Susceptibility
Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA
1 To whom correspondence should be addressed at Department of Botany and Plant Pathology, Cordley Hall 2082, Oregon State University, Corvallis, Oregon 97331–2902, USA. E-mail ciuffetL{at}science.oregonstate.edu, fax (541) 737-3573, tel. (541) 737-2188.
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Abstract |
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 TOP Abstract INTRODUCTIONRESULTSDISCUSSIONMETHODSSUPPLEMENTARY DATAFUNDING |
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To obtain greater insight into the molecular events underlying plant disease susceptibility, we studied transcriptome changes induced by a host-selective toxin of Pyrenophora tritici-repentis, Ptr ToxA (ToxA), on its host plant, wheat. Transcriptional profiling of ToxA-treated leaves of a ToxA-sensitive wheat cultivar was performed using the GeneChip® Wheat Genome Array. An improved and up-to-date annotation of the wheat microarray was generated and a new tool for array data analysis (BRAT) was developed, and both are available for public use via a web-based interface. Our data indicate that massive transcriptional reprogramming occurs due to ToxA treatment, including cellular responses typically associated with defense. In addition, this study supports previous results indicating that ToxA-induced cell death is triggered by impairment of the photosynthetic machinery and accumulation of reactive oxygen species. Based on results of this study, we propose that ToxA acts as both an elicitor and a virulence factor.
Key Words: Gene expression • transcriptome analysis • defense responses • fungal pathogenesis • plant–microbe interactions • host-selective toxin
Received for publication March 18, 2009. Accepted for publication June 8, 2009.
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INTRODUCTION |
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TOPAbstractINTRODUCTIONRESULTSDISCUSSIONMETHODSSUPPLEMENTARY DATAFUNDING |
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Complex molecular interactions between a plant and microbe determine whether disease is the ultimate outcome. This complexity arises from diverse strategies employed by microorganisms to ensure their survival and the multiple plant responses triggered by these potential invaders. The plant recognizes the presence of a pathogen or non-pathogen by detecting a broad spectrum of elicitors (Bent and Mackey, 2007; Dixon et al., 1994; Hahn, 1996). These elicitors induce defense-associated responses characterized by the production of signaling molecules such as salicylic acid (SA), jasmonic acid (JA), nitric oxide, ethylene, and reactive oxygen species (ROS) (Berger et al., 2007; Dangl, 1998; Garcia-Brugger et al., 2006; Glazebrook, 2005; Kunkel and Brooks, 2002; Montesano et al., 2003). Some of the downstream events induced by these signaling pathways lead to transcriptional activation of defense-related genes that result in cell wall strengthening, accumulation of pathogenicity-related (PR) proteins and antimicrobial compounds (Berger et al., 2007; Kunkel and Brooks, 2002; Montesano et al., 2003; Van Loon et al., 2006).
A more specific type of plant–pathogen interaction involves the recognition of the product of a pathogen avirulence (Avr) gene by the product of a complementary plant resistance (R) gene (Bent and Mackey, 2007; Bonas and Lahaye, 2002; Flor, 1971; Jones and Dangl, 2006; Staskawicz et al., 1995). When both genes are present, the outcome of the plant–pathogen interaction is host resistance (incompatibility), and the absence of either the R and/or Avr gene leads to host susceptibility (compatibility). Often, the gene-for-gene dictated resistance response is associated with a localized cell death known as the hypersensitive response (HR) (Glazebrook, 1999; Heath, 2000; Morel and Dangl, 1997; Mur et al., 2008). In addition, these interactions also induce responses similar to elicitors (Dixon et al., 1994). Although the recognition and signal transduction processes associated with the gene-for-gene interaction have been widely studied (Martin, 1999; Nimchuk et al., 2003; Staskawicz et al., 1995; Zhang and Klessig, 2001), the physiological nature of plant disease susceptibility remains far less understood.
A subset of plant pathogenic fungi produce a wide range of compounds that act as pathogenicity (virulence) determinants, commonly referred as host-selective toxins (HSTs) (Walton, 1996; Wolpert et al., 2002). HSTs are usually active against plants that are susceptible to the pathogen that synthesizes them. Typically, a single gene/locus in the host conditions sensitivity to an HST and susceptibility to the pathogen (Gamba et al., 1998; Lorang et al., 2007; Nagy and Bennetzen, 2008; Strelkov and Lamari, 2003; Wolpert et al., 2002). In this respect, susceptibility is the outcome of an ‘inverse’ gene-for-gene interaction. Whereas the classical gene-for-gene is descriptive of many interactions with biotrophic plant pathogens, the inverse gene-for-gene appears to be applicable to many necrotrophic plant–pathogen interactions where HSTs act as major virulence factors (Friesen et al., 2007; Friesen et al., 2008; Glazebrook, 2005; Strelkov and Lamari, 2003; Wolpert et al., 2002). Recent studies have directly demonstrated or strongly implicated that genes homologous to R genes can also confer susceptibility to toxin-producing fungi (Lorang et al., 2007; Nagy and Bennetzen, 2008). While HSTs evoke susceptibility, they also induce molecular and biochemical responses similar to those induced by avirulence factors (Wolpert et al., 2002). For instance, victorin, an HST produced by Cochliobolus victorae, induces callose deposition, phytoalexin accumulation, ethylene production, and respiratory burst among others (Lorang et al., 2007; Mayama et al., 1986; Shain and Wheeler, 1975; Walton and Earle, 1985; Wolpert et al., 2002). Thus, one of the questions that emerges when considering the nature of plant disease susceptibility is whether the plant responses leading to susceptibility are similar to the responses triggering plant resistance (Wolpert et al., 2002). One approach to address this question is to evaluate the effect of a variety of HSTs on the transcriptome of their corresponding host.
