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First published online December 14, 2007; 10.1105/tpc.107.053827 The Plant Cell 19:4046-4060 (2007) © 2007 American Society of Plant Biologists Natural Variation in RPS2-Mediated Resistance among Arabidopsis Accessions: Correlation between Gene Expression Profiles and Phenotypic Responses[W]
a Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, Minnesota 55108 2 Address correspondence to katagiri{at}umn.edu.
Natural variation in gene expression (expression traits or e-traits) is increasingly used for the discovery of genes controlling traits. An important question is whether a particular e-trait is correlated with a phenotypic trait. Here, we examined the correlations between phenotypic traits and e-traits among 10 Arabidopsis thaliana accessions. We studied defense against Pseudomonas syringae pv tomato DC3000 (Pst), with a focus on resistance gene–mediated resistance triggered by the type III effector protein AvrRpt2. As phenotypic traits, we measured growth of the bacteria and extent of the hypersensitive response (HR) as measured by electrolyte leakage. Genetic variation among accessions affected growth of Pst both with (Pst avrRpt2) and without (Pst) the AvrRpt2 effector. Variation in HR was not correlated with variation in bacterial growth. We also collected gene expression profiles 6 h after mock and Pst avrRpt2 inoculation using a custom microarray. Clusters of genes whose expression levels are correlated with bacterial growth or electrolyte leakage were identified. Thus, we demonstrated that variation in gene expression profiles of Arabidopsis accessions collected at one time point under one experimental condition has the power to explain variation in phenotypic responses to pathogen attack.
Genetic variation among wild-type populations of plants is an important source of information about biological traits. The information potential of these natural accessions has long been recognized and is increasingly exploited to uncover genetic loci controlling biological traits, for example, using quantitative trait loci (QTL) analysis (Perchepied et al., 2006  ). Similarly, gene expression can be used to dissect complex traits in combination with natural variation, for example, by expression QTL (eQTL) analyses in which the expression level of a gene is considered as a trait (expression trait or e-trait) (Schadt et al., 2003; Keurentjes et al., 2007). Gene expression profiling technology is usually used for study of e-traits and eQTLs because it allows simultaneous detection and scoring of numerous e-traits and, consequently, numerous eQTLs. For use of e-traits and eQTLs to be successful for discovery of genetic loci controlling biological traits, it is essential to understand (1) how genotypic variation is correlated with variation in gene expression profiles and (2) how variation in gene expression profiles is correlated with phenotypic variation. Recently, the correlation between genotypic variation and variation in gene expression profiles was studied in detail in seven Arabidopsis thaliana accessions (Kliebenstein et al., 2006). Here, we investigate the correlation between variation in gene expression profiles and phenotypic variation among 10 Arabidopsis accessions.
We investigated natural variation in the context of the plant response to pathogen attack. During early stages of infection, plants recognize molecules that are common among large groups of microbes, called microbe-associated molecular patterns (MAMPs), as signals of pathogen attack and turn on defense responses (Jones and Dangl, 2006
We focused on resistance mediated by the Arabidopsis R gene RPS2 against infection by the bacterial pathogen P. syringae pv tomato DC3000 (Pst) carrying the type III effector gene avrRpt2 (Pst avrRpt2). The interaction between the products of the avrRpt2 gene and the RPS2 resistance gene is one of the best-studied examples of R gene–mediated resistance (Axtell and Staskawicz, 2003 We analyzed gene expression profiles of plants infected with Pst avrRpt2 and sampled at a single time point. We found variation in these expression profiles among accessions. We also observed variation in phenotypes, such as growth of Pst avrRpt2, growth of Pst, and extent of the HR. Remarkably, for each of these phenotypes, we could identify subsets of gene expression profiles that were well correlated with phenotype. This finding indicates that the loci that control e-traits could also control biological traits and justifies the eQTL approach for discovery of QTLs that control important traits.
