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First published online August 19, 2004; 10.1105/tpc.104.023382 © 2004 American Society of Plant Biologists
Interaction-Dependent Gene Expression in Mla-Specified Response to Barley Powdery Mildew
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMETHODSREFERENCESNOTE ADDED IN PROOF |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMETHODSREFERENCESNOTE ADDED IN PROOF |
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; Tyler, 2002; Jones and Takemoto, 2004; Zipfel et al., 2004). General elicitors, which include proteins, glycoproteins, peptides, carbohydrates, and lipids, signal the presence of the pathogen and are able to trigger defense responses in a non-cultivar–specific manner (Nürnberger, 1999; Heath, 2000; Nürnberger and Brunner, 2002; Tyler, 2002; Hahlbrock et al., 2003; Montesano et al., 2003). By contrast, specific effectors, encoded by pathogen avirulence genes, trigger cultivar-specific responses resulting in hyperactivation of basal defense, which is often accompanied by hypersensitive cell death (Dangl and Jones, 2001; Nimchuk et al., 2003). This specific recognition in plant–pathogen interactions conforms to the gene-for-gene hypothesis and is determined by direct (Tang et al., 1996; Jia et al., 2000) or indirect (Kim et al., 2002; Mackey et al., 2002, 2003; Axtell and Staskawicz, 2003) interaction of host resistance (R) proteins and cognate pathogen-derived avirulence (AVR) effectors (Dangl and Jones, 2001).
The most prevalent class of plant R proteins contain highly conserved motifs, including an N-terminal coiled-coil or Toll/Interleukin-1 receptor-like domain, a nucleotide binding site, and C-terminal, Leu-rich repeats (Dangl and Jones, 2001), whereas the structures of known pathogen AVR effectors lack significant similarity (Bonas and Lahaye, 2002; Collmer et al., 2002). Many pathogen AVR proteins not only elicit defense responses but also possess virulence functions that contribute to the induction of susceptibility (Abramovitch et al., 2003; Hauck et al., 2003; Jamir et al., 2004; Jones and Takemoto, 2004). With many different virulence effectors produced during pathogenesis, molecular recognition has been the key determinant of the possible outcome of plant–microbe interactions (Jones and Takemoto, 2004). Although many studies have been conducted on plant perception of pathogen-derived molecules, the link between the recognition of general and specific elicitors in the expression of compatibility and incompatibility responses remains poorly understood.
Powdery mildew of barley (Hordeum vulgare), caused by Blumeria graminis f. sp hordei (Bgh), is an ideal system to explore the interactions of obligate fungal biotrophs with their cereal hosts. Stages of Bgh infection in barley are well characterized (Ellingboe, 1972; Kunoh, 1982; Jørgensen, 1988; Clark et al., 1993; Hall et al., 1999), and each stage is a potential recognition point with the possible release of pathogen or plant-derived signaling molecules. Induction of localized responses in underlying barley cells has been shown as early as Bgh conidiospore germination, specifically during primary germ tube formation (Kunoh, 1982; Kruger et al., 2003). Although there is an indication of early fungal recognition by the host, initial conidiospore adhesion, germination, and development of appressoria are not fundamentally different in incompatible and compatible barley–Bgh interactions (Boyd et al., 1995). Significant variation occurs, however, at the later stages of infection with the termination of fungal growth in incompatible interactions as opposed to the successful penetration and formation of haustoria leading to conidiophore development in compatible interactions (Boyd et al., 1995; Panstruga and Schulze-Lefert, 2002, 2003).
Specific recognition in barley–Bgh interactions is triggered in a gene-for-gene manner by genes designated Ml (Jørgensen, 1994; Schulze-Lefert and Vogel, 2000; Wise, 2000). Approximately 30 distinct resistance specificities have been identified at the Mla locus on chromosome 5 (1H) (Jørgensen, 1994). Mla1, Mla6, and Mla13 normally confer rapid and absolute resistance, whereas others, such as Mla7, Mla10, and Mla12, confer an intermediate response (Wise and Ellingboe, 1983; Jørgensen, 1994; Wei et al., 1999; Shen et al., 2003; Halterman and Wise, 2004). Cloned Mla alleles belong to the coiled-coil, nucleotide binding site, Leu-rich repeat class of genes implicated in specific recognition between the host and pathogen (reviewed in Jones, 2001). A unique feature of this host–pathogen interaction is that 92 to 97% similar MLA proteins may or may not require the RAR1/SGT1/HSP90 Skp1-Cullin-F-box ubiquitin ligase complex to activate downstream components (Azevedo et al., 2002; Shen et al., 2003; Shirasu and Schulze-Lefert, 2003; Halterman and Wise, 2004).