Pyrenophora tritici-repentis (PTR), a necrotrophic foliar pathogen responsible for the tan spot disease of wheat, produces several HSTs including proteinaceous and non-proteinaceous HSTs (Ballance et al., 1989; Effertz et al., 2002; Strelkov et al., 1999; Tomas et al., 1990; Tuori et al., 1995). Ptr ToxA [(syn. Ptr toxin, Ptr necrosis toxin, and ToxA) (Ciuffetti et al., 1998)] was the first isolated proteinaceous HST and is the most studied HST of PTR. Isolates of PTR that produce ToxA, as well as purified ToxA, induce necrosis in ToxA-sensitive wheat cultivars (Ballance et al., 1989; Ciuffetti et al., 1997; Tomas et al., 1990; Tuori et al., 1995) in an ‘inverse’ gene-for-gene fashion (Gamba et al., 1998; Lamari and Bernier, 1991; Strelkov and Lamari, 2003; Wolpert et al., 2002). The ToxA protein (13.2 kDa) is the product of a single copy gene, ToxA (Ballance et al., 1996; Ciuffetti et al., 1997; Tuori et al., 2000) and is both necessary and sufficient for pathogenesis on ToxA-sensitive wheat cultivars (Ciuffetti et al., 1997). Wheat sensitivity to this toxin is conditioned by Tsn1, a single locus present on the 5BL chromosome (Anderson et al., 1999; Faris et al., 1996; Gamba et al., 1998; Stock et al., 1996).
The ability to purify native (Ballance et al., 1989; Tomas et al., 1990; Tuori et al., 1995) and heterologously expressed active toxin (Tuori et al., 2000) that produces the necrotic symptoms reminiscent of tan spot disease has provided a powerful tool to study ToxA site- and mode-of-action. ToxA is internalized into toxin-sensitive mesophyll cells (Manning and Ciuffetti, 2005) and its internalization is mediated by the presence of an Arg–Gly–Asp (RGD) motif (Manning et al., 2008). ToxA localizes intracellularly to the chloroplast (Manning and Ciuffetti, 2005) and in vitro experiments suggest that ToxA interacts with the chloroplast-localized protein, ToxA-BP1 (Manning et al., 2007), which has been postulated to be involved in photosystem (PS) II biogenesis/degradation and/or thylakoid formation (Keren et al., 2005; Wang et al., 2004). Recent data demonstrate that ToxA treatment induces changes in PS I and II and consequently leads to the light-dependent accumulation of ROS in the chloroplast (Manning et al., 2009). While the microscopy and biochemical approaches have proven very useful towards elucidating ToxA site- and mode-of-action, there is little information on cellular responses that lead to ToxA-induced cell death. The advantage of inducing disease symptoms solely by infiltrating ToxA, combined with the recent findings on ToxA mode-of-action, make this system an excellent candidate to study the cellular responses implicated in disease susceptibility.
To begin to understand the plant responses triggered by ToxA treatment over time, we compared the global transcriptional changes between mock- and ToxA-treated leaves of the ToxA-sensitive cultivar, Katepwa. To obtain data, we employed the Affymetrix GeneChip® Wheat Genome Array. As the information that can be used for annotation of the GeneChip® Wheat Genome Array is constantly evolving, the array was re-annotated to reflect the most recent information available. A high confidence and statistically significant dataset was obtained for differentially expressed genes using four different statistical methods. In order to determine groups of genes that were most regulated, broad and detailed GO classifications and GO term enrichment analysis (Beissbarth and Speed, 2004; Zheng and Wang, 2008; Eden et al., 2009) were applied to our data. This study supports the hypothesis that disease susceptibility responses to ToxA are similar to the defense responses typically associated with resistance, in a classical gene-for-gene interaction. Also, our findings are consistent with the proposed model for ToxA mode-of-action in which ToxA-induced cell death is triggered by impairment of the PS machinery and subsequent accumulation of ROS (Manning et al., 2009; Manning and Ciuffetti, 2005).
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RESULTS |
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TOPAbstractINTRODUCTIONRESULTSDISCUSSIONMETHODSSUPPLEMENTARY DATAFUNDING |
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ToxA-Induced Effects on Global Gene Expression
Protease protection assays and immunolocalization experiments indicate that ToxA is internalized into sensitive mesophyll cells within 2 h of infiltration (hpi) and is clearly visible in the interior of the cell after 4 h (Manning and Ciuffetti, 2005). Symptom severity and the area affected by ToxA treatment depend on the amount of infiltrated ToxA. ToxA at 0.5 µM is sufficient to induce necrosis to the edges of the infiltration zone (Manning et al., 2008). To ensure that the amounts of ToxA were saturating, we used 1 µM ToxA. Leaves treated with 1 µM ToxA exhibit the first macroscopic signs of ToxA-induced necrosis within 14 hpi when tissue collapse begins, and necrosis reaches its fullest extent at approximately 48 h (Figure 1A). Based on these findings, we selected four time points (0, 3, 9, and 14 hpi) for a transcriptional profile comparison between ToxA- and mock-treated (H2O) ToxA-sensitive wheat leaves that encompass the interval when most cells have internalized toxin, but is prior to large-scale ToxA-induced tissue collapse and cell death.
