Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. Syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity.

Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways.

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Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions. Plants as sessile organisms are subject to numerous attacks from microbes during their lifetime.

As a result, plants have evolved a sophisticated immune system that enables each cell to monitor every invasion by microbes and to mount an appropriate defense response when necessary. Typical immune responses include the generation of reactive oxygen species, activation of the MAPK pathway, deposition of callose and the production of phytohormones, involving complicated transcriptional reprogramming,. These immune responses are interconnected and collaborative for resisting pathogens. Microarray technology has provided a powerful approach for analyzing genome-wide gene expression profiling during plant immune responses. Typically, differentially expressed genes (DEGs) during plant immune responses are identified as potential plant defense-related gene candidates.

Differential expression analysis considers each gene individually, while their potential interactions are ignored. However, genes or their protein products do not act in isolation; instead, they are interrelated with each other and act in close coordination,. Taking interactions between genes into account, various approaches based on gene sets, coexpression,,, machine learning and biological networks have been adopted to analyze transcriptional datasets involved in plant immunity,. For example, a large-scale immune coexpression network was constructed to identify immune-related functional modules involved in plant defense. Previously, we employed an advanced machine learning method to integrate Arabidopsis gene networks with transcriptome data.

Through comprehensive network analysis, we revealed shared and distinct network organizations between pattern-triggered immunity and effector-triggered immunity in Arabidopsis. Recently, differential coexpression analysis is emerging as an important complement to the traditional differential expression analysis,. Instead of focusing on individual genes, the goal of differential coexpression analysis is to identify gene pairs with different expression correlation between two conditions (such as healthy and diseased samples).

One of the possible mechanisms behind differential coexpression is that the regulation of a pair of genes may be disrupted/activated under a certain condition. A number of approaches have been developed to detect differential coexpression. Some methods focus on the detection of differential coexpression gene sets,, whereas others aim to identify differential coexpression gene pairs. In 2013, Amar et al. Developed a method called DICER to detect differentially coexpressed gene sets using a probabilistic model. DICER not only identifies a gene set with correlation significantly altered between two conditions but also provides pairs of gene sets with changed correlational relationships. Based on Fisher’s z-test, Fukushima et al.

Explored tomato gene function via differential coexpression analysis. Later, they published an R package called DiffCorr to identify differential coexpression gene pairs. Implemented several methods in the R package DCGL to identify differential coexpression gene pairs, differentially coexpressed genes (DCGs) and transcription factors (TFs) that regulate differential coexpression.

Differential coexpression analysis has been widely used to analyze human disease-related transcriptional data. Applied differential coexpression analysis to detect major cellular changes in tumor cells and found gene groups whose co-regulation may contribute to malignant transformation. Hudson et al. Successfully inferred myostatin as the gene containing the causal mutation of their interested phenotype through differential coexpression analysis. Recently, Reznik and Sander studied differential coexpression across metabolic pathways in both breast and kidney tumors, and they integrated regulatory information to study the drivers ( i.e., TF) of these changes.

Although a plethora of transcriptional datasets measuring Arabidopsis gene expression during plant immunity are available, to the best of our knowledge no study has been conducted to analyze plant immune responses using differential coexpression analysis. Pseudomonas syringae is a Gram-negative bacterial pathogen that causes diseases in a wide range of plant species.

The Arabidopsis- P. Syringae interaction is recognized as one of the most important model systems for understanding plant-pathogen interactions. Transcriptomics studies focusing on this model system have already broadened our understanding of plant-pathogen interactions. For example, de Torres-Zabala et al.

Generated a high-resolution time series expression profile [Gene Expression Omnibus (GEO) accession number: ] measuring Arabidopsis gene expression following either mock, P. Tomato DC3000 or P. Tomato DC3000 hrpA challenges. They analyzed the expression changes of nuclear-encoded chloroplast-targeted Arabidopsis genes and showed that chloroplast was a key component of early immune responses.

In combination with hormone profiling, reverse genetics and RNA-seq analyses, they also explored the dynamics, interaction and contribution of jasmonic acid, coronatine (COR) and jasmonate ZIM-domain (JAZ) proteins to P. Syringae disease progression. Moreover, Lewis et al. Conducted a comparative analysis on the same transcriptional data for exploring the transcriptional dynamics during microbial-associated molecular pattern-triggered immunity induced by P. Tomato DC3000 hrpA treatment and effector-triggered susceptibility caused by P.