The well-defined stages of powdery mildew disease development provide multiple possibilities to interrogate the regulation of host genes in response to Mla-specified incompatible and compatible barley–Bgh interactions (Ellingboe, 1972; Kunoh, 1982; Jørgensen, 1988; Clark et al., 1993). Information on transcript abundance can be used to describe a cellular state and predict functional involvement of genes in the interactions among plants and pathogens (Maleck et al., 2000; Schenk et al., 2000; Mysore et al., 2002; Wan et al., 2002; Puthoff et al., 2003; Tao et al., 2003; van Wees et al., 2003; Whitham et al., 2003; Eulgem et al., 2004). In this report, we analyzed the molecular mechanisms of gene-specific plant-biotrophic fungus interactions. The newly developed Barley1 GeneChip probe array (Close et al., 2004) was used in conjunction with a mixed linear model analysis to evaluate in parallel 22,792 barley genes over the course of powdery mildew infection. Twenty-two of the 22,792 host genes (P value < 0.0001) exhibited nearly identical expression patterns among all incompatible and compatible interactions up to 16 h after inoculation (hai), coinciding with Bgh conidiospore germination and appressorial germ tube growth. By contrast, divergent expression was observed from 16 to 32 hai during attempted penetration of host epidermal cells and Bgh haustorial formation, with notable suppression of most transcripts identified as differentially expressed in compatible interactions. Based on these results, we propose a model that links the recognition of general elicitors and specific avirulence proteins in the expression of plant defense responses, supporting the hypothesis that host-specific resistance evolved from the recognition and prevention of the pathogen's suppression of plant basal defense.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMETHODSREFERENCESNOTE ADDED IN PROOF |
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; Panstruga, 2004). We were therefore interested in making various expression profile comparisons over time to assess the robustness of our system and also to provide independent biological validation of related phenotypic outcomes. Our first goal was to focus on genes (other than R genes) whose expression might be used to distinguish incompatibility from compatibility. This was done in two different ways: (1) by comparing two near-isogenic host lines with contrasting Mla alleles challenged with one Bgh isolate, with or without the matching AvrMla gene, and (2) by comparing the effects of two alternate Bgh isolates, again with or without the corresponding AvrMla gene, inoculated onto one host harboring a single Mla allele. The second objective was to investigate genes that may distinguish Rar1-dependent versus Rar1-independent incompatible responses specified by different Mla alleles. Therefore, as shown in Figure 1, our experimental design contained three near-isogenic barley lines, with introgressed Mla6, Mla13, and Mla1 alleles, to be challenged with either Bgh isolate 5874 (AvrMla6 and AvrMla1) or K1 (AvrMla13 and AvrMla1). This design, similar to a classic quadratic check (Flor, 1971), resulted in two independent host-isolate combinations for each of the above questions we wished to address. The experiment was conducted in three independent biological replications using a standard split-split-plot design (Kuehl, 2000) with replications as blocks, Bgh isolate as the whole-plot factor, plant genotype as the split-plot factor, and time as the split-split-plot factor (see Methods). First leaves of inoculated barley seedlings were harvested at 0, 8, 16, 20, 24, and 32 hai. One Barley1 GeneChip (Close et al., 2004) was used for each of the 108 split-split-plot experimental units corresponding to 3 replications x 3 genotypes x 2 isolates x 6 time points.
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Significant Divergent Expression Occurs after Haustorial-Plasma Membrane Contact in Compatible and Incompatible Interactions
To narrow our focus to the genes having the highest potential for being involved in response pathways that distinguish compatibility from incompatibility, we conducted F-tests that compared the average time-course expression profile of each gene in incompatible interactions [Mla6/AvrMla6 (5874) and Mla13/AvrMla13 (K1)] to its average time-course expression profile in compatible interactions [Mla6/avrMla6 (K1) and Mla13/avrMla13 (5874)] as part of a mixed linear model analysis of the 22,792 barley probe sets. Note that this comparison focuses on the four leftmost cells in Figure 1. Thus, both the incompatible and compatible mRNA expression profiles are averages of Mla6 and Mla13 plant materials inoculated with Bgh isolates 5874 and K1. This quadratic check structure allows the comparison between compatible and incompatible interactions to potentially be free of both genotype and isolate main effects as an explanation for the pattern differences observed in this experiment.