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In order to compare expression levels of mRNAs from ToxA- and mock-treated leaves, we used the GeneChip® Wheat Genome Array. The GeneChip® Wheat Genome Array contains 61 127 probesets, representing 55 052 transcripts. To obtain up-to-date and maximum annotation coverage, the wheat array probesets were re-annotated by aligning the probeset targets to available wheat cDNAs, transcript assemblies, and ESTs (http://plantta.jcvi.org) using BLAT (Kent, 2002). To be considered a match, two sequences had to show >90% identity over a region of at least 60% of the length of the Affymetrix probeset target, which was the query sequence. For further analysis and functional annotation efforts, only the single best match for each probeset target was considered. When available, the annotations of the wheat sequences were retained; otherwise, the corresponding wheat sequences were mapped to their best match in the rice proteome (TIGR v5) using blastx (e-value cutoff of 1E-06), achieving coverage of
70% of the probesets. The descriptive and gene ontology (GO) annotations of the best-matching rice proteins and corresponding rice genes were assigned to the wheat probesets (Supplemental Table 1). Our improved annotation of the Affymetrix wheat microarray is available for public use at http://wheat.cgrb.oregonstate.edu. A high confidence dataset containing probesets displaying more than a two-fold change in mRNA levels was used for further analysis. Because genes are often represented by multiple probesets, for each gene family of interest, we present data that reflect both the number of genes and probesets (genes/ps). The statistically significant dataset consisted of 7065 differentially expressed probesets that corresponded to 4497 genes (Supplemental Table 2). Massive transcriptional reprogramming occurs between 3 and 14 h after ToxA treatment, as shown by the number of probesets displaying higher or lower mRNA levels compared to the control over time (Figure 1B). These changes in mRNA levels also will be referred to as up- or down-regulated, respectively. The ratio of up- to down-regulated probesets decreases from 1.6 to 0.9 to 0.8 at 3, 9, and 14 hpi, respectively. This transcriptional shift correlates with ToxA-induced tissue collapse between 9 and 14 hpi (Figure 1A). To obtain a global view of ToxA-induced transcriptional changes, the dataset was placed into broad GO categories and numbers of probesets with increased and decreased mRNA levels in each category were compared (Figure 2). In every category, the number of differentially expressed probesets increased over time, with the exception of probesets associated with developmental processes. One striking observation at 3 hpi is the up-regulation of cell wall and extracellular components (Figure 2A), which is a typical response to pathogen invasion (Thordal-Christensen, 2003). The number of up-regulated probesets belonging to these categories continues to increase over time. This is in contrast to proteins present in ribosomes, plastids, and chloroplasts, where down-regulated probesets increase over time. While probesets associated with the nucleus display increase rather than decrease in levels of mRNA at 3 hpi, this pattern is reversed at 9 and 14 hpi. Transcript accumulation of the components of the ER and Golgi apparatus peaked at 9 hpi. Enzymatic activities (kinases, hydrolases, transferases, nucleotide binding) are amongst the most up-regulated categories at 3 hpi (Figure 2B) and present a drastic change in the ratio of up/down-regulated probesets over time. For example, for kinase activity, the ratio of up/down-regulated probesets changes from 16 to 2.8 to 1.5 at 3, 9, and 14 hpi, respectively. Functions associated with transcription and translation are less affected at 3 hpi; however, the number of probesets are increasingly down-regulated over time. For instance, the ratio of up/down-regulated probesets for transcription factors shifts from 13.5 to 1.2 to 1.1 at 3, 9, and 14 hpi, respectively. Other categories that are predominantly up-regulated over time include electron transport energy pathways, response to stress, response to abiotic and biotic stimulus, and cell organization and biogenesis (Figure 2C).
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Gene Ontology (GO) term enrichment analysis has become a widely used approach for interpretation of microarray experiments and other large-scale transcriptomic datasets. Several methods and tools to perform GO term enrichment analysis have been developed (Beissbarth and Speed, 2004; Zheng and Wang, 2008; Eden et al., 2009), including several tools developed specifically for plant species with sequenced genomes or available microarray platforms (Toufighi et al., 2005; Zhou and Su, 2007; Goffard and Weiller, 2007). In general, these tools seek to identify enrichment of GO terms in a target set of genes of interest compared to a background set or pre-calculated background statistics. We used a Perl script to determine whether specific GO terms were significantly over-represented among differentially expressed genes (Supplemental Table 3). For this analysis, each time point was separated into sets of up- and down-regulated probesets. We modeled the background frequency of each GO term among all probesets on the wheat array using a random sampling approach. A Z-score was calculated for each GO term by comparing the number of occurrences of that GO term in the set of differentially expressed probesets against a background distribution derived by random sampling of all probesets represented on the microarray. Z-scores were converted to one-tailed p-values, and, to adjust for multiple testing, we applied a Benjamini and Hochberg p-value correction (Benjamini and Hochberg, 1995) to the p-values. According to this analysis, GO categories encompassing different kinase activities were over-represented among up-regulated probesets at 3 hpi. These categories continued to be over-represented throughout the experiment. Additional over-represented GO categories among up-regulated probesets at 3 hpi are involved with processes such as sugar binding, carbohydrate transport, oxidative stress responses, peroxidase and chitinase activity. At 9 hpi, the over-represented GO categories among up-regulated probesets relate to the L-phenylalanine catabolic process, cell wall catabolism, ATP binding, and electron transport. At this time, over-represented down-regulated probesets include genes involved in photosynthesis/light harvesting complexes. At 14 hpi, electron transport and photosynthesis/light harvesting complexes continue to be over-represented amongst up- and down-regulated probesets, respectively. Additional over-represented, up-regulated processes at 14 hpi correspond to iron binding, monooxygenase, and oxidoreductase activities. This categorization helped to determine groups of genes of interest for further analysis.
ToxA-Induced Effects on Specific Gene Families
The newly annotated GeneChip® Wheat Genome Array database was searched by key words to generate a list of probesets that correspond to genes of interest. This list was used as a query in the table of significantly up- or down-regulated probesets and a final list of differentially expressed probesets for genes of interests was used for data evaluation. Data for gene families used for Figures 3–8
, Table 1, and mentioned in the text are presented in Supplemental Table 4 and include probeset IDs, corresponding gene identifiers, gene names, and fold changes with p-values for 3, 9, and 14 hpi.
