In planta bacterial multi-omics analysis illuminates regulatory principles underlying plant-pathogen interactions

Understanding how gene expression is regulated in plant pathogens is crucial for pest control and thus global food security. An integrated understanding of bacterial gene regulation in the host is dependent on multi-omic datasets, but these are largely lacking. Here, we simultaneously characterized the transcriptome and proteome of a foliar bacterial pathogen, Pseudomonas syringae, in Arabidopsis thaliana and identified a number of bacterial processes influenced by plant immunity at the mRNA and the protein level. We found instances of both concordant and discordant regulation of bacterial mRNAs and proteins. Notably, the tip component of bacterial type III secretion system was selectively suppressed by the plant salicylic acid pathway at the protein level, suggesting protein-level targeting of the bacterial virulence system by plant immunity. Furthermore, gene co-expression analysis illuminated previously unknown gene regulatory modules underlying bacterial virulence and their regulatory hierarchy. Collectively, the integrated in planta bacterial omics approach provides molecular insights into multiple layers of bacterial gene regulation that contribute to bacterial growth in planta and elucidate the role of plant immunity in controlling pathogens.


Introduction
The growth of bacterial pathogens in plants and disease development are determined by genetically encoded bacterial virulence system and plant immune system (1). Despite the wealth of knowledge available concerning these systems in isolation, interactions between the two-especially how plant immunity affects bacterial function-are poorly understood (2).
Previously, it was shown that in planta transcriptomics of a bacterial pathogen can be used to 40 identify bacterial mRNAs whose expression is influenced by plant immune activation (3).
Although transcriptome analysis is a useful and widely used approach to elucidate cellular function, it has been well established that mRNA expression does not always reflect protein expression, and thus it becomes clear that a better understanding of cellular behavior requires direct interrogation of protein expression (4,5). A previous study showed that proteome 45 analysis of leaf commensal bacteria can reveal metabolic changes in bacteria residing on the leaf surface (6). However, the capacity of proteomics to describe plant-associated bacteria remains limited (7). For instance, analyzing bacterial responses in the intercellular space (apoplast) of leaves, which is an important niche for various commensal and pathogenic bacteria, poses a major challenge because the large preponderance of plant material relative 50 to bacterial material confounds analysis. To date, there is no proteome study of bacterial pathogens in the leaf apoplast, and thus, we lack comprehensive knowledge of proteins that are affected by the host plants. Moreover, due to the lack of comparative analyses between different modalities of bacterial responses in planta, little is known about the flow of bacterial genetic information (mRNA and protein expression) that is important for virulence 55 and how this is affected by plant immunity.
Here, we simultaneously profiled bacterial transcriptomes and proteomes in planta and identified bacterial processes influenced by plant immunity at the mRNA and protein levels at early and late stages of infection. Comparative analysis of transcriptomes and proteomes 60 revealed that changes in bacterial mRNA and protein expression are correlated in general.
However, proteins involved in the tip component of the bacterial type III secretion system were selectively suppressed by plant immunity at the protein level, implying the direct targeting of an essential virulence component by plant immunity. Furthermore, gene regulatory network analysis of bacteria showed previously unknown gene regulatory modules that mediate bacterial virulence in planta. Together, this study reveals the multi-layered regulatory mechanisms that underlie interactions between plants and bacterial pathogens.

In planta transcriptome and proteome profiling of P. syringae
We profiled the transcriptome and proteome of the bacterial pathogen Pseudomonas syringae 70 pv. tomato DC3000 (Pto) in Arabidopsis thaliana leaves using RNA-seq and liquid chromatography-mass spectrometry (LC-MS), respectively. Bacterial information was enriched by isolating bacterial cells from infected plant leaves using a previously established method (Fig. 1A) (3). Briefly, infected leaves were crushed and incubated in a buffer that stops bacterial metabolism and protects bacterial RNA from degradation. After bacterial cells 75 and plant cells were separated by centrifugation, RNA and protein were extracted from isolated bacteria and subjected to RNA-seq and LC-MS analysis, respectively (Fig. 1A). Both