A total of 28 probe sets had P values < 0.0001 for the comparison of expression patterns across incompatible and compatible interactions. Using the method of Storey and Tibshirani (2003), this set of 28 genes was associated with a false discovery rate of under 7%. Transcript profiles of 22 of these 28 genes exhibited consistent patterns of expression within host–pathogen interaction categories and, thus, were selected for further analysis. The pattern of mRNA accumulation was highly similar in both incompatible and compatible interactions up to 16 hai and divergent thereafter (Table 1, Figure 2; see Supplemental Tables 1 and 2 online). Such a pattern is particularly interesting given that haustoria of avirulent Bgh make contact with the host cell plasma membrane at 
16 h. These 22 genes exhibit the same basic divergence between incompatible and compatible expression patterns for all pairwise comparisons of contrasting barley–Bgh interactions in the four leftmost cells of Figure 1 (see Figure 2B for examples pertinent to Mla6-specified interactions). Thus, this further strengthens the argument that the expression of these genes is a feature that can be used to distinguish susceptible and resistant responses. Highly similar expression patterns were observed in genes of predicted function as well as the unknowns (Figure 2). Seven of the predicted proteins could be modeled onto the last step of shikimate pathway leading to the synthesis of phenylpropanoid phytoalexins and lignins (Figure 3). Other genes had predicted functions in ethylene biosynthesis, cellular metabolism, and oxidative stress. Three showed no significant sequence similarities in the public databases, whereas two shared significant similarities to genes with unknown function in the rice genome (Table 1).
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, 2003; Zhou et al., 2001; Shen et al., 2003; Halterman and Wise, 2004). We used the same basic strategy as described above to determine variations in transcript abundance after pathogen inoculation in host genotypes that differ in Rar1 dependency. The mean mRNA expression of each gene in the two Rar1-dependent incompatible interactions [Mla6/AvrMla6 (5874) + Mla13/AvrMla13 (K1)] was compared with the mean mRNA expression in the two Rar1-independent incompatible interactions [Mla1/AvrMla1 (5874) + Mla1/AvrMla1 (K1)] over the first 32 h of powdery mildew infection. Just as in the case of incompatible versus compatible interactions described above, this comparison is potentially free of Bgh isolate main effects. However, linkage drag and other possible genomic introgression in the unlinked regions could be partly responsible for the observed pattern differences because the Rar1-dependent versus Rar1-independent comparison is confounded with variations between the near-isogenic lines with introgressed Mla1, Mla6, and Mla13 alleles.
A total of 41 probe sets exhibited pattern differences significant at the 0.0001 level. Using the method of Storey and Tibshirani (2003), this set of 41 genes was associated with a false discovery rate of under 5%. Of these 41 genes, only 14 genes had consistent patterns of expression in two host-isolate combinations within Rar1-dependent and -independent categories (Figure 4; see Supplemental Tables 3 and 4 online). As shown in Table 2, most genes are predicted to function in signal transduction, regulation of gene expression, and plant defense. Six genes were of unknown function; two of these six shared significant similarities to annotated sequences in the rice genome. Genes encoding predicted histone H2B.2 and a receptor-like kinase were found to be highly upregulated in Mla-specified Rar1-independent incompatible interactions with Bgh. By contrast, transcripts of predicted genes that encode proteinase inhibitors, precursor of PR5 (csAtPR5), eukaryotic initiation factor subunit, RNA binding protein, Ras-related GTP binding protein, and six predicted proteins with unknown function were found to be highly abundant in Mla-specified Rar1-dependent interactions with Bgh.
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As illustrated in Figure 5B, time point–specific responses of the 14 genes after pathogen inoculation were grouped based on Rar1 dependency with Mla1/AvrMla1-specified mRNA expression patterns distinct from those specified for Mla6/AvrMla6 and Mla13/AvrMla13. The overall clustering of the selected genes separated expression at 0 hai from that of 8 to 32 hai, suggesting that the differential gene expression occurred at the early stages after pathogen inoculation. In addition, gene expression profiles after 0 hai within the Rar1-dependent group were found to be clustered based on genotypes, separating Mla6/AvrMla6 from Mla13/AvrMla13 interactions.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMETHODSREFERENCESNOTE ADDED IN PROOF |
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; Schulze-Lefert and Panstruga, 2003). For several plant pathogenic fungi, including Bgh, early stages of infection are not fundamentally different among incompatible and compatible interactions with the host (Boyd et al., 1995; Hardham, 2001; Tucker and Talbot, 2001; Mendgen and Hahn, 2002). However, differences become apparent during the more advanced stages of infection, resulting in termination of fungal growth in incompatible interactions as opposed to maturation of haustoria, establishment of secondary hyphae, and subsequent conidiophore development in compatible interactions (Wise and Ellingboe, 1983; Boyd et al., 1995; Panstruga and Schulze-Lefert, 2002, 2003).