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Receptors, Kinases, and Transcription Factors
Plant receptors play a key role in the recognition and transduction of pathogen signals that evoke reprogramming of cellular metabolism. Because of their importance in signal transduction, we examined transcriptional profiles of several classes of receptors, signal transduction partner proteins, and proteins involved in receptor endocytosis. Microarray analysis revealed that a wide range of receptors is differentially regulated in response to ToxA treatment (Figure 3). The maximum number of genes/probesets, as well as the corresponding fold change, varies amongst different receptor families. Overall expression patterns for the presented groups show that the majority of the genes/probesets are up-regulated at all three time points, with an increasing number of down-regulated genes/probesets at 14 hpi (Figure 3).
One of the groups differentially expressed was the Brassinosteroid insensitive 1-associated receptor kinase 1 (BAK1) family. The brassinosteroid signaling pathway includes association of BRI1 with BAK1 (Nam and Li, 2002). BAK1 has been demonstrated to be a regulator of disease resistance (Chinchilla et al., 2007; Krishna, 2003) and responsible for controlling ROS production, cell death, and limiting pathogen infection. Five putative genes for BAK1 are represented by eight probesets, with up to a 42-fold increase in transcript levels at 3 hpi. A similar pattern emerges for the receptor kinase (RK) group, including receptor-like kinases (RLK). However, where the BAK1 family exhibits a decrease in fold change over time, for the RK family, the range of fold change increases. In contrast, the greatest number of up-regulated genes/probesets for the cell wall-associated kinase (WAK) family is apparent at 9 hpi but the range of fold change is similar at all times. Other groups that show differential expression due to ToxA treatment include the giberellin and serine/threonine-protein kinase receptors, which were primarily up-regulated (Figure 3), and the glutamate receptor that showed transcriptional repression over time (Table 1). ToxA internalization has been shown to require an RGD-motif (Manning et al., 2008) present on a solvent-exposed loop that is structurally similar to a loop in fibronectin, a protein that binds to plasma membrane receptors via the RGD-motif (Pierschbacher and Ruoslahti, 1984; Sarma et al., 2005). Furthermore, it has been shown that RGD-containing proteins can interact with a lectin-like receptor kinase on plant plasma membranes (Gouget et al., 2006). Interestingly, lectin-like receptor kinases and some proteins involved in endocytosis, such as SNARE family proteins, including vesicle transport v-SNARE and syntaxin, show increased levels of mRNA (Figure 3).
Stress responses triggered during pathogen infection are attributed to complex signaling regulatory networks and changes in the activity of transcription factors (Singh et al., 2002). As previously mentioned, our analysis detected differential expression of a large number of kinases, among which the probesets corresponding to MAPKs showed increased numbers of probesets up-regulated over time (Table 1). Results also suggest that calcium signaling is induced upon ToxA treatment. Levels of Calcineurin-B-like, calmodulin-like proteins, and calcium-dependent protein kinase transcripts are increased at 9 and 14 hpi (Table 1). The activation of MAPK and calcium signaling cascades was accompanied by the differential expression of three transcription factor superfamilies related to plant defense. The WRKY transcription factor superfamily had increased levels of mRNA at 3, 9, and 14 hpi. Most dehydration-responsive element (DRE)-binding protein genes/probesets showed decreased levels, whereas most ethylene-responsive-element-binding proteins (EREBP) showed increased levels of mRNA at 9 and 14 hpi (Table 1).
Phenylpropanoid Pathway and Cell-Wall Lignification-Related Genes
The phenylpropanoid pathway produces secondary metabolic compounds, such as lignins, salicylates, coumarins, flavonoids, and phytoalexins (Dixon and Paiva, 1995; Weisshaar and Jenkins, 1998), and its activation has been implicated in plant defense (Dixon et al., 2002). The initial steps of the pathway for the production of these various compounds rely on the conversion of phenylalanine to precursor molecules by enzymes including phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), and 4-coumarate:coenzyme A ligase (4CCoAL), all of which display an increase in levels of mRNA in ToxA-treated leaves reaching maximum increase at 9 hpi (Figure 4). Whether this leads to the synthesis of flavonoid compounds and phytolalexins is unclear. The transcripts levels of the key enzyme required for their biosynthesis, chalcone isomerase, are decreased at all time points. However, mRNA levels for other enzymes that are required for their production, including chalcone synthase and flavoinoid-3-monooxygenase/hydrolases, are decreased at 3 hpi but are later increased at 9 and 14 hpi and, for isoflavone reductase, increased at 14 hpi (Table 1). Another branch of the phenylpropanoid pathway is responsible for lignin synthesis (Dixon and Paiva, 1995; Weisshaar and Jenkins, 1998). Lignification is one of the mechanisms to strengthen the cell wall during the plant defense response (Cano-Delgado et al., 2003; Vance et al., 1980). Caffeoyl-CoA O-methyltransferase (CCoAM) and cinnamyl alcohol dehydrogenase (CAD) are involved in the last steps in the synthesis of lignin monomers (Boerjan et al., 2003). Levels of mRNA for these genes are increased following ToxA treatment, suggesting that lignin production is one of the possible outcomes of ToxA-induced regulation of the phenylpropanoid pathway (Figure 4). Furthermore, our data show activation of different classes of peroxidases (Figure 8) that can contribute to lignin polymer formation (Boerjan et al., 2003).
Enzymes Involved in Synthesis of Salicylic Acid, Jasmonic Acid, and Ethylene
It has been suggested that synthesis of salicylic acid (SA) occurs via two alternative pathways, PAL- and isochorismate synthase-dependent (Loake and Grant, 2007; Metraux, 2002; Wildermuth et al., 2001). According to our results, it is not clear whether ToxA treatment induces SA accumulation because benzoic acid 2-hydoxylase, an enzyme required for PAL-dependent pathway production of SA, is not represented on the microarray. In addition, isochorismate synthase was not significantly regulated.