Dynamic regulation of bacterial function across different conditions
Many biological processes of Pto were differentially regulated in distinct conditions (Fig.   S1). To gain further insights into these biological functions, we grouped Pto genes into gene ontology (GO) terms and calculated standardized GO expression scores; GO terms expressed 110 in highly condition-dependent manners were then selected ( Fig. 2A and 2B; see Materials and Methods). The GO term "pathogenesis" was one of the most dynamically regulated processes at both the transcriptome and proteome levels ( Fig. 2A and 2B). These mRNAs and proteins were strongly induced in planta at 6 hpi; their expression remained high at 48 hpi, and a clear host genotype effect was observed at this time point; i.e., expression was higher in 115 the mutants of the salicylic acid (SA) pathway (sid2, pad4, pad4 side2) compared with the wild type Col-0 ( Fig. 2C and 2D), suggesting that SA-mediated immunity suppresses pathogenesis-related factors at the transcript level at 48 hpi. Pto AvrRpt2 induces effectortriggered immunity (ETI) upon recognition of the effector AvrRpt2 by the receptor RPS2 (9,10), and SA pathways are important components of this ETI (11). We found that successful 120 activation of ETI strongly induces mRNAs/proteins related to "catalase activity" at 6 hpi ( Fig. S2B), which probably reflects bacterial responses to oxidative burst, a hallmark of ETI responses. Interestingly these mRNAs and proteins were even more highly expressed in virulent Pto at 48 hpi (Fig. S2B), implying that Pto experiences oxidative stress at later infection stages. Taken together, our multi-omic Pto dataset uncovered dynamic regulation of 125 various biological processes across different conditions at the mRNA and protein levels.
Previously, we showed that bacterial genes related to the iron acquisition pathway (iron starvation genes) are highly induced in susceptible plants and strongly suppressed by the activation of pattern-triggered immunity (PTI) and/or ETI at 6 hpi at the mRNA level (3) 130 (Fig. 2E). Expression of these genes was lower at 48 hpi compared with 6 hpi (Fig. 2E), but still higher than under in vitro conditions. Expression of iron starvation genes is known to be regulated by the master regulator protein Fur, which typically functions as a transcriptional repressor when bound by Fe(II) (12). Interestingly, the accumulation of Fur protein was negatively correlated with the expression of iron starvation genes in three distinct conditions 135 (in vitro, in planta 6 hpi, and in planta 48 hpi) ( Fig. 2E and 2F). This implies a previously unknown mechanism of bacterial iron acquisition, by which accumulation of the Fur protein might also contribute to regulation of iron-starvation genes. Hierarchical clustering of selected gene ontology (GO) terms in transcriptome (A) and proteome (B) data. Transcriptome and proteome data were standardized using z-scores (log 2 ) and mean z-scores of mRNAs/proteins involved in individual GO terms were shown. For the full GO expression data, see Data S7 and S8. (C and D) Box plots show expression (z-score) of mRNAs (C) and proteins (D) related to "pathogenesis". (E) Box plots show expression (z-145 score) of mRNAs previously described as "iron-responsive" (13). (A-E) Light and dark gray sidebars represent Pto and Pto AvrRpt2, respectively. Black, orange, and brown sidebars represent in vitro (KB), in planta (Col-0) 6 hpi, and in planta 48 hpi, respectively. (F) Expression of the Fur protein in Pto based on the proteome data (normalized iBAQ value (log 2 )). Different letters indicate statistically significant differences (adjusted p-value < 0.01; 150 Benjamini-Hochberg method).