In the analysis presented in this report, early recognition of Bgh resulted in nearly identical transcript accumulation of the identified genes up to 16 hai in all incompatible and compatible interactions (Figures 2, 5, and 6; see Supplemental Tables 1 and 2 online). This finding is consistent with the observations that fungal attachment and germination are accompanied by the release of proteins, carbohydrates, lipids, glycoproteins, and peptides from the spores (Tucker and Talbot, 2001), and many of these molecules can trigger general host defense responses (Kiba et al., 1999). In addition, primary germ tubes are also capable of breaching host epidermal walls leading to the initiation of cytoplasmic aggregates in underlying host cells (Kunoh, 1982). Not surprisingly, these general responses to the initial phases of fungal infection have been implicated in non-host resistance as well (Heath, 2000; Jones and Takemoto, 2004). Thus, it is likely that plants take advantage of this early detection of biotic elicitors to rapidly initiate responses that could lead to pathogen rejection. For example, perception of flagellin, a general elicitor in Arabidopsis thaliana, induces expression of numerous defense-related genes and contributes to bacterial disease resistance (Zipfel et al., 2004). Therefore, the initial induction of multicomponent defense responses most likely occurs at the early stages of infection and is possibly the result of synergistic effects of recognition of multiple pathogen-derived molecules.
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; Schulze-Lefert and Panstruga, 2003). Thus, the ability of the pathogen to effectively and coordinately inhibit host nonspecific defense is likely an essential component of successful pathogenesis. However, many suppressors not only block host defense responses but can also act, in contrary fashion, as an elicitor (Shiraishi et al., 1994). Supprescins produced by Mycosphaerella pinodes, the causal agent of Mycosphaerella blight on peas (Pisum sativum), inhibit host defense responses and alternatively function as avirulence factors when applied to plants other than pea (Yoshioka et al., 1990; Toyoda et al., 1993; Shiraishi et al., 1997; Toyoda et al., 2002). Indeed, elicitors of plant disease resistance are often virulence factors from the pathogen (Collmer, 1998; Kjemtrup et al., 2000; Tsiamis et al., 2000; White et al., 2000; Bonas and Lahaye, 2002; Nimchuk et al., 2003), many of which were recently shown to inhibit programmed cell death and cell wall–based extracellular defense (Abramovitch et al., 2003; Hauck et al., 2003; Jamir et al., 2004). Taken together, these examples illustrate the dual functions of many pathogen-derived molecules in plant–microbe interactions, suggesting dynamic evolution in the attack and counterattack strategies of the two organisms.
By contrast, expression of the identified genes generally increased or remained steady from 16 to 32 hai in incompatible interactions (Figure 2). This observation suggests that one of the outcomes of specific recognition of a cognate avirulence effector by the host is the maintenance of increased levels of defense-related transcripts. This is consistent with the phenomenon of induced resistance, wherein preinoculation of plants with a nonpathogen or an avirulent isolate often elicits resistance at the infection site to a virulent isolate (Kunoh, 2002). Because differences in incompatible and compatible responses occurred only after 16 hai, this would support the hypothesis that delivery and recognition of Bgh avirulence effectors most likely occurs during membrane-to-membrane contact after penetration and during early haustorial development (Halterman et al., 2003). Thus, to sustain defense transcript accumulation, the host appears to have evolved a mechanism to counteract the pathogen's suppression of basal defense or their cellular effects in a cultivar-specific manner (Figure 6; Ellis and Dodds, 2003; Parker, 2003).
The interaction between Bgh and attacked host cells is largely a cell-autonomous event (Shirasu et al., 1999; Panstruga, 2004); thus, the overall interpretation outlined in Figure 6 illustrates the immediate changes in gene expression that occur in the challenged epidermal cells. Our observed interaction-independent transcript accumulation is consistent with the fact that only infected cells are in direct contact with the spores and therefore are capable of perception of the pathogen-associated molecules. In addition, based on the evidence obtained from gene expression analysis using RNA isolated from epidermal cells, mesophyll cells, and whole leaf tissue (Gregersen et al., 1997), suppression of defense-related transcripts has been suggested to occur only in physical proximity to haustoria (Schulze-Lefert and Panstruga, 2003). Hence, although there are reports of the possible systemic spread of defense from infected cells in barley–Bgh interactions (Gregersen et al., 1997; Kunoh, 2002), the relationship between localized perception of signals from the pathogen and the subsequent systemic perception of signals from the attacked host cells remains unclear.