Phenotypic Characterization 1: Arabidopsis Accessions Show Variation in Resistance against Pst and Pst avrRpt2 We analyzed variation in growth, defined as the number of colony-forming units (cfu) of bacteria present in 1 cm2 of Arabidopsis leaf tissue of Pst avrRpt2 in 10 different Arabidopsis accessions. The accessions were chosen based on different geographic and climatic origins (see Supplemental Figure 1 online) and well represent known genetic variation among Arabidopsis accessions (Nordborg et al., 2005 ; Kliebenstein et al., 2006). As a control, we also assessed the growth of Pst avrRpt2 in the rps2 mutant. This mutant carries the null rps2-101C allele in the Columbia (Col-0) background (Mindrinos et al., 1994). Among the 45 possible pairwise comparisons among accessions, 39 showed significant differences in growth of Pst avrRpt2 (q < 0.05; Table 1
). The rps2 mutant supported the highest growth of Pst avrRpt2 (Figure 1
). Thus, there is considerable quantitative variation in growth of Pst avrRpt2 among these 10 accessions (Figure 1, Table 1). Broad-sense heritability indicates what proportion of the phenotypic variation (in this case, variation in the bacterial growth) can be explained by the genotypic variation (in this case, the accession difference). The remaining portion of the phenotypic variation could be caused by some other source of variation, such as uncontrolled environmental difference among replicated experiments. The broad-sense heritability of the resistance against Pst avrRpt2 1 and 2 d after inoculation was 58 and 70%, respectively (see Supplemental Table 1 online). Thus, a large part of the observed variation in growth of Pst avrRpt2 could be explained by genetic differences among Arabidopsis accessions.
To determine whether the variation in growth of Pst avrRpt2 is due to variation in AvrRpt2-induced responses, such as R gene–mediated resistance and virulence effects of AvrRpt2, we also analyzed growth of Pst without avrRpt2. Again, a large part of the observed variation in growth of Pst could be attributed to genetic differences among Arabidopsis accessions, as the broad-sense heritabilities were 50 and 43% 1 and 2 d after inoculation, respectively (see Supplemental Table 1 online). However, due to larger variation within the accessions, the broad-sense heritability was lower than that of resistance against Pst avrRpt2.
To determine which accessions show AvrRpt2-induced resistance, we compared the growth of these two bacterial strains in each accession. Two days after inoculation, all accessions except Van-0 showed more growth of Pst than Pst avrRpt2 (q < 0.05), demonstrating that in these accessions AvrRpt2 triggers R gene–mediated resistance. By contrast, Pst avrRpt2 grew better than Pst in the rps2 mutant, demonstrating the contribution of AvrRpt2 to virulence in Col-0 plants that do not recognize AvrRpt2 (Kim et al., 2005
To investigate the differences in resistance to Pst avrRpt2 among accessions, we used pairwise comparisons to test for differences in growth of Pst, differences in growth of Pst avrRpt2, and differences in
Phenotypic Characterization 2: Arabidopsis Accessions Show Variation in Extent of the HR
As HR is considered an induced plant defense response, we tested for a correlation between HR and bacterial growth among accessions. We compared HR measured by conductivity after inoculation with Pst avrRpt2 from 0 to 24 h at 3-h intervals with titer of Pst avrRpt2 0, 1, and 2 d after inoculation. Only data from the eight accessions that showed AvrRpt2-induced HR (excluding Kin-0 and Van-0) were included. The bacterial titer 0 d after inoculation and conductivity 0 h after inoculation were included as negative controls: no correlations between HR and bacterial growth at zero time points were expected, and indeed no significant correlation was found. In fact, the broad-sense heritabilities of the bacterial titer on zero day and the conductivity at zero hour were <6% (see Supplemental Table 1 online). More interestingly, there was no significant correlation between conductivity at any time point and titer of Pst avrRpt2 at any day (maximum r2 = 0.40, df = 6, q = 0.44) or between  cfu and conductivity (maximum r2 = 0.55, df = 6, q = 0.17) among accessions. Thus, variation in HR does not correlate with variation in resistance to Pst avrRpt2.