Comparative analysis of bacterial transcriptomes and proteomes
To compare global expression patterns of genes and proteins, the transcriptome and proteome data of all 15 conditions were standardized and combined, and hierarchical clustering was 155 performed (Fig. S3A). Strikingly, transcriptome and proteome data were clustered together in three major conditions (in vitro and in planta 6/48 hpi) (Fig. S3A), indicating that the global patterns of bacterial gene expression and protein expression are similar both in vitro and in planta. Since many of the samples for transcriptome and proteome data were prepared independently, the overall agreement between transcriptome and proteome data indicates the 160 high accuracy of both sets of omics data.
We compared RNA-seq and proteome data in each condition. In King's B medium (KB) and minimal medium (MM) conditions, transcriptomes and proteomes were moderately correlated (R 2 = 0.52 and 0.43, respectively) ( Fig. S3B), which is consistent with previous 165 studies in Escherichia coli (R 2 = 0.42 -0.57) (14)(15)(16). A similar level of correlation was observed in planta with slightly higher correlation at 6 hpi (R 2 = 0.51 -0.55) than at 48 hpi (R 2 = 0.39 -0.47) (Fig. S3B). Thus, Pto mRNA and protein expression are moderately correlated both in vitro and in planta. 170 We further compared the fold changes in the RNA-seq and proteome data between Pto in vitro (KB) and each of the other conditions (Fig. 3A). In all conditions, expression changes in transcriptome and proteome were moderately correlated (R 2 = 0.52 -0.63), suggesting that protein expression changes closely mirror changes in bacterial mRNA levels during infection to both resistant and susceptible plants. Of 1,068 mRNAs/proteins detected in both RNA-seq 175 and proteome analyses, 111 mRNAs/proteins (10.4%) were significantly induced at both transcriptome and proteome levels in planta at 6 hpi compared with in KB (Fig. 3B). GO analysis showed that "pathogenesis-related process" was enriched among these mRNAs/proteins (Fig. 3B), indicating the transcription-driven activation of pathogenesis programs upon plant infection. On the other hand, there were cases where expression of 180 mRNAs and proteins were discordant. Interestingly, more proteins were down-regulated (168 proteins) than up-regulated (39 proteins) in a protein-specific manner (Fig. 3B). This may be explained by a prominent role of protein degradation or translation inhibition in bacteria in planta (Fig. 3B). GO analysis showed that "cell wall biogenesis"-related proteins were suppressed only at the protein level (Fig. 3B). In contrast, more mRNAs were up-regulated 185 (207 proteins) than down-regulated (49 proteins) in an mRNA-specific manner (Fig. 3B).
This implies that upregulation of specific mRNAs is a key response of Pto at an early stage of infection, and that the induction of mRNAs is not yet reflected in protein abundance at this point. We also compared the fold changes in the transcriptome and proteome profiles between pad4 sid2 and Col-0 at 48 hpi. GO enrichment analysis showed that bacterial 190 processes related to "chemotaxis" were highly expressed in pad4 sid2 at the protein level, but not the mRNA level (Fig. S3C). This suggests that chemotaxis-related processes are suppressed by plant SA-mediated immunity at the protein level, and this may be important for bacterial growth inhibition. Collectively, genome-wide comparisons between mRNA and protein expression illuminate the multifaceted control of bacterial gene expression in planta.

195
Component-specific targeting of the type III secretion system by plant

SA pathways
GO enrichment analysis showed that the plant SA pathway suppresses a significant number of bacterial proteins related to pathogenesis including proteins comprising the type III 200 secretion system (T3SS) (Fig. S1D). The T3SS is an essential component by which Pto translocates effectors into plant cells to subvert plant immunity and become virulent (17). We found that the impact of the SA pathways was apparent almost exclusively in proteins comprising the tip of the T3SS, namely HrpZ, HrpK, and HrpW (Fig. 3C). This implies that the SA pathways selectively target the tip of bacterial T3SS. To confirm this observation, we 205 performed immunoblotting using protein directly extracted from infected leaves without physical bacterial isolation. When protein loading was normalized by HrcC expression, HrpZ accumulated more highly in pad4 sid2 plants than in Col-0 plants, indicating differential effects of the SA pathway on different components of the type III secretion system ( Fig. 3D and Fig. S4A). This also suggests that the bacterial isolation process did not introduce 210 artefacts in the proteome data. To test if differential expression of HrpZ is due to different bacterial populations in plants, we compared HrpZ protein abundance between Col-0 at 48 hpi and pad4 sid2 at 24 hpi, time points at which bacterial population densities were comparable (Fig. S2C). Also in this comparison, HrpZ protein accumulated to higher levels (1.6-fold) in pad4 sid2 than in Col-0 ( Fig. S4B), suggesting that bacterial population does not solely explain differences in HrpZ protein expression and thus that SA-mediated immunity might directly target this protein. Furthermore, the difference in relative protein accumulation (hrpZ/hrcC) between Col-0 and pad4 sid2 at 48 hpi (5.9-fold) could not be solely explained by differences in mRNA expression (1.7-fold) ( Fig. S4B and Fig. 3E). Taken together, the results suggest that SA-mediated immunity selectively affects expression of the tip 220 component of the T3SS at the protein level.