Interplay of Nonspecific and Specific Transcriptional Responses in Gene-for-Gene Resistance
Many race-specific, race-nonspecific, and non-host resistance responses share similar downstream components (Dangl and Jones, 2001; Nürnberger and Brunner, 2002; Tyler, 2002; Hahlbrock et al., 2003; Jones and Takemoto, 2004). The defense signaling proteins SGT1, NDR1, PAD4, and EDS1 are required in both host-specific and broad-spectrum disease resistance (Century et al., 1997; Xiao et al., 2001; Fellbrich et al., 2002; Peart et al., 2002a, 2002b; Peng et al., 2003; Yun et al., 2003). In addition, mitogen-activated protein kinase cascades are triggered not only by R–AVR interactions but also by general pathogen-derived molecules, such as flagellin, fungal cell wall fragments, elicitin (conserved 98–amino acid steorol binding protein from Phytophthora and Phythium species), Pep13 (a peptide fragment within cell wall glycoprotein from Phytophthora sojae), and necrosis-inducing Phytophthora protein 1 (reviewed in Zhang and Klessig, 2001; Brunner et al., 2002; Fellbrich et al., 2002; Jones and Takemoto, 2004; Menke et al., 2004).
In this study, early induction of nonspecific defense-related transcripts appears to be linked to Mla-specified gene-for-gene resistance. Indeed, seven genes that were coordinately upregulated are predicted to function in the last step of the shikimate pathway leading to the biosynthesis of phenylpropanoid phytoalexins and lignin, important chemical and structural defenses (Figure 3; Oelofse and Dubery, 1996; Tyler, 2002; Hahlbrock et al., 2003). These genes are downregulated 16 hai in compatible interactions, but accumulation is sustained in incompatible interactions. In the shikimate pathway, chorismate synthase catalyzes the conversion of 5-enol-pyruvylshikimate-3-phosphate to chorismate (Ahn et al., 2003). The resulting chorismate can then be catalyzed by chorismate mutase, leading to the production of Phe and Tyr (Guo et al., 2001). Alternatively, chorismate can also be converted to anthranilate by anthranilate synthase, leading to the production of Trp (Bohlmann et al., 1996). In the first step in the monolignol biosynthetic branch of the phenylpropanoid pathway, cinnamoyl-CoA reductase catalyzes the conversion of cinnamoyl-CoA into corresponding cinnamaldehydes for lignin biosynthesis (Lauvergeat et al., 2001). Regulation of the cinnamoyl CoA reductase encoding gene affects the levels of lignin in Arabidopsis (Lauvergeat et al., 2001; Goujun et al., 2003). In addition, N-hydroxycinnamoyl/benzoyl transferase and anthranilate N-benzoyl transferase convert N-benzoylanthranilate from anthranilate, a precursor of several sets of dianthramides in carnation (Dianthus caryophyllus) treated with fungal elicitor (Yang et al., 1997). Another antifungal protein, hordatine, is derived from barley hydroxycinnamoylagmatines, which are produced by the catalysis of agmatine and hydrocinnamoyl-CoA thiolesters with agmatine coumaroyl transferase (Burhenne et al., 2003). Preferential accumulation of p-coumaroyl-hydroxyagmatine after powdery mildew infection has been shown in barley plants undergoing both non-host and host-specific resistance (von Ropenack et al., 1998; Stein and Somerville, 2002).