A number of phospholipases and lipooxygenases (PL, LOX), along with enzymes required for jasmonic acid (JA) synthesis such as allene oxide synthase (AOS), allene oxide cyclase (AOC), and 12-oxophytodienoate reductase (OPDAR), showed increased mRNA levels at 9 and 14 hpi (Figure 5). This indicates that biosynthesis of JA is very likely to occur due to ToxA treatment. In support of ethylene synthesis, our data showed increased levels of mRNA for key enzymes in the pathway like 1-aminocyclopropane-1-carboxylase oxidase, aminocyclopropane-1-carboxylase synthase, and S-adenosylmethionine synthase 1 at 9 and 14 hpi (Table 1).
PR Proteins and Other Defense-Related Genes
One of the hallmarks of the plant defense response is up-regulation of pathogenesis-related (PR) genes. There are a total of 331 probesets on the Wheat Genome Array that represent different classes of PR genes. Approximately 28% of all PR genes/probesets are affected by ToxA treatment (Figure 6). Most of the PR genes/probesets are up-regulated starting at 3 hpi and their levels of expression increase over time (Figure 6). Glucanases (PR-2) and chitinases (PR-4) are the largest groups of PR proteins displaying differential expression. The majority of glucanases belong to the family of β-1,3-glucanases (glucan endo-1,3-β-glucosidases). Not all glucanases were up-regulated and among down-regulated gene/probesets were lichenases and (1–3,1–4)-β-glucanases (Figure 6). Other defense-related genes that displayed differential expression are the cell-wall degrading enzyme polygalacturonase and polygalactorunase inhibitor, both showing higher mRNA levels at 3 hpi (Table 1).
Photosystems and Oxidative Stress
Recent data on the effects of ToxA on chloroplast protein profile using Blue Native gel electrophoresis showed a significant reduction in photosynthetic (PS) complexes. These changes were accompanied by an accumulation of reactive oxygen species (ROS) in the chloroplast (Manning et al., 2009). This reduction in PS complexes could be the result of damage due to the production of ROS, a decrease in the regeneration efficiency due to transcriptional repression, or both. To determine whether a decrease in transcription of the proteins required to assemble active photosynthetic complexes is a factor in this decrease, we examined the expression levels for genes that represent components of PS I and II. The data indicate that as early as 3 hpi, there are decreased levels of mRNA of two chlorophyll a/b binding-proteins, and the number of genes/ps corresponding to chlorophyll a/b binding-proteins, as well as the their level of repression, increase over time (Figure 7). Furthermore, there is a significant decrease in the expression levels of genes encoding proteins present in the reaction centers of both PS I and PS II at 9 and 14 hpi. Regulation of the genes encoding the electron carrier ferredoxin was also affected by ToxA treatment; three were down-regulated and one was up-regulated. Ferredoxin–NADP reductase and ferredoxin–thioredoxin reductase were also down-regulated at 9 and 14 hpi. Therefore, it is likely that a decrease in protein production contributes to changes in the PS complex protein profile attributable to ToxA treatment.
Production of ROS is not only important as a signaling mechanism for defense, but is also damaging to the organism if it leads to oxidative stress. To overcome oxidative changes that occur in response to cellular stress, cells contain an arsenal of detoxifying enzymes. Analysis of the microarray data showed differential expression of the genes responsive to oxidative changes in the cell (Figure 8). Peroxidases and glutathione-S-transferase were the first of these genes that showed higher levels of mRNA at 3 hpi. Phospholipid glutathione peroxidase, ascorbate peroxidase, and superoxide dismutase were differentially regulated at 9 and 14 hpi. Although mechanisms have been engaged to attempt to detoxify ToxA-induced accumulation of ROS, these mechanisms are insufficient and ROS accumulation eventually leads to cell death (Manning et al., 2009).
Validation of the Microarray Data by RT–PCR
To confirm genes/probesets expression patterns derived from microarray data analysis, we carried out RT–PCR with several probesets that represent the previously discussed biochemical and molecular responses activated upon ToxA treatment. Probesets were chosen to represent various fold change ranges and expression patterns over time. Differential expression detected by RT–PCR (Figure 9A) was consistent with the microarray expression profiling of each of the selected probesets (Figure 9B). Experiments were repeated at least two times, with similar results, for each probeset, using two biological replicates.
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DISCUSSION |
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TOPAbstractINTRODUCTIONRESULTSDISCUSSIONMETHODSSUPPLEMENTARY DATAFUNDING |
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To better understand the molecular basis of disease susceptibility and acquire additional information on ToxA mode-of-action, we investigated ToxA-induced transcriptional responses over time. In order to catalog a potentially broad spectrum of events, we employed the Affymetrix Wheat Genome Array to compare the abundance of transcripts from ToxA- and mock-treated leaves. As there are limited wheat genes available for annotation of the array, an improved and up-to-date annotation was performed based on wheat ESTs and homology to rice loci, resulting in 70% coverage of all probesets on the wheat array. This allowed us to obtain a robust dataset and also provides a tool for the broader plant research community. It should be noted that as new wheat sequence information accumulates and protein functional data become available, the annotations should be updated.