Gene co-expression analysis predicts bacterial gene regulatory logic
Despite the distinct regulation of certain specific mRNAs and proteins, the overall moderate 245 correlation between transcriptome and proteome patterns of Pto suggests that mRNA expression can be a good indicator of bacterial functional expression in planta. Also, transcriptome-based analysis would be aided by additional transcriptome data available from a previous study (8) and by the fact that a greater number of Pto mRNAs were detected compared to proteins (Fig. 1C). Therefore, we reasoned that investigating the regulatory 250 network governing bacterial mRNA expression would help deepen our understanding of bacterial functional regulation. To deconvolute the gene regulatory network of Pto, we used 125 transcriptome datasets of Pto profiled in 38 conditions (generated in a previous study (3) and this study). A correlation matrix of 4,765 genes revealed highly correlated gene clusters ( Fig. S5A), some of which were enriched with known functions (Fig. 4A). Then, we built a 255 gene co-expression network based on the correlation scores and annotated genes with known functions; this allowed us to conclude that genes sharing the same functions tend to be coexpressed (Fig. 4B). For instance, genes related to pathogenesis (mostly T3SS and effector genes), flagellum, and iron starvation responses were found in separate and highly coexpressed gene clusters ( Fig. 4A and 4B). Intriguingly, genes involved in coronatine 260 biosynthesis and alginate biosynthesis were clustered very closely together, suggesting that these processes might share the same regulatory mechanism (Fig. S5B). On the other hand, genes related to coronatine biosynthesis and the T3SS were only mildly correlated with each other (Fig. S5B), although it has been shown that the expression of corR, the master regulator of coronatine biosynthesis genes, is dependent on HrpL, the master regulator of the T3SS (18). This suggests that there might be additional regulators that govern the expression patterns of genes related to coronatine biosynthesis and the T3SS. We also found that some genes annotated as effectors were not co-expressed with the majority of effectors (Fig. S5B), suggesting that they function in different contexts or that they do not function as effectors.
Strong anti-correlation was observed between "siderophore transport" genes, which are iron-270 repressive, and "ferric iron binding" genes, which are involved in iron-inducible bacterioferritin (Fig. S5C), indicating that this analysis could capture known expression patterns.
We anticipated that groups of highly co-expressed genes contain transcriptional regulators 275 (TRs) and their targets. Indeed, hrpL and the iron starvation sigma factor pvdS were coexpressed with their targets, T3SS genes and iron starvation genes, respectively (Fig. 4B). To test whether gene co-expression data can predict gene regulatory hierarchy, we selected three putative TRs, PSPTO_0384, PSPTO_3050, and PSPTO_3467, whose functions have not been characterized, and generated Pto strains that overexpress each of the TRs. We then 280 analyzed the expression of predicted target genes which were highly co-expressed with individual TRs. Remarkably, for the three TRs, all or most of predicted target genes were highly expressed in the TR overexpression lines in vitro (Fig. 4C), supporting the predicted regulatory hierarchy. This was further confirmed in planta at 6 hpi for PSPTO_3050, but overexpression of PSPTO_0384 or PSPTO_4908 induced only a small number of the 285 predicted targets (Fig. S6A). This is probably explained by the fact that genes highly coexpressed with these two TRs are already strongly induced in wild type Pto at 6 hpi ( Fig.   S6B), and thus, overexpression of these TRs did not lead to further induction of the predicted target genes. Notably, all the TR-overexpressing Pto grew significantly better than wild type Pto in Col-0 plants ( Fig. 4D and Fig. S6C), suggesting that the three TRs and some of their

Discussion
In this study, we analyzed the transcriptome and proteome of the bacterial pathogen Pto both in vitro and in planta under various conditions. This is, to our knowledge, the first study integrating transcriptomes and proteomes of bacteria in a plant host. We found that bacterial mRNA and protein expression were moderately correlated in liquid media and in resistant 315 and susceptible plants (Fig. 3A). Our data indicate that population-level changes in bacterial transcriptomes can serve as a reliable predictor of the proteome changes elicited by plant colonization. Previous studies using plants, yeasts, and mammals showed varying degrees of correlation between transcriptomes and proteomes, but in most cases, the correlation was considerably lower than for the bacterial pathogen Pto shown in this study (19)(20)(21)(22)(23). This mRNA co-expression analysis identified groups of highly co-expressed bacterial mRNAs with known and unknown functions. Using mRNA co-expression data, we could predict relationships between three putative TRs and mRNAs whose expression is affected by these 345 TRs (Fig. 4C). The TRs and their potential target mRNAs are induced in planta at an early and/or a late time point of infection (Fig. S6B) and overexpression of the TRs led to enhanced bacterial growth in planta (Fig. 4D). Therefore, our approach has the capability to identify previously unknown gene modules that are important for virulence in plants. mRNAs co-expressed with the TR PSPTO_0384 are suppressed by plants that have engaged PTI 350 (flg22 pre-treatment) (Fig. S6B), which is similar to known virulence related genes such as the T3SS and effectors (3). Thus, the induction of these mRNAs might be important for virulence and this might explain the enhanced growth of PSPTO_0384-ox strains in planta (Fig. 4D). Interestingly, mRNAs co-expressed with either of the other two TRs PSPTO_0350 or PSPTO_3467 were induced by PTI activation, while overexpression of these TRs led to 355 enhanced bacterial growth (Fig. 4D). It is possible that these genes are involved in stress adaptation and contribute to bacterial growth in plants. Previously, we observed that PTI activation induces a number of mRNAs in Pto (800 mRNAs) whose functions are not well understood (3). Investigating such genes might help us identify previously uncharacterized genes related to bacterial stress adaptation and/or virulence in plants. Taken together, in 360 planta bacterial multi-omics represents a new strategy for studying the molecular mechanisms underlying bacterial virulence and plant immunity.