Tomato DC3000 challenge. To date, the GEO database contains over 400 samples related to Arabidopsis immune responses to the infection of P. Syringae, which provides a great opportunity to revisit the transcriptomics data through some new computational methods and thus convert these data into new biological discoveries. In this work, we focused on two sub-series from. We first showed that differential coexpression was a common phenomenon during plant immune responses.

Then, we identified 1,315 genes ( i.e., DCGs) that frequently changed their coexpression partners. To explore the biological significance of differential coexpression, we identified some potential TFs regulating differential coexpression in plant immune responses with the assistance of known Arabidopsis gene regulatory networks. Furthermore, we investigated differential coexpression in the context of metabolic pathways.

These results further indicated that the Arabidopsis gene network has been extensively rewired in response to infections by plant pathogens. Differential coexpression is extensive during plant immune responses The microarray data is composed of 156 distinct samples from 13 time points in three conditions: mock treatments or infections by either virulent P.

Syringae pv tomato DC3000 or the corresponding nonpathogenic hrpA mutant, with four replicates for each condition,. In our work, we focused on mock and virulent P. Syringae pv tomato DC3000 treatments. Therefore, 104 samples from were used in this work, including 52 samples from mock-treated control and 52 samples infected by bacteria (). We only used 6,775 genes with an expression variance larger than 0. Garmin Topo Espana V5 Pro Unlocked Mapsource City there. 2 in either of the two conditions.

The R package DiffCorr was used to identify differentially coexpresssed gene pairs. Briefly, Spearman correlation coefficients (SCCs) and associated p-values for all possible gene pairs among these 6,775 genes were calculated for each condition. Then, the difference in correlation between two conditions for every gene pair was further evaluated using Δ Z. A pair of genes is defined as differentially coexpressed if the expression correlations of the gene pair under two conditions (pathogen infection and mock treatment) are significantly different (see the Materials and Methods section for details). In total, we obtained 124,115 differential coexpression gene pairs among 4,748 genes (The complete list of these differential coexpression gene pairs is available at: ). Illustrated two examples of differentially coexpressed gene pairs from our work.

In the first example, the strength of coexpression between AT5G14420 and AT5G01820 increased from −0.03 to 0.96 after infection. With respect to the AT1G68440 and AT1G54130 gene pair in the second example, the strength of coexpression was reversed between control (−0.83) and infection (0.89). Overview of the differential coexpression analysis. To investigate whether these drastically changed coexpression patterns are biologically significant, we followed the strategy of Amar et al. To shuffle data and then assessed the distribution of coexpression differences for all possible gene pairs on the microarray. The coexpression difference for a gene pair is measured using Δ Z (, see the Materials and Methods section for details).

A larger absolute value of Δ Z indicates a larger coexpression difference. If the differential coexpression is common, the real biological data will have larger coexpression differences than the shuffled data. We permuted the 104 samples by shuffling sample labels ( i.e., infection or control) and calculated Δ Z based on the shuffled data.

As expected, the absolute values of Δ Z are larger in real data compared with shuffled data (Student’s t-test, p-value. DCGs are heavily involved in plant immune responses In our analysis, we defined genes that were frequently differentially coexpressed with other genes as DCGs. To identify DCGs, we tested the enrichment of differentially coexpressed gene pairs for each gene. For the gene i that participated in k i differential coexpression gene pairs, the p-value was calculated based on a binomial probability model (see in the Materials and Methods section). If the corrected p-value of gene i was smaller than a given cutoff of 0.05, gene i was assigned as a DCG.

According to the criterion, we totally obtained 1,315 DCGs (). Gene Ontology (GO) enrichment analysis of 1,315 DCGs revealed that the most significantly enriched GO term was “response to stimulus” (hypergeometric test, p-value = 9.80 × 10 −20, ), indicating the extensive involvement of DCGs in plant responses to stimuli.