Upon pathogen recognition, plants activate complex signaling pathways that lead to a broad array of responses. This study provides a baseline of the changes in plant gene expression due to a single pathogenicity factor, ToxA. Our results demonstrate that massive transcriptional reprogramming is already occurring at 3 hpi and continues throughout the time course. An early transcriptional activation at 3 hpi (Figure 2B and 2C) is followed by an increase in protein targeting machinery (ER and Golgi) at 9 hpi (Figure 2A). The transcriptional changes at 14 hpi (Figure 1B), the latest time point tested, coincide with the onset of macroscopic symptom development. The down-regulation of ribosomal (Figure 2A) and transcriptional activities (Figure 2C) observed at 9 and 14 hpi suggest that by 14 hpi, the cellular machinery is shutting down.
Even though a receptor for ToxA has not yet been found, genetic studies and ToxA structural and functional analyses strongly support the existence of a high-affinity binding receptor in the toxin-sensitive wheat cells. The binding of ToxA to its putative receptor appears to be mediated by an RGD motif and likely determines toxin uptake into the cell (Manning and Ciuffetti, 2005; Manning et al., 2008). Based on these data, we examined the differential expression of multiple receptor families. Lectin receptor-like kinases, which are part of the large family of receptor-like kinases (Krupa et al., 2006), were up-regulated upon ToxA treatment (Figure 3). This is highly relevant because lectin receptor-like kinases were identified in Arabidopsis as the site of interaction for RGD-containing peptides and proteins (Gouget et al., 2006) and members of this family mediate interactions with various pathogen-produced proteins (Gouget et al., 2006; Sasabe et al., 2007; Senchou et al., 2004). Previous studies suggest that ToxA uptake occurs via a receptor-mediated-like endocytosis (Manning and Ciuffetti, 2005). Accordingly, we noticed up-regulation of genes involved in endocytosis such as vesicle transport v-SNARE and syntaxin family genes (Figure 3).
The plant cell wall–plasma membrane–cytoskeleton continuum participates in the perception and transduction of environmental and developmental cues important for stress responses (Levitt, 1983; Roberts and Haigler, 1989; Schindler et al., 1989; Wyatt and Carpita, 1993; Zhu et al., 1993). Thus, this interface may play a role in the recognition of ToxA and its internalization. WAKs are important components of the cell wall–plasma membrane interface, as they physically link the cell wall and plasma membrane and facilitate communication between the extracellular matrix and the cytosol (Anderson et al., 2001; Verica and He, 2002). We found that WAKs were transcriptionally activated in response to ToxA, consistent with their role in responses to pathogens (He et al., 1998). Due to their structure and function in signaling, we can not discard the possibility that WAKs may be part of the mechanism exploited by ToxA to enter the cell or could play a role in the recognition of ToxA.
Increased transcript levels of serine/threonine-protein kinase receptors, RLKs, MAPKs, and calcium-related proteins (Figure 3 and Table 1) suggest that perception and transduction cascades are switched on within 3 h of ToxA treatment. This is consistent with data indicating that Ca++ channel blockers and protein kinase inhibitors can reduce electrolyte leakage, an early response to ToxA treatment (Rasmussen et al., 2004). Since MAPKs are a converging point in the defense signaling network in response to abiotic and biotic stresses (Zhang and Klessig, 2001), the activation of MAPK cascade components upon treatment with ToxA is not surprising. MAPK signaling can lead to phosphorylation of transcription factors, which regulate gene expression and therefore cellular metabolism (Zhang and Klessig, 2001). Among the differentially expressed transcription factors with increased levels of mRNA, we identified members of the WRKY transcription factor superfamily and EREBPs. EREBPs can be regulated by a number of stimuli, including JA, SA, ethylene, and the presence of virulent or avirulent pathogens (Gutterson and Reuber, 2004). Our microarray analysis indicates that biosynthesis of JA and ethylene is likely to be induced in response to ToxA. Therefore, the up-regulation of EREBPs could be explained by the accumulation of these signaling molecules. WRKY transcription factors are induced by fungal elicitors (Rushton et al., 1996), SA (Yang et al., 1999), and infection with different pathogens (Eulgem et al., 2000). They play a role in the transcriptional activation of disease resistance genes including several PR genes (Eulgem et al., 2000; Kim and Zhang, 2004; Yamamoto et al., 2004). This correlates with the evident increased levels of mRNA for PR genes in the presence of ToxA.
The BAK1 family and gibberellin-like receptors were also up-regulated upon ToxA treatment (Figure 3). Interestingly, plant hormones such as giberrellin and brassinosteroid have been linked to plant defense (Lopez et al., 2008; Nakashita et al., 2003; Robert-Seilaniantz et al., 2007) and may dictate the outcome of host–pathogen interactions by their effect on the SA- and JA-dependent signaling pathways (Robert-Seilaniantz et al., 2007). Furthermore, there is evidence that pathogens alter the plant hormonal balance as part of their invading strategy (Robert-Seilaniantz et al., 2007). Our analysis suggests that ToxA interferes with hormone signaling networks; it is possible that PTR via ToxA activity manipulates plant hormone signaling to divert cell energy and thus increase access to nutrients.
The biosynthesis of some plant defense-related secondary metabolites such as phenylpropanoids and phytoalexins is mediated by the conversion of phenylalanine to coumaroyl-CoA in the phenylpropanoid pathway (Dixon, 2001; Weisshaar and Jenkins, 1998; Zabala et al., 2006; Dixon and Paiva, 1995; Golkari et al., 2007). ToxA treatment triggers transcriptional activation of genes encoding enzymes that participate in the phenylpropanoid pathway such as PAL, C4H, and 4CCoAL (Weisshaar and Jenkins, 1998) (Figure 4). The activation of this pathway has been reported to occur in the presence of elicitors, plant pathogens, SA, and methyl jasmonate (Dixon and Steele, 1999; Shinya et al., 2006; Dalkin et al., 1990; Golkari et al., 2007; Salzman et al., 2005; Boddu et al., 2006). Similarly to ToxA, microarray analysis of gene expression changes in tomato after treatment with the toxin Fusicoccin produced by Fusicoccum amygdali demonstrated induced expression of PAL (Frick and Schaller, 2002). The host-selective toxin victorin produced by Cochliobolus victoriae induces accumulation of phytoalexins (Mayama et al., 1986; Lorang et al., 2007), implying that the phenylpropanoid pathway is activated upon the presence of this toxin. It is unclear from our microarray data whether phytoalexin accumulation is triggered by ToxA treatment. However, downstream enzymatic reactions of the phenylpropanoid pathway that lead to lignin biosynthesis and cell wall fortification (Ro and Douglas, 2004) and require genes encoding CCoAM and CAD suggest that cell-wall modifications take place upon ToxA treatment.