Coordinated suppression of these nonspecific defense-related transcripts suggests that the potential targets of virulence functions are regulators of general or basal defense. It is therefore possible that host-specific resistance may have evolved from the recognition of the virulence effects on another host protein regulating early non-specific defense. This is in agreement with the guard hypothesis wherein the R protein is guarding another host protein that is the target of an avirulence effector (Van der Biezen and Jones, 1998; Dangl and Jones, 2001). This is also consistent with indirect pathogen recognition through disappearance of RIN4 (RPM1 interacting protein), a regulator of basal defense and a target of several unrelated avirulence effectors (Mackey et al., 2002, 2003; Axtell and Staskawicz, 2003). Perturbation of RIN4 by AvrRpm1/AvrB and AvrRpt2 modulates RPM1- and RPS2-mediated resistance, respectively. Taken together, the above examples support the idea that recognition of general and specific pathogen-derived molecules are linked in gene-for-gene disease resistance.
mRNA Expression in Mla-Specified Rar1-Dependent and -Independent Barley–Bgh Interactions
An additional layer of defense regulation was revealed by the comparison of Rar1-dependent and -independent incompatible interactions. Separation of mRNA expression patterns of the selected genes was dependent on whether or not the Mla allele required Rar1 to effect resistance (Figure 5). Some of the upregulated genes in Rar1-dependent interactions were found to have predicted functions in defense (Table 2, Figure 4; see Supplemental Tables 3 and 4 online). Proteinase inhibitors have been shown to have an antifungal activity and also to inhibit the growth of Botrytis cinerae, Fusarium solani f. sp pisi, and Alternaria brassicicola (Lorito et al., 1994; Joshi et al., 1999; Heath, 2000). Another gene associated with Rar1-dependent barley–Bgh interaction encodes a predicted Ras-related GTP binding protein, and overexpression of this class of gene in tobacco (Nicotiana tabacum) produced an abnormally high level of salicylic acid with associated increase in acidic pathogenesis-related proteins conferring resistance to tobacco mosaic virus infection (Sano et al., 1994). By contrast, genes associated with Rar1-independent interactions revealed sequence similarity to predicted receptor-like kinase and histone H2B-2. Ubiquitination of receptor-like kinases and histones (Cock et al., 2002; Jason et al., 2002) is very common in eukaryotic cells, but its association with the protein degradation process probably does not require RAR1 in Mla1–AvrMla1 interactions. Recently, phosphorylation of histone H2-B was correlated with cells undergoing programmed cell death in vertebrates (Cheung et al., 2003). Thus, functional analyses of these 14 differentially expressed genes are necessary to determine their possible involvement in Rar1-dependent and -independent plant–pathogen interactions.
In summary, we predict the possible interplay of plant recognition of general and specific pathogen-derived molecules in the expression and evolution of host-specific resistance. A rapid increase in mRNA accumulation of the identified genes was triggered early after pathogen challenge, with the significant suppression of these transcripts in plants undergoing susceptible reactions. Further study of the mechanism of suppression of the differentially expressed genes should provide new insights in the understanding of compatibility and incompatibility in host–pathogen interactions.
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METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMETHODSREFERENCESNOTE ADDED IN PROOF |
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; Wei et al., 1999; AvrMla1 and AvrMla6) and K1 (Zhou et al., 2001; AvrMla1 and AvrMla13) were propagated on Hordeum vulgare cv Manchuria (C.I. 2330) in separate growth chambers at 18°C (16 h light/8 h darkness).
Plant Material
The Moseman Cereal Introduction (C.I.) lines used in these experiments each have unique Mla alleles introgressed into the universal susceptible cultivar Manchuria. Introgression was accomplished by backcrossing four times and selecting for the appropriate resistance specificity at each generation followed by selfing the heterozygous plants 12 to 15 generations, again selecting for the appropriate resistance specificity at each generation, resulting in 6% of the donor parent genome remaining in the backcrossed lines (Moseman, 1972).
Experimental Design
Incompatible and compatible barley powdery mildew interactions were generated by all pairwise combinations of the C.I. 16151, C.I. 16155, and C.I. 16137 near isogenic lines (containing Mla6, Mla13, and Mla1 resistance alleles, respectively) and the two Bgh isolates 5874 (AvrMla6 and AvrMla1) and K1 (AvrMla13 and AvrMla1). For each replication, individual genotypes were planted in separate 20 x 30-cm flats using sterilized potting soil. Each experimental flat consisted of six rows of 15 seedlings, with rows randomly assigned to one of six harvest times (0, 8, 16, 20, 24, and 32 hai). Seedlings were grown to the 2nd leaf stage with 1st leaf unfolded (GRO: 0007060), and inoculation was performed at 4 PM Central Standard Time by tipping the flats at 45° and dusting the plants with a high density of fresh conidiospores (84 ± 19 spores/mm2). This procedure was repeated from the opposite angle to ensure that a high proportion of the cells are in contact with the fungus. This conidial density per unit leaf area routinely results in >50% of epidermal cells that are successfully infected (Bushnell, 2002; Collinge et al., 2002). Groups of flats were placed at 18°C (8 h darkness, 16 h light, and 8 h darkness) in separate controlled growth chambers corresponding to the Bgh isolate. Rows of plants were harvested at their assigned harvest times and flash frozen in liquid nitrogen. The entire experiment was repeated three times in a standard split-split-plot design with 108 experimental units (Kuehl, 2000).