Other biological processes related to plant defense responses have also been enriched in DCGs, such as “defense response” and “cell wall modification”. In addition to GO enrichment analysis, we compared 1,315 DCGs with 1,393 curated defense-related genes (see the Materials and Methods section for details) and found that DCGs were significantly enriched in defense-related genes (hypergeometric test, p-value = 2.73 × 10 −09). This observation highlighted the important functional roles of DCGs in plant defense responses. For 129 plant defense-related DCGs, some have already been proved to play vital roles in plant immunity to P. For example, FLS2, which encodes an LRR receptor-like kinase involved in the perception of flagellin in Arabidopsis, was differentially coexpressed with 229 genes. The observation reflected that the coexpression between FLS2 and these 229 genes were extensively altered after P.

Syringae infection. Other important defense-related genes included the R gene ADR1, a JA synthetase JAR1, and so on. Notably, the DCG with the largest number of differential coexpression events was AT3G03440, which was involved in 1,001 differential coexpression gene pairs.

AT3G03440 encodes a chloroplast-located armadillo (ARM) repeat family protein, but its physiological function remains elusive. Even so, some studies have suggested their potential functional importance in many biological processes, including disease resistance. Through analyzing its 1,001 differentially coexpressed partners, “defense response” was found to be over-represented (hypergeometric test, p-value = 4.39 × 10 −3, ).

Based on these observations, AT3G03440 may be a good candidate to study plant immune responses to P. Differential coexpression analysis provides additional information that is complementary to differential expression analysis Differential expression analysis is a routine method to conduct microarray data analysis. Generally, differential expression analysis treats each gene separately and does not consider its relationship with potential interaction partners. In this context, it is interesting to decipher the relationship between differential coexpression and differential expression based on the transcriptional dataset used in this work. We first calculated the magnitude of differential expression (measured by average log 2-fold change) for each DCG (). Then, we compared the magnitude of differential expression with the number of differentially coexpressed gene pairs on the identified 1,315 DCGs. As shown in, the correlation between these two measures was generally weak (SCC = 0.17).

The result was consistent with the previous observation that differential expression analysis sheds little light on differential coexpression analysis. We also compared 1,315 DCGs with 3,398 DEGs () detected using the R package maSigPro (see the Materials and Methods section). The overlap between DCGs and DEGs was generally large, but there were 582 DCGs that were not detected as differentially expressed by maSigPro (, ).

Moreover, we also calculated the overlap between DEGs and DCGs from another two datasets, i.e., the dataset of G. Orontii infection () and the dataset of B. Cinerea infection ().

Using the same analysis procedures performed on, we obtained 154 DEGs and 404 DCGs from, and 4,817 DEGs and 1,676 DCGs from. DCGs from and were both enriched for defense-related genes (hypergeometric test, p-value. The relationship between differential expression and differential coexpression. TFs are major players in regulating transcriptional reprogramming during plant immunity. Therefore, we analyzed the relationship among DCGs, DEGs and plant defense-related genes in the context of TFs. In total, we obtained 1,996 TFs from the Arabidopsis Gene Regulatory Information Server (AGRIS) and the Plant Transcription Factor Database (PlantTFDB).

As shown in, DCGs and DEGs were both significantly enriched in defense-related TFs (hypergeometric test, p-value = 1.28 × 10 −4 for TFs from DCGs and p-value. Potential TFs regulate the observed differential coexpression Direct identification of TFs involved in plant immunity through differential expression analysis is insufficient, given that TFs also tend to be stably expressed and regulated at the protein level. The disruption of TF binding sites can affect the expression correlation between its targets, which is recognized as a potential regulatory mechanism of differential coexpression,. To account for this phenomenon, we explored the underlying regulatory mechanisms of differential coexpression in plant immune responses to P. Syringae by integrating the known regulatory network data available in public databases.

After removing redundant interactions, we collected 50,824 TF-target binary interactions between 529 TFs and 22,793 target genes from the Arabidopsis Transcriptional Regulatory Map (ATRM), AGRIS and AthaMap (). In total, 13,879 interactions between 333 TFs and 5,371 targets were retained for further analysis by filtering out stably expressed genes under each condition (variance. Transcription factors with targets that significantly form differential coexpression gene pairs. For these 20 TFs with unknown roles in plant immunity, we assessed whether any indirect evidence is available indicating that they may be involved in plant immunity. By analyzing their targets, we found that the targets of 9 TFs are significantly enriched with defense-related GO terms (), suggesting a potential role for those TFs in regulating defense-related gene network.