Variation in RPS2 Coding Sequence or Expression Cannot Fully Explain Variation in Phenotypic Responses among Arabidopsis Accessions
Some of the observed phenotypic variation may be explained by variation in RPS2 sequence. For example, the RPS2 sequence of Van-0 is identical to that of Po-1. Like Van-0, Po-1 does not show RPS2-mediated resistance (Banerjee et al., 2001 ). Based on sequence analyses of RPS2, Mauricio et al. (2003) divided Arabidopsis accessions into two clades. One clade, which includes Po-1, consists mainly of susceptible accessions; the other clade consists of resistant accessions. Similarly, the RPS2 sequence of Kin-0 is identical to that of Yo-0. Like Kin-0, Yo-0 shows only marginal RPS2-mediated resistance (Mauricio et al., 2003). Both examples illustrate that RPS2 sequence may explain part of the observed variation, although it is also possible that these accessions share sequence identity at other loci that can explain this variation. However, accessions with the same RPS2 sequence, such as Est-1, Mt-0, and Ws-2, showed significant differences with respect to bacterial growth and HR. Thus, variation in RPS2 coding sequence cannot explain all the phenotypic variation observed. RPS2 expression showed moderate variation among mock-treated accessions (a 2.8-fold difference between highest and lowest expression) and low variation among Pst avrRpt2–treated accessions (a 1.5-fold difference between highest and lowest expression; Figure 3). The broad-sense heritability of RPS2 expression was higher in mock-inoculated than in Pst avrRpt2–inoculated accessions (49 and 19%, respectively; see Supplemental Table 1 online). Overall, Pst avrRpt2 inoculation affects RPS2 expression with on average a 1.4-fold induction (df = 1, F = 6.5, P = 0.015 for the treatment factor in ANOVA). There is significant variation in the effect of Pst avrRpt2 on RPS2 expression among accessions (df = 8, F = 2.5, P = 0.027 for the genotype:treatment interaction in ANOVA). Comparing RPS2 expression after the Pst avrRpt2 treatment only, there were no significant differences among accessions. Comparing RPS2 expression after mock treatment only, Tsu-1 was significantly different from Est-1 (q = 0.041) and Kas-1 (q = 0.041). Thus, much of the variation in RPS2 expression among accessions is due to variation between Est-1 and Tsu-1 and Kas-1 and Tsu-1 after mock inoculation. Additionally, RPS2 expression 6 h after inoculation with Pst avrRpt2 was not significantly correlated with growth of Pst avrRpt2 at day one or day two (r2 = 0.41 and 0.40, respectively, df = 7 and q = 0.10 for both), and no significant correlations were found between expression of RPS2 after Pst avrRpt2 inoculation and HR at any time point (maximum r2 = 0.44, df = 6, q = 0.60). Thus, variation in RPS2 expression among accessions 6 h after inoculation with Pst avrRpt2 cannot explain the variation of growth of Pst avrRpt2 or the variation in HR.
Inoculation with Pst avrRpt2 Strongly Affects the Variation in Gene Expression Profiles among Accessions Noise reduction by removing genes that did not show gene expression levels higher than a negative control (a probe that does not match any Arabidopsis or Pst sequence) in any of the genotype treatment combinations removed 105 genes from the data set (P < 0.01), leaving 466 genes (see Supplemental Figure 5 online). Of these 466 genes, 436 were induced or repressed by Pst avrRpt2 treatment in at least one accession (q < 0.05), and more than one-third of these (167) were responsive in all accessions. As the majority of the genes on the miniarray were selected based on their induction or repression upon pathogen or viral attack, it is not surprising that most of these genes are responsive to Pst avrRpt2 treatment in at least one accession. Subsequently, we analyzed the gene expression profiles by pairwise comparisons of accessions (Table 1). Of the 466 genes, 409 showed variation in at least one pair of Pst avrRpt2–inoculated accessions (q < 0.05). Thus, considerable variation exists among gene expression profiles after inoculation with Pst avrRpt2. However, some of this variation may not depend on the interaction with Pst avrRpt2. Indeed, 386 genes showed variation in at least one pair of mock-treated accessions (q < 0.05). To assess whether genes are differentially induced or repressed by treatment with Pst avrRpt2, we compared the log ratio of expression values in Pst avrRpt2–inoculated and mock-treated plants between accession pairs for each gene: 284 genes showed variation in the log ratio of Pst avrRpt2–inoculated and mock-treated plants in at least one pair of accessions (q < 0.05). Of these 284 genes