Materials and Methods
Plant materials and growth conditions 455 The Arabidopsis thaliana accession Col-0 was the background of all A. thaliana mutants used in this study. The A. thaliana mutants rpm1-3 rps2-101C (27), pad4-1 (28), sid2-2 (29), and pad4 sid2 (11) were described previously. Plants were grown in a chamber at 22°C with a 10-h light period and 60% relative humidity for 24 days and then in another chamber at 22°C with a 12-h light period and 60% relative humidity. For all experiments, 31-to 33-day-460 old plants were used.

Preparation of in vitro bacterial samples
Bacteria were grown in either King's B medium or type III-inducible medium (32) (50 mM KH 2 PO 4 ; 7.6 mM (NH 4 ) 2 SO 4 ; 1.7 mM NaCl; 1.7 mM MgCl 2 6H 2 O; 10 mM fructose) at 28ºC 480 until they reached OD 600 = 0.65 (exponential phase). Upon harvesting the bacterial culture, 0.1 volumes of 5% phenol and 95% ethanol were added. The culture was then centrifuged to harvest the bacterial pellet, followed by total RNA and/or protein extraction.

Bacterial infection of plant leaves and sampling
Pto stains were cultured in King's B medium at 28°C at 200 rpm. Bacteria were harvested by centrifugation and resuspended in sterile water to OD 600 of 0.5 (∼2.5 × 10 8 cfu/mL) and 0.005 (∼2.5 × 10 6 cfu/mL) for harvesting at 6 hpi and 48 hpi, respectively. In total, 80-100 A.
thaliana leaves (four leaves per plant) were syringe-inoculated with bacterial suspensions using a needleless syringe. The infected leaves were harvested at 6 hpi or 48 hpi, immediately 490 frozen in liquid nitrogen, and stored at -80°C. The bacterial growth assay was performed as described before (33).

In planta bacterial transcriptomics
Sample preparation and RNA sequencing 495 In planta bacterial transcriptome analysis was conducted as described previously (34).
Briefly, bacterial cells were isolated from plant leaves, followed by RNA extraction, DNase treatment, rRNA depletion, and cDNA library preparation. The cDNA libraries were sequenced using an Illumina HiSeq 3000 system with a 150-bp strand-specific single-end read, resulting in ∼10 million reads per sample. The resulting reads were mapped onto the Pto 500 DC3000 genome/CDS (Pseudomonas Genome Database) using Bowtie2 (35). Mapped reads were counted with the Python package HTSeq (36). The RNA-seq data used in this study are  (37). Genes with q-value <0.01 and log 2 fold change > 2 were defined as differentially expressed genes. The prcomp function was used for principal component analysis. Hierarchical clustering was performed using the dist and hclust functions in the R environment or using Cluster3.0 software (38). Heatmaps were created with the heatmap3 or pheatmap function in the R environment or using TreeView (39). Enriched GO terms were 520 identified using the BiNGO plugin for Cytoscape (40). Scatterplots and boxplots were generated using the R-package ggplot2. Correlation matrices were made by cor function and the correlation heatmap was drawn by pheatmap in the R environment. Gene correlation networks were created in Cytoscape with the yFiles Layout Algorithm. 525 Gene co-expression analysis RNA-seq data obtained in a previous study (8) and the present study were combined (see Data S12 for the full dataset). Data were TMM-normalized and voom-(log 2 ) transformed.
Hierarchical clustering was done using the dist and hclust functions in the R environment or using the Cluster3.0 software (38). Heatmaps were created with the TreeView (39). Enriched 615 gene ontology terms were identified using BiNGO plugin for cytoscape (40).
Gene ontology (GO) analysis mRNA/protein expression data were standardized using z-score and a GO expression matrix was generated by taking the mean z-score for each GO term. To select GO terms that show distinct expression patterns among different conditions, we performed statistical tests in all pairwise comparisons among 15 conditions for each GO term and manually curated GO terms with high numbers of significant pairs (redundant GO terms were avoided).
RT-qPCR analysis 625 RT-qPCR was performed using the SuperScript One-Step RT-PCR system kit (Invitrogen).
As inputs, 30 ng of DNase-treated RNA extracted from infected leaves were used for analyzing bacterial genes.
Total protein extraction and immunoblotting analysis