For example, the MADS TF AGL15 was not differentially expressed between control and infection conditions (). However, the expression correlations between one of its targets ( HYS1) and the other 87 targets were significantly increased after infection by P. Interestingly, those targets regulated by AGL15 are enriched in defense-related functions ().

The role of HYS1 in plant immunity has been previously studied, but how it is regulated is not clear. Based on the fact that the promoter of HYS1 can bind with AGL15 and the increased coexpression between HYS1 and other targets of AGL15, it is reasonable to assume AGL15 may play a role when Arabidopsis is infected by P. Extensive gene coexpression rewiring in metabolic pathways during plant immune responses From the GO annotation of DCGs, we noticed that metabolic processes were significantly overrepresented (). Plant metabolism constitutes integral part of the plant immune system. The primary metabolic pathways, such as photosynthesis, photorespiration and TCA cycle, provide the energy required for the defense responses. The indispensable contribution of secondary metabolites to plant immunity, such as phytoalexin and various phenolic compounds, has also been well established,. Considering the important role of metabolites in plant immunity, we wonder how the coexpression pattern changes within metabolic pathways during plant immune responses to P.

For this purpose, we first collected 606 metabolic pathways from the AraCyc database (V13.0) deposited in the Plant Metabolic Network (PMN) project. Then, we analyzed the difference in correlation between infection and control for all possible gene pairs from a common pathway. We found that the absolute differences in correlation of metabolic gene pairs were larger for a pathway compared with a group of randomly selected genes (, Wilcoxon test, p-value = 3.49 × 10 −63). Based on this finding, we hypothesized that the coordination of metabolic genes in the same metabolic pathway was widely influenced during plant immune responses to P. Differential coexpression in metabolic pathways. Then, we were interested in which metabolic pathway significantly changes coexpression patterns between its genes during plant immune responses to P.

For this purpose, we assigned a pathway score to each pathway by measuring the difference in coexpression between control and infection conditions (see the Materials and Methods section). A larger pathway score indicated a larger difference in coexpression for the pathway. We used the concept of dysregulated pathways to represent those pathways. In total, 36 dysregulated pathways were identified () and divided into three categories according their super pathways in PMN, including biosynthesis, degradation/utilization/assimilation, and generation of precursor metabolites and energy (). As a comparison, the Gene Set Enrichment Analysis (GSEA) method was used to identify metabolic pathways with significantly changed expression levels, and 80 metabolic pathways were identified (nominal GSEA p-value. Dysregulated pathways with significantly changed expression correlations. We further explored the relationship between dysregulated pathways and differentially expressed pathways.

Regarding the 16 pathways detected as significant by both of our method and GSEA, it is possible that the coordinated changes in expression of genes in the pathway resulted in the altered gene coexpression. For example, the metabolic pathway “photosynthesis light reactions” (Pathway ID: PWY-101) was identified as differentially expressed as well as dysregulated ( i.e., differentially coexpressed). The average correlation among genes within this pathway increased from 0.36 to 0.67 after pathogen infection, whereas most genes in this pathway (30 out of 35) were significantly down-regulated (). Increasing evidences showed that photosynthesis is inhibited during immune responses.

Not surprisingly, the original study reported the down-regulation of genes located in chloroplasts during plant responses to P. Therefore, genes in PWY-101 were coordinately down-regulated during plant immune responses (). For the 20 pathways specifically identified by our method (not differentially expressed between two conditions), we also observed some plant defense-related pathways. For example, the glutathione biosynthesis pathway (Pathway ID: GLUTATHIONESYN-PWY) was detected as a dysregulated pathway by our method. There were two enzymes in GLUTATHIONESYN-PWY, and the coexpression between them was increased from −0.41 (control) to 0.85 (infection) (). Glutathione participates in detoxification and signaling in plant defense against biotic and abiotic stresses. The abundance of glutathione increases in the plants infected with avirulent pathogens or insect Mayetiola destructor.