that are differentially induced or repressed among accessions, 90 genes show similar basal expression levels but reach different expression levels after treatment with Pst avrRpt2, 49 genes show different basal expression levels but reach a similar expression level after treatment with Pst avrRpt2, and 139 show differences in both basal expression levels and expression levels after treatment with Pst avrRpt2. Thus, considerable variation exists among expression profiles of Pst avrRpt2–inoculated accessions, and much of this variation is caused by differential induction or repression of genes. More detailed information on specific accession pairs is shown in Table 1. In some accession pairs, very few genes were differentially induced or repressed (e.g., Cvi-1 and Tsu-1: two genes), whereas other accession pairs showed extensive differences in gene induction or repression (e.g., Col-0 and Kas-1: 146 genes). As described above, in general more genes were differentially induced or repressed due to differences in Pst avrRpt2–inoculated plants than to differences in mock-inoculated plants (53% versus 27%). However, this also varied considerably among accession pairs. For example, between Col-0 and Kas-1, only 13% (19/146) of the differentially induced or repressed genes were different in Pst avrRpt2–inoculated plants only, while 69% were different in mock-inoculated plants only, the remaining genes being differentially expressed after both treatments. By contrast, between Col-0 and Ws-2, 73% (74/101) of the differentially induced or repressed genes were different in Pst avrRpt2–inoculated plants only, and just 4% were different in mock-inoculated plants only. Thus, when interested in gene expression changes related to resistance against Pst avrRpt2, it may be more worthwhile to compare, for example, Col-0 and Ws-2 than Col-0 and Kas-1.
The variation in expression profiles among accessions was further explored using the algorithm locally linear embedding graph generator (LEGG). LEGG uses locally linear embedding (Roweis and Saul, 2000
In summary, there is extensive variation in gene expression profiles among accessions after inoculation with Pst avrRpt2, and on average 49% of this variation can be explained by genetic differences among accessions (see Supplemental Table 1 online). Although some of the variation is present after mock inoculation, most of it is specifically caused by differential induction or repression after treatment with Pst avrRpt2. When comparing pairs of accessions, substantial deviations from this general trend can be found, with some accession pairs mainly showing differences after mock inoculation, whereas other accession pairs mainly show differences after inoculation with Pst avrRpt2. These results are indicative of significant phenotypic plasticity among accessions. This is illustrated in the LEGG analysis: accessions that are strongly connected after mock inoculation are not strongly connected after inoculation with Pst avrRpt2 and vice versa.
HR, Bacterial Growth, and RPS2 Expression Show Distinct Correlations with the Expression of Clusters of Genes Separation of signal from noise is a problem with analyses of many possible predictors (i.e., genes). To address this problem, we used two sequential methods. First, we ran an ANOVA on the Pst avrRpt2–inoculated data set only and selected genes (360) that showed a significant genotype effect, thus selecting genes that show significant variation in expression among Pst avrRpt2–inoculated accessions. Second, we clustered genes using hierarchical clustering (see Methods), thus drastically reducing the number of comparisons between gene expression and phenotypic data and thereby increasing statistical power. This resulted in 28 clusters containing three or more genes (see Supplemental Table 2 online). Genes within a cluster showed correlated expression profiles over the different accessions and thus likely share upstream signaling factors, for example, regulation by the same transcription factor. Additionally, clustering filters out possible effects of sequence variation that affect binding of cRNA to the probes of the miniarray: It is likely that the measured expression differences of some genes are actually (partially) due to different probe binding properties of the mRNA because of sequence differences among Arabidopsis accessions. However, it is highly unlikely that similar patterns of probe efficiency variation across the accessions occur in more than one gene. Selection of clusters with multiple genes therefore eliminates this type of false signal. Thus, clustering has statistical, biological, and methodological advantages.