Barley1 GeneChip Probe Array
The Barley1 GeneChip probe array (part number 900515) is distributed by Affymetrix (Santa Clara, CA). The array includes 22,792 probe sets derived from worldwide contribution of 350,000 high-quality ESTs clustered from 84 cDNA libraries, in addition to 1145 barley gene sequences from the NCBI nonredundant database (Close et al., 2004). Three of the 84 libraries were derived from Mla6- and Mla13-specified race-specific incompatible interactions with Bgh, another library was derived from epidermal peels of mlo-5 broad-spectrum resistant plants challenged with Bgh, and two additional libraries were derived from susceptible interactions with Bgh and Fusarium graminearum (http://harvest.ucr.edu/Barley1.htm). The performance of the Barley1 GeneChip is consistent with other Affymetrix GeneChip probe arrays with respect to low false change rate for technical replicates and a broad linear detection range (Close et al., 2004). Array annotation information is hosted on the NetAffx data analysis center at affymetrix.com as well as the Harvest:Barley (http://harvest.ucr.edu/Barley1.htm) and BarleyBase (http://barleybase.org/) databases.
Target Synthesis and GeneChip Hybridization
Total RNA was isolated using a hot (60°C) phenol/guanidine thiocyanate method. Trizol-like reagent was made from 38% saturated phenol, pH 4.3 (Fisher Scientific, Pittsburg, PA), 1 M guanidine thiocyanate (Fisher Scientific), 1 M ammonium thiocyanate (Fisher Scientific), 0.1 M sodium acetate, pH 5.0, and 5% glycerol (Fisher Scientific). RNA purified further using the RNeasy Midi kit (Qiagen, Valencia, CA) yielded the most consistent cDNA synthesis and cRNA labeling among large numbers of samples. Probe synthesis, labeling, and hybridization protocols were followed as described in the Affymetrix manual (Affymetrix) and performed at the Iowa State University GeneChip Core facility (http://www.public.iastate.edu/qnzhou/Genechip.htm). Ten micrograms of purified RNA with a 260:280 ratio of 2.0 was used for cDNA synthesis using the Super-Script Choice system kit (Invitrogen, Carlsbad, CA) and GeneChip T7-oligo(dT) promoter primer kit (Affymetrix). Double-stranded cDNA was purified using the gene sample cleanup module, and 5 µL of purified cDNA was used to generate biotynilated cRNA target using the Enzo BioArray HighYield RNA transcript labeling kit (Affymetrix). Labeled cRNA was purified using the Affymetrix gene sample cleanup module, and the concentration of cRNA was determined using a Bio-Rad spectrophotometer (Hercules, CA) and adjusted based on the total RNA used as starting material. Twenty micrograms of cRNA at a final concentration of 0.5 µg/µL was fragmented in 5x fragmentation buffer at 94°C for 35 min. Quality of cDNA, cRNA, and fragmented cRNA was verified at each step on an Agilent 2100 bioanalyzer equipped with an RNA Nano LabChip (Agilent Technologies, Palo Alto, CA). Fifteen micrograms of fragmented cRNA was used to make each hybridization cocktail and 10 µg equivalent was hybridized to a GeneChip. Hybridization was performed at 60°C for 16 h in an Affymetrix hybridization oven model 640, GeneChips were washed and stained with streptavidin-phycoerythrin using the fluidics protocol EukGE-WS2 in the Affymetrix GeneChip fluidics station model 400, and stained chips were immediately scanned with an Agilent 2500A GeneArray scanner. All detailed protocols can be accessed online at http://barleypop.vrac.iastate.edu/BarleyBase/experiment_dataquery.php?class=protocolandname=any within the BarleyBase database (http://barleybase.org/).