Another characteristic response to pathogen attack is the induction of expression of PR proteins. The role of PR proteins in defense is often linked to their antimicrobial properties (van Loon and Van Strien, 1999; Golkari et al., 2007; Pritsch et al., 2000; Vigers et al., 1992). We identified up-regulation of several classes of genes encoding PR proteins, including 1,3-β-glucanases, chitinases, and thaumatin-like proteins (PR-5) (Figure 6). Although the induction of PR proteins in response to pathogens is well documented, there are only a few reports of their induction in response to toxins and HSTs. Microarray analyses determined that Fusicoccin in tomato and AAL-toxin in Arabidopsis induce expression of several PR-proteins (Frick and Schaller, 2002; Gechev et al., 2004). Victorin has also been demonstrated to induce expression of PR proteins (Hoat et al., 2006; Lorang et al., 2007).
Transcripts of genes involved in the biosynthesis of JA and ethylene were more abundant in the plants treated with ToxA at 9 and 14 hpi (Figure 5). In contrast, we did not find clear indication of SA synthesis. Further experiments are required to address whether the observed changes in gene expression triggered by ToxA result in accumulation of SA, JA, and ethylene. The signaling pathways driven by these molecules do not function independently and complex positive and negative regulatory interactions occur amongst them (Kunkel and Brooks, 2002). Nevertheless, the antagonistic role of JA- and ethylene-dependent signaling pathways to the SA-dependent pathway appears to be a common theme, although synergistic effects among them can take place (Kunkel and Brooks, 2002; Bari and Jones, 2009). Ethylene accumulation has been shown in response to victorin (Wolpert et al., 2002), fusicoccin (Malerba et al., 1995; Frick and Schaller, 2002), and AAL-toxin (Moore et al., 1999; Gechev et al., 2004). Another role for ethylene is as a signal to intensify cell death during the susceptible response (Lund et al., 1998). Thus, pathogens producing these toxins may benefit from host ethylene synthesis, as it enhances cell death.
ToxA-induced cell death appears to be linked to disruption of the photosynthetic machinery. This is supported by the light-dependent symptom development (Manning et al., 2005), ToxA localization to chloroplast, thylakoid disruption (Freeman, 1995), and chlorophyll loss (Manning et al., 2004). Additionally, protein analysis of ToxA-treated leaves shows a light-dependent reduction in RuBisCo, and depletion of components of photosystems (PS) I and II in light and dark (Manning et al., 2009). Under light conditions, the reduction in proteins of both PS could be attributed to the production of ROS in the chloroplast, whereas PS changes occurring in the dark are suggested to be due to ToxA interaction with chloroplast-localized ToxA-binding protein 1 (ToxABP1) (Manning et al., 2007). By sequence comparison, ToxABP1 is homologous to thylakoid formation 1 (Thf1) protein, which plays a role in thylakoid formation and PSII biogenesis (Keren, 2005; Wang, 2004). Our microarray data indicate that ToxABP1 is down-regulated at 14 hpi. This, together with the possible functional repression of ToxABP1 due to its interaction with ToxA, could prevent regeneration of PS components, leading to disruption of electron transport and ROS accumulation. ToxA-induced, light-dependent accumulation of ROS is significantly detectable at 18 hpi (Manning, 2009); however, differential expression of the enzymes associated with oxidative stress occurs earlier than 18 hpi. This could be explained by a limited sensitivity of the assay to detect ROS or the up-regulation of cytosolic enzymes responsible for maintaining the redox balance of the cell at 9 hpi (Figure 8). Additionally, transcriptional repression of chloroplast detoxifying enzymes such as APX, SOD, and GPX can result in accumulation of ROS in the chloroplast.
Taken together, our results show that toxin sensitivity evokes cellular processes related to resistance, suggesting that pathogen resistance and susceptibility have converging or overlapping signaling pathways and responses. These results agree with previous observations in which many of the early gene expression changes that happen during gene-for-gene responses occur during susceptible interactions as well (Glazebrook, 2005). For example, susceptibility to C. victoriae in Arabidopsis is conditioned by LOV1, a member of the NBS-LRR resistance gene family. LOV1 mediates responses typically associated with defense, even though mutations in defense response pathways do not affect susceptibility to C. victoriae (Lorang et al., 2007). What is the purpose of these defense-associated responses? Are these responses a futile effort for survival? It is plausible that the defense-associated responses do not have an impact in ToxA-mediated susceptibility. Much needs to be done to clarify the role of resistance responses in the context of susceptibility and what factors ultimately determine host survival or death. One aspect in determining the outcome of plant–pathogen interaction seems to relate to the timing at which resistance responses are activated (Glazebrook, 2005). It is not clear whether ToxA entry into the sensitive cell precedes the induction of defense responses. Once inside the cell, ToxA could serve as the weapon that allows PTR to defeat the plant defense responses. Transcriptome profiling during the first 3 hpi should provide an insight on the timing of ToxA perception and activation of downstream responses. Based on our findings, we conclude that ToxA acts as an elicitor in addition to its role as a virulence factor (effector) and host-selective toxin.