After clustering the genes, we analyzed the correlation between the expression of each cluster and phenotypic data (both bacterial growth and HR) and RPS2 expression. For bacterial growth, we used titers of the two bacterial strains on different days; for electrolyte leakage, we used the electrolyte leakage of Pst avrRpt2–inoculated plants in 3-h intervals; and for RPS2 expression, we used the data obtained after inoculation with Pst avrRpt2. Most phenotypic data correlated with the expression of at least one gene cluster, suggesting that genes in these clusters, or other genes not present on the miniarray but controlled by the same regulator, may influence the correlated phenotypic responses (Table 3
, Figures 4C to 4F). For example, five clusters were correlated with growth of Pst on both day one and day two, and five other clusters were correlated with growth of Pst on day two only (q < 0.05). These correlations make biological sense if these genes induced or repressed by Pst avrRpt2 are also induced or repressed by Pst. Indeed, previous analyses of gene expression profile changes of Col-0 after inoculation with Pst and Pst avrRpt2 indicated that many genes are responsive to both treatments, albeit to a different extent and on a different time scale (Tao et al., 2003
As the genes on the small-scale microarray were selected for broad representation of diverse expression patterns defined in Col-0 and for easy measurement of expression levels, rather than representation of biological processes, the selected genes are unlikely to adequately represent biological processes of potential interest. Furthermore, many clusters are so small that they are not appropriate for statistical analysis of biological processes associated with gene clusters. For these reasons, we did not attempt to associate biological processes with the clusters. However, these results do demonstrate that gene expression profiling at a single time point after Pst avrRpt2 infection contains information that can be correlated to several phenotypic responses. Additionally, these gene expression profiles likely contain information about the structure of the underlying signaling network. The correlation analyses also gives us some insight into how the signaling network connects different phenotypic responses, as several clusters show correlation with multiple phenotypic responses. For example, cluster 18 is specifically correlated with growth of Pst avrRpt2 and cluster 35 is specifically correlated with growth of Pst. Other clusters are correlated with more than one phenotypic response, such as cluster 29, which is correlated with growth of both bacterial strains, and cluster 25, which is correlated with HR and with growth of Pst. Thus, clusters correlating with several phenotypic responses may indicate overlaps in the signaling networks influencing the different phenotypes. Interestingly, of the 10 clusters correlated with growth of Pst, seven overlapped with clusters correlated with RPS2 expression, suggesting a role of RPS2 in basal resistance or coregulation of RPS2 with basal resistance. It was reported that an inducer of the basal defense response, flg22, induces RPS2 expression (Zipfel et al., 2004
As a negative control, we also investigated whether any patterns were significantly correlated with bacterial titer at day zero, right after inoculation. No patterns correlated with bacterial titer at day zero for either bacterial strain (q We performed the same analyses using gene expression profiles after mock inoculation. ANOVA resulted in the selection of 349 genes that showed significant variation in expression levels among accessions after mock treatment. Gene clustering resulted in 15 clusters of three or more genes. None of these clusters showed significant correlation with titer of either Pst or Pst avrRpt2 at any time point, but one cluster correlated with electrolyte leakage at 21 and 24 h (Table 3). Clearly, few differences in phenotypic responses can be explained by differences in expression profiles after mock inoculation. Thus, it appears that differences in basal gene expression levels do not strongly affect the phenotypic variation. By contrast, certain expression changes after Pst avrRpt2 infection are strongly correlated with phenotypic variation. This implies that an eQTL approach using uninfected plants has a very limited chance of detecting loci involved in resistance. To visualize the relationships between gene expression patterns, HR, and bacterial growth, we used LEGG to embed the phenotypic data in a gene expression graph. As LEGG uses dimensionality reduction, not all significant correlations between gene expression clusters are represented by direct links in the LEGG analyses. Still, the LEGG analysis of combined phenotypic and gene expression data reflects and integrates many of the results from the statistical analyses (Figure 4B). First, Figure 4B shows many more links between the gene expression graph and bacterial growth than between the gene expression graph and HR. The gene cluster connected with HR is not strongly connected to the rest of the network. Thus, these results confirm the lack of strong correlation between HR and bacterial growth and show that this lack of correlation is reflected in separate parts of the gene expression graph that are associated with either phenotypic response. RPS2 expression level is embedded in the center of the graph and shows no direct connections with phenotypic data. Again, this fits with the observation that RPS2 expression is not correlated with the phenotypic data. Thus, it appears that variation in signaling downstream or independent of RPS2 is causing much of the observed variation in phenotypic responses to Pst avrRpt2 among accessions.