Normalization
Before logging the data, MAS 5.0 signal measures on each GeneChip were scaled to a target intensity of 500. No additional normalization was employed. We chose to use this relatively simple normalization method to preserve independence among our measures of gene expression across GeneChips, which plays a key role in the mixed model analyses that we have implemented. More complex methods of normalization and expression calculation induce dependencies across GeneChips by making the transcript abundance measure for a gene on any one GeneChip a function of the perfect match and, in some cases, mismatch probe intensities observed for the gene on all GeneChips in the experiment. Examples of more complex normalization strategies that induce dependencies include the D-chip method (Li and Wong, 2001) and the robust multiarray average measure of expression (Irizarry et al., 2003). Although these methods have been shown to produce relatively good measures of expression, it is not clear how the dependencies created using these methods would impact the validity of subsequent statistical analyses. Furthermore, GeneChip versus GeneChip scatter plots of log signal measures (data not shown) provided no evidence that complex normalization was needed for our data.
Data Analysis
A mixed linear model analysis (Wolfinger et al., 2001) was conducted for each of the 22,792 probe sets on the Barley1 GeneChip using the SAS mixed procedure. The natural logarithm of the Affymetrix MAS 5.0 signal measure of gene expression was used as the response variable in the mixed linear model analyses to stabilize variance within genes and obtain approximate normality for random effects required for valid statistical inference. The mixed linear model included terms for the fixed effects of genotype, isolate, time point, and all interactions between these three factors as well as random effects for replications and random interactions corresponding to whole-plot, split-plot, and split-split-plot experimental units. We used contrast statements with the SAS mixed procedure to identify genes whose differences in average expression between compatible and incompatible interactions varied significantly across time. For each time point, we estimated the average expression of the gene in the compatible interactions [Mla6/virMla6 (K1) and Mla13/virMla13 (5874)] and compared that with the average expression of the gene in the incompatible interactions [Mla6/AvrMla6 (5874) and Mla13/AvrMla13 (K1)]. These time-specific differences between the average expressions (d0hai, d8hai, d16hai, d20hai, d24hai, and d32hai) were tested for equality using an F-statistic. Formally, the null hypothesis of this test may be written as H0: 
0hai = 8hai = 16hai = 20hai = 24hai = 32hai, where is the true difference estimated by d. Genes whose time-specific differences varied significantly (P value < 0.0001) across time points were identified as differentially expressed. Note that genes for which H0 is true are those whose average expression patterns are the same in both compatible and incompatible interactions. Thus, rejection of H0 for a gene indicates a pattern of expression in compatible interactions that differs from its pattern in incompatible interactions. The P value < 0.0001 threshold for significance was chosen to obtain a list of which the proportion of false positive results would be low. We then estimated the proportion of false positive results to be <7% for the 0.0001 threshold using the method described by Storey and Tibshirani (2003).
The same basic strategy was used to identify genes involved in pathways that distinguish Mla-specified Rar1-dependent from Rar1-independent interactions. The mean mRNA expression levels in Mla6/AvrMla6 (5874) and Mla13/AvrMla13 (K1) were compared with the mean mRNA expression levels in Mla1/AvrMla1 (5874) and Mla1/AvrMla1 (K1) to identify genes whose transcriptional differences varied significantly (P value < 0.0001) across time.
Cluster Analysis
Average scaled signal intensities were calculated from three replications using Microsoft Excel 2002. Data matrices were constructed with genes in rows and time points of the different genotype-isolate combinations in columns. For the clustering of 22 (Figure 2) and 14 (Figure 4) gene sets, data matrices were loaded in the GeneSpring 5.1 (Silicon Genetics, Redwood City, CA) software. Signal intensities were standardized based on the median for each gene. A Pearson correlation and hierarchical clustering were both used in creating the gene tree. For the time point–specific clustering of 22 and 14 (Figure 5) gene sets, data matrices were loaded in the NTSYSpc statistical software version 2.1 (Exeter Software, Setauket, NY). Similarities were calculated using a Pearson correlation. Correlation matrix was used in the unweighted pair group mean arithmetic cluster algorithm.
Data Access
All detailed data and protocols from these experiments have been deposited in BarleyBase (http://barleybase.org/), a MIAME-compliant expression database for cereal GeneChips (Brazma et al., 2001; http://www.mged.org/Workgroups/MIAME/miame.html). Files are categorized under accession number BB4 and can be analyzed online using the current tools in BarleyBase, downloaded as batch files in MAGE-ML, CSV, CEL, DAT, or expression data formats at the Download Center or downloaded as individual CEL, CHP, DAT, or EXP files under "browse experiments." Data files have also been deposited as accession number E-MEXP-142 in ArrayExpress (http://www.ebi.ac.uk/arrayexpress).
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Acknowledgments |
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Footnotes |
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Online version contains Web-only data. 
Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.104.023382.
Received April 10, 2004; accepted June 26, 2004.
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