Conditions used in this study
Note that this is not a time-course study as the doses of starting bacterial inocula are different for the two time points (OD 600 =0.5 for 6 hpi and OD 600 =0.005 for 48 hpi). Pto AvrRpt2 was 655 not used for the sampling at 48 hpi because this strain caused tissue collapse in the leaves in this condition.
Bacterial proteins differentially expressed under various conditions (related to Fig. S1) We analyzed mRNAs and proteins whose expression was significantly changed between the 660 in vitro (KB) and in planta (Col-0) conditions at 6 hpi or 48 hpi. GO enrichment analysis showed that mRNAs as well as proteins related to "pathogenesis", "translation", and "cell wall organization or biogenesis" were induced both at 6 and 48 hpi (Cluster I and II in Fig.   S1A and Cluster I in Fig. S1B). Interestingly, in the proteome data, tRNA synthases and ribosomal proteins, both of which are related to protein translation, showed the opposite 665 expression patterns, i.e., tRNA synthases were suppressed at 48 hpi, while ribosomal proteins were induced (Cluster I and III in Fig. S1B). Whether bacterial translation efficiency is reduced or enhanced at 48 hpi compared with the other conditions remains to be elucidated. mRNAs and proteins annotated as "response to oxidative stress" were induced at 48 hpi (Cluster III in Fig. S1A and cluster IV in Fig. S1B (Fig. S1D). Gene ontology (GO) enrichment analysis revealed that pathogenesis-related proteins ("Interaction with host") were highly expressed in the SA mutants (Cluster II in Fig.   S1D), suggesting that the SA immune pathway suppresses the expression of pathogenesis-related proteins. On the other hand, expression of "translation"-related proteins (ribosomal proteins) was suppressed in the SA mutants (Cluster I in Fig. S1D). Taken together, the SA 685 pathway affects expression of bacterial proteins related to bacterial virulence and basic metabolism in planta.

GO analysis
As shown in Fig. S1, bacterial functions differentially expressed in different conditions could 690 be studied by GO enrichment analysis following statistical tests. However, this analysis is highly dependent on the thresholds applied for selecting differentially expressed mRNAs or proteins, and thus some important information can be lost before GO enrichment analysis.
Moreover, it is difficult to compare the global expression pattern of GO terms across many conditions in this approach. To gain insights into biological functions that are differentially 695 regulated in various conditions, we annotated the Pto genome with GO terms and calculated GO expression scores (see Materials and Methods). This enabled quantitative analysis of bacterial functions across various conditions (Fig. 2).   and proteome data were standardized using z-scores (log 2 ) and combined, followed by hierarchical clustering. mRNAs/proteins detected in all conditions were subject to analysis. Light and dark green sidebars represent transcriptome and proteome data, respectively. Black, orange, and brown sidebars represent in vitro (King's B (KB)), in planta (Col-0) 6 hpi, and in planta 48 hpi, respectively. MM, minimal medium; ps, pad4 sid2; rr, rpm1 rps2. (B) 730 Comparisons between transcriptome and proteome data in each condition. Pearson's correlation coefficients were shown. (C) mRNA/protein expression fold changes between Col-0 and pad4 sid2 at 48 hpi were compared. mRNAs/proteins differentially expressed (DE; FDR < 0.01; |log 2 FC| > 2) in both, either, and neither ("Unchanged") of the transcriptome and proteome studies were grouped and colored. mRNAs/proteins differentially expressed in the 735 opposite direction were colored in blue ("Inconsistent"). The numbers of mRNAs/proteins are shown for each category. List of gene ontology (GO) terms enriched in the group of proteins that are significantly induced in planta at both mRNA and protein levels or proteins that are significantly suppressed in planta only at the protein level. For the full gene list and GO list, see Data S10 and S11. (B and C) mRNAs/proteins detected in both the transcriptome 740 and proteome in each condition or comparison were used for this analysis.