Phenotypic Characterization and Gene Expression Profiling of Arabidopsis Accessions Show Distinct and Shared Mechanisms of Resistance against Pst and Pst avrRpt2 We have demonstrated that variation exists in RPS2-mediated resistance among Arabidopsis accessions (Figure 1, Table 1). Such variation has been reported previously and as far as the accessions used overlap, our data confirm previous results (Caicedo et al., 1999 ; Mauricio et al., 2003). By analyzing variation in cfu and variation in growth of Pst and Pst avrRpt2, we can separate variation in resistance to Pst between accession pairs into four distinct classes. Some of the variation in resistance to Pst and Pst avrRpt2 among accessions suggests that the molecular mechanisms underlying basal and R gene–mediated resistance in Arabidopsis have shared features. The strong interconnectedness of gene expression clusters that are associated with growth of the two bacterial strains (Figure 4B) also support overlapping molecular mechanisms. Obviously, this overlap is not complete, with some variation specifically affecting one bacterial strain but not the other. Thus, our results with natural accessions of Arabidopsis corroborate the conclusion that basal and R gene–mediated resistance in Arabidopsis have both distinct and shared features (Tao et al., 2003).
Phenotypic Characterization and Gene Expression Profiling of Arabidopsis Accessions Show Uncoupling of HR and Resistance
Gene Expression Profiling Identifies Robust yet Variable Responses to Pst avrRpt2
Even though many genes are responsive in all accessions, there is considerable variation in the extent of this response (Table 1). For example, when considering the average response of all 436 responsive genes, Ler-0 and Kas-1 are least responsive to Pst avrRpt2 (average absolute fold changes of 1.2 and 1.3; Figure 5
), whereas Col-0 is most responsive (average absolute fold change of 2.0). Limiting the analyses to the 167 genes that are responsive in all accessions does not change these results dramatically (Figure 5). Thus, it is not only the number of responsive genes but also the extent of induction or repression that varies among accessions. In the previously mentioned study including the same accessions except for Kas-1, Ler-0, and Ws-2, van Leeuwen et al. (2007)
From Correlation to Causal Effect In this article, we have demonstrated that variation in gene expression patterns and phenotypic responses among natural accessions is correlated. This correlation indicates that variation in phenotypic plasticity is reflected in variation in gene expression patterns and that these gene expression patterns contain predictive information on phenotypic responses. However, correlations do not necessarily signify causal effects. To demonstrate causal effects, we need to identify the trans-acting genetic loci that affect clusters of genes and assess their effects on phenotypic variation. Identification of these loci can be achieved using a segregating population from a cross between two parental accessions in an eQTL approach. The information from expression profiles can aid us in selecting the parental accessions. For example, when interested in identifying trans-acting genetic loci that specifically affect R gene–mediated resistance, it may be more worthwhile to select Col-0 and Ws-2 as parental lines rather than Col-0 and Kas-1, even though Col-0 and Kas-1 show more significant phenotypic differences. This is because between Col-0 and Kas-1, differential induction or repression of genes is mainly caused by differences in expression profiles after mock inoculation, whereas differential induction or repression of genes between Col-0 and Ws-2 is mainly caused by differences in expression profiles after inoculation with Pst avrRpt2. Thus, we can quickly screen many candidate parental lines and select the most promising combinations using the results presented here. We are currently undertaking an eQTL approach to identify regulatory loci affecting clusters of genes and assess the effect of these loci on Arabidopsis resistance against Pst avrRpt2.
Plants and Bacteria Arabidopsis thaliana accessions were chosen to represent diverse geographic origins. Seeds of Arabidopsis accessions Col-0 (ABRC stock number CS22625), Cvi-1 (CS8580), Est-1 (CS22629), Kas-1 (CS22638), Kin-0 (22,654), Ler-0 (CS20), Mt-0 (CS22642), Tsu-1 (CS1640), Van-0 (CS22627), Ws-2 (CS2360), and the rps2 mutant rps2-101C (Col-0 background) (Mindrinos et al., 1994 ) were suspended in 0.1% agar for a minimum of 4 d at 4°C. Cold-treated seeds were transferred to 12 x 12 x 5-cm (l x w x h) pots filled with twice-autoclaved, sterile soil (either BM-2 soil from Berger Peat Moss or LG3 germinating mix from Sun Gro Horticulture). Plants were grown in a controlled-environment chamber at 22°C, with 80% RH and a 10/14 h (R gene expression and gene expression profiling) or 12/12 h (all other experiments) light/dark cycle. Plants used were either 5 (R gene expression and gene expression profiling) or 4 (all other experiments) weeks old. We noticed that Col-0 plants used in this study (CS22625) reproducibly showed higher bacterial growth than Col-0 plants that were used for the genome sequencing (CS60000). We do not know genetic difference underlying this phenotypic difference.