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Plant Growth and Experimental Conditions
Plants were grown in a growth chamber set to 16 h of light at 22°C and 8 h of darkness at 18°C. ToxA was produced and purified as described by Touri et al. (1995). For each data point, ToxA (1 µM) or H2O infiltrations were carried out on eight secondary leaves of the ToxA-sensitive cultivar Katepwa using a modified-Harborg device (Hagborg, 1970). A 4-cm leaf area surrounding the infiltration center point was collected at 0, 3, 9, and 14 h post infiltration (hpi), ground in liquid nitrogen and stored at –80°C for later RNA isolation. The experiment was repeated in three biological replicates.
RNA Isolation
Total RNA for all replicate samples was isolated on the same day using the RNeasy Plant Mini Kit (Qiagen, Chatsworth, CA) following the manufacturer's instructions. RNA concentration was measured on a NanoDrop® ND-1000 UV-Vis spectrophotometer (NanoDrop Technologies, Wilmington, DE) and resulted in the range of 800–1200 ng per µl in 30 µl. RNA integrity was assayed using the RNA 6000 Nano LabChip kit on the Agilent Bioanalyzer 2100 (Agilent Technologies, Inc., Palo Alto, CA).
RNA Labeling and Affymetrix Expression Array Processing
RNA integrity screening, probe synthesis, hybridization and scanning were conducted by the Center for Genome Research and Biocomputing Core Laboratories at Oregon State University, Corvallis, OR. Biotinylated complementary RNA (cRNA) was generated for each treatment group from 5 µg of RNA following the One-Cycle Target Labeling protocol (Affymetrix, Santa Clara, CA) from the GeneChip® Expression Analysis Technical Manual (701021 Rev. 5). Biotinylated cRNA was synthesized from double stranded cDNA using T7 RNA polymerase and a biotin-conjugated pseudouridine containing nucleotide mixture provided in the IVT Labeling Kit (Affymetrix). Prior to hybridization, the cRNA was purified with GeneChip® Sample Cleanup Modules (Affymetrix), and fragmented. 10 µg from each experimental sample were hybridized for 16 h to wheat genome arrays in an Affymetrix GeneChip® Hybridization Oven 640. Affymetrix GeneChip® Fluidics Station 450 was used to wash and stain the arrays with streptavidin–phycoerythrin (Moleculer Probes, Eugene, OR), followed by biotinylated anti-streptavidin (Vector Laboratories, Burlingame, CA) according to the standard antibody amplification protocol for eukaryotic targets. Arrays were scanned with an Affymetrix GeneChip® Scanner 3000 at 570 nm. The Affymetrix eukaryotic hybridization control kit and Poly-A RNA control kit were used to ensure efficiency of hybridization and cRNA amplification. Each array image was visually screened to discount for signal artifacts, scratches, or debris.
Data Analysis
Microarray data quality was analyzed using standard tools implemented in the Bioconductor packages simpleaffy and affyPLM. All microarrays were normalized together using RMA (Bolstad et al., 2003). Differentially expressed probesets were identified using four methods: PaGE (Grant et al., 2005), SAM (Tusher et al., 2001), LIMMA (Smyth, 2004; Wettenhall et al., 2006), and BRAT (http://brat.cgrb.oregonstate.edu/). Differentially expressed probesets predicted to be statistically significant by all four methods were considered as a high confidence dataset (Supplemental Figure 1). Probesets with increases or decreases in mRNA levels less than two-fold were removed.
Validation of Microarray Experiment by RT–PCR
Microarray data corresponding to genes of interest were validated by reverse transcriptase (RT)–PCR. Probesets showing significantly high differential expression between mock and ToxA treatments were selected for validation. Target sequences were obtained at the TIGR Wheat Annotation Database (www.tigr.org/tdb/e2k1/tae1/index.shtml) and used to design specific primers (Table 1). Total RNA prepared for microarray data analysis was utilized to perform RT–PCR. cDNA was synthesized from 1 µg of total RNA using the iScriptTM cDNA Synthesis Kit (Bio-Rad, Hercules, CA) following the manufacturer's protocol and stored at –20°C. PCR samples were assembled in a 50-µl reaction as follows: 1.5 µl of 10 µM forward and reverse primer, 5 µl 10X Buffer A (Thermo Fisher Scientific Inc., Fair Lawn, NJ), 1.5 µl 10 mM dNTP (Promega, Madison, WI), 1 µl Taq polymerase and 1 µl cDNA template. PCR programs included the following steps: 95°C for 2 min, 30 cycles of 30 s at 95°C, 30 s at primers’ optimal temperature, 30 s at 72°C, and 5 min at 95°C. Primers’ optimal temperature was determined using a 55–60°C temperature gradient. The amplification of the 18S ribosomal RNA gene was performed as a control.
Data Availability
All microarray data have been deposited in ArrayExpress under accession number E-MEXP-2203. These data are also available online at http://wheat.cgrb.oregonstate.edu. Software tools used in this study are available at http://mocklerlab-tools.cgrb.oregonstate.edu/.
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Supplementary Data are available at Molecular Plant Online.
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This project is supported by a grant to L.M.C. from the National Research Initiative (NRI) Microbial Biology: Microbial Associations with Plants Program of the USDA Cooperative State Research, Education and Extension Service (CSREES, grant number 2006–55600–16619). Funding for M.F.B. was provided by the National Science Foundation under a grant awarded in 2007. This work was partially supported by the Oregon State University Computational and Genome Biology Initiative and startup funds to T.C.M.
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Acknowledgements |
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We would like to thank Anne-Marie Girard from Center for Genome Research and Biocomputing for microarray processing and data acquisition and Dr. Thomas J. Wolpert for helpful comments. No conflict of interest declared.
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