The increased correlation of GLUTATHIONESYN-PWY under pathogen infection may favor the production of glutathione, which contributes to plant immune responses to P. Pathways with roles in plant immunity that have not been reported may serve as good candidates for further analysis. Our expression correlation-based method found that aerobic respiration III (Pathway ID: PWY-4302) was dysregulated during plant immune responses to P. Syringae, but the GSEA analysis recognized it as non-differentially expressed.

Within this pathway, a majority of genes (93%) displayed a stable expression pattern in control and infection conditions. Regarding the expression correlation between genes in this pathway, we observed large changes in correlation between a subset of genes in this pathway ().

Although genes in the subset exhibited minimal changes in gene expression, the expression correlation between these genes significantly changed. In general, the generation of energy and release of ROS during aerobic respiration may indicate the potential role of this pathway in plant immunity. Further validation is needed to decipher how these coexpression changes influence plant immune responses to P. Comparison of current work with previous studies Our current work is primarily based on the dataset of, and the resultant new findings have been clearly described in the aforementioned sections.

Here, we focus on discussing the technical differences between our work and the original studies based on the same microarray data,. In these original studies, de Torres Zabala et al. Focused on the expression of nuclear-encoded chloroplast-targeted genes. The group further analyzed the expression pattern of genes related to jasmonate signaling. Aimed to determine the difference between transcriptional reprogramming associated with microbial-associated molecular pattern-triggered immunity and effector-triggered susceptibility. In contrast to these original studies, in this work was used to conduct differential coexpression analysis. A series of computational analyses were performed in our work, including DCG analysis, metabolic pathway analysis and transcriptional regulation analysis.

As reported in Lewis et al., three techniques were utilized to identify DEGs. We also compared the list of 9,782 DEGs between mock-infiltrated leaves and DC3000-infiltrated leaves identified by the original study of Lewis et al.

With 1,315 DCGs detected in this work. We found that 238 out of 1,315 DCGs were not included in the DEG list of Lewis et al. (2015) (), further indicating the complementarity between DEGs and DCGs. Regarding the module/pathway analysis, Lewis et al. Used coexpression-based clustering method to identify gene clusters coexpressed within each treatment (mock treatment, infection by virulent P.

Syringae or avirulent P. Syringae with hrpA mutant).

In our analysis, we first demonstrated extensive gene coexpression rewiring in metabolic pathways during plant immune responses and then identified 36 dysregulated metabolic pathways with significant changes in expression correlation between normal growth and pathogen infection (). This finding differs from the coexpression-based clustering analysis performed by Lewis et al. With respect to the coregulation analysis, Lewis et al. Aimed to predict the specific regulation of pathogen-response genes, whereas we sought to identify potential TFs regulating the observed differential expression during plant immune responses. In summary, our current analyses exhibit technical complementarity to the three original studies based on the same dataset and thus provide new insights into our understanding of plant immunity. Cd Installation Hp Psc 1610 Tout En Un Coup on this page. Conclusions Plants are challenged by various pathogens during all phases of their development.

To effectively defend against these infections, plants have evolved a powerful immune system, and extensive transcriptional reprogramming exists during this process. In this work, we initiated a study to investigate Arabidopsis immune responses to the bacterium P. Syringae through differential coexpression analysis. We concluded that differential coexpression was a common phenomenon during plant immunity. Moreover, we found that DCGs, which are complementary to DEGs, also played important roles in plant immune responses. By integrating regulatory networks into our analysis, we identified several TFs that may regulate this differential coexpression during plant immune responses. We noticed extensive differential coexpression of metabolic genes during plant immunity and identified 36 dysregulated metabolic pathways.

In the future, the transcriptional data measuring plant responses to pathogen infection will be increasingly available, allowing us to perform the current analysis using sizable expression data and achieve more robust results. In the meantime, the flourish of transcriptional data will also allow us to answer some specific biological questions in the context of differential coexpression. For instance, comparative analysis of differential coexpression during plant immune responses to different pathogens should be an important topic. It is our expectation that the differential coexpression analysis can boost the study of plant immune response-related transcriptomics and provide new insights into deciphering the molecular mechanisms of plant-pathogen interactions.

Data collection Normalized expression dataset (GEO accession number: ) were directly downloaded from the GEO database. Was designed to measure Arabidopsis gene expression at a high resolution under three conditions: mock treatment, or infections with either P.