Pseudomonas syringae pv tomato DC3000 strains containing either avrRpt2 (Pst avrRpt2) or the empty pLAFR3 plasmid vector (Pst) were grown for 2 d on King's B plates containing appropriate antibiotics and subsequently were grown overnight in liquid King's B, again containing appropriate antibiotics (25 µg/mL rifampicin and 10 µg/mL tertracycline). Inoculum was prepared and hand-infiltrated as described by Katagiri et al. (2002)
Bacterial Growth Analysis
Results were analyzed separately by day using a fixed-effects linear model: log2(Cijk)
The results from the linear model were used for two-tailed t tests using Benjamini and Hochberg false discovery rate (BH-FDR) multiple tests correction per hypothesis tested (Benjamini and Hochberg, 1995
Electrolyte Leakage Analysis
Results were analyzed by fitting a polynomial linear model through the electrolyte leakage curves of individual plants and using a mixed-effect linear model on the coefficients of these curves: Cijk
RPS2 Sequencing
RPS2 Expression Analysis The results from the linear model were used for two-tailed t tests with BH-FDR multiple tests correction per hypothesis tested.
Gene Expression Profiling
The results from the linear model were used to determine which genes show different treatment-dependent expression per genotype pair using two-tailed t tests with BH-FDR multiple tests correction. For selection of genes varying among accessions per treatment, the raw data set was split into two sets, each containing the raw data of one treatment. Per treatment data set, the following fixed-effects linear model was used to determine which genes show significant genotype effects: log2(Eik)
Correlation Analysis Using LEGG
Clustering of Genes Based on Expression Profiles of Different Accessions
Accession Numbers
Supplemental Data
We thank all the undergraduate students who helped in this project, in particular Katie Wolf and Heather Cohen. We thank Ken Vernick for sharing his GenePix 4000B scanner and the Minnesota Supercomputing Institute for GenePix software. We thank all the members of the Katagiri and Glazebrook labs, especially Jane Glazebrook, and Jetske de Boer and Robert Stupar for their critical comments on the manuscript. R.M.P.V.P. was supported in part by the Netherlands Organization for Scientific Research under a Talent Fellowship. M.S. is a recipient of a Research Fellowship of the Japan Society for the Promotion of Science for Young Scientists. This project was supported by the National Research Initiative of the USDA Cooperative State Research, Education, and Extension Service (Grant 2004-35301-14525 to F.K.).
1 Current address: Keygene, PO Box 216, 6700 AE Wageningen, The Netherlands.  The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Fumiaki Katagiri (katagiri{at}umn.edu).
[W] Online version contains Web-only data. www.plantcell.org/cgi/doi/10.1105/tpc.107.053827 Received June 28, 2007; Revision received November 5, 2007. accepted November 15, 2007.
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ABSTRACT
INTRODUCTION
3 h, 95% confidence intervals do not include a 
0.62). These results strengthen our conclusion that gene expression profiles contain information that is relevant to the phenotypic responses. 
ijk, where C = cfu/cm2, G = genotype; T = treatment; R = replicate; 
), the n nearest neighboring profile vectors were identified (n, the number of neighboring vectors to explore; 
). Second, the coefficients in the linear combination of the neighboring vectors that have the highest similarities to the seed vector were determined, that is, maximize 
for each i when 
. This can be achieved by solving a set of simultaneous first-order equations for each i, 
, where Bi is an n by n matrix of 
, and 
. The coefficients 
can be determined by multiplying both sides of the equations with 
from the left and then scale for 
were included into Bi one by one in the order of similarity to