Tomato DC3000 or the corresponding nonpathogenic hrpA mutant,. The detailed experimental procedures of the pathogen treatments and microarray data processing were documented by Lewis et al.. Probe sets were mapped to their corresponding AGI (Arabidopsis Genome Initiative) gene identifiers according to the annotation file from GEO, and replicated probes of the same gene were averaged. After data processing, we obtained 23,974 unique genes. Arabidopsis metabolic pathways were obtained from the AraCyc database at PMN (). Genes in each pathway were filtered using expression data, and only pathways with genes detected on the microarray were retained for analysis.

Arabidopsis transcriptional regulatory networks were collected from ATRM, AGRIS and AthaMap, and TFs were collected from PlantTFDB and AGRIS. For predicted regulatory networks from AthaMap, only TFs with binding sites determined by pattern-based screenings were considered in our work. Plant defense-related genes were collected through analyzing annotation information from the TAIR GO.Slim files and the literature. We first downloaded the GO.Slim files from the FTP site of TAIR (). Then, for each record in the annotation file, if the description of a gene contained at least one of the following biological process keywords, i.e., “systemic acquired resistance”, “disease resistance”, and “immune” and “defense”, we selected the gene as a plant defense-related gene. Moreover, Tsuda and Somssich recently provided a list of TFs associated with plant immunity in their review article. These TFs were also assigned as defense-related genes.

Furthermore, collected defense-related genes were filtered using expression data, and only genes detected on the microarray were reserved. Detection of differentially coexpressed gene pairs Differentially coexpressed gene pairs were detected using the R package DiffCorr, and the workflow is summarized as follows ().

First, the obtained microarray data were divided into infection and control groups. Prior to the calculation of the coexpression correlation coefficient, we removed genes with expression variance less than 0.2 across both infection samples and control samples. Then, for each group, SCCs and Benjamini-Hochberg adjusted p-values for all possible gene pairs were calculated. Here, a gene pair was defined as significantly coexpressed if its corrected p-value was less than a given cutoff of 10 −8.

Next, the difference in correlation of a gene pair ( i, j) between infection and control conditions was evaluated by the following equation. Where N I and N C was the sample number in the infection and control groups, respectively; Z I and Z C were Fisher’s transformation of SCC between the gene pair ( i, j) under infection and control conditions, respectively, and had been used in previous differential coexpression analysis.

The Δ Z followed a Gaussian distribution, and the associated p-value was evaluated based on the distribution and corrected using the Benjamini-Hochberg correction. If the corrected p-value is less than 10 −8, the difference in correlation was regarded as significant. Finally, we defined a pair of genes as differentially coexpressed if they exhibited both a significant difference in correlation and a significant correlation under at least one of the two conditions. Identification of DEGs The Bioconductor package maSigPro was used to identify DEGs. The obtained 104 samples of 52 infection samples and 52 control samples were used as input.

The maSigPro method follows a two-step regression strategy to identify DEGs from time-course microarray data. Regarding the parameters of maSigPro, Q value and “alfa” were set as 0.01, and a default R 2 threshold of 0.6 was adopted. After MaSigPro analysis, a cutoff of abs (log 2-fold change) ≥0.58 ( i.e., at least 1.5-fold change) was applied to designate genes as differentially expressed. Where Δ Z k,l was calculated using representing difference in correlation for the gene pair ( k, l) from G i.

Then, we generated a random pathway score distribution for each pathway to evaluate the significance of the pathway score. For a pathway with m genes, we randomly selected m genes from the microarray and calculated a pathway score using. We repeated the process 1,000 times to form a random pathway score distribution. An empirical p-value for the pathway was calculated as the fraction of permutation values that were greater than or equal to the observed value and corrected for multiple testing using the Benjamini-Hochberg correction.

Finally, a pathway was defined as a dysregulated pathway in plant immune responses to P. Syringae if its corrected p-value was less than a given cutoff. GSEA was also used to identify metabolic pathways with significantly changed expression levels and was performed using the GSEA software. The minimum and maximum gene set sizes were set to 5 and 1,000, respectively. The metric for ranking genes was set to “Diff_of_Classes”.

1000 permutations were used to calculate p-value and the permutation type was set to “Gene_set”. All other parameters were set as default. A cutoff of 0.05 was used to define statistical significance.